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---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: pharma_classification
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. -->
# pharma_classification
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5315
- Accuracy: 0.9581
- F1: 0.9506
## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 30000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.0035 | 5.99 | 5000 | 0.2892 | 0.9539 | 0.9554 |
| 0.0137 | 11.98 | 10000 | 0.2620 | 0.9641 | 0.9600 |
| 0.0 | 17.96 | 15000 | 0.4022 | 0.9611 | 0.9586 |
| 0.0001 | 23.95 | 20000 | 0.3838 | 0.9611 | 0.9552 |
| 0.0 | 29.94 | 25000 | 0.4363 | 0.9575 | 0.9490 |
| 0.0 | 35.93 | 30000 | 0.5315 | 0.9581 | 0.9506 |
### Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.2.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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