---
base_model: allenai/scibert_scivocab_cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: scibert_ner_drugname
  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. -->

# scibert_ner_drugname

This model is a fine-tuned version of [allenai/scibert_scivocab_cased](https://huggingface.co/allenai/scibert_scivocab_cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1243
- Precision: 0.7631
- Recall: 0.8520
- F1: 0.8051
- Accuracy: 0.9722

## 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: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0733        | 1.0   | 120  | 0.1176          | 0.6466    | 0.7713 | 0.7035 | 0.9583   |
| 0.0069        | 2.0   | 240  | 0.1126          | 0.6757    | 0.7848 | 0.7261 | 0.9654   |
| 0.0521        | 3.0   | 360  | 0.0949          | 0.7461    | 0.8565 | 0.7975 | 0.9707   |
| 0.0217        | 4.0   | 480  | 0.0972          | 0.7171    | 0.8296 | 0.7692 | 0.9718   |
| 0.001         | 5.0   | 600  | 0.1111          | 0.7422    | 0.8520 | 0.7933 | 0.9707   |
| 0.0044        | 6.0   | 720  | 0.1138          | 0.7664    | 0.8386 | 0.8009 | 0.9715   |
| 0.0011        | 7.0   | 840  | 0.1155          | 0.7449    | 0.8251 | 0.7830 | 0.9699   |
| 0.0006        | 8.0   | 960  | 0.1213          | 0.7344    | 0.8430 | 0.7850 | 0.9716   |
| 0.0289        | 9.0   | 1080 | 0.1238          | 0.7661    | 0.8520 | 0.8068 | 0.9718   |
| 0.0096        | 10.0  | 1200 | 0.1243          | 0.7631    | 0.8520 | 0.8051 | 0.9722   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2