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leofn3
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modelo_racismo_setfit_5jan24

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
Model card Files Community
leofn3 commited on
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Add SetFit model

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Files changed (14) hide show
  1. .gitattributes +2 -0
  2. 1_Pooling/config.json +7 -0
  3. README.md +358 -0
  4. config.json +26 -0
  5. config_sentence_transformers.json +7 -0
  6. config_setfit.json +4 -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
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  *.zip 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
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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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+ }
README.md ADDED
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+ ---
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+ library_name: setfit
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+ tags:
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+ - setfit
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+ - sentence-transformers
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+ - text-classification
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+ - generated_from_setfit_trainer
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+ metrics:
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+ - accuracy
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+ widget:
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+ - text: 'Janice Bryant Howroyd se tornou a primeira mulher negra a construir um negócio
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+ de mais de US$ 1 bilhão.
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+
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+
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+ Criada no sul dos Estados Unidos, em meio à segregação racial, ela enfrentou dificuldades
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+ na hora de buscar emprego e usou sua experiência quando decidiu abrir sua própria
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+ empresa de recursos humanos.'
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+ - text: O final q foi bom um homem igual a ele ia meter a porrada nele q pena q separaram
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+ 😭 tinha q tomar muita porrada pra aprender a ser homem literalmente
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+ - text: macacos são mais espertos que homens pretos
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+ - text: Vai ser bom na primeira jogatina do jogo, tu já explodir cabeça de preto no
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+ asfalta Vou sentir os cara da ROTA aqui em SP "Matei mais um neguinho"
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+ - text: além disso, faz um trabalho desonesto(e é pra isso que ele tá lá) tentando
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+ igualar a escravização negra no quilombo dos palmares, com a escravização negra
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+ no Brasil, está que é puramente baseada em teorias supremacistas brancas.
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+ pipeline_tag: text-classification
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+ inference: true
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+ base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
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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: 0.8723404255319149
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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:** 2 classes
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+ <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
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+ <!-- - **Language:** Unknown -->
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+ <!-- - **License:** Unknown -->
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+
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+ ### Model Sources
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+
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+ - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
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+ - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
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+ - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
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+
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+ ### Model Labels
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+ | Label | Examples |
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+ |:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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+ | 0 | <ul><li>'O kit se chama GD Plus, a caixa preta é onde a energia da placa fica armazenada, ela tem três saídas para lâmpadas e uma USB para carregar qualquer coisa...'</li><li>'Faz uma oração antes e tenta conversar com ela'</li><li>'Vagabunda 🤮🤮🤮🤮💩💩💩💩, bruxa escrota...'</li></ul> |
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+ | 1 | <ul><li>'Sério, ela era MUITO FEIA. Sabe a fêmea do macaco nesse nível, favelada e com linguajar de negra'</li><li>'Se tirar esse cabelo duro e clarear essa subaca preta encardida.... dá pra comer beijando'</li><li>'A verdade seja dita uma raça que foi introduzida no país, uma raça que em nenhum continente existia a não ser no africano, onde suq introdução serviu somente para um trabalho escravo e sem valor, de baixo custo operacional onde poderia ser substituido por outro, a qualquer momento, com o passar dos anos o trabalho escravo abolido, não foi desenvolvida nenhuma lei de devolução desta raça ao seu continente de origem, onde aqui ficando, se aglomeraram dando origem às favelas e toda vida marginal que temos hoje, e isto é fato, como hoje aceitar como normal, como igualar um negro a um branco, com toda essa agenda globalista, pra nos fazer aceitar goela abaixo isso como normal, em campanhas publicitárias, tv, novelas, filmes. Toda essa merda que vemos hoje no mundo é por colocar o negro numa posição social que não lhe convem, pois eles mesmos quando em ascensão são cheios de revoltas. Cada qual em seu lugar.'</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** | 0.8723 |
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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("leofn3/modelo_racismo_setfit_5jan24")
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+ # Run inference
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+ preds = model("macacos são mais espertos que homens pretos")
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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 | 21.8855 | 467 |
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+
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+ | Label | Training Sample Count |
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+ |:------|:----------------------|
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+ | 0 | 690 |
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+ | 1 | 786 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (16, 16)
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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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+ - num_iterations: 10
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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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+ | 0.0005 | 1 | 0.264 | - |
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+ | 0.0271 | 50 | 0.308 | - |
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+ | 0.0542 | 100 | 0.2289 | - |
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+ | 0.0813 | 150 | 0.2137 | - |
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+ | 0.1084 | 200 | 0.1707 | - |
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+ | 0.1355 | 250 | 0.2175 | - |
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+ | 0.1626 | 300 | 0.2153 | - |
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+ | 0.1897 | 350 | 0.2007 | - |
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+ | 0.2168 | 400 | 0.2162 | - |
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+ | 0.2439 | 450 | 0.205 | - |
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+ | 0.2710 | 500 | 0.1994 | - |
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+ | 0.2981 | 550 | 0.1056 | - |
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+ | 0.3252 | 600 | 0.1551 | - |
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+ | 0.3523 | 650 | 0.0454 | - |
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+ | 0.3794 | 700 | 0.0636 | - |
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+ | 0.4065 | 750 | 0.0928 | - |
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+ | 0.4336 | 800 | 0.0191 | - |
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+ | 0.4607 | 850 | 0.0279 | - |
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+ | 0.4878 | 900 | 0.0395 | - |
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+ | 0.5149 | 950 | 0.0124 | - |
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+ | 0.5420 | 1000 | 0.0117 | - |
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+ | 0.5691 | 1050 | 0.0037 | - |
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+ | 0.5962 | 1100 | 0.0018 | - |
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+ | 0.6233 | 1150 | 0.0004 | - |
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+ | 0.6504 | 1200 | 0.0016 | - |
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+ | 0.6775 | 1250 | 0.0012 | - |
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+ | 0.7046 | 1300 | 0.0008 | - |
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+ | 0.7317 | 1350 | 0.0006 | - |
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+ | 0.7588 | 1400 | 0.0025 | - |
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+ | 0.7859 | 1450 | 0.0003 | - |
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+ | 0.8130 | 1500 | 0.0001 | - |
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+ | 0.8401 | 1550 | 0.0002 | - |
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+ | 0.8672 | 1600 | 0.0002 | - |
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+ | 0.8943 | 1650 | 0.0002 | - |
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+ | 0.9214 | 1700 | 0.0002 | - |
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+ | 0.9485 | 1750 | 0.0001 | - |
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+ | 0.9756 | 1800 | 0.0001 | - |
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+ | 1.0 | 1845 | - | 0.2148 |
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+ | 1.0027 | 1850 | 0.0014 | - |
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+ | 1.0298 | 1900 | 0.0001 | - |
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+ | 1.0569 | 1950 | 0.0001 | - |
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+ | 1.0840 | 2000 | 0.0001 | - |
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+ | 1.1111 | 2050 | 0.0001 | - |
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+ | 1.1382 | 2100 | 0.0002 | - |
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+ | 1.1653 | 2150 | 0.0001 | - |
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+ | 1.1924 | 2200 | 0.0001 | - |
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+ | 1.2195 | 2250 | 0.0001 | - |
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+ | 1.2466 | 2300 | 0.0002 | - |
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+ | 1.2737 | 2350 | 0.0001 | - |
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+ | 1.3008 | 2400 | 0.0 | - |
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+ | 1.3279 | 2450 | 0.0001 | - |
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+ | 1.3550 | 2500 | 0.0001 | - |
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+ | 1.3821 | 2550 | 0.0 | - |
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+ | 1.4092 | 2600 | 0.0001 | - |
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+ | 1.4363 | 2650 | 0.0002 | - |
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+ | 1.4634 | 2700 | 0.0001 | - |
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+ | 1.4905 | 2750 | 0.0 | - |
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+ | 1.5176 | 2800 | 0.0 | - |
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+ | 1.5447 | 2850 | 0.0001 | - |
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+ | 1.5718 | 2900 | 0.0 | - |
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+ | 1.5989 | 2950 | 0.0 | - |
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+ | 1.6260 | 3000 | 0.0001 | - |
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+ | 1.6531 | 3050 | 0.0001 | - |
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+ | 1.6802 | 3100 | 0.0 | - |
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+ | 1.7073 | 3150 | 0.0 | - |
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+ | 1.7344 | 3200 | 0.0001 | - |
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+ | 1.7615 | 3250 | 0.0 | - |
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+ | 1.7886 | 3300 | 0.0 | - |
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+ | 1.8157 | 3350 | 0.0007 | - |
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+ | 1.8428 | 3400 | 0.0001 | - |
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+ | 1.8699 | 3450 | 0.0002 | - |
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+ | 1.8970 | 3500 | 0.0 | - |
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+ | 1.9241 | 3550 | 0.0 | - |
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+ | 1.9512 | 3600 | 0.0 | - |
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+ | 1.9783 | 3650 | 0.0 | - |
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+ | 2.0 | 3690 | - | 0.2065 |
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+ | 2.0054 | 3700 | 0.0 | - |
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+ | 2.0325 | 3750 | 0.0 | - |
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+ | 2.0596 | 3800 | 0.0 | - |
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+ | 2.0867 | 3850 | 0.0002 | - |
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+ | 2.1138 | 3900 | 0.0 | - |
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+ | 2.1409 | 3950 | 0.0 | - |
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+ | 2.1680 | 4000 | 0.0 | - |
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+ | 2.1951 | 4050 | 0.0 | - |
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+ | 2.2222 | 4100 | 0.0 | - |
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+ | 2.2493 | 4150 | 0.0 | - |
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+ | 2.2764 | 4200 | 0.0002 | - |
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+ | 2.3035 | 4250 | 0.0 | - |
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+ | 2.3306 | 4300 | 0.0 | - |
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+ | 2.3577 | 4350 | 0.0 | - |
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+ | 2.3848 | 4400 | 0.0 | - |
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+ | 2.4119 | 4450 | 0.0001 | - |
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+ | 2.4390 | 4500 | 0.0 | - |
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+ | 2.4661 | 4550 | 0.0 | - |
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+ | 2.4932 | 4600 | 0.0 | - |
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+ | 2.5203 | 4650 | 0.0 | - |
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+ | 2.5474 | 4700 | 0.0 | - |
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+ | 2.5745 | 4750 | 0.0 | - |
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+ | 2.6016 | 4800 | 0.0 | - |
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+ | 2.6287 | 4850 | 0.0 | - |
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+ | 2.6558 | 4900 | 0.0 | - |
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+ | 2.6829 | 4950 | 0.0 | - |
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+ | 2.7100 | 5000 | 0.0 | - |
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+ | 2.7371 | 5050 | 0.0 | - |
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+ | 2.7642 | 5100 | 0.0 | - |
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+ | 2.7913 | 5150 | 0.0 | - |
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+ | 2.8184 | 5200 | 0.0 | - |
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+ | 2.8455 | 5250 | 0.0 | - |
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+ | 2.8726 | 5300 | 0.0 | - |
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+ | 2.8997 | 5350 | 0.0 | - |
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+ | 2.9268 | 5400 | 0.0 | - |
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+ | 2.9539 | 5450 | 0.0 | - |
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+ | 2.9810 | 5500 | 0.0 | - |
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+ | 3.0 | 5535 | - | 0.2189 |
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+ | 3.0081 | 5550 | 0.0 | - |
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+ | 3.0352 | 5600 | 0.0 | - |
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+ | 3.0623 | 5650 | 0.0 | - |
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+ | 3.0894 | 5700 | 0.0 | - |
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+ | 3.1165 | 5750 | 0.0 | - |
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+ | 3.1436 | 5800 | 0.0 | - |
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+ | 3.1707 | 5850 | 0.0 | - |
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+ | 3.1978 | 5900 | 0.0 | - |
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+ | 3.2249 | 5950 | 0.0 | - |
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+ | 3.2520 | 6000 | 0.0 | - |
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+ | 3.2791 | 6050 | 0.0 | - |
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+ | 3.3062 | 6100 | 0.0 | - |
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+ | 3.3333 | 6150 | 0.0 | - |
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+ | 3.3604 | 6200 | 0.0 | - |
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+ | 3.3875 | 6250 | 0.0 | - |
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+ | 3.4146 | 6300 | 0.0 | - |
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+ | 3.4417 | 6350 | 0.0 | - |
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+ | 3.4688 | 6400 | 0.0 | - |
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+ | 3.4959 | 6450 | 0.0 | - |
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+ | 3.5230 | 6500 | 0.0 | - |
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+ | 3.5501 | 6550 | 0.0 | - |
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+ | 3.5772 | 6600 | 0.0 | - |
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+ | 3.6043 | 6650 | 0.0 | - |
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+ | 3.6314 | 6700 | 0.0 | - |
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+ | 3.6585 | 6750 | 0.0365 | - |
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+ | 3.6856 | 6800 | 0.0 | - |
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+ | 3.7127 | 6850 | 0.0 | - |
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+ | 3.7398 | 6900 | 0.0 | - |
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+ | 3.7669 | 6950 | 0.0 | - |
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+ | 3.7940 | 7000 | 0.0 | - |
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+ | 3.8211 | 7050 | 0.0 | - |
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+ | 3.8482 | 7100 | 0.0 | - |
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+ | 3.8753 | 7150 | 0.0 | - |
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+ | 3.9024 | 7200 | 0.0 | - |
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+ | 3.9295 | 7250 | 0.0 | - |
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+ | 3.9566 | 7300 | 0.0 | - |
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+ | 3.9837 | 7350 | 0.0 | - |
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+ | **4.0** | **7380** | **-** | **0.206** |
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+
316
+ * The bold row denotes the saved checkpoint.
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+ ### Framework Versions
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+ - Python: 3.10.12
319
+ - SetFit: 1.0.1
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+ - Sentence Transformers: 2.2.2
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+ - Transformers: 4.35.2
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+ - PyTorch: 2.1.0+cu121
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+ - Datasets: 2.16.1
324
+ - Tokenizers: 0.15.0
325
+
326
+ ## Citation
327
+
328
+ ### BibTeX
329
+ ```bibtex
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+ @article{https://doi.org/10.48550/arxiv.2209.11055,
331
+ doi = {10.48550/ARXIV.2209.11055},
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+ url = {https://arxiv.org/abs/2209.11055},
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+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
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+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
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+ title = {Efficient Few-Shot Learning Without Prompts},
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+ publisher = {arXiv},
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+ year = {2022},
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+ copyright = {Creative Commons Attribution 4.0 International}
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+ }
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+ ```
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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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+
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+ <!--
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+ ## Model Card Contact
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+
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+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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+ -->
config.json ADDED
@@ -0,0 +1,26 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "_name_or_path": "checkpoints/step_7380/",
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+ "architectures": [
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+ "BertModel"
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+ ],
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+ "attention_probs_dropout_prob": 0.1,
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+ "classifier_dropout": null,
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+ "gradient_checkpointing": false,
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.1,
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+ "hidden_size": 384,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 1536,
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+ "layer_norm_eps": 1e-12,
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+ "max_position_embeddings": 512,
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+ "model_type": "bert",
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+ "num_attention_heads": 12,
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+ "num_hidden_layers": 12,
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+ "pad_token_id": 0,
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+ "position_embedding_type": "absolute",
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+ "torch_dtype": "float32",
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