| ---
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| language:
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| - vi
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| license: mit
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| library_name: transformers
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| pipeline_tag: text-classification
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| base_model: intfloat/multilingual-e5-small
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| tags:
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| - vietnamese
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| - custom-code
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| - transformers
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| - multilingual-e5
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| - absa
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| - hosrev
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| - healthcare
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| - text-classification
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| - aspect-based-sentiment-analysis
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| ---
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|
|
| # m-e5-small-hosrev
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|
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| ## Overview
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|
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| Vietnamese aspect-category sentiment classification model for hospital reviews. The model predicts sentiment for 13 aspect categories covering facilities, medical staff, and overall experience.
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|
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| ## Model Details
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|
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| - Base model: `intfloat/multilingual-e5-small`
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| - Architecture: `absa`
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| - Checkpoint source: `hosrev-e5-small-best.pt`
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| - Sequence length used during training/inference pipeline: `256`
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| - Number of aspect categories: `13`
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|
|
| ## Label Schema
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|
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| - `0`: aspect not mentioned
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| - `1`: positive
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| - `2`: negative
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| - `3`: neutral
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|
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| ## Aspect Categories
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| - `Cơ sở vật chất#Chất lượng`
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| - `Cơ sở vật chất#Khác`
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| - `Cơ sở vật chất#Không gian`
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| - `Cơ sở vật chất#Vệ sinh`
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| - `Nhân viên y tế#Chất lượng`
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| - `Nhân viên y tế#Khác`
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| - `Nhân viên y tế#Thái độ`
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| - `Trải nghiệm chung#Chất lượng`
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| - `Trải nghiệm chung#Giá`
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| - `Trải nghiệm chung#Khác`
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| - `Trải nghiệm chung#Không gian`
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| - `Trải nghiệm chung#Thái độ`
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| - `Trải nghiệm chung#Vệ sinh`
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|
|
| ## Dataset
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|
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| - Dataset: `HosRev`
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| HosRev is a Vietnamese hospital review dataset for Aspect-Category Sentiment Analysis (ACSA). The data contains reviews of hospitals in Ho Chi Minh City with aspect-category sentiment labels.
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|
|
| ### Data Format
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|
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| - `Review` is the input text column.
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| - Each remaining column is one aspect-category label encoded as `0/1/2/3`.
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|
|
| ### Splits
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|
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| - Train: `4566` samples
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| - Validation: `978` samples
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| - Test: `979` samples
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|
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| ## Checkpoint Metrics
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|
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| - `loss`: `0.2834`
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| - `accuracy`: `0.8970`
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|
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| ## Usage
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| Load the model with `trust_remote_code=True` because this repository contains custom modeling code.
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|
|
| ```python
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| from transformers import AutoModelForSequenceClassification, AutoTokenizer
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|
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| repo_id = "NeoCyber/m-e5-small-hosrev"
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| tokenizer = AutoTokenizer.from_pretrained(repo_id)
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| model = AutoModelForSequenceClassification.from_pretrained(
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| repo_id,
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| trust_remote_code=True,
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| )
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|
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| texts = ["Bác sĩ nhiệt tình và giải thích rất dễ hiểu."]
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| inputs = tokenizer(texts, return_tensors="pt", truncation=True, padding=True)
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| outputs = model(**inputs)
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| predictions = model.decode_predictions(outputs.logits)
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| print(predictions)
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| ```
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|
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| ## Notes
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|
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| - The repository includes custom `configuration_*.py` and `modeling_*.py` files required by `transformers` AutoClasses.
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| - `outputs.logits` has shape `[batch_size, num_aspects, 4]` and `model.decode_predictions(...)` maps logits back to aspect-level labels.
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|
|
