modelId
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81
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list | pipeline_tag
stringclasses 17
values | config
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int64 0
59.7M
| first_commit
timestamp[ns, tz=UTC] | card
stringlengths 51
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AnonymousSub/specter-emanuals-model | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
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} | 6 | null | ---
license: apache-2.0
duplicated_from: PaddlePaddle/ci-test-ernie-model
---
this model is for CI testing in paddlenlp repo.
As you can guess, PaddleNLP will play with π€ Huggingface. |
AnonymousSub/unsup-consert-base | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
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} | 6 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: nmt-mpst-id-en-lr_0.0001-ep_10-seq_128_bs-32
results: []
---
<!-- 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. -->
# nmt-mpst-id-en-lr_0.0001-ep_10-seq_128_bs-32
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2914
- Bleu: 0.0708
- Meteor: 0.2054
## 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: 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Meteor |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| No log | 1.0 | 202 | 2.8210 | 0.0313 | 0.1235 |
| No log | 2.0 | 404 | 2.6712 | 0.0398 | 0.1478 |
| 3.0646 | 3.0 | 606 | 2.5543 | 0.0483 | 0.1661 |
| 3.0646 | 4.0 | 808 | 2.4735 | 0.0537 | 0.1751 |
| 2.6866 | 5.0 | 1010 | 2.4120 | 0.0591 | 0.1855 |
| 2.6866 | 6.0 | 1212 | 2.3663 | 0.0618 | 0.1906 |
| 2.6866 | 7.0 | 1414 | 2.3324 | 0.0667 | 0.1993 |
| 2.5034 | 8.0 | 1616 | 2.3098 | 0.0684 | 0.2023 |
| 2.5034 | 9.0 | 1818 | 2.2969 | 0.0696 | 0.2042 |
| 2.4271 | 10.0 | 2020 | 2.2914 | 0.0708 | 0.2054 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.13.2
|
Apisate/DialoGPT-small-jordan | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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} | 12 | null | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: finetuned_twitter_targeted_insult_LSTM
results: []
---
<!-- 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. -->
# finetuned_twitter_targeted_insult_LSTM
This model is a fine-tuned version of [LYTinn/lstm-finetuning-sentiment-model-3000-samples](https://huggingface.co/LYTinn/lstm-finetuning-sentiment-model-3000-samples) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6314
- Accuracy: 0.6394
- F1: 0.6610
- Precision: 0.6262
- Recall: 0.6998
## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.13.2
|
Apisate/Discord-Ai-Bot | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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"GPT2LMHeadModel"
],
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} | 11 | null | ---
language:
- en
license: creativeml-openrail-m
thumbnail: "https://huggingface.co/an303042/Jocelyn_Hobbie_Diffusion/resolve/main/00026-865999057.png"
tags:
- stable-diffusion
- text-to-image
- image-to-image
- diffusers
---
### Jocelyn Hobbie Diffusion v1
This model was created to celebrate the works of Jocelyn Hobbie - A wonderful contemporary artist.
Check out her works @ www.jocelynhobbie.com and @jocelynhobbie
**Token to use is "jclnhbe style" **
### 𧨠Diffusers
This model can be used just like any other Stable Diffusion model. For more information,
please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion).
## License
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
### Examples
|
ArBert/albert-base-v2-finetuned-ner-agglo-twitter | [
"pytorch",
"tensorboard",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
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"AlbertForTokenClassification"
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} | 27 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-issues-128
results: []
---
<!-- 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-issues-128
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2449
## 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: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.099 | 1.0 | 291 | 1.6946 |
| 1.6396 | 2.0 | 582 | 1.4288 |
| 1.4875 | 3.0 | 873 | 1.3893 |
| 1.399 | 4.0 | 1164 | 1.3812 |
| 1.341 | 5.0 | 1455 | 1.2004 |
| 1.2803 | 6.0 | 1746 | 1.2738 |
| 1.2397 | 7.0 | 2037 | 1.2645 |
| 1.199 | 8.0 | 2328 | 1.2092 |
| 1.166 | 9.0 | 2619 | 1.1871 |
| 1.1406 | 10.0 | 2910 | 1.2244 |
| 1.1293 | 11.0 | 3201 | 1.2061 |
| 1.1037 | 12.0 | 3492 | 1.1621 |
| 1.0824 | 13.0 | 3783 | 1.2540 |
| 1.0738 | 14.0 | 4074 | 1.1703 |
| 1.0625 | 15.0 | 4365 | 1.1195 |
| 1.0628 | 16.0 | 4656 | 1.2449 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.10.3
|
ArBert/albert-base-v2-finetuned-ner-agglo | [
"pytorch",
"tensorboard",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"AlbertForTokenClassification"
],
"model_type": "albert",
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} | 8 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9255
- name: F1
type: f1
value: 0.9256889016417648
---
<!-- 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. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2222
- Accuracy: 0.9255
- F1: 0.9257
## 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: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.7962 | 1.0 | 250 | 0.3167 | 0.903 | 0.8984 |
| 0.2475 | 2.0 | 500 | 0.2222 | 0.9255 | 0.9257 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
ArBert/albert-base-v2-finetuned-ner-gmm-twitter | [
"pytorch",
"tensorboard",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
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} | 8 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
---
### noggles9000 on Stable Diffusion via Dreambooth trained on the [fast-DreamBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
#### Model by alxdfy
This your the Stable Diffusion model fine-tuned the noggles9000 concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the `instance_prompt(s)`: **nounfootball.jpg**
You can also train your own concepts and upload them to the library by using [the fast-DremaBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb).
You can run your new concept via A1111 Colab :[Fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Or you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts)
Sample pictures of this concept:
nounfootball.jpg
|
ArBert/albert-base-v2-finetuned-ner-gmm | [
"pytorch",
"tensorboard",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
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} | 8 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- rock-glacier-dataset
metrics:
- accuracy
model-index:
- name: hf_train_output
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: rock-glacier-dataset
type: rock-glacier-dataset
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9258241758241759
---
<!-- 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. -->
# hf_train_output
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the rock-glacier-dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3894
- Accuracy: 0.9258
## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5619 | 0.55 | 50 | 0.5432 | 0.7692 |
| 0.4582 | 1.1 | 100 | 0.4435 | 0.8352 |
| 0.3548 | 1.65 | 150 | 0.3739 | 0.8599 |
| 0.217 | 2.2 | 200 | 0.2913 | 0.9093 |
| 0.1709 | 2.75 | 250 | 0.2619 | 0.9148 |
| 0.0919 | 3.3 | 300 | 0.2475 | 0.9148 |
| 0.0652 | 3.85 | 350 | 0.3275 | 0.8901 |
| 0.0495 | 4.4 | 400 | 0.2515 | 0.9093 |
| 0.0321 | 4.95 | 450 | 0.2878 | 0.9066 |
| 0.0247 | 5.49 | 500 | 0.2612 | 0.9148 |
| 0.017 | 6.04 | 550 | 0.2687 | 0.9176 |
| 0.0131 | 6.59 | 600 | 0.3062 | 0.9093 |
| 0.0113 | 7.14 | 650 | 0.2587 | 0.9231 |
| 0.0099 | 7.69 | 700 | 0.2815 | 0.9203 |
| 0.009 | 8.24 | 750 | 0.2675 | 0.9286 |
| 0.0084 | 8.79 | 800 | 0.2711 | 0.9286 |
| 0.0077 | 9.34 | 850 | 0.2663 | 0.9313 |
| 0.0073 | 9.89 | 900 | 0.3003 | 0.9258 |
| 0.0069 | 10.44 | 950 | 0.2758 | 0.9313 |
| 0.0064 | 10.99 | 1000 | 0.2999 | 0.9258 |
| 0.0061 | 11.54 | 1050 | 0.2931 | 0.9313 |
| 0.0057 | 12.09 | 1100 | 0.2989 | 0.9313 |
| 0.0056 | 12.64 | 1150 | 0.2974 | 0.9313 |
| 0.0053 | 13.19 | 1200 | 0.3099 | 0.9258 |
| 0.005 | 13.74 | 1250 | 0.3131 | 0.9313 |
| 0.0049 | 14.29 | 1300 | 0.3201 | 0.9258 |
| 0.0046 | 14.84 | 1350 | 0.3109 | 0.9313 |
| 0.0045 | 15.38 | 1400 | 0.3168 | 0.9313 |
| 0.0043 | 15.93 | 1450 | 0.3226 | 0.9231 |
| 0.0042 | 16.48 | 1500 | 0.3234 | 0.9231 |
| 0.0041 | 17.03 | 1550 | 0.3283 | 0.9258 |
| 0.0039 | 17.58 | 1600 | 0.3304 | 0.9258 |
| 0.0038 | 18.13 | 1650 | 0.3321 | 0.9231 |
| 0.0037 | 18.68 | 1700 | 0.3362 | 0.9231 |
| 0.0036 | 19.23 | 1750 | 0.3307 | 0.9286 |
| 0.0035 | 19.78 | 1800 | 0.3357 | 0.9231 |
| 0.0034 | 20.33 | 1850 | 0.3244 | 0.9313 |
| 0.0033 | 20.88 | 1900 | 0.3497 | 0.9231 |
| 0.0032 | 21.43 | 1950 | 0.3443 | 0.9231 |
| 0.0031 | 21.98 | 2000 | 0.3398 | 0.9286 |
| 0.003 | 22.53 | 2050 | 0.3388 | 0.9286 |
| 0.003 | 23.08 | 2100 | 0.3399 | 0.9286 |
| 0.0029 | 23.63 | 2150 | 0.3548 | 0.9231 |
| 0.0028 | 24.18 | 2200 | 0.3475 | 0.9286 |
| 0.0028 | 24.73 | 2250 | 0.3480 | 0.9286 |
| 0.0027 | 25.27 | 2300 | 0.3542 | 0.9231 |
| 0.0026 | 25.82 | 2350 | 0.3589 | 0.9231 |
| 0.0026 | 26.37 | 2400 | 0.3449 | 0.9286 |
| 0.0025 | 26.92 | 2450 | 0.3604 | 0.9231 |
| 0.0025 | 27.47 | 2500 | 0.3493 | 0.9286 |
| 0.0024 | 28.02 | 2550 | 0.3631 | 0.9258 |
| 0.0024 | 28.57 | 2600 | 0.3590 | 0.9258 |
| 0.0023 | 29.12 | 2650 | 0.3604 | 0.9258 |
| 0.0023 | 29.67 | 2700 | 0.3667 | 0.9258 |
| 0.0022 | 30.22 | 2750 | 0.3571 | 0.9286 |
| 0.0022 | 30.77 | 2800 | 0.3660 | 0.9258 |
| 0.0021 | 31.32 | 2850 | 0.3638 | 0.9286 |
| 0.0021 | 31.87 | 2900 | 0.3729 | 0.9258 |
| 0.0021 | 32.42 | 2950 | 0.3706 | 0.9258 |
| 0.002 | 32.97 | 3000 | 0.3669 | 0.9286 |
| 0.002 | 33.52 | 3050 | 0.3740 | 0.9258 |
| 0.002 | 34.07 | 3100 | 0.3693 | 0.9286 |
| 0.002 | 34.62 | 3150 | 0.3700 | 0.9286 |
| 0.0019 | 35.16 | 3200 | 0.3752 | 0.9258 |
| 0.0019 | 35.71 | 3250 | 0.3753 | 0.9258 |
| 0.0019 | 36.26 | 3300 | 0.3721 | 0.9286 |
| 0.0018 | 36.81 | 3350 | 0.3764 | 0.9258 |
| 0.0018 | 37.36 | 3400 | 0.3758 | 0.9258 |
| 0.0018 | 37.91 | 3450 | 0.3775 | 0.9258 |
| 0.0018 | 38.46 | 3500 | 0.3812 | 0.9258 |
| 0.0018 | 39.01 | 3550 | 0.3817 | 0.9258 |
| 0.0017 | 39.56 | 3600 | 0.3815 | 0.9258 |
| 0.0017 | 40.11 | 3650 | 0.3825 | 0.9258 |
| 0.0017 | 40.66 | 3700 | 0.3852 | 0.9258 |
| 0.0017 | 41.21 | 3750 | 0.3854 | 0.9258 |
| 0.0017 | 41.76 | 3800 | 0.3823 | 0.9258 |
| 0.0016 | 42.31 | 3850 | 0.3829 | 0.9258 |
| 0.0016 | 42.86 | 3900 | 0.3873 | 0.9258 |
| 0.0016 | 43.41 | 3950 | 0.3842 | 0.9258 |
| 0.0016 | 43.96 | 4000 | 0.3857 | 0.9258 |
| 0.0016 | 44.51 | 4050 | 0.3873 | 0.9258 |
| 0.0016 | 45.05 | 4100 | 0.3878 | 0.9258 |
| 0.0016 | 45.6 | 4150 | 0.3881 | 0.9258 |
| 0.0016 | 46.15 | 4200 | 0.3888 | 0.9258 |
| 0.0016 | 46.7 | 4250 | 0.3891 | 0.9258 |
| 0.0016 | 47.25 | 4300 | 0.3878 | 0.9258 |
| 0.0016 | 47.8 | 4350 | 0.3890 | 0.9258 |
| 0.0016 | 48.35 | 4400 | 0.3890 | 0.9258 |
| 0.0015 | 48.9 | 4450 | 0.3895 | 0.9258 |
| 0.0015 | 49.45 | 4500 | 0.3896 | 0.9258 |
| 0.0015 | 50.0 | 4550 | 0.3894 | 0.9258 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.13.2
|
ArBert/albert-base-v2-finetuned-ner-kmeans-twitter | [
"pytorch",
"tensorboard",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
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} | 10 | null | ---
license: cc-by-sa-4.0
---
# π Technicolor-50s Diffusion
## Style Description
- highly-saturated postcard-like colors, flat high-key lighting, strong rim-lighting, 40s and 50s lifestyle
## Sample Output (Raw Output)
<sub>tchnclr style, a closeup portrait of Brenda Song, happy beaming content, glitter, glittery
Negative prompt: b&w, lowres, text, error, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, ugly
Steps: 40, Sampler: Euler, CFG scale: 7, Seed: 1638627547, Size: 512x512, Model hash: ed87e89c, Variation seed: 3476746822, Variation seed strength: 0.2</sub>
<sub>Use PNG block tool to view the prompts and settings used to product these images</sub>
## Recommended Usage
- Your prompt must include "tchnclr style"
- Use CFG of 7 or 8 for best results
- The model was trained with and excels at closeup portraits of men and women
- Try including "glitter" in your prompt!
- Putting "b&w" as a negative prompt will help ensure color image
## Known Limitations
- It strongly tries to insert 40s and 50s hairstyles, clothing, and scenery
- As you can see from the examples, you can insert some modernity and blend with other styles. But if your prompt insists on modern elements, the technicolor effect may disappear.
- The model tends to turn men into women. It also likes to add hats!
## Training Process
20 images from movies filmed in technicolor, 200 photo-like classifiers, 6000 steps, using the Dreambooth Extension for Automatic1111. |
ArBert/bert-base-uncased-finetuned-ner-kmeans | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
]
| token-classification | {
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"BertForTokenClassification"
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} | 6 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- lst20
model-index:
- name: premodel
results: []
---
<!-- 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. -->
# premodel
This model is a fine-tuned version of [Geotrend/bert-base-th-cased](https://huggingface.co/Geotrend/bert-base-th-cased) on the lst20 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.13.2
|
ArBert/bert-base-uncased-finetuned-ner | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"BertForTokenClassification"
],
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} | 8 | 2021-08-03T13:49:01Z | ---
tags:
- text-to-image
library_name: generic
---
# Text To Image repository template
This is a template repository for text to image to support generic inference with Hugging Face Hub generic Inference API. There are two required steps
1. Specify the requirements by defining a `requirements.txt` file.
2. Implement the `pipeline.py` `__init__` and `__call__` methods. These methods are called by the Inference API. The `__init__` method should load the model and preload all the elements needed for inference (model, processors, tokenizers, etc.). This is only called once. The `__call__` method performs the actual inference. Make sure to follow the same input/output specifications defined in the template for the pipeline to work.
Example repos
* https://huggingface.co/osanseviero/BigGAN-deep-128/blob/main/pipeline.py
## How to start
First create a repo in https://hf.co/new.
Then clone this template and push it to your repo.
```
git clone https://huggingface.co/templates/text-to-image
cd text-to-image
git remote set-url origin https://huggingface.co/$YOUR_USER/$YOUR_REPO_NAME
git push --force
``` |
ArBert/roberta-base-finetuned-ner-gmm | []
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} | 0 | 2022-11-19T08:52:06Z | ---
license: odbl
datasets: gojiteji/QRsst2
---
This is a diffusion model fine-tuned with [QRsst2](https://huggingface.co/datasets/gojiteji/QRsst2).
This model generates a QR code from text.
Please clone this repository and replace [LambdaLabsML's example's inference code ](https://github.com/LambdaLabsML/examples/blob/767e1101b0125202871812ec7e1b5c46aa9c8d95/stable-diffusion-finetuning/pokemon_finetune.ipynb). checkpoint filename check pint name with `main.ckpt`.
The below images are examples of an input :`The way to get started is to quit talking and begin doing.`
sample code is here:https://github.com/gojiteji/text2QR/blob/main/samplecode.ipynb
|
ArBert/roberta-base-finetuned-ner-kmeans-twitter | [
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"autotrain_compatible"
]
| token-classification | {
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"RobertaForTokenClassification"
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} | 10 | null | Access to model CosmicAvatar/cosmic_avatar_stable_diffusion_v_1_5 is restricted and you are not in the authorized list. Visit https://huggingface.co/CosmicAvatar/cosmic_avatar_stable_diffusion_v_1_5 to ask for access. |
ArBert/roberta-base-finetuned-ner-kmeans | [
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"dataset:conll2003",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"RobertaForTokenClassification"
],
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}
} | 8 | null | ---
license: creativeml-openrail-m
---
### anglaLudicMindTwo on Stable Diffusion via Dreambooth trained on the [fast-DreamBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
#### Model by Cacau s your the Stable Diffusion model fine-tuned the anglaLudicMindTwo concept taught to Stable Diffusion with Dreambooth.
You can also train your own concepts and upload them to the library by using [the fast-DremaBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb).
You can run your new concept via A1111 Colab :[Fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Or you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts)
sample_pictures.png.
 |
ArJakusz/DialoGPT-small-stark | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
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} | 8 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: KubiakJakub01/finetuned-distilbert-base-uncased
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# KubiakJakub01/finetuned-distilbert-base-uncased
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2767
- Validation Loss: 0.4326
- Train Accuracy: 0.8319
- Epoch: 2
## 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:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1140, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.4680 | 0.4008 | 0.8378 | 0 |
| 0.3475 | 0.4017 | 0.8385 | 1 |
| 0.2767 | 0.4326 | 0.8319 | 2 |
### Framework versions
- Transformers 4.21.3
- TensorFlow 2.9.1
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Arcktosh/DialoGPT-small-rick | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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"GPT2LMHeadModel"
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} | 8 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: raisvaza/distilbert-base-uncased-finetuned-ner
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# raisvaza/distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0352
- Validation Loss: 0.0607
- Train Precision: 0.9246
- Train Recall: 0.9330
- Train F1: 0.9288
- Train Accuracy: 0.9832
- Epoch: 2
## 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2631, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch |
|:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:|
| 0.1955 | 0.0720 | 0.8998 | 0.9157 | 0.9077 | 0.9792 | 0 |
| 0.0557 | 0.0620 | 0.9200 | 0.9271 | 0.9235 | 0.9822 | 1 |
| 0.0352 | 0.0607 | 0.9246 | 0.9330 | 0.9288 | 0.9832 | 2 |
### Framework versions
- Transformers 4.24.0
- TensorFlow 2.9.2
- Datasets 2.7.0
- Tokenizers 0.13.2
|
AriakimTaiyo/DialoGPT-cultured-Kumiko | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
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} | 8 | null | ---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: huggan/smithsonian_butterflies_subset
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [π€ Diffusers](https://github.com/huggingface/diffusers) library
on the `huggan/smithsonian_butterflies_subset` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16
### Training results
π [TensorBoard logs](https://huggingface.co/vicky10011001/ddpm-butterflies-128/tensorboard?#scalars)
|
AriakimTaiyo/DialoGPT-medium-Kumiko | [
"conversational"
]
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} | 0 | null | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="LidoHon/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
AriakimTaiyo/DialoGPT-revised-Kumiko | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
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} | 6 | null | ---
library_name: sklearn
tags:
- sklearn
- skops
- tabular-classification
widget:
structuredData:
x0:
- 5.8
- 6.0
- 5.5
x1:
- 2.8
- 2.2
- 4.2
x2:
- 5.1
- 4.0
- 1.4
x3:
- 2.4
- 1.0
- 0.2
---
# Model description
[More Information Needed]
## Intended uses & limitations
[More Information Needed]
## Training Procedure
### Hyperparameters
The model is trained with below hyperparameters.
<details>
<summary> Click to expand </summary>
| Hyperparameter | Value |
|--------------------------|---------|
| bootstrap | True |
| ccp_alpha | 0.0 |
| class_weight | |
| criterion | gini |
| max_depth | |
| max_features | sqrt |
| max_leaf_nodes | |
| max_samples | |
| min_impurity_decrease | 0.0 |
| min_samples_leaf | 1 |
| min_samples_split | 2 |
| min_weight_fraction_leaf | 0.0 |
| n_estimators | 100 |
| n_jobs | |
| oob_score | False |
| random_state | |
| verbose | 0 |
| warm_start | False |
</details>
### Model Plot
The model plot is below.
<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: "βΈ";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "βΎ";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-1" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" checked><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">RandomForestClassifier</label><div class="sk-toggleable__content"><pre>RandomForestClassifier()</pre></div></div></div></div></div>
##Β Evaluation Results
You can find the details about evaluation process and the evaluation results.
| Metric | Value |
|----------|---------|
# How to Get Started with the Model
Use the code below to get started with the model.
<details>
<summary> Click to expand </summary>
```python
[More Information Needed]
```
</details>
# Model Card Authors
This model card is written by following authors:
[More Information Needed]
# Model Card Contact
You can contact the model card authors through following channels:
[More Information Needed]
# Citation
Below you can find information related to citation.
**BibTeX:**
```
[More Information Needed]
``` |
Aries/T5_question_generation | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
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} | 13 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: bert-indo-base-stance-cls
results: []
---
<!-- 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-indo-base-stance-cls
This model is a fine-tuned version of [indobenchmark/indobert-base-p1](https://huggingface.co/indobenchmark/indobert-base-p1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0156
- Accuracy: 0.6892
- Precision: 0.6848
- Recall: 0.6892
- F1: 0.6859
- Against: {'precision': 0.6185567010309279, 'recall': 0.5555555555555556, 'f1-score': 0.5853658536585366, 'support': 216}
- For: {'precision': 0.7280453257790368, 'recall': 0.7764350453172205, 'f1-score': 0.7514619883040935, 'support': 331}
## 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: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Against | For |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-----------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------:|
| No log | 1.0 | 137 | 0.6423 | 0.6581 | 0.6894 | 0.6581 | 0.5917 | {'precision': 0.7543859649122807, 'recall': 0.19907407407407407, 'f1-score': 0.31501831501831506, 'support': 216} | {'precision': 0.6469387755102041, 'recall': 0.9577039274924471, 'f1-score': 0.7722289890377587, 'support': 331} |
| No log | 2.0 | 274 | 0.6146 | 0.6600 | 0.6691 | 0.6600 | 0.6628 | {'precision': 0.5614754098360656, 'recall': 0.6342592592592593, 'f1-score': 0.5956521739130436, 'support': 216} | {'precision': 0.7392739273927392, 'recall': 0.676737160120846, 'f1-score': 0.7066246056782334, 'support': 331} |
| No log | 3.0 | 411 | 0.7572 | 0.6545 | 0.6734 | 0.6545 | 0.6583 | {'precision': 0.550561797752809, 'recall': 0.6805555555555556, 'f1-score': 0.608695652173913, 'support': 216} | {'precision': 0.7535714285714286, 'recall': 0.6374622356495468, 'f1-score': 0.6906710310965631, 'support': 331} |
| 0.4855 | 4.0 | 548 | 0.7405 | 0.6892 | 0.6842 | 0.6892 | 0.6851 | {'precision': 0.6210526315789474, 'recall': 0.5462962962962963, 'f1-score': 0.5812807881773399, 'support': 216} | {'precision': 0.7254901960784313, 'recall': 0.7824773413897281, 'f1-score': 0.7529069767441859, 'support': 331} |
| 0.4855 | 5.0 | 685 | 1.1222 | 0.6856 | 0.6828 | 0.6856 | 0.6839 | {'precision': 0.6078431372549019, 'recall': 0.5740740740740741, 'f1-score': 0.5904761904761905, 'support': 216} | {'precision': 0.7317784256559767, 'recall': 0.7583081570996979, 'f1-score': 0.7448071216617211, 'support': 331} |
| 0.4855 | 6.0 | 822 | 1.4960 | 0.6892 | 0.6830 | 0.6892 | 0.6827 | {'precision': 0.6292134831460674, 'recall': 0.5185185185185185, 'f1-score': 0.5685279187817258, 'support': 216} | {'precision': 0.7181571815718157, 'recall': 0.8006042296072508, 'f1-score': 0.7571428571428572, 'support': 331} |
| 0.4855 | 7.0 | 959 | 1.6304 | 0.6801 | 0.6886 | 0.6801 | 0.6827 | {'precision': 0.5843621399176955, 'recall': 0.6574074074074074, 'f1-score': 0.6187363834422658, 'support': 216} | {'precision': 0.756578947368421, 'recall': 0.6948640483383686, 'f1-score': 0.7244094488188976, 'support': 331} |
| 0.1029 | 8.0 | 1096 | 1.8381 | 0.6673 | 0.6727 | 0.6673 | 0.6693 | {'precision': 0.5726495726495726, 'recall': 0.6203703703703703, 'f1-score': 0.5955555555555555, 'support': 216} | {'precision': 0.7380191693290735, 'recall': 0.6978851963746223, 'f1-score': 0.717391304347826, 'support': 331} |
| 0.1029 | 9.0 | 1233 | 1.9474 | 0.6929 | 0.6876 | 0.6929 | 0.6881 | {'precision': 0.6290322580645161, 'recall': 0.5416666666666666, 'f1-score': 0.582089552238806, 'support': 216} | {'precision': 0.7257617728531855, 'recall': 0.7915407854984894, 'f1-score': 0.7572254335260115, 'support': 331} |
| 0.1029 | 10.0 | 1370 | 2.0156 | 0.6892 | 0.6848 | 0.6892 | 0.6859 | {'precision': 0.6185567010309279, 'recall': 0.5555555555555556, 'f1-score': 0.5853658536585366, 'support': 216} | {'precision': 0.7280453257790368, 'recall': 0.7764350453172205, 'f1-score': 0.7514619883040935, 'support': 331} |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.13.2
|
Arnold/wav2vec2-large-xlsr-turkish-demo-colab | []
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} | 0 | null | ---
tags:
- spacy
- token-classification
language:
- de
model-index:
- name: de_fnhd_nerdh
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.9629324547
- name: NER Recall
type: recall
value: 0.9504065041
- name: NER F Score
type: f_score
value: 0.9566284779
---
Deutsche NER-Pipeline fΓΌr frΓΌhneuhochdeutsche Texte (2.Version)
| Feature | Description |
| --- | --- |
| **Name** | `de_fnhd_nerdh` |
| **Version** | `0.0.2` |
| **spaCy** | `>=3.4.1,<3.5.0` |
| **Default Pipeline** | `tok2vec`, `ner` |
| **Components** | `tok2vec`, `ner` |
| **Vectors** | 500000 keys, 500000 unique vectors (300 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [ih]() |
### Label Scheme
<details>
<summary>View label scheme (5 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `OBJEKT`, `ORGANISATION`, `ORT`, `PERSON`, `ZEIT` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `ENTS_F` | 95.66 |
| `ENTS_P` | 96.29 |
| `ENTS_R` | 95.04 |
| `TOK2VEC_LOSS` | 25311.59 |
| `NER_LOSS` | 15478.32 | |
Arpita/opus-mt-en-ro-finetuned-syn-to-react | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
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} | 9 | null | ---
license: wtfpl
---
ΡΡΠΎ ΡΠ°ΠΉΠ½ΡΡΠ½ sberai ruGPT3 small (125 ΠΌΠ»Π½ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ²) Π½Π° ΠΎΡΡΠ΅Π΄Π°ΠΊΡΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
ΠΏΡΠΏΠ°Ρ
, ΡΠ΄Π΅Π»Π°Π½Π½ΡΡ
ΠΈΠ· Π½ΡΠΆΠ΄ΠΈΠΊΠΎΠ² (ΡΠΈΡΡ Ρ
Π°Ρ
, Π΄ΠΆΡΠ½Π³Π»ΠΈ, ΠΆΡΠΆΠ΄ΠΈΠΊΠΈ; Π²ΡΠ΅Π³ΠΎ ΠΎΠΊΠΎΠ»ΠΎ 30 ΠΌΠΈΠ½ΡΡ, ΡΡΠ°Π½ΡΠΊΡΠΈΠ±ΠΈΡΠΎΠ²Π°Π½Π½ΡΠ΅ ΡΠ΅ΡΠ΅Π· openai whisper large). ΡΠ°Π·ΠΌΠ΅Ρ Π±Π»ΠΎΠΊΠ° ΠΏΡΠΈ ΡΠ°ΠΉΠ½ΡΡΠ½Π΅ 1024, 25 ΡΠΏΠΎΡ
. Π²ΡΠ΅ ΡΠΊΡΠΈΠΏΡΡ ΠΏΠΎ ΠΈΠ½ΡΠ΅ΡΠ΅Π½ΡΡ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΡΡΡ https://github.com/ai-forever/ru-gpts, ΡΠ΅ΡΠ΅Π· transformers Π²ΠΏΠΎΠ»Π½Π΅ ΡΠ΅Π±Π΅ ΡΠ°Π±ΠΎΡΠ°Π΅Ρ Π½Π° 4 Π³Π± Π²ΠΈΠ΄Π΅ΠΎΠΏΠ°ΠΌΡΡΠΈ, Π½Π° 2 Π΄ΡΠΌΠ°Ρ ΡΠΎΠΆΠ΅ Π·Π°ΡΠ°Π±ΠΎΡΠ°Π΅Ρ.
-ΠΊΠ°ΠΊ Π·Π°ΠΏΡΡΡΠΈΡΡ ΡΠ΅ΡΠ΅Π· transformers?
Π·Π°ΠΏΡΡΠΊΠ°Π΅ΠΌ ΡΡΡΠΎΠΊΠΈ Π½ΠΈΠΆΠ΅ Π² jupyterΠ΅
from transformers import pipeline, set_seed
set_seed(32)
generator = pipeline('text-generation', model="4eJIoBek/ruGPT3_small_nujdiki_fithah", do_sample=True, max_length=350)
generator("ΠΠ»Π΅ΠΊΡΠ°Π½Π΄Ρ Π‘Π΅ΡΠ³Π΅Π΅Π²ΠΈΡ ΠΡΡΠΊΠΈΠ½ ΠΈΠ·Π²Π΅ΡΡΠ΅Π½ ΡΠ°ΠΊΠΆΠ΅ Π±Π»Π°Π³ΠΎΠ΄Π°ΡΡ ΡΠ²ΠΎΠΈΠΌ ΡΠΊΠ°Π·ΠΊΠ°ΠΌ, ΠΊΠΎΡΠΎΡΡΠ΅ Π²ΠΊΠ»ΡΡΠ°ΡΡ Π² ΡΠ΅Π±Ρ: ")
ΠΈ Π²ΡΡ ΡΠ°Π±ΠΎΡΠ°Π΅Ρ ΠΈ Π²ΠΎΠΎΠ±ΡΠ΅ Π½ΠΈΡ
ΡΡΠ²ΠΎ
Π§ΡΠΎ ΠΏΡΠΈΠΌΠ΅ΡΠ°ΡΠ΅Π»ΡΠ½ΠΎ, ΡΡΠ° ΠΌΠΎΠ΄Π΅Π»Ρ ΠΈΠ½ΠΎΠ³Π΄Π° Π²ΡΠ΄Π°ΡΡ ΡΠ°ΠΊΠΈΠ΅ ΠΏΠ΅ΡΡΠΎΡΠΌΠ°Π½ΡΡ, ΡΡΠΎ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΈΠ½ΠΎΠ³Π΄Π° Π΄Π°ΠΆΠ΅ ΠΏΡΡΠ΅Π²ΠΎΡΡ
ΠΎΠ΄ΡΡ ΠΏΠΎ ΡΠ°ΡΠΊΠ°ΡΡΠΈΡΠ½ΠΎΡΡΠΈ ΠΎΡΠΈΠ³ΠΈΠ½Π°Π»ΡΠ½ΡΠ΅ Π½ΡΠΆΠ΄ΠΈΠΊΠΈ, Π½Π°ΠΏΡΠΈΠΌΠ΅Ρ:
ΠΡΠΈΡ
ΠΈΠΊΠ° ΠΆΠ΅Π½ΡΠΈΠ½Ρ ΡΠΎΡΡΠΎΠΈΡ ΠΈΠ· Π΄Π²ΡΡ
ΡΠ»Π΅ΠΌΠ΅Π½ΡΠΎΠ²: Π²ΠΎΠ·Π±ΡΠΆΠ΄Π΅Π½ΠΈΡ Π°ΠΏΠΏΠ΅ΡΠΈΡΠ° (Ρ
ΠΎΡΡ Π΅ΡΡΡ)ΠΈ ΡΡΡΠ°Ρ
Π° ΡΠΌΠ΅ΡΡΠΈ(ΠΏΡΡΠ°ΡΡΡ Π·Π°ΠΏΠΈΡ
Π½ΡΡΡ ΡΠ΅Π±Ρ Π² ΡΠΊΠ°ΡΡΠΈΠΊ). ΠΡΠ»ΠΈ Π²Ρ Π±ΠΎΠΈΡΠ΅ΡΡ ΡΠΌΠ΅ΡΠ΅ΡΡ ΠΎΡ ΠΏΠ΅ΡΠ΅ΠΎΡ
Π»Π°ΠΆΠ΄Π΅Π½ΠΈΡ ΠΈΠ»ΠΈ ΠΏΠ΅ΡΠ΅Π³ΡΠ΅Π²Π° ΡΠ΅Π»Π° Π΅ΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΠΌ ΠΏΡΡΠ΅ΠΌ ΠΏΡΠΈ ΠΊΡΠΏΠ°Π½ΠΈΠΈ Π½Π° ΠΏΠ»ΡΠΆΠ΅ ΡΠΎ Π»ΡΡΡΠ΅ Π½Π΅ Π½Π°Π΄ΠΎ Π΄Π΅Π»Π°ΡΡ ΡΡΠΎΠ³ΠΎ ΠΈΠ±ΠΎ ΠΏΠΎΡΠΎΠΌ ΡΠ³ΠΎΡΠΈΡΡ Π·Π°ΠΆΠΈΠ²ΠΎ Π²ΠΌΠ΅ΡΡΠ΅ ΡΠΎ ΡΡΡΠ»ΠΎΠΌ ΠΊΠΎΡΠΎΡΡΠΉ Π±ΡΠ΄Π΅ΡΠ΅ ΡΡΠ°Π²ΠΈΡΡ ΡΡΠ΄ΠΎΠΌ Π΄ΡΡΠ³ Π΄ΡΡΠ³Ρ ΡΠ°ΠΊ ΡΡΠΎΠ± ΠΎΠ½ ΠΊΠ°ΡΠ°Π»ΡΡ Π²Π°ΡΠΈΡ
ΡΡΡΠΏΠ½Π΅ΠΉ Π½ΠΎΠ³ Π½ΠΎ ΡΡΠΎ Π±ΡΠ΄Π΅Ρ ΡΠΎΠ²ΡΠ΅ΠΌ Π΄ΡΡΠ³Π°Ρ ΠΈΡΡΠΎΡΠΈΡ ΡΠ΅ΠΌ Π²Π°ΡΠΈ ΡΡΠΎΠ½Ρ ΠΏΠΎ ΡΠ΅Π»Π΅ΡΠΎΠ½Ρ Π·Π½Π°Ρ ΡΡΠΎ Ρ Π²Π°Ρ Π΄ΡΠΎΠΆΠ°Ρ ΡΡΠΊΠΈ Π±Π΅ΡΠΈΡΠ΅ ΡΠΎΠΏΠΎΡ ΠΎΡΡΡΠΈΠ΅ΠΌ Π²Π½ΠΈΠ· ΠΏΠ°Π΄Π°ΠΉΡΠ΅ ΡΠ²Π΅ΡΡ
Ρ Π±Π»ΠΈΠ½ Π½Ρ Π΅ΡΠ»ΠΈ ΡΠΆ ΠΎΡΠ΅Π½Ρ Ρ
ΠΎΡΠ΅ΡΡΡ ΠΌΡΡΠ° Π½Π°Π»Π΅ΠΉΡΠ΅ ΠΊΠΈΠΏΡΡΠΊΠ° ΠΏΠΎΠ΄ ΠΊΡΡΡΠΊΡ ΠΊΠ°ΡΡΡΡΠ»ΠΈ Π·Π°Π»Π΅ΠΉΡΠ΅ ΠΊΠ°ΡΡΡΡΠ»Ρ ΠΊΡΡΡΠΊΠΎΠΉ ΠΏΠ΅ΡΠ΅Π²Π΅ΡΠ½ΠΈΡΠ΅ Π²Π°ΡΠ΅Π½ΡΠ΅ ΡΡΠ°Π²ΡΡΠ΅ ΡΠ»ΠΊΡ ΠΏΠΎΡΡΠ°Π²ΠΈΡΠ΅ Π±ΡΡΡΠ»ΠΊΡ Π²ΠΎΠ΄ΠΊΠΈ Π½Π°ΠΊΡΠΎΠΉΡΠ΅ Π²ΡΡ ΠΊΡΡΡΠΊΠ°ΠΌΠΈ Π·Π°ΠΊΡΠΎΠΉ Π΄Π²Π΅ΡΡ ΠΏΠΎΠ»ΠΎΡΠ΅Π½ΡΠ΅ΠΌ Π»ΡΠ³ ΡΠΏΠ°ΡΡ ΡΠ΅ΠΉΡΠ°Ρ ΠΏΡΠΈΠ½Π΅ΡΡ Π²Π΅Π»ΠΎΡΠΈΠΏΠ΅Π΄ ΡΡΠΏΠ»ΡΠΉ ΡΠ½ΠΈΠ»ΡΡ ΠΌΠ½Π΅ ΡΠ΅Π³ΠΎΠ΄Π½Ρ ΠΏΡΠΈΡΠ½ΠΈΠ»ΡΡ ΡΠΌΠ΅Ρ Π΄Π΅Π΄ΡΡΠΊΠ° Ρ ΡΠΎΠ΄ΠΈΠ»ΡΡ 25 ΠΈΡΠ½Ρ Π±ΡΠ» ΡΠ±ΠΈΡΡΠΉ Π½ΠΎΠΆΠΎΠΌ ΠΎΡΡΠ° ΠΌΠΎΠ΅Π³ΠΎ Π±ΡΠ°ΡΠ° Π΄Π΅Π΄Π° Π²Π°Π·Π΅Π»ΠΈΠ½Π° ΠΊΡΠ°ΡΠ½Π°Ρ ΠΆΠΈΠ΄ΠΊΠΎΡΡΡ Π·Π°ΡΡΡΠ»Π° |
ArthurcJP/DialoGPT-small-YODA | []
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} | 0 | null | ---
license: cc0-1.0
inference: false
language:
- en
tags:
- stable-diffusion
- text-to-image
---
# Stable Diffusion fine tuned on art by [Nekro](https://www.artstation.com/nekro)
### Usage
Use by adding the keyword "nekrofaerie" to the prompt. The model was trained with the "faerie" classname, which can also be added to the prompt.
## Samples
The top 2 images are "pure", the rest could be mixed with other artists or modifiers. I hope it still gives you an idea of what kind of styles can be created with this model.
<img src="https://huggingface.co/Froddan/nekrofaerie/resolve/main/index.png" width="256px"/>
<img src="https://huggingface.co/Froddan/nekrofaerie/resolve/main/index2.png" width="256px"/>
<img src="https://huggingface.co/Froddan/nekrofaerie/resolve/main/tmp04o1t4b_.png" width="256px"/>
<img src="https://huggingface.co/Froddan/nekrofaerie/resolve/main/tmp41igywg4.png" width="256px"/>
<img src="https://huggingface.co/Froddan/nekrofaerie/resolve/main/tmpbkj8sqmh.png" width="256px"/>
<img src="https://huggingface.co/Froddan/nekrofaerie/resolve/main/tmphk34pib0.png" width="256px"/>
<img src="https://huggingface.co/Froddan/nekrofaerie/resolve/main/dog_octane.png" width="256px"/>
<img src="https://huggingface.co/Froddan/nekrofaerie/resolve/main/dog_octane2.png" width="256px"/>
<img src="https://huggingface.co/Froddan/nekrofaerie/resolve/main/greg_mucha2.png" width="256px"/>
### 𧨠Diffusers
This model can be used just like any other Stable Diffusion model. For more information,
please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion).
|
Ateeb/QA | [
"pytorch",
"distilbert",
"question-answering",
"transformers",
"autotrain_compatible"
]
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} | 4 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
---
### mPred Dreambooth model trained by CiroN2022 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb)
Sample pictures of this concept:
mPred
.jpg)
|
Augustvember/WokkaBot99 | []
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} | 0 | null | ---
tags:
- text-generation
library_name: transformers
---
## Model description
Based on Jellywibble/dalio-pretrained-book-bs4-seed1 which was pre-trained on the Dalio Principles Book
Finetuned on handwritten conversations Jellywibble/dalio_handwritten-conversations
## Dataset Used
Jellywibble/dalio_handwritten-conversations
## Training Parameters
- Deepspeed on 4xA40 GPUs
- Ensuring EOS token `<s>` appears only at the beginning of each 'This is a conversation where Ray ...'
- Gradient Accumulation steps = 1 (Effective batch size of 4)
- 2e-6 Learning Rate, AdamW optimizer
- Block size of 1000
- Trained for 1 Epoch (additional epochs yielded worse Hellaswag result)
## Metrics
- Hellaswag Perplexity: 29.83
- Eval accuracy: 58.1%
- Eval loss: 1.883
- Checkpoint 9 uploaded
- Wandb run: https://wandb.ai/jellywibble/huggingface/runs/157eehn9?workspace=user-jellywibble |
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} | 0 | null | ---
license: unknown
---
# Silvery Trait finetuned style Model
Produced from publicly available pictures in landscape, portrait and square format.
Using words found in `prompt_words.md` within your prompt will produce better results. Other words can be used also but will tend to produce "weaker" results. Combining the use of the Aesthetic Gradient file provided in the `easthetic_embeddings` folder can greatly enhance the results.
## Model info
The models included was trained on "multi-resolution" images.
## Using the model
* common subject prompt tokens: `<wathever>, by asd artstyle`
## Example prompts
`a sheep, symmetry, by asd artstyle`:
* without easthetic_embeddings
<img src="https://huggingface.co/cyburn/silvery_trait/resolve/main/1.jpg" alt="Picture." width="500"/>
* with easthetic_embeddings
<img src="https://huggingface.co/cyburn/silvery_trait/resolve/main/2.jpg" alt="Picture." width="500"/>
`crow, skull, symmetry, flower, feather, circle, by asd artstyle`
* without easthetic_embeddings
<img src="https://huggingface.co/cyburn/silvery_trait/resolve/main/3.jpg" alt="Picture." width="500"/>
* with easthetic_embeddings
<img src="https://huggingface.co/cyburn/silvery_trait/resolve/main/4.jpg" alt="Picture." width="500"/> |
Axon/resnet34-v1 | [
"dataset:ImageNet",
"arxiv:1512.03385",
"Axon",
"Elixir",
"license:apache-2.0"
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- rotten_tomatoes
metrics:
- accuracy
model-index:
- name: distilbert-imdb
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: rotten_tomatoes
type: rotten_tomatoes
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8405253283302064
---
<!-- 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. -->
# distilbert-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the rotten_tomatoes dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3716
- Accuracy: 0.8405
## 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
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4034 | 1.0 | 534 | 0.3716 | 0.8405 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.13.2
|
Ayah/GPT2-DBpedia | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
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} | 6 | null | ---
license: creativeml-openrail-m
---
This is a Dreambooth Stable Diffusion model, trained on grey shaded images from 3d modeling programs like Zbrush, Mudbox, Sculptris, etc.
The token prompt is: **zsculptport**
The (optional) class prompt is: **sculpture**
Example prompt:
spectacular realistic detailed (zsculptport) sculpture of beautiful alien elf woman creature. ultra detailed, cinematic. sepia [by artist todd mcfarlane]
Negative prompt: lumpy, smeared, noisy, messy, ugly, distorted, colour, painting, ((watercolour)), blurry, (high contrast)
Steps: 45, Sampler: DPM++ 2S a Karras, CFG scale: 10, Size: 768x960, Denoising strength: 0.32, First pass size: 512x640
some cherrypicked sample results:
 |
Aybars/XLM_Turkish | [
"pytorch",
"xlm-roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"XLMRobertaForQuestionAnswering"
],
"model_type": "xlm-roberta",
"task_specific_params": {
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},
"summarization": {
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}
} | 4 | null | ---
license: apache-2.0
tags:
- text generation
- judge
- jury
- executioner
datasets:
- biglam/old_bailey_proceedings
model-index:
- name: distilgpt2_jje
results: []
widget:
- text: "HAROLD KUMAR"
example_title: "morning sun"
- text: "Obama, he of no first name,"
example_title: "Obama"
- text: "John Smith,"
example_title: "generic"
parameters:
min_length: 16
max_length: 96
no_repeat_ngram_size: 1
do_sample: True
---
# distilgpt2_jje (judge, jury, executioner)
`distilgpt2` fine-tuned on the `biglam/old_bailey_proceedings` dataset for two epochs.
|
Ayham/bert_gpt2_summarization_cnndm_new | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
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},
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}
} | 8 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
---
### elonmusk01 Dreambooth model trained by cormacncheese with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb)
Sample pictures of this concept:
|
Ayham/bert_roberta_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
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}
} | 3 | null | ---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
datasets:
- cnn_dailymail
model-index:
- name: t5-small-finetuned-summarization-cnn-ver2
results: []
---
<!-- 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. -->
# t5-small-finetuned-summarization-cnn-ver2
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dailymail dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0084
- Bertscore-mean-precision: 0.8859
- Bertscore-mean-recall: 0.8592
- Bertscore-mean-f1: 0.8721
- Bertscore-median-precision: 0.8855
- Bertscore-median-recall: 0.8578
- Bertscore-median-f1: 0.8718
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bertscore-mean-precision | Bertscore-mean-recall | Bertscore-mean-f1 | Bertscore-median-precision | Bertscore-median-recall | Bertscore-median-f1 |
|:-------------:|:-----:|:----:|:---------------:|:------------------------:|:---------------------:|:-----------------:|:--------------------------:|:-----------------------:|:-------------------:|
| 2.0422 | 1.0 | 718 | 2.0139 | 0.8853 | 0.8589 | 0.8717 | 0.8857 | 0.8564 | 0.8715 |
| 1.9481 | 2.0 | 1436 | 2.0085 | 0.8863 | 0.8591 | 0.8723 | 0.8858 | 0.8577 | 0.8718 |
| 1.9231 | 3.0 | 2154 | 2.0084 | 0.8859 | 0.8592 | 0.8721 | 0.8855 | 0.8578 | 0.8718 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.13.2
|
Ayham/distilbert_gpt2_summarization_cnndm | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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}
} | 6 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results: []
---
<!-- 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-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5658
- Precision: 1.0
- Recall: 1.0
- F1: 1.0
- Accuracy: 1.0
## 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: 2e-05
- 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
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:|
| No log | 1.0 | 1 | 2.8381 | 1.0 | 1.0 | 1.0 | 1.0 |
| No log | 2.0 | 2 | 2.6555 | 1.0 | 1.0 | 1.0 | 1.0 |
| No log | 3.0 | 3 | 2.5658 | 1.0 | 1.0 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Ayham/distilbert_roberta_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
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},
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}
} | 14 | 2022-11-20T02:22:58Z | ---
license: creativeml-openrail-m
---
Trained on "kobold", "lizardfolk", and "dragonborn". Using Dreambooth, trained for 6000, 10000, or 14000 steps. I recommend using the 14000 step model with a CFG 4-8. You may need to use the models that were trained for fewer steps if you're having difficulty getting certain elements in the image (e.g. hats).
You can also use a higher CFG if attempting to generate inked images. E.g: CFG 9 and "photo octane 3d render" in the negative prompt:
 |
Ayham/roberta_bert_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
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"task_specific_params": {
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},
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},
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}
} | 12 | null | ---
tags:
- text-generation
library_name: transformers
---
## Model description
Dalio Bot Pre-trained on Principles, fine-tuned on handwritten examples.
Pre-trained model: Jellywibble/dalio-pretrained-book-bs4-seed1 (based-off OPT30B)
Fine-tuning dataset: Jellywibble/dalio_handwritten-conversations
## Model Parameters
- 4xA40 (eff. batch size = 4)
- base_mode_name Jellywibble/dalio-pretrained-book-bs4-seed1
- dataset_name Jellywibble/dalio_handwritten-conversations
- block size 500
- per_device_train_batch_size 1
- gradient_accumulation steps 1
- learning_rate 2e-6
- seed 28
- validation split percentage 20
- hellaswag_sample_size 100
## Metrics
- Hellaswag Perplexity: 29.9
- Eval acc: 57.1%
- Eval loss: 1.971
- wandb: https://wandb.ai/jellywibble/huggingface/runs/12lgyt20?workspace=user-jellywibble
- Checkpoint 10 selected and uploaded |
Ayham/roberta_gpt2_new_max64_summarization_cnndm | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
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},
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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}
} | 4 | 2022-11-20T02:51:48Z | ---
license: openrail
library_name: diffusers
tags:
- TPU
- JAX
- Flax
- stable-diffusion
- text-to-image
language:
- en
---
|
Ayham/robertagpt2_cnn | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
} | 4 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: deberta-classifier-feedback-1024
results: []
---
<!-- 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. -->
# deberta-classifier-feedback-1024
This model is a fine-tuned version of [TTian/deberta-mlm-feedback-1024](https://huggingface.co/TTian/deberta-mlm-feedback-1024) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6246
## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.038 | 0.04 | 10 | 0.8470 |
| 0.8858 | 0.08 | 20 | 0.7317 |
| 0.8166 | 0.13 | 30 | 0.8127 |
| 0.7791 | 0.17 | 40 | 0.8111 |
| 0.7977 | 0.21 | 50 | 0.7540 |
| 0.7815 | 0.25 | 60 | 0.7204 |
| 0.7467 | 0.3 | 70 | 0.7446 |
| 0.7525 | 0.34 | 80 | 0.7522 |
| 0.716 | 0.38 | 90 | 0.7542 |
| 0.7617 | 0.42 | 100 | 0.7095 |
| 0.7618 | 0.47 | 110 | 0.7147 |
| 0.7297 | 0.51 | 120 | 0.8648 |
| 0.7797 | 0.55 | 130 | 0.7150 |
| 0.7466 | 0.59 | 140 | 0.7360 |
| 0.745 | 0.64 | 150 | 0.6842 |
| 0.718 | 0.68 | 160 | 0.7408 |
| 0.7455 | 0.72 | 170 | 0.7029 |
| 0.7476 | 0.76 | 180 | 0.7106 |
| 0.695 | 0.81 | 190 | 0.6781 |
| 0.6603 | 0.85 | 200 | 0.7713 |
| 0.7763 | 0.89 | 210 | 0.7619 |
| 0.6858 | 0.93 | 220 | 0.7252 |
| 0.6567 | 0.97 | 230 | 0.7017 |
| 0.6529 | 1.02 | 240 | 0.7030 |
| 0.6752 | 1.06 | 250 | 0.6717 |
| 0.7078 | 1.1 | 260 | 0.6868 |
| 0.6428 | 1.14 | 270 | 0.6694 |
| 0.6173 | 1.19 | 280 | 0.7137 |
| 0.6753 | 1.23 | 290 | 0.7363 |
| 0.6326 | 1.27 | 300 | 0.6808 |
| 0.6241 | 1.31 | 310 | 0.6855 |
| 0.6717 | 1.36 | 320 | 0.6627 |
| 0.633 | 1.4 | 330 | 0.7079 |
| 0.6541 | 1.44 | 340 | 0.6475 |
| 0.5998 | 1.48 | 350 | 0.7008 |
| 0.7088 | 1.53 | 360 | 0.6558 |
| 0.6209 | 1.57 | 370 | 0.6536 |
| 0.6159 | 1.61 | 380 | 0.6805 |
| 0.6297 | 1.65 | 390 | 0.6617 |
| 0.6506 | 1.69 | 400 | 0.6459 |
| 0.6397 | 1.74 | 410 | 0.6450 |
| 0.6181 | 1.78 | 420 | 0.7158 |
| 0.6609 | 1.82 | 430 | 0.6336 |
| 0.6066 | 1.86 | 440 | 0.6232 |
| 0.6418 | 1.91 | 450 | 0.6272 |
| 0.6499 | 1.95 | 460 | 0.6268 |
| 0.6021 | 1.99 | 470 | 0.6431 |
| 0.5899 | 2.03 | 480 | 0.6395 |
| 0.5524 | 2.08 | 490 | 0.6278 |
| 0.5182 | 2.12 | 500 | 0.6690 |
| 0.5768 | 2.16 | 510 | 0.6400 |
| 0.5326 | 2.2 | 520 | 0.6386 |
| 0.5641 | 2.25 | 530 | 0.6759 |
| 0.5794 | 2.29 | 540 | 0.6483 |
| 0.5341 | 2.33 | 550 | 0.6273 |
| 0.5604 | 2.37 | 560 | 0.6393 |
| 0.529 | 2.42 | 570 | 0.6389 |
| 0.5433 | 2.46 | 580 | 0.6272 |
| 0.5574 | 2.5 | 590 | 0.6387 |
| 0.5279 | 2.54 | 600 | 0.6613 |
| 0.5066 | 2.58 | 610 | 0.6376 |
| 0.5235 | 2.63 | 620 | 0.6449 |
| 0.516 | 2.67 | 630 | 0.6285 |
| 0.5888 | 2.71 | 640 | 0.6391 |
| 0.5326 | 2.75 | 650 | 0.6226 |
| 0.5486 | 2.8 | 660 | 0.6373 |
| 0.5176 | 2.84 | 670 | 0.6272 |
| 0.5038 | 2.88 | 680 | 0.6235 |
| 0.5335 | 2.92 | 690 | 0.6266 |
| 0.557 | 2.97 | 700 | 0.6246 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.13.2
|
Ayham/xlmroberta_gpt2_summarization_xsum | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
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"EncoderDecoderModel"
],
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"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
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}
} | 9 | 2022-11-20T03:56:10Z | ---
language:
- en
tags:
- pytorch
- causal-lm
- pythia
- pythia_v0
license: apache-2.0
datasets:
- the_pile
---
The *Pythia Scaling Suite* is a collection of models developed to facilitate
interpretability research. It contains two sets of eight models of sizes
70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two
models: one trained on the Pile, and one trained on the Pile after the dataset
has been globally deduplicated. All 8 model sizes are trained on the exact
same data, in the exact same order. All Pythia models are available
[on Hugging Face](https://huggingface.co/models?other=pythia).
The Pythia model suite was deliberately designed to promote scientific
research on large language models, especially interpretability research.
Despite not centering downstream performance as a design goal, we find the
models <a href="#evaluations">match or exceed</a> the performance of
similar and same-sized models, such as those in the OPT and GPT-Neo suites.
Please note that all models in the *Pythia* suite were renamed in January
2023. For clarity, a <a href="#naming-convention-and-parameter-count">table
comparing the old and new names</a> is provided in this model card, together
with exact parameter counts.
## Pythia-2.8B
### Model Details
- Developed by: [EleutherAI](http://eleuther.ai)
- Model type: Transformer-based Language Model
- Language: English
- Learn more: [Pythia's GitHub repository](https://github.com/EleutherAI/pythia)
for training procedure, config files, and details on how to use.
- Library: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox)
- License: Apache 2.0
- Contact: to ask questions about this model, join the [EleutherAI
Discord](https://discord.gg/zBGx3azzUn), and post them in `#release-discussion`.
Please read the existing *Pythia* documentation before asking about it in the
EleutherAI Discord. For general correspondence: [contact@eleuther.
ai](mailto:[email protected]).
<figure>
| Pythia model | Non-Embedding Params | Layers | Model Dim | Heads | Batch Size | Learning Rate | Equivalent Models |
| -----------: | -------------------: | :----: | :-------: | :---: | :--------: | :-------------------: | :--------------------: |
| 70M | 18,915,328 | 6 | 512 | 8 | 2M | 1.0 x 10<sup>-3</sup> | β |
| 160M | 85,056,000 | 12 | 768 | 12 | 4M | 6.0 x 10<sup>-4</sup> | GPT-Neo 125M, OPT-125M |
| 410M | 302,311,424 | 24 | 1024 | 16 | 4M | 3.0 x 10<sup>-4</sup> | OPT-350M |
| 1.0B | 805,736,448 | 16 | 2048 | 8 | 2M | 3.0 x 10<sup>-4</sup> | β |
| 1.4B | 1,208,602,624 | 24 | 2048 | 16 | 4M | 2.0 x 10<sup>-4</sup> | GPT-Neo 1.3B, OPT-1.3B |
| 2.8B | 2,517,652,480 | 32 | 2560 | 32 | 2M | 1.6 x 10<sup>-4</sup> | GPT-Neo 2.7B, OPT-2.7B |
| 6.9B | 6,444,163,072 | 32 | 4096 | 32 | 2M | 1.2 x 10<sup>-4</sup> | OPT-6.7B |
| 12B | 11,327,027,200 | 36 | 5120 | 40 | 2M | 1.2 x 10<sup>-4</sup> | β |
<figcaption>Engineering details for the <i>Pythia Suite</i>. Deduped and
non-deduped models of a given size have the same hyperparameters. βEquivalentβ
models have <b>exactly</b> the same architecture, and the same number of
non-embedding parameters.</figcaption>
</figure>
### Uses and Limitations
#### Intended Use
The primary intended use of Pythia is research on the behavior, functionality,
and limitations of large language models. This suite is intended to provide
a controlled setting for performing scientific experiments. To enable the
study of how language models change over the course of training, we provide
143 evenly spaced intermediate checkpoints per model. These checkpoints are
hosted on Hugging Face as branches. Note that branch `143000` corresponds
exactly to the model checkpoint on the `main` branch of each model.
You may also further fine-tune and adapt Pythia-2.8B for deployment,
as long as your use is in accordance with the Apache 2.0 license. Pythia
models work with the Hugging Face [Transformers
Library](https://huggingface.co/docs/transformers/index). If you decide to use
pre-trained Pythia-2.8B as a basis for your fine-tuned model, please
conduct your own risk and bias assessment.
#### Out-of-scope use
The Pythia Suite is **not** intended for deployment. It is not a in itself
a product and cannot be used for human-facing interactions.
Pythia models are English-language only, and are not suitable for translation
or generating text in other languages.
Pythia-2.8B has not been fine-tuned for downstream contexts in which
language models are commonly deployed, such as writing genre prose,
or commercial chatbots. This means Pythia-2.8B will **not**
respond to a given prompt the way a product like ChatGPT does. This is because,
unlike this model, ChatGPT was fine-tuned using methods such as Reinforcement
Learning from Human Feedback (RLHF) to better βunderstandβ human instructions.
#### Limitations and biases
The core functionality of a large language model is to take a string of text
and predict the next token. The token deemed statistically most likely by the
model need not produce the most βaccurateβ text. Never rely on
Pythia-2.8B to produce factually accurate output.
This model was trained on [the Pile](https://pile.eleuther.ai/), a dataset
known to contain profanity and texts that are lewd or otherwise offensive.
See [Section 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a
discussion of documented biases with regards to gender, religion, and race.
Pythia-2.8B may produce socially unacceptable or undesirable text, *even if*
the prompt itself does not include anything explicitly offensive.
If you plan on using text generated through, for example, the Hosted Inference
API, we recommend having a human curate the outputs of this language model
before presenting it to other people. Please inform your audience that the
text was generated by Pythia-2.8B.
### Quickstart
Pythia models can be loaded and used via the following code, demonstrated here
for the third `pythia-70m-deduped` checkpoint:
```python
from transformers import GPTNeoXForCausalLM, AutoTokenizer
model = GPTNeoXForCausalLM.from_pretrained(
"EleutherAI/pythia-70m-deduped",
revision="step3000",
cache_dir="./pythia-70m-deduped/step3000",
)
tokenizer = AutoTokenizer.from_pretrained(
"EleutherAI/pythia-70m-deduped",
revision="step3000",
cache_dir="./pythia-70m-deduped/step3000",
)
inputs = tokenizer("Hello, I am", return_tensors="pt")
tokens = model.generate(**inputs)
tokenizer.decode(tokens[0])
```
Revision/branch `step143000` corresponds exactly to the model checkpoint on
the `main` branch of each model.<br>
For more information on how to use all Pythia models, see [documentation on
GitHub](https://github.com/EleutherAI/pythia).
### Training
#### Training data
[The Pile](https://pile.eleuther.ai/) is a 825GiB general-purpose dataset in
English. It was created by EleutherAI specifically for training large language
models. It contains texts from 22 diverse sources, roughly broken down into
five categories: academic writing (e.g. arXiv), internet (e.g. CommonCrawl),
prose (e.g. Project Gutenberg), dialogue (e.g. YouTube subtitles), and
miscellaneous (e.g. GitHub, Enron Emails). See [the Pile
paper](https://arxiv.org/abs/2101.00027) for a breakdown of all data sources,
methodology, and a discussion of ethical implications. Consult [the
datasheet](https://arxiv.org/abs/2201.07311) for more detailed documentation
about the Pile and its component datasets. The Pile can be downloaded from
the [official website](https://pile.eleuther.ai/), or from a [community
mirror](https://the-eye.eu/public/AI/pile/).<br>
The Pile was **not** deduplicated before being used to train Pythia-2.8B.
#### Training procedure
All models were trained on the exact same data, in the exact same order. Each
model saw 299,892,736,000 tokens during training, and 143 checkpoints for each
model are saved every 2,097,152,000 tokens, spaced evenly throughout training.
This corresponds to training for just under 1 epoch on the Pile for
non-deduplicated models, and about 1.5 epochs on the deduplicated Pile.
All *Pythia* models trained for the equivalent of 143000 steps at a batch size
of 2,097,152 tokens. Two batch sizes were used: 2M and 4M. Models with a batch
size of 4M tokens listed were originally trained for 71500 steps instead, with
checkpoints every 500 steps. The checkpoints on Hugging Face are renamed for
consistency with all 2M batch models, so `step1000` is the first checkpoint
for `pythia-1.4b` that was saved (corresponding to step 500 in training), and
`step1000` is likewise the first `pythia-6.9b` checkpoint that was saved
(corresponding to 1000 βactualβ steps).<br>
See [GitHub](https://github.com/EleutherAI/pythia) for more details on training
procedure, including [how to reproduce
it](https://github.com/EleutherAI/pythia/blob/main/README.md#reproducing-training).<br>
Pythia uses the same tokenizer as [GPT-NeoX-
20B](https://huggingface.co/EleutherAI/gpt-neox-20b).
### Evaluations
All 16 *Pythia* models were evaluated using the [LM Evaluation
Harness](https://github.com/EleutherAI/lm-evaluation-harness). You can access
the results by model and step at `results/json/*` in the [GitHub
repository](https://github.com/EleutherAI/pythia/tree/main/results/json).<br>
Expand the sections below to see plots of evaluation results for all
Pythia and Pythia-deduped models compared with OPT and BLOOM.
<details>
<summary>LAMBADA β OpenAI</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/lambada_openai.png" style="width:auto"/>
</details>
<details>
<summary>Physical Interaction: Question Answering (PIQA)</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/piqa.png" style="width:auto"/>
</details>
<details>
<summary>WinoGrande</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/winogrande.png" style="width:auto"/>
</details>
<details>
<summary>AI2 Reasoning ChallengeβChallenge Set</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/arc_challenge.png" style="width:auto"/>
</details>
<details>
<summary>SciQ</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/sciq.png" style="width:auto"/>
</details>
### Naming convention and parameter count
*Pythia* models were renamed in January 2023. It is possible that the old
naming convention still persists in some documentation by accident. The
current naming convention (70M, 160M, etc.) is based on total parameter count.
<figure style="width:32em">
| current Pythia suffix | old suffix | total params | non-embedding params |
| --------------------: | ---------: | -------------: | -------------------: |
| 70M | 19M | 70,426,624 | 18,915,328 |
| 160M | 125M | 162,322,944 | 85,056,000 |
| 410M | 350M | 405,334,016 | 302,311,424 |
| 1B | 800M | 1,011,781,632 | 805,736,448 |
| 1.4B | 1.3B | 1,414,647,808 | 1,208,602,624 |
| 2.8B | 2.7B | 2,775,208,960 | 2,517,652,480 |
| 6.9B | 6.7B | 6,857,302,016 | 6,444,163,072 |
| 12B | 13B | 11,846,072,320 | 11,327,027,200 |
</figure> |
Ayham/xlnet_bert_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
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}
}
} | 7 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 244.69 +/- 29.72
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Ayham/xlnet_gpt2_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"no_repeat_ngram_size": null,
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},
"text-generation": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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}
}
} | 8 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
---
### seraphm Dreambooth model trained by mint designer at alvdansen with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook trained on 1500 steps and 12 images. Aesthetic diverse dataset which should allow the character to be used seamlessly in a multitude of aesthetics.
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb)
Sample pictures of this concept:
prompt (credit Astria.ai) serphm prince :: by Martine Johanna and Simon StΓ₯lenhag and Chie Yoshii and Casey Weldon and wlop :: ornate, dynamic, particulate, rich colors, intricate, elegant, highly detailed, centered, artstation, smooth, sharp focus, octane render, 3d
00170.jpg
00169.jpg
Follow our other work, and additional style drops: http://alvdansen.com
|
Ayham/xlnet_gpt_xsum | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
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},
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}
}
} | 11 | null | ---
tags:
- generated_from_keras_callback
model-index:
- name: amitjohn007/mpnet-finetuned
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# amitjohn007/mpnet-finetuned
This model is a fine-tuned version of [shaina/covid_qa_mpnet](https://huggingface.co/shaina/covid_qa_mpnet) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5882
- Epoch: 2
## 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 16602, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 1.0499 | 0 |
| 0.7289 | 1 |
| 0.5882 | 2 |
### Framework versions
- Transformers 4.24.0
- TensorFlow 2.9.2
- Datasets 2.7.0
- Tokenizers 0.13.2
|
Ayham/xlnet_roberta_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
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"max_length": null
},
"summarization": {
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}
}
} | 10 | null | # FAST-RIR: FAST NEURAL DIFFUSE ROOM IMPULSE RESPONSE GENERATOR (ICASSP 2022)
This is the official implementation of our neural-network-based fast diffuse room impulse response generator ([**FAST-RIR**](https://arxiv.org/pdf/2110.04057.pdf)) for generating room impulse responses (RIRs) for a given rectangular acoustic environment. Our model is inspired by [**StackGAN**](https://github.com/hanzhanggit/StackGAN-Pytorch) architecture. The audio examples and spectrograms of the generated RIRs are available [here](https://anton-jeran.github.io/FRIR/).
**NEWS : We have genaralized our FAST-RIR to generate RIRs for any 3D indoor scenes represented using meshes. Official code of our network [**MESH2IR**](https://anton-jeran.github.io/M2IR/) is available.**
## Requirements
```
Python3.6
Pytorch
python-dateutil
easydict
pandas
torchfile
gdown
librosa
soundfile
acoustics
wavefile
wavfile
pyyaml==5.4.1
pickle
```
## Embedding
Each normalized embedding is created as follows: If you are using our trained model, you may need to use extra parameter Correction(CRR).
```
Listener Position = LP
Source Position = SP
Room Dimension = RD
Reverberation Time = T60
Correction = CRR
CRR = 0.1 if 0.5<T60<0.6
CRR = 0.2 if T60>0.6
CRR = 0 otherwise
Embedding = ([LP_X,LP_Y,LP_Z,SP_X,SP_Y,SP_Z,RD_X,RD_Y,RD_Z,(T60+CRR)] /5) - 1
```
## Generete RIRs using trained model
Download the trained model using this command
```
source download_generate.sh
```
Create normalized embeddings list in pickle format. You can run following command to generate an example embedding list
```
python3 example1.py
```
Run the following command inside **code_new** to generate RIRs corresponding to the normalized embeddings list. You can find generated RIRs inside **code_new/Generated_RIRs**
```
python3 main.py --cfg cfg/RIR_eval.yml --gpu 0
```
## Range
Our trained NN-DAS is capable of generating RIRs with the following range accurately.
```
Room Dimension X --> 8m to 11m
Room Dimesnion Y --> 6m to 8m
Room Dimension Z --> 2.5m to 3.5m
Listener Position --> Any position within the room
Speaker Position --> Any position within the room
Reverberation time --> 0.2s to 0.7s
```
## Training the Model
Run the following command to download the training dataset we created using a [**Diffuse Acoustic Simulator**](https://github.com/GAMMA-UMD/pygsound). You also can train the model using your dataset.
```
source download_data.sh
```
Run the following command to train the model. You can pass what GPUs to be used for training as an input argument. In this example, I am using 2 GPUs.
```
python3 main.py --cfg cfg/RIR_s1.yml --gpu 0,1
```
## Related Works
1) [**IR-GAN: Room Impulse Response Generator for Far-field Speech Recognition (INTERSPEECH2021)**](https://github.com/anton-jeran/IR-GAN)
2) [**TS-RIR: Translated synthetic room impulse responses for speech augmentation (IEEE ASRU 2021)**](https://github.com/GAMMA-UMD/TS-RIR)
## Citations
If you use our **FAST-RIR** for your research, please consider citing
```
@INPROCEEDINGS{9747846,
author={Ratnarajah, Anton and Zhang, Shi-Xiong and Yu, Meng and Tang, Zhenyu and Manocha, Dinesh and Yu, Dong},
booktitle={ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
title={Fast-Rir: Fast Neural Diffuse Room Impulse Response Generator},
year={2022},
volume={},
number={},
pages={571-575},
doi={10.1109/ICASSP43922.2022.9747846}}
```
Our work is inspired by
```
@inproceedings{han2017stackgan,
Author = {Han Zhang and Tao Xu and Hongsheng Li and Shaoting Zhang and Xiaogang Wang and Xiaolei Huang and Dimitris Metaxas},
Title = {StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks},
Year = {2017},
booktitle = {{ICCV}},
}
```
If you use our training dataset generated using [**Diffuse Acoustic Simulator**](https://github.com/GAMMA-UMD/pygsound) in your research, please consider citing
```
@inproceedings{9052932,
author={Z. {Tang} and L. {Chen} and B. {Wu} and D. {Yu} and D. {Manocha}},
booktitle={ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
title={Improving Reverberant Speech Training Using Diffuse Acoustic Simulation},
year={2020},
volume={},
number={},
pages={6969-6973},
}
```
|
Ayjayo/DialoGPT-medium-AyjayoAI | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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}
} | 12 | null | ---
tags:
- text-to-image
---
Stable diffusion model for Sanrio and Sanrio characters. Includes Hello Kitty, My Melody, Kuromi, Cinnamoroll, Badtz-Maru, Pompompurin, and Keroppi. Tags are "Sanrio" and the character's name respectively. To use, just download the ckpt file. Download it [here.](https://huggingface.co/Kafke/sanrio/resolve/main/sanrio.ckpt)
Examples:
```
hello kitty
Steps: 20, Sampler: Euler a, CFG scale: 9, Seed: 207838508, Size: 512x512, ENSD: 31337
```
<img src="https://huggingface.co/Kafke/sanrio/resolve/main/hellokitty.png" width="512px"/>
```
my melody in a field of flowers, picking flowers, sanrio
Steps: 20, Sampler: Euler a, CFG scale: 10, Seed: 1003504798, Size: 512x512, ENSD: 31337
```
<img src="https://huggingface.co/Kafke/sanrio/resolve/main/mymelody.png" width="512px"/>
```
plushie of cinnamoroll
Steps: 20, Sampler: Euler a, CFG scale: 12, Seed: 4279840297, Size: 512x512, ENSD: 31337
```
<img src="https://huggingface.co/Kafke/sanrio/resolve/main/cinnamoroll.png" width="512px"/>
```
badtz-maru
Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 3378087276, Size: 512x512, ENSD: 31337
```
<img src="https://huggingface.co/Kafke/sanrio/resolve/main/badtz.png" width="512px"/>
```
totoro sanrio
Steps: 20, Sampler: Euler a, CFG scale: 8, Seed: 3046772456, Size: 512x512, ENSD: 31337
```
<img src="https://huggingface.co/Kafke/sanrio/resolve/main/totoro.png" width="512px"/> |
Ayran/DialoGPT-medium-harry-potter-1-through-4-plus-6-e18 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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},
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},
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},
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}
}
} | 12 | null | Access to model CosmicAvatar/cosmic_avatar_waifu_diffusion_v1_3 is restricted and you are not in the authorized list. Visit https://huggingface.co/CosmicAvatar/cosmic_avatar_waifu_diffusion_v1_3 to ask for access. |
Ayta/Haha | []
| null | {
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}
} | 0 | 2022-11-20T07:45:13Z | ---
license: cc-by-4.0
language: ta
---
## TamilBERT
TamilBERT is a Tamil BERT model trained on publicly available Tamil monolingual datasets.
Preliminary details on the dataset, models, and baseline results can be found in our [<a href='https://arxiv.org/abs/2211.11418'> paper </a>] .
Citing:
```
@article{joshi2022l3cubehind,
title={L3Cube-HindBERT and DevBERT: Pre-Trained BERT Transformer models for Devanagari based Hindi and Marathi Languages},
author={Joshi, Raviraj},
journal={arXiv preprint arXiv:2211.11418},
year={2022}
}
``` |
AyushPJ/ai-club-inductions-21-nlp-ELECTRA-base-squad | [
"pytorch",
"electra",
"question-answering",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"ElectraForQuestionAnswering"
],
"model_type": "electra",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
"translation_en_to_de": {
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"prefix": null
},
"translation_en_to_fr": {
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},
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"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 12 | 2022-11-20T08:12:25Z | ---
license: cc-by-4.0
language: as
---
## AssameseBERT
AssameseBERT is an Assamese BERT model trained on publicly available Assamese monolingual datasets.
Preliminary details on the dataset, models, and baseline results can be found in our [<a href='https://arxiv.org/abs/2211.11418'> paper </a>] .
Citing:
```
@article{joshi2022l3cubehind,
title={L3Cube-HindBERT and DevBERT: Pre-Trained BERT Transformer models for Devanagari based Hindi and Marathi Languages},
author={Joshi, Raviraj},
journal={arXiv preprint arXiv:2211.11418},
year={2022}
}
``` |
Bala/model_name | []
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},
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}
}
} | 0 | null | ---
language:
- it
tags:
- mbart-50
license: apache-2.0
datasets:
- PortMEDIA-Lang
metrics:
- cer
- cver
---
This model is `mbart-large-50-many-to-many-mmt` model fine-tuned on the text part of [PortMEDIA-Lang](https://catalogue.elra.info/en-us/repository/browse/ELRA-S0371/) spoken language understanding dataset.
The scores on the test set are 40.76% and 43.93% for CER and CVER respectively.
|
Baybars/wav2vec2-xls-r-1b-turkish | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"tr",
"dataset:common_voice",
"transformers",
"common_voice",
"generated_from_trainer"
]
| automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 13 | null | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 15.50 +/- 11.93
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
BeIR/query-gen-msmarco-t5-large-v1 | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"prefix": "summarize: "
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 1,225 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: bert-base-uncased-finetuned-squadv
results: []
---
<!-- 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-squadv
This model is a fine-tuned version of [monakth/bert-base-uncased-finetuned-squad](https://huggingface.co/monakth/bert-base-uncased-finetuned-squad) on the squad_v2 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: 2e-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: 3
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
BigSalmon/MrLincoln13 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"max_length": null,
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"no_repeat_ngram_size": null,
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},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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},
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"prefix": null
}
}
} | 9 | 2022-11-20T22:46:14Z | ---
license: mit
---
### Filename_2 on Stable Diffusion
This is the `<filename>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
BigSalmon/MrLincoln3 | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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"prefix": null
},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 17 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 1060 with parameters:
```
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 2,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 5.2895295964701986e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 1060,
"warmup_steps": 106,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
BigSalmon/MrLincolnBerta | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 8 | null | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="xaeroq/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
BigSalmon/Neo | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPTNeoForCausalLM"
],
"model_type": "gpt_neo",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 13 | 2022-11-21T00:01:52Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 125 with parameters:
```
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 6.112815904337237e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 125,
"warmup_steps": 13,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
BigSalmon/T5F | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
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"T5ForConditionalGeneration"
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"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 6 | 2022-11-21T01:58:47Z | ---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: huggan/smithsonian_butterflies_subset
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [π€ Diffusers](https://github.com/huggingface/diffusers) library
on the `huggan/smithsonian_butterflies_subset` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16
### Training results
π [TensorBoard logs](https://huggingface.co/DONG19/ddpm-butterflies-128/tensorboard?#scalars)
|
Binbin/test | []
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} | 0 | null | ---
tags:
- text-generation
library_name: transformers
widget:
- text: "This is a conversation where Ray Dalio is giving advice on being a manager and building a successful team.\nUser: Hi Ray, thanks for talking with me today. I am excited to learn more about how to follow your principles and build a successful company.\nRay: No problem, I am happy to help. What situation are you facing?\nUser: It feels like I keep making decisions without thinking first - I do something without thinking and then I face the consequences afterwards.\nRay:"
example_title: "Q&A"
- text: "Itβs easy to tell an open-minded person from a closed-minded person because they act very differently. Here are some cues to tell you whether you or others are being closed-minded: "
example_title: "Principles"
---
## Model Description
Pre-training on cleaned version of Principles
- removing numeric references to footnotes
- removing numeric counts, i.e. 1) ... 2) ... 3) ...
- correcting gramma, i.e. full stops must be followed by a space
- finetuning OPT-30B model on the dataset above
- Dataset location: Jellywibble/dalio-principles-cleaned-v3
## Metrics
- Checkpoint 8 served
- Hellaswag Perplexity: 30.65
- 2.289 eval loss
wandb link: https://wandb.ai/jellywibble/huggingface/runs/2jqc504o?workspace=user-jellywibble
## Model Parameters
Trained on 4xA40, effective batchsize = 8
- base_model_name facebook/opt-30b
- dataset_name Jellywibble/dalio-principles-cleaned-v3
- block_size 1024
- gradient_accumulation_steps 2
- per_device_train_batch_size 1
- seed 2
- num_train_epochs 1
- learning_rate 3e-6
## Notes
- It is important for the effective batch size to be at least 8
- Learning rate higher than 3e-6 will result in massive overfitting, i.e. much worse Hellaswag metrics |
Blazeolmo/Scrabunzi | []
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} | 0 | 2022-11-21T04:02:34Z | ---
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
widget:
- text: "A high tech solarpunk utopia in the Amazon rainforest"
example_title: Amazon rainforest
- text: "A pikachu fine dining with a view to the Eiffel Tower"
example_title: Pikachu in Paris
- text: "A mecha robot in a favela in expressionist style"
example_title: Expressionist robot
- text: "an insect robot preparing a delicious meal"
example_title: Insect robot
- text: "A small cabin on top of a snowy mountain in the style of Disney, artstation"
example_title: Snowy disney cabin
extra_gated_prompt: |-
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claim no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
Please read the full license carefully here: https://huggingface.co/spaces/CompVis/stable-diffusion-license
extra_gated_heading: Please read the LICENSE to access this model
---
# Stable Diffusion v1-4 Model Card
Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.
For more information about how Stable Diffusion functions, please have a look at [π€'s Stable Diffusion with π§¨Diffusers blog](https://huggingface.co/blog/stable_diffusion).
The **Stable-Diffusion-v1-4** checkpoint was initialized with the weights of the [Stable-Diffusion-v1-2](https:/steps/huggingface.co/CompVis/stable-diffusion-v1-2)
checkpoint and subsequently fine-tuned on 225k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
This weights here are intended to be used with the 𧨠Diffusers library. If you are looking for the weights to be loaded into the CompVis Stable Diffusion codebase, [come here](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original)
## Model Details
- **Developed by:** Robin Rombach, Patrick Esser
- **Model type:** Diffusion-based text-to-image generation model
- **Language(s):** English
- **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based.
- **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487).
- **Resources for more information:** [GitHub Repository](https://github.com/CompVis/stable-diffusion), [Paper](https://arxiv.org/abs/2112.10752).
- **Cite as:**
@InProceedings{Rombach_2022_CVPR,
author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
title = {High-Resolution Image Synthesis With Latent Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10684-10695}
}
## Examples
We recommend using [π€'s Diffusers library](https://github.com/huggingface/diffusers) to run Stable Diffusion.
### PyTorch
```bash
pip install --upgrade diffusers transformers scipy
```
Run this command to log in with your HF Hub token if you haven't before:
```bash
huggingface-cli login
```
Running the pipeline with the default PNDM scheduler:
```python
import torch
from diffusers import StableDiffusionPipeline
model_id = "CompVis/stable-diffusion-v1-4"
device = "cuda"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, revision="fp16")
pipe = pipe.to(device)
prompt = "a photo of an astronaut riding a horse on mars"
image = pipe(prompt).images[0]
image.save("astronaut_rides_horse.png")
```
**Note**:
If you are limited by GPU memory and have less than 4GB of GPU RAM available, please make sure to load the StableDiffusionPipeline in float16 precision instead of the default float32 precision as done above. You can do so by telling diffusers to expect the weights to be in float16 precision:
```py
import torch
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, revision="fp16")
pipe = pipe.to(device)
pipe.enable_attention_slicing()
prompt = "a photo of an astronaut riding a horse on mars"
image = pipe(prompt).images[0]
image.save("astronaut_rides_horse.png")
```
To swap out the noise scheduler, pass it to `from_pretrained`:
```python
from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler
model_id = "CompVis/stable-diffusion-v1-4"
# Use the Euler scheduler here instead
scheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16, revision="fp16")
pipe = pipe.to("cuda")
prompt = "a photo of an astronaut riding a horse on mars"
image = pipe(prompt).images[0]
image.save("astronaut_rides_horse.png")
```
### JAX/Flax
To use StableDiffusion on TPUs and GPUs for faster inference you can leverage JAX/Flax.
Running the pipeline with default PNDMScheduler
```python
import jax
import numpy as np
from flax.jax_utils import replicate
from flax.training.common_utils import shard
from diffusers import FlaxStableDiffusionPipeline
pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4", revision="flax", dtype=jax.numpy.bfloat16
)
prompt = "a photo of an astronaut riding a horse on mars"
prng_seed = jax.random.PRNGKey(0)
num_inference_steps = 50
num_samples = jax.device_count()
prompt = num_samples * [prompt]
prompt_ids = pipeline.prepare_inputs(prompt)
# shard inputs and rng
params = replicate(params)
prng_seed = jax.random.split(prng_seed, 8)
prompt_ids = shard(prompt_ids)
images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images
images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
```
**Note**:
If you are limited by TPU memory, please make sure to load the `FlaxStableDiffusionPipeline` in `bfloat16` precision instead of the default `float32` precision as done above. You can do so by telling diffusers to load the weights from "bf16" branch.
```python
import jax
import numpy as np
from flax.jax_utils import replicate
from flax.training.common_utils import shard
from diffusers import FlaxStableDiffusionPipeline
pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4", revision="bf16", dtype=jax.numpy.bfloat16
)
prompt = "a photo of an astronaut riding a horse on mars"
prng_seed = jax.random.PRNGKey(0)
num_inference_steps = 50
num_samples = jax.device_count()
prompt = num_samples * [prompt]
prompt_ids = pipeline.prepare_inputs(prompt)
# shard inputs and rng
params = replicate(params)
prng_seed = jax.random.split(prng_seed, 8)
prompt_ids = shard(prompt_ids)
images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images
images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
```
# Uses
## Direct Use
The model is intended for research purposes only. Possible research areas and
tasks include
- Safe deployment of models which have the potential to generate harmful content.
- Probing and understanding the limitations and biases of generative models.
- Generation of artworks and use in design and other artistic processes.
- Applications in educational or creative tools.
- Research on generative models.
Excluded uses are described below.
### Misuse, Malicious Use, and Out-of-Scope Use
_Note: This section is taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), but applies in the same way to Stable Diffusion v1_.
The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
#### Out-of-Scope Use
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
#### Misuse and Malicious Use
Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:
- Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
- Intentionally promoting or propagating discriminatory content or harmful stereotypes.
- Impersonating individuals without their consent.
- Sexual content without consent of the people who might see it.
- Mis- and disinformation
- Representations of egregious violence and gore
- Sharing of copyrighted or licensed material in violation of its terms of use.
- Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.
## Limitations and Bias
### Limitations
- The model does not achieve perfect photorealism
- The model cannot render legible text
- The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to βA red cube on top of a blue sphereβ
- Faces and people in general may not be generated properly.
- The model was trained mainly with English captions and will not work as well in other languages.
- The autoencoding part of the model is lossy
- The model was trained on a large-scale dataset
[LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material
and is not fit for product use without additional safety mechanisms and
considerations.
- No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the training data.
The training data can be searched at [https://rom1504.github.io/clip-retrieval/](https://rom1504.github.io/clip-retrieval/) to possibly assist in the detection of memorized images.
### Bias
While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/),
which consists of images that are primarily limited to English descriptions.
Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for.
This affects the overall output of the model, as white and western cultures are often set as the default. Further, the
ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts.
### Safety Module
The intended use of this model is with the [Safety Checker](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) in Diffusers.
This checker works by checking model outputs against known hard-coded NSFW concepts.
The concepts are intentionally hidden to reduce the likelihood of reverse-engineering this filter.
Specifically, the checker compares the class probability of harmful concepts in the embedding space of the `CLIPTextModel` *after generation* of the images.
The concepts are passed into the model with the generated image and compared to a hand-engineered weight for each NSFW concept.
## Training
**Training Data**
The model developers used the following dataset for training the model:
- LAION-2B (en) and subsets thereof (see next section)
**Training Procedure**
Stable Diffusion v1-4 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training,
- Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4
- Text prompts are encoded through a ViT-L/14 text-encoder.
- The non-pooled output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention.
- The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet.
We currently provide four checkpoints, which were trained as follows.
- [`stable-diffusion-v1-1`](https://huggingface.co/CompVis/stable-diffusion-v1-1): 237,000 steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en).
194,000 steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`).
- [`stable-diffusion-v1-2`](https://huggingface.co/CompVis/stable-diffusion-v1-2): Resumed from `stable-diffusion-v1-1`.
515,000 steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en,
filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an [improved aesthetics estimator](https://github.com/christophschuhmann/improved-aesthetic-predictor)).
- [`stable-diffusion-v1-3`](https://huggingface.co/CompVis/stable-diffusion-v1-3): Resumed from `stable-diffusion-v1-2`. 195,000 steps at resolution `512x512` on "laion-improved-aesthetics" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
- [`stable-diffusion-v1-4`](https://huggingface.co/CompVis/stable-diffusion-v1-4) Resumed from `stable-diffusion-v1-2`.225,000 steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
- **Hardware:** 32 x 8 x A100 GPUs
- **Optimizer:** AdamW
- **Gradient Accumulations**: 2
- **Batch:** 32 x 8 x 2 x 4 = 2048
- **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant
## Evaluation Results
Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0,
5.0, 6.0, 7.0, 8.0) and 50 PLMS sampling
steps show the relative improvements of the checkpoints:
Evaluated using 50 PLMS steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores.
## Environmental Impact
**Stable Diffusion v1** **Estimated Emissions**
Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.
- **Hardware Type:** A100 PCIe 40GB
- **Hours used:** 150000
- **Cloud Provider:** AWS
- **Compute Region:** US-east
- **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 11250 kg CO2 eq.
## Citation
```bibtex
@InProceedings{Rombach_2022_CVPR,
author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
title = {High-Resolution Image Synthesis With Latent Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10684-10695}
}
```
*This model card was written by: Robin Rombach and Patrick Esser and is based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).* |
BritishLibraryLabs/bl-books-genre | [
"pytorch",
"distilbert",
"text-classification",
"multilingual",
"dataset:blbooksgenre",
"transformers",
"genre",
"books",
"library",
"historic",
"glam ",
"lam",
"license:mit",
"has_space"
]
| text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
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},
"translation_en_to_fr": {
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},
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}
}
} | 76 | null | the EXACT BERRY mix model, download from the direct source?
all we know is... it is here..
good at nsfw |
Broadus20/DialoGPT-small-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
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"no_repeat_ngram_size": null,
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"prefix": null
},
"text-generation": {
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},
"translation_en_to_fr": {
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},
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}
} | 9 | null | This amalgamation appears to be a fusion of waifu diffusion v1.3 and Novel AI,
though the precise methodology and circumstances of their merger remain unknown.
Despite this lack of information,
the existence of such a hybrid entity cannot be denied
π€ |
Buntan/BuntanAI | []
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} | 0 | null | ---
language:
- tr
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Small tr - Kshitiz-Khandelwal
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
args: 'config: tr, split: test'
metrics:
- name: Wer
type: wer
value: 22.351594186023945
---
<!-- 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. -->
# Whisper Small tr - Kshitiz-Khandelwal
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2475
- Wer: 22.3516
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.2152 | 0.22 | 500 | 0.2900 | 25.5283 |
| 0.1656 | 0.44 | 1000 | 0.2639 | 23.8575 |
| 0.1853 | 0.66 | 1500 | 0.2475 | 22.3516 |
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
CAMeL-Lab/bert-base-arabic-camelbert-ca | [
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} | 580 | null | ---
tags:
- tabular-classification
- sklearn
dataset:
- titanic
widget:
structuredData:
PassengerId:
- 1191
Pclass:
- 1
Name:
- Sherlock Holmes
Sex:
- male
SibSp:
- 0
Parch:
- 0
Ticket:
- C.A.29395
Fare:
- 12
Cabin:
- F44
Embarked:
- S
---
## Titanic (Survived/Not Survived) - Binary Classification
### How to use
```python
from huggingface_hub import hf_hub_url, cached_download
import joblib
import pandas as pd
import numpy as np
from tensorflow.keras.models import load_model
REPO_ID = 'danupurnomo/dummy-titanic'
PIPELINE_FILENAME = 'final_pipeline.pkl'
TF_FILENAME = 'titanic_model.h5'
model_pipeline = joblib.load(cached_download(
hf_hub_url(REPO_ID, PIPELINE_FILENAME)
))
model_seq = load_model(cached_download(
hf_hub_url(REPO_ID, TF_FILENAME)
))
```
### Example A New Data
```python
new_data = {
'PassengerId': 1191,
'Pclass': 1,
'Name': 'Sherlock Holmes',
'Sex': 'male',
'Age': 30,
'SibSp': 0,
'Parch': 0,
'Ticket': 'C.A.29395',
'Fare': 12,
'Cabin': 'F44',
'Embarked': 'S'
}
new_data = pd.DataFrame([new_data])
```
### Transform Inference-Set
```python
new_data_transform = model_pipeline.transform(new_data)
```
### Predict using Neural Networks
```python
y_pred_inf_single = model_seq.predict(new_data_transform)
y_pred_inf_single = np.where(y_pred_inf_single >= 0.5, 1, 0)
print('Result : ', y_pred_inf_single)
# [[0]]
``` |
CBreit00/DialoGPT_small_Rick | []
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} | 0 | 2022-11-21T10:48:22Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
---
### Zlikwid Dreambooth model trained by Zlikwid with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb)
Sample pictures of this concept:
20221120210939
|
CLAck/indo-mixed | [
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"marian",
"text2text-generation",
"en",
"id",
"dataset:ALT",
"transformers",
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"license:apache-2.0",
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} | 15 | 2022-11-21T11:02:20Z | ---
tags:
- generated_from_trainer
model-index:
- name: NEW_OCR_10_8wangchanberta-base-att-spm-uncased
results: []
---
<!-- 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. -->
# NEW_OCR_10_8wangchanberta-base-att-spm-uncased
This model is a fine-tuned version of [airesearch/wangchanberta-base-att-spm-uncased](https://huggingface.co/airesearch/wangchanberta-base-att-spm-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0147
## 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: 2e-05
- 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
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 0.108 | 1.0 | 10701 | 0.0167 |
| 0.0161 | 2.0 | 21402 | 0.0140 |
| 0.0126 | 3.0 | 32103 | 0.0130 |
| 0.0105 | 4.0 | 42804 | 0.0125 |
| 0.009 | 5.0 | 53505 | 0.0135 |
| 0.008 | 6.0 | 64206 | 0.0137 |
| 0.0074 | 7.0 | 74907 | 0.0139 |
| 0.0064 | 8.0 | 85608 | 0.0143 |
| 0.0058 | 9.0 | 96309 | 0.0147 |
| 0.0054 | 10.0 | 107010 | 0.0147 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.12.1+cu113
- Datasets 1.17.0
- Tokenizers 0.10.3
|
CLAck/indo-pure | [
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} | 4 | 2022-11-21T11:07:13Z | hey friends welcome in this applied NLP tutorial we're going to learn how to fine tune a text generation model one second how to push the text generated like the fine-tuned model into hugging face model Hub and in this process we are also going to explore the stable diffusion part of it so this is a combination of a lot of different things.
the model is uploaded to hugging face model Hub and the model I'm calling it SD prompt generator GPT Neo because this is a prompt generator for stable diffusion so if you want to create something using stable division the AI are generated so you ideally need to give a very detailed prompt.
ideally is as you can see from the name it says SD prompt generator GPT Neo so we're going to use GPT Neo model to fine tune our prompts so that we have created a text generation model where we can give a prompt text and that will generate new prompt or a new extended prompt better prompt for us so what are we going to do we are going to take a set of existing stable diffusion prompts and we have got a 124 in 124 million stay GPT Neo model and we are going to fine tune that model based on this data and then we are going to finally save that model and then push the model into hugging phase model Hub |
CLS/WubiBERT_models | []
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} | 0 | 2022-11-21T11:22:21Z | # Model Details: QuaLA-MiniLM
The article discusses the challenge of making transformer-based models efficient enough for practical use, given their size and computational requirements. The authors propose a new approach called **QuaLA-MiniLM**, which combines knowledge distillation, the length-adaptive transformer (LAT) technique, and low-bit quantization. We expand the Dynamic-TinyBERT approach. This approach trains a single model that can adapt to any inference scenario with a given computational budget, achieving a superior accuracy-efficiency trade-off on the SQuAD1.1 dataset. The authors compare their approach to other efficient methods and find that it achieves up to an **x8.8 speedup with less than 1% accuracy loss**. They also provide their code publicly on GitHub. The article also discusses other related work in the field, including dynamic transformers and other knowledge distillation approaches.
The model card has been written in combination by Intel.
### QuaLA-MiniLM training process
Figure showing QuaLA-MiniLM training process. To run the model with the best accuracy-efficiency tradeoff per a specific computational budget, we set the length configuration to the best setting found by an evolutionary search to match our computational constraint.
### Model license
Licensed under MIT license.
| Model Detail | Description |
| ---- | --- |
| language: | en |
| Model Authors Company | Intel |
| Date | May 4, 2023 |
| Version | 1 |
| Type | NLP - Tiny language model|
| Architecture | "In this work we expand Dynamic-TinyBERT to generate a much more highly efficient model. First, we use a much smaller MiniLM model which was distilled from a RoBERTa-Large teacher rather than BERT-base. Second, we apply the LAT method to make the model length-adaptive, and finally we further enhance the modelβs efficiency by applying 8-bit quantization. The resultant QuaLAMiniLM (Quantized Length-Adaptive MiniLM) model outperforms BERT-base with only 30% of parameters, and demonstrates an accuracy-speedup tradeoff that is superior to any other efficiency approach (up to x8.8 speedup with <1% accuracy loss) on the challenging SQuAD1.1 benchmark. Following the concept presented by LAT, it provides a wide range of accuracy-efficiency tradeoff points while alleviating the need to retrain it for each point along the accuracy-efficiency curve." |
| Paper or Other Resources | https://arxiv.org/pdf/2210.17114.pdf |
| License | TBD |
| Questions or Comments | [Community Tab](https://huggingface.co/Intel/bert-base-uncased-sparse-90-unstructured-pruneofa/discussions) and [Intel Developers Discord](https://discord.gg/rv2Gp55UJQ) |
| Intended Use | Description |
| --- | --- |
| Primary intended uses | TBD |
| Primary intended users | Anyone who needs an efficient tiny language model for other downstream tasks.|
|Out-of-scope uses|The model should not be used to intentionally create hostile or alienating environments for people.|
### How to use
Code examples coming soon!
```python
import ...
```
For more code examples, refer to the GitHub Repo.
### Metrics (Model Performance):
Inference performance on the SQuAD1.1 evaluation dataset. For all the length-adaptive
(LA) models we show the performance both of running the model without token-dropping, and of
running the model in a token-dropping configuration according to the optimal length configuration
found to meet our accuracy constraint.
|Model | Model size (Mb) |Tokens per layer |Accuracy (F1) | Latency (ms) | FLOPs | Speedup|
| --- | --- | --- | --- | --- | --- | --- |
|BERT-base |415.4723 |(384,384,384,384,384,384) |88.5831 |56.5679 |3.53E+10 |1x|
|TinyBERT-ours |253.2077 |(384,384,384,384,384,384) |88.3959 |32.4038 |1.77E+10 |1.74x|
|QuaTinyBERT-ours |132.0665 |(384,384,384,384,384,384) |87.6755 |15.5850 1.77E+10 |3.63x|
|MiniLMv2-ours |115.0473 |(384,384,384,384,384,384) |88.7016 |18.2312 |4.76E+09 |3.10x|
|QuaMiniLMv2-ours |84.8602 |(384,384,384,384,384,384) |88.5463 |9.1466 |4.76E+09 |6.18x|
|LA-MiniLM |115.0473 |(384,384,384,384,384,384) |89.2811 |16.9900 |4.76E+09 |3.33x|
|LA-MiniLM |115.0473 |(269, 253, 252, 202, 104, 34) |87.7637 |11.4428 |2.49E+09 |4.94x|
|QuaLA-MiniLM |84.8596 |(384,384,384,384,384,384) |88.8593 |7.4443 |4.76E+09 |7.6x|
|QuaLA-MiniLM |84.8596 |(315,251,242,159,142,33) |87.6828 |6.4146 |2.547E+09 |8.8x|
### Training and Evaluation Data
| Training and Evaluation Data | Description |
| --- | --- |
|Datasets|SQuAD1.1 dataset |
|Motivation | To build an efficient and accurate base model for several downstream language tasks. |
### Ethical Considerations
|Ethical Considerations|Description|
| --- | --- |
|Data | SQuAD1.1 dataset |
| Human life | The model is not intended to inform decisions central to human life or flourishing. It is an aggregated set of labelled Wikipedia articles. |
|Mitigations| No additional risk mitigation strategies were considered during model development. |
|Risks and harms| Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al., 2021, and Bender et al., 2021). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. Beyond this, the extent of the risks involved by using the model remain unknown. |
### Caveats and Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. There are no additional caveats or recommendations for this model.
### BibTeX entry and citation info
| comments | description |
| --- | --- |
| comments: | In this version we added reference to the source code in the abstract. arXiv admin note: text overlap with arXiv:2111.09645 |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2210.17114 [cs.CL]|
| - | (or arXiv:2210.17114v2 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2210.17114|
|
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} | 33 | null | ---
license: apache-2.0
tags:
- vision
- depth-estimation
- generated_from_trainer
model-index:
- name: glpn-nyu-finetuned-diode-221121-113853
results: []
---
<!-- 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. -->
# glpn-nyu-finetuned-diode-221121-113853
This model is a fine-tuned version of [vinvino02/glpn-nyu](https://huggingface.co/vinvino02/glpn-nyu) on the diode-subset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3384
- Mae: 0.2739
- Rmse: 0.3959
- Abs Rel: 0.3230
- Log Mae: 0.1148
- Log Rmse: 0.1651
- Delta1: 0.5576
- Delta2: 0.8345
- Delta3: 0.9398
## 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: 24
- eval_batch_size: 48
- seed: 2022
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mae | Rmse | Abs Rel | Log Mae | Log Rmse | Delta1 | Delta2 | Delta3 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:-------:|:--------:|:------:|:------:|:------:|
| 0.7523 | 1.0 | 72 | 0.5772 | 0.7466 | 0.9116 | 0.9709 | 0.2568 | 0.3040 | 0.1373 | 0.3324 | 0.6328 |
| 0.4281 | 2.0 | 144 | 0.3849 | 0.3324 | 0.4673 | 0.3934 | 0.1349 | 0.1874 | 0.4681 | 0.7753 | 0.9142 |
| 0.3906 | 3.0 | 216 | 0.3660 | 0.3048 | 0.4418 | 0.3593 | 0.1258 | 0.1800 | 0.5225 | 0.7997 | 0.9195 |
| 0.3766 | 4.0 | 288 | 0.3556 | 0.2923 | 0.4275 | 0.3383 | 0.1209 | 0.1741 | 0.5450 | 0.8157 | 0.9259 |
| 0.3744 | 5.0 | 360 | 0.3539 | 0.2899 | 0.4171 | 0.3435 | 0.1208 | 0.1724 | 0.5355 | 0.8173 | 0.9307 |
| 0.328 | 6.0 | 432 | 0.3498 | 0.2860 | 0.4109 | 0.3418 | 0.1196 | 0.1709 | 0.5402 | 0.8193 | 0.9334 |
| 0.3166 | 7.0 | 504 | 0.3451 | 0.2793 | 0.4110 | 0.3203 | 0.1166 | 0.1677 | 0.5583 | 0.8286 | 0.9331 |
| 0.2639 | 8.0 | 576 | 0.3475 | 0.2823 | 0.4083 | 0.3341 | 0.1182 | 0.1695 | 0.5469 | 0.8251 | 0.9337 |
| 0.2802 | 9.0 | 648 | 0.3422 | 0.2779 | 0.4030 | 0.3249 | 0.1163 | 0.1667 | 0.5524 | 0.8287 | 0.9366 |
| 0.2701 | 10.0 | 720 | 0.3411 | 0.2781 | 0.3962 | 0.3316 | 0.1168 | 0.1664 | 0.5446 | 0.8286 | 0.9396 |
| 0.2232 | 11.0 | 792 | 0.3408 | 0.2755 | 0.3998 | 0.3259 | 0.1154 | 0.1665 | 0.5578 | 0.8332 | 0.9383 |
| 0.2921 | 12.0 | 864 | 0.3391 | 0.2749 | 0.3975 | 0.3220 | 0.1152 | 0.1652 | 0.5553 | 0.8332 | 0.9390 |
| 0.2837 | 13.0 | 936 | 0.3400 | 0.2745 | 0.3979 | 0.3251 | 0.1150 | 0.1660 | 0.5587 | 0.8347 | 0.9386 |
| 0.2922 | 14.0 | 1008 | 0.3370 | 0.2728 | 0.3965 | 0.3184 | 0.1142 | 0.1644 | 0.5602 | 0.8359 | 0.9401 |
| 0.2921 | 15.0 | 1080 | 0.3384 | 0.2739 | 0.3959 | 0.3230 | 0.1148 | 0.1651 | 0.5576 | 0.8345 | 0.9398 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu116
- Tokenizers 0.13.2
|
CLTL/icf-levels-ber | [
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
"license:mit"
]
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} | 33 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wispher2
results: []
---
<!-- 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. -->
# wispher2
This model is a fine-tuned version of [openai/whisper-base.en](https://huggingface.co/openai/whisper-base.en) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8125
- Wer: 50.1754
## 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: 8
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.7532 | 1.12 | 100 | 0.8125 | 50.1754 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1
- Datasets 2.7.1
- Tokenizers 0.13.2
|
CLTL/icf-levels-enr | [
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
"license:mit"
]
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} | 30 | null | ---
tags:
- generated_from_trainer
model-index:
- name: base-on-torgo0003
results: []
---
<!-- 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-on-torgo0003
This model is a fine-tuned version of [yongjian/wav2vec2-large-a](https://huggingface.co/yongjian/wav2vec2-large-a) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6579
- Wer: 0.7547
## 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: 4
- 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 28.1611 | 0.46 | 500 | 3.4550 | 1.0163 |
| 3.2238 | 0.92 | 1000 | 2.8781 | 1.0411 |
| 2.8617 | 1.39 | 1500 | 2.9896 | 1.0028 |
| 2.5841 | 1.85 | 2000 | 2.3744 | 1.2896 |
| 2.2029 | 2.31 | 2500 | 1.8598 | 1.2722 |
| 1.9976 | 2.77 | 3000 | 1.6505 | 1.2513 |
| 1.7817 | 3.23 | 3500 | 1.5291 | 1.2294 |
| 1.6484 | 3.69 | 4000 | 1.4635 | 1.2106 |
| 1.56 | 4.16 | 4500 | 1.4251 | 1.1989 |
| 1.417 | 4.62 | 5000 | 1.4040 | 1.1904 |
| 1.2884 | 5.08 | 5500 | 1.2734 | 1.1568 |
| 1.2788 | 5.54 | 6000 | 1.2242 | 1.1384 |
| 1.2159 | 6.0 | 6500 | 1.0561 | 1.1349 |
| 1.1125 | 6.46 | 7000 | 1.1001 | 1.1175 |
| 1.1495 | 6.93 | 7500 | 1.0409 | 1.1112 |
| 1.0222 | 7.39 | 8000 | 1.0525 | 1.0952 |
| 1.0104 | 7.85 | 8500 | 1.0184 | 1.1048 |
| 0.9956 | 8.31 | 9000 | 1.0438 | 1.1196 |
| 0.8747 | 8.77 | 9500 | 1.0736 | 1.1005 |
| 0.8437 | 9.23 | 10000 | 1.0041 | 1.0768 |
| 0.861 | 9.7 | 10500 | 0.9407 | 1.0496 |
| 0.8238 | 10.16 | 11000 | 0.9237 | 1.0697 |
| 0.7806 | 10.62 | 11500 | 0.8706 | 1.0343 |
| 0.7475 | 11.08 | 12000 | 0.9576 | 1.0407 |
| 0.6963 | 11.54 | 12500 | 0.9195 | 1.0159 |
| 0.7624 | 12.0 | 13000 | 0.8102 | 1.0060 |
| 0.6311 | 12.47 | 13500 | 0.8208 | 0.9897 |
| 0.6649 | 12.93 | 14000 | 0.7699 | 0.9968 |
| 0.6025 | 13.39 | 14500 | 0.7834 | 0.9547 |
| 0.5691 | 13.85 | 15000 | 0.7414 | 0.9632 |
| 0.532 | 14.31 | 15500 | 0.7056 | 0.9473 |
| 0.5572 | 14.77 | 16000 | 0.8136 | 0.9929 |
| 0.5455 | 15.24 | 16500 | 0.7355 | 0.9264 |
| 0.5369 | 15.7 | 17000 | 0.7531 | 0.9352 |
| 0.4771 | 16.16 | 17500 | 0.7527 | 0.9228 |
| 0.4778 | 16.62 | 18000 | 0.7312 | 0.9218 |
| 0.4384 | 17.08 | 18500 | 0.6774 | 0.8913 |
| 0.4619 | 17.54 | 19000 | 0.6888 | 0.8896 |
| 0.4341 | 18.01 | 19500 | 0.7068 | 0.9030 |
| 0.4164 | 18.47 | 20000 | 0.6484 | 0.8754 |
| 0.3883 | 18.93 | 20500 | 0.6388 | 0.8676 |
| 0.4135 | 19.39 | 21000 | 0.6732 | 0.8683 |
| 0.4121 | 19.85 | 21500 | 0.6354 | 0.8591 |
| 0.3694 | 20.31 | 22000 | 0.6751 | 0.8581 |
| 0.367 | 20.78 | 22500 | 0.6487 | 0.8411 |
| 0.3798 | 21.24 | 23000 | 0.5955 | 0.8312 |
| 0.3249 | 21.7 | 23500 | 0.6209 | 0.8230 |
| 0.3182 | 22.16 | 24000 | 0.7341 | 0.8212 |
| 0.3196 | 22.62 | 24500 | 0.6533 | 0.8106 |
| 0.297 | 23.08 | 25000 | 0.7163 | 0.8177 |
| 0.3021 | 23.55 | 25500 | 0.7209 | 0.8149 |
| 0.3248 | 24.01 | 26000 | 0.6268 | 0.8018 |
| 0.3013 | 24.47 | 26500 | 0.7014 | 0.7915 |
| 0.2986 | 24.93 | 27000 | 0.7306 | 0.8028 |
| 0.2913 | 25.39 | 27500 | 0.6866 | 0.7912 |
| 0.2706 | 25.85 | 28000 | 0.6860 | 0.7851 |
| 0.2572 | 26.32 | 28500 | 0.6478 | 0.7752 |
| 0.2794 | 26.78 | 29000 | 0.6308 | 0.7703 |
| 0.2796 | 27.24 | 29500 | 0.6302 | 0.7653 |
| 0.2604 | 27.7 | 30000 | 0.6638 | 0.7621 |
| 0.2367 | 28.16 | 30500 | 0.6492 | 0.7593 |
| 0.2383 | 28.62 | 31000 | 0.6560 | 0.7614 |
| 0.2495 | 29.09 | 31500 | 0.6577 | 0.7593 |
| 0.2513 | 29.55 | 32000 | 0.6579 | 0.7547 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 1.18.3
- Tokenizers 0.13.2
|
CLTL/icf-levels-etn | [
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
"license:mit"
]
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"RobertaForSequenceClassification"
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} | 31 | 2022-11-21T11:45:11Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.924
- name: F1
type: f1
value: 0.923935334776563
---
<!-- 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. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2162
- Accuracy: 0.924
- F1: 0.9239
## 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: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8203 | 1.0 | 250 | 0.3095 | 0.905 | 0.9019 |
| 0.2468 | 2.0 | 500 | 0.2162 | 0.924 | 0.9239 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.13.2
|
CLTL/icf-levels-mbw | [
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
"license:mit"
]
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} | 30 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 1000 with parameters:
```
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 2,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 6.468596158458052e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 1000,
"warmup_steps": 100,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
CLTL/icf-levels-stm | [
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
"license:mit"
]
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} | 32 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-base.en
results: []
---
<!-- 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. -->
# whisper-base.en
This model is a fine-tuned version of [openai/whisper-base.en](https://huggingface.co/openai/whisper-base.en) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8125
- Wer: 50.1754
## 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: 8
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.7532 | 1.12 | 100 | 0.8125 | 50.1754 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.13.2
|
CM-CA/Cartman | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: t5-small-finetuned-xsum-introduction
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xsum
type: xsum
args: default
metrics:
- name: Rouge1
type: rouge
value: 28.1828
---
<!-- 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. -->
# t5-small-finetuned-xsum-introduction
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4784
- Rouge1: 28.1828
- Rouge2: 7.6948
- Rougel: 22.1413
- Rougelsum: 22.1467
- Gen Len: 18.8272
## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.7155 | 1.0 | 12753 | 2.4784 | 28.1828 | 7.6948 | 22.1413 | 22.1467 | 18.8272 |
### Framework versions
- Transformers 4.11.0
- Pytorch 1.11.0a0+b6df043
- Datasets 2.6.1
- Tokenizers 0.10.3
|
CNT-UPenn/Bio_ClinicalBERT_for_seizureFreedom_classification | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
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"BertForSequenceClassification"
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} | 28 | null | pet = input('whats your pet')
if pet == 'pig':
print ('you are the pig ') |
CTBC/ATS | []
| null | {
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} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- dutch_social
model-index:
- name: dutch_threeway_sentiment_classification_v2
results: []
---
<!-- 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. -->
# dutch_threeway_sentiment_classification_v2
This model is a fine-tuned version of [MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli](https://huggingface.co/MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli) on the dutch_social 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: 2e-05
- 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
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.13.2
|
Calamarii/calamari | []
| null | {
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: test_trainer3
results: []
---
<!-- 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. -->
# test_trainer3
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown 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: 8
- eval_batch_size: 8
- seed: 42
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 125 | 1.8785 | 0.396 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cpu
- Tokenizers 0.11.6
|
CalvinHuang/mt5-small-finetuned-amazon-en-es | [
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"transformers",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
]
| summarization | {
"architectures": [
"MT5ForConditionalGeneration"
],
"model_type": "mt5",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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}
} | 16 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: taozexi/distilgpt2-finetuned-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# taozexi/distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.8582
- Validation Loss: 3.6762
- Epoch: 0
## 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.8582 | 3.6762 | 0 |
### Framework versions
- Transformers 4.24.0
- TensorFlow 2.9.2
- Datasets 2.7.0
- Tokenizers 0.13.2
|
Cameron/BERT-eec-emotion | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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}
} | 36 | 2022-11-21T12:43:55Z | ---
title: Hassanblend1.4
emoji: π
colorFrom: green
colorTo: indigo
sdk: gradio
sdk_version: 3.11.0
app_file: app.py
pinned: false
thumbnail: "https://i.imgur.com/PVThZvk.png"
license: creativeml-openrail-m
tags:
- stable-diffusion
- text-to-image
inference: true
---
# HassanBlend1.4 - fantasy.ai
Fantasy.ai is the official and exclusive hosted AI generation platform that holds a commercial use license for HassanBlend, you can use their service at https://Fantasy.ai/
I am hassan, I created HassansBlend, the latest version currently is 1.4. I continue to iterate and improve on this model over time. Feel free to check out our discord or rentry page for more examples with prompts and outputs generated.
I have also some custom created content such as enhancement hypernetworks/embeddings etc for patreons or KoFi subscribers only on my pages below
<b> Links </b><br>
<b>Patreon</b>
<a href="https://www.patreon.com/sd_hassan" target="_blank"><img src="https://i.imgur.com/sR32SqJ.jpg"></img></a>
<b>KoFi</b>
<a href="https://ko-fi.com/sdhassan" target="_blank"><img src="https://i.imgur.com/0P7CTN4.png"></img></a>
<b>Discord</b>
<a href="https://discord.gg/sdmodelers" target="_blank"><img src="https://i.imgur.com/HC1iHwg.png"></img></a>
### Quicklinks:
* [Latest Setup](https://rentry.org/sdhassan#current-setup)
* [HassanBlend Model Finetune Updates](https://rentry.org/sdhassan#hassanblend-finetuning-updates)
* [Latest Patreon Posts](https://rentry.org/sdhassan#patreon-posts)
* [Models](https://rentry.org/sdhassan#merged-models)
* [HassanBlend1.4](https://rentry.org/sdhassan#hassanblend14-downloads)
* [Prompts](https://rentry.org/sdhassan#prompts)
* [Photorealistic Tips](https://rentry.org/sdhassan#tips-for-photorealistic-images)
* [Embeddings](https://rentry.org/sdhassan#embeddings)
* [Hypernetworks](https://rentry.org/sdhassan#hypernetworks)
* [Wildcards](https://rentry.org/sdhassan#wildcards-i-made)
* [MyTools](https://rentry.org/sdhassan#my-tools)
* [Settings I use](https://rentry.org/sdhassan#settings)
Model details and examples with sample prompts: https://rentry.org/sdhassan
# Gradio Demo
We support a [Gradio](https://github.com/gradio-app/gradio) Web UI to run hassanblend1.4:
[](https://huggingface.co/spaces/akhaliq/hassanblend1.4)
|
Cameron/BERT-mdgender-wizard | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
}
} | 30 | 2022-11-21T13:20:21Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 40 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 40,
"warmup_steps": 4,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
Canadiancaleb/DialoGPT-small-jesse | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
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} | 9 | 2022-11-21T13:29:29Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-base-finetuned-liputan6-coba-coba
results: []
---
<!-- 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. -->
# mt5-base-finetuned-liputan6-coba-coba
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6428
- Rouge1: 0.37
- Rouge2: 0.2512
- Rougel: 0.3302
- Rougelsum: 0.3479
## 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: 5.6e-06
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|
| 10.8402 | 1.0 | 4970 | 0.9818 | 0.2754 | 0.1704 | 0.2475 | 0.2597 |
| 1.2001 | 2.0 | 9940 | 0.6614 | 0.3682 | 0.2521 | 0.3291 | 0.3461 |
| 0.9427 | 3.0 | 14910 | 0.6428 | 0.37 | 0.2512 | 0.3302 | 0.3479 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.13.2
|
Canadiancaleb/DialoGPT-small-walter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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} | 13 | 2022-11-21T13:30:57Z | # Prompt
Add 'ttp style' in your prompt to activate "This is The Police" style
# Samples
<img src="https://i.imgur.com/CfszL4y.png"/>
<img src="https://i.imgur.com/KaSPTtQ.png"/>
<img src="https://i.imgur.com/nO19Oog.png"/>
<img src="https://i.imgur.com/IRlwhEC.png"/>
<img src="https://i.imgur.com/vmfh4AR.png"/>
<img src="https://i.imgur.com/kETfqDj.png"/>
This is very early version, model does not understand some things yet. |
Canadiancaleb/jessebot | []
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} | 0 | null | the trigger word in automatic is the embedding name eg akudamadrivestyle-500
Its my first attempt, its good looking but nothing spectacular
0.05 training rate, 500 steps, 5 images of average quality |
CapitainData/wav2vec2-large-xlsr-turkish-demo-colab | []
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} | 0 | 2022-11-21T13:35:21Z | # Dragon diffusion
This is a Dreambooth model trained to make better dragons.
I plan to make updates with better models in the future.
---
license: mit
---
|
Captain272/lstm | []
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} | 0 | 2022-11-21T13:56:15Z | just an experimental embedding on a doodle that was put through img2img
psychedelic is the prompt trigger, or psy
1000 steps on 3 images, 0.05 training rate
|
Carlork314/Carlos | []
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} | 0 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 88 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 6.444852180284075e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 88,
"warmup_steps": 9,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
Carlork314/Xd | []
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} | 0 | 2022-11-21T14:03:57Z | ---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1596.00 +/- 357.12
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Carolhuehuehuehue/Sla | []
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} | 0 | null | Nightmare Creature/Human model. Prompt being "NghtmrCrtFrk". It's great making nightmare fuel creatures, humans and other living things. You try non-living things also, but you usually just end up with bizarre creatures still. Steps can be anything. Sometimes 20 does amazing, sometimes 150 does amazing. CFG can be anything also, but again results may vary. The more details you put, the better and more disgusting the prompt. Different samplers give's you various results.
thumbnail: 
|
dccuchile/albert-large-spanish-finetuned-qa-mlqa | [
"pytorch",
"albert",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
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"AlbertForQuestionAnswering"
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} | 5 | 2022-11-21T14:52:15Z | ---
datasets:
- relbert/semeval2012_relational_similarity_v6
model-index:
- name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-nce-0
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.796984126984127
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4037433155080214
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3916913946587537
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6859366314619233
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.784
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.42105263157894735
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4583333333333333
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9010094922404701
- name: F1 (macro)
type: f1_macro
value: 0.8947571278975387
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8227699530516432
- name: F1 (macro)
type: f1_macro
value: 0.6007828127513786
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6164680390032503
- name: F1 (macro)
type: f1_macro
value: 0.5989494559912151
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9572928983793559
- name: F1 (macro)
type: f1_macro
value: 0.8821535108627934
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8743340645565655
- name: F1 (macro)
type: f1_macro
value: 0.8719695915031801
---
# relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-nce-0
RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on
[relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-nce-0/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.4037433155080214
- Accuracy on SAT: 0.3916913946587537
- Accuracy on BATS: 0.6859366314619233
- Accuracy on U2: 0.42105263157894735
- Accuracy on U4: 0.4583333333333333
- Accuracy on Google: 0.784
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-nce-0/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9010094922404701
- Micro F1 score on CogALexV: 0.8227699530516432
- Micro F1 score on EVALution: 0.6164680390032503
- Micro F1 score on K&H+N: 0.9572928983793559
- Micro F1 score on ROOT09: 0.8743340645565655
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-nce-0/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.796984126984127
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-nce-0")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-base
- max_length: 64
- mode: average
- data: relbert/semeval2012_relational_similarity_v6
- split: train
- split_eval: validation
- template_mode: manual
- loss_function: nce_logout
- classification_loss: False
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 6
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 0
- exclude_relation: None
- n_sample: 320
- gradient_accumulation: 8
- relation_level: None
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-nce-0/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
dccuchile/bert-base-spanish-wwm-cased-finetuned-pos | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
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"BertForTokenClassification"
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} | 1 | null | ---
license: mit
---
### dapSciBERT
DapSciBERT is a BERT-like model trained based on the domain adaptive pretraining method ([Gururangan et al.](https://aclanthology.org/2020.acl-main.740/)) for the patent domain. Allenai/scibert_scivocab_uncased is used as base for the training. The training dataset used consists of a corpus of 10,000,000
patent abstracts that have been filed between 1998-2020 in US and European patent offices as well as the World Intellectual Property Organization. |
dccuchile/bert-base-spanish-wwm-uncased-finetuned-pawsx | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
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} | 24 | 2022-11-21T16:17:48Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
---
### Open Potion Bottle v1 Dreambooth model trained by [piEsposito](https://twitter.com/piesposi_to) with open weights, configs and prompts (as it should be)
- Concept: `potionbottle`
You can run this concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb)
Sample pictures of this concept:
`potionbottle`
<img src="https://huggingface.co/piEsposito/openpotionbottle-v1/resolve/main/WhatsApp%20Image%202022-11-21%20at%2011.13.02.jpg" width=256/>
<img src="https://huggingface.co/piEsposito/openpotionbottle-v1/resolve/main/WhatsApp%20Image%202022-11-21%20at%2011.25.15.jpg" width=256/>
<img src="https://huggingface.co/piEsposito/openpotionbottle-v1/resolve/main/WhatsApp%20Image%202022-11-21%20at%2012.34.18.jpg" width=256/>
<img src="https://huggingface.co/piEsposito/openpotionbottle-v1/resolve/main/WhatsApp%20Image%202022-11-21%20at%2011.19.28.jpg" width=256/>
## Usage examples
- Prompt: green potionbottle, perfectly ornated, intricate details, 3d render vray, beautiful, trending on artstation
- CFG Scale: 10
- Scheduler: `diffusers.EulerAncestralDiscreteScheduler`
- Steps: 30
<img src="https://huggingface.co/piEsposito/openpotionbottle-v1/resolve/main/green%20potionbottle%2C%20perfectly%20ornated%2C%20intricate%20details%2C%203d%20render%20vray%2C%20beautiful%2C%20trending%20on%20artstation.png" width=512/>
- Prompt: fantasy dragon as potionbottle, perfectly ornated, intricate details, 3d render vray, beautiful, trending on artstation
- CFG Scale: 10
- Scheduler: `diffusers.EulerAncestralDiscreteScheduler`
- Steps: 30
<img src="https://huggingface.co/piEsposito/openpotionbottle-v1/resolve/main/fantasy%20dragon%20as%20potionbottle%2C%20perfectly%20ornated%2C%20intricate%20details%2C%203d%20render%20vray%2C%20beautiful%2C%20trending%20on%20artstation.png" width=512/>
#### By https://twitter.com/piesposi_to
|
dccuchile/bert-base-spanish-wwm-uncased-finetuned-pos | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
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} | 5 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 24 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 2,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 6.474612215184842e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 24,
"warmup_steps": 3,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
dccuchile/bert-base-spanish-wwm-uncased-finetuned-xnli | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
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"BertForSequenceClassification"
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}
} | 36 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: t5-small-finetuned-xsum-purpose-system
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xsum
type: xsum
args: default
metrics:
- name: Rouge1
type: rouge
value: 28.1918
---
<!-- 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. -->
# t5-small-finetuned-xsum-purpose-system
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4784
- Rouge1: 28.1918
- Rouge2: 7.6941
- Rougel: 22.1356
- Rougelsum: 22.1486
- Gen Len: 18.8272
## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.7155 | 1.0 | 12753 | 2.4784 | 28.1918 | 7.6941 | 22.1356 | 22.1486 | 18.8272 |
### Framework versions
- Transformers 4.11.0
- Pytorch 1.11.0a0+b6df043
- Datasets 2.6.1
- Tokenizers 0.10.3
|
dccuchile/distilbert-base-spanish-uncased-finetuned-ner | [
"pytorch",
"distilbert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"DistilBertForTokenClassification"
],
"model_type": "distilbert",
"task_specific_params": {
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},
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}
} | 28 | null | Nightmare Mr. Bean Babys Toilet. Prompt being " NghtmrBbyFrk". This one was requested after my pictures, so I delivered. The results will really just give you Mr. Bean Baby and his toilet. The model is semi-limited. So lets say you put "cat", the baby will likely have some cat features like cat ears. Or you can say things like "blue baby" to make him blue. It's not the greatest model. Steps can be anything, in this case just more detail with more steps as per normal. CFG needs to be between 3-7. Any sampler.
thumbnail
 |
dccuchile/distilbert-base-spanish-uncased-finetuned-pawsx | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
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},
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},
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},
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},
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}
}
} | 29 | 2022-11-21T16:40:51Z | ---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: huggan/smithsonian_butterflies_subset
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [π€ Diffusers](https://github.com/huggingface/diffusers) library
on the `huggan/smithsonian_butterflies_subset` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16
### Training results
π [TensorBoard logs](https://huggingface.co/wangjksjtu/ddpm-butterflies-128/tensorboard?#scalars)
|
CennetOguz/distilbert-base-uncased-finetuned-recipe-accelerate-1 | [
"pytorch",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
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},
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}
} | 1 | 2022-11-21T16:58:56Z | ---
language:
- sv
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: whisper-large-sv
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: sv-SE
split: train[:1%]+validation[:1%]
args: sv-SE
metrics:
- name: Wer
type: wer
value: 30.935251798561154
---
<!-- 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. -->
# whisper-large-sv
This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5259
- Wer: 30.9353
## 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: 1
- 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: 1
- training_steps: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 4.5521 | 0.04 | 5 | 3.5048 | 48.2014 |
| 1.8009 | 0.08 | 10 | 1.5259 | 30.9353 |
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.6.1
- Tokenizers 0.13.1
|
CennetOguz/distilbert-base-uncased-finetuned-recipe | [
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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}
} | 2 | 2022-11-21T17:12:05Z | ---
license: creativeml-openrail-m
thumbnail: "https://huggingface.co/Linaqruf/hitokomoru/resolve/main/thumbnail.png"
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
# Hitokomoru Diffusion
**Welcome to Hitokomoru Diffusion** - a latent diffusion model that has been trained on Japanese Artist artwork, [γγγγγ/Hitokomoru](https://www.pixiv.net/en/users/30837811). The current model has been fine-tuned with a learning rate of `2.0e-6` for `20000 training steps`/`80 Epochs` on `255 images` collected from Danbooru. The model is trained using [NovelAI Aspect Ratio Bucketing Tool](https://github.com/NovelAI/novelai-aspect-ratio-bucketing) so that it can be trained at non-square resolutions. Like other anime-style Stable Diffusion models, it also supports Danbooru tags to generate images.
e.g. **_1girl, white hair, golden eyes, beautiful eyes, detail, flower meadow, cumulonimbus clouds, lighting, detailed sky, garden_**
There is 4 variations of this model available so far:
- `hitokomoru-5000.ckpt for the checkpoint trained on 5,000 steps.`
- `hitokomoru-10000.ckpt for the checkpoint trained on 10,000 steps.`
- `hitokomoru-15000.ckpt for the checkpoint trained on 15,000 steps.`
- `hitokomoru-20000.ckpt for the checkpoint trained on 20,000 steps.`
# Dataset
You can find `datasets` used to train this model and the `last-state` folder for resume training [here](https://huggingface.co/datasets/Linaqruf/hitokomoru-tag)
## 𧨠Diffusers
This model can be used just like any other Stable Diffusion model. For more information,
please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion).
You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [MPS](https://huggingface.co/docs/diffusers/optimization/mps) and/or [FLAX/JAX]().
```python
from diffusers import StableDiffusionPipeline
import torch
model_id = "Linaqruf/hitokomoru-diffusion"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "hatsune_miku"
image = pipe(prompt).images[0]
image.save("./hatsune_miku.png")
```
## Examples
Below are some examples of images generated using this model:
#### Using Hitokomoru-5000-pruned.ckpt
**Anime Girl:**
**Anime Boy:**
#### Using Hitokomoru-10000-pruned.ckpt
**Anime Girl:**
**Anime Boy:**
#### Using Hitokomoru-15000-pruned.ckpt
**Anime Girl:**
**Anime Boy:**
#### Using Hitokomoru-20000-pruned.ckpt
**Anime Girl:**
**Anime Boy:**
### Prompt and settings for Example Images
**Anime Girl:**
```
(masterpiece:1.05),illustration,beautiful detailed,colourful,finely detailed,dramatic light,intricate details,1 girl, 1990, 1980, hatsune miku
Negative prompt:
nsfw, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry,missing fingers,bad hands,missing arms, long neck, Humpbacked,shadow,long body, Abnormal fingers,
Steps: 32, Sampler: Euler, CFG scale: 10, Seed: 2319346364, Size: 512x768, Model hash: 2700c435, Batch size: 2, Batch pos: 0, Clip skip: 2
```
**Anime Boy:**
```
Authentic and detailed face(man:1.2763)(boymasterpiece:1.1025), (best quality:1.1025), (ultra-detailed:1.1025), (illustration:1.1025), (tousled hair:1.1025), (frill:0.907) , white cutter shirt, (one boy:1.05), (solo:1.05) chest, detailed wet clothes, empty stare, pants, (flowers:1.05), beautifully detailed sky, beautifully detailed water, leaves, detailed and beautiful sea
Negative prompt:
(big breasts:1.2763)(breast:1.1025)}(woman:1.2155)} little girl,(3d:1.1576)(girl:1.629), nsfw, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry,missing fingers,bad hands,missing arms, long neck, Humpbacked
Steps: 40, Sampler: DPM++ 2M Karras, CFG scale: 7, Seed: 1870332858, Size: 512x768, Model hash: 2700c435, Batch size: 2, Batch pos: 0, Clip skip: 2
```
## License
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
## What's next?
- Hitokomoru Diffusion V2 soon, will added scenery datasets from MidJourney I hope it's working, because I'm getting bored the result is always simple background
## Credit
- [γγγγγ/Hitokomoru](https://www.pixiv.net/en/users/30837811) for Datasets
- Just for my part
## Big Thanks to
- [Kohya](https://twitter.com/kohya_ss) with their [Kohya Trainer](https://note.com/kohya_ss/n/ne17e34dd51bf)
- Peeps on SD Training Labs Discord Server
- [ptsearch.info](https://www.ptsearch.info/) for prompt references |
Chaddmckay/Cdm | []
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} | 0 | 2022-11-21T17:21:03Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 960 with parameters:
```
{'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 2,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 5.317107976265941e-06
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 960,
"warmup_steps": 96,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
Chae/botman | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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} | 5 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 300 with parameters:
```
{'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2.431769382015511e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 300,
"warmup_steps": 30,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
Chaewon/mmnt_decoder_en | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
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},
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"do_sample": true,
"max_length": 50
},
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},
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}
}
} | 12 | 2022-11-21T17:25:12Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
---
### cybertruck01 Dreambooth model trained by cormacncheese with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb)
Sample pictures of this concept:
|
Chaewon/mnmt_decoder_en | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
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},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
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},
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}
}
} | 8 | 2022-11-21T17:25:41Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: train
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.93188
- name: F1
type: f1
value: 0.9322889745934556
---
<!-- 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. -->
# finetuning-sentiment-model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2309
- Accuracy: 0.9319
- F1: 0.9323
## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.13.2
|
Chakita/KNUBert | [
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
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},
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},
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},
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},
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},
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}
}
} | 20 | 2022-11-21T17:46:49Z | "Nightmare Combined Model" was an attempt at mixing all four of my recent models. Prompt is "NghtmrFrk" It's pretty amazing if you want a bit of all the models in one model instead. It gives you some truly nightmare worthy stuff, be creative with what you type in. Or just have no prompt but NghtmrFrk for random horror! Even though this is a combined model, you may want to try the models I have separately if your are looking for a certain style specifically. CFG keep low, steps can be anything. Same with sampler.
|
Chakita/KROBERT | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"masked-lm",
"fill-in-the-blanks",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
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"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 7 | 2022-11-21T17:53:37Z | ---
license: apache-2.0
tags:
- Question Answering
metrics:
- squad
model-index:
- name: consciousAI/question-answering-roberta-base-s-v2
results: []
---
# Question Answering
The model is intended to be used for Q&A task, given the question & context, the model would attempt to infer the answer text, answer span & confidence score.<br>
Model is encoder-only (deepset/roberta-base-squad2) with QuestionAnswering LM Head, fine-tuned on SQUADx dataset with **exact_match:** 84.83 & **f1:** 91.80 performance scores.
[Live Demo: Question Answering Encoders vs Generative](https://huggingface.co/spaces/consciousAI/question_answering)
Please follow this link for [Encoder based Question Answering V1](https://huggingface.co/consciousAI/question-answering-roberta-base-s/)
<br>Please follow this link for [Generative Question Answering](https://huggingface.co/consciousAI/question-answering-generative-t5-v1-base-s-q-c/)
Example code:
```
from transformers import pipeline
model_checkpoint = "consciousAI/question-answering-roberta-base-s-v2"
context = """
π€ Transformers is backed by the three most popular deep learning libraries β Jax, PyTorch and TensorFlow β with a seamless integration
between them. It's straightforward to train your models with one before loading them for inference with the other.
"""
question = "Which deep learning libraries back π€ Transformers?"
question_answerer = pipeline("question-answering", model=model_checkpoint)
question_answerer(question=question, context=context)
```
## Training and evaluation data
SQUAD Split
## Training procedure
Preprocessing:
1. SQUAD Data longer chunks were sub-chunked with input context max-length 384 tokens and stride as 128 tokens.
2. Target answers readjusted for sub-chunks, sub-chunks with no-answers or partial answers were set to target answer span as (0,0)
Metrics:
1. Adjusted accordingly to handle sub-chunking.
2. n best = 20
3. skip answers with length zero or higher than max answer length (30)
### Training hyperparameters
Custom Training Loop:
The following hyperparameters were used during training:
- learning_rate: 2e-5
- train_batch_size: 32
- eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
{'exact_match': 84.83443708609272, 'f1': 91.79987545811638}
### Framework versions
- Transformers 4.23.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.13.0
|
Chakita/KannadaBERT | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"masked-lm",
"fill-in-the-blanks",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
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},
"translation_en_to_ro": {
"early_stopping": null,
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}
} | 5 | 2022-11-21T17:58:15Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 103 with parameters:
```
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 2,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 3.0969347665304716e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 103,
"warmup_steps": 11,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
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