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vipinbansal179
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SetFit_sms_Analyzer1

Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Eval Results
Model card Files Community
vipinbansal179 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 +965 -1
  3. config.json +24 -0
  4. config_sentence_transformers.json +7 -0
  5. config_setfit.json +4 -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 +51 -0
  11. tokenizer.json +0 -0
  12. tokenizer_config.json +72 -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": 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 CHANGED
@@ -1,3 +1,967 @@
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  ---
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- license: apache-2.0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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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+ widget:
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+ - text: 'pay rs.20.00 / c 91xx3402 ganeshramkudisodebur 22 - 09 - 2023 . ref:3648483126
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+ . query ? click http://m.paytm.me/care : ppbl'
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+ - text: undelivered!\nyour hdfc bank debit card 9875 / c 8494\nreason ch shift . case
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+ address change , update seamless card delivery > > hdfcbk.io/a/0nzoo052
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+ - text: rs 5000.00 debit / c upi 23 - 09 - 2023 14:21:12 vpa 35890012004230@cnrb -
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+ ( upi ref 363290511260)-federal bank
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+ - text: 472448 otp set hdfc bank 4 digit login pin . share otp you?call 18002586161
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+ pipeline_tag: text-classification
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+ inference: true
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+ base_model: sentence-transformers/all-mpnet-base-v2
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+ model-index:
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+ - name: SetFit with sentence-transformers/all-mpnet-base-v2
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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: 0.9715909090909091
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+ name: Accuracy
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  ---
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+
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+ # SetFit with sentence-transformers/all-mpnet-base-v2
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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 [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) 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:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)
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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:** 384 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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+ | 2 | <ul><li>'840989 otp proceed canara bank mobile banking . valid 15 minute . share otp . - canara bank . kbl8a1ju0mt'</li><li>'cheque . 000102 issue riya collection rs . 12,000.00 present / c xxxxx546157 return unpaid insufficient fund . team idfc bank'</li><li>'avl bal / c xxxx0959 10 - jul-2022 06:06:24 inr 0.00 . combine avl bal inr 0.00 . use mb app track / c - kotak bank'</li></ul> |
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+ | 0 | <ul><li>'/ c . xxxxxxxx7146 debit rs.11933.00 16 - 09 - 23 / c xxxxxxxx4716 credit ( imp ref 325908759095 ) . warm regard , yes bank'</li><li>'send rs.290.00 kotak bank ac x4524 bharatpe90727843812@yesbankltd 13-10-23.upi ref 328684167136 . , kotak.com/fraud'</li><li>'rs.295 transfer / c ... 4322 : lien_marking_fo . total bal : rs.188.8cr . avlbl amt : rs.609.97(28 - 06 - 2022 16:39:53 ) - bank baroda'</li></ul> |
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+ | 1 | <ul><li>'rs 15000credite / c xx4524via neft neofirst technology india private- utr ref hsbcn23276508097 ; avail . bal.:rs 215180.62kotak bank'</li><li>'/ c : xx6775 credit rs.60.00 14 - 11 - 2023 10:47:49 upi - id 8733076955@omni ( upi ref 331800008439).-canara bank'</li><li>'rs.28 credit / c ... 7783 upi/323962847509 kiwicashback_ax . total bal : rs.122751.36cr . avlbl amt : rs.94671.36(27 - 08 - 2023 15:37:01 ) - bank baroda'</li></ul> |
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+
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+ ## Evaluation
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+
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+ ### Metrics
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+ | Label | Accuracy |
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+ |:--------|:---------|
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+ | **all** | 0.9716 |
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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("vipinbansal179/SetFit_sms_Analyzer1")
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+ # Run inference
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+ preds = model("472448 otp set hdfc bank 4 digit login pin . share otp you?call 18002586161")
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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 | 4 | 23.17 | 65 |
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+
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+ | Label | Training Sample Count |
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+ |:------|:----------------------|
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+ | 0 | 231 |
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+ | 1 | 131 |
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+ | 2 | 338 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (16, 16)
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+ - num_epochs: (2, 2)
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+ - max_steps: -1
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+ - sampling_strategy: oversampling
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+ - body_learning_rate: (2e-05, 1e-05)
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+ - head_learning_rate: 0.01
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+ - loss: CosineSimilarityLoss
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+ - distance_metric: cosine_distance
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+ - margin: 0.25
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+ - end_to_end: False
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+ - use_amp: False
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+ - warmup_proportion: 0.1
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+ - seed: 42
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+ - eval_max_steps: -1
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+ - load_best_model_at_end: True
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+
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+ ### Training Results
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+ | Epoch | Step | Training Loss | Validation Loss |
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+ |:-------:|:---------:|:-------------:|:---------------:|
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+ | 0.0001 | 1 | 0.2945 | - |
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+ | 0.0026 | 50 | 0.3574 | - |
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+ | 0.0052 | 100 | 0.2512 | - |
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+ | 0.0079 | 150 | 0.2319 | - |
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+ | 0.0105 | 200 | 0.2787 | - |
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+ | 0.0131 | 250 | 0.2129 | - |
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+ | 0.0157 | 300 | 0.2189 | - |
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+ | 0.0183 | 350 | 0.0857 | - |
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+ | 0.0210 | 400 | 0.0932 | - |
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+ | 0.0236 | 450 | 0.065 | - |
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+ | 0.0262 | 500 | 0.0553 | - |
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+ | 0.0288 | 550 | 0.0674 | - |
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+ | 0.0314 | 600 | 0.0239 | - |
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+ | 0.0341 | 650 | 0.0054 | - |
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+ | 0.0367 | 700 | 0.0025 | - |
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+ | 0.0393 | 750 | 0.002 | - |
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+ | 0.0419 | 800 | 0.0007 | - |
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+ | 0.0446 | 850 | 0.001 | - |
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+ | 0.0472 | 900 | 0.0008 | - |
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+ | 0.0498 | 950 | 0.0008 | - |
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+ | 0.0524 | 1000 | 0.0003 | - |
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+ | 0.0550 | 1050 | 0.0012 | - |
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+ | 0.0577 | 1100 | 0.002 | - |
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+ | 0.0603 | 1150 | 0.0192 | - |
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+ | 0.0629 | 1200 | 0.0041 | - |
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+ | 0.0655 | 1250 | 0.0002 | - |
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+ | 0.0681 | 1300 | 0.0001 | - |
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+ | 0.0708 | 1350 | 0.0001 | - |
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+ | 0.0734 | 1400 | 0.0001 | - |
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+ | 0.0760 | 1450 | 0.0004 | - |
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+ | 0.0786 | 1500 | 0.0003 | - |
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+ | 0.0812 | 1550 | 0.0002 | - |
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+ | 0.0839 | 1600 | 0.0004 | - |
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+ | 0.0865 | 1650 | 0.0002 | - |
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+ | 0.0891 | 1700 | 0.0002 | - |
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+ | 0.0917 | 1750 | 0.0001 | - |
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+ | 0.0943 | 1800 | 0.0001 | - |
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+ | 0.0970 | 1850 | 0.0001 | - |
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+ | 0.0996 | 1900 | 0.0001 | - |
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+ | 0.1022 | 1950 | 0.0001 | - |
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347
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348
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349
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350
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351
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352
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353
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354
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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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366
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367
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370
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491
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494
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509
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511
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517
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520
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523
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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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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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542
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543
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544
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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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554
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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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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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575
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576
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577
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578
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579
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580
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581
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582
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585
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592
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595
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596
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597
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598
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599
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600
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602
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603
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604
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608
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610
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614
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617
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618
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621
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623
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624
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626
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627
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628
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629
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630
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631
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632
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633
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634
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635
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636
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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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656
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657
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658
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659
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660
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663
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664
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665
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668
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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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+
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+ * The bold row denotes the saved checkpoint.
926
+ ### Framework Versions
927
+ - Python: 3.10.12
928
+ - SetFit: 1.0.1
929
+ - Sentence Transformers: 2.2.2
930
+ - Transformers: 4.36.0
931
+ - PyTorch: 2.0.0
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+ - Datasets: 2.16.1
933
+ - Tokenizers: 0.15.0
934
+
935
+ ## Citation
936
+
937
+ ### BibTeX
938
+ ```bibtex
939
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
940
+ doi = {10.48550/ARXIV.2209.11055},
941
+ url = {https://arxiv.org/abs/2209.11055},
942
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
943
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
944
+ title = {Efficient Few-Shot Learning Without Prompts},
945
+ publisher = {arXiv},
946
+ year = {2022},
947
+ copyright = {Creative Commons Attribution 4.0 International}
948
+ }
949
+ ```
950
+
951
+ <!--
952
+ ## Glossary
953
+
954
+ *Clearly define terms in order to be accessible across audiences.*
955
+ -->
956
+
957
+ <!--
958
+ ## Model Card Authors
959
+
960
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
961
+ -->
962
+
963
+ <!--
964
+ ## Model Card Contact
965
+
966
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
967
+ -->
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