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nazhan
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bge-large-en-v1.5-brahmaputra-iter-9-2nd-1-epoch

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
Model card Files Community
nazhan commited on
Commit
1fbc451
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1 Parent(s): 1e70472

Add SetFit model

Browse files
Files changed (13) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +909 -0
  3. config.json +32 -0
  4. config_sentence_transformers.json +10 -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": 1024,
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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-large-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: you're very lucky.
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+ - text: Show me operating cash flow trends.
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+ - text: Join data_asset_kpi_is and data_asset_kpi_cf tables.
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+ - text: Can I have max EBIT_Margin?
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+ - text: I'm not inclined to generate further data sets.
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+ inference: true
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+ model-index:
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+ - name: SetFit with BAAI/bge-large-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: 0.9829059829059829
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+ name: Accuracy
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+ ---
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+
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+ # SetFit with BAAI/bge-large-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-large-en-v1.5](https://huggingface.co/BAAI/bge-large-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-large-en-v1.5](https://huggingface.co/BAAI/bge-large-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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+ | Lookup_1 | <ul><li>'Analyze product category revenue impact.'</li><li>'Analyze Product-wise Financial Performance Metrics.'</li><li>'Get M&A deal size by company.'</li></ul> |
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+ | Aggregation | <ul><li>'Group the products by color and find the average price for each color.'</li><li>'Get me count Product.'</li><li>'Show me forecast accuracy and group by version.'</li></ul> |
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+ | Lookup | <ul><li>'What are the products with a price below 20?'</li><li>'Can you get me the products that are out of stock?'</li><li>'Get me the list of employees who joined the company after January 2023.'</li></ul> |
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+ | Viewtables | <ul><li>'What are the different types of tables that can be found within the starhub_data_asset database?'</li><li>'What is the complete list of tables in the starhub_data_asset database that can be accessed without needing to perform any table joining operations?'</li><li>'What is the list of tables that a new user should familiarize themselves with when accessing the starhub_data_asset database?'</li></ul> |
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+ | Tablejoin | <ul><li>'Can you join the Products and Orders tables to track revenue by product category?'</li><li>'Could you combine table data from Orders and Products to identify which products were ordered most frequently?'</li><li>'Show me a join of key performance metrics and cash flow tables.'</li></ul> |
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+ | Generalreply | <ul><li>"Oh, I'm a big fan of indie rock. What about you? What's your favorite type of music?"</li><li>'It was pretty good! How about yours?'</li><li>"Oh, that's a tough question! I have a few favorites, but if I had to pick just one, it would be The Shawshank Redemption. What about you, what's your favorite movie?"</li></ul> |
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+ | Rejection | <ul><li>"I don't need to filter this data set."</li><li>"Let's not generate more data entries."</li><li>"Please don't filter the list."</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** | 0.9829 |
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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-large-en-v1.5-brahmaputra-iter-9-2nd-1-epoch")
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+ # Run inference
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+ preds = model("you're very lucky.")
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+ ```
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+
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+ <!--
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+ ### Downstream Use
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+
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+ *List how someone could finetune this model on their own dataset.*
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+ -->
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+
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+ <!--
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+ ### Out-of-Scope Use
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+
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+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
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+ -->
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+
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+ <!--
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+ ## Bias, Risks and Limitations
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+
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+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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+ -->
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+
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+ <!--
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+ ### Recommendations
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+
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+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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+ -->
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+
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+ ## Training Details
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+
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+ ### Training Set Metrics
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+ | Training set | Min | Median | Max |
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+ |:-------------|:----|:-------|:----|
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+ | Word count | 2 | 8.8397 | 53 |
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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 | 69 |
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+ | Aggregation | 282 |
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+ | Lookup | 64 |
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+ | Generalreply | 69 |
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+ | Viewtables | 76 |
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+ | Lookup_1 | 147 |
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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 | 0.23 | - |
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+ | 0.0014 | 50 | 0.196 | - |
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+ | 0.0028 | 100 | 0.1679 | - |
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+ | 0.0043 | 150 | 0.156 | - |
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+ | 0.0057 | 200 | 0.2 | - |
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+ | 0.0071 | 250 | 0.0765 | - |
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+ | 0.0085 | 300 | 0.167 | - |
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+ | 0.0100 | 350 | 0.1154 | - |
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+ | 0.0114 | 400 | 0.0625 | - |
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+ | 0.0128 | 450 | 0.0666 | - |
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+ | 0.0142 | 500 | 0.0515 | - |
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+ | 0.0157 | 550 | 0.0178 | - |
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+ | 0.0171 | 600 | 0.0068 | - |
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+ | 0.0185 | 650 | 0.0174 | - |
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+ | 0.0199 | 700 | 0.0136 | - |
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+ | 0.0214 | 750 | 0.0066 | - |
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+ | 0.0228 | 800 | 0.0052 | - |
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+ | 0.0242 | 850 | 0.0045 | - |
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+ | 0.0256 | 900 | 0.003 | - |
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+ | 0.0271 | 950 | 0.0031 | - |
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+ | 0.0285 | 1000 | 0.0035 | - |
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+ | 0.0299 | 1050 | 0.0032 | - |
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+ | 0.0313 | 1100 | 0.0031 | - |
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+ | 0.0328 | 1150 | 0.0029 | - |
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+ | 0.0342 | 1200 | 0.0023 | - |
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+ | 0.0356 | 1250 | 0.0012 | - |
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+ | 0.0370 | 1300 | 0.0025 | - |
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+ | 0.0385 | 1350 | 0.0019 | - |
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+ | 0.0399 | 1400 | 0.0023 | - |
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+ | 0.0413 | 1450 | 0.0016 | - |
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+ | 0.0427 | 1500 | 0.0018 | - |
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+ | 0.0441 | 1550 | 0.0019 | - |
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+ | 0.0456 | 1600 | 0.0012 | - |
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+ | 0.0470 | 1650 | 0.0012 | - |
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+ | 0.0484 | 1700 | 0.0013 | - |
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+ | 0.0498 | 1750 | 0.0011 | - |
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+ | 0.0513 | 1800 | 0.001 | - |
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+ | 0.0527 | 1850 | 0.0013 | - |
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+ | 0.0541 | 1900 | 0.0014 | - |
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+ | 0.0555 | 1950 | 0.0008 | - |
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+ | 0.0570 | 2000 | 0.0009 | - |
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+ | 0.0584 | 2050 | 0.0009 | - |
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+ | 0.0598 | 2100 | 0.0009 | - |
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+ | 0.0612 | 2150 | 0.0012 | - |
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+ | 0.0627 | 2200 | 0.0008 | - |
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+ | 0.0641 | 2250 | 0.0011 | - |
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+ | 0.0655 | 2300 | 0.0006 | - |
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+ | 0.0669 | 2350 | 0.0011 | - |
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+ | 0.0684 | 2400 | 0.0007 | - |
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+ | 0.0698 | 2450 | 0.0009 | - |
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+ | 0.0712 | 2500 | 0.0007 | - |
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+ | 0.0798 | 2800 | 0.0007 | - |
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+ | 0.0840 | 2950 | 0.0006 | - |
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+ | 0.0855 | 3000 | 0.0006 | - |
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+ | 0.0883 | 3100 | 0.0005 | - |
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+ | 0.0897 | 3150 | 0.0007 | - |
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+ | 0.0911 | 3200 | 0.0005 | - |
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+ | 0.0926 | 3250 | 0.0007 | - |
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+ | 0.0940 | 3300 | 0.0007 | - |
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+ | 0.0968 | 3400 | 0.0007 | - |
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+ | 0.1211 | 4250 | 0.0009 | - |
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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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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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383
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414
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431
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548
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685
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686
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688
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689
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690
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692
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695
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699
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700
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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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742
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+ | **1.0** | **35108** | **-** | **0.03** |
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+
867
+ * The bold row denotes the saved checkpoint.
868
+ ### Framework Versions
869
+ - Python: 3.11.9
870
+ - SetFit: 1.1.0.dev0
871
+ - Sentence Transformers: 3.0.1
872
+ - Transformers: 4.44.2
873
+ - PyTorch: 2.4.0+cu121
874
+ - Datasets: 2.21.0
875
+ - Tokenizers: 0.19.1
876
+
877
+ ## Citation
878
+
879
+ ### BibTeX
880
+ ```bibtex
881
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
882
+ doi = {10.48550/ARXIV.2209.11055},
883
+ url = {https://arxiv.org/abs/2209.11055},
884
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
885
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
886
+ title = {Efficient Few-Shot Learning Without Prompts},
887
+ publisher = {arXiv},
888
+ year = {2022},
889
+ copyright = {Creative Commons Attribution 4.0 International}
890
+ }
891
+ ```
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+
893
+ <!--
894
+ ## Glossary
895
+
896
+ *Clearly define terms in order to be accessible across audiences.*
897
+ -->
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+
899
+ <!--
900
+ ## Model Card Authors
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+
902
+ *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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+ -->
904
+
905
+ <!--
906
+ ## Model Card Contact
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+
908
+ *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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