Add SetFit model
Browse files- README.md +26 -18
- config.json +1 -1
- config_setfit.json +2 -2
- model.safetensors +1 -1
- model_head.pkl +1 -1
README.md
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@@ -9,11 +9,13 @@ 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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- text:
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- text: What are the benefits of using cloud storage?
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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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---
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@@ -60,17 +62,17 @@ The model has been trained using an efficient few-shot learning technique that i
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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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## 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("What
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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 Hyperparameters
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- batch_size: (4, 4)
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- num_epochs: (
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- max_steps: -1
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- sampling_strategy: oversampling
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- body_learning_rate: (2e-05, 1e-05)
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- load_best_model_at_end: True
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### Training Results
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| Epoch | Step
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| 0.0164 | 1
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| 0.8197 | 50
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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: 'Xác suất để trúng giải thưởng khi bạn mua một tờ vé số là 0.05%. Giả sử mỗi
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ngày bạn mua 1 tờ vé số, vậy
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chúng ta cần bao nhiêu ngày (trung bình) để có 98% cơ hội trúng?'
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- text: Briefly describe the concept of photosynthesis.
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- text: What are the benefits of using cloud storage?
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- text: Write a Python function that checks if a given number is prime.
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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: 0.25
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name: Accuracy
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---
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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>'Which of the following is a Code-Based Test Coverage Metrics(E. F. Miller, 1977 dissertation)?\nCâu hỏi 1Trả lời\n\na.\nC1c: Every condition outcome\n\nb.\nMMCC: Multiple Module condition coverage\n\nc.\nCx - Every "x" statement ("x" can be single, double, triple)\n\nd.\nC2: C0 coverage + loop coverage'</li><li>'Analyze the time complexity of the merge sort algorithm.'</li><li>'For the expression "(a AND (b OR c))", which of the following test-cases is Multiple Condition Coverage (MCC)?\nCâu hỏi 8Trả lời\n\na.\n04 test cases in (a,b,c) format: "(true,true,true)", "(true,true,false)", "(true,false,true)" and "(false,true,true)"\n\nb.\n02 test cases in (a,b,c) format: "(true,true,true)" and "(false,true,false)"\n\nc.\n06 test cases in (a,b,c)format: "(true,true,true)", "(true,true,false)", "(true,false,true)", "(true,false,false)", "(false,true,true)", and "(false,false,false)"\n\nd.\n08 test cases for all combination of a=true/false, b=true/false, c=true/false'</li></ul> |
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| 0 | <ul><li>'Viết một hàm Python tính giai thừa của một số.'</li><li>'I have this math problem: Solve for x in the equation 2x + 5 = 11. Show the steps involved.'</li><li>'Nêu ngắn gọn về quá trình quang hợp.'</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** | 0.25 |
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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("What are the benefits of using cloud storage?")
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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 | 26.7143 | 115 |
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| Label | Training Sample Count |
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|:------|:----------------------|
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### Training Hyperparameters
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- batch_size: (4, 4)
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- num_epochs: (4, 4)
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- max_steps: -1
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- sampling_strategy: oversampling
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- body_learning_rate: (2e-05, 1e-05)
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- load_best_model_at_end: True
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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 | 0.353 | - |
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| 0.8197 | 50 | 0.2404 | - |
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| 1.0 | 61 | - | 0.0838 |
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| 1.6393 | 100 | 0.0044 | - |
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| 2.0 | 122 | - | 0.0572 |
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| 2.4590 | 150 | 0.0017 | - |
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| **3.0** | **183** | **-** | **0.0523** |
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| 3.2787 | 200 | 0.0055 | - |
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| 4.0 | 244 | - | 0.0541 |
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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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{
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"_name_or_path": "checkpoints/
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"architectures": [
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"BertModel"
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],
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{
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"_name_or_path": "checkpoints/step_183",
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"architectures": [
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"BertModel"
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],
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config_setfit.json
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{
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"normalize_embeddings": false,
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"labels": [
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}
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{
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"labels": [
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"normalize_embeddings": false
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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:
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size 90864192
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version https://git-lfs.github.com/spec/v1
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oid sha256:418ffc704b439c53c1f05f00dadf247b8136a6cd9b2445a3a3a5fa0d76ca913d
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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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oid sha256:
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size 3935
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version https://git-lfs.github.com/spec/v1
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oid sha256:1a9488d8a6934272a8d3056e003576e8aeb95f784f01720be30d7ade10e85fbc
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size 3935
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