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skylord
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pharma_classification

Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
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skylord/pharma_classification

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Files changed (3) hide show
  1. README.md +12 -10
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -5,6 +5,7 @@ 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: pharma_classification
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  results: []
@@ -17,8 +18,9 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.6035
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- - Accuracy: 0.9664
 
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  ## Model description
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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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- | 0.0194 | 5.99 | 5000 | 0.2594 | 0.9635 |
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- | 0.0 | 11.98 | 10000 | 0.4335 | 0.9641 |
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- | 0.0 | 17.96 | 15000 | 0.5338 | 0.9641 |
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- | 0.0 | 23.95 | 20000 | 0.4973 | 0.9664 |
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- | 0.0 | 29.94 | 25000 | 0.5737 | 0.9664 |
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- | 0.0 | 35.93 | 30000 | 0.6035 | 0.9664 |
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  ### Framework versions
 
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  - generated_from_trainer
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  metrics:
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  - accuracy
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+ - f1
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  model-index:
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  - name: pharma_classification
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  results: []
 
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  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5315
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+ - Accuracy: 0.9581
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+ - F1: 0.9506
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  ## Model description
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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+ |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
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+ | 0.0035 | 5.99 | 5000 | 0.2892 | 0.9539 | 0.9554 |
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+ | 0.0137 | 11.98 | 10000 | 0.2620 | 0.9641 | 0.9600 |
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+ | 0.0 | 17.96 | 15000 | 0.4022 | 0.9611 | 0.9586 |
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+ | 0.0001 | 23.95 | 20000 | 0.3838 | 0.9611 | 0.9552 |
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+ | 0.0 | 29.94 | 25000 | 0.4363 | 0.9575 | 0.9490 |
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+ | 0.0 | 35.93 | 30000 | 0.5315 | 0.9581 | 0.9506 |
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  ### Framework versions
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