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
• 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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Macro avg precision	Macro avg recall	Macro avg f1	Weighted avg precision	Weighted avg recall	Weighted avg f1	Accuracy
No log	1.0	125	0.3898	0.8417	0.7849	0.8078	0.9119	0.9112	0.9101	0.9112
No log	2.0	250	0.1768	0.8765	0.8683	0.8720	0.9561	0.9560	0.9559	0.9560
No log	3.0	375	0.1217	0.8972	0.8892	0.8929	0.9701	0.9701	0.9699	0.9701
0.4709	4.0	500	0.0841	0.9057	0.9064	0.9059	0.9802	0.9800	0.9800	0.9800
0.4709	5.0	625	0.0649	0.9128	0.9133	0.9130	0.9854	0.9853	0.9853	0.9853
0.4709	6.0	750	0.0513	0.9147	0.9170	0.9158	0.9878	0.9877	0.9877	0.9877
0.4709	7.0	875	0.0423	0.9199	0.9180	0.9189	0.9900	0.9900	0.9900	0.9900
0.0857	8.0	1000	0.0350	0.9226	0.9207	0.9216	0.9921	0.9921	0.9921	0.9921
0.0857	9.0	1125	0.0318	0.9237	0.9219	0.9228	0.9932	0.9932	0.9932	0.9932
0.0857	10.0	1250	0.0303	0.9235	0.9228	0.9231	0.9935	0.9935	0.9935	0.9935

Framework versions

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