Push model using huggingface_hub.

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text
+unigram.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

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+{
+  "word_embedding_dimension": 384,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

@@ -0,0 +1,278 @@
+---
+base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
+library_name: setfit
+metrics:
+- accuracy
+pipeline_tag: text-classification
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: Is it available?
+- text: Est-il possible de fixer une visite?
+- text: Where is it located?
+- text: Pouvez-vous me parler des projets disponibles?
+- text: What’s the process to reserve?
+inference: true
+model-index:
+- name: SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 1.0
+      name: Accuracy
+---
+
+# SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-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/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2)
+- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
+- **Maximum Sequence Length:** 128 tokens
+- **Number of Classes:** 9 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                                                                                                                                                                  |
+|:------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| schedule_a_visit        | <ul><li>'I’d like to schedule a visit'</li><li>'Je voudrais planifier une visite'</li><li>'Puis-je programmer une visite?'</li></ul>                                      |
+| check_availability      | <ul><li>'Est-ce encore disponible?'</li><li>'Is this still available?'</li><li>'Can I check availability?'</li></ul>                                                      |
+| amenities_and_features  | <ul><li>'Parlez-moi des fonctionnalités du bien'</li><li>'Tell me the features of the property'</li><li>'Quels sont les équipements disponibles?'</li></ul>               |
+| payment_plan            | <ul><li>'Pouvez-vous me parler du plan de paiement?'</li><li>'Quels sont les modes de paiement disponibles?'</li><li>'What are the payment options?'</li></ul>            |
+| reservation_process     | <ul><li>'Tell me about the reservation process'</li><li>'Pouvez-vous m’expliquer le processus de réservation?'</li><li>'Comment puis-je faire une réservation?'</li></ul> |
+| location_details        | <ul><li>'Où est-ce situé?'</li><li>'Can you tell me the location details?'</li><li>'What’s the address?'</li></ul>                                                        |
+| pricing_details         | <ul><li>'How much does it cost?'</li><li>'Tell me the pricing details'</li><li>'Combien ça coûte?'</li></ul>                                                              |
+| option_process          | <ul><li>'Tell me about the option process'</li><li>'Parlez-moi du processus des options'</li><li>'Quels sont mes choix?'</li></ul>                                        |
+| information_on_projects | <ul><li>'Can you give me information about the projects?'</li><li>'I need details on the available projects'</li><li>'Quels sont les projets disponibles ?'</li></ul>     |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 1.0      |
+
+## 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("ali170506/chab")
+# Run inference
+preds = model("Is it available?")
+```
+
+<!--
+### 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   | 3   | 5.2222 | 8   |
+
+| Label                   | Training Sample Count |
+|:------------------------|:----------------------|
+| information_on_projects | 3                     |
+| pricing_details         | 3                     |
+| location_details        | 3                     |
+| amenities_and_features  | 3                     |
+| check_availability      | 3                     |
+| schedule_a_visit        | 3                     |
+| reservation_process     | 3                     |
+| option_process          | 3                     |
+| payment_plan            | 3                     |
+
+### Training Hyperparameters
+- batch_size: (4, 4)
+- num_epochs: (4, 4)
+- max_steps: -1
+- sampling_strategy: oversampling
+- 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
+- seed: 42
+- eval_max_steps: -1
+- load_best_model_at_end: True
+
+### Training Results
+| Epoch   | Step    | Training Loss | Validation Loss |
+|:-------:|:-------:|:-------------:|:---------------:|
+| 0.0062  | 1       | 0.0311        | -               |
+| 0.0617  | 10      | 0.0989        | -               |
+| 0.1235  | 20      | 0.0036        | -               |
+| 0.1852  | 30      | 0.0121        | -               |
+| 0.2469  | 40      | 0.0209        | -               |
+| 0.3086  | 50      | 0.001         | -               |
+| 0.3704  | 60      | 0.0067        | -               |
+| 0.4321  | 70      | 0.017         | -               |
+| 0.4938  | 80      | 0.0037        | -               |
+| 0.5556  | 90      | 0.012         | -               |
+| 0.6173  | 100     | 0.0009        | -               |
+| 0.6790  | 110     | 0.0044        | -               |
+| 0.7407  | 120     | 0.0014        | -               |
+| 0.8025  | 130     | 0.0006        | -               |
+| 0.8642  | 140     | 0.0016        | -               |
+| 0.9259  | 150     | 0.0024        | -               |
+| 0.9877  | 160     | 0.0011        | -               |
+| 1.0     | 162     | -             | 0.0164          |
+| 1.0494  | 170     | 0.0019        | -               |
+| 1.1111  | 180     | 0.0017        | -               |
+| 1.1728  | 190     | 0.0004        | -               |
+| 1.2346  | 200     | 0.0008        | -               |
+| 1.2963  | 210     | 0.0012        | -               |
+| 1.3580  | 220     | 0.0009        | -               |
+| 1.4198  | 230     | 0.0006        | -               |
+| 1.4815  | 240     | 0.001         | -               |
+| 1.5432  | 250     | 0.0009        | -               |
+| 1.6049  | 260     | 0.0015        | -               |
+| 1.6667  | 270     | 0.0016        | -               |
+| 1.7284  | 280     | 0.0009        | -               |
+| 1.7901  | 290     | 0.0005        | -               |
+| 1.8519  | 300     | 0.0009        | -               |
+| 1.9136  | 310     | 0.0009        | -               |
+| 1.9753  | 320     | 0.0008        | -               |
+| 2.0     | 324     | -             | 0.0138          |
+| 2.0370  | 330     | 0.0011        | -               |
+| 2.0988  | 340     | 0.0016        | -               |
+| 2.1605  | 350     | 0.0006        | -               |
+| 2.2222  | 360     | 0.0012        | -               |
+| 2.2840  | 370     | 0.0014        | -               |
+| 2.3457  | 380     | 0.0009        | -               |
+| 2.4074  | 390     | 0.0008        | -               |
+| 2.4691  | 400     | 0.0003        | -               |
+| 2.5309  | 410     | 0.0002        | -               |
+| 2.5926  | 420     | 0.0007        | -               |
+| 2.6543  | 430     | 0.001         | -               |
+| 2.7160  | 440     | 0.0008        | -               |
+| 2.7778  | 450     | 0.0008        | -               |
+| 2.8395  | 460     | 0.0003        | -               |
+| 2.9012  | 470     | 0.0004        | -               |
+| 2.9630  | 480     | 0.0003        | -               |
+| **3.0** | **486** | **-**         | **0.0129**      |
+| 3.0247  | 490     | 0.0013        | -               |
+| 3.0864  | 500     | 0.0006        | -               |
+| 3.1481  | 510     | 0.0008        | -               |
+| 3.2099  | 520     | 0.0001        | -               |
+| 3.2716  | 530     | 0.0007        | -               |
+| 3.3333  | 540     | 0.0004        | -               |
+| 3.3951  | 550     | 0.0004        | -               |
+| 3.4568  | 560     | 0.0003        | -               |
+| 3.5185  | 570     | 0.0003        | -               |
+| 3.5802  | 580     | 0.0002        | -               |
+| 3.6420  | 590     | 0.0002        | -               |
+| 3.7037  | 600     | 0.0002        | -               |
+| 3.7654  | 610     | 0.0007        | -               |
+| 3.8272  | 620     | 0.0007        | -               |
+| 3.8889  | 630     | 0.0007        | -               |
+| 3.9506  | 640     | 0.0003        | -               |
+| 4.0     | 648     | -             | 0.0129          |
+
+* The bold row denotes the saved checkpoint.
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.0.3
+- Sentence Transformers: 3.0.1
+- Transformers: 4.37.0
+- PyTorch: 2.4.1+cu121
+- Datasets: 3.0.1
+- Tokenizers: 0.15.2
+
+## 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.*
+-->

config.json

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+}

config_sentence_transformers.json

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+    "transformers": "4.37.0",
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+  },
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+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

config_setfit.json

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+  "normalize_embeddings": false,
+  "labels": [
+    "information_on_projects",
+    "pricing_details",
+    "location_details",
+    "amenities_and_features",
+    "check_availability",
+    "schedule_a_visit",
+    "reservation_process",
+    "option_process",
+    "payment_plan"
+  ]
+}

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sentence_bert_config.json

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special_tokens_map.json

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