---
license: mit
base_model: xlnet-large-cased
tags:
- generated_from_trainer
metrics:
- f1
- recall
- precision
model-index:
- name: task2_xlnet-large-cased_3_4_2e-05_0.01
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# task2_xlnet-large-cased_3_4_2e-05_0.01

This model is a fine-tuned version of [xlnet-large-cased](https://huggingface.co./xlnet-large-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9482
- F1: 0.7790
- Recall: 0.7790
- Precision: 0.7790

## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:---------:|
| 0.8074        | 1.0   | 745  | 0.7084          | 0.7574 | 0.7574 | 0.7574    |
| 0.7665        | 2.0   | 1490 | 0.7881          | 0.7628 | 0.7628 | 0.7628    |
| 0.6739        | 3.0   | 2235 | 0.9482          | 0.7790 | 0.7790 | 0.7790    |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3