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Hulyyy
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req-quality-setfit-128

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Model card Files
xet
 Community
Hulyyy commited on
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Browse files
Files changed (13) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +521 -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 +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 +65 -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: The TCS software shall have an open architecture and be capable of being hosted
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+ on computers that are typically supported by the using Service.
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+ - text: 3 It shall be possible to deregister up to ten functional numbers to items
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+ of equipment physically connected to the Cab radio within 30 seconds. (M)
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+ - text: 1 The EIRENE system shall enable users to originate and receive calls by functional
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+ number. (M)
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+ - text: The product shall store new conference rooms.
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+ - text: Before authomatic transition to Shunting, ETCS shall request confirmation
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+ from the driver.
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+ metrics:
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+ - micro_f1
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+ - macro_f1
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+ - hamming_accuracy
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+ - subset_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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+ model-index:
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+ - name: SetFit
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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: micro_f1
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+ value: 0.5705128205128205
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+ name: Micro_F1
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+ - type: macro_f1
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+ value: 0.5793040286957435
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+ name: Macro_F1
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+ - type: hamming_accuracy
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+ value: 0.8897119341563786
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+ name: Hamming_Accuracy
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+ - type: subset_accuracy
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+ value: 0.5720164609053497
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+ name: Subset_Accuracy
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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 WeightedBinaryRelevanceHead 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 WeightedBinaryRelevanceHead instance
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+ - **Maximum Sequence Length:** 256 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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+ ## Evaluation
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+
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+ ### Metrics
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+ | Label | Micro_F1 | Macro_F1 | Hamming_Accuracy | Subset_Accuracy |
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+ |:--------|:---------|:---------|:-----------------|:----------------|
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+ | **all** | 0.5705 | 0.5793 | 0.8897 | 0.5720 |
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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("Hulyyy/req-quality-setfit-128")
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+ # Run inference
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+ preds = model("The product shall store new conference rooms.")
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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 | 19.3840 | 32 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (64, 64)
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+ - num_epochs: (3, 3)
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+ - max_steps: -1
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+ - sampling_strategy: oversampling
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+ - num_iterations: 150
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+ - body_learning_rate: (1e-05, 1e-05)
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+ - head_learning_rate: 1e-05
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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: True
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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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+ | 0.0002 | 1 | 0.3465 | - |
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+ | 0.0094 | 50 | 0.3586 | - |
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+ | 0.0187 | 100 | 0.3442 | - |
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+ | 0.0281 | 150 | 0.3214 | - |
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+ | 0.0375 | 200 | 0.2907 | - |
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+ | 0.0469 | 250 | 0.2647 | - |
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+ | 0.0562 | 300 | 0.2612 | - |
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+ | 0.0656 | 350 | 0.2543 | - |
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+ | 0.0750 | 400 | 0.2507 | - |
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+ | 0.0843 | 450 | 0.2483 | - |
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+ | 0.0937 | 500 | 0.2435 | - |
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+ | 0.1031 | 550 | 0.2409 | - |
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+ | 0.1125 | 600 | 0.2329 | - |
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+ | 0.1218 | 650 | 0.2318 | - |
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+ | 0.1312 | 700 | 0.2292 | - |
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+ | 0.1406 | 750 | 0.226 | - |
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+ | 0.1500 | 800 | 0.2194 | - |
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+ | 0.1593 | 850 | 0.2184 | - |
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+ | 0.1687 | 900 | 0.2135 | - |
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+ | 0.1781 | 950 | 0.2133 | - |
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+ | 0.1874 | 1000 | 0.2052 | - |
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+ | 0.1968 | 1050 | 0.2019 | - |
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+ | 0.2062 | 1100 | 0.1977 | - |
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+ | 0.2156 | 1150 | 0.196 | - |
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+ | 0.2249 | 1200 | 0.185 | - |
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+ | 0.2343 | 1250 | 0.1812 | - |
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+ | 0.2437 | 1300 | 0.1747 | - |
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+ | 0.2530 | 1350 | 0.1756 | - |
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+ | 0.2624 | 1400 | 0.1634 | - |
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+ | 0.2718 | 1450 | 0.158 | - |
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+ | 0.2812 | 1500 | 0.1502 | - |
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+ | 0.2905 | 1550 | 0.1433 | - |
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+ | 0.2999 | 1600 | 0.1305 | - |
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+ | 0.3093 | 1650 | 0.1333 | - |
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+ | 0.3187 | 1700 | 0.1115 | - |
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+ | 0.3280 | 1750 | 0.1073 | - |
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+ | 0.3374 | 1800 | 0.1016 | - |
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+ | 0.3468 | 1850 | 0.0978 | - |
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+ | 0.3561 | 1900 | 0.0861 | - |
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+ | 0.3655 | 1950 | 0.0767 | - |
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+ | 0.3749 | 2000 | 0.0733 | - |
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+ | 0.3843 | 2050 | 0.0676 | - |
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+ | 0.3936 | 2100 | 0.061 | - |
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+ | 0.4030 | 2150 | 0.0594 | - |
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+ | 0.4124 | 2200 | 0.0575 | - |
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+ | 0.4217 | 2250 | 0.0526 | - |
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+ | 0.4311 | 2300 | 0.0484 | - |
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+ | 0.4405 | 2350 | 0.043 | - |
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+ | 0.4499 | 2400 | 0.0424 | - |
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+ | 0.4592 | 2450 | 0.0396 | - |
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+ | 0.4686 | 2500 | 0.0435 | - |
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+ | 0.4780 | 2550 | 0.0361 | - |
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+ | 0.4873 | 2600 | 0.0354 | - |
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+ | 0.4967 | 2650 | 0.0372 | - |
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+ | 0.5061 | 2700 | 0.0342 | - |
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+ | 0.5155 | 2750 | 0.0342 | - |
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+ | 0.5248 | 2800 | 0.0314 | - |
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+ | 0.5342 | 2850 | 0.0296 | - |
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+ | 0.5436 | 2900 | 0.0316 | - |
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+ | 0.5530 | 2950 | 0.0295 | - |
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+ | 0.5623 | 3000 | 0.0271 | - |
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+ | 0.5717 | 3050 | 0.0278 | - |
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+ | 0.5811 | 3100 | 0.0293 | - |
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+ | 0.5904 | 3150 | 0.025 | - |
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+ | 0.5998 | 3200 | 0.024 | - |
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+ | 0.6092 | 3250 | 0.0233 | - |
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+ | 0.6186 | 3300 | 0.0237 | - |
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+ | 0.6279 | 3350 | 0.0249 | - |
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+ | 0.6373 | 3400 | 0.0228 | - |
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+ | 0.6467 | 3450 | 0.0265 | - |
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+ | 0.6560 | 3500 | 0.0208 | - |
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+ | 0.6654 | 3550 | 0.0249 | - |
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+ | 0.6748 | 3600 | 0.0241 | - |
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+ | 0.6842 | 3650 | 0.0211 | - |
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+ | 0.6935 | 3700 | 0.0202 | - |
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+ | 0.7029 | 3750 | 0.0212 | - |
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+ | 0.7123 | 3800 | 0.0203 | - |
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+ | 0.7216 | 3850 | 0.0206 | - |
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+ | 0.7310 | 3900 | 0.0188 | - |
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+ | 0.7404 | 3950 | 0.0192 | - |
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+ | 0.7498 | 4000 | 0.0193 | - |
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+ | 0.7591 | 4050 | 0.0182 | - |
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+ | 0.7685 | 4100 | 0.0177 | - |
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+ | 0.7779 | 4150 | 0.0154 | - |
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+ | 0.7873 | 4200 | 0.0147 | - |
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+ | 0.7966 | 4250 | 0.0148 | - |
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+ | 0.8060 | 4300 | 0.0139 | - |
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+ | 0.8154 | 4350 | 0.0129 | - |
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+ | 0.8247 | 4400 | 0.0125 | - |
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+ | 0.8435 | 4500 | 0.0112 | - |
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+ | 0.8529 | 4550 | 0.0104 | - |
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+ | 1.0028 | 5350 | 0.0069 | - |
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+ | 1.0122 | 5400 | 0.0059 | - |
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+ | 1.1528 | 6150 | 0.004 | - |
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+ | 1.3215 | 7050 | 0.0037 | - |
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+ | 1.3871 | 7400 | 0.0032 | - |
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+ | 1.4058 | 7500 | 0.0039 | - |
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+
480
+ ### Framework Versions
481
+ - Python: 3.13.7
482
+ - SetFit: 1.1.3
483
+ - Sentence Transformers: 5.1.1
484
+ - Transformers: 4.57.0
485
+ - PyTorch: 2.8.0+cu129
486
+ - Datasets: 4.2.0
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+ - Tokenizers: 0.22.1
488
+
489
+ ## Citation
490
+
491
+ ### BibTeX
492
+ ```bibtex
493
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
494
+ doi = {10.48550/ARXIV.2209.11055},
495
+ url = {https://arxiv.org/abs/2209.11055},
496
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
497
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
498
+ title = {Efficient Few-Shot Learning Without Prompts},
499
+ publisher = {arXiv},
500
+ year = {2022},
501
+ copyright = {Creative Commons Attribution 4.0 International}
502
+ }
503
+ ```
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+
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+ <!--
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+ ## Glossary
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+
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+ *Clearly define terms in order to be accessible across audiences.*
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+ -->
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+
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+ <!--
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+ ## Model Card Authors
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+
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+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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+ -->
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+
517
+ <!--
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+ ## Model Card Contact
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+
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+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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+ -->
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