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yigagilbert
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salt_language_Classification

Text Classification
Transformers
TensorBoard
Safetensors
t5
Generated from Trainer
Eval Results
text-generation-inference
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End of training

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  1. README.md +62 -57
  2. model.safetensors +1 -1
  3. runs/May30_07-28-46_145a99fee08a/events.out.tfevents.1717054132.145a99fee08a.992.0 +3 -0
  4. runs/May30_07-33-18_145a99fee08a/events.out.tfevents.1717054406.145a99fee08a.992.1 +3 -0
  5. runs/May30_07-33-18_145a99fee08a/events.out.tfevents.1717056242.145a99fee08a.992.2 +3 -0
  6. training_args.bin +1 -1
README.md CHANGED
@@ -1,11 +1,12 @@
1
  ---
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  license: apache-2.0
 
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  tags:
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  - generated_from_trainer
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- base_model: google/t5-efficient-tiny
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  datasets:
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  - generator
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  metrics:
 
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  - precision
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  - recall
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  - f1
@@ -13,8 +14,8 @@ model-index:
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  - name: salt_language_Classification
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  results:
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  - task:
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- type: text-classification
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  name: Text Classification
 
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  dataset:
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  name: generator
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  type: generator
@@ -22,15 +23,18 @@ model-index:
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  split: train
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  args: default
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  metrics:
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- - type: precision
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- value: 1.0
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- name: Precision
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- - type: recall
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- value: 1.0
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- name: Recall
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- - type: f1
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- value: 1.0
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- name: F1
 
 
 
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -40,10 +44,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [google/t5-efficient-tiny](https://huggingface.co/google/t5-efficient-tiny) on the generator dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0
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- - Precision: 1.0
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- - Recall: 1.0
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- - F1: 1.0
 
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  ## Model description
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@@ -73,48 +78,48 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
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- |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:---:|
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- | 0.0 | 0.025 | 500 | 0.0 | 1.0 | 1.0 | 1.0 |
79
- | 0.0 | 0.05 | 1000 | 0.0 | 1.0 | 1.0 | 1.0 |
80
- | 0.0 | 0.075 | 1500 | 0.0 | 1.0 | 1.0 | 1.0 |
81
- | 0.0 | 0.1 | 2000 | 0.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 0.125 | 2500 | 0.0 | 1.0 | 1.0 | 1.0 |
83
- | 0.0 | 0.15 | 3000 | 0.0 | 1.0 | 1.0 | 1.0 |
84
- | 0.0 | 0.175 | 3500 | 0.0 | 1.0 | 1.0 | 1.0 |
85
- | 0.0 | 0.2 | 4000 | 0.0 | 1.0 | 1.0 | 1.0 |
86
- | 0.0 | 0.225 | 4500 | 0.0 | 1.0 | 1.0 | 1.0 |
87
- | 0.0 | 0.25 | 5000 | 0.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 0.275 | 5500 | 0.0 | 1.0 | 1.0 | 1.0 |
89
- | 0.0 | 0.3 | 6000 | 0.0 | 1.0 | 1.0 | 1.0 |
90
- | 0.0 | 0.325 | 6500 | 0.0 | 1.0 | 1.0 | 1.0 |
91
- | 0.0 | 0.35 | 7000 | 0.0 | 1.0 | 1.0 | 1.0 |
92
- | 0.0 | 0.375 | 7500 | 0.0 | 1.0 | 1.0 | 1.0 |
93
- | 0.0 | 0.4 | 8000 | 0.0 | 1.0 | 1.0 | 1.0 |
94
- | 0.0 | 0.425 | 8500 | 0.0 | 1.0 | 1.0 | 1.0 |
95
- | 0.0 | 0.45 | 9000 | 0.0 | 1.0 | 1.0 | 1.0 |
96
- | 0.0 | 0.475 | 9500 | 0.0 | 1.0 | 1.0 | 1.0 |
97
- | 0.0 | 0.5 | 10000 | 0.0 | 1.0 | 1.0 | 1.0 |
98
- | 0.0 | 0.525 | 10500 | 0.0 | 1.0 | 1.0 | 1.0 |
99
- | 0.0 | 0.55 | 11000 | 0.0 | 1.0 | 1.0 | 1.0 |
100
- | 0.0 | 0.575 | 11500 | 0.0 | 1.0 | 1.0 | 1.0 |
101
- | 0.0 | 0.6 | 12000 | 0.0 | 1.0 | 1.0 | 1.0 |
102
- | 0.0 | 0.625 | 12500 | 0.0 | 1.0 | 1.0 | 1.0 |
103
- | 0.0 | 0.65 | 13000 | 0.0 | 1.0 | 1.0 | 1.0 |
104
- | 0.0 | 0.675 | 13500 | 0.0 | 1.0 | 1.0 | 1.0 |
105
- | 0.0 | 0.7 | 14000 | 0.0 | 1.0 | 1.0 | 1.0 |
106
- | 0.0 | 0.725 | 14500 | 0.0 | 1.0 | 1.0 | 1.0 |
107
- | 0.0 | 0.75 | 15000 | 0.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 0.775 | 15500 | 0.0 | 1.0 | 1.0 | 1.0 |
109
- | 0.0 | 0.8 | 16000 | 0.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 0.825 | 16500 | 0.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 0.85 | 17000 | 0.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 0.875 | 17500 | 0.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 0.9 | 18000 | 0.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 0.925 | 18500 | 0.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 0.95 | 19000 | 0.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 0.975 | 19500 | 0.0 | 1.0 | 1.0 | 1.0 |
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- | 0.0 | 1.0 | 20000 | 0.0 | 1.0 | 1.0 | 1.0 |
118
 
119
 
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  ### Framework versions
 
1
  ---
2
  license: apache-2.0
3
+ base_model: google/t5-efficient-tiny
4
  tags:
5
  - generated_from_trainer
 
6
  datasets:
7
  - generator
8
  metrics:
9
+ - accuracy
10
  - precision
11
  - recall
12
  - f1
 
14
  - name: salt_language_Classification
15
  results:
16
  - task:
 
17
  name: Text Classification
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+ type: text-classification
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  dataset:
20
  name: generator
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  type: generator
 
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  split: train
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  args: default
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  metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9781586021505376
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+ - name: Precision
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+ type: precision
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+ value: 0.9786579334649282
32
+ - name: Recall
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+ type: recall
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+ value: 0.9781586021505376
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+ - name: F1
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+ type: f1
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+ value: 0.97818824673623
38
  ---
39
 
40
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
44
 
45
  This model is a fine-tuned version of [google/t5-efficient-tiny](https://huggingface.co/google/t5-efficient-tiny) on the generator dataset.
46
  It achieves the following results on the evaluation set:
47
+ - Loss: 0.0615
48
+ - Accuracy: 0.9782
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+ - Precision: 0.9787
50
+ - Recall: 0.9782
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+ - F1: 0.9782
52
 
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  ## Model description
54
 
 
78
 
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  ### Training results
80
 
81
+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
82
+ |:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
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+ | 0.2011 | 0.025 | 500 | 0.4979 | 0.8733 | 0.9001 | 0.8733 | 0.8714 |
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+ | 0.234 | 0.05 | 1000 | 0.1886 | 0.9345 | 0.9354 | 0.9345 | 0.9345 |
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+ | 0.2083 | 0.075 | 1500 | 0.1833 | 0.9328 | 0.9391 | 0.9328 | 0.9328 |
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+ | 0.1838 | 0.1 | 2000 | 0.1457 | 0.9476 | 0.9479 | 0.9476 | 0.9475 |
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+ | 0.1737 | 0.125 | 2500 | 0.1659 | 0.9409 | 0.9438 | 0.9409 | 0.9411 |
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+ | 0.1591 | 0.15 | 3000 | 0.1450 | 0.9516 | 0.9524 | 0.9516 | 0.9517 |
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+ | 0.1571 | 0.175 | 3500 | 0.1351 | 0.9459 | 0.9485 | 0.9459 | 0.9461 |
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+ | 0.1513 | 0.2 | 4000 | 0.1510 | 0.9456 | 0.9515 | 0.9456 | 0.9460 |
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+ | 0.1439 | 0.225 | 4500 | 0.1339 | 0.9546 | 0.9578 | 0.9546 | 0.9547 |
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+ | 0.1394 | 0.25 | 5000 | 0.1052 | 0.9657 | 0.9658 | 0.9657 | 0.9656 |
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+ | 0.1472 | 0.275 | 5500 | 0.1088 | 0.9610 | 0.9629 | 0.9610 | 0.9609 |
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+ | 0.1385 | 0.3 | 6000 | 0.0792 | 0.9694 | 0.9696 | 0.9694 | 0.9694 |
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+ | 0.1349 | 0.325 | 6500 | 0.1063 | 0.9610 | 0.9632 | 0.9610 | 0.9613 |
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+ | 0.1215 | 0.35 | 7000 | 0.0855 | 0.9688 | 0.9694 | 0.9688 | 0.9687 |
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+ | 0.133 | 0.375 | 7500 | 0.1049 | 0.9630 | 0.9640 | 0.9630 | 0.9630 |
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+ | 0.1226 | 0.4 | 8000 | 0.0938 | 0.9667 | 0.9675 | 0.9667 | 0.9667 |
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+ | 0.1222 | 0.425 | 8500 | 0.1134 | 0.9570 | 0.9604 | 0.9570 | 0.9573 |
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+ | 0.1165 | 0.45 | 9000 | 0.0997 | 0.9688 | 0.9697 | 0.9688 | 0.9687 |
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+ | 0.1174 | 0.475 | 9500 | 0.1002 | 0.9661 | 0.9680 | 0.9661 | 0.9659 |
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+ | 0.1165 | 0.5 | 10000 | 0.0807 | 0.9728 | 0.9728 | 0.9728 | 0.9728 |
103
+ | 0.1065 | 0.525 | 10500 | 0.0750 | 0.9745 | 0.9754 | 0.9745 | 0.9746 |
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+ | 0.1089 | 0.55 | 11000 | 0.0896 | 0.9688 | 0.9703 | 0.9688 | 0.9689 |
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+ | 0.1125 | 0.575 | 11500 | 0.0632 | 0.9782 | 0.9787 | 0.9782 | 0.9782 |
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+ | 0.11 | 0.6 | 12000 | 0.0775 | 0.9691 | 0.9708 | 0.9691 | 0.9692 |
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+ | 0.1028 | 0.625 | 12500 | 0.0833 | 0.9698 | 0.9708 | 0.9698 | 0.9698 |
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+ | 0.1052 | 0.65 | 13000 | 0.0663 | 0.9751 | 0.9755 | 0.9751 | 0.9751 |
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+ | 0.1068 | 0.675 | 13500 | 0.0648 | 0.9772 | 0.9774 | 0.9772 | 0.9772 |
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+ | 0.1029 | 0.7 | 14000 | 0.0962 | 0.9688 | 0.9706 | 0.9688 | 0.9689 |
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+ | 0.1014 | 0.725 | 14500 | 0.0686 | 0.9772 | 0.9775 | 0.9772 | 0.9771 |
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+ | 0.0978 | 0.75 | 15000 | 0.0802 | 0.9745 | 0.9752 | 0.9745 | 0.9745 |
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+ | 0.095 | 0.775 | 15500 | 0.0646 | 0.9758 | 0.9763 | 0.9758 | 0.9758 |
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+ | 0.0996 | 0.8 | 16000 | 0.0711 | 0.9758 | 0.9761 | 0.9758 | 0.9758 |
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+ | 0.0967 | 0.825 | 16500 | 0.0683 | 0.9761 | 0.9768 | 0.9761 | 0.9761 |
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+ | 0.0939 | 0.85 | 17000 | 0.0572 | 0.9792 | 0.9795 | 0.9792 | 0.9791 |
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+ | 0.0966 | 0.875 | 17500 | 0.0527 | 0.9792 | 0.9794 | 0.9792 | 0.9791 |
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+ | 0.0925 | 0.9 | 18000 | 0.0581 | 0.9798 | 0.9802 | 0.9798 | 0.9799 |
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+ | 0.0945 | 0.925 | 18500 | 0.0693 | 0.9768 | 0.9776 | 0.9768 | 0.9768 |
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+ | 0.0923 | 0.95 | 19000 | 0.0615 | 0.9785 | 0.9790 | 0.9785 | 0.9785 |
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+ | 0.0896 | 0.975 | 19500 | 0.0643 | 0.9758 | 0.9766 | 0.9758 | 0.9758 |
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+ | 0.0979 | 1.0 | 20000 | 0.0619 | 0.9765 | 0.9770 | 0.9765 | 0.9765 |
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  ### Framework versions
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