Pavarissy/mdeberta-v3-ud-thai-pud-upos

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mdeberta-v3-ud-thai-pud-upos

This model is a fine-tuned version of microsoft/mdeberta-v3-base on the universal_dependencies dataset. It achieves the following results on the evaluation set:

• —Loss: 0.0303
• —Macro avg precision: 0.9235
• —Macro avg recall: 0.9228
• —Macro avg f1: 0.9231
• —Weighted avg precision: 0.9935
• —Weighted avg recall: 0.9935
• —Weighted avg f1: 0.9935
• —Accuracy: 0.9935

Model description

This model is train on thai UD Thai PUD corpus with Universal Part-of-speech (UPOS) tag to help with pos tagging in Thai language.

Example

from transformers import AutoModelForTokenClassification, AutoTokenizer, TokenClassificationPipeline

model = AutoModelForTokenClassification.from_pretrained("Pavarissy/mdeberta-v3-ud-thai-pud-upos")
tokenizer = AutoTokenizer.from_pretrained("Pavarissy/mdeberta-v3-ud-thai-pud-upos")

pipeline = TokenClassificationPipeline(model=model, tokenizer=tokenizer, grouped_entities=True)
outputs = pipeline("ประเทศไทย อยู่ใน ทวีป เอเชีย")
print(outputs)
# [{'entity_group': 'PROPN', 'score': 0.9946701, 'word': 'ประเทศไทย', 'start': 0, 'end': 9}, {'entity_group': 'VERB', 'score': 0.85809743, 'word': 'อยู่ใน', 'start': 9, 'end': 16}, {'entity_group': 'NOUN', 'score': 0.99632, 'word': 'ทวีป', 'start': 16, 'end': 21}, {'entity_group': 'PROPN', 'score': 0.9961184, 'word': 'เอเชีย', 'start': 21, 'end': 28}]

Training hyperparameters

The following hyperparameters were used during training:

• —learning_rate: 2e-05
• —trainbatchsize: 8
• —evalbatchsize: 8
• —seed: 42
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: linear
• —num_epochs: 10

Training results

Framework versions

• —Transformers 4.34.1
• —Pytorch 2.1.0+cu118
• —Datasets 2.14.6
• —Tokenizers 0.14.1