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

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

Browse files
Files changed (13) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +920 -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: Get me var Product_Profitability.
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+ - text: What’s the best way to merge the Orders and Employees tables to identify the
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+ top-performing departments?
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+ - text: Please show min Total Company Revenue.
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+ - text: Get me avg Intangible Assets.
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+ - text: Can I join the Customers and Orders tables to find out which customers have
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+ the highest lifetime value?
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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: 0.5726495726495726
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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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+ | Generalreply | <ul><li>'How was your day today?'</li><li>'Oh, I have a lot of hobbies actually! But if I had to pick one, I would say that my favorite is probably reading. I love getting lost in a good book and discovering new worlds and characters. How about you?'</li><li>'Honestly, I hope to achieve a lot in the next 5 years. I want to continue growing in my career and learn new skills. I also aspire to travel more and experience different cultures. Overall, my goal is to be happy and fulfilled in both my personal and professional life. How about you? What are your hopes for the next 5 years?'</li></ul> |
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+ | Lookup_1 | <ul><li>'i want to get trend analysis and group by product'</li><li>'Show me data_asset_001_pcc details.'</li><li>'Analyze Product-wise EBIT Margin Trend.'</li></ul> |
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+ | Tablejoin | <ul><li>'Join data_asset_001_kpm with data_asset_kpi_is.'</li><li>'Can I merge cash flow and key performance metrics tables?'</li><li>'Join product category comparison and trend analysis tables.'</li></ul> |
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+ | Rejection | <ul><li>"I'm not interested in filtering this collection."</li><li>"I don't want to create any new data outputs."</li><li>"I don't want to perform any filtering."</li></ul> |
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+ | Aggregation | <ul><li>'Can I have avg Cost_Broadband?'</li><li>'Please show min % YoY Change.'</li><li>'Get me avg Earning_per_Cost.'</li></ul> |
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+ | Viewtables | <ul><li>'What tables are included in the starhub_data_asset database that relate to customer complaints?'</li><li>'I need to see a list of tables that contain information about network outages.'</li><li>'What are the available tables in the starhub_data_asset database that are relevant to financial reporting?'</li></ul> |
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+ | Lookup | <ul><li>'Filter by orders placed by customer ID 102 and get me the order dates.'</li><li>'Show me the orders placed on January 1st, 2024.'</li><li>"Get me the phone number of the customer with the first name 'Alice'."</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** | 0.5726 |
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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-9-1-epoch")
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+ # Run inference
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+ preds = model("Get me avg Intangible Assets.")
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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 | 8.7792 | 62 |
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+
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+ | Label | Training Sample Count |
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+ |:-------------|:----------------------|
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+ | Tablejoin | 126 |
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+ | Rejection | 72 |
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+ | Aggregation | 221 |
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+ | Lookup | 62 |
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+ | Generalreply | 60 |
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+ | Viewtables | 73 |
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+ | Lookup_1 | 224 |
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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 | 0.2059 | - |
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+ | 0.0014 | 50 | 0.1956 | - |
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+ | 0.0028 | 100 | 0.207 | - |
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+ | 0.0042 | 150 | 0.1783 | - |
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+ | 0.0056 | 200 | 0.1517 | - |
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+ | 0.0070 | 250 | 0.1795 | - |
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+ | 0.0084 | 300 | 0.1227 | - |
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+ | 0.0098 | 350 | 0.063 | - |
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+ | 0.0112 | 400 | 0.0451 | - |
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+ | 0.0126 | 450 | 0.0408 | - |
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+ | 0.0140 | 500 | 0.0576 | - |
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+ | 0.0155 | 550 | 0.0178 | - |
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+ | 0.0169 | 600 | 0.0244 | - |
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+ | 0.0183 | 650 | 0.0072 | - |
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+ | 0.0197 | 700 | 0.0223 | - |
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+ | 0.0211 | 750 | 0.0046 | - |
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+ | 0.0225 | 800 | 0.003 | - |
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+ | 0.0239 | 850 | 0.004 | - |
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+ | 0.0253 | 900 | 0.0042 | - |
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+ | 0.0267 | 950 | 0.0047 | - |
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+ | 0.0281 | 1000 | 0.0045 | - |
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+ | 0.0295 | 1050 | 0.0032 | - |
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+ | 0.0309 | 1100 | 0.0021 | - |
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+ | 0.0323 | 1150 | 0.0028 | - |
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+ | 0.0337 | 1200 | 0.0022 | - |
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+ | 0.0351 | 1250 | 0.0024 | - |
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+ | 0.0365 | 1300 | 0.0019 | - |
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+ | 0.0379 | 1350 | 0.002 | - |
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+ | 0.0393 | 1400 | 0.0015 | - |
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+ | 0.0407 | 1450 | 0.0016 | - |
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+ | 0.0421 | 1500 | 0.0014 | - |
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+ | 0.0436 | 1550 | 0.0013 | - |
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+ | 0.0450 | 1600 | 0.0016 | - |
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+ | 0.0464 | 1650 | 0.0011 | - |
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+ | 0.0478 | 1700 | 0.0012 | - |
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+ | 0.0492 | 1750 | 0.0011 | - |
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+ | 0.0506 | 1800 | 0.0015 | - |
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+ | 0.0520 | 1850 | 0.0016 | - |
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+ | 0.0534 | 1900 | 0.0012 | - |
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+ | 0.0548 | 1950 | 0.0008 | - |
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+ | 0.0562 | 2000 | 0.0011 | - |
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+ | 0.0576 | 2050 | 0.001 | - |
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+ | 0.0590 | 2100 | 0.001 | - |
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+ | 0.0604 | 2150 | 0.0008 | - |
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+ | 0.0618 | 2200 | 0.0009 | - |
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+ | 0.0632 | 2250 | 0.0007 | - |
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+ | 0.0646 | 2300 | 0.0008 | - |
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+ | 0.0660 | 2350 | 0.0006 | - |
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+ | 0.0674 | 2400 | 0.0007 | - |
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+ | 0.0688 | 2450 | 0.0008 | - |
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+ | 0.0702 | 2500 | 0.0006 | - |
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+ | 0.0717 | 2550 | 0.0007 | - |
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+ | 0.0731 | 2600 | 0.0006 | - |
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+ | 0.0745 | 2650 | 0.0007 | - |
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+ | 0.0759 | 2700 | 0.0005 | - |
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+ | 0.0773 | 2750 | 0.0006 | - |
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+ | 0.0787 | 2800 | 0.0007 | - |
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+ | 0.0801 | 2850 | 0.0007 | - |
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+ | 0.0815 | 2900 | 0.0005 | - |
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+ | 0.0829 | 2950 | 0.0008 | - |
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+ | 0.0843 | 3000 | 0.0005 | - |
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+ | 0.0857 | 3050 | 0.0007 | - |
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+ | 0.0871 | 3100 | 0.0006 | - |
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+ | 0.0885 | 3150 | 0.0005 | - |
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+ | 0.0899 | 3200 | 0.0007 | - |
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+ | 0.0913 | 3250 | 0.0005 | - |
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+ | 0.0927 | 3300 | 0.0004 | - |
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+ | 0.0941 | 3350 | 0.0005 | - |
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+ | 0.0955 | 3400 | 0.0003 | - |
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+ | 0.0969 | 3450 | 0.0004 | - |
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+ | 0.0983 | 3500 | 0.0004 | - |
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+ | 0.0998 | 3550 | 0.0004 | - |
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+ | 0.1012 | 3600 | 0.0004 | - |
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+ | 0.1026 | 3650 | 0.0004 | - |
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+ | 0.1040 | 3700 | 0.0004 | - |
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+ | 0.1054 | 3750 | 0.0004 | - |
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+ | 0.1068 | 3800 | 0.0003 | - |
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+ | 0.1082 | 3850 | 0.0003 | - |
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+ | 0.1096 | 3900 | 0.0005 | - |
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+ | 0.1124 | 4000 | 0.0005 | - |
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+ | 0.1138 | 4050 | 0.0003 | - |
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+ | 0.1152 | 4100 | 0.0006 | - |
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+ | 0.1180 | 4200 | 0.0003 | - |
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+ | 0.1194 | 4250 | 0.0004 | - |
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+ | 0.1208 | 4300 | 0.0003 | - |
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+ | 0.1222 | 4350 | 0.0004 | - |
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+ | 0.1236 | 4400 | 0.0003 | - |
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+ | 0.1264 | 4500 | 0.0004 | - |
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+ | 0.1279 | 4550 | 0.0003 | - |
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+ | 0.1293 | 4600 | 0.0005 | - |
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+ | 0.1307 | 4650 | 0.0004 | - |
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+ | 0.1321 | 4700 | 0.0003 | - |
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+ | 0.1335 | 4750 | 0.0004 | - |
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+ | 0.1349 | 4800 | 0.0003 | - |
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+ | 0.1363 | 4850 | 0.0003 | - |
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+ | 0.1377 | 4900 | 0.0003 | - |
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+ | 0.1391 | 4950 | 0.0003 | - |
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+ | 0.1405 | 5000 | 0.0003 | - |
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+ | 0.1419 | 5050 | 0.0003 | - |
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+ | 0.1433 | 5100 | 0.0004 | - |
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+ | 0.1447 | 5150 | 0.0003 | - |
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+ | 0.1461 | 5200 | 0.0004 | - |
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+ | 0.1475 | 5250 | 0.0004 | - |
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+ | 0.1489 | 5300 | 0.0003 | - |
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+ | 0.1503 | 5350 | 0.0003 | - |
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+ | 0.1517 | 5400 | 0.0003 | - |
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+ | 0.1531 | 5450 | 0.0003 | - |
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+ | 0.1545 | 5500 | 0.0002 | - |
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+ | 0.1560 | 5550 | 0.0003 | - |
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+ | 0.1574 | 5600 | 0.0003 | - |
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+ | 0.1588 | 5650 | 0.0003 | - |
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+ | 0.1602 | 5700 | 0.0002 | - |
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+ | 0.1616 | 5750 | 0.0002 | - |
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+ | 0.1630 | 5800 | 0.0003 | - |
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+ | 0.1644 | 5850 | 0.0002 | - |
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+ | 0.1658 | 5900 | 0.0003 | - |
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+ | 0.1672 | 5950 | 0.0002 | - |
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+ | 0.1686 | 6000 | 0.0002 | - |
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+ | 0.1700 | 6050 | 0.0002 | - |
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+ | 0.1714 | 6100 | 0.0002 | - |
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+ | 0.1728 | 6150 | 0.0003 | - |
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+ | 0.1742 | 6200 | 0.0003 | - |
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+ | 0.1756 | 6250 | 0.0003 | - |
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+ | 0.1770 | 6300 | 0.0003 | - |
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+ | 0.1784 | 6350 | 0.0002 | - |
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+ | 0.1798 | 6400 | 0.0003 | - |
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+ | 0.1812 | 6450 | 0.0002 | - |
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+ | 0.1826 | 6500 | 0.0003 | - |
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+ | 0.1841 | 6550 | 0.0002 | - |
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+ | 0.1855 | 6600 | 0.0002 | - |
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+ | 0.1869 | 6650 | 0.0002 | - |
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+ | 0.1883 | 6700 | 0.0002 | - |
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+ | 0.1897 | 6750 | 0.0003 | - |
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+ | 0.1911 | 6800 | 0.0003 | - |
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+ | 0.1925 | 6850 | 0.0002 | - |
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+ | 0.1939 | 6900 | 0.0002 | - |
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+ | 0.1953 | 6950 | 0.0002 | - |
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+ | 0.1967 | 7000 | 0.0002 | - |
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+ | 0.1981 | 7050 | 0.0001 | - |
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+ | 0.1995 | 7100 | 0.0002 | - |
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+ | 0.2009 | 7150 | 0.0002 | - |
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+ | 0.2023 | 7200 | 0.0002 | - |
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+ | 0.2037 | 7250 | 0.0002 | - |
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+ | 0.2051 | 7300 | 0.0002 | - |
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+ | 0.2065 | 7350 | 0.0001 | - |
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+ | 0.2079 | 7400 | 0.0002 | - |
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+ | 0.2093 | 7450 | 0.0024 | - |
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+ | 0.2107 | 7500 | 0.0718 | - |
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+ | 0.2122 | 7550 | 0.1 | - |
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+ | 0.2136 | 7600 | 0.1876 | - |
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+ | 0.2150 | 7650 | 0.1006 | - |
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+ | 0.2164 | 7700 | 0.163 | - |
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+ | 0.2178 | 7750 | 0.1008 | - |
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+ | 0.2192 | 7800 | 0.1073 | - |
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+ | 0.2206 | 7850 | 0.2059 | - |
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+ | 0.2220 | 7900 | 0.112 | - |
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+ | 0.2234 | 7950 | 0.1103 | - |
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+ | 0.2248 | 8000 | 0.1921 | - |
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+ | 0.2262 | 8050 | 0.0641 | - |
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+ | 0.2276 | 8100 | 0.0992 | - |
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+ | 0.2290 | 8150 | 0.2486 | - |
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+ | 0.2304 | 8200 | 0.1716 | - |
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+ | 0.2318 | 8250 | 0.142 | - |
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+ | 0.2332 | 8300 | 0.1431 | - |
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+ | 0.2346 | 8350 | 0.1774 | - |
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+ | 0.2360 | 8400 | 0.1537 | - |
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+ | 0.2374 | 8450 | 0.1902 | - |
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+ | 0.2388 | 8500 | 0.1015 | - |
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+ | 0.2402 | 8550 | 0.1401 | - |
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+ | 0.2417 | 8600 | 0.2599 | - |
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+ | 0.2431 | 8650 | 0.261 | - |
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+ | 0.2445 | 8700 | 0.1861 | - |
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+ | 0.2459 | 8750 | 0.1743 | - |
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+ | 0.2473 | 8800 | 0.1705 | - |
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+ | 0.2487 | 8850 | 0.1752 | - |
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+ | 0.2501 | 8900 | 0.0914 | - |
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+ | 0.2515 | 8950 | 0.1651 | - |
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+ | 0.2529 | 9000 | 0.1165 | - |
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+ | 0.2543 | 9050 | 0.2675 | - |
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+ | 0.2557 | 9100 | 0.0953 | - |
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+ | 0.2571 | 9150 | 0.0713 | - |
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+ | 0.2585 | 9200 | 0.1782 | - |
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+ | 0.2599 | 9250 | 0.1995 | - |
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+ | 0.2613 | 9300 | 0.2393 | - |
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+ | 0.2627 | 9350 | 0.1734 | - |
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+ | 0.2641 | 9400 | 0.2222 | - |
353
+ | 0.2655 | 9450 | 0.3005 | - |
354
+ | 0.2669 | 9500 | 0.2252 | - |
355
+ | 0.2683 | 9550 | 0.2498 | - |
356
+ | 0.2698 | 9600 | 0.3293 | - |
357
+ | 0.2712 | 9650 | 0.2422 | - |
358
+ | 0.2726 | 9700 | 0.1943 | - |
359
+ | 0.2740 | 9750 | 0.2497 | - |
360
+ | 0.2754 | 9800 | 0.2538 | - |
361
+ | 0.2768 | 9850 | 0.2114 | - |
362
+ | 0.2782 | 9900 | 0.1719 | - |
363
+ | 0.2796 | 9950 | 0.2453 | - |
364
+ | 0.2810 | 10000 | 0.2571 | - |
365
+ | 0.2824 | 10050 | 0.2267 | - |
366
+ | 0.2838 | 10100 | 0.2274 | - |
367
+ | 0.2852 | 10150 | 0.2441 | - |
368
+ | 0.2866 | 10200 | 0.2536 | - |
369
+ | 0.2880 | 10250 | 0.236 | - |
370
+ | 0.2894 | 10300 | 0.204 | - |
371
+ | 0.2908 | 10350 | 0.2636 | - |
372
+ | 0.2922 | 10400 | 0.2562 | - |
373
+ | 0.2936 | 10450 | 0.2437 | - |
374
+ | 0.2950 | 10500 | 0.2395 | - |
375
+ | 0.2964 | 10550 | 0.2616 | - |
376
+ | 0.2979 | 10600 | 0.272 | - |
377
+ | 0.2993 | 10650 | 0.2637 | - |
378
+ | 0.3007 | 10700 | 0.2503 | - |
379
+ | 0.3021 | 10750 | 0.2401 | - |
380
+ | 0.3035 | 10800 | 0.2485 | - |
381
+ | 0.3049 | 10850 | 0.2521 | - |
382
+ | 0.3063 | 10900 | 0.256 | - |
383
+ | 0.3077 | 10950 | 0.2363 | - |
384
+ | 0.3091 | 11000 | 0.2482 | - |
385
+ | 0.3105 | 11050 | 0.2533 | - |
386
+ | 0.3119 | 11100 | 0.2598 | - |
387
+ | 0.3133 | 11150 | 0.2572 | - |
388
+ | 0.3147 | 11200 | 0.2631 | - |
389
+ | 0.3161 | 11250 | 0.2399 | - |
390
+ | 0.3175 | 11300 | 0.2509 | - |
391
+ | 0.3189 | 11350 | 0.2447 | - |
392
+ | 0.3203 | 11400 | 0.2395 | - |
393
+ | 0.3217 | 11450 | 0.2439 | - |
394
+ | 0.3231 | 11500 | 0.2497 | - |
395
+ | 0.3245 | 11550 | 0.2377 | - |
396
+ | 0.3260 | 11600 | 0.2452 | - |
397
+ | 0.3274 | 11650 | 0.2361 | - |
398
+ | 0.3288 | 11700 | 0.2431 | - |
399
+ | 0.3302 | 11750 | 0.2462 | - |
400
+ | 0.3316 | 11800 | 0.2438 | - |
401
+ | 0.3330 | 11850 | 0.2498 | - |
402
+ | 0.3344 | 11900 | 0.262 | - |
403
+ | 0.3358 | 11950 | 0.2451 | - |
404
+ | 0.3372 | 12000 | 0.251 | - |
405
+ | 0.3386 | 12050 | 0.2605 | - |
406
+ | 0.3400 | 12100 | 0.2477 | - |
407
+ | 0.3414 | 12150 | 0.2417 | - |
408
+ | 0.3428 | 12200 | 0.2566 | - |
409
+ | 0.3442 | 12250 | 0.2373 | - |
410
+ | 0.3456 | 12300 | 0.2444 | - |
411
+ | 0.3470 | 12350 | 0.2589 | - |
412
+ | 0.3484 | 12400 | 0.2491 | - |
413
+ | 0.3498 | 12450 | 0.2438 | - |
414
+ | 0.3512 | 12500 | 0.2519 | - |
415
+ | 0.3526 | 12550 | 0.2406 | - |
416
+ | 0.3541 | 12600 | 0.2472 | - |
417
+ | 0.3555 | 12650 | 0.2447 | - |
418
+ | 0.3569 | 12700 | 0.2677 | - |
419
+ | 0.3583 | 12750 | 0.2486 | - |
420
+ | 0.3597 | 12800 | 0.2585 | - |
421
+ | 0.3611 | 12850 | 0.2539 | - |
422
+ | 0.3625 | 12900 | 0.2556 | - |
423
+ | 0.3639 | 12950 | 0.2653 | - |
424
+ | 0.3653 | 13000 | 0.2583 | - |
425
+ | 0.3667 | 13050 | 0.2308 | - |
426
+ | 0.3681 | 13100 | 0.2586 | - |
427
+ | 0.3695 | 13150 | 0.2384 | - |
428
+ | 0.3709 | 13200 | 0.2645 | - |
429
+ | 0.3723 | 13250 | 0.2394 | - |
430
+ | 0.3737 | 13300 | 0.2575 | - |
431
+ | 0.3751 | 13350 | 0.2418 | - |
432
+ | 0.3765 | 13400 | 0.2414 | - |
433
+ | 0.3779 | 13450 | 0.2516 | - |
434
+ | 0.3793 | 13500 | 0.2571 | - |
435
+ | 0.3807 | 13550 | 0.2352 | - |
436
+ | 0.3822 | 13600 | 0.2584 | - |
437
+ | 0.3836 | 13650 | 0.2561 | - |
438
+ | 0.3850 | 13700 | 0.2672 | - |
439
+ | 0.3864 | 13750 | 0.2574 | - |
440
+ | 0.3878 | 13800 | 0.2398 | - |
441
+ | 0.3892 | 13850 | 0.2359 | - |
442
+ | 0.3906 | 13900 | 0.2397 | - |
443
+ | 0.3920 | 13950 | 0.2582 | - |
444
+ | 0.3934 | 14000 | 0.2468 | - |
445
+ | 0.3948 | 14050 | 0.2702 | - |
446
+ | 0.3962 | 14100 | 0.2547 | - |
447
+ | 0.3976 | 14150 | 0.2382 | - |
448
+ | 0.3990 | 14200 | 0.255 | - |
449
+ | 0.4004 | 14250 | 0.2382 | - |
450
+ | 0.4018 | 14300 | 0.2516 | - |
451
+ | 0.4032 | 14350 | 0.236 | - |
452
+ | 0.4046 | 14400 | 0.2499 | - |
453
+ | 0.4060 | 14450 | 0.2606 | - |
454
+ | 0.4074 | 14500 | 0.2514 | - |
455
+ | 0.4088 | 14550 | 0.2442 | - |
456
+ | 0.4103 | 14600 | 0.2516 | - |
457
+ | 0.4117 | 14650 | 0.2439 | - |
458
+ | 0.4131 | 14700 | 0.2547 | - |
459
+ | 0.4145 | 14750 | 0.2522 | - |
460
+ | 0.4159 | 14800 | 0.2421 | - |
461
+ | 0.4173 | 14850 | 0.2461 | - |
462
+ | 0.4187 | 14900 | 0.2663 | - |
463
+ | 0.4201 | 14950 | 0.259 | - |
464
+ | 0.4215 | 15000 | 0.2526 | - |
465
+ | 0.4229 | 15050 | 0.2527 | - |
466
+ | 0.4243 | 15100 | 0.2547 | - |
467
+ | 0.4257 | 15150 | 0.2696 | - |
468
+ | 0.4271 | 15200 | 0.2399 | - |
469
+ | 0.4285 | 15250 | 0.2557 | - |
470
+ | 0.4299 | 15300 | 0.2581 | - |
471
+ | 0.4313 | 15350 | 0.2402 | - |
472
+ | 0.4327 | 15400 | 0.2658 | - |
473
+ | 0.4341 | 15450 | 0.2491 | - |
474
+ | 0.4355 | 15500 | 0.2434 | - |
475
+ | 0.4369 | 15550 | 0.2511 | - |
476
+ | 0.4384 | 15600 | 0.2448 | - |
477
+ | 0.4398 | 15650 | 0.262 | - |
478
+ | 0.4412 | 15700 | 0.2549 | - |
479
+ | 0.4426 | 15750 | 0.2546 | - |
480
+ | 0.4440 | 15800 | 0.2444 | - |
481
+ | 0.4454 | 15850 | 0.2551 | - |
482
+ | 0.4468 | 15900 | 0.247 | - |
483
+ | 0.4482 | 15950 | 0.253 | - |
484
+ | 0.4496 | 16000 | 0.2615 | - |
485
+ | 0.4510 | 16050 | 0.2514 | - |
486
+ | 0.4524 | 16100 | 0.2587 | - |
487
+ | 0.4538 | 16150 | 0.2591 | - |
488
+ | 0.4552 | 16200 | 0.249 | - |
489
+ | 0.4566 | 16250 | 0.2459 | - |
490
+ | 0.4580 | 16300 | 0.2582 | - |
491
+ | 0.4594 | 16350 | 0.243 | - |
492
+ | 0.4608 | 16400 | 0.2493 | - |
493
+ | 0.4622 | 16450 | 0.2306 | - |
494
+ | 0.4636 | 16500 | 0.2561 | - |
495
+ | 0.4650 | 16550 | 0.2363 | - |
496
+ | 0.4664 | 16600 | 0.2412 | - |
497
+ | 0.4679 | 16650 | 0.2454 | - |
498
+ | 0.4693 | 16700 | 0.2575 | - |
499
+ | 0.4707 | 16750 | 0.2369 | - |
500
+ | 0.4721 | 16800 | 0.245 | - |
501
+ | 0.4735 | 16850 | 0.2591 | - |
502
+ | 0.4749 | 16900 | 0.2582 | - |
503
+ | 0.4763 | 16950 | 0.2629 | - |
504
+ | 0.4777 | 17000 | 0.2393 | - |
505
+ | 0.4791 | 17050 | 0.2563 | - |
506
+ | 0.4805 | 17100 | 0.2511 | - |
507
+ | 0.4819 | 17150 | 0.2538 | - |
508
+ | 0.4833 | 17200 | 0.2464 | - |
509
+ | 0.4847 | 17250 | 0.2511 | - |
510
+ | 0.4861 | 17300 | 0.244 | - |
511
+ | 0.4875 | 17350 | 0.2688 | - |
512
+ | 0.4889 | 17400 | 0.2729 | - |
513
+ | 0.4903 | 17450 | 0.2523 | - |
514
+ | 0.4917 | 17500 | 0.2507 | - |
515
+ | 0.4931 | 17550 | 0.2527 | - |
516
+ | 0.4945 | 17600 | 0.2478 | - |
517
+ | 0.4960 | 17650 | 0.26 | - |
518
+ | 0.4974 | 17700 | 0.2526 | - |
519
+ | 0.4988 | 17750 | 0.2549 | - |
520
+ | 0.5002 | 17800 | 0.2496 | - |
521
+ | 0.5016 | 17850 | 0.2537 | - |
522
+ | 0.5030 | 17900 | 0.2644 | - |
523
+ | 0.5044 | 17950 | 0.2633 | - |
524
+ | 0.5058 | 18000 | 0.2515 | - |
525
+ | 0.5072 | 18050 | 0.2551 | - |
526
+ | 0.5086 | 18100 | 0.2427 | - |
527
+ | 0.5100 | 18150 | 0.2615 | - |
528
+ | 0.5114 | 18200 | 0.2455 | - |
529
+ | 0.5128 | 18250 | 0.2615 | - |
530
+ | 0.5142 | 18300 | 0.2558 | - |
531
+ | 0.5156 | 18350 | 0.2483 | - |
532
+ | 0.5170 | 18400 | 0.2618 | - |
533
+ | 0.5184 | 18450 | 0.2404 | - |
534
+ | 0.5198 | 18500 | 0.2562 | - |
535
+ | 0.5212 | 18550 | 0.259 | - |
536
+ | 0.5226 | 18600 | 0.246 | - |
537
+ | 0.5241 | 18650 | 0.2529 | - |
538
+ | 0.5255 | 18700 | 0.2526 | - |
539
+ | 0.5269 | 18750 | 0.2381 | - |
540
+ | 0.5283 | 18800 | 0.2648 | - |
541
+ | 0.5297 | 18850 | 0.2628 | - |
542
+ | 0.5311 | 18900 | 0.2528 | - |
543
+ | 0.5325 | 18950 | 0.2447 | - |
544
+ | 0.5339 | 19000 | 0.2467 | - |
545
+ | 0.5353 | 19050 | 0.2487 | - |
546
+ | 0.5367 | 19100 | 0.2494 | - |
547
+ | 0.5381 | 19150 | 0.2441 | - |
548
+ | 0.5395 | 19200 | 0.2507 | - |
549
+ | 0.5409 | 19250 | 0.2494 | - |
550
+ | 0.5423 | 19300 | 0.2501 | - |
551
+ | 0.5437 | 19350 | 0.2586 | - |
552
+ | 0.5451 | 19400 | 0.2677 | - |
553
+ | 0.5465 | 19450 | 0.2558 | - |
554
+ | 0.5479 | 19500 | 0.2444 | - |
555
+ | 0.5493 | 19550 | 0.251 | - |
556
+ | 0.5507 | 19600 | 0.2545 | - |
557
+ | 0.5522 | 19650 | 0.2464 | - |
558
+ | 0.5536 | 19700 | 0.2565 | - |
559
+ | 0.5550 | 19750 | 0.2674 | - |
560
+ | 0.5564 | 19800 | 0.2483 | - |
561
+ | 0.5578 | 19850 | 0.241 | - |
562
+ | 0.5592 | 19900 | 0.2504 | - |
563
+ | 0.5606 | 19950 | 0.2655 | - |
564
+ | 0.5620 | 20000 | 0.2484 | - |
565
+ | 0.5634 | 20050 | 0.254 | - |
566
+ | 0.5648 | 20100 | 0.2482 | - |
567
+ | 0.5662 | 20150 | 0.2644 | - |
568
+ | 0.5676 | 20200 | 0.2694 | - |
569
+ | 0.5690 | 20250 | 0.258 | - |
570
+ | 0.5704 | 20300 | 0.2587 | - |
571
+ | 0.5718 | 20350 | 0.2571 | - |
572
+ | 0.5732 | 20400 | 0.2464 | - |
573
+ | 0.5746 | 20450 | 0.2531 | - |
574
+ | 0.5760 | 20500 | 0.2504 | - |
575
+ | 0.5774 | 20550 | 0.2551 | - |
576
+ | 0.5788 | 20600 | 0.253 | - |
577
+ | 0.5803 | 20650 | 0.2374 | - |
578
+ | 0.5817 | 20700 | 0.2405 | - |
579
+ | 0.5831 | 20750 | 0.2435 | - |
580
+ | 0.5845 | 20800 | 0.2569 | - |
581
+ | 0.5859 | 20850 | 0.2533 | - |
582
+ | 0.5873 | 20900 | 0.2508 | - |
583
+ | 0.5887 | 20950 | 0.2508 | - |
584
+ | 0.5901 | 21000 | 0.2531 | - |
585
+ | 0.5915 | 21050 | 0.2381 | - |
586
+ | 0.5929 | 21100 | 0.2009 | - |
587
+ | 0.5943 | 21150 | 0.0899 | - |
588
+ | 0.5957 | 21200 | 0.3046 | - |
589
+ | 0.5971 | 21250 | 0.2006 | - |
590
+ | 0.5985 | 21300 | 0.2289 | - |
591
+ | 0.5999 | 21350 | 0.1581 | - |
592
+ | 0.6013 | 21400 | 0.1769 | - |
593
+ | 0.6027 | 21450 | 0.2377 | - |
594
+ | 0.6041 | 21500 | 0.1988 | - |
595
+ | 0.6055 | 21550 | 0.2543 | - |
596
+ | 0.6069 | 21600 | 0.2517 | - |
597
+ | 0.6084 | 21650 | 0.2191 | - |
598
+ | 0.6098 | 21700 | 0.2803 | - |
599
+ | 0.6112 | 21750 | 0.2984 | - |
600
+ | 0.6126 | 21800 | 0.1915 | - |
601
+ | 0.6140 | 21850 | 0.189 | - |
602
+ | 0.6154 | 21900 | 0.1302 | - |
603
+ | 0.6168 | 21950 | 0.203 | - |
604
+ | 0.6182 | 22000 | 0.2038 | - |
605
+ | 0.6196 | 22050 | 0.134 | - |
606
+ | 0.6210 | 22100 | 0.1904 | - |
607
+ | 0.6224 | 22150 | 0.1477 | - |
608
+ | 0.6238 | 22200 | 0.1338 | - |
609
+ | 0.6252 | 22250 | 0.0709 | - |
610
+ | 0.6266 | 22300 | 0.0902 | - |
611
+ | 0.6280 | 22350 | 0.2025 | - |
612
+ | 0.6294 | 22400 | 0.0991 | - |
613
+ | 0.6308 | 22450 | 0.1321 | - |
614
+ | 0.6322 | 22500 | 0.1356 | - |
615
+ | 0.6336 | 22550 | 0.1682 | - |
616
+ | 0.6350 | 22600 | 0.2064 | - |
617
+ | 0.6365 | 22650 | 0.2 | - |
618
+ | 0.6379 | 22700 | 0.2105 | - |
619
+ | 0.6393 | 22750 | 0.2074 | - |
620
+ | 0.6407 | 22800 | 0.1901 | - |
621
+ | 0.6421 | 22850 | 0.1914 | - |
622
+ | 0.6435 | 22900 | 0.1831 | - |
623
+ | 0.6449 | 22950 | 0.1423 | - |
624
+ | 0.6463 | 23000 | 0.2502 | - |
625
+ | 0.6477 | 23050 | 0.1655 | - |
626
+ | 0.6491 | 23100 | 0.1585 | - |
627
+ | 0.6505 | 23150 | 0.2122 | - |
628
+ | 0.6519 | 23200 | 0.217 | - |
629
+ | 0.6533 | 23250 | 0.1704 | - |
630
+ | 0.6547 | 23300 | 0.189 | - |
631
+ | 0.6561 | 23350 | 0.1333 | - |
632
+ | 0.6575 | 23400 | 0.1863 | - |
633
+ | 0.6589 | 23450 | 0.2089 | - |
634
+ | 0.6603 | 23500 | 0.1261 | - |
635
+ | 0.6617 | 23550 | 0.1655 | - |
636
+ | 0.6631 | 23600 | 0.1721 | - |
637
+ | 0.6645 | 23650 | 0.083 | - |
638
+ | 0.6660 | 23700 | 0.1166 | - |
639
+ | 0.6674 | 23750 | 0.146 | - |
640
+ | 0.6688 | 23800 | 0.0423 | - |
641
+ | 0.6702 | 23850 | 0.1781 | - |
642
+ | 0.6716 | 23900 | 0.121 | - |
643
+ | 0.6730 | 23950 | 0.1624 | - |
644
+ | 0.6744 | 24000 | 0.1483 | - |
645
+ | 0.6758 | 24050 | 0.1479 | - |
646
+ | 0.6772 | 24100 | 0.2285 | - |
647
+ | 0.6786 | 24150 | 0.2084 | - |
648
+ | 0.6800 | 24200 | 0.12 | - |
649
+ | 0.6814 | 24250 | 0.115 | - |
650
+ | 0.6828 | 24300 | 0.1331 | - |
651
+ | 0.6842 | 24350 | 0.0971 | - |
652
+ | 0.6856 | 24400 | 0.0846 | - |
653
+ | 0.6870 | 24450 | 0.2254 | - |
654
+ | 0.6884 | 24500 | 0.1348 | - |
655
+ | 0.6898 | 24550 | 0.0633 | - |
656
+ | 0.6912 | 24600 | 0.1207 | - |
657
+ | 0.6926 | 24650 | 0.2109 | - |
658
+ | 0.6941 | 24700 | 0.0768 | - |
659
+ | 0.6955 | 24750 | 0.108 | - |
660
+ | 0.6969 | 24800 | 0.0665 | - |
661
+ | 0.6983 | 24850 | 0.0601 | - |
662
+ | 0.6997 | 24900 | 0.1922 | - |
663
+ | 0.7011 | 24950 | 0.1517 | - |
664
+ | 0.7025 | 25000 | 0.1049 | - |
665
+ | 0.7039 | 25050 | 0.1122 | - |
666
+ | 0.7053 | 25100 | 0.0973 | - |
667
+ | 0.7067 | 25150 | 0.1547 | - |
668
+ | 0.7081 | 25200 | 0.115 | - |
669
+ | 0.7095 | 25250 | 0.1881 | - |
670
+ | 0.7109 | 25300 | 0.2144 | - |
671
+ | 0.7123 | 25350 | 0.0567 | - |
672
+ | 0.7137 | 25400 | 0.0917 | - |
673
+ | 0.7151 | 25450 | 0.1404 | - |
674
+ | 0.7165 | 25500 | 0.019 | - |
675
+ | 0.7179 | 25550 | 0.1382 | - |
676
+ | 0.7193 | 25600 | 0.0727 | - |
677
+ | 0.7207 | 25650 | 0.1125 | - |
678
+ | 0.7222 | 25700 | 0.1133 | - |
679
+ | 0.7236 | 25750 | 0.0987 | - |
680
+ | 0.7250 | 25800 | 0.1915 | - |
681
+ | 0.7264 | 25850 | 0.09 | - |
682
+ | 0.7278 | 25900 | 0.1462 | - |
683
+ | 0.7292 | 25950 | 0.0881 | - |
684
+ | 0.7306 | 26000 | 0.1026 | - |
685
+ | 0.7320 | 26050 | 0.1079 | - |
686
+ | 0.7334 | 26100 | 0.1639 | - |
687
+ | 0.7348 | 26150 | 0.1229 | - |
688
+ | 0.7362 | 26200 | 0.3261 | - |
689
+ | 0.7376 | 26250 | 0.1426 | - |
690
+ | 0.7390 | 26300 | 0.0773 | - |
691
+ | 0.7404 | 26350 | 0.1607 | - |
692
+ | 0.7418 | 26400 | 0.1354 | - |
693
+ | 0.7432 | 26450 | 0.1512 | - |
694
+ | 0.7446 | 26500 | 0.1875 | - |
695
+ | 0.7460 | 26550 | 0.1403 | - |
696
+ | 0.7474 | 26600 | 0.1287 | - |
697
+ | 0.7488 | 26650 | 0.1892 | - |
698
+ | 0.7503 | 26700 | 0.166 | - |
699
+ | 0.7517 | 26750 | 0.2385 | - |
700
+ | 0.7531 | 26800 | 0.1445 | - |
701
+ | 0.7545 | 26850 | 0.0969 | - |
702
+ | 0.7559 | 26900 | 0.0948 | - |
703
+ | 0.7573 | 26950 | 0.0589 | - |
704
+ | 0.7587 | 27000 | 0.2326 | - |
705
+ | 0.7601 | 27050 | 0.1438 | - |
706
+ | 0.7615 | 27100 | 0.1032 | - |
707
+ | 0.7629 | 27150 | 0.0784 | - |
708
+ | 0.7643 | 27200 | 0.1478 | - |
709
+ | 0.7657 | 27250 | 0.1872 | - |
710
+ | 0.7671 | 27300 | 0.0672 | - |
711
+ | 0.7685 | 27350 | 0.0725 | - |
712
+ | 0.7699 | 27400 | 0.0771 | - |
713
+ | 0.7713 | 27450 | 0.2575 | - |
714
+ | 0.7727 | 27500 | 0.133 | - |
715
+ | 0.7741 | 27550 | 0.1222 | - |
716
+ | 0.7755 | 27600 | 0.1207 | - |
717
+ | 0.7769 | 27650 | 0.0973 | - |
718
+ | 0.7784 | 27700 | 0.2186 | - |
719
+ | 0.7798 | 27750 | 0.1648 | - |
720
+ | 0.7812 | 27800 | 0.1128 | - |
721
+ | 0.7826 | 27850 | 0.1626 | - |
722
+ | 0.7840 | 27900 | 0.1768 | - |
723
+ | 0.7854 | 27950 | 0.1806 | - |
724
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725
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+ | **1.0** | **35588** | **-** | **0.1207** |
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+
878
+ * The bold row denotes the saved checkpoint.
879
+ ### Framework Versions
880
+ - Python: 3.11.9
881
+ - SetFit: 1.1.0.dev0
882
+ - Sentence Transformers: 3.0.1
883
+ - Transformers: 4.44.2
884
+ - PyTorch: 2.4.0+cu121
885
+ - Datasets: 2.21.0
886
+ - Tokenizers: 0.19.1
887
+
888
+ ## Citation
889
+
890
+ ### BibTeX
891
+ ```bibtex
892
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
893
+ doi = {10.48550/ARXIV.2209.11055},
894
+ url = {https://arxiv.org/abs/2209.11055},
895
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
896
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
897
+ title = {Efficient Few-Shot Learning Without Prompts},
898
+ publisher = {arXiv},
899
+ year = {2022},
900
+ copyright = {Creative Commons Attribution 4.0 International}
901
+ }
902
+ ```
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+
904
+ <!--
905
+ ## Glossary
906
+
907
+ *Clearly define terms in order to be accessible across audiences.*
908
+ -->
909
+
910
+ <!--
911
+ ## Model Card Authors
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+
913
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
914
+ -->
915
+
916
+ <!--
917
+ ## Model Card Contact
918
+
919
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
920
+ -->
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