first commit

custom_model.py

@@ -0,0 +1,479 @@
+import logging
+from typing import Optional, Tuple, Union
+
+import torch
+from torch import nn
+from torch.nn import MSELoss, CrossEntropyLoss, BCEWithLogitsLoss
+from transformers import GPT2Model, GPT2ForSequenceClassification, RobertaForSequenceClassification, RobertaModel
+from transformers.modeling_outputs import SequenceClassifierOutputWithPast, SequenceClassifierOutput
+
+
+LOGGER = logging.getLogger(__name__)
+
+
+# RoBERTa - simple example
+
+
+class RobertaClassificationHeadCustomSimple(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        hidden_size = config.hidden_size
+        self.dense_1 = nn.Linear(hidden_size, 2 * hidden_size)
+        self.dense_2 = nn.Linear(2 * hidden_size, hidden_size)
+        classifier_dropout = (
+            config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
+        )
+        self.dropout = nn.Dropout(classifier_dropout)
+        self.out_proj = nn.Linear(hidden_size, config.num_labels)
+
+    def forward(self, features, **kwargs):
+        x = features[:, 0, :]  # take <s> token (equiv. to [CLS])
+
+        x = self.dense_1(x)
+        x = torch.relu(x)
+        x = self.dropout(x)
+
+        x = self.dense_2(x)
+        x = torch.relu(x)
+        x = self.dropout(x)
+
+        x = self.out_proj(x)
+        return x
+
+
+class RobertaForSequenceClassificationCustomSimple(RobertaForSequenceClassification):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.config = config
+
+        self.roberta = RobertaModel(config, add_pooling_layer=False)
+        self.classifier = RobertaClassificationHeadCustomSimple(config)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+
+# RoBERTa - Example 1
+
+
+class RobertaClassificationHeadCustom(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        hidden_size = config.hidden_size * 2
+
+        self.dense_1 = nn.Linear(hidden_size, 2 * hidden_size)
+        self.dense_2 = nn.Linear(2 * hidden_size, hidden_size)
+        classifier_dropout = (
+            config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
+        )
+        self.dropout = nn.Dropout(classifier_dropout)
+        self.out_proj = nn.Linear(hidden_size, config.num_labels)
+
+    def forward(self, features, **kwargs):
+        if "hidden_states" in kwargs and kwargs["hidden_states"] is not None:
+            x = torch.cat(
+                (
+                    features[:, 0, :],
+                    # take <s> token (equiv. to [CLS]) from hidden states from last layer
+                    kwargs["hidden_states"][-2][:, 0, :],
+                ),
+                dim=1,
+            )
+        else:
+            raise RuntimeError("Missing hidden state to process forward")
+
+        x = self.dense_1(x)
+        x = torch.relu(x)
+        x = self.dropout(x)
+
+        x = self.dense_2(x)
+        x = torch.relu(x)
+        x = self.dropout(x)
+
+        x = self.out_proj(x)
+        return x
+
+
+class RobertaForSequenceClassificationCustom(RobertaForSequenceClassification):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.config = config
+
+        self.roberta = RobertaModel(config, add_pooling_layer=False)
+        self.classifier = RobertaClassificationHeadCustom(config)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        token_type_ids: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.roberta(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=self.config.use_hidden_states,
+            return_dict=return_dict,
+        )
+        sequence_output = outputs[0]
+        logits = self.classifier(sequence_output, hidden_states=outputs.hidden_states)
+        del outputs.hidden_states
+
+        loss = None
+        if labels is not None:
+            # move labels to correct device to enable model parallelism
+            labels = labels.to(logits.device)
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+# RoBERTa - Example 2
+
+
+class RobertaClassificationHeadCustomAlternative(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        hidden_size = config.hidden_size
+
+        self.dense_1_input = nn.Linear(hidden_size, 2 * hidden_size)
+        self.dense_1_hidden = nn.Linear(hidden_size, 2 * hidden_size)
+        self.dense_2 = nn.Linear(4 * hidden_size, hidden_size)
+        classifier_dropout = (
+            config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
+        )
+        self.dropout = nn.Dropout(classifier_dropout)
+        self.out_proj = nn.Linear(hidden_size, config.num_labels)
+
+    def forward(self, features, **kwargs):
+        x = features[:, 0, :]  # take <s> token (equiv. to [CLS])
+        if "hidden_states" in kwargs and kwargs["hidden_states"] is not None:
+            # take <s> token (equiv. to [CLS]) from hidden states from last layer
+            hidden = kwargs["hidden_states"][-1][:, 0, :]
+        else:
+            raise RuntimeError("Missing hidden state to process forward")
+
+        x = self.dense_1_input(x)
+        x = torch.relu(x)
+        x = self.dropout(x)
+
+        hidden = self.dense_1_hidden(hidden)
+        hidden = torch.relu(hidden)
+        hidden = self.dropout(hidden)
+
+        x = torch.cat((x, hidden), dim=1)
+        x = self.dense_2(x)
+        x = torch.relu(x)
+        x = self.dropout(x)
+
+        x = self.out_proj(x)
+        return x
+
+
+class RobertaForSequenceClassificationCustomAlternative(RobertaForSequenceClassification):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.config = config
+
+        self.roberta = RobertaModel(config, add_pooling_layer=False)
+        self.classifier = RobertaClassificationHeadCustomAlternative(config)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        token_type_ids: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.roberta(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=self.config.use_hidden_states,
+            return_dict=return_dict,
+        )
+        sequence_output = outputs[0]
+        logits = self.classifier(sequence_output, hidden_states=outputs.hidden_states)
+        del outputs.hidden_states
+
+        loss = None
+        if labels is not None:
+            # move labels to correct device to enable model parallelism
+            labels = labels.to(logits.device)
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+# GPT-2 - simple example #
+
+
+class GPT2ClassificationHeadCustomSimple(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        hidden_size = config.n_embd
+        self.dense_1 = nn.Linear(hidden_size, 2 * hidden_size)
+        self.dense_2 = nn.Linear(2 * hidden_size, hidden_size)
+        self.dropout = nn.Dropout(config.resid_pdrop)
+        self.out_proj = nn.Linear(hidden_size, config.num_labels, bias=False)
+
+    def forward(self, x):
+        x = self.dense_1(x)
+        x = torch.relu(x)
+        x = self.dropout(x)
+
+        x = self.dense_2(x)
+        x = torch.relu(x)
+        x = self.dropout(x)
+
+        x = self.out_proj(x)
+        return x
+
+
+class GPT2ForSequenceClassificationCustomSimple(GPT2ForSequenceClassification):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.transformer = GPT2Model(config)
+        self.score = GPT2ClassificationHeadCustomSimple(config)
+
+        # Model parallel
+        self.model_parallel = False
+        self.device_map = None
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+
+# GPT-2 - Example 1 #
+
+
+class GPT2ClassificationHeadCustom(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        hidden_size = config.n_embd
+        self.dense_1_input = nn.Linear(hidden_size, 2 * hidden_size)
+        self.dense_1_hidden = nn.Linear(hidden_size, 2 * hidden_size)
+        self.dense_2 = nn.Linear(4 * hidden_size, hidden_size)
+        self.dropout = nn.Dropout(config.resid_pdrop)
+        self.out_proj = nn.Linear(hidden_size, config.num_labels, bias=False)
+
+    def forward(self, x, **kwargs):
+        if "hidden_states" in kwargs and kwargs["hidden_states"] is not None:
+            # Get hidden states from last layer
+            hidden = kwargs["hidden_states"][-1]
+        else:
+            raise RuntimeError("Missing hidden state to process forward")
+
+        x = self.dense_1_input(x)
+        x = torch.relu(x)
+        x = self.dropout(x)
+
+        hidden = self.dense_1_hidden(hidden)
+        hidden = torch.relu(hidden)
+        hidden = self.dropout(hidden)
+
+        x = torch.cat((x, hidden), dim=2)
+        x = self.dense_2(x)
+        x = torch.relu(x)
+        x = self.dropout(x)
+
+        x = self.out_proj(x)
+        return x
+
+
+class GPT2ForSequenceClassificationCustom(GPT2ForSequenceClassification):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.transformer = GPT2Model(config)
+        self.score = GPT2ClassificationHeadCustom(config)
+
+        # Model parallel
+        self.model_parallel = False
+        self.device_map = None
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        token_type_ids: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        transformer_outputs = self.transformer(
+            input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=self.config.use_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = transformer_outputs[0]
+        logits = self.score(hidden_states, hidden_states=transformer_outputs.hidden_states)
+        del transformer_outputs.hidden_states
+
+        if input_ids is not None:
+            batch_size, sequence_length = input_ids.shape[:2]
+        else:
+            batch_size, sequence_length = inputs_embeds.shape[:2]
+
+        assert (
+            self.config.pad_token_id is not None or batch_size == 1
+        ), "Cannot handle batch sizes > 1 if no padding token is defined."
+        if self.config.pad_token_id is None:
+            sequence_lengths = -1
+        else:
+            if input_ids is not None:
+                sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).long().argmax(-1) - 1).to(
+                    logits.device
+                )
+            else:
+                sequence_lengths = -1
+                LOGGER.warning(
+                    f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
+                    "unexpected if using padding tokens in conjunction with `inputs_embeds.`"
+                )
+
+        pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
+
+        loss = None
+        if labels is not None:
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(pooled_logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(pooled_logits, labels)
+        if not return_dict:
+            output = (pooled_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )

prepare_ag_news.py

@@ -0,0 +1,104 @@
+#!/usr/bin/env python3
+
+import json
+import logging
+from pathlib import Path
+
+from datasets import load_dataset
+
+LOGGER = logging.getLogger(__name__)
+
+def save_limited_data(file_path: Path) -> None:
+    all_text = file_path.read_text().split("\n")
+    text = all_text[:2500] + all_text[-2500:]
+    save_path = file_path.parent / f"{file_path.stem}-5k.json"
+    save_path.write_text("\n".join(text))
+    LOGGER.info(f"Saved limited ({len(text)}) version in: {save_path}")
+
+def save_as_translations(original_save_path: Path, data_to_save: list[dict], label_translation: dict) -> None:
+    file_name = "s2s-" + original_save_path.name
+    file_path = original_save_path.parent / file_name
+
+    LOGGER.info(f"Saving into: {file_path}")
+    with open(file_path, "wt") as f_write:
+        for data_line in data_to_save:
+            label = data_line["label"]
+            new_label = label_translation.get(label, str(label))
+            data_line["label"] = new_label
+            data_line_str = json.dumps(data_line)
+            f_write.write(f"{data_line_str}\n")
+
+    save_limited_data(file_path)
+
+def main() -> None:
+    logging.basicConfig(level=logging.INFO)
+    loaded_data = load_dataset("ag_news")
+    LOGGER.info(f"Loaded dataset ag_news: {loaded_data}")
+
+    save_path = Path("data/")
+    save_train_path = save_path / "train.json"
+    save_valid_path = save_path / "valid.json"
+    save_test_path = save_path / "test.json"
+    if not save_path.exists():
+        save_path.mkdir()
+
+    # Read train and validation data
+    data_train, data_valid, data_test = [], [], []
+    for source_data, dataset in [
+        (loaded_data["train"], data_train),
+        (loaded_data["test"], data_valid),
+    ]:
+        for i, data in enumerate(source_data):
+            data_line = {
+                "label": int(data["label"]),
+                "text": data["text"],
+            }
+            dataset.append(data_line)
+    LOGGER.info(f"Train: {len(data_train):6d}")
+
+    # Split validation set into 2 classes for validation and test splitting
+    data_class_1, data_class_2, data_class_3, data_class_4 = [], [], [], []
+    for data in data_valid:
+        label = data["label"]
+        if label == 0:
+            data_class_1.append(data)
+        elif label == 1:
+            data_class_2.append(data)
+        elif label == 2:
+            data_class_3.append(data)
+        elif label == 3:
+            data_class_4.append(data)
+
+    LOGGER.info(f"Label 1: {len(data_class_1):6d}")
+    LOGGER.info(f"Label 2: {len(data_class_2):6d}")
+    LOGGER.info(f"Label 3: {len(data_class_3):6d}")
+    LOGGER.info(f"Label 3: {len(data_class_4):6d}")
+
+    # Split 2 classes into validation and test
+    size_half_class_1 = int(len(data_class_1) / 2)
+    size_half_class_2 = int(len(data_class_2) / 2)
+    size_half_class_3 = int(len(data_class_3) / 2)
+    size_half_class_4 = int(len(data_class_4) / 2)
+    data_valid = data_class_1[:size_half_class_1] + data_class_2[:size_half_class_2] + data_class_3[:size_half_class_3] + data_class_4[:size_half_class_4]
+    data_test = data_class_1[size_half_class_1:] + data_class_2[size_half_class_2:] + data_class_3[size_half_class_3:] + data_class_4[size_half_class_4:]
+    LOGGER.info(f"Valid: {len(data_valid):6d}")
+    LOGGER.info(f"Test : {len(data_test):6d}")
+
+    # Save files
+    for file_path, data_to_save in [
+        (save_train_path, data_train),
+        (save_valid_path, data_valid),
+        (save_test_path, data_test),
+    ]:
+        LOGGER.info(f"Saving into: {file_path}")
+        label_translation = {0: "negative", 1: "positive"}
+        with open(file_path, "wt") as f_write:
+            for data_line in data_to_save:
+                data_line_str = json.dumps(data_line)
+                f_write.write(f"{data_line_str}\n")
+
+        save_limited_data(file_path)
+        save_as_translations(file_path, data_to_save, label_translation)
+
+if __name__ == "__main__":
+    main()

run_glue.py

@@ -0,0 +1,692 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2020 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" Finetuning the library models for sequence classification on GLUE."""
+# You can also adapt this script on your own text classification task. Pointers for this are left as comments.
+
+import logging
+import os
+import random
+import sys
+import warnings
+from dataclasses import dataclass, field
+from typing import Optional
+
+import datasets
+import evaluate
+import numpy as np
+from datasets import load_dataset
+
+import transformers
+from transformers import (
+    AutoConfig,
+    AutoModelForSequenceClassification,
+    AutoTokenizer,
+    DataCollatorWithPadding,
+    EvalPrediction,
+    HfArgumentParser,
+    PretrainedConfig,
+    Trainer,
+    TrainingArguments,
+    default_data_collator,
+    set_seed,
+)
+from transformers.trainer_utils import get_last_checkpoint
+from transformers.utils import check_min_version, send_example_telemetry
+from transformers.utils.versions import require_version
+
+from custom_model import RobertaForSequenceClassificationCustomSimple, RobertaForSequenceClassificationCustom, \
+    RobertaForSequenceClassificationCustomAlternative, GPT2ForSequenceClassificationCustomSimple, \
+    GPT2ForSequenceClassificationCustom
+from save_on_end_epoch import SaveOnEndEpochTrainerCallback
+
+MODEL_NAME_TO_CLASS = {
+    "roberta_simple": RobertaForSequenceClassificationCustomSimple,
+    "roberta_hidden": RobertaForSequenceClassificationCustom,
+    "roberta_hidden_v2": RobertaForSequenceClassificationCustomAlternative,
+    "gpt2_simple": GPT2ForSequenceClassificationCustomSimple,
+    "gpt2_hidden": GPT2ForSequenceClassificationCustom,
+}
+
+# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+check_min_version("4.34.0")
+
+require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
+
+task_to_keys = {
+    "cola": ("sentence", None),
+    "mnli": ("premise", "hypothesis"),
+    "mrpc": ("sentence1", "sentence2"),
+    "qnli": ("question", "sentence"),
+    "qqp": ("question1", "question2"),
+    "rte": ("sentence1", "sentence2"),
+    "sst2": ("sentence", None),
+    "stsb": ("sentence1", "sentence2"),
+    "wnli": ("sentence1", "sentence2"),
+}
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class DataTrainingArguments:
+    """
+    Arguments pertaining to what data we are going to input our model for training and eval.
+
+    Using `HfArgumentParser` we can turn this class
+    into argparse arguments to be able to specify them on
+    the command line.
+    """
+
+    task_name: Optional[str] = field(
+        default=None,
+        metadata={"help": "The name of the task to train on: " + ", ".join(task_to_keys.keys())},
+    )
+    dataset_name: Optional[str] = field(
+        default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
+    )
+    dataset_config_name: Optional[str] = field(
+        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
+    )
+    max_seq_length: int = field(
+        default=128,
+        metadata={
+            "help": (
+                "The maximum total input sequence length after tokenization. Sequences longer "
+                "than this will be truncated, sequences shorter will be padded."
+            )
+        },
+    )
+    overwrite_cache: bool = field(
+        default=False, metadata={"help": "Overwrite the cached preprocessed datasets or not."}
+    )
+    pad_to_max_length: bool = field(
+        default=True,
+        metadata={
+            "help": (
+                "Whether to pad all samples to `max_seq_length`. "
+                "If False, will pad the samples dynamically when batching to the maximum length in the batch."
+            )
+        },
+    )
+    max_train_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "For debugging purposes or quicker training, truncate the number of training examples to this "
+                "value if set."
+            )
+        },
+    )
+    max_eval_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
+                "value if set."
+            )
+        },
+    )
+    max_predict_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "For debugging purposes or quicker training, truncate the number of prediction examples to this "
+                "value if set."
+            )
+        },
+    )
+    train_file: Optional[str] = field(
+        default=None, metadata={"help": "A csv or a json file containing the training data."}
+    )
+    validation_file: Optional[str] = field(
+        default=None, metadata={"help": "A csv or a json file containing the validation data."}
+    )
+    test_file: Optional[str] = field(default=None, metadata={"help": "A csv or a json file containing the test data."})
+
+    def __post_init__(self):
+        if self.task_name is not None:
+            self.task_name = self.task_name.lower()
+            if self.task_name not in task_to_keys.keys():
+                raise ValueError("Unknown task, you should pick one in " + ",".join(task_to_keys.keys()))
+        elif self.dataset_name is not None:
+            pass
+        elif self.train_file is None or self.validation_file is None:
+            raise ValueError("Need either a GLUE task, a training/validation file or a dataset name.")
+        else:
+            train_extension = self.train_file.split(".")[-1]
+            assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
+            validation_extension = self.validation_file.split(".")[-1]
+            assert (
+                validation_extension == train_extension
+            ), "`validation_file` should have the same extension (csv or json) as `train_file`."
+
+
+@dataclass
+class ModelArguments:
+    """
+    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
+    """
+
+    model_name_or_path: str = field(
+        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
+    )
+    config_name: Optional[str] = field(
+        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
+    )
+    tokenizer_name: Optional[str] = field(
+        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
+    )
+    cache_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
+    )
+    use_fast_tokenizer: bool = field(
+        default=True,
+        metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
+    )
+    model_revision: str = field(
+        default="main",
+        metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
+    )
+    token: str = field(
+        default=None,
+        metadata={
+            "help": (
+                "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
+                "generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
+            )
+        },
+    )
+    use_auth_token: bool = field(
+        default=None,
+        metadata={
+            "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`."
+        },
+    )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+                "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+                "execute code present on the Hub on your local machine."
+            )
+        },
+    )
+    ignore_mismatched_sizes: bool = field(
+        default=False,
+        metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."},
+    )
+    custom_model: str = field(
+        default=None,
+        metadata={
+            "help": "Use custom implementation from available list",
+            "choices": list(MODEL_NAME_TO_CLASS.keys()),
+        },
+    )
+
+
+def main():
+    # See all possible arguments in src/transformers/training_args.py
+    # or by passing the --help flag to this script.
+    # We now keep distinct sets of args, for a cleaner separation of concerns.
+
+    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
+    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
+        # If we pass only one argument to the script and it's the path to a json file,
+        # let's parse it to get our arguments.
+        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
+    else:
+        model_args, data_args, training_args = parser.parse_args_into_dataclasses()
+
+    if model_args.use_auth_token is not None:
+        warnings.warn("The `use_auth_token` argument is deprecated and will be removed in v4.34.", FutureWarning)
+        if model_args.token is not None:
+            raise ValueError("`token` and `use_auth_token` are both specified. Please set only the argument `token`.")
+        model_args.token = model_args.use_auth_token
+
+    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
+    # information sent is the one passed as arguments along with your Python/PyTorch versions.
+    send_example_telemetry("run_glue", model_args, data_args)
+
+    # Setup logging
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        handlers=[logging.StreamHandler(sys.stdout)],
+    )
+
+    if training_args.should_log:
+        # The default of training_args.log_level is passive, so we set log level at info here to have that default.
+        transformers.utils.logging.set_verbosity_info()
+
+    log_level = training_args.get_process_log_level()
+    logger.setLevel(log_level)
+    datasets.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.enable_default_handler()
+    transformers.utils.logging.enable_explicit_format()
+
+    # Log on each process the small summary:
+    logger.warning(
+        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+        + f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
+    )
+    logger.info(f"Training/evaluation parameters {training_args}")
+
+    # Detecting last checkpoint.
+    last_checkpoint = None
+    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
+        last_checkpoint = get_last_checkpoint(training_args.output_dir)
+        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
+            raise ValueError(
+                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
+                "Use --overwrite_output_dir to overcome."
+            )
+        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
+            logger.info(
+                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
+                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
+            )
+
+    # Set seed before initializing model.
+    set_seed(training_args.seed)
+
+    # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
+    # or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
+    #
+    # For CSV/JSON files, this script will use as labels the column called 'label' and as pair of sentences the
+    # sentences in columns called 'sentence1' and 'sentence2' if such column exists or the first two columns not named
+    # label if at least two columns are provided.
+    #
+    # If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
+    # single column. You can easily tweak this behavior (see below)
+    #
+    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
+    # download the dataset.
+    if data_args.task_name is not None:
+        # Downloading and loading a dataset from the hub.
+        raw_datasets = load_dataset(
+            "glue",
+            data_args.task_name,
+            cache_dir=model_args.cache_dir,
+            token=model_args.token,
+        )
+    elif data_args.dataset_name is not None:
+        # Downloading and loading a dataset from the hub.
+        raw_datasets = load_dataset(
+            data_args.dataset_name,
+            data_args.dataset_config_name,
+            cache_dir=model_args.cache_dir,
+            token=model_args.token,
+        )
+    else:
+        # Loading a dataset from your local files.
+        # CSV/JSON training and evaluation files are needed.
+        data_files = {"train": data_args.train_file, "validation": data_args.validation_file}
+
+        # Get the test dataset: you can provide your own CSV/JSON test file (see below)
+        # when you use `do_predict` without specifying a GLUE benchmark task.
+        if training_args.do_predict:
+            if data_args.test_file is not None:
+                train_extension = data_args.train_file.split(".")[-1]
+                test_extension = data_args.test_file.split(".")[-1]
+                assert (
+                    test_extension == train_extension
+                ), "`test_file` should have the same extension (csv or json) as `train_file`."
+                data_files["test"] = data_args.test_file
+            else:
+                raise ValueError("Need either a GLUE task or a test file for `do_predict`.")
+
+        for key in data_files.keys():
+            logger.info(f"load a local file for {key}: {data_files[key]}")
+
+        if data_args.train_file.endswith(".csv"):
+            # Loading a dataset from local csv files
+            raw_datasets = load_dataset(
+                "csv",
+                data_files=data_files,
+                cache_dir=model_args.cache_dir,
+                token=model_args.token,
+            )
+        else:
+            # Loading a dataset from local json files
+            raw_datasets = load_dataset(
+                "json",
+                data_files=data_files,
+                cache_dir=model_args.cache_dir,
+                token=model_args.token,
+            )
+    # See more about loading any type of standard or custom dataset at
+    # https://huggingface.co/docs/datasets/loading_datasets.html.
+
+    # Labels
+    if data_args.task_name is not None:
+        is_regression = data_args.task_name == "stsb"
+        if not is_regression:
+            label_list = raw_datasets["train"].features["label"].names
+            num_labels = len(label_list)
+        else:
+            num_labels = 1
+    else:
+        # Trying to have good defaults here, don't hesitate to tweak to your needs.
+        is_regression = raw_datasets["train"].features["label"].dtype in ["float32", "float64"]
+        if is_regression:
+            num_labels = 1
+        else:
+            # A useful fast method:
+            # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.unique
+            label_list = raw_datasets["train"].unique("label")
+            label_list.sort()  # Let's sort it for determinism
+            num_labels = len(label_list)
+
+    # Load pretrained model and tokenizer
+    #
+    # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
+    # download model & vocab.
+    config = AutoConfig.from_pretrained(
+        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
+        num_labels=num_labels,
+        finetuning_task=data_args.task_name,
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
+    )
+    tokenizer = AutoTokenizer.from_pretrained(
+        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
+        cache_dir=model_args.cache_dir,
+        use_fast=model_args.use_fast_tokenizer,
+        revision=model_args.model_revision,
+        token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
+    )
+    custom_model = model_args.custom_model
+    if custom_model is not None:
+        # Check model and implementation is the same
+        if 'roberta' in custom_model and 'roberta' not in model_args.model_name_or_path:
+            raise RuntimeError('Model and custom implementation should be the same type: RoBERTa')
+        elif 'gpt2' in custom_model and 'gpt2' not in model_args.model_name_or_path:
+            raise RuntimeError('Model and custom implementation should be the same type: GPT-2')
+
+        # Set custom configuration in model configuration
+        config.use_hidden_states = 'hidden' in custom_model
+        logger.info(f'Using hidden states in model: {config.use_hidden_states}')
+
+        # Get class to initialize model
+        model_cls = MODEL_NAME_TO_CLASS[custom_model]
+    else:
+        model_cls = AutoModelForSequenceClassification
+
+    logger.info(f'Using implementation from class: {model_cls.__name__}')
+    model = model_cls.from_pretrained(
+        model_args.model_name_or_path,
+        from_tf=bool(".ckpt" in model_args.model_name_or_path),
+        config=config,
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
+        ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,
+    )
+
+    if 'gpt2' in tokenizer.name_or_path and tokenizer.pad_token is None:
+        logger.info(f'Set PAD token to EOS: {tokenizer.eos_token}')
+        tokenizer._pad_token = tokenizer.eos_token
+        model.config.pad_token_id = model.config.eos_token_id
+
+    # Preprocessing the raw_datasets
+    if data_args.task_name is not None:
+        sentence1_key, sentence2_key = task_to_keys[data_args.task_name]
+    else:
+        # Again, we try to have some nice defaults but don't hesitate to tweak to your use case.
+        non_label_column_names = [name for name in raw_datasets["train"].column_names if name != "label"]
+        if "sentence1" in non_label_column_names and "sentence2" in non_label_column_names:
+            sentence1_key, sentence2_key = "sentence1", "sentence2"
+        else:
+            if len(non_label_column_names) >= 2:
+                sentence1_key, sentence2_key = non_label_column_names[:2]
+            else:
+                sentence1_key, sentence2_key = non_label_column_names[0], None
+
+    # Padding strategy
+    if data_args.pad_to_max_length:
+        padding = "max_length"
+    else:
+        # We will pad later, dynamically at batch creation, to the max sequence length in each batch
+        padding = False
+
+    # Some models have set the order of the labels to use, so let's make sure we do use it.
+    label_to_id = None
+    if (
+        model.config.label2id != PretrainedConfig(num_labels=num_labels).label2id
+        and data_args.task_name is not None
+        and not is_regression
+    ):
+        # Some have all caps in their config, some don't.
+        label_name_to_id = {k.lower(): v for k, v in model.config.label2id.items()}
+        if sorted(label_name_to_id.keys()) == sorted(label_list):
+            label_to_id = {i: int(label_name_to_id[label_list[i]]) for i in range(num_labels)}
+        else:
+            logger.warning(
+                "Your model seems to have been trained with labels, but they don't match the dataset: ",
+                f"model labels: {sorted(label_name_to_id.keys())}, dataset labels: {sorted(label_list)}."
+                "\nIgnoring the model labels as a result.",
+            )
+    elif data_args.task_name is None and not is_regression:
+        label_to_id = {v: i for i, v in enumerate(label_list)}
+
+    if label_to_id is not None:
+        model.config.label2id = label_to_id
+        model.config.id2label = {id: label for label, id in config.label2id.items()}
+    elif data_args.task_name is not None and not is_regression:
+        model.config.label2id = {l: i for i, l in enumerate(label_list)}
+        model.config.id2label = {id: label for label, id in config.label2id.items()}
+
+    if data_args.max_seq_length > tokenizer.model_max_length:
+        logger.warning(
+            f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"
+            f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
+        )
+    max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)
+
+    def preprocess_function(examples):
+        # Tokenize the texts
+        args = (
+            (examples[sentence1_key],) if sentence2_key is None else (examples[sentence1_key], examples[sentence2_key])
+        )
+        result = tokenizer(*args, padding=padding, max_length=max_seq_length, truncation=True)
+
+        # Map labels to IDs (not necessary for GLUE tasks)
+        if label_to_id is not None and "label" in examples:
+            result["label"] = [(label_to_id[l] if l != -1 else -1) for l in examples["label"]]
+        return result
+
+    with training_args.main_process_first(desc="dataset map pre-processing"):
+        raw_datasets = raw_datasets.map(
+            preprocess_function,
+            batched=True,
+            load_from_cache_file=not data_args.overwrite_cache,
+            desc="Running tokenizer on dataset",
+        )
+    if training_args.do_train:
+        if "train" not in raw_datasets:
+            raise ValueError("--do_train requires a train dataset")
+        train_dataset = raw_datasets["train"]
+        if data_args.max_train_samples is not None:
+            max_train_samples = min(len(train_dataset), data_args.max_train_samples)
+            train_dataset = train_dataset.select(range(max_train_samples))
+
+    if training_args.do_eval:
+        if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
+            raise ValueError("--do_eval requires a validation dataset")
+        eval_dataset = raw_datasets["validation_matched" if data_args.task_name == "mnli" else "validation"]
+        if data_args.max_eval_samples is not None:
+            max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
+            eval_dataset = eval_dataset.select(range(max_eval_samples))
+
+    if training_args.do_predict or data_args.task_name is not None or data_args.test_file is not None:
+        if "test" not in raw_datasets and "test_matched" not in raw_datasets:
+            raise ValueError("--do_predict requires a test dataset")
+        predict_dataset = raw_datasets["test_matched" if data_args.task_name == "mnli" else "test"]
+        if data_args.max_predict_samples is not None:
+            max_predict_samples = min(len(predict_dataset), data_args.max_predict_samples)
+            predict_dataset = predict_dataset.select(range(max_predict_samples))
+
+    # Log a few random samples from the training set:
+    if training_args.do_train:
+        for index in random.sample(range(len(train_dataset)), 3):
+            logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
+
+    # Get the metric function
+    if data_args.task_name is not None:
+        metric = evaluate.load("glue", data_args.task_name)
+    elif is_regression:
+        metric = evaluate.load("mse")
+    else:
+        metric = evaluate.load("accuracy")
+
+    # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
+    # predictions and label_ids field) and has to return a dictionary string to float.
+    def compute_metrics(p: EvalPrediction):
+        preds = p.predictions[0] if isinstance(p.predictions, tuple) else p.predictions
+        preds = np.squeeze(preds) if is_regression else np.argmax(preds, axis=1)
+        result = metric.compute(predictions=preds, references=p.label_ids)
+        if len(result) > 1:
+            result["combined_score"] = np.mean(list(result.values())).item()
+        return result
+
+    # Data collator will default to DataCollatorWithPadding when the tokenizer is passed to Trainer, so we change it if
+    # we already did the padding.
+    if data_args.pad_to_max_length:
+        data_collator = default_data_collator
+    elif training_args.fp16:
+        data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8)
+    else:
+        data_collator = None
+
+    # Initialize our Trainer
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        train_dataset=train_dataset if training_args.do_train else None,
+        eval_dataset=eval_dataset if training_args.do_eval else None,
+        compute_metrics=compute_metrics,
+        tokenizer=tokenizer,
+        data_collator=data_collator,
+        callbacks=[SaveOnEndEpochTrainerCallback()]
+    )
+
+    # Training
+    if training_args.do_train:
+        checkpoint = None
+        if training_args.resume_from_checkpoint is not None:
+            checkpoint = training_args.resume_from_checkpoint
+        elif last_checkpoint is not None:
+            checkpoint = last_checkpoint
+        train_result = trainer.train(resume_from_checkpoint=checkpoint)
+        metrics = train_result.metrics
+        max_train_samples = (
+            data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
+        )
+        metrics["train_samples"] = min(max_train_samples, len(train_dataset))
+
+        trainer.save_model()  # Saves the tokenizer too for easy upload
+
+        trainer.log_metrics("train", metrics)
+        trainer.save_metrics("train", metrics)
+        trainer.save_state()
+
+    # Evaluation
+    if training_args.do_eval:
+        logger.info("*** Evaluate ***")
+
+        # Loop to handle MNLI double evaluation (matched, mis-matched)
+        tasks = [data_args.task_name]
+        eval_datasets = [eval_dataset]
+        if data_args.task_name == "mnli":
+            tasks.append("mnli-mm")
+            valid_mm_dataset = raw_datasets["validation_mismatched"]
+            if data_args.max_eval_samples is not None:
+                max_eval_samples = min(len(valid_mm_dataset), data_args.max_eval_samples)
+                valid_mm_dataset = valid_mm_dataset.select(range(max_eval_samples))
+            eval_datasets.append(valid_mm_dataset)
+            combined = {}
+
+        for eval_dataset, task in zip(eval_datasets, tasks):
+            metrics = trainer.evaluate(eval_dataset=eval_dataset)
+
+            max_eval_samples = (
+                data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
+            )
+            metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
+
+            if task == "mnli-mm":
+                metrics = {k + "_mm": v for k, v in metrics.items()}
+            if task is not None and "mnli" in task:
+                combined.update(metrics)
+
+            trainer.log_metrics("eval", metrics)
+            trainer.save_metrics("eval", combined if task is not None and "mnli" in task else metrics)
+
+    if training_args.do_predict:
+        logger.info("*** Predict ***")
+
+        # Loop to handle MNLI double evaluation (matched, mis-matched)
+        tasks = [data_args.task_name]
+        predict_datasets = [predict_dataset]
+        if data_args.task_name == "mnli":
+            tasks.append("mnli-mm")
+            predict_datasets.append(raw_datasets["test_mismatched"])
+
+        for predict_dataset, task in zip(predict_datasets, tasks):
+            # Removing the `label` columns because it contains -1 and Trainer won't like that.
+            predict_dataset = predict_dataset.remove_columns("label")
+            predictions = trainer.predict(predict_dataset, metric_key_prefix="predict").predictions
+            predictions = np.squeeze(predictions) if is_regression else np.argmax(predictions, axis=1)
+
+            output_predict_file = os.path.join(training_args.output_dir, f"predict_results_{task}.txt")
+            if trainer.is_world_process_zero():
+                with open(output_predict_file, "w") as writer:
+                    logger.info(f"***** Predict results {task} *****")
+                    writer.write("index\tprediction\n")
+                    for index, item in enumerate(predictions):
+                        if is_regression:
+                            writer.write(f"{index}\t{item:3.3f}\n")
+                        else:
+                            item = label_list[item]
+                            writer.write(f"{index}\t{item}\n")
+
+    kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "text-classification"}
+    if data_args.task_name is not None:
+        kwargs["language"] = "en"
+        kwargs["dataset_tags"] = "glue"
+        kwargs["dataset_args"] = data_args.task_name
+        kwargs["dataset"] = f"GLUE {data_args.task_name.upper()}"
+
+    if training_args.push_to_hub:
+        trainer.push_to_hub(**kwargs)
+    else:
+        trainer.create_model_card(**kwargs)
+
+
+def _mp_fn(index):
+    # For xla_spawn (TPUs)
+    main()
+
+
+if __name__ == "__main__":
+    main()

save_on_end_epoch.py

@@ -0,0 +1,25 @@
+from pathlib import Path
+from typing import Any
+
+from transformers import TrainerCallback, TrainerControl, TrainerState, TrainingArguments
+from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR
+
+
+class SaveOnEndEpochTrainerCallback(TrainerCallback):
+    def on_epoch_end(
+        self, args: TrainingArguments, state: TrainerState, control: TrainerControl, **kwargs: Any
+    ) -> None:
+        training_steps = state.global_step
+
+        # Do not save if was not trained
+        if training_steps <= 0:
+            return
+
+        save_path = Path(args.output_dir) / f"{PREFIX_CHECKPOINT_DIR}-{training_steps}"
+        # Skip if checkpoint exists - no need to save
+        if save_path.exists():
+            return
+
+        control.should_log = True
+        control.should_evaluate = True
+        control.should_save = True