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luis-cardoso-q
/
kotodama-multilingual-v3

Text Classification
setfit
Safetensors
sentence-transformers
xlm-roberta
generated_from_setfit_trainer
Eval Results
Model card Files Community
luis-cardoso-q commited on
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Add SetFit model

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Files changed (13) hide show
  1. .gitattributes +1 -0
  2. 1_Pooling/config.json +10 -0
  3. README.md +587 -0
  4. config.json +27 -0
  5. config_sentence_transformers.json +9 -0
  6. config_setfit.json +12 -0
  7. model.safetensors +3 -0
  8. model_head.pkl +3 -0
  9. modules.json +14 -0
  10. sentence_bert_config.json +4 -0
  11. special_tokens_map.json +51 -0
  12. tokenizer.json +3 -0
  13. tokenizer_config.json +61 -0
.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
1_Pooling/config.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
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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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+ "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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+ base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
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+ metrics:
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+ - accuracy
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+ widget:
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+ - text: 'loan repayment '
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+ - text: 2023-F48
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+ - text: 'acompte '
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+ - text: 2023-12-1165548
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+ - text: Facture 20230040
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+ pipeline_tag: text-classification
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+ inference: true
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+ model-index:
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+ - name: SetFit with sentence-transformers/paraphrase-multilingual-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: 0.73568281938326
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+ name: Accuracy
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+ ---
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+
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+ # SetFit with sentence-transformers/paraphrase-multilingual-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/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-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/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-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:** 128 tokens
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+ - **Number of Classes:** 7 classes
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+ <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
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+ <!-- - **Language:** Unknown -->
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+ <!-- - **License:** Unknown -->
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+
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+ ### Model Sources
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+
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+ - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
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+ - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
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+ - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
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+
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+ ### Model Labels
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+ | Label | Examples |
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+ |:------------------|:---------------------------------------------------------------------------------------------------------------------------------|
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+ | company name | <ul><li>'Werbeverguetung Ford Mustang GT Cabrio Mueller Marc'</li><li>'ELEVEN PARIS'</li><li>'Saldo quota iscrizione '</li></ul> |
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+ | salary | <ul><li>'stipendio gianoli gen 23'</li><li>'Salaire Fevrier'</li><li>'salaire'</li></ul> |
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+ | invoice | <ul><li>'PAIEMENT DE FACTURE FAT00014728'</li><li>'fattura 3/24'</li><li>'saldo fattura'</li></ul> |
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+ | random characters | <ul><li>'arretrati'</li><li>'Identnummer 00480257'</li><li>'CEP AGENCE'</li></ul> |
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+ | refund | <ul><li>'versement'</li><li>'Remboursement frais transport'</li><li>'REMB FRAIS V / V LONDRES'</li></ul> |
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+ | buying | <ul><li>'voiture'</li><li>'fourniture'</li><li>'fournisseur '</li></ul> |
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+ | rent | <ul><li>'loyer'</li><li>'loyer mars 2023'</li><li>'loyer'</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** | 0.7357 |
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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("luis-cardoso-q/kotodama-multilingual-v3")
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+ # Run inference
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+ preds = model("2023-F48")
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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 | 1 | 2.6689 | 16 |
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+
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+ | Label | Training Sample Count |
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+ |:------------------|:----------------------|
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+ | buying | 25 |
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+ | company name | 73 |
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+ | invoice | 128 |
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+ | random characters | 128 |
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+ | refund | 87 |
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+ | rent | 38 |
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+ | salary | 128 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (16, 16)
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+ - num_epochs: (1, 1)
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+ - max_steps: -1
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+ - sampling_strategy: oversampling
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+ - body_learning_rate: (2e-05, 1e-05)
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+ - head_learning_rate: 0.01
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+ - loss: CosineSimilarityLoss
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+ - distance_metric: cosine_distance
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+ - margin: 0.25
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+ - end_to_end: False
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+ - use_amp: False
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+ - warmup_proportion: 0.1
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+ - seed: 42
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+ - eval_max_steps: -1
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+ - load_best_model_at_end: True
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+
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+ ### Training Results
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+ | Epoch | Step | Training Loss | Validation Loss |
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+ |:-------:|:---------:|:-------------:|:---------------:|
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+ | 0.0001 | 1 | 0.2604 | - |
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+ | 0.0026 | 50 | 0.3244 | - |
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+ | 0.0053 | 100 | 0.2233 | - |
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+ | 0.0079 | 150 | 0.2034 | - |
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+ | 0.0105 | 200 | 0.2998 | - |
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+ | 0.0131 | 250 | 0.2074 | - |
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+ | 0.0158 | 300 | 0.1682 | - |
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+ | 0.0184 | 350 | 0.1815 | - |
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+ | 0.0210 | 400 | 0.155 | - |
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+ | 0.0237 | 450 | 0.16 | - |
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+ | 0.0263 | 500 | 0.117 | - |
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+ | 0.0289 | 550 | 0.1685 | - |
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+ | 0.0315 | 600 | 0.0348 | - |
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+ | 0.0342 | 650 | 0.0912 | - |
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+ | 0.0368 | 700 | 0.0217 | - |
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+ | 0.0394 | 750 | 0.0417 | - |
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+ | 0.0421 | 800 | 0.0592 | - |
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+ | 0.0447 | 850 | 0.047 | - |
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+ | 0.0473 | 900 | 0.0914 | - |
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+ | 0.0499 | 950 | 0.0116 | - |
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+ | 0.0526 | 1000 | 0.022 | - |
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+ | 0.0552 | 1050 | 0.0018 | - |
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+ | 0.0578 | 1100 | 0.0159 | - |
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+ | 0.0605 | 1150 | 0.0097 | - |
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+ | 0.0631 | 1200 | 0.066 | - |
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+ | 0.0657 | 1250 | 0.0027 | - |
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+ | 0.0683 | 1300 | 0.003 | - |
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+ | 0.0710 | 1350 | 0.0146 | - |
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+ | 0.0736 | 1400 | 0.009 | - |
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+ | 0.0762 | 1450 | 0.0016 | - |
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+ | 0.0789 | 1500 | 0.001 | - |
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+ | 0.0815 | 1550 | 0.019 | - |
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+ | 0.0841 | 1600 | 0.0015 | - |
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+ | 0.0867 | 1650 | 0.0003 | - |
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+ | 0.0894 | 1700 | 0.0929 | - |
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+ | 0.0920 | 1750 | 0.013 | - |
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+ | 0.0946 | 1800 | 0.0007 | - |
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+ | 0.0973 | 1850 | 0.0413 | - |
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+ | 0.0999 | 1900 | 0.0922 | - |
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+ | 0.1025 | 1950 | 0.0009 | - |
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+ | 0.1051 | 2000 | 0.001 | - |
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+ | 0.1078 | 2050 | 0.0007 | - |
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+ | 0.1104 | 2100 | 0.0086 | - |
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+ | 0.1130 | 2150 | 0.0017 | - |
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+ | 0.1157 | 2200 | 0.0048 | - |
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+ | 0.1183 | 2250 | 0.0002 | - |
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+ | 0.1209 | 2300 | 0.0518 | - |
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+ | 0.1235 | 2350 | 0.0271 | - |
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+ | 0.1262 | 2400 | 0.0138 | - |
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+ | 0.1288 | 2450 | 0.0136 | - |
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+ | 0.1314 | 2500 | 0.0444 | - |
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+ | 0.1341 | 2550 | 0.0096 | - |
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+ | 0.1367 | 2600 | 0.0064 | - |
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+ | 0.1393 | 2650 | 0.0092 | - |
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+ | 0.1419 | 2700 | 0.0012 | - |
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+ | 0.1446 | 2750 | 0.0044 | - |
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+ | 0.1472 | 2800 | 0.0121 | - |
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+ | 0.1498 | 2850 | 0.0004 | - |
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+ | 0.1525 | 2900 | 0.0002 | - |
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+ | 0.1551 | 2950 | 0.0008 | - |
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+ | 0.1577 | 3000 | 0.0034 | - |
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+ | 0.1603 | 3050 | 0.0002 | - |
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+ | 0.1630 | 3100 | 0.0152 | - |
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+ | 0.1656 | 3150 | 0.0195 | - |
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+ | 0.1682 | 3200 | 0.0005 | - |
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+ | 0.1709 | 3250 | 0.0002 | - |
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+ | 0.1735 | 3300 | 0.0343 | - |
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+ | 0.1761 | 3350 | 0.0095 | - |
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+ | 0.1787 | 3400 | 0.0354 | - |
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+ | 0.1919 | 3650 | 0.0003 | - |
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+ | 0.1945 | 3700 | 0.0024 | - |
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+ | 0.1972 | 3750 | 0.06 | - |
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+ | 0.1998 | 3800 | 0.0006 | - |
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+ | 0.2024 | 3850 | 0.0003 | - |
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+ | 0.2050 | 3900 | 0.0311 | - |
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+ | 0.2077 | 3950 | 0.023 | - |
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+ | 0.2103 | 4000 | 0.0039 | - |
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+ | 0.2129 | 4050 | 0.0085 | - |
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+ | 0.2156 | 4100 | 0.0036 | - |
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+ | 0.2182 | 4150 | 0.0015 | - |
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+ | 0.2208 | 4200 | 0.0584 | - |
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+ | 0.2234 | 4250 | 0.0004 | - |
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+ | 0.2287 | 4350 | 0.0001 | - |
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+ | 0.2313 | 4400 | 0.0044 | - |
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+ | 0.2445 | 4650 | 0.0033 | - |
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+ | 0.2471 | 4700 | 0.0005 | - |
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+ | 0.2524 | 4800 | 0.046 | - |
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+ | 0.2550 | 4850 | 0.0028 | - |
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+ | 0.2576 | 4900 | 0.0014 | - |
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+ | 0.2655 | 5050 | 0.0005 | - |
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+ | 0.2708 | 5150 | 0.0002 | - |
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+ | 0.2760 | 5250 | 0.0111 | - |
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+ | 0.2786 | 5300 | 0.0286 | - |
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+ | 0.2892 | 5500 | 0.0691 | - |
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+ | 0.2918 | 5550 | 0.0403 | - |
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+ | 0.2944 | 5600 | 0.0002 | - |
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+ | 0.2970 | 5650 | 0.0057 | - |
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+ | 0.2997 | 5700 | 0.0047 | - |
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+ | 0.3023 | 5750 | 0.0322 | - |
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+ | 0.3049 | 5800 | 0.0097 | - |
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+ | 0.3076 | 5850 | 0.0012 | - |
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+ | 0.3102 | 5900 | 0.0047 | - |
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+ | 0.3128 | 5950 | 0.0925 | - |
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+ | 0.3154 | 6000 | 0.0562 | - |
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+ | 0.3181 | 6050 | 0.0058 | - |
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+ | 0.3207 | 6100 | 0.0001 | - |
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+ | 0.3233 | 6150 | 0.0029 | - |
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+ | 0.3260 | 6200 | 0.0001 | - |
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+ | 0.3286 | 6250 | 0.0035 | - |
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+ | 0.3312 | 6300 | 0.0013 | - |
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+ | 0.3338 | 6350 | 0.0152 | - |
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+ | 0.3365 | 6400 | 0.0004 | - |
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+ | 0.3391 | 6450 | 0.0114 | - |
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+ | 0.3417 | 6500 | 0.0906 | - |
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+ | 0.3444 | 6550 | 0.0005 | - |
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+ | 0.3470 | 6600 | 0.0028 | - |
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+ | 0.3496 | 6650 | 0.0395 | - |
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+ | 0.3522 | 6700 | 0.0001 | - |
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+ | 0.3549 | 6750 | 0.0044 | - |
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+ | 0.3575 | 6800 | 0.0121 | - |
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+ | 0.3601 | 6850 | 0.0012 | - |
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+ | 0.3628 | 6900 | 0.0193 | - |
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+ | 0.3654 | 6950 | 0.0014 | - |
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+ | 0.3680 | 7000 | 0.0001 | - |
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+ | 0.3706 | 7050 | 0.0618 | - |
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+ | 0.3733 | 7100 | 0.0066 | - |
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+ | 0.3759 | 7150 | 0.0426 | - |
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+ | 0.3785 | 7200 | 0.0281 | - |
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+ | 0.3812 | 7250 | 0.0254 | - |
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+ | 0.3838 | 7300 | 0.0008 | - |
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+ | 0.3864 | 7350 | 0.0047 | - |
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+ | 0.3890 | 7400 | 0.0088 | - |
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+ | 0.3917 | 7450 | 0.0004 | - |
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+ | 0.3943 | 7500 | 0.0054 | - |
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+ | 0.3969 | 7550 | 0.0371 | - |
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+ | 0.3996 | 7600 | 0.0001 | - |
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+ | 0.4022 | 7650 | 0.0082 | - |
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+ | 0.4101 | 7800 | 0.0115 | - |
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+ | 0.4153 | 7900 | 0.0001 | - |
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+ | 0.4180 | 7950 | 0.0002 | - |
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+ | 0.4206 | 8000 | 0.0098 | - |
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+ | 0.4232 | 8050 | 0.0001 | - |
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+ | 0.4258 | 8100 | 0.0 | - |
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+ | 0.4285 | 8150 | 0.0104 | - |
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+ | 0.4311 | 8200 | 0.0564 | - |
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+ | 0.4337 | 8250 | 0.0002 | - |
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+ | 0.4364 | 8300 | 0.0176 | - |
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+ | 0.4442 | 8450 | 0.0053 | - |
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+ | 0.4469 | 8500 | 0.0629 | - |
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+ | 0.4521 | 8600 | 0.0003 | - |
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+ | 0.4548 | 8650 | 0.0025 | - |
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+ | 0.4574 | 8700 | 0.0032 | - |
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+ | 0.4626 | 8800 | 0.0001 | - |
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+ | 0.5073 | 9650 | 0.018 | - |
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+ | **1.0** | **19021** | **-** | **0.2343** |
544
+
545
+ * The bold row denotes the saved checkpoint.
546
+ ### Framework Versions
547
+ - Python: 3.10.12
548
+ - SetFit: 1.0.3
549
+ - Sentence Transformers: 2.4.0
550
+ - Transformers: 4.38.1
551
+ - PyTorch: 2.1.0+cu118
552
+ - Datasets: 2.17.1
553
+ - Tokenizers: 0.15.2
554
+
555
+ ## Citation
556
+
557
+ ### BibTeX
558
+ ```bibtex
559
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
560
+ doi = {10.48550/ARXIV.2209.11055},
561
+ url = {https://arxiv.org/abs/2209.11055},
562
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
563
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
564
+ title = {Efficient Few-Shot Learning Without Prompts},
565
+ publisher = {arXiv},
566
+ year = {2022},
567
+ copyright = {Creative Commons Attribution 4.0 International}
568
+ }
569
+ ```
570
+
571
+ <!--
572
+ ## Glossary
573
+
574
+ *Clearly define terms in order to be accessible across audiences.*
575
+ -->
576
+
577
+ <!--
578
+ ## Model Card Authors
579
+
580
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
581
+ -->
582
+
583
+ <!--
584
+ ## Model Card Contact
585
+
586
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
587
+ -->
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