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