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tvganesh
/
philosophy_model

Text2Text Generation
Transformers
PyTorch
t5
Generated from Trainer
Inference Endpoints
text-generation-inference
Model card Files Community
tvganesh commited on
Commit
4c9e0d6
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1 Parent(s): 593010e

End of training

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Files changed (3) hide show
  1. README.md +22 -12
  2. pytorch_model.bin +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -17,7 +17,7 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0095
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  - Rouge1: 0.81
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  - Rouge2: 0.8004
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  - Rougel: 0.8107
@@ -47,22 +47,32 @@ The following hyperparameters were used during training:
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.99) and epsilon=1e-06
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  - lr_scheduler_type: linear
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- - num_epochs: 10
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
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- | No log | 1.0 | 13 | 2.1513 | 0.3915 | 0.1976 | 0.3412 | 0.34 | 18.5 |
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- | No log | 2.0 | 26 | 1.3805 | 0.4442 | 0.2633 | 0.3928 | 0.3926 | 18.5 |
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- | No log | 3.0 | 39 | 0.7527 | 0.5254 | 0.3897 | 0.4895 | 0.4877 | 17.9 |
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- | No log | 4.0 | 52 | 0.4230 | 0.596 | 0.4779 | 0.571 | 0.573 | 18.2667 |
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- | No log | 5.0 | 65 | 0.2280 | 0.7253 | 0.6706 | 0.7186 | 0.7163 | 18.5667 |
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- | No log | 6.0 | 78 | 0.1540 | 0.7379 | 0.7006 | 0.7375 | 0.7393 | 18.5 |
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- | No log | 7.0 | 91 | 0.0511 | 0.7954 | 0.7767 | 0.7922 | 0.7904 | 18.5 |
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- | No log | 8.0 | 104 | 0.0223 | 0.7909 | 0.7716 | 0.7871 | 0.7856 | 18.5 |
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- | No log | 9.0 | 117 | 0.0123 | 0.7962 | 0.7777 | 0.7969 | 0.7955 | 18.5 |
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- | No log | 10.0 | 130 | 0.0095 | 0.81 | 0.8004 | 0.8107 | 0.809 | 18.5 |
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0005
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  - Rouge1: 0.81
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  - Rouge2: 0.8004
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  - Rougel: 0.8107
 
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.99) and epsilon=1e-06
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  - lr_scheduler_type: linear
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+ - num_epochs: 20
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
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+ | No log | 1.0 | 13 | 2.2462 | 0.3632 | 0.1462 | 0.3114 | 0.3126 | 18.3333 |
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+ | No log | 2.0 | 26 | 1.4611 | 0.459 | 0.3039 | 0.4178 | 0.4178 | 18.5667 |
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+ | No log | 3.0 | 39 | 0.8323 | 0.5465 | 0.4259 | 0.5247 | 0.5208 | 17.1333 |
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+ | No log | 4.0 | 52 | 0.4723 | 0.6161 | 0.5176 | 0.601 | 0.6004 | 18.3667 |
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+ | No log | 5.0 | 65 | 0.3121 | 0.6812 | 0.6078 | 0.6747 | 0.6714 | 18.2333 |
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+ | No log | 6.0 | 78 | 0.1546 | 0.7418 | 0.7023 | 0.7338 | 0.7313 | 18.0667 |
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+ | No log | 7.0 | 91 | 0.1121 | 0.7832 | 0.763 | 0.7802 | 0.7789 | 18.5 |
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+ | No log | 8.0 | 104 | 0.0699 | 0.8014 | 0.7882 | 0.8027 | 0.8009 | 18.5333 |
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+ | No log | 9.0 | 117 | 0.0459 | 0.7958 | 0.7805 | 0.7946 | 0.7917 | 18.5 |
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+ | No log | 10.0 | 130 | 0.0517 | 0.8091 | 0.7958 | 0.8105 | 0.809 | 18.4667 |
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+ | No log | 11.0 | 143 | 0.0358 | 0.7994 | 0.7852 | 0.7973 | 0.7946 | 18.5 |
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+ | No log | 12.0 | 156 | 0.0418 | 0.7799 | 0.7548 | 0.7747 | 0.7732 | 18.2667 |
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+ | No log | 13.0 | 169 | 0.0107 | 0.81 | 0.8004 | 0.8107 | 0.809 | 18.5 |
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+ | No log | 14.0 | 182 | 0.0065 | 0.8077 | 0.7971 | 0.8094 | 0.8075 | 18.5 |
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+ | No log | 15.0 | 195 | 0.0178 | 0.808 | 0.796 | 0.8094 | 0.8075 | 18.3667 |
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+ | No log | 16.0 | 208 | 0.0017 | 0.81 | 0.8004 | 0.8107 | 0.809 | 18.5 |
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+ | No log | 17.0 | 221 | 0.0055 | 0.81 | 0.8004 | 0.8107 | 0.809 | 18.5 |
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+ | No log | 18.0 | 234 | 0.0020 | 0.81 | 0.8004 | 0.8107 | 0.809 | 18.5 |
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+ | No log | 19.0 | 247 | 0.0006 | 0.81 | 0.8004 | 0.8107 | 0.809 | 18.5 |
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+ | No log | 20.0 | 260 | 0.0005 | 0.81 | 0.8004 | 0.8107 | 0.809 | 18.5 |
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  ### Framework versions
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