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Hulyyy
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req-quality-setfit-2

Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Model card Files
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Files changed (13) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +657 -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: Shunting mode is the term used to describe the application that will regulate and control
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+ user access to facilities and features in the mobile while it is being used for shunting
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+ communications. (I)
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+ - text: RAM scrub shall [SRS275] not scrub the area used for telemetry data.
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+ - text: 2.4.4 It should be possible for the network to prevent the identity of certain
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+ users from being displayed on the mobile, either when being called, calling or
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+ both. (O) Priority and pre-emption
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+ - text: If acknowledgement is specified the driver shall acknowledge transfer from
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+ Full Supervision to Partial Supervision within 5 seconds
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+ - text: 5.3.6 Requirements on electromagnetic emissions for the Cab radio are
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+ to be more stringent than those defined for other radio types due to close proximity
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+ to other train-mounted control and protection equipment, and higher transmission
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+ power. (I)
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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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+ base_model: sentence-transformers/all-MiniLM-L6-v2
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+ model-index:
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+ - name: SetFit with sentence-transformers/all-MiniLM-L6-v2
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+ results:
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+ - task:
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+ type: text-classification
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+ name: Text Classification
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+ dataset:
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+ name: Unknown
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+ type: unknown
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+ split: test
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+ metrics:
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+ - type: accuracy
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+ value: 0.5268817204301075
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+ name: Accuracy
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+ ---
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+
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+ # SetFit with sentence-transformers/all-MiniLM-L6-v2
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+
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+ This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) as the Sentence Transformer embedding model. A ClassifierChain instance is used for classification.
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+
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+ The model has been trained using an efficient few-shot learning technique that involves:
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+
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+ 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
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+ 2. Training a classification head with features from the fine-tuned Sentence Transformer.
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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** SetFit
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+ - **Sentence Transformer body:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
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+ - **Classification head:** a ClassifierChain 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 | Accuracy |
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+ |:--------|:---------|
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+ | **all** | 0.5269 |
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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-2")
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+ # Run inference
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+ preds = model("RAM scrub shall [SRS275] not scrub the area used for telemetry data.")
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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 | 4 | 42.6799 | 1156 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (256, 256)
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+ - num_epochs: (2, 2)
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+ - max_steps: -1
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+ - sampling_strategy: oversampling
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+ - num_iterations: 1000
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+ - body_learning_rate: (3e-05, 3e-05)
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+ - head_learning_rate: 3e-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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+ | 0.0001 | 1 | 0.3496 | - |
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+ | 0.0043 | 50 | 0.3562 | - |
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+ | 0.0086 | 100 | 0.3152 | - |
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+ | 0.0129 | 150 | 0.2646 | - |
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+ | 0.0173 | 200 | 0.2472 | - |
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+ | 0.0216 | 250 | 0.2392 | - |
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+ | 0.0259 | 300 | 0.2328 | - |
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+ | 0.0302 | 350 | 0.2269 | - |
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+ | 0.0345 | 400 | 0.2215 | - |
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+ | 0.0388 | 450 | 0.2137 | - |
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+ | 0.0431 | 500 | 0.2062 | - |
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+ | 0.0474 | 550 | 0.1998 | - |
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+ | 0.0518 | 600 | 0.1891 | - |
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+ | 0.0561 | 650 | 0.1813 | - |
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+ | 0.0604 | 700 | 0.1727 | - |
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+ | 0.0647 | 750 | 0.1611 | - |
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+ | 0.0690 | 800 | 0.152 | - |
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+ | 0.0733 | 850 | 0.1407 | - |
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+ | 0.0776 | 900 | 0.1339 | - |
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+ | 0.0819 | 950 | 0.1247 | - |
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+ | 0.0863 | 1000 | 0.1189 | - |
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+ | 0.0906 | 1050 | 0.1108 | - |
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+ | 0.0949 | 1100 | 0.1037 | - |
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+ | 0.0992 | 1150 | 0.1005 | - |
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+ | 0.1035 | 1200 | 0.0968 | - |
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+ | 0.1078 | 1250 | 0.0918 | - |
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+ | 0.1121 | 1300 | 0.0916 | - |
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+ | 0.1164 | 1350 | 0.0878 | - |
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+ | 0.1208 | 1400 | 0.0869 | - |
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+ | 0.1251 | 1450 | 0.0829 | - |
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+ | 0.1294 | 1500 | 0.0825 | - |
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+ | 0.1337 | 1550 | 0.0818 | - |
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+ | 0.1380 | 1600 | 0.0813 | - |
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+ | 0.1423 | 1650 | 0.0793 | - |
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+ | 0.2415 | 2800 | 0.0529 | - |
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+ | 0.2544 | 2950 | 0.0535 | - |
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+ | 0.2588 | 3000 | 0.0539 | - |
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+ | 0.2631 | 3050 | 0.0526 | - |
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+ | 0.2674 | 3100 | 0.0514 | - |
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+ | 0.2717 | 3150 | 0.0535 | - |
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+ | 0.2760 | 3200 | 0.0531 | - |
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+ | 0.2803 | 3250 | 0.0518 | - |
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+ | 0.2846 | 3300 | 0.0534 | - |
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+
616
+ ### Framework Versions
617
+ - Python: 3.13.7
618
+ - SetFit: 1.1.3
619
+ - Sentence Transformers: 5.1.1
620
+ - Transformers: 4.57.0
621
+ - PyTorch: 2.8.0+cu129
622
+ - Datasets: 4.2.0
623
+ - Tokenizers: 0.22.1
624
+
625
+ ## Citation
626
+
627
+ ### BibTeX
628
+ ```bibtex
629
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
630
+ doi = {10.48550/ARXIV.2209.11055},
631
+ url = {https://arxiv.org/abs/2209.11055},
632
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
633
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
634
+ title = {Efficient Few-Shot Learning Without Prompts},
635
+ publisher = {arXiv},
636
+ year = {2022},
637
+ copyright = {Creative Commons Attribution 4.0 International}
638
+ }
639
+ ```
640
+
641
+ <!--
642
+ ## Glossary
643
+
644
+ *Clearly define terms in order to be accessible across audiences.*
645
+ -->
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+
647
+ <!--
648
+ ## Model Card Authors
649
+
650
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
651
+ -->
652
+
653
+ <!--
654
+ ## Model Card Contact
655
+
656
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
657
+ -->
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+ }
43
+ },
44
+ "clean_up_tokenization_spaces": false,
45
+ "cls_token": "[CLS]",
46
+ "do_basic_tokenize": true,
47
+ "do_lower_case": true,
48
+ "extra_special_tokens": {},
49
+ "mask_token": "[MASK]",
50
+ "max_length": 128,
51
+ "model_max_length": 256,
52
+ "never_split": null,
53
+ "pad_to_multiple_of": null,
54
+ "pad_token": "[PAD]",
55
+ "pad_token_type_id": 0,
56
+ "padding_side": "right",
57
+ "sep_token": "[SEP]",
58
+ "stride": 0,
59
+ "strip_accents": null,
60
+ "tokenize_chinese_chars": true,
61
+ "tokenizer_class": "BertTokenizer",
62
+ "truncation_side": "right",
63
+ "truncation_strategy": "longest_first",
64
+ "unk_token": "[UNK]"
65
+ }
vocab.txt ADDED
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