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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 +57 -57
  2. config.json +1 -1
  3. config_sentence_transformers.json +1 -1
  4. config_setfit.json +2 -2
  5. model.safetensors +1 -1
  6. model_head.pkl +1 -1
README.md CHANGED
@@ -9,12 +9,12 @@ 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: Sí, la próxima vez que vayas, cuenta conmigo. He querido salir y hacer
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- más actividades en la naturaleza.'
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- - text: 'query: I''m man, I''m leaving now.'
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- - text: 'query: Ja, forse possiamo fare un giro in bicicletta insieme.'
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- - text: 'query: Mak saya suruh balik, jumpa lagi.'
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- - text: 'query: İnanılmaz, bu harika! Bir ayı gördüğüne inanamıyorum!'
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  pipeline_tag: text-classification
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  inference: true
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  ---
@@ -47,10 +47,10 @@ 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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- | 1 | <ul><li>'query: Tja, måste dra nu, ses senare.'</li><li>'query: Ispričavam se, moram sada otići.'</li><li>'query: Przepraszam, muszę już iść.'</li></ul> |
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- | 0 | <ul><li>'query: Sveiki, kā jums klājas?'</li><li>'query: அதிர்ச்சிகரமானது, அது மிகவும் அருமையாக இருக்கிறது! நீ கரடியை பார்த்தது எனக்கு நம்பிக்கையே வரவில்லை!'</li><li>'query: Ég hef það fínt, takk. Og þú?'</li></ul> |
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  ## Uses
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@@ -70,7 +70,7 @@ 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: I'm man, I'm leaving now.")
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  ```
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  <!--
@@ -102,17 +102,17 @@ preds = model("query: I'm man, I'm leaving now.")
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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 | 7.6965 | 31 |
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  | Label | Training Sample Count |
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  |:------|:----------------------|
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- | 0 | 902 |
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- | 1 | 910 |
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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: -1
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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
@@ -130,53 +130,53 @@ preds = model("query: I'm man, I'm leaving now.")
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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 | 0.3613 | - |
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- | 0.0005 | 50 | 0.3577 | - |
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- | 0.0010 | 100 | 0.3511 | 0.3413 |
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- | 0.0015 | 150 | 0.3372 | - |
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- | 0.0019 | 200 | 0.3447 | 0.3347 |
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- | 0.0024 | 250 | 0.3349 | - |
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- | 0.0029 | 300 | 0.3326 | 0.3224 |
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- | 0.0034 | 350 | 0.3372 | - |
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- | 0.0039 | 400 | 0.3185 | 0.3039 |
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- | 0.0044 | 450 | 0.2828 | - |
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- | 0.0049 | 500 | 0.3055 | 0.2774 |
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- | 0.0054 | 550 | 0.2594 | - |
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- | 0.0058 | 600 | 0.2779 | 0.2489 |
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- | 0.0063 | 650 | 0.2486 | - |
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- | 0.0068 | 700 | 0.2321 | 0.22 |
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- | 0.0073 | 750 | 0.1838 | - |
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- | 0.0078 | 800 | 0.1845 | 0.2075 |
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- | 0.0083 | 850 | 0.1899 | - |
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- | 0.0088 | 900 | 0.2147 | 0.2025 |
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- | 0.0093 | 950 | 0.1644 | - |
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- | 0.0097 | 1000 | 0.2019 | 0.1821 |
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- | 0.0102 | 1050 | 0.2309 | - |
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- | 0.0107 | 1100 | 0.2084 | 0.1784 |
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- | 0.0112 | 1150 | 0.1508 | - |
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- | 0.0117 | 1200 | 0.1064 | 0.1453 |
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- | 0.0122 | 1250 | 0.1376 | - |
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- | 0.0127 | 1300 | 0.0828 | 0.121 |
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- | 0.0132 | 1350 | 0.1628 | - |
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- | 0.0136 | 1400 | 0.1308 | 0.1018 |
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- | 0.0141 | 1450 | 0.0566 | - |
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- | 0.0146 | 1500 | 0.0953 | 0.0767 |
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- | 0.0151 | 1550 | 0.1607 | - |
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- | 0.0156 | 1600 | 0.1322 | 0.0625 |
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- | 0.0161 | 1650 | 0.0861 | - |
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- | 0.0166 | 1700 | 0.0926 | 0.0423 |
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- | 0.0171 | 1750 | 0.0338 | - |
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- | 0.0175 | 1800 | 0.1029 | 0.0344 |
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- | 0.0180 | 1850 | 0.0442 | - |
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- | 0.0185 | 1900 | 0.019 | 0.0256 |
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- | 0.0190 | 1950 | 0.0489 | - |
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- | 0.0195 | 2000 | 0.0675 | 0.0187 |
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  ### Framework Versions
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  - Python: 3.10.11
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  - SetFit: 1.0.3
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  - Sentence Transformers: 2.7.0
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- - Transformers: 4.39.0
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  - PyTorch: 2.4.0
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  - Datasets: 2.20.0
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  - Tokenizers: 0.15.2
 
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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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  ---
 
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  - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
48
 
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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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55
  ## Uses
56
 
 
70
  # 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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  <!--
 
102
  ### 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: 2000
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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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  ### Training Results
131
  | Epoch | Step | Training Loss | Validation Loss |
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  |:------:|:----:|:-------------:|:---------------:|
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+ | 0.0002 | 1 | 0.3683 | - |
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+ | 0.0125 | 50 | 0.3256 | - |
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+ | 0.0250 | 100 | 0.211 | 0.1998 |
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+ | 0.0375 | 150 | 0.1668 | - |
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+ | 0.0500 | 200 | 0.0788 | 0.0571 |
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+ | 0.0625 | 250 | 0.0644 | - |
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+ | 0.0750 | 300 | 0.0232 | 0.0286 |
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+ | 0.0875 | 350 | 0.0024 | - |
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+ | 0.1000 | 400 | 0.0014 | 0.0945 |
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+ | 0.1125 | 450 | 0.0007 | - |
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+ | 0.1250 | 500 | 0.0008 | 0.1036 |
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+ | 0.1375 | 550 | 0.0005 | - |
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+ | 0.1500 | 600 | 0.0005 | 0.098 |
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+ | 0.1625 | 650 | 0.0003 | - |
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+ | 0.1750 | 700 | 0.0005 | 0.1056 |
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+ | 0.1875 | 750 | 0.0004 | - |
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+ | 0.2000 | 800 | 0.0006 | 0.1044 |
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+ | 0.2124 | 850 | 0.0005 | - |
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+ | 0.2249 | 900 | 0.0004 | 0.1072 |
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+ | 0.2374 | 950 | 0.0003 | - |
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+ | 0.2499 | 1000 | 0.0001 | 0.0993 |
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+ | 0.2624 | 1050 | 0.0003 | - |
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+ | 0.2749 | 1100 | 0.0003 | 0.1114 |
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+ | 0.2874 | 1150 | 0.0002 | - |
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+ | 0.2999 | 1200 | 0.0002 | 0.1078 |
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+ | 0.3124 | 1250 | 0.0001 | - |
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+ | 0.3249 | 1300 | 0.0002 | 0.0908 |
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+ | 0.3374 | 1350 | 0.0002 | - |
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+ | 0.3499 | 1400 | 0.0002 | 0.1019 |
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+ | 0.3624 | 1450 | 0.0001 | - |
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+ | 0.3749 | 1500 | 0.0002 | 0.11 |
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+ | 0.3874 | 1550 | 0.0002 | - |
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+ | 0.3999 | 1600 | 0.0001 | 0.1031 |
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+ | 0.4124 | 1650 | 0.0001 | - |
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+ | 0.4249 | 1700 | 0.0001 | 0.0996 |
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+ | 0.4374 | 1750 | 0.0002 | - |
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+ | 0.4499 | 1800 | 0.0001 | 0.0903 |
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+ | 0.4624 | 1850 | 0.0002 | - |
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+ | 0.4749 | 1900 | 0.0001 | 0.0901 |
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+ | 0.4874 | 1950 | 0.0002 | - |
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+ | 0.4999 | 2000 | 0.0001 | 0.0854 |
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175
  ### Framework Versions
176
  - Python: 3.10.11
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  - SetFit: 1.0.3
178
  - Sentence Transformers: 2.7.0
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+ - Transformers: 4.39.3
180
  - PyTorch: 2.4.0
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  - Datasets: 2.20.0
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  - Tokenizers: 0.15.2
config.json CHANGED
@@ -19,7 +19,7 @@
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  "position_embedding_type": "absolute",
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  "tokenizer_class": "XLMRobertaTokenizer",
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  "torch_dtype": "float32",
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- "transformers_version": "4.39.0",
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  "type_vocab_size": 2,
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  "use_cache": true,
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  "vocab_size": 250037
 
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  "position_embedding_type": "absolute",
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  "tokenizer_class": "XLMRobertaTokenizer",
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  "torch_dtype": "float32",
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+ "transformers_version": "4.39.3",
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  "type_vocab_size": 2,
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  "use_cache": true,
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  "vocab_size": 250037
config_sentence_transformers.json CHANGED
@@ -1,7 +1,7 @@
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  {
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  "__version__": {
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  "sentence_transformers": "2.7.0",
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- "transformers": "4.39.0",
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  "pytorch": "2.4.0"
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  },
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  "prompts": {},
 
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  {
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  "__version__": {
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  "sentence_transformers": "2.7.0",
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+ "transformers": "4.39.3",
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  "pytorch": "2.4.0"
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  },
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  "prompts": {},
config_setfit.json CHANGED
@@ -1,4 +1,4 @@
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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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+ "normalize_embeddings": false,
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+ "labels": null
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  }
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