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STEMO-SetFit

Text Classification
setfit
Safetensors
sentence-transformers
xlm-roberta
generated_from_setfit_trainer
text-embeddings-inference
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  11. sentencepiece.bpe.model +3 -0
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  13. tokenizer.json +3 -0
  14. tokenizer_config.json +61 -0
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+ ---
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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: Really @chirjathya බිය නොවන්න. මා අනුගමනය කරන්න.
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+ - text: සිහිනයක් පමනකැයි සිතන්නද බිය වුනුපතිනියක වී සැරසිලාහද බැඳි කුමරු ගෙට යන්නට
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+ මහ හුගක් ආශාවල්ඇතිය ලඟ සියල් සොඳුරු පැතුමන්කඩාවැටුනත් දෙපා මුලඅහිංසකාවියේ මහ දෙය|K|
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+ pic.twitter.com/1w1nwGWXQC
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+ - text: அய்யா உங்களை விட அவரை நன்கு அறிந்தவர் இருக்கமுடியாது. அவர்களின் மறைவு உங்களுக்கு
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+ பெரிய மனவேதனை தரும் அவர்களின் என்பதில் ஐயம் இல்லை.
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+ - text: '@Helayaa_max @gihan_ddde_alwis හිත හදාාාගන්නෙමීදුක දරාගන්නෙමී:face_with_tears_of_joy::face_with_tears_of_joy::face_with_tears_of_joy:'
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+ - text: බලන්න මේ චුටි පැටියා ට අධික විදිය (සංවේදී කිසිම කෝපය මේක බලන්න එපා ගහන කෙනක්
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+ තියන අයත් බලන්න
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+ metrics:
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+ - accuracy
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+ pipeline_tag: text-classification
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+ library_name: setfit
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+ inference: true
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+ base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
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+ ---
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+
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+ # SetFit with sentence-transformers/paraphrase-multilingual-mpnet-base-v2
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+
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+ This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
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+
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+ The model has been trained using an efficient few-shot learning technique that involves:
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+
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+ 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
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+ 2. Training a classification head with features from the fine-tuned Sentence Transformer.
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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** SetFit
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+ - **Sentence Transformer body:** [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2)
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+ - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
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+ - **Maximum Sequence Length:** 128 tokens
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+ - **Number of Classes:** 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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+ | Happy | <ul><li>'Thanks For Tune In .අද ඇත්තටම සතුටු දවසක් :France: :grinning_face_with_big_eyes:.විකාශනයේ නිමාව! බායි …'</li><li>'@nwty_ck @imasha_fernando කොහොම හරි කමක් නෑ. සෙට් වෙන විදියකට ආතල් එකේ ඉන්නයි ඕන. හෙට මැරුනත් දුක නැති විදියකට.:smiling_face_with_smiling_eyes::smiling_face_with_smiling_eyes::smiling_face_with_smiling_eyes::smiling_face_with_smiling_eyes:'</li><li>'@Soulchild_Rishi සතුටු වෙන්න ඔනෙනෙ එහෙම හරි නාන්න වෙන එක ගැන..'</li></ul> |
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+ | Anger | <ul><li>'හදිසි\u200d කෝපය මරු කැදවයි..ඊයේ (09) රාත්\u200dරී කතරගම, කඳසුරිගම ප්\u200dරදේශයේ පුද්ගලයෙකු බිරිඳට තියුණු ආයුධයකින් පහරදී...'</li><li>'@dinu_nilmini @itzJambole ඔහොම ද මෙච්චර කරපු අක්කාට සලකන්නේ දුක හිතුණා ජම්බෝලස් ඒකට නම් :loudly_crying_face::loudly_crying_face::loudly_crying_face::loudly_crying_face::loudly_crying_face:'</li><li>'ඉක්මන් කෝපය...'</li></ul> |
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+ | Sadness | <ul><li>'@IamHisAnchor සමාව දෙන්න..එපා තියාගන්න:worried_face: දුක..'</li><li>'@otaradel you just looked at the picture and didnt read the caption සියලු සතුන්ට වේදනාව, දුක හා මරණ බිය දැනෙයි.සියලු සතුන්ට කරුණාවන්ත වෙමු.#LiveKind #BeKind #AllLivesMatter'</li><li>'බෑ මෙහෙම විඳගන්න තනියෙන් දුක හිර කරන්මට හරිම පාළුයි මගෙ රත්තරං:shushing_face::face_with_crossed-out_eyes::astonished_face::astonished_face::astonished_face::man_gesturing_OK:'</li></ul> |
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+ | Fear | <ul><li>'‘වියත් මග’ කිසියම් රැස්වීමක් ෂැංග්\u200dරිලා හෝටලයේ තිබුණා ය කියා ලංකාවට ඒකාධිපතියකු ආවොත් මාර ආතල් එකක් ලැබේ ය කියා සිතන අය උෂාර් වී සිටින අතර, ගෝඨාභය රාජපක්ෂ ජනාධිපති වුණොත් ලංකාව මිනී කනත්තක් වේ යයි සිතෙන අයට අධික බිය ඇති වී ගුද මාර්ගය හීන් වී ඇත.'</li><li>'අදත් රෑ වෙනකං යාළුවො ඇවිත් කතා කරල යාවි. හෙටත් අනිද්දත්. හත්දවසෙ දානෙ ඉවරවෙනකං. සුදු කොඩි දාපු ඩීපි වෙනස් වේවි. ඉන්පස්සෙ උඹේ දුක බලන්න කවුරුත් නැතිවේවි.'</li><li>'කශ්ශපගෙ බය සීගිරිය උනා..අට වෙනි පුදුමය එයම උනා.. බයෙන් බයෙන් thrill ගත් රට මේකද.. #sajje'</li></ul> |
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+ | Surprise | <ul><li>'වෙන එකෙක්ට මෙහෙම උනානම් දුකක් නෑ Charith Thambavita ..:face_with_tears_of_joy: පවුල් පිටින් දඩ අලි වෙච්ච උබටම මේක උනු එකයි මට දුක.. අවුලක් නෑ අවුරුද්දකට සැරයක් ජාත්\u200dයන්තරේ ජයගන්න එක උබේ හැකියාවනේ.. ට්\u200dරිප් එක ඉවර කරලා ඉක්මනට වරෙන් සහෝදරයා... …'</li><li>'ඔබ පුදුමය පත් වන දෙයක් සිතුවාද ? කැනඩාවේ හිටපු ආරක්ෂක අමාත්\u200dය Paul Hellyer විසින් හෙළි කර තිබෙනවා “ වසර දහස් ගණන්...'</li><li>'ලොව 8 වෙනි පුදුමය නැවත සොයාගනියි, එය අතුරුදහන් වුනේ වසර 131 පෙරයි - 8th ... via @YouTube'</li></ul> |
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+ | Disgust | <ul><li>'@NightWingzzz අප්\u200dරසන්නයි නේ!'</li><li>'@nirowa74 :grinning_face::grinning_face::grinning_face:ඒ විතරක් නෙමේ අයියෙ ඔය සේරමත් කරලා සිංහලයයි බෞද්ධාගමත් මේ රටෙන් තුරන් කරපු දවසට ඔය හිටං තවත් සතුටු වෙයි'</li><li>'@යහපාලන ආණ්ඩුවේ @officialunp වුර්තිය සමිති යොදා අප්\u200dරසන්න වීගෙන ආණ්ඩු විරෝධය වෙනස් කිරීමේ පහත් ක්\u200dරියාවලියක කුමන්ත��\u200dරණකාරී වැඩපිළිවෙලක් නොවේද මේ. තමන්ගේම ජාතික සේවක සංගමයට පොලිසිය යොදා පහර දීම විහිළුවක් නොවේද? … #SriLanka #SriLankan @Neetwit @sajithpremadasa pic.twitter.com/6PPPLk5t4s'</li></ul> |
61
+
62
+ ## Uses
63
+
64
+ ### Direct Use for Inference
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+
66
+ First install the SetFit library:
67
+
68
+ ```bash
69
+ 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("setfit_model_id")
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+ # Run inference
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+ preds = model("Really @chirjathya බිය නොවන්න. මා අනුගමනය කරන්න.")
81
+ ```
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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 | 16.2605 | 53 |
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+
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+ | Label | Training Sample Count |
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+ |:---------|:----------------------|
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+ | Happy | 1610 |
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+ | Anger | 1610 |
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+ | Sadness | 1610 |
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+ | Fear | 1610 |
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+ | Surprise | 1610 |
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+ | Disgust | 1610 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (16, 16)
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+ - num_epochs: (1, 1)
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+ - max_steps: -1
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+ - sampling_strategy: oversampling
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+ - num_iterations: 20
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+ - body_learning_rate: (1e-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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+ - l2_weight: 0.01
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+ - seed: 42
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+ - evaluation_strategy: epoch
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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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+ | 0.0000 | 1 | 0.2776 | - |
147
+ | 0.0021 | 50 | 0.2549 | - |
148
+ | 0.0041 | 100 | 0.2624 | - |
149
+ | 0.0062 | 150 | 0.2501 | - |
150
+ | 0.0083 | 200 | 0.2594 | - |
151
+ | 0.0104 | 250 | 0.2484 | - |
152
+ | 0.0124 | 300 | 0.259 | - |
153
+ | 0.0145 | 350 | 0.2403 | - |
154
+ | 0.0166 | 400 | 0.2257 | - |
155
+ | 0.0186 | 450 | 0.2218 | - |
156
+ | 0.0207 | 500 | 0.2148 | - |
157
+ | 0.0228 | 550 | 0.2021 | - |
158
+ | 0.0248 | 600 | 0.1823 | - |
159
+ | 0.0269 | 650 | 0.1759 | - |
160
+ | 0.0290 | 700 | 0.1682 | - |
161
+ | 0.0311 | 750 | 0.1668 | - |
162
+ | 0.0331 | 800 | 0.1527 | - |
163
+ | 0.0352 | 850 | 0.1353 | - |
164
+ | 0.0373 | 900 | 0.1293 | - |
165
+ | 0.0393 | 950 | 0.1169 | - |
166
+ | 0.0414 | 1000 | 0.1125 | - |
167
+ | 0.0435 | 1050 | 0.1137 | - |
168
+ | 0.0455 | 1100 | 0.1081 | - |
169
+ | 0.0476 | 1150 | 0.0986 | - |
170
+ | 0.0497 | 1200 | 0.089 | - |
171
+ | 0.0518 | 1250 | 0.0808 | - |
172
+ | 0.0538 | 1300 | 0.0735 | - |
173
+ | 0.0559 | 1350 | 0.0753 | - |
174
+ | 0.0580 | 1400 | 0.0751 | - |
175
+ | 0.0600 | 1450 | 0.064 | - |
176
+ | 0.0621 | 1500 | 0.0674 | - |
177
+ | 0.0642 | 1550 | 0.0521 | - |
178
+ | 0.0663 | 1600 | 0.0438 | - |
179
+ | 0.0683 | 1650 | 0.0406 | - |
180
+ | 0.0704 | 1700 | 0.0347 | - |
181
+ | 0.0725 | 1750 | 0.028 | - |
182
+ | 0.0745 | 1800 | 0.0251 | - |
183
+ | 0.0766 | 1850 | 0.0376 | - |
184
+ | 0.0787 | 1900 | 0.0265 | - |
185
+ | 0.0807 | 1950 | 0.0294 | - |
186
+ | 0.0828 | 2000 | 0.0203 | - |
187
+ | 0.0849 | 2050 | 0.021 | - |
188
+ | 0.0870 | 2100 | 0.018 | - |
189
+ | 0.0890 | 2150 | 0.0171 | - |
190
+ | 0.0911 | 2200 | 0.01 | - |
191
+ | 0.0932 | 2250 | 0.0108 | - |
192
+ | 0.0952 | 2300 | 0.0092 | - |
193
+ | 0.0973 | 2350 | 0.0079 | - |
194
+ | 0.0994 | 2400 | 0.0107 | - |
195
+ | 0.1014 | 2450 | 0.016 | - |
196
+ | 0.1035 | 2500 | 0.01 | - |
197
+ | 0.1056 | 2550 | 0.0041 | - |
198
+ | 0.1077 | 2600 | 0.0071 | - |
199
+ | 0.1097 | 2650 | 0.0103 | - |
200
+ | 0.1118 | 2700 | 0.0067 | - |
201
+ | 0.1139 | 2750 | 0.0062 | - |
202
+ | 0.1159 | 2800 | 0.0071 | - |
203
+ | 0.1180 | 2850 | 0.0064 | - |
204
+ | 0.1201 | 2900 | 0.0089 | - |
205
+ | 0.1222 | 2950 | 0.0065 | - |
206
+ | 0.1242 | 3000 | 0.0031 | - |
207
+ | 0.1263 | 3050 | 0.0038 | - |
208
+ | 0.1284 | 3100 | 0.005 | - |
209
+ | 0.1304 | 3150 | 0.0038 | - |
210
+ | 0.1325 | 3200 | 0.0077 | - |
211
+ | 0.1346 | 3250 | 0.0022 | - |
212
+ | 0.1366 | 3300 | 0.0022 | - |
213
+ | 0.1387 | 3350 | 0.0008 | - |
214
+ | 0.1408 | 3400 | 0.0047 | - |
215
+ | 0.1429 | 3450 | 0.0024 | - |
216
+ | 0.1449 | 3500 | 0.0062 | - |
217
+ | 0.1470 | 3550 | 0.0038 | - |
218
+ | 0.1491 | 3600 | 0.0016 | - |
219
+ | 0.1511 | 3650 | 0.0011 | - |
220
+ | 0.1532 | 3700 | 0.0026 | - |
221
+ | 0.1553 | 3750 | 0.0005 | - |
222
+ | 0.1573 | 3800 | 0.002 | - |
223
+ | 0.1594 | 3850 | 0.0007 | - |
224
+ | 0.1615 | 3900 | 0.0003 | - |
225
+ | 0.1636 | 3950 | 0.0038 | - |
226
+ | 0.1656 | 4000 | 0.0042 | - |
227
+ | 0.1677 | 4050 | 0.0028 | - |
228
+ | 0.1698 | 4100 | 0.0021 | - |
229
+ | 0.1718 | 4150 | 0.0005 | - |
230
+ | 0.1739 | 4200 | 0.0003 | - |
231
+ | 0.1760 | 4250 | 0.0032 | - |
232
+ | 0.1781 | 4300 | 0.0018 | - |
233
+ | 0.1801 | 4350 | 0.0012 | - |
234
+ | 0.1822 | 4400 | 0.0023 | - |
235
+ | 0.1843 | 4450 | 0.0013 | - |
236
+ | 0.1863 | 4500 | 0.0028 | - |
237
+ | 0.1884 | 4550 | 0.0025 | - |
238
+ | 0.1905 | 4600 | 0.0029 | - |
239
+ | 0.1925 | 4650 | 0.0052 | - |
240
+ | 0.1946 | 4700 | 0.0007 | - |
241
+ | 0.1967 | 4750 | 0.0059 | - |
242
+ | 0.1988 | 4800 | 0.002 | - |
243
+ | 0.2008 | 4850 | 0.0029 | - |
244
+ | 0.2029 | 4900 | 0.0028 | - |
245
+ | 0.2050 | 4950 | 0.0017 | - |
246
+ | 0.2070 | 5000 | 0.0025 | - |
247
+ | 0.2091 | 5050 | 0.0031 | - |
248
+ | 0.2112 | 5100 | 0.0026 | - |
249
+ | 0.2133 | 5150 | 0.0013 | - |
250
+ | 0.2153 | 5200 | 0.0012 | - |
251
+ | 0.2174 | 5250 | 0.0023 | - |
252
+ | 0.2195 | 5300 | 0.0001 | - |
253
+ | 0.2215 | 5350 | 0.0018 | - |
254
+ | 0.2236 | 5400 | 0.0013 | - |
255
+ | 0.2257 | 5450 | 0.0034 | - |
256
+ | 0.2277 | 5500 | 0.005 | - |
257
+ | 0.2298 | 5550 | 0.0012 | - |
258
+ | 0.2319 | 5600 | 0.0017 | - |
259
+ | 0.2340 | 5650 | 0.0019 | - |
260
+ | 0.2360 | 5700 | 0.0028 | - |
261
+ | 0.2381 | 5750 | 0.0013 | - |
262
+ | 0.2402 | 5800 | 0.0025 | - |
263
+ | 0.2422 | 5850 | 0.001 | - |
264
+ | 0.2443 | 5900 | 0.0019 | - |
265
+ | 0.2464 | 5950 | 0.0036 | - |
266
+ | 0.2484 | 6000 | 0.0004 | - |
267
+ | 0.2505 | 6050 | 0.0013 | - |
268
+ | 0.2526 | 6100 | 0.003 | - |
269
+ | 0.2547 | 6150 | 0.0016 | - |
270
+ | 0.2567 | 6200 | 0.0001 | - |
271
+ | 0.2588 | 6250 | 0.0021 | - |
272
+ | 0.2609 | 6300 | 0.0017 | - |
273
+ | 0.2629 | 6350 | 0.0033 | - |
274
+ | 0.2650 | 6400 | 0.0005 | - |
275
+ | 0.2671 | 6450 | 0.0025 | - |
276
+ | 0.2692 | 6500 | 0.0056 | - |
277
+ | 0.2712 | 6550 | 0.0039 | - |
278
+ | 0.2733 | 6600 | 0.0002 | - |
279
+ | 0.2754 | 6650 | 0.0025 | - |
280
+ | 0.2774 | 6700 | 0.0005 | - |
281
+ | 0.2795 | 6750 | 0.0002 | - |
282
+ | 0.2816 | 6800 | 0.0011 | - |
283
+ | 0.2836 | 6850 | 0.0007 | - |
284
+ | 0.2857 | 6900 | 0.0014 | - |
285
+ | 0.2878 | 6950 | 0.0021 | - |
286
+ | 0.2899 | 7000 | 0.0002 | - |
287
+ | 0.2919 | 7050 | 0.0039 | - |
288
+ | 0.2940 | 7100 | 0.0013 | - |
289
+ | 0.2961 | 7150 | 0.0001 | - |
290
+ | 0.2981 | 7200 | 0.0017 | - |
291
+ | 0.3002 | 7250 | 0.0036 | - |
292
+ | 0.3023 | 7300 | 0.0017 | - |
293
+ | 0.3043 | 7350 | 0.0025 | - |
294
+ | 0.3064 | 7400 | 0.0015 | - |
295
+ | 0.3085 | 7450 | 0.003 | - |
296
+ | 0.3106 | 7500 | 0.0015 | - |
297
+ | 0.3126 | 7550 | 0.0002 | - |
298
+ | 0.3147 | 7600 | 0.003 | - |
299
+ | 0.3168 | 7650 | 0.0003 | - |
300
+ | 0.3188 | 7700 | 0.0001 | - |
301
+ | 0.3209 | 7750 | 0.0005 | - |
302
+ | 0.3230 | 7800 | 0.0007 | - |
303
+ | 0.3251 | 7850 | 0.0014 | - |
304
+ | 0.3271 | 7900 | 0.0013 | - |
305
+ | 0.3292 | 7950 | 0.0 | - |
306
+ | 0.3313 | 8000 | 0.0013 | - |
307
+ | 0.3333 | 8050 | 0.0002 | - |
308
+ | 0.3354 | 8100 | 0.0 | - |
309
+ | 0.3375 | 8150 | 0.0 | - |
310
+ | 0.3395 | 8200 | 0.0 | - |
311
+ | 0.3416 | 8250 | 0.0012 | - |
312
+ | 0.3437 | 8300 | 0.0001 | - |
313
+ | 0.3458 | 8350 | 0.0002 | - |
314
+ | 0.3478 | 8400 | 0.0038 | - |
315
+ | 0.3499 | 8450 | 0.0013 | - |
316
+ | 0.3520 | 8500 | 0.0014 | - |
317
+ | 0.3540 | 8550 | 0.0015 | - |
318
+ | 0.3561 | 8600 | 0.002 | - |
319
+ | 0.3582 | 8650 | 0.0001 | - |
320
+ | 0.3602 | 8700 | 0.0006 | - |
321
+ | 0.3623 | 8750 | 0.0031 | - |
322
+ | 0.3644 | 8800 | 0.0005 | - |
323
+ | 0.3665 | 8850 | 0.0004 | - |
324
+ | 0.3685 | 8900 | 0.0007 | - |
325
+ | 0.3706 | 8950 | 0.0024 | - |
326
+ | 0.3727 | 9000 | 0.0004 | - |
327
+ | 0.3747 | 9050 | 0.001 | - |
328
+ | 0.3768 | 9100 | 0.0001 | - |
329
+ | 0.3789 | 9150 | 0.0022 | - |
330
+ | 0.3810 | 9200 | 0.0013 | - |
331
+ | 0.3830 | 9250 | 0.0013 | - |
332
+ | 0.3851 | 9300 | 0.0031 | - |
333
+ | 0.3872 | 9350 | 0.0032 | - |
334
+ | 0.3892 | 9400 | 0.0005 | - |
335
+ | 0.3913 | 9450 | 0.0002 | - |
336
+ | 0.3934 | 9500 | 0.0 | - |
337
+ | 0.3954 | 9550 | 0.0004 | - |
338
+ | 0.3975 | 9600 | 0.0012 | - |
339
+ | 0.3996 | 9650 | 0.0011 | - |
340
+ | 0.4017 | 9700 | 0.0001 | - |
341
+ | 0.4037 | 9750 | 0.002 | - |
342
+ | 0.4058 | 9800 | 0.0015 | - |
343
+ | 0.4079 | 9850 | 0.0012 | - |
344
+ | 0.4099 | 9900 | 0.0018 | - |
345
+ | 0.4120 | 9950 | 0.0001 | - |
346
+ | 0.4141 | 10000 | 0.0002 | - |
347
+ | 0.4161 | 10050 | 0.0013 | - |
348
+ | 0.4182 | 10100 | 0.0017 | - |
349
+ | 0.4203 | 10150 | 0.001 | - |
350
+ | 0.4224 | 10200 | 0.0001 | - |
351
+ | 0.4244 | 10250 | 0.0001 | - |
352
+ | 0.4265 | 10300 | 0.0014 | - |
353
+ | 0.4286 | 10350 | 0.0001 | - |
354
+ | 0.4306 | 10400 | 0.001 | - |
355
+ | 0.4327 | 10450 | 0.0008 | - |
356
+ | 0.4348 | 10500 | 0.0004 | - |
357
+ | 0.4369 | 10550 | 0.0003 | - |
358
+ | 0.4389 | 10600 | 0.0012 | - |
359
+ | 0.4410 | 10650 | 0.0007 | - |
360
+ | 0.4431 | 10700 | 0.0 | - |
361
+ | 0.4451 | 10750 | 0.0 | - |
362
+ | 0.4472 | 10800 | 0.0004 | - |
363
+ | 0.4493 | 10850 | 0.0001 | - |
364
+ | 0.4513 | 10900 | 0.0 | - |
365
+ | 0.4534 | 10950 | 0.0004 | - |
366
+ | 0.4555 | 11000 | 0.0013 | - |
367
+ | 0.4576 | 11050 | 0.0 | - |
368
+ | 0.4596 | 11100 | 0.0016 | - |
369
+ | 0.4617 | 11150 | 0.0 | - |
370
+ | 0.4638 | 11200 | 0.0002 | - |
371
+ | 0.4658 | 11250 | 0.0011 | - |
372
+ | 0.4679 | 11300 | 0.0004 | - |
373
+ | 0.4700 | 11350 | 0.0009 | - |
374
+ | 0.4720 | 11400 | 0.0 | - |
375
+ | 0.4741 | 11450 | 0.0014 | - |
376
+ | 0.4762 | 11500 | 0.001 | - |
377
+ | 0.4783 | 11550 | 0.0028 | - |
378
+ | 0.4803 | 11600 | 0.0001 | - |
379
+ | 0.4824 | 11650 | 0.0009 | - |
380
+ | 0.4845 | 11700 | 0.0035 | - |
381
+ | 0.4865 | 11750 | 0.0002 | - |
382
+ | 0.4886 | 11800 | 0.0001 | - |
383
+ | 0.4907 | 11850 | 0.0 | - |
384
+ | 0.4928 | 11900 | 0.0001 | - |
385
+ | 0.4948 | 11950 | 0.0 | - |
386
+ | 0.4969 | 12000 | 0.0 | - |
387
+ | 0.4990 | 12050 | 0.0 | - |
388
+ | 0.5010 | 12100 | 0.0 | - |
389
+ | 0.5031 | 12150 | 0.0 | - |
390
+ | 0.5052 | 12200 | 0.0 | - |
391
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+
631
+ ### Framework Versions
632
+ - Python: 3.12.12
633
+ - SetFit: 1.1.3
634
+ - Sentence Transformers: 5.2.3
635
+ - Transformers: 4.44.2
636
+ - PyTorch: 2.10.0+cu128
637
+ - Datasets: 4.0.0
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+ - Tokenizers: 0.19.1
639
+
640
+ ## Citation
641
+
642
+ ### BibTeX
643
+ ```bibtex
644
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
645
+ doi = {10.48550/ARXIV.2209.11055},
646
+ url = {https://arxiv.org/abs/2209.11055},
647
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
648
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
649
+ title = {Efficient Few-Shot Learning Without Prompts},
650
+ publisher = {arXiv},
651
+ year = {2022},
652
+ copyright = {Creative Commons Attribution 4.0 International}
653
+ }
654
+ ```
655
+
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+ <!--
657
+ ## Glossary
658
+
659
+ *Clearly define terms in order to be accessible across audiences.*
660
+ -->
661
+
662
+ <!--
663
+ ## Model Card Authors
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+
665
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
666
+ -->
667
+
668
+ <!--
669
+ ## Model Card Contact
670
+
671
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
672
+ -->
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25
+ "special": true
26
+ },
27
+ "3": {
28
+ "content": "<unk>",
29
+ "lstrip": false,
30
+ "normalized": false,
31
+ "rstrip": false,
32
+ "single_word": false,
33
+ "special": true
34
+ },
35
+ "250001": {
36
+ "content": "<mask>",
37
+ "lstrip": true,
38
+ "normalized": false,
39
+ "rstrip": false,
40
+ "single_word": false,
41
+ "special": true
42
+ }
43
+ },
44
+ "bos_token": "<s>",
45
+ "clean_up_tokenization_spaces": true,
46
+ "cls_token": "<s>",
47
+ "eos_token": "</s>",
48
+ "mask_token": "<mask>",
49
+ "max_length": 128,
50
+ "model_max_length": 128,
51
+ "pad_to_multiple_of": null,
52
+ "pad_token": "<pad>",
53
+ "pad_token_type_id": 0,
54
+ "padding_side": "right",
55
+ "sep_token": "</s>",
56
+ "stride": 0,
57
+ "tokenizer_class": "XLMRobertaTokenizer",
58
+ "truncation_side": "right",
59
+ "truncation_strategy": "longest_first",
60
+ "unk_token": "<unk>"
61
+ }