README.md

---
license: mit
base_model: xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: fine-tuned-NLI-mnli_original-with-xlm-roberta-large
  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. -->

# fine-tuned-NLI-mnli_original-with-xlm-roberta-large

This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3833
- Accuracy: 0.8879
- F1: 0.8881

## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- 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 | Accuracy | F1     |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|
| 0.3931        | 0.4997 | 1533  | 0.3416          | 0.8697   | 0.8695 |
| 0.3529        | 0.9993 | 3066  | 0.3214          | 0.8825   | 0.8829 |
| 0.2985        | 1.4990 | 4599  | 0.3312          | 0.8872   | 0.8877 |
| 0.299         | 1.9987 | 6132  | 0.3209          | 0.8881   | 0.8884 |
| 0.2349        | 2.4984 | 7665  | 0.3322          | 0.8851   | 0.8856 |
| 0.2433        | 2.9980 | 9198  | 0.3324          | 0.8866   | 0.8869 |
| 0.1912        | 3.4977 | 10731 | 0.3833          | 0.8879   | 0.8881 |


### Framework versions

- Transformers 4.42.3
- Pytorch 1.13.1+cu117
- Datasets 2.2.0
- Tokenizers 0.19.1