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nazhan
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bge-small-en-v1.5-brahmaputra-iter-10

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
Model card Files Community
nazhan commited on
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Add SetFit model

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Files changed (13) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +907 -0
  3. config.json +31 -0
  4. config_sentence_transformers.json +9 -0
  5. config_setfit.json +12 -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": 384,
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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 ADDED
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+ ---
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+ base_model: BAAI/bge-small-en-v1.5
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+ library_name: setfit
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+ metrics:
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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: What tables are included in the starhub_data_asset database that relate to
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+ customer complaints?
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+ - text: What are the tables that I can access in the starhub_data_asset database?
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+ - text: Can I have avg Cost_Efficiency
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+ - text: Analyze product category revenue impact.
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+ - text: Retrieve data_asset_kpi_ma_product details.
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+ inference: true
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+ model-index:
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+ - name: SetFit with BAAI/bge-small-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: accuracy
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+ value: 0.9914529914529915
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+ name: Accuracy
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+ ---
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+
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+ # SetFit with BAAI/bge-small-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-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) 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-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5)
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+ - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
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+ - **Maximum Sequence Length:** 512 tokens
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+ - **Number of Classes:** 7 classes
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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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+ ### Model Labels
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+ | Label | Examples |
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+ |:-------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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+ | Generalreply | <ul><li>'Can you recommend a good movie to watch?'</li><li>"Oh, that's a tough one! There are so many good memories to choose from. But if I had to pick just one, I think it would be spending summers at my grandparent's house. We would play board games, make homemade ice cream, and have big family dinners. It was always so much fun!"</li><li>'Oh, I love reading books! My favorite genre is definitely fantasy. How about you? What kind of books do you like to read?'</li></ul> |
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+ | Lookup_1 | <ul><li>'Get me data_asset_kpi_cf cash flow.'</li><li>'Display data_asset_001_pcc for electronics category.'</li><li>'Calculate Gross Profit Margin Trends.'</li></ul> |
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+ | Lookup | <ul><li>"What are the products in the 'Clothing' category?"</li><li>"Get me the phone numbers of customers with the last name 'Johnson'."</li><li>"Can you filter by employees who have the last name 'Brown'?"</li></ul> |
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+ | Aggregation | <ul><li>'Get me max Accumulated Amortisation and Impairment.'</li><li>'Can I have mode of Revenue'</li><li>'Show me count company_name'</li></ul> |
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+ | Tablejoin | <ul><li>'Could you merge the Orders and Employees tables to identify which employees have processed high-value orders?'</li><li>'Could you connect the Products and Orders tables to analyze sales data by product category?'</li><li>'How can I connect the Customers and Orders tables to find customers who made purchases during a specific promotion?'</li></ul> |
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+ | Viewtables | <ul><li>'What are the tables in the starhub_data_asset database that a user can join to perform a sales analysis?'</li><li>'What tables can be found in the asset-tracking section of the starhub_data_asset database?'</li><li>'What tables exist in the starhub_data_asset database?'</li></ul> |
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+ | Rejection | <ul><li>"Let's avoid creating any new data sets."</li><li>"I'd prefer to avoid generating data fields."</li><li>"I'm not interested in filtering this collection."</li></ul> |
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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** | 0.9915 |
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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("nazhan/bge-small-en-v1.5-brahmaputra-iter-10")
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+ # Run inference
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+ preds = model("Can I have avg Cost_Efficiency")
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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 | 8.6563 | 62 |
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+
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+ | Label | Training Sample Count |
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+ |:-------------|:----------------------|
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+ | Tablejoin | 129 |
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+ | Rejection | 77 |
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+ | Aggregation | 282 |
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+ | Lookup | 60 |
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+ | Generalreply | 63 |
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+ | Viewtables | 74 |
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+ | Lookup_1 | 150 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (16, 16)
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+ - num_epochs: (1, 1)
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+ - max_steps: -1
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+ - sampling_strategy: oversampling
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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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+ - 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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+ | 0.0000 | 1 | 0.2038 | - |
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+ | 0.0014 | 50 | 0.2019 | - |
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+ | 0.0029 | 100 | 0.1983 | - |
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+ | 0.0043 | 150 | 0.206 | - |
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+ | 0.0057 | 200 | 0.2268 | - |
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+ | 0.0071 | 250 | 0.2025 | - |
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+ | 0.0086 | 300 | 0.2041 | - |
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+ | 0.0100 | 350 | 0.1426 | - |
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+ | 0.0114 | 400 | 0.1513 | - |
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+ | 0.0129 | 450 | 0.1215 | - |
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+ | 0.0143 | 500 | 0.1426 | - |
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+ | 0.0157 | 550 | 0.0859 | - |
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+ | 0.0172 | 600 | 0.0486 | - |
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+ | 0.0186 | 650 | 0.0378 | - |
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+ | 0.0200 | 700 | 0.0519 | - |
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+ | 0.0214 | 750 | 0.0717 | - |
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+ | 0.0229 | 800 | 0.1161 | - |
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+ | 0.0243 | 850 | 0.0771 | - |
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+ | 0.0257 | 900 | 0.074 | - |
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+ | 0.0272 | 950 | 0.0567 | - |
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+ | 0.0286 | 1000 | 0.0223 | - |
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+ | 0.0300 | 1050 | 0.0266 | - |
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+ | 0.0315 | 1100 | 0.0261 | - |
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+ | 0.0329 | 1150 | 0.0333 | - |
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+ | 0.0343 | 1200 | 0.0107 | - |
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+ | 0.0357 | 1250 | 0.0123 | - |
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+ | 0.0372 | 1300 | 0.0193 | - |
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+ | 0.0386 | 1350 | 0.0039 | - |
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+ | 0.0400 | 1400 | 0.0079 | - |
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+ | 0.0415 | 1450 | 0.0035 | - |
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+ | 0.0429 | 1500 | 0.003 | - |
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+ | 0.0443 | 1550 | 0.0041 | - |
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+ | 0.0457 | 1600 | 0.0038 | - |
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+ | 0.0472 | 1650 | 0.002 | - |
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+ | 0.0486 | 1700 | 0.0028 | - |
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+ | 0.0500 | 1750 | 0.0056 | - |
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+ | 0.0515 | 1800 | 0.0035 | - |
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+ | 0.0529 | 1850 | 0.0027 | - |
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+ | 0.0543 | 1900 | 0.0028 | - |
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+ | 0.0558 | 1950 | 0.0028 | - |
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+ | 0.0572 | 2000 | 0.0019 | - |
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+ | 0.0586 | 2050 | 0.0046 | - |
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+ | 0.0600 | 2100 | 0.0017 | - |
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+ | 0.0615 | 2150 | 0.0016 | - |
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+ | 0.0629 | 2200 | 0.0022 | - |
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+ | 0.0643 | 2250 | 0.002 | - |
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+ | 0.0658 | 2300 | 0.0029 | - |
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+ | 0.0672 | 2350 | 0.0032 | - |
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+ | 0.0686 | 2400 | 0.0018 | - |
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+ | 0.0701 | 2450 | 0.0015 | - |
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+ | 0.0715 | 2500 | 0.0015 | - |
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+ | 0.0729 | 2550 | 0.0016 | - |
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+ | 0.0743 | 2600 | 0.0012 | - |
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+ | 0.0758 | 2650 | 0.0014 | - |
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+ | 0.0772 | 2700 | 0.0015 | - |
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+ | 0.0786 | 2750 | 0.0018 | - |
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+ | 0.0801 | 2800 | 0.0012 | - |
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+ | 0.0815 | 2850 | 0.0009 | - |
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+ | 0.0829 | 2900 | 0.001 | - |
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+ | 0.0843 | 2950 | 0.0011 | - |
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+ | 0.0858 | 3000 | 0.0011 | - |
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+ | 0.0872 | 3050 | 0.001 | - |
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+ | 0.0886 | 3100 | 0.0012 | - |
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+ | 0.0901 | 3150 | 0.0006 | - |
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+ | 0.0915 | 3200 | 0.0013 | - |
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+ | 0.0929 | 3250 | 0.0007 | - |
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+ | 0.0944 | 3300 | 0.0007 | - |
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+ | 0.0958 | 3350 | 0.0009 | - |
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+ | 0.0972 | 3400 | 0.0008 | - |
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+ | 0.0986 | 3450 | 0.0005 | - |
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+ | 0.1001 | 3500 | 0.001 | - |
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+ | 0.1015 | 3550 | 0.001 | - |
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+ | 0.1029 | 3600 | 0.0008 | - |
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+ | 0.1044 | 3650 | 0.0007 | - |
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+ | 0.1058 | 3700 | 0.0006 | - |
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+ | 0.1086 | 3800 | 0.0012 | - |
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+ | 0.1101 | 3850 | 0.0007 | - |
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+ | 0.1301 | 4550 | 0.0008 | - |
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+ | 0.1330 | 4650 | 0.0007 | - |
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+ | 0.1372 | 4800 | 0.0006 | - |
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+ | 0.1387 | 4850 | 0.0008 | - |
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+ | 0.1401 | 4900 | 0.0008 | - |
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+ | 0.1415 | 4950 | 0.0004 | - |
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+ | 0.1430 | 5000 | 0.0005 | - |
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+ | 0.1458 | 5100 | 0.0007 | - |
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+ | 0.1472 | 5150 | 0.0006 | - |
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+ | 0.1487 | 5200 | 0.0006 | - |
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+ | 0.1501 | 5250 | 0.0004 | - |
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+ | 0.1515 | 5300 | 0.0005 | - |
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+ | 0.1530 | 5350 | 0.0007 | - |
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+ | 0.1544 | 5400 | 0.0007 | - |
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+ | 0.1558 | 5450 | 0.0005 | - |
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+ | 0.1573 | 5500 | 0.0007 | - |
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+ | 0.1587 | 5550 | 0.0004 | - |
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+ | 0.1601 | 5600 | 0.0004 | - |
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+ | 0.1687 | 5900 | 0.0007 | - |
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+ | 0.1701 | 5950 | 0.0005 | - |
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+ | 0.1730 | 6050 | 0.0003 | - |
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+ | 0.1773 | 6200 | 0.0007 | - |
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+ | 0.1930 | 6750 | 0.0004 | - |
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+ | 0.1944 | 6800 | 0.0004 | - |
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+ | 0.1959 | 6850 | 0.0004 | - |
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+ | 0.1973 | 6900 | 0.0003 | - |
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+ | 0.1987 | 6950 | 0.0004 | - |
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+ | 0.2016 | 7050 | 0.0003 | - |
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+ | 0.2030 | 7100 | 0.0003 | - |
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+ | 0.2688 | 9400 | 0.0552 | - |
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+ | 0.2731 | 9550 | 0.0004 | - |
355
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356
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357
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358
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359
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360
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361
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362
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363
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364
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365
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366
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367
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368
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369
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370
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371
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372
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373
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374
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375
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376
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377
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378
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379
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380
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381
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382
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383
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384
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385
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386
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387
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388
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389
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390
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391
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392
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393
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394
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395
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396
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397
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398
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399
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400
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401
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402
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403
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404
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405
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406
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407
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408
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409
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410
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411
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412
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413
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414
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415
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416
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417
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418
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419
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420
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421
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422
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423
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424
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425
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426
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427
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428
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429
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430
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431
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432
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433
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434
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435
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436
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437
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438
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439
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440
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441
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442
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443
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444
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445
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446
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447
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448
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449
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450
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451
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452
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453
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454
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455
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456
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457
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458
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459
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460
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461
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462
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463
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464
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465
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466
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467
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468
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469
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470
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471
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472
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473
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474
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475
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476
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477
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478
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479
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480
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481
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482
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483
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484
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485
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486
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487
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488
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489
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490
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491
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492
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493
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494
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495
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496
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497
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498
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499
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500
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501
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502
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503
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504
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505
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506
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507
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508
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509
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510
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511
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512
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513
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514
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515
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516
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517
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518
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519
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520
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521
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522
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523
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524
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525
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526
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527
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528
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529
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530
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531
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532
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533
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534
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535
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536
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537
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538
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539
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540
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541
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542
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543
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544
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545
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546
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547
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548
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549
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550
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551
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552
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553
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554
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555
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556
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557
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558
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559
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560
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561
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562
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563
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564
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565
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566
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567
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568
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569
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570
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571
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572
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573
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574
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575
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576
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577
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578
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579
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580
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581
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582
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583
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584
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585
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586
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587
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588
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589
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590
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591
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592
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593
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594
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595
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596
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597
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598
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599
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600
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601
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602
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603
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604
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605
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606
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607
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608
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609
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610
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611
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612
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613
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614
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615
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616
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617
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618
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619
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620
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621
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622
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623
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624
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625
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626
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627
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628
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629
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630
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631
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632
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633
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634
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635
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636
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637
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638
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639
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640
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641
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642
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643
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644
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645
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646
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647
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648
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649
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650
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651
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652
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653
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654
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655
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656
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657
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658
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659
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660
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661
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662
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663
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664
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665
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666
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667
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668
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669
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670
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671
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672
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673
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674
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675
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676
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677
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678
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679
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680
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681
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682
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683
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684
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685
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686
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687
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688
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689
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690
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691
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692
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693
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694
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695
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696
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697
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698
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699
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700
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701
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702
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703
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704
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705
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706
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707
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708
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709
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710
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711
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712
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713
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714
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715
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716
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717
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718
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719
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720
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721
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722
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723
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724
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725
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726
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727
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728
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729
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730
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731
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732
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733
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734
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735
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736
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737
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738
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739
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740
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741
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+ | **1.0** | **34975** | **-** | **0.0221** |
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+
865
+ * The bold row denotes the saved checkpoint.
866
+ ### Framework Versions
867
+ - Python: 3.11.9
868
+ - SetFit: 1.0.3
869
+ - Sentence Transformers: 2.7.0
870
+ - Transformers: 4.42.4
871
+ - PyTorch: 2.4.0+cu121
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+ - Datasets: 2.21.0
873
+ - Tokenizers: 0.19.1
874
+
875
+ ## Citation
876
+
877
+ ### BibTeX
878
+ ```bibtex
879
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
880
+ doi = {10.48550/ARXIV.2209.11055},
881
+ url = {https://arxiv.org/abs/2209.11055},
882
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
883
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
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+ title = {Efficient Few-Shot Learning Without Prompts},
885
+ publisher = {arXiv},
886
+ year = {2022},
887
+ copyright = {Creative Commons Attribution 4.0 International}
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+ }
889
+ ```
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+
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+ <!--
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+ ## Glossary
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+
894
+ *Clearly define terms in order to be accessible across audiences.*
895
+ -->
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+
897
+ <!--
898
+ ## Model Card Authors
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+
900
+ *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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+ -->
902
+
903
+ <!--
904
+ ## Model Card Contact
905
+
906
+ *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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