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--- |
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license: apache-2.0 |
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base_model: bert-base-uncased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: Bert-Lab4 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Bert-Lab4 |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5647 |
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- Accuracy: 0.73 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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We use 100 data from the glue/sst2, since we train on Colab. |
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https://drive.google.com/file/d/1Ej0-bDkfXmlNPynypS-xDbEEGK9hI06l/view?usp=sharing |
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## How to use |
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```python |
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from datasets import load_dataset |
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from transformers import AutoTokenizer, DataCollatorWithPadding |
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raw_datasets = load_dataset("glue", "sst2") |
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checkpoint = "ChiJuiChen/Bert-Lab4" |
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tokenizer = AutoTokenizer.from_pretrained(checkpoint) |
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def tokenize_function(example): |
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return tokenizer(example["sentence"], truncation=True) |
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tokenized_datasets = raw_datasets.map(tokenize_function, batched=True) |
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small_train_dataset = tokenized_datasets["train"].shuffle(seed=42).select(range(100)) |
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small_eval_dataset = tokenized_datasets["validation"].shuffle(seed=42).select(range(100)) |
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data_collator = DataCollatorWithPadding(tokenizer=tokenizer) |
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from transformers import TrainingArguments |
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training_args = TrainingArguments(output_dir="ChiJuiChen/Bert-Lab4", |
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evaluation_strategy="epoch", |
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hub_model_id="ChiJuiChen/Bert-Lab4") |
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from transformers import AutoModelForSequenceClassification |
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model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2) |
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from transformers import Trainer |
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trainer = Trainer( |
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model, |
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training_args, |
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train_dataset=small_train_dataset, # if using cpu |
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eval_dataset=small_eval_dataset, # if using cpu |
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data_collator=data_collator, |
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tokenizer=tokenizer, |
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compute_metrics=compute_metrics, |
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) |
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# Evaluation |
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predictions = trainer.predict(small_eval_dataset) |
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print(predictions.predictions.shape, predictions.label_ids.shape) |
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preds = np.argmax(predictions.predictions, axis=-1) |
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import evaluate |
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metric = evaluate.load("glue", "sst2") |
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metric.compute(predictions=preds, references=predictions.label_ids) |
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``` |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 3.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| No log | 1.0 | 13 | 0.6383 | 0.59 | |
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| No log | 2.0 | 26 | 0.5867 | 0.71 | |
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| No log | 3.0 | 39 | 0.5647 | 0.73 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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