| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: bert-base-uncased |
| tags: |
| - generated_from_trainer |
| - text-classification |
| - transformers |
| - bert |
| metrics: |
| - accuracy |
| model-index: |
| - name: bert-finetuned-sst2 |
| 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-finetuned-sst2 |
|
|
| 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: 0.3812 |
| - Accuracy: 0.9083 |
|
|
| # Load model directly |
| ```python |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification |
| |
| tokenizer = AutoTokenizer.from_pretrained("execbat/bert-finetuned-sst2") |
| model = AutoModelForSequenceClassification.from_pretrained("execbat/bert-finetuned-sst2") |
| ``` |
|
|
| ## Use a pipeline as a high-level helper |
| ```python |
| from transformers import pipeline |
| |
| label_tags = {'LABEL_0' : "NEGATIVE", |
| 'LABEL_1' : "POSITIVE"} |
| |
| pipe = pipeline("text-classification", model="execbat/bert-finetuned-sst2") |
| result = pipe(["what a horrible day!", "what a wonderfull day!"]) |
| encoded_result = [label_tags[i["label"]] for i in result] |
| print(encoded_result) |
| ``` |
| ```python |
| ['NEGATIVE', 'POSITIVE'] |
| ``` |
|
|
| ## Model description |
|
|
| More information needed |
|
|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
|
|
| More information needed |
|
|
| ## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: linear |
| - num_epochs: 3.0 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| |:-------------:|:-----:|:-----:|:---------------:|:--------:| |
| | 0.269 | 1.0 | 8419 | 0.5041 | 0.8716 | |
| | 0.1854 | 2.0 | 16838 | 0.4296 | 0.8968 | |
| | 0.0993 | 3.0 | 25257 | 0.3812 | 0.9083 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.49.0 |
| - Pytorch 2.6.0 |
| - Datasets 3.3.2 |
| - Tokenizers 0.21.0 |
|
|
