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--- |
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license: apache-2.0 |
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base_model: distilbert-base-cased |
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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_clf_results |
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results: [] |
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datasets: |
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- app_reviews |
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language: |
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- en |
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library_name: transformers |
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pipeline_tag: text-classification |
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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_clf_results |
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This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9611 |
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- Accuracy: 0.7011 |
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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: 16 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 4 |
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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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| 1.0767 | 1.0 | 5401 | 0.8447 | 0.7087 | |
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| 0.6523 | 2.0 | 10803 | 0.8287 | 0.7156 | |
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| 0.7209 | 3.0 | 16204 | 0.8852 | 0.7121 | |
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| 0.4274 | 4.0 | 21604 | 0.9611 | 0.7011 | |
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### Code Implementation |
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``` |
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from transformers import AutoTokenizer |
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from transformers import AutoModelForSequenceClassification |
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tokenizer = AutoTokenizer.from_pretrained("Andyrasika/bert_clf_results") |
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inputs = tokenizer(prompt, return_tensors="pt") |
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model = AutoModelForSequenceClassification.from_pretrained("Andyrasika/bert_clf_results") |
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with torch.no_grad(): |
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logits = model(**inputs).logits |
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predicted_class_id = logits.argmax().item() |
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model.config.id2label[predicted_class_id] |
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``` |
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Output |
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``` |
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'LABEL_4' |
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``` |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.16.0 |
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- Tokenizers 0.15.0 |
