---
license: mit
base_model: cointegrated/rubert-tiny2
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_awesome_wnut_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_wnut_model_

This model is a fine-tuned version of [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1997
- Precision: 0.3076
- Recall: 0.4690
- F1: 0.3716
- Accuracy: 0.9322

## 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: 7
- eval_batch_size: 7
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 24

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.621         | 2.41  | 140  | 0.4025          | 0.0       | 0.0    | 0.0    | 0.9050   |
| 0.3224        | 4.83  | 280  | 0.2750          | 0.2036    | 0.2074 | 0.2055 | 0.9118   |
| 0.2421        | 7.24  | 420  | 0.2326          | 0.2706    | 0.3406 | 0.3016 | 0.9220   |
| 0.2061        | 9.66  | 560  | 0.2146          | 0.2968    | 0.4102 | 0.3444 | 0.9269   |
| 0.1779        | 12.07 | 700  | 0.2037          | 0.3125    | 0.4257 | 0.3604 | 0.9306   |
| 0.1606        | 14.48 | 840  | 0.2042          | 0.3044    | 0.4613 | 0.3668 | 0.9298   |
| 0.1544        | 16.9  | 980  | 0.2001          | 0.3101    | 0.4690 | 0.3734 | 0.9310   |
| 0.1402        | 19.31 | 1120 | 0.1991          | 0.3130    | 0.4690 | 0.3755 | 0.9316   |
| 0.139         | 21.72 | 1260 | 0.1997          | 0.3076    | 0.4690 | 0.3716 | 0.9322   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2