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m-aliabbas1
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medicare_idrak_ab

Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
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Add SetFit model

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Files changed (13) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +805 -0
  3. config.json +24 -0
  4. config_sentence_transformers.json +10 -0
  5. config_setfit.json +34 -0
  6. model.safetensors +3 -0
  7. model_head.pkl +3 -0
  8. modules.json +14 -0
  9. sentence_bert_config.json +4 -0
  10. special_tokens_map.json +51 -0
  11. tokenizer.json +0 -0
  12. tokenizer_config.json +59 -0
  13. vocab.txt +0 -0
1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 768,
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+ "pooling_mode_cls_token": false,
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+ "pooling_mode_mean_tokens": true,
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+ "pooling_mode_max_tokens": false,
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+ "pooling_mode_mean_sqrt_len_tokens": false,
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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
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+ }
README.md ADDED
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+ ---
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+ library_name: setfit
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+ tags:
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+ - setfit
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+ - sentence-transformers
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+ - text-classification
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+ - generated_from_setfit_trainer
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+ base_model: sentence-transformers/paraphrase-mpnet-base-v2
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+ metrics:
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+ - accuracy
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+ widget:
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+ - text: i'm busy with a project can we talk later
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+ - text: are you situated in india by any chance
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+ - text: '35'
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+ - text: at the tone please record your message
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+ - text: i told you not to keep going
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+ pipeline_tag: text-classification
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+ inference: true
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+ ---
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+
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+ # SetFit with sentence-transformers/paraphrase-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-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-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-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-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:** 512 tokens
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+ - **Number of Classes:** 29 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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+ | decline | <ul><li>'no i am not registered with medicare for part a or part b'</li><li>"no i'm not in"</li><li>"thank you very much but i don't think i will need that"</li></ul> |
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+ | provide_age | <ul><li>'my age is 36'</li><li>"i'm 62 years old"</li><li>'49'</li></ul> |
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+ | complain_calls | <ul><li>'stop disrupting my life with these calls'</li><li>'your constant calls are causing me distress'</li><li>'same as i was last time you called'</li></ul> |
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+ | already | <ul><li>'i already charged it should be good to go'</li><li>'already took care of it no worries'</li><li>'got the app installed already'</li></ul> |
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+ | Not_Interested | <ul><li>"oh but i'm not interested"</li><li>"no i don't want to talk i don't want to talk"</li><li>"i'm not interested in making any decisions right now"</li></ul> |
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+ | DNC | <ul><li>'i asked you to stop calling me'</li><li>"i've had enough no more calls"</li><li>'would you take me off your list please'</li></ul> |
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+ | where_get_number | <ul><li>"i never provided my number to you what's going on"</li><li>'who is responsible for sharing my number with you'</li><li>"what's the source of my contact details in your database"</li></ul> |
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+ | language_barrier | <ul><li>"speak espanol i'm lost"</li><li>"sorry i'm not speaking english"</li><li>'no english'</li></ul> |
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+ | answering_machine | <ul><li>'this is the voicemail system record your message after the beep'</li><li>'this is the voicemail speak your message after the beep'</li><li>'you have reached a law source number that is no longer in service please check the number you have dialed'</li></ul> |
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+ | BUSY | <ul><li>"i'm engaged in a conference call can we talk later"</li><li>"right now i'm not able to talk right now bye"</li><li>"i don't have time"</li></ul> |
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+ | where_are_you_calling_from | <ul><li>'is the philippines where your organization is based'</li><li>'where can i find your headquarters'</li><li>"can you confirm if you're in canada"</li></ul> |
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+ | scam | <ul><li>"that's none of your business"</li><li>"i don't think i'm going to say it"</li><li>'scammers are clever what measures do you have to counteract them'</li></ul> |
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+ | affirmation | <ul><li>'yeah you are'</li><li>"for sure that's correct"</li><li>'i have secured both medicare part a and part b coverage'</li></ul> |
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+ | transfer_request | <ul><li>'i want to discuss this with someone higher up'</li><li>'transfer my call to your superior please'</li><li>'i require assistance from your manager immediately'</li></ul> |
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+ | abusive | <ul><li>'what the fuck you calling me for'</li><li>'and you rather the fucking guy you fucked up'</li><li>'crikey'</li></ul> |
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+ | calling_about | <ul><li>'why are you getting in touch with me'</li><li>"what's the main subject of discussion in this call"</li><li>"what's the rationale behind this call"</li></ul> |
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+ | GreetBack | <ul><li>"what's crackin' how you been"</li><li>"i'm doing good how are you doing"</li><li>"hi i'm fine how's your day been so far"</li></ul> |
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+ | say_again | <ul><li>'can you please say that again'</li><li>'sorry i need you to repeat that'</li><li>'what was that can you repeat it'</li></ul> |
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+ | sorry_greeting | <ul><li>"to be honest i'm feeling a bit down"</li><li>"i'm not really in a great mood"</li><li>"i'm not feeling very joyful right now"</li></ul> |
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+ | not_decision_maker | <ul><li>'decisions like this require a different approach'</li><li>"decisions of this nature aren't mine to make"</li><li>'decisions in this regard are not mine to make'</li></ul> |
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+ | hold_a_sec | <ul><li>'please stay on the line while i check'</li><li>'i need to consult with my colleague hold on'</li><li>"i'll be right back don't disconnect"</li></ul> |
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+ | interested | <ul><li>'your topic has piqued my curiosity do continue'</li><li>'go'</li><li>"i'm all ears talk"</li></ul> |
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+ | greetings | <ul><li>"i'm doing great thank you"</li><li>"hi there hope you're having a splendid day"</li><li>'rest well and recharge good night'</li></ul> |
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+ | who_are_you | <ul><li>'start by telling me who you are'</li><li>'can you tell me your given name'</li><li>"what's your name and position"</li></ul> |
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+ | can_you_email | <ul><li>'can you send an email with the instructions'</li><li>'can you email me the contract terms'</li><li>'is email the preferred way to receive updates'</li></ul> |
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+ | DNQ | <ul><li>"i'm not the right fit for this"</li><li>"i'm not the right person for this"</li><li>"it's not the right fit for me"</li></ul> |
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+ | are_you_bot | <ul><li>'is this interaction with an automated system'</li><li>'is this interaction with a human or a bot'</li><li>'is this interaction with a robot or human'</li></ul> |
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+ | other | <ul><li>'i love exploring the different neighborhoods of our city each has its own charm'</li><li>'no i have to buy a car'</li><li>'i was just reading an article about the latest technological innovations'</li></ul> |
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+ | weather | <ul><li>"how's the climate today"</li><li>'tell me what the weather is like'</li><li>"how's the weather in the morning"</li></ul> |
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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("m-aliabbas1/medicare_idrak_ab")
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+ # Run inference
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+ preds = model("35")
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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 | 7.3794 | 109 |
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+
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+ | Label | Training Sample Count |
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+ |:---------------------------|:----------------------|
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+ | BUSY | 528 |
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+ | DNC | 585 |
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+ | DNQ | 96 |
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+ | GreetBack | 224 |
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+ | Not_Interested | 497 |
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+ | abusive | 145 |
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+ | affirmation | 306 |
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+ | already | 70 |
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+ | answering_machine | 316 |
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+ | are_you_bot | 205 |
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+ | calling_about | 147 |
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+ | can_you_email | 116 |
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+ | complain_calls | 65 |
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+ | decline | 455 |
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+ | greetings | 82 |
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+ | hold_a_sec | 79 |
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+ | interested | 94 |
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+ | language_barrier | 163 |
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+ | not_decision_maker | 83 |
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+ | other | 56 |
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+ | provide_age | 355 |
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+ | say_again | 83 |
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+ | scam | 110 |
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+ | sorry_greeting | 102 |
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+ | transfer_request | 73 |
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+ | weather | 129 |
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+ | where_are_you_calling_from | 250 |
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+ | where_get_number | 127 |
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+ | who_are_you | 221 |
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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: 40
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+ - body_learning_rate: (2e-05, 2e-05)
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+ - head_learning_rate: 2e-05
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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: False
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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 | 0.2366 | - |
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+ | 0.0017 | 50 | 0.1785 | - |
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+ | 0.0035 | 100 | 0.1604 | - |
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+ | 0.0052 | 150 | 0.1877 | - |
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+ | 0.0069 | 200 | 0.1209 | - |
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+ | 0.0087 | 250 | 0.161 | - |
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+ | 0.0104 | 300 | 0.1261 | - |
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+ | 0.0121 | 350 | 0.153 | - |
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+ | 0.0139 | 400 | 0.1333 | - |
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+ | 0.0156 | 450 | 0.0675 | - |
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+ | 0.0174 | 500 | 0.0623 | - |
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+ | 0.0191 | 550 | 0.1324 | - |
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+ | 0.0208 | 600 | 0.0481 | - |
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+ | 0.0226 | 650 | 0.0894 | - |
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+ | 0.0243 | 700 | 0.0484 | - |
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+ | 0.0260 | 750 | 0.0683 | - |
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+ | 0.0278 | 800 | 0.1025 | - |
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+ | 0.0295 | 850 | 0.028 | - |
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+ | 0.0312 | 900 | 0.0218 | - |
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+ | 0.0330 | 950 | 0.0078 | - |
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+ | 0.0347 | 1000 | 0.0682 | - |
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+ | 0.0364 | 1050 | 0.0094 | - |
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+ | 0.0382 | 1100 | 0.0836 | - |
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+ | 0.0399 | 1150 | 0.0858 | - |
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+ | 0.0417 | 1200 | 0.0115 | - |
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+ | 0.0434 | 1250 | 0.0738 | - |
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+ | 0.0451 | 1300 | 0.009 | - |
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+ | 0.0469 | 1350 | 0.044 | - |
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+ | 0.0486 | 1400 | 0.059 | - |
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+ | 0.0503 | 1450 | 0.0271 | - |
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+ | 0.0521 | 1500 | 0.1249 | - |
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+ | 0.0538 | 1550 | 0.0032 | - |
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+ | 0.0555 | 1600 | 0.0897 | - |
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+ | 0.0573 | 1650 | 0.0758 | - |
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+ | 0.0590 | 1700 | 0.0573 | - |
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+ | 0.0607 | 1750 | 0.0063 | - |
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+ | 0.0625 | 1800 | 0.011 | - |
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+ | 0.0642 | 1850 | 0.005 | - |
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+ | 0.0659 | 1900 | 0.0545 | - |
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+ | 0.0677 | 1950 | 0.0216 | - |
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+ | 0.0694 | 2000 | 0.0059 | - |
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+ | 0.0712 | 2050 | 0.0043 | - |
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+ | 0.0729 | 2100 | 0.0109 | - |
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+ | 0.0746 | 2150 | 0.0049 | - |
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+ | 0.0764 | 2200 | 0.012 | - |
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+ | 0.0781 | 2250 | 0.0012 | - |
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+ | 0.0798 | 2300 | 0.0284 | - |
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+ | 0.0816 | 2350 | 0.0089 | - |
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+ | 0.0833 | 2400 | 0.0023 | - |
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+ | 0.0850 | 2450 | 0.0234 | - |
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+ | 0.0868 | 2500 | 0.0463 | - |
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+ | 0.0885 | 2550 | 0.0647 | - |
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+ | 0.0902 | 2600 | 0.0578 | - |
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+ | 0.0920 | 2650 | 0.0119 | - |
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+ | 0.0937 | 2700 | 0.0562 | - |
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+ | 0.0955 | 2750 | 0.0009 | - |
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+ | 0.0972 | 2800 | 0.0573 | - |
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+ | 0.0989 | 2850 | 0.0042 | - |
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+ | 0.1007 | 2900 | 0.0028 | - |
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+ | 0.1024 | 2950 | 0.0048 | - |
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+ | 0.1041 | 3000 | 0.1124 | - |
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+ | 0.1059 | 3050 | 0.0022 | - |
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+ | 0.1076 | 3100 | 0.0033 | - |
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+ | 0.1093 | 3150 | 0.0029 | - |
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+ | 0.1111 | 3200 | 0.0281 | - |
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+ | 0.1128 | 3250 | 0.0474 | - |
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+ | 0.1145 | 3300 | 0.0059 | - |
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+ | 0.1163 | 3350 | 0.0198 | - |
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+ | 0.1180 | 3400 | 0.128 | - |
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+ | 0.1198 | 3450 | 0.0092 | - |
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+ | 0.1215 | 3500 | 0.0023 | - |
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+ | 0.1232 | 3550 | 0.044 | - |
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+ | 0.1250 | 3600 | 0.0333 | - |
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+ | 0.1267 | 3650 | 0.0014 | - |
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+ | 0.1284 | 3700 | 0.0019 | - |
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+ | 0.1302 | 3750 | 0.0514 | - |
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+ | 0.1319 | 3800 | 0.0004 | - |
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+ | 0.1336 | 3850 | 0.0022 | - |
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+ | 0.1354 | 3900 | 0.0012 | - |
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+ | 0.1371 | 3950 | 0.0598 | - |
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+ | 0.1388 | 4000 | 0.0013 | - |
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+ | 0.1406 | 4050 | 0.0597 | - |
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+ | 0.1423 | 4100 | 0.0004 | - |
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+ | 0.1440 | 4150 | 0.0038 | - |
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+ | 0.1458 | 4200 | 0.0523 | - |
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+ | 0.1475 | 4250 | 0.0481 | - |
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+ | 0.1493 | 4300 | 0.1062 | - |
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+ | 0.1510 | 4350 | 0.0033 | - |
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+ | 0.1527 | 4400 | 0.0007 | - |
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+ | 0.1545 | 4450 | 0.0002 | - |
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+ | 0.1562 | 4500 | 0.0009 | - |
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+ | 0.1579 | 4550 | 0.0021 | - |
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+ | 0.1597 | 4600 | 0.0013 | - |
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+ | 0.1614 | 4650 | 0.0012 | - |
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+ | 0.1631 | 4700 | 0.0012 | - |
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+ | 0.1649 | 4750 | 0.0016 | - |
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+ | 0.1666 | 4800 | 0.0002 | - |
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+ | 0.1683 | 4850 | 0.0005 | - |
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+ | 0.1701 | 4900 | 0.0039 | - |
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+ | 0.1718 | 4950 | 0.0013 | - |
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+ | 0.1736 | 5000 | 0.0022 | - |
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+ | 0.1753 | 5050 | 0.0006 | - |
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+ | 0.1770 | 5100 | 0.002 | - |
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+ | 0.1788 | 5150 | 0.0004 | - |
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+ | 0.1805 | 5200 | 0.0009 | - |
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+ | 0.1822 | 5250 | 0.0004 | - |
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+ | 0.1840 | 5300 | 0.0006 | - |
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+ | 0.1857 | 5350 | 0.0107 | - |
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+ | 0.1874 | 5400 | 0.0002 | - |
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+ | 0.1892 | 5450 | 0.0006 | - |
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+ | 0.1909 | 5500 | 0.0017 | - |
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+ | 0.1926 | 5550 | 0.0049 | - |
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+ | 0.1944 | 5600 | 0.0006 | - |
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+ | 0.1961 | 5650 | 0.0138 | - |
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+ | 0.1978 | 5700 | 0.011 | - |
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+ | 0.1996 | 5750 | 0.0042 | - |
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+ | 0.2013 | 5800 | 0.0017 | - |
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+ | 0.2031 | 5850 | 0.0011 | - |
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+ | 0.2048 | 5900 | 0.0103 | - |
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+ | 0.2065 | 5950 | 0.0008 | - |
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+ | 0.2083 | 6000 | 0.0615 | - |
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+ | 0.2100 | 6050 | 0.0539 | - |
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+ | 0.2117 | 6100 | 0.0016 | - |
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+ | 0.2135 | 6150 | 0.0005 | - |
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+ | 0.2152 | 6200 | 0.0004 | - |
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+ | 0.2169 | 6250 | 0.0296 | - |
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+ | 0.2187 | 6300 | 0.0003 | - |
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+ | 0.2204 | 6350 | 0.0023 | - |
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+ | 0.2221 | 6400 | 0.0306 | - |
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+ | 0.2239 | 6450 | 0.0496 | - |
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+ | 0.2256 | 6500 | 0.0433 | - |
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+ | 0.2274 | 6550 | 0.0005 | - |
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+ | 0.2291 | 6600 | 0.0109 | - |
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+ | 0.2308 | 6650 | 0.0354 | - |
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+ | 0.2326 | 6700 | 0.0007 | - |
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+ | 0.2343 | 6750 | 0.0003 | - |
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+ | 0.2360 | 6800 | 0.0006 | - |
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+ | 0.2378 | 6850 | 0.0002 | - |
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+ | 0.2395 | 6900 | 0.0014 | - |
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+ | 0.2412 | 6950 | 0.0005 | - |
326
+ | 0.2430 | 7000 | 0.0002 | - |
327
+ | 0.2447 | 7050 | 0.0394 | - |
328
+ | 0.2464 | 7100 | 0.0006 | - |
329
+ | 0.2482 | 7150 | 0.0005 | - |
330
+ | 0.2499 | 7200 | 0.0002 | - |
331
+ | 0.2516 | 7250 | 0.0017 | - |
332
+ | 0.2534 | 7300 | 0.0004 | - |
333
+ | 0.2551 | 7350 | 0.0018 | - |
334
+ | 0.2569 | 7400 | 0.0184 | - |
335
+ | 0.2586 | 7450 | 0.0003 | - |
336
+ | 0.2603 | 7500 | 0.0515 | - |
337
+ | 0.2621 | 7550 | 0.0003 | - |
338
+ | 0.2638 | 7600 | 0.0013 | - |
339
+ | 0.2655 | 7650 | 0.0609 | - |
340
+ | 0.2673 | 7700 | 0.0017 | - |
341
+ | 0.2690 | 7750 | 0.0003 | - |
342
+ | 0.2707 | 7800 | 0.0011 | - |
343
+ | 0.2725 | 7850 | 0.0016 | - |
344
+ | 0.2742 | 7900 | 0.003 | - |
345
+ | 0.2759 | 7950 | 0.1212 | - |
346
+ | 0.2777 | 8000 | 0.0001 | - |
347
+ | 0.2794 | 8050 | 0.0004 | - |
348
+ | 0.2812 | 8100 | 0.0003 | - |
349
+ | 0.2829 | 8150 | 0.0608 | - |
350
+ | 0.2846 | 8200 | 0.0002 | - |
351
+ | 0.2864 | 8250 | 0.0003 | - |
352
+ | 0.2881 | 8300 | 0.0022 | - |
353
+ | 0.2898 | 8350 | 0.0052 | - |
354
+ | 0.2916 | 8400 | 0.0003 | - |
355
+ | 0.2933 | 8450 | 0.0001 | - |
356
+ | 0.2950 | 8500 | 0.0007 | - |
357
+ | 0.2968 | 8550 | 0.0336 | - |
358
+ | 0.2985 | 8600 | 0.0071 | - |
359
+ | 0.3002 | 8650 | 0.0002 | - |
360
+ | 0.3020 | 8700 | 0.0002 | - |
361
+ | 0.3037 | 8750 | 0.0107 | - |
362
+ | 0.3054 | 8800 | 0.0006 | - |
363
+ | 0.3072 | 8850 | 0.002 | - |
364
+ | 0.3089 | 8900 | 0.001 | - |
365
+ | 0.3107 | 8950 | 0.0002 | - |
366
+ | 0.3124 | 9000 | 0.0002 | - |
367
+ | 0.3141 | 9050 | 0.0021 | - |
368
+ | 0.3159 | 9100 | 0.0545 | - |
369
+ | 0.3176 | 9150 | 0.0007 | - |
370
+ | 0.3193 | 9200 | 0.0152 | - |
371
+ | 0.3211 | 9250 | 0.0003 | - |
372
+ | 0.3228 | 9300 | 0.0005 | - |
373
+ | 0.3245 | 9350 | 0.053 | - |
374
+ | 0.3263 | 9400 | 0.0031 | - |
375
+ | 0.3280 | 9450 | 0.0002 | - |
376
+ | 0.3297 | 9500 | 0.0002 | - |
377
+ | 0.3315 | 9550 | 0.0002 | - |
378
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379
+ | 0.3350 | 9650 | 0.0023 | - |
380
+ | 0.3367 | 9700 | 0.0011 | - |
381
+ | 0.3384 | 9750 | 0.0003 | - |
382
+ | 0.3402 | 9800 | 0.0003 | - |
383
+ | 0.3419 | 9850 | 0.0005 | - |
384
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385
+ | 0.3454 | 9950 | 0.0028 | - |
386
+ | 0.3471 | 10000 | 0.0016 | - |
387
+ | 0.3488 | 10050 | 0.0008 | - |
388
+ | 0.3506 | 10100 | 0.001 | - |
389
+ | 0.3523 | 10150 | 0.0005 | - |
390
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391
+ | 0.3558 | 10250 | 0.0002 | - |
392
+ | 0.3575 | 10300 | 0.0003 | - |
393
+ | 0.3593 | 10350 | 0.0003 | - |
394
+ | 0.3610 | 10400 | 0.0009 | - |
395
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396
+ | 0.3645 | 10500 | 0.0001 | - |
397
+ | 0.3662 | 10550 | 0.0002 | - |
398
+ | 0.3679 | 10600 | 0.0003 | - |
399
+ | 0.3697 | 10650 | 0.0002 | - |
400
+ | 0.3714 | 10700 | 0.0006 | - |
401
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402
+ | 0.3749 | 10800 | 0.0005 | - |
403
+ | 0.3766 | 10850 | 0.0009 | - |
404
+ | 0.3783 | 10900 | 0.0604 | - |
405
+ | 0.3801 | 10950 | 0.0002 | - |
406
+ | 0.3818 | 11000 | 0.0013 | - |
407
+ | 0.3835 | 11050 | 0.0001 | - |
408
+ | 0.3853 | 11100 | 0.0005 | - |
409
+ | 0.3870 | 11150 | 0.0007 | - |
410
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411
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412
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413
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414
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415
+ | 0.3974 | 11450 | 0.0001 | - |
416
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417
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418
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419
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420
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421
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422
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423
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424
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425
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426
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427
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428
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429
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430
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431
+ | 0.4252 | 12250 | 0.0205 | - |
432
+ | 0.4269 | 12300 | 0.0004 | - |
433
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434
+ | 0.4304 | 12400 | 0.0001 | - |
435
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436
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437
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438
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439
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440
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441
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442
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443
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444
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445
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446
+ | 0.4512 | 13000 | 0.0007 | - |
447
+ | 0.4530 | 13050 | 0.0001 | - |
448
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449
+ | 0.4564 | 13150 | 0.0002 | - |
450
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451
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452
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453
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454
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455
+ | 0.4669 | 13450 | 0.0002 | - |
456
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457
+ | 0.4703 | 13550 | 0.0023 | - |
458
+ | 0.4721 | 13600 | 0.0004 | - |
459
+ | 0.4738 | 13650 | 0.0001 | - |
460
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461
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462
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463
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464
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465
+ | 0.4842 | 13950 | 0.027 | - |
466
+ | 0.4859 | 14000 | 0.0002 | - |
467
+ | 0.4877 | 14050 | 0.0001 | - |
468
+ | 0.4894 | 14100 | 0.0002 | - |
469
+ | 0.4911 | 14150 | 0.0003 | - |
470
+ | 0.4929 | 14200 | 0.0001 | - |
471
+ | 0.4946 | 14250 | 0.0001 | - |
472
+ | 0.4964 | 14300 | 0.0002 | - |
473
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474
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475
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476
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477
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478
+ | 0.5068 | 14600 | 0.0008 | - |
479
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480
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481
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482
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483
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484
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485
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486
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487
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488
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489
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490
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491
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492
+ | 0.5311 | 15300 | 0.0001 | - |
493
+ | 0.5328 | 15350 | 0.0003 | - |
494
+ | 0.5345 | 15400 | 0.0003 | - |
495
+ | 0.5363 | 15450 | 0.0002 | - |
496
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497
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498
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499
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500
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501
+ | 0.5467 | 15750 | 0.0108 | - |
502
+ | 0.5484 | 15800 | 0.0001 | - |
503
+ | 0.5502 | 15850 | 0.0002 | - |
504
+ | 0.5519 | 15900 | 0.0001 | - |
505
+ | 0.5536 | 15950 | 0.0014 | - |
506
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507
+ | 0.5571 | 16050 | 0.0003 | - |
508
+ | 0.5588 | 16100 | 0.0008 | - |
509
+ | 0.5606 | 16150 | 0.0333 | - |
510
+ | 0.5623 | 16200 | 0.0018 | - |
511
+ | 0.5640 | 16250 | 0.0002 | - |
512
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513
+ | 0.5675 | 16350 | 0.0001 | - |
514
+ | 0.5692 | 16400 | 0.0001 | - |
515
+ | 0.5710 | 16450 | 0.0003 | - |
516
+ | 0.5727 | 16500 | 0.0001 | - |
517
+ | 0.5745 | 16550 | 0.0073 | - |
518
+ | 0.5762 | 16600 | 0.0012 | - |
519
+ | 0.5779 | 16650 | 0.0002 | - |
520
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521
+ | 0.5814 | 16750 | 0.0022 | - |
522
+ | 0.5831 | 16800 | 0.0003 | - |
523
+ | 0.5849 | 16850 | 0.0002 | - |
524
+ | 0.5866 | 16900 | 0.0001 | - |
525
+ | 0.5883 | 16950 | 0.0019 | - |
526
+ | 0.5901 | 17000 | 0.0003 | - |
527
+ | 0.5918 | 17050 | 0.0001 | - |
528
+ | 0.5935 | 17100 | 0.0003 | - |
529
+ | 0.5953 | 17150 | 0.0001 | - |
530
+ | 0.5970 | 17200 | 0.0001 | - |
531
+ | 0.5988 | 17250 | 0.0167 | - |
532
+ | 0.6005 | 17300 | 0.0002 | - |
533
+ | 0.6022 | 17350 | 0.0001 | - |
534
+ | 0.6040 | 17400 | 0.0001 | - |
535
+ | 0.6057 | 17450 | 0.0242 | - |
536
+ | 0.6074 | 17500 | 0.0015 | - |
537
+ | 0.6092 | 17550 | 0.0009 | - |
538
+ | 0.6109 | 17600 | 0.0001 | - |
539
+ | 0.6126 | 17650 | 0.0001 | - |
540
+ | 0.6144 | 17700 | 0.0001 | - |
541
+ | 0.6161 | 17750 | 0.0001 | - |
542
+ | 0.6178 | 17800 | 0.0001 | - |
543
+ | 0.6196 | 17850 | 0.0113 | - |
544
+ | 0.6213 | 17900 | 0.0001 | - |
545
+ | 0.6230 | 17950 | 0.0005 | - |
546
+ | 0.6248 | 18000 | 0.0017 | - |
547
+ | 0.6265 | 18050 | 0.0001 | - |
548
+ | 0.6283 | 18100 | 0.0001 | - |
549
+ | 0.6300 | 18150 | 0.0003 | - |
550
+ | 0.6317 | 18200 | 0.0001 | - |
551
+ | 0.6335 | 18250 | 0.0004 | - |
552
+ | 0.6352 | 18300 | 0.0001 | - |
553
+ | 0.6369 | 18350 | 0.0001 | - |
554
+ | 0.6387 | 18400 | 0.0021 | - |
555
+ | 0.6404 | 18450 | 0.0001 | - |
556
+ | 0.6421 | 18500 | 0.0002 | - |
557
+ | 0.6439 | 18550 | 0.0006 | - |
558
+ | 0.6456 | 18600 | 0.0001 | - |
559
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560
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561
+ | 0.6508 | 18750 | 0.0001 | - |
562
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563
+ | 0.6543 | 18850 | 0.0002 | - |
564
+ | 0.6560 | 18900 | 0.001 | - |
565
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566
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567
+ | 0.6612 | 19050 | 0.0001 | - |
568
+ | 0.6630 | 19100 | 0.0001 | - |
569
+ | 0.6647 | 19150 | 0.0002 | - |
570
+ | 0.6664 | 19200 | 0.0001 | - |
571
+ | 0.6682 | 19250 | 0.0001 | - |
572
+ | 0.6699 | 19300 | 0.0064 | - |
573
+ | 0.6716 | 19350 | 0.0001 | - |
574
+ | 0.6734 | 19400 | 0.0001 | - |
575
+ | 0.6751 | 19450 | 0.0001 | - |
576
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577
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578
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579
+ | 0.6821 | 19650 | 0.0001 | - |
580
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581
+ | 0.6855 | 19750 | 0.0002 | - |
582
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583
+ | 0.6890 | 19850 | 0.0001 | - |
584
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585
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586
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587
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588
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589
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590
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591
+ | 0.7029 | 20250 | 0.0001 | - |
592
+ | 0.7046 | 20300 | 0.0011 | - |
593
+ | 0.7064 | 20350 | 0.0001 | - |
594
+ | 0.7081 | 20400 | 0.0001 | - |
595
+ | 0.7098 | 20450 | 0.0001 | - |
596
+ | 0.7116 | 20500 | 0.0057 | - |
597
+ | 0.7133 | 20550 | 0.0 | - |
598
+ | 0.7150 | 20600 | 0.0001 | - |
599
+ | 0.7168 | 20650 | 0.0001 | - |
600
+ | 0.7185 | 20700 | 0.0001 | - |
601
+ | 0.7202 | 20750 | 0.0001 | - |
602
+ | 0.7220 | 20800 | 0.0001 | - |
603
+ | 0.7237 | 20850 | 0.0001 | - |
604
+ | 0.7254 | 20900 | 0.0002 | - |
605
+ | 0.7272 | 20950 | 0.0001 | - |
606
+ | 0.7289 | 21000 | 0.0001 | - |
607
+ | 0.7306 | 21050 | 0.0 | - |
608
+ | 0.7324 | 21100 | 0.0002 | - |
609
+ | 0.7341 | 21150 | 0.0001 | - |
610
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611
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612
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613
+ | 0.7411 | 21350 | 0.0001 | - |
614
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615
+ | 0.7445 | 21450 | 0.0001 | - |
616
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617
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618
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619
+ | 0.7515 | 21650 | 0.0001 | - |
620
+ | 0.7532 | 21700 | 0.0001 | - |
621
+ | 0.7549 | 21750 | 0.0002 | - |
622
+ | 0.7567 | 21800 | 0.0001 | - |
623
+ | 0.7584 | 21850 | 0.0013 | - |
624
+ | 0.7602 | 21900 | 0.0001 | - |
625
+ | 0.7619 | 21950 | 0.0002 | - |
626
+ | 0.7636 | 22000 | 0.0 | - |
627
+ | 0.7654 | 22050 | 0.0001 | - |
628
+ | 0.7671 | 22100 | 0.0002 | - |
629
+ | 0.7688 | 22150 | 0.0001 | - |
630
+ | 0.7706 | 22200 | 0.0002 | - |
631
+ | 0.7723 | 22250 | 0.0001 | - |
632
+ | 0.7740 | 22300 | 0.0001 | - |
633
+ | 0.7758 | 22350 | 0.0002 | - |
634
+ | 0.7775 | 22400 | 0.0001 | - |
635
+ | 0.7792 | 22450 | 0.0013 | - |
636
+ | 0.7810 | 22500 | 0.0001 | - |
637
+ | 0.7827 | 22550 | 0.0002 | - |
638
+ | 0.7844 | 22600 | 0.0002 | - |
639
+ | 0.7862 | 22650 | 0.0069 | - |
640
+ | 0.7879 | 22700 | 0.0001 | - |
641
+ | 0.7897 | 22750 | 0.0001 | - |
642
+ | 0.7914 | 22800 | 0.0001 | - |
643
+ | 0.7931 | 22850 | 0.0001 | - |
644
+ | 0.7949 | 22900 | 0.0001 | - |
645
+ | 0.7966 | 22950 | 0.0001 | - |
646
+ | 0.7983 | 23000 | 0.0001 | - |
647
+ | 0.8001 | 23050 | 0.0002 | - |
648
+ | 0.8018 | 23100 | 0.0001 | - |
649
+ | 0.8035 | 23150 | 0.0001 | - |
650
+ | 0.8053 | 23200 | 0.0001 | - |
651
+ | 0.8070 | 23250 | 0.0001 | - |
652
+ | 0.8087 | 23300 | 0.0001 | - |
653
+ | 0.8105 | 23350 | 0.0001 | - |
654
+ | 0.8122 | 23400 | 0.0027 | - |
655
+ | 0.8140 | 23450 | 0.0001 | - |
656
+ | 0.8157 | 23500 | 0.0001 | - |
657
+ | 0.8174 | 23550 | 0.0027 | - |
658
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659
+ | 0.8209 | 23650 | 0.0002 | - |
660
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661
+ | 0.8244 | 23750 | 0.0003 | - |
662
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663
+ | 0.8278 | 23850 | 0.0001 | - |
664
+ | 0.8296 | 23900 | 0.0001 | - |
665
+ | 0.8313 | 23950 | 0.0001 | - |
666
+ | 0.8330 | 24000 | 0.0014 | - |
667
+ | 0.8348 | 24050 | 0.0083 | - |
668
+ | 0.8365 | 24100 | 0.0001 | - |
669
+ | 0.8383 | 24150 | 0.0001 | - |
670
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671
+ | 0.8417 | 24250 | 0.0001 | - |
672
+ | 0.8435 | 24300 | 0.0001 | - |
673
+ | 0.8452 | 24350 | 0.0001 | - |
674
+ | 0.8469 | 24400 | 0.0 | - |
675
+ | 0.8487 | 24450 | 0.0001 | - |
676
+ | 0.8504 | 24500 | 0.0001 | - |
677
+ | 0.8521 | 24550 | 0.022 | - |
678
+ | 0.8539 | 24600 | 0.0001 | - |
679
+ | 0.8556 | 24650 | 0.0001 | - |
680
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681
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682
+ | 0.8608 | 24800 | 0.0002 | - |
683
+ | 0.8625 | 24850 | 0.0001 | - |
684
+ | 0.8643 | 24900 | 0.0001 | - |
685
+ | 0.8660 | 24950 | 0.0001 | - |
686
+ | 0.8678 | 25000 | 0.0002 | - |
687
+ | 0.8695 | 25050 | 0.0001 | - |
688
+ | 0.8712 | 25100 | 0.0001 | - |
689
+ | 0.8730 | 25150 | 0.0001 | - |
690
+ | 0.8747 | 25200 | 0.0001 | - |
691
+ | 0.8764 | 25250 | 0.0007 | - |
692
+ | 0.8782 | 25300 | 0.0001 | - |
693
+ | 0.8799 | 25350 | 0.0001 | - |
694
+ | 0.8816 | 25400 | 0.0002 | - |
695
+ | 0.8834 | 25450 | 0.0001 | - |
696
+ | 0.8851 | 25500 | 0.0001 | - |
697
+ | 0.8868 | 25550 | 0.0001 | - |
698
+ | 0.8886 | 25600 | 0.0006 | - |
699
+ | 0.8903 | 25650 | 0.0003 | - |
700
+ | 0.8921 | 25700 | 0.0001 | - |
701
+ | 0.8938 | 25750 | 0.0002 | - |
702
+ | 0.8955 | 25800 | 0.0001 | - |
703
+ | 0.8973 | 25850 | 0.0001 | - |
704
+ | 0.8990 | 25900 | 0.0015 | - |
705
+ | 0.9007 | 25950 | 0.0005 | - |
706
+ | 0.9025 | 26000 | 0.0001 | - |
707
+ | 0.9042 | 26050 | 0.0056 | - |
708
+ | 0.9059 | 26100 | 0.0001 | - |
709
+ | 0.9077 | 26150 | 0.0001 | - |
710
+ | 0.9094 | 26200 | 0.0001 | - |
711
+ | 0.9111 | 26250 | 0.0001 | - |
712
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713
+ | 0.9146 | 26350 | 0.0001 | - |
714
+ | 0.9163 | 26400 | 0.0002 | - |
715
+ | 0.9181 | 26450 | 0.0001 | - |
716
+ | 0.9198 | 26500 | 0.0003 | - |
717
+ | 0.9216 | 26550 | 0.0001 | - |
718
+ | 0.9233 | 26600 | 0.0001 | - |
719
+ | 0.9250 | 26650 | 0.0002 | - |
720
+ | 0.9268 | 26700 | 0.0001 | - |
721
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722
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723
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724
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726
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730
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731
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732
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733
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734
+ | 0.9511 | 27400 | 0.0001 | - |
735
+ | 0.9528 | 27450 | 0.0001 | - |
736
+ | 0.9545 | 27500 | 0.0001 | - |
737
+ | 0.9563 | 27550 | 0.0035 | - |
738
+ | 0.9580 | 27600 | 0.0001 | - |
739
+ | 0.9597 | 27650 | 0.0002 | - |
740
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741
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742
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743
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744
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745
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746
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747
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748
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762
+ | 0.9997 | 28800 | 0.0 | - |
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+
764
+ ### Framework Versions
765
+ - Python: 3.10.12
766
+ - SetFit: 1.0.3
767
+ - Sentence Transformers: 3.0.1
768
+ - Transformers: 4.39.0
769
+ - PyTorch: 2.3.0+cu121
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+ - Datasets: 2.19.2
771
+ - Tokenizers: 0.15.2
772
+
773
+ ## Citation
774
+
775
+ ### BibTeX
776
+ ```bibtex
777
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
778
+ doi = {10.48550/ARXIV.2209.11055},
779
+ url = {https://arxiv.org/abs/2209.11055},
780
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
781
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
782
+ title = {Efficient Few-Shot Learning Without Prompts},
783
+ publisher = {arXiv},
784
+ year = {2022},
785
+ copyright = {Creative Commons Attribution 4.0 International}
786
+ }
787
+ ```
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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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+
801
+ <!--
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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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+ -->
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