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fadyabdo
/
botpress_football_sft_model

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
Model card Files Community
fadyabdo commited on
Commit
4f859a1
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1 Parent(s): 922ff98

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Files changed (14) hide show
  1. .gitattributes +2 -0
  2. 1_Pooling/config.json +10 -0
  3. README.md +539 -0
  4. config.json +26 -0
  5. config_sentence_transformers.json +10 -0
  6. config_setfit.json +11 -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 +64 -0
  14. unigram.json +3 -0
.gitattributes CHANGED
@@ -33,3 +33,5 @@ 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
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+ unigram.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": 384,
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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
@@ -0,0 +1,539 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
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+ library_name: setfit
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+ metrics:
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+ - accuracy
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+ pipeline_tag: text-classification
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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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+ widget:
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+ - text: au revoir
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+ - text: quand auront lieu les matchs de Aston Villa
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+ - text: any upcoming fixtures for Juventus
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+ - text: qui êtes-vous
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+ - text: what is the score of Brentford match
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+ inference: true
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+ model-index:
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+ - name: SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-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: 1.0
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+ name: Accuracy
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+ ---
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+
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+ # SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-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-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-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-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-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:** 6 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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+ | matches-match_time | <ul><li>'Norwich City vs Newcastle United'</li><li>'will Manchester United play with chelsea'</li><li>'est-ce que Manchester United jouera avec chelsea'</li></ul> |
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+ | matches-match_result | <ul><li>'Liverpool and West Ham result'</li><li>'what is the score of Wolverhampton match'</li><li>'who won in Liverpool vs Newcastle United match'</li></ul> |
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+ | greet-who_are_you | <ul><li>'how can you help me'</li><li>"pourquoi j'ai besoin de toi"</li><li>'je ne te comprends pas'</li></ul> |
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+ | matches-team_next_match | <ul><li>'Real Madrid fixtures'</li><li>'quels sont les prochains matchs de Borussia Dortmund'</li><li>'próximos partidos de Atletico Madrid'</li></ul> |
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+ | greet-good_bye | <ul><li>'See you later'</li><li>'A plus tard'</li><li>'stop'</li></ul> |
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+ | greet-hi | <ul><li>'Hello buddy'</li><li>'Salut'</li><li>'Hey'</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** | 1.0 |
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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("fadyabdo/botpress_football_sft_model")
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+ # Run inference
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+ preds = model("au revoir")
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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 | 5.2 | 10 |
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+
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+ | Label | Training Sample Count |
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+ |:------------------------|:----------------------|
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+ | greet-hi | 5 |
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+ | greet-who_are_you | 7 |
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+ | greet-good_bye | 5 |
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+ | matches-team_next_match | 21 |
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+ | matches-match_time | 12 |
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+ | matches-match_result | 15 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (4, 4)
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+ - num_epochs: (4, 4)
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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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+ | 0.0012 | 1 | 0.1544 | - |
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+ | 0.0121 | 10 | 0.0658 | - |
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+ | 0.0241 | 20 | 0.1235 | - |
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+ | 0.0362 | 30 | 0.2422 | - |
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+ | 0.0483 | 40 | 0.2876 | - |
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+ | 0.0603 | 50 | 0.1208 | - |
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+ | 0.0724 | 60 | 0.1358 | - |
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+ | 0.0844 | 70 | 0.1494 | - |
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+ | 0.0965 | 80 | 0.1284 | - |
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+ | 0.1086 | 90 | 0.1107 | - |
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+ | 0.1206 | 100 | 0.2395 | - |
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+ | 0.1327 | 110 | 0.0661 | - |
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+ | 0.1448 | 120 | 0.1554 | - |
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+ | 0.1568 | 130 | 0.0258 | - |
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+ | 0.1689 | 140 | 0.0279 | - |
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+ | 0.1809 | 150 | 0.1162 | - |
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+ | 0.1930 | 160 | 0.0244 | - |
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+ | 0.2051 | 170 | 0.0221 | - |
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+ | 0.2171 | 180 | 0.0813 | - |
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+ | 0.2292 | 190 | 0.0188 | - |
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+ | 0.2413 | 200 | 0.03 | - |
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+ | 0.2533 | 210 | 0.0019 | - |
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+ | 0.2654 | 220 | 0.0076 | - |
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+ | 0.2774 | 230 | 0.01 | - |
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+ | 0.2895 | 240 | 0.0025 | - |
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+ | 0.3016 | 250 | 0.0705 | - |
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+ | 0.3136 | 260 | 0.0044 | - |
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+ | 0.3257 | 270 | 0.0038 | - |
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+ | 0.3378 | 280 | 0.006 | - |
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+ | 0.3498 | 290 | 0.0018 | - |
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+ | 0.3619 | 300 | 0.0003 | - |
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+ | 0.3739 | 310 | 0.0007 | - |
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+ | 0.3860 | 320 | 0.0128 | - |
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+ | 0.3981 | 330 | 0.0022 | - |
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+ | 0.4101 | 340 | 0.0008 | - |
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+ | 0.4222 | 350 | 0.004 | - |
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+ | 0.4343 | 360 | 0.0006 | - |
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+ | 0.4463 | 370 | 0.0007 | - |
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+ | 0.4584 | 380 | 0.0005 | - |
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+ | 0.4704 | 390 | 0.0057 | - |
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+ | 0.4825 | 400 | 0.0007 | - |
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+ | 0.4946 | 410 | 0.0022 | - |
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+ | 0.5066 | 420 | 0.0012 | - |
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+ | 0.5187 | 430 | 0.0009 | - |
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+ | 0.5308 | 440 | 0.0004 | - |
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+ | 0.5428 | 450 | 0.0032 | - |
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+ | 0.5549 | 460 | 0.0007 | - |
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+ | 0.5669 | 470 | 0.0008 | - |
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+ | 0.5790 | 480 | 0.0005 | - |
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+ | 0.5911 | 490 | 0.0005 | - |
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+ | 0.6031 | 500 | 0.0008 | - |
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+ | 0.6152 | 510 | 0.0008 | - |
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+ | 0.6273 | 520 | 0.0004 | - |
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+ | 0.6393 | 530 | 0.0015 | - |
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+ | 0.6514 | 540 | 0.0002 | - |
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+ | 0.6634 | 550 | 0.0006 | - |
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+ | 0.6755 | 560 | 0.0015 | - |
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+ | 0.6876 | 570 | 0.0024 | - |
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+ | 0.6996 | 580 | 0.0004 | - |
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+ | 0.7117 | 590 | 0.0005 | - |
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+ | 0.7238 | 600 | 0.0011 | - |
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+ | 0.7358 | 610 | 0.0008 | - |
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+ | 0.7479 | 620 | 0.0002 | - |
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+ | 0.7600 | 630 | 0.0006 | - |
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+ | 0.7720 | 640 | 0.0003 | - |
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+ | 0.7841 | 650 | 0.0002 | - |
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+ | 0.7961 | 660 | 0.0007 | - |
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+ | 0.8082 | 670 | 0.0009 | - |
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+ | 0.8203 | 680 | 0.0002 | - |
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+ | 0.8323 | 690 | 0.0006 | - |
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+ | 0.8444 | 700 | 0.0015 | - |
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+ | 0.8565 | 710 | 0.0003 | - |
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+ | 0.8685 | 720 | 0.0003 | - |
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+ | 0.8806 | 730 | 0.0003 | - |
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+ | 0.8926 | 740 | 0.0015 | - |
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+ | 0.9047 | 750 | 0.0003 | - |
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+ | 0.9168 | 760 | 0.0005 | - |
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+ | 0.9288 | 770 | 0.0002 | - |
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+ | 0.9409 | 780 | 0.0003 | - |
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+ | 0.9530 | 790 | 0.0002 | - |
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+ | 0.9650 | 800 | 0.0004 | - |
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+ | 0.9771 | 810 | 0.0003 | - |
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+ | 0.9891 | 820 | 0.001 | - |
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+ | 1.0 | 829 | - | 0.0216 |
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+ | 1.0012 | 830 | 0.0003 | - |
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+ | 1.0133 | 840 | 0.0007 | - |
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+ | 1.0253 | 850 | 0.0004 | - |
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+ | 1.0374 | 860 | 0.0001 | - |
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+ | 1.0495 | 870 | 0.0008 | - |
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+ | 1.0615 | 880 | 0.0003 | - |
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+ | 1.0736 | 890 | 0.0006 | - |
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+ | 1.0856 | 900 | 0.0001 | - |
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+ | 1.0977 | 910 | 0.0018 | - |
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+ | 1.1098 | 920 | 0.0 | - |
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+ | 1.1218 | 930 | 0.0001 | - |
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+ | 1.1339 | 940 | 0.0007 | - |
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+ | 1.1460 | 950 | 0.0009 | - |
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+ | 1.1580 | 960 | 0.0004 | - |
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+ | 1.1701 | 970 | 0.0003 | - |
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+ | 1.1821 | 980 | 0.0015 | - |
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+ | 1.1942 | 990 | 0.0002 | - |
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+ | 1.2063 | 1000 | 0.0005 | - |
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+ | 1.2183 | 1010 | 0.0002 | - |
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+ | 1.2304 | 1020 | 0.0003 | - |
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+ | 1.2425 | 1030 | 0.0001 | - |
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+ | 1.2545 | 1040 | 0.0002 | - |
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+ | 1.2666 | 1050 | 0.0004 | - |
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+ | 1.2786 | 1060 | 0.0001 | - |
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+ | 1.2907 | 1070 | 0.0002 | - |
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+ | 1.3028 | 1080 | 0.0001 | - |
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+ | 1.3148 | 1090 | 0.0002 | - |
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+ | 1.3269 | 1100 | 0.0001 | - |
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+ | 1.3390 | 1110 | 0.0002 | - |
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+ | 1.3510 | 1120 | 0.0003 | - |
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+ | 1.3631 | 1130 | 0.0001 | - |
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+ | 1.3752 | 1140 | 0.0001 | - |
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+ | 1.3872 | 1150 | 0.0001 | - |
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+ | 1.3993 | 1160 | 0.0002 | - |
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+ | 1.4113 | 1170 | 0.0001 | - |
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+ | 1.4234 | 1180 | 0.0005 | - |
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+ | 1.4355 | 1190 | 0.0002 | - |
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+ | 1.4475 | 1200 | 0.0002 | - |
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+ | 1.4596 | 1210 | 0.0002 | - |
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+ | 1.4717 | 1220 | 0.0001 | - |
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+ | 1.4837 | 1230 | 0.0001 | - |
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+ | 1.4958 | 1240 | 0.0001 | - |
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+ | 1.5078 | 1250 | 0.0001 | - |
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+ | 1.5199 | 1260 | 0.001 | - |
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+ | 1.5320 | 1270 | 0.0001 | - |
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+ | 1.5440 | 1280 | 0.0003 | - |
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+ | 1.5561 | 1290 | 0.0001 | - |
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+ | 1.5682 | 1300 | 0.0002 | - |
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+ | 1.5802 | 1310 | 0.0005 | - |
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+ | 1.5923 | 1320 | 0.0002 | - |
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+ | 1.6043 | 1330 | 0.0001 | - |
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+ | 1.6164 | 1340 | 0.0004 | - |
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+ | 1.6285 | 1350 | 0.0002 | - |
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+ | 1.6405 | 1360 | 0.0001 | - |
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+ | 1.6526 | 1370 | 0.0004 | - |
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+ | 1.6647 | 1380 | 0.0003 | - |
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+ | 1.6767 | 1390 | 0.0002 | - |
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+ | 1.6888 | 1400 | 0.0001 | - |
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+ | 1.7008 | 1410 | 0.0008 | - |
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+ | 1.7129 | 1420 | 0.0003 | - |
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+ | 1.7250 | 1430 | 0.0005 | - |
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+ | 1.7370 | 1440 | 0.0001 | - |
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+ | 1.7491 | 1450 | 0.0001 | - |
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+ | 1.7612 | 1460 | 0.0001 | - |
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+ | 1.7732 | 1470 | 0.0007 | - |
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+ | 1.7853 | 1480 | 0.0001 | - |
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+ | 1.7973 | 1490 | 0.0002 | - |
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+ | 1.8094 | 1500 | 0.0001 | - |
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+ | 1.8215 | 1510 | 0.001 | - |
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+ | 1.8335 | 1520 | 0.0002 | - |
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+ | 1.8456 | 1530 | 0.0003 | - |
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+ | 1.8577 | 1540 | 0.0004 | - |
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+ | 1.8697 | 1550 | 0.0005 | - |
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+ | 1.8818 | 1560 | 0.0001 | - |
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+ | 1.8938 | 1570 | 0.0006 | - |
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+ | 1.9059 | 1580 | 0.0005 | - |
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+ | 1.9180 | 1590 | 0.0002 | - |
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+ | 1.9300 | 1600 | 0.0002 | - |
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+ | 1.9421 | 1610 | 0.0001 | - |
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+ | 1.9542 | 1620 | 0.0003 | - |
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+ | 1.9662 | 1630 | 0.0005 | - |
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+ | 1.9783 | 1640 | 0.0007 | - |
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+ | 1.9903 | 1650 | 0.0001 | - |
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+ | 2.0 | 1658 | - | 0.0186 |
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+ | 2.0024 | 1660 | 0.0 | - |
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+ | 2.0145 | 1670 | 0.0001 | - |
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+ | 2.0265 | 1680 | 0.0002 | - |
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+ | 2.0386 | 1690 | 0.0001 | - |
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+ | 2.0507 | 1700 | 0.0002 | - |
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+ | 2.0869 | 1730 | 0.0002 | - |
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+ | 2.0989 | 1740 | 0.0001 | - |
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+ | 2.1110 | 1750 | 0.0002 | - |
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+ | 2.1230 | 1760 | 0.0001 | - |
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+ | 2.1351 | 1770 | 0.0003 | - |
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+ | 2.1472 | 1780 | 0.0006 | - |
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+ | 2.1592 | 1790 | 0.0001 | - |
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+ | 2.1713 | 1800 | 0.0002 | - |
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+ | 2.1834 | 1810 | 0.0002 | - |
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+ | 2.1954 | 1820 | 0.0001 | - |
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+ | 2.2075 | 1830 | 0.0 | - |
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+ | 2.2195 | 1840 | 0.0001 | - |
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+ | 2.2316 | 1850 | 0.0002 | - |
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+ | 2.2437 | 1860 | 0.0004 | - |
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+ | 2.2557 | 1870 | 0.0003 | - |
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+ | 2.2678 | 1880 | 0.0002 | - |
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+ | 2.2799 | 1890 | 0.0002 | - |
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+ | 2.2919 | 1900 | 0.0004 | - |
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+ | 2.3040 | 1910 | 0.0002 | - |
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+ | 2.3160 | 1920 | 0.0001 | - |
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+ | 2.3281 | 1930 | 0.0 | - |
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+ | 2.3402 | 1940 | 0.0002 | - |
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+ | 2.3522 | 1950 | 0.0001 | - |
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+ | 2.3643 | 1960 | 0.0 | - |
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+ | 2.3764 | 1970 | 0.0003 | - |
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+ | 2.3884 | 1980 | 0.0002 | - |
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+ | 2.4005 | 1990 | 0.0001 | - |
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+ | 2.4125 | 2000 | 0.0003 | - |
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+ | 2.4246 | 2010 | 0.0003 | - |
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+ | 2.4367 | 2020 | 0.0002 | - |
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+ | 2.4487 | 2030 | 0.0002 | - |
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+ | 2.4608 | 2040 | 0.0002 | - |
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+ | 2.4729 | 2050 | 0.0001 | - |
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+ | 2.4849 | 2060 | 0.0001 | - |
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+ | 2.4970 | 2070 | 0.0002 | - |
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+ | 2.5090 | 2080 | 0.0 | - |
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+ | 2.5211 | 2090 | 0.0002 | - |
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+ | 2.5332 | 2100 | 0.0004 | - |
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+ | 4.0 | 3316 | - | 0.0187 |
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+
497
+ * The bold row denotes the saved checkpoint.
498
+ ### Framework Versions
499
+ - Python: 3.10.12
500
+ - SetFit: 1.0.3
501
+ - Sentence Transformers: 3.0.1
502
+ - Transformers: 4.37.0
503
+ - PyTorch: 2.3.0+cu121
504
+ - Datasets: 2.20.0
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+ - Tokenizers: 0.15.2
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+
507
+ ## Citation
508
+
509
+ ### BibTeX
510
+ ```bibtex
511
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
512
+ doi = {10.48550/ARXIV.2209.11055},
513
+ url = {https://arxiv.org/abs/2209.11055},
514
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
515
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
516
+ title = {Efficient Few-Shot Learning Without Prompts},
517
+ publisher = {arXiv},
518
+ year = {2022},
519
+ copyright = {Creative Commons Attribution 4.0 International}
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+ }
521
+ ```
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+
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+ <!--
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+ ## Glossary
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+
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+ *Clearly define terms in order to be accessible across audiences.*
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+ -->
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+
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+ <!--
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+ ## Model Card Authors
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+
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+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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+ -->
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+
535
+ <!--
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+ ## Model Card Contact
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+
538
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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+ -->
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