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Zlovoblachko
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L1-classifier

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Model card Files
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Zlovoblachko commited on
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Add SetFit model

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Files changed (13) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +935 -0
  3. config.json +26 -0
  4. config_sentence_transformers.json +9 -0
  5. config_setfit.json +10 -0
  6. model.safetensors +3 -0
  7. model_head.pkl +3 -0
  8. modules.json +20 -0
  9. sentence_bert_config.json +4 -0
  10. special_tokens_map.json +37 -0
  11. tokenizer.json +0 -0
  12. tokenizer_config.json +64 -0
  13. vocab.txt +0 -0
1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 384,
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+ "pooling_mode_cls_token": false,
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+ "pooling_mode_mean_tokens": true,
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+ "pooling_mode_max_tokens": false,
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+ "pooling_mode_mean_sqrt_len_tokens": false,
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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
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+ }
README.md ADDED
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+ ---
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+ library_name: setfit
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+ tags:
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+ - setfit
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+ - sentence-transformers
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+ - text-classification
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+ - generated_from_setfit_trainer
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+ metrics:
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+ - accuracy
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+ widget:
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+ - text: It doesn't depend on hi-teck evangelism.
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+ - text: But in the all region we see gender unequal; in 2000 boys have education often
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+ then girls on 15 millions.
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+ - text: There is opinion, that universities should have equal amount of male and female
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+ students in every subject in society.
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+ - text: A building's style may say a lot about its history.
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+ - text: Manufactured goods by rail is the same amount as by road, Machinery transported
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+ by road has minimal percent in second chart.
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+ pipeline_tag: text-classification
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+ inference: true
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+ base_model: sentence-transformers/all-MiniLM-L6-v2
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+ model-index:
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+ - name: SetFit with sentence-transformers/all-MiniLM-L6-v2
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+ results:
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+ - task:
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+ type: text-classification
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+ name: Text Classification
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+ dataset:
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+ name: Unknown
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+ type: unknown
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+ split: test
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+ metrics:
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+ - type: accuracy
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+ value: 0.592741935483871
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+ name: Accuracy
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+ ---
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+
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+ # SetFit with sentence-transformers/all-MiniLM-L6-v2
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+
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+ This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-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.
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+
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+ The model has been trained using an efficient few-shot learning technique that involves:
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+
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+ 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
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+ 2. Training a classification head with features from the fine-tuned Sentence Transformer.
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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** SetFit
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+ - **Sentence Transformer body:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
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+ - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
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+ - **Maximum Sequence Length:** 256 tokens
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+ - **Number of Classes:** 5 classes
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+ <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
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+ <!-- - **Language:** Unknown -->
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+ <!-- - **License:** Unknown -->
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+
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+ ### Model Sources
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+
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+ - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
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+ - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
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+ - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
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+
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+ ### Model Labels
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+ | Label | Examples |
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+ |:-----------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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+ | Copying expression | <ul><li>'What is needed to improve the situation with widespread using of gadjets is definite action should be encouraged and promoted by means of avoiding them.'</li><li>'Inside every of us are our passions.'</li><li>'The number of 15-59 year old people will increase for 11% but the number of 0-14 will fall and become 37%.'</li></ul> |
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+ | Synonyms | <ul><li>'But some persons consider that the institutes should accept the equal amount of girls and boys in every faculty.'</li><li>'Nowadays problem of ecology and environment is rather acute and many people are alarmed by it.'</li><li>'The amount of people over 65 was rising between 1940 and the end of 1970s.'</li></ul> |
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+ | Tense semantics | <ul><li>'After that the figure uncreases dramatically from 180 billions in 2009 to approximately 279 billions in 2011.'</li><li>'On the contrary, in 2014 the UK book market demonstrate minimum income, only 2,6 and 1,8 billion dollars for print book and eBook, correspondely.'</li><li>'It is not clear, what it is depends on, but after the higest point in 42% in Japan the percentage get down to 30%.'</li></ul> |
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+ | Word form transmission | <ul><li>'A lot of people from music and cinema industry lose money due to somebody sends pirate copies to the internet.'</li><li>'The deal was worth $2 billions .'</li><li>'According to the projections numbers of people in the age of 15-60 years will show a considerable increase in 2050 by 11 per cent, such as people aged 60 and more years by 2,1 percent.'</li></ul> |
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+ | Transliteration | <ul><li>'According to the statistic a lot of people with some horible diseases can get cvalificate help only in Japan.'</li><li>"It doesn't depend on hi-teck evangelism."</li><li>'GMO technologies are believed to be dangerous.'</li></ul> |
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+
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+ ## Evaluation
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+
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+ ### Metrics
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+ | Label | Accuracy |
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+ |:--------|:---------|
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+ | **all** | 0.5927 |
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+
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+ ## Uses
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+
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+ ### Direct Use for Inference
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+
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+ First install the SetFit library:
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+
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+ ```bash
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+ pip install setfit
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+ ```
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+
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+ Then you can load this model and run inference.
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+
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+ ```python
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+ from setfit import SetFitModel
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+
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+ # Download from the 🤗 Hub
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+ model = SetFitModel.from_pretrained("Zlovoblachko/L1-classifier")
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+ # Run inference
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+ preds = model("It doesn't depend on hi-teck evangelism.")
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+ ```
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+
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+ <!--
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+ ### Downstream Use
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+
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+ *List how someone could finetune this model on their own dataset.*
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+ -->
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+
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+ <!--
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+ ### Out-of-Scope Use
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+
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+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
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+ -->
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+
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+ <!--
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+ ## Bias, Risks and Limitations
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+
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+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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+ -->
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+
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+ <!--
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+ ### Recommendations
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+
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+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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+ -->
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+
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+ ## Training Details
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+
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+ ### Training Set Metrics
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+ | Training set | Min | Median | Max |
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+ |:-------------|:----|:-------|:----|
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+ | Word count | 2 | 20.788 | 54 |
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+
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+ | Label | Training Sample Count |
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+ |:-----------------------|:----------------------|
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+ | Synonyms | 91 |
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+ | Copying expression | 55 |
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+ | Tense semantics | 57 |
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+ | Word form transmission | 32 |
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+ | Transliteration | 15 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (32, 32)
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+ - num_epochs: (15, 15)
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+ - max_steps: -1
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+ - sampling_strategy: oversampling
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+ - body_learning_rate: (2e-05, 1e-05)
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+ - head_learning_rate: 0.01
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+ - loss: CosineSimilarityLoss
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+ - distance_metric: cosine_distance
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+ - margin: 0.25
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+ - end_to_end: False
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+ - use_amp: False
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+ - warmup_proportion: 0.1
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+ - seed: 42
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+ - eval_max_steps: -1
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+ - load_best_model_at_end: False
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+
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+ ### Training Results
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+ | Epoch | Step | Training Loss | Validation Loss |
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+ |:-------:|:-----:|:-------------:|:---------------:|
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+ | 0.0043 | 1 | 0.3438 | - |
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+ | 0.0342 | 50 | 0.2906 | - |
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+ | 0.0685 | 100 | 0.2761 | - |
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+ | 0.1027 | 150 | 0.2696 | - |
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+ | 0.1370 | 200 | 0.2381 | - |
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+ | 0.1712 | 250 | 0.2542 | - |
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+ | 0.2055 | 300 | 0.1781 | - |
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+ | 0.2397 | 350 | 0.2067 | - |
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+ | 0.2740 | 400 | 0.222 | - |
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+ | 0.3082 | 450 | 0.2372 | - |
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+ | 0.3425 | 500 | 0.193 | - |
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+ | 0.3767 | 550 | 0.2399 | - |
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+ | 0.4110 | 600 | 0.1712 | - |
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+ | 0.4452 | 650 | 0.1697 | - |
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+ | 0.4795 | 700 | 0.1507 | - |
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+ | 0.5137 | 750 | 0.0947 | - |
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+ | 0.5479 | 800 | 0.0722 | - |
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+ | 0.5822 | 850 | 0.0975 | - |
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+ | 0.6164 | 900 | 0.035 | - |
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+ | 0.6507 | 950 | 0.0114 | - |
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+ | 0.6849 | 1000 | 0.0332 | - |
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+ | 0.7192 | 1050 | 0.0274 | - |
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+ | 0.7534 | 1100 | 0.0126 | - |
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+ | 0.7877 | 1150 | 0.0267 | - |
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+ | 0.8219 | 1200 | 0.0194 | - |
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+ | 0.8562 | 1250 | 0.0206 | - |
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+ | 0.8904 | 1300 | 0.0228 | - |
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+ | 0.9247 | 1350 | 0.0076 | - |
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+ | 0.9589 | 1400 | 0.0342 | - |
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+ | 0.9932 | 1450 | 0.0252 | - |
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+ | 1.0274 | 1500 | 0.0164 | - |
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+ | 1.0616 | 1550 | 0.0049 | - |
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+ | 1.0959 | 1600 | 0.0043 | - |
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+ | 1.1301 | 1650 | 0.0114 | - |
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+ | 1.1644 | 1700 | 0.03 | - |
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+ | 1.1986 | 1750 | 0.0026 | - |
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+ | 1.2329 | 1800 | 0.0012 | - |
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+ | 1.2671 | 1850 | 0.0073 | - |
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+ | 1.3014 | 1900 | 0.0146 | - |
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+ | 1.3356 | 1950 | 0.001 | - |
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+ | 1.3699 | 2000 | 0.0088 | - |
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+ | 1.4041 | 2050 | 0.0031 | - |
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+ | 1.4384 | 2100 | 0.0125 | - |
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+ | 1.4726 | 2150 | 0.0357 | - |
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+ | 1.5068 | 2200 | 0.0186 | - |
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+ | 1.5411 | 2250 | 0.0178 | - |
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+ | 1.5753 | 2300 | 0.0071 | - |
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+ | 1.6096 | 2350 | 0.0186 | - |
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+ | 1.6438 | 2400 | 0.0077 | - |
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+ | 1.6781 | 2450 | 0.0183 | - |
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+ | 1.7123 | 2500 | 0.0007 | - |
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+ | 1.7466 | 2550 | 0.0007 | - |
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+ | 1.7808 | 2600 | 0.0052 | - |
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+ | 1.8151 | 2650 | 0.0077 | - |
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+ | 1.8493 | 2700 | 0.0421 | - |
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+ | 1.8836 | 2750 | 0.0272 | - |
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+ | 1.9178 | 2800 | 0.0144 | - |
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+ | 1.9521 | 2850 | 0.0038 | - |
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+ | 1.9863 | 2900 | 0.0043 | - |
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+ | 2.0205 | 2950 | 0.0187 | - |
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+ | 2.0548 | 3000 | 0.0075 | - |
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+ | 2.0890 | 3050 | 0.0151 | - |
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+ | 2.1233 | 3100 | 0.0114 | - |
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+ | 2.1575 | 3150 | 0.0022 | - |
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+ | 2.1918 | 3200 | 0.0007 | - |
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+ | 2.2260 | 3250 | 0.0196 | - |
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+ | 2.2603 | 3300 | 0.0266 | - |
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+ | 2.2945 | 3350 | 0.0139 | - |
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+ | 2.3288 | 3400 | 0.0169 | - |
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+ | 2.3630 | 3450 | 0.0124 | - |
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+ | 2.3973 | 3500 | 0.0018 | - |
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+ | 2.4315 | 3550 | 0.0242 | - |
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+ | 2.4658 | 3600 | 0.0402 | - |
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+ | 2.5 | 3650 | 0.0015 | - |
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+ | 2.5342 | 3700 | 0.0042 | - |
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+ | 2.5685 | 3750 | 0.0437 | - |
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+ | 2.6027 | 3800 | 0.006 | - |
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+ | 2.6370 | 3850 | 0.0005 | - |
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+ | 2.6712 | 3900 | 0.0118 | - |
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+ | 2.7055 | 3950 | 0.0166 | - |
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+ | 2.7397 | 4000 | 0.025 | - |
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+ | 2.7740 | 4050 | 0.0167 | - |
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+ | 2.8082 | 4100 | 0.0285 | - |
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+ | 2.8425 | 4150 | 0.0048 | - |
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+ | 2.8767 | 4200 | 0.0149 | - |
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+ | 2.9110 | 4250 | 0.0078 | - |
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+ | 2.9452 | 4300 | 0.0097 | - |
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+ | 2.9795 | 4350 | 0.0068 | - |
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+ | 3.0137 | 4400 | 0.0235 | - |
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+ | 3.0479 | 4450 | 0.0004 | - |
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+ | 3.0822 | 4500 | 0.0355 | - |
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+ | 3.1164 | 4550 | 0.0237 | - |
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+ | 3.1507 | 4600 | 0.0004 | - |
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+ | 3.1849 | 4650 | 0.0003 | - |
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+ | 3.2192 | 4700 | 0.0038 | - |
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+ | 3.2534 | 4750 | 0.0002 | - |
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+ | 3.2877 | 4800 | 0.0105 | - |
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+ | 3.3219 | 4850 | 0.0055 | - |
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+ | 3.3562 | 4900 | 0.0282 | - |
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+ | 3.3904 | 4950 | 0.0105 | - |
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+ | 3.4247 | 5000 | 0.0362 | - |
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+ | 3.4589 | 5050 | 0.0004 | - |
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+ | 3.4932 | 5100 | 0.0229 | - |
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+ | 3.5274 | 5150 | 0.0092 | - |
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+ | 3.5616 | 5200 | 0.033 | - |
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+ | 3.5959 | 5250 | 0.0003 | - |
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+ | 3.6301 | 5300 | 0.0444 | - |
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+ | 3.6644 | 5350 | 0.0181 | - |
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+ | 3.6986 | 5400 | 0.0254 | - |
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+ | 3.7329 | 5450 | 0.0057 | - |
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+ | 3.7671 | 5500 | 0.0511 | - |
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+ | 3.8014 | 5550 | 0.0024 | - |
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+ | 3.8356 | 5600 | 0.0195 | - |
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+ | 3.8699 | 5650 | 0.0202 | - |
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+ | 3.9041 | 5700 | 0.0003 | - |
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+ | 3.9384 | 5750 | 0.0322 | - |
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+ | 3.9726 | 5800 | 0.0123 | - |
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+ | 4.0068 | 5850 | 0.0002 | - |
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+ | 4.0411 | 5900 | 0.0002 | - |
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+ | 4.0753 | 5950 | 0.008 | - |
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+ | 4.1096 | 6000 | 0.0053 | - |
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+ | 4.1438 | 6050 | 0.0003 | - |
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+ | 4.1781 | 6100 | 0.0213 | - |
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+ | 4.2123 | 6150 | 0.0046 | - |
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+ | 4.2466 | 6200 | 0.0331 | - |
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+ | 4.2808 | 6250 | 0.0078 | - |
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+ | 4.3151 | 6300 | 0.0042 | - |
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+ | 4.3493 | 6350 | 0.0234 | - |
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+ | 4.3836 | 6400 | 0.0043 | - |
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+ | 4.4178 | 6450 | 0.0253 | - |
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+ | 4.4521 | 6500 | 0.0303 | - |
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+ | 4.4863 | 6550 | 0.004 | - |
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+ | 4.5205 | 6600 | 0.0166 | - |
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+ | 4.5548 | 6650 | 0.0269 | - |
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+ | 4.5890 | 6700 | 0.0079 | - |
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+ | 4.6233 | 6750 | 0.0001 | - |
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+ | 4.6575 | 6800 | 0.0002 | - |
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+ | 4.6918 | 6850 | 0.0002 | - |
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+ | 4.7260 | 6900 | 0.0199 | - |
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+ | 4.7603 | 6950 | 0.0282 | - |
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+ | 4.7945 | 7000 | 0.0016 | - |
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+ | 4.8288 | 7050 | 0.0068 | - |
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+ | 4.8630 | 7100 | 0.0054 | - |
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+ | 4.8973 | 7150 | 0.036 | - |
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+ | 4.9315 | 7200 | 0.0054 | - |
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+ | 4.9658 | 7250 | 0.0174 | - |
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+ | 5.0 | 7300 | 0.0001 | - |
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+ | 5.0342 | 7350 | 0.0123 | - |
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+ | 5.0685 | 7400 | 0.0218 | - |
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+ | 5.1027 | 7450 | 0.0162 | - |
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+ | 5.1370 | 7500 | 0.0181 | - |
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+ | 5.1712 | 7550 | 0.0001 | - |
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+ | 5.2055 | 7600 | 0.0201 | - |
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+ | 5.2397 | 7650 | 0.0232 | - |
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+ | 5.2740 | 7700 | 0.0003 | - |
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+ | 5.3082 | 7750 | 0.0002 | - |
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+ | 5.3425 | 7800 | 0.0094 | - |
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+ | 5.3767 | 7850 | 0.0151 | - |
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+ | 5.4110 | 7900 | 0.0099 | - |
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+ | 5.4452 | 7950 | 0.01 | - |
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+ | 5.4795 | 8000 | 0.0378 | - |
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+ | 5.5137 | 8050 | 0.0199 | - |
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+ | 5.5479 | 8100 | 0.0201 | - |
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+ | 5.5822 | 8150 | 0.0242 | - |
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+ | 5.6164 | 8200 | 0.0015 | - |
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+ | 5.6507 | 8250 | 0.0002 | - |
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+ | 5.6849 | 8300 | 0.0047 | - |
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+ | 5.7192 | 8350 | 0.0002 | - |
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+ | 5.7534 | 8400 | 0.0001 | - |
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+ | 5.7877 | 8450 | 0.0215 | - |
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+ | 5.8219 | 8500 | 0.0159 | - |
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+ | 5.8562 | 8550 | 0.0001 | - |
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+ | 5.8904 | 8600 | 0.0194 | - |
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+ | 5.9247 | 8650 | 0.0058 | - |
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+ | 5.9589 | 8700 | 0.0001 | - |
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+ | 5.9932 | 8750 | 0.0164 | - |
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+ | 6.0274 | 8800 | 0.0272 | - |
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+ | 6.0616 | 8850 | 0.0001 | - |
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+ | 6.0959 | 8900 | 0.0031 | - |
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+ | 6.1301 | 8950 | 0.0154 | - |
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+ | 6.1644 | 9000 | 0.0403 | - |
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+ | 6.1986 | 9050 | 0.0035 | - |
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+ | 6.2329 | 9100 | 0.0001 | - |
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+ | 6.2671 | 9150 | 0.0061 | - |
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+ | 6.3014 | 9200 | 0.0118 | - |
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+ | 6.3356 | 9250 | 0.0031 | - |
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+ | 6.3699 | 9300 | 0.0001 | - |
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+ | 6.4041 | 9350 | 0.0098 | - |
349
+ | 6.4384 | 9400 | 0.0001 | - |
350
+ | 6.4726 | 9450 | 0.0343 | - |
351
+ | 6.5068 | 9500 | 0.017 | - |
352
+ | 6.5411 | 9550 | 0.0025 | - |
353
+ | 6.5753 | 9600 | 0.0001 | - |
354
+ | 6.6096 | 9650 | 0.0181 | - |
355
+ | 6.6438 | 9700 | 0.0191 | - |
356
+ | 6.6781 | 9750 | 0.0186 | - |
357
+ | 6.7123 | 9800 | 0.0001 | - |
358
+ | 6.7466 | 9850 | 0.0002 | - |
359
+ | 6.7808 | 9900 | 0.0001 | - |
360
+ | 6.8151 | 9950 | 0.0086 | - |
361
+ | 6.8493 | 10000 | 0.0377 | - |
362
+ | 6.8836 | 10050 | 0.0167 | - |
363
+ | 6.9178 | 10100 | 0.0034 | - |
364
+ | 6.9521 | 10150 | 0.0054 | - |
365
+ | 6.9863 | 10200 | 0.0048 | - |
366
+ | 7.0205 | 10250 | 0.0219 | - |
367
+ | 7.0548 | 10300 | 0.0001 | - |
368
+ | 7.0890 | 10350 | 0.0001 | - |
369
+ | 7.1233 | 10400 | 0.0262 | - |
370
+ | 7.1575 | 10450 | 0.0069 | - |
371
+ | 7.1918 | 10500 | 0.0001 | - |
372
+ | 7.2260 | 10550 | 0.0158 | - |
373
+ | 7.2603 | 10600 | 0.0192 | - |
374
+ | 7.2945 | 10650 | 0.0098 | - |
375
+ | 7.3288 | 10700 | 0.0001 | - |
376
+ | 7.3630 | 10750 | 0.0002 | - |
377
+ | 7.3973 | 10800 | 0.0021 | - |
378
+ | 7.4315 | 10850 | 0.0252 | - |
379
+ | 7.4658 | 10900 | 0.0383 | - |
380
+ | 7.5 | 10950 | 0.0001 | - |
381
+ | 7.5342 | 11000 | 0.0001 | - |
382
+ | 7.5685 | 11050 | 0.0491 | - |
383
+ | 7.6027 | 11100 | 0.0076 | - |
384
+ | 7.6370 | 11150 | 0.0089 | - |
385
+ | 7.6712 | 11200 | 0.0162 | - |
386
+ | 7.7055 | 11250 | 0.0163 | - |
387
+ | 7.7397 | 11300 | 0.0188 | - |
388
+ | 7.7740 | 11350 | 0.0141 | - |
389
+ | 7.8082 | 11400 | 0.0277 | - |
390
+ | 7.8425 | 11450 | 0.0001 | - |
391
+ | 7.8767 | 11500 | 0.0001 | - |
392
+ | 7.9110 | 11550 | 0.0055 | - |
393
+ | 7.9452 | 11600 | 0.0029 | - |
394
+ | 7.9795 | 11650 | 0.0001 | - |
395
+ | 8.0137 | 11700 | 0.0186 | - |
396
+ | 8.0479 | 11750 | 0.0037 | - |
397
+ | 8.0822 | 11800 | 0.0205 | - |
398
+ | 8.1164 | 11850 | 0.0217 | - |
399
+ | 8.1507 | 11900 | 0.0036 | - |
400
+ | 8.1849 | 11950 | 0.0039 | - |
401
+ | 8.2192 | 12000 | 0.0001 | - |
402
+ | 8.2534 | 12050 | 0.0055 | - |
403
+ | 8.2877 | 12100 | 0.0027 | - |
404
+ | 8.3219 | 12150 | 0.0029 | - |
405
+ | 8.3562 | 12200 | 0.0279 | - |
406
+ | 8.3904 | 12250 | 0.0139 | - |
407
+ | 8.4247 | 12300 | 0.04 | - |
408
+ | 8.4589 | 12350 | 0.003 | - |
409
+ | 8.4932 | 12400 | 0.0161 | - |
410
+ | 8.5274 | 12450 | 0.0001 | - |
411
+ | 8.5616 | 12500 | 0.035 | - |
412
+ | 8.5959 | 12550 | 0.0021 | - |
413
+ | 8.6301 | 12600 | 0.0355 | - |
414
+ | 8.6644 | 12650 | 0.0139 | - |
415
+ | 8.6986 | 12700 | 0.0183 | - |
416
+ | 8.7329 | 12750 | 0.0041 | - |
417
+ | 8.7671 | 12800 | 0.0354 | - |
418
+ | 8.8014 | 12850 | 0.0 | - |
419
+ | 8.8356 | 12900 | 0.0197 | - |
420
+ | 8.8699 | 12950 | 0.0189 | - |
421
+ | 8.9041 | 13000 | 0.0063 | - |
422
+ | 8.9384 | 13050 | 0.0309 | - |
423
+ | 8.9726 | 13100 | 0.0029 | - |
424
+ | 9.0068 | 13150 | 0.0027 | - |
425
+ | 9.0411 | 13200 | 0.0018 | - |
426
+ | 9.0753 | 13250 | 0.0104 | - |
427
+ | 9.1096 | 13300 | 0.0057 | - |
428
+ | 9.1438 | 13350 | 0.0051 | - |
429
+ | 9.1781 | 13400 | 0.0172 | - |
430
+ | 9.2123 | 13450 | 0.0001 | - |
431
+ | 9.2466 | 13500 | 0.0347 | - |
432
+ | 9.2808 | 13550 | 0.0024 | - |
433
+ | 9.3151 | 13600 | 0.0147 | - |
434
+ | 9.3493 | 13650 | 0.0218 | - |
435
+ | 9.3836 | 13700 | 0.0028 | - |
436
+ | 9.4178 | 13750 | 0.0205 | - |
437
+ | 9.4521 | 13800 | 0.0215 | - |
438
+ | 9.4863 | 13850 | 0.0001 | - |
439
+ | 9.5205 | 13900 | 0.0157 | - |
440
+ | 9.5548 | 13950 | 0.0227 | - |
441
+ | 9.5890 | 14000 | 0.0001 | - |
442
+ | 9.6233 | 14050 | 0.0048 | - |
443
+ | 9.6575 | 14100 | 0.0106 | - |
444
+ | 9.6918 | 14150 | 0.0077 | - |
445
+ | 9.7260 | 14200 | 0.0225 | - |
446
+ | 9.7603 | 14250 | 0.0173 | - |
447
+ | 9.7945 | 14300 | 0.0028 | - |
448
+ | 9.8288 | 14350 | 0.0022 | - |
449
+ | 9.8630 | 14400 | 0.003 | - |
450
+ | 9.8973 | 14450 | 0.0355 | - |
451
+ | 9.9315 | 14500 | 0.0001 | - |
452
+ | 9.9658 | 14550 | 0.0187 | - |
453
+ | 10.0 | 14600 | 0.0001 | - |
454
+ | 0.0007 | 1 | 0.0055 | - |
455
+ | 0.0342 | 50 | 0.0127 | - |
456
+ | 0.0685 | 100 | 0.0206 | - |
457
+ | 0.1027 | 150 | 0.0195 | - |
458
+ | 0.1370 | 200 | 0.0238 | - |
459
+ | 0.1712 | 250 | 0.0029 | - |
460
+ | 0.2055 | 300 | 0.0204 | - |
461
+ | 0.2397 | 350 | 0.0174 | - |
462
+ | 0.2740 | 400 | 0.0001 | - |
463
+ | 0.3082 | 450 | 0.0023 | - |
464
+ | 0.3425 | 500 | 0.0001 | - |
465
+ | 0.3767 | 550 | 0.0254 | - |
466
+ | 0.4110 | 600 | 0.0029 | - |
467
+ | 0.4452 | 650 | 0.0082 | - |
468
+ | 0.4795 | 700 | 0.0411 | - |
469
+ | 0.5137 | 750 | 0.0159 | - |
470
+ | 0.5479 | 800 | 0.0207 | - |
471
+ | 0.5822 | 850 | 0.0173 | - |
472
+ | 0.6164 | 900 | 0.0001 | - |
473
+ | 0.6507 | 950 | 0.0018 | - |
474
+ | 0.6849 | 1000 | 0.0059 | - |
475
+ | 0.7192 | 1050 | 0.0014 | - |
476
+ | 0.7534 | 1100 | 0.0022 | - |
477
+ | 0.7877 | 1150 | 0.0187 | - |
478
+ | 0.8219 | 1200 | 0.0158 | - |
479
+ | 0.8562 | 1250 | 0.0025 | - |
480
+ | 0.8904 | 1300 | 0.0113 | - |
481
+ | 0.9247 | 1350 | 0.0007 | - |
482
+ | 0.9589 | 1400 | 0.004 | - |
483
+ | 0.9932 | 1450 | 0.0216 | - |
484
+ | 1.0274 | 1500 | 0.0213 | - |
485
+ | 1.0616 | 1550 | 0.0044 | - |
486
+ | 1.0959 | 1600 | 0.0025 | - |
487
+ | 1.1301 | 1650 | 0.0154 | - |
488
+ | 1.1644 | 1700 | 0.038 | - |
489
+ | 1.1986 | 1750 | 0.0001 | - |
490
+ | 1.2329 | 1800 | 0.0004 | - |
491
+ | 1.2671 | 1850 | 0.0065 | - |
492
+ | 1.3014 | 1900 | 0.0087 | - |
493
+ | 1.3356 | 1950 | 0.0001 | - |
494
+ | 1.3699 | 2000 | 0.0039 | - |
495
+ | 1.4041 | 2050 | 0.0005 | - |
496
+ | 1.4384 | 2100 | 0.0087 | - |
497
+ | 1.4726 | 2150 | 0.0369 | - |
498
+ | 1.5068 | 2200 | 0.0157 | - |
499
+ | 1.5411 | 2250 | 0.0094 | - |
500
+ | 1.5753 | 2300 | 0.0042 | - |
501
+ | 1.6096 | 2350 | 0.018 | - |
502
+ | 1.6438 | 2400 | 0.014 | - |
503
+ | 1.6781 | 2450 | 0.0161 | - |
504
+ | 1.7123 | 2500 | 0.0011 | - |
505
+ | 1.7466 | 2550 | 0.0001 | - |
506
+ | 1.7808 | 2600 | 0.004 | - |
507
+ | 1.8151 | 2650 | 0.0048 | - |
508
+ | 1.8493 | 2700 | 0.0403 | - |
509
+ | 1.8836 | 2750 | 0.0254 | - |
510
+ | 1.9178 | 2800 | 0.0124 | - |
511
+ | 1.9521 | 2850 | 0.0028 | - |
512
+ | 1.9863 | 2900 | 0.0026 | - |
513
+ | 2.0205 | 2950 | 0.0171 | - |
514
+ | 2.0548 | 3000 | 0.0049 | - |
515
+ | 2.0890 | 3050 | 0.0092 | - |
516
+ | 2.1233 | 3100 | 0.0134 | - |
517
+ | 2.1575 | 3150 | 0.0021 | - |
518
+ | 2.1918 | 3200 | 0.0001 | - |
519
+ | 2.2260 | 3250 | 0.0153 | - |
520
+ | 2.2603 | 3300 | 0.0253 | - |
521
+ | 2.2945 | 3350 | 0.0095 | - |
522
+ | 2.3288 | 3400 | 0.0144 | - |
523
+ | 2.3630 | 3450 | 0.0064 | - |
524
+ | 2.3973 | 3500 | 0.0013 | - |
525
+ | 2.4315 | 3550 | 0.0216 | - |
526
+ | 2.4658 | 3600 | 0.0387 | - |
527
+ | 2.5 | 3650 | 0.0018 | - |
528
+ | 2.5342 | 3700 | 0.0034 | - |
529
+ | 2.5685 | 3750 | 0.0428 | - |
530
+ | 2.6027 | 3800 | 0.0055 | - |
531
+ | 2.6370 | 3850 | 0.0001 | - |
532
+ | 2.6712 | 3900 | 0.0154 | - |
533
+ | 2.7055 | 3950 | 0.0176 | - |
534
+ | 2.7397 | 4000 | 0.0213 | - |
535
+ | 2.7740 | 4050 | 0.016 | - |
536
+ | 2.8082 | 4100 | 0.0293 | - |
537
+ | 2.8425 | 4150 | 0.0034 | - |
538
+ | 2.8767 | 4200 | 0.0119 | - |
539
+ | 2.9110 | 4250 | 0.0061 | - |
540
+ | 2.9452 | 4300 | 0.0068 | - |
541
+ | 2.9795 | 4350 | 0.006 | - |
542
+ | 3.0137 | 4400 | 0.0211 | - |
543
+ | 3.0479 | 4450 | 0.0001 | - |
544
+ | 3.0822 | 4500 | 0.0303 | - |
545
+ | 3.1164 | 4550 | 0.0225 | - |
546
+ | 3.1507 | 4600 | 0.0001 | - |
547
+ | 3.1849 | 4650 | 0.0002 | - |
548
+ | 3.2192 | 4700 | 0.0031 | - |
549
+ | 3.2534 | 4750 | 0.0001 | - |
550
+ | 3.2877 | 4800 | 0.0103 | - |
551
+ | 3.3219 | 4850 | 0.0055 | - |
552
+ | 3.3562 | 4900 | 0.0297 | - |
553
+ | 3.3904 | 4950 | 0.0121 | - |
554
+ | 3.4247 | 5000 | 0.0348 | - |
555
+ | 3.4589 | 5050 | 0.0003 | - |
556
+ | 3.4932 | 5100 | 0.0212 | - |
557
+ | 3.5274 | 5150 | 0.0077 | - |
558
+ | 3.5616 | 5200 | 0.0339 | - |
559
+ | 3.5959 | 5250 | 0.0001 | - |
560
+ | 3.6301 | 5300 | 0.0444 | - |
561
+ | 3.6644 | 5350 | 0.0167 | - |
562
+ | 3.6986 | 5400 | 0.0245 | - |
563
+ | 3.7329 | 5450 | 0.005 | - |
564
+ | 3.7671 | 5500 | 0.047 | - |
565
+ | 3.8014 | 5550 | 0.0021 | - |
566
+ | 3.8356 | 5600 | 0.019 | - |
567
+ | 3.8699 | 5650 | 0.0187 | - |
568
+ | 3.9041 | 5700 | 0.0001 | - |
569
+ | 3.9384 | 5750 | 0.0328 | - |
570
+ | 3.9726 | 5800 | 0.0097 | - |
571
+ | 4.0068 | 5850 | 0.0001 | - |
572
+ | 4.0411 | 5900 | 0.0001 | - |
573
+ | 4.0753 | 5950 | 0.0078 | - |
574
+ | 4.1096 | 6000 | 0.0057 | - |
575
+ | 4.1438 | 6050 | 0.0002 | - |
576
+ | 4.1781 | 6100 | 0.0218 | - |
577
+ | 4.2123 | 6150 | 0.0038 | - |
578
+ | 4.2466 | 6200 | 0.0337 | - |
579
+ | 4.2808 | 6250 | 0.0065 | - |
580
+ | 4.3151 | 6300 | 0.0033 | - |
581
+ | 4.3493 | 6350 | 0.0228 | - |
582
+ | 4.3836 | 6400 | 0.0033 | - |
583
+ | 4.4178 | 6450 | 0.0244 | - |
584
+ | 4.4521 | 6500 | 0.027 | - |
585
+ | 4.4863 | 6550 | 0.0027 | - |
586
+ | 4.5205 | 6600 | 0.0153 | - |
587
+ | 4.5548 | 6650 | 0.0241 | - |
588
+ | 4.5890 | 6700 | 0.0071 | - |
589
+ | 4.6233 | 6750 | 0.0001 | - |
590
+ | 4.6575 | 6800 | 0.0 | - |
591
+ | 4.6918 | 6850 | 0.0001 | - |
592
+ | 4.7260 | 6900 | 0.0203 | - |
593
+ | 4.7603 | 6950 | 0.0273 | - |
594
+ | 4.7945 | 7000 | 0.0017 | - |
595
+ | 4.8288 | 7050 | 0.0062 | - |
596
+ | 4.8630 | 7100 | 0.0043 | - |
597
+ | 4.8973 | 7150 | 0.0346 | - |
598
+ | 4.9315 | 7200 | 0.005 | - |
599
+ | 4.9658 | 7250 | 0.0182 | - |
600
+ | 5.0 | 7300 | 0.0001 | - |
601
+ | 5.0342 | 7350 | 0.0108 | - |
602
+ | 5.0685 | 7400 | 0.0218 | - |
603
+ | 5.1027 | 7450 | 0.0163 | - |
604
+ | 5.1370 | 7500 | 0.0195 | - |
605
+ | 5.1712 | 7550 | 0.0001 | - |
606
+ | 5.2055 | 7600 | 0.0195 | - |
607
+ | 5.2397 | 7650 | 0.0222 | - |
608
+ | 5.2740 | 7700 | 0.0002 | - |
609
+ | 5.3082 | 7750 | 0.0001 | - |
610
+ | 5.3425 | 7800 | 0.0078 | - |
611
+ | 5.3767 | 7850 | 0.0158 | - |
612
+ | 5.4110 | 7900 | 0.0081 | - |
613
+ | 5.4452 | 7950 | 0.0087 | - |
614
+ | 5.4795 | 8000 | 0.0372 | - |
615
+ | 5.5137 | 8050 | 0.019 | - |
616
+ | 5.5479 | 8100 | 0.0188 | - |
617
+ | 5.5822 | 8150 | 0.0238 | - |
618
+ | 5.6164 | 8200 | 0.0018 | - |
619
+ | 5.6507 | 8250 | 0.0001 | - |
620
+ | 5.6849 | 8300 | 0.0046 | - |
621
+ | 5.7192 | 8350 | 0.0001 | - |
622
+ | 5.7534 | 8400 | 0.0001 | - |
623
+ | 5.7877 | 8450 | 0.0216 | - |
624
+ | 5.8219 | 8500 | 0.0164 | - |
625
+ | 5.8562 | 8550 | 0.0 | - |
626
+ | 5.8904 | 8600 | 0.018 | - |
627
+ | 5.9247 | 8650 | 0.0059 | - |
628
+ | 5.9589 | 8700 | 0.0001 | - |
629
+ | 5.9932 | 8750 | 0.0168 | - |
630
+ | 6.0274 | 8800 | 0.0259 | - |
631
+ | 6.0616 | 8850 | 0.0001 | - |
632
+ | 6.0959 | 8900 | 0.0029 | - |
633
+ | 6.1301 | 8950 | 0.0159 | - |
634
+ | 6.1644 | 9000 | 0.041 | - |
635
+ | 6.1986 | 9050 | 0.0035 | - |
636
+ | 6.2329 | 9100 | 0.0001 | - |
637
+ | 6.2671 | 9150 | 0.005 | - |
638
+ | 6.3014 | 9200 | 0.0101 | - |
639
+ | 6.3356 | 9250 | 0.0027 | - |
640
+ | 6.3699 | 9300 | 0.0 | - |
641
+ | 6.4041 | 9350 | 0.0094 | - |
642
+ | 6.4384 | 9400 | 0.0001 | - |
643
+ | 6.4726 | 9450 | 0.0335 | - |
644
+ | 6.5068 | 9500 | 0.0168 | - |
645
+ | 6.5411 | 9550 | 0.0025 | - |
646
+ | 6.5753 | 9600 | 0.0001 | - |
647
+ | 6.6096 | 9650 | 0.0185 | - |
648
+ | 6.6438 | 9700 | 0.0188 | - |
649
+ | 6.6781 | 9750 | 0.0187 | - |
650
+ | 6.7123 | 9800 | 0.0001 | - |
651
+ | 6.7466 | 9850 | 0.0002 | - |
652
+ | 6.7808 | 9900 | 0.0001 | - |
653
+ | 6.8151 | 9950 | 0.0087 | - |
654
+ | 6.8493 | 10000 | 0.0371 | - |
655
+ | 6.8836 | 10050 | 0.0172 | - |
656
+ | 6.9178 | 10100 | 0.0028 | - |
657
+ | 6.9521 | 10150 | 0.0055 | - |
658
+ | 6.9863 | 10200 | 0.0043 | - |
659
+ | 7.0205 | 10250 | 0.0219 | - |
660
+ | 7.0548 | 10300 | 0.0 | - |
661
+ | 7.0890 | 10350 | 0.0001 | - |
662
+ | 7.1233 | 10400 | 0.026 | - |
663
+ | 7.1575 | 10450 | 0.0067 | - |
664
+ | 7.1918 | 10500 | 0.0001 | - |
665
+ | 7.2260 | 10550 | 0.0162 | - |
666
+ | 7.2603 | 10600 | 0.019 | - |
667
+ | 7.2945 | 10650 | 0.0093 | - |
668
+ | 7.3288 | 10700 | 0.0001 | - |
669
+ | 7.3630 | 10750 | 0.0002 | - |
670
+ | 7.3973 | 10800 | 0.002 | - |
671
+ | 7.4315 | 10850 | 0.0247 | - |
672
+ | 7.4658 | 10900 | 0.0394 | - |
673
+ | 7.5 | 10950 | 0.0001 | - |
674
+ | 7.5342 | 11000 | 0.0001 | - |
675
+ | 7.5685 | 11050 | 0.0503 | - |
676
+ | 7.6027 | 11100 | 0.0066 | - |
677
+ | 7.6370 | 11150 | 0.0087 | - |
678
+ | 7.6712 | 11200 | 0.0165 | - |
679
+ | 7.7055 | 11250 | 0.0164 | - |
680
+ | 7.7397 | 11300 | 0.019 | - |
681
+ | 7.7740 | 11350 | 0.0143 | - |
682
+ | 7.8082 | 11400 | 0.0282 | - |
683
+ | 7.8425 | 11450 | 0.0001 | - |
684
+ | 7.8767 | 11500 | 0.0 | - |
685
+ | 7.9110 | 11550 | 0.0049 | - |
686
+ | 7.9452 | 11600 | 0.0028 | - |
687
+ | 7.9795 | 11650 | 0.0001 | - |
688
+ | 8.0137 | 11700 | 0.0184 | - |
689
+ | 8.0479 | 11750 | 0.0038 | - |
690
+ | 8.0822 | 11800 | 0.0211 | - |
691
+ | 8.1164 | 11850 | 0.0217 | - |
692
+ | 8.1507 | 11900 | 0.0035 | - |
693
+ | 8.1849 | 11950 | 0.0039 | - |
694
+ | 8.2192 | 12000 | 0.0 | - |
695
+ | 8.2534 | 12050 | 0.0055 | - |
696
+ | 8.2877 | 12100 | 0.0027 | - |
697
+ | 8.3219 | 12150 | 0.0031 | - |
698
+ | 8.3562 | 12200 | 0.0271 | - |
699
+ | 8.3904 | 12250 | 0.0138 | - |
700
+ | 8.4247 | 12300 | 0.0413 | - |
701
+ | 8.4589 | 12350 | 0.0029 | - |
702
+ | 8.4932 | 12400 | 0.0161 | - |
703
+ | 8.5274 | 12450 | 0.0 | - |
704
+ | 8.5616 | 12500 | 0.0352 | - |
705
+ | 8.5959 | 12550 | 0.0018 | - |
706
+ | 8.6301 | 12600 | 0.0363 | - |
707
+ | 8.6644 | 12650 | 0.0136 | - |
708
+ | 8.6986 | 12700 | 0.0175 | - |
709
+ | 8.7329 | 12750 | 0.0045 | - |
710
+ | 8.7671 | 12800 | 0.036 | - |
711
+ | 8.8014 | 12850 | 0.0001 | - |
712
+ | 8.8356 | 12900 | 0.0188 | - |
713
+ | 8.8699 | 12950 | 0.0192 | - |
714
+ | 8.9041 | 13000 | 0.0059 | - |
715
+ | 8.9384 | 13050 | 0.0298 | - |
716
+ | 8.9726 | 13100 | 0.0026 | - |
717
+ | 9.0068 | 13150 | 0.0027 | - |
718
+ | 9.0411 | 13200 | 0.0017 | - |
719
+ | 9.0753 | 13250 | 0.0103 | - |
720
+ | 9.1096 | 13300 | 0.0061 | - |
721
+ | 9.1438 | 13350 | 0.0043 | - |
722
+ | 9.1781 | 13400 | 0.0189 | - |
723
+ | 9.2123 | 13450 | 0.0001 | - |
724
+ | 9.2466 | 13500 | 0.0363 | - |
725
+ | 9.2808 | 13550 | 0.0019 | - |
726
+ | 9.3151 | 13600 | 0.0141 | - |
727
+ | 9.3493 | 13650 | 0.0213 | - |
728
+ | 9.3836 | 13700 | 0.0029 | - |
729
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733
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734
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735
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736
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737
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+
894
+ ### Framework Versions
895
+ - Python: 3.10.12
896
+ - SetFit: 1.1.0.dev0
897
+ - Sentence Transformers: 2.6.1
898
+ - Transformers: 4.38.2
899
+ - PyTorch: 2.2.1+cu121
900
+ - Datasets: 2.18.0
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+ - Tokenizers: 0.15.2
902
+
903
+ ## Citation
904
+
905
+ ### BibTeX
906
+ ```bibtex
907
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
908
+ doi = {10.48550/ARXIV.2209.11055},
909
+ url = {https://arxiv.org/abs/2209.11055},
910
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
911
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
912
+ title = {Efficient Few-Shot Learning Without Prompts},
913
+ publisher = {arXiv},
914
+ year = {2022},
915
+ copyright = {Creative Commons Attribution 4.0 International}
916
+ }
917
+ ```
918
+
919
+ <!--
920
+ ## Glossary
921
+
922
+ *Clearly define terms in order to be accessible across audiences.*
923
+ -->
924
+
925
+ <!--
926
+ ## Model Card Authors
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+
928
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
929
+ -->
930
+
931
+ <!--
932
+ ## Model Card Contact
933
+
934
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
935
+ -->
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