---
license: mit
base_model: xlnet-large-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlnet-lg-cased-ms-ner-test
  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. -->

# xlnet-lg-cased-ms-ner-test

This model is a fine-tuned version of [xlnet-large-cased](https://huggingface.co/xlnet-large-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1308
- Precision: 0.8828
- Recall: 0.9077
- F1: 0.8951
- Accuracy: 0.9814

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.137         | 1.0   | 3615  | 0.1313          | 0.7971    | 0.7986 | 0.7979 | 0.9663   |
| 0.0761        | 2.0   | 7230  | 0.0894          | 0.8564    | 0.8773 | 0.8667 | 0.9781   |
| 0.0459        | 3.0   | 10845 | 0.0946          | 0.8718    | 0.8918 | 0.8817 | 0.9803   |
| 0.021         | 4.0   | 14460 | 0.1091          | 0.8795    | 0.9017 | 0.8905 | 0.9808   |
| 0.013         | 5.0   | 18075 | 0.1308          | 0.8828    | 0.9077 | 0.8951 | 0.9814   |


### Framework versions

- Transformers 4.39.3
- Pytorch 1.12.0
- Datasets 2.18.0
- Tokenizers 0.15.2