Text Classification
Transformers
Safetensors
Vietnamese
vnsabsa
feature-extraction
finance
custom_code
Instructions to use ptdat/vn-smartphone-absa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ptdat/vn-smartphone-absa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ptdat/vn-smartphone-absa", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ptdat/vn-smartphone-absa", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Upload model
Browse files- README.md +6 -6
- config.json +15 -0
- model.safetensors +3 -0
README.md
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---
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datasets:
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- SEACrowd/uit_visfd
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language:
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- vi
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metrics:
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- accuracy
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library_name: transformers
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pipeline_tag: text-classification
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tags:
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- finance
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---
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language:
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- vi
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library_name: transformers
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tags:
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- finance
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datasets:
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- SEACrowd/uit_visfd
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metrics:
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- accuracy
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pipeline_tag: text-classification
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---
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config.json
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{
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"architectures": [
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"VnSmartphoneBERTModel"
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],
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"embed_dim": 768,
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"encoder_dropout": 0.1,
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"fc_dropout": 0.4,
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"fc_hidden_size": 128,
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"model_type": "bert",
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"num_encoders": 4,
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"num_heads": 8,
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"torch_dtype": "float32",
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"transformers_version": "4.41.2",
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"vocab_size": 5272
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:03507112afa77d32d0b67f80efddf4ccbdbc9e6f2c4c90c40957fbd959f15c3e
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size 81635196
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