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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GUE_prom_prom_core_all-seqsight_32768_512_30M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_30M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_30M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_all](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_all) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4119
- F1 Score: 0.8126
- Accuracy: 0.8128
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.526 | 0.54 | 200 | 0.4698 | 0.7801 | 0.7804 |
| 0.4634 | 1.08 | 400 | 0.4577 | 0.7913 | 0.7917 |
| 0.4461 | 1.62 | 600 | 0.4453 | 0.7968 | 0.7968 |
| 0.4374 | 2.16 | 800 | 0.4477 | 0.7964 | 0.7965 |
| 0.4359 | 2.7 | 1000 | 0.4441 | 0.7915 | 0.7917 |
| 0.434 | 3.24 | 1200 | 0.4382 | 0.7943 | 0.7944 |
| 0.4296 | 3.78 | 1400 | 0.4438 | 0.7894 | 0.7902 |
| 0.4213 | 4.32 | 1600 | 0.4387 | 0.7961 | 0.7961 |
| 0.4245 | 4.86 | 1800 | 0.4342 | 0.7989 | 0.7992 |
| 0.4215 | 5.41 | 2000 | 0.4407 | 0.7971 | 0.7973 |
| 0.4186 | 5.95 | 2200 | 0.4384 | 0.8010 | 0.8010 |
| 0.4214 | 6.49 | 2400 | 0.4297 | 0.8011 | 0.8012 |
| 0.4125 | 7.03 | 2600 | 0.4311 | 0.7999 | 0.8 |
| 0.4136 | 7.57 | 2800 | 0.4317 | 0.8031 | 0.8032 |
| 0.4061 | 8.11 | 3000 | 0.4325 | 0.8029 | 0.8029 |
| 0.4051 | 8.65 | 3200 | 0.4266 | 0.8050 | 0.8051 |
| 0.4079 | 9.19 | 3400 | 0.4302 | 0.8036 | 0.8037 |
| 0.4033 | 9.73 | 3600 | 0.4303 | 0.8067 | 0.8069 |
| 0.4077 | 10.27 | 3800 | 0.4298 | 0.8075 | 0.8076 |
| 0.3999 | 10.81 | 4000 | 0.4400 | 0.7994 | 0.7997 |
| 0.3983 | 11.35 | 4200 | 0.4293 | 0.8044 | 0.8044 |
| 0.4002 | 11.89 | 4400 | 0.4298 | 0.8091 | 0.8093 |
| 0.3956 | 12.43 | 4600 | 0.4288 | 0.8074 | 0.8074 |
| 0.3981 | 12.97 | 4800 | 0.4251 | 0.8073 | 0.8073 |
| 0.3934 | 13.51 | 5000 | 0.4284 | 0.8029 | 0.8032 |
| 0.391 | 14.05 | 5200 | 0.4226 | 0.8069 | 0.8069 |
| 0.3899 | 14.59 | 5400 | 0.4223 | 0.8072 | 0.8073 |
| 0.389 | 15.14 | 5600 | 0.4329 | 0.8036 | 0.8039 |
| 0.3889 | 15.68 | 5800 | 0.4265 | 0.8090 | 0.8091 |
| 0.3851 | 16.22 | 6000 | 0.4256 | 0.8128 | 0.8128 |
| 0.39 | 16.76 | 6200 | 0.4199 | 0.8141 | 0.8142 |
| 0.3855 | 17.3 | 6400 | 0.4224 | 0.8128 | 0.8128 |
| 0.3837 | 17.84 | 6600 | 0.4264 | 0.8089 | 0.8090 |
| 0.3788 | 18.38 | 6800 | 0.4269 | 0.8105 | 0.8108 |
| 0.3818 | 18.92 | 7000 | 0.4178 | 0.8118 | 0.8118 |
| 0.3773 | 19.46 | 7200 | 0.4217 | 0.8128 | 0.8128 |
| 0.3852 | 20.0 | 7400 | 0.4199 | 0.8123 | 0.8123 |
| 0.3773 | 20.54 | 7600 | 0.4241 | 0.8140 | 0.8140 |
| 0.377 | 21.08 | 7800 | 0.4221 | 0.8135 | 0.8135 |
| 0.3771 | 21.62 | 8000 | 0.4172 | 0.8137 | 0.8137 |
| 0.3737 | 22.16 | 8200 | 0.4188 | 0.8138 | 0.8139 |
| 0.3791 | 22.7 | 8400 | 0.4218 | 0.8157 | 0.8159 |
| 0.375 | 23.24 | 8600 | 0.4180 | 0.8135 | 0.8135 |
| 0.3746 | 23.78 | 8800 | 0.4204 | 0.8147 | 0.8147 |
| 0.3718 | 24.32 | 9000 | 0.4194 | 0.8133 | 0.8133 |
| 0.3724 | 24.86 | 9200 | 0.4185 | 0.8152 | 0.8152 |
| 0.3792 | 25.41 | 9400 | 0.4188 | 0.8152 | 0.8152 |
| 0.3688 | 25.95 | 9600 | 0.4202 | 0.8137 | 0.8137 |
| 0.3752 | 26.49 | 9800 | 0.4192 | 0.8142 | 0.8142 |
| 0.3675 | 27.03 | 10000 | 0.4197 | 0.8152 | 0.8152 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_prom_prom_core_all-seqsight_32768_512_30M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_prom_prom_core_all-seqsight_32768_512_30M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_30M",
"region:us"
] | null | 2024-04-30T03:57:36+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GUE_prom_prom_core_notata-seqsight_32768_512_30M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_30M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_30M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_notata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_notata) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3958
- F1 Score: 0.8215
- Accuracy: 0.8216
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.5688 | 0.6 | 200 | 0.4438 | 0.7950 | 0.7950 |
| 0.4751 | 1.2 | 400 | 0.4215 | 0.8086 | 0.8087 |
| 0.4623 | 1.81 | 600 | 0.4112 | 0.8140 | 0.8140 |
| 0.4522 | 2.41 | 800 | 0.4044 | 0.8165 | 0.8165 |
| 0.4376 | 3.01 | 1000 | 0.4067 | 0.8142 | 0.8144 |
| 0.4289 | 3.61 | 1200 | 0.3959 | 0.8225 | 0.8225 |
| 0.4281 | 4.22 | 1400 | 0.3935 | 0.8259 | 0.8259 |
| 0.4234 | 4.82 | 1600 | 0.3896 | 0.8248 | 0.8248 |
| 0.4157 | 5.42 | 1800 | 0.3959 | 0.8224 | 0.8227 |
| 0.4163 | 6.02 | 2000 | 0.3876 | 0.8281 | 0.8282 |
| 0.4148 | 6.63 | 2200 | 0.3845 | 0.8263 | 0.8263 |
| 0.407 | 7.23 | 2400 | 0.3879 | 0.8266 | 0.8268 |
| 0.4094 | 7.83 | 2600 | 0.3816 | 0.8284 | 0.8285 |
| 0.4058 | 8.43 | 2800 | 0.3845 | 0.8288 | 0.8289 |
| 0.4076 | 9.04 | 3000 | 0.3835 | 0.8276 | 0.8278 |
| 0.404 | 9.64 | 3200 | 0.3804 | 0.8289 | 0.8289 |
| 0.4027 | 10.24 | 3400 | 0.3834 | 0.8272 | 0.8272 |
| 0.4007 | 10.84 | 3600 | 0.3822 | 0.8276 | 0.8276 |
| 0.4028 | 11.45 | 3800 | 0.3810 | 0.8284 | 0.8283 |
| 0.3969 | 12.05 | 4000 | 0.3801 | 0.8296 | 0.8297 |
| 0.397 | 12.65 | 4200 | 0.3798 | 0.8313 | 0.8314 |
| 0.3971 | 13.25 | 4400 | 0.3810 | 0.8287 | 0.8287 |
| 0.4005 | 13.86 | 4600 | 0.3810 | 0.8297 | 0.8297 |
| 0.3972 | 14.46 | 4800 | 0.3787 | 0.8312 | 0.8312 |
| 0.395 | 15.06 | 5000 | 0.3808 | 0.8293 | 0.8293 |
| 0.3937 | 15.66 | 5200 | 0.3778 | 0.8319 | 0.8319 |
| 0.3923 | 16.27 | 5400 | 0.3820 | 0.8263 | 0.8263 |
| 0.3958 | 16.87 | 5600 | 0.3809 | 0.8334 | 0.8336 |
| 0.3927 | 17.47 | 5800 | 0.3810 | 0.8340 | 0.8342 |
| 0.4006 | 18.07 | 6000 | 0.3772 | 0.8326 | 0.8327 |
| 0.3938 | 18.67 | 6200 | 0.3770 | 0.8315 | 0.8315 |
| 0.3956 | 19.28 | 6400 | 0.3783 | 0.8323 | 0.8323 |
| 0.393 | 19.88 | 6600 | 0.3764 | 0.8330 | 0.8331 |
| 0.387 | 20.48 | 6800 | 0.3787 | 0.8326 | 0.8327 |
| 0.3946 | 21.08 | 7000 | 0.3773 | 0.8348 | 0.8349 |
| 0.3921 | 21.69 | 7200 | 0.3794 | 0.8319 | 0.8319 |
| 0.3879 | 22.29 | 7400 | 0.3774 | 0.8325 | 0.8325 |
| 0.3905 | 22.89 | 7600 | 0.3763 | 0.8334 | 0.8334 |
| 0.3904 | 23.49 | 7800 | 0.3772 | 0.8315 | 0.8315 |
| 0.3934 | 24.1 | 8000 | 0.3778 | 0.8311 | 0.8312 |
| 0.392 | 24.7 | 8200 | 0.3770 | 0.8333 | 0.8334 |
| 0.3864 | 25.3 | 8400 | 0.3780 | 0.8321 | 0.8321 |
| 0.394 | 25.9 | 8600 | 0.3767 | 0.8323 | 0.8323 |
| 0.3916 | 26.51 | 8800 | 0.3772 | 0.8314 | 0.8314 |
| 0.3912 | 27.11 | 9000 | 0.3768 | 0.8319 | 0.8319 |
| 0.3964 | 27.71 | 9200 | 0.3762 | 0.8327 | 0.8327 |
| 0.3816 | 28.31 | 9400 | 0.3774 | 0.8324 | 0.8325 |
| 0.3897 | 28.92 | 9600 | 0.3771 | 0.8325 | 0.8325 |
| 0.3947 | 29.52 | 9800 | 0.3770 | 0.8317 | 0.8317 |
| 0.3875 | 30.12 | 10000 | 0.3770 | 0.8321 | 0.8321 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_prom_prom_core_notata-seqsight_32768_512_30M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_prom_prom_core_notata-seqsight_32768_512_30M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_30M",
"region:us"
] | null | 2024-04-30T03:58:26+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GUE_prom_prom_core_notata-seqsight_32768_512_30M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_30M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_30M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_notata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_notata) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3852
- F1 Score: 0.8319
- Accuracy: 0.8319
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.5333 | 0.6 | 200 | 0.4216 | 0.8146 | 0.8146 |
| 0.4398 | 1.2 | 400 | 0.3978 | 0.8268 | 0.8268 |
| 0.4208 | 1.81 | 600 | 0.3987 | 0.8246 | 0.8251 |
| 0.4123 | 2.41 | 800 | 0.3841 | 0.8294 | 0.8295 |
| 0.4028 | 3.01 | 1000 | 0.3850 | 0.8281 | 0.8283 |
| 0.3961 | 3.61 | 1200 | 0.3779 | 0.8319 | 0.8321 |
| 0.4018 | 4.22 | 1400 | 0.3790 | 0.8326 | 0.8327 |
| 0.3964 | 4.82 | 1600 | 0.3751 | 0.8346 | 0.8347 |
| 0.3894 | 5.42 | 1800 | 0.3777 | 0.8347 | 0.8347 |
| 0.3928 | 6.02 | 2000 | 0.3744 | 0.8357 | 0.8359 |
| 0.3918 | 6.63 | 2200 | 0.3715 | 0.8381 | 0.8381 |
| 0.3859 | 7.23 | 2400 | 0.3764 | 0.8332 | 0.8336 |
| 0.3899 | 7.83 | 2600 | 0.3694 | 0.8370 | 0.8370 |
| 0.3852 | 8.43 | 2800 | 0.3808 | 0.8304 | 0.8310 |
| 0.3878 | 9.04 | 3000 | 0.3694 | 0.8344 | 0.8346 |
| 0.3816 | 9.64 | 3200 | 0.3685 | 0.8362 | 0.8363 |
| 0.3819 | 10.24 | 3400 | 0.3709 | 0.8351 | 0.8351 |
| 0.3797 | 10.84 | 3600 | 0.3684 | 0.8357 | 0.8357 |
| 0.3816 | 11.45 | 3800 | 0.3699 | 0.8360 | 0.8361 |
| 0.3772 | 12.05 | 4000 | 0.3678 | 0.8370 | 0.8370 |
| 0.3768 | 12.65 | 4200 | 0.3701 | 0.8358 | 0.8359 |
| 0.3755 | 13.25 | 4400 | 0.3707 | 0.8357 | 0.8359 |
| 0.3789 | 13.86 | 4600 | 0.3703 | 0.8362 | 0.8363 |
| 0.3754 | 14.46 | 4800 | 0.3700 | 0.8363 | 0.8364 |
| 0.376 | 15.06 | 5000 | 0.3677 | 0.8378 | 0.8379 |
| 0.3703 | 15.66 | 5200 | 0.3680 | 0.8364 | 0.8364 |
| 0.3713 | 16.27 | 5400 | 0.3706 | 0.8381 | 0.8381 |
| 0.3742 | 16.87 | 5600 | 0.3715 | 0.8354 | 0.8357 |
| 0.3702 | 17.47 | 5800 | 0.3728 | 0.8331 | 0.8334 |
| 0.377 | 18.07 | 6000 | 0.3687 | 0.8370 | 0.8372 |
| 0.3728 | 18.67 | 6200 | 0.3677 | 0.8361 | 0.8363 |
| 0.3731 | 19.28 | 6400 | 0.3682 | 0.8393 | 0.8393 |
| 0.3703 | 19.88 | 6600 | 0.3669 | 0.8383 | 0.8383 |
| 0.3648 | 20.48 | 6800 | 0.3682 | 0.8388 | 0.8389 |
| 0.3724 | 21.08 | 7000 | 0.3701 | 0.8339 | 0.8342 |
| 0.3694 | 21.69 | 7200 | 0.3700 | 0.8359 | 0.8359 |
| 0.3643 | 22.29 | 7400 | 0.3686 | 0.8361 | 0.8363 |
| 0.3662 | 22.89 | 7600 | 0.3673 | 0.8400 | 0.8400 |
| 0.3676 | 23.49 | 7800 | 0.3664 | 0.8384 | 0.8385 |
| 0.371 | 24.1 | 8000 | 0.3677 | 0.8378 | 0.8379 |
| 0.3679 | 24.7 | 8200 | 0.3681 | 0.8359 | 0.8361 |
| 0.3629 | 25.3 | 8400 | 0.3699 | 0.8372 | 0.8374 |
| 0.3714 | 25.9 | 8600 | 0.3659 | 0.8374 | 0.8374 |
| 0.3667 | 26.51 | 8800 | 0.3668 | 0.8377 | 0.8378 |
| 0.3682 | 27.11 | 9000 | 0.3667 | 0.8389 | 0.8389 |
| 0.3728 | 27.71 | 9200 | 0.3660 | 0.8384 | 0.8385 |
| 0.3587 | 28.31 | 9400 | 0.3679 | 0.8371 | 0.8372 |
| 0.3643 | 28.92 | 9600 | 0.3673 | 0.8390 | 0.8391 |
| 0.3697 | 29.52 | 9800 | 0.3665 | 0.8387 | 0.8387 |
| 0.3631 | 30.12 | 10000 | 0.3667 | 0.8381 | 0.8381 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_prom_prom_core_notata-seqsight_32768_512_30M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_prom_prom_core_notata-seqsight_32768_512_30M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_30M",
"region:us"
] | null | 2024-04-30T03:58:26+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GUE_prom_prom_core_notata-seqsight_32768_512_30M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_30M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_30M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_notata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_notata) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3860
- F1 Score: 0.8313
- Accuracy: 0.8314
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.5172 | 0.6 | 200 | 0.4052 | 0.8219 | 0.8219 |
| 0.4211 | 1.2 | 400 | 0.3859 | 0.8273 | 0.8274 |
| 0.4089 | 1.81 | 600 | 0.4006 | 0.8219 | 0.8227 |
| 0.4032 | 2.41 | 800 | 0.3774 | 0.8310 | 0.8312 |
| 0.3961 | 3.01 | 1000 | 0.3809 | 0.8325 | 0.8329 |
| 0.3881 | 3.61 | 1200 | 0.3729 | 0.8339 | 0.8340 |
| 0.3944 | 4.22 | 1400 | 0.3775 | 0.8313 | 0.8314 |
| 0.388 | 4.82 | 1600 | 0.3727 | 0.8340 | 0.8342 |
| 0.3809 | 5.42 | 1800 | 0.3754 | 0.8379 | 0.8379 |
| 0.3842 | 6.02 | 2000 | 0.3709 | 0.8349 | 0.8351 |
| 0.3817 | 6.63 | 2200 | 0.3669 | 0.8379 | 0.8379 |
| 0.3762 | 7.23 | 2400 | 0.3732 | 0.8332 | 0.8336 |
| 0.3786 | 7.83 | 2600 | 0.3687 | 0.8385 | 0.8385 |
| 0.3739 | 8.43 | 2800 | 0.3753 | 0.8322 | 0.8327 |
| 0.3763 | 9.04 | 3000 | 0.3642 | 0.8394 | 0.8395 |
| 0.3695 | 9.64 | 3200 | 0.3650 | 0.8389 | 0.8389 |
| 0.3688 | 10.24 | 3400 | 0.3669 | 0.8378 | 0.8378 |
| 0.3665 | 10.84 | 3600 | 0.3633 | 0.8372 | 0.8372 |
| 0.3676 | 11.45 | 3800 | 0.3668 | 0.8368 | 0.8368 |
| 0.3631 | 12.05 | 4000 | 0.3644 | 0.8397 | 0.8396 |
| 0.361 | 12.65 | 4200 | 0.3676 | 0.8368 | 0.8368 |
| 0.3589 | 13.25 | 4400 | 0.3670 | 0.8377 | 0.8378 |
| 0.3636 | 13.86 | 4600 | 0.3679 | 0.8385 | 0.8385 |
| 0.3584 | 14.46 | 4800 | 0.3690 | 0.8341 | 0.8342 |
| 0.3592 | 15.06 | 5000 | 0.3640 | 0.8373 | 0.8374 |
| 0.3507 | 15.66 | 5200 | 0.3666 | 0.8372 | 0.8372 |
| 0.3542 | 16.27 | 5400 | 0.3716 | 0.8389 | 0.8389 |
| 0.3544 | 16.87 | 5600 | 0.3714 | 0.8382 | 0.8385 |
| 0.3499 | 17.47 | 5800 | 0.3699 | 0.8398 | 0.8400 |
| 0.3593 | 18.07 | 6000 | 0.3667 | 0.8380 | 0.8381 |
| 0.3515 | 18.67 | 6200 | 0.3696 | 0.8403 | 0.8404 |
| 0.3535 | 19.28 | 6400 | 0.3689 | 0.8381 | 0.8381 |
| 0.3485 | 19.88 | 6600 | 0.3658 | 0.8381 | 0.8381 |
| 0.344 | 20.48 | 6800 | 0.3670 | 0.8411 | 0.8412 |
| 0.3513 | 21.08 | 7000 | 0.3681 | 0.8372 | 0.8374 |
| 0.3481 | 21.69 | 7200 | 0.3709 | 0.8385 | 0.8385 |
| 0.3405 | 22.29 | 7400 | 0.3695 | 0.8355 | 0.8357 |
| 0.3456 | 22.89 | 7600 | 0.3676 | 0.8370 | 0.8370 |
| 0.3438 | 23.49 | 7800 | 0.3669 | 0.8379 | 0.8379 |
| 0.3483 | 24.1 | 8000 | 0.3690 | 0.8378 | 0.8379 |
| 0.3444 | 24.7 | 8200 | 0.3709 | 0.8379 | 0.8381 |
| 0.3416 | 25.3 | 8400 | 0.3708 | 0.8378 | 0.8379 |
| 0.3489 | 25.9 | 8600 | 0.3669 | 0.8362 | 0.8363 |
| 0.3433 | 26.51 | 8800 | 0.3689 | 0.8381 | 0.8381 |
| 0.346 | 27.11 | 9000 | 0.3683 | 0.8372 | 0.8372 |
| 0.3487 | 27.71 | 9200 | 0.3676 | 0.8373 | 0.8374 |
| 0.3361 | 28.31 | 9400 | 0.3696 | 0.8377 | 0.8378 |
| 0.3389 | 28.92 | 9600 | 0.3697 | 0.8368 | 0.8368 |
| 0.3451 | 29.52 | 9800 | 0.3687 | 0.8360 | 0.8361 |
| 0.3386 | 30.12 | 10000 | 0.3689 | 0.8370 | 0.8370 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_prom_prom_core_notata-seqsight_32768_512_30M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_prom_prom_core_notata-seqsight_32768_512_30M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_30M",
"region:us"
] | null | 2024-04-30T03:59:07+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GUE_prom_prom_core_tata-seqsight_32768_512_30M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_30M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_30M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_tata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_tata) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4587
- F1 Score: 0.8090
- Accuracy: 0.8091
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
| 0.6029 | 5.13 | 200 | 0.5668 | 0.7003 | 0.7015 |
| 0.5502 | 10.26 | 400 | 0.5582 | 0.7241 | 0.7243 |
| 0.532 | 15.38 | 600 | 0.5570 | 0.7391 | 0.7406 |
| 0.5121 | 20.51 | 800 | 0.5333 | 0.7373 | 0.7374 |
| 0.4906 | 25.64 | 1000 | 0.5225 | 0.7435 | 0.7439 |
| 0.4734 | 30.77 | 1200 | 0.4972 | 0.7716 | 0.7716 |
| 0.4596 | 35.9 | 1400 | 0.4787 | 0.7732 | 0.7732 |
| 0.4473 | 41.03 | 1600 | 0.4591 | 0.7813 | 0.7814 |
| 0.4287 | 46.15 | 1800 | 0.4504 | 0.7910 | 0.7912 |
| 0.4199 | 51.28 | 2000 | 0.4420 | 0.8026 | 0.8026 |
| 0.4101 | 56.41 | 2200 | 0.4387 | 0.8022 | 0.8026 |
| 0.4061 | 61.54 | 2400 | 0.4289 | 0.8075 | 0.8075 |
| 0.3985 | 66.67 | 2600 | 0.4362 | 0.8088 | 0.8091 |
| 0.396 | 71.79 | 2800 | 0.4231 | 0.8156 | 0.8157 |
| 0.3906 | 76.92 | 3000 | 0.4260 | 0.8123 | 0.8124 |
| 0.3821 | 82.05 | 3200 | 0.4278 | 0.8139 | 0.8140 |
| 0.3798 | 87.18 | 3400 | 0.4294 | 0.8138 | 0.8140 |
| 0.3791 | 92.31 | 3600 | 0.4262 | 0.8189 | 0.8189 |
| 0.3705 | 97.44 | 3800 | 0.4277 | 0.8254 | 0.8254 |
| 0.3731 | 102.56 | 4000 | 0.4143 | 0.8271 | 0.8271 |
| 0.367 | 107.69 | 4200 | 0.4146 | 0.8270 | 0.8271 |
| 0.3664 | 112.82 | 4400 | 0.4136 | 0.8352 | 0.8352 |
| 0.3603 | 117.95 | 4600 | 0.4128 | 0.8352 | 0.8352 |
| 0.3595 | 123.08 | 4800 | 0.4159 | 0.8271 | 0.8271 |
| 0.3567 | 128.21 | 5000 | 0.4183 | 0.8271 | 0.8271 |
| 0.3594 | 133.33 | 5200 | 0.4097 | 0.8336 | 0.8336 |
| 0.3548 | 138.46 | 5400 | 0.4106 | 0.8352 | 0.8352 |
| 0.3499 | 143.59 | 5600 | 0.4125 | 0.8352 | 0.8352 |
| 0.3511 | 148.72 | 5800 | 0.4116 | 0.8336 | 0.8336 |
| 0.3431 | 153.85 | 6000 | 0.4205 | 0.8220 | 0.8222 |
| 0.3477 | 158.97 | 6200 | 0.4071 | 0.8320 | 0.8320 |
| 0.3424 | 164.1 | 6400 | 0.4106 | 0.8352 | 0.8352 |
| 0.3432 | 169.23 | 6600 | 0.4101 | 0.8369 | 0.8369 |
| 0.3408 | 174.36 | 6800 | 0.4169 | 0.8253 | 0.8254 |
| 0.3386 | 179.49 | 7000 | 0.4072 | 0.8401 | 0.8401 |
| 0.3398 | 184.62 | 7200 | 0.4102 | 0.8385 | 0.8385 |
| 0.3337 | 189.74 | 7400 | 0.4126 | 0.8352 | 0.8352 |
| 0.3374 | 194.87 | 7600 | 0.4090 | 0.8368 | 0.8369 |
| 0.3315 | 200.0 | 7800 | 0.4102 | 0.8369 | 0.8369 |
| 0.3346 | 205.13 | 8000 | 0.4109 | 0.8303 | 0.8303 |
| 0.3326 | 210.26 | 8200 | 0.4078 | 0.8352 | 0.8352 |
| 0.3358 | 215.38 | 8400 | 0.4076 | 0.8271 | 0.8271 |
| 0.3342 | 220.51 | 8600 | 0.4106 | 0.8319 | 0.8320 |
| 0.333 | 225.64 | 8800 | 0.4104 | 0.8352 | 0.8352 |
| 0.3329 | 230.77 | 9000 | 0.4093 | 0.8320 | 0.8320 |
| 0.3291 | 235.9 | 9200 | 0.4103 | 0.8369 | 0.8369 |
| 0.3333 | 241.03 | 9400 | 0.4072 | 0.8336 | 0.8336 |
| 0.3282 | 246.15 | 9600 | 0.4084 | 0.8336 | 0.8336 |
| 0.3275 | 251.28 | 9800 | 0.4101 | 0.8352 | 0.8352 |
| 0.3313 | 256.41 | 10000 | 0.4090 | 0.8336 | 0.8336 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_prom_prom_core_tata-seqsight_32768_512_30M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_prom_prom_core_tata-seqsight_32768_512_30M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_30M",
"region:us"
] | null | 2024-04-30T03:59:13+00:00 |
text-to-audio | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# fil_b64_le3_s4000
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5467
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:--------:|:----:|:---------------:|
| 0.4644 | 22.2222 | 500 | 0.4340 |
| 0.6468 | 44.4444 | 1000 | 0.7537 |
| 1.5805 | 66.6667 | 1500 | 1.5453 |
| 1.5766 | 88.8889 | 2000 | 1.5454 |
| 1.5747 | 111.1111 | 2500 | 1.5428 |
| 1.578 | 133.3333 | 3000 | 1.5456 |
| 1.5761 | 155.5556 | 3500 | 1.5494 |
| 1.5728 | 177.7778 | 4000 | 1.5467 |
### Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "microsoft/speecht5_tts", "model-index": [{"name": "fil_b64_le3_s4000", "results": []}]} | mikhail-panzo/fil_b64_le3_s4000 | null | [
"transformers",
"tensorboard",
"safetensors",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"base_model:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T03:59:21+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GUE_prom_prom_core_tata-seqsight_32768_512_30M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_30M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_30M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_tata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_tata) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4756
- F1 Score: 0.8433
- Accuracy: 0.8434
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
| 0.5808 | 5.13 | 200 | 0.5600 | 0.7181 | 0.7194 |
| 0.5091 | 10.26 | 400 | 0.5245 | 0.7621 | 0.7635 |
| 0.4588 | 15.38 | 600 | 0.4656 | 0.7916 | 0.7928 |
| 0.4185 | 20.51 | 800 | 0.4389 | 0.8106 | 0.8108 |
| 0.3931 | 25.64 | 1000 | 0.4436 | 0.8104 | 0.8108 |
| 0.3732 | 30.77 | 1200 | 0.4187 | 0.8189 | 0.8189 |
| 0.3553 | 35.9 | 1400 | 0.4304 | 0.8151 | 0.8157 |
| 0.3396 | 41.03 | 1600 | 0.4030 | 0.8266 | 0.8271 |
| 0.3258 | 46.15 | 1800 | 0.4102 | 0.8351 | 0.8352 |
| 0.3141 | 51.28 | 2000 | 0.4127 | 0.8385 | 0.8385 |
| 0.3008 | 56.41 | 2200 | 0.4153 | 0.8335 | 0.8336 |
| 0.2934 | 61.54 | 2400 | 0.4077 | 0.8303 | 0.8303 |
| 0.2792 | 66.67 | 2600 | 0.4119 | 0.8336 | 0.8336 |
| 0.2787 | 71.79 | 2800 | 0.4028 | 0.8319 | 0.8320 |
| 0.2682 | 76.92 | 3000 | 0.4231 | 0.8400 | 0.8401 |
| 0.2581 | 82.05 | 3200 | 0.4253 | 0.8384 | 0.8385 |
| 0.2543 | 87.18 | 3400 | 0.4510 | 0.8281 | 0.8287 |
| 0.2517 | 92.31 | 3600 | 0.4290 | 0.8434 | 0.8434 |
| 0.2414 | 97.44 | 3800 | 0.4335 | 0.8319 | 0.8320 |
| 0.2361 | 102.56 | 4000 | 0.4184 | 0.8416 | 0.8418 |
| 0.2357 | 107.69 | 4200 | 0.4296 | 0.8319 | 0.8320 |
| 0.2353 | 112.82 | 4400 | 0.4464 | 0.8352 | 0.8352 |
| 0.2264 | 117.95 | 4600 | 0.4482 | 0.8254 | 0.8254 |
| 0.2233 | 123.08 | 4800 | 0.4609 | 0.8350 | 0.8352 |
| 0.2191 | 128.21 | 5000 | 0.4606 | 0.8302 | 0.8303 |
| 0.2165 | 133.33 | 5200 | 0.4362 | 0.8336 | 0.8336 |
| 0.2145 | 138.46 | 5400 | 0.4555 | 0.8385 | 0.8385 |
| 0.2141 | 143.59 | 5600 | 0.4448 | 0.8350 | 0.8352 |
| 0.208 | 148.72 | 5800 | 0.4553 | 0.8303 | 0.8303 |
| 0.2004 | 153.85 | 6000 | 0.4639 | 0.8270 | 0.8271 |
| 0.1984 | 158.97 | 6200 | 0.4570 | 0.8320 | 0.8320 |
| 0.1998 | 164.1 | 6400 | 0.4635 | 0.8352 | 0.8352 |
| 0.2 | 169.23 | 6600 | 0.4776 | 0.8317 | 0.8320 |
| 0.195 | 174.36 | 6800 | 0.4860 | 0.8366 | 0.8369 |
| 0.1875 | 179.49 | 7000 | 0.4813 | 0.8270 | 0.8271 |
| 0.1932 | 184.62 | 7200 | 0.4951 | 0.8352 | 0.8352 |
| 0.1906 | 189.74 | 7400 | 0.4936 | 0.8366 | 0.8369 |
| 0.186 | 194.87 | 7600 | 0.4896 | 0.8254 | 0.8254 |
| 0.1817 | 200.0 | 7800 | 0.4967 | 0.8270 | 0.8271 |
| 0.1844 | 205.13 | 8000 | 0.5009 | 0.8318 | 0.8320 |
| 0.1813 | 210.26 | 8200 | 0.4859 | 0.8270 | 0.8271 |
| 0.1862 | 215.38 | 8400 | 0.4870 | 0.8303 | 0.8303 |
| 0.1762 | 220.51 | 8600 | 0.4989 | 0.8303 | 0.8303 |
| 0.1789 | 225.64 | 8800 | 0.5017 | 0.8334 | 0.8336 |
| 0.1788 | 230.77 | 9000 | 0.5011 | 0.8270 | 0.8271 |
| 0.1763 | 235.9 | 9200 | 0.4996 | 0.8221 | 0.8222 |
| 0.1769 | 241.03 | 9400 | 0.4952 | 0.8270 | 0.8271 |
| 0.1758 | 246.15 | 9600 | 0.5050 | 0.8318 | 0.8320 |
| 0.1739 | 251.28 | 9800 | 0.5046 | 0.8302 | 0.8303 |
| 0.1773 | 256.41 | 10000 | 0.4985 | 0.8303 | 0.8303 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_prom_prom_core_tata-seqsight_32768_512_30M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_prom_prom_core_tata-seqsight_32768_512_30M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_30M",
"region:us"
] | null | 2024-04-30T04:01:48+00:00 |
text-generation | transformers |
# Model Card for Model ID
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## Model Details
### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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## How to Get Started with the Model
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#### Speeds, Sizes, Times [optional]
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<!-- Relevant interpretability work for the model goes here -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | lunarsylph/stablecell_v55 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T04:02:13+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GUE_prom_prom_300_all-seqsight_32768_512_30M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_30M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_30M) on the [mahdibaghbanzadeh/GUE_prom_prom_300_all](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_300_all) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2233
- F1 Score: 0.9091
- Accuracy: 0.9091
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.4264 | 0.54 | 200 | 0.2903 | 0.8858 | 0.8858 |
| 0.3006 | 1.08 | 400 | 0.2596 | 0.8981 | 0.8981 |
| 0.2764 | 1.62 | 600 | 0.2417 | 0.9047 | 0.9047 |
| 0.2554 | 2.16 | 800 | 0.2420 | 0.9067 | 0.9068 |
| 0.2497 | 2.7 | 1000 | 0.2326 | 0.9073 | 0.9073 |
| 0.242 | 3.24 | 1200 | 0.2334 | 0.9061 | 0.9061 |
| 0.2433 | 3.78 | 1400 | 0.2248 | 0.9108 | 0.9108 |
| 0.2411 | 4.32 | 1600 | 0.2225 | 0.9118 | 0.9118 |
| 0.2329 | 4.86 | 1800 | 0.2215 | 0.9115 | 0.9115 |
| 0.2302 | 5.41 | 2000 | 0.2211 | 0.9132 | 0.9132 |
| 0.2306 | 5.95 | 2200 | 0.2171 | 0.9127 | 0.9127 |
| 0.2304 | 6.49 | 2400 | 0.2172 | 0.9135 | 0.9135 |
| 0.2293 | 7.03 | 2600 | 0.2164 | 0.9130 | 0.9130 |
| 0.2251 | 7.57 | 2800 | 0.2149 | 0.9120 | 0.9120 |
| 0.2249 | 8.11 | 3000 | 0.2143 | 0.9135 | 0.9135 |
| 0.2238 | 8.65 | 3200 | 0.2129 | 0.9128 | 0.9128 |
| 0.2175 | 9.19 | 3400 | 0.2118 | 0.9147 | 0.9147 |
| 0.2172 | 9.73 | 3600 | 0.2080 | 0.9139 | 0.9139 |
| 0.224 | 10.27 | 3800 | 0.2063 | 0.9144 | 0.9144 |
| 0.2162 | 10.81 | 4000 | 0.2058 | 0.9160 | 0.9160 |
| 0.218 | 11.35 | 4200 | 0.2037 | 0.9186 | 0.9186 |
| 0.2158 | 11.89 | 4400 | 0.2046 | 0.9171 | 0.9171 |
| 0.2118 | 12.43 | 4600 | 0.2055 | 0.9157 | 0.9157 |
| 0.2142 | 12.97 | 4800 | 0.2031 | 0.9182 | 0.9182 |
| 0.2116 | 13.51 | 5000 | 0.2036 | 0.9191 | 0.9191 |
| 0.2147 | 14.05 | 5200 | 0.2027 | 0.9179 | 0.9179 |
| 0.2106 | 14.59 | 5400 | 0.2012 | 0.9193 | 0.9193 |
| 0.2094 | 15.14 | 5600 | 0.1992 | 0.9203 | 0.9203 |
| 0.2089 | 15.68 | 5800 | 0.2003 | 0.9179 | 0.9179 |
| 0.2124 | 16.22 | 6000 | 0.1985 | 0.9198 | 0.9198 |
| 0.2083 | 16.76 | 6200 | 0.1997 | 0.9208 | 0.9208 |
| 0.2121 | 17.3 | 6400 | 0.1997 | 0.9191 | 0.9191 |
| 0.2094 | 17.84 | 6600 | 0.1996 | 0.9193 | 0.9193 |
| 0.2024 | 18.38 | 6800 | 0.1999 | 0.9201 | 0.9201 |
| 0.2116 | 18.92 | 7000 | 0.1975 | 0.9196 | 0.9196 |
| 0.2087 | 19.46 | 7200 | 0.1978 | 0.9211 | 0.9211 |
| 0.2052 | 20.0 | 7400 | 0.1964 | 0.9230 | 0.9230 |
| 0.2071 | 20.54 | 7600 | 0.1988 | 0.9209 | 0.9209 |
| 0.204 | 21.08 | 7800 | 0.1966 | 0.9213 | 0.9213 |
| 0.2057 | 21.62 | 8000 | 0.1982 | 0.9211 | 0.9211 |
| 0.2051 | 22.16 | 8200 | 0.1969 | 0.9209 | 0.9209 |
| 0.1994 | 22.7 | 8400 | 0.1984 | 0.9223 | 0.9223 |
| 0.2098 | 23.24 | 8600 | 0.1964 | 0.9211 | 0.9211 |
| 0.2024 | 23.78 | 8800 | 0.1972 | 0.9213 | 0.9213 |
| 0.2056 | 24.32 | 9000 | 0.1976 | 0.9206 | 0.9206 |
| 0.2018 | 24.86 | 9200 | 0.1981 | 0.9213 | 0.9213 |
| 0.2014 | 25.41 | 9400 | 0.1974 | 0.9216 | 0.9216 |
| 0.2022 | 25.95 | 9600 | 0.1972 | 0.9221 | 0.9221 |
| 0.2029 | 26.49 | 9800 | 0.1961 | 0.9218 | 0.9218 |
| 0.203 | 27.03 | 10000 | 0.1966 | 0.9221 | 0.9221 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_prom_prom_300_all-seqsight_32768_512_30M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_prom_prom_300_all-seqsight_32768_512_30M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_30M",
"region:us"
] | null | 2024-04-30T04:03:04+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GUE_prom_prom_core_tata-seqsight_32768_512_30M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_30M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_30M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_tata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_tata) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6278
- F1 Score: 0.8418
- Accuracy: 0.8418
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
| 0.5627 | 5.13 | 200 | 0.5373 | 0.7354 | 0.7357 |
| 0.4667 | 10.26 | 400 | 0.5145 | 0.7635 | 0.7667 |
| 0.41 | 15.38 | 600 | 0.4455 | 0.8053 | 0.8059 |
| 0.3673 | 20.51 | 800 | 0.4164 | 0.8221 | 0.8222 |
| 0.3324 | 25.64 | 1000 | 0.4136 | 0.8270 | 0.8271 |
| 0.3039 | 30.77 | 1200 | 0.3964 | 0.8417 | 0.8418 |
| 0.2827 | 35.9 | 1400 | 0.3928 | 0.8434 | 0.8434 |
| 0.2594 | 41.03 | 1600 | 0.3932 | 0.8332 | 0.8336 |
| 0.2454 | 46.15 | 1800 | 0.3887 | 0.8467 | 0.8467 |
| 0.2301 | 51.28 | 2000 | 0.4146 | 0.8483 | 0.8483 |
| 0.2181 | 56.41 | 2200 | 0.4038 | 0.8434 | 0.8434 |
| 0.2026 | 61.54 | 2400 | 0.4016 | 0.8434 | 0.8434 |
| 0.1905 | 66.67 | 2600 | 0.4172 | 0.8482 | 0.8483 |
| 0.1809 | 71.79 | 2800 | 0.4441 | 0.8385 | 0.8385 |
| 0.1741 | 76.92 | 3000 | 0.4264 | 0.8465 | 0.8467 |
| 0.1585 | 82.05 | 3200 | 0.4547 | 0.8367 | 0.8369 |
| 0.1519 | 87.18 | 3400 | 0.5098 | 0.8342 | 0.8352 |
| 0.1542 | 92.31 | 3600 | 0.4655 | 0.8597 | 0.8597 |
| 0.1399 | 97.44 | 3800 | 0.4824 | 0.8515 | 0.8515 |
| 0.1333 | 102.56 | 4000 | 0.4525 | 0.8564 | 0.8564 |
| 0.1288 | 107.69 | 4200 | 0.4617 | 0.8564 | 0.8564 |
| 0.125 | 112.82 | 4400 | 0.5046 | 0.8499 | 0.8499 |
| 0.1209 | 117.95 | 4600 | 0.4963 | 0.8532 | 0.8532 |
| 0.1125 | 123.08 | 4800 | 0.5106 | 0.8481 | 0.8483 |
| 0.1095 | 128.21 | 5000 | 0.5352 | 0.8563 | 0.8564 |
| 0.1101 | 133.33 | 5200 | 0.5064 | 0.8630 | 0.8630 |
| 0.1038 | 138.46 | 5400 | 0.5283 | 0.8548 | 0.8548 |
| 0.1004 | 143.59 | 5600 | 0.5361 | 0.8515 | 0.8515 |
| 0.0947 | 148.72 | 5800 | 0.5382 | 0.8548 | 0.8548 |
| 0.0909 | 153.85 | 6000 | 0.5442 | 0.8466 | 0.8467 |
| 0.0932 | 158.97 | 6200 | 0.5373 | 0.8564 | 0.8564 |
| 0.0874 | 164.1 | 6400 | 0.5438 | 0.8532 | 0.8532 |
| 0.0867 | 169.23 | 6600 | 0.5365 | 0.8630 | 0.8630 |
| 0.0862 | 174.36 | 6800 | 0.5783 | 0.8482 | 0.8483 |
| 0.0791 | 179.49 | 7000 | 0.5920 | 0.8564 | 0.8564 |
| 0.0822 | 184.62 | 7200 | 0.5735 | 0.8581 | 0.8581 |
| 0.0794 | 189.74 | 7400 | 0.5573 | 0.8613 | 0.8613 |
| 0.0752 | 194.87 | 7600 | 0.5611 | 0.8581 | 0.8581 |
| 0.0731 | 200.0 | 7800 | 0.5884 | 0.8564 | 0.8564 |
| 0.0727 | 205.13 | 8000 | 0.5914 | 0.8531 | 0.8532 |
| 0.0719 | 210.26 | 8200 | 0.5868 | 0.8515 | 0.8515 |
| 0.0703 | 215.38 | 8400 | 0.5914 | 0.8548 | 0.8548 |
| 0.0686 | 220.51 | 8600 | 0.5958 | 0.8564 | 0.8564 |
| 0.0716 | 225.64 | 8800 | 0.6047 | 0.8515 | 0.8515 |
| 0.0723 | 230.77 | 9000 | 0.5930 | 0.8564 | 0.8564 |
| 0.0646 | 235.9 | 9200 | 0.6007 | 0.8597 | 0.8597 |
| 0.0678 | 241.03 | 9400 | 0.5928 | 0.8548 | 0.8548 |
| 0.0662 | 246.15 | 9600 | 0.5984 | 0.8581 | 0.8581 |
| 0.0668 | 251.28 | 9800 | 0.5998 | 0.8581 | 0.8581 |
| 0.0669 | 256.41 | 10000 | 0.5949 | 0.8515 | 0.8515 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_prom_prom_core_tata-seqsight_32768_512_30M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_prom_prom_core_tata-seqsight_32768_512_30M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_30M",
"region:us"
] | null | 2024-04-30T04:03:04+00:00 |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# selfbiorag-7b-dpo-full-sft-wo-healthsearch_qa
This model is a fine-tuned version of [Minbyul/selfbiorag-7b-wo-healthsearch_qa-sft](https://huggingface.co/Minbyul/selfbiorag-7b-wo-healthsearch_qa-sft) on the HuggingFaceH4/ultrafeedback_binarized dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4465
- Rewards/chosen: -0.5929
- Rewards/rejected: -1.6772
- Rewards/accuracies: 0.7846
- Rewards/margins: 1.0843
- Logps/rejected: -1480.8429
- Logps/chosen: -984.8102
- Logits/rejected: -3.4642
- Logits/chosen: -2.6475
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.1.2
- Datasets 2.14.6
- Tokenizers 0.15.2
| {"tags": ["alignment-handbook", "trl", "dpo", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["HuggingFaceH4/ultrafeedback_binarized"], "base_model": "Minbyul/selfbiorag-7b-wo-healthsearch_qa-sft", "model-index": [{"name": "selfbiorag-7b-dpo-full-sft-wo-healthsearch_qa", "results": []}]} | Minbyul/selfbiorag-7b-dpo-full-sft-wo-healthsearch_qa | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"alignment-handbook",
"trl",
"dpo",
"generated_from_trainer",
"dataset:HuggingFaceH4/ultrafeedback_binarized",
"base_model:Minbyul/selfbiorag-7b-wo-healthsearch_qa-sft",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T04:03:26+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GUE_prom_prom_300_all-seqsight_32768_512_30M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_30M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_30M) on the [mahdibaghbanzadeh/GUE_prom_prom_300_all](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_300_all) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2083
- F1 Score: 0.9155
- Accuracy: 0.9155
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.3754 | 0.54 | 200 | 0.2521 | 0.9011 | 0.9012 |
| 0.2585 | 1.08 | 400 | 0.2312 | 0.9098 | 0.9098 |
| 0.2443 | 1.62 | 600 | 0.2205 | 0.9133 | 0.9133 |
| 0.2314 | 2.16 | 800 | 0.2192 | 0.9123 | 0.9123 |
| 0.2293 | 2.7 | 1000 | 0.2133 | 0.9140 | 0.9140 |
| 0.2236 | 3.24 | 1200 | 0.2145 | 0.9142 | 0.9142 |
| 0.2247 | 3.78 | 1400 | 0.2088 | 0.9172 | 0.9172 |
| 0.2214 | 4.32 | 1600 | 0.2041 | 0.9188 | 0.9187 |
| 0.2143 | 4.86 | 1800 | 0.2076 | 0.9182 | 0.9182 |
| 0.2114 | 5.41 | 2000 | 0.2012 | 0.9214 | 0.9215 |
| 0.2122 | 5.95 | 2200 | 0.1988 | 0.9235 | 0.9235 |
| 0.2113 | 6.49 | 2400 | 0.1976 | 0.9242 | 0.9242 |
| 0.209 | 7.03 | 2600 | 0.1972 | 0.9206 | 0.9206 |
| 0.2032 | 7.57 | 2800 | 0.1967 | 0.9208 | 0.9208 |
| 0.2066 | 8.11 | 3000 | 0.1971 | 0.9228 | 0.9228 |
| 0.2018 | 8.65 | 3200 | 0.1946 | 0.9247 | 0.9247 |
| 0.1975 | 9.19 | 3400 | 0.1939 | 0.9240 | 0.9240 |
| 0.1968 | 9.73 | 3600 | 0.1925 | 0.9248 | 0.9248 |
| 0.2032 | 10.27 | 3800 | 0.1913 | 0.9223 | 0.9223 |
| 0.1969 | 10.81 | 4000 | 0.1899 | 0.9260 | 0.9260 |
| 0.1965 | 11.35 | 4200 | 0.1903 | 0.9253 | 0.9253 |
| 0.1948 | 11.89 | 4400 | 0.1922 | 0.9250 | 0.925 |
| 0.1927 | 12.43 | 4600 | 0.1906 | 0.9248 | 0.9248 |
| 0.1933 | 12.97 | 4800 | 0.1895 | 0.9245 | 0.9245 |
| 0.1907 | 13.51 | 5000 | 0.1934 | 0.9243 | 0.9243 |
| 0.1943 | 14.05 | 5200 | 0.1900 | 0.9257 | 0.9257 |
| 0.1884 | 14.59 | 5400 | 0.1902 | 0.9255 | 0.9255 |
| 0.189 | 15.14 | 5600 | 0.1914 | 0.9233 | 0.9233 |
| 0.1887 | 15.68 | 5800 | 0.1912 | 0.9230 | 0.9230 |
| 0.1919 | 16.22 | 6000 | 0.1898 | 0.9241 | 0.9242 |
| 0.1891 | 16.76 | 6200 | 0.1873 | 0.9265 | 0.9265 |
| 0.1909 | 17.3 | 6400 | 0.1891 | 0.9253 | 0.9253 |
| 0.1898 | 17.84 | 6600 | 0.1876 | 0.9260 | 0.9260 |
| 0.1819 | 18.38 | 6800 | 0.1878 | 0.9252 | 0.9252 |
| 0.189 | 18.92 | 7000 | 0.1877 | 0.9270 | 0.9270 |
| 0.1888 | 19.46 | 7200 | 0.1873 | 0.9269 | 0.9269 |
| 0.1837 | 20.0 | 7400 | 0.1869 | 0.9279 | 0.9279 |
| 0.1864 | 20.54 | 7600 | 0.1869 | 0.9280 | 0.9280 |
| 0.1821 | 21.08 | 7800 | 0.1850 | 0.9287 | 0.9287 |
| 0.185 | 21.62 | 8000 | 0.1865 | 0.9280 | 0.9280 |
| 0.1847 | 22.16 | 8200 | 0.1857 | 0.9279 | 0.9279 |
| 0.1796 | 22.7 | 8400 | 0.1863 | 0.9275 | 0.9275 |
| 0.1875 | 23.24 | 8600 | 0.1861 | 0.9257 | 0.9257 |
| 0.1818 | 23.78 | 8800 | 0.1854 | 0.9267 | 0.9267 |
| 0.1853 | 24.32 | 9000 | 0.1859 | 0.9263 | 0.9264 |
| 0.181 | 24.86 | 9200 | 0.1857 | 0.9274 | 0.9274 |
| 0.18 | 25.41 | 9400 | 0.1860 | 0.9270 | 0.9270 |
| 0.18 | 25.95 | 9600 | 0.1859 | 0.9265 | 0.9265 |
| 0.18 | 26.49 | 9800 | 0.1858 | 0.9277 | 0.9277 |
| 0.1807 | 27.03 | 10000 | 0.1858 | 0.9274 | 0.9274 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_prom_prom_300_all-seqsight_32768_512_30M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_prom_prom_300_all-seqsight_32768_512_30M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_30M",
"region:us"
] | null | 2024-04-30T04:03:47+00:00 |
null | transformers |
# Model Card for Model ID
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## Model Details
### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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[More Information Needed] | {"library_name": "transformers", "tags": []} | HenryCai1129/adapter-llama-adaptertoxic2nontoxic-2k-search-50-0.0003 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T04:04:09+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GUE_EMP_H3K14ac-seqsight_32768_512_30M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_30M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_30M) on the [mahdibaghbanzadeh/GUE_EMP_H3K14ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K14ac) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5313
- F1 Score: 0.7362
- Accuracy: 0.7349
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.6211 | 0.97 | 200 | 0.6106 | 0.6738 | 0.6741 |
| 0.5868 | 1.93 | 400 | 0.5878 | 0.6990 | 0.6977 |
| 0.5796 | 2.9 | 600 | 0.5923 | 0.6891 | 0.6887 |
| 0.5747 | 3.86 | 800 | 0.5552 | 0.7198 | 0.7195 |
| 0.57 | 4.83 | 1000 | 0.5918 | 0.6950 | 0.6944 |
| 0.5651 | 5.8 | 1200 | 0.5861 | 0.7025 | 0.7014 |
| 0.5631 | 6.76 | 1400 | 0.5698 | 0.7107 | 0.7089 |
| 0.5593 | 7.73 | 1600 | 0.5561 | 0.7273 | 0.7256 |
| 0.5561 | 8.7 | 1800 | 0.5569 | 0.7253 | 0.7234 |
| 0.5523 | 9.66 | 2000 | 0.5574 | 0.7266 | 0.7247 |
| 0.5528 | 10.63 | 2200 | 0.5828 | 0.6924 | 0.6920 |
| 0.5442 | 11.59 | 2400 | 0.5509 | 0.7280 | 0.7262 |
| 0.5464 | 12.56 | 2600 | 0.5646 | 0.7185 | 0.7168 |
| 0.5458 | 13.53 | 2800 | 0.5711 | 0.7114 | 0.7101 |
| 0.5415 | 14.49 | 3000 | 0.5805 | 0.6999 | 0.6992 |
| 0.5403 | 15.46 | 3200 | 0.5439 | 0.7296 | 0.7280 |
| 0.5397 | 16.43 | 3400 | 0.5778 | 0.7075 | 0.7068 |
| 0.5392 | 17.39 | 3600 | 0.5535 | 0.7241 | 0.7222 |
| 0.5353 | 18.36 | 3800 | 0.5459 | 0.7355 | 0.7337 |
| 0.5361 | 19.32 | 4000 | 0.5543 | 0.7229 | 0.7210 |
| 0.5324 | 20.29 | 4200 | 0.5556 | 0.7271 | 0.7253 |
| 0.5351 | 21.26 | 4400 | 0.5629 | 0.7185 | 0.7171 |
| 0.5328 | 22.22 | 4600 | 0.5702 | 0.7138 | 0.7129 |
| 0.5348 | 23.19 | 4800 | 0.5479 | 0.7319 | 0.7301 |
| 0.532 | 24.15 | 5000 | 0.5561 | 0.7257 | 0.7241 |
| 0.5272 | 25.12 | 5200 | 0.5689 | 0.7164 | 0.7153 |
| 0.5292 | 26.09 | 5400 | 0.5568 | 0.7270 | 0.7253 |
| 0.5311 | 27.05 | 5600 | 0.5823 | 0.7017 | 0.7017 |
| 0.5285 | 28.02 | 5800 | 0.5512 | 0.7289 | 0.7271 |
| 0.5264 | 28.99 | 6000 | 0.5661 | 0.7172 | 0.7162 |
| 0.5261 | 29.95 | 6200 | 0.5738 | 0.7123 | 0.7116 |
| 0.5263 | 30.92 | 6400 | 0.5544 | 0.7275 | 0.7259 |
| 0.5249 | 31.88 | 6600 | 0.5614 | 0.7196 | 0.7183 |
| 0.5209 | 32.85 | 6800 | 0.5667 | 0.7203 | 0.7192 |
| 0.5282 | 33.82 | 7000 | 0.5726 | 0.7109 | 0.7104 |
| 0.5241 | 34.78 | 7200 | 0.5687 | 0.7146 | 0.7138 |
| 0.5249 | 35.75 | 7400 | 0.5636 | 0.7219 | 0.7207 |
| 0.5203 | 36.71 | 7600 | 0.5581 | 0.7285 | 0.7271 |
| 0.523 | 37.68 | 7800 | 0.5587 | 0.7257 | 0.7244 |
| 0.5222 | 38.65 | 8000 | 0.5512 | 0.7306 | 0.7289 |
| 0.5262 | 39.61 | 8200 | 0.5592 | 0.7269 | 0.7256 |
| 0.5176 | 40.58 | 8400 | 0.5688 | 0.7169 | 0.7162 |
| 0.5192 | 41.55 | 8600 | 0.5576 | 0.7276 | 0.7262 |
| 0.5205 | 42.51 | 8800 | 0.5582 | 0.7254 | 0.7241 |
| 0.5211 | 43.48 | 9000 | 0.5708 | 0.7156 | 0.7150 |
| 0.5205 | 44.44 | 9200 | 0.5639 | 0.7210 | 0.7198 |
| 0.5225 | 45.41 | 9400 | 0.5589 | 0.7257 | 0.7244 |
| 0.5169 | 46.38 | 9600 | 0.5664 | 0.7184 | 0.7174 |
| 0.5211 | 47.34 | 9800 | 0.5622 | 0.7220 | 0.7207 |
| 0.5202 | 48.31 | 10000 | 0.5599 | 0.7254 | 0.7241 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_EMP_H3K14ac-seqsight_32768_512_30M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3K14ac-seqsight_32768_512_30M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_30M",
"region:us"
] | null | 2024-04-30T04:04:32+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GUE_prom_prom_300_all-seqsight_32768_512_30M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_30M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_30M) on the [mahdibaghbanzadeh/GUE_prom_prom_300_all](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_300_all) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2076
- F1 Score: 0.9187
- Accuracy: 0.9187
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.341 | 0.54 | 200 | 0.2333 | 0.9073 | 0.9073 |
| 0.2436 | 1.08 | 400 | 0.2192 | 0.9174 | 0.9174 |
| 0.2313 | 1.62 | 600 | 0.2097 | 0.9194 | 0.9194 |
| 0.2187 | 2.16 | 800 | 0.2053 | 0.9180 | 0.9181 |
| 0.2162 | 2.7 | 1000 | 0.2020 | 0.9177 | 0.9177 |
| 0.2101 | 3.24 | 1200 | 0.2039 | 0.9167 | 0.9167 |
| 0.2088 | 3.78 | 1400 | 0.1930 | 0.9220 | 0.9220 |
| 0.205 | 4.32 | 1600 | 0.1914 | 0.9250 | 0.925 |
| 0.1994 | 4.86 | 1800 | 0.1913 | 0.9240 | 0.9240 |
| 0.1945 | 5.41 | 2000 | 0.1888 | 0.9253 | 0.9253 |
| 0.1957 | 5.95 | 2200 | 0.1876 | 0.9262 | 0.9262 |
| 0.1944 | 6.49 | 2400 | 0.1859 | 0.9250 | 0.925 |
| 0.1918 | 7.03 | 2600 | 0.1878 | 0.9284 | 0.9284 |
| 0.1845 | 7.57 | 2800 | 0.1859 | 0.9272 | 0.9272 |
| 0.19 | 8.11 | 3000 | 0.1883 | 0.9280 | 0.9280 |
| 0.1826 | 8.65 | 3200 | 0.1845 | 0.9252 | 0.9252 |
| 0.1782 | 9.19 | 3400 | 0.1850 | 0.9282 | 0.9282 |
| 0.1782 | 9.73 | 3600 | 0.1849 | 0.9277 | 0.9277 |
| 0.1832 | 10.27 | 3800 | 0.1808 | 0.9274 | 0.9274 |
| 0.1778 | 10.81 | 4000 | 0.1840 | 0.9292 | 0.9292 |
| 0.1761 | 11.35 | 4200 | 0.1820 | 0.9279 | 0.9279 |
| 0.1748 | 11.89 | 4400 | 0.1829 | 0.9299 | 0.9299 |
| 0.1724 | 12.43 | 4600 | 0.1817 | 0.9296 | 0.9296 |
| 0.1712 | 12.97 | 4800 | 0.1806 | 0.9301 | 0.9301 |
| 0.1685 | 13.51 | 5000 | 0.1847 | 0.9275 | 0.9275 |
| 0.1719 | 14.05 | 5200 | 0.1840 | 0.9262 | 0.9262 |
| 0.1656 | 14.59 | 5400 | 0.1836 | 0.9302 | 0.9302 |
| 0.1659 | 15.14 | 5600 | 0.1828 | 0.9302 | 0.9302 |
| 0.1655 | 15.68 | 5800 | 0.1821 | 0.9277 | 0.9277 |
| 0.1673 | 16.22 | 6000 | 0.1802 | 0.9309 | 0.9309 |
| 0.1626 | 16.76 | 6200 | 0.1844 | 0.9270 | 0.9270 |
| 0.1659 | 17.3 | 6400 | 0.1824 | 0.9301 | 0.9301 |
| 0.1643 | 17.84 | 6600 | 0.1810 | 0.9289 | 0.9289 |
| 0.1558 | 18.38 | 6800 | 0.1833 | 0.9279 | 0.9279 |
| 0.1622 | 18.92 | 7000 | 0.1815 | 0.9285 | 0.9285 |
| 0.1613 | 19.46 | 7200 | 0.1819 | 0.9301 | 0.9301 |
| 0.1575 | 20.0 | 7400 | 0.1808 | 0.9289 | 0.9289 |
| 0.1588 | 20.54 | 7600 | 0.1826 | 0.9279 | 0.9279 |
| 0.1533 | 21.08 | 7800 | 0.1782 | 0.9297 | 0.9297 |
| 0.1546 | 21.62 | 8000 | 0.1805 | 0.9294 | 0.9294 |
| 0.1552 | 22.16 | 8200 | 0.1813 | 0.9297 | 0.9297 |
| 0.1511 | 22.7 | 8400 | 0.1811 | 0.9301 | 0.9301 |
| 0.155 | 23.24 | 8600 | 0.1812 | 0.9279 | 0.9279 |
| 0.1544 | 23.78 | 8800 | 0.1797 | 0.9292 | 0.9292 |
| 0.1552 | 24.32 | 9000 | 0.1822 | 0.9290 | 0.9291 |
| 0.151 | 24.86 | 9200 | 0.1815 | 0.9285 | 0.9285 |
| 0.1515 | 25.41 | 9400 | 0.1818 | 0.9280 | 0.9280 |
| 0.1504 | 25.95 | 9600 | 0.1817 | 0.9292 | 0.9292 |
| 0.1505 | 26.49 | 9800 | 0.1814 | 0.9289 | 0.9289 |
| 0.1485 | 27.03 | 10000 | 0.1815 | 0.9291 | 0.9291 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_prom_prom_300_all-seqsight_32768_512_30M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_prom_prom_300_all-seqsight_32768_512_30M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_30M",
"region:us"
] | null | 2024-04-30T04:04:32+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GUE_EMP_H3K14ac-seqsight_32768_512_30M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_30M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_30M) on the [mahdibaghbanzadeh/GUE_EMP_H3K14ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K14ac) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5169
- F1 Score: 0.7454
- Accuracy: 0.7446
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.609 | 0.97 | 200 | 0.5883 | 0.6958 | 0.6944 |
| 0.5754 | 1.93 | 400 | 0.5632 | 0.7136 | 0.7116 |
| 0.5648 | 2.9 | 600 | 0.5999 | 0.6804 | 0.6817 |
| 0.5582 | 3.86 | 800 | 0.5436 | 0.7305 | 0.7295 |
| 0.546 | 4.83 | 1000 | 0.5875 | 0.6947 | 0.6950 |
| 0.5417 | 5.8 | 1200 | 0.5696 | 0.7109 | 0.7098 |
| 0.5368 | 6.76 | 1400 | 0.5383 | 0.7370 | 0.7352 |
| 0.5324 | 7.73 | 1600 | 0.5430 | 0.7368 | 0.7349 |
| 0.5267 | 8.7 | 1800 | 0.5540 | 0.7249 | 0.7234 |
| 0.5227 | 9.66 | 2000 | 0.5485 | 0.7307 | 0.7292 |
| 0.5245 | 10.63 | 2200 | 0.5652 | 0.7168 | 0.7162 |
| 0.5132 | 11.59 | 2400 | 0.5336 | 0.7442 | 0.7425 |
| 0.5154 | 12.56 | 2600 | 0.5633 | 0.7208 | 0.7198 |
| 0.5141 | 13.53 | 2800 | 0.5603 | 0.7262 | 0.7253 |
| 0.5085 | 14.49 | 3000 | 0.5610 | 0.7188 | 0.7177 |
| 0.5053 | 15.46 | 3200 | 0.5305 | 0.7470 | 0.7452 |
| 0.5071 | 16.43 | 3400 | 0.5553 | 0.7242 | 0.7231 |
| 0.5043 | 17.39 | 3600 | 0.5345 | 0.7398 | 0.7380 |
| 0.501 | 18.36 | 3800 | 0.5264 | 0.7479 | 0.7464 |
| 0.5007 | 19.32 | 4000 | 0.5324 | 0.7437 | 0.7419 |
| 0.4968 | 20.29 | 4200 | 0.5485 | 0.7323 | 0.7307 |
| 0.5002 | 21.26 | 4400 | 0.5446 | 0.7343 | 0.7328 |
| 0.4953 | 22.22 | 4600 | 0.5511 | 0.7294 | 0.7280 |
| 0.4959 | 23.19 | 4800 | 0.5296 | 0.7426 | 0.7410 |
| 0.4942 | 24.15 | 5000 | 0.5398 | 0.7310 | 0.7292 |
| 0.4882 | 25.12 | 5200 | 0.5566 | 0.7251 | 0.7241 |
| 0.4882 | 26.09 | 5400 | 0.5433 | 0.7341 | 0.7325 |
| 0.4913 | 27.05 | 5600 | 0.5629 | 0.7201 | 0.7192 |
| 0.4871 | 28.02 | 5800 | 0.5371 | 0.7329 | 0.7310 |
| 0.4834 | 28.99 | 6000 | 0.5448 | 0.7275 | 0.7259 |
| 0.4846 | 29.95 | 6200 | 0.5552 | 0.7260 | 0.7247 |
| 0.4837 | 30.92 | 6400 | 0.5311 | 0.7418 | 0.7401 |
| 0.4817 | 31.88 | 6600 | 0.5389 | 0.7307 | 0.7289 |
| 0.4774 | 32.85 | 6800 | 0.5524 | 0.7299 | 0.7283 |
| 0.4852 | 33.82 | 7000 | 0.5469 | 0.7304 | 0.7289 |
| 0.48 | 34.78 | 7200 | 0.5522 | 0.7268 | 0.7253 |
| 0.4839 | 35.75 | 7400 | 0.5412 | 0.7374 | 0.7356 |
| 0.4745 | 36.71 | 7600 | 0.5460 | 0.7322 | 0.7304 |
| 0.4787 | 37.68 | 7800 | 0.5444 | 0.7288 | 0.7271 |
| 0.4737 | 38.65 | 8000 | 0.5371 | 0.7383 | 0.7365 |
| 0.4814 | 39.61 | 8200 | 0.5405 | 0.7328 | 0.7310 |
| 0.4688 | 40.58 | 8400 | 0.5516 | 0.7291 | 0.7274 |
| 0.4735 | 41.55 | 8600 | 0.5391 | 0.7347 | 0.7328 |
| 0.4747 | 42.51 | 8800 | 0.5424 | 0.7337 | 0.7319 |
| 0.4764 | 43.48 | 9000 | 0.5544 | 0.7271 | 0.7256 |
| 0.4729 | 44.44 | 9200 | 0.5512 | 0.7294 | 0.7277 |
| 0.4761 | 45.41 | 9400 | 0.5433 | 0.7328 | 0.7310 |
| 0.4701 | 46.38 | 9600 | 0.5481 | 0.7318 | 0.7301 |
| 0.4746 | 47.34 | 9800 | 0.5451 | 0.7331 | 0.7313 |
| 0.4722 | 48.31 | 10000 | 0.5435 | 0.7340 | 0.7322 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_EMP_H3K14ac-seqsight_32768_512_30M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3K14ac-seqsight_32768_512_30M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_30M",
"region:us"
] | null | 2024-04-30T04:04:45+00:00 |
text-classification | transformers | ## TextAttack Model Card
This `distilbert` model was fine-tuned using TextAttack. The model was fine-tuned
for 3 epochs with a batch size of 8,
a maximum sequence length of 512, and an initial learning rate of 3e-05.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.937, as measured by the
eval set accuracy, found after 3 epochs.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack). | {"language": ["zh"], "license": "apache-2.0", "metrics": ["accuracy"], "pipeline_tag": "text-classification"} | WangA/distilbert-base-finetuned-jd | null | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"zh",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T04:05:11+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GUE_EMP_H3K14ac-seqsight_32768_512_30M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_30M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_30M) on the [mahdibaghbanzadeh/GUE_EMP_H3K14ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K14ac) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5324
- F1 Score: 0.7516
- Accuracy: 0.7504
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.5995 | 0.97 | 200 | 0.5710 | 0.7127 | 0.7107 |
| 0.5654 | 1.93 | 400 | 0.5500 | 0.7193 | 0.7177 |
| 0.5486 | 2.9 | 600 | 0.5834 | 0.6900 | 0.6905 |
| 0.5401 | 3.86 | 800 | 0.5375 | 0.7422 | 0.7404 |
| 0.5259 | 4.83 | 1000 | 0.5876 | 0.7014 | 0.7023 |
| 0.521 | 5.8 | 1200 | 0.5590 | 0.7225 | 0.7216 |
| 0.5147 | 6.76 | 1400 | 0.5281 | 0.7434 | 0.7416 |
| 0.5094 | 7.73 | 1600 | 0.5283 | 0.7437 | 0.7419 |
| 0.5015 | 8.7 | 1800 | 0.5503 | 0.7247 | 0.7238 |
| 0.4981 | 9.66 | 2000 | 0.5451 | 0.7333 | 0.7319 |
| 0.4975 | 10.63 | 2200 | 0.5551 | 0.7293 | 0.7286 |
| 0.4858 | 11.59 | 2400 | 0.5316 | 0.7425 | 0.7407 |
| 0.4862 | 12.56 | 2600 | 0.5598 | 0.7285 | 0.7274 |
| 0.4828 | 13.53 | 2800 | 0.5533 | 0.7315 | 0.7304 |
| 0.4771 | 14.49 | 3000 | 0.5429 | 0.7427 | 0.7410 |
| 0.4717 | 15.46 | 3200 | 0.5272 | 0.7488 | 0.7470 |
| 0.4684 | 16.43 | 3400 | 0.5541 | 0.7354 | 0.7340 |
| 0.4673 | 17.39 | 3600 | 0.5392 | 0.7413 | 0.7395 |
| 0.4619 | 18.36 | 3800 | 0.5252 | 0.7443 | 0.7434 |
| 0.4598 | 19.32 | 4000 | 0.5366 | 0.7507 | 0.7492 |
| 0.4536 | 20.29 | 4200 | 0.5497 | 0.7350 | 0.7331 |
| 0.4546 | 21.26 | 4400 | 0.5486 | 0.7365 | 0.7346 |
| 0.45 | 22.22 | 4600 | 0.5716 | 0.7334 | 0.7322 |
| 0.4466 | 23.19 | 4800 | 0.5486 | 0.7357 | 0.7340 |
| 0.444 | 24.15 | 5000 | 0.5740 | 0.7280 | 0.7265 |
| 0.437 | 25.12 | 5200 | 0.5811 | 0.7251 | 0.7244 |
| 0.4377 | 26.09 | 5400 | 0.5717 | 0.7311 | 0.7295 |
| 0.4379 | 27.05 | 5600 | 0.6015 | 0.7226 | 0.7219 |
| 0.4321 | 28.02 | 5800 | 0.5653 | 0.7304 | 0.7286 |
| 0.4275 | 28.99 | 6000 | 0.5599 | 0.7352 | 0.7334 |
| 0.4268 | 29.95 | 6200 | 0.5907 | 0.7252 | 0.7241 |
| 0.423 | 30.92 | 6400 | 0.5557 | 0.7391 | 0.7374 |
| 0.4214 | 31.88 | 6600 | 0.5636 | 0.7364 | 0.7346 |
| 0.4147 | 32.85 | 6800 | 0.5935 | 0.7273 | 0.7259 |
| 0.4206 | 33.82 | 7000 | 0.5936 | 0.7247 | 0.7238 |
| 0.4144 | 34.78 | 7200 | 0.5917 | 0.7253 | 0.7241 |
| 0.4164 | 35.75 | 7400 | 0.5744 | 0.7347 | 0.7328 |
| 0.4071 | 36.71 | 7600 | 0.5950 | 0.7295 | 0.7280 |
| 0.4095 | 37.68 | 7800 | 0.5915 | 0.7254 | 0.7238 |
| 0.4024 | 38.65 | 8000 | 0.5928 | 0.7303 | 0.7286 |
| 0.4133 | 39.61 | 8200 | 0.5809 | 0.7229 | 0.7210 |
| 0.399 | 40.58 | 8400 | 0.5939 | 0.7310 | 0.7292 |
| 0.399 | 41.55 | 8600 | 0.5873 | 0.7320 | 0.7301 |
| 0.4036 | 42.51 | 8800 | 0.5890 | 0.7307 | 0.7289 |
| 0.4041 | 43.48 | 9000 | 0.6023 | 0.7283 | 0.7268 |
| 0.3987 | 44.44 | 9200 | 0.5997 | 0.7263 | 0.7247 |
| 0.4011 | 45.41 | 9400 | 0.5952 | 0.7288 | 0.7271 |
| 0.3971 | 46.38 | 9600 | 0.5951 | 0.7300 | 0.7283 |
| 0.4 | 47.34 | 9800 | 0.5905 | 0.7259 | 0.7241 |
| 0.3954 | 48.31 | 10000 | 0.5908 | 0.7277 | 0.7259 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_EMP_H3K14ac-seqsight_32768_512_30M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3K14ac-seqsight_32768_512_30M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_30M",
"region:us"
] | null | 2024-04-30T04:05:16+00:00 |
null | null | {} | Viswes/OpenLlama-quantized | null | [
"region:us"
] | null | 2024-04-30T04:05:26+00:00 |
|
null | null | {"license": "mit"} | rasheduzzaman/llama3_8b_bangla_question_pair_fine_tune_unsloth | null | [
"license:mit",
"region:us"
] | null | 2024-04-30T04:06:12+00:00 |
|
null | null |
# Model Card for Model ID
This code implements Support Vector Machines (SVMs) with two different kernels: linear and RBF. A model card should mention that the model is an SVM and potentially specify the available kernels.
## Model Details
The code demonstrates how the model is trained using the SVC class from scikit-learn. A model card's training details section might mention scikit-learn as a training framework.
### Model Description
This model is a Support Vector Machine (SVM) classifier implemented using scikit-learn. It can be used for binary classification tasks where the data can be separated by a hyperplane in a high-dimensional space. The model offers two kernel choices: linear and RBF (Radial Basis Function). The linear kernel is suitable for data that is already linearly separable, while the RBF kernel can handle non-linearly separable data by mapping it to a higher-dimensional space.
Here are some key aspects of this model:
Classification Task: Binary classification (separating data points into two classes).
Kernel Choices: Linear and RBF.
Implementation Library: scikit-learn.
Additionally, consider including these details if relevant:
Limitations of SVMs, such as potentially high computational cost for training large datasets or difficulty interpreting the model's decisions.
Specific use cases where this type of SVM might be suitable (e.g., image classification with low-dimensional data for linear kernel, or text classification for RBF kernel).
Remember to replace or adjust the details based on your specific implementation and use case.
### Model Sources [optional]
Akif
## Uses
Direct Use
This SVM model can be directly used for binary classification tasks where the data can be separated by a hyperplane. Here are some potential applications:
Spam filtering: Classifying emails as spam or not spam based on features like sender address, keywords, and content.
Image categorization: Classifying images into two categories, such as cat vs. dog or handwritten digit recognition (classifying digits 0-9).
Sentiment analysis: Classifying text data as positive or negative sentiment.
General requirements for direct use:
The data needs to be well-defined with clear features that distinguish the two classes.
The data should be balanced, meaning there are roughly equal numbers of data points for each class.
Downstream Use [optional]
This SVM model can also be a building block for more complex machine learning pipelines. Here's an example:
You could use this model as a first stage filter in a multi-class classification problem. The SVM could classify data points into broad categories, and then a separate model could handle further classification within those categories.
General requirements for downstream use:
The downstream task should benefit from the binary classification performed by the SVM.
The data used downstream should be compatible with the output of the SVM.
Out-of-Scope Use
While this SVM can be a powerful tool, it's essential to consider limitations:
High dimensionality: The SVM might not perform well with very high-dimensional data due to the curse of dimensionality.
Non-linear data: The linear kernel might not be suitable for data that is not linearly separable. In such cases, the RBF kernel or other kernel functions might be needed.
Imbalanced data: The model's performance can be skewed if the data has a significant class imbalance (one class having many more data points than the other).
It's important to avoid using this model for tasks where these limitations could significantly impact its effectiveness.
### Direct Use
This SVM model can be directly applied to binary classification tasks where the data can be well-represented in a high-dimensional space and separated by a hyperplane. Here are some potential applications:
Spam Filtering: Classifying emails as spam or not spam based on features like sender address, keywords, and content. This could be useful for personal email filtering or as a building block in more sophisticated spam filtering systems.
Image Categorization: Classifying images into two broad categories, such as cat vs. dog or handwritten digit recognition (classifying digits 0-9). This could be used for simple image sorting tasks or as a preliminary step in more complex image recognition pipelines.
Sentiment Analysis: Classifying text data as positive or negative sentiment. This could be helpful for analyzing customer reviews, social media posts, or other textual data to understand overall sentiment.
General requirements for direct use:
Data Suitability: The data should have clear features that effectively distinguish the two classes the model is designed to separate. Features might be numerical or categorical, depending on the task.
Data Balance: Ideally, the data should be balanced, meaning there are roughly equal numbers of data points for each class (positive and negative). Imbalanced data can bias the model towards the majority class.
Interpretability Needs: If you need to understand the model's reasoning behind its classifications, a linear kernel SVM might be preferable as it offers more interpretability compared to the RBF kernel.
Additional Considerations:
SVMs can be computationally expensive to train for very large datasets. Consider this when dealing with massive amounts of data.
While SVMs are powerful classifiers, they might not be the best choice for all binary classification problems. Explore other algorithms like decision trees or random forests if the data is highly complex or not easily separable by a hyperplane.
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
Bias, Risks, and Limitations
Here's a possible description for the "Bias, Risks, and Limitations" section of your model card:
Bias:
Training Data Bias: Like any machine learning model, this SVM is susceptible to bias present in the training data. If the training data is skewed towards one class or if certain features are not representative of the real world, the model's predictions can be biased.
Algorithmic Bias: SVMs themselves might exhibit bias depending on the kernel used. For instance, linear SVMs can struggle with non-linear data distributions, potentially favoring certain regions of the feature space.
Risks:
Misclassification: The model might misclassify data points, especially if the data is noisy or not well-separated. This can lead to errors in downstream applications.
Overfitting: If the model is trained on a small dataset or with overly complex hyperparameters, it might overfit the training data and perform poorly on unseen data.
Limitations:
High Dimensionality: SVMs can become computationally expensive and less effective when dealing with very high-dimensional data due to the "curse of dimensionality."
Non-linear Data: The linear kernel SVM is limited to linearly separable data. For more complex, non-linear relationships, the RBF kernel might be necessary, but it can be less interpretable.
Imbalanced Data: The model's performance can be skewed if the data has a significant class imbalance (one class having many more data points than the other).
General Mitigation Strategies:
Use high-quality, balanced training data that represents the real-world distribution of the target variable.
Carefully select and tune hyperparameters to avoid overfitting.
Consider using techniques like cross-validation to evaluate the model's generalizability.
Be aware of the limitations of SVMs and choose alternative algorithms if the data is high-dimensional, non-linear, or imbalanced.
It's important to understand these potential biases, risks, and limitations before deploying this SVM model in real-world applications.
[More Information Needed]
### Recommendations
Recommendations
To mitigate the potential biases, risks, and limitations discussed in the previous section, here are some recommendations for users of this SVM model:
Data Considerations:
Data Quality and Balance: Ensure the training data used for the SVM is high-quality, free from errors, and balanced between the two classes. Techniques like data cleaning and oversampling/undersampling can be used to address imbalances.
Data Representativeness: The training data should accurately represent the real-world distribution of data the model will encounter during deployment. Consider potential biases in data collection processes and explore mitigating strategies.
Model Training and Evaluation:
Hyperparameter Tuning: Carefully tune the hyperparameters of the SVM (e.g., regularization parameter, kernel parameters) to achieve a good balance between training accuracy and generalization performance. Techniques like grid search or randomized search can be helpful.
Cross-Validation: Evaluate the model's performance using techniques like k-fold cross-validation to get a more robust estimate of its generalizability to unseen data.
Alternative Models:
Consider Alternatives: If the data is high-dimensional, non-linear, or imbalanced, explore alternative classification algorithms like decision trees, random forests, or gradient boosting that might be more suitable for such scenarios.
Monitoring and Improvement:
Monitor Performance: Continuously monitor the model's performance in deployment and retrain it with new data or adjusted hyperparameters if its accuracy degrades over time.
Additionally:
Document Biases: Document any identified biases in the training data or the model itself. This transparency is crucial for responsible model development and deployment.
Responsible Use: Be aware of the potential societal impacts of using this model and ensure its application aligns with ethical considerations.
By following these recommendations, users can help mitigate the risks and limitations associated with this SVM model and promote its fair and effective use.
## How to Get Started with the Model
Use the code below to get started with the model.
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.datasets import make_classification
# Generate synthetic dataset
X, y = make_classification(n_samples=100, n_features=2, n_informative=2, n_redundant=0, n_classes=2, n_clusters_per_class=1, random_state=42)
# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Support Vector Machine without kernel (linear kernel)
svm_linear = SVC(kernel='linear')
svm_linear.fit(X_train, y_train)
linear_train_acc = svm_linear.score(X_train, y_train)
linear_test_acc = svm_linear.score(X_test, y_test)
# Support Vector Machine with radial basis function (RBF) kernel
svm_rbf = SVC(kernel='rbf')
svm_rbf.fit(X_train, y_train)
rbf_train_acc = svm_rbf.score(X_train, y_train)
rbf_test_acc = svm_rbf.score(X_test, y_test)
# Visualize decision boundary for linear SVM
plt.figure(figsize=(10, 5))
plt.subplot(1, 2, 1)
plt.scatter(X[:, 0], X[:, 1], c=y, cmap='coolwarm', edgecolors='k', s=100)
plt.title("Linear SVM")
plt.xlabel("Feature 1")
plt.ylabel("Feature 2")
# Plot decision boundary
ax = plt.gca()
xlim = ax.get_xlim()
ylim = ax.get_ylim()
# Create grid to evaluate model
xx = np.linspace(xlim[0], xlim[1], 30)
yy = np.linspace(ylim[0], ylim[1], 30)
YY, XX = np.meshgrid(yy, xx)
xy = np.vstack([XX.ravel(), YY.ravel()]).T
Z = svm_linear.decision_function(xy).reshape(XX.shape)
# Plot decision boundary and margins
ax.contour(XX, YY, Z, colors='k', levels=[-1, 0, 1], alpha=0.5, linestyles=['--', '-', '--'])
ax.scatter(svm_linear.support_vectors_[:, 0], svm_linear.support_vectors_[:, 1], s=100,
linewidth=1, facecolors='none', edgecolors='k')
plt.subplot(1, 2, 2)
plt.scatter(X[:, 0], X[:, 1], c=y, cmap='coolwarm', edgecolors='k', s=100)
plt.title("RBF SVM")
plt.xlabel("Feature 1")
plt.ylabel("Feature 2")
# Plot decision boundary
ax = plt.gca()
xlim = ax.get_xlim()
ylim = ax.get_ylim()
# Create grid to evaluate model
xx = np.linspace(xlim[0], xlim[1], 30)
yy = np.linspace(ylim[0], ylim[1], 30)
YY, XX = np.meshgrid(yy, xx)
xy = np.vstack([XX.ravel(), YY.ravel()]).T
Z = svm_rbf.decision_function(xy).reshape(XX.shape)
# Plot decision boundary and margins
ax.contour(XX, YY, Z, colors='k', levels=[-1, 0, 1], alpha=0.5, linestyles=['--', '-', '--'])
ax.scatter(svm_rbf.support_vectors_[:, 0], svm_rbf.support_vectors_[:, 1], s=100,
linewidth=1, facecolors='none', edgecolors='k')
plt.tight_layout()
plt.show()
# Print accuracy scores
print("Linear SVM - Training Accuracy: {:.2f}, Test Accuracy: {:.2f}".format(linear_train_acc, linear_test_acc))
print("RBF SVM - Training Accuracy: {:.2f}, Test Accuracy: {:.2f}".format(rbf_train_acc, rbf_test_acc))
# Example usage after training the model (replace with your specific logic)
def predict_new_data(X_new):
predictions = svm_model.predict(X_new)
return predictions
# Example usage
X_new = np.array([[1.5, 2.0]]) # Replace with your new data point
predictions = predict_new_data(X_new)
print("Predicted class:", predictions[0])
### Training Data
Electric_Vehicle_Population_Data.csv
[More Information Needed]
### Testing Data, Factors & Metrics
#### Testing Hyperparameters
The code trains two SVMs:
Linear SVM: Uses the 'linear' kernel.
RBF SVM: Uses the 'rbf' kernel.
[More Information Needed]
#### Software
Visual Studio - Python
## Model Card Contact
[email protected] | {} | Ironclad313/SVM | null | [
"region:us"
] | null | 2024-04-30T04:06:21+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GUE_EMP_H3K4me2-seqsight_32768_512_30M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_30M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_30M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me2) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6031
- F1 Score: 0.6608
- Accuracy: 0.6631
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.6575 | 1.04 | 200 | 0.6330 | 0.6290 | 0.6494 |
| 0.6292 | 2.08 | 400 | 0.6402 | 0.6381 | 0.6354 |
| 0.6232 | 3.12 | 600 | 0.6226 | 0.6552 | 0.6644 |
| 0.6211 | 4.17 | 800 | 0.6234 | 0.6564 | 0.6588 |
| 0.6211 | 5.21 | 1000 | 0.6321 | 0.6407 | 0.6383 |
| 0.6172 | 6.25 | 1200 | 0.6235 | 0.6562 | 0.6569 |
| 0.6115 | 7.29 | 1400 | 0.6385 | 0.6413 | 0.6386 |
| 0.6146 | 8.33 | 1600 | 0.6308 | 0.6486 | 0.6471 |
| 0.613 | 9.38 | 1800 | 0.6341 | 0.6422 | 0.6396 |
| 0.6114 | 10.42 | 2000 | 0.6247 | 0.6487 | 0.6481 |
| 0.6126 | 11.46 | 2200 | 0.6182 | 0.6578 | 0.6601 |
| 0.6066 | 12.5 | 2400 | 0.6252 | 0.6533 | 0.6533 |
| 0.6057 | 13.54 | 2600 | 0.6179 | 0.6597 | 0.6637 |
| 0.6096 | 14.58 | 2800 | 0.6155 | 0.6599 | 0.6654 |
| 0.609 | 15.62 | 3000 | 0.6201 | 0.6517 | 0.6536 |
| 0.6048 | 16.67 | 3200 | 0.6248 | 0.6520 | 0.6514 |
| 0.6053 | 17.71 | 3400 | 0.6165 | 0.6619 | 0.6654 |
| 0.6042 | 18.75 | 3600 | 0.6202 | 0.6526 | 0.6533 |
| 0.6058 | 19.79 | 3800 | 0.6191 | 0.6552 | 0.6556 |
| 0.5999 | 20.83 | 4000 | 0.6295 | 0.6545 | 0.6523 |
| 0.603 | 21.88 | 4200 | 0.6291 | 0.6500 | 0.6481 |
| 0.602 | 22.92 | 4400 | 0.6283 | 0.6527 | 0.6507 |
| 0.6012 | 23.96 | 4600 | 0.6303 | 0.6523 | 0.6500 |
| 0.6001 | 25.0 | 4800 | 0.6210 | 0.6581 | 0.6579 |
| 0.6001 | 26.04 | 5000 | 0.6215 | 0.6582 | 0.6575 |
| 0.6007 | 27.08 | 5200 | 0.6239 | 0.6571 | 0.6559 |
| 0.5995 | 28.12 | 5400 | 0.6180 | 0.6592 | 0.6592 |
| 0.6006 | 29.17 | 5600 | 0.6224 | 0.6565 | 0.6549 |
| 0.5955 | 30.21 | 5800 | 0.6266 | 0.6581 | 0.6566 |
| 0.599 | 31.25 | 6000 | 0.6228 | 0.6594 | 0.6582 |
| 0.5979 | 32.29 | 6200 | 0.6203 | 0.6604 | 0.6595 |
| 0.5957 | 33.33 | 6400 | 0.6253 | 0.6591 | 0.6575 |
| 0.5987 | 34.38 | 6600 | 0.6141 | 0.6647 | 0.6667 |
| 0.5951 | 35.42 | 6800 | 0.6181 | 0.6620 | 0.6621 |
| 0.5965 | 36.46 | 7000 | 0.6154 | 0.6632 | 0.6644 |
| 0.5937 | 37.5 | 7200 | 0.6227 | 0.6565 | 0.6553 |
| 0.5943 | 38.54 | 7400 | 0.6190 | 0.6629 | 0.6628 |
| 0.5991 | 39.58 | 7600 | 0.6188 | 0.6627 | 0.6624 |
| 0.596 | 40.62 | 7800 | 0.6263 | 0.6566 | 0.6546 |
| 0.5932 | 41.67 | 8000 | 0.6229 | 0.6598 | 0.6582 |
| 0.5933 | 42.71 | 8200 | 0.6159 | 0.6631 | 0.6647 |
| 0.5952 | 43.75 | 8400 | 0.6189 | 0.6620 | 0.6618 |
| 0.5955 | 44.79 | 8600 | 0.6236 | 0.6585 | 0.6572 |
| 0.5914 | 45.83 | 8800 | 0.6193 | 0.6607 | 0.6605 |
| 0.595 | 46.88 | 9000 | 0.6241 | 0.6624 | 0.6608 |
| 0.5925 | 47.92 | 9200 | 0.6216 | 0.6605 | 0.6595 |
| 0.5937 | 48.96 | 9400 | 0.6204 | 0.6612 | 0.6605 |
| 0.5929 | 50.0 | 9600 | 0.6214 | 0.6611 | 0.6601 |
| 0.5922 | 51.04 | 9800 | 0.6216 | 0.6608 | 0.6598 |
| 0.5916 | 52.08 | 10000 | 0.6214 | 0.6604 | 0.6595 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_EMP_H3K4me2-seqsight_32768_512_30M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3K4me2-seqsight_32768_512_30M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_30M",
"region:us"
] | null | 2024-04-30T04:07:25+00:00 |
null | diffusers | {} | xiaozaa/Diffusion-Tryon-Trainer | null | [
"diffusers",
"safetensors",
"region:us"
] | null | 2024-04-30T04:08:09+00:00 |
|
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GUE_EMP_H3K4me2-seqsight_32768_512_30M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_30M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_30M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me2) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5980
- F1 Score: 0.6814
- Accuracy: 0.6820
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.651 | 1.04 | 200 | 0.6255 | 0.6438 | 0.6553 |
| 0.622 | 2.08 | 400 | 0.6356 | 0.6361 | 0.6334 |
| 0.6165 | 3.12 | 600 | 0.6170 | 0.6501 | 0.6660 |
| 0.6148 | 4.17 | 800 | 0.6253 | 0.6527 | 0.6530 |
| 0.6136 | 5.21 | 1000 | 0.6172 | 0.6597 | 0.6618 |
| 0.6088 | 6.25 | 1200 | 0.6159 | 0.6636 | 0.6686 |
| 0.6019 | 7.29 | 1400 | 0.6237 | 0.6572 | 0.6559 |
| 0.604 | 8.33 | 1600 | 0.6284 | 0.6570 | 0.6553 |
| 0.5998 | 9.38 | 1800 | 0.6439 | 0.6466 | 0.6442 |
| 0.5968 | 10.42 | 2000 | 0.6108 | 0.6591 | 0.6611 |
| 0.5993 | 11.46 | 2200 | 0.6101 | 0.6627 | 0.6660 |
| 0.5902 | 12.5 | 2400 | 0.6159 | 0.6649 | 0.6660 |
| 0.5879 | 13.54 | 2600 | 0.6134 | 0.6637 | 0.6650 |
| 0.59 | 14.58 | 2800 | 0.6106 | 0.6691 | 0.6722 |
| 0.5897 | 15.62 | 3000 | 0.6170 | 0.6682 | 0.6680 |
| 0.584 | 16.67 | 3200 | 0.6184 | 0.6671 | 0.6657 |
| 0.5818 | 17.71 | 3400 | 0.6140 | 0.6724 | 0.6722 |
| 0.5827 | 18.75 | 3600 | 0.6075 | 0.6756 | 0.6777 |
| 0.5826 | 19.79 | 3800 | 0.6120 | 0.6771 | 0.6774 |
| 0.576 | 20.83 | 4000 | 0.6182 | 0.6697 | 0.6680 |
| 0.579 | 21.88 | 4200 | 0.6227 | 0.6610 | 0.6588 |
| 0.5766 | 22.92 | 4400 | 0.6199 | 0.6693 | 0.6676 |
| 0.574 | 23.96 | 4600 | 0.6246 | 0.6611 | 0.6588 |
| 0.5733 | 25.0 | 4800 | 0.6145 | 0.6750 | 0.6745 |
| 0.5718 | 26.04 | 5000 | 0.6181 | 0.6714 | 0.6706 |
| 0.5735 | 27.08 | 5200 | 0.6164 | 0.6725 | 0.6712 |
| 0.571 | 28.12 | 5400 | 0.6126 | 0.6756 | 0.6748 |
| 0.5714 | 29.17 | 5600 | 0.6112 | 0.6778 | 0.6774 |
| 0.5653 | 30.21 | 5800 | 0.6211 | 0.6701 | 0.6686 |
| 0.5691 | 31.25 | 6000 | 0.6191 | 0.6703 | 0.6686 |
| 0.5659 | 32.29 | 6200 | 0.6093 | 0.6780 | 0.6787 |
| 0.5674 | 33.33 | 6400 | 0.6177 | 0.6727 | 0.6716 |
| 0.5646 | 34.38 | 6600 | 0.6116 | 0.6733 | 0.6735 |
| 0.5638 | 35.42 | 6800 | 0.6152 | 0.6729 | 0.6722 |
| 0.564 | 36.46 | 7000 | 0.6129 | 0.6750 | 0.6748 |
| 0.5601 | 37.5 | 7200 | 0.6183 | 0.6719 | 0.6703 |
| 0.5607 | 38.54 | 7400 | 0.6166 | 0.6745 | 0.6735 |
| 0.566 | 39.58 | 7600 | 0.6112 | 0.6797 | 0.6804 |
| 0.5596 | 40.62 | 7800 | 0.6226 | 0.6673 | 0.6654 |
| 0.5599 | 41.67 | 8000 | 0.6182 | 0.6694 | 0.6676 |
| 0.5576 | 42.71 | 8200 | 0.6144 | 0.6782 | 0.6787 |
| 0.5594 | 43.75 | 8400 | 0.6165 | 0.6723 | 0.6716 |
| 0.5617 | 44.79 | 8600 | 0.6195 | 0.6689 | 0.6673 |
| 0.556 | 45.83 | 8800 | 0.6149 | 0.6732 | 0.6729 |
| 0.5588 | 46.88 | 9000 | 0.6242 | 0.6629 | 0.6608 |
| 0.5548 | 47.92 | 9200 | 0.6218 | 0.6665 | 0.6647 |
| 0.5566 | 48.96 | 9400 | 0.6186 | 0.6722 | 0.6712 |
| 0.5551 | 50.0 | 9600 | 0.6230 | 0.6675 | 0.6657 |
| 0.5542 | 51.04 | 9800 | 0.6213 | 0.6676 | 0.6660 |
| 0.5549 | 52.08 | 10000 | 0.6214 | 0.6679 | 0.6663 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_EMP_H3K4me2-seqsight_32768_512_30M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3K4me2-seqsight_32768_512_30M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_30M",
"region:us"
] | null | 2024-04-30T04:08:40+00:00 |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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#### Speeds, Sizes, Times [optional]
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | fruk19/hidaka_model | null | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T04:09:05+00:00 |
text-classification | transformers | {} | KalaiselvanD/albert_model__30_1 | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T04:09:34+00:00 |
|
null | null | {"license": "apache-2.0"} | Slothy34634534562634/Caseoh | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-04-30T04:12:31+00:00 |
|
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# aligner-v1-llama3-01
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4361
- Rewards/chosen: -0.0331
- Rewards/rejected: -0.0349
- Rewards/accuracies: 0.8333
- Rewards/margins: 0.0018
- Logps/rejected: -0.3493
- Logps/chosen: -0.3313
- Logits/rejected: -1.5592
- Logits/chosen: -1.5485
- Nll Loss: 1.3699
- Log Odds Ratio: -0.6618
- Log Odds Chosen: 0.0646
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 8e-06
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | Nll Loss | Log Odds Ratio | Log Odds Chosen |
|:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|:--------:|:--------------:|:---------------:|
| 2.8628 | 0.2105 | 15 | 2.7068 | -0.1219 | -0.1183 | 0.0 | -0.0036 | -1.1830 | -1.2191 | -1.8565 | -1.8340 | 2.6349 | -0.7190 | -0.0509 |
| 2.1044 | 0.4211 | 30 | 2.0553 | -0.0702 | -0.0687 | 0.1667 | -0.0015 | -0.6871 | -0.7024 | -1.6352 | -1.6218 | 1.9845 | -0.7082 | -0.0296 |
| 1.6915 | 0.6316 | 45 | 1.6323 | -0.0431 | -0.0436 | 0.8333 | 0.0006 | -0.4364 | -0.4305 | -1.6833 | -1.6715 | 1.5639 | -0.6842 | 0.0185 |
| 1.4279 | 0.8421 | 60 | 1.4361 | -0.0331 | -0.0349 | 0.8333 | 0.0018 | -0.3493 | -0.3313 | -1.5592 | -1.5485 | 1.3699 | -0.6618 | 0.0646 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"license": "other", "library_name": "peft", "tags": ["trl", "orpo", "generated_from_trainer"], "base_model": "meta-llama/Meta-Llama-3-8B", "model-index": [{"name": "aligner-v1-llama3-01", "results": []}]} | Ksgk-fy/aligner-v1-llama3-01 | null | [
"peft",
"safetensors",
"trl",
"orpo",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B",
"license:other",
"region:us"
] | null | 2024-04-30T04:14:00+00:00 |
null | null | {} | ttc0000/mistral_Progressive_Home_Homesite_text_scan_lora_r64_a128_info_extract | null | [
"safetensors",
"region:us"
] | null | 2024-04-30T04:14:38+00:00 |
|
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GUE_EMP_H3K4me2-seqsight_32768_512_30M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_30M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_30M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me2) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5996
- F1 Score: 0.6706
- Accuracy: 0.6735
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.6466 | 1.04 | 200 | 0.6215 | 0.6422 | 0.6605 |
| 0.6183 | 2.08 | 400 | 0.6391 | 0.6355 | 0.6328 |
| 0.6117 | 3.12 | 600 | 0.6127 | 0.6578 | 0.6657 |
| 0.6078 | 4.17 | 800 | 0.6438 | 0.6425 | 0.6399 |
| 0.603 | 5.21 | 1000 | 0.6093 | 0.6701 | 0.6751 |
| 0.5969 | 6.25 | 1200 | 0.6119 | 0.6672 | 0.6683 |
| 0.5883 | 7.29 | 1400 | 0.6109 | 0.6679 | 0.6686 |
| 0.5884 | 8.33 | 1600 | 0.6236 | 0.6656 | 0.6637 |
| 0.5821 | 9.38 | 1800 | 0.6329 | 0.6546 | 0.6520 |
| 0.5761 | 10.42 | 2000 | 0.6073 | 0.6728 | 0.6777 |
| 0.5791 | 11.46 | 2200 | 0.6121 | 0.6784 | 0.6804 |
| 0.5684 | 12.5 | 2400 | 0.6158 | 0.6737 | 0.6745 |
| 0.5641 | 13.54 | 2600 | 0.6354 | 0.6682 | 0.6663 |
| 0.5652 | 14.58 | 2800 | 0.6163 | 0.6714 | 0.6722 |
| 0.5626 | 15.62 | 3000 | 0.6323 | 0.6655 | 0.6637 |
| 0.5534 | 16.67 | 3200 | 0.6317 | 0.6589 | 0.6569 |
| 0.5497 | 17.71 | 3400 | 0.6289 | 0.6551 | 0.6527 |
| 0.5498 | 18.75 | 3600 | 0.6250 | 0.6695 | 0.6680 |
| 0.549 | 19.79 | 3800 | 0.6511 | 0.6484 | 0.6458 |
| 0.5396 | 20.83 | 4000 | 0.6248 | 0.6676 | 0.6660 |
| 0.541 | 21.88 | 4200 | 0.6431 | 0.6582 | 0.6556 |
| 0.535 | 22.92 | 4400 | 0.6522 | 0.6578 | 0.6553 |
| 0.5304 | 23.96 | 4600 | 0.6437 | 0.6591 | 0.6566 |
| 0.5291 | 25.0 | 4800 | 0.6536 | 0.6485 | 0.6458 |
| 0.5236 | 26.04 | 5000 | 0.6384 | 0.6638 | 0.6618 |
| 0.5239 | 27.08 | 5200 | 0.6368 | 0.6631 | 0.6608 |
| 0.5184 | 28.12 | 5400 | 0.6363 | 0.6645 | 0.6624 |
| 0.5166 | 29.17 | 5600 | 0.6427 | 0.6558 | 0.6533 |
| 0.5082 | 30.21 | 5800 | 0.6549 | 0.6571 | 0.6549 |
| 0.512 | 31.25 | 6000 | 0.6498 | 0.6568 | 0.6543 |
| 0.5049 | 32.29 | 6200 | 0.6523 | 0.6551 | 0.6527 |
| 0.5058 | 33.33 | 6400 | 0.6637 | 0.6555 | 0.6530 |
| 0.5015 | 34.38 | 6600 | 0.6583 | 0.6577 | 0.6556 |
| 0.5006 | 35.42 | 6800 | 0.6677 | 0.6462 | 0.6435 |
| 0.4944 | 36.46 | 7000 | 0.6683 | 0.6509 | 0.6484 |
| 0.4917 | 37.5 | 7200 | 0.6583 | 0.6577 | 0.6553 |
| 0.4896 | 38.54 | 7400 | 0.6636 | 0.6519 | 0.6494 |
| 0.4953 | 39.58 | 7600 | 0.6492 | 0.6679 | 0.6676 |
| 0.4872 | 40.62 | 7800 | 0.6830 | 0.6417 | 0.6393 |
| 0.4851 | 41.67 | 8000 | 0.6629 | 0.6530 | 0.6504 |
| 0.4814 | 42.71 | 8200 | 0.6695 | 0.6476 | 0.6452 |
| 0.4805 | 43.75 | 8400 | 0.6657 | 0.6546 | 0.6520 |
| 0.4817 | 44.79 | 8600 | 0.6747 | 0.6540 | 0.6514 |
| 0.4768 | 45.83 | 8800 | 0.6611 | 0.6608 | 0.6588 |
| 0.4798 | 46.88 | 9000 | 0.6750 | 0.6527 | 0.6500 |
| 0.4736 | 47.92 | 9200 | 0.6768 | 0.6533 | 0.6507 |
| 0.4757 | 48.96 | 9400 | 0.6713 | 0.6570 | 0.6546 |
| 0.4723 | 50.0 | 9600 | 0.6803 | 0.6533 | 0.6507 |
| 0.4724 | 51.04 | 9800 | 0.6813 | 0.6530 | 0.6504 |
| 0.4699 | 52.08 | 10000 | 0.6809 | 0.6540 | 0.6514 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_EMP_H3K4me2-seqsight_32768_512_30M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3K4me2-seqsight_32768_512_30M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_30M",
"region:us"
] | null | 2024-04-30T04:15:12+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GUE_EMP_H3K9ac-seqsight_32768_512_30M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_30M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_30M) on the [mahdibaghbanzadeh/GUE_EMP_H3K9ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K9ac) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5056
- F1 Score: 0.7602
- Accuracy: 0.7600
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.6135 | 1.15 | 200 | 0.6051 | 0.6809 | 0.6837 |
| 0.568 | 2.3 | 400 | 0.6276 | 0.6593 | 0.6693 |
| 0.549 | 3.45 | 600 | 0.6638 | 0.6411 | 0.6556 |
| 0.5469 | 4.6 | 800 | 0.6112 | 0.6717 | 0.6797 |
| 0.5401 | 5.75 | 1000 | 0.5847 | 0.7014 | 0.7046 |
| 0.5366 | 6.9 | 1200 | 0.5835 | 0.7028 | 0.7053 |
| 0.5323 | 8.05 | 1400 | 0.5841 | 0.6957 | 0.6992 |
| 0.5291 | 9.2 | 1600 | 0.6242 | 0.6636 | 0.6743 |
| 0.5265 | 10.34 | 1800 | 0.5708 | 0.7085 | 0.7100 |
| 0.5274 | 11.49 | 2000 | 0.5831 | 0.6970 | 0.7003 |
| 0.5156 | 12.64 | 2200 | 0.6086 | 0.6808 | 0.6884 |
| 0.5236 | 13.79 | 2400 | 0.5860 | 0.6945 | 0.6992 |
| 0.5169 | 14.94 | 2600 | 0.5761 | 0.6992 | 0.7028 |
| 0.5151 | 16.09 | 2800 | 0.5545 | 0.7202 | 0.7208 |
| 0.5141 | 17.24 | 3000 | 0.5720 | 0.7112 | 0.7136 |
| 0.5113 | 18.39 | 3200 | 0.5723 | 0.7048 | 0.7082 |
| 0.5117 | 19.54 | 3400 | 0.5575 | 0.7116 | 0.7132 |
| 0.5076 | 20.69 | 3600 | 0.5581 | 0.7156 | 0.7172 |
| 0.504 | 21.84 | 3800 | 0.5462 | 0.7226 | 0.7233 |
| 0.5049 | 22.99 | 4000 | 0.5607 | 0.7121 | 0.7139 |
| 0.5039 | 24.14 | 4200 | 0.5326 | 0.7287 | 0.7283 |
| 0.4962 | 25.29 | 4400 | 0.5532 | 0.7228 | 0.7236 |
| 0.5032 | 26.44 | 4600 | 0.5572 | 0.7174 | 0.7190 |
| 0.4971 | 27.59 | 4800 | 0.5615 | 0.7163 | 0.7182 |
| 0.498 | 28.74 | 5000 | 0.5526 | 0.7201 | 0.7218 |
| 0.5028 | 29.89 | 5200 | 0.5424 | 0.7264 | 0.7269 |
| 0.4958 | 31.03 | 5400 | 0.5537 | 0.7191 | 0.7208 |
| 0.4966 | 32.18 | 5600 | 0.5343 | 0.7258 | 0.7254 |
| 0.49 | 33.33 | 5800 | 0.5416 | 0.7284 | 0.7283 |
| 0.5015 | 34.48 | 6000 | 0.5405 | 0.7266 | 0.7269 |
| 0.491 | 35.63 | 6200 | 0.5331 | 0.7275 | 0.7272 |
| 0.4951 | 36.78 | 6400 | 0.5474 | 0.7234 | 0.7244 |
| 0.4922 | 37.93 | 6600 | 0.5390 | 0.7253 | 0.7254 |
| 0.4898 | 39.08 | 6800 | 0.5376 | 0.7285 | 0.7287 |
| 0.4914 | 40.23 | 7000 | 0.5362 | 0.7279 | 0.7280 |
| 0.4917 | 41.38 | 7200 | 0.5389 | 0.7275 | 0.7280 |
| 0.4898 | 42.53 | 7400 | 0.5419 | 0.7280 | 0.7287 |
| 0.4915 | 43.68 | 7600 | 0.5351 | 0.7296 | 0.7298 |
| 0.4878 | 44.83 | 7800 | 0.5439 | 0.7257 | 0.7265 |
| 0.4901 | 45.98 | 8000 | 0.5424 | 0.7277 | 0.7283 |
| 0.4884 | 47.13 | 8200 | 0.5406 | 0.7268 | 0.7272 |
| 0.4885 | 48.28 | 8400 | 0.5398 | 0.7269 | 0.7272 |
| 0.4862 | 49.43 | 8600 | 0.5342 | 0.7296 | 0.7294 |
| 0.4879 | 50.57 | 8800 | 0.5392 | 0.7285 | 0.7287 |
| 0.488 | 51.72 | 9000 | 0.5415 | 0.7299 | 0.7305 |
| 0.484 | 52.87 | 9200 | 0.5435 | 0.7290 | 0.7298 |
| 0.4876 | 54.02 | 9400 | 0.5411 | 0.7292 | 0.7298 |
| 0.4855 | 55.17 | 9600 | 0.5401 | 0.7289 | 0.7294 |
| 0.4856 | 56.32 | 9800 | 0.5371 | 0.7309 | 0.7312 |
| 0.4865 | 57.47 | 10000 | 0.5389 | 0.7301 | 0.7305 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_EMP_H3K9ac-seqsight_32768_512_30M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3K9ac-seqsight_32768_512_30M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_30M",
"region:us"
] | null | 2024-04-30T04:16:01+00:00 |
text-generation | transformers |
# Model Card for Model ID
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## Model Details
### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed] | {"library_name": "transformers", "tags": []} | kyounghyun/EEVE-Korean-Instruct-2.8B-v1.0-20240430-2 | null | [
"transformers",
"safetensors",
"phi",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-04-30T04:16:37+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GUE_EMP_H3K9ac-seqsight_32768_512_30M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_30M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_30M) on the [mahdibaghbanzadeh/GUE_EMP_H3K9ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K9ac) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4849
- F1 Score: 0.7717
- Accuracy: 0.7711
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.5939 | 1.15 | 200 | 0.5881 | 0.6942 | 0.6963 |
| 0.5479 | 2.3 | 400 | 0.6719 | 0.6133 | 0.6351 |
| 0.5277 | 3.45 | 600 | 0.6097 | 0.6835 | 0.6902 |
| 0.5221 | 4.6 | 800 | 0.5649 | 0.7057 | 0.7082 |
| 0.5142 | 5.75 | 1000 | 0.5473 | 0.7279 | 0.7283 |
| 0.508 | 6.9 | 1200 | 0.5458 | 0.7246 | 0.7247 |
| 0.5044 | 8.05 | 1400 | 0.5579 | 0.7147 | 0.7164 |
| 0.4986 | 9.2 | 1600 | 0.5739 | 0.6996 | 0.7049 |
| 0.4961 | 10.34 | 1800 | 0.5555 | 0.7246 | 0.7258 |
| 0.4955 | 11.49 | 2000 | 0.5400 | 0.7335 | 0.7337 |
| 0.4864 | 12.64 | 2200 | 0.5728 | 0.7042 | 0.7092 |
| 0.491 | 13.79 | 2400 | 0.5279 | 0.7362 | 0.7362 |
| 0.485 | 14.94 | 2600 | 0.5321 | 0.7306 | 0.7312 |
| 0.4823 | 16.09 | 2800 | 0.5368 | 0.7319 | 0.7330 |
| 0.4823 | 17.24 | 3000 | 0.5420 | 0.7306 | 0.7319 |
| 0.476 | 18.39 | 3200 | 0.5328 | 0.7314 | 0.7316 |
| 0.479 | 19.54 | 3400 | 0.5289 | 0.7342 | 0.7348 |
| 0.4729 | 20.69 | 3600 | 0.5216 | 0.7380 | 0.7377 |
| 0.4698 | 21.84 | 3800 | 0.5466 | 0.7280 | 0.7301 |
| 0.4707 | 22.99 | 4000 | 0.5451 | 0.7244 | 0.7269 |
| 0.4704 | 24.14 | 4200 | 0.5234 | 0.7425 | 0.7424 |
| 0.4603 | 25.29 | 4400 | 0.5452 | 0.7390 | 0.7398 |
| 0.467 | 26.44 | 4600 | 0.5324 | 0.7397 | 0.7398 |
| 0.4605 | 27.59 | 4800 | 0.5406 | 0.7382 | 0.7391 |
| 0.4615 | 28.74 | 5000 | 0.5333 | 0.7400 | 0.7406 |
| 0.4659 | 29.89 | 5200 | 0.5364 | 0.7409 | 0.7413 |
| 0.46 | 31.03 | 5400 | 0.5299 | 0.7435 | 0.7438 |
| 0.4581 | 32.18 | 5600 | 0.5224 | 0.7462 | 0.7460 |
| 0.4506 | 33.33 | 5800 | 0.5321 | 0.7459 | 0.7456 |
| 0.4614 | 34.48 | 6000 | 0.5286 | 0.7471 | 0.7470 |
| 0.451 | 35.63 | 6200 | 0.5194 | 0.7483 | 0.7478 |
| 0.4568 | 36.78 | 6400 | 0.5335 | 0.7438 | 0.7442 |
| 0.4509 | 37.93 | 6600 | 0.5496 | 0.7319 | 0.7330 |
| 0.4498 | 39.08 | 6800 | 0.5295 | 0.7479 | 0.7478 |
| 0.4497 | 40.23 | 7000 | 0.5345 | 0.7447 | 0.7449 |
| 0.4491 | 41.38 | 7200 | 0.5461 | 0.7390 | 0.7398 |
| 0.4512 | 42.53 | 7400 | 0.5352 | 0.7394 | 0.7398 |
| 0.4487 | 43.68 | 7600 | 0.5305 | 0.7478 | 0.7478 |
| 0.4472 | 44.83 | 7800 | 0.5382 | 0.7427 | 0.7431 |
| 0.4482 | 45.98 | 8000 | 0.5231 | 0.7488 | 0.7485 |
| 0.4459 | 47.13 | 8200 | 0.5408 | 0.7394 | 0.7398 |
| 0.4456 | 48.28 | 8400 | 0.5319 | 0.7460 | 0.7460 |
| 0.4418 | 49.43 | 8600 | 0.5314 | 0.7468 | 0.7467 |
| 0.4449 | 50.57 | 8800 | 0.5351 | 0.7463 | 0.7463 |
| 0.4438 | 51.72 | 9000 | 0.5454 | 0.7432 | 0.7438 |
| 0.4416 | 52.87 | 9200 | 0.5444 | 0.7409 | 0.7416 |
| 0.4431 | 54.02 | 9400 | 0.5426 | 0.7393 | 0.7398 |
| 0.441 | 55.17 | 9600 | 0.5405 | 0.7413 | 0.7416 |
| 0.4411 | 56.32 | 9800 | 0.5347 | 0.7437 | 0.7438 |
| 0.4409 | 57.47 | 10000 | 0.5362 | 0.7444 | 0.7445 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_EMP_H3K9ac-seqsight_32768_512_30M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3K9ac-seqsight_32768_512_30M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_30M",
"region:us"
] | null | 2024-04-30T04:16:47+00:00 |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-vivos
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5366
- Wer: 0.3320
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 7.987 | 0.66 | 500 | 3.5460 | 1.0 |
| 3.4026 | 1.31 | 1000 | 3.0685 | 1.0 |
| 1.6402 | 1.97 | 1500 | 0.7959 | 0.7082 |
| 0.8229 | 2.62 | 2000 | 0.5581 | 0.5326 |
| 0.6392 | 3.28 | 2500 | 0.4779 | 0.4738 |
| 0.5532 | 3.94 | 3000 | 0.4415 | 0.4491 |
| 0.4937 | 4.59 | 3500 | 0.4318 | 0.4312 |
| 0.4506 | 5.25 | 4000 | 0.4284 | 0.4134 |
| 0.4099 | 5.91 | 4500 | 0.4405 | 0.4267 |
| 0.3848 | 6.56 | 5000 | 0.4097 | 0.3987 |
| 0.3683 | 7.22 | 5500 | 0.4239 | 0.4031 |
| 0.3485 | 7.87 | 6000 | 0.4383 | 0.3926 |
| 0.3313 | 8.53 | 6500 | 0.4779 | 0.3846 |
| 0.321 | 9.19 | 7000 | 0.4623 | 0.3895 |
| 0.3058 | 9.84 | 7500 | 0.4668 | 0.3906 |
| 0.2869 | 10.5 | 8000 | 0.4817 | 0.3749 |
| 0.2828 | 11.15 | 8500 | 0.4777 | 0.3789 |
| 0.2724 | 11.81 | 9000 | 0.4915 | 0.3649 |
| 0.2527 | 12.47 | 9500 | 0.4671 | 0.3670 |
| 0.2588 | 13.12 | 10000 | 0.4693 | 0.3612 |
| 0.2405 | 13.78 | 10500 | 0.4375 | 0.3579 |
| 0.2409 | 14.44 | 11000 | 0.4643 | 0.3595 |
| 0.2247 | 15.09 | 11500 | 0.5445 | 0.3626 |
| 0.2257 | 15.75 | 12000 | 0.4474 | 0.3513 |
| 0.2101 | 16.4 | 12500 | 0.4327 | 0.3502 |
| 0.2118 | 17.06 | 13000 | 0.4830 | 0.3534 |
| 0.1991 | 17.72 | 13500 | 0.4832 | 0.3454 |
| 0.193 | 18.37 | 14000 | 0.4878 | 0.3547 |
| 0.1909 | 19.03 | 14500 | 0.4777 | 0.3506 |
| 0.1869 | 19.69 | 15000 | 0.4722 | 0.3455 |
| 0.1801 | 20.34 | 15500 | 0.4891 | 0.3477 |
| 0.1749 | 21.0 | 16000 | 0.5065 | 0.3446 |
| 0.1715 | 21.65 | 16500 | 0.5381 | 0.3447 |
| 0.1669 | 22.31 | 17000 | 0.4946 | 0.3459 |
| 0.1674 | 22.97 | 17500 | 0.4968 | 0.3425 |
| 0.1579 | 23.62 | 18000 | 0.5210 | 0.3370 |
| 0.1566 | 24.28 | 18500 | 0.5318 | 0.3385 |
| 0.1565 | 24.93 | 19000 | 0.4959 | 0.3381 |
| 0.1517 | 25.59 | 19500 | 0.5181 | 0.3393 |
| 0.1452 | 26.25 | 20000 | 0.5222 | 0.3359 |
| 0.1419 | 26.9 | 20500 | 0.5316 | 0.3333 |
| 0.1389 | 27.56 | 21000 | 0.5094 | 0.3302 |
| 0.1422 | 28.22 | 21500 | 0.5327 | 0.3346 |
| 0.1365 | 28.87 | 22000 | 0.5436 | 0.3320 |
| 0.1337 | 29.53 | 22500 | 0.5366 | 0.3320 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "facebook/wav2vec2-xls-r-300m", "model-index": [{"name": "wav2vec2-base-vivos", "results": []}]} | Lasion/wav2vec2-xls-r-300m-vivos | null | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T04:19:12+00:00 |
null | transformers |
# Uploaded model
- **Developed by:** arvnoodle
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Phi-3-mini-4k-instruct
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "trl"], "base_model": "unsloth/Phi-3-mini-4k-instruct"} | arvnoodle/hcl-phi3-it-3b-xml-json | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/Phi-3-mini-4k-instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T04:19:50+00:00 |
text-generation | transformers |
# Model Card for Model ID
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## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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### Training Data
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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- This section describes the evaluation protocols and provides the results. -->
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#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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## Citation [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | cilantro9246/7chhuyp | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T04:19:50+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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### Model Architecture and Objective
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[More Information Needed]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | shallow6414/2p8jfrv | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T04:20:10+00:00 |
null | null | {} | KirtiKousik/New_loras | null | [
"region:us"
] | null | 2024-04-30T04:20:50+00:00 |
|
text-generation | transformers |
# TooManyMix_LLM
TooManyMix_LLM is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [jdqwoi/TooManyMixed-LLM_01](https://huggingface.co/jdqwoi/TooManyMixed-LLM_01)
* [jdqwoi/TooManyMixed-LLM_02](https://huggingface.co/jdqwoi/TooManyMixed-LLM_02)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: jdqwoi/TooManyMixed-LLM_01
layer_range: [0, 32]
- model: jdqwoi/TooManyMixed-LLM_02
layer_range: [0, 32]
merge_method: slerp
base_model: jdqwoi/TooManyMixed-LLM_01
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "jdqwoi/TooManyMix_LLM"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"tags": ["merge", "mergekit", "lazymergekit", "jdqwoi/TooManyMixed-LLM_01", "jdqwoi/TooManyMixed-LLM_02"], "base_model": ["jdqwoi/TooManyMixed-LLM_01", "jdqwoi/TooManyMixed-LLM_02"]} | jdqwoi/TooManyMix_LLM | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"jdqwoi/TooManyMixed-LLM_01",
"jdqwoi/TooManyMixed-LLM_02",
"base_model:jdqwoi/TooManyMixed-LLM_01",
"base_model:jdqwoi/TooManyMixed-LLM_02",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T04:21:02+00:00 |
text-classification | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | atsizelti/turkish_org_classifier_16k | null | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T04:21:43+00:00 |
text-generation | transformers |
# Model Card for Model ID
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## How to Get Started with the Model
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[More Information Needed] | {"library_name": "transformers", "tags": []} | MohammadOthman/OpenHermes-2.5-Mistral-7B-Orca-DPO | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T04:21:48+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GUE_EMP_H3K9ac-seqsight_32768_512_30M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_30M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_30M) on the [mahdibaghbanzadeh/GUE_EMP_H3K9ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K9ac) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5096
- F1 Score: 0.7734
- Accuracy: 0.7729
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.5817 | 1.15 | 200 | 0.5913 | 0.6887 | 0.6927 |
| 0.5339 | 2.3 | 400 | 0.6205 | 0.6522 | 0.6657 |
| 0.5132 | 3.45 | 600 | 0.5668 | 0.7166 | 0.7186 |
| 0.5061 | 4.6 | 800 | 0.5335 | 0.7329 | 0.7334 |
| 0.4999 | 5.75 | 1000 | 0.5319 | 0.7328 | 0.7330 |
| 0.4901 | 6.9 | 1200 | 0.5360 | 0.7338 | 0.7341 |
| 0.487 | 8.05 | 1400 | 0.5536 | 0.7241 | 0.7262 |
| 0.4783 | 9.2 | 1600 | 0.5589 | 0.7149 | 0.7186 |
| 0.4752 | 10.34 | 1800 | 0.5505 | 0.7303 | 0.7319 |
| 0.4725 | 11.49 | 2000 | 0.5447 | 0.7361 | 0.7370 |
| 0.4628 | 12.64 | 2200 | 0.5480 | 0.7317 | 0.7334 |
| 0.4647 | 13.79 | 2400 | 0.5368 | 0.7442 | 0.7452 |
| 0.4587 | 14.94 | 2600 | 0.5183 | 0.7536 | 0.7531 |
| 0.4518 | 16.09 | 2800 | 0.5482 | 0.7373 | 0.7388 |
| 0.4483 | 17.24 | 3000 | 0.5362 | 0.7485 | 0.7492 |
| 0.4448 | 18.39 | 3200 | 0.5329 | 0.7519 | 0.7521 |
| 0.4423 | 19.54 | 3400 | 0.5261 | 0.7483 | 0.7481 |
| 0.4362 | 20.69 | 3600 | 0.5187 | 0.7569 | 0.7564 |
| 0.4329 | 21.84 | 3800 | 0.5539 | 0.7347 | 0.7370 |
| 0.4273 | 22.99 | 4000 | 0.5805 | 0.7244 | 0.7280 |
| 0.4263 | 24.14 | 4200 | 0.5338 | 0.7522 | 0.7521 |
| 0.4153 | 25.29 | 4400 | 0.5495 | 0.7534 | 0.7535 |
| 0.4194 | 26.44 | 4600 | 0.5493 | 0.7572 | 0.7571 |
| 0.4125 | 27.59 | 4800 | 0.5311 | 0.7544 | 0.7546 |
| 0.4093 | 28.74 | 5000 | 0.5474 | 0.7483 | 0.7488 |
| 0.4153 | 29.89 | 5200 | 0.5588 | 0.7436 | 0.7438 |
| 0.4062 | 31.03 | 5400 | 0.5699 | 0.7401 | 0.7413 |
| 0.4034 | 32.18 | 5600 | 0.5563 | 0.7472 | 0.7478 |
| 0.3941 | 33.33 | 5800 | 0.5614 | 0.7547 | 0.7546 |
| 0.4054 | 34.48 | 6000 | 0.5466 | 0.7500 | 0.7499 |
| 0.3897 | 35.63 | 6200 | 0.5369 | 0.7565 | 0.7560 |
| 0.3964 | 36.78 | 6400 | 0.5498 | 0.7498 | 0.7499 |
| 0.3841 | 37.93 | 6600 | 0.5737 | 0.7442 | 0.7449 |
| 0.3878 | 39.08 | 6800 | 0.5691 | 0.7422 | 0.7424 |
| 0.3843 | 40.23 | 7000 | 0.5700 | 0.7392 | 0.7398 |
| 0.3824 | 41.38 | 7200 | 0.5768 | 0.7391 | 0.7398 |
| 0.3807 | 42.53 | 7400 | 0.5628 | 0.7473 | 0.7474 |
| 0.3792 | 43.68 | 7600 | 0.5603 | 0.7478 | 0.7478 |
| 0.3783 | 44.83 | 7800 | 0.5697 | 0.7431 | 0.7434 |
| 0.3768 | 45.98 | 8000 | 0.5539 | 0.7477 | 0.7474 |
| 0.3742 | 47.13 | 8200 | 0.5758 | 0.7421 | 0.7424 |
| 0.3746 | 48.28 | 8400 | 0.5785 | 0.7392 | 0.7395 |
| 0.3716 | 49.43 | 8600 | 0.5693 | 0.7489 | 0.7488 |
| 0.3702 | 50.57 | 8800 | 0.5805 | 0.7424 | 0.7427 |
| 0.3675 | 51.72 | 9000 | 0.5923 | 0.7381 | 0.7388 |
| 0.369 | 52.87 | 9200 | 0.5896 | 0.7385 | 0.7391 |
| 0.3655 | 54.02 | 9400 | 0.5891 | 0.7405 | 0.7409 |
| 0.3646 | 55.17 | 9600 | 0.5869 | 0.7422 | 0.7427 |
| 0.3627 | 56.32 | 9800 | 0.5785 | 0.7466 | 0.7467 |
| 0.3617 | 57.47 | 10000 | 0.5803 | 0.7440 | 0.7442 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_EMP_H3K9ac-seqsight_32768_512_30M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3K9ac-seqsight_32768_512_30M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_30M",
"region:us"
] | null | 2024-04-30T04:21:52+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GUE_EMP_H3K4me3-seqsight_32768_512_30M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_30M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_30M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me3) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6279
- F1 Score: 0.6600
- Accuracy: 0.6614
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.6724 | 0.87 | 200 | 0.6653 | 0.6048 | 0.6076 |
| 0.6521 | 1.74 | 400 | 0.6557 | 0.6151 | 0.6158 |
| 0.6529 | 2.61 | 600 | 0.6530 | 0.6176 | 0.6177 |
| 0.6433 | 3.48 | 800 | 0.6474 | 0.6226 | 0.6223 |
| 0.645 | 4.35 | 1000 | 0.6444 | 0.6231 | 0.6239 |
| 0.6426 | 5.22 | 1200 | 0.6666 | 0.5973 | 0.6065 |
| 0.6386 | 6.09 | 1400 | 0.6468 | 0.6261 | 0.6266 |
| 0.6351 | 6.96 | 1600 | 0.6556 | 0.6185 | 0.6204 |
| 0.6332 | 7.83 | 1800 | 0.6383 | 0.6358 | 0.6359 |
| 0.6323 | 8.7 | 2000 | 0.6411 | 0.6274 | 0.6274 |
| 0.6294 | 9.57 | 2200 | 0.6424 | 0.6283 | 0.6296 |
| 0.6282 | 10.43 | 2400 | 0.6412 | 0.6303 | 0.6310 |
| 0.6257 | 11.3 | 2600 | 0.6514 | 0.6153 | 0.6198 |
| 0.6248 | 12.17 | 2800 | 0.6429 | 0.6282 | 0.6291 |
| 0.6216 | 13.04 | 3000 | 0.6408 | 0.6285 | 0.6304 |
| 0.6217 | 13.91 | 3200 | 0.6472 | 0.6253 | 0.6299 |
| 0.6203 | 14.78 | 3400 | 0.6342 | 0.6275 | 0.6283 |
| 0.6178 | 15.65 | 3600 | 0.6449 | 0.6316 | 0.6340 |
| 0.6169 | 16.52 | 3800 | 0.6425 | 0.6311 | 0.6334 |
| 0.6175 | 17.39 | 4000 | 0.6414 | 0.6335 | 0.6356 |
| 0.6187 | 18.26 | 4200 | 0.6366 | 0.6324 | 0.6334 |
| 0.6142 | 19.13 | 4400 | 0.6372 | 0.6350 | 0.6364 |
| 0.6144 | 20.0 | 4600 | 0.6373 | 0.6328 | 0.6345 |
| 0.6143 | 20.87 | 4800 | 0.6336 | 0.6365 | 0.6367 |
| 0.6121 | 21.74 | 5000 | 0.6438 | 0.6295 | 0.6329 |
| 0.6126 | 22.61 | 5200 | 0.6392 | 0.6326 | 0.6359 |
| 0.6123 | 23.48 | 5400 | 0.6446 | 0.6300 | 0.6348 |
| 0.6108 | 24.35 | 5600 | 0.6339 | 0.6372 | 0.6383 |
| 0.6109 | 25.22 | 5800 | 0.6554 | 0.6262 | 0.6345 |
| 0.6076 | 26.09 | 6000 | 0.6478 | 0.6272 | 0.6329 |
| 0.6098 | 26.96 | 6200 | 0.6392 | 0.6312 | 0.6351 |
| 0.6086 | 27.83 | 6400 | 0.6554 | 0.6260 | 0.6351 |
| 0.6064 | 28.7 | 6600 | 0.6385 | 0.6337 | 0.6364 |
| 0.6092 | 29.57 | 6800 | 0.6343 | 0.6386 | 0.6410 |
| 0.6032 | 30.43 | 7000 | 0.6460 | 0.6329 | 0.6386 |
| 0.6104 | 31.3 | 7200 | 0.6428 | 0.6317 | 0.6372 |
| 0.6078 | 32.17 | 7400 | 0.6475 | 0.6331 | 0.6402 |
| 0.6052 | 33.04 | 7600 | 0.6336 | 0.6376 | 0.6405 |
| 0.6053 | 33.91 | 7800 | 0.6369 | 0.6342 | 0.6378 |
| 0.6076 | 34.78 | 8000 | 0.6369 | 0.6351 | 0.6389 |
| 0.6026 | 35.65 | 8200 | 0.6360 | 0.6346 | 0.6380 |
| 0.6033 | 36.52 | 8400 | 0.6402 | 0.6370 | 0.6416 |
| 0.605 | 37.39 | 8600 | 0.6368 | 0.6354 | 0.6391 |
| 0.601 | 38.26 | 8800 | 0.6395 | 0.6352 | 0.6397 |
| 0.6053 | 39.13 | 9000 | 0.6395 | 0.6378 | 0.6429 |
| 0.6045 | 40.0 | 9200 | 0.6345 | 0.6374 | 0.6405 |
| 0.6022 | 40.87 | 9400 | 0.6321 | 0.6417 | 0.6440 |
| 0.604 | 41.74 | 9600 | 0.6334 | 0.6407 | 0.6435 |
| 0.6062 | 42.61 | 9800 | 0.6341 | 0.6374 | 0.6408 |
| 0.6016 | 43.48 | 10000 | 0.6344 | 0.6380 | 0.6413 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_EMP_H3K4me3-seqsight_32768_512_30M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3K4me3-seqsight_32768_512_30M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_30M",
"region:us"
] | null | 2024-04-30T04:22:22+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GUE_EMP_H3K4me3-seqsight_32768_512_30M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_30M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_30M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me3) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6199
- F1 Score: 0.6667
- Accuracy: 0.6698
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.6672 | 0.87 | 200 | 0.6615 | 0.6132 | 0.6152 |
| 0.6439 | 1.74 | 400 | 0.6459 | 0.6231 | 0.6231 |
| 0.6412 | 2.61 | 600 | 0.6482 | 0.6209 | 0.6234 |
| 0.6295 | 3.48 | 800 | 0.6386 | 0.6372 | 0.6372 |
| 0.6282 | 4.35 | 1000 | 0.6344 | 0.6327 | 0.6323 |
| 0.6234 | 5.22 | 1200 | 0.6662 | 0.6025 | 0.6160 |
| 0.6176 | 6.09 | 1400 | 0.6671 | 0.6179 | 0.6291 |
| 0.6138 | 6.96 | 1600 | 0.6507 | 0.6316 | 0.6370 |
| 0.6085 | 7.83 | 1800 | 0.6236 | 0.6435 | 0.6432 |
| 0.6093 | 8.7 | 2000 | 0.6283 | 0.6425 | 0.6446 |
| 0.6027 | 9.57 | 2200 | 0.6225 | 0.6476 | 0.6492 |
| 0.6013 | 10.43 | 2400 | 0.6278 | 0.6439 | 0.6457 |
| 0.5974 | 11.3 | 2600 | 0.6335 | 0.6411 | 0.6465 |
| 0.5978 | 12.17 | 2800 | 0.6289 | 0.6457 | 0.6497 |
| 0.5916 | 13.04 | 3000 | 0.6242 | 0.6445 | 0.6476 |
| 0.5914 | 13.91 | 3200 | 0.6235 | 0.6489 | 0.6524 |
| 0.5896 | 14.78 | 3400 | 0.6243 | 0.6465 | 0.6505 |
| 0.5864 | 15.65 | 3600 | 0.6296 | 0.6491 | 0.6538 |
| 0.5849 | 16.52 | 3800 | 0.6174 | 0.6572 | 0.6592 |
| 0.5837 | 17.39 | 4000 | 0.6279 | 0.6473 | 0.6503 |
| 0.585 | 18.26 | 4200 | 0.6204 | 0.6579 | 0.6611 |
| 0.5802 | 19.13 | 4400 | 0.6223 | 0.6572 | 0.6598 |
| 0.5784 | 20.0 | 4600 | 0.6207 | 0.6516 | 0.6554 |
| 0.5788 | 20.87 | 4800 | 0.6239 | 0.6594 | 0.6630 |
| 0.5772 | 21.74 | 5000 | 0.6308 | 0.6471 | 0.6519 |
| 0.5765 | 22.61 | 5200 | 0.6179 | 0.6564 | 0.6590 |
| 0.5741 | 23.48 | 5400 | 0.6391 | 0.6392 | 0.6495 |
| 0.5735 | 24.35 | 5600 | 0.6255 | 0.6541 | 0.6582 |
| 0.5715 | 25.22 | 5800 | 0.6391 | 0.6390 | 0.6481 |
| 0.5686 | 26.09 | 6000 | 0.6380 | 0.6459 | 0.6527 |
| 0.5695 | 26.96 | 6200 | 0.6258 | 0.6469 | 0.6541 |
| 0.5671 | 27.83 | 6400 | 0.6481 | 0.6306 | 0.6435 |
| 0.5667 | 28.7 | 6600 | 0.6278 | 0.6508 | 0.6554 |
| 0.567 | 29.57 | 6800 | 0.6250 | 0.6557 | 0.6598 |
| 0.5628 | 30.43 | 7000 | 0.6341 | 0.6460 | 0.6533 |
| 0.5685 | 31.3 | 7200 | 0.6270 | 0.6499 | 0.6546 |
| 0.5663 | 32.17 | 7400 | 0.6295 | 0.6484 | 0.6546 |
| 0.5633 | 33.04 | 7600 | 0.6262 | 0.6493 | 0.6546 |
| 0.5621 | 33.91 | 7800 | 0.6226 | 0.6564 | 0.6606 |
| 0.5644 | 34.78 | 8000 | 0.6256 | 0.6548 | 0.6587 |
| 0.5589 | 35.65 | 8200 | 0.6265 | 0.6565 | 0.6614 |
| 0.5588 | 36.52 | 8400 | 0.6334 | 0.6470 | 0.6543 |
| 0.5623 | 37.39 | 8600 | 0.6259 | 0.6523 | 0.6571 |
| 0.5561 | 38.26 | 8800 | 0.6353 | 0.6506 | 0.6573 |
| 0.5623 | 39.13 | 9000 | 0.6298 | 0.6524 | 0.6582 |
| 0.5584 | 40.0 | 9200 | 0.6240 | 0.6541 | 0.6582 |
| 0.5577 | 40.87 | 9400 | 0.6227 | 0.6544 | 0.6579 |
| 0.5591 | 41.74 | 9600 | 0.6240 | 0.6546 | 0.6584 |
| 0.563 | 42.61 | 9800 | 0.6244 | 0.6533 | 0.6576 |
| 0.5566 | 43.48 | 10000 | 0.6258 | 0.6543 | 0.6587 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_EMP_H3K4me3-seqsight_32768_512_30M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3K4me3-seqsight_32768_512_30M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_30M",
"region:us"
] | null | 2024-04-30T04:22:46+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GUE_EMP_H3K4me3-seqsight_32768_512_30M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_30M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_30M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me3) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6248
- F1 Score: 0.6761
- Accuracy: 0.6777
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.6637 | 0.87 | 200 | 0.6550 | 0.6195 | 0.6209 |
| 0.6376 | 1.74 | 400 | 0.6438 | 0.6310 | 0.6326 |
| 0.6322 | 2.61 | 600 | 0.6353 | 0.6408 | 0.6416 |
| 0.6198 | 3.48 | 800 | 0.6305 | 0.6444 | 0.6448 |
| 0.6141 | 4.35 | 1000 | 0.6260 | 0.6433 | 0.6435 |
| 0.6064 | 5.22 | 1200 | 0.6410 | 0.6384 | 0.6448 |
| 0.5977 | 6.09 | 1400 | 0.6605 | 0.6232 | 0.6375 |
| 0.5927 | 6.96 | 1600 | 0.6323 | 0.6466 | 0.6524 |
| 0.5833 | 7.83 | 1800 | 0.6178 | 0.6596 | 0.6606 |
| 0.5848 | 8.7 | 2000 | 0.6235 | 0.6560 | 0.6592 |
| 0.5737 | 9.57 | 2200 | 0.6151 | 0.6564 | 0.6582 |
| 0.5733 | 10.43 | 2400 | 0.6241 | 0.6578 | 0.6601 |
| 0.5658 | 11.3 | 2600 | 0.6331 | 0.6475 | 0.6546 |
| 0.5669 | 12.17 | 2800 | 0.6178 | 0.6548 | 0.6576 |
| 0.5576 | 13.04 | 3000 | 0.6209 | 0.6604 | 0.6628 |
| 0.5577 | 13.91 | 3200 | 0.6188 | 0.6523 | 0.6560 |
| 0.5507 | 14.78 | 3400 | 0.6287 | 0.6479 | 0.6533 |
| 0.5483 | 15.65 | 3600 | 0.6358 | 0.6585 | 0.6625 |
| 0.5452 | 16.52 | 3800 | 0.6206 | 0.6618 | 0.6633 |
| 0.5422 | 17.39 | 4000 | 0.6360 | 0.6548 | 0.6579 |
| 0.5416 | 18.26 | 4200 | 0.6539 | 0.6452 | 0.6533 |
| 0.5367 | 19.13 | 4400 | 0.6351 | 0.6593 | 0.6617 |
| 0.5333 | 20.0 | 4600 | 0.6371 | 0.6540 | 0.6598 |
| 0.5308 | 20.87 | 4800 | 0.6404 | 0.6613 | 0.6639 |
| 0.5284 | 21.74 | 5000 | 0.6435 | 0.6561 | 0.6598 |
| 0.5246 | 22.61 | 5200 | 0.6354 | 0.6653 | 0.6671 |
| 0.5236 | 23.48 | 5400 | 0.6640 | 0.6461 | 0.6541 |
| 0.5195 | 24.35 | 5600 | 0.6482 | 0.6523 | 0.6568 |
| 0.5162 | 25.22 | 5800 | 0.6601 | 0.6504 | 0.6552 |
| 0.5139 | 26.09 | 6000 | 0.6628 | 0.6573 | 0.6620 |
| 0.5121 | 26.96 | 6200 | 0.6513 | 0.6523 | 0.6571 |
| 0.5083 | 27.83 | 6400 | 0.6792 | 0.6408 | 0.65 |
| 0.5081 | 28.7 | 6600 | 0.6416 | 0.6605 | 0.6628 |
| 0.5059 | 29.57 | 6800 | 0.6477 | 0.6586 | 0.6617 |
| 0.5005 | 30.43 | 7000 | 0.6573 | 0.6549 | 0.6590 |
| 0.5098 | 31.3 | 7200 | 0.6490 | 0.6573 | 0.6611 |
| 0.5007 | 32.17 | 7400 | 0.6486 | 0.6604 | 0.6625 |
| 0.4976 | 33.04 | 7600 | 0.6524 | 0.6543 | 0.6582 |
| 0.4946 | 33.91 | 7800 | 0.6502 | 0.6584 | 0.6617 |
| 0.4978 | 34.78 | 8000 | 0.6525 | 0.6635 | 0.6658 |
| 0.4894 | 35.65 | 8200 | 0.6656 | 0.6563 | 0.6592 |
| 0.4883 | 36.52 | 8400 | 0.6661 | 0.6535 | 0.6576 |
| 0.4944 | 37.39 | 8600 | 0.6607 | 0.6583 | 0.6617 |
| 0.4869 | 38.26 | 8800 | 0.6732 | 0.6511 | 0.6565 |
| 0.4922 | 39.13 | 9000 | 0.6633 | 0.6551 | 0.6587 |
| 0.4886 | 40.0 | 9200 | 0.6539 | 0.6564 | 0.6590 |
| 0.4866 | 40.87 | 9400 | 0.6586 | 0.6583 | 0.6609 |
| 0.4864 | 41.74 | 9600 | 0.6606 | 0.6561 | 0.6592 |
| 0.4919 | 42.61 | 9800 | 0.6595 | 0.6565 | 0.6595 |
| 0.4856 | 43.48 | 10000 | 0.6606 | 0.6585 | 0.6617 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_EMP_H3K4me3-seqsight_32768_512_30M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3K4me3-seqsight_32768_512_30M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_30M",
"region:us"
] | null | 2024-04-30T04:24:47+00:00 |
reinforcement-learning | ml-agents |
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: Rudolph314/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| {"library_name": "ml-agents", "tags": ["SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos"]} | Rudolph314/poca-SoccerTwos | null | [
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] | null | 2024-04-30T04:24:53+00:00 |
text-generation | transformers |
# Finetune Mistral, Gemma, Llama 2-5x faster with 70% less memory via Unsloth!
Directly quantized 4bit model with `bitsandbytes`. Built with Meta Llama 3
We have a Google Colab Tesla T4 notebook for Llama-3 8b here: https://colab.research.google.com/drive/135ced7oHytdxu3N2DNe1Z0kqjyYIkDXp?usp=sharing
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord%20button.png" width="200"/>](https://discord.gg/u54VK8m8tk)
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/buy%20me%20a%20coffee%20button.png" width="200"/>](https://ko-fi.com/unsloth)
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
## ✨ Finetune for Free
All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face.
| Unsloth supports | Free Notebooks | Performance | Memory use |
|-----------------|--------------------------------------------------------------------------------------------------------------------------|-------------|----------|
| **Llama-3 8b** | [▶️ Start on Colab](https://colab.research.google.com/drive/135ced7oHytdxu3N2DNe1Z0kqjyYIkDXp?usp=sharing) | 2.4x faster | 58% less |
| **Gemma 7b** | [▶️ Start on Colab](https://colab.research.google.com/drive/10NbwlsRChbma1v55m8LAPYG15uQv6HLo?usp=sharing) | 2.4x faster | 58% less |
| **Mistral 7b** | [▶️ Start on Colab](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing) | 2.2x faster | 62% less |
| **Llama-2 7b** | [▶️ Start on Colab](https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?usp=sharing) | 2.2x faster | 43% less |
| **TinyLlama** | [▶️ Start on Colab](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing) | 3.9x faster | 74% less |
| **CodeLlama 34b** A100 | [▶️ Start on Colab](https://colab.research.google.com/drive/1y7A0AxE3y8gdj4AVkl2aZX47Xu3P1wJT?usp=sharing) | 1.9x faster | 27% less |
| **Mistral 7b** 1xT4 | [▶️ Start on Kaggle](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook) | 5x faster\* | 62% less |
| **DPO - Zephyr** | [▶️ Start on Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) | 1.9x faster | 19% less |
- This [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing) is useful for ShareGPT ChatML / Vicuna templates.
- This [text completion notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing) is for raw text. This [DPO notebook](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) replicates Zephyr.
- \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster. | {"language": ["en"], "license": "llama2", "library_name": "transformers", "tags": ["unsloth", "transformers", "llama", "llama-3"]} | Akirami/vanilla-llama-3-8b-bnb-4bit | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"unsloth",
"llama-3",
"en",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-04-30T04:25:17+00:00 |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisperFinetune
This model is a fine-tuned version of [openai/whisper-tiny.en](https://huggingface.co/openai/whisper-tiny.en) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5745
- Wer: 28.7011
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 128
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 2.8818 | 0.2778 | 10 | 1.9210 | 39.4679 |
| 0.7557 | 0.5556 | 20 | 0.6926 | 27.2926 |
| 0.5718 | 0.8333 | 30 | 0.5717 | 23.7559 |
| 0.421 | 1.1111 | 40 | 0.5161 | 21.5023 |
| 0.3088 | 1.3889 | 50 | 0.5103 | 21.0955 |
| 0.3415 | 1.6667 | 60 | 0.5155 | 21.6901 |
| 0.3434 | 1.9444 | 70 | 0.5176 | 30.0156 |
| 0.1371 | 2.2222 | 80 | 0.5303 | 20.6886 |
| 0.1349 | 2.5 | 90 | 0.5589 | 22.0344 |
| 0.1461 | 2.7778 | 100 | 0.5745 | 28.7011 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.1.dev0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "openai/whisper-tiny.en", "model-index": [{"name": "whisperFinetune", "results": []}]} | shljessie/whisperFinetune | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-tiny.en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T04:25:36+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GUE_EMP_H4-seqsight_32768_512_30M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_30M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_30M) on the [mahdibaghbanzadeh/GUE_EMP_H4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H4) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2787
- F1 Score: 0.8962
- Accuracy: 0.8960
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
| 0.4361 | 2.17 | 200 | 0.3195 | 0.8804 | 0.8802 |
| 0.3195 | 4.35 | 400 | 0.3192 | 0.8772 | 0.8768 |
| 0.3052 | 6.52 | 600 | 0.3107 | 0.8745 | 0.8741 |
| 0.3046 | 8.7 | 800 | 0.3134 | 0.8765 | 0.8761 |
| 0.2905 | 10.87 | 1000 | 0.3050 | 0.8825 | 0.8823 |
| 0.2882 | 13.04 | 1200 | 0.3107 | 0.8738 | 0.8734 |
| 0.2853 | 15.22 | 1400 | 0.3008 | 0.8840 | 0.8836 |
| 0.2813 | 17.39 | 1600 | 0.3047 | 0.8772 | 0.8768 |
| 0.2758 | 19.57 | 1800 | 0.2978 | 0.8900 | 0.8898 |
| 0.2777 | 21.74 | 2000 | 0.3170 | 0.8725 | 0.8720 |
| 0.2722 | 23.91 | 2200 | 0.3090 | 0.8772 | 0.8768 |
| 0.2693 | 26.09 | 2400 | 0.3118 | 0.8786 | 0.8782 |
| 0.2696 | 28.26 | 2600 | 0.2978 | 0.8866 | 0.8864 |
| 0.2645 | 30.43 | 2800 | 0.2999 | 0.8840 | 0.8836 |
| 0.2651 | 32.61 | 3000 | 0.3025 | 0.8840 | 0.8836 |
| 0.2593 | 34.78 | 3200 | 0.2983 | 0.8839 | 0.8836 |
| 0.2579 | 36.96 | 3400 | 0.3068 | 0.8840 | 0.8836 |
| 0.2566 | 39.13 | 3600 | 0.3051 | 0.8820 | 0.8816 |
| 0.2533 | 41.3 | 3800 | 0.2938 | 0.8934 | 0.8932 |
| 0.2536 | 43.48 | 4000 | 0.3004 | 0.8839 | 0.8836 |
| 0.2569 | 45.65 | 4200 | 0.2923 | 0.8886 | 0.8884 |
| 0.2477 | 47.83 | 4400 | 0.2996 | 0.8880 | 0.8877 |
| 0.249 | 50.0 | 4600 | 0.2921 | 0.8900 | 0.8898 |
| 0.2485 | 52.17 | 4800 | 0.2950 | 0.8846 | 0.8843 |
| 0.2513 | 54.35 | 5000 | 0.3077 | 0.8807 | 0.8802 |
| 0.2448 | 56.52 | 5200 | 0.3044 | 0.8827 | 0.8823 |
| 0.243 | 58.7 | 5400 | 0.2998 | 0.8861 | 0.8857 |
| 0.2423 | 60.87 | 5600 | 0.3085 | 0.8875 | 0.8871 |
| 0.2435 | 63.04 | 5800 | 0.3065 | 0.8834 | 0.8830 |
| 0.241 | 65.22 | 6000 | 0.2984 | 0.8895 | 0.8891 |
| 0.24 | 67.39 | 6200 | 0.3087 | 0.8875 | 0.8871 |
| 0.2387 | 69.57 | 6400 | 0.2938 | 0.8915 | 0.8912 |
| 0.2418 | 71.74 | 6600 | 0.2994 | 0.8895 | 0.8891 |
| 0.2411 | 73.91 | 6800 | 0.2972 | 0.8922 | 0.8919 |
| 0.2367 | 76.09 | 7000 | 0.3017 | 0.8895 | 0.8891 |
| 0.2353 | 78.26 | 7200 | 0.3026 | 0.8868 | 0.8864 |
| 0.2358 | 80.43 | 7400 | 0.2961 | 0.8901 | 0.8898 |
| 0.236 | 82.61 | 7600 | 0.2898 | 0.8928 | 0.8925 |
| 0.2363 | 84.78 | 7800 | 0.3005 | 0.8902 | 0.8898 |
| 0.2322 | 86.96 | 8000 | 0.2967 | 0.8881 | 0.8877 |
| 0.2365 | 89.13 | 8200 | 0.2971 | 0.8895 | 0.8891 |
| 0.2345 | 91.3 | 8400 | 0.2962 | 0.8888 | 0.8884 |
| 0.2311 | 93.48 | 8600 | 0.2978 | 0.8902 | 0.8898 |
| 0.2326 | 95.65 | 8800 | 0.2956 | 0.8915 | 0.8912 |
| 0.2361 | 97.83 | 9000 | 0.2949 | 0.8915 | 0.8912 |
| 0.2363 | 100.0 | 9200 | 0.3017 | 0.8888 | 0.8884 |
| 0.2318 | 102.17 | 9400 | 0.2972 | 0.8881 | 0.8877 |
| 0.2341 | 104.35 | 9600 | 0.2977 | 0.8895 | 0.8891 |
| 0.2316 | 106.52 | 9800 | 0.2960 | 0.8901 | 0.8898 |
| 0.2336 | 108.7 | 10000 | 0.2960 | 0.8901 | 0.8898 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_EMP_H4-seqsight_32768_512_30M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H4-seqsight_32768_512_30M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_30M",
"region:us"
] | null | 2024-04-30T04:26:10+00:00 |
null | null | {} | BIctminh/TriageAutomationModel_vTestRun | null | [
"region:us"
] | null | 2024-04-30T04:26:23+00:00 |
|
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Text Classification
## Validation Metrics
loss: 1.065280795097351
f1_macro: 0.2095479509928179
f1_micro: 0.4584103512014787
f1_weighted: 0.2881768494245037
precision_macro: 0.1528034504004929
precision_micro: 0.4584103512014787
precision_weighted: 0.21014005008866307
recall_macro: 0.3333333333333333
recall_micro: 0.4584103512014787
recall_weighted: 0.4584103512014787
accuracy: 0.4584103512014787
| {"tags": ["autotrain", "text-classification"], "datasets": ["autotrain-4vbeh-1p6bd/autotrain-data"], "widget": [{"text": "I love AutoTrain"}]} | AnirudhVV/autotrain-4vbeh-1p6bd | null | [
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"autotrain",
"dataset:autotrain-4vbeh-1p6bd/autotrain-data",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T04:29:37+00:00 |
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Le prix de 2 bouteilles de bonbons gélifiés Summer Keto ACV est de 49,95 £ par bouteille. Pour bénéficier de réductions supplémentaires, vous pouvez essayer le pack de 4 bouteilles pour seulement 39,95 £ par bouteille. Le pack super économique de bonbons gélifiés Summer Keto ACV Gummies est disponible au prix de 39,95 £ par bouteille. Chaque commande que vous passez sur le site officiel de Summer Keto ACV Gummies est éligible à la livraison gratuite au Royaume-Uni.
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## Avantages des bonbons gélifiés Summer Keto ACV
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## **[Cliquez ici pour acheter maintenant sur le site officiel de Summer Keto ACV Gummies](https://adtocart.xyz/summer-keto-fr)** | {} | VKapseln475/SummerKetoACV102 | null | [
"region:us"
] | null | 2024-04-30T04:31:06+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GUE_EMP_H4-seqsight_32768_512_30M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_30M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_30M) on the [mahdibaghbanzadeh/GUE_EMP_H4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H4) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2796
- F1 Score: 0.8974
- Accuracy: 0.8973
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
| 0.3949 | 2.17 | 200 | 0.3073 | 0.8806 | 0.8802 |
| 0.2985 | 4.35 | 400 | 0.3049 | 0.8813 | 0.8809 |
| 0.2837 | 6.52 | 600 | 0.2934 | 0.8898 | 0.8898 |
| 0.28 | 8.7 | 800 | 0.3121 | 0.8732 | 0.8727 |
| 0.2662 | 10.87 | 1000 | 0.2953 | 0.8906 | 0.8905 |
| 0.2628 | 13.04 | 1200 | 0.2932 | 0.8804 | 0.8802 |
| 0.2545 | 15.22 | 1400 | 0.2913 | 0.8853 | 0.8850 |
| 0.2481 | 17.39 | 1600 | 0.2990 | 0.8820 | 0.8816 |
| 0.2409 | 19.57 | 1800 | 0.2841 | 0.8961 | 0.8960 |
| 0.24 | 21.74 | 2000 | 0.2988 | 0.8895 | 0.8891 |
| 0.2333 | 23.91 | 2200 | 0.2900 | 0.8887 | 0.8884 |
| 0.2279 | 26.09 | 2400 | 0.2836 | 0.8901 | 0.8898 |
| 0.2268 | 28.26 | 2600 | 0.2898 | 0.8891 | 0.8891 |
| 0.221 | 30.43 | 2800 | 0.2876 | 0.8901 | 0.8898 |
| 0.2186 | 32.61 | 3000 | 0.2898 | 0.8928 | 0.8925 |
| 0.2119 | 34.78 | 3200 | 0.2841 | 0.8921 | 0.8919 |
| 0.214 | 36.96 | 3400 | 0.3009 | 0.8929 | 0.8925 |
| 0.2104 | 39.13 | 3600 | 0.2906 | 0.8858 | 0.8857 |
| 0.2023 | 41.3 | 3800 | 0.2903 | 0.8886 | 0.8884 |
| 0.2025 | 43.48 | 4000 | 0.3002 | 0.8894 | 0.8891 |
| 0.2039 | 45.65 | 4200 | 0.2933 | 0.8879 | 0.8877 |
| 0.1957 | 47.83 | 4400 | 0.3002 | 0.8837 | 0.8836 |
| 0.1974 | 50.0 | 4600 | 0.2938 | 0.8851 | 0.8850 |
| 0.1917 | 52.17 | 4800 | 0.3058 | 0.8833 | 0.8830 |
| 0.1949 | 54.35 | 5000 | 0.3103 | 0.8840 | 0.8836 |
| 0.1884 | 56.52 | 5200 | 0.3055 | 0.8860 | 0.8857 |
| 0.1859 | 58.7 | 5400 | 0.3017 | 0.8888 | 0.8884 |
| 0.1825 | 60.87 | 5600 | 0.3068 | 0.8847 | 0.8843 |
| 0.183 | 63.04 | 5800 | 0.3102 | 0.8840 | 0.8836 |
| 0.178 | 65.22 | 6000 | 0.3112 | 0.8874 | 0.8871 |
| 0.1789 | 67.39 | 6200 | 0.3041 | 0.8901 | 0.8898 |
| 0.1766 | 69.57 | 6400 | 0.3150 | 0.8874 | 0.8871 |
| 0.1778 | 71.74 | 6600 | 0.3118 | 0.8908 | 0.8905 |
| 0.1749 | 73.91 | 6800 | 0.3055 | 0.8893 | 0.8891 |
| 0.1711 | 76.09 | 7000 | 0.3166 | 0.8901 | 0.8898 |
| 0.1709 | 78.26 | 7200 | 0.3134 | 0.8900 | 0.8898 |
| 0.1681 | 80.43 | 7400 | 0.3146 | 0.8886 | 0.8884 |
| 0.1696 | 82.61 | 7600 | 0.3152 | 0.8871 | 0.8871 |
| 0.1688 | 84.78 | 7800 | 0.3242 | 0.8888 | 0.8884 |
| 0.1638 | 86.96 | 8000 | 0.3179 | 0.8880 | 0.8877 |
| 0.1663 | 89.13 | 8200 | 0.3152 | 0.8853 | 0.8850 |
| 0.1662 | 91.3 | 8400 | 0.3132 | 0.8859 | 0.8857 |
| 0.161 | 93.48 | 8600 | 0.3181 | 0.8866 | 0.8864 |
| 0.1647 | 95.65 | 8800 | 0.3166 | 0.8885 | 0.8884 |
| 0.1635 | 97.83 | 9000 | 0.3175 | 0.8852 | 0.8850 |
| 0.1643 | 100.0 | 9200 | 0.3221 | 0.8874 | 0.8871 |
| 0.1608 | 102.17 | 9400 | 0.3188 | 0.8859 | 0.8857 |
| 0.1634 | 104.35 | 9600 | 0.3203 | 0.8867 | 0.8864 |
| 0.161 | 106.52 | 9800 | 0.3191 | 0.8845 | 0.8843 |
| 0.1615 | 108.7 | 10000 | 0.3195 | 0.8873 | 0.8871 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_EMP_H4-seqsight_32768_512_30M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H4-seqsight_32768_512_30M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_30M",
"region:us"
] | null | 2024-04-30T04:32:35+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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[More Information Needed]
#### Factors
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | terry69/tiny-llama-20p-full | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T04:32:53+00:00 |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# WAV2VEC-FINETUNE-TAMIL-1
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_11_0 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 20
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice_11_0"], "base_model": "facebook/wav2vec2-xls-r-300m", "model-index": [{"name": "WAV2VEC-FINETUNE-TAMIL-1", "results": []}]} | Vignesh-M/WAV2VEC-FINETUNE-TAMIL-1 | null | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_11_0",
"base_model:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T04:32:57+00:00 |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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#### Metrics
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### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
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[More Information Needed]
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## Glossary [optional]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | amks313/llama2_qa | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T04:33:05+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GUE_EMP_H4-seqsight_32768_512_30M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_30M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_30M) on the [mahdibaghbanzadeh/GUE_EMP_H4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H4) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3352
- F1 Score: 0.8852
- Accuracy: 0.8850
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
| 0.3712 | 2.17 | 200 | 0.3065 | 0.8834 | 0.8830 |
| 0.2877 | 4.35 | 400 | 0.2900 | 0.8919 | 0.8919 |
| 0.2685 | 6.52 | 600 | 0.2932 | 0.8851 | 0.8850 |
| 0.2599 | 8.7 | 800 | 0.3087 | 0.8786 | 0.8782 |
| 0.2416 | 10.87 | 1000 | 0.2910 | 0.8803 | 0.8802 |
| 0.2365 | 13.04 | 1200 | 0.3027 | 0.8813 | 0.8809 |
| 0.2243 | 15.22 | 1400 | 0.2986 | 0.8800 | 0.8795 |
| 0.2134 | 17.39 | 1600 | 0.3094 | 0.8868 | 0.8864 |
| 0.2025 | 19.57 | 1800 | 0.2979 | 0.8901 | 0.8898 |
| 0.2007 | 21.74 | 2000 | 0.3136 | 0.8759 | 0.8754 |
| 0.1901 | 23.91 | 2200 | 0.3097 | 0.8874 | 0.8871 |
| 0.182 | 26.09 | 2400 | 0.3008 | 0.8940 | 0.8939 |
| 0.1777 | 28.26 | 2600 | 0.3158 | 0.8948 | 0.8946 |
| 0.1681 | 30.43 | 2800 | 0.3206 | 0.8813 | 0.8809 |
| 0.1622 | 32.61 | 3000 | 0.3273 | 0.8933 | 0.8932 |
| 0.1515 | 34.78 | 3200 | 0.3242 | 0.8962 | 0.8960 |
| 0.1531 | 36.96 | 3400 | 0.3262 | 0.8901 | 0.8898 |
| 0.1447 | 39.13 | 3600 | 0.3404 | 0.8858 | 0.8857 |
| 0.1353 | 41.3 | 3800 | 0.3585 | 0.8798 | 0.8795 |
| 0.131 | 43.48 | 4000 | 0.3822 | 0.8805 | 0.8802 |
| 0.1291 | 45.65 | 4200 | 0.3702 | 0.8827 | 0.8830 |
| 0.12 | 47.83 | 4400 | 0.3924 | 0.8783 | 0.8782 |
| 0.1165 | 50.0 | 4600 | 0.3935 | 0.8794 | 0.8795 |
| 0.1134 | 52.17 | 4800 | 0.4138 | 0.8731 | 0.8727 |
| 0.1109 | 54.35 | 5000 | 0.4328 | 0.8758 | 0.8754 |
| 0.1051 | 56.52 | 5200 | 0.3864 | 0.8798 | 0.8795 |
| 0.0994 | 58.7 | 5400 | 0.4100 | 0.8805 | 0.8802 |
| 0.095 | 60.87 | 5600 | 0.4347 | 0.8764 | 0.8761 |
| 0.0976 | 63.04 | 5800 | 0.4336 | 0.8762 | 0.8761 |
| 0.0899 | 65.22 | 6000 | 0.4530 | 0.8710 | 0.8706 |
| 0.0908 | 67.39 | 6200 | 0.4437 | 0.8724 | 0.8720 |
| 0.084 | 69.57 | 6400 | 0.4855 | 0.8724 | 0.8720 |
| 0.0867 | 71.74 | 6600 | 0.4605 | 0.8804 | 0.8802 |
| 0.0779 | 73.91 | 6800 | 0.4823 | 0.8716 | 0.8713 |
| 0.0805 | 76.09 | 7000 | 0.4758 | 0.8750 | 0.8747 |
| 0.0778 | 78.26 | 7200 | 0.4791 | 0.8743 | 0.8741 |
| 0.0709 | 80.43 | 7400 | 0.4960 | 0.8743 | 0.8741 |
| 0.0718 | 82.61 | 7600 | 0.4910 | 0.8823 | 0.8823 |
| 0.0748 | 84.78 | 7800 | 0.5038 | 0.8784 | 0.8782 |
| 0.0677 | 86.96 | 8000 | 0.5160 | 0.8764 | 0.8761 |
| 0.0685 | 89.13 | 8200 | 0.5056 | 0.8757 | 0.8754 |
| 0.0682 | 91.3 | 8400 | 0.5076 | 0.8729 | 0.8727 |
| 0.0644 | 93.48 | 8600 | 0.5138 | 0.8743 | 0.8741 |
| 0.0647 | 95.65 | 8800 | 0.5183 | 0.8775 | 0.8775 |
| 0.0661 | 97.83 | 9000 | 0.5152 | 0.8750 | 0.8747 |
| 0.0658 | 100.0 | 9200 | 0.5207 | 0.8764 | 0.8761 |
| 0.0607 | 102.17 | 9400 | 0.5205 | 0.8771 | 0.8768 |
| 0.0627 | 104.35 | 9600 | 0.5205 | 0.8777 | 0.8775 |
| 0.0604 | 106.52 | 9800 | 0.5226 | 0.8743 | 0.8741 |
| 0.0596 | 108.7 | 10000 | 0.5268 | 0.8757 | 0.8754 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_EMP_H4-seqsight_32768_512_30M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H4-seqsight_32768_512_30M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_30M",
"region:us"
] | null | 2024-04-30T04:33:12+00:00 |
text-classification | transformers | {} | KalaiselvanD/albert_test_model | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T04:33:24+00:00 |
|
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GUE_EMP_H3-seqsight_32768_512_30M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_30M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_30M) on the [mahdibaghbanzadeh/GUE_EMP_H3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3166
- F1 Score: 0.8704
- Accuracy: 0.8704
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
| 0.4895 | 2.13 | 200 | 0.4117 | 0.8179 | 0.8183 |
| 0.38 | 4.26 | 400 | 0.3987 | 0.8195 | 0.8196 |
| 0.3604 | 6.38 | 600 | 0.3889 | 0.8288 | 0.8290 |
| 0.3455 | 8.51 | 800 | 0.3671 | 0.8444 | 0.8444 |
| 0.3305 | 10.64 | 1000 | 0.3545 | 0.8530 | 0.8530 |
| 0.3153 | 12.77 | 1200 | 0.3509 | 0.8490 | 0.8490 |
| 0.3084 | 14.89 | 1400 | 0.3471 | 0.8524 | 0.8524 |
| 0.2992 | 17.02 | 1600 | 0.3392 | 0.8577 | 0.8577 |
| 0.2955 | 19.15 | 1800 | 0.3475 | 0.8550 | 0.8550 |
| 0.2881 | 21.28 | 2000 | 0.3450 | 0.8536 | 0.8537 |
| 0.2886 | 23.4 | 2200 | 0.3340 | 0.8591 | 0.8591 |
| 0.2802 | 25.53 | 2400 | 0.3377 | 0.8591 | 0.8591 |
| 0.2854 | 27.66 | 2600 | 0.3288 | 0.8677 | 0.8677 |
| 0.2832 | 29.79 | 2800 | 0.3375 | 0.8597 | 0.8597 |
| 0.2779 | 31.91 | 3000 | 0.3364 | 0.8644 | 0.8644 |
| 0.2714 | 34.04 | 3200 | 0.3363 | 0.8637 | 0.8637 |
| 0.272 | 36.17 | 3400 | 0.3365 | 0.8650 | 0.8651 |
| 0.2693 | 38.3 | 3600 | 0.3322 | 0.8691 | 0.8691 |
| 0.2693 | 40.43 | 3800 | 0.3393 | 0.8682 | 0.8684 |
| 0.2687 | 42.55 | 4000 | 0.3355 | 0.8704 | 0.8704 |
| 0.266 | 44.68 | 4200 | 0.3295 | 0.8711 | 0.8711 |
| 0.2622 | 46.81 | 4400 | 0.3366 | 0.8683 | 0.8684 |
| 0.2648 | 48.94 | 4600 | 0.3383 | 0.8676 | 0.8677 |
| 0.2635 | 51.06 | 4800 | 0.3306 | 0.8677 | 0.8677 |
| 0.2598 | 53.19 | 5000 | 0.3522 | 0.8614 | 0.8617 |
| 0.2634 | 55.32 | 5200 | 0.3305 | 0.8691 | 0.8691 |
| 0.2626 | 57.45 | 5400 | 0.3378 | 0.8664 | 0.8664 |
| 0.2566 | 59.57 | 5600 | 0.3363 | 0.8683 | 0.8684 |
| 0.2604 | 61.7 | 5800 | 0.3259 | 0.8717 | 0.8717 |
| 0.2559 | 63.83 | 6000 | 0.3541 | 0.8628 | 0.8631 |
| 0.2574 | 65.96 | 6200 | 0.3417 | 0.8683 | 0.8684 |
| 0.2549 | 68.09 | 6400 | 0.3428 | 0.8689 | 0.8691 |
| 0.2529 | 70.21 | 6600 | 0.3406 | 0.8670 | 0.8671 |
| 0.2563 | 72.34 | 6800 | 0.3388 | 0.8675 | 0.8677 |
| 0.2518 | 74.47 | 7000 | 0.3564 | 0.8567 | 0.8570 |
| 0.2496 | 76.6 | 7200 | 0.3428 | 0.8696 | 0.8697 |
| 0.255 | 78.72 | 7400 | 0.3416 | 0.8676 | 0.8677 |
| 0.2503 | 80.85 | 7600 | 0.3381 | 0.8703 | 0.8704 |
| 0.2505 | 82.98 | 7800 | 0.3454 | 0.8649 | 0.8651 |
| 0.2503 | 85.11 | 8000 | 0.3388 | 0.8676 | 0.8677 |
| 0.2493 | 87.23 | 8200 | 0.3319 | 0.8711 | 0.8711 |
| 0.249 | 89.36 | 8400 | 0.3409 | 0.8670 | 0.8671 |
| 0.2503 | 91.49 | 8600 | 0.3386 | 0.8690 | 0.8691 |
| 0.2497 | 93.62 | 8800 | 0.3395 | 0.8710 | 0.8711 |
| 0.2502 | 95.74 | 9000 | 0.3442 | 0.8655 | 0.8657 |
| 0.2477 | 97.87 | 9200 | 0.3356 | 0.8703 | 0.8704 |
| 0.2492 | 100.0 | 9400 | 0.3363 | 0.8697 | 0.8697 |
| 0.2471 | 102.13 | 9600 | 0.3394 | 0.8683 | 0.8684 |
| 0.2484 | 104.26 | 9800 | 0.3398 | 0.8670 | 0.8671 |
| 0.2488 | 106.38 | 10000 | 0.3397 | 0.8676 | 0.8677 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_EMP_H3-seqsight_32768_512_30M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3-seqsight_32768_512_30M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_30M",
"region:us"
] | null | 2024-04-30T04:33:58+00:00 |
null | null | {"license": "apache-2.0"} | deepapaikar/pretrained_GPT124M | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-04-30T04:36:05+00:00 |
|
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 1er_mod_eval
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6113
- Accuracy: 0.175
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.6316 | 0.5 | 5 | 1.6332 | 0.175 |
| 1.6988 | 1.0 | 10 | 1.6113 | 0.175 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "bert-base-cased", "model-index": [{"name": "1er_mod_eval", "results": []}]} | edchaud/1er_mod_eval | null | [
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T04:37:44+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | abc88767/model14 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T04:38:33+00:00 |
text-classification | transformers | {} | Emmytheo/bert-finetuned-hate-speech-jigsaw-toxic-comments | null | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T04:38:35+00:00 |
|
text-generation | transformers |
# Phi-3-128K-Instruct-ov-fp16-int4-asym
## Model Description
This is a version of the original [Phi-3-128K-Instruct](https://huggingface.co/microsoft/Phi-3-128k-instruct) model, converted to OpenVINO™ IR (Intermediate Representation) format for optimized inference on Intel® hardware. This model is created using the procedures detailed in the [OpenVINO™ Notebooks](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks) repository.
## Intended Use
This model is designed for advanced natural language understanding and generation tasks, ideal for developers and researchers in both academic and commercial settings who require efficient AI capabilities for devices with limited computational power. It is not intended for use in creating or promoting harmful or illegal content, in accordance with the guidelines outlined in the Phi-3 Acceptable Use Policy.
## Licensing and Redistribution
This model is released under the [MIT license](https://huggingface.co/microsoft/Phi-3-128k-instruct/resolve/main/LICENSE).
## Weight Compression Parameters
For more information on the parameters, refer to the [OpenVINO™ 2024.1.0 documentation](https://docs.openvino.ai/2024/openvino-workflow/model-optimization-guide/weight-compression.html)
* mode: **INT4_ASYM**
* group_size: **128**
* ratio: **0.8**
## Running Model Inference
Install packages required for using [Optimum Intel](https://huggingface.co/docs/optimum/intel/index) integration with the OpenVINO™ backend:
```python
pip install --upgrade --upgrade-strategy eager "optimum[openvino]"
from optimum.intel.openvino import OVModelForCausalLM
from transformers import AutoTokenizer
model_id = "microsoft/Phi-3-128K-Instruct-ov-fp32-int4-asym"
# Initialize the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = OVModelForCausalLM.from_pretrained(model_id)
pipeline = transformers.pipeline("text-generation", model=model, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto")
pipeline("i am in paris, plan me a 2 week trip")
```
| {"language": ["en"], "license": "mit", "library_name": "transformers", "tags": ["OpenVINO", "Phi-3", "PyTorch", "weight_compression", "optimum-intel"], "pipeline_tag": "text-generation"} | nsbendre25/Phi-3-mini-128k-instruct-ov-fp16-int4-asym | null | [
"transformers",
"openvino",
"phi3",
"text-generation",
"OpenVINO",
"Phi-3",
"PyTorch",
"weight_compression",
"optimum-intel",
"conversational",
"custom_code",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T04:38:36+00:00 |
null | span-marker | [Growth Matrix Reviews](https://jciodev.microsoftcrmportals.com/forums/general-discussion/56e0e0e2-f105-ef11-a81c-6045bd0b2619) It's vital to take note of that not all upgrade techniques are therapeutically or experimentally demonstrated, and numerous items advertised for these reasons might need guideline or logical proof supporting their adequacy and wellbeing. Prior to considering any type of male upgrade, it's significant to talk with a medical care proficient to grasp expected dangers, viability, and legitimate use. Furthermore, solid way of life decisions like standard activity, a decent eating regimen, overseeing pressure, and sufficient rest can emphatically influence sexual wellbeing and execution.
VISIT HERE FOR OFFICIAL WEBSITE:-https://jciodev.microsoftcrmportals.com/forums/general-discussion/56e0e0e2-f105-ef11-a81c-6045bd0b2619
| {"language": ["en"], "license": "afl-3.0", "library_name": "span-marker", "tags": ["Growth Matrix Reviews"]} | growthmatrixreviews/growthmatrixreviews | null | [
"span-marker",
"Growth Matrix Reviews",
"en",
"license:afl-3.0",
"region:us"
] | null | 2024-04-30T04:38:43+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GUE_EMP_H3-seqsight_32768_512_30M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_30M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_30M) on the [mahdibaghbanzadeh/GUE_EMP_H3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3326
- F1 Score: 0.8657
- Accuracy: 0.8657
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
| 0.4518 | 2.13 | 200 | 0.3904 | 0.8321 | 0.8323 |
| 0.3475 | 4.26 | 400 | 0.3838 | 0.8313 | 0.8317 |
| 0.3167 | 6.38 | 600 | 0.4000 | 0.8234 | 0.8243 |
| 0.2998 | 8.51 | 800 | 0.3542 | 0.8422 | 0.8424 |
| 0.2901 | 10.64 | 1000 | 0.3432 | 0.8583 | 0.8584 |
| 0.2814 | 12.77 | 1200 | 0.3466 | 0.8603 | 0.8604 |
| 0.2758 | 14.89 | 1400 | 0.3572 | 0.8555 | 0.8557 |
| 0.2684 | 17.02 | 1600 | 0.3379 | 0.8644 | 0.8644 |
| 0.2648 | 19.15 | 1800 | 0.3645 | 0.8554 | 0.8557 |
| 0.2584 | 21.28 | 2000 | 0.3568 | 0.8622 | 0.8624 |
| 0.2561 | 23.4 | 2200 | 0.3326 | 0.8704 | 0.8704 |
| 0.2466 | 25.53 | 2400 | 0.3611 | 0.8607 | 0.8611 |
| 0.2535 | 27.66 | 2600 | 0.3273 | 0.8697 | 0.8697 |
| 0.2485 | 29.79 | 2800 | 0.3265 | 0.8710 | 0.8711 |
| 0.2408 | 31.91 | 3000 | 0.3381 | 0.8697 | 0.8697 |
| 0.236 | 34.04 | 3200 | 0.3314 | 0.8717 | 0.8717 |
| 0.2353 | 36.17 | 3400 | 0.3567 | 0.8648 | 0.8651 |
| 0.2295 | 38.3 | 3600 | 0.3450 | 0.8715 | 0.8717 |
| 0.2286 | 40.43 | 3800 | 0.3548 | 0.8708 | 0.8711 |
| 0.229 | 42.55 | 4000 | 0.3523 | 0.8702 | 0.8704 |
| 0.2232 | 44.68 | 4200 | 0.3357 | 0.8704 | 0.8704 |
| 0.2184 | 46.81 | 4400 | 0.3455 | 0.8743 | 0.8744 |
| 0.2212 | 48.94 | 4600 | 0.3579 | 0.8654 | 0.8657 |
| 0.2195 | 51.06 | 4800 | 0.3319 | 0.8744 | 0.8744 |
| 0.2131 | 53.19 | 5000 | 0.3678 | 0.8688 | 0.8691 |
| 0.2172 | 55.32 | 5200 | 0.3406 | 0.8717 | 0.8717 |
| 0.2145 | 57.45 | 5400 | 0.3620 | 0.8683 | 0.8684 |
| 0.2086 | 59.57 | 5600 | 0.3550 | 0.8670 | 0.8671 |
| 0.2104 | 61.7 | 5800 | 0.3386 | 0.8711 | 0.8711 |
| 0.2066 | 63.83 | 6000 | 0.3741 | 0.8640 | 0.8644 |
| 0.2072 | 65.96 | 6200 | 0.3680 | 0.8681 | 0.8684 |
| 0.2037 | 68.09 | 6400 | 0.3723 | 0.8654 | 0.8657 |
| 0.2017 | 70.21 | 6600 | 0.3713 | 0.8668 | 0.8671 |
| 0.2042 | 72.34 | 6800 | 0.3558 | 0.8681 | 0.8684 |
| 0.1993 | 74.47 | 7000 | 0.3915 | 0.8612 | 0.8617 |
| 0.1957 | 76.6 | 7200 | 0.3658 | 0.8716 | 0.8717 |
| 0.1982 | 78.72 | 7400 | 0.3823 | 0.8666 | 0.8671 |
| 0.1966 | 80.85 | 7600 | 0.3718 | 0.8628 | 0.8631 |
| 0.1935 | 82.98 | 7800 | 0.3755 | 0.8634 | 0.8637 |
| 0.1943 | 85.11 | 8000 | 0.3707 | 0.8641 | 0.8644 |
| 0.1925 | 87.23 | 8200 | 0.3586 | 0.8683 | 0.8684 |
| 0.1939 | 89.36 | 8400 | 0.3771 | 0.8634 | 0.8637 |
| 0.1907 | 91.49 | 8600 | 0.3762 | 0.8634 | 0.8637 |
| 0.194 | 93.62 | 8800 | 0.3665 | 0.8662 | 0.8664 |
| 0.1916 | 95.74 | 9000 | 0.3781 | 0.8621 | 0.8624 |
| 0.1885 | 97.87 | 9200 | 0.3667 | 0.8669 | 0.8671 |
| 0.19 | 100.0 | 9400 | 0.3722 | 0.8622 | 0.8624 |
| 0.1891 | 102.13 | 9600 | 0.3752 | 0.8641 | 0.8644 |
| 0.1892 | 104.26 | 9800 | 0.3738 | 0.8641 | 0.8644 |
| 0.1854 | 106.38 | 10000 | 0.3722 | 0.8641 | 0.8644 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_EMP_H3-seqsight_32768_512_30M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3-seqsight_32768_512_30M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_30M",
"region:us"
] | null | 2024-04-30T04:39:12+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GUE_EMP_H3-seqsight_32768_512_30M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_30M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_30M) on the [mahdibaghbanzadeh/GUE_EMP_H3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3600
- F1 Score: 0.8564
- Accuracy: 0.8564
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
| 0.426 | 2.13 | 200 | 0.3677 | 0.8409 | 0.8410 |
| 0.3148 | 4.26 | 400 | 0.3429 | 0.8577 | 0.8577 |
| 0.2892 | 6.38 | 600 | 0.3671 | 0.8459 | 0.8464 |
| 0.2772 | 8.51 | 800 | 0.3247 | 0.8710 | 0.8711 |
| 0.2667 | 10.64 | 1000 | 0.3338 | 0.8683 | 0.8684 |
| 0.2551 | 12.77 | 1200 | 0.3291 | 0.8677 | 0.8677 |
| 0.2481 | 14.89 | 1400 | 0.3790 | 0.8484 | 0.8490 |
| 0.2397 | 17.02 | 1600 | 0.3280 | 0.8663 | 0.8664 |
| 0.2329 | 19.15 | 1800 | 0.3701 | 0.8601 | 0.8604 |
| 0.2215 | 21.28 | 2000 | 0.3518 | 0.8723 | 0.8724 |
| 0.2175 | 23.4 | 2200 | 0.3751 | 0.8594 | 0.8597 |
| 0.2053 | 25.53 | 2400 | 0.3631 | 0.8662 | 0.8664 |
| 0.2058 | 27.66 | 2600 | 0.3505 | 0.8770 | 0.8771 |
| 0.1974 | 29.79 | 2800 | 0.3614 | 0.8661 | 0.8664 |
| 0.1889 | 31.91 | 3000 | 0.3470 | 0.8730 | 0.8731 |
| 0.1809 | 34.04 | 3200 | 0.3540 | 0.8730 | 0.8731 |
| 0.1762 | 36.17 | 3400 | 0.3803 | 0.8663 | 0.8664 |
| 0.1633 | 38.3 | 3600 | 0.3846 | 0.8716 | 0.8717 |
| 0.1618 | 40.43 | 3800 | 0.4367 | 0.8564 | 0.8570 |
| 0.1529 | 42.55 | 4000 | 0.4538 | 0.8628 | 0.8631 |
| 0.1468 | 44.68 | 4200 | 0.4200 | 0.8616 | 0.8617 |
| 0.1404 | 46.81 | 4400 | 0.4299 | 0.8683 | 0.8684 |
| 0.1377 | 48.94 | 4600 | 0.4680 | 0.8511 | 0.8517 |
| 0.1316 | 51.06 | 4800 | 0.4302 | 0.8650 | 0.8651 |
| 0.1266 | 53.19 | 5000 | 0.4679 | 0.8588 | 0.8591 |
| 0.1245 | 55.32 | 5200 | 0.4582 | 0.8650 | 0.8651 |
| 0.1218 | 57.45 | 5400 | 0.4607 | 0.8676 | 0.8677 |
| 0.1138 | 59.57 | 5600 | 0.4858 | 0.8622 | 0.8624 |
| 0.1141 | 61.7 | 5800 | 0.4473 | 0.8664 | 0.8664 |
| 0.1122 | 63.83 | 6000 | 0.5141 | 0.8606 | 0.8611 |
| 0.1058 | 65.96 | 6200 | 0.5234 | 0.8634 | 0.8637 |
| 0.0999 | 68.09 | 6400 | 0.5177 | 0.8594 | 0.8597 |
| 0.0962 | 70.21 | 6600 | 0.5301 | 0.8642 | 0.8644 |
| 0.0984 | 72.34 | 6800 | 0.4981 | 0.8663 | 0.8664 |
| 0.0956 | 74.47 | 7000 | 0.5309 | 0.8643 | 0.8644 |
| 0.0919 | 76.6 | 7200 | 0.5333 | 0.8690 | 0.8691 |
| 0.0925 | 78.72 | 7400 | 0.5550 | 0.8580 | 0.8584 |
| 0.0899 | 80.85 | 7600 | 0.5366 | 0.8615 | 0.8617 |
| 0.0874 | 82.98 | 7800 | 0.5441 | 0.8616 | 0.8617 |
| 0.0848 | 85.11 | 8000 | 0.5362 | 0.8616 | 0.8617 |
| 0.0829 | 87.23 | 8200 | 0.5478 | 0.8649 | 0.8651 |
| 0.0831 | 89.36 | 8400 | 0.5718 | 0.8595 | 0.8597 |
| 0.0793 | 91.49 | 8600 | 0.5700 | 0.8649 | 0.8651 |
| 0.083 | 93.62 | 8800 | 0.5573 | 0.8642 | 0.8644 |
| 0.0805 | 95.74 | 9000 | 0.5617 | 0.8622 | 0.8624 |
| 0.076 | 97.87 | 9200 | 0.5696 | 0.8642 | 0.8644 |
| 0.079 | 100.0 | 9400 | 0.5691 | 0.8609 | 0.8611 |
| 0.0798 | 102.13 | 9600 | 0.5755 | 0.8602 | 0.8604 |
| 0.0766 | 104.26 | 9800 | 0.5702 | 0.8609 | 0.8611 |
| 0.0728 | 106.38 | 10000 | 0.5732 | 0.8635 | 0.8637 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_EMP_H3-seqsight_32768_512_30M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3-seqsight_32768_512_30M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_30M",
"region:us"
] | null | 2024-04-30T04:40:00+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GUE_EMP_H4ac-seqsight_32768_512_30M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_30M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_30M) on the [mahdibaghbanzadeh/GUE_EMP_H4ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H4ac) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5836
- F1 Score: 0.7005
- Accuracy: 0.7003
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.6436 | 0.93 | 200 | 0.6088 | 0.6696 | 0.6698 |
| 0.6105 | 1.87 | 400 | 0.6151 | 0.6631 | 0.6669 |
| 0.5996 | 2.8 | 600 | 0.5981 | 0.6807 | 0.6812 |
| 0.5969 | 3.74 | 800 | 0.6018 | 0.6711 | 0.6733 |
| 0.59 | 4.67 | 1000 | 0.6090 | 0.6696 | 0.6730 |
| 0.5862 | 5.61 | 1200 | 0.6141 | 0.6667 | 0.6716 |
| 0.5843 | 6.54 | 1400 | 0.5930 | 0.6870 | 0.6880 |
| 0.5743 | 7.48 | 1600 | 0.5876 | 0.6932 | 0.6933 |
| 0.5819 | 8.41 | 1800 | 0.5908 | 0.6893 | 0.6900 |
| 0.5781 | 9.35 | 2000 | 0.5984 | 0.6864 | 0.6883 |
| 0.5703 | 10.28 | 2200 | 0.5909 | 0.6911 | 0.6915 |
| 0.574 | 11.21 | 2400 | 0.5908 | 0.6928 | 0.6935 |
| 0.5695 | 12.15 | 2600 | 0.5897 | 0.6913 | 0.6921 |
| 0.5663 | 13.08 | 2800 | 0.5944 | 0.6927 | 0.6938 |
| 0.5677 | 14.02 | 3000 | 0.5944 | 0.6928 | 0.6941 |
| 0.5652 | 14.95 | 3200 | 0.5875 | 0.6959 | 0.6965 |
| 0.5625 | 15.89 | 3400 | 0.6009 | 0.6904 | 0.6930 |
| 0.5608 | 16.82 | 3600 | 0.5798 | 0.7017 | 0.7018 |
| 0.5643 | 17.76 | 3800 | 0.5829 | 0.6979 | 0.6982 |
| 0.5605 | 18.69 | 4000 | 0.5820 | 0.7003 | 0.7006 |
| 0.5563 | 19.63 | 4200 | 0.5911 | 0.6991 | 0.7006 |
| 0.5581 | 20.56 | 4400 | 0.5751 | 0.7047 | 0.7047 |
| 0.5548 | 21.5 | 4600 | 0.6061 | 0.6908 | 0.6941 |
| 0.5565 | 22.43 | 4800 | 0.5817 | 0.7041 | 0.7044 |
| 0.5538 | 23.36 | 5000 | 0.5969 | 0.6963 | 0.6982 |
| 0.5559 | 24.3 | 5200 | 0.5749 | 0.7042 | 0.7041 |
| 0.5535 | 25.23 | 5400 | 0.5772 | 0.7069 | 0.7070 |
| 0.5542 | 26.17 | 5600 | 0.5775 | 0.7041 | 0.7044 |
| 0.5504 | 27.1 | 5800 | 0.5838 | 0.7056 | 0.7065 |
| 0.55 | 28.04 | 6000 | 0.5729 | 0.7078 | 0.7076 |
| 0.5521 | 28.97 | 6200 | 0.5890 | 0.7016 | 0.7032 |
| 0.5493 | 29.91 | 6400 | 0.5800 | 0.7049 | 0.7053 |
| 0.5514 | 30.84 | 6600 | 0.5974 | 0.6998 | 0.7026 |
| 0.5486 | 31.78 | 6800 | 0.5761 | 0.7106 | 0.7109 |
| 0.549 | 32.71 | 7000 | 0.5780 | 0.7092 | 0.7097 |
| 0.5467 | 33.64 | 7200 | 0.5782 | 0.7081 | 0.7088 |
| 0.5481 | 34.58 | 7400 | 0.5793 | 0.7079 | 0.7085 |
| 0.5495 | 35.51 | 7600 | 0.5756 | 0.7081 | 0.7085 |
| 0.5441 | 36.45 | 7800 | 0.5772 | 0.7088 | 0.7091 |
| 0.5498 | 37.38 | 8000 | 0.5855 | 0.7066 | 0.7082 |
| 0.545 | 38.32 | 8200 | 0.5817 | 0.7076 | 0.7085 |
| 0.5459 | 39.25 | 8400 | 0.5762 | 0.7082 | 0.7085 |
| 0.5488 | 40.19 | 8600 | 0.5737 | 0.7139 | 0.7141 |
| 0.5465 | 41.12 | 8800 | 0.5785 | 0.7087 | 0.7094 |
| 0.5475 | 42.06 | 9000 | 0.5754 | 0.7108 | 0.7111 |
| 0.5452 | 42.99 | 9200 | 0.5809 | 0.7059 | 0.7067 |
| 0.5453 | 43.93 | 9400 | 0.5809 | 0.7073 | 0.7082 |
| 0.5474 | 44.86 | 9600 | 0.5764 | 0.7098 | 0.7103 |
| 0.5441 | 45.79 | 9800 | 0.5775 | 0.7103 | 0.7109 |
| 0.5448 | 46.73 | 10000 | 0.5792 | 0.7084 | 0.7091 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_EMP_H4ac-seqsight_32768_512_30M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H4ac-seqsight_32768_512_30M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_30M",
"region:us"
] | null | 2024-04-30T04:40:31+00:00 |
text-generation | transformers |
# TooManyMix_LLM_01
TooManyMix_LLM_01 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [jdqwoi/TooManyMixed-LLM_03](https://huggingface.co/jdqwoi/TooManyMixed-LLM_03)
* [jdqwoi/TooManyMix_LLM](https://huggingface.co/jdqwoi/TooManyMix_LLM)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: jdqwoi/TooManyMixed-LLM_03
layer_range: [0, 32]
- model: jdqwoi/TooManyMix_LLM
layer_range: [0, 32]
merge_method: slerp
base_model: jdqwoi/TooManyMixed-LLM_03
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "jdqwoi/TooManyMix_LLM_01"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"tags": ["merge", "mergekit", "lazymergekit", "jdqwoi/TooManyMixed-LLM_03", "jdqwoi/TooManyMix_LLM"], "base_model": ["jdqwoi/TooManyMixed-LLM_03", "jdqwoi/TooManyMix_LLM"]} | jdqwoi/TooManyMix_LLM_01 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"jdqwoi/TooManyMixed-LLM_03",
"jdqwoi/TooManyMix_LLM",
"base_model:jdqwoi/TooManyMixed-LLM_03",
"base_model:jdqwoi/TooManyMix_LLM",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T04:41:10+00:00 |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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| {"library_name": "transformers", "tags": []} | dahye1/generator_2 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T04:41:52+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GUE_EMP_H4ac-seqsight_32768_512_30M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_30M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_30M) on the [mahdibaghbanzadeh/GUE_EMP_H4ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H4ac) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5741
- F1 Score: 0.7114
- Accuracy: 0.7111
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.6317 | 0.93 | 200 | 0.6072 | 0.6730 | 0.6742 |
| 0.5992 | 1.87 | 400 | 0.6159 | 0.6676 | 0.6727 |
| 0.5842 | 2.8 | 600 | 0.5923 | 0.6918 | 0.6921 |
| 0.5787 | 3.74 | 800 | 0.5891 | 0.6928 | 0.6938 |
| 0.5703 | 4.67 | 1000 | 0.5910 | 0.6946 | 0.6959 |
| 0.564 | 5.61 | 1200 | 0.6009 | 0.6853 | 0.6880 |
| 0.5623 | 6.54 | 1400 | 0.5970 | 0.6860 | 0.6889 |
| 0.5491 | 7.48 | 1600 | 0.5792 | 0.7026 | 0.7026 |
| 0.5555 | 8.41 | 1800 | 0.5778 | 0.7047 | 0.7050 |
| 0.551 | 9.35 | 2000 | 0.5788 | 0.7050 | 0.7056 |
| 0.5405 | 10.28 | 2200 | 0.5726 | 0.7108 | 0.7106 |
| 0.5426 | 11.21 | 2400 | 0.5767 | 0.7086 | 0.7091 |
| 0.5388 | 12.15 | 2600 | 0.5831 | 0.7062 | 0.7070 |
| 0.5343 | 13.08 | 2800 | 0.5828 | 0.7091 | 0.7097 |
| 0.5344 | 14.02 | 3000 | 0.5646 | 0.7151 | 0.7150 |
| 0.5299 | 14.95 | 3200 | 0.5724 | 0.7176 | 0.7176 |
| 0.5268 | 15.89 | 3400 | 0.5760 | 0.7141 | 0.7150 |
| 0.5257 | 16.82 | 3600 | 0.5654 | 0.7171 | 0.7173 |
| 0.5286 | 17.76 | 3800 | 0.5759 | 0.7137 | 0.7150 |
| 0.5247 | 18.69 | 4000 | 0.5598 | 0.7201 | 0.7199 |
| 0.5196 | 19.63 | 4200 | 0.5636 | 0.7180 | 0.7182 |
| 0.5192 | 20.56 | 4400 | 0.5582 | 0.7233 | 0.7232 |
| 0.5188 | 21.5 | 4600 | 0.5939 | 0.7065 | 0.7097 |
| 0.5183 | 22.43 | 4800 | 0.5602 | 0.7235 | 0.7232 |
| 0.5143 | 23.36 | 5000 | 0.5759 | 0.7175 | 0.7185 |
| 0.5179 | 24.3 | 5200 | 0.5599 | 0.7257 | 0.7255 |
| 0.5131 | 25.23 | 5400 | 0.5583 | 0.7229 | 0.7226 |
| 0.5141 | 26.17 | 5600 | 0.5610 | 0.7247 | 0.7243 |
| 0.5096 | 27.1 | 5800 | 0.5611 | 0.7246 | 0.7243 |
| 0.5089 | 28.04 | 6000 | 0.5564 | 0.7255 | 0.7252 |
| 0.5104 | 28.97 | 6200 | 0.5749 | 0.7219 | 0.7226 |
| 0.507 | 29.91 | 6400 | 0.5643 | 0.7247 | 0.7246 |
| 0.5104 | 30.84 | 6600 | 0.5732 | 0.7212 | 0.7220 |
| 0.5071 | 31.78 | 6800 | 0.5577 | 0.7264 | 0.7261 |
| 0.5051 | 32.71 | 7000 | 0.5633 | 0.7284 | 0.7282 |
| 0.5034 | 33.64 | 7200 | 0.5653 | 0.7236 | 0.7240 |
| 0.5036 | 34.58 | 7400 | 0.5598 | 0.7263 | 0.7261 |
| 0.506 | 35.51 | 7600 | 0.5645 | 0.7270 | 0.7270 |
| 0.5009 | 36.45 | 7800 | 0.5634 | 0.7263 | 0.7261 |
| 0.5045 | 37.38 | 8000 | 0.5727 | 0.7228 | 0.7235 |
| 0.5012 | 38.32 | 8200 | 0.5647 | 0.7285 | 0.7284 |
| 0.5002 | 39.25 | 8400 | 0.5619 | 0.7266 | 0.7264 |
| 0.5018 | 40.19 | 8600 | 0.5651 | 0.7261 | 0.7258 |
| 0.5016 | 41.12 | 8800 | 0.5630 | 0.7295 | 0.7293 |
| 0.4991 | 42.06 | 9000 | 0.5662 | 0.7278 | 0.7276 |
| 0.4986 | 42.99 | 9200 | 0.5685 | 0.7269 | 0.7270 |
| 0.4984 | 43.93 | 9400 | 0.5664 | 0.7279 | 0.7279 |
| 0.4996 | 44.86 | 9600 | 0.5637 | 0.7274 | 0.7273 |
| 0.4991 | 45.79 | 9800 | 0.5643 | 0.7286 | 0.7284 |
| 0.4985 | 46.73 | 10000 | 0.5655 | 0.7258 | 0.7258 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_EMP_H4ac-seqsight_32768_512_30M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H4ac-seqsight_32768_512_30M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_30M",
"region:us"
] | null | 2024-04-30T04:42:22+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GUE_EMP_H4ac-seqsight_32768_512_30M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_30M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_30M) on the [mahdibaghbanzadeh/GUE_EMP_H4ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H4ac) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5936
- F1 Score: 0.7152
- Accuracy: 0.7152
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.6213 | 0.93 | 200 | 0.6249 | 0.6653 | 0.6698 |
| 0.5891 | 1.87 | 400 | 0.5998 | 0.6870 | 0.6891 |
| 0.5713 | 2.8 | 600 | 0.5835 | 0.6988 | 0.6988 |
| 0.5616 | 3.74 | 800 | 0.5721 | 0.7055 | 0.7053 |
| 0.5515 | 4.67 | 1000 | 0.5745 | 0.7055 | 0.7056 |
| 0.5432 | 5.61 | 1200 | 0.5905 | 0.7016 | 0.7038 |
| 0.5421 | 6.54 | 1400 | 0.5781 | 0.7094 | 0.7109 |
| 0.5277 | 7.48 | 1600 | 0.5684 | 0.7096 | 0.7097 |
| 0.5329 | 8.41 | 1800 | 0.5651 | 0.7150 | 0.7150 |
| 0.5273 | 9.35 | 2000 | 0.5605 | 0.7217 | 0.7214 |
| 0.5175 | 10.28 | 2200 | 0.5610 | 0.7234 | 0.7232 |
| 0.5185 | 11.21 | 2400 | 0.5701 | 0.7167 | 0.7170 |
| 0.5126 | 12.15 | 2600 | 0.5806 | 0.7168 | 0.7176 |
| 0.5074 | 13.08 | 2800 | 0.5609 | 0.7203 | 0.7199 |
| 0.5061 | 14.02 | 3000 | 0.5711 | 0.7234 | 0.7235 |
| 0.5012 | 14.95 | 3200 | 0.5722 | 0.7222 | 0.7220 |
| 0.4972 | 15.89 | 3400 | 0.5637 | 0.7255 | 0.7252 |
| 0.4918 | 16.82 | 3600 | 0.5738 | 0.7182 | 0.7185 |
| 0.4956 | 17.76 | 3800 | 0.6154 | 0.7044 | 0.7085 |
| 0.4889 | 18.69 | 4000 | 0.5638 | 0.7244 | 0.7240 |
| 0.485 | 19.63 | 4200 | 0.5662 | 0.7257 | 0.7258 |
| 0.4795 | 20.56 | 4400 | 0.5650 | 0.7260 | 0.7258 |
| 0.4792 | 21.5 | 4600 | 0.6026 | 0.7131 | 0.7150 |
| 0.4754 | 22.43 | 4800 | 0.5727 | 0.7231 | 0.7229 |
| 0.4695 | 23.36 | 5000 | 0.5847 | 0.7255 | 0.7255 |
| 0.4752 | 24.3 | 5200 | 0.5807 | 0.7292 | 0.7293 |
| 0.4688 | 25.23 | 5400 | 0.5726 | 0.7211 | 0.7208 |
| 0.4657 | 26.17 | 5600 | 0.5799 | 0.7229 | 0.7226 |
| 0.4601 | 27.1 | 5800 | 0.5873 | 0.7202 | 0.7202 |
| 0.4591 | 28.04 | 6000 | 0.5771 | 0.7235 | 0.7232 |
| 0.4593 | 28.97 | 6200 | 0.5979 | 0.7212 | 0.7214 |
| 0.4538 | 29.91 | 6400 | 0.5846 | 0.7192 | 0.7191 |
| 0.459 | 30.84 | 6600 | 0.5857 | 0.7239 | 0.7240 |
| 0.4502 | 31.78 | 6800 | 0.5823 | 0.7269 | 0.7267 |
| 0.4505 | 32.71 | 7000 | 0.5879 | 0.7273 | 0.7270 |
| 0.446 | 33.64 | 7200 | 0.5924 | 0.7256 | 0.7258 |
| 0.4463 | 34.58 | 7400 | 0.5940 | 0.7288 | 0.7287 |
| 0.4453 | 35.51 | 7600 | 0.6015 | 0.7184 | 0.7191 |
| 0.4406 | 36.45 | 7800 | 0.6008 | 0.7262 | 0.7261 |
| 0.4444 | 37.38 | 8000 | 0.6006 | 0.7247 | 0.7249 |
| 0.4367 | 38.32 | 8200 | 0.5996 | 0.7273 | 0.7270 |
| 0.4409 | 39.25 | 8400 | 0.5946 | 0.7255 | 0.7252 |
| 0.4369 | 40.19 | 8600 | 0.6044 | 0.7248 | 0.7246 |
| 0.4382 | 41.12 | 8800 | 0.5966 | 0.7254 | 0.7252 |
| 0.437 | 42.06 | 9000 | 0.5995 | 0.7255 | 0.7252 |
| 0.4313 | 42.99 | 9200 | 0.6036 | 0.7240 | 0.7240 |
| 0.432 | 43.93 | 9400 | 0.5994 | 0.7241 | 0.7240 |
| 0.4341 | 44.86 | 9600 | 0.6011 | 0.7242 | 0.7240 |
| 0.4323 | 45.79 | 9800 | 0.6003 | 0.7234 | 0.7232 |
| 0.4315 | 46.73 | 10000 | 0.6008 | 0.7236 | 0.7235 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_EMP_H4ac-seqsight_32768_512_30M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H4ac-seqsight_32768_512_30M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_30M",
"region:us"
] | null | 2024-04-30T04:42:47+00:00 |
null | null | {"license": "llama3"} | probemedicalandyonseimailab/Meta-Llama-3-70B | null | [
"license:llama3",
"region:us"
] | null | 2024-04-30T04:43:05+00:00 |
|
null | null | {"license": "llama3"} | probemedicalandyonseimailab/Meta-Llama-3-70B-Instruct | null | [
"license:llama3",
"region:us"
] | null | 2024-04-30T04:44:18+00:00 |
|
text-classification | transformers | {} | scott-routledge/bert-hotpotqa-classifier-labels3 | null | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T04:44:35+00:00 |
|
null | null | {} | RaindropCake/Test | null | [
"region:us"
] | null | 2024-04-30T04:44:56+00:00 |
|
text-generation | transformers | {} | Moon-Ahn/kllama_finetune_hyunwook | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T04:45:44+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
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[More Information Needed]
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
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<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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## Citation [optional]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | shallow6414/2vrd6jk | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T04:48:01+00:00 |
null | null | {"license": "mit"} | paricdac/indictodayt | null | [
"license:mit",
"region:us"
] | null | 2024-04-30T04:48:23+00:00 |
|
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Shared by [optional]:** [More Information Needed]
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### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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#### Metrics
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[More Information Needed]
### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
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### Compute Infrastructure
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#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | Vignesh-M/WAV2VEC-FINETUNE-TAMIL-2 | null | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T04:49:05+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GUE_EMP_H3K79me3-seqsight_32768_512_30M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_30M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_30M) on the [mahdibaghbanzadeh/GUE_EMP_H3K79me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K79me3) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4466
- F1 Score: 0.8058
- Accuracy: 0.8065
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.5547 | 1.1 | 200 | 0.4825 | 0.7918 | 0.7916 |
| 0.4917 | 2.21 | 400 | 0.4899 | 0.7754 | 0.7798 |
| 0.4836 | 3.31 | 600 | 0.4668 | 0.7886 | 0.7909 |
| 0.4719 | 4.42 | 800 | 0.4647 | 0.7895 | 0.7920 |
| 0.4714 | 5.52 | 1000 | 0.4744 | 0.7840 | 0.7878 |
| 0.4633 | 6.63 | 1200 | 0.4693 | 0.7860 | 0.7895 |
| 0.4661 | 7.73 | 1400 | 0.4584 | 0.7939 | 0.7961 |
| 0.4582 | 8.84 | 1600 | 0.4690 | 0.7859 | 0.7892 |
| 0.4589 | 9.94 | 1800 | 0.4467 | 0.8093 | 0.8100 |
| 0.4563 | 11.05 | 2000 | 0.4576 | 0.7913 | 0.7940 |
| 0.4549 | 12.15 | 2200 | 0.4516 | 0.7962 | 0.7982 |
| 0.45 | 13.26 | 2400 | 0.4518 | 0.7955 | 0.7979 |
| 0.4493 | 14.36 | 2600 | 0.4465 | 0.8053 | 0.8065 |
| 0.4487 | 15.47 | 2800 | 0.4629 | 0.7857 | 0.7895 |
| 0.4443 | 16.57 | 3000 | 0.4441 | 0.8086 | 0.8096 |
| 0.4471 | 17.68 | 3200 | 0.4448 | 0.8041 | 0.8055 |
| 0.4413 | 18.78 | 3400 | 0.4418 | 0.8082 | 0.8093 |
| 0.4419 | 19.89 | 3600 | 0.4554 | 0.7908 | 0.7937 |
| 0.4418 | 20.99 | 3800 | 0.4519 | 0.7977 | 0.7999 |
| 0.4383 | 22.1 | 4000 | 0.4429 | 0.8049 | 0.8062 |
| 0.4406 | 23.2 | 4200 | 0.4459 | 0.7999 | 0.8017 |
| 0.4405 | 24.31 | 4400 | 0.4483 | 0.7978 | 0.7996 |
| 0.4309 | 25.41 | 4600 | 0.4468 | 0.8040 | 0.8055 |
| 0.4346 | 26.52 | 4800 | 0.4417 | 0.8070 | 0.8079 |
| 0.436 | 27.62 | 5000 | 0.4414 | 0.8049 | 0.8062 |
| 0.4321 | 28.73 | 5200 | 0.4410 | 0.7999 | 0.8013 |
| 0.4302 | 29.83 | 5400 | 0.4402 | 0.8049 | 0.8058 |
| 0.4306 | 30.94 | 5600 | 0.4401 | 0.8094 | 0.8100 |
| 0.4315 | 32.04 | 5800 | 0.4400 | 0.8112 | 0.8117 |
| 0.4298 | 33.15 | 6000 | 0.4405 | 0.8075 | 0.8083 |
| 0.4282 | 34.25 | 6200 | 0.4412 | 0.8056 | 0.8065 |
| 0.4279 | 35.36 | 6400 | 0.4453 | 0.8001 | 0.8017 |
| 0.4281 | 36.46 | 6600 | 0.4380 | 0.8112 | 0.8117 |
| 0.4287 | 37.57 | 6800 | 0.4388 | 0.8050 | 0.8058 |
| 0.4261 | 38.67 | 7000 | 0.4404 | 0.8031 | 0.8041 |
| 0.4252 | 39.78 | 7200 | 0.4400 | 0.8061 | 0.8069 |
| 0.4295 | 40.88 | 7400 | 0.4403 | 0.8059 | 0.8069 |
| 0.4267 | 41.99 | 7600 | 0.4398 | 0.8078 | 0.8086 |
| 0.4247 | 43.09 | 7800 | 0.4424 | 0.8023 | 0.8034 |
| 0.4272 | 44.2 | 8000 | 0.4402 | 0.8049 | 0.8058 |
| 0.4262 | 45.3 | 8200 | 0.4404 | 0.8068 | 0.8076 |
| 0.4258 | 46.41 | 8400 | 0.4402 | 0.8056 | 0.8065 |
| 0.427 | 47.51 | 8600 | 0.4411 | 0.8034 | 0.8044 |
| 0.424 | 48.62 | 8800 | 0.4419 | 0.8048 | 0.8058 |
| 0.4228 | 49.72 | 9000 | 0.4408 | 0.8056 | 0.8065 |
| 0.4277 | 50.83 | 9200 | 0.4422 | 0.8023 | 0.8034 |
| 0.4227 | 51.93 | 9400 | 0.4412 | 0.8056 | 0.8065 |
| 0.4236 | 53.04 | 9600 | 0.4406 | 0.8056 | 0.8065 |
| 0.423 | 54.14 | 9800 | 0.4401 | 0.8067 | 0.8076 |
| 0.4235 | 55.25 | 10000 | 0.4407 | 0.8056 | 0.8065 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_EMP_H3K79me3-seqsight_32768_512_30M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3K79me3-seqsight_32768_512_30M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_30M",
"region:us"
] | null | 2024-04-30T04:49:25+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | cilantro9246/iaztpsp | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T04:49:57+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GUE_EMP_H3K79me3-seqsight_32768_512_30M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_30M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_30M) on the [mahdibaghbanzadeh/GUE_EMP_H3K79me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K79me3) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4411
- F1 Score: 0.8133
- Accuracy: 0.8141
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.5327 | 1.1 | 200 | 0.4630 | 0.7998 | 0.7999 |
| 0.4772 | 2.21 | 400 | 0.4671 | 0.7860 | 0.7892 |
| 0.4691 | 3.31 | 600 | 0.4594 | 0.7946 | 0.7972 |
| 0.4565 | 4.42 | 800 | 0.4593 | 0.7883 | 0.7913 |
| 0.4538 | 5.52 | 1000 | 0.4621 | 0.7888 | 0.7923 |
| 0.4425 | 6.63 | 1200 | 0.4565 | 0.7961 | 0.7985 |
| 0.4437 | 7.73 | 1400 | 0.4494 | 0.8024 | 0.8037 |
| 0.4349 | 8.84 | 1600 | 0.4628 | 0.7919 | 0.7947 |
| 0.4339 | 9.94 | 1800 | 0.4360 | 0.8091 | 0.8093 |
| 0.4311 | 11.05 | 2000 | 0.4547 | 0.7985 | 0.7999 |
| 0.4304 | 12.15 | 2200 | 0.4329 | 0.8139 | 0.8141 |
| 0.4252 | 13.26 | 2400 | 0.4445 | 0.8036 | 0.8051 |
| 0.4245 | 14.36 | 2600 | 0.4365 | 0.8106 | 0.8110 |
| 0.4233 | 15.47 | 2800 | 0.4471 | 0.8053 | 0.8069 |
| 0.4189 | 16.57 | 3000 | 0.4404 | 0.8129 | 0.8128 |
| 0.4213 | 17.68 | 3200 | 0.4339 | 0.8127 | 0.8131 |
| 0.416 | 18.78 | 3400 | 0.4448 | 0.8031 | 0.8044 |
| 0.4149 | 19.89 | 3600 | 0.4380 | 0.8077 | 0.8089 |
| 0.4151 | 20.99 | 3800 | 0.4530 | 0.7974 | 0.7992 |
| 0.4111 | 22.1 | 4000 | 0.4380 | 0.8083 | 0.8089 |
| 0.414 | 23.2 | 4200 | 0.4378 | 0.8077 | 0.8086 |
| 0.4123 | 24.31 | 4400 | 0.4519 | 0.8034 | 0.8051 |
| 0.4032 | 25.41 | 4600 | 0.4410 | 0.8138 | 0.8145 |
| 0.407 | 26.52 | 4800 | 0.4456 | 0.8074 | 0.8083 |
| 0.4098 | 27.62 | 5000 | 0.4381 | 0.8089 | 0.8100 |
| 0.4034 | 28.73 | 5200 | 0.4416 | 0.8064 | 0.8076 |
| 0.4008 | 29.83 | 5400 | 0.4387 | 0.8096 | 0.8103 |
| 0.4018 | 30.94 | 5600 | 0.4414 | 0.8102 | 0.8107 |
| 0.4035 | 32.04 | 5800 | 0.4388 | 0.8140 | 0.8145 |
| 0.4009 | 33.15 | 6000 | 0.4430 | 0.8059 | 0.8069 |
| 0.3978 | 34.25 | 6200 | 0.4480 | 0.8061 | 0.8069 |
| 0.3978 | 35.36 | 6400 | 0.4439 | 0.8096 | 0.8107 |
| 0.3984 | 36.46 | 6600 | 0.4370 | 0.8154 | 0.8159 |
| 0.3977 | 37.57 | 6800 | 0.4420 | 0.8096 | 0.8107 |
| 0.3951 | 38.67 | 7000 | 0.4418 | 0.8112 | 0.8121 |
| 0.3937 | 39.78 | 7200 | 0.4430 | 0.8099 | 0.8107 |
| 0.3953 | 40.88 | 7400 | 0.4407 | 0.8107 | 0.8114 |
| 0.3939 | 41.99 | 7600 | 0.4414 | 0.8118 | 0.8124 |
| 0.3927 | 43.09 | 7800 | 0.4443 | 0.8122 | 0.8131 |
| 0.3962 | 44.2 | 8000 | 0.4435 | 0.8130 | 0.8138 |
| 0.3926 | 45.3 | 8200 | 0.4420 | 0.8142 | 0.8148 |
| 0.3907 | 46.41 | 8400 | 0.4434 | 0.8128 | 0.8135 |
| 0.3941 | 47.51 | 8600 | 0.4474 | 0.8085 | 0.8096 |
| 0.3897 | 48.62 | 8800 | 0.4443 | 0.8116 | 0.8124 |
| 0.3908 | 49.72 | 9000 | 0.4452 | 0.8105 | 0.8114 |
| 0.3948 | 50.83 | 9200 | 0.4475 | 0.8081 | 0.8093 |
| 0.3896 | 51.93 | 9400 | 0.4439 | 0.8112 | 0.8121 |
| 0.3891 | 53.04 | 9600 | 0.4432 | 0.8105 | 0.8114 |
| 0.3887 | 54.14 | 9800 | 0.4431 | 0.8116 | 0.8124 |
| 0.3879 | 55.25 | 10000 | 0.4447 | 0.8108 | 0.8117 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_EMP_H3K79me3-seqsight_32768_512_30M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3K79me3-seqsight_32768_512_30M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_30M",
"region:us"
] | null | 2024-04-30T04:50:07+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Codellama-finetuned-code
This model was trained from scratch on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 20
### Training results
### Framework versions
- PEFT 0.6.0.dev0
- Transformers 4.40.1
- Pytorch 2.2.2+cpu
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"library_name": "peft", "tags": ["generated_from_trainer"], "model-index": [{"name": "Codellama-finetuned-code", "results": []}]} | elinaparajuli/Codellama-finetuned-code | null | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"region:us"
] | null | 2024-04-30T04:50:29+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GUE_EMP_H3K79me3-seqsight_32768_512_30M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_30M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_30M) on the [mahdibaghbanzadeh/GUE_EMP_H3K79me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K79me3) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4398
- F1 Score: 0.8086
- Accuracy: 0.8093
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.5218 | 1.1 | 200 | 0.4583 | 0.8048 | 0.8058 |
| 0.4698 | 2.21 | 400 | 0.4506 | 0.8055 | 0.8069 |
| 0.458 | 3.31 | 600 | 0.4579 | 0.7928 | 0.7954 |
| 0.442 | 4.42 | 800 | 0.4569 | 0.7951 | 0.7975 |
| 0.4381 | 5.52 | 1000 | 0.4499 | 0.8073 | 0.8093 |
| 0.4275 | 6.63 | 1200 | 0.4617 | 0.7983 | 0.8010 |
| 0.4286 | 7.73 | 1400 | 0.4585 | 0.7964 | 0.7989 |
| 0.4188 | 8.84 | 1600 | 0.4737 | 0.7904 | 0.7940 |
| 0.4157 | 9.94 | 1800 | 0.4370 | 0.8080 | 0.8086 |
| 0.4119 | 11.05 | 2000 | 0.4548 | 0.7998 | 0.8013 |
| 0.409 | 12.15 | 2200 | 0.4337 | 0.8122 | 0.8128 |
| 0.4028 | 13.26 | 2400 | 0.4539 | 0.7992 | 0.8013 |
| 0.4002 | 14.36 | 2600 | 0.4392 | 0.8158 | 0.8162 |
| 0.3978 | 15.47 | 2800 | 0.4501 | 0.8042 | 0.8058 |
| 0.3899 | 16.57 | 3000 | 0.4450 | 0.8023 | 0.8024 |
| 0.3895 | 17.68 | 3200 | 0.4433 | 0.8122 | 0.8124 |
| 0.3836 | 18.78 | 3400 | 0.4752 | 0.7933 | 0.7961 |
| 0.3794 | 19.89 | 3600 | 0.4526 | 0.8081 | 0.8096 |
| 0.378 | 20.99 | 3800 | 0.4687 | 0.7927 | 0.7951 |
| 0.3705 | 22.1 | 4000 | 0.4535 | 0.8100 | 0.8107 |
| 0.3705 | 23.2 | 4200 | 0.4610 | 0.8054 | 0.8065 |
| 0.3683 | 24.31 | 4400 | 0.4735 | 0.7959 | 0.7982 |
| 0.3578 | 25.41 | 4600 | 0.4592 | 0.8075 | 0.8079 |
| 0.3582 | 26.52 | 4800 | 0.4762 | 0.7985 | 0.7999 |
| 0.3595 | 27.62 | 5000 | 0.4640 | 0.8012 | 0.8024 |
| 0.3503 | 28.73 | 5200 | 0.4704 | 0.8008 | 0.8024 |
| 0.3448 | 29.83 | 5400 | 0.4623 | 0.8059 | 0.8065 |
| 0.3459 | 30.94 | 5600 | 0.4716 | 0.8021 | 0.8031 |
| 0.342 | 32.04 | 5800 | 0.4681 | 0.8033 | 0.8041 |
| 0.339 | 33.15 | 6000 | 0.4785 | 0.7992 | 0.8006 |
| 0.335 | 34.25 | 6200 | 0.4910 | 0.7936 | 0.7947 |
| 0.3323 | 35.36 | 6400 | 0.4938 | 0.8028 | 0.8044 |
| 0.3324 | 36.46 | 6600 | 0.4806 | 0.8061 | 0.8072 |
| 0.3283 | 37.57 | 6800 | 0.4998 | 0.7955 | 0.7975 |
| 0.327 | 38.67 | 7000 | 0.4950 | 0.7969 | 0.7989 |
| 0.3219 | 39.78 | 7200 | 0.5078 | 0.7965 | 0.7989 |
| 0.3193 | 40.88 | 7400 | 0.4910 | 0.7944 | 0.7954 |
| 0.3203 | 41.99 | 7600 | 0.4877 | 0.8009 | 0.8020 |
| 0.3157 | 43.09 | 7800 | 0.5048 | 0.7975 | 0.7992 |
| 0.3195 | 44.2 | 8000 | 0.4973 | 0.7969 | 0.7982 |
| 0.3137 | 45.3 | 8200 | 0.4954 | 0.7987 | 0.7999 |
| 0.3148 | 46.41 | 8400 | 0.4947 | 0.7992 | 0.8003 |
| 0.3134 | 47.51 | 8600 | 0.5113 | 0.7963 | 0.7982 |
| 0.3075 | 48.62 | 8800 | 0.5066 | 0.7974 | 0.7989 |
| 0.3102 | 49.72 | 9000 | 0.5083 | 0.7986 | 0.8003 |
| 0.3115 | 50.83 | 9200 | 0.5146 | 0.7956 | 0.7979 |
| 0.3053 | 51.93 | 9400 | 0.5082 | 0.7981 | 0.7996 |
| 0.3046 | 53.04 | 9600 | 0.5085 | 0.7984 | 0.7999 |
| 0.305 | 54.14 | 9800 | 0.5078 | 0.7989 | 0.8003 |
| 0.3031 | 55.25 | 10000 | 0.5112 | 0.7991 | 0.8006 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_EMP_H3K79me3-seqsight_32768_512_30M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3K79me3-seqsight_32768_512_30M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_30M",
"region:us"
] | null | 2024-04-30T04:50:42+00:00 |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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## How to Get Started with the Model
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | hi000000/insta_merged_llama2_koen | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T04:51:34+00:00 |
null | null | {} | doggywastaken/segformer-b0-finetuned-bmri-prep-2 | null | [
"region:us"
] | null | 2024-04-30T04:51:40+00:00 |
|
text-classification | transformers | {} | Emmytheo/Deberta-v3-finetuned-hate-speech-jigsaw-toxic-comments | null | [
"transformers",
"tensorboard",
"safetensors",
"deberta-v2",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T04:52:45+00:00 |
|
multiple-choice | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-swag
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the swag dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["swag"], "base_model": "bert-base-uncased", "model-index": [{"name": "bert-base-uncased-finetuned-swag", "results": []}]} | souraviithmds/bert-base-uncased-finetuned-swag | null | [
"transformers",
"safetensors",
"bert",
"multiple-choice",
"generated_from_trainer",
"dataset:swag",
"base_model:bert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T04:54:59+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GUE_EMP_H3K4me1-seqsight_32768_512_30M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_30M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_30M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me1](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me1) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5440
- F1 Score: 0.7349
- Accuracy: 0.7383
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.6424 | 1.01 | 200 | 0.6210 | 0.6590 | 0.6664 |
| 0.6155 | 2.02 | 400 | 0.6108 | 0.6790 | 0.6828 |
| 0.6067 | 3.03 | 600 | 0.6060 | 0.6894 | 0.6929 |
| 0.6019 | 4.04 | 800 | 0.6108 | 0.6895 | 0.6960 |
| 0.595 | 5.05 | 1000 | 0.5984 | 0.7027 | 0.7061 |
| 0.5902 | 6.06 | 1200 | 0.6042 | 0.6975 | 0.7052 |
| 0.5843 | 7.07 | 1400 | 0.5939 | 0.7048 | 0.7109 |
| 0.5808 | 8.08 | 1600 | 0.5890 | 0.7063 | 0.7124 |
| 0.5763 | 9.09 | 1800 | 0.5817 | 0.7119 | 0.7172 |
| 0.5732 | 10.1 | 2000 | 0.5760 | 0.7151 | 0.7188 |
| 0.5686 | 11.11 | 2200 | 0.5833 | 0.7101 | 0.7162 |
| 0.5668 | 12.12 | 2400 | 0.5794 | 0.7143 | 0.7188 |
| 0.5665 | 13.13 | 2600 | 0.5766 | 0.7160 | 0.7213 |
| 0.5648 | 14.14 | 2800 | 0.5742 | 0.7160 | 0.7216 |
| 0.5622 | 15.15 | 3000 | 0.5733 | 0.7134 | 0.7191 |
| 0.562 | 16.16 | 3200 | 0.5752 | 0.7125 | 0.7194 |
| 0.5568 | 17.17 | 3400 | 0.5767 | 0.7167 | 0.7222 |
| 0.558 | 18.18 | 3600 | 0.5650 | 0.7216 | 0.7257 |
| 0.5545 | 19.19 | 3800 | 0.5738 | 0.7115 | 0.7178 |
| 0.557 | 20.2 | 4000 | 0.5695 | 0.7176 | 0.7232 |
| 0.5529 | 21.21 | 4200 | 0.5781 | 0.7164 | 0.7235 |
| 0.5537 | 22.22 | 4400 | 0.5648 | 0.7220 | 0.7266 |
| 0.5492 | 23.23 | 4600 | 0.5744 | 0.7160 | 0.7232 |
| 0.5556 | 24.24 | 4800 | 0.5680 | 0.7210 | 0.7276 |
| 0.5482 | 25.25 | 5000 | 0.5586 | 0.7319 | 0.7355 |
| 0.5513 | 26.26 | 5200 | 0.5580 | 0.7296 | 0.7333 |
| 0.5481 | 27.27 | 5400 | 0.5586 | 0.7264 | 0.7311 |
| 0.5485 | 28.28 | 5600 | 0.5556 | 0.7332 | 0.7364 |
| 0.5508 | 29.29 | 5800 | 0.5675 | 0.7203 | 0.7270 |
| 0.5437 | 30.3 | 6000 | 0.5591 | 0.7288 | 0.7333 |
| 0.5467 | 31.31 | 6200 | 0.5600 | 0.7276 | 0.7330 |
| 0.5478 | 32.32 | 6400 | 0.5695 | 0.7179 | 0.7251 |
| 0.5459 | 33.33 | 6600 | 0.5659 | 0.7203 | 0.7273 |
| 0.5442 | 34.34 | 6800 | 0.5652 | 0.7222 | 0.7289 |
| 0.5435 | 35.35 | 7000 | 0.5568 | 0.7291 | 0.7330 |
| 0.5473 | 36.36 | 7200 | 0.5567 | 0.7278 | 0.7326 |
| 0.5456 | 37.37 | 7400 | 0.5559 | 0.7296 | 0.7345 |
| 0.5413 | 38.38 | 7600 | 0.5552 | 0.7327 | 0.7364 |
| 0.5418 | 39.39 | 7800 | 0.5554 | 0.7306 | 0.7348 |
| 0.5437 | 40.4 | 8000 | 0.5586 | 0.7302 | 0.7348 |
| 0.5427 | 41.41 | 8200 | 0.5597 | 0.7251 | 0.7311 |
| 0.544 | 42.42 | 8400 | 0.5618 | 0.7230 | 0.7292 |
| 0.5416 | 43.43 | 8600 | 0.5600 | 0.7245 | 0.7301 |
| 0.5392 | 44.44 | 8800 | 0.5574 | 0.7291 | 0.7339 |
| 0.5426 | 45.45 | 9000 | 0.5568 | 0.7291 | 0.7336 |
| 0.5408 | 46.46 | 9200 | 0.5592 | 0.7247 | 0.7301 |
| 0.5447 | 47.47 | 9400 | 0.5584 | 0.7262 | 0.7317 |
| 0.5387 | 48.48 | 9600 | 0.5595 | 0.7242 | 0.7298 |
| 0.5456 | 49.49 | 9800 | 0.5580 | 0.7264 | 0.7317 |
| 0.5423 | 50.51 | 10000 | 0.5575 | 0.7263 | 0.7314 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_EMP_H3K4me1-seqsight_32768_512_30M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3K4me1-seqsight_32768_512_30M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_30M",
"region:us"
] | null | 2024-04-30T04:56:06+00:00 |
null | null | {} | iamlola/gaga2 | null | [
"region:us"
] | null | 2024-04-30T04:56:40+00:00 |
|
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GUE_EMP_H3K4me1-seqsight_32768_512_30M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_30M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_30M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me1](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me1) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5415
- F1 Score: 0.7414
- Accuracy: 0.7440
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.6328 | 1.01 | 200 | 0.6200 | 0.6632 | 0.6723 |
| 0.6045 | 2.02 | 400 | 0.6039 | 0.6923 | 0.6979 |
| 0.5895 | 3.03 | 600 | 0.5887 | 0.7041 | 0.7090 |
| 0.5776 | 4.04 | 800 | 0.5877 | 0.7050 | 0.7118 |
| 0.5679 | 5.05 | 1000 | 0.5737 | 0.7163 | 0.7203 |
| 0.5625 | 6.06 | 1200 | 0.5807 | 0.7063 | 0.7143 |
| 0.5572 | 7.07 | 1400 | 0.5668 | 0.7217 | 0.7263 |
| 0.5537 | 8.08 | 1600 | 0.5677 | 0.7156 | 0.7219 |
| 0.5499 | 9.09 | 1800 | 0.5558 | 0.7256 | 0.7301 |
| 0.5459 | 10.1 | 2000 | 0.5559 | 0.7248 | 0.7298 |
| 0.5406 | 11.11 | 2200 | 0.5610 | 0.7191 | 0.7257 |
| 0.5389 | 12.12 | 2400 | 0.5545 | 0.7272 | 0.7317 |
| 0.5364 | 13.13 | 2600 | 0.5530 | 0.7344 | 0.7390 |
| 0.5367 | 14.14 | 2800 | 0.5472 | 0.7384 | 0.7424 |
| 0.531 | 15.15 | 3000 | 0.5516 | 0.7329 | 0.7377 |
| 0.5313 | 16.16 | 3200 | 0.5513 | 0.7280 | 0.7336 |
| 0.5251 | 17.17 | 3400 | 0.5542 | 0.7397 | 0.7431 |
| 0.5274 | 18.18 | 3600 | 0.5477 | 0.7361 | 0.7396 |
| 0.5238 | 19.19 | 3800 | 0.5453 | 0.7375 | 0.7418 |
| 0.5233 | 20.2 | 4000 | 0.5501 | 0.7348 | 0.7390 |
| 0.5211 | 21.21 | 4200 | 0.5565 | 0.7316 | 0.7371 |
| 0.5185 | 22.22 | 4400 | 0.5518 | 0.7428 | 0.7459 |
| 0.5173 | 23.23 | 4600 | 0.5546 | 0.7332 | 0.7393 |
| 0.5206 | 24.24 | 4800 | 0.5519 | 0.7273 | 0.7333 |
| 0.513 | 25.25 | 5000 | 0.5423 | 0.7419 | 0.7443 |
| 0.517 | 26.26 | 5200 | 0.5424 | 0.7448 | 0.7459 |
| 0.5136 | 27.27 | 5400 | 0.5468 | 0.7393 | 0.7437 |
| 0.5117 | 28.28 | 5600 | 0.5417 | 0.7437 | 0.7456 |
| 0.517 | 29.29 | 5800 | 0.5555 | 0.7292 | 0.7361 |
| 0.5067 | 30.3 | 6000 | 0.5485 | 0.7369 | 0.7405 |
| 0.5108 | 31.31 | 6200 | 0.5455 | 0.7386 | 0.7421 |
| 0.5093 | 32.32 | 6400 | 0.5500 | 0.7384 | 0.7431 |
| 0.5081 | 33.33 | 6600 | 0.5540 | 0.7344 | 0.7405 |
| 0.5065 | 34.34 | 6800 | 0.5463 | 0.7391 | 0.7434 |
| 0.5053 | 35.35 | 7000 | 0.5452 | 0.7414 | 0.7437 |
| 0.5087 | 36.36 | 7200 | 0.5438 | 0.7406 | 0.7443 |
| 0.5064 | 37.37 | 7400 | 0.5428 | 0.7414 | 0.7453 |
| 0.503 | 38.38 | 7600 | 0.5419 | 0.7449 | 0.7472 |
| 0.5019 | 39.39 | 7800 | 0.5426 | 0.7453 | 0.7475 |
| 0.5036 | 40.4 | 8000 | 0.5470 | 0.7425 | 0.7459 |
| 0.504 | 41.41 | 8200 | 0.5476 | 0.7403 | 0.7449 |
| 0.5043 | 42.42 | 8400 | 0.5495 | 0.7403 | 0.7446 |
| 0.5015 | 43.43 | 8600 | 0.5458 | 0.7423 | 0.7459 |
| 0.5001 | 44.44 | 8800 | 0.5451 | 0.7407 | 0.7443 |
| 0.4993 | 45.45 | 9000 | 0.5444 | 0.7440 | 0.7472 |
| 0.4984 | 46.46 | 9200 | 0.5479 | 0.7404 | 0.7443 |
| 0.5039 | 47.47 | 9400 | 0.5467 | 0.7407 | 0.7446 |
| 0.4951 | 48.48 | 9600 | 0.5482 | 0.7432 | 0.7472 |
| 0.5047 | 49.49 | 9800 | 0.5462 | 0.7425 | 0.7462 |
| 0.5003 | 50.51 | 10000 | 0.5462 | 0.7419 | 0.7456 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_EMP_H3K4me1-seqsight_32768_512_30M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3K4me1-seqsight_32768_512_30M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_30M",
"region:us"
] | null | 2024-04-30T04:57:03+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GUE_EMP_H3K4me1-seqsight_32768_512_30M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_30M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_30M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me1](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me1) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5543
- F1 Score: 0.7439
- Accuracy: 0.7475
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.6259 | 1.01 | 200 | 0.6208 | 0.6637 | 0.6771 |
| 0.5922 | 2.02 | 400 | 0.5916 | 0.7069 | 0.7121 |
| 0.5733 | 3.03 | 600 | 0.5755 | 0.7112 | 0.7172 |
| 0.5603 | 4.04 | 800 | 0.5784 | 0.7122 | 0.7184 |
| 0.5535 | 5.05 | 1000 | 0.5569 | 0.7314 | 0.7339 |
| 0.5474 | 6.06 | 1200 | 0.5674 | 0.7250 | 0.7314 |
| 0.54 | 7.07 | 1400 | 0.5611 | 0.7265 | 0.7307 |
| 0.5362 | 8.08 | 1600 | 0.5579 | 0.7254 | 0.7304 |
| 0.533 | 9.09 | 1800 | 0.5576 | 0.7329 | 0.7367 |
| 0.5272 | 10.1 | 2000 | 0.5598 | 0.7325 | 0.7371 |
| 0.5203 | 11.11 | 2200 | 0.5623 | 0.7305 | 0.7361 |
| 0.519 | 12.12 | 2400 | 0.5546 | 0.7339 | 0.7383 |
| 0.5147 | 13.13 | 2600 | 0.5691 | 0.7282 | 0.7348 |
| 0.5137 | 14.14 | 2800 | 0.5500 | 0.7414 | 0.7449 |
| 0.5054 | 15.15 | 3000 | 0.5548 | 0.7336 | 0.7367 |
| 0.5047 | 16.16 | 3200 | 0.5563 | 0.7367 | 0.7405 |
| 0.4981 | 17.17 | 3400 | 0.5729 | 0.7420 | 0.7446 |
| 0.4977 | 18.18 | 3600 | 0.5607 | 0.7344 | 0.7377 |
| 0.4931 | 19.19 | 3800 | 0.5522 | 0.7401 | 0.7427 |
| 0.4916 | 20.2 | 4000 | 0.5615 | 0.7304 | 0.7352 |
| 0.4868 | 21.21 | 4200 | 0.5671 | 0.7371 | 0.7399 |
| 0.4823 | 22.22 | 4400 | 0.5698 | 0.7400 | 0.7421 |
| 0.4801 | 23.23 | 4600 | 0.5699 | 0.7365 | 0.7396 |
| 0.4808 | 24.24 | 4800 | 0.5764 | 0.7242 | 0.7298 |
| 0.4722 | 25.25 | 5000 | 0.5625 | 0.7366 | 0.7390 |
| 0.4713 | 26.26 | 5200 | 0.5592 | 0.7387 | 0.7405 |
| 0.4719 | 27.27 | 5400 | 0.5751 | 0.7263 | 0.7320 |
| 0.4665 | 28.28 | 5600 | 0.5683 | 0.7362 | 0.7390 |
| 0.4684 | 29.29 | 5800 | 0.5813 | 0.7159 | 0.7235 |
| 0.4598 | 30.3 | 6000 | 0.5790 | 0.7356 | 0.7383 |
| 0.4585 | 31.31 | 6200 | 0.5718 | 0.7383 | 0.7405 |
| 0.4574 | 32.32 | 6400 | 0.5792 | 0.7262 | 0.7307 |
| 0.4569 | 33.33 | 6600 | 0.5909 | 0.7291 | 0.7355 |
| 0.4521 | 34.34 | 6800 | 0.5689 | 0.7324 | 0.7358 |
| 0.4514 | 35.35 | 7000 | 0.5756 | 0.7393 | 0.7408 |
| 0.4542 | 36.36 | 7200 | 0.5706 | 0.7291 | 0.7320 |
| 0.4493 | 37.37 | 7400 | 0.5764 | 0.7293 | 0.7339 |
| 0.4437 | 38.38 | 7600 | 0.5780 | 0.7354 | 0.7371 |
| 0.4425 | 39.39 | 7800 | 0.5812 | 0.7362 | 0.7380 |
| 0.4409 | 40.4 | 8000 | 0.5924 | 0.7354 | 0.7383 |
| 0.4422 | 41.41 | 8200 | 0.5874 | 0.7302 | 0.7348 |
| 0.4413 | 42.42 | 8400 | 0.5901 | 0.7336 | 0.7371 |
| 0.4389 | 43.43 | 8600 | 0.5838 | 0.7328 | 0.7355 |
| 0.4356 | 44.44 | 8800 | 0.5858 | 0.7302 | 0.7330 |
| 0.4337 | 45.45 | 9000 | 0.5856 | 0.7316 | 0.7339 |
| 0.434 | 46.46 | 9200 | 0.5912 | 0.7297 | 0.7330 |
| 0.4394 | 47.47 | 9400 | 0.5868 | 0.7265 | 0.7298 |
| 0.4297 | 48.48 | 9600 | 0.5887 | 0.7288 | 0.7323 |
| 0.4369 | 49.49 | 9800 | 0.5892 | 0.7280 | 0.7314 |
| 0.4349 | 50.51 | 10000 | 0.5883 | 0.7291 | 0.7320 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_EMP_H3K4me1-seqsight_32768_512_30M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3K4me1-seqsight_32768_512_30M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_30M",
"region:us"
] | null | 2024-04-30T04:57:49+00:00 |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Text Classification
## Validation Metrics
loss: 1.0650867223739624
f1_macro: 0.2095479509928179
f1_micro: 0.4584103512014787
f1_weighted: 0.2881768494245037
precision_macro: 0.1528034504004929
precision_micro: 0.4584103512014787
precision_weighted: 0.21014005008866307
recall_macro: 0.3333333333333333
recall_micro: 0.4584103512014787
recall_weighted: 0.4584103512014787
accuracy: 0.4584103512014787
| {"tags": ["autotrain", "text-classification"], "datasets": ["autotrain-byt8e-zygc3/autotrain-data"], "widget": [{"text": "I love AutoTrain"}]} | Akhil-9640/autotrain-byt8e-zygc3 | null | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"autotrain",
"dataset:autotrain-byt8e-zygc3/autotrain-data",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T04:58:14+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mistral_envs_claim_finetune1
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 5
- total_train_batch_size: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 100
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.1.0a0+29c30b1
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "mistralai/Mistral-7B-Instruct-v0.2", "model-index": [{"name": "mistral_envs_claim_finetune1", "results": []}]} | Haimee/mistral_envs_claim_finetune1 | null | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"license:apache-2.0",
"region:us"
] | null | 2024-04-30T04:58:22+00:00 |
text-generation | null |
# Smart-Lemon-Cookie-7B
These are GGUFs for the following model:
https://huggingface.co/FallenMerick/Smart-Lemon-Cookie-7B | {"tags": ["quantized", "4-bit", "6-bit", "8-bit", "GGUF", "merge", "mistral", "text-generation"], "model_name": "Smart-Lemon-Cookie-7B", "base_model": ["FallenMerick/Smart-Lemon-Cookie-7B"], "model_type": "mistral", "pipeline_tag": "text-generation"} | FallenMerick/Smart-Lemon-Cookie-7B-GGUF | null | [
"gguf",
"quantized",
"4-bit",
"6-bit",
"8-bit",
"GGUF",
"merge",
"mistral",
"text-generation",
"base_model:FallenMerick/Smart-Lemon-Cookie-7B",
"region:us"
] | null | 2024-04-30T04:59:51+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GUE_EMP_H3K36me3-seqsight_32768_512_30M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_30M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_30M) on the [mahdibaghbanzadeh/GUE_EMP_H3K36me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K36me3) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5059
- F1 Score: 0.7648
- Accuracy: 0.7689
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.5967 | 0.92 | 200 | 0.5618 | 0.7187 | 0.7205 |
| 0.5486 | 1.83 | 400 | 0.5608 | 0.7245 | 0.7276 |
| 0.5437 | 2.75 | 600 | 0.5551 | 0.7270 | 0.7308 |
| 0.5434 | 3.67 | 800 | 0.5425 | 0.7363 | 0.7388 |
| 0.5285 | 4.59 | 1000 | 0.5445 | 0.7306 | 0.7351 |
| 0.5253 | 5.5 | 1200 | 0.5368 | 0.7363 | 0.7408 |
| 0.5228 | 6.42 | 1400 | 0.5290 | 0.7489 | 0.7517 |
| 0.5189 | 7.34 | 1600 | 0.5323 | 0.7486 | 0.7526 |
| 0.5096 | 8.26 | 1800 | 0.5406 | 0.7426 | 0.7480 |
| 0.514 | 9.17 | 2000 | 0.5273 | 0.7471 | 0.7517 |
| 0.5067 | 10.09 | 2200 | 0.5402 | 0.7334 | 0.7411 |
| 0.5037 | 11.01 | 2400 | 0.5333 | 0.7423 | 0.7491 |
| 0.5029 | 11.93 | 2600 | 0.5189 | 0.7584 | 0.7626 |
| 0.5012 | 12.84 | 2800 | 0.5240 | 0.7515 | 0.7569 |
| 0.4992 | 13.76 | 3000 | 0.5303 | 0.7487 | 0.7549 |
| 0.4945 | 14.68 | 3200 | 0.5157 | 0.7587 | 0.7623 |
| 0.4989 | 15.6 | 3400 | 0.5272 | 0.7480 | 0.7546 |
| 0.494 | 16.51 | 3600 | 0.5181 | 0.7580 | 0.7623 |
| 0.4956 | 17.43 | 3800 | 0.5110 | 0.7616 | 0.7655 |
| 0.4948 | 18.35 | 4000 | 0.5128 | 0.7577 | 0.7620 |
| 0.4895 | 19.27 | 4200 | 0.5187 | 0.7564 | 0.7612 |
| 0.4906 | 20.18 | 4400 | 0.5268 | 0.7515 | 0.7577 |
| 0.4897 | 21.1 | 4600 | 0.5191 | 0.7559 | 0.7609 |
| 0.4906 | 22.02 | 4800 | 0.5228 | 0.7518 | 0.7577 |
| 0.4886 | 22.94 | 5000 | 0.5138 | 0.7570 | 0.7618 |
| 0.491 | 23.85 | 5200 | 0.5219 | 0.7544 | 0.7603 |
| 0.4864 | 24.77 | 5400 | 0.5209 | 0.7532 | 0.7589 |
| 0.4877 | 25.69 | 5600 | 0.5162 | 0.7584 | 0.7632 |
| 0.4857 | 26.61 | 5800 | 0.5114 | 0.7611 | 0.7652 |
| 0.4835 | 27.52 | 6000 | 0.5288 | 0.7529 | 0.7592 |
| 0.4856 | 28.44 | 6200 | 0.5212 | 0.7543 | 0.7600 |
| 0.4822 | 29.36 | 6400 | 0.5268 | 0.7526 | 0.7589 |
| 0.4843 | 30.28 | 6600 | 0.5184 | 0.7567 | 0.7623 |
| 0.4828 | 31.19 | 6800 | 0.5090 | 0.7641 | 0.7678 |
| 0.4841 | 32.11 | 7000 | 0.5186 | 0.7572 | 0.7626 |
| 0.4829 | 33.03 | 7200 | 0.5139 | 0.7603 | 0.7649 |
| 0.4811 | 33.94 | 7400 | 0.5169 | 0.7599 | 0.7646 |
| 0.4815 | 34.86 | 7600 | 0.5167 | 0.7572 | 0.7623 |
| 0.4821 | 35.78 | 7800 | 0.5166 | 0.7570 | 0.7623 |
| 0.4837 | 36.7 | 8000 | 0.5127 | 0.7610 | 0.7655 |
| 0.48 | 37.61 | 8200 | 0.5224 | 0.7556 | 0.7618 |
| 0.4814 | 38.53 | 8400 | 0.5110 | 0.7612 | 0.7655 |
| 0.4804 | 39.45 | 8600 | 0.5167 | 0.7603 | 0.7655 |
| 0.4793 | 40.37 | 8800 | 0.5207 | 0.7599 | 0.7652 |
| 0.4834 | 41.28 | 9000 | 0.5159 | 0.7587 | 0.7640 |
| 0.4779 | 42.2 | 9200 | 0.5116 | 0.7625 | 0.7666 |
| 0.4795 | 43.12 | 9400 | 0.5130 | 0.7631 | 0.7675 |
| 0.4823 | 44.04 | 9600 | 0.5153 | 0.7594 | 0.7646 |
| 0.4815 | 44.95 | 9800 | 0.5167 | 0.7593 | 0.7646 |
| 0.4804 | 45.87 | 10000 | 0.5155 | 0.7592 | 0.7643 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_EMP_H3K36me3-seqsight_32768_512_30M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3K36me3-seqsight_32768_512_30M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_30M",
"region:us"
] | null | 2024-04-30T04:59:56+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GUE_EMP_H3K36me3-seqsight_32768_512_30M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_30M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_30M) on the [mahdibaghbanzadeh/GUE_EMP_H3K36me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K36me3) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4964
- F1 Score: 0.7769
- Accuracy: 0.7801
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.5823 | 0.92 | 200 | 0.5639 | 0.7133 | 0.7179 |
| 0.5372 | 1.83 | 400 | 0.5540 | 0.7230 | 0.7285 |
| 0.5235 | 2.75 | 600 | 0.5354 | 0.7390 | 0.7443 |
| 0.5191 | 3.67 | 800 | 0.5246 | 0.7483 | 0.7534 |
| 0.5011 | 4.59 | 1000 | 0.5301 | 0.7489 | 0.7549 |
| 0.494 | 5.5 | 1200 | 0.5277 | 0.7459 | 0.7529 |
| 0.4944 | 6.42 | 1400 | 0.5062 | 0.7589 | 0.7626 |
| 0.4915 | 7.34 | 1600 | 0.5254 | 0.7531 | 0.7589 |
| 0.4831 | 8.26 | 1800 | 0.5174 | 0.7620 | 0.7669 |
| 0.487 | 9.17 | 2000 | 0.5059 | 0.7629 | 0.7675 |
| 0.4817 | 10.09 | 2200 | 0.5418 | 0.7252 | 0.7365 |
| 0.478 | 11.01 | 2400 | 0.5097 | 0.7571 | 0.7629 |
| 0.4762 | 11.93 | 2600 | 0.4893 | 0.7771 | 0.7790 |
| 0.475 | 12.84 | 2800 | 0.5069 | 0.7606 | 0.7658 |
| 0.473 | 13.76 | 3000 | 0.5181 | 0.7560 | 0.7620 |
| 0.4686 | 14.68 | 3200 | 0.4987 | 0.7713 | 0.7744 |
| 0.4693 | 15.6 | 3400 | 0.5017 | 0.7633 | 0.7678 |
| 0.4672 | 16.51 | 3600 | 0.5163 | 0.7602 | 0.7655 |
| 0.4657 | 17.43 | 3800 | 0.4967 | 0.7720 | 0.7749 |
| 0.4646 | 18.35 | 4000 | 0.4947 | 0.7722 | 0.7755 |
| 0.46 | 19.27 | 4200 | 0.5023 | 0.7722 | 0.7755 |
| 0.4609 | 20.18 | 4400 | 0.5151 | 0.7575 | 0.7629 |
| 0.4586 | 21.1 | 4600 | 0.5014 | 0.7714 | 0.7752 |
| 0.4594 | 22.02 | 4800 | 0.5005 | 0.7715 | 0.7749 |
| 0.4568 | 22.94 | 5000 | 0.4909 | 0.7720 | 0.7752 |
| 0.4577 | 23.85 | 5200 | 0.5041 | 0.7606 | 0.7658 |
| 0.4524 | 24.77 | 5400 | 0.5124 | 0.7607 | 0.7663 |
| 0.4551 | 25.69 | 5600 | 0.5014 | 0.7685 | 0.7726 |
| 0.4524 | 26.61 | 5800 | 0.4908 | 0.7769 | 0.7792 |
| 0.4503 | 27.52 | 6000 | 0.5047 | 0.7684 | 0.7721 |
| 0.4508 | 28.44 | 6200 | 0.5004 | 0.7686 | 0.7729 |
| 0.4465 | 29.36 | 6400 | 0.5062 | 0.7672 | 0.7718 |
| 0.4482 | 30.28 | 6600 | 0.5013 | 0.7644 | 0.7695 |
| 0.4461 | 31.19 | 6800 | 0.4921 | 0.7735 | 0.7764 |
| 0.4477 | 32.11 | 7000 | 0.4983 | 0.7706 | 0.7744 |
| 0.4471 | 33.03 | 7200 | 0.4909 | 0.7739 | 0.7772 |
| 0.4451 | 33.94 | 7400 | 0.4961 | 0.7743 | 0.7775 |
| 0.4447 | 34.86 | 7600 | 0.4964 | 0.7718 | 0.7755 |
| 0.4448 | 35.78 | 7800 | 0.4944 | 0.7742 | 0.7772 |
| 0.4471 | 36.7 | 8000 | 0.4942 | 0.7723 | 0.7758 |
| 0.4426 | 37.61 | 8200 | 0.5033 | 0.7642 | 0.7689 |
| 0.4447 | 38.53 | 8400 | 0.4944 | 0.7739 | 0.7772 |
| 0.4396 | 39.45 | 8600 | 0.5002 | 0.7694 | 0.7735 |
| 0.4405 | 40.37 | 8800 | 0.5046 | 0.7694 | 0.7735 |
| 0.4449 | 41.28 | 9000 | 0.4965 | 0.7716 | 0.7755 |
| 0.44 | 42.2 | 9200 | 0.4938 | 0.7752 | 0.7784 |
| 0.4396 | 43.12 | 9400 | 0.4936 | 0.7776 | 0.7804 |
| 0.4438 | 44.04 | 9600 | 0.4961 | 0.7715 | 0.7752 |
| 0.4417 | 44.95 | 9800 | 0.4998 | 0.7675 | 0.7718 |
| 0.44 | 45.87 | 10000 | 0.4976 | 0.7711 | 0.7749 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_EMP_H3K36me3-seqsight_32768_512_30M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3K36me3-seqsight_32768_512_30M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_30M",
"region:us"
] | null | 2024-04-30T05:00:14+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gemma-1.1-2b-it-genai-kb
This model is a fine-tuned version of [google/gemma-1.1-2b-it](https://huggingface.co/google/gemma-1.1-2b-it) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 5.2164
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 1 | 5.2201 |
| No log | 2.0 | 3 | 5.2164 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"license": "gemma", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "google/gemma-1.1-2b-it", "model-index": [{"name": "gemma-1.1-2b-it-genai-kb", "results": []}]} | cohesionet/gemma-1.1-2b-it-genai-kb | null | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:google/gemma-1.1-2b-it",
"license:gemma",
"region:us"
] | null | 2024-04-30T05:00:17+00:00 |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# base-nsp-100000
This model is a fine-tuned version of [mhr2004/plm-nsp-100000](https://huggingface.co/mhr2004/plm-nsp-100000) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8933
- Accuracy: 0.4877
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9735 | 1.0 | 183 | 0.8668 | 0.4811 |
| 0.8972 | 2.0 | 366 | 0.8636 | 0.4784 |
| 0.8381 | 3.0 | 549 | 0.8927 | 0.4613 |
| 0.8088 | 4.0 | 732 | 0.9399 | 0.4586 |
| 0.793 | 5.0 | 915 | 0.9159 | 0.4856 |
| 0.767 | 6.0 | 1098 | 0.9487 | 0.4793 |
| 0.7457 | 7.0 | 1281 | 0.9372 | 0.4946 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mhr2004/plm-nsp-100000", "model-index": [{"name": "base-nsp-100000", "results": []}]} | mhr2004/base-nsp-100000 | null | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:mhr2004/plm-nsp-100000",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T05:01:25+00:00 |
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