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pupugu02
/
absa-setfit-resto-aspect

Text Classification
setfit
Safetensors
sentence-transformers
bert
absa
generated_from_setfit_trainer
Model card Files Community
pupugu02 commited on
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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 +1318 -0
  3. config.json +47 -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 +14 -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
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
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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
@@ -0,0 +1,1318 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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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+ - 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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+ metrics:
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+ - accuracy
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+ widget:
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+ - text: Tempatnya:Tempatnya lumayan bagus di jalan lintas propinsi
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+ - text: orang:Makanan disini sangat enak , pelayanannya juga Ramah, saat saya datang
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+ tidak begtu banyak orang .. kamar mandi juga bersih aku akan kembali lagi kesini
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+ - text: Pelayanan:Tempatnya strategis, makanan enak. Pelayanan cepat, pokoknya maknyuuus
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+ deh. Kapan2 nanti pgn mampir lagi sambil nongki sama temen2
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+ - text: Restoran:Restoran cepat saji dengan pelayanan yang baik, bersih dan tidak
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+ terlalu mahal. Ayam Goreng Pedas adalah hidangan favoritku. Burger Double Cheese
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+ bisa menjadi makanan bawa pulang terbaik bagi orang-orang yang sedang terburu-buru
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+ - text: pelayanan:Alhamdulillah… Makananannya enak, suasananya lumayan tenang, pelayanan
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+ dari Teh Putri dan Teh Andini luar biasa membantu… …
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+ pipeline_tag: text-classification
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+ inference: false
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+ ---
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+
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+ # SetFit Aspect Model
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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). 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:** [Unknown](https://huggingface.co/unknown) -->
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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:** id_core_news_trf
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+ - **SetFitABSA Aspect Model:** [pupugu02/absa-setfit-resto-aspect](https://huggingface.co/pupugu02/absa-setfit-resto-aspect)
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+ - **SetFitABSA Polarity Model:** [pupugu02/absa-setfit-resto-polarity](https://huggingface.co/pupugu02/absa-setfit-resto-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>'tempat:Mcd selalu jadi tempat ternyaman untuk me time, selain tempat yang Sangat bersih dan nyaman, mcdonals juga selalu menjaga kualitas makanan. Bagi saya mcd sangat affdorable dan worth it, selain itu paling digemari oleh kalangan anak muda dan anak anak sangat menyukai ayam nya.'</li><li>'tempat:Makanan nya enak n harga nya juga murah.. View nya bagus dan ada tempat buat bermain anak2.. Naek beca ato perahu angsa..'</li><li>'pelayanan:McDonald’s adalah menjadi restoran cepat saji dengan pelayanan terbaik di dunia. Untuk mencapai visi ini, McDonald’s selalu menjamin mutu produk-produknya, memberikan pelayanan yang memuaskan, menawarkan kebersihan dan keamanan produk …'</li></ul> |
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+ | no aspect | <ul><li>'Mcd:Mcd selalu jadi tempat ternyaman untuk me time, selain tempat yang Sangat bersih dan nyaman, mcdonals juga selalu menjaga kualitas makanan. Bagi saya mcd sangat affdorable dan worth it, selain itu paling digemari oleh kalangan anak muda dan anak anak sangat menyukai ayam nya.'</li><li>'tempat:Mcd selalu jadi tempat ternyaman untuk me time, selain tempat yang Sangat bersih dan nyaman, mcdonals juga selalu menjaga kualitas makanan. Bagi saya mcd sangat affdorable dan worth it, selain itu paling digemari oleh kalangan anak muda dan anak anak sangat menyukai ayam nya.'</li><li>'me time:Mcd selalu jadi tempat ternyaman untuk me time, selain tempat yang Sangat bersih dan nyaman, mcdonals juga selalu menjaga kualitas makanan. Bagi saya mcd sangat affdorable dan worth it, selain itu paling digemari oleh kalangan anak muda dan anak anak sangat menyukai ayam nya.'</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 AbsaModel
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+
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+ # Download from the 🤗 Hub
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+ model = AbsaModel.from_pretrained(
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+ "pupugu02/absa-setfit-resto-aspect",
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+ "pupugu02/absa-setfit-resto-polarity",
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+ )
88
+ # Run inference
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+ preds = model("The food was great, but the venue is just way too busy.")
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+ ```
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+
92
+ <!--
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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 | 2 | 28.1010 | 61 |
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+
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+ | Label | Training Sample Count |
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+ |:----------|:----------------------|
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+ | no aspect | 2653 |
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+ | aspect | 406 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (128, 128)
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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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+ - 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: 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 | 0.3793 | - |
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+ | 0.0009 | 50 | 0.3641 | - |
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+ | 0.0018 | 100 | 0.3647 | - |
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+ | 0.0027 | 150 | 0.3221 | - |
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+ | 0.0036 | 200 | 0.2554 | - |
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+ | 0.0044 | 250 | 0.2202 | - |
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+ | 0.0053 | 300 | 0.1798 | - |
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+ | 0.0062 | 350 | 0.1139 | - |
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+ | 0.0071 | 400 | 0.1069 | - |
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+ | 0.0080 | 450 | 0.127 | - |
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+ | 0.0089 | 500 | 0.1267 | - |
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+ | 0.0098 | 550 | 0.0727 | - |
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+ | 0.0107 | 600 | 0.1005 | - |
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+ | 0.0115 | 650 | 0.0816 | - |
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+ | 0.0124 | 700 | 0.0915 | - |
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+ | 0.0133 | 750 | 0.0931 | - |
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+ | 0.0142 | 800 | 0.0555 | - |
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+ | 0.0151 | 850 | 0.0567 | - |
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+ | 0.0160 | 900 | 0.0599 | - |
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+ | 0.0169 | 950 | 0.0594 | - |
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+ | 0.0178 | 1000 | 0.0586 | - |
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+ | 0.0187 | 1050 | 0.0639 | - |
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+ | 0.0195 | 1100 | 0.0357 | - |
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+ | 0.0204 | 1150 | 0.0363 | - |
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+ | 0.0213 | 1200 | 0.0316 | - |
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+ | 0.0222 | 1250 | 0.0372 | - |
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+ | 0.0231 | 1300 | 0.0296 | - |
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+ | 0.0240 | 1350 | 0.0178 | - |
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+ | 0.0249 | 1400 | 0.0157 | - |
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+ | 0.0258 | 1450 | 0.0206 | - |
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+ | 0.0266 | 1500 | 0.0131 | - |
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+ | 0.0275 | 1550 | 0.018 | - |
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+ | 0.0284 | 1600 | 0.0175 | - |
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+ | 0.0293 | 1650 | 0.024 | - |
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+ | 0.0302 | 1700 | 0.0313 | - |
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+ | 0.0311 | 1750 | 0.0125 | - |
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+ | 0.0320 | 1800 | 0.0319 | - |
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+ | 0.0329 | 1850 | 0.0214 | - |
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+ | 0.0337 | 1900 | 0.0031 | - |
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+ | 0.0346 | 1950 | 0.0109 | - |
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+ | 0.0355 | 2000 | 0.011 | - |
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+ | 0.0364 | 2050 | 0.0019 | - |
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+ | 0.0373 | 2100 | 0.0256 | - |
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+ | 0.0382 | 2150 | 0.0091 | - |
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+ | 0.0391 | 2200 | 0.0123 | - |
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+ | 0.0400 | 2250 | 0.0077 | - |
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+ | 0.0409 | 2300 | 0.0073 | - |
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+ | 0.0417 | 2350 | 0.01 | - |
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+ | 0.0426 | 2400 | 0.0111 | - |
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+ | 0.0435 | 2450 | 0.0134 | - |
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+ | 0.0444 | 2500 | 0.0146 | - |
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+ | 0.0453 | 2550 | 0.0116 | - |
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+ | 0.0462 | 2600 | 0.0066 | - |
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+ | 0.0471 | 2650 | 0.0045 | - |
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+ | 0.0480 | 2700 | 0.0049 | - |
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+ | 0.0488 | 2750 | 0.0022 | - |
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+ | 0.0497 | 2800 | 0.0093 | - |
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+ | 0.0506 | 2850 | 0.0084 | - |
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+ | 0.0515 | 2900 | 0.0174 | - |
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+ | 0.0524 | 2950 | 0.0104 | - |
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+ | 0.0533 | 3000 | 0.0143 | - |
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+ | 0.0542 | 3050 | 0.0089 | - |
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+ | 0.0551 | 3100 | 0.0099 | - |
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+ | 0.0560 | 3150 | 0.0041 | - |
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+ | 0.0568 | 3200 | 0.0006 | - |
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+ | 0.0577 | 3250 | 0.0212 | - |
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+ | 0.0586 | 3300 | 0.0097 | - |
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+ | 0.0595 | 3350 | 0.0122 | - |
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+ | 0.0604 | 3400 | 0.0064 | - |
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+ | 0.0613 | 3450 | 0.0222 | - |
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+ | 0.0622 | 3500 | 0.0044 | - |
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+ | 0.0631 | 3550 | 0.0036 | - |
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+ | 0.0639 | 3600 | 0.0091 | - |
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+ | 0.0648 | 3650 | 0.0121 | - |
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+ | 0.0657 | 3700 | 0.0086 | - |
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+ | 0.0666 | 3750 | 0.0123 | - |
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+ | 0.0675 | 3800 | 0.0188 | - |
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+ | 0.0684 | 3850 | 0.0107 | - |
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+ | 0.0693 | 3900 | 0.0156 | - |
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+ | 0.0702 | 3950 | 0.0013 | - |
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+ | 0.0710 | 4000 | 0.0183 | - |
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+ | 0.0719 | 4050 | 0.0181 | - |
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+ | 0.0728 | 4100 | 0.0221 | - |
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+ | 0.0737 | 4150 | 0.0071 | - |
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+ | 0.0746 | 4200 | 0.0143 | - |
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+ | 0.0755 | 4250 | 0.0185 | - |
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+ | 0.0764 | 4300 | 0.0077 | - |
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+ | 0.0773 | 4350 | 0.0142 | - |
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+ | 0.0782 | 4400 | 0.0011 | - |
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+ | 0.0790 | 4450 | 0.0048 | - |
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+ | 0.0799 | 4500 | 0.0284 | - |
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+ | 0.0808 | 4550 | 0.0131 | - |
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+ | 0.0817 | 4600 | 0.0176 | - |
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+ | 0.0826 | 4650 | 0.0201 | - |
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+ | 0.0835 | 4700 | 0.0124 | - |
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+ | 0.0844 | 4750 | 0.0161 | - |
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+ | 0.0853 | 4800 | 0.0086 | - |
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+ | 0.0861 | 4850 | 0.0059 | - |
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+ | 0.0870 | 4900 | 0.0013 | - |
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+ | 0.0879 | 4950 | 0.0131 | - |
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+ | 0.0888 | 5000 | 0.0049 | - |
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+ | 0.0897 | 5050 | 0.0254 | - |
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+ | 0.0906 | 5100 | 0.0003 | - |
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+ | 0.0915 | 5150 | 0.0081 | - |
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+ | 0.0924 | 5200 | 0.0084 | - |
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+ | 0.0933 | 5250 | 0.0094 | - |
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+ | 0.0941 | 5300 | 0.0055 | - |
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+ | 0.0950 | 5350 | 0.002 | - |
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+ | 0.0959 | 5400 | 0.0145 | - |
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+ | 0.0968 | 5450 | 0.0071 | - |
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+ | 0.0977 | 5500 | 0.0132 | - |
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+ | 0.0986 | 5550 | 0.0396 | - |
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+ | 0.0995 | 5600 | 0.0137 | - |
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+ | 0.1004 | 5650 | 0.0126 | - |
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+ | 0.1012 | 5700 | 0.0133 | - |
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+ | 0.1021 | 5750 | 0.0044 | - |
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+ | 0.1030 | 5800 | 0.0082 | - |
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+ | 0.1039 | 5850 | 0.0081 | - |
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+ | 0.1048 | 5900 | 0.0129 | - |
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+ | 0.1057 | 5950 | 0.0105 | - |
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+ | 0.1066 | 6000 | 0.0205 | - |
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+ | 0.1075 | 6050 | 0.0056 | - |
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+ | 0.1083 | 6100 | 0.0174 | - |
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+ | 0.1092 | 6150 | 0.0128 | - |
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+ | 0.1101 | 6200 | 0.0044 | - |
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+ | 0.1110 | 6250 | 0.0015 | - |
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+ | 0.1119 | 6300 | 0.0074 | - |
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+ | 0.1128 | 6350 | 0.0095 | - |
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+ | 0.1137 | 6400 | 0.0106 | - |
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+ | 0.1146 | 6450 | 0.0129 | - |
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+ | 0.1155 | 6500 | 0.0174 | - |
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+ | 0.1163 | 6550 | 0.0113 | - |
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+ | 0.1172 | 6600 | 0.0095 | - |
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+ | 0.1181 | 6650 | 0.0016 | - |
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+ | 0.1190 | 6700 | 0.0074 | - |
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+ | 0.1199 | 6750 | 0.0049 | - |
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+ | 0.1208 | 6800 | 0.0178 | - |
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+ | 0.1217 | 6850 | 0.0176 | - |
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+ | 0.1226 | 6900 | 0.0014 | - |
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+ | 0.1234 | 6950 | 0.0113 | - |
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+ | 0.1243 | 7000 | 0.0069 | - |
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+ | 0.1252 | 7050 | 0.0064 | - |
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+ | 0.1261 | 7100 | 0.0054 | - |
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+ | 0.1270 | 7150 | 0.008 | - |
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+ | 0.1279 | 7200 | 0.0327 | - |
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+ | 0.1288 | 7250 | 0.0104 | - |
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+ | 0.1297 | 7300 | 0.0102 | - |
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+ | 0.1306 | 7350 | 0.0163 | - |
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+ | 0.1314 | 7400 | 0.0126 | - |
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+ | 0.1323 | 7450 | 0.0014 | - |
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+ | 0.1332 | 7500 | 0.0053 | - |
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+ | 0.1341 | 7550 | 0.0102 | - |
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+ | 0.1350 | 7600 | 0.0223 | - |
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+ | 0.1359 | 7650 | 0.0083 | - |
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+ | 0.1368 | 7700 | 0.003 | - |
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+ | 0.1377 | 7750 | 0.0185 | - |
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+ | 0.1385 | 7800 | 0.0083 | - |
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+ | 0.1394 | 7850 | 0.0148 | - |
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+ | 0.1403 | 7900 | 0.0172 | - |
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+ | 0.1412 | 7950 | 0.0199 | - |
308
+ | 0.1421 | 8000 | 0.0082 | - |
309
+ | 0.1430 | 8050 | 0.0091 | - |
310
+ | 0.1439 | 8100 | 0.0098 | - |
311
+ | 0.1448 | 8150 | 0.0165 | - |
312
+ | 0.1456 | 8200 | 0.0087 | - |
313
+ | 0.1465 | 8250 | 0.001 | - |
314
+ | 0.1474 | 8300 | 0.0113 | - |
315
+ | 0.1483 | 8350 | 0.007 | - |
316
+ | 0.1492 | 8400 | 0.0034 | - |
317
+ | 0.1501 | 8450 | 0.0029 | - |
318
+ | 0.1510 | 8500 | 0.0235 | - |
319
+ | 0.1519 | 8550 | 0.0088 | - |
320
+ | 0.1528 | 8600 | 0.0164 | - |
321
+ | 0.1536 | 8650 | 0.0071 | - |
322
+ | 0.1545 | 8700 | 0.0111 | - |
323
+ | 0.1554 | 8750 | 0.004 | - |
324
+ | 0.1563 | 8800 | 0.0068 | - |
325
+ | 0.1572 | 8850 | 0.0093 | - |
326
+ | 0.1581 | 8900 | 0.0015 | - |
327
+ | 0.1590 | 8950 | 0.0056 | - |
328
+ | 0.1599 | 9000 | 0.0122 | - |
329
+ | 0.1607 | 9050 | 0.018 | - |
330
+ | 0.1616 | 9100 | 0.0165 | - |
331
+ | 0.1625 | 9150 | 0.02 | - |
332
+ | 0.1634 | 9200 | 0.0149 | - |
333
+ | 0.1643 | 9250 | 0.0057 | - |
334
+ | 0.1652 | 9300 | 0.0072 | - |
335
+ | 0.1661 | 9350 | 0.0073 | - |
336
+ | 0.1670 | 9400 | 0.0097 | - |
337
+ | 0.1679 | 9450 | 0.0059 | - |
338
+ | 0.1687 | 9500 | 0.0072 | - |
339
+ | 0.1696 | 9550 | 0.0076 | - |
340
+ | 0.1705 | 9600 | 0.0102 | - |
341
+ | 0.1714 | 9650 | 0.0077 | - |
342
+ | 0.1723 | 9700 | 0.0016 | - |
343
+ | 0.1732 | 9750 | 0.0176 | - |
344
+ | 0.1741 | 9800 | 0.0039 | - |
345
+ | 0.1750 | 9850 | 0.0085 | - |
346
+ | 0.1758 | 9900 | 0.008 | - |
347
+ | 0.1767 | 9950 | 0.0113 | - |
348
+ | 0.1776 | 10000 | 0.0093 | - |
349
+ | 0.1785 | 10050 | 0.0229 | - |
350
+ | 0.1794 | 10100 | 0.0063 | - |
351
+ | 0.1803 | 10150 | 0.0062 | - |
352
+ | 0.1812 | 10200 | 0.001 | - |
353
+ | 0.1821 | 10250 | 0.0115 | - |
354
+ | 0.1829 | 10300 | 0.0012 | - |
355
+ | 0.1838 | 10350 | 0.0106 | - |
356
+ | 0.1847 | 10400 | 0.0095 | - |
357
+ | 0.1856 | 10450 | 0.0287 | - |
358
+ | 0.1865 | 10500 | 0.003 | - |
359
+ | 0.1874 | 10550 | 0.0141 | - |
360
+ | 0.1883 | 10600 | 0.0051 | - |
361
+ | 0.1892 | 10650 | 0.0028 | - |
362
+ | 0.1901 | 10700 | 0.0208 | - |
363
+ | 0.1909 | 10750 | 0.01 | - |
364
+ | 0.1918 | 10800 | 0.008 | - |
365
+ | 0.1927 | 10850 | 0.0119 | - |
366
+ | 0.1936 | 10900 | 0.0229 | - |
367
+ | 0.1945 | 10950 | 0.0037 | - |
368
+ | 0.1954 | 11000 | 0.0132 | - |
369
+ | 0.1963 | 11050 | 0.0074 | - |
370
+ | 0.1972 | 11100 | 0.0073 | - |
371
+ | 0.1980 | 11150 | 0.0154 | - |
372
+ | 0.1989 | 11200 | 0.0086 | - |
373
+ | 0.1998 | 11250 | 0.0206 | - |
374
+ | 0.2007 | 11300 | 0.0067 | - |
375
+ | 0.2016 | 11350 | 0.0142 | - |
376
+ | 0.2025 | 11400 | 0.0104 | - |
377
+ | 0.2034 | 11450 | 0.011 | - |
378
+ | 0.2043 | 11500 | 0.0113 | - |
379
+ | 0.2052 | 11550 | 0.0048 | - |
380
+ | 0.2060 | 11600 | 0.0008 | - |
381
+ | 0.2069 | 11650 | 0.0056 | - |
382
+ | 0.2078 | 11700 | 0.0017 | - |
383
+ | 0.2087 | 11750 | 0.0263 | - |
384
+ | 0.2096 | 11800 | 0.0198 | - |
385
+ | 0.2105 | 11850 | 0.0052 | - |
386
+ | 0.2114 | 11900 | 0.0055 | - |
387
+ | 0.2123 | 11950 | 0.0039 | - |
388
+ | 0.2131 | 12000 | 0.0058 | - |
389
+ | 0.2140 | 12050 | 0.0206 | - |
390
+ | 0.2149 | 12100 | 0.0122 | - |
391
+ | 0.2158 | 12150 | 0.009 | - |
392
+ | 0.2167 | 12200 | 0.0019 | - |
393
+ | 0.2176 | 12250 | 0.0067 | - |
394
+ | 0.2185 | 12300 | 0.0059 | - |
395
+ | 0.2194 | 12350 | 0.0107 | - |
396
+ | 0.2202 | 12400 | 0.0003 | - |
397
+ | 0.2211 | 12450 | 0.0131 | - |
398
+ | 0.2220 | 12500 | 0.0069 | - |
399
+ | 0.2229 | 12550 | 0.0019 | - |
400
+ | 0.2238 | 12600 | 0.0012 | - |
401
+ | 0.2247 | 12650 | 0.0104 | - |
402
+ | 0.2256 | 12700 | 0.0051 | - |
403
+ | 0.2265 | 12750 | 0.0062 | - |
404
+ | 0.2274 | 12800 | 0.004 | - |
405
+ | 0.2282 | 12850 | 0.0121 | - |
406
+ | 0.2291 | 12900 | 0.0144 | - |
407
+ | 0.2300 | 12950 | 0.015 | - |
408
+ | 0.2309 | 13000 | 0.0191 | - |
409
+ | 0.2318 | 13050 | 0.0152 | - |
410
+ | 0.2327 | 13100 | 0.006 | - |
411
+ | 0.2336 | 13150 | 0.01 | - |
412
+ | 0.2345 | 13200 | 0.0123 | - |
413
+ | 0.2353 | 13250 | 0.0029 | - |
414
+ | 0.2362 | 13300 | 0.0036 | - |
415
+ | 0.2371 | 13350 | 0.0005 | - |
416
+ | 0.2380 | 13400 | 0.0065 | - |
417
+ | 0.2389 | 13450 | 0.0139 | - |
418
+ | 0.2398 | 13500 | 0.0056 | - |
419
+ | 0.2407 | 13550 | 0.0079 | - |
420
+ | 0.2416 | 13600 | 0.0024 | - |
421
+ | 0.2425 | 13650 | 0.0062 | - |
422
+ | 0.2433 | 13700 | 0.0082 | - |
423
+ | 0.2442 | 13750 | 0.0007 | - |
424
+ | 0.2451 | 13800 | 0.0093 | - |
425
+ | 0.2460 | 13850 | 0.007 | - |
426
+ | 0.2469 | 13900 | 0.009 | - |
427
+ | 0.2478 | 13950 | 0.0164 | - |
428
+ | 0.2487 | 14000 | 0.027 | - |
429
+ | 0.2496 | 14050 | 0.0243 | - |
430
+ | 0.2504 | 14100 | 0.0014 | - |
431
+ | 0.2513 | 14150 | 0.0072 | - |
432
+ | 0.2522 | 14200 | 0.014 | - |
433
+ | 0.2531 | 14250 | 0.0139 | - |
434
+ | 0.2540 | 14300 | 0.005 | - |
435
+ | 0.2549 | 14350 | 0.0129 | - |
436
+ | 0.2558 | 14400 | 0.0153 | - |
437
+ | 0.2567 | 14450 | 0.0035 | - |
438
+ | 0.2575 | 14500 | 0.0062 | - |
439
+ | 0.2584 | 14550 | 0.0017 | - |
440
+ | 0.2593 | 14600 | 0.0232 | - |
441
+ | 0.2602 | 14650 | 0.016 | - |
442
+ | 0.2611 | 14700 | 0.0011 | - |
443
+ | 0.2620 | 14750 | 0.0083 | - |
444
+ | 0.2629 | 14800 | 0.0202 | - |
445
+ | 0.2638 | 14850 | 0.0084 | - |
446
+ | 0.2647 | 14900 | 0.0132 | - |
447
+ | 0.2655 | 14950 | 0.0148 | - |
448
+ | 0.2664 | 15000 | 0.0061 | - |
449
+ | 0.2673 | 15050 | 0.0062 | - |
450
+ | 0.2682 | 15100 | 0.0118 | - |
451
+ | 0.2691 | 15150 | 0.0066 | - |
452
+ | 0.2700 | 15200 | 0.0007 | - |
453
+ | 0.2709 | 15250 | 0.0085 | - |
454
+ | 0.2718 | 15300 | 0.0093 | - |
455
+ | 0.2726 | 15350 | 0.0197 | - |
456
+ | 0.2735 | 15400 | 0.014 | - |
457
+ | 0.2744 | 15450 | 0.0023 | - |
458
+ | 0.2753 | 15500 | 0.0005 | - |
459
+ | 0.2762 | 15550 | 0.0059 | - |
460
+ | 0.2771 | 15600 | 0.0024 | - |
461
+ | 0.2780 | 15650 | 0.0139 | - |
462
+ | 0.2789 | 15700 | 0.0107 | - |
463
+ | 0.2798 | 15750 | 0.0063 | - |
464
+ | 0.2806 | 15800 | 0.0092 | - |
465
+ | 0.2815 | 15850 | 0.0212 | - |
466
+ | 0.2824 | 15900 | 0.0121 | - |
467
+ | 0.2833 | 15950 | 0.0011 | - |
468
+ | 0.2842 | 16000 | 0.013 | - |
469
+ | 0.2851 | 16050 | 0.0126 | - |
470
+ | 0.2860 | 16100 | 0.0107 | - |
471
+ | 0.2869 | 16150 | 0.0162 | - |
472
+ | 0.2877 | 16200 | 0.0109 | - |
473
+ | 0.2886 | 16250 | 0.0129 | - |
474
+ | 0.2895 | 16300 | 0.002 | - |
475
+ | 0.2904 | 16350 | 0.0098 | - |
476
+ | 0.2913 | 16400 | 0.0058 | - |
477
+ | 0.2922 | 16450 | 0.0186 | - |
478
+ | 0.2931 | 16500 | 0.0092 | - |
479
+ | 0.2940 | 16550 | 0.007 | - |
480
+ | 0.2948 | 16600 | 0.0147 | - |
481
+ | 0.2957 | 16650 | 0.0066 | - |
482
+ | 0.2966 | 16700 | 0.0137 | - |
483
+ | 0.2975 | 16750 | 0.0064 | - |
484
+ | 0.2984 | 16800 | 0.0085 | - |
485
+ | 0.2993 | 16850 | 0.0184 | - |
486
+ | 0.3002 | 16900 | 0.0187 | - |
487
+ | 0.3011 | 16950 | 0.0026 | - |
488
+ | 0.3020 | 17000 | 0.0087 | - |
489
+ | 0.3028 | 17050 | 0.0161 | - |
490
+ | 0.3037 | 17100 | 0.002 | - |
491
+ | 0.3046 | 17150 | 0.0007 | - |
492
+ | 0.3055 | 17200 | 0.0083 | - |
493
+ | 0.3064 | 17250 | 0.0057 | - |
494
+ | 0.3073 | 17300 | 0.007 | - |
495
+ | 0.3082 | 17350 | 0.0063 | - |
496
+ | 0.3091 | 17400 | 0.0021 | - |
497
+ | 0.3099 | 17450 | 0.0014 | - |
498
+ | 0.3108 | 17500 | 0.0156 | - |
499
+ | 0.3117 | 17550 | 0.0072 | - |
500
+ | 0.3126 | 17600 | 0.0017 | - |
501
+ | 0.3135 | 17650 | 0.002 | - |
502
+ | 0.3144 | 17700 | 0.0055 | - |
503
+ | 0.3153 | 17750 | 0.005 | - |
504
+ | 0.3162 | 17800 | 0.0067 | - |
505
+ | 0.3171 | 17850 | 0.0007 | - |
506
+ | 0.3179 | 17900 | 0.0019 | - |
507
+ | 0.3188 | 17950 | 0.0125 | - |
508
+ | 0.3197 | 18000 | 0.0157 | - |
509
+ | 0.3206 | 18050 | 0.0087 | - |
510
+ | 0.3215 | 18100 | 0.0051 | - |
511
+ | 0.3224 | 18150 | 0.0102 | - |
512
+ | 0.3233 | 18200 | 0.0147 | - |
513
+ | 0.3242 | 18250 | 0.0161 | - |
514
+ | 0.3250 | 18300 | 0.0068 | - |
515
+ | 0.3259 | 18350 | 0.0199 | - |
516
+ | 0.3268 | 18400 | 0.0101 | - |
517
+ | 0.3277 | 18450 | 0.0132 | - |
518
+ | 0.3286 | 18500 | 0.0028 | - |
519
+ | 0.3295 | 18550 | 0.0127 | - |
520
+ | 0.3304 | 18600 | 0.0089 | - |
521
+ | 0.3313 | 18650 | 0.0111 | - |
522
+ | 0.3321 | 18700 | 0.006 | - |
523
+ | 0.3330 | 18750 | 0.0062 | - |
524
+ | 0.3339 | 18800 | 0.0006 | - |
525
+ | 0.3348 | 18850 | 0.0063 | - |
526
+ | 0.3357 | 18900 | 0.0005 | - |
527
+ | 0.3366 | 18950 | 0.0024 | - |
528
+ | 0.3375 | 19000 | 0.019 | - |
529
+ | 0.3384 | 19050 | 0.0101 | - |
530
+ | 0.3393 | 19100 | 0.0073 | - |
531
+ | 0.3401 | 19150 | 0.0031 | - |
532
+ | 0.3410 | 19200 | 0.0108 | - |
533
+ | 0.3419 | 19250 | 0.0024 | - |
534
+ | 0.3428 | 19300 | 0.0137 | - |
535
+ | 0.3437 | 19350 | 0.0137 | - |
536
+ | 0.3446 | 19400 | 0.0064 | - |
537
+ | 0.3455 | 19450 | 0.0057 | - |
538
+ | 0.3464 | 19500 | 0.0076 | - |
539
+ | 0.3472 | 19550 | 0.0065 | - |
540
+ | 0.3481 | 19600 | 0.0008 | - |
541
+ | 0.3490 | 19650 | 0.0048 | - |
542
+ | 0.3499 | 19700 | 0.005 | - |
543
+ | 0.3508 | 19750 | 0.0047 | - |
544
+ | 0.3517 | 19800 | 0.0219 | - |
545
+ | 0.3526 | 19850 | 0.0181 | - |
546
+ | 0.3535 | 19900 | 0.0059 | - |
547
+ | 0.3544 | 19950 | 0.0071 | - |
548
+ | 0.3552 | 20000 | 0.0058 | - |
549
+ | 0.3561 | 20050 | 0.0113 | - |
550
+ | 0.3570 | 20100 | 0.0063 | - |
551
+ | 0.3579 | 20150 | 0.0082 | - |
552
+ | 0.3588 | 20200 | 0.0203 | - |
553
+ | 0.3597 | 20250 | 0.0027 | - |
554
+ | 0.3606 | 20300 | 0.0118 | - |
555
+ | 0.3615 | 20350 | 0.0165 | - |
556
+ | 0.3623 | 20400 | 0.0095 | - |
557
+ | 0.3632 | 20450 | 0.0084 | - |
558
+ | 0.3641 | 20500 | 0.0101 | - |
559
+ | 0.3650 | 20550 | 0.014 | - |
560
+ | 0.3659 | 20600 | 0.0062 | - |
561
+ | 0.3668 | 20650 | 0.004 | - |
562
+ | 0.3677 | 20700 | 0.0131 | - |
563
+ | 0.3686 | 20750 | 0.0123 | - |
564
+ | 0.3694 | 20800 | 0.0093 | - |
565
+ | 0.3703 | 20850 | 0.0232 | - |
566
+ | 0.3712 | 20900 | 0.0027 | - |
567
+ | 0.3721 | 20950 | 0.0156 | - |
568
+ | 0.3730 | 21000 | 0.0034 | - |
569
+ | 0.3739 | 21050 | 0.0143 | - |
570
+ | 0.3748 | 21100 | 0.0058 | - |
571
+ | 0.3757 | 21150 | 0.0115 | - |
572
+ | 0.3766 | 21200 | 0.0051 | - |
573
+ | 0.3774 | 21250 | 0.0008 | - |
574
+ | 0.3783 | 21300 | 0.0007 | - |
575
+ | 0.3792 | 21350 | 0.0035 | - |
576
+ | 0.3801 | 21400 | 0.0056 | - |
577
+ | 0.3810 | 21450 | 0.0128 | - |
578
+ | 0.3819 | 21500 | 0.0079 | - |
579
+ | 0.3828 | 21550 | 0.0127 | - |
580
+ | 0.3837 | 21600 | 0.0085 | - |
581
+ | 0.3845 | 21650 | 0.0173 | - |
582
+ | 0.3854 | 21700 | 0.0015 | - |
583
+ | 0.3863 | 21750 | 0.0184 | - |
584
+ | 0.3872 | 21800 | 0.0091 | - |
585
+ | 0.3881 | 21850 | 0.0007 | - |
586
+ | 0.3890 | 21900 | 0.0035 | - |
587
+ | 0.3899 | 21950 | 0.011 | - |
588
+ | 0.3908 | 22000 | 0.004 | - |
589
+ | 0.3917 | 22050 | 0.012 | - |
590
+ | 0.3925 | 22100 | 0.0109 | - |
591
+ | 0.3934 | 22150 | 0.0129 | - |
592
+ | 0.3943 | 22200 | 0.0056 | - |
593
+ | 0.3952 | 22250 | 0.0095 | - |
594
+ | 0.3961 | 22300 | 0.0107 | - |
595
+ | 0.3970 | 22350 | 0.0083 | - |
596
+ | 0.3979 | 22400 | 0.0102 | - |
597
+ | 0.3988 | 22450 | 0.0138 | - |
598
+ | 0.3996 | 22500 | 0.0053 | - |
599
+ | 0.4005 | 22550 | 0.0075 | - |
600
+ | 0.4014 | 22600 | 0.0079 | - |
601
+ | 0.4023 | 22650 | 0.0063 | - |
602
+ | 0.4032 | 22700 | 0.0011 | - |
603
+ | 0.4041 | 22750 | 0.0048 | - |
604
+ | 0.4050 | 22800 | 0.0012 | - |
605
+ | 0.4059 | 22850 | 0.001 | - |
606
+ | 0.4067 | 22900 | 0.0088 | - |
607
+ | 0.4076 | 22950 | 0.0087 | - |
608
+ | 0.4085 | 23000 | 0.0007 | - |
609
+ | 0.4094 | 23050 | 0.0247 | - |
610
+ | 0.4103 | 23100 | 0.0065 | - |
611
+ | 0.4112 | 23150 | 0.0037 | - |
612
+ | 0.4121 | 23200 | 0.0011 | - |
613
+ | 0.4130 | 23250 | 0.0114 | - |
614
+ | 0.4139 | 23300 | 0.0109 | - |
615
+ | 0.4147 | 23350 | 0.0135 | - |
616
+ | 0.4156 | 23400 | 0.0164 | - |
617
+ | 0.4165 | 23450 | 0.0069 | - |
618
+ | 0.4174 | 23500 | 0.006 | - |
619
+ | 0.4183 | 23550 | 0.0069 | - |
620
+ | 0.4192 | 23600 | 0.0047 | - |
621
+ | 0.4201 | 23650 | 0.0149 | - |
622
+ | 0.4210 | 23700 | 0.0113 | - |
623
+ | 0.4218 | 23750 | 0.0066 | - |
624
+ | 0.4227 | 23800 | 0.0113 | - |
625
+ | 0.4236 | 23850 | 0.0111 | - |
626
+ | 0.4245 | 23900 | 0.0207 | - |
627
+ | 0.4254 | 23950 | 0.0206 | - |
628
+ | 0.4263 | 24000 | 0.0136 | - |
629
+ | 0.4272 | 24050 | 0.009 | - |
630
+ | 0.4281 | 24100 | 0.0122 | - |
631
+ | 0.4290 | 24150 | 0.0065 | - |
632
+ | 0.4298 | 24200 | 0.0074 | - |
633
+ | 0.4307 | 24250 | 0.0207 | - |
634
+ | 0.4316 | 24300 | 0.0004 | - |
635
+ | 0.4325 | 24350 | 0.0125 | - |
636
+ | 0.4334 | 24400 | 0.0147 | - |
637
+ | 0.4343 | 24450 | 0.0025 | - |
638
+ | 0.4352 | 24500 | 0.0116 | - |
639
+ | 0.4361 | 24550 | 0.0101 | - |
640
+ | 0.4369 | 24600 | 0.005 | - |
641
+ | 0.4378 | 24650 | 0.0018 | - |
642
+ | 0.4387 | 24700 | 0.0125 | - |
643
+ | 0.4396 | 24750 | 0.0027 | - |
644
+ | 0.4405 | 24800 | 0.0213 | - |
645
+ | 0.4414 | 24850 | 0.0162 | - |
646
+ | 0.4423 | 24900 | 0.0062 | - |
647
+ | 0.4432 | 24950 | 0.0089 | - |
648
+ | 0.4440 | 25000 | 0.0056 | - |
649
+ | 0.4449 | 25050 | 0.0141 | - |
650
+ | 0.4458 | 25100 | 0.0123 | - |
651
+ | 0.4467 | 25150 | 0.0003 | - |
652
+ | 0.4476 | 25200 | 0.0157 | - |
653
+ | 0.4485 | 25250 | 0.03 | - |
654
+ | 0.4494 | 25300 | 0.0067 | - |
655
+ | 0.4503 | 25350 | 0.0009 | - |
656
+ | 0.4512 | 25400 | 0.0125 | - |
657
+ | 0.4520 | 25450 | 0.0066 | - |
658
+ | 0.4529 | 25500 | 0.0133 | - |
659
+ | 0.4538 | 25550 | 0.0064 | - |
660
+ | 0.4547 | 25600 | 0.0104 | - |
661
+ | 0.4556 | 25650 | 0.0043 | - |
662
+ | 0.4565 | 25700 | 0.0068 | - |
663
+ | 0.4574 | 25750 | 0.0067 | - |
664
+ | 0.4583 | 25800 | 0.0088 | - |
665
+ | 0.4591 | 25850 | 0.0039 | - |
666
+ | 0.4600 | 25900 | 0.0122 | - |
667
+ | 0.4609 | 25950 | 0.0181 | - |
668
+ | 0.4618 | 26000 | 0.0058 | - |
669
+ | 0.4627 | 26050 | 0.0208 | - |
670
+ | 0.4636 | 26100 | 0.0107 | - |
671
+ | 0.4645 | 26150 | 0.0066 | - |
672
+ | 0.4654 | 26200 | 0.0072 | - |
673
+ | 0.4663 | 26250 | 0.0046 | - |
674
+ | 0.4671 | 26300 | 0.0004 | - |
675
+ | 0.4680 | 26350 | 0.0021 | - |
676
+ | 0.4689 | 26400 | 0.013 | - |
677
+ | 0.4698 | 26450 | 0.0141 | - |
678
+ | 0.4707 | 26500 | 0.001 | - |
679
+ | 0.4716 | 26550 | 0.001 | - |
680
+ | 0.4725 | 26600 | 0.0023 | - |
681
+ | 0.4734 | 26650 | 0.0106 | - |
682
+ | 0.4742 | 26700 | 0.0023 | - |
683
+ | 0.4751 | 26750 | 0.0082 | - |
684
+ | 0.4760 | 26800 | 0.0014 | - |
685
+ | 0.4769 | 26850 | 0.0063 | - |
686
+ | 0.4778 | 26900 | 0.0022 | - |
687
+ | 0.4787 | 26950 | 0.0059 | - |
688
+ | 0.4796 | 27000 | 0.0011 | - |
689
+ | 0.4805 | 27050 | 0.0082 | - |
690
+ | 0.4813 | 27100 | 0.0034 | - |
691
+ | 0.4822 | 27150 | 0.0107 | - |
692
+ | 0.4831 | 27200 | 0.0004 | - |
693
+ | 0.4840 | 27250 | 0.0112 | - |
694
+ | 0.4849 | 27300 | 0.0079 | - |
695
+ | 0.4858 | 27350 | 0.0069 | - |
696
+ | 0.4867 | 27400 | 0.0116 | - |
697
+ | 0.4876 | 27450 | 0.0069 | - |
698
+ | 0.4885 | 27500 | 0.0103 | - |
699
+ | 0.4893 | 27550 | 0.0072 | - |
700
+ | 0.4902 | 27600 | 0.0029 | - |
701
+ | 0.4911 | 27650 | 0.0073 | - |
702
+ | 0.4920 | 27700 | 0.0143 | - |
703
+ | 0.4929 | 27750 | 0.0024 | - |
704
+ | 0.4938 | 27800 | 0.0142 | - |
705
+ | 0.4947 | 27850 | 0.0119 | - |
706
+ | 0.4956 | 27900 | 0.01 | - |
707
+ | 0.4964 | 27950 | 0.0057 | - |
708
+ | 0.4973 | 28000 | 0.015 | - |
709
+ | 0.4982 | 28050 | 0.0108 | - |
710
+ | 0.4991 | 28100 | 0.0054 | - |
711
+ | 0.5 | 28150 | 0.0017 | - |
712
+ | 0.5009 | 28200 | 0.0079 | - |
713
+ | 0.5018 | 28250 | 0.0232 | - |
714
+ | 0.5027 | 28300 | 0.0108 | - |
715
+ | 0.5036 | 28350 | 0.0024 | - |
716
+ | 0.5044 | 28400 | 0.0094 | - |
717
+ | 0.5053 | 28450 | 0.0142 | - |
718
+ | 0.5062 | 28500 | 0.0133 | - |
719
+ | 0.5071 | 28550 | 0.0048 | - |
720
+ | 0.5080 | 28600 | 0.0108 | - |
721
+ | 0.5089 | 28650 | 0.0061 | - |
722
+ | 0.5098 | 28700 | 0.0241 | - |
723
+ | 0.5107 | 28750 | 0.0105 | - |
724
+ | 0.5115 | 28800 | 0.0162 | - |
725
+ | 0.5124 | 28850 | 0.0012 | - |
726
+ | 0.5133 | 28900 | 0.0074 | - |
727
+ | 0.5142 | 28950 | 0.0055 | - |
728
+ | 0.5151 | 29000 | 0.007 | - |
729
+ | 0.5160 | 29050 | 0.0031 | - |
730
+ | 0.5169 | 29100 | 0.0105 | - |
731
+ | 0.5178 | 29150 | 0.0039 | - |
732
+ | 0.5187 | 29200 | 0.011 | - |
733
+ | 0.5195 | 29250 | 0.0192 | - |
734
+ | 0.5204 | 29300 | 0.0021 | - |
735
+ | 0.5213 | 29350 | 0.0033 | - |
736
+ | 0.5222 | 29400 | 0.0082 | - |
737
+ | 0.5231 | 29450 | 0.0068 | - |
738
+ | 0.5240 | 29500 | 0.0038 | - |
739
+ | 0.5249 | 29550 | 0.0051 | - |
740
+ | 0.5258 | 29600 | 0.0068 | - |
741
+ | 0.5266 | 29650 | 0.0084 | - |
742
+ | 0.5275 | 29700 | 0.0201 | - |
743
+ | 0.5284 | 29750 | 0.0088 | - |
744
+ | 0.5293 | 29800 | 0.0111 | - |
745
+ | 0.5302 | 29850 | 0.006 | - |
746
+ | 0.5311 | 29900 | 0.0123 | - |
747
+ | 0.5320 | 29950 | 0.0067 | - |
748
+ | 0.5329 | 30000 | 0.0053 | - |
749
+ | 0.5337 | 30050 | 0.0052 | - |
750
+ | 0.5346 | 30100 | 0.0075 | - |
751
+ | 0.5355 | 30150 | 0.0126 | - |
752
+ | 0.5364 | 30200 | 0.0005 | - |
753
+ | 0.5373 | 30250 | 0.0076 | - |
754
+ | 0.5382 | 30300 | 0.012 | - |
755
+ | 0.5391 | 30350 | 0.0148 | - |
756
+ | 0.5400 | 30400 | 0.0072 | - |
757
+ | 0.5409 | 30450 | 0.0123 | - |
758
+ | 0.5417 | 30500 | 0.0073 | - |
759
+ | 0.5426 | 30550 | 0.0051 | - |
760
+ | 0.5435 | 30600 | 0.0052 | - |
761
+ | 0.5444 | 30650 | 0.0095 | - |
762
+ | 0.5453 | 30700 | 0.0062 | - |
763
+ | 0.5462 | 30750 | 0.0083 | - |
764
+ | 0.5471 | 30800 | 0.013 | - |
765
+ | 0.5480 | 30850 | 0.0072 | - |
766
+ | 0.5488 | 30900 | 0.0089 | - |
767
+ | 0.5497 | 30950 | 0.0073 | - |
768
+ | 0.5506 | 31000 | 0.0056 | - |
769
+ | 0.5515 | 31050 | 0.0026 | - |
770
+ | 0.5524 | 31100 | 0.006 | - |
771
+ | 0.5533 | 31150 | 0.0079 | - |
772
+ | 0.5542 | 31200 | 0.0138 | - |
773
+ | 0.5551 | 31250 | 0.0111 | - |
774
+ | 0.5560 | 31300 | 0.0104 | - |
775
+ | 0.5568 | 31350 | 0.0204 | - |
776
+ | 0.5577 | 31400 | 0.0058 | - |
777
+ | 0.5586 | 31450 | 0.0107 | - |
778
+ | 0.5595 | 31500 | 0.0027 | - |
779
+ | 0.5604 | 31550 | 0.0208 | - |
780
+ | 0.5613 | 31600 | 0.001 | - |
781
+ | 0.5622 | 31650 | 0.0062 | - |
782
+ | 0.5631 | 31700 | 0.0116 | - |
783
+ | 0.5639 | 31750 | 0.0034 | - |
784
+ | 0.5648 | 31800 | 0.0059 | - |
785
+ | 0.5657 | 31850 | 0.0118 | - |
786
+ | 0.5666 | 31900 | 0.0116 | - |
787
+ | 0.5675 | 31950 | 0.0074 | - |
788
+ | 0.5684 | 32000 | 0.0026 | - |
789
+ | 0.5693 | 32050 | 0.0122 | - |
790
+ | 0.5702 | 32100 | 0.014 | - |
791
+ | 0.5710 | 32150 | 0.0004 | - |
792
+ | 0.5719 | 32200 | 0.0169 | - |
793
+ | 0.5728 | 32250 | 0.0076 | - |
794
+ | 0.5737 | 32300 | 0.0066 | - |
795
+ | 0.5746 | 32350 | 0.0106 | - |
796
+ | 0.5755 | 32400 | 0.0063 | - |
797
+ | 0.5764 | 32450 | 0.0154 | - |
798
+ | 0.5773 | 32500 | 0.0062 | - |
799
+ | 0.5782 | 32550 | 0.0126 | - |
800
+ | 0.5790 | 32600 | 0.0077 | - |
801
+ | 0.5799 | 32650 | 0.0106 | - |
802
+ | 0.5808 | 32700 | 0.0179 | - |
803
+ | 0.5817 | 32750 | 0.0082 | - |
804
+ | 0.5826 | 32800 | 0.0064 | - |
805
+ | 0.5835 | 32850 | 0.0179 | - |
806
+ | 0.5844 | 32900 | 0.0063 | - |
807
+ | 0.5853 | 32950 | 0.0157 | - |
808
+ | 0.5861 | 33000 | 0.0237 | - |
809
+ | 0.5870 | 33050 | 0.0272 | - |
810
+ | 0.5879 | 33100 | 0.0091 | - |
811
+ | 0.5888 | 33150 | 0.0023 | - |
812
+ | 0.5897 | 33200 | 0.0114 | - |
813
+ | 0.5906 | 33250 | 0.001 | - |
814
+ | 0.5915 | 33300 | 0.0089 | - |
815
+ | 0.5924 | 33350 | 0.0077 | - |
816
+ | 0.5933 | 33400 | 0.0139 | - |
817
+ | 0.5941 | 33450 | 0.0021 | - |
818
+ | 0.5950 | 33500 | 0.0098 | - |
819
+ | 0.5959 | 33550 | 0.0009 | - |
820
+ | 0.5968 | 33600 | 0.002 | - |
821
+ | 0.5977 | 33650 | 0.0082 | - |
822
+ | 0.5986 | 33700 | 0.0204 | - |
823
+ | 0.5995 | 33750 | 0.0026 | - |
824
+ | 0.6004 | 33800 | 0.0116 | - |
825
+ | 0.6012 | 33850 | 0.0063 | - |
826
+ | 0.6021 | 33900 | 0.005 | - |
827
+ | 0.6030 | 33950 | 0.0111 | - |
828
+ | 0.6039 | 34000 | 0.0013 | - |
829
+ | 0.6048 | 34050 | 0.0153 | - |
830
+ | 0.6057 | 34100 | 0.0223 | - |
831
+ | 0.6066 | 34150 | 0.0094 | - |
832
+ | 0.6075 | 34200 | 0.012 | - |
833
+ | 0.6083 | 34250 | 0.0202 | - |
834
+ | 0.6092 | 34300 | 0.0015 | - |
835
+ | 0.6101 | 34350 | 0.0063 | - |
836
+ | 0.6110 | 34400 | 0.0052 | - |
837
+ | 0.6119 | 34450 | 0.0034 | - |
838
+ | 0.6128 | 34500 | 0.0075 | - |
839
+ | 0.6137 | 34550 | 0.0062 | - |
840
+ | 0.6146 | 34600 | 0.0061 | - |
841
+ | 0.6155 | 34650 | 0.0176 | - |
842
+ | 0.6163 | 34700 | 0.0074 | - |
843
+ | 0.6172 | 34750 | 0.0006 | - |
844
+ | 0.6181 | 34800 | 0.0056 | - |
845
+ | 0.6190 | 34850 | 0.0059 | - |
846
+ | 0.6199 | 34900 | 0.0069 | - |
847
+ | 0.6208 | 34950 | 0.0081 | - |
848
+ | 0.6217 | 35000 | 0.0161 | - |
849
+ | 0.6226 | 35050 | 0.0223 | - |
850
+ | 0.6234 | 35100 | 0.0118 | - |
851
+ | 0.6243 | 35150 | 0.0024 | - |
852
+ | 0.6252 | 35200 | 0.0106 | - |
853
+ | 0.6261 | 35250 | 0.0135 | - |
854
+ | 0.6270 | 35300 | 0.0068 | - |
855
+ | 0.6279 | 35350 | 0.0062 | - |
856
+ | 0.6288 | 35400 | 0.0081 | - |
857
+ | 0.6297 | 35450 | 0.0166 | - |
858
+ | 0.6306 | 35500 | 0.0055 | - |
859
+ | 0.6314 | 35550 | 0.0211 | - |
860
+ | 0.6323 | 35600 | 0.0032 | - |
861
+ | 0.6332 | 35650 | 0.0017 | - |
862
+ | 0.6341 | 35700 | 0.0012 | - |
863
+ | 0.6350 | 35750 | 0.0185 | - |
864
+ | 0.6359 | 35800 | 0.0056 | - |
865
+ | 0.6368 | 35850 | 0.0008 | - |
866
+ | 0.6377 | 35900 | 0.0072 | - |
867
+ | 0.6385 | 35950 | 0.007 | - |
868
+ | 0.6394 | 36000 | 0.0082 | - |
869
+ | 0.6403 | 36050 | 0.0061 | - |
870
+ | 0.6412 | 36100 | 0.002 | - |
871
+ | 0.6421 | 36150 | 0.0084 | - |
872
+ | 0.6430 | 36200 | 0.0111 | - |
873
+ | 0.6439 | 36250 | 0.0091 | - |
874
+ | 0.6448 | 36300 | 0.0112 | - |
875
+ | 0.6456 | 36350 | 0.0074 | - |
876
+ | 0.6465 | 36400 | 0.0056 | - |
877
+ | 0.6474 | 36450 | 0.0071 | - |
878
+ | 0.6483 | 36500 | 0.007 | - |
879
+ | 0.6492 | 36550 | 0.0011 | - |
880
+ | 0.6501 | 36600 | 0.0052 | - |
881
+ | 0.6510 | 36650 | 0.016 | - |
882
+ | 0.6519 | 36700 | 0.0103 | - |
883
+ | 0.6528 | 36750 | 0.0119 | - |
884
+ | 0.6536 | 36800 | 0.0114 | - |
885
+ | 0.6545 | 36850 | 0.0093 | - |
886
+ | 0.6554 | 36900 | 0.0017 | - |
887
+ | 0.6563 | 36950 | 0.001 | - |
888
+ | 0.6572 | 37000 | 0.0067 | - |
889
+ | 0.6581 | 37050 | 0.0025 | - |
890
+ | 0.6590 | 37100 | 0.0059 | - |
891
+ | 0.6599 | 37150 | 0.0066 | - |
892
+ | 0.6607 | 37200 | 0.0181 | - |
893
+ | 0.6616 | 37250 | 0.01 | - |
894
+ | 0.6625 | 37300 | 0.0192 | - |
895
+ | 0.6634 | 37350 | 0.0039 | - |
896
+ | 0.6643 | 37400 | 0.0207 | - |
897
+ | 0.6652 | 37450 | 0.0005 | - |
898
+ | 0.6661 | 37500 | 0.0006 | - |
899
+ | 0.6670 | 37550 | 0.0056 | - |
900
+ | 0.6679 | 37600 | 0.0122 | - |
901
+ | 0.6687 | 37650 | 0.0049 | - |
902
+ | 0.6696 | 37700 | 0.0113 | - |
903
+ | 0.6705 | 37750 | 0.0086 | - |
904
+ | 0.6714 | 37800 | 0.0064 | - |
905
+ | 0.6723 | 37850 | 0.0008 | - |
906
+ | 0.6732 | 37900 | 0.0025 | - |
907
+ | 0.6741 | 37950 | 0.0167 | - |
908
+ | 0.6750 | 38000 | 0.0018 | - |
909
+ | 0.6758 | 38050 | 0.0034 | - |
910
+ | 0.6767 | 38100 | 0.0011 | - |
911
+ | 0.6776 | 38150 | 0.0067 | - |
912
+ | 0.6785 | 38200 | 0.0147 | - |
913
+ | 0.6794 | 38250 | 0.0169 | - |
914
+ | 0.6803 | 38300 | 0.0159 | - |
915
+ | 0.6812 | 38350 | 0.0081 | - |
916
+ | 0.6821 | 38400 | 0.0157 | - |
917
+ | 0.6829 | 38450 | 0.0088 | - |
918
+ | 0.6838 | 38500 | 0.0075 | - |
919
+ | 0.6847 | 38550 | 0.0131 | - |
920
+ | 0.6856 | 38600 | 0.0048 | - |
921
+ | 0.6865 | 38650 | 0.0104 | - |
922
+ | 0.6874 | 38700 | 0.0059 | - |
923
+ | 0.6883 | 38750 | 0.0035 | - |
924
+ | 0.6892 | 38800 | 0.0025 | - |
925
+ | 0.6901 | 38850 | 0.0062 | - |
926
+ | 0.6909 | 38900 | 0.019 | - |
927
+ | 0.6918 | 38950 | 0.013 | - |
928
+ | 0.6927 | 39000 | 0.0097 | - |
929
+ | 0.6936 | 39050 | 0.0062 | - |
930
+ | 0.6945 | 39100 | 0.0114 | - |
931
+ | 0.6954 | 39150 | 0.0149 | - |
932
+ | 0.6963 | 39200 | 0.003 | - |
933
+ | 0.6972 | 39250 | 0.003 | - |
934
+ | 0.6980 | 39300 | 0.011 | - |
935
+ | 0.6989 | 39350 | 0.0055 | - |
936
+ | 0.6998 | 39400 | 0.0082 | - |
937
+ | 0.7007 | 39450 | 0.0007 | - |
938
+ | 0.7016 | 39500 | 0.0113 | - |
939
+ | 0.7025 | 39550 | 0.0156 | - |
940
+ | 0.7034 | 39600 | 0.0108 | - |
941
+ | 0.7043 | 39650 | 0.0059 | - |
942
+ | 0.7052 | 39700 | 0.0045 | - |
943
+ | 0.7060 | 39750 | 0.0016 | - |
944
+ | 0.7069 | 39800 | 0.0092 | - |
945
+ | 0.7078 | 39850 | 0.0123 | - |
946
+ | 0.7087 | 39900 | 0.0095 | - |
947
+ | 0.7096 | 39950 | 0.0027 | - |
948
+ | 0.7105 | 40000 | 0.0161 | - |
949
+ | 0.7114 | 40050 | 0.006 | - |
950
+ | 0.7123 | 40100 | 0.0107 | - |
951
+ | 0.7131 | 40150 | 0.0064 | - |
952
+ | 0.7140 | 40200 | 0.0118 | - |
953
+ | 0.7149 | 40250 | 0.0124 | - |
954
+ | 0.7158 | 40300 | 0.0054 | - |
955
+ | 0.7167 | 40350 | 0.0141 | - |
956
+ | 0.7176 | 40400 | 0.0048 | - |
957
+ | 0.7185 | 40450 | 0.0021 | - |
958
+ | 0.7194 | 40500 | 0.0025 | - |
959
+ | 0.7202 | 40550 | 0.0108 | - |
960
+ | 0.7211 | 40600 | 0.0111 | - |
961
+ | 0.7220 | 40650 | 0.0201 | - |
962
+ | 0.7229 | 40700 | 0.0056 | - |
963
+ | 0.7238 | 40750 | 0.0215 | - |
964
+ | 0.7247 | 40800 | 0.0108 | - |
965
+ | 0.7256 | 40850 | 0.0055 | - |
966
+ | 0.7265 | 40900 | 0.0078 | - |
967
+ | 0.7274 | 40950 | 0.0106 | - |
968
+ | 0.7282 | 41000 | 0.0089 | - |
969
+ | 0.7291 | 41050 | 0.0029 | - |
970
+ | 0.7300 | 41100 | 0.03 | - |
971
+ | 0.7309 | 41150 | 0.0067 | - |
972
+ | 0.7318 | 41200 | 0.0065 | - |
973
+ | 0.7327 | 41250 | 0.0012 | - |
974
+ | 0.7336 | 41300 | 0.0142 | - |
975
+ | 0.7345 | 41350 | 0.0027 | - |
976
+ | 0.7353 | 41400 | 0.0069 | - |
977
+ | 0.7362 | 41450 | 0.0126 | - |
978
+ | 0.7371 | 41500 | 0.002 | - |
979
+ | 0.7380 | 41550 | 0.0027 | - |
980
+ | 0.7389 | 41600 | 0.0027 | - |
981
+ | 0.7398 | 41650 | 0.0204 | - |
982
+ | 0.7407 | 41700 | 0.0114 | - |
983
+ | 0.7416 | 41750 | 0.0059 | - |
984
+ | 0.7425 | 41800 | 0.0024 | - |
985
+ | 0.7433 | 41850 | 0.0062 | - |
986
+ | 0.7442 | 41900 | 0.0059 | - |
987
+ | 0.7451 | 41950 | 0.0092 | - |
988
+ | 0.7460 | 42000 | 0.0009 | - |
989
+ | 0.7469 | 42050 | 0.0055 | - |
990
+ | 0.7478 | 42100 | 0.0088 | - |
991
+ | 0.7487 | 42150 | 0.0025 | - |
992
+ | 0.7496 | 42200 | 0.0016 | - |
993
+ | 0.7504 | 42250 | 0.0059 | - |
994
+ | 0.7513 | 42300 | 0.0077 | - |
995
+ | 0.7522 | 42350 | 0.0072 | - |
996
+ | 0.7531 | 42400 | 0.025 | - |
997
+ | 0.7540 | 42450 | 0.0111 | - |
998
+ | 0.7549 | 42500 | 0.0165 | - |
999
+ | 0.7558 | 42550 | 0.0114 | - |
1000
+ | 0.7567 | 42600 | 0.0049 | - |
1001
+ | 0.7575 | 42650 | 0.0099 | - |
1002
+ | 0.7584 | 42700 | 0.0051 | - |
1003
+ | 0.7593 | 42750 | 0.0058 | - |
1004
+ | 0.7602 | 42800 | 0.0132 | - |
1005
+ | 0.7611 | 42850 | 0.0184 | - |
1006
+ | 0.7620 | 42900 | 0.0043 | - |
1007
+ | 0.7629 | 42950 | 0.0138 | - |
1008
+ | 0.7638 | 43000 | 0.0197 | - |
1009
+ | 0.7647 | 43050 | 0.0085 | - |
1010
+ | 0.7655 | 43100 | 0.0012 | - |
1011
+ | 0.7664 | 43150 | 0.0107 | - |
1012
+ | 0.7673 | 43200 | 0.0161 | - |
1013
+ | 0.7682 | 43250 | 0.0059 | - |
1014
+ | 0.7691 | 43300 | 0.0067 | - |
1015
+ | 0.7700 | 43350 | 0.0257 | - |
1016
+ | 0.7709 | 43400 | 0.0128 | - |
1017
+ | 0.7718 | 43450 | 0.012 | - |
1018
+ | 0.7726 | 43500 | 0.0016 | - |
1019
+ | 0.7735 | 43550 | 0.0004 | - |
1020
+ | 0.7744 | 43600 | 0.002 | - |
1021
+ | 0.7753 | 43650 | 0.0208 | - |
1022
+ | 0.7762 | 43700 | 0.0229 | - |
1023
+ | 0.7771 | 43750 | 0.0103 | - |
1024
+ | 0.7780 | 43800 | 0.0027 | - |
1025
+ | 0.7789 | 43850 | 0.006 | - |
1026
+ | 0.7798 | 43900 | 0.0082 | - |
1027
+ | 0.7806 | 43950 | 0.0014 | - |
1028
+ | 0.7815 | 44000 | 0.01 | - |
1029
+ | 0.7824 | 44050 | 0.0113 | - |
1030
+ | 0.7833 | 44100 | 0.0111 | - |
1031
+ | 0.7842 | 44150 | 0.0108 | - |
1032
+ | 0.7851 | 44200 | 0.011 | - |
1033
+ | 0.7860 | 44250 | 0.0198 | - |
1034
+ | 0.7869 | 44300 | 0.0114 | - |
1035
+ | 0.7877 | 44350 | 0.001 | - |
1036
+ | 0.7886 | 44400 | 0.0083 | - |
1037
+ | 0.7895 | 44450 | 0.006 | - |
1038
+ | 0.7904 | 44500 | 0.0115 | - |
1039
+ | 0.7913 | 44550 | 0.0106 | - |
1040
+ | 0.7922 | 44600 | 0.0064 | - |
1041
+ | 0.7931 | 44650 | 0.0141 | - |
1042
+ | 0.7940 | 44700 | 0.0089 | - |
1043
+ | 0.7948 | 44750 | 0.0054 | - |
1044
+ | 0.7957 | 44800 | 0.0134 | - |
1045
+ | 0.7966 | 44850 | 0.0172 | - |
1046
+ | 0.7975 | 44900 | 0.003 | - |
1047
+ | 0.7984 | 44950 | 0.0173 | - |
1048
+ | 0.7993 | 45000 | 0.0079 | - |
1049
+ | 0.8002 | 45050 | 0.0171 | - |
1050
+ | 0.8011 | 45100 | 0.0003 | - |
1051
+ | 0.8020 | 45150 | 0.0029 | - |
1052
+ | 0.8028 | 45200 | 0.008 | - |
1053
+ | 0.8037 | 45250 | 0.0204 | - |
1054
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1055
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+
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+ ### Framework Versions
1277
+ - Python: 3.10.12
1278
+ - SetFit: 1.0.3
1279
+ - Sentence Transformers: 3.0.0
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+ - spaCy: 3.7.4
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+ - Transformers: 4.36.2
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+ - PyTorch: 2.3.0+cu121
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+ - Datasets: 2.19.2
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+ - Tokenizers: 0.15.2
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+
1286
+ ## Citation
1287
+
1288
+ ### BibTeX
1289
+ ```bibtex
1290
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
1291
+ doi = {10.48550/ARXIV.2209.11055},
1292
+ url = {https://arxiv.org/abs/2209.11055},
1293
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
1294
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
1295
+ title = {Efficient Few-Shot Learning Without Prompts},
1296
+ publisher = {arXiv},
1297
+ year = {2022},
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+ copyright = {Creative Commons Attribution 4.0 International}
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+ }
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+ ```
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+
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+ <!--
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+ ## Glossary
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+
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+ *Clearly define terms in order to be accessible across audiences.*
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+ ## Model Card Authors
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+ -->
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+
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+ <!--
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+ ## Model Card Contact
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+
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+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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+ -->
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+ "special": true
42
+ }
43
+ },
44
+ "clean_up_tokenization_spaces": true,
45
+ "cls_token": "[CLS]",
46
+ "do_basic_tokenize": true,
47
+ "do_lower_case": true,
48
+ "mask_token": "[MASK]",
49
+ "max_length": 512,
50
+ "model_max_length": 512,
51
+ "never_split": null,
52
+ "pad_to_multiple_of": null,
53
+ "pad_token": "[PAD]",
54
+ "pad_token_type_id": 0,
55
+ "padding_side": "right",
56
+ "sep_token": "[SEP]",
57
+ "stride": 0,
58
+ "strip_accents": null,
59
+ "tokenize_chinese_chars": true,
60
+ "tokenizer_class": "BertTokenizer",
61
+ "truncation_side": "right",
62
+ "truncation_strategy": "longest_first",
63
+ "unk_token": "[UNK]"
64
+ }
vocab.txt ADDED
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