Push model using huggingface_hub.

.gitattributes

@@ -33,3 +33,4 @@ 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

1_Pooling/config.json

@@ -0,0 +1,7 @@
+{
+  "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
+}

README.md

@@ -0,0 +1,269 @@
+---
+library_name: setfit
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+metrics:
+- accuracy
+widget:
+- text: Quels sont les recours possibles en cas de conflit entre un employeur et un
+    employé ?
+- text: Comment déclarer mes impôts et taxes ?
+- text: Quelles sont les règles de tenue de la comptabilité ?
+- text: Quels sont les frais associés à cette procédure ?
+- text: Quelles sont les procédures de recours possibles contre une décision administrative
+    ?
+pipeline_tag: text-classification
+inference: true
+base_model: intfloat/multilingual-e5-small
+model-index:
+- name: SetFit with intfloat/multilingual-e5-small
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.9473684210526315
+      name: Accuracy
+---
+
+# SetFit with intfloat/multilingual-e5-small
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) 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:** [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small)
+- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
+- **Maximum Sequence Length:** 512 tokens
+- **Number of Classes:** 2 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                                                                                                                                                                                                                                                |
+|:------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| follow_up   | <ul><li>'Quels sont les régimes matrimoniaux possibles ?'</li><li>'Quelles sont les conséquences économiques ou sociales de cette loi ?'</li><li>"Est-ce que cette loi s'applique à mon cas particulier ?"</li></ul>                                    |
+| independent | <ul><li>'Quelles sont les règles en matière de temps de travail et de congés ?'</li><li>"Quels sont les types de structures d'entreprise autorisés en Algérie ?"</li><li>'Quels sont les droits et obligations des travailleurs en Algérie ?'</li></ul> |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.9474   |
+
+## 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("super-cinnamon/fewshot-followup-multi-e5")
+# Run inference
+preds = model("Comment déclarer mes impôts et taxes ?")
+```
+
+<!--
+### 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   | 2   | 9.76   | 16  |
+
+| Label       | Training Sample Count |
+|:------------|:----------------------|
+| independent | 39                    |
+| follow_up   | 36                    |
+
+### Training Hyperparameters
+- batch_size: (8, 8)
+- num_epochs: (10, 10)
+- 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: False
+
+### Training Results
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0028 | 1    | 0.3779        | -               |
+| 0.1381 | 50   | 0.3395        | -               |
+| 0.2762 | 100  | 0.1385        | -               |
+| 0.4144 | 150  | 0.1179        | -               |
+| 0.5525 | 200  | 0.0172        | -               |
+| 0.6906 | 250  | 0.0006        | -               |
+| 0.8287 | 300  | 0.0014        | -               |
+| 0.9669 | 350  | 0.0004        | -               |
+| 1.1050 | 400  | 0.0002        | -               |
+| 1.2431 | 450  | 0.0002        | -               |
+| 1.3812 | 500  | 0.0002        | -               |
+| 1.5193 | 550  | 0.0005        | -               |
+| 1.6575 | 600  | 0.0001        | -               |
+| 1.7956 | 650  | 0.0001        | -               |
+| 1.9337 | 700  | 0.0001        | -               |
+| 2.0718 | 750  | 0.0002        | -               |
+| 2.2099 | 800  | 0.0001        | -               |
+| 2.3481 | 850  | 0.0002        | -               |
+| 2.4862 | 900  | 0.0003        | -               |
+| 2.6243 | 950  | 0.0001        | -               |
+| 2.7624 | 1000 | 0.0001        | -               |
+| 2.9006 | 1050 | 0.0001        | -               |
+| 3.0387 | 1100 | 0.0           | -               |
+| 3.1768 | 1150 | 0.0001        | -               |
+| 3.3149 | 1200 | 0.0001        | -               |
+| 3.4530 | 1250 | 0.0001        | -               |
+| 3.5912 | 1300 | 0.0001        | -               |
+| 3.7293 | 1350 | 0.0           | -               |
+| 3.8674 | 1400 | 0.0001        | -               |
+| 4.0055 | 1450 | 0.0001        | -               |
+| 4.1436 | 1500 | 0.0001        | -               |
+| 4.2818 | 1550 | 0.0002        | -               |
+| 4.4199 | 1600 | 0.0001        | -               |
+| 4.5580 | 1650 | 0.0001        | -               |
+| 4.6961 | 1700 | 0.0002        | -               |
+| 4.8343 | 1750 | 0.0           | -               |
+| 4.9724 | 1800 | 0.0001        | -               |
+| 5.1105 | 1850 | 0.0           | -               |
+| 5.2486 | 1900 | 0.0001        | -               |
+| 5.3867 | 1950 | 0.0           | -               |
+| 5.5249 | 2000 | 0.0           | -               |
+| 5.6630 | 2050 | 0.0001        | -               |
+| 5.8011 | 2100 | 0.0           | -               |
+| 5.9392 | 2150 | 0.0           | -               |
+| 6.0773 | 2200 | 0.0001        | -               |
+| 6.2155 | 2250 | 0.0001        | -               |
+| 6.3536 | 2300 | 0.0001        | -               |
+| 6.4917 | 2350 | 0.0           | -               |
+| 6.6298 | 2400 | 0.0           | -               |
+| 6.7680 | 2450 | 0.0           | -               |
+| 6.9061 | 2500 | 0.0           | -               |
+| 7.0442 | 2550 | 0.0           | -               |
+| 7.1823 | 2600 | 0.0001        | -               |
+| 7.3204 | 2650 | 0.0           | -               |
+| 7.4586 | 2700 | 0.0           | -               |
+| 7.5967 | 2750 | 0.0001        | -               |
+| 7.7348 | 2800 | 0.0           | -               |
+| 7.8729 | 2850 | 0.0001        | -               |
+| 8.0110 | 2900 | 0.0           | -               |
+| 8.1492 | 2950 | 0.0           | -               |
+| 8.2873 | 3000 | 0.0           | -               |
+| 8.4254 | 3050 | 0.0           | -               |
+| 8.5635 | 3100 | 0.0001        | -               |
+| 8.7017 | 3150 | 0.0           | -               |
+| 8.8398 | 3200 | 0.0001        | -               |
+| 8.9779 | 3250 | 0.0           | -               |
+| 9.1160 | 3300 | 0.0           | -               |
+| 9.2541 | 3350 | 0.0           | -               |
+| 9.3923 | 3400 | 0.0           | -               |
+| 9.5304 | 3450 | 0.0           | -               |
+| 9.6685 | 3500 | 0.0           | -               |
+| 9.8066 | 3550 | 0.0           | -               |
+| 9.9448 | 3600 | 0.0           | -               |
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.0.1
+- Sentence Transformers: 2.2.2
+- Transformers: 4.35.2
+- PyTorch: 2.1.0+cu118
+- Datasets: 2.15.0
+- Tokenizers: 0.15.0
+
+## 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

@@ -0,0 +1,26 @@
+{
+  "_name_or_path": "/root/.cache/torch/sentence_transformers/intfloat_multilingual-e5-small/",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "torch_dtype": "float32",
+  "transformers_version": "4.35.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 250037
+}

config_sentence_transformers.json

@@ -0,0 +1,7 @@
+{
+  "__version__": {
+    "sentence_transformers": "2.2.2",
+    "transformers": "4.35.2",
+    "pytorch": "2.1.0+cu118"
+  }
+}

config_setfit.json

@@ -0,0 +1,7 @@
+{
+  "labels": [
+    "independent",
+    "follow_up"
+  ],
+  "normalize_embeddings": false
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5edb798d3bd404c37447047f5afda2cdad776e104cc8272895926d747735ca75
+size 470637416

model_head.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:21c3b1bfac2f76b5384939f137e1092bd4f06771646265e3952af3c4161c44a0
+size 3919

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

sentencepiece.bpe.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cfc8146abe2a0488e9e2a0c56de7952f7c11ab059eca145a0a727afce0db2865
+size 5069051

special_tokens_map.json

@@ -0,0 +1,15 @@
+{
+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "unk_token": "<unk>"
+}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:39feb9863a378165ab9c5c689047203d789422966c0c58721c5309fd039a8edc
+size 17083074

tokenizer_config.json

@@ -0,0 +1,55 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "250001": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "unk_token": "<unk>"
+}