---
license: mit
base_model: xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: SCIFACT_inference_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. -->

# SCIFACT_inference_model

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: 1.2496
- Accuracy: 0.8819

## 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: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 378  | 1.0485          | 0.4724   |
| 1.0382        | 2.0   | 756  | 1.3964          | 0.6063   |
| 0.835         | 3.0   | 1134 | 0.9168          | 0.8268   |
| 0.6801        | 4.0   | 1512 | 0.7524          | 0.8425   |
| 0.6801        | 5.0   | 1890 | 1.0672          | 0.8346   |
| 0.4291        | 6.0   | 2268 | 0.9599          | 0.8425   |
| 0.2604        | 7.0   | 2646 | 0.8691          | 0.8661   |
| 0.1932        | 8.0   | 3024 | 1.3162          | 0.8268   |
| 0.1932        | 9.0   | 3402 | 1.3200          | 0.8583   |
| 0.0974        | 10.0  | 3780 | 1.1566          | 0.8740   |
| 0.1051        | 11.0  | 4158 | 1.1568          | 0.8819   |
| 0.0433        | 12.0  | 4536 | 1.2013          | 0.8661   |
| 0.0433        | 13.0  | 4914 | 1.1557          | 0.8819   |
| 0.034         | 14.0  | 5292 | 1.3044          | 0.8661   |
| 0.0303        | 15.0  | 5670 | 1.2496          | 0.8819   |


### Framework versions

- Transformers 4.34.1
- Pytorch 1.13.1+cu116
- Datasets 2.14.6
- Tokenizers 0.14.1