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st-karlos-efood
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setfit-multilabel-example-classifier-chain

Text Classification
setfit
Safetensors
sentence-transformers
xlm-roberta
generated_from_setfit_trainer
Eval Results
Model card Files Community
st-karlos-efood commited on
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Add SetFit model

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Files changed (14) hide show
  1. .gitattributes +1 -0
  2. 1_Pooling/config.json +7 -0
  3. README.md +519 -0
  4. config.json +29 -0
  5. config_sentence_transformers.json +7 -0
  6. config_setfit.json +4 -0
  7. model.safetensors +3 -0
  8. model_head.pkl +3 -0
  9. modules.json +14 -0
  10. sentence_bert_config.json +4 -0
  11. sentencepiece.bpe.model +3 -0
  12. special_tokens_map.json +15 -0
  13. tokenizer.json +3 -0
  14. tokenizer_config.json +61 -0
.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
1_Pooling/config.json ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
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+ {
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+ "word_embedding_dimension": 768,
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+ "pooling_mode_cls_token": false,
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+ "pooling_mode_mean_tokens": true,
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+ "pooling_mode_max_tokens": false,
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+ "pooling_mode_mean_sqrt_len_tokens": false
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+ }
README.md ADDED
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+ ---
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+ library_name: setfit
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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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+ datasets:
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+ - ethos
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+ metrics:
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+ - accuracy
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+ widget:
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+ - text: "This is what happens when you'\x80\x99re a bitch and a cunt. Looks like\
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+ \ that guy called it correctly. Only a bitch and a cunt would think she could\
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+ \ lay hands on another with no repercussions. Maybe after that seizure she won'\x80\
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+ \x99t be as much of a bitchy cunt."
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+ - text: What if we send every men to mars to start a new life there! They are useless
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+ here (Of course milk them first to store their juices)
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+ - text: Honestly, go fuck yourself! bitch!
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+ - text: Hindus take my ass please
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+ - text: Im going to choke you with your cross necklace idiotic religious pig
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+ pipeline_tag: text-classification
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+ inference: false
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+ base_model: lighteternal/stsb-xlm-r-greek-transfer
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+ model-index:
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+ - name: SetFit with lighteternal/stsb-xlm-r-greek-transfer
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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: ethos
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+ type: ethos
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+ split: test
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+ metrics:
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+ - type: accuracy
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+ value: 0.208
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+ name: Accuracy
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+ ---
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+
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+ # SetFit with lighteternal/stsb-xlm-r-greek-transfer
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+
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+ This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [ethos](https://huggingface.co/datasets/ethos) dataset that can be used for Text Classification. This SetFit model uses [lighteternal/stsb-xlm-r-greek-transfer](https://huggingface.co/lighteternal/stsb-xlm-r-greek-transfer) as the Sentence Transformer embedding model. A ClassifierChain instance is used for classification.
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+
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+ The model has been trained using an efficient few-shot learning technique that involves:
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+
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+ 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
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+ 2. Training a classification head with features from the fine-tuned Sentence Transformer.
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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** SetFit
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+ - **Sentence Transformer body:** [lighteternal/stsb-xlm-r-greek-transfer](https://huggingface.co/lighteternal/stsb-xlm-r-greek-transfer)
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+ - **Classification head:** a ClassifierChain instance
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+ - **Maximum Sequence Length:** 400 tokens
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+ <!-- - **Number of Classes:** Unknown -->
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+ - **Training Dataset:** [ethos](https://huggingface.co/datasets/ethos)
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+ <!-- - **Language:** Unknown -->
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+ <!-- - **License:** Unknown -->
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+
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+ ### Model Sources
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+
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+ - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
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+ - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
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+ - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
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+
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+ ## Evaluation
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+
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+ ### Metrics
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+ | Label | Accuracy |
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+ |:--------|:---------|
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+ | **all** | 0.208 |
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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("st-karlos-efood/setfit-multilabel-example-classifier-chain")
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+ # Run inference
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+ preds = model("Hindus take my ass please")
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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 | 3 | 9.9307 | 61 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (32, 32)
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+ - num_epochs: (10, 10)
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+ - max_steps: -1
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+ - sampling_strategy: oversampling
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+ - num_iterations: 10
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+ - body_learning_rate: (2e-05, 2e-05)
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+ - head_learning_rate: 2e-05
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+ - loss: CosineSimilarityLoss
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+ - distance_metric: cosine_distance
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+ - margin: 0.25
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+ - end_to_end: False
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+ - use_amp: 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: False
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+
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+ ### Training Results
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+ | Epoch | Step | Training Loss | Validation Loss |
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+ |:------:|:-----:|:-------------:|:---------------:|
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+ | 0.0006 | 1 | 0.2027 | - |
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+ | 0.0305 | 50 | 0.2092 | - |
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+ | 0.0609 | 100 | 0.1605 | - |
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+ | 0.0914 | 150 | 0.1726 | - |
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+ | 0.1219 | 200 | 0.1322 | - |
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+ | 0.1523 | 250 | 0.1252 | - |
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+ | 0.1828 | 300 | 0.1404 | - |
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+ | 0.2133 | 350 | 0.0927 | - |
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+ | 0.2438 | 400 | 0.1039 | - |
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+ | 0.2742 | 450 | 0.0904 | - |
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+ | 0.3047 | 500 | 0.1194 | - |
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+ | 0.3352 | 550 | 0.1024 | - |
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+ | 0.3656 | 600 | 0.151 | - |
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+ | 0.3961 | 650 | 0.0842 | - |
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+ | 0.4266 | 700 | 0.1158 | - |
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+ | 0.4570 | 750 | 0.214 | - |
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+ | 0.4875 | 800 | 0.1167 | - |
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+ | 0.5180 | 850 | 0.1174 | - |
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+ | 0.5484 | 900 | 0.1567 | - |
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+ | 0.5789 | 950 | 0.0726 | - |
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+ | 0.6094 | 1000 | 0.0741 | - |
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+ | 0.6399 | 1050 | 0.0841 | - |
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+ | 0.6703 | 1100 | 0.0606 | - |
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+ | 0.7008 | 1150 | 0.1005 | - |
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+ | 0.7313 | 1200 | 0.1236 | - |
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+ | 0.7617 | 1250 | 0.141 | - |
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+ | 0.7922 | 1300 | 0.1611 | - |
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+ | 0.8227 | 1350 | 0.1068 | - |
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+ | 0.8531 | 1400 | 0.0542 | - |
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+ | 0.8836 | 1450 | 0.1635 | - |
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+ | 0.9141 | 1500 | 0.106 | - |
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+ | 0.9445 | 1550 | 0.0817 | - |
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+ | 0.9750 | 1600 | 0.1157 | - |
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+ | 1.0055 | 1650 | 0.1031 | - |
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+ | 1.0360 | 1700 | 0.0969 | - |
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+ | 1.0664 | 1750 | 0.0742 | - |
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+ | 1.0969 | 1800 | 0.0697 | - |
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+ | 1.1274 | 1850 | 0.1072 | - |
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+ | 1.1578 | 1900 | 0.0593 | - |
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+ | 1.1883 | 1950 | 0.1102 | - |
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+ | 1.2188 | 2000 | 0.1586 | - |
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+ | 1.2492 | 2050 | 0.1523 | - |
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+ | 1.2797 | 2100 | 0.0921 | - |
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+ | 1.3102 | 2150 | 0.0634 | - |
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+ | 1.3406 | 2200 | 0.073 | - |
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+ | 1.3711 | 2250 | 0.1131 | - |
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+ | 1.4016 | 2300 | 0.0493 | - |
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+ | 1.4321 | 2350 | 0.106 | - |
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+ | 1.4625 | 2400 | 0.0585 | - |
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+ | 1.4930 | 2450 | 0.1058 | - |
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+ | 1.5235 | 2500 | 0.0892 | - |
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+ | 1.5539 | 2550 | 0.0649 | - |
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+ | 1.5844 | 2600 | 0.0481 | - |
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+ | 1.6149 | 2650 | 0.1359 | - |
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+ | 1.6453 | 2700 | 0.0734 | - |
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+ | 1.6758 | 2750 | 0.0762 | - |
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+ | 1.7063 | 2800 | 0.1082 | - |
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+ | 1.7367 | 2850 | 0.1274 | - |
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+ | 1.7672 | 2900 | 0.0724 | - |
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+ | 1.7977 | 2950 | 0.0842 | - |
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+ | 1.8282 | 3000 | 0.1558 | - |
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+ | 1.8586 | 3050 | 0.071 | - |
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+ | 1.8891 | 3100 | 0.1716 | - |
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+ | 1.9196 | 3150 | 0.1078 | - |
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+ | 1.9500 | 3200 | 0.1037 | - |
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+ | 1.9805 | 3250 | 0.0773 | - |
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+ | 2.0110 | 3300 | 0.0706 | - |
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+ | 2.0414 | 3350 | 0.1577 | - |
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+ | 2.0719 | 3400 | 0.0825 | - |
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+ | 2.1024 | 3450 | 0.1227 | - |
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+ | 2.1328 | 3500 | 0.1069 | - |
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+ | 2.1633 | 3550 | 0.1037 | - |
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+ | 2.1938 | 3600 | 0.0595 | - |
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+ | 2.2243 | 3650 | 0.0569 | - |
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+ | 2.2547 | 3700 | 0.0967 | - |
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+ | 2.2852 | 3750 | 0.0632 | - |
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+ | 2.3157 | 3800 | 0.1014 | - |
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+ | 2.3461 | 3850 | 0.0868 | - |
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+ | 2.3766 | 3900 | 0.0986 | - |
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+ | 2.4071 | 3950 | 0.0585 | - |
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+ | 2.4375 | 4000 | 0.063 | - |
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+ | 2.4680 | 4050 | 0.1124 | - |
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+ | 2.4985 | 4100 | 0.0444 | - |
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+ | 2.5289 | 4150 | 0.1547 | - |
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+ | 2.5594 | 4200 | 0.1087 | - |
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+ | 2.5899 | 4250 | 0.0946 | - |
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+ | 2.6204 | 4300 | 0.0261 | - |
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+ | 2.6508 | 4350 | 0.0414 | - |
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+ | 2.6813 | 4400 | 0.0715 | - |
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+ | 2.7118 | 4450 | 0.0831 | - |
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+ | 2.7422 | 4500 | 0.0779 | - |
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+ | 2.8336 | 4650 | 0.0926 | - |
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+ | 2.9250 | 4800 | 0.0536 | - |
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+ | 3.0165 | 4950 | 0.0361 | - |
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+ | 3.0469 | 5000 | 0.0592 | - |
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+ | 3.0774 | 5050 | 0.0656 | - |
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+ | 3.1688 | 5200 | 0.1037 | - |
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+ | 3.2297 | 5300 | 0.0511 | - |
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+ | 3.2602 | 5350 | 0.0427 | - |
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+ | 3.2907 | 5400 | 0.1067 | - |
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+ | 3.3211 | 5450 | 0.0807 | - |
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+ | 3.3516 | 5500 | 0.0815 | - |
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+ | 3.3821 | 5550 | 0.1016 | - |
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+ | 3.4126 | 5600 | 0.1034 | - |
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+ | 3.4430 | 5650 | 0.1257 | - |
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+ | 3.4735 | 5700 | 0.0877 | - |
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+ | 3.5040 | 5750 | 0.0808 | - |
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+ | 3.5344 | 5800 | 0.0926 | - |
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+ | 3.5649 | 5850 | 0.0967 | - |
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+ | 3.5954 | 5900 | 0.0401 | - |
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+ | 3.6258 | 5950 | 0.0547 | - |
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+ | 3.6563 | 6000 | 0.0872 | - |
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+ | 3.6868 | 6050 | 0.0808 | - |
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+ | 3.7172 | 6100 | 0.1125 | - |
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+ | 3.7477 | 6150 | 0.1431 | - |
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+ | 3.7782 | 6200 | 0.1039 | - |
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+ | 3.8087 | 6250 | 0.061 | - |
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+ | 3.8391 | 6300 | 0.1022 | - |
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+ | 3.8696 | 6350 | 0.0394 | - |
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+ | 3.9001 | 6400 | 0.0892 | - |
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+ | 3.9610 | 6500 | 0.0793 | - |
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+ | 3.9915 | 6550 | 0.0462 | - |
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+ | 4.0219 | 6600 | 0.0686 | - |
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+ | 4.0524 | 6650 | 0.0506 | - |
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+ | 4.0829 | 6700 | 0.1012 | - |
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+ | 4.1133 | 6750 | 0.0852 | - |
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+ | 4.1438 | 6800 | 0.0729 | - |
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+ | 4.1743 | 6850 | 0.1007 | - |
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+ | 4.2048 | 6900 | 0.0431 | - |
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+ | 4.2352 | 6950 | 0.0683 | - |
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+ | 4.2657 | 7000 | 0.0712 | - |
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+ | 4.2962 | 7050 | 0.0732 | - |
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+ | 4.3266 | 7100 | 0.0374 | - |
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+ | 4.3571 | 7150 | 0.1015 | - |
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+ | 4.3876 | 7200 | 0.15 | - |
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+ | 4.4180 | 7250 | 0.0852 | - |
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+ | 4.4485 | 7300 | 0.0714 | - |
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+ | 4.4790 | 7350 | 0.0587 | - |
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+ | 4.5094 | 7400 | 0.1335 | - |
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+ | 4.5399 | 7450 | 0.1123 | - |
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+ | 4.5704 | 7500 | 0.0538 | - |
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+ | 4.6009 | 7550 | 0.0989 | - |
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+ | 4.6313 | 7600 | 0.0878 | - |
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+ | 4.6618 | 7650 | 0.0963 | - |
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+ | 4.6923 | 7700 | 0.0991 | - |
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+ | 4.7227 | 7750 | 0.0776 | - |
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+ | 4.7532 | 7800 | 0.0663 | - |
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+ | 4.7837 | 7850 | 0.0696 | - |
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+ | 4.8141 | 7900 | 0.0704 | - |
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+ | 4.8446 | 7950 | 0.0626 | - |
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+ | 4.8751 | 8000 | 0.0657 | - |
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+ | 4.9055 | 8050 | 0.0567 | - |
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+ | 4.9360 | 8100 | 0.0619 | - |
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+ | 4.9665 | 8150 | 0.0792 | - |
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+ | 4.9970 | 8200 | 0.0671 | - |
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+ | 5.0274 | 8250 | 0.1068 | - |
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+ | 5.0579 | 8300 | 0.1111 | - |
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+ | 5.0884 | 8350 | 0.0968 | - |
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+ | 5.1188 | 8400 | 0.0577 | - |
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+ | 5.1493 | 8450 | 0.0934 | - |
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+ | 5.1798 | 8500 | 0.0854 | - |
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+ | 5.2102 | 8550 | 0.0587 | - |
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+ | 5.2407 | 8600 | 0.048 | - |
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+ | 5.3321 | 8750 | 0.107 | - |
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+ | 5.3626 | 8800 | 0.0662 | - |
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+ | 5.3931 | 8850 | 0.0799 | - |
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+ | 5.4235 | 8900 | 0.0948 | - |
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+ | 5.4540 | 8950 | 0.087 | - |
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+ | 5.5454 | 9100 | 0.0911 | - |
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+ | 5.5759 | 9150 | 0.1268 | - |
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+ | 5.6063 | 9200 | 0.1042 | - |
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+ | 5.6368 | 9250 | 0.0642 | - |
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+ | 5.7892 | 9500 | 0.1038 | - |
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+ | 5.8196 | 9550 | 0.0842 | - |
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+ | 5.8501 | 9600 | 0.0623 | - |
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+ | 5.8806 | 9650 | 0.0642 | - |
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+ | 5.9110 | 9700 | 0.0902 | - |
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+ | 5.9415 | 9750 | 0.0994 | - |
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+ | 6.3071 | 10350 | 0.036 | - |
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+ | 6.3376 | 10400 | 0.0524 | - |
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+ | 6.3681 | 10450 | 0.0927 | - |
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+ | 6.3985 | 10500 | 0.0934 | - |
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+ | 6.4290 | 10550 | 0.0788 | - |
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+ | 6.4595 | 10600 | 0.0842 | - |
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+ | 6.4899 | 10650 | 0.0703 | - |
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+ | 6.5204 | 10700 | 0.0684 | - |
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+ | 6.5509 | 10750 | 0.0759 | - |
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+ | 6.5814 | 10800 | 0.0271 | - |
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+ | 6.6118 | 10850 | 0.0391 | - |
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+ | 6.6423 | 10900 | 0.0895 | - |
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+ | 6.6728 | 10950 | 0.054 | - |
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+ | 6.7032 | 11000 | 0.0987 | - |
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+ | 6.7642 | 11100 | 0.0822 | - |
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+ | 6.7946 | 11150 | 0.0986 | - |
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+
478
+ ### Framework Versions
479
+ - Python: 3.10.12
480
+ - SetFit: 1.0.3
481
+ - Sentence Transformers: 2.2.2
482
+ - Transformers: 4.35.2
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+ - PyTorch: 2.1.0+cu121
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+ - Datasets: 2.16.1
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+ - Tokenizers: 0.15.0
486
+
487
+ ## Citation
488
+
489
+ ### BibTeX
490
+ ```bibtex
491
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
492
+ doi = {10.48550/ARXIV.2209.11055},
493
+ url = {https://arxiv.org/abs/2209.11055},
494
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
495
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
496
+ title = {Efficient Few-Shot Learning Without Prompts},
497
+ publisher = {arXiv},
498
+ year = {2022},
499
+ copyright = {Creative Commons Attribution 4.0 International}
500
+ }
501
+ ```
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+
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+ <!--
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+ ## Glossary
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+
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+ *Clearly define terms in order to be accessible across audiences.*
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+ -->
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+
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+ <!--
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+ ## Model Card Authors
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+
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+ *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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+ -->
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+
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+ <!--
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+ ## Model Card Contact
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+
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+ *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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