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

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+{
+  "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
+}

README.md

@@ -0,0 +1,231 @@
+---
+library_name: setfit
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+metrics:
+- accuracy
+widget:
+- text: considering the use of so-called “fractional citations” in which one divides
+    the number of citations associated with a given paper by the number of authors
+    on that paper [33–38];
+- text: Indeed, this is only one of a number of such practical inconsistencies inherent
+    in the traditional h-index; other similar inconsistencies are discussed in Refs.
+    [3, 4].
+- text: One of the referees recommends mentioning Quesada (2008) as another characterization
+    of the Hirsch index relying as well on monotonicity.
+- text: considering the use of so-called “fractional citations” in which one divides
+    the number of citations associated with a given paper by the number of authors
+    on that paper [33–38];
+- text: increasing the weighting of very highly-cited papers, either through the introduction
+    of intrinsic weighting factors or the development of entirely new indices which
+    mix the h-index with other more traditional indices (such as total citation count)
+    [3, 4, 7, 8, 26–32];
+pipeline_tag: text-classification
+inference: true
+base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
+model-index:
+- name: SetFit with sentence-transformers/paraphrase-multilingual-mpnet-base-v2
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.6111111111111112
+      name: Accuracy
+---
+
+# SetFit with sentence-transformers/paraphrase-multilingual-mpnet-base-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-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-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-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
+|:-----------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| ccro:BasedOn     | <ul><li>'The axiomatizations presented in Quesada (2010, 2011) also dispense with strong monotonicity.'</li></ul>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
+| ccro:Basedon     | <ul><li>'A formal mathematical description of the h-index introduced by Hirsch (2005)'</li><li>'Woeginger (2008a, b) and Quesada (2009, 2010) have already suggested characterizations of the Hirsch index'</li><li>'Woeginger (2008a, b) and Quesada (2009, 2010) have already suggested characterizations of the Hirsch index'</li></ul>                                                                                                                                                                                                                                                                                                                                                                                                   |
+| ccro:Compare     | <ul><li>'Instead, a variety of studies [8, 9] have shown that the h index by and large agrees with other objective and subjective measures of scientific quality in a variety of different disciplines (10–15),'</li><li>'Instead, a variety of studies [8, 9] have shown that the h index by and large agrees with other objective and subjective measures of scientific quality in a variety of different disciplines (10–15),'</li><li>'Instead, a variety of studies [8, 9] have shown that the h index by and large agrees with other objective and subjective measures of scientific quality in a variety of different disciplines (10–15),'</li></ul>                                                                                 |
+| ccro:Contrast    | <ul><li>'Hirsch (2005) argues that two individuals with similar Hirsch-index are comparable in terms of their overall scientific impact, even if their total number of papers or their total number of citations is very different.'</li><li>'The three differ from Woeginger’s (2008a) characterization in requiring fewer axioms (three instead of five)'</li><li>'Marchant (2009), instead of characterizing the index itself, characterizes the ranking that the Hirsch index induces on outputs.'</li></ul>                                                                                                                                                                                                                             |
+| ccro:Criticize   | <ul><li>'The h-index does not take into account that some papers may have extraordinarily many citations, and the g-index tries to compensate for this; see also Egghe (2006b) and Tol (2008).'</li><li>'The h-index does not take into account that some papers may have extraordinarily many citations, and the g-index tries to compensate for this; see also Egghe (2006b) and Tol (2008).'</li><li>'Woeginger (2008a, p. 227) stresses that his axioms should be interpreted within the context of MON.'</li></ul>                                                                                                                                                                                                                      |
+| ccro:Discuss     | <ul><li>'The relation between N and h will depend on the detailed form of the particular distribution (HI0501-01)'</li><li>'As discussed by Redner (HI0501-03), most papers earn their citations over a limited period of popularity and then they are no longer cited.'</li><li>'It is also possible that papers "drop out" and then later come back into the h count, as would occur for the kind of papers termed "sleeping beauties" (HI0501-04).'</li></ul>                                                                                                                                                                                                                                                                             |
+| ccro:Extend      | <ul><li>'In [3] the analogous formula for the g-index has been proved'</li></ul>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
+| ccro:Incorporate | <ul><li>'In this paper, we provide an axiomatic characterization of the Hirsch-index, in very much the same spirit as Arrow (1950, 1951), May (1952), and Moulin (1988) did for numerous other problems in mathematical decision making.'</li><li>'In this paper, we provide an axiomatic characterization of the Hirsch-index, in very much the same spirit as Arrow (1950, 1951), May (1952), and Moulin (1988) did for numerous other problems in mathematical decision making.'</li><li>'In this paper, we provide an axiomatic characterization of the Hirsch-index, in very much the same spirit as Arrow (1950, 1951), May (1952), and Moulin (1988) did for numerous other problems in mathematical decision making.'</li></ul>      |
+| ccro:Negate      | <ul><li>'Recently, Lehmann et al. (2, 3) have argued that the mean number of citations per paper (nc = Nc/Np) is a superior indicator.'</li><li>'If one chose instead to use as indicator of scientific achievement the mean number of citations per paper [following Lehmann et al. (2, 3)], our results suggest that (as in the stock market) ‘‘past performance is not predictive of future performance.’’'</li><li>'It has been argued in the literature that one drawback of the h index is that it does not give enough ‘‘credit’’ to very highly cited papers, and various modifications have been proposed to correct this, in particular, Egghe’s g index (4), Jin et al.’s AR index (5), and Komulski’s H(2) index (6).'</li></ul> |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.6111   |
+
+## 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("Corran/CCRO2")
+# Run inference
+preds = model("One of the referees recommends mentioning Quesada (2008) as another characterization of the Hirsch index relying as well on monotonicity.")
+```
+
+<!--
+### 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   | 6   | 25.7812 | 53  |
+
+| Label            | Training Sample Count |
+|:-----------------|:----------------------|
+| ccro:BasedOn     | 1                     |
+| ccro:Basedon     | 11                    |
+| ccro:Compare     | 21                    |
+| ccro:Contrast    | 3                     |
+| ccro:Criticize   | 4                     |
+| ccro:Discuss     | 37                    |
+| ccro:Extend      | 1                     |
+| ccro:Incorporate | 14                    |
+| ccro:Negate      | 4                     |
+
+### Training Hyperparameters
+- batch_size: (60, 60)
+- num_epochs: (1, 1)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 100
+- 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 |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0042 | 1    | 0.2507        | -               |
+| 0.2083 | 50   | 0.0639        | -               |
+| 0.4167 | 100  | 0.0017        | -               |
+| 0.625  | 150  | 0.0016        | -               |
+| 0.8333 | 200  | 0.0059        | -               |
+| 0.0031 | 1    | 0.0051        | -               |
+| 0.1562 | 50   | 0.0005        | -               |
+| 0.3125 | 100  | 0.001         | -               |
+| 0.4688 | 150  | 0.0001        | -               |
+| 0.625  | 200  | 0.0           | -               |
+| 0.7812 | 250  | 0.0           | -               |
+| 0.9375 | 300  | 0.0001        | -               |
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.0.3
+- Sentence Transformers: 2.2.2
+- Transformers: 4.35.2
+- PyTorch: 2.1.0+cu121
+- Datasets: 2.16.1
+- 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

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+{
+  "_name_or_path": "/root/.cache/torch/sentence_transformers/sentence-transformers_paraphrase-multilingual-mpnet-base-v2/",
+  "architectures": [
+    "XLMRobertaModel"
+  ],
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+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
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+  "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": "4.7.0",
+    "pytorch": "1.9.0+cu102"
+  }
+}

config_setfit.json

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+{
+  "normalize_embeddings": false,
+  "labels": [
+    "ccro:BasedOn",
+    "ccro:Basedon",
+    "ccro:Compare",
+    "ccro:Contrast",
+    "ccro:Criticize",
+    "ccro:Discuss",
+    "ccro:Extend",
+    "ccro:Incorporate",
+    "ccro:Negate"
+  ]
+}

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modules.json

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+  }
+]

sentence_bert_config.json

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+  "do_lower_case": false
+}

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special_tokens_map.json

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