projekt-glebokie/ROBERTA.ipynb at a9e6944d12c0b2c0e2868d79fa58fdbb3407176d

Andrzej Preibisz 8cc5715a73 Init commit + roberta

2023-02-08 21:45:52 +01:00

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! pip install datasets transformers torch scikit-learn

Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
device

'cuda'

from datasets import load_dataset
from transformers import RobertaConfig, RobertaForSequenceClassification, RobertaTokenizer, Trainer, TrainingArguments
import torch
from sklearn.metrics import accuracy_score, precision_recall_fscore_support


# Initializing a RoBERTa configuration
configuration = RobertaConfig(vocab_size=2048)
model = RobertaForSequenceClassification(configuration).from_pretrained("roberta-base", num_labels=6)

model.cuda()
tokenizer = RobertaTokenizer.from_pretrained("roberta-base")


def tokenization(batched_text):
    return tokenizer(batched_text['text'], padding = True, truncation=True)

def compute_metrics(pred):
    labels = pred.label_ids
    preds = pred.predictions.argmax(-1)
    precision, recall, f1, _ = precision_recall_fscore_support(labels, preds, average='micro')
    acc = accuracy_score(labels, preds)
    return {
        'accuracy': acc,
        'f1': f1,
        'precision': precision,
        'recall': recall
    }

# Accessing the model configuration
configuration = model.config
dataset = load_dataset("emotion")

train_data= dataset["train"]
test_data = dataset["test"]
eval_data = dataset["validation"]

train_data = train_data.map(tokenization, batched=True, batch_size=len(train_data))
eval_data = eval_data.map(tokenization, batched=True, batch_size=len(eval_data))

train_data.set_format('torch', columns=['input_ids', 'attention_mask', 'label'])
eval_data.set_format('torch', columns=['input_ids', 'attention_mask', 'label'])


training_args = TrainingArguments(
    output_dir="./output",
    num_train_epochs=5,
    per_device_train_batch_size = 32,
    gradient_accumulation_steps = 16,    
    per_device_eval_batch_size= 32,
    evaluation_strategy = "epoch",
    save_strategy = "epoch",
    disable_tqdm = False, 
    load_best_model_at_end=True,
    warmup_steps=10,
    weight_decay=0.01,
    logging_steps = 4,
    fp16 = True,
    dataloader_num_workers = 2,
    run_name = 'roberta-classification'
)

trainer = Trainer(
    model=model,
    args=training_args,
    compute_metrics=compute_metrics,
    train_dataset=train_data,
    eval_dataset=eval_data,

)

trainer.train()

#for line in test_data:

#  inputs = tokenizer(line.get('text'), return_tensors="pt")
#  labels = torch.tensor([1]).unsqueeze(0)  # Batch size 1
#  outputs = model(**inputs, labels=labels)
#  _, predictions = torch.max(outputs[1], 1)
#  print(predictions)

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Some weights of the model checkpoint at roberta-base were not used when initializing RobertaForSequenceClassification: ['roberta.pooler.dense.bias', 'lm_head.bias', 'lm_head.layer_norm.bias', 'roberta.pooler.dense.weight', 'lm_head.decoder.weight', 'lm_head.layer_norm.weight', 'lm_head.dense.weight', 'lm_head.dense.bias']
- This IS expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.dense.weight', 'classifier.out_proj.weight', 'classifier.dense.bias', 'classifier.out_proj.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

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WARNING:datasets.builder:No config specified, defaulting to: emotion/split

Downloading and preparing dataset emotion/split to /root/.cache/huggingface/datasets/emotion/split/1.0.0/cca5efe2dfeb58c1d098e0f9eeb200e9927d889b5a03c67097275dfb5fe463bd...

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Dataset emotion downloaded and prepared to /root/.cache/huggingface/datasets/emotion/split/1.0.0/cca5efe2dfeb58c1d098e0f9eeb200e9927d889b5a03c67097275dfb5fe463bd. Subsequent calls will reuse this data.

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Using cuda_amp half precision backend
The following columns in the training set don't have a corresponding argument in `RobertaForSequenceClassification.forward` and have been ignored: text. If text are not expected by `RobertaForSequenceClassification.forward`,  you can safely ignore this message.
/usr/local/lib/python3.8/dist-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning
  warnings.warn(
***** Running training *****
  Num examples = 16000
  Num Epochs = 5
  Instantaneous batch size per device = 32
  Total train batch size (w. parallel, distributed & accumulation) = 512
  Gradient Accumulation steps = 16
  Total optimization steps = 155
  Number of trainable parameters = 124650246

[155/155 06:28, Epoch 4/5]

Epoch	Training Loss	Validation Loss	Accuracy	F1	Precision	Recall
0	1.089600	0.782759	0.711500	0.711500	0.711500	0.711500
1	0.380000	0.267057	0.900000	0.900000	0.900000	0.900000
2	0.186000	0.183795	0.923000	0.923000	0.923000	0.923000
3	0.156900	0.163963	0.934500	0.934500	0.934500	0.934500
4	0.130100	0.160831	0.933500	0.933500	0.933500	0.933500

The following columns in the evaluation set don't have a corresponding argument in `RobertaForSequenceClassification.forward` and have been ignored: text. If text are not expected by `RobertaForSequenceClassification.forward`,  you can safely ignore this message.
***** Running Evaluation *****
  Num examples = 2000
  Batch size = 32
Saving model checkpoint to ./output/checkpoint-31
Configuration saved in ./output/checkpoint-31/config.json
Model weights saved in ./output/checkpoint-31/pytorch_model.bin
The following columns in the evaluation set don't have a corresponding argument in `RobertaForSequenceClassification.forward` and have been ignored: text. If text are not expected by `RobertaForSequenceClassification.forward`,  you can safely ignore this message.
***** Running Evaluation *****
  Num examples = 2000
  Batch size = 32
Saving model checkpoint to ./output/checkpoint-62
Configuration saved in ./output/checkpoint-62/config.json
Model weights saved in ./output/checkpoint-62/pytorch_model.bin
The following columns in the evaluation set don't have a corresponding argument in `RobertaForSequenceClassification.forward` and have been ignored: text. If text are not expected by `RobertaForSequenceClassification.forward`,  you can safely ignore this message.
***** Running Evaluation *****
  Num examples = 2000
  Batch size = 32
Saving model checkpoint to ./output/checkpoint-93
Configuration saved in ./output/checkpoint-93/config.json
Model weights saved in ./output/checkpoint-93/pytorch_model.bin
The following columns in the evaluation set don't have a corresponding argument in `RobertaForSequenceClassification.forward` and have been ignored: text. If text are not expected by `RobertaForSequenceClassification.forward`,  you can safely ignore this message.
***** Running Evaluation *****
  Num examples = 2000
  Batch size = 32
Saving model checkpoint to ./output/checkpoint-124
Configuration saved in ./output/checkpoint-124/config.json
Model weights saved in ./output/checkpoint-124/pytorch_model.bin
The following columns in the evaluation set don't have a corresponding argument in `RobertaForSequenceClassification.forward` and have been ignored: text. If text are not expected by `RobertaForSequenceClassification.forward`,  you can safely ignore this message.
***** Running Evaluation *****
  Num examples = 2000
  Batch size = 32
Saving model checkpoint to ./output/checkpoint-155
Configuration saved in ./output/checkpoint-155/config.json
Model weights saved in ./output/checkpoint-155/pytorch_model.bin


Training completed. Do not forget to share your model on huggingface.co/models =)


Loading best model from ./output/checkpoint-155 (score: 0.1608307659626007).

TrainOutput(global_step=155, training_loss=0.495477742725803, metrics={'train_runtime': 393.8758, 'train_samples_per_second': 203.11, 'train_steps_per_second': 0.394, 'total_flos': 3612118290333696.0, 'train_loss': 0.495477742725803, 'epoch': 4.99})

i = 0
sum_preds = 0
model = model.to('cpu')
for line in test_data:

  inputs = tokenizer(line.get('text'), return_tensors="pt")
  labels = torch.tensor([1]).unsqueeze(0)  # Batch size 1
  outputs = model(**inputs, labels=labels)
  _, predictions = torch.max(outputs[1], 1)
  a = int(predictions.int())
  b = line.get('label')
  i += 1
  sum_preds += int(a == b)

print(f"ACCURACY: {(sum_preds/i * 100)}")

ACCURACY: 93.15

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