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Omar-Nasr
/
setfitmodel

Text Classification
setfit
Safetensors
sentence-transformers
roberta
generated_from_setfit_trainer
Eval Results
Model card Files Community
Omar-Nasr commited on
Commit
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1 Parent(s): 5662982

Add SetFit model

Browse files
Files changed (4) hide show
  1. README.md +45 -1002
  2. config.json +1 -1
  3. model.safetensors +1 -1
  4. model_head.pkl +2 -2
README.md CHANGED
@@ -9,16 +9,19 @@ base_model: sentence-transformers/all-roberta-large-v1
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  metrics:
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  - accuracy
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  widget:
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- - text: ' I''m a runner and I have to say you''re wrong'
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- - text: ' I walk everywhere on foot and I feel like the greatest loser in my city
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- I have tendonitis in both knees from long distance running (my last favorite hobby
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- which I can''t even do anymore) I''ve wasted my prime years'
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- - text: I can't water the garden because my neighbours are watching
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- - text: ' That''s kind of the nature of my volunteer work, but you could volunteer
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- with a food bank or boys and girls club, which would involve more social interaction
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- Just breaking that cycle by going for a short walk around the neighbourhood is
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- a good idea'
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- - text: ' Like jumping into a pool, or starting an assignment'
 
 
 
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  pipeline_tag: text-classification
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  inference: true
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  model-index:
@@ -33,7 +36,7 @@ model-index:
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  split: test
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  metrics:
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  - type: accuracy
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- value: 0.4766666666666667
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  name: Accuracy
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  ---
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@@ -65,19 +68,19 @@ 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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- | 3.0 | <ul><li>"It's whenever I look someone in the eye, when I'm on my bike or when I'm walking somewhere, I feel like they look at me and ridicule me in their headsIt's whenever I look someone in the eye, when I'm on my bike or when I'm walking somewhere, I feel like they look at me and ridicule me in their heads"</li><li>" While I ended up making progress, it wasn't as fast as I had hoped and I still had a lot of trouble doing some things (such as jogging in public)"</li><li>' My social anxiety has got a lot worse as there’s been a lot of recent trauma and I can’t even go outside anymore, a social worker cane to visit and agreed that she’d help me get counselling at home, I’m hoping soon I’ll start getting better'</li></ul> |
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- | 0.0 | <ul><li>' holy shit the food runs with people visiting lol'</li><li>" The problem with doing this is you're just laying there and not focusing on any outside activities and thus you become prey to hell lot of negative thoughts"</li><li>' You might feel relaxed not having to deal with people, but trust me humans are social creatures and in the long run you’re going to become miserable from loneliness'</li></ul> |
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- | 1.0 | <ul><li>' I run 8'</li><li>" Is there anything actually wrong with your legs, or are you just imagining things? Are they really staring at you, or is it in your head? If you can't deal with going out on your own, try going outside with someone you're comfortable with"</li><li>' Being outside is an extra plus!! Keep going :) '</li></ul> |
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- | 2.0 | <ul><li>' It summer time and I have plans to hike and do other fun stuff but i cant do them myself '</li><li>' She knows that I barely go outside and tries to get me to go to out but only with muslims cause she believes that other kids are bad for me'</li><li>' It depends on if you live in an area where getting around on a bike is a viable option I’ve heard of people delivering Doordash or Postmates on bicycles but it generally is only more viable if you live in an urban area where restaurants and delivery addresses are closer together, in a suburban area that is more car-focused it’s difficult Though if deliveries are slow they will usually make you do other tasks around the restaurant, whereas with apps you can either run multiple apps at once or just fuck around between deliveries if it’s slow '</li></ul> |
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  ## Evaluation
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  ### Metrics
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  | Label | Accuracy |
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  |:--------|:---------|
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- | **all** | 0.4767 |
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  ## Uses
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@@ -97,7 +100,7 @@ from setfit import SetFitModel
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  # Download from the 🤗 Hub
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  model = SetFitModel.from_pretrained("Omar-Nasr/setfitmodel")
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  # Run inference
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- preds = model(" I'm a runner and I have to say you're wrong")
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  ```
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  <!--
@@ -127,20 +130,20 @@ preds = model(" I'm a runner and I have to say you're wrong")
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  ## Training Details
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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 | 3 | 40.3722 | 1083 |
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  | Label | Training Sample Count |
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  |:------|:----------------------|
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- | 0.0 | 90 |
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- | 1.0 | 90 |
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- | 2.0 | 90 |
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- | 3.0 | 90 |
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  ### Training Hyperparameters
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  - batch_size: (8, 8)
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- - num_epochs: (4, 4)
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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)
@@ -156,981 +159,21 @@ preds = model(" I'm a runner and I have to say you're wrong")
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  - load_best_model_at_end: False
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  ### Training Results
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- | Epoch | Step | Training Loss | Validation Loss |
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- |:------:|:-----:|:-------------:|:---------------:|
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- | 0.0001 | 1 | 0.2936 | - |
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- | 0.0041 | 50 | 0.3404 | - |
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- | 0.0082 | 100 | 0.2465 | - |
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- | 0.0123 | 150 | 0.287 | - |
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- | 0.0165 | 200 | 0.1955 | - |
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- | 0.0206 | 250 | 0.2187 | - |
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- | 0.0247 | 300 | 0.2239 | - |
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- | 0.0288 | 350 | 0.2709 | - |
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- | 0.0329 | 400 | 0.2919 | - |
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- | 0.0370 | 450 | 0.2689 | - |
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- | 0.0412 | 500 | 0.176 | - |
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- | 0.0453 | 550 | 0.2699 | - |
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- | 0.0494 | 600 | 0.2538 | - |
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- | 0.0535 | 650 | 0.2029 | - |
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- | 0.0576 | 700 | 0.2566 | - |
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- | 0.0617 | 750 | 0.1106 | - |
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- | 0.0658 | 800 | 0.1625 | - |
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- | 0.0700 | 850 | 0.1276 | - |
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- | 0.0741 | 900 | 0.1603 | - |
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- | 0.0782 | 950 | 0.0294 | - |
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- | 0.0823 | 1000 | 0.011 | - |
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- | 0.0864 | 1050 | 0.1924 | - |
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- | 0.0905 | 1100 | 0.0149 | - |
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- | 0.0947 | 1150 | 0.1249 | - |
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- | 0.0988 | 1200 | 0.1251 | - |
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- | 0.1029 | 1250 | 0.008 | - |
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- | 0.1070 | 1300 | 0.0008 | - |
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- | 0.1111 | 1350 | 0.009 | - |
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- | 0.1152 | 1400 | 0.0007 | - |
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- | 0.1193 | 1450 | 0.0013 | - |
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- | 0.1235 | 1500 | 0.0016 | - |
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- | 0.1276 | 1550 | 0.0042 | - |
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- | 0.1317 | 1600 | 0.0003 | - |
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- | 0.1358 | 1650 | 0.0002 | - |
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- | 0.1399 | 1700 | 0.0003 | - |
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- | 0.1440 | 1750 | 0.0002 | - |
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- | 0.1481 | 1800 | 0.0002 | - |
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- | 0.1523 | 1850 | 0.0002 | - |
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- | 0.1564 | 1900 | 0.0003 | - |
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- | 0.1605 | 1950 | 0.0001 | - |
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- | 0.1646 | 2000 | 0.0001 | - |
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- | 0.1687 | 2050 | 0.0002 | - |
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- | 0.1728 | 2100 | 0.0001 | - |
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- | 0.1770 | 2150 | 0.0001 | - |
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- | 0.1811 | 2200 | 0.0001 | - |
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- | 0.1852 | 2250 | 0.0001 | - |
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- | 0.1893 | 2300 | 0.0001 | - |
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- | 0.1934 | 2350 | 0.0002 | - |
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- | 0.1975 | 2400 | 0.0001 | - |
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- | 0.2016 | 2450 | 0.0002 | - |
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- | 0.2058 | 2500 | 0.0001 | - |
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- | 0.2099 | 2550 | 0.0001 | - |
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- | 0.2140 | 2600 | 0.0001 | - |
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- | 0.2181 | 2650 | 0.0 | - |
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- | 0.2222 | 2700 | 0.0001 | - |
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- | 0.2263 | 2750 | 0.0001 | - |
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- | 0.2305 | 2800 | 0.0001 | - |
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- | 0.2346 | 2850 | 0.0 | - |
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- | 0.2387 | 2900 | 0.0 | - |
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- | 0.2428 | 2950 | 0.0001 | - |
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- | 0.2469 | 3000 | 0.0001 | - |
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- | 0.2510 | 3050 | 0.0002 | - |
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- | 0.2551 | 3100 | 0.0001 | - |
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- | 0.2593 | 3150 | 0.0 | - |
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- | 0.2634 | 3200 | 0.0 | - |
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- | 0.2675 | 3250 | 0.0001 | - |
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- | 0.2716 | 3300 | 0.0001 | - |
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- | 0.2757 | 3350 | 0.0 | - |
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- | 0.3251 | 3950 | 0.0001 | - |
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- | 0.3292 | 4000 | 0.0 | - |
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- | 0.3333 | 4050 | 0.0001 | - |
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- | 0.3416 | 4150 | 0.0 | - |
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- | 0.3457 | 4200 | 0.482 | - |
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- | 0.3498 | 4250 | 0.0384 | - |
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- | 0.3539 | 4300 | 0.1998 | - |
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- | 0.3580 | 4350 | 0.2232 | - |
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- | 0.3621 | 4400 | 0.1794 | - |
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- | 0.3663 | 4450 | 0.0911 | - |
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- | 0.3704 | 4500 | 0.1016 | - |
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- | 0.3745 | 4550 | 0.1266 | - |
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- | 0.3786 | 4600 | 0.0004 | - |
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- | 0.3827 | 4650 | 0.0005 | - |
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- | 0.4733 | 5750 | 0.0001 | - |
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- | 1.1934 | 14500 | 0.0 | - |
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- | 1.1975 | 14550 | 0.0 | - |
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- | 1.2016 | 14600 | 0.0 | - |
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- | 1.2058 | 14650 | 0.0146 | - |
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- | 1.2099 | 14700 | 0.4649 | - |
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- | 1.2140 | 14750 | 0.0596 | - |
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- | 1.2181 | 14800 | 0.0007 | - |
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- | 1.2222 | 14850 | 0.0006 | - |
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- | 1.2305 | 14950 | 0.0026 | - |
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- | 1.2346 | 15000 | 0.0 | - |
462
- | 1.2387 | 15050 | 0.0 | - |
463
- | 1.2428 | 15100 | 0.0001 | - |
464
- | 1.2469 | 15150 | 0.0847 | - |
465
- | 1.2510 | 15200 | 0.0 | - |
466
- | 1.2551 | 15250 | 0.0475 | - |
467
- | 1.2593 | 15300 | 0.0006 | - |
468
- | 1.2634 | 15350 | 0.0001 | - |
469
- | 1.2675 | 15400 | 0.0 | - |
470
- | 1.2716 | 15450 | 0.0 | - |
471
- | 1.2757 | 15500 | 0.0002 | - |
472
- | 1.2798 | 15550 | 0.0001 | - |
473
- | 1.2840 | 15600 | 0.0 | - |
474
- | 1.2881 | 15650 | 0.0 | - |
475
- | 1.2922 | 15700 | 0.0 | - |
476
- | 1.2963 | 15750 | 0.0 | - |
477
- | 1.3004 | 15800 | 0.0 | - |
478
- | 1.3045 | 15850 | 0.0004 | - |
479
- | 1.3086 | 15900 | 0.0 | - |
480
- | 1.3128 | 15950 | 0.0 | - |
481
- | 1.3169 | 16000 | 0.0 | - |
482
- | 1.3210 | 16050 | 0.0 | - |
483
- | 1.3251 | 16100 | 0.0 | - |
484
- | 1.3292 | 16150 | 0.0 | - |
485
- | 1.3333 | 16200 | 0.0 | - |
486
- | 1.3374 | 16250 | 0.0 | - |
487
- | 1.3416 | 16300 | 0.0 | - |
488
- | 1.3457 | 16350 | 0.0 | - |
489
- | 1.3498 | 16400 | 0.0 | - |
490
- | 1.3539 | 16450 | 0.0 | - |
491
- | 1.3580 | 16500 | 0.0 | - |
492
- | 1.3621 | 16550 | 0.0 | - |
493
- | 1.3663 | 16600 | 0.0 | - |
494
- | 1.3704 | 16650 | 0.0001 | - |
495
- | 1.3745 | 16700 | 0.0 | - |
496
- | 1.3786 | 16750 | 0.0 | - |
497
- | 1.3827 | 16800 | 0.0 | - |
498
- | 1.3868 | 16850 | 0.0001 | - |
499
- | 1.3909 | 16900 | 0.0 | - |
500
- | 1.3951 | 16950 | 0.0 | - |
501
- | 1.3992 | 17000 | 0.0 | - |
502
- | 1.4033 | 17050 | 0.0 | - |
503
- | 1.4074 | 17100 | 0.0 | - |
504
- | 1.4115 | 17150 | 0.0 | - |
505
- | 1.4156 | 17200 | 0.0 | - |
506
- | 1.4198 | 17250 | 0.0 | - |
507
- | 1.4239 | 17300 | 0.0 | - |
508
- | 1.4280 | 17350 | 0.0 | - |
509
- | 1.4321 | 17400 | 0.0 | - |
510
- | 1.4362 | 17450 | 0.0 | - |
511
- | 1.4403 | 17500 | 0.0 | - |
512
- | 1.4444 | 17550 | 0.0 | - |
513
- | 1.4486 | 17600 | 0.0 | - |
514
- | 1.4527 | 17650 | 0.0 | - |
515
- | 1.4568 | 17700 | 0.0 | - |
516
- | 1.4609 | 17750 | 0.0 | - |
517
- | 1.4650 | 17800 | 0.0 | - |
518
- | 1.4691 | 17850 | 0.0 | - |
519
- | 1.4733 | 17900 | 0.0 | - |
520
- | 1.4774 | 17950 | 0.0002 | - |
521
- | 1.4815 | 18000 | 0.0 | - |
522
- | 1.4856 | 18050 | 0.0 | - |
523
- | 1.4897 | 18100 | 0.0 | - |
524
- | 1.4938 | 18150 | 0.0 | - |
525
- | 1.4979 | 18200 | 0.0 | - |
526
- | 1.5021 | 18250 | 0.0 | - |
527
- | 1.5062 | 18300 | 0.0 | - |
528
- | 1.5103 | 18350 | 0.0 | - |
529
- | 1.5144 | 18400 | 0.0 | - |
530
- | 1.5185 | 18450 | 0.0 | - |
531
- | 1.5226 | 18500 | 0.0 | - |
532
- | 1.5267 | 18550 | 0.0 | - |
533
- | 1.5309 | 18600 | 0.0 | - |
534
- | 1.5350 | 18650 | 0.0 | - |
535
- | 1.5391 | 18700 | 0.0 | - |
536
- | 1.5432 | 18750 | 0.0 | - |
537
- | 1.5473 | 18800 | 0.0 | - |
538
- | 1.5514 | 18850 | 0.0 | - |
539
- | 1.5556 | 18900 | 0.0 | - |
540
- | 1.5597 | 18950 | 0.0 | - |
541
- | 1.5638 | 19000 | 0.0 | - |
542
- | 1.5679 | 19050 | 0.0 | - |
543
- | 1.5720 | 19100 | 0.0 | - |
544
- | 1.5761 | 19150 | 0.0 | - |
545
- | 1.5802 | 19200 | 0.0 | - |
546
- | 1.5844 | 19250 | 0.0 | - |
547
- | 1.5885 | 19300 | 0.0 | - |
548
- | 1.5926 | 19350 | 0.0 | - |
549
- | 1.5967 | 19400 | 0.0 | - |
550
- | 1.6008 | 19450 | 0.0 | - |
551
- | 1.6049 | 19500 | 0.0 | - |
552
- | 1.6091 | 19550 | 0.0 | - |
553
- | 1.6132 | 19600 | 0.0 | - |
554
- | 1.6173 | 19650 | 0.0022 | - |
555
- | 1.6214 | 19700 | 0.0981 | - |
556
- | 1.6255 | 19750 | 0.0 | - |
557
- | 1.6296 | 19800 | 0.0006 | - |
558
- | 1.6337 | 19850 | 0.0 | - |
559
- | 1.6379 | 19900 | 0.0 | - |
560
- | 1.6420 | 19950 | 0.0 | - |
561
- | 1.6461 | 20000 | 0.0 | - |
562
- | 1.6502 | 20050 | 0.0 | - |
563
- | 1.6543 | 20100 | 0.0 | - |
564
- | 1.6584 | 20150 | 0.0 | - |
565
- | 1.6626 | 20200 | 0.0 | - |
566
- | 1.6667 | 20250 | 0.0173 | - |
567
- | 1.6708 | 20300 | 0.0001 | - |
568
- | 1.6749 | 20350 | 0.0 | - |
569
- | 1.6790 | 20400 | 0.0 | - |
570
- | 1.6831 | 20450 | 0.0001 | - |
571
- | 1.6872 | 20500 | 0.0 | - |
572
- | 1.6914 | 20550 | 0.0 | - |
573
- | 1.6955 | 20600 | 0.0 | - |
574
- | 1.6996 | 20650 | 0.0 | - |
575
- | 1.7037 | 20700 | 0.0 | - |
576
- | 1.7078 | 20750 | 0.0001 | - |
577
- | 1.7119 | 20800 | 0.0 | - |
578
- | 1.7160 | 20850 | 0.0 | - |
579
- | 1.7202 | 20900 | 0.0 | - |
580
- | 1.7243 | 20950 | 0.0 | - |
581
- | 1.7284 | 21000 | 0.0 | - |
582
- | 1.7325 | 21050 | 0.0 | - |
583
- | 1.7366 | 21100 | 0.0 | - |
584
- | 1.7407 | 21150 | 0.0 | - |
585
- | 1.7449 | 21200 | 0.0 | - |
586
- | 1.7490 | 21250 | 0.0 | - |
587
- | 1.7531 | 21300 | 0.0 | - |
588
- | 1.7572 | 21350 | 0.0 | - |
589
- | 1.7613 | 21400 | 0.0 | - |
590
- | 1.7654 | 21450 | 0.0 | - |
591
- | 1.7695 | 21500 | 0.0 | - |
592
- | 1.7737 | 21550 | 0.0 | - |
593
- | 1.7778 | 21600 | 0.0 | - |
594
- | 1.7819 | 21650 | 0.0 | - |
595
- | 1.7860 | 21700 | 0.0 | - |
596
- | 1.7901 | 21750 | 0.0 | - |
597
- | 1.7942 | 21800 | 0.0 | - |
598
- | 1.7984 | 21850 | 0.0 | - |
599
- | 1.8025 | 21900 | 0.0 | - |
600
- | 1.8066 | 21950 | 0.0 | - |
601
- | 1.8107 | 22000 | 0.0 | - |
602
- | 1.8148 | 22050 | 0.0 | - |
603
- | 1.8189 | 22100 | 0.0 | - |
604
- | 1.8230 | 22150 | 0.0 | - |
605
- | 1.8272 | 22200 | 0.0 | - |
606
- | 1.8313 | 22250 | 0.0 | - |
607
- | 1.8354 | 22300 | 0.0 | - |
608
- | 1.8395 | 22350 | 0.0 | - |
609
- | 1.8436 | 22400 | 0.0 | - |
610
- | 1.8477 | 22450 | 0.0 | - |
611
- | 1.8519 | 22500 | 0.0 | - |
612
- | 1.8560 | 22550 | 0.0 | - |
613
- | 1.8601 | 22600 | 0.0 | - |
614
- | 1.8642 | 22650 | 0.0 | - |
615
- | 1.8683 | 22700 | 0.0 | - |
616
- | 1.8724 | 22750 | 0.0 | - |
617
- | 1.8765 | 22800 | 0.0 | - |
618
- | 1.8807 | 22850 | 0.0 | - |
619
- | 1.8848 | 22900 | 0.0 | - |
620
- | 1.8889 | 22950 | 0.0 | - |
621
- | 1.8930 | 23000 | 0.0 | - |
622
- | 1.8971 | 23050 | 0.0007 | - |
623
- | 1.9012 | 23100 | 0.0 | - |
624
- | 1.9053 | 23150 | 0.0 | - |
625
- | 1.9095 | 23200 | 0.0 | - |
626
- | 1.9136 | 23250 | 0.0 | - |
627
- | 1.9177 | 23300 | 0.0 | - |
628
- | 1.9218 | 23350 | 0.0 | - |
629
- | 1.9259 | 23400 | 0.0 | - |
630
- | 1.9300 | 23450 | 0.0 | - |
631
- | 1.9342 | 23500 | 0.0 | - |
632
- | 1.9383 | 23550 | 0.0 | - |
633
- | 1.9424 | 23600 | 0.0 | - |
634
- | 1.9465 | 23650 | 0.0 | - |
635
- | 1.9506 | 23700 | 0.0 | - |
636
- | 1.9547 | 23750 | 0.0 | - |
637
- | 1.9588 | 23800 | 0.0 | - |
638
- | 1.9630 | 23850 | 0.0 | - |
639
- | 1.9671 | 23900 | 0.0 | - |
640
- | 1.9712 | 23950 | 0.0 | - |
641
- | 1.9753 | 24000 | 0.0 | - |
642
- | 1.9794 | 24050 | 0.0 | - |
643
- | 1.9835 | 24100 | 0.0 | - |
644
- | 1.9877 | 24150 | 0.0 | - |
645
- | 1.9918 | 24200 | 0.0 | - |
646
- | 1.9959 | 24250 | 0.0 | - |
647
- | 2.0 | 24300 | 0.0 | - |
648
- | 2.0041 | 24350 | 0.0001 | - |
649
- | 2.0082 | 24400 | 0.0 | - |
650
- | 2.0123 | 24450 | 0.0004 | - |
651
- | 2.0165 | 24500 | 0.0 | - |
652
- | 2.0206 | 24550 | 0.0001 | - |
653
- | 2.0247 | 24600 | 0.0 | - |
654
- | 2.0288 | 24650 | 0.0 | - |
655
- | 2.0329 | 24700 | 0.0 | - |
656
- | 2.0370 | 24750 | 0.0 | - |
657
- | 2.0412 | 24800 | 0.0 | - |
658
- | 2.0453 | 24850 | 0.0 | - |
659
- | 2.0494 | 24900 | 0.0 | - |
660
- | 2.0535 | 24950 | 0.0 | - |
661
- | 2.0576 | 25000 | 0.0 | - |
662
- | 2.0617 | 25050 | 0.0 | - |
663
- | 2.0658 | 25100 | 0.0 | - |
664
- | 2.0700 | 25150 | 0.0 | - |
665
- | 2.0741 | 25200 | 0.0 | - |
666
- | 2.0782 | 25250 | 0.0 | - |
667
- | 2.0823 | 25300 | 0.0 | - |
668
- | 2.0864 | 25350 | 0.0 | - |
669
- | 2.0905 | 25400 | 0.0 | - |
670
- | 2.0947 | 25450 | 0.0 | - |
671
- | 2.0988 | 25500 | 0.0 | - |
672
- | 2.1029 | 25550 | 0.0 | - |
673
- | 2.1070 | 25600 | 0.0 | - |
674
- | 2.1111 | 25650 | 0.0 | - |
675
- | 2.1152 | 25700 | 0.0 | - |
676
- | 2.1193 | 25750 | 0.0 | - |
677
- | 2.1235 | 25800 | 0.0 | - |
678
- | 2.1276 | 25850 | 0.0 | - |
679
- | 2.1317 | 25900 | 0.0 | - |
680
- | 2.1358 | 25950 | 0.0 | - |
681
- | 2.1399 | 26000 | 0.0 | - |
682
- | 2.1440 | 26050 | 0.0 | - |
683
- | 2.1481 | 26100 | 0.0 | - |
684
- | 2.1523 | 26150 | 0.0 | - |
685
- | 2.1564 | 26200 | 0.0 | - |
686
- | 2.1605 | 26250 | 0.0 | - |
687
- | 2.1646 | 26300 | 0.0 | - |
688
- | 2.1687 | 26350 | 0.0 | - |
689
- | 2.1728 | 26400 | 0.0 | - |
690
- | 2.1770 | 26450 | 0.0 | - |
691
- | 2.1811 | 26500 | 0.0 | - |
692
- | 2.1852 | 26550 | 0.0 | - |
693
- | 2.1893 | 26600 | 0.0 | - |
694
- | 2.1934 | 26650 | 0.0 | - |
695
- | 2.1975 | 26700 | 0.0 | - |
696
- | 2.2016 | 26750 | 0.0 | - |
697
- | 2.2058 | 26800 | 0.0 | - |
698
- | 2.2099 | 26850 | 0.0 | - |
699
- | 2.2140 | 26900 | 0.0 | - |
700
- | 2.2181 | 26950 | 0.0 | - |
701
- | 2.2222 | 27000 | 0.0 | - |
702
- | 2.2263 | 27050 | 0.0 | - |
703
- | 2.2305 | 27100 | 0.0 | - |
704
- | 2.2346 | 27150 | 0.0 | - |
705
- | 2.2387 | 27200 | 0.0 | - |
706
- | 2.2428 | 27250 | 0.0 | - |
707
- | 2.2469 | 27300 | 0.0 | - |
708
- | 2.2510 | 27350 | 0.0 | - |
709
- | 2.2551 | 27400 | 0.0 | - |
710
- | 2.2593 | 27450 | 0.0 | - |
711
- | 2.2634 | 27500 | 0.0 | - |
712
- | 2.2675 | 27550 | 0.0 | - |
713
- | 2.2716 | 27600 | 0.0 | - |
714
- | 2.2757 | 27650 | 0.0 | - |
715
- | 2.2798 | 27700 | 0.0 | - |
716
- | 2.2840 | 27750 | 0.0 | - |
717
- | 2.2881 | 27800 | 0.0 | - |
718
- | 2.2922 | 27850 | 0.0 | - |
719
- | 2.2963 | 27900 | 0.0 | - |
720
- | 2.3004 | 27950 | 0.0 | - |
721
- | 2.3045 | 28000 | 0.0 | - |
722
- | 2.3086 | 28050 | 0.0 | - |
723
- | 2.3128 | 28100 | 0.0 | - |
724
- | 2.3169 | 28150 | 0.0 | - |
725
- | 2.3210 | 28200 | 0.0 | - |
726
- | 2.3251 | 28250 | 0.0 | - |
727
- | 2.3292 | 28300 | 0.0 | - |
728
- | 2.3333 | 28350 | 0.0 | - |
729
- | 2.3374 | 28400 | 0.0 | - |
730
- | 2.3416 | 28450 | 0.0 | - |
731
- | 2.3457 | 28500 | 0.0 | - |
732
- | 2.3498 | 28550 | 0.0 | - |
733
- | 2.3539 | 28600 | 0.0 | - |
734
- | 2.3580 | 28650 | 0.0 | - |
735
- | 2.3621 | 28700 | 0.0 | - |
736
- | 2.3663 | 28750 | 0.0 | - |
737
- | 2.3704 | 28800 | 0.0 | - |
738
- | 2.3745 | 28850 | 0.0 | - |
739
- | 2.3786 | 28900 | 0.0 | - |
740
- | 2.3827 | 28950 | 0.0 | - |
741
- | 2.3868 | 29000 | 0.0 | - |
742
- | 2.3909 | 29050 | 0.0 | - |
743
- | 2.3951 | 29100 | 0.0 | - |
744
- | 2.3992 | 29150 | 0.0 | - |
745
- | 2.4033 | 29200 | 0.0 | - |
746
- | 2.4074 | 29250 | 0.0 | - |
747
- | 2.4115 | 29300 | 0.0 | - |
748
- | 2.4156 | 29350 | 0.0 | - |
749
- | 2.4198 | 29400 | 0.0 | - |
750
- | 2.4239 | 29450 | 0.0 | - |
751
- | 2.4280 | 29500 | 0.0 | - |
752
- | 2.4321 | 29550 | 0.0 | - |
753
- | 2.4362 | 29600 | 0.0 | - |
754
- | 2.4403 | 29650 | 0.0 | - |
755
- | 2.4444 | 29700 | 0.0 | - |
756
- | 2.4486 | 29750 | 0.0 | - |
757
- | 2.4527 | 29800 | 0.0 | - |
758
- | 2.4568 | 29850 | 0.0 | - |
759
- | 2.4609 | 29900 | 0.0 | - |
760
- | 2.4650 | 29950 | 0.0 | - |
761
- | 2.4691 | 30000 | 0.0 | - |
762
- | 2.4733 | 30050 | 0.0 | - |
763
- | 2.4774 | 30100 | 0.0 | - |
764
- | 2.4815 | 30150 | 0.0 | - |
765
- | 2.4856 | 30200 | 0.0 | - |
766
- | 2.4897 | 30250 | 0.0 | - |
767
- | 2.4938 | 30300 | 0.0 | - |
768
- | 2.4979 | 30350 | 0.0 | - |
769
- | 2.5021 | 30400 | 0.0 | - |
770
- | 2.5062 | 30450 | 0.0 | - |
771
- | 2.5103 | 30500 | 0.0 | - |
772
- | 2.5144 | 30550 | 0.0 | - |
773
- | 2.5185 | 30600 | 0.0 | - |
774
- | 2.5226 | 30650 | 0.0 | - |
775
- | 2.5267 | 30700 | 0.0 | - |
776
- | 2.5309 | 30750 | 0.0 | - |
777
- | 2.5350 | 30800 | 0.0 | - |
778
- | 2.5391 | 30850 | 0.0 | - |
779
- | 2.5432 | 30900 | 0.0 | - |
780
- | 2.5473 | 30950 | 0.0 | - |
781
- | 2.5514 | 31000 | 0.0 | - |
782
- | 2.5556 | 31050 | 0.0 | - |
783
- | 2.5597 | 31100 | 0.0 | - |
784
- | 2.5638 | 31150 | 0.0 | - |
785
- | 2.5679 | 31200 | 0.0 | - |
786
- | 2.5720 | 31250 | 0.0 | - |
787
- | 2.5761 | 31300 | 0.0 | - |
788
- | 2.5802 | 31350 | 0.0 | - |
789
- | 2.5844 | 31400 | 0.0 | - |
790
- | 2.5885 | 31450 | 0.0 | - |
791
- | 2.5926 | 31500 | 0.0 | - |
792
- | 2.5967 | 31550 | 0.0 | - |
793
- | 2.6008 | 31600 | 0.0 | - |
794
- | 2.6049 | 31650 | 0.0 | - |
795
- | 2.6091 | 31700 | 0.0 | - |
796
- | 2.6132 | 31750 | 0.0 | - |
797
- | 2.6173 | 31800 | 0.0 | - |
798
- | 2.6214 | 31850 | 0.0 | - |
799
- | 2.6255 | 31900 | 0.0 | - |
800
- | 2.6296 | 31950 | 0.0 | - |
801
- | 2.6337 | 32000 | 0.0 | - |
802
- | 2.6379 | 32050 | 0.0 | - |
803
- | 2.6420 | 32100 | 0.0 | - |
804
- | 2.6461 | 32150 | 0.0 | - |
805
- | 2.6502 | 32200 | 0.0 | - |
806
- | 2.6543 | 32250 | 0.0 | - |
807
- | 2.6584 | 32300 | 0.0 | - |
808
- | 2.6626 | 32350 | 0.0 | - |
809
- | 2.6667 | 32400 | 0.0 | - |
810
- | 2.6708 | 32450 | 0.0 | - |
811
- | 2.6749 | 32500 | 0.0 | - |
812
- | 2.6790 | 32550 | 0.0 | - |
813
- | 2.6831 | 32600 | 0.0 | - |
814
- | 2.6872 | 32650 | 0.0 | - |
815
- | 2.6914 | 32700 | 0.0 | - |
816
- | 2.6955 | 32750 | 0.0 | - |
817
- | 2.6996 | 32800 | 0.0 | - |
818
- | 2.7037 | 32850 | 0.0 | - |
819
- | 2.7078 | 32900 | 0.0 | - |
820
- | 2.7119 | 32950 | 0.0 | - |
821
- | 2.7160 | 33000 | 0.0 | - |
822
- | 2.7202 | 33050 | 0.0 | - |
823
- | 2.7243 | 33100 | 0.0 | - |
824
- | 2.7284 | 33150 | 0.0 | - |
825
- | 2.7325 | 33200 | 0.0 | - |
826
- | 2.7366 | 33250 | 0.0 | - |
827
- | 2.7407 | 33300 | 0.0 | - |
828
- | 2.7449 | 33350 | 0.0 | - |
829
- | 2.7490 | 33400 | 0.0 | - |
830
- | 2.7531 | 33450 | 0.0 | - |
831
- | 2.7572 | 33500 | 0.0 | - |
832
- | 2.7613 | 33550 | 0.0967 | - |
833
- | 2.7654 | 33600 | 0.0 | - |
834
- | 2.7695 | 33650 | 0.0 | - |
835
- | 2.7737 | 33700 | 0.0 | - |
836
- | 2.7778 | 33750 | 0.0 | - |
837
- | 2.7819 | 33800 | 0.0 | - |
838
- | 2.7860 | 33850 | 0.0 | - |
839
- | 2.7901 | 33900 | 0.0 | - |
840
- | 2.7942 | 33950 | 0.0 | - |
841
- | 2.7984 | 34000 | 0.0 | - |
842
- | 2.8025 | 34050 | 0.0 | - |
843
- | 2.8066 | 34100 | 0.0 | - |
844
- | 2.8107 | 34150 | 0.0 | - |
845
- | 2.8148 | 34200 | 0.0 | - |
846
- | 2.8189 | 34250 | 0.0 | - |
847
- | 2.8230 | 34300 | 0.0 | - |
848
- | 2.8272 | 34350 | 0.0 | - |
849
- | 2.8313 | 34400 | 0.0 | - |
850
- | 2.8354 | 34450 | 0.0 | - |
851
- | 2.8395 | 34500 | 0.0 | - |
852
- | 2.8436 | 34550 | 0.0 | - |
853
- | 2.8477 | 34600 | 0.0 | - |
854
- | 2.8519 | 34650 | 0.0 | - |
855
- | 2.8560 | 34700 | 0.0 | - |
856
- | 2.8601 | 34750 | 0.0 | - |
857
- | 2.8642 | 34800 | 0.0 | - |
858
- | 2.8683 | 34850 | 0.0 | - |
859
- | 2.8724 | 34900 | 0.0 | - |
860
- | 2.8765 | 34950 | 0.0 | - |
861
- | 2.8807 | 35000 | 0.0 | - |
862
- | 2.8848 | 35050 | 0.0 | - |
863
- | 2.8889 | 35100 | 0.0 | - |
864
- | 2.8930 | 35150 | 0.0 | - |
865
- | 2.8971 | 35200 | 0.0 | - |
866
- | 2.9012 | 35250 | 0.0 | - |
867
- | 2.9053 | 35300 | 0.0 | - |
868
- | 2.9095 | 35350 | 0.0 | - |
869
- | 2.9136 | 35400 | 0.0 | - |
870
- | 2.9177 | 35450 | 0.0 | - |
871
- | 2.9218 | 35500 | 0.0 | - |
872
- | 2.9259 | 35550 | 0.0 | - |
873
- | 2.9300 | 35600 | 0.0 | - |
874
- | 2.9342 | 35650 | 0.0 | - |
875
- | 2.9383 | 35700 | 0.0 | - |
876
- | 2.9424 | 35750 | 0.0 | - |
877
- | 2.9465 | 35800 | 0.0 | - |
878
- | 2.9506 | 35850 | 0.0 | - |
879
- | 2.9547 | 35900 | 0.0 | - |
880
- | 2.9588 | 35950 | 0.0012 | - |
881
- | 2.9630 | 36000 | 0.0 | - |
882
- | 2.9671 | 36050 | 0.0 | - |
883
- | 2.9712 | 36100 | 0.0 | - |
884
- | 2.9753 | 36150 | 0.0 | - |
885
- | 2.9794 | 36200 | 0.0 | - |
886
- | 2.9835 | 36250 | 0.0 | - |
887
- | 2.9877 | 36300 | 0.0 | - |
888
- | 2.9918 | 36350 | 0.0 | - |
889
- | 2.9959 | 36400 | 0.0 | - |
890
- | 3.0 | 36450 | 0.0 | - |
891
- | 3.0041 | 36500 | 0.0 | - |
892
- | 3.0082 | 36550 | 0.0 | - |
893
- | 3.0123 | 36600 | 0.0 | - |
894
- | 3.0165 | 36650 | 0.0 | - |
895
- | 3.0206 | 36700 | 0.0 | - |
896
- | 3.0247 | 36750 | 0.0 | - |
897
- | 3.0288 | 36800 | 0.0 | - |
898
- | 3.0329 | 36850 | 0.0 | - |
899
- | 3.0370 | 36900 | 0.0 | - |
900
- | 3.0412 | 36950 | 0.0 | - |
901
- | 3.0453 | 37000 | 0.0 | - |
902
- | 3.0494 | 37050 | 0.0 | - |
903
- | 3.0535 | 37100 | 0.0 | - |
904
- | 3.0576 | 37150 | 0.0 | - |
905
- | 3.0617 | 37200 | 0.0 | - |
906
- | 3.0658 | 37250 | 0.0 | - |
907
- | 3.0700 | 37300 | 0.0 | - |
908
- | 3.0741 | 37350 | 0.0 | - |
909
- | 3.0782 | 37400 | 0.0 | - |
910
- | 3.0823 | 37450 | 0.0 | - |
911
- | 3.0864 | 37500 | 0.0 | - |
912
- | 3.0905 | 37550 | 0.0 | - |
913
- | 3.0947 | 37600 | 0.0 | - |
914
- | 3.0988 | 37650 | 0.0 | - |
915
- | 3.1029 | 37700 | 0.0 | - |
916
- | 3.1070 | 37750 | 0.0 | - |
917
- | 3.1111 | 37800 | 0.0 | - |
918
- | 3.1152 | 37850 | 0.0 | - |
919
- | 3.1193 | 37900 | 0.0 | - |
920
- | 3.1235 | 37950 | 0.0 | - |
921
- | 3.1276 | 38000 | 0.0 | - |
922
- | 3.1317 | 38050 | 0.0 | - |
923
- | 3.1358 | 38100 | 0.0 | - |
924
- | 3.1399 | 38150 | 0.0 | - |
925
- | 3.1440 | 38200 | 0.0 | - |
926
- | 3.1481 | 38250 | 0.0 | - |
927
- | 3.1523 | 38300 | 0.0 | - |
928
- | 3.1564 | 38350 | 0.0 | - |
929
- | 3.1605 | 38400 | 0.0 | - |
930
- | 3.1646 | 38450 | 0.0 | - |
931
- | 3.1687 | 38500 | 0.0 | - |
932
- | 3.1728 | 38550 | 0.0 | - |
933
- | 3.1770 | 38600 | 0.0 | - |
934
- | 3.1811 | 38650 | 0.0 | - |
935
- | 3.1852 | 38700 | 0.0 | - |
936
- | 3.1893 | 38750 | 0.0 | - |
937
- | 3.1934 | 38800 | 0.0 | - |
938
- | 3.1975 | 38850 | 0.0 | - |
939
- | 3.2016 | 38900 | 0.0 | - |
940
- | 3.2058 | 38950 | 0.0 | - |
941
- | 3.2099 | 39000 | 0.0 | - |
942
- | 3.2140 | 39050 | 0.0 | - |
943
- | 3.2181 | 39100 | 0.0 | - |
944
- | 3.2222 | 39150 | 0.0 | - |
945
- | 3.2263 | 39200 | 0.0 | - |
946
- | 3.2305 | 39250 | 0.0 | - |
947
- | 3.2346 | 39300 | 0.0 | - |
948
- | 3.2387 | 39350 | 0.0 | - |
949
- | 3.2428 | 39400 | 0.0 | - |
950
- | 3.2469 | 39450 | 0.0 | - |
951
- | 3.2510 | 39500 | 0.0 | - |
952
- | 3.2551 | 39550 | 0.0 | - |
953
- | 3.2593 | 39600 | 0.0 | - |
954
- | 3.2634 | 39650 | 0.0 | - |
955
- | 3.2675 | 39700 | 0.0 | - |
956
- | 3.2716 | 39750 | 0.0 | - |
957
- | 3.2757 | 39800 | 0.0 | - |
958
- | 3.2798 | 39850 | 0.0 | - |
959
- | 3.2840 | 39900 | 0.0 | - |
960
- | 3.2881 | 39950 | 0.0 | - |
961
- | 3.2922 | 40000 | 0.0 | - |
962
- | 3.2963 | 40050 | 0.0 | - |
963
- | 3.3004 | 40100 | 0.0 | - |
964
- | 3.3045 | 40150 | 0.0 | - |
965
- | 3.3086 | 40200 | 0.0 | - |
966
- | 3.3128 | 40250 | 0.0 | - |
967
- | 3.3169 | 40300 | 0.0 | - |
968
- | 3.3210 | 40350 | 0.0 | - |
969
- | 3.3251 | 40400 | 0.0 | - |
970
- | 3.3292 | 40450 | 0.0 | - |
971
- | 3.3333 | 40500 | 0.0 | - |
972
- | 3.3374 | 40550 | 0.0 | - |
973
- | 3.3416 | 40600 | 0.0 | - |
974
- | 3.3457 | 40650 | 0.0 | - |
975
- | 3.3498 | 40700 | 0.0 | - |
976
- | 3.3539 | 40750 | 0.0 | - |
977
- | 3.3580 | 40800 | 0.0 | - |
978
- | 3.3621 | 40850 | 0.0 | - |
979
- | 3.3663 | 40900 | 0.0002 | - |
980
- | 3.3704 | 40950 | 0.0 | - |
981
- | 3.3745 | 41000 | 0.0 | - |
982
- | 3.3786 | 41050 | 0.0 | - |
983
- | 3.3827 | 41100 | 0.0 | - |
984
- | 3.3868 | 41150 | 0.0 | - |
985
- | 3.3909 | 41200 | 0.0 | - |
986
- | 3.3951 | 41250 | 0.0 | - |
987
- | 3.3992 | 41300 | 0.0 | - |
988
- | 3.4033 | 41350 | 0.0 | - |
989
- | 3.4074 | 41400 | 0.0 | - |
990
- | 3.4115 | 41450 | 0.0 | - |
991
- | 3.4156 | 41500 | 0.0 | - |
992
- | 3.4198 | 41550 | 0.0 | - |
993
- | 3.4239 | 41600 | 0.0 | - |
994
- | 3.4280 | 41650 | 0.0 | - |
995
- | 3.4321 | 41700 | 0.0 | - |
996
- | 3.4362 | 41750 | 0.0 | - |
997
- | 3.4403 | 41800 | 0.0 | - |
998
- | 3.4444 | 41850 | 0.0 | - |
999
- | 3.4486 | 41900 | 0.0 | - |
1000
- | 3.4527 | 41950 | 0.0 | - |
1001
- | 3.4568 | 42000 | 0.0 | - |
1002
- | 3.4609 | 42050 | 0.0 | - |
1003
- | 3.4650 | 42100 | 0.0 | - |
1004
- | 3.4691 | 42150 | 0.0 | - |
1005
- | 3.4733 | 42200 | 0.0 | - |
1006
- | 3.4774 | 42250 | 0.0 | - |
1007
- | 3.4815 | 42300 | 0.0 | - |
1008
- | 3.4856 | 42350 | 0.0 | - |
1009
- | 3.4897 | 42400 | 0.0 | - |
1010
- | 3.4938 | 42450 | 0.0 | - |
1011
- | 3.4979 | 42500 | 0.0 | - |
1012
- | 3.5021 | 42550 | 0.0 | - |
1013
- | 3.5062 | 42600 | 0.0 | - |
1014
- | 3.5103 | 42650 | 0.0 | - |
1015
- | 3.5144 | 42700 | 0.0 | - |
1016
- | 3.5185 | 42750 | 0.0 | - |
1017
- | 3.5226 | 42800 | 0.0 | - |
1018
- | 3.5267 | 42850 | 0.0 | - |
1019
- | 3.5309 | 42900 | 0.0 | - |
1020
- | 3.5350 | 42950 | 0.0 | - |
1021
- | 3.5391 | 43000 | 0.0 | - |
1022
- | 3.5432 | 43050 | 0.0 | - |
1023
- | 3.5473 | 43100 | 0.0 | - |
1024
- | 3.5514 | 43150 | 0.0 | - |
1025
- | 3.5556 | 43200 | 0.0 | - |
1026
- | 3.5597 | 43250 | 0.0 | - |
1027
- | 3.5638 | 43300 | 0.0 | - |
1028
- | 3.5679 | 43350 | 0.0 | - |
1029
- | 3.5720 | 43400 | 0.0 | - |
1030
- | 3.5761 | 43450 | 0.0 | - |
1031
- | 3.5802 | 43500 | 0.0 | - |
1032
- | 3.5844 | 43550 | 0.0 | - |
1033
- | 3.5885 | 43600 | 0.0 | - |
1034
- | 3.5926 | 43650 | 0.0 | - |
1035
- | 3.5967 | 43700 | 0.0 | - |
1036
- | 3.6008 | 43750 | 0.0 | - |
1037
- | 3.6049 | 43800 | 0.0 | - |
1038
- | 3.6091 | 43850 | 0.0 | - |
1039
- | 3.6132 | 43900 | 0.0 | - |
1040
- | 3.6173 | 43950 | 0.0 | - |
1041
- | 3.6214 | 44000 | 0.0 | - |
1042
- | 3.6255 | 44050 | 0.0 | - |
1043
- | 3.6296 | 44100 | 0.0 | - |
1044
- | 3.6337 | 44150 | 0.0 | - |
1045
- | 3.6379 | 44200 | 0.0 | - |
1046
- | 3.6420 | 44250 | 0.0 | - |
1047
- | 3.6461 | 44300 | 0.0 | - |
1048
- | 3.6502 | 44350 | 0.0 | - |
1049
- | 3.6543 | 44400 | 0.0 | - |
1050
- | 3.6584 | 44450 | 0.0 | - |
1051
- | 3.6626 | 44500 | 0.0 | - |
1052
- | 3.6667 | 44550 | 0.0 | - |
1053
- | 3.6708 | 44600 | 0.0 | - |
1054
- | 3.6749 | 44650 | 0.0 | - |
1055
- | 3.6790 | 44700 | 0.0 | - |
1056
- | 3.6831 | 44750 | 0.0 | - |
1057
- | 3.6872 | 44800 | 0.0 | - |
1058
- | 3.6914 | 44850 | 0.0 | - |
1059
- | 3.6955 | 44900 | 0.0 | - |
1060
- | 3.6996 | 44950 | 0.0 | - |
1061
- | 3.7037 | 45000 | 0.0 | - |
1062
- | 3.7078 | 45050 | 0.0 | - |
1063
- | 3.7119 | 45100 | 0.0 | - |
1064
- | 3.7160 | 45150 | 0.0 | - |
1065
- | 3.7202 | 45200 | 0.0 | - |
1066
- | 3.7243 | 45250 | 0.0 | - |
1067
- | 3.7284 | 45300 | 0.0 | - |
1068
- | 3.7325 | 45350 | 0.0 | - |
1069
- | 3.7366 | 45400 | 0.0 | - |
1070
- | 3.7407 | 45450 | 0.0 | - |
1071
- | 3.7449 | 45500 | 0.0 | - |
1072
- | 3.7490 | 45550 | 0.0 | - |
1073
- | 3.7531 | 45600 | 0.0 | - |
1074
- | 3.7572 | 45650 | 0.0 | - |
1075
- | 3.7613 | 45700 | 0.0 | - |
1076
- | 3.7654 | 45750 | 0.0 | - |
1077
- | 3.7695 | 45800 | 0.0 | - |
1078
- | 3.7737 | 45850 | 0.0 | - |
1079
- | 3.7778 | 45900 | 0.0 | - |
1080
- | 3.7819 | 45950 | 0.0 | - |
1081
- | 3.7860 | 46000 | 0.0 | - |
1082
- | 3.7901 | 46050 | 0.0 | - |
1083
- | 3.7942 | 46100 | 0.0 | - |
1084
- | 3.7984 | 46150 | 0.0 | - |
1085
- | 3.8025 | 46200 | 0.0 | - |
1086
- | 3.8066 | 46250 | 0.0 | - |
1087
- | 3.8107 | 46300 | 0.0 | - |
1088
- | 3.8148 | 46350 | 0.0 | - |
1089
- | 3.8189 | 46400 | 0.0 | - |
1090
- | 3.8230 | 46450 | 0.0 | - |
1091
- | 3.8272 | 46500 | 0.0 | - |
1092
- | 3.8313 | 46550 | 0.0 | - |
1093
- | 3.8354 | 46600 | 0.0 | - |
1094
- | 3.8395 | 46650 | 0.0 | - |
1095
- | 3.8436 | 46700 | 0.0 | - |
1096
- | 3.8477 | 46750 | 0.0 | - |
1097
- | 3.8519 | 46800 | 0.0 | - |
1098
- | 3.8560 | 46850 | 0.0 | - |
1099
- | 3.8601 | 46900 | 0.0 | - |
1100
- | 3.8642 | 46950 | 0.0 | - |
1101
- | 3.8683 | 47000 | 0.0 | - |
1102
- | 3.8724 | 47050 | 0.0 | - |
1103
- | 3.8765 | 47100 | 0.0 | - |
1104
- | 3.8807 | 47150 | 0.0 | - |
1105
- | 3.8848 | 47200 | 0.0 | - |
1106
- | 3.8889 | 47250 | 0.0 | - |
1107
- | 3.8930 | 47300 | 0.0 | - |
1108
- | 3.8971 | 47350 | 0.0 | - |
1109
- | 3.9012 | 47400 | 0.0 | - |
1110
- | 3.9053 | 47450 | 0.0 | - |
1111
- | 3.9095 | 47500 | 0.0 | - |
1112
- | 3.9136 | 47550 | 0.0 | - |
1113
- | 3.9177 | 47600 | 0.0 | - |
1114
- | 3.9218 | 47650 | 0.0 | - |
1115
- | 3.9259 | 47700 | 0.0 | - |
1116
- | 3.9300 | 47750 | 0.0 | - |
1117
- | 3.9342 | 47800 | 0.0 | - |
1118
- | 3.9383 | 47850 | 0.0 | - |
1119
- | 3.9424 | 47900 | 0.0 | - |
1120
- | 3.9465 | 47950 | 0.0 | - |
1121
- | 3.9506 | 48000 | 0.0 | - |
1122
- | 3.9547 | 48050 | 0.0 | - |
1123
- | 3.9588 | 48100 | 0.0 | - |
1124
- | 3.9630 | 48150 | 0.0 | - |
1125
- | 3.9671 | 48200 | 0.0 | - |
1126
- | 3.9712 | 48250 | 0.0 | - |
1127
- | 3.9753 | 48300 | 0.0 | - |
1128
- | 3.9794 | 48350 | 0.0 | - |
1129
- | 3.9835 | 48400 | 0.0 | - |
1130
- | 3.9877 | 48450 | 0.0 | - |
1131
- | 3.9918 | 48500 | 0.0 | - |
1132
- | 3.9959 | 48550 | 0.0 | - |
1133
- | 4.0 | 48600 | 0.0 | - |
1134
 
1135
  ### Framework Versions
1136
  - Python: 3.10.13
 
9
  metrics:
10
  - accuracy
11
  widget:
12
+ - text: '" I hope this kind of cognitive therapy does work in the long run and thoughts
13
+ would dwindle away'
14
+ - text: ' It''s right by the ocean so it was really nice being outside the whole time
15
+ The aquarium building is closed for now so they set up displays outside Anyway
16
+ as far as animal fields, I guess that depends on if you want to do something like
17
+ animal husbandry (with pets, farm animals, or wild animals) or be involved with
18
+ a more wildlife restoration or management side The aquarium building is closed
19
+ for now so they set up displays outside'
20
+ - text: ' Just like a child running from a horror house with her eyes closed Just
21
+ like a child running from a horror house with her eyes closed'
22
+ - text: ' I have to go outside and meet people personally'
23
+ - text: ' I like to come home from a long day of work and get away from the real world
24
+ by watching my fav baseball team, kdramas, or even play online games'
25
  pipeline_tag: text-classification
26
  inference: true
27
  model-index:
 
36
  split: test
37
  metrics:
38
  - type: accuracy
39
+ value: 0.5633333333333334
40
  name: Accuracy
41
  ---
42
 
 
68
  - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
69
 
70
  ### Model Labels
71
+ | Label | Examples |
72
+ |:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
73
+ | 1 | <ul><li>' I went camping by myself at a site near a beach I went camping by myself at a site near a beach I enjoyed being by myself, hiking out and swimming, etc I enjoyed being by myself, hiking out and swimming, etc I went camping by myself at a site near a beach I went camping by myself at a site near a beach I went camping by myself at a site near a beach I went camping by myself at a site near a beach'</li><li>' I regularly leave the house for shopping or to go jogging'</li><li>'get fresh air or smoothies'</li></ul> |
74
+ | 2 | <ul><li>'in the middle of the night when no one is outside here and the air is fresh and cold is the only time where i can actually enjoy leaving the house'</li><li>" I've been socially anxious for so long that I don't even know what to say and what to do when I actually go outside and interact with people It's just a stream of question and uncertainties"</li><li>"Yep I only go running once it's dark (around 7pm) because then people can't see me very well and don't judge me"</li></ul> |
75
+ | 3 | <ul><li>"Tips For Fear of Outdoors It's embarrassing to say at my age but I've never gone really outside by myself Sometimes I'd walk around town by myself but I've never gone anywhere else without a friend or family member"</li><li>' while potheads, sex addicts and bullies and all this shit get to live the fun, free life without worry of what tbey do next The majority of people who lead this sort of life will end up regretting it in the long run This is because i always feel like i walk weird, Have a very asymetrical face (ugly), underdeveloped jaw, i run weird my whole body just doesnt look right'</li><li>" But mostly I'm nervous about walking around downtown"</li></ul> |
76
+ | 0 | <ul><li>' But reflecting on past experiences with it- some moments can be quite comical from an outside view'</li><li>" I realised in my core inner nature I'm actually a super energetic person, and I have the potential to be friendly and outgoing"</li><li>" Well a few years ago I moved away from one of the few towns I've called home to start a life with my partner on the other side of the country (UK) and due to that stopped talking to the few friends I had as I was struggling to keep my life afloat just with work and figuring out the ins and outs of running a household"</li></ul> |
77
 
78
  ## Evaluation
79
 
80
  ### Metrics
81
  | Label | Accuracy |
82
  |:--------|:---------|
83
+ | **all** | 0.5633 |
84
 
85
  ## Uses
86
 
 
100
  # Download from the 🤗 Hub
101
  model = SetFitModel.from_pretrained("Omar-Nasr/setfitmodel")
102
  # Run inference
103
+ preds = model(" I have to go outside and meet people personally")
104
  ```
105
 
106
  <!--
 
130
  ## Training Details
131
 
132
  ### Training Set Metrics
133
+ | Training set | Min | Median | Max |
134
+ |:-------------|:----|:--------|:----|
135
+ | Word count | 5 | 45.6875 | 250 |
136
 
137
  | Label | Training Sample Count |
138
  |:------|:----------------------|
139
+ | 0 | 20 |
140
+ | 1 | 20 |
141
+ | 2 | 20 |
142
+ | 3 | 20 |
143
 
144
  ### Training Hyperparameters
145
  - batch_size: (8, 8)
146
+ - num_epochs: (1, 1)
147
  - max_steps: -1
148
  - sampling_strategy: oversampling
149
  - body_learning_rate: (2e-05, 1e-05)
 
159
  - load_best_model_at_end: False
160
 
161
  ### Training Results
162
+ | Epoch | Step | Training Loss | Validation Loss |
163
+ |:------:|:----:|:-------------:|:---------------:|
164
+ | 0.0017 | 1 | 0.1832 | - |
165
+ | 0.0833 | 50 | 0.2602 | - |
166
+ | 0.1667 | 100 | 0.2547 | - |
167
+ | 0.25 | 150 | 0.2396 | - |
168
+ | 0.3333 | 200 | 0.4516 | - |
169
+ | 0.4167 | 250 | 0.1316 | - |
170
+ | 0.5 | 300 | 0.391 | - |
171
+ | 0.5833 | 350 | 0.072 | - |
172
+ | 0.6667 | 400 | 0.0973 | - |
173
+ | 0.75 | 450 | 0.1348 | - |
174
+ | 0.8333 | 500 | 0.0962 | - |
175
+ | 0.9167 | 550 | 0.301 | - |
176
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