---
base_model: allenai/scibert_scivocab_uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: scibert-ner
  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

This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1809
- Precision: 0.4499
- Recall: 0.4637
- F1: 0.4567
- Accuracy: 0.9536

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 60   | 0.1967          | 0.3563    | 0.3184 | 0.3363 | 0.9509   |
| No log        | 2.0   | 120  | 0.1726          | 0.4077    | 0.3855 | 0.3963 | 0.9525   |
| No log        | 3.0   | 180  | 0.1723          | 0.4204    | 0.4721 | 0.4447 | 0.9529   |
| No log        | 4.0   | 240  | 0.1775          | 0.4248    | 0.4735 | 0.4478 | 0.9526   |
| No log        | 5.0   | 300  | 0.1809          | 0.4499    | 0.4637 | 0.4567 | 0.9536   |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1