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

This model is a fine-tuned version of [allenai/scibert_scivocab_cased](https://huggingface.co/allenai/scibert_scivocab_cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3459
- Precision: 0.5666
- Recall: 0.5191
- F1: 0.5418
- Accuracy: 0.9363

## 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: 8
- eval_batch_size: 8
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 121  | 0.3648          | 0.3157    | 0.3390 | 0.3269 | 0.8945   |
| No log        | 2.0   | 242  | 0.3177          | 0.5280    | 0.3348 | 0.4097 | 0.9253   |
| No log        | 3.0   | 363  | 0.2599          | 0.5143    | 0.4326 | 0.4700 | 0.9315   |
| No log        | 4.0   | 484  | 0.2825          | 0.5360    | 0.4227 | 0.4726 | 0.9336   |
| 0.2574        | 5.0   | 605  | 0.2968          | 0.5473    | 0.4922 | 0.5183 | 0.9350   |
| 0.2574        | 6.0   | 726  | 0.3193          | 0.5857    | 0.4894 | 0.5332 | 0.9377   |
| 0.2574        | 7.0   | 847  | 0.3327          | 0.5513    | 0.4879 | 0.5177 | 0.9356   |
| 0.2574        | 8.0   | 968  | 0.3315          | 0.5658    | 0.5121 | 0.5376 | 0.9363   |
| 0.0678        | 9.0   | 1089 | 0.3413          | 0.5465    | 0.5163 | 0.5310 | 0.9361   |
| 0.0678        | 10.0  | 1210 | 0.3459          | 0.5666    | 0.5191 | 0.5418 | 0.9363   |


### Framework versions

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