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google_t5_language_ID

Transformers
Safetensors
t5
text2text-generation
Generated from Trainer
Eval Results (legacy)
text-generation-inference
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yigagilbert/google_t5_language_ID

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Files changed (3) hide show
  1. README.md +24 -18
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -12,8 +12,8 @@ model-index:
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  - name: google_t5_language_ID
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  results:
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  - task:
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- type: text2text-generation
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  name: Sequence-to-sequence Language Modeling
 
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  dataset:
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  name: generator
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  type: generator
@@ -21,9 +21,9 @@ model-index:
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  split: train
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  args: default
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  metrics:
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- - type: accuracy
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- value: 0.6282717434980809
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- name: Accuracy
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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
@@ -33,12 +33,12 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) on the generator dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.4188
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- - Accuracy: 0.6283
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- - F1 Macro: 0.5525
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- - F1 Weighted: 0.5729
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- - Precision Macro: 0.6438
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- - Recall Macro: 0.6058
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  ## Model description
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@@ -72,14 +72,20 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Weighted | Precision Macro | Recall Macro |
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  |:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|:-----------:|:---------------:|:------------:|
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- | 0.1526 | 0.0083 | 500 | 0.6953 | 0.4727 | 0.4232 | 0.4388 | 0.5151 | 0.4558 |
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- | 0.0743 | 0.0167 | 1000 | 0.9594 | 0.3863 | 0.3125 | 0.3125 | 0.4760 | 0.3863 |
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- | 0.0491 | 0.025 | 1500 | 0.9981 | 0.4410 | 0.3424 | 0.3677 | 0.5306 | 0.4106 |
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- | 0.047 | 0.0333 | 2000 | 0.5014 | 0.6161 | 0.5001 | 0.5556 | 0.5811 | 0.5544 |
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- | 0.0559 | 0.0417 | 2500 | 0.4182 | 0.6452 | 0.5724 | 0.5936 | 0.6326 | 0.6222 |
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- | 0.023 | 0.05 | 3000 | 0.5246 | 0.5914 | 0.5244 | 0.5439 | 0.5739 | 0.5703 |
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- | 0.0432 | 0.0583 | 3500 | 0.4539 | 0.6216 | 0.5600 | 0.5807 | 0.6434 | 0.5994 |
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- | 0.0601 | 0.0667 | 4000 | 0.4188 | 0.6283 | 0.5525 | 0.5729 | 0.6438 | 0.6058 |
 
 
 
 
 
 
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  ### Framework versions
 
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  - name: google_t5_language_ID
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  results:
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  - task:
 
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  name: Sequence-to-sequence Language Modeling
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+ type: text2text-generation
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  dataset:
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  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: 0.6179074697593216
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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
 
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  This model is a fine-tuned version of [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) on the generator dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5429
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+ - Accuracy: 0.6179
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+ - F1 Macro: 0.3389
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+ - F1 Weighted: 0.5774
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+ - Precision Macro: 0.3873
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+ - Recall Macro: 0.3627
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Weighted | Precision Macro | Recall Macro |
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  |:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|:-----------:|:---------------:|:------------:|
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+ | 0.1943 | 0.0083 | 500 | 0.6981 | 0.4018 | 0.3139 | 0.3488 | 0.4624 | 0.3616 |
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+ | 0.0812 | 0.0167 | 1000 | 0.7371 | 0.4086 | 0.3323 | 0.3446 | 0.5179 | 0.3940 |
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+ | 0.049 | 0.025 | 1500 | 0.7806 | 0.4534 | 0.3793 | 0.3793 | 0.5316 | 0.4534 |
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+ | 0.0518 | 0.0333 | 2000 | 0.5042 | 0.5845 | 0.5071 | 0.5258 | 0.5576 | 0.5637 |
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+ | 0.0452 | 0.0417 | 2500 | 0.5120 | 0.6204 | 0.5554 | 0.5554 | 0.6496 | 0.6204 |
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+ | 0.0288 | 0.05 | 3000 | 0.4798 | 0.6018 | 0.5230 | 0.5618 | 0.6077 | 0.5603 |
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+ | 0.0341 | 0.0583 | 3500 | 0.4764 | 0.6098 | 0.5456 | 0.5658 | 0.6528 | 0.5881 |
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+ | 0.0762 | 0.0667 | 4000 | 0.4389 | 0.6251 | 0.5296 | 0.5688 | 0.6091 | 0.5820 |
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+ | 0.0189 | 0.075 | 4500 | 0.4167 | 0.6681 | 0.6068 | 0.6068 | 0.7167 | 0.6681 |
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+ | 0.0235 | 0.0833 | 5000 | 0.4673 | 0.6599 | 0.6018 | 0.6018 | 0.7393 | 0.6599 |
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+ | 0.0274 | 0.0917 | 5500 | 0.3304 | 0.6958 | 0.6102 | 0.6555 | 0.6868 | 0.6478 |
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+ | 0.0198 | 0.1 | 6000 | 0.4752 | 0.6569 | 0.5877 | 0.6095 | 0.7165 | 0.6335 |
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+ | 0.0246 | 0.1083 | 6500 | 0.4657 | 0.6540 | 0.5800 | 0.6015 | 0.6400 | 0.6306 |
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+ | 0.0241 | 0.1167 | 7000 | 0.5429 | 0.6179 | 0.3389 | 0.5774 | 0.3873 | 0.3627 |
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  ### Framework versions
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