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pacoreyes
/
StanceFit

Text Classification
setfit
PyTorch
sentence-transformers
mpnet
generated_from_setfit_trainer
Model card Files Community
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  1. README.md +28 -650
  2. config.json +1 -1
  3. config_sentence_transformers.json +2 -2
  4. model_head.pkl +2 -2
  5. modules.json +0 -6
  6. pytorch_model.bin +1 -1
  7. sentence_bert_config.json +1 -1
  8. tokenizer.json +78 -19
  9. tokenizer_config.json +59 -8
README.md CHANGED
@@ -7,28 +7,29 @@ tags:
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  - generated_from_setfit_trainer
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  metrics:
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  - accuracy
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- widget:
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- - text: I opposed this war in Iraq from the start, and I have never, ever wavered
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- in that opposition.
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- - text: This Central American democracy, peace, and recovery initiative, which I call
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- the Jackson plan, will be designed to bring democracy, peace, and prosperity to
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- Central America.
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- - text: Yesterday we opened another front on the war on terrorism as we began conventional
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- military operations designed to destroy terrorist training camps and military
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- installations of the Taliban Government.
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- - text: The threats against our critical infrastructure are increasingly complex and
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- nuanced, and we all must be prepared to better protect ourselves from malicious
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- actors threatening our cyber and physical security.
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- - text: Well, I am opposed to discrimination in any form, but I am -but I do not favor
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- creating special rights for any group.
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  pipeline_tag: text-classification
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  inference: true
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- base_model: sentence-transformers/all-mpnet-base-v2
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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- # SetFit with sentence-transformers/all-mpnet-base-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-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-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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@@ -39,10 +40,10 @@ The model has been trained using an efficient few-shot learning technique that i
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  ### Model Description
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  - **Model Type:** SetFit
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- - **Sentence Transformer body:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)
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  - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
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- - **Maximum Sequence Length:** 384 tokens
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- - **Number of Classes:** 2 classes
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  <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
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  <!-- - **Language:** Unknown -->
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  <!-- - **License:** Unknown -->
@@ -53,11 +54,12 @@ The model has been trained using an efficient few-shot learning technique that i
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  - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
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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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- | 0 | <ul><li>'I support free-trade, but I also support working to achieve better deals for America.'</li><li>'Our space program will, indeed, help rekindle public interest in science and mathematics, revitalize an area of our educational system that has become disturbingly weak.'</li><li>'The other third goes to fund democracy, the things that we Americans believe would lead to better decisions.'</li></ul> |
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- | 1 | <ul><li>'Well, I think terrorism is the hardest thing to curtail.'</li><li>'For over a year, I have ordered our military to take thousands of airstrikes against ISIL targets.'</li><li>'In my discussion with President Bush this morning, I have made clear that we are opposed to terrorism of all forms.'</li></ul> |
 
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  ## Uses
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@@ -77,7 +79,7 @@ from setfit import SetFitModel
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  # Download from the 🤗 Hub
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  model = SetFitModel.from_pretrained("setfit_model_id")
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  # Run inference
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- preds = model("I opposed this war in Iraq from the start, and I have never, ever wavered in that opposition.")
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  ```
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  <!--
@@ -106,630 +108,6 @@ preds = model("I opposed this war in Iraq from the start, and I have never, ever
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  ## Training Details
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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 | 3 | 23.6564 | 46 |
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-
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- | Label | Training Sample Count |
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- |:------|:----------------------|
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- | 0 | 486 |
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- | 1 | 486 |
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-
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- ### Training Hyperparameters
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- - batch_size: (16, 16)
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- - num_epochs: (1, 1)
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- - max_steps: -1
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- - sampling_strategy: oversampling
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- - body_learning_rate: (1.003444469523018e-06, 1.003444469523018e-06)
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- - head_learning_rate: 0.01
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- - loss: CosineSimilarityLoss
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- - distance_metric: cosine_distance
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- - margin: 0.25
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- - end_to_end: False
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- - use_amp: False
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- - warmup_proportion: 0.1
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- - seed: 37
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- - eval_max_steps: -1
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- - load_best_model_at_end: True
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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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- | 0.0000 | 1 | 0.3371 | - |
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- | 0.0017 | 50 | 0.3042 | - |
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- | 0.0034 | 100 | 0.2146 | - |
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- | 0.0051 | 150 | 0.2119 | - |
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- | 0.0068 | 200 | 0.3006 | - |
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- | 0.0084 | 250 | 0.2619 | - |
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- | 0.0101 | 300 | 0.2862 | - |
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- | 0.0118 | 350 | 0.2587 | - |
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- | 0.0135 | 400 | 0.1888 | - |
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- | 0.0152 | 450 | 0.2727 | - |
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- | 0.0169 | 500 | 0.2586 | 0.2538 |
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- | 0.0186 | 550 | 0.2382 | - |
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- | 0.0203 | 600 | 0.2268 | - |
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- | 0.0220 | 650 | 0.2547 | - |
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- | 0.0237 | 700 | 0.2011 | - |
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- | 0.0253 | 750 | 0.1975 | - |
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- | 0.0270 | 800 | 0.2417 | - |
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- | 0.0287 | 850 | 0.2558 | - |
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- | 0.0304 | 900 | 0.227 | - |
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- | 0.0321 | 950 | 0.2148 | - |
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- | 0.0338 | 1000 | 0.2035 | 0.1979 |
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- | 0.0355 | 1050 | 0.2029 | - |
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- | 0.0372 | 1100 | 0.1874 | - |
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- | 0.0389 | 1150 | 0.1489 | - |
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- | 0.0406 | 1200 | 0.0767 | - |
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- | 0.0422 | 1250 | 0.0995 | - |
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- | 0.0439 | 1300 | 0.1205 | - |
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- | 0.0456 | 1350 | 0.0752 | - |
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- | 0.0473 | 1400 | 0.0546 | - |
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- | 0.0490 | 1450 | 0.0255 | - |
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- | 0.0507 | 1500 | 0.0232 | 0.026 |
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- | 0.0524 | 1550 | 0.0292 | - |
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- | 0.0541 | 1600 | 0.0277 | - |
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- | 0.0558 | 1650 | 0.038 | - |
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- | 0.0575 | 1700 | 0.0073 | - |
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- | 0.0591 | 1750 | 0.0276 | - |
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- | 0.0608 | 1800 | 0.019 | - |
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- | 0.0625 | 1850 | 0.015 | - |
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- | 0.0642 | 1900 | 0.0063 | - |
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- | 0.0659 | 1950 | 0.0031 | - |
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- | 0.0676 | 2000 | 0.0035 | 0.0032 |
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- | 0.0693 | 2050 | 0.0041 | - |
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- | 0.0710 | 2100 | 0.003 | - |
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- | 0.0727 | 2150 | 0.0051 | - |
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- | 0.0744 | 2200 | 0.0062 | - |
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- | 0.0760 | 2250 | 0.0029 | - |
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- | 0.0777 | 2300 | 0.0063 | - |
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- | 0.0794 | 2350 | 0.0061 | - |
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- | 0.0811 | 2400 | 0.0024 | - |
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- | 0.0828 | 2450 | 0.0015 | - |
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- | 0.0845 | 2500 | 0.0009 | 0.001 |
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- | 0.0862 | 2550 | 0.0015 | - |
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- | 0.0879 | 2600 | 0.0012 | - |
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- | 0.0896 | 2650 | 0.0011 | - |
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- | 0.0913 | 2700 | 0.0007 | - |
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- | 0.0929 | 2750 | 0.0007 | - |
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- | 0.0946 | 2800 | 0.0007 | - |
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- | 0.0963 | 2850 | 0.0007 | - |
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- | 0.0980 | 2900 | 0.0006 | - |
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- | 0.0997 | 2950 | 0.0008 | - |
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- | 0.1014 | 3000 | 0.0005 | 0.0004 |
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- | 0.1031 | 3050 | 0.0006 | - |
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- | 0.1960 | 5800 | 0.0002 | - |
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- | 0.1977 | 5850 | 0.001 | - |
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- | 0.2197 | 6500 | 0.0001 | 0.0 |
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- | 0.4293 | 12700 | 0.0 | - |
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- | 0.4309 | 12750 | 0.0 | - |
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- | 0.4326 | 12800 | 0.0 | - |
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- | 0.4394 | 13000 | 0.0 | 0.0 |
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- | 0.4597 | 13600 | 0.0 | - |
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- | 0.4647 | 13750 | 0.0 | - |
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- | 0.4664 | 13800 | 0.0 | - |
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- | 0.4681 | 13850 | 0.0 | - |
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- | 0.4698 | 13900 | 0.0 | - |
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- | 0.4732 | 14000 | 0.0 | 0.0 |
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- | 0.4800 | 14200 | 0.0 | - |
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- | 0.4833 | 14300 | 0.0 | - |
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- | 0.4884 | 14450 | 0.0 | - |
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- | 0.4901 | 14500 | 0.0 | 0.0 |
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- | 0.4918 | 14550 | 0.0 | - |
431
- | 0.4935 | 14600 | 0.0 | - |
432
- | 0.4952 | 14650 | 0.0 | - |
433
- | 0.4969 | 14700 | 0.0 | - |
434
- | 0.4985 | 14750 | 0.0001 | - |
435
- | 0.5002 | 14800 | 0.0 | - |
436
- | 0.5019 | 14850 | 0.0 | - |
437
- | 0.5036 | 14900 | 0.0 | - |
438
- | 0.5053 | 14950 | 0.0 | - |
439
- | 0.5070 | 15000 | 0.0 | 0.0 |
440
- | 0.5087 | 15050 | 0.0 | - |
441
- | 0.5104 | 15100 | 0.0 | - |
442
- | 0.5121 | 15150 | 0.0 | - |
443
- | 0.5138 | 15200 | 0.0 | - |
444
- | 0.5154 | 15250 | 0.0 | - |
445
- | 0.5171 | 15300 | 0.0 | - |
446
- | 0.5188 | 15350 | 0.0 | - |
447
- | 0.5205 | 15400 | 0.0 | - |
448
- | 0.5222 | 15450 | 0.0 | - |
449
- | 0.5239 | 15500 | 0.0 | 0.0 |
450
- | 0.5256 | 15550 | 0.0 | - |
451
- | 0.5273 | 15600 | 0.0 | - |
452
- | 0.5290 | 15650 | 0.0 | - |
453
- | 0.5307 | 15700 | 0.0 | - |
454
- | 0.5323 | 15750 | 0.0 | - |
455
- | 0.5340 | 15800 | 0.0 | - |
456
- | 0.5357 | 15850 | 0.0 | - |
457
- | 0.5374 | 15900 | 0.0 | - |
458
- | 0.5391 | 15950 | 0.0 | - |
459
- | 0.5408 | 16000 | 0.0 | 0.0 |
460
- | 0.5425 | 16050 | 0.0 | - |
461
- | 0.5442 | 16100 | 0.0 | - |
462
- | 0.5459 | 16150 | 0.0 | - |
463
- | 0.5476 | 16200 | 0.0 | - |
464
- | 0.5492 | 16250 | 0.0 | - |
465
- | 0.5509 | 16300 | 0.0 | - |
466
- | 0.5526 | 16350 | 0.0 | - |
467
- | 0.5543 | 16400 | 0.0 | - |
468
- | 0.5560 | 16450 | 0.0 | - |
469
- | 0.5577 | 16500 | 0.0 | 0.0 |
470
- | 0.5594 | 16550 | 0.0 | - |
471
- | 0.5611 | 16600 | 0.0 | - |
472
- | 0.5628 | 16650 | 0.0 | - |
473
- | 0.5645 | 16700 | 0.0 | - |
474
- | 0.5661 | 16750 | 0.0 | - |
475
- | 0.5678 | 16800 | 0.0 | - |
476
- | 0.5695 | 16850 | 0.0 | - |
477
- | 0.5712 | 16900 | 0.0 | - |
478
- | 0.5729 | 16950 | 0.0 | - |
479
- | 0.5746 | 17000 | 0.0 | 0.0 |
480
- | 0.5763 | 17050 | 0.0 | - |
481
- | 0.5780 | 17100 | 0.0 | - |
482
- | 0.5797 | 17150 | 0.0 | - |
483
- | 0.5814 | 17200 | 0.0 | - |
484
- | 0.5830 | 17250 | 0.0 | - |
485
- | 0.5847 | 17300 | 0.0 | - |
486
- | 0.5864 | 17350 | 0.0 | - |
487
- | 0.5881 | 17400 | 0.0 | - |
488
- | 0.5898 | 17450 | 0.0 | - |
489
- | 0.5915 | 17500 | 0.0 | 0.0 |
490
- | 0.5932 | 17550 | 0.0 | - |
491
- | 0.5949 | 17600 | 0.0 | - |
492
- | 0.5966 | 17650 | 0.0 | - |
493
- | 0.5983 | 17700 | 0.0 | - |
494
- | 0.5999 | 17750 | 0.0 | - |
495
- | 0.6016 | 17800 | 0.0 | - |
496
- | 0.6033 | 17850 | 0.0 | - |
497
- | 0.6050 | 17900 | 0.0 | - |
498
- | 0.6067 | 17950 | 0.0 | - |
499
- | 0.6084 | 18000 | 0.0 | 0.0 |
500
- | 0.6101 | 18050 | 0.0 | - |
501
- | 0.6118 | 18100 | 0.0 | - |
502
- | 0.6135 | 18150 | 0.0 | - |
503
- | 0.6152 | 18200 | 0.0 | - |
504
- | 0.6168 | 18250 | 0.0 | - |
505
- | 0.6185 | 18300 | 0.0 | - |
506
- | 0.6202 | 18350 | 0.0 | - |
507
- | 0.6219 | 18400 | 0.0 | - |
508
- | 0.6236 | 18450 | 0.0 | - |
509
- | 0.6253 | 18500 | 0.0 | 0.0 |
510
- | 0.6270 | 18550 | 0.0 | - |
511
- | 0.6287 | 18600 | 0.0 | - |
512
- | 0.6304 | 18650 | 0.0 | - |
513
- | 0.6321 | 18700 | 0.0 | - |
514
- | 0.6337 | 18750 | 0.0 | - |
515
- | 0.6354 | 18800 | 0.0 | - |
516
- | 0.6371 | 18850 | 0.0 | - |
517
- | 0.6388 | 18900 | 0.0 | - |
518
- | 0.6405 | 18950 | 0.0 | - |
519
- | 0.6422 | 19000 | 0.0 | 0.0 |
520
- | 0.6439 | 19050 | 0.0 | - |
521
- | 0.6456 | 19100 | 0.0 | - |
522
- | 0.6473 | 19150 | 0.0 | - |
523
- | 0.6490 | 19200 | 0.0 | - |
524
- | 0.6506 | 19250 | 0.0 | - |
525
- | 0.6523 | 19300 | 0.0 | - |
526
- | 0.6540 | 19350 | 0.0 | - |
527
- | 0.6557 | 19400 | 0.0 | - |
528
- | 0.6574 | 19450 | 0.0 | - |
529
- | 0.6591 | 19500 | 0.0 | 0.0 |
530
- | 0.6608 | 19550 | 0.0 | - |
531
- | 0.6625 | 19600 | 0.0 | - |
532
- | 0.6642 | 19650 | 0.0 | - |
533
- | 0.6659 | 19700 | 0.0 | - |
534
- | 0.6675 | 19750 | 0.0 | - |
535
- | 0.6692 | 19800 | 0.0 | - |
536
- | 0.6709 | 19850 | 0.0 | - |
537
- | 0.6726 | 19900 | 0.0 | - |
538
- | 0.6743 | 19950 | 0.0 | - |
539
- | 0.6760 | 20000 | 0.0 | 0.0 |
540
- | 0.6777 | 20050 | 0.0 | - |
541
- | 0.6794 | 20100 | 0.0 | - |
542
- | 0.6811 | 20150 | 0.0 | - |
543
- | 0.6828 | 20200 | 0.0 | - |
544
- | 0.6844 | 20250 | 0.0 | - |
545
- | 0.6861 | 20300 | 0.0 | - |
546
- | 0.6878 | 20350 | 0.0 | - |
547
- | 0.6895 | 20400 | 0.0 | - |
548
- | 0.6912 | 20450 | 0.0 | - |
549
- | 0.6929 | 20500 | 0.0 | 0.0 |
550
- | 0.6946 | 20550 | 0.0 | - |
551
- | 0.6963 | 20600 | 0.0 | - |
552
- | 0.6980 | 20650 | 0.0 | - |
553
- | 0.6997 | 20700 | 0.0 | - |
554
- | 0.7013 | 20750 | 0.0 | - |
555
- | 0.7030 | 20800 | 0.0 | - |
556
- | 0.7047 | 20850 | 0.0 | - |
557
- | 0.7064 | 20900 | 0.0 | - |
558
- | 0.7081 | 20950 | 0.0 | - |
559
- | 0.7098 | 21000 | 0.0 | 0.0 |
560
- | 0.7115 | 21050 | 0.0 | - |
561
- | 0.7132 | 21100 | 0.0 | - |
562
- | 0.7149 | 21150 | 0.0 | - |
563
- | 0.7166 | 21200 | 0.0 | - |
564
- | 0.7182 | 21250 | 0.0 | - |
565
- | 0.7199 | 21300 | 0.0 | - |
566
- | 0.7216 | 21350 | 0.0 | - |
567
- | 0.7233 | 21400 | 0.0 | - |
568
- | 0.7250 | 21450 | 0.0 | - |
569
- | **0.7267** | **21500** | **0.0** | **0.0** |
570
- | 0.7284 | 21550 | 0.0 | - |
571
- | 0.7301 | 21600 | 0.0 | - |
572
- | 0.7318 | 21650 | 0.0 | - |
573
- | 0.7335 | 21700 | 0.0 | - |
574
- | 0.7351 | 21750 | 0.0 | - |
575
- | 0.7368 | 21800 | 0.0 | - |
576
- | 0.7385 | 21850 | 0.0 | - |
577
- | 0.7402 | 21900 | 0.0 | - |
578
- | 0.7419 | 21950 | 0.0 | - |
579
- | 0.7436 | 22000 | 0.0 | 0.0 |
580
- | 0.7453 | 22050 | 0.0 | - |
581
- | 0.7470 | 22100 | 0.0 | - |
582
- | 0.7487 | 22150 | 0.0 | - |
583
- | 0.7504 | 22200 | 0.0 | - |
584
- | 0.7520 | 22250 | 0.0 | - |
585
- | 0.7537 | 22300 | 0.0 | - |
586
- | 0.7554 | 22350 | 0.0 | - |
587
- | 0.7571 | 22400 | 0.0 | - |
588
- | 0.7588 | 22450 | 0.0 | - |
589
- | 0.7605 | 22500 | 0.0 | 0.0 |
590
- | 0.7622 | 22550 | 0.0 | - |
591
- | 0.7639 | 22600 | 0.0 | - |
592
- | 0.7656 | 22650 | 0.0 | - |
593
- | 0.7673 | 22700 | 0.0 | - |
594
- | 0.7689 | 22750 | 0.0 | - |
595
- | 0.7706 | 22800 | 0.0 | - |
596
- | 0.7723 | 22850 | 0.0 | - |
597
- | 0.7740 | 22900 | 0.0 | - |
598
- | 0.7757 | 22950 | 0.0 | - |
599
- | 0.7774 | 23000 | 0.0 | 0.0 |
600
- | 0.7791 | 23050 | 0.0 | - |
601
- | 0.7808 | 23100 | 0.0 | - |
602
- | 0.7825 | 23150 | 0.0 | - |
603
- | 0.7842 | 23200 | 0.0 | - |
604
- | 0.7858 | 23250 | 0.0 | - |
605
- | 0.7875 | 23300 | 0.0 | - |
606
- | 0.7892 | 23350 | 0.0 | - |
607
- | 0.7909 | 23400 | 0.0 | - |
608
- | 0.7926 | 23450 | 0.0 | - |
609
- | 0.7943 | 23500 | 0.0 | 0.0 |
610
- | 0.7960 | 23550 | 0.0 | - |
611
- | 0.7977 | 23600 | 0.0 | - |
612
- | 0.7994 | 23650 | 0.0 | - |
613
- | 0.8011 | 23700 | 0.0 | - |
614
- | 0.8027 | 23750 | 0.0 | - |
615
- | 0.8044 | 23800 | 0.0 | - |
616
- | 0.8061 | 23850 | 0.0 | - |
617
- | 0.8078 | 23900 | 0.0 | - |
618
- | 0.8095 | 23950 | 0.0 | - |
619
- | 0.8112 | 24000 | 0.0 | 0.0 |
620
- | 0.8129 | 24050 | 0.0 | - |
621
- | 0.8146 | 24100 | 0.0 | - |
622
- | 0.8163 | 24150 | 0.0 | - |
623
- | 0.8180 | 24200 | 0.0 | - |
624
- | 0.8196 | 24250 | 0.0 | - |
625
- | 0.8213 | 24300 | 0.0 | - |
626
- | 0.8230 | 24350 | 0.0 | - |
627
- | 0.8247 | 24400 | 0.0 | - |
628
- | 0.8264 | 24450 | 0.0 | - |
629
- | 0.8281 | 24500 | 0.0 | 0.0 |
630
- | 0.8298 | 24550 | 0.0 | - |
631
- | 0.8315 | 24600 | 0.0 | - |
632
- | 0.8332 | 24650 | 0.0 | - |
633
- | 0.8349 | 24700 | 0.0 | - |
634
- | 0.8365 | 24750 | 0.0 | - |
635
- | 0.8382 | 24800 | 0.0 | - |
636
- | 0.8399 | 24850 | 0.0 | - |
637
- | 0.8416 | 24900 | 0.0 | - |
638
- | 0.8433 | 24950 | 0.0 | - |
639
- | 0.8450 | 25000 | 0.0 | 0.0 |
640
- | 0.8467 | 25050 | 0.0 | - |
641
- | 0.8484 | 25100 | 0.0 | - |
642
- | 0.8501 | 25150 | 0.0 | - |
643
- | 0.8518 | 25200 | 0.0 | - |
644
- | 0.8534 | 25250 | 0.0 | - |
645
- | 0.8551 | 25300 | 0.0 | - |
646
- | 0.8568 | 25350 | 0.0 | - |
647
- | 0.8585 | 25400 | 0.0 | - |
648
- | 0.8602 | 25450 | 0.0 | - |
649
- | 0.8619 | 25500 | 0.0 | 0.0 |
650
- | 0.8636 | 25550 | 0.0 | - |
651
- | 0.8653 | 25600 | 0.0 | - |
652
- | 0.8670 | 25650 | 0.0 | - |
653
- | 0.8687 | 25700 | 0.0 | - |
654
- | 0.8703 | 25750 | 0.0 | - |
655
- | 0.8720 | 25800 | 0.0 | - |
656
- | 0.8737 | 25850 | 0.0 | - |
657
- | 0.8754 | 25900 | 0.0 | - |
658
- | 0.8771 | 25950 | 0.0 | - |
659
- | 0.8788 | 26000 | 0.0 | 0.0 |
660
- | 0.8805 | 26050 | 0.0 | - |
661
- | 0.8822 | 26100 | 0.0 | - |
662
- | 0.8839 | 26150 | 0.0 | - |
663
- | 0.8856 | 26200 | 0.0 | - |
664
- | 0.8872 | 26250 | 0.0 | - |
665
- | 0.8889 | 26300 | 0.0 | - |
666
- | 0.8906 | 26350 | 0.0 | - |
667
- | 0.8923 | 26400 | 0.0 | - |
668
- | 0.8940 | 26450 | 0.0 | - |
669
- | 0.8957 | 26500 | 0.0 | 0.0 |
670
- | 0.8974 | 26550 | 0.0 | - |
671
- | 0.8991 | 26600 | 0.0 | - |
672
- | 0.9008 | 26650 | 0.0 | - |
673
- | 0.9025 | 26700 | 0.0 | - |
674
- | 0.9041 | 26750 | 0.0 | - |
675
- | 0.9058 | 26800 | 0.0 | - |
676
- | 0.9075 | 26850 | 0.0 | - |
677
- | 0.9092 | 26900 | 0.0 | - |
678
- | 0.9109 | 26950 | 0.0 | - |
679
- | 0.9126 | 27000 | 0.0 | 0.0 |
680
- | 0.9143 | 27050 | 0.0 | - |
681
- | 0.9160 | 27100 | 0.0 | - |
682
- | 0.9177 | 27150 | 0.0 | - |
683
- | 0.9194 | 27200 | 0.0 | - |
684
- | 0.9210 | 27250 | 0.0 | - |
685
- | 0.9227 | 27300 | 0.0 | - |
686
- | 0.9244 | 27350 | 0.0 | - |
687
- | 0.9261 | 27400 | 0.0 | - |
688
- | 0.9278 | 27450 | 0.0 | - |
689
- | 0.9295 | 27500 | 0.0 | 0.0 |
690
- | 0.9312 | 27550 | 0.0 | - |
691
- | 0.9329 | 27600 | 0.0 | - |
692
- | 0.9346 | 27650 | 0.0 | - |
693
- | 0.9363 | 27700 | 0.0 | - |
694
- | 0.9379 | 27750 | 0.0 | - |
695
- | 0.9396 | 27800 | 0.0 | - |
696
- | 0.9413 | 27850 | 0.0 | - |
697
- | 0.9430 | 27900 | 0.0 | - |
698
- | 0.9447 | 27950 | 0.0 | - |
699
- | 0.9464 | 28000 | 0.0 | 0.0 |
700
- | 0.9481 | 28050 | 0.0 | - |
701
- | 0.9498 | 28100 | 0.0 | - |
702
- | 0.9515 | 28150 | 0.0 | - |
703
- | 0.9532 | 28200 | 0.0 | - |
704
- | 0.9548 | 28250 | 0.0 | - |
705
- | 0.9565 | 28300 | 0.0 | - |
706
- | 0.9582 | 28350 | 0.0 | - |
707
- | 0.9599 | 28400 | 0.0 | - |
708
- | 0.9616 | 28450 | 0.0 | - |
709
- | 0.9633 | 28500 | 0.0 | 0.0 |
710
- | 0.9650 | 28550 | 0.0 | - |
711
- | 0.9667 | 28600 | 0.0 | - |
712
- | 0.9684 | 28650 | 0.0 | - |
713
- | 0.9701 | 28700 | 0.0 | - |
714
- | 0.9717 | 28750 | 0.0 | - |
715
- | 0.9734 | 28800 | 0.0 | - |
716
- | 0.9751 | 28850 | 0.0 | - |
717
- | 0.9768 | 28900 | 0.0 | - |
718
- | 0.9785 | 28950 | 0.0 | - |
719
- | 0.9802 | 29000 | 0.0 | 0.0 |
720
- | 0.9819 | 29050 | 0.0 | - |
721
- | 0.9836 | 29100 | 0.0 | - |
722
- | 0.9853 | 29150 | 0.0 | - |
723
- | 0.9870 | 29200 | 0.0 | - |
724
- | 0.9886 | 29250 | 0.0 | - |
725
- | 0.9903 | 29300 | 0.0 | - |
726
- | 0.9920 | 29350 | 0.0 | - |
727
- | 0.9937 | 29400 | 0.0 | - |
728
- | 0.9954 | 29450 | 0.0 | - |
729
- | 0.9971 | 29500 | 0.0 | 0.0 |
730
- | 0.9988 | 29550 | 0.0 | - |
731
-
732
- * The bold row denotes the saved checkpoint.
733
  ### Framework Versions
734
  - Python: 3.10.11
735
  - SetFit: 1.0.1
 
7
  - generated_from_setfit_trainer
8
  metrics:
9
  - accuracy
10
+ widget: []
 
 
 
 
 
 
 
 
 
 
 
 
 
11
  pipeline_tag: text-classification
12
  inference: true
13
+ base_model: sentence-transformers/paraphrase-mpnet-base-v2
14
+ model-index:
15
+ - name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
16
+ results:
17
+ - task:
18
+ type: text-classification
19
+ name: Text Classification
20
+ dataset:
21
+ name: Unknown
22
+ type: unknown
23
+ split: test
24
+ metrics:
25
+ - type: accuracy
26
+ value: 1.0
27
+ name: Accuracy
28
  ---
29
 
30
+ # SetFit with sentence-transformers/paraphrase-mpnet-base-v2
31
 
32
+ This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-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.
33
 
34
  The model has been trained using an efficient few-shot learning technique that involves:
35
 
 
40
 
41
  ### Model Description
42
  - **Model Type:** SetFit
43
+ - **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
44
  - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
45
+ - **Maximum Sequence Length:** 512 tokens
46
+ <!-- - **Number of Classes:** Unknown -->
47
  <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
48
  <!-- - **Language:** Unknown -->
49
  <!-- - **License:** Unknown -->
 
54
  - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
55
  - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
56
 
57
+ ## Evaluation
58
+
59
+ ### Metrics
60
+ | Label | Accuracy |
61
+ |:--------|:---------|
62
+ | **all** | 1.0 |
63
 
64
  ## Uses
65
 
 
79
  # Download from the 🤗 Hub
80
  model = SetFitModel.from_pretrained("setfit_model_id")
81
  # Run inference
82
+ preds = model("I loved the spiderman movie!")
83
  ```
84
 
85
  <!--
 
108
 
109
  ## Training Details
110
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
111
  ### Framework Versions
112
  - Python: 3.10.11
113
  - SetFit: 1.0.1
config.json CHANGED
@@ -1,5 +1,5 @@
1
  {
2
- "_name_or_path": "checkpoints/step_21500/",
3
  "architectures": [
4
  "MPNetModel"
5
  ],
 
1
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