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--- |
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license: apache-2.0 |
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base_model: Geotrend/bert-base-th-cased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: pos_thai |
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results: [] |
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language: th |
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widget: |
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- text: ภาษาไทย ง่าย นิดเดียว |
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example_title: test1 |
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- text: >- |
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หนุ่ม เลี้ยง ควาย ใน อิสราเอล เผย รายได้ ต่อ เดือน ทำงาน 4 ปี สร้าง บ้าน ได้ |
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1 หลัง |
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example_title: test2 |
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datasets: |
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- lunarlist/tagging_thai |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# pos_thai |
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This model is a fine-tuned version of [Geotrend/bert-base-th-cased](https://huggingface.co/Geotrend/bert-base-th-cased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0935 |
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- Precision: 0.9525 |
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- Recall: 0.9540 |
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- F1: 0.9533 |
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- Accuracy: 0.9693 |
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## Model description |
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This model is train on thai pos_tag datasets to help with pos tagging in Thai language. |
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## Example |
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~~~ |
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from transformers import AutoModelForTokenClassification, AutoTokenizer, TokenClassificationPipeline |
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model = AutoModelForTokenClassification.from_pretrained("lunarlist/pos_thai") |
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tokenizer = AutoTokenizer.from_pretrained("lunarlist/pos_thai") |
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pipeline = TokenClassificationPipeline(model=model, tokenizer=tokenizer, grouped_entities=True) |
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outputs = pipeline("ภาษาไทย ง่าย นิดเดียว") |
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print(outputs) |
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~~~ |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 2 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.1124 | 1.0 | 7344 | 0.1048 | 0.9505 | 0.9478 | 0.9492 | 0.9670 | |
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| 0.0866 | 2.0 | 14688 | 0.0935 | 0.9525 | 0.9540 | 0.9533 | 0.9693 | |
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### Framework versions |
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- Transformers 4.34.1 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.1 |
