monentiadev
/

en-input-classifier

@@ -1,206 +1,190 @@
----
-tags:
-- setfit
-- sentence-transformers
-- text-classification
-- generated_from_setfit_trainer
-widget:
-- text: poopchute
-- text: Made
-- text: prox
-- text: What happens, uncle, everything in order?
-- text: I need Maritima Avenue to reduce congestion
-metrics:
-- accuracy
-pipeline_tag: text-classification
-library_name: setfit
-inference: true
-base_model: sentence-transformers/all-MiniLM-L6-v2
----
-# SetFit with sentence-transformers/all-MiniLM-L6-v2
-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.
-The model has been trained using an efficient few-shot learning technique that involves:
-1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
-2. Training a classification head with features from the fine-tuned Sentence Transformer.
-## Model Details
-### Model Description
-- **Model Type:** SetFit
-- **Sentence Transformer body:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
-- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
-- **Maximum Sequence Length:** 256 tokens
-- **Number of Classes:** 4 classes
-<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
-<!-- - **Language:** Unknown -->
-<!-- - **License:** Unknown -->
-### Model Sources
-- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
-- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
-- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
-### Model Labels
-| Label        | Examples                                                                                                                                                       |
-|:-------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| request      | <ul><li>'necessary lingerie'</li><li>'necessary material for today'</li><li>'I finished the room 234'</li></ul>                                                |
-| conversation | <ul><li>"What's up, uncle, all good?"</li><li>'Good, how is the thing going?!'</li><li>'Hello how are you'</li></ul>                                           |
-| help         | <ul><li>'Please help'</li><li>"Help I don't know what I can do"</li><li>'Hello, what can I do'</li></ul>                                                       |
-| censorship   | <ul><li>'You are a useless complete, you are useless'</li><li>'Always saying stupidities, better shut up'</li><li>'Your single existence is a shame'</li></ul> |
-## Uses
-### Direct Use for Inference
-First install the SetFit library:
-```bash
-pip install setfit
-```
-Then you can load this model and run inference.
-```python
-from setfit import SetFitModel
-# Download from the 🤗 Hub
-model = SetFitModel.from_pretrained("monentiadev/en-input-classifier")
-# Run inference
-preds = model("Made")
-```
-<!--
-### Downstream Use
-*List how someone could finetune this model on their own dataset.*
--->
-<!--
-### Out-of-Scope Use
-*List how the model may foreseeably be misused and address what users ought not to do with the model.*
--->
-<!--
-## Bias, Risks and Limitations
-*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
--->
-<!--
-### Recommendations
-*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
--->
-## Training Details
-### Training Set Metrics
-| Training set | Min | Median | Max |
-|:-------------|:----|:-------|:----|
-| Word count   | 1   | 5.1483 | 40  |
-| Label        | Training Sample Count |
-|:-------------|:----------------------|
-| censorship   | 576                   |
-| conversation | 123                   |
-| help         | 204                   |
-| request      | 520                   |
-### Training Hyperparameters
-- batch_size: (128, 128)
-- num_epochs: (3, 3)
-- max_steps: -1
-- sampling_strategy: oversampling
-- num_iterations: 20
-- body_learning_rate: (2e-05, 1e-05)
-- head_learning_rate: 0.01
-- loss: CosineSimilarityLoss
-- distance_metric: cosine_distance
-- margin: 0.25
-- end_to_end: False
-- use_amp: False
-- warmup_proportion: 0.1
-- l2_weight: 0.01
-- seed: 42
-- eval_max_steps: -1
-- load_best_model_at_end: False
-### Training Results
-| Epoch  | Step | Training Loss | Validation Loss |
-|:------:|:----:|:-------------:|:---------------:|
-| 0.0022 | 1    | 0.3104        | -               |
-| 0.1124 | 50   | 0.3267        | -               |
-| 0.2247 | 100  | 0.2008        | -               |
-| 0.3371 | 150  | 0.0842        | -               |
-| 0.4494 | 200  | 0.0218        | -               |
-| 0.5618 | 250  | 0.0103        | -               |
-| 0.6742 | 300  | 0.0052        | -               |
-| 0.7865 | 350  | 0.0034        | -               |
-| 0.8989 | 400  | 0.0025        | -               |
-| 1.0112 | 450  | 0.0019        | -               |
-| 1.1236 | 500  | 0.0019        | -               |
-| 1.2360 | 550  | 0.0017        | -               |
-| 1.3483 | 600  | 0.001         | -               |
-| 1.4607 | 650  | 0.001         | -               |
-| 1.5730 | 700  | 0.0011        | -               |
-| 1.6854 | 750  | 0.0009        | -               |
-| 1.7978 | 800  | 0.001         | -               |
-| 1.9101 | 850  | 0.0007        | -               |
-| 2.0225 | 900  | 0.0008        | -               |
-| 2.1348 | 950  | 0.0007        | -               |
-| 2.2472 | 1000 | 0.0007        | -               |
-| 2.3596 | 1050 | 0.0006        | -               |
-| 2.4719 | 1100 | 0.0006        | -               |
-| 2.5843 | 1150 | 0.0006        | -               |
-| 2.6966 | 1200 | 0.0006        | -               |
-| 2.8090 | 1250 | 0.0006        | -               |
-| 2.9213 | 1300 | 0.0006        | -               |
-### Framework Versions
-- Python: 3.10.0
-- SetFit: 1.1.2
-- Sentence Transformers: 5.0.0
-- Transformers: 4.53.1
-- PyTorch: 2.7.1+cu126
-- Datasets: 2.19.2
-- Tokenizers: 0.21.2
-## Citation
-### BibTeX
-```bibtex
-@article{https://doi.org/10.48550/arxiv.2209.11055,
-    doi = {10.48550/ARXIV.2209.11055},
-    url = {https://arxiv.org/abs/2209.11055},
-    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
-    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
-    title = {Efficient Few-Shot Learning Without Prompts},
-    publisher = {arXiv},
-    year = {2022},
-    copyright = {Creative Commons Attribution 4.0 International}
-}
-```
-<!--
-## Glossary
-*Clearly define terms in order to be accessible across audiences.*
--->
-<!--
-## Model Card Authors
-*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
--->
-<!--
-## Model Card Contact
-*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
 -->

+---
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: poopchute
+- text: Made
+- text: prox
+- text: What happens, uncle, everything in order?
+- text: I need Maritima Avenue to reduce congestion
+metrics:
+- accuracy
+pipeline_tag: text-classification
+library_name: setfit
+inference: true
+base_model: sentence-transformers/all-MiniLM-L6-v2
+---
+# SetFit with sentence-transformers/all-MiniLM-L6-v2
+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.
+The model has been trained using an efficient few-shot learning technique that involves:
+1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
+2. Training a classification head with features from the fine-tuned Sentence Transformer.
+## Model Details
+### Model Description
+- **Model Type:** SetFit
+- **Sentence Transformer body:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
+- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
+- **Maximum Sequence Length:** 256 tokens
+- **Number of Classes:** 4 classes
+<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+### Model Sources
+- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
+- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
+- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
+### Model Labels
+| Label        | Examples                                                                                                                                                       |
+|:-------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| request      | <ul><li>'necessary lingerie'</li><li>'necessary material for today'</li><li>'I finished the room 234'</li></ul>                                                |
+| conversation | <ul><li>"What's up, uncle, all good?"</li><li>'Good, how is the thing going?!'</li><li>'Hello how are you'</li></ul>                                           |
+| help         | <ul><li>'Please help'</li><li>"Help I don't know what I can do"</li><li>'Hello, what can I do'</li></ul>                                                       |
+| censorship   | <ul><li>'You are a useless complete, you are useless'</li><li>'Always saying stupidities, better shut up'</li><li>'Your single existence is a shame'</li></ul> |
+## Uses
+### Direct Use for Inference
+First install the SetFit library:
+```bash
+pip install setfit
+```
+Then you can load this model and run inference.
+```python
+from setfit import SetFitModel
+# Download from the 🤗 Hub
+model = SetFitModel.from_pretrained("monentiadev/en-input-classifier")
+# Run inference
+preds = model("Hello")
+```
+<!--
+### Downstream Use
+*List how someone could finetune this model on their own dataset.*
+-->
+<!--
+### Out-of-Scope Use
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+<!--
+## Bias, Risks and Limitations
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+<!--
+### Recommendations
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+## Training Details
+### Training Set Metrics
+| Training set | Min | Median | Max |
+|:-------------|:----|:-------|:----|
+| Word count   | 1   | 5.1483 | 40  |
+| Label        | Training Sample Count |
+|:-------------|:----------------------|
+| censorship   | 576                   |
+| conversation | 123                   |
+| help         | 204                   |
+| request      | 520                   |
+### Training Hyperparameters
+- batch_size: (128, 128)
+- num_epochs: (3, 3)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 20
+- body_learning_rate: (2e-05, 1e-05)
+- head_learning_rate: 0.01
+- loss: CosineSimilarityLoss
+- distance_metric: cosine_distance
+- margin: 0.25
+- end_to_end: False
+- use_amp: False
+- warmup_proportion: 0.1
+- l2_weight: 0.01
+- seed: 42
+- eval_max_steps: -1
+- load_best_model_at_end: False
+### Training Results
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0022 | 1    | 0.3104        | -               |
+| 0.1124 | 50   | 0.3267        | -               |
+| 0.2247 | 100  | 0.2008        | -               |
+| 0.3371 | 150  | 0.0842        | -               |
+| 0.4494 | 200  | 0.0218        | -               |
+| 0.5618 | 250  | 0.0103        | -               |
+| 0.6742 | 300  | 0.0052        | -               |
+| 0.7865 | 350  | 0.0034        | -               |
+| 0.8989 | 400  | 0.0025        | -               |
+| 1.0112 | 450  | 0.0019        | -               |
+| 1.1236 | 500  | 0.0019        | -               |
+| 1.2360 | 550  | 0.0017        | -               |
+| 1.3483 | 600  | 0.001         | -               |
+| 1.4607 | 650  | 0.001         | -               |
+| 1.5730 | 700  | 0.0011        | -               |
+| 1.6854 | 750  | 0.0009        | -               |
+| 1.7978 | 800  | 0.001         | -               |
+| 1.9101 | 850  | 0.0007        | -               |
+| 2.0225 | 900  | 0.0008        | -               |
+| 2.1348 | 950  | 0.0007        | -               |
+| 2.2472 | 1000 | 0.0007        | -               |
+| 2.3596 | 1050 | 0.0006        | -               |
+| 2.4719 | 1100 | 0.0006        | -               |
+| 2.5843 | 1150 | 0.0006        | -               |
+| 2.6966 | 1200 | 0.0006        | -               |
+| 2.8090 | 1250 | 0.0006        | -               |
+| 2.9213 | 1300 | 0.0006        | -               |
+### Framework Versions
+- Python: 3.10.0
+- SetFit: 1.1.2
+- Sentence Transformers: 5.0.0
+- Transformers: 4.53.1
+- PyTorch: 2.7.1+cu126
+- Datasets: 2.19.2
+- Tokenizers: 0.21.2
+<!--
+## Glossary
+*Clearly define terms in order to be accessible across audiences.*
+-->
+<!--
+## Model Card Authors
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+<!--
+## Model Card Contact
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
 -->