---
library_name: transformers
base_model: allenai/scibert_scivocab_uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my_awesome_model
  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. -->

# my_awesome_model

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: 0.1958
- Accuracy: 0.9164

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.1482        | 0.0507 | 100  | 0.2884          | 0.9044   |
| 0.1465        | 0.1014 | 200  | 0.4802          | 0.8739   |
| 0.1523        | 0.1521 | 300  | 0.2684          | 0.8976   |
| 0.1454        | 0.2027 | 400  | 0.4407          | 0.8696   |
| 0.141         | 0.2534 | 500  | 0.2469          | 0.9106   |
| 0.1112        | 0.3041 | 600  | 0.3704          | 0.8927   |
| 0.1368        | 0.3548 | 700  | 0.3799          | 0.8878   |
| 0.0952        | 0.4055 | 800  | 0.3520          | 0.9041   |
| 0.1225        | 0.4562 | 900  | 0.3819          | 0.8882   |
| 0.1245        | 0.5068 | 1000 | 0.2261          | 0.9101   |
| 0.1479        | 0.5575 | 1100 | 0.3054          | 0.8844   |
| 0.1434        | 0.6082 | 1200 | 0.2268          | 0.9194   |
| 0.1622        | 0.6589 | 1300 | 0.2455          | 0.9053   |
| 0.1789        | 0.7096 | 1400 | 0.2411          | 0.8991   |
| 0.1832        | 0.7603 | 1500 | 0.2224          | 0.9120   |
| 0.1855        | 0.8109 | 1600 | 0.2102          | 0.9105   |
| 0.1818        | 0.8616 | 1700 | 0.1893          | 0.9211   |
| 0.1823        | 0.9123 | 1800 | 0.2166          | 0.9092   |
| 0.1632        | 0.9630 | 1900 | 0.1958          | 0.9164   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0
- Datasets 3.0.0
- Tokenizers 0.19.1