| ---
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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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| - uni_vsfc
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| - uit-vsfc
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| - education
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| - multitask
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| - text-classification
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| ---
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|
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| # m-e5-small-uit-vsfc-uni
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|
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| ## Overview
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|
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| Vietnamese multi-task text classification model for student feedback. The model jointly predicts sentiment and topic labels from a single sentence.
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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: `uni_vsfc`
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| - Checkpoint source: `uit-vsfc-uni-e5-small-best.pt`
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| - Sequence length used during training/inference pipeline: `256`
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| - Tasks: `sentiment, topic`
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|
|
| ## Label Schema
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|
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| - `sentiment`: `0 = negative`, `1 = neutral`, `2 = positive`
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| - `topic`: `0 = lecturer`, `1 = training_program`, `2 = facility`, `3 = others`
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|
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| ## Task Heads
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| - `sentiment`: `3` classes
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| - `topic`: `4` classes
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|
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| ## Dataset
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| - Dataset: `Vietnamese Students' Feedback Corpus (UIT-VSFC)`
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| Vietnamese Students' Feedback Corpus (UIT-VSFC) contains more than 16,000 human-annotated student feedback sentences with sentiment and topic labels.
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|
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| ### Data Format
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|
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| - `sentence` is the input text column.
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| - `sentiment` is a 3-class label and `topic` is a 4-class label.
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|
|
| ### Splits
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|
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| - Train: `11426` samples
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| - Validation: `1583` samples
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| - Test: `3166` samples
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|
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| ## Checkpoint Metrics
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|
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| - `loss`: `0.2894`
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| - `accuracy`: `0.9005`
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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-uit-vsfc-uni"
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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 = ["slide giáo trình đầy đủ ."]
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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_by_task)
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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_by_task` contains one tensor per task, and `outputs.logits` is the concatenated tensor.
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|
