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 +1,9 @@
+{
+  "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
+}

README.md

@@ -0,0 +1,317 @@
+---
+library_name: setfit
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+metrics:
+- accuracy
+widget:
+- text: παστα ατομικη
+- text: mikel mini croissant σοκολατα
+- text: tasty nat nut παστελι σουσαμι
+- text: σκιουφιχτα σαλτσα ντοματας μυζηθρα σαλτσα ντοματας ελιες καππαρη μυζηθρα
+- text: κρασι ροζε λιανος
+pipeline_tag: text-classification
+inference: false
+base_model: lighteternal/stsb-xlm-r-greek-transfer
+model-index:
+- name: SetFit with lighteternal/stsb-xlm-r-greek-transfer
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.1588785046728972
+      name: Accuracy
+---
+
+# SetFit with lighteternal/stsb-xlm-r-greek-transfer
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [lighteternal/stsb-xlm-r-greek-transfer](https://huggingface.co/lighteternal/stsb-xlm-r-greek-transfer) as the Sentence Transformer embedding model. A OneVsRestClassifier 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:** [lighteternal/stsb-xlm-r-greek-transfer](https://huggingface.co/lighteternal/stsb-xlm-r-greek-transfer)
+- **Classification head:** a OneVsRestClassifier instance
+- **Maximum Sequence Length:** 400 tokens
+<!-- - **Number of Classes:** Unknown -->
+<!-- - **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)
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.1589   |
+
+## 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("st-karlos-efood/setfit-multilabel-one-vs-rest-feb-2024")
+# Run inference
+preds = model("παστα ατομικη")
+```
+
+<!--
+### 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   | 8.6048 | 116 |
+
+### Training Hyperparameters
+- batch_size: (48, 48)
+- num_epochs: (5, 5)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 10
+- body_learning_rate: (2e-05, 2e-05)
+- head_learning_rate: 2e-05
+- 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 |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0008 | 1    | 0.2009        | -               |
+| 0.0377 | 50   | 0.1674        | -               |
+| 0.0754 | 100  | 0.1593        | -               |
+| 0.1131 | 150  | 0.1793        | -               |
+| 0.1508 | 200  | 0.176         | -               |
+| 0.1885 | 250  | 0.1818        | -               |
+| 0.2262 | 300  | 0.1209        | -               |
+| 0.2640 | 350  | 0.1546        | -               |
+| 0.3017 | 400  | 0.0996        | -               |
+| 0.3394 | 450  | 0.1108        | -               |
+| 0.3771 | 500  | 0.1163        | -               |
+| 0.4148 | 550  | 0.1102        | -               |
+| 0.4525 | 600  | 0.1477        | -               |
+| 0.4902 | 650  | 0.0973        | -               |
+| 0.5279 | 700  | 0.1324        | -               |
+| 0.5656 | 750  | 0.1792        | -               |
+| 0.6033 | 800  | 0.1026        | -               |
+| 0.6410 | 850  | 0.1461        | -               |
+| 0.6787 | 900  | 0.117         | -               |
+| 0.7164 | 950  | 0.0907        | -               |
+| 0.7541 | 1000 | 0.0904        | -               |
+| 0.7919 | 1050 | 0.1168        | -               |
+| 0.8296 | 1100 | 0.0831        | -               |
+| 0.8673 | 1150 | 0.0623        | -               |
+| 0.9050 | 1200 | 0.0802        | -               |
+| 0.9427 | 1250 | 0.0802        | -               |
+| 0.9804 | 1300 | 0.1212        | -               |
+| 1.0181 | 1350 | 0.0872        | -               |
+| 1.0558 | 1400 | 0.1068        | -               |
+| 1.0935 | 1450 | 0.0975        | -               |
+| 1.1312 | 1500 | 0.096         | -               |
+| 1.1689 | 1550 | 0.0649        | -               |
+| 1.2066 | 1600 | 0.1004        | -               |
+| 1.2443 | 1650 | 0.0818        | -               |
+| 1.2821 | 1700 | 0.0714        | -               |
+| 1.3198 | 1750 | 0.0875        | -               |
+| 1.3575 | 1800 | 0.0893        | -               |
+| 1.3952 | 1850 | 0.1132        | -               |
+| 1.4329 | 1900 | 0.1127        | -               |
+| 1.4706 | 1950 | 0.0707        | -               |
+| 1.5083 | 2000 | 0.0819        | -               |
+| 1.5460 | 2050 | 0.0954        | -               |
+| 1.5837 | 2100 | 0.0948        | -               |
+| 1.6214 | 2150 | 0.0953        | -               |
+| 1.6591 | 2200 | 0.0813        | -               |
+| 1.6968 | 2250 | 0.0974        | -               |
+| 1.7345 | 2300 | 0.0785        | -               |
+| 1.7722 | 2350 | 0.086         | -               |
+| 1.8100 | 2400 | 0.0808        | -               |
+| 1.8477 | 2450 | 0.1014        | -               |
+| 1.8854 | 2500 | 0.112         | -               |
+| 1.9231 | 2550 | 0.0765        | -               |
+| 1.9608 | 2600 | 0.0694        | -               |
+| 1.9985 | 2650 | 0.0915        | -               |
+| 2.0362 | 2700 | 0.087         | -               |
+| 2.0739 | 2750 | 0.0831        | -               |
+| 2.1116 | 2800 | 0.1223        | -               |
+| 2.1493 | 2850 | 0.0897        | -               |
+| 2.1870 | 2900 | 0.0937        | -               |
+| 2.2247 | 2950 | 0.0862        | -               |
+| 2.2624 | 3000 | 0.0977        | -               |
+| 2.3002 | 3050 | 0.0563        | -               |
+| 2.3379 | 3100 | 0.1197        | -               |
+| 2.3756 | 3150 | 0.095         | -               |
+| 2.4133 | 3200 | 0.0702        | -               |
+| 2.4510 | 3250 | 0.0823        | -               |
+| 2.4887 | 3300 | 0.1309        | -               |
+| 2.5264 | 3350 | 0.0612        | -               |
+| 2.5641 | 3400 | 0.0994        | -               |
+| 2.6018 | 3450 | 0.0904        | -               |
+| 2.6395 | 3500 | 0.0678        | -               |
+| 2.6772 | 3550 | 0.0896        | -               |
+| 2.7149 | 3600 | 0.0753        | -               |
+| 2.7526 | 3650 | 0.0997        | -               |
+| 2.7903 | 3700 | 0.0956        | -               |
+| 2.8281 | 3750 | 0.1016        | -               |
+| 2.8658 | 3800 | 0.0784        | -               |
+| 2.9035 | 3850 | 0.0911        | -               |
+| 2.9412 | 3900 | 0.0485        | -               |
+| 2.9789 | 3950 | 0.1078        | -               |
+| 3.0166 | 4000 | 0.0659        | -               |
+| 3.0543 | 4050 | 0.0802        | -               |
+| 3.0920 | 4100 | 0.12          | -               |
+| 3.1297 | 4150 | 0.0519        | -               |
+| 3.1674 | 4200 | 0.047         | -               |
+| 3.2051 | 4250 | 0.0906        | -               |
+| 3.2428 | 4300 | 0.0999        | -               |
+| 3.2805 | 4350 | 0.059         | -               |
+| 3.3183 | 4400 | 0.0533        | -               |
+| 3.3560 | 4450 | 0.1033        | -               |
+| 3.3937 | 4500 | 0.0871        | -               |
+| 3.4314 | 4550 | 0.065         | -               |
+| 3.4691 | 4600 | 0.1487        | -               |
+| 3.5068 | 4650 | 0.0542        | -               |
+| 3.5445 | 4700 | 0.0846        | -               |
+| 3.5822 | 4750 | 0.0756        | -               |
+| 3.6199 | 4800 | 0.0518        | -               |
+| 3.6576 | 4850 | 0.1035        | -               |
+| 3.6953 | 4900 | 0.1129        | -               |
+| 3.7330 | 4950 | 0.1319        | -               |
+| 3.7707 | 5000 | 0.0804        | -               |
+| 3.8084 | 5050 | 0.108         | -               |
+| 3.8462 | 5100 | 0.1246        | -               |
+| 3.8839 | 5150 | 0.0923        | -               |
+| 3.9216 | 5200 | 0.1048        | -               |
+| 3.9593 | 5250 | 0.0951        | -               |
+| 3.9970 | 5300 | 0.1015        | -               |
+| 4.0347 | 5350 | 0.0888        | -               |
+| 4.0724 | 5400 | 0.0917        | -               |
+| 4.1101 | 5450 | 0.0823        | -               |
+| 4.1478 | 5500 | 0.0882        | -               |
+| 4.1855 | 5550 | 0.0807        | -               |
+| 4.2232 | 5600 | 0.0997        | -               |
+| 4.2609 | 5650 | 0.0782        | -               |
+| 4.2986 | 5700 | 0.1165        | -               |
+| 4.3363 | 5750 | 0.0837        | -               |
+| 4.3741 | 5800 | 0.1098        | -               |
+| 4.4118 | 5850 | 0.0564        | -               |
+| 4.4495 | 5900 | 0.0715        | -               |
+| 4.4872 | 5950 | 0.0858        | -               |
+| 4.5249 | 6000 | 0.0889        | -               |
+| 4.5626 | 6050 | 0.0719        | -               |
+| 4.6003 | 6100 | 0.1076        | -               |
+| 4.6380 | 6150 | 0.1044        | -               |
+| 4.6757 | 6200 | 0.0914        | -               |
+| 4.7134 | 6250 | 0.1078        | -               |
+| 4.7511 | 6300 | 0.1137        | -               |
+| 4.7888 | 6350 | 0.0666        | -               |
+| 4.8265 | 6400 | 0.1009        | -               |
+| 4.8643 | 6450 | 0.0537        | -               |
+| 4.9020 | 6500 | 0.0576        | -               |
+| 4.9397 | 6550 | 0.1366        | -               |
+| 4.9774 | 6600 | 0.1009        | -               |
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.0.3
+- Sentence Transformers: 2.3.1
+- Transformers: 4.35.2
+- PyTorch: 2.1.0+cu121
+- Datasets: 2.17.0
+- Tokenizers: 0.15.1
+
+## 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

@@ -0,0 +1,29 @@
+{
+  "_name_or_path": "lighteternal/stsb-xlm-r-greek-transfer",
+  "architectures": [
+    "XLMRobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.35.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "2.0.0",
+    "transformers": "4.10.0",
+    "pytorch": "1.7.1"
+  }
+}

config_setfit.json

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+{
+  "labels": null,
+  "normalize_embeddings": false
+}

model.safetensors

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model_head.pkl

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modules.json

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+  {
+    "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

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+{
+  "max_seq_length": 400,
+  "do_lower_case": false
+}

sentencepiece.bpe.model

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special_tokens_map.json

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+  "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

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tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+    }
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+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "max_length": 400,
+  "model_max_length": 512,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<unk>"
+}