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---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
base_model: distilbert-base-uncased
model-index:
- name: destilbert_uncased_fever_nli
  results: []
datasets:
- fever
language:
- en
library_name: transformers
---

<!-- 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. -->

# destilbert_uncased_fever_nli

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on a subset of [fever_nli](https://huggingface.co/datasets/pietrolesci/nli_fever) dataset by using the first 7.5k datapoints per each label from the training split.
It achieves the following results on the evaluation set:
- Loss: 2.1829
- F1: 0.7045

## 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: 0.0001
- train_batch_size: 64
- eval_batch_size: 64
- 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 | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log        | 1.0   | 352  | 0.7894          | 0.7029 |
| 0.5462        | 2.0   | 704  | 0.9908          | 0.7097 |
| 0.2922        | 3.0   | 1056 | 1.0831          | 0.6924 |
| 0.2922        | 4.0   | 1408 | 1.2833          | 0.7044 |
| 0.142         | 5.0   | 1760 | 1.4096          | 0.7008 |
| 0.0695        | 6.0   | 2112 | 1.5585          | 0.7013 |
| 0.0695        | 7.0   | 2464 | 1.7262          | 0.7015 |
| 0.0434        | 8.0   | 2816 | 2.0138          | 0.7016 |
| 0.0204        | 9.0   | 3168 | 2.0912          | 0.7012 |
| 0.011         | 10.0  | 3520 | 2.1829          | 0.7045 |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2