Hugging Face's logo

karpov3
/
menu-lilt-model-XLM-v3

Token Classification
Transformers
TensorBoard
Safetensors
lilt
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
karpov3 commited on
Commit
1b07a6a
•
1 Parent(s): d4ae55b

End of training

Browse files
Files changed (8) hide show
  1. .gitattributes +1 -0
  2. README.md +87 -0
  3. logs/events.out.tfevents.1713974668.4d1528901d50.487.0 +2 -2
  4. model.safetensors +1 -1
  5. preprocessor_config.json +25 -0
  6. special_tokens_map.json +51 -0
  7. tokenizer.json +3 -0
  8. tokenizer_config.json +77 -0
.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
33
  *.zip filter=lfs diff=lfs merge=lfs -text
34
  *.zst filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
33
  *.zip filter=lfs diff=lfs merge=lfs -text
34
  *.zst filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
36
+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
README.md ADDED
@@ -0,0 +1,87 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: mit
3
+ base_model: SCUT-DLVCLab/lilt-infoxlm-base
4
+ tags:
5
+ - generated_from_trainer
6
+ model-index:
7
+ - name: menu-lilt-model-XLM-v3
8
+ results: []
9
+ ---
10
+
11
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
12
+ should probably proofread and complete it, then remove this comment. -->
13
+
14
+ # menu-lilt-model-XLM-v3
15
+
16
+ This model is a fine-tuned version of [SCUT-DLVCLab/lilt-infoxlm-base](https://huggingface.co/SCUT-DLVCLab/lilt-infoxlm-base) on the None dataset.
17
+ It achieves the following results on the evaluation set:
18
+ - Loss: 0.0006
19
+ - Created: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 18}
20
+ - Created Label: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 18}
21
+ - Day Menu Label: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 126}
22
+ - Diet: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 101}
23
+ - Meal: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 4824}
24
+ - Meal Label: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 42}
25
+ - Meal Note Label: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 209}
26
+ - Menu Name: {'precision': 0.9130434782608695, 'recall': 0.9545454545454546, 'f1': 0.9333333333333332, 'number': 22}
27
+ - School Type: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3}
28
+ - Tag Value: {'precision': 0.974025974025974, 'recall': 0.9868421052631579, 'f1': 0.9803921568627451, 'number': 76}
29
+ - Validity: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 54}
30
+ - Validity Detail: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3}
31
+ - Weekday: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 275}
32
+ - Week Count: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 230}
33
+ - Overall Precision: 0.9993
34
+ - Overall Recall: 0.9997
35
+ - Overall F1: 0.9995
36
+ - Overall Accuracy: 0.9999
37
+
38
+ ## Model description
39
+
40
+ More information needed
41
+
42
+ ## Intended uses & limitations
43
+
44
+ More information needed
45
+
46
+ ## Training and evaluation data
47
+
48
+ More information needed
49
+
50
+ ## Training procedure
51
+
52
+ ### Training hyperparameters
53
+
54
+ The following hyperparameters were used during training:
55
+ - learning_rate: 5e-05
56
+ - train_batch_size: 8
57
+ - eval_batch_size: 8
58
+ - seed: 42
59
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
60
+ - lr_scheduler_type: linear
61
+ - training_steps: 2500
62
+ - mixed_precision_training: Native AMP
63
+
64
+ ### Training results
65
+
66
+ | Training Loss | Epoch | Step | Validation Loss | Created | Created Label | Day Menu Label | Diet | Meal | Meal Label | Meal Note Label | Menu Name | School Type | Tag Value | Validity | Validity Detail | Weekday | Week Count | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
67
+ |:-------------:|:-------:|:----:|:---------------:|:----------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------:|:-----------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------:|:-----------------------------------------------------------:|:-----------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
68
+ | 0.8513 | 4.5455 | 200 | 0.0523 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 18} | {'precision': 0.7222222222222222, 'recall': 0.7222222222222222, 'f1': 0.7222222222222222, 'number': 18} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 126} | {'precision': 0.8738738738738738, 'recall': 0.9603960396039604, 'f1': 0.9150943396226415, 'number': 101} | {'precision': 0.9637207340223581, 'recall': 0.947139303482587, 'f1': 0.9553580763199163, 'number': 4824} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 42} | {'precision': 0.9858490566037735, 'recall': 1.0, 'f1': 0.9928741092636578, 'number': 209} | {'precision': 0.5588235294117647, 'recall': 0.8636363636363636, 'f1': 0.6785714285714287, 'number': 22} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.9358974358974359, 'recall': 0.9605263157894737, 'f1': 0.948051948051948, 'number': 76} | {'precision': 0.7692307692307693, 'recall': 0.9259259259259259, 'f1': 0.8403361344537816, 'number': 54} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 275} | {'precision': 0.9704641350210971, 'recall': 1.0, 'f1': 0.9850107066381156, 'number': 230} | 0.9604 | 0.9533 | 0.9568 | 0.9884 |
69
+ | 0.0262 | 9.0909 | 400 | 0.0083 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 18} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 18} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 126} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 101} | {'precision': 0.9972972972972973, 'recall': 0.9944029850746269, 'f1': 0.9958480381980485, 'number': 4824} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 42} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 209} | {'precision': 0.6666666666666666, 'recall': 0.9090909090909091, 'f1': 0.7692307692307692, 'number': 22} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.9868421052631579, 'recall': 0.9868421052631579, 'f1': 0.9868421052631579, 'number': 76} | {'precision': 0.9473684210526315, 'recall': 1.0, 'f1': 0.972972972972973, 'number': 54} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 275} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 230} | 0.9938 | 0.9940 | 0.9939 | 0.9980 |
70
+ | 0.0067 | 13.6364 | 600 | 0.0034 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 18} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 18} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 126} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 101} | {'precision': 0.9977164210089268, 'recall': 0.996268656716418, 'f1': 0.9969920132766311, 'number': 4824} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 42} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 209} | {'precision': 0.9130434782608695, 'recall': 0.9545454545454546, 'f1': 0.9333333333333332, 'number': 22} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 76} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 54} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 275} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 230} | 0.9978 | 0.9968 | 0.9973 | 0.9993 |
71
+ | 0.0046 | 18.1818 | 800 | 0.0023 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 18} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 18} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 126} | {'precision': 0.9803921568627451, 'recall': 0.9900990099009901, 'f1': 0.9852216748768472, 'number': 101} | {'precision': 0.9987557030277893, 'recall': 0.9983416252072969, 'f1': 0.9985486211901307, 'number': 4824} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 42} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 209} | {'precision': 0.8333333333333334, 'recall': 0.9090909090909091, 'f1': 0.8695652173913043, 'number': 22} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3} | {'precision': 0.9868421052631579, 'recall': 0.9868421052631579, 'f1': 0.9868421052631579, 'number': 76} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 54} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 275} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 230} | 0.9978 | 0.9980 | 0.9979 | 0.9994 |
72
+ | 0.002 | 22.7273 | 1000 | 0.0026 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 18} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 18} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 126} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 101} | {'precision': 0.9983395599833956, 'recall': 0.9970978441127695, 'f1': 0.9977183157021364, 'number': 4824} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 42} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 209} | {'precision': 0.9130434782608695, 'recall': 0.9545454545454546, 'f1': 0.9333333333333332, 'number': 22} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 76} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 54} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 275} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 230} | 0.9983 | 0.9975 | 0.9979 | 0.9995 |
73
+ | 0.0015 | 27.2727 | 1200 | 0.0017 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 18} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 18} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 126} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 101} | {'precision': 0.9991701244813278, 'recall': 0.9983416252072969, 'f1': 0.9987557030277893, 'number': 4824} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 42} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 209} | {'precision': 0.9130434782608695, 'recall': 0.9545454545454546, 'f1': 0.9333333333333332, 'number': 22} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3} | {'precision': 0.9868421052631579, 'recall': 0.9868421052631579, 'f1': 0.9868421052631579, 'number': 76} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 54} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 275} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 230} | 0.9988 | 0.9983 | 0.9986 | 0.9995 |
74
+ | 0.0012 | 31.8182 | 1400 | 0.0026 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 18} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 18} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 126} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 101} | {'precision': 0.9997926601700187, 'recall': 0.9995854063018242, 'f1': 0.9996890224940397, 'number': 4824} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 42} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 209} | {'precision': 0.8695652173913043, 'recall': 0.9090909090909091, 'f1': 0.888888888888889, 'number': 22} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3} | {'precision': 0.9868421052631579, 'recall': 0.9868421052631579, 'f1': 0.9868421052631579, 'number': 76} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 54} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 275} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 230} | 0.9992 | 0.9992 | 0.9992 | 0.9995 |
75
+ | 0.0011 | 36.3636 | 1600 | 0.0012 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 18} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 18} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 126} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 101} | {'precision': 0.9993777224642191, 'recall': 0.9987562189054726, 'f1': 0.9990668740279938, 'number': 4824} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 42} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 209} | {'precision': 0.9130434782608695, 'recall': 0.9545454545454546, 'f1': 0.9333333333333332, 'number': 22} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 76} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 54} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 275} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 230} | 0.9992 | 0.9988 | 0.999 | 0.9997 |
76
+ | 0.0008 | 40.9091 | 1800 | 0.0008 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 18} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 18} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 126} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 101} | {'precision': 0.9997926601700187, 'recall': 0.9995854063018242, 'f1': 0.9996890224940397, 'number': 4824} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 42} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 209} | {'precision': 0.9130434782608695, 'recall': 0.9545454545454546, 'f1': 0.9333333333333332, 'number': 22} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3} | {'precision': 0.9868421052631579, 'recall': 0.9868421052631579, 'f1': 0.9868421052631579, 'number': 76} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 54} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 275} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 230} | 0.9993 | 0.9993 | 0.9993 | 0.9997 |
77
+ | 0.0006 | 45.4545 | 2000 | 0.0009 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 18} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 18} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 126} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 101} | {'precision': 0.9997926601700187, 'recall': 0.9995854063018242, 'f1': 0.9996890224940397, 'number': 4824} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 42} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 209} | {'precision': 0.9130434782608695, 'recall': 0.9545454545454546, 'f1': 0.9333333333333332, 'number': 22} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3} | {'precision': 0.9868421052631579, 'recall': 0.9868421052631579, 'f1': 0.9868421052631579, 'number': 76} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 54} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 275} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 230} | 0.9993 | 0.9993 | 0.9993 | 0.9997 |
78
+ | 0.0005 | 50.0 | 2200 | 0.0006 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 18} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 18} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 126} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 101} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 4824} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 42} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 209} | {'precision': 0.9130434782608695, 'recall': 0.9545454545454546, 'f1': 0.9333333333333332, 'number': 22} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3} | {'precision': 0.9868421052631579, 'recall': 0.9868421052631579, 'f1': 0.9868421052631579, 'number': 76} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 54} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 275} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 230} | 0.9995 | 0.9997 | 0.9996 | 0.9998 |
79
+ | 0.0005 | 54.5455 | 2400 | 0.0006 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 18} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 18} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 126} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 101} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 4824} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 42} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 209} | {'precision': 0.9130434782608695, 'recall': 0.9545454545454546, 'f1': 0.9333333333333332, 'number': 22} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3} | {'precision': 0.974025974025974, 'recall': 0.9868421052631579, 'f1': 0.9803921568627451, 'number': 76} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 54} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 275} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 230} | 0.9993 | 0.9997 | 0.9995 | 0.9999 |
80
+
81
+
82
+ ### Framework versions
83
+
84
+ - Transformers 4.40.0
85
+ - Pytorch 2.2.1+cu121
86
+ - Datasets 2.19.0
87
+ - Tokenizers 0.19.1
logs/events.out.tfevents.1713974668.4d1528901d50.487.0 CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:9d6a04464706c649b9a15b9ebcdf9141a43ad1489cd6039b38b87e00b98f1c8d
3
- size 15117
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:13358d6d6f0ebfadc91c5549080931f34b60c83a2832d45f3e356d8b1fece35b
3
+ size 15471
model.safetensors CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:7e2d4548690b054bb737d4f8c32c21acc8f657ad7be44b339a776f9b92bcd40b
3
  size 1134406060
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:3b8717da670fe83a102b1a3d681154f0ab448ecf06a72d070ac01353a338eba1
3
  size 1134406060
preprocessor_config.json ADDED
@@ -0,0 +1,25 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_valid_processor_keys": [
3
+ "images",
4
+ "do_resize",
5
+ "size",
6
+ "resample",
7
+ "apply_ocr",
8
+ "ocr_lang",
9
+ "tesseract_config",
10
+ "return_tensors",
11
+ "data_format",
12
+ "input_data_format"
13
+ ],
14
+ "apply_ocr": false,
15
+ "do_resize": true,
16
+ "image_processor_type": "LayoutLMv2FeatureExtractor",
17
+ "ocr_lang": null,
18
+ "processor_class": "LayoutXLMProcessor",
19
+ "resample": 2,
20
+ "size": {
21
+ "height": 224,
22
+ "width": 224
23
+ },
24
+ "tesseract_config": ""
25
+ }
special_tokens_map.json ADDED
@@ -0,0 +1,51 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "bos_token": {
3
+ "content": "<s>",
4
+ "lstrip": false,
5
+ "normalized": false,
6
+ "rstrip": false,
7
+ "single_word": false
8
+ },
9
+ "cls_token": {
10
+ "content": "<s>",
11
+ "lstrip": false,
12
+ "normalized": false,
13
+ "rstrip": false,
14
+ "single_word": false
15
+ },
16
+ "eos_token": {
17
+ "content": "</s>",
18
+ "lstrip": false,
19
+ "normalized": false,
20
+ "rstrip": false,
21
+ "single_word": false
22
+ },
23
+ "mask_token": {
24
+ "content": "<mask>",
25
+ "lstrip": true,
26
+ "normalized": false,
27
+ "rstrip": false,
28
+ "single_word": false
29
+ },
30
+ "pad_token": {
31
+ "content": "<pad>",
32
+ "lstrip": false,
33
+ "normalized": false,
34
+ "rstrip": false,
35
+ "single_word": false
36
+ },
37
+ "sep_token": {
38
+ "content": "</s>",
39
+ "lstrip": false,
40
+ "normalized": false,
41
+ "rstrip": false,
42
+ "single_word": false
43
+ },
44
+ "unk_token": {
45
+ "content": "<unk>",
46
+ "lstrip": false,
47
+ "normalized": false,
48
+ "rstrip": false,
49
+ "single_word": false
50
+ }
51
+ }
tokenizer.json ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:4508a62680ca24eff6a8ba74127c053afe12f9b8fe33296729bce17426e460d4
3
+ size 17083001
tokenizer_config.json ADDED
@@ -0,0 +1,77 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "added_tokens_decoder": {
3
+ "0": {
4
+ "content": "<s>",
5
+ "lstrip": false,
6
+ "normalized": false,
7
+ "rstrip": false,
8
+ "single_word": false,
9
+ "special": true
10
+ },
11
+ "1": {
12
+ "content": "<pad>",
13
+ "lstrip": false,
14
+ "normalized": false,
15
+ "rstrip": false,
16
+ "single_word": false,
17
+ "special": true
18
+ },
19
+ "2": {
20
+ "content": "</s>",
21
+ "lstrip": false,
22
+ "normalized": false,
23
+ "rstrip": false,
24
+ "single_word": false,
25
+ "special": true
26
+ },
27
+ "3": {
28
+ "content": "<unk>",
29
+ "lstrip": false,
30
+ "normalized": false,
31
+ "rstrip": false,
32
+ "single_word": false,
33
+ "special": true
34
+ },
35
+ "250001": {
36
+ "content": "<mask>",
37
+ "lstrip": true,
38
+ "normalized": false,
39
+ "rstrip": false,
40
+ "single_word": false,
41
+ "special": true
42
+ }
43
+ },
44
+ "bos_token": "<s>",
45
+ "clean_up_tokenization_spaces": true,
46
+ "cls_token": "<s>",
47
+ "cls_token_box": [
48
+ 0,
49
+ 0,
50
+ 0,
51
+ 0
52
+ ],
53
+ "eos_token": "</s>",
54
+ "mask_token": "<mask>",
55
+ "model_max_length": 1000000000000000019884624838656,
56
+ "only_label_first_subword": true,
57
+ "pad_token": "<pad>",
58
+ "pad_token_box": [
59
+ 0,
60
+ 0,
61
+ 0,
62
+ 0
63
+ ],
64
+ "pad_token_label": -100,
65
+ "processor_class": "LayoutXLMProcessor",
66
+ "return_special_tokens_mask": true,
67
+ "return_tensors": "pt",
68
+ "sep_token": "</s>",
69
+ "sep_token_box": [
70
+ 1000,
71
+ 1000,
72
+ 1000,
73
+ 1000
74
+ ],
75
+ "tokenizer_class": "LayoutXLMTokenizer",
76
+ "unk_token": "<unk>"
77
+ }