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timestamp[us, tz=UTC]date 2020-02-15 11:33:14
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| library_name
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lizaboiarchuk/bert-tiny-oa-finetuned | lizaboiarchuk | 2022-09-18T19:05:02Z | 83 | 0 | transformers | [
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2022-09-18T07:27:29Z | ---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: lizaboiarchuk/bert-tiny-oa-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. -->
# lizaboiarchuk/bert-tiny-oa-finetuned
This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 4.0626
- Validation Loss: 3.7514
- Epoch: 4
## 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: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -525, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 4.6311 | 4.1088 | 0 |
| 4.2579 | 3.7859 | 1 |
| 4.0635 | 3.7253 | 2 |
| 4.0658 | 3.6842 | 3 |
| 4.0626 | 3.7514 | 4 |
### Framework versions
- Transformers 4.22.1
- TensorFlow 2.8.2
- Tokenizers 0.12.1
|
sd-concepts-library/laala-character | sd-concepts-library | 2022-09-18T17:07:01Z | 0 | 0 | null | [
"license:mit",
"region:us"
]
| null | 2022-09-18T17:06:58Z | ---
license: mit
---
### laala-character on Stable Diffusion
This is the `<laala>` 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`:
|
baptiste/deberta-finetuned-ner-connll-late-stop | baptiste | 2022-09-18T16:28:32Z | 108 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"deberta",
"token-classification",
"generated_from_trainer",
"dataset:wikiann",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2022-09-18T15:35:43Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: deberta-finetuned-ner-connll-late-stop
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wikiann
type: wikiann
config: en
split: train
args: en
metrics:
- name: Precision
type: precision
value: 0.830192600803658
- name: Recall
type: recall
value: 0.8470945850417079
- name: F1
type: f1
value: 0.8385584324702589
- name: Accuracy
type: accuracy
value: 0.9228861596598961
---
<!-- 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-finetuned-ner-connll-late-stop
This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5259
- Precision: 0.8302
- Recall: 0.8471
- F1: 0.8386
- Accuracy: 0.9229
## 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: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3408 | 1.0 | 1875 | 0.3639 | 0.7462 | 0.7887 | 0.7669 | 0.8966 |
| 0.2435 | 2.0 | 3750 | 0.2933 | 0.8104 | 0.8332 | 0.8217 | 0.9178 |
| 0.1822 | 3.0 | 5625 | 0.3034 | 0.8147 | 0.8388 | 0.8266 | 0.9221 |
| 0.1402 | 4.0 | 7500 | 0.3667 | 0.8275 | 0.8474 | 0.8374 | 0.9235 |
| 0.1013 | 5.0 | 9375 | 0.4290 | 0.8285 | 0.8448 | 0.8366 | 0.9227 |
| 0.0677 | 6.0 | 11250 | 0.4914 | 0.8259 | 0.8473 | 0.8365 | 0.9231 |
| 0.0439 | 7.0 | 13125 | 0.5259 | 0.8302 | 0.8471 | 0.8386 | 0.9229 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
yuntian-deng/latex2im | yuntian-deng | 2022-09-18T14:29:45Z | 5 | 0 | diffusers | [
"diffusers",
"en",
"dataset:yuntian-deng/im2latex-100k",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
]
| null | 2022-09-12T01:31:47Z | ---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: yuntian-deng/im2latex-100k
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. -->
# latex2im
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `yuntian-deng/im2latex-100k` 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: no
### Training results
📈 [TensorBoard logs](https://huggingface.co/yuntian-deng/latex2im/tensorboard?#scalars)
|
sd-concepts-library/rail-scene | sd-concepts-library | 2022-09-18T14:28:03Z | 0 | 1 | null | [
"license:mit",
"region:us"
]
| null | 2022-09-18T14:27:48Z | ---
license: mit
---
### Rail Scene on Stable Diffusion
This is the `<rail-pov>` 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`:
|
jayanta/aaraki-vit-base-patch16-224-in21k-finetuned-cifar10 | jayanta | 2022-09-18T14:16:57Z | 220 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2022-09-17T11:53:40Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: mit-b2-finetuned-memes
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8523956723338485
- task:
type: image-classification
name: Image Classification
dataset:
type: custom
name: custom
split: test
metrics:
- type: f1
value: 0.8580847578266328
name: F1
- type: precision
value: 0.8587893412503379
name: Precision
- type: recall
value: 0.8593508500772797
name: Recall
---
<!-- 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. -->
# mit-b2-finetuned-memes
This model is a fine-tuned version of [aaraki/vit-base-patch16-224-in21k-finetuned-cifar10](https://huggingface.co/aaraki/vit-base-patch16-224-in21k-finetuned-cifar10) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4137
- Accuracy: 0.8524
## 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: 0.00012
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9727 | 0.99 | 40 | 0.8400 | 0.7334 |
| 0.5305 | 1.99 | 80 | 0.5147 | 0.8284 |
| 0.3124 | 2.99 | 120 | 0.4698 | 0.8145 |
| 0.2263 | 3.99 | 160 | 0.3892 | 0.8563 |
| 0.1453 | 4.99 | 200 | 0.3874 | 0.8570 |
| 0.1255 | 5.99 | 240 | 0.4097 | 0.8470 |
| 0.0989 | 6.99 | 280 | 0.3860 | 0.8570 |
| 0.0755 | 7.99 | 320 | 0.4141 | 0.8539 |
| 0.08 | 8.99 | 360 | 0.4049 | 0.8594 |
| 0.0639 | 9.99 | 400 | 0.4137 | 0.8524 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Anoop03031988/bert_emo_classifier | Anoop03031988 | 2022-09-18T14:14:24Z | 104 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-09-18T13:44:40Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
model-index:
- name: bert_emo_classifier
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_emo_classifier
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2652
## 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.8874 | 0.25 | 500 | 0.4256 |
| 0.3255 | 0.5 | 1000 | 0.3233 |
| 0.2754 | 0.75 | 1500 | 0.2736 |
| 0.242 | 1.0 | 2000 | 0.2263 |
| 0.1661 | 1.25 | 2500 | 0.2118 |
| 0.1614 | 1.5 | 3000 | 0.1812 |
| 0.1434 | 1.75 | 3500 | 0.1924 |
| 0.1629 | 2.0 | 4000 | 0.1766 |
| 0.1066 | 2.25 | 4500 | 0.2100 |
| 0.1313 | 2.5 | 5000 | 0.1996 |
| 0.1113 | 2.75 | 5500 | 0.2185 |
| 0.115 | 3.0 | 6000 | 0.2406 |
| 0.0697 | 3.25 | 6500 | 0.2485 |
| 0.0835 | 3.5 | 7000 | 0.2391 |
| 0.0637 | 3.75 | 7500 | 0.2695 |
| 0.0707 | 4.0 | 8000 | 0.2652 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.10.3
|
LanYiU/codeparrot-ds | LanYiU | 2022-09-18T13:10:45Z | 109 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2022-09-18T11:27:34Z | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: codeparrot-ds
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. -->
# codeparrot-ds
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6886
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.4802 | 0.93 | 5000 | 1.6886 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Shaz/augh | Shaz | 2022-09-18T12:49:50Z | 0 | 0 | null | [
"region:us"
]
| null | 2022-09-17T19:10:50Z | import requests
API_URL = "https://api-inference.huggingface.co/models/gpt2"
headers = {"Authorization": f"Bearer {API_TOKEN}"}
def query(payload):
response = requests.post(API_URL, headers=headers, json=payload)
return response.json()
output = query({
"inputs": "Can you please let us know more details about your ",
}) |
domenicrosati/deberta-v3-large-finetuned-paws-paraphrase-detector | domenicrosati | 2022-09-18T11:58:59Z | 118 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"dataset:paws",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-09-17T15:59:17Z | ---
license: mit
tags:
- text-classification
- generated_from_trainer
datasets:
- paws
metrics:
- f1
- precision
- recall
model-index:
- name: deberta-v3-large-finetuned-paws-paraphrase-detector
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: paws
type: paws
args: labeled_final
metrics:
- name: F1
type: f1
value: 0.9426698284279537
- name: Precision
type: precision
value: 0.9300853289292595
- name: Recall
type: recall
value: 0.9555995475113123
---
<!-- 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-v3-large-finetuned-paws-paraphrase-detector
Feel free to use for paraphrase detection tasks!
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the paws dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3046
- F1: 0.9427
- Precision: 0.9301
- Recall: 0.9556
## 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: 6e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:---------:|:------:|
| 0.1492 | 1.0 | 6176 | 0.1650 | 0.9537 | 0.9385 | 0.9695 |
| 0.1018 | 2.0 | 12352 | 0.1968 | 0.9544 | 0.9427 | 0.9664 |
| 0.0482 | 3.0 | 18528 | 0.2419 | 0.9521 | 0.9388 | 0.9658 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
sd-concepts-library/lizardman | sd-concepts-library | 2022-09-18T11:42:28Z | 0 | 3 | null | [
"license:mit",
"region:us"
]
| null | 2022-09-18T11:42:22Z | ---
license: mit
---
### Lizardman on Stable Diffusion
This is the `PlaceholderTokenLizardman` 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`:
|
venkateshdas/roberta-base-squad2-ta-qna-roberta10e | venkateshdas | 2022-09-18T11:00:43Z | 118 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
]
| question-answering | 2022-09-18T10:37:07Z | ---
license: cc-by-4.0
tags:
- generated_from_trainer
model-index:
- name: roberta-base-squad2-ta-qna-roberta10e
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. -->
# roberta-base-squad2-ta-qna-roberta10e
This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2952
## 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: 3e-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
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 87 | 0.5890 |
| No log | 2.0 | 174 | 0.4194 |
| No log | 3.0 | 261 | 0.3104 |
| No log | 4.0 | 348 | 0.3037 |
| No log | 5.0 | 435 | 0.2723 |
| 0.2889 | 6.0 | 522 | 0.2368 |
| 0.2889 | 7.0 | 609 | 0.2974 |
| 0.2889 | 8.0 | 696 | 0.2923 |
| 0.2889 | 9.0 | 783 | 0.2936 |
| 0.2889 | 10.0 | 870 | 0.2952 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
huggingtweets/perpetualg00se | huggingtweets | 2022-09-18T10:25:36Z | 109 | 1 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2022-09-18T10:20:59Z | ---
language: en
thumbnail: http://www.huggingtweets.com/perpetualg00se/1663496719106/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1245588692573409281/mGWMt1q7_400x400.png')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">PerpetualG00se</div>
<div style="text-align: center; font-size: 14px;">@perpetualg00se</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from PerpetualG00se.
| Data | PerpetualG00se |
| --- | --- |
| Tweets downloaded | 3166 |
| Retweets | 514 |
| Short tweets | 628 |
| Tweets kept | 2024 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/32gxsmj0/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @perpetualg00se's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/17rf9oo3) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/17rf9oo3/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/perpetualg00se')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
venkateshdas/roberta-base-squad2-ta-qna-roberta3e | venkateshdas | 2022-09-18T10:22:29Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
]
| question-answering | 2022-09-18T10:13:04Z | ---
license: cc-by-4.0
tags:
- generated_from_trainer
model-index:
- name: roberta-base-squad2-ta-qna-roberta3e
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. -->
# roberta-base-squad2-ta-qna-roberta3e
This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4671
## 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: 3e-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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 87 | 0.5221 |
| No log | 2.0 | 174 | 0.4408 |
| No log | 3.0 | 261 | 0.4671 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
debbiesoon/prot_bert_bfd-disoDNA | debbiesoon | 2022-09-18T06:50:23Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2022-09-18T04:33:19Z | ---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: prot_bert_bfd-disoDNA
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. -->
# prot_bert_bfd-disoDNA
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1323
- Precision: 0.9442
- Recall: 0.9717
- F1: 0.9578
## Model description
This is a token classification model designed to predict the intrinsically disordered regions of amino acid sequences on the level of DNA disorder annotation.
## Intended uses & limitations
This model works on amino acid sequences that are spaced between characters.
'0': No disorder
'1': Disordered
Example Inputs :
D E A Q F K E C Y D T C H K E C S D K G N G F T F C E M K C D T D C S V K D V K E K L E N Y K P K N
M A S E E L Q K D L E E V K V L L E K A T R K R V R D A L T A E K S K I E T E I K N K M Q Q K S Q K K A E L L D N E K P A A V V A P I T T G Y T D G I S Q I S L
M D V F M K G L S K A K E G V V A A A E K T K Q G V A E A A G K T K E G V L Y V G S K T K E G V V H G V A T V A E K T K E Q V T N V G G A V V T G V T A V A Q K T V E G A G S I A A A T G F V K K D Q L G K N E E G A P Q E G I L E D M P V D P D N E A Y E M P S E E G Y Q D Y E P E A
M E L V L K D A Q S A L T V S E T T F G R D F N E A L V H Q V V V A Y A A G A R Q G T R A Q K T R A E V T G S G K K P W R Q K G T G R A R S G S I K S P I W R S G G V T F A
A R P Q D H S Q K V N K K M Y R G A L K S I L S E L V R Q D R L I V V E K F S V E A P K T K L L A Q K L K D M A L E D V L I I T G E L D E N L F L A A R N L H K V D V R D A T G I D P V S L I A F D K V V M T A D A V K Q V E E M L A
M S D K P D M A E I E K F D K S K L K K T E T Q E K N P L P S K E T I E Q E K Q A G E S
## Training and evaluation data
Training and evaluation data were retrieved from https://www.csuligroup.com/DeepDISOBind/#Materials (Accessed March 2022).
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.0213 | 1.0 | 61 | 0.1322 | 0.9442 | 0.9717 | 0.9578 |
| 0.0212 | 2.0 | 122 | 0.1322 | 0.9442 | 0.9717 | 0.9578 |
| 0.1295 | 3.0 | 183 | 0.1323 | 0.9442 | 0.9717 | 0.9578 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
sd-concepts-library/dsmuses | sd-concepts-library | 2022-09-18T06:37:28Z | 0 | 0 | null | [
"license:mit",
"region:us"
]
| null | 2022-09-18T06:37:17Z | ---
license: mit
---
### DSmuses on Stable Diffusion
This is the `<DSmuses>` 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 a `style`:
|
NeonBohdan/stt-polyglot-en | NeonBohdan | 2022-09-18T06:32:07Z | 0 | 0 | null | [
"tflite",
"license:apache-2.0",
"region:us"
]
| null | 2022-03-02T23:29:04Z | ---
license: apache-2.0
---
|
rosskrasner/testcatdog | rosskrasner | 2022-09-18T03:56:03Z | 0 | 0 | fastai | [
"fastai",
"region:us"
]
| null | 2022-09-14T03:29:28Z | ---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
pikodemo/ppo-LunarLander-v2 | pikodemo | 2022-09-18T00:11:48Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-09-17T14:59:15Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: -553.66 +/- 175.78
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **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
...
```
|
sd-concepts-library/valorantstyle | sd-concepts-library | 2022-09-17T23:55:16Z | 0 | 20 | null | [
"license:mit",
"region:us"
]
| null | 2022-09-17T23:55:05Z | ---
license: mit
---
### valorantstyle on Stable Diffusion
This is the `<valorant>` 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 a `style`:
|
anechaev/Reinforce-U5Pixelcopter | anechaev | 2022-09-17T22:11:25Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-09-17T22:11:15Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-U5Pixelcopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 17.10 +/- 15.09
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
|
Bistolero/1ep_seq_25_6b | Bistolero | 2022-09-17T21:23:44Z | 111 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:gem",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-09-17T21:07:40Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- gem
model-index:
- name: kapakapa
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. -->
# kapakapa
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the gem 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: 15
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 14
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
anechaev/Reinforce-U5CartPole | anechaev | 2022-09-17T20:43:09Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-09-17T20:41:20Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-U5CartPole
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 46.40 +/- 7.76
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
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
|
sd-concepts-library/3d-female-cyborgs | sd-concepts-library | 2022-09-17T20:15:59Z | 0 | 39 | null | [
"license:mit",
"region:us"
]
| null | 2022-09-17T20:15:45Z | ---
license: mit
---
### 3d Female Cyborgs on Stable Diffusion
This is the `<A female cyborg>` 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 a `style`:
|
tavakolih/all-MiniLM-L6-v2-pubmed-full | tavakolih | 2022-09-17T19:59:09Z | 1,201 | 9 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"dataset:pubmed",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2022-09-17T19:59:01Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
datasets:
- pubmed
---
# tavakolih/all-MiniLM-L6-v2-pubmed-full
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 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('tavakolih/all-MiniLM-L6-v2-pubmed-full')
embeddings = model.encode(sentences)
print(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=tavakolih/all-MiniLM-L6-v2-pubmed-full)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 221 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 10,
"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": null,
"warmup_steps": 10000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
sd-concepts-library/max-foley | sd-concepts-library | 2022-09-17T19:35:09Z | 0 | 2 | null | [
"license:mit",
"region:us"
]
| null | 2022-09-17T19:34:45Z | ---
license: mit
---
### Max Foley on Stable Diffusion
This is the `<max-foley>` 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 a `style`:
|
tkuye/skills-classifier | tkuye | 2022-09-17T19:16:20Z | 117 | 0 | transformers | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-09-17T17:56:54Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: skills-classifier
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. -->
# skills-classifier
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:
- Loss: 0.3051
- Accuracy: 0.9242
## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 312 | 0.2713 | 0.9058 |
| 0.361 | 2.0 | 624 | 0.2539 | 0.9182 |
| 0.361 | 3.0 | 936 | 0.2802 | 0.9238 |
| 0.1532 | 4.0 | 1248 | 0.3058 | 0.9202 |
| 0.0899 | 5.0 | 1560 | 0.3051 | 0.9242 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
dumitrescustefan/gpt-neo-romanian-780m | dumitrescustefan | 2022-09-17T18:24:19Z | 260 | 12 | transformers | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"romanian",
"text generation",
"causal lm",
"gpt-neo",
"ro",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2022-08-29T15:31:26Z | ---
language:
- ro
license: mit # Example: apache-2.0 or any license from https://hf.co/docs/hub/repositories-licenses
tags:
- romanian
- text generation
- causal lm
- gpt-neo
---
# GPT-Neo Romanian 780M
This model is a GPT-Neo transformer decoder model designed using EleutherAI's replication of the GPT-3 architecture.
It was trained on a thoroughly cleaned corpus of Romanian text of about 40GB composed of Oscar, Opus, Wikipedia, literature and various other bits and pieces of text, joined together and deduplicated. It was trained for about a month, totaling 1.5M steps on a v3-32 TPU machine.
### Authors:
* Dumitrescu Stefan
* Mihai Ilie
### Evaluation
Evaluation to be added soon, also on [https://github.com/dumitrescustefan/Romanian-Transformers](https://github.com/dumitrescustefan/Romanian-Transformers)
### Acknowledgements
Thanks [TPU Research Cloud](https://sites.research.google/trc/about/) for the TPUv3 machine needed to train this model!
|
sd-concepts-library/mechasoulall | sd-concepts-library | 2022-09-17T17:44:02Z | 0 | 21 | null | [
"license:mit",
"region:us"
]
| null | 2022-09-17T17:43:55Z | ---
license: mit
---
### mechasoulall on Stable Diffusion
This is the `<mechasoulall>` 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 a `style`:
|
RICHPOOL/RICHPOOL_MINER | RICHPOOL | 2022-09-17T17:42:59Z | 0 | 0 | null | [
"region:us"
]
| null | 2022-09-17T17:39:16Z | ### 开源矿工-瑞池专业版
开源-绿色-无抽水
huggingface 下载分流
#### 原软件源代码
https://github.com/ntminer/NtMiner
#### 授权协议
The LGPL license。
|
sd-concepts-library/durer-style | sd-concepts-library | 2022-09-17T16:36:56Z | 0 | 7 | null | [
"license:mit",
"region:us"
]
| null | 2022-09-17T16:36:49Z | ---
license: mit
---
### durer style on Stable Diffusion
This is the `<drr-style>` 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 a `style`:
|
sd-concepts-library/led-toy | sd-concepts-library | 2022-09-17T16:33:57Z | 0 | 0 | null | [
"license:mit",
"region:us"
]
| null | 2022-09-17T16:33:50Z | ---
license: mit
---
### led-toy on Stable Diffusion
This is the `<led-toy>` 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`:
|
sd-concepts-library/she-hulk-law-art | sd-concepts-library | 2022-09-17T16:10:47Z | 0 | 2 | null | [
"license:mit",
"region:us"
]
| null | 2022-09-17T16:10:35Z | ---
license: mit
---
### She-Hulk Law Art on Stable Diffusion
This is the `<shehulk-style>` 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 a `style`:
|
theojolliffe/pegasus-model-3-x25 | theojolliffe | 2022-09-17T15:48:03Z | 108 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-09-17T14:27:08Z | ---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: pegasus-model-3-x25
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. -->
# pegasus-model-3-x25
This model is a fine-tuned version of [theojolliffe/pegasus-cnn_dailymail-v4-e1-e4-feedback](https://huggingface.co/theojolliffe/pegasus-cnn_dailymail-v4-e1-e4-feedback) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5668
- Rouge1: 61.9972
- Rouge2: 48.1531
- Rougel: 48.845
- Rougelsum: 59.5019
- Gen Len: 123.0814
## 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: 1
- eval_batch_size: 1
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:------:|:---------:|:--------:|
| 1.144 | 1.0 | 883 | 0.5668 | 61.9972 | 48.1531 | 48.845 | 59.5019 | 123.0814 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
sd-concepts-library/lxj-o4 | sd-concepts-library | 2022-09-17T15:14:59Z | 0 | 1 | null | [
"license:mit",
"region:us"
]
| null | 2022-09-17T15:14:54Z | ---
license: mit
---
### lxj-o4 on Stable Diffusion
This is the `<csp>` 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 a `style`:
|
Eksperymenty/Pong-PLE-v0 | Eksperymenty | 2022-09-17T14:44:18Z | 0 | 0 | null | [
"Pong-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-09-17T14:44:08Z | ---
tags:
- Pong-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Pong-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pong-PLE-v0
type: Pong-PLE-v0
metrics:
- type: mean_reward
value: -16.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pong-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pong-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
|
sd-concepts-library/sorami-style | sd-concepts-library | 2022-09-17T13:31:17Z | 0 | 2 | null | [
"license:mit",
"region:us"
]
| null | 2022-09-17T13:31:10Z | ---
license: mit
---
### Sorami style on Stable Diffusion
This is the `<sorami-style>` 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 a `style`:
|
test1234678/distilbert-base-uncased-distilled-clinc | test1234678 | 2022-09-17T12:34:43Z | 108 | 0 | transformers | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-09-17T07:24:42Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-distilled-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
config: plus
split: train
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9461290322580646
---
<!-- 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-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2712
- Accuracy: 0.9461
## 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: 48
- eval_batch_size: 48
- 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2629 | 1.0 | 318 | 1.6048 | 0.7368 |
| 1.2437 | 2.0 | 636 | 0.8148 | 0.8565 |
| 0.6604 | 3.0 | 954 | 0.4768 | 0.9161 |
| 0.4054 | 4.0 | 1272 | 0.3548 | 0.9352 |
| 0.2987 | 5.0 | 1590 | 0.3084 | 0.9419 |
| 0.2549 | 6.0 | 1908 | 0.2909 | 0.9435 |
| 0.232 | 7.0 | 2226 | 0.2804 | 0.9458 |
| 0.221 | 8.0 | 2544 | 0.2749 | 0.9458 |
| 0.2145 | 9.0 | 2862 | 0.2722 | 0.9468 |
| 0.2112 | 10.0 | 3180 | 0.2712 | 0.9461 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.10.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
jayanta/resnet50-finetuned-memes | jayanta | 2022-09-17T12:04:12Z | 176 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"resnet",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2022-09-15T14:19:11Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: resnet50-finetuned-memes
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.5741885625965997
- task:
type: image-classification
name: Image Classification
dataset:
type: custom
name: custom
split: test
metrics:
- type: f1
value: 0.47811617701687364
name: F1
- type: precision
value: 0.43689216537139497
name: Precision
- type: recall
value: 0.5695517774343122
name: Recall
---
<!-- 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. -->
# resnet50-finetuned-memes
This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0625
- Accuracy: 0.5742
## 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: 0.00012
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4795 | 0.99 | 40 | 1.4641 | 0.4382 |
| 1.3455 | 1.99 | 80 | 1.3281 | 0.4389 |
| 1.262 | 2.99 | 120 | 1.2583 | 0.4583 |
| 1.1975 | 3.99 | 160 | 1.1978 | 0.4876 |
| 1.1358 | 4.99 | 200 | 1.1614 | 0.5139 |
| 1.1273 | 5.99 | 240 | 1.1316 | 0.5379 |
| 1.0379 | 6.99 | 280 | 1.1024 | 0.5464 |
| 1.041 | 7.99 | 320 | 1.0927 | 0.5580 |
| 0.9952 | 8.99 | 360 | 1.0790 | 0.5541 |
| 1.0146 | 9.99 | 400 | 1.0625 | 0.5742 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Shamus/NLLB-600m-vie_Latn-to-eng_Latn | Shamus | 2022-09-17T11:54:50Z | 107 | 1 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"m2m_100",
"text2text-generation",
"generated_from_trainer",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-09-17T03:28:00Z | ---
license: cc-by-nc-4.0
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: NLLB-600m-vie_Latn-to-eng_Latn
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. -->
# NLLB-600m-vie_Latn-to-eng_Latn
This model is a fine-tuned version of [facebook/nllb-200-distilled-600M](https://huggingface.co/facebook/nllb-200-distilled-600M) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1189
- Bleu: 36.6767
- Gen Len: 47.504
## 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: 3
- eval_batch_size: 3
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 1.9294 | 2.24 | 1000 | 1.5970 | 23.6201 | 48.1 |
| 1.4 | 4.47 | 2000 | 1.3216 | 28.9526 | 45.156 |
| 1.2071 | 6.71 | 3000 | 1.2245 | 32.5538 | 46.576 |
| 1.0893 | 8.95 | 4000 | 1.1720 | 34.265 | 46.052 |
| 1.0064 | 11.19 | 5000 | 1.1497 | 34.9249 | 46.508 |
| 0.9562 | 13.42 | 6000 | 1.1331 | 36.4619 | 47.244 |
| 0.9183 | 15.66 | 7000 | 1.1247 | 36.4723 | 47.26 |
| 0.8858 | 17.9 | 8000 | 1.1198 | 36.7058 | 47.376 |
| 0.8651 | 20.13 | 9000 | 1.1201 | 36.7897 | 47.496 |
| 0.8546 | 22.37 | 10000 | 1.1189 | 36.6767 | 47.504 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
huggingtweets/arrington-jespow-lightcrypto | huggingtweets | 2022-09-17T11:11:37Z | 109 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2022-09-17T11:09:39Z | ---
language: en
thumbnail: http://www.huggingtweets.com/arrington-jespow-lightcrypto/1663413092521/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1478019214212747264/LZmNClhs_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1484988558024720385/WAv0tlyD_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1481313178302754821/eeHGWpUF_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">light & Jesse Powell & Michael Arrington 🏴☠️</div>
<div style="text-align: center; font-size: 14px;">@arrington-jespow-lightcrypto</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from light & Jesse Powell & Michael Arrington 🏴☠️.
| Data | light | Jesse Powell | Michael Arrington 🏴☠️ |
| --- | --- | --- | --- |
| Tweets downloaded | 3237 | 3237 | 3243 |
| Retweets | 352 | 490 | 892 |
| Short tweets | 392 | 168 | 718 |
| Tweets kept | 2493 | 2579 | 1633 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3ozhl36a/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @arrington-jespow-lightcrypto's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2vhxitdi) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2vhxitdi/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/arrington-jespow-lightcrypto')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
LanYiU/distilbert-base-uncased-finetuned-imdb | LanYiU | 2022-09-17T11:04:50Z | 161 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2022-09-17T10:55:23Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
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. -->
# distilbert-base-uncased-finetuned-imdb
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: 2.4738
## 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
- distributed_type: tpu
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7 | 1.0 | 157 | 2.4988 |
| 2.5821 | 2.0 | 314 | 2.4242 |
| 2.541 | 3.0 | 471 | 2.4371 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.9.0+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Eksperymenty/Reinforce-CartPole-v1 | Eksperymenty | 2022-09-17T10:09:00Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-09-17T10:07:54Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 445.10 +/- 56.96
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
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
|
Hammad7/plag-col-rev-en-v2 | Hammad7 | 2022-09-17T09:58:44Z | 102 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"plagiarism",
"cross-encoder",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-09-15T11:51:58Z | ---
license: apache-2.0
language:
- en
tags:
- plagiarism
- cross-encoder
---
## Usage:
from sentence_transformers.cross_encoder import CrossEncoder
model = CrossEncoder('Hammad7/plag-col-rev-en-v2')
model.predict(["duplicate first paragraph","original second paragraph"]) |
michael20at/dqn-SpaceInvadersNoFrameskip-v4 | michael20at | 2022-09-17T08:25:21Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-09-17T08:24:50Z | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 246.00 +/- 104.47
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga michael20at -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga michael20at
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
ameerazam08/wav2vec2-xlsr-ravdess-speech-emotion-recognition-new | ameerazam08 | 2022-09-17T05:57:54Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2022-09-17T04:09:37Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: wav2vec2-xlsr-ravdess-speech-emotion-recognition-new
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. -->
# wav2vec2-xlsr-ravdess-speech-emotion-recognition-new
This model is a fine-tuned version of [lighteternal/wav2vec2-large-xlsr-53-greek](https://huggingface.co/lighteternal/wav2vec2-large-xlsr-53-greek) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8209
- Accuracy: 0.625
## 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: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1484 | 0.42 | 10 | 1.1682 | 0.4375 |
| 1.1195 | 0.83 | 20 | 1.0696 | 0.5417 |
| 0.9414 | 1.25 | 30 | 1.0196 | 0.5 |
| 0.8109 | 1.67 | 40 | 0.7216 | 0.6875 |
| 0.724 | 2.08 | 50 | 0.7667 | 0.6875 |
| 0.6081 | 2.5 | 60 | 0.7912 | 0.6458 |
| 0.6073 | 2.92 | 70 | 0.8209 | 0.625 |
### Framework versions
- Transformers 4.23.0.dev0
- Pytorch 1.12.1+cu102
- Datasets 2.4.1.dev0
- Tokenizers 0.12.1
|
sd-concepts-library/armor-concept | sd-concepts-library | 2022-09-17T02:19:43Z | 0 | 5 | null | [
"license:mit",
"region:us"
]
| null | 2022-09-17T02:19:30Z | ---
license: mit
---
### armor-concept on Stable Diffusion
This is the `<armor-concept>` 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 a `style`:
|
sd-concepts-library/dtv-pkmn | sd-concepts-library | 2022-09-17T01:25:50Z | 0 | 5 | null | [
"license:mit",
"region:us"
]
| null | 2022-09-13T23:08:57Z | ---
license: mit
---
### dtv-pkmn on Stable Diffusion
This is the `<dtv-pkm2>` 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).
`"hyperdetailed fantasy (monster) (dragon-like) character on top of a rock in the style of <dtv-pkm2> . extremely detailed, amazing artwork with depth and realistic CINEMATIC lighting, matte painting"`
Here is the new concept you will be able to use as a `style`:
|
g30rv17ys/ddpm-geeve-drusen-1000-128 | g30rv17ys | 2022-09-16T22:59:42Z | 4 | 0 | diffusers | [
"diffusers",
"tensorboard",
"en",
"dataset:imagefolder",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
]
| null | 2022-09-16T20:36:25Z | ---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: imagefolder
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-geeve-drusen-1000-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `imagefolder` 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
📈 [TensorBoard logs](https://huggingface.co/geevegeorge/ddpm-geeve-drusen-1000-128/tensorboard?#scalars)
|
g30rv17ys/ddpm-geeve-dme-1000-128 | g30rv17ys | 2022-09-16T22:45:49Z | 0 | 0 | diffusers | [
"diffusers",
"tensorboard",
"en",
"dataset:imagefolder",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
]
| null | 2022-09-16T20:29:37Z | ---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: imagefolder
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-geeve-dme-1000-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `imagefolder` 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
📈 [TensorBoard logs](https://huggingface.co/geevegeorge/ddpm-geeve-dme-1000-128/tensorboard?#scalars)
|
g30rv17ys/ddpm-geeve-cnv-1000-128 | g30rv17ys | 2022-09-16T22:44:56Z | 1 | 0 | diffusers | [
"diffusers",
"tensorboard",
"en",
"dataset:imagefolder",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
]
| null | 2022-09-16T20:19:10Z | ---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: imagefolder
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-geeve-cnv-1000-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `imagefolder` 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
📈 [TensorBoard logs](https://huggingface.co/geevegeorge/ddpm-geeve-cnv-1000-128/tensorboard?#scalars)
|
sd-concepts-library/belen | sd-concepts-library | 2022-09-16T22:35:23Z | 0 | 2 | null | [
"license:mit",
"region:us"
]
| null | 2022-09-16T22:35:19Z | ---
license: mit
---
### belen on Stable Diffusion
This is the `<belen>` 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 a `style`:
|
sd-concepts-library/jamie-hewlett-style | sd-concepts-library | 2022-09-16T22:32:42Z | 0 | 14 | null | [
"license:mit",
"region:us"
]
| null | 2022-09-16T22:32:38Z | ---
license: mit
---
### Jamie Hewlett Style on Stable Diffusion
This is the `<hewlett>` 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 a `style`:
|
rhiga/a2c-AntBulletEnv-v0 | rhiga | 2022-09-16T22:26:26Z | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-09-16T22:25:06Z | ---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- metrics:
- type: mean_reward
value: 1742.04 +/- 217.69
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
---
# **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
...
```
|
davidaf3/ReverseNutrition_TFIngPort | davidaf3 | 2022-09-16T21:05:21Z | 20 | 0 | generic | [
"generic",
"image-classification",
"license:mit",
"region:us"
]
| image-classification | 2022-08-26T17:12:04Z | ---
license: mit
pipeline_tag: image-classification
library_name: generic
---
|
sd-concepts-library/lugal-ki-en | sd-concepts-library | 2022-09-16T19:32:47Z | 0 | 14 | null | [
"license:mit",
"region:us"
]
| null | 2022-09-16T05:58:43Z | ---
title: Lugal Ki EN
emoji: 🪐
colorFrom: gray
colorTo: red
sdk: gradio
sdk_version: 3.3
app_file: app.py
pinned: false
license: mit
---
### Lugal ki en on Stable Diffusion
This is the `<lugal-ki-en>` 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 a `style`:
|
sd-concepts-library/harmless-ai-1 | sd-concepts-library | 2022-09-16T19:24:49Z | 0 | 0 | null | [
"license:mit",
"region:us"
]
| null | 2022-09-16T19:24:43Z | ---
license: mit
---
### harmless-ai-1 on Stable Diffusion
This is the `<bee-style>` 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 a `style`:
,+The+computer+is+the+enemy+of+transhumanity,+detailed,+beautiful+masterpiece,+unreal+engine,+4k-0.024599999999999973.png)
,+The+computer+is+the+enemy+of+transhumanity,+detailed,+beautiful+masterpiece,+unreal+engine,+4k-0.02-3024.png)
|
sd-concepts-library/rj-palmer | sd-concepts-library | 2022-09-16T19:09:00Z | 0 | 5 | null | [
"license:mit",
"region:us"
]
| null | 2022-09-16T19:08:57Z | ---
license: mit
---
### RJ Palmer on Stable Diffusion
This is the `<rj-palmer>` 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 a `style`:
|
sanchit-gandhi/wav2vec2-ctc-earnings22-baseline-5-gram | sanchit-gandhi | 2022-09-16T18:50:03Z | 70 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-09-16T18:34:22Z | Unrolled PT and FX weights of https://huggingface.co/sanchit-gandhi/flax-wav2vec2-ctc-earnings22-baseline/tree/main |
MayaGalvez/bert-base-multilingual-cased-finetuned-pos | MayaGalvez | 2022-09-16T18:35:53Z | 104 | 1 | transformers | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2022-09-16T18:16:35Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-multilingual-cased-finetuned-pos
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-multilingual-cased-finetuned-pos
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1736
- Precision: 0.9499
- Recall: 0.9504
- F1: 0.9501
- Accuracy: 0.9551
## 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: 3e-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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.7663 | 0.27 | 200 | 0.2047 | 0.9318 | 0.9312 | 0.9315 | 0.9388 |
| 0.5539 | 0.53 | 400 | 0.1815 | 0.9381 | 0.9404 | 0.9392 | 0.9460 |
| 0.5222 | 0.8 | 600 | 0.1787 | 0.9400 | 0.9424 | 0.9412 | 0.9468 |
| 0.5084 | 1.07 | 800 | 0.1591 | 0.9470 | 0.9463 | 0.9467 | 0.9519 |
| 0.4703 | 1.33 | 1000 | 0.1622 | 0.9456 | 0.9458 | 0.9457 | 0.9510 |
| 0.5005 | 1.6 | 1200 | 0.1666 | 0.9470 | 0.9464 | 0.9467 | 0.9519 |
| 0.4677 | 1.87 | 1400 | 0.1583 | 0.9483 | 0.9483 | 0.9483 | 0.9532 |
| 0.4704 | 2.13 | 1600 | 0.1635 | 0.9472 | 0.9475 | 0.9473 | 0.9528 |
| 0.4639 | 2.4 | 1800 | 0.1569 | 0.9475 | 0.9488 | 0.9482 | 0.9536 |
| 0.4627 | 2.67 | 2000 | 0.1605 | 0.9474 | 0.9478 | 0.9476 | 0.9527 |
| 0.4608 | 2.93 | 2200 | 0.1535 | 0.9485 | 0.9495 | 0.9490 | 0.9538 |
| 0.4306 | 3.2 | 2400 | 0.1646 | 0.9489 | 0.9487 | 0.9488 | 0.9536 |
| 0.4583 | 3.47 | 2600 | 0.1642 | 0.9488 | 0.9495 | 0.9491 | 0.9539 |
| 0.453 | 3.73 | 2800 | 0.1646 | 0.9498 | 0.9505 | 0.9501 | 0.9554 |
| 0.4347 | 4.0 | 3000 | 0.1629 | 0.9494 | 0.9504 | 0.9499 | 0.9552 |
| 0.4425 | 4.27 | 3200 | 0.1738 | 0.9495 | 0.9502 | 0.9498 | 0.9550 |
| 0.4335 | 4.53 | 3400 | 0.1733 | 0.9499 | 0.9506 | 0.9503 | 0.9550 |
| 0.4306 | 4.8 | 3600 | 0.1736 | 0.9499 | 0.9504 | 0.9501 | 0.9551 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1
|
philschmid/all-MiniLM-L6-v2-optimum-embeddings | philschmid | 2022-09-16T14:40:25Z | 8 | 7 | generic | [
"generic",
"onnx",
"sentence-embeddings",
"endpoints-template",
"optimum",
"license:mit",
"endpoints_compatible",
"region:us"
]
| null | 2022-08-31T19:15:30Z | ---
license: mit
tags:
- sentence-embeddings
- endpoints-template
- optimum
library_name: generic
---
# Optimized and Quantized [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) with a custom pipeline.py
This repository implements a `custom` task for `sentence-embeddings` for 🤗 Inference Endpoints for accelerated inference using [🤗 Optimum](https://huggingface.co/docs/optimum/index). The code for the customized pipeline is in the [pipeline.py](https://huggingface.co/philschmid/all-MiniLM-L6-v2-optimum-embeddings/blob/main/pipeline.py).
In the [how to create your own optimized and quantized model](#how-to-create-your-own-optimized-and-quantized-model) you will learn how the model was converted & optimized, it is based on the [Accelerate Sentence Transformers with Hugging Face Optimum](https://www.philschmid.de/optimize-sentence-transformers) blog post. It also includes how to create your custom pipeline and test it. There is also a [notebook](https://huggingface.co/philschmid/all-MiniLM-L6-v2-optimum-embeddings/blob/main/convert.ipynb) included.
To use deploy this model a an Inference Endpoint you have to select `Custom` as task to use the `pipeline.py` file. -> _double check if it is selected_
### expected Request payload
```json
{
"inputs": "The sky is a blue today and not gray",
}
```
below is an example on how to run a request using Python and `requests`.
## Run Request
```python
import json
from typing import List
import requests as r
import base64
ENDPOINT_URL = ""
HF_TOKEN = ""
def predict(document_string:str=None):
payload = {"inputs": document_string}
response = r.post(
ENDPOINT_URL, headers={"Authorization": f"Bearer {HF_TOKEN}"}, json=payload
)
return response.json()
prediction = predict(
path_to_image="The sky is a blue today and not gray"
)
```
expected output
```python
{'embeddings': [[-0.021580450236797333,
0.021715054288506508,
0.00979710929095745,
-0.0005379787762649357,
0.04682469740509987,
-0.013600599952042103,
...
}
```
## How to create your own optimized and quantized model
Steps:
[1. Convert model to ONNX](#1-convert-model-to-onnx)
[2. Optimize & quantize model with Optimum](#2-optimize--quantize-model-with-optimum)
[3. Create Custom Handler for Inference Endpoints](#3-create-custom-handler-for-inference-endpoints)
Helpful links:
* [Accelerate Sentence Transformers with Hugging Face Optimum](https://www.philschmid.de/optimize-sentence-transformers)
* [Create Custom Handler Endpoints](https://link-to-docs)
## Setup & Installation
```python
%%writefile requirements.txt
optimum[onnxruntime]==1.3.0
mkl-include
mkl
```
install requirements
```python
!pip install -r requirements.txt
```
## 1. Convert model to ONNX
```python
from optimum.onnxruntime import ORTModelForFeatureExtraction
from transformers import AutoTokenizer
from pathlib import Path
model_id="sentence-transformers/all-MiniLM-L6-v2"
onnx_path = Path(".")
# load vanilla transformers and convert to onnx
model = ORTModelForFeatureExtraction.from_pretrained(model_id, from_transformers=True)
tokenizer = AutoTokenizer.from_pretrained(model_id)
# save onnx checkpoint and tokenizer
model.save_pretrained(onnx_path)
tokenizer.save_pretrained(onnx_path)
```
## 2. Optimize & quantize model with Optimum
```python
from optimum.onnxruntime import ORTOptimizer, ORTQuantizer
from optimum.onnxruntime.configuration import OptimizationConfig, AutoQuantizationConfig
# create ORTOptimizer and define optimization configuration
optimizer = ORTOptimizer.from_pretrained(model_id, feature=model.pipeline_task)
optimization_config = OptimizationConfig(optimization_level=99) # enable all optimizations
# apply the optimization configuration to the model
optimizer.export(
onnx_model_path=onnx_path / "model.onnx",
onnx_optimized_model_output_path=onnx_path / "model-optimized.onnx",
optimization_config=optimization_config,
)
# create ORTQuantizer and define quantization configuration
dynamic_quantizer = ORTQuantizer.from_pretrained(model_id, feature=model.pipeline_task)
dqconfig = AutoQuantizationConfig.avx512_vnni(is_static=False, per_channel=False)
# apply the quantization configuration to the model
model_quantized_path = dynamic_quantizer.export(
onnx_model_path=onnx_path / "model-optimized.onnx",
onnx_quantized_model_output_path=onnx_path / "model-quantized.onnx",
quantization_config=dqconfig,
)
```
## 3. Create Custom Handler for Inference Endpoints
```python
%%writefile pipeline.py
from typing import Dict, List, Any
from optimum.onnxruntime import ORTModelForFeatureExtraction
from transformers import AutoTokenizer
import torch.nn.functional as F
import torch
# copied from the model card
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)
class PreTrainedPipeline():
def __init__(self, path=""):
# load the optimized model
self.model = ORTModelForFeatureExtraction.from_pretrained(path, file_name="model-quantized.onnx")
self.tokenizer = AutoTokenizer.from_pretrained(path)
def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
"""
Args:
data (:obj:):
includes the input data and the parameters for the inference.
Return:
A :obj:`list`:. The list contains the embeddings of the inference inputs
"""
inputs = data.get("inputs", data)
# tokenize the input
encoded_inputs = self.tokenizer(inputs, padding=True, truncation=True, return_tensors='pt')
# run the model
outputs = self.model(**encoded_inputs)
# Perform pooling
sentence_embeddings = mean_pooling(outputs, encoded_inputs['attention_mask'])
# Normalize embeddings
sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
# postprocess the prediction
return {"embeddings": sentence_embeddings.tolist()}
```
test custom pipeline
```python
from pipeline import PreTrainedPipeline
# init handler
my_handler = PreTrainedPipeline(path=".")
# prepare sample payload
request = {"inputs": "I am quite excited how this will turn out"}
# test the handler
%timeit my_handler(request)
```
results
```
1.55 ms ± 2.04 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)
```
|
sd-concepts-library/seraphimmoonshadow-art | sd-concepts-library | 2022-09-16T14:14:16Z | 0 | 0 | null | [
"license:mit",
"region:us"
]
| null | 2022-09-16T04:38:59Z | ---
license: mit
---
### seraphimmoonshadow-art on Stable Diffusion
This is the `<seraphimmoonshadow-art>` 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).
AHAHAHAHHAHHAHHAHAHAH...............................................................welllllll. My own art, failing me.
<img src="https://cdn.discordapp.com/attachments/1011389373775876116/1020201262244970527/kindaaaaa.png">
|
Dazzid/xlm-roberta-base-finetuned-panx-de | Dazzid | 2022-09-16T13:24:45Z | 124 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2022-09-16T13:00:23Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8648740833380706
---
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1365
- F1: 0.8649
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2553 | 1.0 | 525 | 0.1575 | 0.8279 |
| 0.1284 | 2.0 | 1050 | 0.1386 | 0.8463 |
| 0.0813 | 3.0 | 1575 | 0.1365 | 0.8649 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
dwisaji/SentimentBert | dwisaji | 2022-09-16T12:09:42Z | 161 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-09-16T12:01:39Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: SentimentBert
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. -->
# SentimentBert
This model is a fine-tuned version of [cahya/bert-base-indonesian-522M](https://huggingface.co/cahya/bert-base-indonesian-522M) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2005
- Accuracy: 0.965
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 275 | 0.7807 | 0.715 |
| 0.835 | 2.0 | 550 | 1.0588 | 0.635 |
| 0.835 | 3.0 | 825 | 0.2764 | 0.94 |
| 0.5263 | 4.0 | 1100 | 0.1913 | 0.97 |
| 0.5263 | 5.0 | 1375 | 0.2005 | 0.965 |
### Framework versions
- Transformers 4.22.0
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
g30rv17ys/ddpm-geeve-128 | g30rv17ys | 2022-09-16T10:13:42Z | 0 | 0 | diffusers | [
"diffusers",
"tensorboard",
"en",
"dataset:imagefolder",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
]
| null | 2022-09-16T07:46:35Z | ---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: imagefolder
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-geeve-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `imagefolder` 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
📈 [TensorBoard logs](https://huggingface.co/geevegeorge/ddpm-geeve-128/tensorboard?#scalars)
|
huijian222/a2c-AntBulletEnv-v0 | huijian222 | 2022-09-16T09:10:32Z | 2 | 0 | stable-baselines3 | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-09-16T09:09:15Z | ---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- metrics:
- type: mean_reward
value: 1245.42 +/- 483.73
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
---
# **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
...
```
|
dwisaji/Modelroberta | dwisaji | 2022-09-16T09:03:17Z | 161 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:indonlu",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-09-16T08:46:21Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- indonlu
model-index:
- name: Modelroberta
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. -->
# Modelroberta
This model is a fine-tuned version of [cahya/roberta-base-indonesian-522M](https://huggingface.co/cahya/roberta-base-indonesian-522M) on the indonlu 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: 5
### Training results
### Framework versions
- Transformers 4.22.0
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
yogeshchandrasekharuni/parrot_paraphraser_on_T5-finetuned-xsum-v0 | yogeshchandrasekharuni | 2022-09-16T08:16:06Z | 113 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-09-16T07:30:17Z | ---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: parrot_paraphraser_on_T5-finetuned-xsum-v0
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. -->
# parrot_paraphraser_on_T5-finetuned-xsum-v0
This model is a fine-tuned version of [prithivida/parrot_paraphraser_on_T5](https://huggingface.co/prithivida/parrot_paraphraser_on_T5) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4418
- Rouge1: 79.364
- Rouge2: 74.776
- Rougel: 78.997
- Rougelsum: 78.7013
- Gen Len: 18.6789
## 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: 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: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 216 | 0.5427 | 79.4586 | 74.9115 | 78.8483 | 78.6557 | 18.6972 |
| No log | 2.0 | 432 | 0.4922 | 79.5229 | 74.8555 | 78.7762 | 78.5797 | 18.6881 |
| 0.5974 | 3.0 | 648 | 0.4628 | 79.4743 | 74.7622 | 78.7621 | 78.5631 | 18.6881 |
| 0.5974 | 4.0 | 864 | 0.4517 | 79.6842 | 75.2876 | 79.3457 | 79.0682 | 18.6881 |
| 0.4292 | 5.0 | 1080 | 0.4451 | 79.6571 | 75.2248 | 79.2939 | 79.0412 | 18.6881 |
| 0.4292 | 6.0 | 1296 | 0.4409 | 79.3363 | 74.6763 | 78.9595 | 78.7335 | 18.6789 |
| 0.3367 | 7.0 | 1512 | 0.4398 | 79.364 | 74.776 | 78.997 | 78.7013 | 18.6789 |
| 0.3367 | 8.0 | 1728 | 0.4407 | 79.364 | 74.776 | 78.997 | 78.7013 | 18.6789 |
| 0.3367 | 9.0 | 1944 | 0.4413 | 79.364 | 74.776 | 78.997 | 78.7013 | 18.6789 |
| 0.3012 | 10.0 | 2160 | 0.4418 | 79.364 | 74.776 | 78.997 | 78.7013 | 18.6789 |
### Framework versions
- Transformers 4.22.0
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
viola77data/recycling | viola77data | 2022-09-16T07:43:19Z | 0 | 2 | tf-keras | [
"tf-keras",
"license:apache-2.0",
"region:us"
]
| null | 2022-09-16T06:19:33Z | ---
license: apache-2.0
---
Recycling Model trained with Keras and Tensorflow on this dataset: https://huggingface.co/datasets/viola77data/recycling-dataset |
Tritkoman/autotrain-gahhaha-1478754178 | Tritkoman | 2022-09-16T06:11:41Z | 85 | 0 | transformers | [
"transformers",
"pytorch",
"autotrain",
"translation",
"es",
"en",
"dataset:Tritkoman/autotrain-data-gahhaha",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
]
| translation | 2022-09-16T05:42:56Z | ---
tags:
- autotrain
- translation
language:
- es
- en
datasets:
- Tritkoman/autotrain-data-gahhaha
co2_eq_emissions:
emissions: 39.86630127427062
---
# Model Trained Using AutoTrain
- Problem type: Translation
- Model ID: 1478754178
- CO2 Emissions (in grams): 39.8663
## Validation Metrics
- Loss: 1.716
- SacreBLEU: 9.095
- Gen len: 11.146 |
fatimaseemab/wav2vec2-urdu | fatimaseemab | 2022-09-16T05:51:23Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-09-16T05:09:29Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-urdu
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. -->
# wav2vec2-urdu
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.22.0
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
sd-concepts-library/osrstiny | sd-concepts-library | 2022-09-16T04:54:51Z | 0 | 0 | null | [
"license:mit",
"region:us"
]
| null | 2022-09-16T04:54:46Z | ---
license: mit
---
### osrstiny on Stable Diffusion
This is the `<osrstiny>` 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 a `style`:
|
SALT-NLP/pfadapter-bert-base-uncased-stsb-combined-value | SALT-NLP | 2022-09-16T04:48:01Z | 1 | 0 | adapter-transformers | [
"adapter-transformers",
"bert",
"en",
"dataset:glue",
"region:us"
]
| null | 2022-09-16T04:47:54Z | ---
tags:
- bert
- adapter-transformers
datasets:
- glue
language:
- en
---
# Adapter `SALT-NLP/pfadapter-bert-base-uncased-stsb-combined-value` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [glue](https://huggingface.co/datasets/glue/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("SALT-NLP/pfadapter-bert-base-uncased-stsb-combined-value", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here --> |
SALT-NLP/pfadapter-bert-base-uncased-sst2-combined-value | SALT-NLP | 2022-09-16T04:35:45Z | 2 | 0 | adapter-transformers | [
"adapter-transformers",
"bert",
"en",
"dataset:glue",
"region:us"
]
| null | 2022-09-16T04:35:38Z | ---
tags:
- adapter-transformers
- bert
datasets:
- glue
language:
- en
---
# Adapter `SALT-NLP/pfadapter-bert-base-uncased-sst2-combined-value` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [glue](https://huggingface.co/datasets/glue/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("SALT-NLP/pfadapter-bert-base-uncased-sst2-combined-value", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here --> |
sd-concepts-library/david-firth-artstyle | sd-concepts-library | 2022-09-16T04:31:20Z | 0 | 3 | null | [
"license:mit",
"region:us"
]
| null | 2022-09-16T04:31:14Z | ---
license: mit
---
### David Firth Artstyle on Stable Diffusion
This is the `<david-firth-artstyle>` 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 a `style`:
|
microsoft/layoutlmv2-base-uncased | microsoft | 2022-09-16T03:40:56Z | 693,838 | 62 | transformers | [
"transformers",
"pytorch",
"layoutlmv2",
"en",
"arxiv:2012.14740",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05Z | ---
language: en
license: cc-by-nc-sa-4.0
---
# LayoutLMv2
**Multimodal (text + layout/format + image) pre-training for document AI**
The documentation of this model in the Transformers library can be found [here](https://huggingface.co/docs/transformers/model_doc/layoutlmv2).
[Microsoft Document AI](https://www.microsoft.com/en-us/research/project/document-ai/) | [GitHub](https://github.com/microsoft/unilm/tree/master/layoutlmv2)
## Introduction
LayoutLMv2 is an improved version of LayoutLM with new pre-training tasks to model the interaction among text, layout, and image in a single multi-modal framework. It outperforms strong baselines and achieves new state-of-the-art results on a wide variety of downstream visually-rich document understanding tasks, including , including FUNSD (0.7895 → 0.8420), CORD (0.9493 → 0.9601), SROIE (0.9524 → 0.9781), Kleister-NDA (0.834 → 0.852), RVL-CDIP (0.9443 → 0.9564), and DocVQA (0.7295 → 0.8672).
[LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding](https://arxiv.org/abs/2012.14740)
Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou, ACL 2021
|
HYPJUDY/layoutlmv3-large-finetuned-funsd | HYPJUDY | 2022-09-16T03:18:44Z | 170 | 4 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"layoutlmv3",
"token-classification",
"arxiv:2204.08387",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2022-04-18T18:06:30Z | ---
license: cc-by-nc-sa-4.0
---
# layoutlmv3-large-finetuned-funsd
The model [layoutlmv3-large-finetuned-funsd](https://huggingface.co/HYPJUDY/layoutlmv3-large-finetuned-funsd) is fine-tuned on the FUNSD dataset initialized from [microsoft/layoutlmv3-large](https://huggingface.co/microsoft/layoutlmv3-large).
This finetuned model achieves an F1 score of 92.15 on the test split of the FUNSD dataset.
[Paper](https://arxiv.org/pdf/2204.08387.pdf) | [Code](https://aka.ms/layoutlmv3) | [Microsoft Document AI](https://www.microsoft.com/en-us/research/project/document-ai/)
If you find LayoutLMv3 helpful, please cite the following paper:
```
@inproceedings{huang2022layoutlmv3,
author={Yupan Huang and Tengchao Lv and Lei Cui and Yutong Lu and Furu Wei},
title={LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking},
booktitle={Proceedings of the 30th ACM International Conference on Multimedia},
year={2022}
}
```
## License
The content of this project itself is licensed under the [Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/).
Portions of the source code are based on the [transformers](https://github.com/huggingface/transformers) project.
[Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct)
|
mikedodge/t5-small-finetuned-xsum | mikedodge | 2022-09-16T02:23:09Z | 117 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-09-15T20:00:32Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: t5-small-finetuned-xsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xsum
type: xsum
config: default
split: train
args: default
metrics:
- name: Rouge1
type: rouge
value: 28.2804
---
<!-- 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
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: 2.4789
- Rouge1: 28.2804
- Rouge2: 7.7039
- Rougel: 22.2002
- Rougelsum: 22.2019
- Gen Len: 18.8238
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.711 | 1.0 | 12753 | 2.4789 | 28.2804 | 7.7039 | 22.2002 | 22.2019 | 18.8238 |
### Framework versions
- Transformers 4.22.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
sd-concepts-library/hydrasuit | sd-concepts-library | 2022-09-16T01:50:23Z | 0 | 0 | null | [
"license:mit",
"region:us"
]
| null | 2022-09-16T01:50:17Z | ---
license: mit
---
### Hydrasuit on Stable Diffusion
This is the `<hydrasuit>` 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`:
|
sd-concepts-library/luinv2 | sd-concepts-library | 2022-09-16T01:04:43Z | 0 | 1 | null | [
"license:mit",
"region:us"
]
| null | 2022-09-16T01:04:31Z | ---
license: mit
---
### luinv2 on Stable Diffusion
This is the `<luin-waifu>` 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`:
|
sd-concepts-library/furrpopasthetic | sd-concepts-library | 2022-09-16T00:48:33Z | 0 | 3 | null | [
"license:mit",
"region:us"
]
| null | 2022-09-15T23:05:54Z | ---
license: mit
---
### furrpopasthetic on Stable Diffusion
This is the `<furpop>` 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).
More information will be provided via my GOOGLE DOCUMENT, which you can check out HERE: https://docs.google.com/document/d/1R2UZi5G-DXiz2HcCrfAFLYJoer_JPDEoZmV7wy1tEz0/edit
Here are some sample images of things I created using this model:
<img src="https://cdn.discordapp.com/attachments/1011389373775876116/1020123619218698301/sofancy.png">
<img src="https://cdn.discordapp.com/attachments/1006210928548773939/1020129494490677309/allthedoggos.png">
<img src="https://cdn.discordapp.com/attachments/1011389373775876116/1020124794420740128/alltheunicorns.png">
<img src="https://cdn.discordapp.com/attachments/1006210928548773939/1020131203543744572/sosweet.png">
<img src="https://cdn.discordapp.com/attachments/1006210928548773939/1020133712119201852/fartoocute.png">
I will be providing information for the model in my Google Doc, so please just check there; thanks!
These are the images that I used for the `style`:
And yes, this is all based on my LSP/romanticism painters, which you can still do by combining the key words outlined in my document.
|
sd-concepts-library/csgo-awp-texture-map | sd-concepts-library | 2022-09-16T00:32:03Z | 0 | 3 | null | [
"license:mit",
"region:us"
]
| null | 2022-09-16T00:31:57Z | ---
license: mit
---
### csgo_awp_texture_map on Stable Diffusion
This is the `<csgo_awp_texture>` 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 a `style`:
|
Isaacp/xlm-roberta-base-finetuned-panx-all | Isaacp | 2022-09-15T23:58:43Z | 101 | 0 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2022-09-15T23:31:10Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-all
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. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1757
- F1: 0.8513
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2986 | 1.0 | 835 | 0.1939 | 0.8077 |
| 0.1547 | 2.0 | 1670 | 0.1813 | 0.8351 |
| 0.1003 | 3.0 | 2505 | 0.1757 | 0.8513 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
rajistics/donut-base-sroiev2 | rajistics | 2022-09-15T23:44:13Z | 45 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"dataset:imagefolder",
"license:mit",
"endpoints_compatible",
"region:us"
]
| image-text-to-text | 2022-09-15T23:08:07Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: donut-base-sroiev2
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. -->
# donut-base-sroiev2
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder 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: 2
- 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
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.23.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Isaacp/xlm-roberta-base-finetuned-panx-en | Isaacp | 2022-09-15T23:30:58Z | 123 | 0 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2022-09-15T23:10:20Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-en
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.en
metrics:
- name: F1
type: f1
value: 0.7032474804031354
---
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3932
- F1: 0.7032
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1504 | 1.0 | 50 | 0.5992 | 0.4786 |
| 0.5147 | 2.0 | 100 | 0.4307 | 0.6468 |
| 0.3717 | 3.0 | 150 | 0.3932 | 0.7032 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Isaacp/xlm-roberta-base-finetuned-panx-it | Isaacp | 2022-09-15T23:10:07Z | 114 | 0 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2022-09-15T22:48:54Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-it
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.it
metrics:
- name: F1
type: f1
value: 0.8245828245828245
---
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2401
- F1: 0.8246
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.8187 | 1.0 | 70 | 0.3325 | 0.7337 |
| 0.2829 | 2.0 | 140 | 0.2554 | 0.8003 |
| 0.1894 | 3.0 | 210 | 0.2401 | 0.8246 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Isaacp/xlm-roberta-base-finetuned-panx-fr | Isaacp | 2022-09-15T22:48:39Z | 103 | 0 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2022-09-15T22:25:15Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-fr
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.fr
metrics:
- name: F1
type: f1
value: 0.8299296953465015
---
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2848
- F1: 0.8299
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5989 | 1.0 | 191 | 0.3383 | 0.7928 |
| 0.2617 | 2.0 | 382 | 0.2966 | 0.8318 |
| 0.1672 | 3.0 | 573 | 0.2848 | 0.8299 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Mcy/bert-base-uncased-finetuned-classification_ds30 | Mcy | 2022-09-15T22:09:50Z | 163 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-09-11T14:34:42Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-uncased-finetuned-classification_ds30
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-classification_ds30
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 41.1515
- Mse: 41.1515
- Mae: 4.7002
- R2: 0.7675
- Accuracy: 0.2685
- Msev: 0.0243
## 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: 16
- 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 | Mse | Mae | R2 | Accuracy | Msev |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:------:|:--------:|:------:|
| 10.1514 | 1.0 | 5215 | 40.1844 | 40.1844 | 4.6065 | 0.7730 | 0.2644 | 0.0249 |
| 3.7754 | 2.0 | 10430 | 39.4067 | 39.4067 | 4.5926 | 0.7774 | 0.2803 | 0.0254 |
| 2.2314 | 3.0 | 15645 | 44.9527 | 44.9527 | 4.8825 | 0.7460 | 0.2680 | 0.0222 |
| 1.6468 | 4.0 | 20860 | 40.3435 | 40.3435 | 4.6496 | 0.7721 | 0.2702 | 0.0248 |
| 1.2442 | 5.0 | 26075 | 40.8178 | 40.8178 | 4.6934 | 0.7694 | 0.2657 | 0.0245 |
| 1.0992 | 6.0 | 31290 | 42.6644 | 42.6644 | 4.7802 | 0.7590 | 0.2620 | 0.0234 |
| 0.9911 | 7.0 | 36505 | 40.0627 | 40.0627 | 4.6277 | 0.7737 | 0.2751 | 0.0250 |
| 0.8167 | 8.0 | 41720 | 40.6918 | 40.6918 | 4.6755 | 0.7701 | 0.2693 | 0.0246 |
| 0.7862 | 9.0 | 46935 | 41.9593 | 41.9593 | 4.7363 | 0.7629 | 0.2693 | 0.0238 |
| 0.8136 | 10.0 | 52150 | 41.1515 | 41.1515 | 4.7002 | 0.7675 | 0.2685 | 0.0243 |
### Framework versions
- Transformers 4.22.0
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
sd-concepts-library/a-hat-kid | sd-concepts-library | 2022-09-15T22:03:52Z | 0 | 1 | null | [
"license:mit",
"region:us"
]
| null | 2022-09-15T22:03:46Z | ---
license: mit
---
### A Hat kid on Stable Diffusion
This is the `<hatintime-kid>` 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`:
|
sd-concepts-library/backrooms | sd-concepts-library | 2022-09-15T21:32:42Z | 0 | 12 | null | [
"license:mit",
"region:us"
]
| null | 2022-09-15T21:32:37Z | ---
license: mit
---
### Backrooms on Stable Diffusion
This is the `<Backrooms>` 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`:
|
sd-concepts-library/naruto | sd-concepts-library | 2022-09-15T20:52:17Z | 0 | 1 | null | [
"license:mit",
"region:us"
]
| null | 2022-09-15T20:52:10Z | ---
license: mit
---
### Naruto on Stable Diffusion
This is the `<Naruto>` 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 a `style`:
|
VanessaSchenkel/pt-unicamp-handcrafted | VanessaSchenkel | 2022-09-15T20:27:04Z | 106 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| translation | 2022-09-15T20:01:33Z | ---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: pt-unicamp-handcrafted
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. -->
# pt-unicamp-handcrafted
This model is a fine-tuned version of [VanessaSchenkel/pt-unicamp-news-t5](https://huggingface.co/VanessaSchenkel/pt-unicamp-news-t5) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7061
- Bleu: 75.3691
## 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: 64
- seed: 42
- 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
### Framework versions
- Transformers 4.22.0
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
sd-concepts-library/liqwid-aquafarmer | sd-concepts-library | 2022-09-15T20:02:21Z | 0 | 1 | null | [
"license:mit",
"region:us"
]
| null | 2022-09-15T20:02:15Z | ---
license: mit
---
### liqwid_aquafarmer on Stable Diffusion
This is the `<aquafarmer>` 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`:
|
richhkust/distilbert-base-uncased-finetuned-cola | richhkust | 2022-09-15T18:55:35Z | 103 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-09-15T17:08:32Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: train
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5332198659134496
---
<!-- 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-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7865
- Matthews Correlation: 0.5332
## 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
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5246 | 1.0 | 535 | 0.5492 | 0.4039 |
| 0.3516 | 2.0 | 1070 | 0.5242 | 0.4703 |
| 0.2369 | 3.0 | 1605 | 0.5779 | 0.5203 |
| 0.1719 | 4.0 | 2140 | 0.7865 | 0.5332 |
| 0.1178 | 5.0 | 2675 | 0.8519 | 0.5298 |
### Framework versions
- Transformers 4.22.0
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
pnr-svc/named-entity-tr | pnr-svc | 2022-09-15T18:05:01Z | 103 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"electra",
"token-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2022-09-15T17:49:34Z | ---
license: mit
widget:
- text: "emanet ürün"
---
tags:
- generated_from_trainer
datasets:
- ner-tr
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: named-entity-tr
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: ner-tr
type: ner-tr
config: NERTR
split: train
args: NERTR
metrics:
- name: Precision
type: precision
value: 0.0
- name: Recall
type: recall
value: 0.0
- name: F1
type: f1
value: 0.0
- name: Accuracy
type: accuracy
value: 0.45027322404371584
---
<!-- 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. -->
# named-entity-tr
This model is a fine-tuned version of [dbmdz/electra-base-turkish-cased-discriminator](https://huggingface.co/dbmdz/electra-base-turkish-cased-discriminator) on the ner-tr dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2782
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- Accuracy: 0.4503
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 9 | 2.7645 | 0.0537 | 0.0777 | 0.0635 | 0.3191 |
| No log | 2.0 | 18 | 2.3464 | 0.0 | 0.0 | 0.0 | 0.4503 |
| No log | 3.0 | 27 | 2.2782 | 0.0 | 0.0 | 0.0 | 0.4503 |
### Framework versions
- Transformers 4.22.0
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
edub0420/autotrain-graphwerk-1472254089 | edub0420 | 2022-09-15T18:00:49Z | 190 | 0 | transformers | [
"transformers",
"pytorch",
"autotrain",
"vision",
"image-classification",
"dataset:edub0420/autotrain-data-graphwerk",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
]
| image-classification | 2022-09-15T17:59:52Z | ---
tags:
- autotrain
- vision
- image-classification
datasets:
- edub0420/autotrain-data-graphwerk
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 0.0037659513202956607
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1472254089
- CO2 Emissions (in grams): 0.0038
## Validation Metrics
- Loss: 0.005
- Accuracy: 1.000
- Precision: 1.000
- Recall: 1.000
- AUC: 1.000
- F1: 1.000 |
reinoudbosch/xlm-roberta-base-finetuned-panx-all | reinoudbosch | 2022-09-15T17:44:39Z | 115 | 0 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2022-09-15T17:33:33Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-all
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. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1739
- F1: 0.8525
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.3 | 1.0 | 835 | 0.1894 | 0.8104 |
| 0.1564 | 2.0 | 1670 | 0.1751 | 0.8423 |
| 0.1032 | 3.0 | 2505 | 0.1739 | 0.8525 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.11.0
- Datasets 2.0.0
- Tokenizers 0.11.0
|
reinoudbosch/xlm-roberta-base-finetuned-panx-en | reinoudbosch | 2022-09-15T17:33:23Z | 116 | 0 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2022-09-15T17:25:18Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-en
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.en
metrics:
- name: F1
type: f1
value: 0.6777777777777778
---
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4025
- F1: 0.6778
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1069 | 1.0 | 50 | 0.5201 | 0.5010 |
| 0.4975 | 2.0 | 100 | 0.4503 | 0.6198 |
| 0.3705 | 3.0 | 150 | 0.4025 | 0.6778 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.11.0
- Datasets 2.0.0
- Tokenizers 0.11.0
|
reinoudbosch/xlm-roberta-base-finetuned-panx-it | reinoudbosch | 2022-09-15T17:25:07Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2022-09-15T17:16:33Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-it
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.it
metrics:
- name: F1
type: f1
value: 0.8085969180859691
---
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2527
- F1: 0.8086
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.8319 | 1.0 | 70 | 0.3179 | 0.7474 |
| 0.2959 | 2.0 | 140 | 0.2695 | 0.7916 |
| 0.2036 | 3.0 | 210 | 0.2527 | 0.8086 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.11.0
- Datasets 2.0.0
- Tokenizers 0.11.0
|
valadhi/swin-tiny-patch4-window7-224-finetuned-agrivision | valadhi | 2022-09-15T17:21:42Z | 59 | 0 | transformers | [
"transformers",
"pytorch",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2022-09-08T14:40:38Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-agrivision
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9202733485193622
---
<!-- 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. -->
# swin-tiny-patch4-window7-224-finetuned-agrivision
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3605
- Accuracy: 0.9203
## 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: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5913 | 1.0 | 31 | 0.7046 | 0.7175 |
| 0.1409 | 2.0 | 62 | 0.8423 | 0.6788 |
| 0.0825 | 3.0 | 93 | 0.6224 | 0.7654 |
| 0.0509 | 4.0 | 124 | 0.4379 | 0.8360 |
| 0.0439 | 5.0 | 155 | 0.1706 | 0.9317 |
| 0.0107 | 6.0 | 186 | 0.1914 | 0.9362 |
| 0.0134 | 7.0 | 217 | 0.2491 | 0.9089 |
| 0.0338 | 8.0 | 248 | 0.2119 | 0.9362 |
| 0.0306 | 9.0 | 279 | 0.4502 | 0.8610 |
| 0.0054 | 10.0 | 310 | 0.4990 | 0.8747 |
| 0.0033 | 11.0 | 341 | 0.2746 | 0.9112 |
| 0.0021 | 12.0 | 372 | 0.2501 | 0.9317 |
| 0.0068 | 13.0 | 403 | 0.1883 | 0.9522 |
| 0.0038 | 14.0 | 434 | 0.3672 | 0.9134 |
| 0.0006 | 15.0 | 465 | 0.2275 | 0.9408 |
| 0.0011 | 16.0 | 496 | 0.3349 | 0.9134 |
| 0.0017 | 17.0 | 527 | 0.3329 | 0.9157 |
| 0.0007 | 18.0 | 558 | 0.2508 | 0.9317 |
| 0.0023 | 19.0 | 589 | 0.2338 | 0.9385 |
| 0.0003 | 20.0 | 620 | 0.3193 | 0.9226 |
| 0.002 | 21.0 | 651 | 0.4604 | 0.9043 |
| 0.0023 | 22.0 | 682 | 0.3338 | 0.9203 |
| 0.005 | 23.0 | 713 | 0.2925 | 0.9271 |
| 0.0001 | 24.0 | 744 | 0.2022 | 0.9522 |
| 0.0002 | 25.0 | 775 | 0.2699 | 0.9339 |
| 0.0007 | 26.0 | 806 | 0.2603 | 0.9385 |
| 0.0005 | 27.0 | 837 | 0.4120 | 0.9134 |
| 0.0003 | 28.0 | 868 | 0.3550 | 0.9203 |
| 0.0008 | 29.0 | 899 | 0.3657 | 0.9203 |
| 0.0 | 30.0 | 930 | 0.3605 | 0.9203 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1
|
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