Add SetFit model

.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 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "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

@@ -1,69 +1,247 @@
 ---
-library_name: transformers
-license: apache-2.0
-base_model: projecte-aina/roberta-base-ca-v2
-tags:
-- generated_from_trainer
+base_model: projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base
+library_name: setfit
 metrics:
 - accuracy
-- precision
-- recall
-- f1
+pipeline_tag: text-classification
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: Quin és el percentatge de bonificació per a les famílies monoparentals o nombroses?
+- text: Salut, tanque's
+- text: Quin és el tema principal de l'informe previ?
+- text: Quin és el destinatari de la sol·licitud de canvi d'ubicació?
+- text: Què es necessita per obtenir una placa de gual?
+inference: true
 model-index:
-- name: fm-tc-authentic
-  results: []
+- name: SetFit with projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.9978448275862069
+      name: Accuracy
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+# SetFit with projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base
 
-# fm-tc-authentic
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base](https://huggingface.co/projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base) 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.
 
-This model is a fine-tuned version of [projecte-aina/roberta-base-ca-v2](https://huggingface.co/projecte-aina/roberta-base-ca-v2) on the None dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.0314
-- Accuracy: 1.0
-- Precision: 1.0
-- Recall: 1.0
-- F1: 1.0
+The model has been trained using an efficient few-shot learning technique that involves:
 
-## Model description
+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.
 
-More information needed
+## Model Details
 
-## Intended uses & limitations
+### Model Description
+- **Model Type:** SetFit
+- **Sentence Transformer body:** [projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base](https://huggingface.co/projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base)
+- **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:** 2 classes
+<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
 
-More information needed
+### Model Sources
 
-## Training and evaluation data
+- **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)
 
-More information needed
+### Model Labels
+| Label | Examples                                                                                                                                                                                                   |
+|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 1     | <ul><li>'Bona nit, com estàs?'</li><li>'Ei, què tal tot?'</li><li>'Hola, com està el temps?'</li></ul>                                                                                                     |
+| 0     | <ul><li>'Quin és el propòsit de la llicència administrativa?'</li><li>'Quin és el benefici de les subvencions per als infants?'</li><li>"Què acredita el certificat d'empadronament col·lectiu?"</li></ul> |
 
-## Training procedure
+## Evaluation
 
-### Training hyperparameters
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.9978   |
 
-The following hyperparameters were used during training:
-- learning_rate: 1e-05
-- train_batch_size: 16
-- eval_batch_size: 16
-- seed: 42
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- lr_scheduler_warmup_steps: 500
-- num_epochs: 2
+## 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("adriansanz/greetings-v2")
+# Run inference
+preds = model("Salut, tanque's")
+```
 
-### Training results
+<!--
+### Downstream Use
 
-| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
-| No log        | 1.0   | 202  | 0.1516          | 0.9992   | 0.9994    | 0.9988 | 0.9991 |
-| No log        | 2.0   | 404  | 0.0314          | 1.0      | 1.0       | 1.0    | 1.0    |
+*List how someone could finetune this model on their own dataset.*
+-->
 
+<!--
+### Out-of-Scope Use
 
-### Framework versions
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
 
-- Transformers 4.44.2
-- Pytorch 2.5.0+cu121
-- Datasets 3.1.0
-- Tokenizers 0.19.1
+<!--
+## 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   | 9.8187 | 23  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 100                   |
+| 1     | 60                    |
+
+### Training Hyperparameters
+- batch_size: (16, 16)
+- num_epochs: (3, 3)
+- 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
+- l2_weight: 0.01
+- seed: 42
+- eval_max_steps: -1
+- load_best_model_at_end: False
+
+### Training Results
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0012 | 1    | 0.2127        | -               |
+| 0.0581 | 50   | 0.1471        | -               |
+| 0.1163 | 100  | 0.0168        | -               |
+| 0.1744 | 150  | 0.001         | -               |
+| 0.2326 | 200  | 0.0004        | -               |
+| 0.2907 | 250  | 0.0002        | -               |
+| 0.3488 | 300  | 0.0001        | -               |
+| 0.4070 | 350  | 0.0001        | -               |
+| 0.4651 | 400  | 0.0001        | -               |
+| 0.5233 | 450  | 0.0001        | -               |
+| 0.5814 | 500  | 0.0001        | -               |
+| 0.6395 | 550  | 0.0001        | -               |
+| 0.6977 | 600  | 0.0001        | -               |
+| 0.7558 | 650  | 0.0           | -               |
+| 0.8140 | 700  | 0.0           | -               |
+| 0.8721 | 750  | 0.0           | -               |
+| 0.9302 | 800  | 0.0           | -               |
+| 0.9884 | 850  | 0.0           | -               |
+| 1.0465 | 900  | 0.0           | -               |
+| 1.1047 | 950  | 0.0           | -               |
+| 1.1628 | 1000 | 0.0           | -               |
+| 1.2209 | 1050 | 0.0           | -               |
+| 1.2791 | 1100 | 0.0           | -               |
+| 1.3372 | 1150 | 0.0           | -               |
+| 1.3953 | 1200 | 0.0           | -               |
+| 1.4535 | 1250 | 0.0           | -               |
+| 1.5116 | 1300 | 0.0           | -               |
+| 1.5698 | 1350 | 0.0           | -               |
+| 1.6279 | 1400 | 0.0           | -               |
+| 1.6860 | 1450 | 0.0           | -               |
+| 1.7442 | 1500 | 0.0           | -               |
+| 1.8023 | 1550 | 0.0           | -               |
+| 1.8605 | 1600 | 0.0           | -               |
+| 1.9186 | 1650 | 0.0           | -               |
+| 1.9767 | 1700 | 0.0           | -               |
+| 2.0349 | 1750 | 0.0           | -               |
+| 2.0930 | 1800 | 0.0           | -               |
+| 2.1512 | 1850 | 0.0           | -               |
+| 2.2093 | 1900 | 0.0           | -               |
+| 2.2674 | 1950 | 0.0           | -               |
+| 2.3256 | 2000 | 0.0           | -               |
+| 2.3837 | 2050 | 0.0           | -               |
+| 2.4419 | 2100 | 0.0           | -               |
+| 2.5    | 2150 | 0.0           | -               |
+| 2.5581 | 2200 | 0.0           | -               |
+| 2.6163 | 2250 | 0.0           | -               |
+| 2.6744 | 2300 | 0.0           | -               |
+| 2.7326 | 2350 | 0.0           | -               |
+| 2.7907 | 2400 | 0.0           | -               |
+| 2.8488 | 2450 | 0.0           | -               |
+| 2.9070 | 2500 | 0.0           | -               |
+| 2.9651 | 2550 | 0.0           | -               |
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.1.0
+- Sentence Transformers: 3.2.1
+- Transformers: 4.44.2
+- PyTorch: 2.5.0+cu121
+- Datasets: 3.1.0
+- Tokenizers: 0.19.1
+
+## 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

@@ -1,7 +1,7 @@
 {
-  "_name_or_path": "projecte-aina/roberta-base-ca-v2",
+  "_name_or_path": "projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base",
   "architectures": [
-    "RobertaForSequenceClassification"
+    "XLMRobertaModel"
   ],
   "attention_probs_dropout_prob": 0.1,
   "bos_token_id": 0,
@@ -11,23 +11,19 @@
   "hidden_act": "gelu",
   "hidden_dropout_prob": 0.1,
   "hidden_size": 768,
-  "id2label": {
-    "0": 0,
-    "1": 1
-  },
   "initializer_range": 0.02,
   "intermediate_size": 3072,
   "layer_norm_eps": 1e-05,
   "max_position_embeddings": 514,
-  "model_type": "roberta",
+  "model_type": "xlm-roberta",
   "num_attention_heads": 12,
   "num_hidden_layers": 12,
+  "output_past": true,
   "pad_token_id": 1,
   "position_embedding_type": "absolute",
-  "problem_type": "single_label_classification",
   "torch_dtype": "float32",
   "transformers_version": "4.44.2",
   "type_vocab_size": 1,
   "use_cache": true,
-  "vocab_size": 50262
+  "vocab_size": 250002
 }

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.2.1",
+    "transformers": "4.44.2",
+    "pytorch": "2.5.0+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

config_setfit.json

@@ -0,0 +1,4 @@
+{
+  "normalize_embeddings": false,
+  "labels": null
+}

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:06945cdccb238d1ebde6feeeccfe25752fadf8327042c418141728f99b125ddf
-size 498603608
+oid sha256:541b2bbe7945748eb9a5102fa753a29424013a6ddb8fdeadd1408d8bca5b9ce0
+size 1112197096

model_head.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d57419310f3170a362600f05ead623a5d6d4793887b9248aa87fab12ab91a414
+size 7007

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 128,
+  "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

@@ -2,49 +2,49 @@
   "bos_token": {
     "content": "<s>",
     "lstrip": false,
-    "normalized": true,
+    "normalized": false,
     "rstrip": false,
     "single_word": false
   },
   "cls_token": {
     "content": "<s>",
     "lstrip": false,
-    "normalized": true,
+    "normalized": false,
     "rstrip": false,
     "single_word": false
   },
   "eos_token": {
     "content": "</s>",
     "lstrip": false,
-    "normalized": true,
+    "normalized": false,
     "rstrip": false,
     "single_word": false
   },
   "mask_token": {
     "content": "<mask>",
     "lstrip": true,
-    "normalized": true,
+    "normalized": false,
     "rstrip": false,
     "single_word": false
   },
   "pad_token": {
     "content": "<pad>",
     "lstrip": false,
-    "normalized": true,
+    "normalized": false,
     "rstrip": false,
     "single_word": false
   },
   "sep_token": {
     "content": "</s>",
     "lstrip": false,
-    "normalized": true,
+    "normalized": false,
     "rstrip": false,
     "single_word": false
   },
   "unk_token": {
     "content": "<unk>",
     "lstrip": false,
-    "normalized": true,
+    "normalized": false,
     "rstrip": false,
     "single_word": false
   }

tokenizer_config.json

@@ -1,10 +1,9 @@
 {
-  "add_prefix_space": true,
   "added_tokens_decoder": {
     "0": {
       "content": "<s>",
       "lstrip": false,
-      "normalized": true,
+      "normalized": false,
       "rstrip": false,
       "single_word": false,
       "special": true
@@ -12,7 +11,7 @@
     "1": {
       "content": "<pad>",
       "lstrip": false,
-      "normalized": true,
+      "normalized": false,
       "rstrip": false,
       "single_word": false,
       "special": true
@@ -20,7 +19,7 @@
     "2": {
       "content": "</s>",
       "lstrip": false,
-      "normalized": true,
+      "normalized": false,
       "rstrip": false,
       "single_word": false,
       "special": true
@@ -28,15 +27,15 @@
     "3": {
       "content": "<unk>",
       "lstrip": false,
-      "normalized": true,
+      "normalized": false,
       "rstrip": false,
       "single_word": false,
       "special": true
     },
-    "4": {
+    "250001": {
       "content": "<mask>",
       "lstrip": true,
-      "normalized": true,
+      "normalized": false,
       "rstrip": false,
       "single_word": false,
       "special": true
@@ -46,13 +45,17 @@
   "clean_up_tokenization_spaces": true,
   "cls_token": "<s>",
   "eos_token": "</s>",
-  "errors": "replace",
   "mask_token": "<mask>",
-  "max_len": 512,
-  "model_max_length": 512,
+  "max_length": 128,
+  "model_max_length": 128,
+  "pad_to_multiple_of": null,
   "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
   "sep_token": "</s>",
-  "tokenizer_class": "RobertaTokenizer",
-  "trim_offsets": true,
+  "stride": 0,
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
   "unk_token": "<unk>"
 }