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---
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
should probably proofread and complete it, then remove this comment. -->
# 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
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