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jamiehudson
/
725_model_v3

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
Model card Files Community
jamiehudson commited on
Commit
a3c2c1a
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1 Parent(s): 3336239

Push model using huggingface_hub.

Browse files
Files changed (13) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +1053 -0
  3. config.json +32 -0
  4. config_sentence_transformers.json +9 -0
  5. config_setfit.json +8 -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 +57 -0
  13. vocab.txt +0 -0
1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 768,
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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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+ 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: 'brand''s product, powered by product, is making waves by potentially surpassing
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+ brand''s product in ai performance. lets not forget massive developments in ai
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+ from brand, brand, brand and 5 new tools here''s what you need to know:'
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+ - text: 'well... brand launches product tomorrow so it''s going to be much more exciting
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+ than 2x! product ca: 0x09e5e172df245529b22686b77e959d3f2937feb0'
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+ - text: 'brand''s product is product''s newest and greatest competitor yet: here''s
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+ how you can use it within product dlvr.it/szs9nh'
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+ - text: bad actors exploit product to write malicious codes product, ever since its
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+ launch in november last year, has been making lots of noise. with creators experimenting
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+ with it and getting varied results, the product became an acceptable product tool
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+ that couldlnkd.in/drbvpbdt
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+ - text: testing out product. i find it incredibly useful. one way to monetize it is
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+ simply to put paid links related to the search
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+ pipeline_tag: text-classification
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+ inference: true
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+ base_model: BAAI/bge-base-en-v1.5
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+ model-index:
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+ - name: SetFit with BAAI/bge-base-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: 0.86
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+ name: Accuracy
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+ - type: f1
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+ value:
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+ - 0.2857142857142857
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+ - 0.5945945945945945
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+ - 0.9195402298850575
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+ name: F1
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+ - type: precision
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+ value:
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+ - 1.0
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+ - 0.9166666666666666
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+ - 0.8547008547008547
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+ name: Precision
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+ - type: recall
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+ value:
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+ - 0.16666666666666666
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+ - 0.44
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+ - 0.9950248756218906
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+ name: Recall
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+ ---
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+
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+ # SetFit with BAAI/bge-base-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-base-en-v1.5](https://huggingface.co/BAAI/bge-base-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-base-en-v1.5](https://huggingface.co/BAAI/bge-base-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:** 3 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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+ | neither | <ul><li>'ai becomes so much easier to spot when you realize it can replicate, but never understand. its why product usually gives its answers in lists. its a standardized format meant to hide its ignorance to prose.'</li><li>"hakeem jeffries' tweets are getting so productian it's not even funny and boring any more. he may have brand cranking these out."</li><li>'have you tried this with product? i did this with music and got amazing results'</li></ul> |
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+ | peak | <ul><li>'thats rad man. i have adhd and dyslexia and some other cognitive disabilities and honestly brand is a lifesaver.'</li><li>"product is like having a coding partner that understands my style, enhancing my productivity significantly. i've even changed the way i code. my code and process is more modular so it's easier to use the output from product in my code base!"</li><li>'product is an incredible tool for explaining concepts in i prompted it to describe how k-means clustering could be applied to an engagement survey. it generated sample data, explained the concept and how the insights could be applied.'</li></ul> |
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+ | pit | <ul><li>'many similar posts popping up on my timeline frustrated with chatproduct not performing to previous levels defeats the purpose of having an ai assitant available 24/7 if it never wants to do any of the tasks you ask of it'</li><li>"the stuff brand gives is entirely too scripted *and* impractical, which is what i'm trying to avoid:/"</li><li>'so disappointed theyve programmed product to think starvation mode is real'</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** | 0.86 | [0.2857142857142857, 0.5945945945945945, 0.9195402298850575] | [1.0, 0.9166666666666666, 0.8547008547008547] | [0.16666666666666666, 0.44, 0.9950248756218906] |
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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("jamiehudson/725_model_v3")
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+ # Run inference
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+ preds = model("brand's product is product's newest and greatest competitor yet: here's how you can use it within product dlvr.it/szs9nh")
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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 | 27.8534 | 91 |
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+
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+ | Label | Training Sample Count |
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+ |:--------|:----------------------|
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+ | pit | 26 |
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+ | peak | 51 |
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+ | neither | 1137 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (32, 32)
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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: 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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+ | 0.0012 | 1 | 0.2612 | - |
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+ | 0.0621 | 50 | 0.2009 | - |
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+ | 0.1242 | 100 | 0.0339 | - |
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+ | 0.1863 | 150 | 0.0062 | - |
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+ | 0.2484 | 200 | 0.0039 | - |
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+ | 0.3106 | 250 | 0.0017 | - |
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+ | 0.3727 | 300 | 0.003 | - |
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+ | 0.4348 | 350 | 0.0015 | - |
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+ | 0.4969 | 400 | 0.002 | - |
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+ | 0.5590 | 450 | 0.0022 | - |
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+ | 0.6211 | 500 | 0.0013 | - |
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+ | 0.6832 | 550 | 0.0013 | - |
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+ | 0.7453 | 600 | 0.0014 | - |
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+ | 0.8075 | 650 | 0.0014 | - |
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+ | 0.8696 | 700 | 0.0012 | - |
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+ | 0.9317 | 750 | 0.0014 | - |
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+ | 0.9938 | 800 | 0.0016 | - |
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+ | 0.0000 | 1 | 0.0897 | - |
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+ | 0.0012 | 50 | 0.1107 | - |
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+ | 0.0025 | 100 | 0.065 | - |
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+ | 0.0037 | 150 | 0.1892 | - |
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+ | 0.0049 | 200 | 0.0774 | - |
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+ | 0.0062 | 250 | 0.0391 | - |
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+ | 0.0074 | 300 | 0.117 | - |
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+ | 0.0086 | 350 | 0.0954 | - |
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+ | 0.0099 | 400 | 0.0292 | - |
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+ | 0.0111 | 450 | 0.0327 | - |
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+ | 0.0123 | 500 | 0.0041 | - |
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+ | 0.0136 | 550 | 0.0018 | - |
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+ | 0.0148 | 600 | 0.03 | - |
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+ | 0.0160 | 650 | 0.0015 | - |
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+ | 0.0173 | 700 | 0.0036 | - |
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+ | 0.0185 | 750 | 0.0182 | - |
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+ | 0.0197 | 800 | 0.0017 | - |
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+ | 0.0210 | 850 | 0.0012 | - |
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+ | 0.0222 | 900 | 0.0014 | - |
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+ | 0.0234 | 950 | 0.0011 | - |
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+ | 0.0247 | 1000 | 0.0014 | - |
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+ | 0.0259 | 1050 | 0.0301 | - |
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+ | 0.0271 | 1100 | 0.001 | - |
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+ | 0.0284 | 1150 | 0.0011 | - |
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+ | 0.0296 | 1200 | 0.0009 | - |
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+ | 0.0308 | 1250 | 0.0011 | - |
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+ | 0.0321 | 1300 | 0.0012 | - |
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+ | 0.0333 | 1350 | 0.001 | - |
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+ | 0.0345 | 1400 | 0.0008 | - |
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+ | 0.0358 | 1450 | 0.005 | - |
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+ | 0.0370 | 1500 | 0.0008 | - |
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+ | 0.0382 | 1550 | 0.0044 | - |
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+ | 0.0395 | 1600 | 0.0008 | - |
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+ | 0.0407 | 1650 | 0.0007 | - |
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+ | 0.0419 | 1700 | 0.0014 | - |
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+ | 0.0432 | 1750 | 0.0006 | - |
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+ | 0.0444 | 1800 | 0.001 | - |
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+ | 0.0456 | 1850 | 0.0007 | - |
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+ | 0.0481 | 1950 | 0.0006 | - |
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+ | 0.0493 | 2000 | 0.0005 | - |
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+ | 0.0518 | 2100 | 0.0041 | - |
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355
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356
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357
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358
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359
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360
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361
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362
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363
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364
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365
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366
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367
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368
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369
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370
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371
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372
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373
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374
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375
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376
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377
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378
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379
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380
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381
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382
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383
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384
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385
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386
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387
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388
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389
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390
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391
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392
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393
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395
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396
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398
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399
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400
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401
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402
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403
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404
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405
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406
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407
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408
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409
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410
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411
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412
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413
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414
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415
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416
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417
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418
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419
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420
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421
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423
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425
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436
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446
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486
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487
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488
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489
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490
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491
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492
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495
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498
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500
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501
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503
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504
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510
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511
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515
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518
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521
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525
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527
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528
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529
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530
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531
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532
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533
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534
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535
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536
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537
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538
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539
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540
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541
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543
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545
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546
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547
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548
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549
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550
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551
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552
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553
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555
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556
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557
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558
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559
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560
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561
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562
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563
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564
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565
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566
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567
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568
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569
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570
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571
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572
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573
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574
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575
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576
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577
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578
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580
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584
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585
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586
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588
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589
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590
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592
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604
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607
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608
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610
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611
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612
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613
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614
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615
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616
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617
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618
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621
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622
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623
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624
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625
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627
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628
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629
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630
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632
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637
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638
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639
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640
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641
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642
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643
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644
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645
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646
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647
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648
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649
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650
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651
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652
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653
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657
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659
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660
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661
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662
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663
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664
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665
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666
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669
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670
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675
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676
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677
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678
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679
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680
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681
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682
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683
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684
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685
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686
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687
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688
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689
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690
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691
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692
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693
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694
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695
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696
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697
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698
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699
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700
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701
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702
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703
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704
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705
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706
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707
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708
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709
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710
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711
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712
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713
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714
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715
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716
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717
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718
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719
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720
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721
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722
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723
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724
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725
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726
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727
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728
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729
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730
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731
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732
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733
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734
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735
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736
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737
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738
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739
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740
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741
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742
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+
1012
+ ### Framework Versions
1013
+ - Python: 3.10.12
1014
+ - SetFit: 1.0.3
1015
+ - Sentence Transformers: 2.5.1
1016
+ - Transformers: 4.38.1
1017
+ - PyTorch: 2.1.0+cu121
1018
+ - Datasets: 2.18.0
1019
+ - Tokenizers: 0.15.2
1020
+
1021
+ ## Citation
1022
+
1023
+ ### BibTeX
1024
+ ```bibtex
1025
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
1026
+ doi = {10.48550/ARXIV.2209.11055},
1027
+ url = {https://arxiv.org/abs/2209.11055},
1028
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
1029
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
1030
+ title = {Efficient Few-Shot Learning Without Prompts},
1031
+ publisher = {arXiv},
1032
+ year = {2022},
1033
+ copyright = {Creative Commons Attribution 4.0 International}
1034
+ }
1035
+ ```
1036
+
1037
+ <!--
1038
+ ## Glossary
1039
+
1040
+ *Clearly define terms in order to be accessible across audiences.*
1041
+ -->
1042
+
1043
+ <!--
1044
+ ## Model Card Authors
1045
+
1046
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
1047
+ -->
1048
+
1049
+ <!--
1050
+ ## Model Card Contact
1051
+
1052
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
1053
+ -->
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tokenizer.json ADDED
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tokenizer_config.json ADDED
@@ -0,0 +1,57 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "added_tokens_decoder": {
3
+ "0": {
4
+ "content": "[PAD]",
5
+ "lstrip": false,
6
+ "normalized": false,
7
+ "rstrip": false,
8
+ "single_word": false,
9
+ "special": true
10
+ },
11
+ "100": {
12
+ "content": "[UNK]",
13
+ "lstrip": false,
14
+ "normalized": false,
15
+ "rstrip": false,
16
+ "single_word": false,
17
+ "special": true
18
+ },
19
+ "101": {
20
+ "content": "[CLS]",
21
+ "lstrip": false,
22
+ "normalized": false,
23
+ "rstrip": false,
24
+ "single_word": false,
25
+ "special": true
26
+ },
27
+ "102": {
28
+ "content": "[SEP]",
29
+ "lstrip": false,
30
+ "normalized": false,
31
+ "rstrip": false,
32
+ "single_word": false,
33
+ "special": true
34
+ },
35
+ "103": {
36
+ "content": "[MASK]",
37
+ "lstrip": false,
38
+ "normalized": false,
39
+ "rstrip": false,
40
+ "single_word": false,
41
+ "special": true
42
+ }
43
+ },
44
+ "clean_up_tokenization_spaces": true,
45
+ "cls_token": "[CLS]",
46
+ "do_basic_tokenize": true,
47
+ "do_lower_case": true,
48
+ "mask_token": "[MASK]",
49
+ "model_max_length": 512,
50
+ "never_split": null,
51
+ "pad_token": "[PAD]",
52
+ "sep_token": "[SEP]",
53
+ "strip_accents": null,
54
+ "tokenize_chinese_chars": true,
55
+ "tokenizer_class": "BertTokenizer",
56
+ "unk_token": "[UNK]"
57
+ }
vocab.txt ADDED
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