Add SetFit model
Browse files- README.md +45 -39
- config.json +1 -1
- model.safetensors +1 -1
- model_head.pkl +1 -1
README.md
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@@ -9,14 +9,34 @@ base_model: sentence-transformers/all-MiniLM-L6-v2
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metrics:
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- accuracy
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widget:
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pipeline_tag: text-classification
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inference: true
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model-index:
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split: test
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metrics:
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- type: accuracy
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value: 0.
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name: Accuracy
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license: mit
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datasets:
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- chibao24/gpt_routing
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language:
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- vi
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- en
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---
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# SetFit with sentence-transformers/all-MiniLM-L6-v2
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This model is gpt routing between gpt.5 and gpt-4o based on my prompt (to reduce cost). You can take a look at the dataset for more information.
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I got the idea from this [LLM classifier](https://github.com/lamini-ai/llm-classifier)
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The model utilizes Few-Shot Learning techniques through SetFit, requiring only 8 examples per class. It can be trained in less than 1 minute on an RTX 3060 graphics card.
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This method provides an efficient solution for developing lightweight models suitable for real-world applications.
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The source code can be found in my repo [mrzaizai2k/LLM-with-RAG](https://github.com/mrzaizai2k/LLM-with-RAG)
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This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
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The model has been trained using an efficient few-shot learning technique that involves:
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- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
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### Model Labels
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| Label | Examples
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| 1 | <ul><li>'
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| 0 | <ul><li>'
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## Evaluation
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### Metrics
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| Label | Accuracy |
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|:--------|:---------|
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| **all** | 0.
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## Uses
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# Download from the 🤗 Hub
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model = SetFitModel.from_pretrained("chibao24/model_routing_few_shot")
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# Run inference
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preds = model("
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```
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<!--
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### Training Set Metrics
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| Training set | Min | Median | Max |
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|:-------------|:----|:--------|:----|
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| Word count | 4 |
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| Label | Training Sample Count |
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|:------|:----------------------|
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### Training Results
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| Epoch | Step | Training Loss | Validation Loss |
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|:-------:|:-------:|:-------------:|:---------------:|
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| 0.0164 | 1 | 0.
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| 0.8197 | 50 | 0.
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| 1.0 | 61 | - | 0.
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| 1.6393 | 100 | 0.
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| 2.0
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| 2.4590 | 150 | 0.
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| 3.2787 | 200 | 0.
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| 4.0 | 244 | - | 0.
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* The bold row denotes the saved checkpoint.
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### Framework Versions
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metrics:
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- accuracy
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widget:
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- text: 'Which of the following is a Code-Based Test Coverage Metrics(E. F. Miller,
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1977 dissertation)?
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Câu hỏi 1Trả lời
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a.
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C1c: Every condition outcome
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b.
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MMCC: Multiple Module condition coverage
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c.
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Cx - Every "x" statement ("x" can be single, double, triple)
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d.
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C2: C0 coverage + loop coverage'
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- text: Phần mềm kiểm thử là gì?
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- text: Giải thích sự khác biệt giữa kiểm thử hộp đen và kiểm thử hộp trắng. Cung
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cấp ví dụ cho từng loại. (ít nhất 150 từ)
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- text: Thủ đô của nước Pháp là gì?
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pipeline_tag: text-classification
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inference: true
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model-index:
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split: test
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metrics:
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- type: accuracy
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value: 0.5
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name: Accuracy
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---
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# SetFit with sentence-transformers/all-MiniLM-L6-v2
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This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
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The model has been trained using an efficient few-shot learning technique that involves:
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- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
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### Model Labels
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| Label | Examples |
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|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| 1 | <ul><li>'Giải thích sự khác biệt giữa mô hình học có giám sát và không giám sát. Cung cấp ví dụ cho từng loại. (ít nhất 150 từ)'</li><li>'Analyze the time complexity of the merge sort algorithm.'</li><li>'Xác suất để trúng giải thưởng khi bạn mua một tờ vé số là 0.05%. Giả sử mỗi ngày bạn mua 1 tờ vé số, vậy\nchúng ta cần bao nhiêu ngày (trung bình) để có 98% cơ hội trúng?'</li></ul> |
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| 0 | <ul><li>'Nêu ngắn gọn về quá trình quang hợp.'</li><li>'Viết một hàm Python tính giai thừa của một số.'</li><li>'Briefly describe the concept of photosynthesis.'</li></ul> |
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## Evaluation
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### Metrics
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| Label | Accuracy |
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|:--------|:---------|
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| **all** | 0.5 |
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## Uses
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# Download from the 🤗 Hub
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model = SetFitModel.from_pretrained("chibao24/model_routing_few_shot")
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# Run inference
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preds = model("Phần mềm kiểm thử là gì?")
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```
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<!--
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### Training Set Metrics
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| Training set | Min | Median | Max |
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|:-------------|:----|:--------|:----|
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| Word count | 4 | 24.7619 | 115 |
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| Label | Training Sample Count |
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|:------|:----------------------|
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### Training Results
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| Epoch | Step | Training Loss | Validation Loss |
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|:-------:|:-------:|:-------------:|:---------------:|
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| 0.0164 | 1 | 0.1956 | - |
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| 0.8197 | 50 | 0.1926 | - |
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| 1.0 | 61 | - | 0.1463 |
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| 1.6393 | 100 | 0.0228 | - |
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| **2.0** | **122** | **-** | **0.0374** |
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| 2.4590 | 150 | 0.017 | - |
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| 3.0 | 183 | - | 0.0507 |
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| 3.2787 | 200 | 0.003 | - |
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| 4.0 | 244 | - | 0.0443 |
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* The bold row denotes the saved checkpoint.
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### Framework Versions
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config.json
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"_name_or_path": "checkpoints/
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"architectures": [
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"BertModel"
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"_name_or_path": "checkpoints/step_122",
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"BertModel"
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],
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model.safetensors
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size 90864192
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model_head.pkl
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version https://git-lfs.github.com/spec/v1
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version https://git-lfs.github.com/spec/v1
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size 3935
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