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thegenerativegeneration
/
stay_or_go_conversation_classifier_s_v2

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
Model card Files Community
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  1. README.md +67 -36
  2. config_setfit.json +2 -2
  3. model.safetensors +1 -1
  4. model_head.pkl +1 -1
README.md CHANGED
@@ -9,12 +9,11 @@ base_model: intfloat/multilingual-e5-small
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  metrics:
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  - accuracy
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  widget:
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- - text: 'query: Interessant. Hast du das schon mal ausprobiert?'
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- - text: 'query: はい、持っていますよ。すぐにメールで送りますね。'
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- - text: 'query: Va bene ci sentiamo dopo Marco buona giornata'
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- - text: 'query: Ζητώ συγγνώμη, πρέπει να αποχωρήσω τώρα.'
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- - text: 'query: Guten Morgen, Maria! Hast du die Präsentation für das Meeting heute
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- fertig?'
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  pipeline_tag: text-classification
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  inference: true
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  model-index:
@@ -61,10 +60,10 @@ The model has been trained using an efficient few-shot learning technique that i
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  - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
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  ### Model Labels
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- | Label | Examples |
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- |:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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- | 0 | <ul><li>'query: สวัสดีค่ะ วันนี้เป็นอย่างไรบ้าง?'</li><li>'query: Jag förstår. Vad tycker du att vi ska göra nu?'</li><li>'query: Hej, wszystko w porządku. Właśnie dostałam nową pracę.'</li></ul> |
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- | 1 | <ul><li>'query: Чудесно, доскоро!'</li><li>'query: Mama mă cheamă, trebuie să mă întorc acasă, pa.'</li><li>'query: Perdó, ja he de marxar.'</li></ul> |
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  ## Evaluation
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@@ -91,7 +90,7 @@ from setfit import SetFitModel
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  # Download from the 🤗 Hub
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  model = SetFitModel.from_pretrained("setfit_model_id")
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  # Run inference
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- preds = model("query: はい、持っていますよ。すぐにメールで送りますね。")
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  ```
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  <!--
@@ -123,23 +122,23 @@ preds = model("query: はい、持っていますよ。すぐにメールで送
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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 | 7.3663 | 21 |
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  | Label | Training Sample Count |
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  |:------|:----------------------|
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- | 0 | 286 |
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- | 1 | 290 |
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  ### Training Hyperparameters
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  - batch_size: (16, 2)
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  - num_epochs: (1, 16)
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- - max_steps: 900
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  - sampling_strategy: undersampling
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- - body_learning_rate: (1e-05, 1e-05)
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  - head_learning_rate: 0.001
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  - loss: CosineSimilarityLoss
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  - distance_metric: cosine_distance
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- - margin: 0.1
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  - end_to_end: False
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  - use_amp: False
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  - warmup_proportion: 0.1
@@ -151,25 +150,57 @@ preds = model("query: はい、持っていますよ。すぐにメールで送
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  ### Training Results
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  | Epoch | Step | Training Loss | Validation Loss |
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  |:------:|:----:|:-------------:|:---------------:|
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- | 0.0006 | 1 | 0.3683 | - |
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- | 0.0278 | 50 | 0.2855 | - |
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- | 0.0555 | 100 | 0.1691 | 0.1598 |
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- | 0.0833 | 150 | 0.0339 | - |
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- | 0.1110 | 200 | 0.0134 | 0.0745 |
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- | 0.1388 | 250 | 0.0309 | - |
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- | 0.1666 | 300 | 0.0076 | 0.0344 |
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- | 0.1943 | 350 | 0.0023 | - |
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- | 0.2221 | 400 | 0.0012 | 0.0849 |
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- | 0.2499 | 450 | 0.0007 | - |
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- | 0.2776 | 500 | 0.0008 | 0.0932 |
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- | 0.3054 | 550 | 0.0005 | - |
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- | 0.3331 | 600 | 0.0005 | 0.0805 |
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- | 0.3609 | 650 | 0.0004 | - |
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- | 0.3887 | 700 | 0.0006 | 0.0951 |
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- | 0.4164 | 750 | 0.0006 | - |
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- | 0.4442 | 800 | 0.0016 | 0.0983 |
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- | 0.4720 | 850 | 0.0008 | - |
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- | 0.4997 | 900 | 0.0005 | 0.092 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework Versions
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  - Python: 3.10.11
 
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  metrics:
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  - accuracy
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  widget:
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+ - text: 'query: Baiklah, kita cakap lagi nanti, Mark. Selamat hari!'
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+ - text: 'query: Tôi xin lỗi nhưng tôi phải đi'
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+ - text: 'query: 次回行くときは、私を連れて行ってください。もっと自然の中で活動したいと思っています。'
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+ - text: 'query: Entschuldigung, ich muss jetzt gehen.'
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+ - text: 'query: Buenos días, ¿cómo están ustedes?'
 
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  pipeline_tag: text-classification
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  inference: true
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  model-index:
 
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  - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
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  ### Model Labels
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+ | Label | Examples |
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+ |:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------|
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+ | 0 | <ul><li>'query: Értem. Mit csinálunk most?'</li><li>'query: Ola Luca, que tal? Rematache o traballo?'</li><li>'query: Lijepo je. Hvala.'</li></ul> |
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+ | 1 | <ul><li>'query: Жөнейін, кейін кездесеміз.'</li><li>'query: Така, ќе се видиме повторно.'</li><li>'query: ठीक है बाद में बात करते हैं मार्क अच्छा दिन'</li></ul> |
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  ## Evaluation
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  # Download from the 🤗 Hub
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  model = SetFitModel.from_pretrained("setfit_model_id")
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  # Run inference
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+ preds = model("query: Tôi xin lỗi nhưng tôi phải đi")
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  ```
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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 | 7.2168 | 25 |
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  | Label | Training Sample Count |
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  |:------|:----------------------|
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+ | 0 | 346 |
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+ | 1 | 346 |
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  ### Training Hyperparameters
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  - batch_size: (16, 2)
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  - num_epochs: (1, 16)
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+ - max_steps: 2500
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  - sampling_strategy: undersampling
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+ - body_learning_rate: (1e-06, 1e-06)
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  - head_learning_rate: 0.001
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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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  ### Training Results
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  | Epoch | Step | Training Loss | Validation Loss |
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  |:------:|:----:|:-------------:|:---------------:|
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+ | 0.0002 | 1 | 0.3607 | - |
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+ | 0.0100 | 50 | 0.3634 | 0.3452 |
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+ | 0.0200 | 100 | 0.3493 | 0.3377 |
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+ | 0.0300 | 150 | 0.3244 | 0.3234 |
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+ | 0.0400 | 200 | 0.3244 | 0.3034 |
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+ | 0.0500 | 250 | 0.2931 | 0.2731 |
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+ | 0.0600 | 300 | 0.2471 | 0.2398 |
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+ | 0.0700 | 350 | 0.237 | 0.2168 |
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+ | 0.0800 | 400 | 0.1964 | 0.2082 |
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+ | 0.0900 | 450 | 0.2319 | 0.198 |
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+ | 0.1000 | 500 | 0.2003 | 0.1968 |
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+ | 0.1100 | 550 | 0.2014 | 0.1968 |
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+ | 0.1200 | 600 | 0.1617 | 0.1879 |
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+ | 0.1300 | 650 | 0.2214 | 0.1798 |
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+ | 0.1400 | 700 | 0.2498 | 0.1768 |
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+ | 0.1500 | 750 | 0.1527 | 0.1764 |
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+ | 0.1600 | 800 | 0.1134 | 0.1733 |
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+ | 0.1700 | 850 | 0.1393 | 0.1614 |
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+ | 0.1800 | 900 | 0.1052 | 0.1549 |
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+ | 0.1900 | 950 | 0.1772 | 0.149 |
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+ | 0.2000 | 1000 | 0.1065 | 0.1504 |
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+ | 0.2100 | 1050 | 0.087 | 0.1392 |
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+ | 0.2200 | 1100 | 0.1416 | 0.1333 |
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+ | 0.2300 | 1150 | 0.0767 | 0.1279 |
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+ | 0.2400 | 1200 | 0.1228 | 0.1243 |
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+ | 0.2500 | 1250 | 0.099 | 0.1128 |
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+ | 0.2599 | 1300 | 0.1125 | 0.1106 |
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+ | 0.2699 | 1350 | 0.1012 | 0.1156 |
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+ | 0.2799 | 1400 | 0.0343 | 0.1022 |
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+ | 0.2899 | 1450 | 0.0814 | 0.1012 |
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+ | 0.2999 | 1500 | 0.0947 | 0.0965 |
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+ | 0.3099 | 1550 | 0.0799 | 0.0964 |
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+ | 0.3199 | 1600 | 0.113 | 0.0942 |
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+ | 0.3299 | 1650 | 0.1125 | 0.0917 |
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+ | 0.3399 | 1700 | 0.0507 | 0.0899 |
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+ | 0.3499 | 1750 | 0.0986 | 0.0938 |
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+ | 0.3599 | 1800 | 0.0885 | 0.0913 |
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+ | 0.3699 | 1850 | 0.0712 | 0.0841 |
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+ | 0.3799 | 1900 | 0.1131 | 0.0851 |
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+ | 0.3899 | 1950 | 0.0701 | 0.0852 |
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+ | 0.3999 | 2000 | 0.0805 | 0.0878 |
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+ | 0.4099 | 2050 | 0.0375 | 0.0814 |
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+ | 0.4199 | 2100 | 0.1236 | 0.0797 |
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+ | 0.4299 | 2150 | 0.0532 | 0.0881 |
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+ | 0.4399 | 2200 | 0.0265 | 0.0806 |
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+ | 0.4499 | 2250 | 0.1268 | 0.0801 |
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+ | 0.4599 | 2300 | 0.0557 | 0.0797 |
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+ | 0.4699 | 2350 | 0.0956 | 0.0832 |
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+ | 0.4799 | 2400 | 0.0671 | 0.081 |
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+ | 0.4899 | 2450 | 0.1394 | 0.0794 |
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+ | 0.4999 | 2500 | 0.1165 | 0.0798 |
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  ### Framework Versions
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  - Python: 3.10.11
config_setfit.json CHANGED
@@ -1,4 +1,4 @@
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  {
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- "normalize_embeddings": false,
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- "labels": null
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  }
 
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+ "labels": null,
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+ "normalize_embeddings": false
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  }
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