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NLBSE2026-java

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
text-embeddings-inference
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  1. 1_Pooling/config.json +10 -0
  2. README.md +844 -0
  3. checkpoint-33850/1_Pooling/config.json +10 -0
  4. checkpoint-33850/README.md +100 -0
  5. checkpoint-33850/config.json +25 -0
  6. checkpoint-33850/config_sentence_transformers.json +14 -0
  7. checkpoint-33850/model.safetensors +3 -0
  8. checkpoint-33850/modules.json +14 -0
  9. checkpoint-33850/optimizer.pt +3 -0
  10. checkpoint-33850/rng_state.pth +3 -0
  11. checkpoint-33850/scheduler.pt +3 -0
  12. checkpoint-33850/sentence_bert_config.json +4 -0
  13. checkpoint-33850/special_tokens_map.json +37 -0
  14. checkpoint-33850/tokenizer.json +0 -0
  15. checkpoint-33850/tokenizer_config.json +58 -0
  16. checkpoint-33850/trainer_state.json +0 -0
  17. checkpoint-33850/training_args.bin +3 -0
  18. checkpoint-33850/vocab.txt +0 -0
  19. config.json +25 -0
  20. config_sentence_transformers.json +14 -0
  21. config_setfit.json +4 -0
  22. model.safetensors +3 -0
  23. model_head.pkl +3 -0
  24. modules.json +14 -0
  25. sentence_bert_config.json +4 -0
  26. special_tokens_map.json +37 -0
  27. tokenizer.json +0 -0
  28. tokenizer_config.json +58 -0
  29. 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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+ 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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+ widget:
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+ - text: '@link FSNamesystem#readLock() | FSPermissionChecker.java'
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+ - text: previous^checkpoint li | TestSaveNamespace.java
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+ - text: // the file doesn't have anything | TaskLog.java
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+ - text: " @param file the file the include directives point to\n\t * @param depth\
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+ \ depth to which includes are followed, should be one of\n\t * {@link #DEPTH_ZERO}\
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+ \ or {@link #DEPTH_INFINITE}\n\t * @return an array of include relations\n\t *\
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+ \ @throws CoreException | IIndex.java"
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+ - text: // quotes are removed | ScannerUtility.java
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+ metrics:
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+ - accuracy
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+ pipeline_tag: text-classification
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+ library_name: setfit
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+ inference: true
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+ ---
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+
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+ # SetFit
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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. A MultiTaskHead 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:** [Unknown](https://huggingface.co/unknown) -->
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+ - **Classification head:** a MultiTaskHead instance
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+ - **Maximum Sequence Length:** 128 tokens
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+ <!-- - **Number of Classes:** Unknown -->
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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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+ ## 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("setfit_model_id")
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+ # Run inference
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+ preds = model("// quotes are removed | ScannerUtility.java")
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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 | 3 | 15.4874 | 299 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (32, 32)
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+ - num_epochs: (5, 5)
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+ - max_steps: -1
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+ - sampling_strategy: oversampling
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+ - num_iterations: 20
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+ - body_learning_rate: (2e-05, 1e-05)
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+ - head_learning_rate: 0.001
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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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+ - l2_weight: 0.01
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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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+ | 0.0001 | 1 | 0.2247 | - |
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+ | 0.0074 | 50 | 0.2914 | - |
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+ | 0.0148 | 100 | 0.2746 | - |
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+ | 0.0222 | 150 | 0.2579 | - |
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+ | 0.0295 | 200 | 0.2499 | - |
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+ | 0.0369 | 250 | 0.2386 | - |
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+ | 0.0443 | 300 | 0.2269 | - |
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+ | 0.0517 | 350 | 0.2171 | - |
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+ | 0.0591 | 400 | 0.1999 | - |
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+ | 0.0665 | 450 | 0.1787 | - |
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+ | 0.0739 | 500 | 0.1647 | - |
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+ | 0.0812 | 550 | 0.1581 | - |
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+ | 0.0886 | 600 | 0.1531 | - |
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+ | 0.0960 | 650 | 0.1475 | - |
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+ | 0.1034 | 700 | 0.1375 | - |
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+ | 0.1108 | 750 | 0.1274 | - |
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+ | 0.1182 | 800 | 0.1312 | - |
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+ | 0.1256 | 850 | 0.1228 | - |
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+ | 0.1329 | 900 | 0.118 | - |
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+ | 0.1403 | 950 | 0.1117 | - |
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+ | 0.1477 | 1000 | 0.1108 | - |
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+ | 0.1551 | 1050 | 0.0941 | - |
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+ | 0.1625 | 1100 | 0.0917 | - |
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+ | 0.1699 | 1150 | 0.0961 | - |
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+ | 0.1773 | 1200 | 0.0896 | - |
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+ | 0.1846 | 1250 | 0.092 | - |
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+ | 0.1920 | 1300 | 0.0895 | - |
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+ | 0.1994 | 1350 | 0.0823 | - |
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+ | 0.2068 | 1400 | 0.0809 | - |
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+ | 0.2142 | 1450 | 0.0766 | - |
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+ | 0.2216 | 1500 | 0.0733 | - |
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+ | 0.2290 | 1550 | 0.0778 | - |
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+ | 0.2363 | 1600 | 0.0715 | - |
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+ | 0.2437 | 1650 | 0.0701 | - |
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+ | 0.2511 | 1700 | 0.0664 | - |
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+ | 0.2585 | 1750 | 0.0645 | - |
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+ | 0.2659 | 1800 | 0.061 | - |
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+ | 0.2733 | 1850 | 0.0625 | - |
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+ | 0.2806 | 1900 | 0.054 | - |
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+ | 0.2880 | 1950 | 0.0612 | - |
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+ | 0.2954 | 2000 | 0.0579 | - |
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+ | 0.3028 | 2050 | 0.0566 | - |
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+ | 0.3102 | 2100 | 0.0495 | - |
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+ | 0.3176 | 2150 | 0.0514 | - |
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+ | 0.3250 | 2200 | 0.0478 | - |
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+ | 0.3323 | 2250 | 0.0484 | - |
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+ | 0.3397 | 2300 | 0.0547 | - |
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+ | 0.3471 | 2350 | 0.0466 | - |
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+ | 0.3545 | 2400 | 0.0454 | - |
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+ | 0.3619 | 2450 | 0.041 | - |
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+ | 0.3693 | 2500 | 0.0395 | - |
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+ | 0.3767 | 2550 | 0.0398 | - |
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+ | 0.3840 | 2600 | 0.0415 | - |
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+ | 0.3914 | 2650 | 0.0367 | - |
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+ | 0.3988 | 2700 | 0.0331 | - |
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+ | 0.4062 | 2750 | 0.0399 | - |
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+ | 0.4136 | 2800 | 0.0342 | - |
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+ | 0.4210 | 2850 | 0.0356 | - |
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+ | 0.4284 | 2900 | 0.0346 | - |
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+ | 0.4357 | 2950 | 0.0326 | - |
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+ | 0.4431 | 3000 | 0.0301 | - |
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+ | 0.4505 | 3050 | 0.0297 | - |
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+ | 0.4579 | 3100 | 0.0318 | - |
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+ | 0.4653 | 3150 | 0.0288 | - |
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+ | 0.4727 | 3200 | 0.0324 | - |
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+ | 0.4801 | 3250 | 0.024 | - |
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+ | 0.4874 | 3300 | 0.0299 | - |
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+ | 0.4948 | 3350 | 0.0315 | - |
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+ | 0.5022 | 3400 | 0.0267 | - |
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+ | 0.5096 | 3450 | 0.0268 | - |
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+ | 0.5170 | 3500 | 0.0231 | - |
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+ | 0.5244 | 3550 | 0.0257 | - |
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+ | 0.5318 | 3600 | 0.023 | - |
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+ | 0.5391 | 3650 | 0.0222 | - |
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+ | 0.5465 | 3700 | 0.0244 | - |
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+ | 0.5539 | 3750 | 0.0218 | - |
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+ | 0.5613 | 3800 | 0.0267 | - |
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+ | 0.5687 | 3850 | 0.0221 | - |
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+ | 0.5761 | 3900 | 0.0169 | - |
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+ | 0.5835 | 3950 | 0.0203 | - |
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+ | 0.5908 | 4000 | 0.0184 | - |
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+ | 0.5982 | 4050 | 0.0175 | - |
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+ | 0.6056 | 4100 | 0.0219 | - |
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+ | 0.6130 | 4150 | 0.0175 | - |
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+ | 0.6204 | 4200 | 0.017 | - |
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+ | 0.6278 | 4250 | 0.0181 | - |
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+ | 0.6352 | 4300 | 0.0164 | - |
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+ | 0.6425 | 4350 | 0.0129 | - |
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+ | 0.7164 | 4850 | 0.012 | - |
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+ | 0.7755 | 5250 | 0.0061 | - |
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+ | 0.7829 | 5300 | 0.0107 | - |
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+ | 0.7903 | 5350 | 0.0093 | - |
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+ | 1.2334 | 8350 | 0.0065 | - |
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+ | 1.3220 | 8950 | 0.0059 | - |
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+ | 1.3294 | 9000 | 0.0036 | - |
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+ | 1.3442 | 9100 | 0.0053 | - |
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+ | 1.3516 | 9150 | 0.0035 | - |
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+ | 1.3589 | 9200 | 0.0073 | - |
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+ | 1.3663 | 9250 | 0.0028 | - |
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+ | 1.6322 | 11050 | 0.0042 | - |
346
+ | 1.6396 | 11100 | 0.0029 | - |
347
+ | 1.6470 | 11150 | 0.005 | - |
348
+ | 1.6544 | 11200 | 0.0059 | - |
349
+ | 1.6617 | 11250 | 0.0061 | - |
350
+ | 1.6691 | 11300 | 0.0037 | - |
351
+ | 1.6765 | 11350 | 0.0034 | - |
352
+ | 1.6839 | 11400 | 0.0058 | - |
353
+ | 1.6913 | 11450 | 0.0057 | - |
354
+ | 1.6987 | 11500 | 0.0053 | - |
355
+ | 1.7061 | 11550 | 0.0038 | - |
356
+ | 1.7134 | 11600 | 0.0055 | - |
357
+ | 1.7208 | 11650 | 0.0053 | - |
358
+ | 1.7282 | 11700 | 0.0046 | - |
359
+ | 1.7356 | 11750 | 0.0038 | - |
360
+ | 1.7430 | 11800 | 0.006 | - |
361
+ | 1.7504 | 11850 | 0.0063 | - |
362
+ | 1.7578 | 11900 | 0.0044 | - |
363
+ | 1.7651 | 11950 | 0.0044 | - |
364
+ | 1.7725 | 12000 | 0.0038 | - |
365
+ | 1.7799 | 12050 | 0.0063 | - |
366
+ | 1.7873 | 12100 | 0.0022 | - |
367
+ | 1.7947 | 12150 | 0.0043 | - |
368
+ | 1.8021 | 12200 | 0.0035 | - |
369
+ | 1.8095 | 12250 | 0.0044 | - |
370
+ | 1.8168 | 12300 | 0.0034 | - |
371
+ | 1.8242 | 12350 | 0.0045 | - |
372
+ | 1.8316 | 12400 | 0.0035 | - |
373
+ | 1.8390 | 12450 | 0.0037 | - |
374
+ | 1.8464 | 12500 | 0.0043 | - |
375
+ | 1.8538 | 12550 | 0.0046 | - |
376
+ | 1.8612 | 12600 | 0.0062 | - |
377
+ | 1.8685 | 12650 | 0.0023 | - |
378
+ | 1.8759 | 12700 | 0.0033 | - |
379
+ | 1.8833 | 12750 | 0.0043 | - |
380
+ | 1.8907 | 12800 | 0.004 | - |
381
+ | 1.8981 | 12850 | 0.0025 | - |
382
+ | 1.9055 | 12900 | 0.0062 | - |
383
+ | 1.9129 | 12950 | 0.0037 | - |
384
+ | 1.9202 | 13000 | 0.0038 | - |
385
+ | 1.9276 | 13050 | 0.0044 | - |
386
+ | 1.9350 | 13100 | 0.003 | - |
387
+ | 1.9424 | 13150 | 0.0037 | - |
388
+ | 1.9498 | 13200 | 0.0034 | - |
389
+ | 1.9572 | 13250 | 0.0029 | - |
390
+ | 1.9645 | 13300 | 0.0019 | - |
391
+ | 1.9719 | 13350 | 0.003 | - |
392
+ | 1.9793 | 13400 | 0.0041 | - |
393
+ | 1.9867 | 13450 | 0.0033 | - |
394
+ | 1.9941 | 13500 | 0.0032 | - |
395
+ | 2.0015 | 13550 | 0.0029 | - |
396
+ | 2.0089 | 13600 | 0.0058 | - |
397
+ | 2.0162 | 13650 | 0.0019 | - |
398
+ | 2.0236 | 13700 | 0.0027 | - |
399
+ | 2.0310 | 13750 | 0.0015 | - |
400
+ | 2.0384 | 13800 | 0.0029 | - |
401
+ | 2.0458 | 13850 | 0.0043 | - |
402
+ | 2.0532 | 13900 | 0.0016 | - |
403
+ | 2.0606 | 13950 | 0.0022 | - |
404
+ | 2.0679 | 14000 | 0.0035 | - |
405
+ | 2.0753 | 14050 | 0.0033 | - |
406
+ | 2.0827 | 14100 | 0.0019 | - |
407
+ | 2.0901 | 14150 | 0.0039 | - |
408
+ | 2.0975 | 14200 | 0.0022 | - |
409
+ | 2.1049 | 14250 | 0.0042 | - |
410
+ | 2.1123 | 14300 | 0.0023 | - |
411
+ | 2.1196 | 14350 | 0.0022 | - |
412
+ | 2.1270 | 14400 | 0.0016 | - |
413
+ | 2.1344 | 14450 | 0.0023 | - |
414
+ | 2.1418 | 14500 | 0.0034 | - |
415
+ | 2.1492 | 14550 | 0.0019 | - |
416
+ | 2.1566 | 14600 | 0.0027 | - |
417
+ | 2.1640 | 14650 | 0.0025 | - |
418
+ | 2.1713 | 14700 | 0.0025 | - |
419
+ | 2.1787 | 14750 | 0.0024 | - |
420
+ | 2.1861 | 14800 | 0.004 | - |
421
+ | 2.1935 | 14850 | 0.0013 | - |
422
+ | 2.2009 | 14900 | 0.0018 | - |
423
+ | 2.2083 | 14950 | 0.0025 | - |
424
+ | 2.2157 | 15000 | 0.0052 | - |
425
+ | 2.2230 | 15050 | 0.0027 | - |
426
+ | 2.2304 | 15100 | 0.0011 | - |
427
+ | 2.2378 | 15150 | 0.0019 | - |
428
+ | 2.2452 | 15200 | 0.0012 | - |
429
+ | 2.2526 | 15250 | 0.0045 | - |
430
+ | 2.2600 | 15300 | 0.0031 | - |
431
+ | 2.2674 | 15350 | 0.0029 | - |
432
+ | 2.2747 | 15400 | 0.0048 | - |
433
+ | 2.2821 | 15450 | 0.0024 | - |
434
+ | 2.2895 | 15500 | 0.0032 | - |
435
+ | 2.2969 | 15550 | 0.0017 | - |
436
+ | 2.3043 | 15600 | 0.0018 | - |
437
+ | 2.3117 | 15650 | 0.0035 | - |
438
+ | 2.3191 | 15700 | 0.0041 | - |
439
+ | 2.3264 | 15750 | 0.0015 | - |
440
+ | 2.3338 | 15800 | 0.003 | - |
441
+ | 2.3412 | 15850 | 0.0016 | - |
442
+ | 2.3486 | 15900 | 0.0027 | - |
443
+ | 2.3560 | 15950 | 0.0024 | - |
444
+ | 2.3634 | 16000 | 0.002 | - |
445
+ | 2.3708 | 16050 | 0.0014 | - |
446
+ | 2.3781 | 16100 | 0.001 | - |
447
+ | 2.3855 | 16150 | 0.0005 | - |
448
+ | 2.3929 | 16200 | 0.0015 | - |
449
+ | 2.4003 | 16250 | 0.0045 | - |
450
+ | 2.4077 | 16300 | 0.0015 | - |
451
+ | 2.4151 | 16350 | 0.0011 | - |
452
+ | 2.4225 | 16400 | 0.0019 | - |
453
+ | 2.4298 | 16450 | 0.0024 | - |
454
+ | 2.4372 | 16500 | 0.002 | - |
455
+ | 2.4446 | 16550 | 0.0016 | - |
456
+ | 2.4520 | 16600 | 0.0015 | - |
457
+ | 2.4594 | 16650 | 0.0021 | - |
458
+ | 2.4668 | 16700 | 0.0025 | - |
459
+ | 2.4742 | 16750 | 0.0021 | - |
460
+ | 2.4815 | 16800 | 0.0029 | - |
461
+ | 2.4889 | 16850 | 0.0014 | - |
462
+ | 2.4963 | 16900 | 0.0029 | - |
463
+ | 2.5037 | 16950 | 0.004 | - |
464
+ | 2.5111 | 17000 | 0.0028 | - |
465
+ | 2.5185 | 17050 | 0.0027 | - |
466
+ | 2.5258 | 17100 | 0.0011 | - |
467
+ | 2.5332 | 17150 | 0.0036 | - |
468
+ | 2.5406 | 17200 | 0.0031 | - |
469
+ | 2.5480 | 17250 | 0.0021 | - |
470
+ | 2.5554 | 17300 | 0.0018 | - |
471
+ | 2.5628 | 17350 | 0.0015 | - |
472
+ | 2.5702 | 17400 | 0.0031 | - |
473
+ | 2.5775 | 17450 | 0.0031 | - |
474
+ | 2.5849 | 17500 | 0.0011 | - |
475
+ | 2.5923 | 17550 | 0.0044 | - |
476
+ | 2.5997 | 17600 | 0.0013 | - |
477
+ | 2.6071 | 17650 | 0.0015 | - |
478
+ | 2.6145 | 17700 | 0.0013 | - |
479
+ | 2.6219 | 17750 | 0.0018 | - |
480
+ | 2.6292 | 17800 | 0.0023 | - |
481
+ | 2.6366 | 17850 | 0.0043 | - |
482
+ | 2.6440 | 17900 | 0.0049 | - |
483
+ | 2.6514 | 17950 | 0.0045 | - |
484
+ | 2.6588 | 18000 | 0.0017 | - |
485
+ | 2.6662 | 18050 | 0.002 | - |
486
+ | 2.6736 | 18100 | 0.0021 | - |
487
+ | 2.6809 | 18150 | 0.0014 | - |
488
+ | 2.6883 | 18200 | 0.0025 | - |
489
+ | 2.6957 | 18250 | 0.0032 | - |
490
+ | 2.7031 | 18300 | 0.0038 | - |
491
+ | 2.7105 | 18350 | 0.0016 | - |
492
+ | 2.7179 | 18400 | 0.0014 | - |
493
+ | 2.7253 | 18450 | 0.0013 | - |
494
+ | 2.7326 | 18500 | 0.0013 | - |
495
+ | 2.7400 | 18550 | 0.0024 | - |
496
+ | 2.7474 | 18600 | 0.0024 | - |
497
+ | 2.7548 | 18650 | 0.0026 | - |
498
+ | 2.7622 | 18700 | 0.0032 | - |
499
+ | 2.7696 | 18750 | 0.0024 | - |
500
+ | 2.7770 | 18800 | 0.0019 | - |
501
+ | 2.7843 | 18850 | 0.0015 | - |
502
+ | 2.7917 | 18900 | 0.0028 | - |
503
+ | 2.7991 | 18950 | 0.0021 | - |
504
+ | 2.8065 | 19000 | 0.0018 | - |
505
+ | 2.8139 | 19050 | 0.0009 | - |
506
+ | 2.8213 | 19100 | 0.0024 | - |
507
+ | 2.8287 | 19150 | 0.0016 | - |
508
+ | 2.8360 | 19200 | 0.001 | - |
509
+ | 2.8434 | 19250 | 0.0016 | - |
510
+ | 2.8508 | 19300 | 0.0009 | - |
511
+ | 2.8582 | 19350 | 0.0025 | - |
512
+ | 2.8656 | 19400 | 0.0026 | - |
513
+ | 2.8730 | 19450 | 0.0018 | - |
514
+ | 2.8804 | 19500 | 0.0012 | - |
515
+ | 2.8877 | 19550 | 0.0012 | - |
516
+ | 2.8951 | 19600 | 0.0018 | - |
517
+ | 2.9025 | 19650 | 0.003 | - |
518
+ | 2.9099 | 19700 | 0.0026 | - |
519
+ | 2.9173 | 19750 | 0.001 | - |
520
+ | 2.9247 | 19800 | 0.0031 | - |
521
+ | 2.9321 | 19850 | 0.0019 | - |
522
+ | 2.9394 | 19900 | 0.0027 | - |
523
+ | 2.9468 | 19950 | 0.001 | - |
524
+ | 2.9542 | 20000 | 0.0025 | - |
525
+ | 2.9616 | 20050 | 0.0017 | - |
526
+ | 2.9690 | 20100 | 0.0033 | - |
527
+ | 2.9764 | 20150 | 0.0006 | - |
528
+ | 2.9838 | 20200 | 0.0026 | - |
529
+ | 2.9911 | 20250 | 0.0011 | - |
530
+ | 2.9985 | 20300 | 0.0021 | - |
531
+ | 3.0059 | 20350 | 0.0039 | - |
532
+ | 3.0133 | 20400 | 0.0003 | - |
533
+ | 3.0207 | 20450 | 0.001 | - |
534
+ | 3.0281 | 20500 | 0.0008 | - |
535
+ | 3.0355 | 20550 | 0.0009 | - |
536
+ | 3.0428 | 20600 | 0.0023 | - |
537
+ | 3.0502 | 20650 | 0.0008 | - |
538
+ | 3.0576 | 20700 | 0.0009 | - |
539
+ | 3.0650 | 20750 | 0.0015 | - |
540
+ | 3.0724 | 20800 | 0.0019 | - |
541
+ | 3.0798 | 20850 | 0.0027 | - |
542
+ | 3.0871 | 20900 | 0.0009 | - |
543
+ | 3.0945 | 20950 | 0.0006 | - |
544
+ | 3.1019 | 21000 | 0.0011 | - |
545
+ | 3.1093 | 21050 | 0.0014 | - |
546
+ | 3.1167 | 21100 | 0.0009 | - |
547
+ | 3.1241 | 21150 | 0.0011 | - |
548
+ | 3.1315 | 21200 | 0.0021 | - |
549
+ | 3.1388 | 21250 | 0.0023 | - |
550
+ | 3.1462 | 21300 | 0.0022 | - |
551
+ | 3.1536 | 21350 | 0.001 | - |
552
+ | 3.1610 | 21400 | 0.0017 | - |
553
+ | 3.1684 | 21450 | 0.0022 | - |
554
+ | 3.1758 | 21500 | 0.0007 | - |
555
+ | 3.1832 | 21550 | 0.0022 | - |
556
+ | 3.1905 | 21600 | 0.0002 | - |
557
+ | 3.1979 | 21650 | 0.0004 | - |
558
+ | 3.2053 | 21700 | 0.001 | - |
559
+ | 3.2127 | 21750 | 0.0033 | - |
560
+ | 3.2201 | 21800 | 0.0011 | - |
561
+ | 3.2275 | 21850 | 0.0003 | - |
562
+ | 3.2349 | 21900 | 0.0004 | - |
563
+ | 3.2422 | 21950 | 0.0004 | - |
564
+ | 3.2496 | 22000 | 0.0014 | - |
565
+ | 3.2570 | 22050 | 0.0012 | - |
566
+ | 3.2644 | 22100 | 0.002 | - |
567
+ | 3.2718 | 22150 | 0.001 | - |
568
+ | 3.2792 | 22200 | 0.0026 | - |
569
+ | 3.2866 | 22250 | 0.0017 | - |
570
+ | 3.2939 | 22300 | 0.0009 | - |
571
+ | 3.3013 | 22350 | 0.0018 | - |
572
+ | 3.3087 | 22400 | 0.0025 | - |
573
+ | 3.3161 | 22450 | 0.0016 | - |
574
+ | 3.3235 | 22500 | 0.0035 | - |
575
+ | 3.3309 | 22550 | 0.0002 | - |
576
+ | 3.3383 | 22600 | 0.0015 | - |
577
+ | 3.3456 | 22650 | 0.002 | - |
578
+ | 3.3530 | 22700 | 0.0021 | - |
579
+ | 3.3604 | 22750 | 0.0024 | - |
580
+ | 3.3678 | 22800 | 0.0015 | - |
581
+ | 3.3752 | 22850 | 0.0021 | - |
582
+ | 3.3826 | 22900 | 0.0022 | - |
583
+ | 3.3900 | 22950 | 0.0015 | - |
584
+ | 3.3973 | 23000 | 0.0015 | - |
585
+ | 3.4047 | 23050 | 0.0016 | - |
586
+ | 3.4121 | 23100 | 0.0008 | - |
587
+ | 3.4195 | 23150 | 0.0021 | - |
588
+ | 3.4269 | 23200 | 0.0021 | - |
589
+ | 3.4343 | 23250 | 0.0017 | - |
590
+ | 3.4417 | 23300 | 0.0015 | - |
591
+ | 3.4490 | 23350 | 0.0004 | - |
592
+ | 3.4564 | 23400 | 0.0007 | - |
593
+ | 3.4638 | 23450 | 0.0004 | - |
594
+ | 3.4712 | 23500 | 0.0014 | - |
595
+ | 3.4786 | 23550 | 0.0003 | - |
596
+ | 3.4860 | 23600 | 0.002 | - |
597
+ | 3.4934 | 23650 | 0.0003 | - |
598
+ | 3.5007 | 23700 | 0.0002 | - |
599
+ | 3.5081 | 23750 | 0.0009 | - |
600
+ | 3.5155 | 23800 | 0.0036 | - |
601
+ | 3.5229 | 23850 | 0.0022 | - |
602
+ | 3.5303 | 23900 | 0.0014 | - |
603
+ | 3.5377 | 23950 | 0.0015 | - |
604
+ | 3.5451 | 24000 | 0.0009 | - |
605
+ | 3.5524 | 24050 | 0.0007 | - |
606
+ | 3.5598 | 24100 | 0.0024 | - |
607
+ | 3.5672 | 24150 | 0.0011 | - |
608
+ | 3.5746 | 24200 | 0.0018 | - |
609
+ | 3.5820 | 24250 | 0.0018 | - |
610
+ | 3.5894 | 24300 | 0.0029 | - |
611
+ | 3.5968 | 24350 | 0.0009 | - |
612
+ | 3.6041 | 24400 | 0.0015 | - |
613
+ | 3.6115 | 24450 | 0.0015 | - |
614
+ | 3.6189 | 24500 | 0.0009 | - |
615
+ | 3.6263 | 24550 | 0.0002 | - |
616
+ | 3.6337 | 24600 | 0.0021 | - |
617
+ | 3.6411 | 24650 | 0.0002 | - |
618
+ | 3.6484 | 24700 | 0.0014 | - |
619
+ | 3.6558 | 24750 | 0.0008 | - |
620
+ | 3.6632 | 24800 | 0.0013 | - |
621
+ | 3.6706 | 24850 | 0.0023 | - |
622
+ | 3.6780 | 24900 | 0.0004 | - |
623
+ | 3.6854 | 24950 | 0.0007 | - |
624
+ | 3.6928 | 25000 | 0.0015 | - |
625
+ | 3.7001 | 25050 | 0.0008 | - |
626
+ | 3.7075 | 25100 | 0.0014 | - |
627
+ | 3.7149 | 25150 | 0.0005 | - |
628
+ | 3.7223 | 25200 | 0.0018 | - |
629
+ | 3.7297 | 25250 | 0.0012 | - |
630
+ | 3.7371 | 25300 | 0.0002 | - |
631
+ | 3.7445 | 25350 | 0.0005 | - |
632
+ | 3.7518 | 25400 | 0.0016 | - |
633
+ | 3.7592 | 25450 | 0.0015 | - |
634
+ | 3.7666 | 25500 | 0.0014 | - |
635
+ | 3.7740 | 25550 | 0.0008 | - |
636
+ | 3.7814 | 25600 | 0.0004 | - |
637
+ | 3.7888 | 25650 | 0.0014 | - |
638
+ | 3.7962 | 25700 | 0.0018 | - |
639
+ | 3.8035 | 25750 | 0.0008 | - |
640
+ | 3.8109 | 25800 | 0.0008 | - |
641
+ | 3.8183 | 25850 | 0.0002 | - |
642
+ | 3.8257 | 25900 | 0.0003 | - |
643
+ | 3.8331 | 25950 | 0.0009 | - |
644
+ | 3.8405 | 26000 | 0.002 | - |
645
+ | 3.8479 | 26050 | 0.0016 | - |
646
+ | 3.8552 | 26100 | 0.0013 | - |
647
+ | 3.8626 | 26150 | 0.0021 | - |
648
+ | 3.8700 | 26200 | 0.0006 | - |
649
+ | 3.8774 | 26250 | 0.0005 | - |
650
+ | 3.8848 | 26300 | 0.0019 | - |
651
+ | 3.8922 | 26350 | 0.0017 | - |
652
+ | 3.8996 | 26400 | 0.0002 | - |
653
+ | 3.9069 | 26450 | 0.0014 | - |
654
+ | 3.9143 | 26500 | 0.0003 | - |
655
+ | 3.9217 | 26550 | 0.0015 | - |
656
+ | 3.9291 | 26600 | 0.001 | - |
657
+ | 3.9365 | 26650 | 0.0002 | - |
658
+ | 3.9439 | 26700 | 0.0002 | - |
659
+ | 3.9513 | 26750 | 0.0002 | - |
660
+ | 3.9586 | 26800 | 0.0006 | - |
661
+ | 3.9660 | 26850 | 0.0003 | - |
662
+ | 3.9734 | 26900 | 0.0016 | - |
663
+ | 3.9808 | 26950 | 0.0008 | - |
664
+ | 3.9882 | 27000 | 0.002 | - |
665
+ | 3.9956 | 27050 | 0.0017 | - |
666
+ | 4.0030 | 27100 | 0.0003 | - |
667
+ | 4.0103 | 27150 | 0.0012 | - |
668
+ | 4.0177 | 27200 | 0.0004 | - |
669
+ | 4.0251 | 27250 | 0.0022 | - |
670
+ | 4.0325 | 27300 | 0.0015 | - |
671
+ | 4.0399 | 27350 | 0.0004 | - |
672
+ | 4.0473 | 27400 | 0.001 | - |
673
+ | 4.0547 | 27450 | 0.0002 | - |
674
+ | 4.0620 | 27500 | 0.0002 | - |
675
+ | 4.0694 | 27550 | 0.0002 | - |
676
+ | 4.0768 | 27600 | 0.0002 | - |
677
+ | 4.0842 | 27650 | 0.0009 | - |
678
+ | 4.0916 | 27700 | 0.0016 | - |
679
+ | 4.0990 | 27750 | 0.0017 | - |
680
+ | 4.1064 | 27800 | 0.0016 | - |
681
+ | 4.1137 | 27850 | 0.0008 | - |
682
+ | 4.1211 | 27900 | 0.0007 | - |
683
+ | 4.1285 | 27950 | 0.0002 | - |
684
+ | 4.1359 | 28000 | 0.0003 | - |
685
+ | 4.1433 | 28050 | 0.0007 | - |
686
+ | 4.1507 | 28100 | 0.002 | - |
687
+ | 4.1581 | 28150 | 0.0013 | - |
688
+ | 4.1654 | 28200 | 0.0003 | - |
689
+ | 4.1728 | 28250 | 0.0001 | - |
690
+ | 4.1802 | 28300 | 0.0008 | - |
691
+ | 4.1876 | 28350 | 0.0019 | - |
692
+ | 4.1950 | 28400 | 0.0017 | - |
693
+ | 4.2024 | 28450 | 0.0007 | - |
694
+ | 4.2097 | 28500 | 0.0021 | - |
695
+ | 4.2171 | 28550 | 0.0005 | - |
696
+ | 4.2245 | 28600 | 0.0009 | - |
697
+ | 4.2319 | 28650 | 0.0019 | - |
698
+ | 4.2393 | 28700 | 0.0006 | - |
699
+ | 4.2467 | 28750 | 0.0011 | - |
700
+ | 4.2541 | 28800 | 0.0005 | - |
701
+ | 4.2614 | 28850 | 0.0008 | - |
702
+ | 4.2688 | 28900 | 0.0006 | - |
703
+ | 4.2762 | 28950 | 0.0006 | - |
704
+ | 4.2836 | 29000 | 0.0008 | - |
705
+ | 4.2910 | 29050 | 0.0014 | - |
706
+ | 4.2984 | 29100 | 0.0003 | - |
707
+ | 4.3058 | 29150 | 0.0002 | - |
708
+ | 4.3131 | 29200 | 0.0009 | - |
709
+ | 4.3205 | 29250 | 0.0001 | - |
710
+ | 4.3279 | 29300 | 0.0002 | - |
711
+ | 4.3353 | 29350 | 0.0008 | - |
712
+ | 4.3427 | 29400 | 0.0003 | - |
713
+ | 4.3501 | 29450 | 0.0009 | - |
714
+ | 4.3575 | 29500 | 0.0008 | - |
715
+ | 4.3648 | 29550 | 0.0009 | - |
716
+ | 4.3722 | 29600 | 0.0012 | - |
717
+ | 4.3796 | 29650 | 0.0004 | - |
718
+ | 4.3870 | 29700 | 0.0015 | - |
719
+ | 4.3944 | 29750 | 0.0011 | - |
720
+ | 4.4018 | 29800 | 0.0003 | - |
721
+ | 4.4092 | 29850 | 0.0014 | - |
722
+ | 4.4165 | 29900 | 0.0001 | - |
723
+ | 4.4239 | 29950 | 0.001 | - |
724
+ | 4.4313 | 30000 | 0.0003 | - |
725
+ | 4.4387 | 30050 | 0.0003 | - |
726
+ | 4.4461 | 30100 | 0.0008 | - |
727
+ | 4.4535 | 30150 | 0.0008 | - |
728
+ | 4.4609 | 30200 | 0.0002 | - |
729
+ | 4.4682 | 30250 | 0.0002 | - |
730
+ | 4.4756 | 30300 | 0.0001 | - |
731
+ | 4.4830 | 30350 | 0.0007 | - |
732
+ | 4.4904 | 30400 | 0.0001 | - |
733
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738
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739
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740
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741
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742
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743
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744
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745
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747
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748
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751
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755
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756
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757
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+ | 5.0 | 33850 | 0.0007 | - |
802
+
803
+ ### Framework Versions
804
+ - Python: 3.10.8
805
+ - SetFit: 1.1.2
806
+ - Sentence Transformers: 5.1.0
807
+ - Transformers: 4.56.0
808
+ - PyTorch: 2.8.0+cu128
809
+ - Datasets: 3.6.0
810
+ - Tokenizers: 0.22.0
811
+
812
+ ## Citation
813
+
814
+ ### BibTeX
815
+ ```bibtex
816
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
817
+ doi = {10.48550/ARXIV.2209.11055},
818
+ url = {https://arxiv.org/abs/2209.11055},
819
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
820
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
821
+ title = {Efficient Few-Shot Learning Without Prompts},
822
+ publisher = {arXiv},
823
+ year = {2022},
824
+ copyright = {Creative Commons Attribution 4.0 International}
825
+ }
826
+ ```
827
+
828
+ <!--
829
+ ## Glossary
830
+
831
+ *Clearly define terms in order to be accessible across audiences.*
832
+ -->
833
+
834
+ <!--
835
+ ## Model Card Authors
836
+
837
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
838
+ -->
839
+
840
+ <!--
841
+ ## Model Card Contact
842
+
843
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
844
+ -->
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checkpoint-33850/README.md ADDED
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1
+ ---
2
+ license: apache-2.0
3
+ library_name: sentence-transformers
4
+ tags:
5
+ - sentence-transformers
6
+ - feature-extraction
7
+ - sentence-similarity
8
+ - transformers
9
+ pipeline_tag: sentence-similarity
10
+ ---
11
+
12
+ # sentence-transformers/paraphrase-MiniLM-L6-v2
13
+
14
+ This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
15
+
16
+
17
+
18
+ ## Usage (Sentence-Transformers)
19
+
20
+ Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
21
+
22
+ ```
23
+ pip install -U sentence-transformers
24
+ ```
25
+
26
+ Then you can use the model like this:
27
+
28
+ ```python
29
+ from sentence_transformers import SentenceTransformer
30
+ sentences = ["This is an example sentence", "Each sentence is converted"]
31
+
32
+ model = SentenceTransformer('sentence-transformers/paraphrase-MiniLM-L6-v2')
33
+ embeddings = model.encode(sentences)
34
+ print(embeddings)
35
+ ```
36
+
37
+
38
+
39
+ ## Usage (HuggingFace Transformers)
40
+ Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
41
+
42
+ ```python
43
+ from transformers import AutoTokenizer, AutoModel
44
+ import torch
45
+
46
+
47
+ #Mean Pooling - Take attention mask into account for correct averaging
48
+ def mean_pooling(model_output, attention_mask):
49
+ token_embeddings = model_output[0] #First element of model_output contains all token embeddings
50
+ input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
51
+ return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
52
+
53
+
54
+ # Sentences we want sentence embeddings for
55
+ sentences = ['This is an example sentence', 'Each sentence is converted']
56
+
57
+ # Load model from HuggingFace Hub
58
+ tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-MiniLM-L6-v2')
59
+ model = AutoModel.from_pretrained('sentence-transformers/paraphrase-MiniLM-L6-v2')
60
+
61
+ # Tokenize sentences
62
+ encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
63
+
64
+ # Compute token embeddings
65
+ with torch.no_grad():
66
+ model_output = model(**encoded_input)
67
+
68
+ # Perform pooling. In this case, max pooling.
69
+ sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
70
+
71
+ print("Sentence embeddings:")
72
+ print(sentence_embeddings)
73
+ ```
74
+
75
+
76
+
77
+ ## Full Model Architecture
78
+ ```
79
+ SentenceTransformer(
80
+ (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
81
+ (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
82
+ )
83
+ ```
84
+
85
+ ## Citing & Authors
86
+
87
+ This model was trained by [sentence-transformers](https://www.sbert.net/).
88
+
89
+ If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084):
90
+ ```bibtex
91
+ @inproceedings{reimers-2019-sentence-bert,
92
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
93
+ author = "Reimers, Nils and Gurevych, Iryna",
94
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
95
+ month = "11",
96
+ year = "2019",
97
+ publisher = "Association for Computational Linguistics",
98
+ url = "http://arxiv.org/abs/1908.10084",
99
+ }
100
+ ```
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+ "normalized": false,
20
+ "rstrip": false,
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+ "single_word": false
22
+ },
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+ "sep_token": {
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+ "content": "[SEP]",
25
+ "lstrip": false,
26
+ "normalized": false,
27
+ "rstrip": false,
28
+ "single_word": false
29
+ },
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+ "unk_token": {
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+ "content": "[UNK]",
32
+ "lstrip": false,
33
+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false
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+ }
37
+ }
tokenizer.json ADDED
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tokenizer_config.json ADDED
@@ -0,0 +1,58 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "added_tokens_decoder": {
3
+ "0": {
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+ "content": "[PAD]",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ },
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+ "100": {
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+ "content": "[UNK]",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ },
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+ "101": {
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+ "content": "[CLS]",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ },
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+ "102": {
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+ "content": "[SEP]",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ },
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+ "103": {
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+ "content": "[MASK]",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ }
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+ },
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+ "clean_up_tokenization_spaces": false,
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+ "cls_token": "[CLS]",
46
+ "do_basic_tokenize": true,
47
+ "do_lower_case": true,
48
+ "extra_special_tokens": {},
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+ "mask_token": "[MASK]",
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+ "model_max_length": 128,
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+ "never_split": null,
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+ "pad_token": "[PAD]",
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+ "sep_token": "[SEP]",
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+ "strip_accents": null,
55
+ "tokenize_chinese_chars": true,
56
+ "tokenizer_class": "BertTokenizer",
57
+ "unk_token": "[UNK]"
58
+ }
vocab.txt ADDED
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