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nlbse26_java

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Model card Files
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  1. 1_Pooling/config.json +10 -0
  2. README.md +841 -0
  3. config.json +25 -0
  4. config_sentence_transformers.json +14 -0
  5. config_setfit.json +4 -0
  6. model.safetensors +3 -0
  7. model_head.pkl +3 -0
  8. modules.json +14 -0
  9. sentence_bert_config.json +4 -0
  10. special_tokens_map.json +37 -0
  11. tokenizer.json +0 -0
  12. tokenizer_config.json +58 -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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+ 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: false
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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 MultiOutputClassifier 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 MultiOutputClassifier 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 | 15.5217 | 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: 0.01
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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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+ - 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 | 0.3106 | - |
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+ | 0.0074 | 50 | 0.3017 | - |
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+ | 0.0148 | 100 | 0.2776 | - |
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+ | 0.0222 | 150 | 0.2638 | - |
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+ | 0.0297 | 200 | 0.2522 | - |
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+ | 0.0371 | 250 | 0.2441 | - |
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+ | 0.0445 | 300 | 0.2316 | - |
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+ | 0.0519 | 350 | 0.2212 | - |
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+ | 0.0593 | 400 | 0.205 | - |
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+ | 0.0667 | 450 | 0.1928 | - |
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+ | 0.0742 | 500 | 0.1815 | - |
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+ | 0.0816 | 550 | 0.1649 | - |
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+ | 0.0890 | 600 | 0.1579 | - |
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+ | 0.0964 | 650 | 0.1499 | - |
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+ | 0.1038 | 700 | 0.1426 | - |
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+ | 0.1112 | 750 | 0.1467 | - |
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+ | 0.1186 | 800 | 0.1393 | - |
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+ | 0.1261 | 850 | 0.1273 | - |
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+ | 0.1335 | 900 | 0.1201 | - |
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+ | 0.1409 | 950 | 0.1202 | - |
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+ | 0.1483 | 1000 | 0.1144 | - |
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+ | 0.1557 | 1050 | 0.112 | - |
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+ | 0.1631 | 1100 | 0.1067 | - |
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+ | 0.1705 | 1150 | 0.0966 | - |
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+ | 0.1780 | 1200 | 0.1005 | - |
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+ | 0.1854 | 1250 | 0.1001 | - |
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+ | 0.1928 | 1300 | 0.0972 | - |
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+ | 0.2002 | 1350 | 0.0866 | - |
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+ | 0.2076 | 1400 | 0.0794 | - |
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+ | 0.2150 | 1450 | 0.0863 | - |
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+ | 0.2225 | 1500 | 0.0831 | - |
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+ | 0.2299 | 1550 | 0.082 | - |
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+ | 0.2373 | 1600 | 0.0856 | - |
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+ | 0.2447 | 1650 | 0.0808 | - |
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+ | 0.2521 | 1700 | 0.0741 | - |
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+ | 0.2595 | 1750 | 0.0768 | - |
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+ | 0.2744 | 1850 | 0.0692 | - |
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+ | 0.2818 | 1900 | 0.07 | - |
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+ | 0.2892 | 1950 | 0.0651 | - |
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+ | 0.2966 | 2000 | 0.0701 | - |
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+ | 0.4227 | 2850 | 0.0369 | - |
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+ | 0.4301 | 2900 | 0.0418 | - |
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+ | 0.4375 | 2950 | 0.0424 | - |
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+ | 0.4449 | 3000 | 0.0378 | - |
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+ | 0.4523 | 3050 | 0.0406 | - |
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+ | 0.4597 | 3100 | 0.0346 | - |
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+ | 0.4672 | 3150 | 0.042 | - |
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+ | 0.4746 | 3200 | 0.0331 | - |
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+ | 0.5635 | 3800 | 0.0276 | - |
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+ | 0.5710 | 3850 | 0.0299 | - |
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+ | 0.5784 | 3900 | 0.0286 | - |
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+ | 0.5858 | 3950 | 0.027 | - |
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+ | 0.5932 | 4000 | 0.0257 | - |
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+ | 0.6006 | 4050 | 0.023 | - |
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+ | 0.6080 | 4100 | 0.0249 | - |
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+ | 0.6155 | 4150 | 0.0217 | - |
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+ | 0.6229 | 4200 | 0.0234 | - |
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+ | 0.6303 | 4250 | 0.0271 | - |
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+ | 0.6377 | 4300 | 0.0211 | - |
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+ | 0.6451 | 4350 | 0.0254 | - |
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+ | 0.6525 | 4400 | 0.0189 | - |
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+ | 1.7425 | 11750 | 0.0068 | - |
360
+ | 1.7500 | 11800 | 0.0079 | - |
361
+ | 1.7574 | 11850 | 0.0073 | - |
362
+ | 1.7648 | 11900 | 0.0068 | - |
363
+ | 1.7722 | 11950 | 0.0099 | - |
364
+ | 1.7796 | 12000 | 0.0069 | - |
365
+ | 1.7870 | 12050 | 0.0061 | - |
366
+ | 1.7945 | 12100 | 0.0096 | - |
367
+ | 1.8019 | 12150 | 0.0065 | - |
368
+ | 1.8093 | 12200 | 0.0119 | - |
369
+ | 1.8167 | 12250 | 0.0067 | - |
370
+ | 1.8241 | 12300 | 0.0084 | - |
371
+ | 1.8315 | 12350 | 0.0053 | - |
372
+ | 1.8389 | 12400 | 0.0074 | - |
373
+ | 1.8464 | 12450 | 0.0067 | - |
374
+ | 1.8538 | 12500 | 0.0061 | - |
375
+ | 1.8612 | 12550 | 0.0056 | - |
376
+ | 1.8686 | 12600 | 0.0051 | - |
377
+ | 1.8760 | 12650 | 0.0043 | - |
378
+ | 1.8834 | 12700 | 0.0066 | - |
379
+ | 1.8908 | 12750 | 0.0065 | - |
380
+ | 1.8983 | 12800 | 0.0048 | - |
381
+ | 1.9057 | 12850 | 0.0047 | - |
382
+ | 1.9131 | 12900 | 0.0065 | - |
383
+ | 1.9205 | 12950 | 0.0064 | - |
384
+ | 1.9279 | 13000 | 0.0056 | - |
385
+ | 1.9353 | 13050 | 0.0088 | - |
386
+ | 1.9428 | 13100 | 0.009 | - |
387
+ | 1.9502 | 13150 | 0.0086 | - |
388
+ | 1.9576 | 13200 | 0.0097 | - |
389
+ | 1.9650 | 13250 | 0.0062 | - |
390
+ | 1.9724 | 13300 | 0.0079 | - |
391
+ | 1.9798 | 13350 | 0.0094 | - |
392
+ | 1.9872 | 13400 | 0.0056 | - |
393
+ | 1.9947 | 13450 | 0.0041 | - |
394
+ | 2.0021 | 13500 | 0.0062 | - |
395
+ | 2.0095 | 13550 | 0.0063 | - |
396
+ | 2.0169 | 13600 | 0.0056 | - |
397
+ | 2.0243 | 13650 | 0.0056 | - |
398
+ | 2.0317 | 13700 | 0.0063 | - |
399
+ | 2.0392 | 13750 | 0.0052 | - |
400
+ | 2.0466 | 13800 | 0.0058 | - |
401
+ | 2.0540 | 13850 | 0.006 | - |
402
+ | 2.0614 | 13900 | 0.0071 | - |
403
+ | 2.0688 | 13950 | 0.0095 | - |
404
+ | 2.0762 | 14000 | 0.0063 | - |
405
+ | 2.0836 | 14050 | 0.0056 | - |
406
+ | 2.0911 | 14100 | 0.0044 | - |
407
+ | 2.0985 | 14150 | 0.0051 | - |
408
+ | 2.1059 | 14200 | 0.0075 | - |
409
+ | 2.1133 | 14250 | 0.0055 | - |
410
+ | 2.1207 | 14300 | 0.0048 | - |
411
+ | 2.1281 | 14350 | 0.0052 | - |
412
+ | 2.1355 | 14400 | 0.0094 | - |
413
+ | 2.1430 | 14450 | 0.0068 | - |
414
+ | 2.1504 | 14500 | 0.004 | - |
415
+ | 2.1578 | 14550 | 0.004 | - |
416
+ | 2.1652 | 14600 | 0.0046 | - |
417
+ | 2.1726 | 14650 | 0.006 | - |
418
+ | 2.1800 | 14700 | 0.0075 | - |
419
+ | 2.1875 | 14750 | 0.0055 | - |
420
+ | 2.1949 | 14800 | 0.0038 | - |
421
+ | 2.2023 | 14850 | 0.0073 | - |
422
+ | 2.2097 | 14900 | 0.0067 | - |
423
+ | 2.2171 | 14950 | 0.0066 | - |
424
+ | 2.2245 | 15000 | 0.007 | - |
425
+ | 2.2319 | 15050 | 0.0047 | - |
426
+ | 2.2394 | 15100 | 0.0057 | - |
427
+ | 2.2468 | 15150 | 0.0041 | - |
428
+ | 2.2542 | 15200 | 0.0058 | - |
429
+ | 2.2616 | 15250 | 0.0082 | - |
430
+ | 2.2690 | 15300 | 0.0049 | - |
431
+ | 2.2764 | 15350 | 0.0053 | - |
432
+ | 2.2838 | 15400 | 0.0055 | - |
433
+ | 2.2913 | 15450 | 0.0056 | - |
434
+ | 2.2987 | 15500 | 0.004 | - |
435
+ | 2.3061 | 15550 | 0.0055 | - |
436
+ | 2.3135 | 15600 | 0.0076 | - |
437
+ | 2.3209 | 15650 | 0.0089 | - |
438
+ | 2.3283 | 15700 | 0.0058 | - |
439
+ | 2.3358 | 15750 | 0.0055 | - |
440
+ | 2.3432 | 15800 | 0.0047 | - |
441
+ | 2.3506 | 15850 | 0.0052 | - |
442
+ | 2.3580 | 15900 | 0.005 | - |
443
+ | 2.3654 | 15950 | 0.0044 | - |
444
+ | 2.3728 | 16000 | 0.0086 | - |
445
+ | 2.3802 | 16050 | 0.0046 | - |
446
+ | 2.3877 | 16100 | 0.0036 | - |
447
+ | 2.3951 | 16150 | 0.0061 | - |
448
+ | 2.4025 | 16200 | 0.0054 | - |
449
+ | 2.4099 | 16250 | 0.0062 | - |
450
+ | 2.4173 | 16300 | 0.0055 | - |
451
+ | 2.4247 | 16350 | 0.0042 | - |
452
+ | 2.4322 | 16400 | 0.0058 | - |
453
+ | 2.4396 | 16450 | 0.0064 | - |
454
+ | 2.4470 | 16500 | 0.0042 | - |
455
+ | 2.4544 | 16550 | 0.0047 | - |
456
+ | 2.4618 | 16600 | 0.0062 | - |
457
+ | 2.4692 | 16650 | 0.0057 | - |
458
+ | 2.4766 | 16700 | 0.0048 | - |
459
+ | 2.4841 | 16750 | 0.0054 | - |
460
+ | 2.4915 | 16800 | 0.0061 | - |
461
+ | 2.4989 | 16850 | 0.0059 | - |
462
+ | 2.5063 | 16900 | 0.0041 | - |
463
+ | 2.5137 | 16950 | 0.0056 | - |
464
+ | 2.5211 | 17000 | 0.0058 | - |
465
+ | 2.5285 | 17050 | 0.0037 | - |
466
+ | 2.5360 | 17100 | 0.0064 | - |
467
+ | 2.5434 | 17150 | 0.0058 | - |
468
+ | 2.5508 | 17200 | 0.006 | - |
469
+ | 2.5582 | 17250 | 0.0089 | - |
470
+ | 2.5656 | 17300 | 0.0045 | - |
471
+ | 2.5730 | 17350 | 0.0046 | - |
472
+ | 2.5805 | 17400 | 0.0047 | - |
473
+ | 2.5879 | 17450 | 0.0029 | - |
474
+ | 2.5953 | 17500 | 0.0068 | - |
475
+ | 2.6027 | 17550 | 0.0036 | - |
476
+ | 2.6101 | 17600 | 0.0037 | - |
477
+ | 2.6175 | 17650 | 0.0042 | - |
478
+ | 2.6249 | 17700 | 0.0056 | - |
479
+ | 2.6324 | 17750 | 0.0084 | - |
480
+ | 2.6398 | 17800 | 0.0045 | - |
481
+ | 2.6472 | 17850 | 0.0065 | - |
482
+ | 2.6546 | 17900 | 0.0038 | - |
483
+ | 2.6620 | 17950 | 0.0051 | - |
484
+ | 2.6694 | 18000 | 0.0057 | - |
485
+ | 2.6769 | 18050 | 0.0037 | - |
486
+ | 2.6843 | 18100 | 0.0042 | - |
487
+ | 2.6917 | 18150 | 0.0052 | - |
488
+ | 2.6991 | 18200 | 0.0053 | - |
489
+ | 2.7065 | 18250 | 0.0054 | - |
490
+ | 2.7139 | 18300 | 0.0025 | - |
491
+ | 2.7213 | 18350 | 0.0045 | - |
492
+ | 2.7288 | 18400 | 0.0039 | - |
493
+ | 2.7362 | 18450 | 0.0064 | - |
494
+ | 2.7436 | 18500 | 0.0031 | - |
495
+ | 2.7510 | 18550 | 0.0057 | - |
496
+ | 2.7584 | 18600 | 0.0052 | - |
497
+ | 2.7658 | 18650 | 0.0049 | - |
498
+ | 2.7732 | 18700 | 0.0062 | - |
499
+ | 2.7807 | 18750 | 0.0041 | - |
500
+ | 2.7881 | 18800 | 0.0062 | - |
501
+ | 2.7955 | 18850 | 0.005 | - |
502
+ | 2.8029 | 18900 | 0.0057 | - |
503
+ | 2.8103 | 18950 | 0.0051 | - |
504
+ | 2.8177 | 19000 | 0.0035 | - |
505
+ | 2.8252 | 19050 | 0.0045 | - |
506
+ | 2.8326 | 19100 | 0.0048 | - |
507
+ | 2.8400 | 19150 | 0.002 | - |
508
+ | 2.8474 | 19200 | 0.0058 | - |
509
+ | 2.8548 | 19250 | 0.0041 | - |
510
+ | 2.8622 | 19300 | 0.0044 | - |
511
+ | 2.8696 | 19350 | 0.0062 | - |
512
+ | 2.8771 | 19400 | 0.0042 | - |
513
+ | 2.8845 | 19450 | 0.0036 | - |
514
+ | 2.8919 | 19500 | 0.005 | - |
515
+ | 2.8993 | 19550 | 0.0056 | - |
516
+ | 2.9067 | 19600 | 0.0056 | - |
517
+ | 2.9141 | 19650 | 0.0039 | - |
518
+ | 2.9215 | 19700 | 0.0058 | - |
519
+ | 2.9290 | 19750 | 0.0053 | - |
520
+ | 2.9364 | 19800 | 0.0058 | - |
521
+ | 2.9438 | 19850 | 0.0044 | - |
522
+ | 2.9512 | 19900 | 0.0028 | - |
523
+ | 2.9586 | 19950 | 0.0046 | - |
524
+ | 2.9660 | 20000 | 0.0059 | - |
525
+ | 2.9735 | 20050 | 0.0049 | - |
526
+ | 2.9809 | 20100 | 0.0048 | - |
527
+ | 2.9883 | 20150 | 0.0039 | - |
528
+ | 2.9957 | 20200 | 0.0062 | - |
529
+ | 3.0031 | 20250 | 0.0034 | - |
530
+ | 3.0105 | 20300 | 0.0048 | - |
531
+ | 3.0179 | 20350 | 0.0035 | - |
532
+ | 3.0254 | 20400 | 0.0048 | - |
533
+ | 3.0328 | 20450 | 0.0063 | - |
534
+ | 3.0402 | 20500 | 0.0047 | - |
535
+ | 3.0476 | 20550 | 0.0041 | - |
536
+ | 3.0550 | 20600 | 0.0046 | - |
537
+ | 3.0624 | 20650 | 0.0041 | - |
538
+ | 3.0699 | 20700 | 0.0052 | - |
539
+ | 3.0773 | 20750 | 0.0031 | - |
540
+ | 3.0847 | 20800 | 0.0042 | - |
541
+ | 3.0921 | 20850 | 0.0045 | - |
542
+ | 3.0995 | 20900 | 0.0049 | - |
543
+ | 3.1069 | 20950 | 0.0033 | - |
544
+ | 3.1143 | 21000 | 0.0064 | - |
545
+ | 3.1218 | 21050 | 0.0039 | - |
546
+ | 3.1292 | 21100 | 0.0058 | - |
547
+ | 3.1366 | 21150 | 0.004 | - |
548
+ | 3.1440 | 21200 | 0.0031 | - |
549
+ | 3.1514 | 21250 | 0.0028 | - |
550
+ | 3.1588 | 21300 | 0.0038 | - |
551
+ | 3.1662 | 21350 | 0.0048 | - |
552
+ | 3.1737 | 21400 | 0.0047 | - |
553
+ | 3.1811 | 21450 | 0.0057 | - |
554
+ | 3.1885 | 21500 | 0.0037 | - |
555
+ | 3.1959 | 21550 | 0.0039 | - |
556
+ | 3.2033 | 21600 | 0.0045 | - |
557
+ | 3.2107 | 21650 | 0.005 | - |
558
+ | 3.2182 | 21700 | 0.0034 | - |
559
+ | 3.2256 | 21750 | 0.0048 | - |
560
+ | 3.2330 | 21800 | 0.0034 | - |
561
+ | 3.2404 | 21850 | 0.0056 | - |
562
+ | 3.2478 | 21900 | 0.0041 | - |
563
+ | 3.2552 | 21950 | 0.0048 | - |
564
+ | 3.2626 | 22000 | 0.0066 | - |
565
+ | 3.2701 | 22050 | 0.0044 | - |
566
+ | 3.2775 | 22100 | 0.0046 | - |
567
+ | 3.2849 | 22150 | 0.0046 | - |
568
+ | 3.2923 | 22200 | 0.0038 | - |
569
+ | 3.2997 | 22250 | 0.0036 | - |
570
+ | 3.3071 | 22300 | 0.0023 | - |
571
+ | 3.3145 | 22350 | 0.0049 | - |
572
+ | 3.3220 | 22400 | 0.0041 | - |
573
+ | 3.3294 | 22450 | 0.0036 | - |
574
+ | 3.3368 | 22500 | 0.004 | - |
575
+ | 3.3442 | 22550 | 0.0054 | - |
576
+ | 3.3516 | 22600 | 0.0033 | - |
577
+ | 3.3590 | 22650 | 0.0054 | - |
578
+ | 3.3665 | 22700 | 0.0056 | - |
579
+ | 3.3739 | 22750 | 0.0051 | - |
580
+ | 3.3813 | 22800 | 0.0033 | - |
581
+ | 3.3887 | 22850 | 0.0046 | - |
582
+ | 3.3961 | 22900 | 0.0052 | - |
583
+ | 3.4035 | 22950 | 0.0043 | - |
584
+ | 3.4109 | 23000 | 0.0051 | - |
585
+ | 3.4184 | 23050 | 0.0036 | - |
586
+ | 3.4258 | 23100 | 0.0051 | - |
587
+ | 3.4332 | 23150 | 0.0061 | - |
588
+ | 3.4406 | 23200 | 0.004 | - |
589
+ | 3.4480 | 23250 | 0.0036 | - |
590
+ | 3.4554 | 23300 | 0.0035 | - |
591
+ | 3.4629 | 23350 | 0.0063 | - |
592
+ | 3.4703 | 23400 | 0.0051 | - |
593
+ | 3.4777 | 23450 | 0.0024 | - |
594
+ | 3.4851 | 23500 | 0.0033 | - |
595
+ | 3.4925 | 23550 | 0.0048 | - |
596
+ | 3.4999 | 23600 | 0.0035 | - |
597
+ | 3.5073 | 23650 | 0.0041 | - |
598
+ | 3.5148 | 23700 | 0.0035 | - |
599
+ | 3.5222 | 23750 | 0.0031 | - |
600
+ | 3.5296 | 23800 | 0.0031 | - |
601
+ | 3.5370 | 23850 | 0.0042 | - |
602
+ | 3.5444 | 23900 | 0.0038 | - |
603
+ | 3.5518 | 23950 | 0.0042 | - |
604
+ | 3.5592 | 24000 | 0.0048 | - |
605
+ | 3.5667 | 24050 | 0.0018 | - |
606
+ | 3.5741 | 24100 | 0.005 | - |
607
+ | 3.5815 | 24150 | 0.0073 | - |
608
+ | 3.5889 | 24200 | 0.0056 | - |
609
+ | 3.5963 | 24250 | 0.0037 | - |
610
+ | 3.6037 | 24300 | 0.0065 | - |
611
+ | 3.6112 | 24350 | 0.005 | - |
612
+ | 3.6186 | 24400 | 0.0051 | - |
613
+ | 3.6260 | 24450 | 0.0039 | - |
614
+ | 3.6334 | 24500 | 0.0055 | - |
615
+ | 3.6408 | 24550 | 0.0035 | - |
616
+ | 3.6482 | 24600 | 0.0039 | - |
617
+ | 3.6556 | 24650 | 0.0044 | - |
618
+ | 3.6631 | 24700 | 0.0045 | - |
619
+ | 3.6705 | 24750 | 0.0029 | - |
620
+ | 3.6779 | 24800 | 0.0025 | - |
621
+ | 3.6853 | 24850 | 0.0032 | - |
622
+ | 3.6927 | 24900 | 0.0035 | - |
623
+ | 3.7001 | 24950 | 0.0045 | - |
624
+ | 3.7075 | 25000 | 0.0043 | - |
625
+ | 3.7150 | 25050 | 0.0035 | - |
626
+ | 3.7224 | 25100 | 0.0039 | - |
627
+ | 3.7298 | 25150 | 0.0036 | - |
628
+ | 3.7372 | 25200 | 0.0035 | - |
629
+ | 3.7446 | 25250 | 0.0043 | - |
630
+ | 3.7520 | 25300 | 0.0027 | - |
631
+ | 3.7595 | 25350 | 0.0034 | - |
632
+ | 3.7669 | 25400 | 0.0045 | - |
633
+ | 3.7743 | 25450 | 0.0031 | - |
634
+ | 3.7817 | 25500 | 0.0033 | - |
635
+ | 3.7891 | 25550 | 0.0045 | - |
636
+ | 3.7965 | 25600 | 0.0046 | - |
637
+ | 3.8039 | 25650 | 0.0026 | - |
638
+ | 3.8114 | 25700 | 0.0053 | - |
639
+ | 3.8188 | 25750 | 0.0033 | - |
640
+ | 3.8262 | 25800 | 0.0046 | - |
641
+ | 3.8336 | 25850 | 0.0035 | - |
642
+ | 3.8410 | 25900 | 0.0045 | - |
643
+ | 3.8484 | 25950 | 0.0036 | - |
644
+ | 3.8559 | 26000 | 0.0035 | - |
645
+ | 3.8633 | 26050 | 0.0037 | - |
646
+ | 3.8707 | 26100 | 0.0024 | - |
647
+ | 3.8781 | 26150 | 0.0049 | - |
648
+ | 3.8855 | 26200 | 0.0028 | - |
649
+ | 3.8929 | 26250 | 0.0055 | - |
650
+ | 3.9003 | 26300 | 0.0029 | - |
651
+ | 3.9078 | 26350 | 0.0052 | - |
652
+ | 3.9152 | 26400 | 0.0043 | - |
653
+ | 3.9226 | 26450 | 0.0042 | - |
654
+ | 3.9300 | 26500 | 0.0059 | - |
655
+ | 3.9374 | 26550 | 0.0038 | - |
656
+ | 3.9448 | 26600 | 0.0047 | - |
657
+ | 3.9522 | 26650 | 0.0043 | - |
658
+ | 3.9597 | 26700 | 0.0034 | - |
659
+ | 3.9671 | 26750 | 0.005 | - |
660
+ | 3.9745 | 26800 | 0.004 | - |
661
+ | 3.9819 | 26850 | 0.0053 | - |
662
+ | 3.9893 | 26900 | 0.0046 | - |
663
+ | 3.9967 | 26950 | 0.0053 | - |
664
+ | 4.0042 | 27000 | 0.0038 | - |
665
+ | 4.0116 | 27050 | 0.0038 | - |
666
+ | 4.0190 | 27100 | 0.0032 | - |
667
+ | 4.0264 | 27150 | 0.0038 | - |
668
+ | 4.0338 | 27200 | 0.0042 | - |
669
+ | 4.0412 | 27250 | 0.005 | - |
670
+ | 4.0486 | 27300 | 0.0031 | - |
671
+ | 4.0561 | 27350 | 0.0041 | - |
672
+ | 4.0635 | 27400 | 0.0033 | - |
673
+ | 4.0709 | 27450 | 0.0028 | - |
674
+ | 4.0783 | 27500 | 0.0054 | - |
675
+ | 4.0857 | 27550 | 0.0038 | - |
676
+ | 4.0931 | 27600 | 0.0037 | - |
677
+ | 4.1005 | 27650 | 0.0037 | - |
678
+ | 4.1080 | 27700 | 0.0033 | - |
679
+ | 4.1154 | 27750 | 0.0041 | - |
680
+ | 4.1228 | 27800 | 0.0038 | - |
681
+ | 4.1302 | 27850 | 0.0029 | - |
682
+ | 4.1376 | 27900 | 0.0047 | - |
683
+ | 4.1450 | 27950 | 0.0038 | - |
684
+ | 4.1525 | 28000 | 0.0041 | - |
685
+ | 4.1599 | 28050 | 0.0036 | - |
686
+ | 4.1673 | 28100 | 0.003 | - |
687
+ | 4.1747 | 28150 | 0.005 | - |
688
+ | 4.1821 | 28200 | 0.0039 | - |
689
+ | 4.1895 | 28250 | 0.005 | - |
690
+ | 4.1969 | 28300 | 0.0035 | - |
691
+ | 4.2044 | 28350 | 0.0036 | - |
692
+ | 4.2118 | 28400 | 0.0053 | - |
693
+ | 4.2192 | 28450 | 0.0041 | - |
694
+ | 4.2266 | 28500 | 0.0042 | - |
695
+ | 4.2340 | 28550 | 0.0058 | - |
696
+ | 4.2414 | 28600 | 0.0035 | - |
697
+ | 4.2489 | 28650 | 0.0036 | - |
698
+ | 4.2563 | 28700 | 0.0041 | - |
699
+ | 4.2637 | 28750 | 0.0046 | - |
700
+ | 4.2711 | 28800 | 0.0048 | - |
701
+ | 4.2785 | 28850 | 0.0035 | - |
702
+ | 4.2859 | 28900 | 0.0041 | - |
703
+ | 4.2933 | 28950 | 0.0034 | - |
704
+ | 4.3008 | 29000 | 0.0022 | - |
705
+ | 4.3082 | 29050 | 0.005 | - |
706
+ | 4.3156 | 29100 | 0.0042 | - |
707
+ | 4.3230 | 29150 | 0.0031 | - |
708
+ | 4.3304 | 29200 | 0.0052 | - |
709
+ | 4.3378 | 29250 | 0.0032 | - |
710
+ | 4.3452 | 29300 | 0.0027 | - |
711
+ | 4.3527 | 29350 | 0.0034 | - |
712
+ | 4.3601 | 29400 | 0.0045 | - |
713
+ | 4.3675 | 29450 | 0.0031 | - |
714
+ | 4.3749 | 29500 | 0.0036 | - |
715
+ | 4.3823 | 29550 | 0.0054 | - |
716
+ | 4.3897 | 29600 | 0.0036 | - |
717
+ | 4.3972 | 29650 | 0.0023 | - |
718
+ | 4.4046 | 29700 | 0.0043 | - |
719
+ | 4.4120 | 29750 | 0.0048 | - |
720
+ | 4.4194 | 29800 | 0.0027 | - |
721
+ | 4.4268 | 29850 | 0.0027 | - |
722
+ | 4.4342 | 29900 | 0.0026 | - |
723
+ | 4.4416 | 29950 | 0.0038 | - |
724
+ | 4.4491 | 30000 | 0.0033 | - |
725
+ | 4.4565 | 30050 | 0.0025 | - |
726
+ | 4.4639 | 30100 | 0.003 | - |
727
+ | 4.4713 | 30150 | 0.0051 | - |
728
+ | 4.4787 | 30200 | 0.0043 | - |
729
+ | 4.4861 | 30250 | 0.0047 | - |
730
+ | 4.4935 | 30300 | 0.0056 | - |
731
+ | 4.5010 | 30350 | 0.0043 | - |
732
+ | 4.5084 | 30400 | 0.0033 | - |
733
+ | 4.5158 | 30450 | 0.0028 | - |
734
+ | 4.5232 | 30500 | 0.0039 | - |
735
+ | 4.5306 | 30550 | 0.0031 | - |
736
+ | 4.5380 | 30600 | 0.0033 | - |
737
+ | 4.5455 | 30650 | 0.0045 | - |
738
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+
800
+ ### Framework Versions
801
+ - Python: 3.10.8
802
+ - SetFit: 1.1.2
803
+ - Sentence Transformers: 5.0.0
804
+ - Transformers: 4.54.1
805
+ - PyTorch: 2.7.1+cu126
806
+ - Datasets: 3.6.0
807
+ - Tokenizers: 0.21.4
808
+
809
+ ## Citation
810
+
811
+ ### BibTeX
812
+ ```bibtex
813
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
814
+ doi = {10.48550/ARXIV.2209.11055},
815
+ url = {https://arxiv.org/abs/2209.11055},
816
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
817
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
818
+ title = {Efficient Few-Shot Learning Without Prompts},
819
+ publisher = {arXiv},
820
+ year = {2022},
821
+ copyright = {Creative Commons Attribution 4.0 International}
822
+ }
823
+ ```
824
+
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+ <!--
826
+ ## Glossary
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+
828
+ *Clearly define terms in order to be accessible across audiences.*
829
+ -->
830
+
831
+ <!--
832
+ ## Model Card Authors
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+
834
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
835
+ -->
836
+
837
+ <!--
838
+ ## Model Card Contact
839
+
840
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
841
+ -->
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