Text Classification
Transformers
Safetensors
Vietnamese
absa_transformer
aspect-based-sentiment-analysis
multilingual-e5
vietnamese
Instructions to use NeoCyber/m-e5-small-hosrev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NeoCyber/m-e5-small-hosrev with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="NeoCyber/m-e5-small-hosrev")# Load model directly from transformers import ABSAForAspectSentimentClassification model = ABSAForAspectSentimentClassification.from_pretrained("NeoCyber/m-e5-small-hosrev", dtype="auto") - Notebooks
- Google Colab
- Kaggle
NeoCyber/m-e5-small-hosrev
Aspect-based sentiment model exported from hosrev-e5-small-best.pt.
Training Metadata
- Base model:
intfloat/multilingual-e5-small - Epoch:
12 - Multi branch:
False - Aspect count:
13 - Sentiment labels:
none, positive, negative, neutral
Training Dataset
- Dataset: HosRev
- Local dataset path:
training/datasets/hosrev - Result source:
training/pipeline/full_pipeline_hosRev.ipynb
Test Metrics (Weighted Avg)
| Report | Precision | Recall | F1 | Support |
|---|---|---|---|---|
| aspect_category | 0.894861 | 0.905712 | 0.897999 | 12727 |
| aspect_category_polarity | 0.887875 | 0.898248 | 0.889022 | 12727 |
Checkpoint Metrics
{
"loss": 0.30365538026424166,
"accuracy": 0.8986943526820828,
"f1": 0.5241892214072907
}
Aspects
- Cơ sở vật chất#Chất lượng
- Cơ sở vật chất#Khác
- Cơ sở vật chất#Không gian
- Cơ sở vật chất#Vệ sinh
- Nhân viên y tế#Chất lượng
- Nhân viên y tế#Khác
- Nhân viên y tế#Thái độ
- Trải nghiệm chung#Chất lượng
- Trải nghiệm chung#Giá
- Trải nghiệm chung#Khác
- Trải nghiệm chung#Không gian
- Trải nghiệm chung#Thái độ
- Trải nghiệm chung#Vệ sinh
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Model tree for NeoCyber/m-e5-small-hosrev
Base model
intfloat/multilingual-e5-small