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w11wo
/
bge-small-en-v1.5-isarcasm

Text Classification
setfit
PyTorch
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
Model card Files
xet
Community
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w11wo commited on
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Add SetFit model

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Files changed (13) hide show
  1. 1_Pooling/config.json +7 -0
  2. README.md +382 -0
  3. config.json +31 -0
  4. config_sentence_transformers.json +7 -0
  5. config_setfit.json +7 -0
  6. model_head.pkl +3 -0
  7. modules.json +20 -0
  8. pytorch_model.bin +3 -0
  9. sentence_bert_config.json +4 -0
  10. special_tokens_map.json +7 -0
  11. tokenizer.json +0 -0
  12. tokenizer_config.json +15 -0
  13. vocab.txt +0 -0
1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 384,
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+ "pooling_mode_cls_token": true,
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+ "pooling_mode_mean_tokens": false,
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+ "pooling_mode_max_tokens": false,
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+ "pooling_mode_mean_sqrt_len_tokens": false
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+ }
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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+ metrics:
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+ - accuracy
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+ - f1
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+ - precision
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+ - recall
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+ widget:
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+ - text: so i am currently stuck in an automatic revolving door .
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+ - text: ah my favorite pastime , watching logan and crying
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+ - text: i have a new instagram account ! go give theollyjackson a follow
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+ - text: guess they are not rich enough to get their precious cars in a garage .
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+ - text: last day in my twenties
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+ pipeline_tag: text-classification
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+ inference: true
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+ base_model: BAAI/bge-small-en-v1.5
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+ model-index:
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+ - name: SetFit with BAAI/bge-small-en-v1.5
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+ results:
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+ - task:
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+ type: text-classification
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+ name: Text Classification
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+ dataset:
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+ name: Unknown
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+ type: unknown
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+ split: test
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+ metrics:
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+ - type: accuracy
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+ value: 0.6617812852311161
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+ name: Accuracy
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+ - type: f1
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+ value: 0.3951612903225807
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+ name: F1
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+ - type: precision
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+ value: 0.2890855457227139
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+ name: Precision
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+ - type: recall
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+ value: 0.6242038216560509
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+ name: Recall
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+ ---
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+
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+ # SetFit with BAAI/bge-small-en-v1.5
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+
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+ This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) as the Sentence Transformer embedding model. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance is used for classification.
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+
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+ The model has been trained using an efficient few-shot learning technique that involves:
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+
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+ 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
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+ 2. Training a classification head with features from the fine-tuned Sentence Transformer.
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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** SetFit
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+ - **Sentence Transformer body:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5)
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+ - **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance
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+ - **Maximum Sequence Length:** 512 tokens
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+ - **Number of Classes:** 2 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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+ | NON_SARCASTIC | <ul><li>'so the newer devices have the ios screenshot i m still on ios but my ipad mini 1 st gen shows the ios screenshot . odd .'</li><li>'why do amazon need a test authorisation when i add a new payment card , as well as the authorisation they get when i actually use it ?'</li><li>'waterboarding sounds like a lot of fun until you find out what it is'</li></ul> |
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+ | SARCASTIC | <ul><li>"have you been reading long ? you are not very good at it . it has nothing to do with who i like , especially since i am not a fan of corbyn anyway . it ' s that in one case someone was literally slapped in the face , and in the other someone wore a milkshake . battery > being annoying"</li><li>'wish one of the many people dressed as killers were actually one n killed me'</li><li>'is it even christmas if there isn t a fight with neighbours and a broken wrist ?'</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 | F1 | Precision | Recall |
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+ |:--------|:---------|:-------|:----------|:-------|
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+ | **all** | 0.6618 | 0.3952 | 0.2891 | 0.6242 |
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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("w11wo/bge-small-en-v1.5-isarcasm")
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+ # Run inference
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+ preds = model("last day in my twenties")
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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 | 19.8489 | 63 |
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+
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+ | Label | Training Sample Count |
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+ |:--------------|:----------------------|
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+ | NON_SARCASTIC | 609 |
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+ | SARCASTIC | 609 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (256, 16)
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+ - num_epochs: (3, 8)
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+ - max_steps: -1
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+ - sampling_strategy: oversampling
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+ - body_learning_rate: (2e-05, 5e-06)
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+ - head_learning_rate: 0.002
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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: True
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+ - use_amp: False
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+ - warmup_proportion: 0.1
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+ - l2_weight: 0.01
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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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+ | 0.0003 | 1 | 0.2571 | - |
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+ | 0.0172 | 50 | 0.251 | - |
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+ | 0.0344 | 100 | 0.2556 | - |
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+ | 0.0517 | 150 | 0.2513 | - |
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+ | 0.0689 | 200 | 0.2531 | - |
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+ | 0.0861 | 250 | 0.2518 | - |
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+ | 0.1033 | 300 | 0.2553 | - |
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+ | 0.1206 | 350 | 0.2501 | - |
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+ | 0.1378 | 400 | 0.2546 | - |
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+ | 0.1550 | 450 | 0.2506 | - |
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+ | 0.1722 | 500 | 0.2317 | - |
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+ | 0.1895 | 550 | 0.093 | - |
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+ | 0.2067 | 600 | 0.0139 | - |
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+ | 0.2239 | 650 | 0.0166 | - |
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+ | 0.2411 | 700 | 0.0053 | - |
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+ | 0.2584 | 750 | 0.0013 | - |
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+ | 0.2756 | 800 | 0.0121 | - |
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+ | 0.2928 | 850 | 0.0096 | - |
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+ | 0.3100 | 900 | 0.0043 | - |
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+ | 0.3272 | 950 | 0.0014 | - |
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+ | 0.3445 | 1000 | 0.0009 | - |
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+ | 0.3617 | 1050 | 0.0117 | - |
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+ | 0.3789 | 1100 | 0.0144 | - |
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+ | 0.3961 | 1150 | 0.0084 | - |
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+ | 0.4134 | 1200 | 0.0006 | - |
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+ | 0.4306 | 1250 | 0.0005 | - |
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+ | 0.4478 | 1300 | 0.0081 | - |
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+ | 0.4650 | 1350 | 0.0144 | - |
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+ | 0.4823 | 1400 | 0.0045 | - |
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+ | 0.4995 | 1450 | 0.0042 | - |
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+ | 0.5167 | 1500 | 0.0005 | - |
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+ | 0.5339 | 1550 | 0.003 | - |
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+ | 0.5512 | 1600 | 0.0004 | - |
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+ | 0.5684 | 1650 | 0.0005 | - |
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+ | 0.5856 | 1700 | 0.0004 | - |
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+ | 0.6028 | 1750 | 0.0004 | - |
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+ | 0.6200 | 1800 | 0.0026 | - |
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+ | 0.6373 | 1850 | 0.0004 | - |
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+ | 0.6545 | 1900 | 0.0004 | - |
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+ | 0.6717 | 1950 | 0.0003 | - |
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+ | 0.6889 | 2000 | 0.0014 | - |
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+ | 0.7062 | 2050 | 0.0004 | - |
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+ | 0.7234 | 2100 | 0.0003 | - |
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+ | 0.7406 | 2150 | 0.0003 | - |
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+ | 0.7578 | 2200 | 0.0004 | - |
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+ | 0.7751 | 2250 | 0.0003 | - |
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+ | 0.7923 | 2300 | 0.0003 | - |
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+ | 0.8095 | 2350 | 0.0003 | - |
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+ | 0.8267 | 2400 | 0.0003 | - |
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+ | 0.8440 | 2450 | 0.0003 | - |
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+ | 0.8612 | 2500 | 0.0003 | - |
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+ | 0.8784 | 2550 | 0.0003 | - |
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+ | 0.8956 | 2600 | 0.0003 | - |
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+ | 0.9128 | 2650 | 0.0003 | - |
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+ | 0.9301 | 2700 | 0.0003 | - |
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+ | 0.9473 | 2750 | 0.0004 | - |
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+ | 0.9645 | 2800 | 0.0003 | - |
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+ | 0.9817 | 2850 | 0.0003 | - |
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+ | 0.9990 | 2900 | 0.0036 | - |
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+ | 1.0162 | 2950 | 0.0003 | - |
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+ | 1.0334 | 3000 | 0.0003 | - |
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+ | 1.0506 | 3050 | 0.0002 | - |
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+ | 1.0679 | 3100 | 0.0002 | - |
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+ | 1.0851 | 3150 | 0.0002 | - |
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+ | 1.1023 | 3200 | 0.0002 | - |
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+ | 1.1195 | 3250 | 0.0002 | - |
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+ | 1.1368 | 3300 | 0.0003 | - |
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+ | 1.1540 | 3350 | 0.0004 | - |
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+ | 1.1712 | 3400 | 0.0002 | - |
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+ | 1.1884 | 3450 | 0.0002 | - |
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+ | 1.2056 | 3500 | 0.0002 | - |
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+ | 1.2229 | 3550 | 0.0002 | - |
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+ | 1.2401 | 3600 | 0.0002 | - |
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+ | 1.2573 | 3650 | 0.0009 | - |
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+ | 1.2745 | 3700 | 0.0002 | - |
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+ | 1.2918 | 3750 | 0.0002 | - |
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+ | 1.3090 | 3800 | 0.0002 | - |
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+ | 1.3262 | 3850 | 0.0002 | - |
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+ | 1.3434 | 3900 | 0.0002 | - |
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+ | 1.3607 | 3950 | 0.0002 | - |
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+ | 1.3779 | 4000 | 0.0002 | - |
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+ | 1.3951 | 4050 | 0.0002 | - |
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+ | 1.4123 | 4100 | 0.0002 | - |
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+ | 1.4296 | 4150 | 0.0002 | - |
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+ | 1.4468 | 4200 | 0.0003 | - |
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+ | 1.4640 | 4250 | 0.0002 | - |
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+ | 1.4812 | 4300 | 0.0002 | - |
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+ | 1.7224 | 5000 | 0.0003 | - |
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+ | 1.7568 | 5100 | 0.0002 | - |
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+ | 1.7740 | 5150 | 0.0002 | - |
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+ | 1.7913 | 5200 | 0.0002 | - |
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+ | 1.8085 | 5250 | 0.0002 | - |
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+ | 1.8257 | 5300 | 0.0038 | - |
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+ | 1.8429 | 5350 | 0.0002 | - |
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+ | 1.8601 | 5400 | 0.0002 | - |
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+ | 1.8774 | 5450 | 0.0002 | - |
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+ | 1.8946 | 5500 | 0.0002 | - |
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+ | 1.9118 | 5550 | 0.0002 | - |
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+ | 1.9463 | 5650 | 0.0002 | - |
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+ | 1.9635 | 5700 | 0.0002 | - |
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+ | 1.9979 | 5800 | 0.0002 | - |
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+ | 2.0152 | 5850 | 0.0001 | - |
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+ | 2.0324 | 5900 | 0.0002 | - |
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+ | 2.0496 | 5950 | 0.0002 | - |
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+ | 2.1529 | 6250 | 0.0002 | - |
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+ | 2.1702 | 6300 | 0.0002 | - |
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+ | 2.1874 | 6350 | 0.0001 | - |
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+ | 2.2046 | 6400 | 0.0001 | - |
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+ | 2.2218 | 6450 | 0.0001 | - |
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+ | 2.2391 | 6500 | 0.0001 | - |
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+ | 2.2563 | 6550 | 0.0001 | - |
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+ | 2.2735 | 6600 | 0.0001 | - |
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+ | 2.2907 | 6650 | 0.0001 | - |
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+ | 2.3080 | 6700 | 0.0001 | - |
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+ | 2.3252 | 6750 | 0.0001 | - |
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+ | 2.3424 | 6800 | 0.0001 | - |
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+ | 2.3596 | 6850 | 0.0001 | - |
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+ | 2.3769 | 6900 | 0.0001 | - |
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+ | 2.3941 | 6950 | 0.0001 | - |
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+ | 2.4113 | 7000 | 0.0001 | - |
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+ | 2.4285 | 7050 | 0.0001 | - |
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+ | 2.4457 | 7100 | 0.0001 | - |
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+ | 2.4630 | 7150 | 0.0001 | - |
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+ | 2.4802 | 7200 | 0.0001 | - |
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+ | 2.4974 | 7250 | 0.0001 | - |
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+ | 2.5146 | 7300 | 0.0001 | - |
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+ | 2.5319 | 7350 | 0.0001 | - |
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+ | 2.5491 | 7400 | 0.0001 | - |
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+ | 2.5663 | 7450 | 0.0001 | - |
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+ | 2.5835 | 7500 | 0.0001 | - |
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+ | 2.6008 | 7550 | 0.0001 | - |
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+ | 2.6180 | 7600 | 0.0001 | - |
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+ | 2.6352 | 7650 | 0.0001 | - |
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+ | 2.6524 | 7700 | 0.0001 | - |
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+ | 2.6697 | 7750 | 0.0001 | - |
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+ | 2.6869 | 7800 | 0.0001 | - |
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+ | 2.7041 | 7850 | 0.0001 | - |
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+ | 2.7213 | 7900 | 0.0001 | - |
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+ | 2.7385 | 7950 | 0.0001 | - |
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+ | 2.7558 | 8000 | 0.0001 | - |
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+ | 2.7730 | 8050 | 0.0001 | - |
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+ | 2.7902 | 8100 | 0.0001 | - |
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+ | 2.8074 | 8150 | 0.0001 | - |
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+ | 2.8247 | 8200 | 0.0001 | - |
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+ | 2.8419 | 8250 | 0.0001 | - |
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+ | 2.8591 | 8300 | 0.0001 | - |
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+ | 2.8763 | 8350 | 0.0001 | - |
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+ | 2.8936 | 8400 | 0.0001 | - |
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+ | 2.9108 | 8450 | 0.0001 | - |
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+ | 2.9280 | 8500 | 0.0001 | - |
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+ | 2.9452 | 8550 | 0.0001 | - |
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+ | 2.9625 | 8600 | 0.0001 | - |
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+ | 2.9797 | 8650 | 0.0001 | - |
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+ | 2.9969 | 8700 | 0.0001 | - |
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+
341
+ ### Framework Versions
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+ - Python: 3.10.12
343
+ - SetFit: 1.0.1
344
+ - Sentence Transformers: 2.2.2
345
+ - Transformers: 4.32.0
346
+ - PyTorch: 2.1.1+cu121
347
+ - Datasets: 2.14.5
348
+ - Tokenizers: 0.13.3
349
+
350
+ ## Citation
351
+
352
+ ### BibTeX
353
+ ```bibtex
354
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
355
+ doi = {10.48550/ARXIV.2209.11055},
356
+ url = {https://arxiv.org/abs/2209.11055},
357
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
358
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
359
+ title = {Efficient Few-Shot Learning Without Prompts},
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+ publisher = {arXiv},
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+ year = {2022},
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+ copyright = {Creative Commons Attribution 4.0 International}
363
+ }
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+ ```
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+
366
+ <!--
367
+ ## Glossary
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+
369
+ *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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+
375
+ *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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+ -->
config.json ADDED
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+ {
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+ "_name_or_path": "/root/.cache/torch/sentence_transformers/BAAI_bge-small-en-v1.5/",
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+ "architectures": [
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+ "BertModel"
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+ ],
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+ "attention_probs_dropout_prob": 0.1,
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+ "classifier_dropout": null,
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.1,
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+ "hidden_size": 384,
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+ "id2label": {
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+ "0": "LABEL_0"
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+ },
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+ "initializer_range": 0.02,
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+ "intermediate_size": 1536,
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+ "label2id": {
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+ "LABEL_0": 0
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+ },
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+ "layer_norm_eps": 1e-12,
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+ "max_position_embeddings": 512,
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+ "model_type": "bert",
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+ "num_attention_heads": 12,
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+ "num_hidden_layers": 12,
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+ "pad_token_id": 0,
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+ "position_embedding_type": "absolute",
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.32.0",
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+ "type_vocab_size": 2,
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+ "use_cache": true,
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+ "vocab_size": 30522
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+ }
config_sentence_transformers.json ADDED
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