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twright8
/
setfit_lobbying_classifier

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
Model card Files Community
twright8 commited on
Commit
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Files changed (13) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +721 -3
  3. config.json +32 -0
  4. config_sentence_transformers.json +10 -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 +64 -0
  13. vocab.txt +0 -0
1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 768,
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+ "pooling_mode_cls_token": true,
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+ "pooling_mode_mean_tokens": false,
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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 CHANGED
@@ -1,3 +1,721 @@
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- ---
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- license: mit
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ base_model: BAAI/bge-base-en-v1.5
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+ library_name: setfit
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+ metrics:
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+ - f1
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+ - accuracy
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+ pipeline_tag: text-classification
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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: Discussion on recent report publication
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+ - text: Growth
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+ - text: The roundtable was arranged in order to provide an overview of the work of
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+ Alliance members and promote international development policy positions to the
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+ Scottish Conservatives. During the meeting we presented the work of SCIAF and
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+ its campaign for a world leading climate change response. In particular SCIAF
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+ explained how climate change is already affecting some of the poorest communities
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+ in the world and is therefore a central concern for international development.
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+ We argued that Scotland needs to do what it can to mitigate climate change.
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+ - text: To introduce Energy UK discuss the energy industries contribution to tackling
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+ climate change and discuss stage 1 of theClimate Change (Emissions Reduction Targets)
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+ (Scotland) Bill. Also discussed the Scottish Government's ambition on electric
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+ vehicles and the role of the energy industry in a successful roll out.
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+ - text: To discuss our key asks on the Climate Change (Emissions Reduction Targets)
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+ (Scotland) Bill in advance of Stage 2 including support for amendments on regional
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+ land use partnerships and land use strategy as means to deliver climate mitigation
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+ for land.
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+ inference: false
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+ model-index:
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+ - name: SetFit with BAAI/bge-base-en-v1.5
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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: f1
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+ value: 0.9667149059334297
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+ name: F1
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+ - type: accuracy
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+ value: 0.9420654911838791
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+ name: Accuracy
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+ ---
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+
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+ # SetFit with BAAI/bge-base-en-v1.5
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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 [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) as the Sentence Transformer embedding model. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) 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:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5)
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+ - **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance
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+ - **Maximum Sequence Length:** 512 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 | F1 | Accuracy |
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+ |:--------|:-------|:---------|
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+ | **all** | 0.9667 | 0.9421 |
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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("twright8/setfit_lobbying_classifier")
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+ # Run inference
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+ preds = model("Growth")
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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 | 1 | 39.4538 | 282 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (16, 2)
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+ - num_epochs: (4, 9)
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+ - max_steps: -1
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+ - sampling_strategy: undersampling
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+ - body_learning_rate: (1.0797496673911536e-05, 3.457046714445997e-05)
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+ - head_learning_rate: 0.0004470582121407239
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+ - loss: CoSENTLoss
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+ - distance_metric: cosine_distance
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+ - margin: 0.25
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+ - end_to_end: True
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+ - use_amp: False
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+ - warmup_proportion: 0.1
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+ - seed: 42
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+ - eval_max_steps: -1
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+ - load_best_model_at_end: True
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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 | 2.097 | - |
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+ | 0.0077 | 50 | 8.5514 | - |
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+ | 0.0155 | 100 | 3.5635 | - |
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+ | 0.0232 | 150 | 2.9266 | - |
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+ | 0.0310 | 200 | 2.1173 | - |
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+ | 0.0387 | 250 | 3.1002 | - |
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+ | 0.0465 | 300 | 3.6942 | - |
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+ | 0.0542 | 350 | 3.4905 | - |
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+ | 0.0620 | 400 | 4.0804 | - |
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+ | 0.0697 | 450 | 1.6071 | - |
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+ | 0.0774 | 500 | 2.3018 | - |
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+ | 0.0852 | 550 | 2.3876 | - |
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+ | 0.0929 | 600 | 0.2511 | - |
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+ | 0.1007 | 650 | 0.2435 | - |
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+ | 0.1084 | 700 | 2.2596 | - |
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+ | 0.1162 | 750 | 1.121 | - |
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+ | 0.1239 | 800 | 0.0907 | - |
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+ | 0.1317 | 850 | 0.2172 | - |
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+ | 0.1394 | 900 | 3.06 | - |
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+ | 0.1471 | 950 | 0.0074 | - |
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+ | 0.1549 | 1000 | 0.457 | - |
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+ | 0.1626 | 1050 | 0.0575 | - |
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+ | 0.1704 | 1100 | 0.0002 | - |
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+ | 0.1781 | 1150 | 0.0003 | - |
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+ | 0.1859 | 1200 | 0.0047 | - |
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+ | 0.1936 | 1250 | 0.0004 | - |
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+ | 0.2014 | 1300 | 0.0006 | - |
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+ | 0.2091 | 1350 | 0.0027 | - |
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+ | 0.2169 | 1400 | 0.0004 | - |
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+ | 0.2246 | 1450 | 0.0009 | - |
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+ | 0.2323 | 1500 | 0.0006 | - |
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+ | 0.2401 | 1550 | 0.0003 | - |
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+ | 0.2478 | 1600 | 0.0077 | - |
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+ | 0.3020 | 1950 | 0.0062 | - |
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+ | 0.3098 | 2000 | 0.0003 | - |
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+ | 0.3253 | 2100 | 0.0685 | - |
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+ | 0.4569 | 2950 | 0.0014 | - |
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+ | 0.4647 | 3000 | 0.0001 | - |
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+ | 0.4802 | 3100 | 0.0002 | - |
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+ | 0.4879 | 3150 | 0.0 | - |
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+ | 0.5034 | 3250 | 0.0 | - |
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+ | **1.0** | **6456** | **-** | **1.3837** |
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+ | 4.0 | 25824 | - | 2.3576 |
678
+
679
+ * The bold row denotes the saved checkpoint.
680
+ ### Framework Versions
681
+ - Python: 3.10.12
682
+ - SetFit: 1.0.3
683
+ - Sentence Transformers: 3.0.1
684
+ - Transformers: 4.39.0
685
+ - PyTorch: 2.3.0+cu121
686
+ - Datasets: 2.20.0
687
+ - Tokenizers: 0.15.2
688
+
689
+ ## Citation
690
+
691
+ ### BibTeX
692
+ ```bibtex
693
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
694
+ doi = {10.48550/ARXIV.2209.11055},
695
+ url = {https://arxiv.org/abs/2209.11055},
696
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
697
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
698
+ title = {Efficient Few-Shot Learning Without Prompts},
699
+ publisher = {arXiv},
700
+ year = {2022},
701
+ copyright = {Creative Commons Attribution 4.0 International}
702
+ }
703
+ ```
704
+
705
+ <!--
706
+ ## Glossary
707
+
708
+ *Clearly define terms in order to be accessible across audiences.*
709
+ -->
710
+
711
+ <!--
712
+ ## Model Card Authors
713
+
714
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
715
+ -->
716
+
717
+ <!--
718
+ ## Model Card Contact
719
+
720
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
721
+ -->
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+ "max_seq_length": 512,
3
+ "do_lower_case": true
4
+ }
special_tokens_map.json ADDED
@@ -0,0 +1,37 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cls_token": {
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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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+ "mask_token": {
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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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+ },
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+ "pad_token": {
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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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+ },
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+ "sep_token": {
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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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+ },
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+ "unk_token": {
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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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+ }
37
+ }
tokenizer.json ADDED
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tokenizer_config.json ADDED
@@ -0,0 +1,64 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
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+ "added_tokens_decoder": {
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+ "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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+ "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": true,
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+ "cls_token": "[CLS]",
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+ "do_basic_tokenize": true,
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+ "do_lower_case": true,
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+ "mask_token": "[MASK]",
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+ "max_length": 512,
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+ "model_max_length": 512,
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+ "never_split": null,
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+ "pad_to_multiple_of": null,
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+ "pad_token": "[PAD]",
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+ "pad_token_type_id": 0,
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+ "padding_side": "right",
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+ "sep_token": "[SEP]",
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+ "stride": 0,
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+ "strip_accents": null,
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+ "tokenize_chinese_chars": true,
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+ "tokenizer_class": "BertTokenizer",
61
+ "truncation_side": "right",
62
+ "truncation_strategy": "longest_first",
63
+ "unk_token": "[UNK]"
64
+ }
vocab.txt ADDED
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