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omymble
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books-full-bge-aspect

Text Classification
setfit
Safetensors
sentence-transformers
bert
absa
generated_from_setfit_trainer
Model card Files Community
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Add SetFit ABSA model

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Files changed (13) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +756 -0
  3. config.json +31 -0
  4. config_sentence_transformers.json +10 -0
  5. config_setfit.json +9 -0
  6. model.safetensors +3 -0
  7. model_head.pkl +3 -0
  8. modules.json +20 -0
  9. sentence_bert_config.json +4 -0
  10. special_tokens_map.json +37 -0
  11. tokenizer.json +0 -0
  12. tokenizer_config.json +64 -0
  13. vocab.txt +0 -0
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": true,
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+ "pooling_mode_mean_tokens": false,
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+ "pooling_mode_max_tokens": false,
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+ "pooling_mode_mean_sqrt_len_tokens": false,
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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
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+ }
README.md ADDED
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+ ---
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+ base_model: BAAI/bge-small-en-v1.5
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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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+ - absa
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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: book:I was hooked!! Garth Nix is a awesome writer and though the book is a
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+ little babyish - its definetly worth a read! I thought the whole minute - hand
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+ - is - a - key part was a real good idea plus the names are so fun! The only thing
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+ I didn't like was that Arthur doesen't take his rightful place as "Monday"
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+ - text: book:The lawyer says he tracked Jack from his book and would like Jack to
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+ investigate the brutal murders of thirty-seven year old Gina Anderson and her
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+ son Joshua in their Seattle home; the house was trashed and the husband a lecturer
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+ at the nearby community college vanished
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+ - text: beings:Arthur with the lesser key to the lower kingdom of the House in hand,
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+ must travel into the House to find a cure for the mysterious plague that is striking
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+ the people of his town and his loved ones and find out why there are beings intent
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+ on getting the key from him, even if it means killing him
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+ - text: figures:But when a fight emerges between the two figures - Mister Monday and
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+ Sneezer - they both disappear without any further regard to Arthur
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+ - text: book:I could not put this book down if my life depended on it! I have never
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+ in my life read a book this fast
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+ inference: false
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+ ---
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+
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+ # SetFit Aspect Model with BAAI/bge-small-en-v1.5
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+
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+ This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. In particular, this model is in charge of filtering aspect span candidates.
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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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+ This model was trained within the context of a larger system for ABSA, which looks like so:
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+
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+ 1. Use a spaCy model to select possible aspect span candidates.
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+ 2. **Use this SetFit model to filter these possible aspect span candidates.**
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+ 3. Use a SetFit model to classify the filtered aspect span candidates.
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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** SetFit
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+ - **Sentence Transformer body:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5)
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+ - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
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+ - **spaCy Model:** en_core_web_lg
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+ - **SetFitABSA Aspect Model:** [omymble/books-full-bge-aspect](https://huggingface.co/omymble/books-full-bge-aspect)
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+ - **SetFitABSA Polarity Model:** [omymble/books-full-bge-polarity](https://huggingface.co/omymble/books-full-bge-polarity)
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+ - **Maximum Sequence Length:** 512 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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+ | aspect | <ul><li>"younger ones:It might be an entertaining point of discussion with a child 12 or older, but it's too dark for younger ones, unless you're ready to talk about true evil, adult motivations, supernatural forces, and fratricide!"</li><li>'Nix:-enjoy the genre of fantasies, of a unknown world, as Nix weaves a wonderful tale of the things that will open your eyes to a different world'</li><li>'mystery:The mystery is secondary to the rest of the story and is only really approached in the remaining 30 pages of the book'</li></ul> |
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+ | no aspect | <ul><li>"point:It might be an entertaining point of discussion with a child 12 or older, but it's too dark for younger ones, unless you're ready to talk about true evil, adult motivations, supernatural forces, and fratricide!"</li><li>"discussion:It might be an entertaining point of discussion with a child 12 or older, but it's too dark for younger ones, unless you're ready to talk about true evil, adult motivations, supernatural forces, and fratricide!"</li><li>"child:It might be an entertaining point of discussion with a child 12 or older, but it's too dark for younger ones, unless you're ready to talk about true evil, adult motivations, supernatural forces, and fratricide!"</li></ul> |
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+
75
+ ## Uses
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+
77
+ ### Direct Use for Inference
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+
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+ First install the SetFit library:
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+
81
+ ```bash
82
+ pip install setfit
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+ ```
84
+
85
+ Then you can load this model and run inference.
86
+
87
+ ```python
88
+ from setfit import AbsaModel
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+
90
+ # Download from the 🤗 Hub
91
+ model = AbsaModel.from_pretrained(
92
+ "omymble/books-full-bge-aspect",
93
+ "omymble/books-full-bge-polarity",
94
+ )
95
+ # Run inference
96
+ preds = model("The food was great, but the venue is just way too busy.")
97
+ ```
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+
99
+ <!--
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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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+
105
+ <!--
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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 | 2 | 25.9648 | 72 |
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+
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+ | Label | Training Sample Count |
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+ |:----------|:----------------------|
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+ | no aspect | 572 |
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+ | aspect | 167 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (64, 64)
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+ - num_epochs: (5, 5)
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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: True
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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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+ | 0.0002 | 1 | 0.2687 | - |
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+ | 0.0090 | 50 | 0.2516 | - |
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+ | 0.0180 | 100 | 0.2619 | - |
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+ | 0.0270 | 150 | 0.2499 | - |
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+ | 0.0360 | 200 | 0.2428 | - |
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+ | 0.0450 | 250 | 0.2443 | - |
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+ | 0.0540 | 300 | 0.246 | - |
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+ | 0.0629 | 350 | 0.249 | - |
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+ | 0.0719 | 400 | 0.2354 | - |
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+ | 0.0809 | 450 | 0.2347 | - |
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+ | 0.0899 | 500 | 0.2154 | - |
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+ | 0.0989 | 550 | 0.2285 | - |
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+ | 0.1079 | 600 | 0.1812 | - |
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+ | 0.1169 | 650 | 0.1446 | - |
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+ | 0.1259 | 700 | 0.165 | - |
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+ | 0.1349 | 750 | 0.1125 | - |
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+ | 0.1439 | 800 | 0.0971 | - |
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+ | 0.1529 | 850 | 0.1059 | - |
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+ | 0.1619 | 900 | 0.0866 | - |
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+ | 0.1709 | 950 | 0.0492 | - |
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+ | **0.1799** | **1000** | **0.0546** | **0.274** |
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+ | 0.1888 | 1050 | 0.037 | - |
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+ | 0.1978 | 1100 | 0.0189 | - |
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+ | 0.2068 | 1150 | 0.0279 | - |
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+ | 0.2158 | 1200 | 0.004 | - |
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+ | 0.2248 | 1250 | 0.0309 | - |
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+ | 0.2338 | 1300 | 0.0049 | - |
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+ | 0.2428 | 1350 | 0.0286 | - |
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+ | 0.2518 | 1400 | 0.0234 | - |
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+ | 0.2608 | 1450 | 0.0158 | - |
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+ | 0.2698 | 1500 | 0.0354 | - |
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+ | 0.2788 | 1550 | 0.0062 | - |
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+ | 0.2878 | 1600 | 0.0172 | - |
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+ | 0.2968 | 1650 | 0.0389 | - |
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+ | 0.3058 | 1700 | 0.0221 | - |
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+ | 0.3147 | 1750 | 0.0065 | - |
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+ | 0.3237 | 1800 | 0.0128 | - |
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+ | 0.3327 | 1850 | 0.0225 | - |
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+ | 0.3417 | 1900 | 0.0021 | - |
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+ | 0.3507 | 1950 | 0.0102 | - |
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+ | 0.3597 | 2000 | 0.012 | 0.3429 |
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+ | 0.3687 | 2050 | 0.0249 | - |
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+ | 0.3777 | 2100 | 0.0054 | - |
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+ | 0.3867 | 2150 | 0.0014 | - |
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+ | 0.3957 | 2200 | 0.0014 | - |
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+ | 0.4047 | 2250 | 0.0143 | - |
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+ | 0.4137 | 2300 | 0.0078 | - |
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+ | 0.4227 | 2350 | 0.0195 | - |
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+ | 0.4317 | 2400 | 0.0006 | - |
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+ | 0.4406 | 2450 | 0.0014 | - |
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+ | 0.4496 | 2500 | 0.0083 | - |
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+ | 0.4586 | 2550 | 0.0141 | - |
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+ | 0.4676 | 2600 | 0.0046 | - |
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+ | 0.4766 | 2650 | 0.01 | - |
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+ | 0.4856 | 2700 | 0.0268 | - |
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+ | 0.4946 | 2750 | 0.0008 | - |
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+ | 0.5036 | 2800 | 0.0076 | - |
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+ | 0.5126 | 2850 | 0.0004 | - |
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+ | 0.5216 | 2900 | 0.0037 | - |
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+ | 0.5306 | 2950 | 0.0005 | - |
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+ | 0.5396 | 3000 | 0.0065 | 0.3565 |
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+ | 0.5486 | 3050 | 0.002 | - |
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+ | 0.5576 | 3100 | 0.0072 | - |
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+ | 0.5665 | 3150 | 0.0141 | - |
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+ | 0.5755 | 3200 | 0.0004 | - |
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+ | 0.5845 | 3250 | 0.0086 | - |
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+ | 0.5935 | 3300 | 0.0098 | - |
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+ | 0.6025 | 3350 | 0.0048 | - |
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+ | 0.6115 | 3400 | 0.0013 | - |
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+ | 0.6205 | 3450 | 0.007 | - |
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+ | 0.6295 | 3500 | 0.0059 | - |
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+ | 0.6385 | 3550 | 0.0174 | - |
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+ | 0.6475 | 3600 | 0.0003 | - |
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+ | 0.6565 | 3650 | 0.0004 | - |
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+ | 0.6655 | 3700 | 0.0032 | - |
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+ | 0.6745 | 3750 | 0.0004 | - |
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+ | 0.6835 | 3800 | 0.0035 | - |
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+ | 0.6924 | 3850 | 0.0019 | - |
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+ | 0.7014 | 3900 | 0.015 | - |
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+ | 0.7104 | 3950 | 0.0204 | - |
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+ | 0.7194 | 4000 | 0.0016 | 0.3404 |
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+ | 0.7284 | 4050 | 0.0003 | - |
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+ | 0.7374 | 4100 | 0.0036 | - |
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+ | 0.7464 | 4150 | 0.0016 | - |
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+ | 0.7554 | 4200 | 0.0104 | - |
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+ | 0.7644 | 4250 | 0.003 | - |
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+ | 0.7734 | 4300 | 0.0159 | - |
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+ | 0.7824 | 4350 | 0.0029 | - |
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+ | 0.7914 | 4400 | 0.0068 | - |
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+ | 0.8004 | 4450 | 0.0021 | - |
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+ | 0.8094 | 4500 | 0.006 | - |
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+ | 0.8183 | 4550 | 0.006 | - |
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+ | 0.8273 | 4600 | 0.0038 | - |
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+ | 0.8363 | 4650 | 0.008 | - |
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+ | 0.8453 | 4700 | 0.0003 | - |
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+ | 0.8543 | 4750 | 0.0126 | - |
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+ | 0.8633 | 4800 | 0.0002 | - |
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+ | 0.8723 | 4850 | 0.0041 | - |
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+ | 0.8813 | 4900 | 0.0002 | - |
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+ | 0.8903 | 4950 | 0.0137 | - |
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+ | 0.8993 | 5000 | 0.0041 | 0.3363 |
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+ | 0.9083 | 5050 | 0.0252 | - |
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+ | 0.9173 | 5100 | 0.0023 | - |
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+ | 0.9263 | 5150 | 0.0062 | - |
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+ | 0.9353 | 5200 | 0.0152 | - |
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+ | 0.9442 | 5250 | 0.0014 | - |
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+ | 0.9532 | 5300 | 0.0224 | - |
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+ | 0.9622 | 5350 | 0.0174 | - |
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+ | 0.9712 | 5400 | 0.0066 | - |
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+ | 0.9802 | 5450 | 0.0002 | - |
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+ | 0.9892 | 5500 | 0.0136 | - |
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+ | 0.9982 | 5550 | 0.0036 | - |
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+ | 1.0072 | 5600 | 0.0102 | - |
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+ | 1.0162 | 5650 | 0.011 | - |
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+ | 1.0252 | 5700 | 0.0035 | - |
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+ | 1.0342 | 5750 | 0.0002 | - |
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+ | 1.0432 | 5800 | 0.0002 | - |
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+ | 1.0522 | 5850 | 0.0044 | - |
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+ | 1.0612 | 5900 | 0.0125 | - |
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+ | 1.0701 | 5950 | 0.0061 | - |
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+ | 1.0791 | 6000 | 0.0165 | 0.3591 |
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+ | 1.0881 | 6050 | 0.006 | - |
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+ | 1.0971 | 6100 | 0.0003 | - |
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+ | 1.1061 | 6150 | 0.0074 | - |
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+ | 1.1151 | 6200 | 0.0019 | - |
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+ | 1.1241 | 6250 | 0.0002 | - |
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+ | 1.1331 | 6300 | 0.0064 | - |
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+ | 1.1421 | 6350 | 0.0127 | - |
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+ | 1.1511 | 6400 | 0.0012 | - |
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+ | 1.1601 | 6450 | 0.0003 | - |
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+ | 1.1691 | 6500 | 0.0251 | - |
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+ | 1.1781 | 6550 | 0.0002 | - |
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+ | 1.1871 | 6600 | 0.0003 | - |
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+ | 1.1960 | 6650 | 0.0002 | - |
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+ | 1.2050 | 6700 | 0.0002 | - |
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+ | 1.2140 | 6750 | 0.0123 | - |
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+ | 1.2230 | 6800 | 0.0055 | - |
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+ | 1.2320 | 6850 | 0.0098 | - |
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+ | 1.2410 | 6900 | 0.0028 | - |
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+ | 1.25 | 6950 | 0.0049 | - |
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+ | 1.2590 | 7000 | 0.0021 | 0.3537 |
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+ | 1.2680 | 7050 | 0.0147 | - |
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+ | 1.2770 | 7100 | 0.003 | - |
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+ | 1.2860 | 7150 | 0.0002 | - |
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+ | 1.2950 | 7200 | 0.0049 | - |
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+ | 1.3040 | 7250 | 0.0033 | - |
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+ | 1.3129 | 7300 | 0.0002 | - |
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+ | 1.3219 | 7350 | 0.0065 | - |
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+ | 1.3309 | 7400 | 0.0043 | - |
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+ | 1.3399 | 7450 | 0.0107 | - |
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+ | 1.3489 | 7500 | 0.0184 | - |
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+ | 1.3579 | 7550 | 0.0116 | - |
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+ | 1.3669 | 7600 | 0.0041 | - |
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+ | 1.3759 | 7650 | 0.0001 | - |
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+ | 1.3849 | 7700 | 0.0001 | - |
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+ | 1.3939 | 7750 | 0.0074 | - |
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+ | 1.4029 | 7800 | 0.0002 | - |
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+ | 1.4119 | 7850 | 0.0087 | - |
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+ | 1.4209 | 7900 | 0.0014 | - |
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+ | 1.4299 | 7950 | 0.0045 | - |
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+ | 1.4388 | 8000 | 0.0018 | 0.3439 |
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+ | 1.4478 | 8050 | 0.0039 | - |
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+ | 1.4568 | 8100 | 0.007 | - |
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+ | 1.4658 | 8150 | 0.0066 | - |
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+ | 1.4748 | 8200 | 0.0101 | - |
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+ | 1.4838 | 8250 | 0.0047 | - |
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+ | 1.4928 | 8300 | 0.0021 | - |
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+ | 1.5018 | 8350 | 0.0002 | - |
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+ | 1.5108 | 8400 | 0.0116 | - |
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+ | 1.5198 | 8450 | 0.0017 | - |
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+ | 1.5288 | 8500 | 0.0032 | - |
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+ | 1.5378 | 8550 | 0.0053 | - |
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+ | 1.5468 | 8600 | 0.0038 | - |
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+ | 1.5558 | 8650 | 0.0001 | - |
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+ | 1.5647 | 8700 | 0.002 | - |
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+ | 1.5737 | 8750 | 0.0065 | - |
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+ | 1.5827 | 8800 | 0.0064 | - |
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+ | 1.5917 | 8850 | 0.0001 | - |
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+ | 1.6007 | 8900 | 0.0049 | - |
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+ | 1.6097 | 8950 | 0.0002 | - |
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+ | 1.6187 | 9000 | 0.0083 | 0.3486 |
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+ | 1.6277 | 9050 | 0.0105 | - |
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+ | 1.6367 | 9100 | 0.0019 | - |
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+ | 1.6457 | 9150 | 0.0002 | - |
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+ | 1.6547 | 9200 | 0.0049 | - |
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+ | 1.6637 | 9250 | 0.0001 | - |
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+ | 1.6727 | 9300 | 0.0097 | - |
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+ | 1.6817 | 9350 | 0.0098 | - |
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+ | 1.6906 | 9400 | 0.0022 | - |
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+ | 1.6996 | 9450 | 0.0142 | - |
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+ | 1.7086 | 9500 | 0.0025 | - |
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+ | 1.7176 | 9550 | 0.0147 | - |
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+ | 1.7266 | 9600 | 0.0086 | - |
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+ | 1.7356 | 9650 | 0.0062 | - |
349
+ | 1.7446 | 9700 | 0.0002 | - |
350
+ | 1.7536 | 9750 | 0.0103 | - |
351
+ | 1.7626 | 9800 | 0.0186 | - |
352
+ | 1.7716 | 9850 | 0.0112 | - |
353
+ | 1.7806 | 9900 | 0.0042 | - |
354
+ | 1.7896 | 9950 | 0.0166 | - |
355
+ | 1.7986 | 10000 | 0.0002 | 0.3571 |
356
+ | 1.8076 | 10050 | 0.0029 | - |
357
+ | 1.8165 | 10100 | 0.0055 | - |
358
+ | 1.8255 | 10150 | 0.0057 | - |
359
+ | 1.8345 | 10200 | 0.0163 | - |
360
+ | 1.8435 | 10250 | 0.0093 | - |
361
+ | 1.8525 | 10300 | 0.0083 | - |
362
+ | 1.8615 | 10350 | 0.0073 | - |
363
+ | 1.8705 | 10400 | 0.0089 | - |
364
+ | 1.8795 | 10450 | 0.0068 | - |
365
+ | 1.8885 | 10500 | 0.0001 | - |
366
+ | 1.8975 | 10550 | 0.0232 | - |
367
+ | 1.9065 | 10600 | 0.0161 | - |
368
+ | 1.9155 | 10650 | 0.0088 | - |
369
+ | 1.9245 | 10700 | 0.0002 | - |
370
+ | 1.9335 | 10750 | 0.0093 | - |
371
+ | 1.9424 | 10800 | 0.0103 | - |
372
+ | 1.9514 | 10850 | 0.002 | - |
373
+ | 1.9604 | 10900 | 0.0113 | - |
374
+ | 1.9694 | 10950 | 0.0055 | - |
375
+ | 1.9784 | 11000 | 0.0148 | 0.3461 |
376
+ | 1.9874 | 11050 | 0.0001 | - |
377
+ | 1.9964 | 11100 | 0.0017 | - |
378
+ | 2.0054 | 11150 | 0.0001 | - |
379
+ | 2.0144 | 11200 | 0.0204 | - |
380
+ | 2.0234 | 11250 | 0.0032 | - |
381
+ | 2.0324 | 11300 | 0.0029 | - |
382
+ | 2.0414 | 11350 | 0.002 | - |
383
+ | 2.0504 | 11400 | 0.0001 | - |
384
+ | 2.0594 | 11450 | 0.005 | - |
385
+ | 2.0683 | 11500 | 0.0001 | - |
386
+ | 2.0773 | 11550 | 0.0051 | - |
387
+ | 2.0863 | 11600 | 0.0095 | - |
388
+ | 2.0953 | 11650 | 0.0093 | - |
389
+ | 2.1043 | 11700 | 0.0171 | - |
390
+ | 2.1133 | 11750 | 0.0059 | - |
391
+ | 2.1223 | 11800 | 0.0026 | - |
392
+ | 2.1313 | 11850 | 0.0092 | - |
393
+ | 2.1403 | 11900 | 0.0002 | - |
394
+ | 2.1493 | 11950 | 0.0069 | - |
395
+ | 2.1583 | 12000 | 0.006 | 0.3572 |
396
+ | 2.1673 | 12050 | 0.009 | - |
397
+ | 2.1763 | 12100 | 0.008 | - |
398
+ | 2.1853 | 12150 | 0.0001 | - |
399
+ | 2.1942 | 12200 | 0.0062 | - |
400
+ | 2.2032 | 12250 | 0.0086 | - |
401
+ | 2.2122 | 12300 | 0.0001 | - |
402
+ | 2.2212 | 12350 | 0.0001 | - |
403
+ | 2.2302 | 12400 | 0.0001 | - |
404
+ | 2.2392 | 12450 | 0.0001 | - |
405
+ | 2.2482 | 12500 | 0.0022 | - |
406
+ | 2.2572 | 12550 | 0.0014 | - |
407
+ | 2.2662 | 12600 | 0.0014 | - |
408
+ | 2.2752 | 12650 | 0.009 | - |
409
+ | 2.2842 | 12700 | 0.0001 | - |
410
+ | 2.2932 | 12750 | 0.0081 | - |
411
+ | 2.3022 | 12800 | 0.0127 | - |
412
+ | 2.3112 | 12850 | 0.0001 | - |
413
+ | 2.3201 | 12900 | 0.0028 | - |
414
+ | 2.3291 | 12950 | 0.0016 | - |
415
+ | 2.3381 | 13000 | 0.0051 | 0.3587 |
416
+ | 2.3471 | 13050 | 0.0044 | - |
417
+ | 2.3561 | 13100 | 0.0133 | - |
418
+ | 2.3651 | 13150 | 0.0043 | - |
419
+ | 2.3741 | 13200 | 0.0001 | - |
420
+ | 2.3831 | 13250 | 0.0017 | - |
421
+ | 2.3921 | 13300 | 0.0095 | - |
422
+ | 2.4011 | 13350 | 0.008 | - |
423
+ | 2.4101 | 13400 | 0.0074 | - |
424
+ | 2.4191 | 13450 | 0.0181 | - |
425
+ | 2.4281 | 13500 | 0.0141 | - |
426
+ | 2.4371 | 13550 | 0.0114 | - |
427
+ | 2.4460 | 13600 | 0.0046 | - |
428
+ | 2.4550 | 13650 | 0.0053 | - |
429
+ | 2.4640 | 13700 | 0.0001 | - |
430
+ | 2.4730 | 13750 | 0.0001 | - |
431
+ | 2.4820 | 13800 | 0.0114 | - |
432
+ | 2.4910 | 13850 | 0.0001 | - |
433
+ | 2.5 | 13900 | 0.0075 | - |
434
+ | 2.5090 | 13950 | 0.0016 | - |
435
+ | 2.5180 | 14000 | 0.0014 | 0.3376 |
436
+ | 2.5270 | 14050 | 0.0075 | - |
437
+ | 2.5360 | 14100 | 0.0001 | - |
438
+ | 2.5450 | 14150 | 0.0001 | - |
439
+ | 2.5540 | 14200 | 0.0013 | - |
440
+ | 2.5629 | 14250 | 0.0001 | - |
441
+ | 2.5719 | 14300 | 0.0082 | - |
442
+ | 2.5809 | 14350 | 0.0021 | - |
443
+ | 2.5899 | 14400 | 0.0001 | - |
444
+ | 2.5989 | 14450 | 0.0001 | - |
445
+ | 2.6079 | 14500 | 0.0016 | - |
446
+ | 2.6169 | 14550 | 0.0001 | - |
447
+ | 2.6259 | 14600 | 0.0001 | - |
448
+ | 2.6349 | 14650 | 0.0058 | - |
449
+ | 2.6439 | 14700 | 0.0223 | - |
450
+ | 2.6529 | 14750 | 0.0001 | - |
451
+ | 2.6619 | 14800 | 0.0001 | - |
452
+ | 2.6709 | 14850 | 0.0249 | - |
453
+ | 2.6799 | 14900 | 0.008 | - |
454
+ | 2.6888 | 14950 | 0.0071 | - |
455
+ | 2.6978 | 15000 | 0.0237 | 0.3769 |
456
+ | 2.7068 | 15050 | 0.0001 | - |
457
+ | 2.7158 | 15100 | 0.0016 | - |
458
+ | 2.7248 | 15150 | 0.0031 | - |
459
+ | 2.7338 | 15200 | 0.0063 | - |
460
+ | 2.7428 | 15250 | 0.0001 | - |
461
+ | 2.7518 | 15300 | 0.0127 | - |
462
+ | 2.7608 | 15350 | 0.0001 | - |
463
+ | 2.7698 | 15400 | 0.0114 | - |
464
+ | 2.7788 | 15450 | 0.0106 | - |
465
+ | 2.7878 | 15500 | 0.0086 | - |
466
+ | 2.7968 | 15550 | 0.0083 | - |
467
+ | 2.8058 | 15600 | 0.0001 | - |
468
+ | 2.8147 | 15650 | 0.0001 | - |
469
+ | 2.8237 | 15700 | 0.0035 | - |
470
+ | 2.8327 | 15750 | 0.0095 | - |
471
+ | 2.8417 | 15800 | 0.0041 | - |
472
+ | 2.8507 | 15850 | 0.0001 | - |
473
+ | 2.8597 | 15900 | 0.0001 | - |
474
+ | 2.8687 | 15950 | 0.0001 | - |
475
+ | 2.8777 | 16000 | 0.0001 | 0.3509 |
476
+ | 2.8867 | 16050 | 0.0001 | - |
477
+ | 2.8957 | 16100 | 0.0124 | - |
478
+ | 2.9047 | 16150 | 0.0083 | - |
479
+ | 2.9137 | 16200 | 0.0017 | - |
480
+ | 2.9227 | 16250 | 0.0001 | - |
481
+ | 2.9317 | 16300 | 0.0042 | - |
482
+ | 2.9406 | 16350 | 0.0058 | - |
483
+ | 2.9496 | 16400 | 0.0001 | - |
484
+ | 2.9586 | 16450 | 0.0001 | - |
485
+ | 2.9676 | 16500 | 0.0021 | - |
486
+ | 2.9766 | 16550 | 0.0025 | - |
487
+ | 2.9856 | 16600 | 0.0068 | - |
488
+ | 2.9946 | 16650 | 0.0099 | - |
489
+ | 3.0036 | 16700 | 0.0015 | - |
490
+ | 3.0126 | 16750 | 0.0086 | - |
491
+ | 3.0216 | 16800 | 0.0162 | - |
492
+ | 3.0306 | 16850 | 0.0001 | - |
493
+ | 3.0396 | 16900 | 0.0181 | - |
494
+ | 3.0486 | 16950 | 0.0083 | - |
495
+ | 3.0576 | 17000 | 0.0045 | 0.346 |
496
+ | 3.0665 | 17050 | 0.0072 | - |
497
+ | 3.0755 | 17100 | 0.0045 | - |
498
+ | 3.0845 | 17150 | 0.005 | - |
499
+ | 3.0935 | 17200 | 0.003 | - |
500
+ | 3.1025 | 17250 | 0.0069 | - |
501
+ | 3.1115 | 17300 | 0.0001 | - |
502
+ | 3.1205 | 17350 | 0.003 | - |
503
+ | 3.1295 | 17400 | 0.0077 | - |
504
+ | 3.1385 | 17450 | 0.0001 | - |
505
+ | 3.1475 | 17500 | 0.0001 | - |
506
+ | 3.1565 | 17550 | 0.0166 | - |
507
+ | 3.1655 | 17600 | 0.0001 | - |
508
+ | 3.1745 | 17650 | 0.0001 | - |
509
+ | 3.1835 | 17700 | 0.0084 | - |
510
+ | 3.1924 | 17750 | 0.0106 | - |
511
+ | 3.2014 | 17800 | 0.0027 | - |
512
+ | 3.2104 | 17850 | 0.0092 | - |
513
+ | 3.2194 | 17900 | 0.0001 | - |
514
+ | 3.2284 | 17950 | 0.0001 | - |
515
+ | 3.2374 | 18000 | 0.0066 | 0.3501 |
516
+ | 3.2464 | 18050 | 0.0037 | - |
517
+ | 3.2554 | 18100 | 0.0035 | - |
518
+ | 3.2644 | 18150 | 0.0029 | - |
519
+ | 3.2734 | 18200 | 0.0017 | - |
520
+ | 3.2824 | 18250 | 0.0001 | - |
521
+ | 3.2914 | 18300 | 0.0034 | - |
522
+ | 3.3004 | 18350 | 0.0121 | - |
523
+ | 3.3094 | 18400 | 0.0051 | - |
524
+ | 3.3183 | 18450 | 0.0024 | - |
525
+ | 3.3273 | 18500 | 0.0019 | - |
526
+ | 3.3363 | 18550 | 0.0014 | - |
527
+ | 3.3453 | 18600 | 0.0167 | - |
528
+ | 3.3543 | 18650 | 0.0097 | - |
529
+ | 3.3633 | 18700 | 0.0025 | - |
530
+ | 3.3723 | 18750 | 0.0065 | - |
531
+ | 3.3813 | 18800 | 0.011 | - |
532
+ | 3.3903 | 18850 | 0.0001 | - |
533
+ | 3.3993 | 18900 | 0.0001 | - |
534
+ | 3.4083 | 18950 | 0.0072 | - |
535
+ | 3.4173 | 19000 | 0.0132 | 0.3511 |
536
+ | 3.4263 | 19050 | 0.0084 | - |
537
+ | 3.4353 | 19100 | 0.0015 | - |
538
+ | 3.4442 | 19150 | 0.0014 | - |
539
+ | 3.4532 | 19200 | 0.011 | - |
540
+ | 3.4622 | 19250 | 0.0083 | - |
541
+ | 3.4712 | 19300 | 0.0073 | - |
542
+ | 3.4802 | 19350 | 0.0024 | - |
543
+ | 3.4892 | 19400 | 0.002 | - |
544
+ | 3.4982 | 19450 | 0.0155 | - |
545
+ | 3.5072 | 19500 | 0.0042 | - |
546
+ | 3.5162 | 19550 | 0.0001 | - |
547
+ | 3.5252 | 19600 | 0.0043 | - |
548
+ | 3.5342 | 19650 | 0.0026 | - |
549
+ | 3.5432 | 19700 | 0.0022 | - |
550
+ | 3.5522 | 19750 | 0.002 | - |
551
+ | 3.5612 | 19800 | 0.0018 | - |
552
+ | 3.5701 | 19850 | 0.0001 | - |
553
+ | 3.5791 | 19900 | 0.0012 | - |
554
+ | 3.5881 | 19950 | 0.002 | - |
555
+ | 3.5971 | 20000 | 0.0089 | 0.3516 |
556
+ | 3.6061 | 20050 | 0.003 | - |
557
+ | 3.6151 | 20100 | 0.0036 | - |
558
+ | 3.6241 | 20150 | 0.0001 | - |
559
+ | 3.6331 | 20200 | 0.0001 | - |
560
+ | 3.6421 | 20250 | 0.0156 | - |
561
+ | 3.6511 | 20300 | 0.0001 | - |
562
+ | 3.6601 | 20350 | 0.0174 | - |
563
+ | 3.6691 | 20400 | 0.0001 | - |
564
+ | 3.6781 | 20450 | 0.011 | - |
565
+ | 3.6871 | 20500 | 0.0001 | - |
566
+ | 3.6960 | 20550 | 0.0047 | - |
567
+ | 3.7050 | 20600 | 0.0132 | - |
568
+ | 3.7140 | 20650 | 0.007 | - |
569
+ | 3.7230 | 20700 | 0.0001 | - |
570
+ | 3.7320 | 20750 | 0.0025 | - |
571
+ | 3.7410 | 20800 | 0.0049 | - |
572
+ | 3.75 | 20850 | 0.0074 | - |
573
+ | 3.7590 | 20900 | 0.002 | - |
574
+ | 3.7680 | 20950 | 0.0112 | - |
575
+ | 3.7770 | 21000 | 0.0001 | 0.3483 |
576
+ | 3.7860 | 21050 | 0.0001 | - |
577
+ | 3.7950 | 21100 | 0.0064 | - |
578
+ | 3.8040 | 21150 | 0.0133 | - |
579
+ | 3.8129 | 21200 | 0.0001 | - |
580
+ | 3.8219 | 21250 | 0.0112 | - |
581
+ | 3.8309 | 21300 | 0.0001 | - |
582
+ | 3.8399 | 21350 | 0.0001 | - |
583
+ | 3.8489 | 21400 | 0.0001 | - |
584
+ | 3.8579 | 21450 | 0.0025 | - |
585
+ | 3.8669 | 21500 | 0.0047 | - |
586
+ | 3.8759 | 21550 | 0.0001 | - |
587
+ | 3.8849 | 21600 | 0.0062 | - |
588
+ | 3.8939 | 21650 | 0.0001 | - |
589
+ | 3.9029 | 21700 | 0.0315 | - |
590
+ | 3.9119 | 21750 | 0.002 | - |
591
+ | 3.9209 | 21800 | 0.0034 | - |
592
+ | 3.9299 | 21850 | 0.004 | - |
593
+ | 3.9388 | 21900 | 0.0046 | - |
594
+ | 3.9478 | 21950 | 0.008 | - |
595
+ | 3.9568 | 22000 | 0.0103 | 0.3474 |
596
+ | 3.9658 | 22050 | 0.0142 | - |
597
+ | 3.9748 | 22100 | 0.0207 | - |
598
+ | 3.9838 | 22150 | 0.0105 | - |
599
+ | 3.9928 | 22200 | 0.0114 | - |
600
+ | 4.0018 | 22250 | 0.002 | - |
601
+ | 4.0108 | 22300 | 0.0121 | - |
602
+ | 4.0198 | 22350 | 0.0001 | - |
603
+ | 4.0288 | 22400 | 0.0058 | - |
604
+ | 4.0378 | 22450 | 0.0045 | - |
605
+ | 4.0468 | 22500 | 0.0001 | - |
606
+ | 4.0558 | 22550 | 0.0086 | - |
607
+ | 4.0647 | 22600 | 0.0121 | - |
608
+ | 4.0737 | 22650 | 0.0045 | - |
609
+ | 4.0827 | 22700 | 0.0001 | - |
610
+ | 4.0917 | 22750 | 0.0046 | - |
611
+ | 4.1007 | 22800 | 0.0076 | - |
612
+ | 4.1097 | 22850 | 0.0001 | - |
613
+ | 4.1187 | 22900 | 0.0154 | - |
614
+ | 4.1277 | 22950 | 0.0108 | - |
615
+ | 4.1367 | 23000 | 0.0058 | 0.3575 |
616
+ | 4.1457 | 23050 | 0.0088 | - |
617
+ | 4.1547 | 23100 | 0.0019 | - |
618
+ | 4.1637 | 23150 | 0.0055 | - |
619
+ | 4.1727 | 23200 | 0.0299 | - |
620
+ | 4.1817 | 23250 | 0.0085 | - |
621
+ | 4.1906 | 23300 | 0.0016 | - |
622
+ | 4.1996 | 23350 | 0.0001 | - |
623
+ | 4.2086 | 23400 | 0.0001 | - |
624
+ | 4.2176 | 23450 | 0.0072 | - |
625
+ | 4.2266 | 23500 | 0.0092 | - |
626
+ | 4.2356 | 23550 | 0.0001 | - |
627
+ | 4.2446 | 23600 | 0.0064 | - |
628
+ | 4.2536 | 23650 | 0.0065 | - |
629
+ | 4.2626 | 23700 | 0.0001 | - |
630
+ | 4.2716 | 23750 | 0.0017 | - |
631
+ | 4.2806 | 23800 | 0.0083 | - |
632
+ | 4.2896 | 23850 | 0.0001 | - |
633
+ | 4.2986 | 23900 | 0.0039 | - |
634
+ | 4.3076 | 23950 | 0.002 | - |
635
+ | 4.3165 | 24000 | 0.0037 | 0.357 |
636
+ | 4.3255 | 24050 | 0.0095 | - |
637
+ | 4.3345 | 24100 | 0.002 | - |
638
+ | 4.3435 | 24150 | 0.017 | - |
639
+ | 4.3525 | 24200 | 0.0086 | - |
640
+ | 4.3615 | 24250 | 0.007 | - |
641
+ | 4.3705 | 24300 | 0.0023 | - |
642
+ | 4.3795 | 24350 | 0.0122 | - |
643
+ | 4.3885 | 24400 | 0.0097 | - |
644
+ | 4.3975 | 24450 | 0.0027 | - |
645
+ | 4.4065 | 24500 | 0.0081 | - |
646
+ | 4.4155 | 24550 | 0.0043 | - |
647
+ | 4.4245 | 24600 | 0.0055 | - |
648
+ | 4.4335 | 24650 | 0.0001 | - |
649
+ | 4.4424 | 24700 | 0.0014 | - |
650
+ | 4.4514 | 24750 | 0.0001 | - |
651
+ | 4.4604 | 24800 | 0.0091 | - |
652
+ | 4.4694 | 24850 | 0.0087 | - |
653
+ | 4.4784 | 24900 | 0.0101 | - |
654
+ | 4.4874 | 24950 | 0.0001 | - |
655
+ | 4.4964 | 25000 | 0.013 | 0.3566 |
656
+ | 4.5054 | 25050 | 0.013 | - |
657
+ | 4.5144 | 25100 | 0.0082 | - |
658
+ | 4.5234 | 25150 | 0.0063 | - |
659
+ | 4.5324 | 25200 | 0.0046 | - |
660
+ | 4.5414 | 25250 | 0.0087 | - |
661
+ | 4.5504 | 25300 | 0.0063 | - |
662
+ | 4.5594 | 25350 | 0.0019 | - |
663
+ | 4.5683 | 25400 | 0.0061 | - |
664
+ | 4.5773 | 25450 | 0.004 | - |
665
+ | 4.5863 | 25500 | 0.0001 | - |
666
+ | 4.5953 | 25550 | 0.0001 | - |
667
+ | 4.6043 | 25600 | 0.0088 | - |
668
+ | 4.6133 | 25650 | 0.0191 | - |
669
+ | 4.6223 | 25700 | 0.0124 | - |
670
+ | 4.6313 | 25750 | 0.0001 | - |
671
+ | 4.6403 | 25800 | 0.0023 | - |
672
+ | 4.6493 | 25850 | 0.0001 | - |
673
+ | 4.6583 | 25900 | 0.0068 | - |
674
+ | 4.6673 | 25950 | 0.0001 | - |
675
+ | 4.6763 | 26000 | 0.0034 | 0.3563 |
676
+ | 4.6853 | 26050 | 0.0138 | - |
677
+ | 4.6942 | 26100 | 0.0001 | - |
678
+ | 4.7032 | 26150 | 0.0068 | - |
679
+ | 4.7122 | 26200 | 0.0091 | - |
680
+ | 4.7212 | 26250 | 0.0001 | - |
681
+ | 4.7302 | 26300 | 0.0152 | - |
682
+ | 4.7392 | 26350 | 0.0064 | - |
683
+ | 4.7482 | 26400 | 0.0021 | - |
684
+ | 4.7572 | 26450 | 0.0088 | - |
685
+ | 4.7662 | 26500 | 0.0001 | - |
686
+ | 4.7752 | 26550 | 0.0042 | - |
687
+ | 4.7842 | 26600 | 0.0022 | - |
688
+ | 4.7932 | 26650 | 0.0065 | - |
689
+ | 4.8022 | 26700 | 0.0039 | - |
690
+ | 4.8112 | 26750 | 0.0039 | - |
691
+ | 4.8201 | 26800 | 0.0001 | - |
692
+ | 4.8291 | 26850 | 0.0155 | - |
693
+ | 4.8381 | 26900 | 0.0021 | - |
694
+ | 4.8471 | 26950 | 0.0039 | - |
695
+ | 4.8561 | 27000 | 0.002 | 0.3555 |
696
+ | 4.8651 | 27050 | 0.0092 | - |
697
+ | 4.8741 | 27100 | 0.0001 | - |
698
+ | 4.8831 | 27150 | 0.0081 | - |
699
+ | 4.8921 | 27200 | 0.0081 | - |
700
+ | 4.9011 | 27250 | 0.0037 | - |
701
+ | 4.9101 | 27300 | 0.0104 | - |
702
+ | 4.9191 | 27350 | 0.0022 | - |
703
+ | 4.9281 | 27400 | 0.004 | - |
704
+ | 4.9371 | 27450 | 0.0076 | - |
705
+ | 4.9460 | 27500 | 0.0043 | - |
706
+ | 4.9550 | 27550 | 0.0142 | - |
707
+ | 4.9640 | 27600 | 0.0126 | - |
708
+ | 4.9730 | 27650 | 0.0038 | - |
709
+ | 4.9820 | 27700 | 0.0107 | - |
710
+ | 4.9910 | 27750 | 0.0019 | - |
711
+ | 5.0 | 27800 | 0.0104 | - |
712
+
713
+ * The bold row denotes the saved checkpoint.
714
+ ### Framework Versions
715
+ - Python: 3.10.12
716
+ - SetFit: 1.0.3
717
+ - Sentence Transformers: 3.0.1
718
+ - spaCy: 3.7.4
719
+ - Transformers: 4.39.0
720
+ - PyTorch: 2.3.1+cu121
721
+ - Datasets: 2.20.0
722
+ - Tokenizers: 0.15.2
723
+
724
+ ## Citation
725
+
726
+ ### BibTeX
727
+ ```bibtex
728
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
729
+ doi = {10.48550/ARXIV.2209.11055},
730
+ url = {https://arxiv.org/abs/2209.11055},
731
+ 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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+ ## Model Card Authors
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+
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+ -->
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+ <!--
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