---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Bert-Lab4
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# Bert-Lab4

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5647
- Accuracy: 0.73

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

We use 100 data from the glue/sst2, since we train on Colab.
https://drive.google.com/file/d/1Ej0-bDkfXmlNPynypS-xDbEEGK9hI06l/view?usp=sharing

## How to use

```python
from datasets import load_dataset
from transformers import AutoTokenizer, DataCollatorWithPadding

raw_datasets = load_dataset("glue", "sst2")
checkpoint = "ChiJuiChen/Bert-Lab4"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)


def tokenize_function(example):
    return tokenizer(example["sentence"], truncation=True)


tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)

small_train_dataset = tokenized_datasets["train"].shuffle(seed=42).select(range(100))
small_eval_dataset = tokenized_datasets["validation"].shuffle(seed=42).select(range(100))

data_collator = DataCollatorWithPadding(tokenizer=tokenizer)

from transformers import TrainingArguments

training_args = TrainingArguments(output_dir="ChiJuiChen/Bert-Lab4",
                                  evaluation_strategy="epoch",
                                  hub_model_id="ChiJuiChen/Bert-Lab4")

from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)

from transformers import Trainer
trainer = Trainer(
    model,
    training_args,
    train_dataset=small_train_dataset, # if using cpu
    eval_dataset=small_eval_dataset, # if using cpu
    data_collator=data_collator,
    tokenizer=tokenizer,
    compute_metrics=compute_metrics,
)

# Evaluation
predictions = trainer.predict(small_eval_dataset)
print(predictions.predictions.shape, predictions.label_ids.shape)
preds = np.argmax(predictions.predictions, axis=-1)

import evaluate
metric = evaluate.load("glue", "sst2")
metric.compute(predictions=preds, references=predictions.label_ids)
```
## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 13   | 0.6383          | 0.59     |
| No log        | 2.0   | 26   | 0.5867          | 0.71     |
| No log        | 3.0   | 39   | 0.5647          | 0.73     |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2