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Michał Kozłowski bf17b12877 fix readme, links in roberta

2023-02-13 16:09:22 +01:00

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RobertaForSequenceClassification model classification training

Colab link better looking output:

https://colab.research.google.com/drive/1pLOmBL5MJGA_BzY3pGoYSXf_bivsoJlX?usp=sharing

Links:

Tensorboard training: https://tensorboard.dev/experiment/Hq95VFzqTQ2CyBb1S4SOpw/#scalars
Huggingface dataset edited: https://huggingface.co/datasets/Zombely/sst2-project-dataset
Huggingface Trained model: https://huggingface.co/Zombely/RobertaForSequenceClassification-sst2

!pip install -q datasets transformers

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from datasets import load_dataset
import torch
from transformers import AutoTokenizer, RobertaForSequenceClassification, RobertaTokenizerFast, TrainingArguments, Trainer
from sklearn.metrics import accuracy_score, precision_recall_fscore_support
from transformers.integrations import TensorBoardCallback

def load_and_process_dataset():
    dataset = load_dataset("sst2")
    dataset.remove_columns('idx')
    del dataset['test']
    dataset['test'] = dataset['validation']
    del dataset['validation']
    split_dataset = dataset['train'].train_test_split(test_size=1600)
    dataset['train'] = split_dataset['train']
    dataset['validation'] = split_dataset['test']
    return dataset

dataset = load_and_process_dataset()
dataset

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Downloading and preparing dataset sst2/default to /root/.cache/huggingface/datasets/sst2/default/2.0.0/9896208a8d85db057ac50c72282bcb8fe755accc671a57dd8059d4e130961ed5...

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Dataset sst2 downloaded and prepared to /root/.cache/huggingface/datasets/sst2/default/2.0.0/9896208a8d85db057ac50c72282bcb8fe755accc671a57dd8059d4e130961ed5. Subsequent calls will reuse this data.

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DatasetDict({
    train: Dataset({
        features: ['idx', 'sentence', 'label'],
        num_rows: 65749
    })
    test: Dataset({
        features: ['idx', 'sentence', 'label'],
        num_rows: 872
    })
    validation: Dataset({
        features: ['idx', 'sentence', 'label'],
        num_rows: 1600
    })
})

train = dataset['train']
validation = dataset['validation']
test = dataset['test']

model = RobertaForSequenceClassification.from_pretrained('roberta-base')
tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base', max_length = 512)

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Some weights of the model checkpoint at roberta-base were not used when initializing RobertaForSequenceClassification: ['lm_head.layer_norm.bias', 'roberta.pooler.dense.weight', 'lm_head.bias', 'roberta.pooler.dense.bias', 'lm_head.dense.weight', 'lm_head.decoder.weight', 'lm_head.layer_norm.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.dense.bias', 'classifier.out_proj.weight', '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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# define a function that will tokenize the model, and will return the relevant inputs for the model
def tokenization(batched_text):
    return tokenizer(batched_text['sentence'], padding = True, truncation=True)


train_data = train.map(tokenization, batched = True, batch_size = len(train))
val_data = validation.map(tokenization, batched = True, batch_size = len(validation))
test_data = test.map(tokenization, batched = True, batch_size = len(test))

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train_data.set_format('torch', columns=['input_ids', 'sentence', 'label'])
val_data.set_format('torch', columns=['input_ids', 'sentence', 'label'])
test_data.set_format('torch', columns=['input_ids', 'sentence', 'label'])

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

# define the training arguments
training_args = TrainingArguments(
    output_dir = './results',
    num_train_epochs=3,
    per_device_train_batch_size = 4,
    gradient_accumulation_steps = 16,    
    per_device_eval_batch_size= 8,
    evaluation_strategy = "epoch",
    disable_tqdm = False, 
    load_best_model_at_end=False,
    warmup_steps=500,
    weight_decay=0.01,
    logging_steps = 8,
    fp16 = True,
    logging_dir='./logs',
    dataloader_num_workers = 2,
    run_name = 'roberta-classification',
    optim="adamw_torch"
)

trainer = Trainer(
    model=model,
    args=training_args,
    compute_metrics=compute_metrics,
    train_dataset=train_data,
    eval_dataset=val_data,
    callbacks=[TensorBoardCallback]
)

You are adding a <class 'transformers.integrations.TensorBoardCallback'> to the callbacks of this Trainer, but there is already one. The currentlist of callbacks is
:DefaultFlowCallback
TensorBoardCallback
Using cuda_amp half precision backend

trainer.train()

The following columns in the training set don't have a corresponding argument in `RobertaForSequenceClassification.forward` and have been ignored: idx, sentence. If idx, sentence are not expected by `RobertaForSequenceClassification.forward`,  you can safely ignore this message.
***** Running training *****
  Num examples = 65749
  Num Epochs = 3
  Instantaneous batch size per device = 4
  Total train batch size (w. parallel, distributed & accumulation) = 64
  Gradient Accumulation steps = 16
  Total optimization steps = 3081
  Number of trainable parameters = 124647170

[3081/3081 42:34, Epoch 2/3]

Epoch	Training Loss	Validation Loss	Accuracy	F1	Precision	Recall
0	0.195700	0.158939	0.936250	0.942761	0.944882	0.940649
1	0.132900	0.146519	0.955000	0.959551	0.962796	0.956327
2	0.039700	0.150718	0.955625	0.960357	0.957684	0.963046

Saving model checkpoint to ./results/checkpoint-500
Configuration saved in ./results/checkpoint-500/config.json
Model weights saved in ./results/checkpoint-500/pytorch_model.bin
Saving model checkpoint to ./results/checkpoint-1000
Configuration saved in ./results/checkpoint-1000/config.json
Model weights saved in ./results/checkpoint-1000/pytorch_model.bin
The following columns in the evaluation set don't have a corresponding argument in `RobertaForSequenceClassification.forward` and have been ignored: idx, sentence. If idx, sentence are not expected by `RobertaForSequenceClassification.forward`,  you can safely ignore this message.
***** Running Evaluation *****
  Num examples = 1600
  Batch size = 8
Saving model checkpoint to ./results/checkpoint-1500
Configuration saved in ./results/checkpoint-1500/config.json
Model weights saved in ./results/checkpoint-1500/pytorch_model.bin
Saving model checkpoint to ./results/checkpoint-2000
Configuration saved in ./results/checkpoint-2000/config.json
Model weights saved in ./results/checkpoint-2000/pytorch_model.bin
The following columns in the evaluation set don't have a corresponding argument in `RobertaForSequenceClassification.forward` and have been ignored: idx, sentence. If idx, sentence are not expected by `RobertaForSequenceClassification.forward`,  you can safely ignore this message.
***** Running Evaluation *****
  Num examples = 1600
  Batch size = 8
Saving model checkpoint to ./results/checkpoint-2500
Configuration saved in ./results/checkpoint-2500/config.json
Model weights saved in ./results/checkpoint-2500/pytorch_model.bin
Saving model checkpoint to ./results/checkpoint-3000
Configuration saved in ./results/checkpoint-3000/config.json
Model weights saved in ./results/checkpoint-3000/pytorch_model.bin
The following columns in the evaluation set don't have a corresponding argument in `RobertaForSequenceClassification.forward` and have been ignored: idx, sentence. If idx, sentence are not expected by `RobertaForSequenceClassification.forward`,  you can safely ignore this message.
***** Running Evaluation *****
  Num examples = 1600
  Batch size = 8


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

TrainOutput(global_step=3081, training_loss=0.19893329894531087, metrics={'train_runtime': 2559.2258, 'train_samples_per_second': 77.073, 'train_steps_per_second': 1.204, 'total_flos': 6790599311126760.0, 'train_loss': 0.19893329894531087, 'epoch': 3.0})

trainer.evaluate()

The following columns in the evaluation set don't have a corresponding argument in `RobertaForSequenceClassification.forward` and have been ignored: idx, sentence. If idx, sentence are not expected by `RobertaForSequenceClassification.forward`,  you can safely ignore this message.
***** Running Evaluation *****
  Num examples = 1600
  Batch size = 8

[200/200 00:03]

{'eval_loss': 0.15071792900562286,
 'eval_accuracy': 0.955625,
 'eval_f1': 0.96035734226689,
 'eval_precision': 0.9576837416481069,
 'eval_recall': 0.9630459126539753,
 'eval_runtime': 3.7924,
 'eval_samples_per_second': 421.898,
 'eval_steps_per_second': 52.737,
 'epoch': 3.0}

trainer.evaluate(test_data)

The following columns in the evaluation set don't have a corresponding argument in `RobertaForSequenceClassification.forward` and have been ignored: idx, sentence. If idx, sentence are not expected by `RobertaForSequenceClassification.forward`,  you can safely ignore this message.
***** Running Evaluation *****
  Num examples = 872
  Batch size = 8

[200/200 00:05]

{'eval_loss': 0.20586328208446503,
 'eval_accuracy': 0.9392201834862385,
 'eval_f1': 0.9407821229050279,
 'eval_precision': 0.9334811529933481,
 'eval_recall': 0.9481981981981982,
 'eval_runtime': 2.3748,
 'eval_samples_per_second': 367.184,
 'eval_steps_per_second': 45.898,
 'epoch': 3.0}

!tensorboard dev upload --logdir logs --name RobertaForSequenceClassification

2023-02-13 13:06:49.916239: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib64-nvidia
2023-02-13 13:06:49.916330: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib64-nvidia
2023-02-13 13:06:49.916358: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.

***** TensorBoard Uploader *****

This will upload your TensorBoard logs to https://tensorboard.dev/ from
the following directory:

logs

This TensorBoard will be visible to everyone. Do not upload sensitive
data.

Your use of this service is subject to Google's Terms of Service
<https://policies.google.com/terms> and Privacy Policy
<https://policies.google.com/privacy>, and TensorBoard.dev's Terms of Service
<https://tensorboard.dev/policy/terms/>.

This notice will not be shown again while you are logged into the uploader.
To log out, run `tensorboard dev auth revoke`.

Continue? (yes/NO) yes

To sign in with the TensorBoard uploader:

1. On your computer or phone, visit:

   https://www.google.com/device

2. Sign in with your Google account, then enter:

   YBVF-QCSV


Upload started and will continue reading any new data as it's added to the logdir.

To stop uploading, press Ctrl-C.

New experiment created. View your TensorBoard at: https://tensorboard.dev/experiment/Hq95VFzqTQ2CyBb1S4SOpw/

[1m[2023-02-13T13:07:05][0m Started scanning logdir.
[1m[2023-02-13T13:07:06][0m Total uploaded: 2412 scalars, 10 tensors (7.1 kB), 0 binary objects


Interrupted. View your TensorBoard at https://tensorboard.dev/experiment/Hq95VFzqTQ2CyBb1S4SOpw/
Traceback (most recent call last):
  File "/usr/local/bin/tensorboard", line 8, in <module>
    sys.exit(run_main())
  File "/usr/local/lib/python3.8/dist-packages/tensorboard/main.py", line 46, in run_main
    app.run(tensorboard.main, flags_parser=tensorboard.configure)
  File "/usr/local/lib/python3.8/dist-packages/absl/app.py", line 308, in run
    _run_main(main, args)
  File "/usr/local/lib/python3.8/dist-packages/absl/app.py", line 254, in _run_main
    sys.exit(main(argv))
  File "/usr/local/lib/python3.8/dist-packages/tensorboard/program.py", line 276, in main
    return runner(self.flags) or 0
  File "/usr/local/lib/python3.8/dist-packages/tensorboard/uploader/uploader_subcommand.py", line 691, in run
    return _run(flags, self._experiment_url_callback)
  File "/usr/local/lib/python3.8/dist-packages/tensorboard/uploader/uploader_subcommand.py", line 124, in _run
    intent.execute(server_info, channel)
  File "/usr/local/lib/python3.8/dist-packages/tensorboard/uploader/uploader_subcommand.py", line 507, in execute
    sys.stdout.write(end_message + "\n")
KeyboardInterrupt
^C

model.save_pretrained("./model")

Configuration saved in ./model/config.json
Model weights saved in ./model/pytorch_model.bin

!huggingface-cli login

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    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .
Token: 
Add token as git credential? (Y/n) y
Token is valid.
[1m[31mCannot authenticate through git-credential as no helper is defined on your machine.
You might have to re-authenticate when pushing to the Hugging Face Hub.
Run the following command in your terminal in case you want to set the 'store' credential helper as default.

git config --global credential.helper store

Read https://git-scm.com/book/en/v2/Git-Tools-Credential-Storage for more details.[0m
Token has not been saved to git credential helper.
Your token has been saved to /root/.cache/huggingface/token
Login successful

model.push_to_hub("Zombely/RobertaForSequenceClassification-sst2")

Configuration saved in /tmp/tmpga7eb38a/config.json
Model weights saved in /tmp/tmpga7eb38a/pytorch_model.bin
Uploading the following files to Zombely/RobertaForSequenceClassification-sst2: config.json,pytorch_model.bin

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CommitInfo(commit_url='https://huggingface.co/Zombely/RobertaForSequenceClassification-sst2/commit/b9c78c4bdd91c2077b01e2109c77c30495c5a9e9', commit_message='Upload RobertaForSequenceClassification', commit_description='', oid='b9c78c4bdd91c2077b01e2109c77c30495c5a9e9', pr_url=None, pr_revision=None, pr_num=None)

!zip model -r model

  adding: model/ (stored 0%)
  adding: model/config.json (deflated 51%)
  adding: model/pytorch_model.bin (deflated 12%)

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RobertaForSequenceClassification model classification training

Colab link better looking output:

Links:

268 KiB

Raw Permalink Blame History