---
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: 0.4445
- Precision: 0.3974
- Recall: 0.3404
- F1: 0.3667
- Accuracy: 0.9140

## 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  | 0.5694          | 0.0808    | 0.0411 | 0.0545 | 0.8648   |
| No log        | 2.0   | 242  | 0.4878          | 0.3169    | 0.1277 | 0.1820 | 0.9020   |
| No log        | 3.0   | 363  | 0.4121          | 0.3422    | 0.2199 | 0.2677 | 0.9097   |
| No log        | 4.0   | 484  | 0.4240          | 0.4273    | 0.2624 | 0.3251 | 0.9147   |
| 0.4235        | 5.0   | 605  | 0.3974          | 0.4187    | 0.2922 | 0.3442 | 0.9159   |
| 0.4235        | 6.0   | 726  | 0.4125          | 0.4146    | 0.3064 | 0.3524 | 0.9140   |
| 0.4235        | 7.0   | 847  | 0.4235          | 0.3713    | 0.3191 | 0.3432 | 0.9136   |
| 0.4235        | 8.0   | 968  | 0.4330          | 0.3794    | 0.3348 | 0.3557 | 0.9132   |
| 0.1633        | 9.0   | 1089 | 0.4481          | 0.3926    | 0.3319 | 0.3597 | 0.9133   |
| 0.1633        | 10.0  | 1210 | 0.4445          | 0.3974    | 0.3404 | 0.3667 | 0.9140   |


### Framework versions

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