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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-01 06:29:04
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
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listlengths 1
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|---|---|---|---|---|---|---|---|---|---|
chrommium/rubert-base-cased-sentence-finetuned-headlines_X
|
chrommium
| 2021-09-16T00:34:06Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: rubert-base-cased-sentence-finetuned-headlines_X
results:
- task:
name: Text Classification
type: text-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.952
---
<!-- 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. -->
# rubert-base-cased-sentence-finetuned-headlines_X
This model is a fine-tuned version of [DeepPavlov/rubert-base-cased-sentence](https://huggingface.co/DeepPavlov/rubert-base-cased-sentence) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2535
- Accuracy: 0.952
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 157 | 0.2759 | 0.912 |
| No log | 2.0 | 314 | 0.2538 | 0.936 |
| No log | 3.0 | 471 | 0.2556 | 0.945 |
| 0.1908 | 4.0 | 628 | 0.2601 | 0.95 |
| 0.1908 | 5.0 | 785 | 0.2535 | 0.952 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
|
dhairya2303/bert-base-uncased-emotion_holler
|
dhairya2303
| 2021-09-15T21:26:03Z | 5 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
{'sadness':0,'joy':1,'love':2,'anger':3,'fear':4,'surprise':5}
|
huggingartists/big-russian-boss
|
huggingartists
| 2021-09-15T16:41:55Z | 15 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/big-russian-boss",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- huggingartists/big-russian-boss
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/d66eeeef006738708df1e52b84c34c14.403x403x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Big Russian Boss</div>
<a href="https://genius.com/artists/big-russian-boss">
<div style="text-align: center; font-size: 14px;">@big-russian-boss</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from Big Russian Boss.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/big-russian-boss).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/big-russian-boss")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1ju9bqqi/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 Big Russian Boss's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/3820n7qx) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/3820n7qx/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='huggingartists/big-russian-boss')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/big-russian-boss")
model = AutoModelWithLMHead.from_pretrained("huggingartists/big-russian-boss")
```
## 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 Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
Narshion/bert-base-multilingual-cased-urgency
|
Narshion
| 2021-09-15T12:27:00Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
---
tags:
- generated_from_trainer
datasets:
- null
model-index:
- name: bert-base-multilingual-cased-urgency
results:
- task:
name: Masked Language Modeling
type: fill-mask
---
<!-- 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-urgency
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/) on the mWACH NEO dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2797
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 4.1408 | 1.0 | 5659 | 3.6705 |
| 2.8777 | 2.0 | 11318 | 2.5536 |
| 2.561 | 3.0 | 16977 | 2.2740 |
### Framework versions
- Transformers 4.10.0
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
huggingartists/mikhail-gorshenev
|
huggingartists
| 2021-09-15T12:07:32Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/mikhail-gorshenev",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- huggingartists/mikhail-gorshenev
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/713c41590244f597dd6484bb61eacc5a.413x413x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Михаил Горшенев (Mikhail Gorshenev)</div>
<a href="https://genius.com/artists/mikhail-gorshenev">
<div style="text-align: center; font-size: 14px;">@mikhail-gorshenev</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from Михаил Горшенев (Mikhail Gorshenev).
Dataset is available [here](https://huggingface.co/datasets/huggingartists/mikhail-gorshenev).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/mikhail-gorshenev")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3h9endcz/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 Михаил Горшенев (Mikhail Gorshenev)'s lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1kdp29bz) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1kdp29bz/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='huggingartists/mikhail-gorshenev')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/mikhail-gorshenev")
model = AutoModelWithLMHead.from_pretrained("huggingartists/mikhail-gorshenev")
```
## 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 Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
huggingartists/till-lindemann
|
huggingartists
| 2021-09-15T11:46:53Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/till-lindemann",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- huggingartists/till-lindemann
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/48d6ca7ca17a9dfc9ad3034e71533a89.1000x1000x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Till Lindemann</div>
<a href="https://genius.com/artists/till-lindemann">
<div style="text-align: center; font-size: 14px;">@till-lindemann</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from Till Lindemann.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/till-lindemann).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/till-lindemann")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/2xh6fyqt/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 Till Lindemann's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/32ohf092) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/32ohf092/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='huggingartists/till-lindemann')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/till-lindemann")
model = AutoModelWithLMHead.from_pretrained("huggingartists/till-lindemann")
```
## 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 Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
huggingartists/face
|
huggingartists
| 2021-09-15T11:08:11Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/face",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- huggingartists/face
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/1dcb4e1dc4242207c27fe5cd0d4090e8.1000x1000x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">FACE</div>
<a href="https://genius.com/artists/face">
<div style="text-align: center; font-size: 14px;">@face</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from FACE.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/face).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/face")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/xtozoqtm/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 FACE's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/knkqp5iy) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/knkqp5iy/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='huggingartists/face')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/face")
model = AutoModelWithLMHead.from_pretrained("huggingartists/face")
```
## 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 Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
blizrys/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-pubmedqa-1
|
blizrys
| 2021-09-15T08:14:01Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- null
metrics:
- accuracy
model-index:
- name: BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-pubmedqa-1
results:
- task:
name: Text Classification
type: text-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.7
---
<!-- 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. -->
# BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-pubmedqa-1
This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6660
- Accuracy: 0.7
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 57 | 0.8471 | 0.58 |
| No log | 2.0 | 114 | 0.8450 | 0.58 |
| No log | 3.0 | 171 | 0.7846 | 0.58 |
| No log | 4.0 | 228 | 0.8649 | 0.58 |
| No log | 5.0 | 285 | 0.7220 | 0.68 |
| No log | 6.0 | 342 | 0.7395 | 0.66 |
| No log | 7.0 | 399 | 0.7198 | 0.72 |
| No log | 8.0 | 456 | 0.6417 | 0.72 |
| 0.7082 | 9.0 | 513 | 0.6265 | 0.74 |
| 0.7082 | 10.0 | 570 | 0.6660 | 0.7 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.0
- Tokenizers 0.10.3
|
huggingtweets/lilnasx
|
huggingtweets
| 2021-09-14T23:54:26Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/lilnasx/1631663662799/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/1430901239110258696/1P0QZ5_7_400x400.jpg')">
</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">MONTERO 🦋</div>
<div style="text-align: center; font-size: 14px;">@lilnasx</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 MONTERO 🦋.
| Data | MONTERO 🦋 |
| --- | --- |
| Tweets downloaded | 3169 |
| Retweets | 883 |
| Short tweets | 796 |
| Tweets kept | 1490 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/z4oke017/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 @lilnasx's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3flqsl4t) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3flqsl4t/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/lilnasx')
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)
|
huggingtweets/nhlrumorsdaily
|
huggingtweets
| 2021-09-14T23:52:40Z | 5 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/nhlrumorsdaily/1631663556170/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/1230668680066891776/NrwCWFUg_400x400.jpg')">
</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">NRD</div>
<div style="text-align: center; font-size: 14px;">@nhlrumorsdaily</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 NRD.
| Data | NRD |
| --- | --- |
| Tweets downloaded | 3247 |
| Retweets | 282 |
| Short tweets | 576 |
| Tweets kept | 2389 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/362t5kc0/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 @nhlrumorsdaily's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/9pxaxgg1) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/9pxaxgg1/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/nhlrumorsdaily')
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)
|
huggingtweets/cutebunnys50
|
huggingtweets
| 2021-09-14T23:47:15Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/cutebunnys50/1631663231129/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/1385023548935258114/UuMXQpjI_400x400.jpg')">
</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">Bunny ✊🏽✊🏾✊🏿 🏳️🌈</div>
<div style="text-align: center; font-size: 14px;">@cutebunnys50</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 Bunny ✊🏽✊🏾✊🏿 🏳️🌈.
| Data | Bunny ✊🏽✊🏾✊🏿 🏳️🌈 |
| --- | --- |
| Tweets downloaded | 3208 |
| Retweets | 2575 |
| Short tweets | 16 |
| Tweets kept | 617 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2t0h4kcz/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 @cutebunnys50's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2ymfrlb8) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2ymfrlb8/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/cutebunnys50')
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)
|
huggingtweets/foodnetwork
|
huggingtweets
| 2021-09-14T23:41:32Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/foodnetwork/1631662887881/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/1395089186538115072/oehHqb54_400x400.jpg')">
</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">Food Network</div>
<div style="text-align: center; font-size: 14px;">@foodnetwork</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 Food Network.
| Data | Food Network |
| --- | --- |
| Tweets downloaded | 3237 |
| Retweets | 938 |
| Short tweets | 49 |
| Tweets kept | 2250 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2x1lok4q/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 @foodnetwork's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2yjxdjcm) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2yjxdjcm/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/foodnetwork')
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)
|
huggingtweets/fluffyguy
|
huggingtweets
| 2021-09-14T23:40:29Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/fluffyguy/1631662825404/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/1346711262869086210/KPshm_gK_400x400.jpg')">
</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">G a b r i e l - I g l e s i a s</div>
<div style="text-align: center; font-size: 14px;">@fluffyguy</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 G a b r i e l - I g l e s i a s.
| Data | G a b r i e l - I g l e s i a s |
| --- | --- |
| Tweets downloaded | 3246 |
| Retweets | 264 |
| Short tweets | 132 |
| Tweets kept | 2850 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/24pz59rj/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 @fluffyguy's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/36h0hs6l) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/36h0hs6l/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/fluffyguy')
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)
|
huggingtweets/lizzo
|
huggingtweets
| 2021-09-14T23:39:31Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/lizzo/1631662767078/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/1422227243498020865/sMYfk77e_400x400.jpg')">
</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">ALL THE RUMORS ARE TRUE</div>
<div style="text-align: center; font-size: 14px;">@lizzo</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 ALL THE RUMORS ARE TRUE.
| Data | ALL THE RUMORS ARE TRUE |
| --- | --- |
| Tweets downloaded | 3095 |
| Retweets | 1412 |
| Short tweets | 420 |
| Tweets kept | 1263 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1iacenbu/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 @lizzo's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1erzu9fc) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1erzu9fc/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/lizzo')
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)
|
huggingtweets/cosm1cgrandma-glitchre-glitchre8
|
huggingtweets
| 2021-09-14T22:32:11Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/cosm1cgrandma-glitchre-glitchre8/1631658643977/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/1265389720047058944/hWPrCwh7_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/1394712172010393608/tkWea9AS_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/1406255548228640781/wzOACSA8_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">SA | Glitchre & glitchre & cosmic gangster</div>
<div style="text-align: center; font-size: 14px;">@cosm1cgrandma-glitchre-glitchre8</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 SA | Glitchre & glitchre & cosmic gangster.
| Data | SA | Glitchre | glitchre | cosmic gangster |
| --- | --- | --- | --- |
| Tweets downloaded | 2920 | 2891 | 2960 |
| Retweets | 347 | 808 | 1410 |
| Short tweets | 872 | 600 | 359 |
| Tweets kept | 1701 | 1483 | 1191 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/15s2bdg3/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 @cosm1cgrandma-glitchre-glitchre8's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3jv76342) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3jv76342/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/cosm1cgrandma-glitchre-glitchre8')
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)
|
huggingtweets/lilbthebasedgod
|
huggingtweets
| 2021-09-14T22:15:23Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/lilbthebasedgod/1631657718769/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/1248509273/39198_1571854573776_1157872547_31663366_5779158_n_400x400.jpg')">
</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">Lil B THE BASEDGOD</div>
<div style="text-align: center; font-size: 14px;">@lilbthebasedgod</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 Lil B THE BASEDGOD.
| Data | Lil B THE BASEDGOD |
| --- | --- |
| Tweets downloaded | 3074 |
| Retweets | 2496 |
| Short tweets | 0 |
| Tweets kept | 578 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/19n5hf1u/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 @lilbthebasedgod's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2dlir6fx) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2dlir6fx/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/lilbthebasedgod')
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)
|
bigscience/tr1-13B-codecarbon
|
bigscience
| 2021-09-14T21:38:31Z | 0 | 1 | null |
[
"region:us"
] | null | 2022-03-02T23:29:05Z |
CodeCarbon wasn't ready until the training was over so we only did an additional 10h run to measure with and then we can extrapolate to the whole training.
This set of records captures the startup time and 2499 iterations in 2 records per gpu, since there was also an intermediary checkpoint saved half-way and we flush the CC
records on each checkpoint saving.
The training had 168000 iterations. Therefore multiply the reported data by 67. This would be quite approximate since we were using 16 nodes when doing
the ramp up, then 64 and only the last 3 weeks 128 nodes.
Caveat emptor: I'm not sure whether CC-reports overlap since each report is per gpu and I think they may be measuring the same thing, other than the gpu itself.
So this requires research.
Each csv file contains a report for a single gpu.
|
deval/distilbert-base-uncased-finetuned-ner
|
deval
| 2021-09-14T19:10:43Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9276788676324229
- name: Recall
type: recall
value: 0.9384718648618414
- name: F1
type: f1
value: 0.9330441552663775
- name: Accuracy
type: accuracy
value: 0.9843836878643939
---
<!-- 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-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0606
- Precision: 0.9277
- Recall: 0.9385
- F1: 0.9330
- Accuracy: 0.9844
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2454 | 1.0 | 878 | 0.0692 | 0.9106 | 0.9212 | 0.9159 | 0.9809 |
| 0.0517 | 2.0 | 1756 | 0.0616 | 0.9203 | 0.9352 | 0.9277 | 0.9834 |
| 0.0314 | 3.0 | 2634 | 0.0606 | 0.9277 | 0.9385 | 0.9330 | 0.9844 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.0
- Tokenizers 0.10.3
|
macedonizer/gr-gpt2
|
macedonizer
| 2021-09-14T16:07:35Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"gr",
"dataset:wiki-gr",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language:
- gr
thumbnail: https://huggingface.co/macedonizer/gr-roberta-base/lets-talk-about-nlp-gr.jpg
license: apache-2.0
datasets:
- wiki-gr
---
# gr-gpt2
Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
and first released at [this page](https://openai.com/blog/better-language-models/).
## Model description
gr-gpt2 is a transformers model pretrained on a very large corpus of Greek data in a self-supervised fashion. This
means it was pretrained on the raw texts only, with no humans labeling them in any way (which is why it can use lots
of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely,
it was trained to guess the next word in sentences.
More precisely, inputs are sequences of the continuous text of a certain length and the targets are the same sequence,
shifted one token (word or piece of a word) to the right. The model uses internally a mask-mechanism to make sure the
predictions for the token `i` only uses the inputs from `1` to `i` but not the future tokens.
This way, the model learns an inner representation of the Greek language that can then be used to extract features
useful for downstream tasks. The model is best at what it was pretrained for, however, which is generating texts from a
prompt.
### How to use
Here is how to use this model to get the features of a given text in PyTorch:
import random
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained('macedonizer/gr-gpt2') \\nnmodel = AutoModelWithLMHead.from_pretrained('macedonizer/gr-gpt2')
input_text = 'Η Αθήνα είναι'
if len(input_text) == 0: \
encoded_input = tokenizer(input_text, return_tensors="pt") \
output = model.generate( \
bos_token_id=random.randint(1, 50000), \
do_sample=True, \
top_k=50, \
max_length=1024, \
top_p=0.95, \
num_return_sequences=1, \
) \
else: \
encoded_input = tokenizer(input_text, return_tensors="pt") \
output = model.generate( \
**encoded_input, \
bos_token_id=random.randint(1, 50000), \
do_sample=True, \
top_k=50, \
max_length=1024, \
top_p=0.95, \
num_return_sequences=1, \
)
decoded_output = [] \
for sample in output: \
decoded_output.append(tokenizer.decode(sample, skip_special_tokens=True))
print(decoded_output)
|
CAMeL-Lab/bert-base-arabic-camelbert-mix
|
CAMeL-Lab
| 2021-09-14T14:34:32Z | 3,211 | 15 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"Arabic",
"Dialect",
"Egyptian",
"Gulf",
"Levantine",
"Classical Arabic",
"MSA",
"Modern Standard Arabic",
"ar",
"arxiv:2103.06678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
---
language:
- ar
license: apache-2.0
tags:
- Arabic
- Dialect
- Egyptian
- Gulf
- Levantine
- Classical Arabic
- MSA
- Modern Standard Arabic
widget:
- text: "الهدف من الحياة هو [MASK] ."
---
# CAMeLBERT: A collection of pre-trained models for Arabic NLP tasks
## Model description
**CAMeLBERT** is a collection of BERT models pre-trained on Arabic texts with different sizes and variants.
We release pre-trained language models for Modern Standard Arabic (MSA), dialectal Arabic (DA), and classical Arabic (CA), in addition to a model pre-trained on a mix of the three.
We also provide additional models that are pre-trained on a scaled-down set of the MSA variant (half, quarter, eighth, and sixteenth).
The details are described in the paper *"[The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://arxiv.org/abs/2103.06678)."*
This model card describes **CAMeLBERT-Mix** (`bert-base-arabic-camelbert-mix`), a model pre-trained on a mixture of these variants: MSA, DA, and CA.
||Model|Variant|Size|#Word|
|-|-|:-:|-:|-:|
|✔|`bert-base-arabic-camelbert-mix`|CA,DA,MSA|167GB|17.3B|
||`bert-base-arabic-camelbert-ca`|CA|6GB|847M|
||`bert-base-arabic-camelbert-da`|DA|54GB|5.8B|
||`bert-base-arabic-camelbert-msa`|MSA|107GB|12.6B|
||`bert-base-arabic-camelbert-msa-half`|MSA|53GB|6.3B|
||`bert-base-arabic-camelbert-msa-quarter`|MSA|27GB|3.1B|
||`bert-base-arabic-camelbert-msa-eighth`|MSA|14GB|1.6B|
||`bert-base-arabic-camelbert-msa-sixteenth`|MSA|6GB|746M|
## Intended uses
You can use the released model for either masked language modeling or next sentence prediction.
However, it is mostly intended to be fine-tuned on an NLP task, such as NER, POS tagging, sentiment analysis, dialect identification, and poetry classification.
We release our fine-tuninig code [here](https://github.com/CAMeL-Lab/CAMeLBERT).
#### How to use
You can use this model directly with a pipeline for masked language modeling:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='CAMeL-Lab/bert-base-arabic-camelbert-mix')
>>> unmasker("الهدف من الحياة هو [MASK] .")
[{'sequence': '[CLS] الهدف من الحياة هو النجاح. [SEP]',
'score': 0.10861027985811234,
'token': 6232,
'token_str': 'النجاح'},
{'sequence': '[CLS] الهدف من الحياة هو.. [SEP]',
'score': 0.07626965641975403,
'token': 18,
'token_str': '.'},
{'sequence': '[CLS] الهدف من الحياة هو الحياة. [SEP]',
'score': 0.05131986364722252,
'token': 3696,
'token_str': 'الحياة'},
{'sequence': '[CLS] الهدف من الحياة هو الموت. [SEP]',
'score': 0.03734956309199333,
'token': 4295,
'token_str': 'الموت'},
{'sequence': '[CLS] الهدف من الحياة هو العمل. [SEP]',
'score': 0.027189988642930984,
'token': 2854,
'token_str': 'العمل'}]
```
*Note*: to download our models, you would need `transformers>=3.5.0`. Otherwise, you could download the models manually.
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained('CAMeL-Lab/bert-base-arabic-camelbert-mix')
model = AutoModel.from_pretrained('CAMeL-Lab/bert-base-arabic-camelbert-mix')
text = "مرحبا يا عالم."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import AutoTokenizer, TFAutoModel
tokenizer = AutoTokenizer.from_pretrained('CAMeL-Lab/bert-base-arabic-camelbert-mix')
model = TFAutoModel.from_pretrained('CAMeL-Lab/bert-base-arabic-camelbert-mix')
text = "مرحبا يا عالم."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Training data
- MSA (Modern Standard Arabic)
- [The Arabic Gigaword Fifth Edition](https://catalog.ldc.upenn.edu/LDC2011T11)
- [Abu El-Khair Corpus](http://www.abuelkhair.net/index.php/en/arabic/abu-el-khair-corpus)
- [OSIAN corpus](https://vlo.clarin.eu/search;jsessionid=31066390B2C9E8C6304845BA79869AC1?1&q=osian)
- [Arabic Wikipedia](https://archive.org/details/arwiki-20190201)
- The unshuffled version of the Arabic [OSCAR corpus](https://oscar-corpus.com/)
- DA (dialectal Arabic)
- A collection of dialectal Arabic data described in [our paper](https://arxiv.org/abs/2103.06678).
- CA (classical Arabic)
- [OpenITI (Version 2020.1.2)](https://zenodo.org/record/3891466#.YEX4-F0zbzc)
## Training procedure
We use [the original implementation](https://github.com/google-research/bert) released by Google for pre-training.
We follow the original English BERT model's hyperparameters for pre-training, unless otherwise specified.
### Preprocessing
- After extracting the raw text from each corpus, we apply the following pre-processing.
- We first remove invalid characters and normalize white spaces using the utilities provided by [the original BERT implementation](https://github.com/google-research/bert/blob/eedf5716ce1268e56f0a50264a88cafad334ac61/tokenization.py#L286-L297).
- We also remove lines without any Arabic characters.
- We then remove diacritics and kashida using [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools).
- Finally, we split each line into sentences with a heuristics-based sentence segmenter.
- We train a WordPiece tokenizer on the entire dataset (167 GB text) with a vocabulary size of 30,000 using [HuggingFace's tokenizers](https://github.com/huggingface/tokenizers).
- We do not lowercase letters nor strip accents.
### Pre-training
- The model was trained on a single cloud TPU (`v3-8`) for one million steps in total.
- The first 90,000 steps were trained with a batch size of 1,024 and the rest was trained with a batch size of 256.
- The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%.
- We use whole word masking and a duplicate factor of 10.
- We set max predictions per sequence to 20 for the dataset with max sequence length of 128 tokens and 80 for the dataset with max sequence length of 512 tokens.
- We use a random seed of 12345, masked language model probability of 0.15, and short sequence probability of 0.1.
- The optimizer used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after.
## Evaluation results
- We evaluate our pre-trained language models on five NLP tasks: NER, POS tagging, sentiment analysis, dialect identification, and poetry classification.
- We fine-tune and evaluate the models using 12 dataset.
- We used Hugging Face's transformers to fine-tune our CAMeLBERT models.
- We used transformers `v3.1.0` along with PyTorch `v1.5.1`.
- The fine-tuning was done by adding a fully connected linear layer to the last hidden state.
- We use \\(F_{1}\\) score as a metric for all tasks.
- Code used for fine-tuning is available [here](https://github.com/CAMeL-Lab/CAMeLBERT).
### Results
| Task | Dataset | Variant | Mix | CA | DA | MSA | MSA-1/2 | MSA-1/4 | MSA-1/8 | MSA-1/16 |
| -------------------- | --------------- | ------- | ----- | ----- | ----- | ----- | ------- | ------- | ------- | -------- |
| NER | ANERcorp | MSA | 80.8% | 67.9% | 74.1% | 82.4% | 82.0% | 82.1% | 82.6% | 80.8% |
| POS | PATB (MSA) | MSA | 98.1% | 97.8% | 97.7% | 98.3% | 98.2% | 98.3% | 98.2% | 98.2% |
| | ARZTB (EGY) | DA | 93.6% | 92.3% | 92.7% | 93.6% | 93.6% | 93.7% | 93.6% | 93.6% |
| | Gumar (GLF) | DA | 97.3% | 97.7% | 97.9% | 97.9% | 97.9% | 97.9% | 97.9% | 97.9% |
| SA | ASTD | MSA | 76.3% | 69.4% | 74.6% | 76.9% | 76.0% | 76.8% | 76.7% | 75.3% |
| | ArSAS | MSA | 92.7% | 89.4% | 91.8% | 93.0% | 92.6% | 92.5% | 92.5% | 92.3% |
| | SemEval | MSA | 69.0% | 58.5% | 68.4% | 72.1% | 70.7% | 72.8% | 71.6% | 71.2% |
| DID | MADAR-26 | DA | 62.9% | 61.9% | 61.8% | 62.6% | 62.0% | 62.8% | 62.0% | 62.2% |
| | MADAR-6 | DA | 92.5% | 91.5% | 92.2% | 91.9% | 91.8% | 92.2% | 92.1% | 92.0% |
| | MADAR-Twitter-5 | MSA | 75.7% | 71.4% | 74.2% | 77.6% | 78.5% | 77.3% | 77.7% | 76.2% |
| | NADI | DA | 24.7% | 17.3% | 20.1% | 24.9% | 24.6% | 24.6% | 24.9% | 23.8% |
| Poetry | APCD | CA | 79.8% | 80.9% | 79.6% | 79.7% | 79.9% | 80.0% | 79.7% | 79.8% |
### Results (Average)
| | Variant | Mix | CA | DA | MSA | MSA-1/2 | MSA-1/4 | MSA-1/8 | MSA-1/16 |
| -------------------- | ------- | ----- | ----- | ----- | ----- | ------- | ------- | ------- | -------- |
| Variant-wise-average<sup>[[1]](#footnote-1)</sup> | MSA | 82.1% | 75.7% | 80.1% | 83.4% | 83.0% | 83.3% | 83.2% | 82.3% |
| | DA | 74.4% | 72.1% | 72.9% | 74.2% | 74.0% | 74.3% | 74.1% | 73.9% |
| | CA | 79.8% | 80.9% | 79.6% | 79.7% | 79.9% | 80.0% | 79.7% | 79.8% |
| Macro-Average | ALL | 78.7% | 74.7% | 77.1% | 79.2% | 79.0% | 79.2% | 79.1% | 78.6% |
<a name="footnote-1">[1]</a>: Variant-wise-average refers to average over a group of tasks in the same language variant.
## Acknowledgements
This research was supported with Cloud TPUs from Google’s TensorFlow Research Cloud (TFRC).
## Citation
```bibtex
@inproceedings{inoue-etal-2021-interplay,
title = "The Interplay of Variant, Size, and Task Type in {A}rabic Pre-trained Language Models",
author = "Inoue, Go and
Alhafni, Bashar and
Baimukan, Nurpeiis and
Bouamor, Houda and
Habash, Nizar",
booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop",
month = apr,
year = "2021",
address = "Kyiv, Ukraine (Online)",
publisher = "Association for Computational Linguistics",
abstract = "In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models. To do so, we build three pre-trained language models across three variants of Arabic: Modern Standard Arabic (MSA), dialectal Arabic, and classical Arabic, in addition to a fourth language model which is pre-trained on a mix of the three. We also examine the importance of pre-training data size by building additional models that are pre-trained on a scaled-down set of the MSA variant. We compare our different models to each other, as well as to eight publicly available models by fine-tuning them on five NLP tasks spanning 12 datasets. Our results suggest that the variant proximity of pre-training data to fine-tuning data is more important than the pre-training data size. We exploit this insight in defining an optimized system selection model for the studied tasks.",
}
```
|
CAMeL-Lab/bert-base-arabic-camelbert-da
|
CAMeL-Lab
| 2021-09-14T14:29:21Z | 1,130 | 28 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
---
language:
- ar
license: apache-2.0
widget:
- text: "الهدف من الحياة هو [MASK] ."
---
# CAMeLBERT: A collection of pre-trained models for Arabic NLP tasks
## Model description
**CAMeLBERT** is a collection of BERT models pre-trained on Arabic texts with different sizes and variants.
We release pre-trained language models for Modern Standard Arabic (MSA), dialectal Arabic (DA), and classical Arabic (CA), in addition to a model pre-trained on a mix of the three.
We also provide additional models that are pre-trained on a scaled-down set of the MSA variant (half, quarter, eighth, and sixteenth).
The details are described in the paper *"[The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://arxiv.org/abs/2103.06678)."*
This model card describes **CAMeLBERT-DA** (`bert-base-arabic-camelbert-da`), a model pre-trained on the DA (dialectal Arabic) dataset.
||Model|Variant|Size|#Word|
|-|-|:-:|-:|-:|
||`bert-base-arabic-camelbert-mix`|CA,DA,MSA|167GB|17.3B|
||`bert-base-arabic-camelbert-ca`|CA|6GB|847M|
|✔|`bert-base-arabic-camelbert-da`|DA|54GB|5.8B|
||`bert-base-arabic-camelbert-msa`|MSA|107GB|12.6B|
||`bert-base-arabic-camelbert-msa-half`|MSA|53GB|6.3B|
||`bert-base-arabic-camelbert-msa-quarter`|MSA|27GB|3.1B|
||`bert-base-arabic-camelbert-msa-eighth`|MSA|14GB|1.6B|
||`bert-base-arabic-camelbert-msa-sixteenth`|MSA|6GB|746M|
## Intended uses
You can use the released model for either masked language modeling or next sentence prediction.
However, it is mostly intended to be fine-tuned on an NLP task, such as NER, POS tagging, sentiment analysis, dialect identification, and poetry classification.
We release our fine-tuninig code [here](https://github.com/CAMeL-Lab/CAMeLBERT).
#### How to use
You can use this model directly with a pipeline for masked language modeling:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='CAMeL-Lab/bert-base-arabic-camelbert-da')
>>> unmasker("الهدف من الحياة هو [MASK] .")
[{'sequence': '[CLS] الهدف من الحياة هو.. [SEP]',
'score': 0.062508225440979,
'token': 18,
'token_str': '.'},
{'sequence': '[CLS] الهدف من الحياة هو الموت. [SEP]',
'score': 0.033172328025102615,
'token': 4295,
'token_str': 'الموت'},
{'sequence': '[CLS] الهدف من الحياة هو الحياة. [SEP]',
'score': 0.029575437307357788,
'token': 3696,
'token_str': 'الحياة'},
{'sequence': '[CLS] الهدف من الحياة هو الرحيل. [SEP]',
'score': 0.02724040113389492,
'token': 11449,
'token_str': 'الرحيل'},
{'sequence': '[CLS] الهدف من الحياة هو الحب. [SEP]',
'score': 0.01564178802073002,
'token': 3088,
'token_str': 'الحب'}]
```
*Note*: to download our models, you would need `transformers>=3.5.0`. Otherwise, you could download the models manually.
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained('CAMeL-Lab/bert-base-arabic-camelbert-da')
model = AutoModel.from_pretrained('CAMeL-Lab/bert-base-arabic-camelbert-da')
text = "مرحبا يا عالم."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import AutoTokenizer, TFAutoModel
tokenizer = AutoTokenizer.from_pretrained('CAMeL-Lab/bert-base-arabic-camelbert-da')
model = TFAutoModel.from_pretrained('CAMeL-Lab/bert-base-arabic-camelbert-da')
text = "مرحبا يا عالم."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Training data
- DA (dialectal Arabic)
- A collection of dialectal Arabic data described in [our paper](https://arxiv.org/abs/2103.06678).
## Training procedure
We use [the original implementation](https://github.com/google-research/bert) released by Google for pre-training.
We follow the original English BERT model's hyperparameters for pre-training, unless otherwise specified.
### Preprocessing
- After extracting the raw text from each corpus, we apply the following pre-processing.
- We first remove invalid characters and normalize white spaces using the utilities provided by [the original BERT implementation](https://github.com/google-research/bert/blob/eedf5716ce1268e56f0a50264a88cafad334ac61/tokenization.py#L286-L297).
- We also remove lines without any Arabic characters.
- We then remove diacritics and kashida using [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools).
- Finally, we split each line into sentences with a heuristics-based sentence segmenter.
- We train a WordPiece tokenizer on the entire dataset (167 GB text) with a vocabulary size of 30,000 using [HuggingFace's tokenizers](https://github.com/huggingface/tokenizers).
- We do not lowercase letters nor strip accents.
### Pre-training
- The model was trained on a single cloud TPU (`v3-8`) for one million steps in total.
- The first 90,000 steps were trained with a batch size of 1,024 and the rest was trained with a batch size of 256.
- The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%.
- We use whole word masking and a duplicate factor of 10.
- We set max predictions per sequence to 20 for the dataset with max sequence length of 128 tokens and 80 for the dataset with max sequence length of 512 tokens.
- We use a random seed of 12345, masked language model probability of 0.15, and short sequence probability of 0.1.
- The optimizer used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after.
## Evaluation results
- We evaluate our pre-trained language models on five NLP tasks: NER, POS tagging, sentiment analysis, dialect identification, and poetry classification.
- We fine-tune and evaluate the models using 12 dataset.
- We used Hugging Face's transformers to fine-tune our CAMeLBERT models.
- We used transformers `v3.1.0` along with PyTorch `v1.5.1`.
- The fine-tuning was done by adding a fully connected linear layer to the last hidden state.
- We use \\(F_{1}\\) score as a metric for all tasks.
- Code used for fine-tuning is available [here](https://github.com/CAMeL-Lab/CAMeLBERT).
### Results
| Task | Dataset | Variant | Mix | CA | DA | MSA | MSA-1/2 | MSA-1/4 | MSA-1/8 | MSA-1/16 |
| -------------------- | --------------- | ------- | ----- | ----- | ----- | ----- | ------- | ------- | ------- | -------- |
| NER | ANERcorp | MSA | 80.8% | 67.9% | 74.1% | 82.4% | 82.0% | 82.1% | 82.6% | 80.8% |
| POS | PATB (MSA) | MSA | 98.1% | 97.8% | 97.7% | 98.3% | 98.2% | 98.3% | 98.2% | 98.2% |
| | ARZTB (EGY) | DA | 93.6% | 92.3% | 92.7% | 93.6% | 93.6% | 93.7% | 93.6% | 93.6% |
| | Gumar (GLF) | DA | 97.3% | 97.7% | 97.9% | 97.9% | 97.9% | 97.9% | 97.9% | 97.9% |
| SA | ASTD | MSA | 76.3% | 69.4% | 74.6% | 76.9% | 76.0% | 76.8% | 76.7% | 75.3% |
| | ArSAS | MSA | 92.7% | 89.4% | 91.8% | 93.0% | 92.6% | 92.5% | 92.5% | 92.3% |
| | SemEval | MSA | 69.0% | 58.5% | 68.4% | 72.1% | 70.7% | 72.8% | 71.6% | 71.2% |
| DID | MADAR-26 | DA | 62.9% | 61.9% | 61.8% | 62.6% | 62.0% | 62.8% | 62.0% | 62.2% |
| | MADAR-6 | DA | 92.5% | 91.5% | 92.2% | 91.9% | 91.8% | 92.2% | 92.1% | 92.0% |
| | MADAR-Twitter-5 | MSA | 75.7% | 71.4% | 74.2% | 77.6% | 78.5% | 77.3% | 77.7% | 76.2% |
| | NADI | DA | 24.7% | 17.3% | 20.1% | 24.9% | 24.6% | 24.6% | 24.9% | 23.8% |
| Poetry | APCD | CA | 79.8% | 80.9% | 79.6% | 79.7% | 79.9% | 80.0% | 79.7% | 79.8% |
### Results (Average)
| | Variant | Mix | CA | DA | MSA | MSA-1/2 | MSA-1/4 | MSA-1/8 | MSA-1/16 |
| -------------------- | ------- | ----- | ----- | ----- | ----- | ------- | ------- | ------- | -------- |
| Variant-wise-average<sup>[[1]](#footnote-1)</sup> | MSA | 82.1% | 75.7% | 80.1% | 83.4% | 83.0% | 83.3% | 83.2% | 82.3% |
| | DA | 74.4% | 72.1% | 72.9% | 74.2% | 74.0% | 74.3% | 74.1% | 73.9% |
| | CA | 79.8% | 80.9% | 79.6% | 79.7% | 79.9% | 80.0% | 79.7% | 79.8% |
| Macro-Average | ALL | 78.7% | 74.7% | 77.1% | 79.2% | 79.0% | 79.2% | 79.1% | 78.6% |
<a name="footnote-1">[1]</a>: Variant-wise-average refers to average over a group of tasks in the same language variant.
## Acknowledgements
This research was supported with Cloud TPUs from Google’s TensorFlow Research Cloud (TFRC).
## Citation
```bibtex
@inproceedings{inoue-etal-2021-interplay,
title = "The Interplay of Variant, Size, and Task Type in {A}rabic Pre-trained Language Models",
author = "Inoue, Go and
Alhafni, Bashar and
Baimukan, Nurpeiis and
Bouamor, Houda and
Habash, Nizar",
booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop",
month = apr,
year = "2021",
address = "Kyiv, Ukraine (Online)",
publisher = "Association for Computational Linguistics",
abstract = "In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models. To do so, we build three pre-trained language models across three variants of Arabic: Modern Standard Arabic (MSA), dialectal Arabic, and classical Arabic, in addition to a fourth language model which is pre-trained on a mix of the three. We also examine the importance of pre-training data size by building additional models that are pre-trained on a scaled-down set of the MSA variant. We compare our different models to each other, as well as to eight publicly available models by fine-tuning them on five NLP tasks spanning 12 datasets. Our results suggest that the variant proximity of pre-training data to fine-tuning data is more important than the pre-training data size. We exploit this insight in defining an optimized system selection model for the studied tasks.",
}
```
|
CAMeL-Lab/bert-base-arabic-camelbert-msa-sixteenth
|
CAMeL-Lab
| 2021-09-14T14:26:07Z | 124 | 3 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
---
language:
- ar
license: apache-2.0
widget:
- text: "الهدف من الحياة هو [MASK] ."
---
# CAMeLBERT: A collection of pre-trained models for Arabic NLP tasks
## Model description
**CAMeLBERT** is a collection of BERT models pre-trained on Arabic texts with different sizes and variants.
We release pre-trained language models for Modern Standard Arabic (MSA), dialectal Arabic (DA), and classical Arabic (CA), in addition to a model pre-trained on a mix of the three.
We also provide additional models that are pre-trained on a scaled-down set of the MSA variant (half, quarter, eighth, and sixteenth).
The details are described in the paper *"[The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://arxiv.org/abs/2103.06678)."*
This model card describes **CAMeLBERT-MSA-sixteenth** (`bert-base-arabic-camelbert-msa-sixteenth`), a model pre-trained on a sixteenth of the full MSA dataset.
||Model|Variant|Size|#Word|
|-|-|:-:|-:|-:|
||`bert-base-arabic-camelbert-mix`|CA,DA,MSA|167GB|17.3B|
||`bert-base-arabic-camelbert-ca`|CA|6GB|847M|
||`bert-base-arabic-camelbert-da`|DA|54GB|5.8B|
||`bert-base-arabic-camelbert-msa`|MSA|107GB|12.6B|
||`bert-base-arabic-camelbert-msa-half`|MSA|53GB|6.3B|
||`bert-base-arabic-camelbert-msa-quarter`|MSA|27GB|3.1B|
||`bert-base-arabic-camelbert-msa-eighth`|MSA|14GB|1.6B|
|✔|`bert-base-arabic-camelbert-msa-sixteenth`|MSA|6GB|746M|
## Intended uses
You can use the released model for either masked language modeling or next sentence prediction.
However, it is mostly intended to be fine-tuned on an NLP task, such as NER, POS tagging, sentiment analysis, dialect identification, and poetry classification.
We release our fine-tuninig code [here](https://github.com/CAMeL-Lab/CAMeLBERT).
#### How to use
You can use this model directly with a pipeline for masked language modeling:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='CAMeL-Lab/bert-base-arabic-camelbert-msa-sixteenth')
>>> unmasker("الهدف من الحياة هو [MASK] .")
[{'sequence': '[CLS] الهدف من الحياة هو التغيير. [SEP]',
'score': 0.08320745080709457,
'token': 7946,
'token_str': 'التغيير'},
{'sequence': '[CLS] الهدف من الحياة هو التعلم. [SEP]',
'score': 0.04305094853043556,
'token': 12554,
'token_str': 'التعلم'},
{'sequence': '[CLS] الهدف من الحياة هو العمل. [SEP]',
'score': 0.0417640283703804,
'token': 2854,
'token_str': 'العمل'},
{'sequence': '[CLS] الهدف من الحياة هو الحياة. [SEP]',
'score': 0.041371218860149384,
'token': 3696,
'token_str': 'الحياة'},
{'sequence': '[CLS] الهدف من الحياة هو المعرفة. [SEP]',
'score': 0.039794355630874634,
'token': 7344,
'token_str': 'المعرفة'}]
```
*Note*: to download our models, you would need `transformers>=3.5.0`. Otherwise, you could download the models manually.
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained('CAMeL-Lab/bert-base-arabic-camelbert-msa-sixteenth')
model = AutoModel.from_pretrained('CAMeL-Lab/bert-base-arabic-camelbert-msa-sixteenth')
text = "مرحبا يا عالم."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import AutoTokenizer, TFAutoModel
tokenizer = AutoTokenizer.from_pretrained('CAMeL-Lab/bert-base-arabic-camelbert-msa-sixteenth')
model = TFAutoModel.from_pretrained('CAMeL-Lab/bert-base-arabic-camelbert-msa-sixteenth')
text = "مرحبا يا عالم."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Training data
- MSA (Modern Standard Arabic)
- [The Arabic Gigaword Fifth Edition](https://catalog.ldc.upenn.edu/LDC2011T11)
- [Abu El-Khair Corpus](http://www.abuelkhair.net/index.php/en/arabic/abu-el-khair-corpus)
- [OSIAN corpus](https://vlo.clarin.eu/search;jsessionid=31066390B2C9E8C6304845BA79869AC1?1&q=osian)
- [Arabic Wikipedia](https://archive.org/details/arwiki-20190201)
- The unshuffled version of the Arabic [OSCAR corpus](https://oscar-corpus.com/)
## Training procedure
We use [the original implementation](https://github.com/google-research/bert) released by Google for pre-training.
We follow the original English BERT model's hyperparameters for pre-training, unless otherwise specified.
### Preprocessing
- After extracting the raw text from each corpus, we apply the following pre-processing.
- We first remove invalid characters and normalize white spaces using the utilities provided by [the original BERT implementation](https://github.com/google-research/bert/blob/eedf5716ce1268e56f0a50264a88cafad334ac61/tokenization.py#L286-L297).
- We also remove lines without any Arabic characters.
- We then remove diacritics and kashida using [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools).
- Finally, we split each line into sentences with a heuristics-based sentence segmenter.
- We train a WordPiece tokenizer on the entire dataset (167 GB text) with a vocabulary size of 30,000 using [HuggingFace's tokenizers](https://github.com/huggingface/tokenizers).
- We do not lowercase letters nor strip accents.
### Pre-training
- The model was trained on a single cloud TPU (`v3-8`) for one million steps in total.
- The first 90,000 steps were trained with a batch size of 1,024 and the rest was trained with a batch size of 256.
- The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%.
- We use whole word masking and a duplicate factor of 10.
- We set max predictions per sequence to 20 for the dataset with max sequence length of 128 tokens and 80 for the dataset with max sequence length of 512 tokens.
- We use a random seed of 12345, masked language model probability of 0.15, and short sequence probability of 0.1.
- The optimizer used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after.
## Evaluation results
- We evaluate our pre-trained language models on five NLP tasks: NER, POS tagging, sentiment analysis, dialect identification, and poetry classification.
- We fine-tune and evaluate the models using 12 dataset.
- We used Hugging Face's transformers to fine-tune our CAMeLBERT models.
- We used transformers `v3.1.0` along with PyTorch `v1.5.1`.
- The fine-tuning was done by adding a fully connected linear layer to the last hidden state.
- We use \\(F_{1}\\) score as a metric for all tasks.
- Code used for fine-tuning is available [here](https://github.com/CAMeL-Lab/CAMeLBERT).
### Results
| Task | Dataset | Variant | Mix | CA | DA | MSA | MSA-1/2 | MSA-1/4 | MSA-1/8 | MSA-1/16 |
| -------------------- | --------------- | ------- | ----- | ----- | ----- | ----- | ------- | ------- | ------- | -------- |
| NER | ANERcorp | MSA | 80.8% | 67.9% | 74.1% | 82.4% | 82.0% | 82.1% | 82.6% | 80.8% |
| POS | PATB (MSA) | MSA | 98.1% | 97.8% | 97.7% | 98.3% | 98.2% | 98.3% | 98.2% | 98.2% |
| | ARZTB (EGY) | DA | 93.6% | 92.3% | 92.7% | 93.6% | 93.6% | 93.7% | 93.6% | 93.6% |
| | Gumar (GLF) | DA | 97.3% | 97.7% | 97.9% | 97.9% | 97.9% | 97.9% | 97.9% | 97.9% |
| SA | ASTD | MSA | 76.3% | 69.4% | 74.6% | 76.9% | 76.0% | 76.8% | 76.7% | 75.3% |
| | ArSAS | MSA | 92.7% | 89.4% | 91.8% | 93.0% | 92.6% | 92.5% | 92.5% | 92.3% |
| | SemEval | MSA | 69.0% | 58.5% | 68.4% | 72.1% | 70.7% | 72.8% | 71.6% | 71.2% |
| DID | MADAR-26 | DA | 62.9% | 61.9% | 61.8% | 62.6% | 62.0% | 62.8% | 62.0% | 62.2% |
| | MADAR-6 | DA | 92.5% | 91.5% | 92.2% | 91.9% | 91.8% | 92.2% | 92.1% | 92.0% |
| | MADAR-Twitter-5 | MSA | 75.7% | 71.4% | 74.2% | 77.6% | 78.5% | 77.3% | 77.7% | 76.2% |
| | NADI | DA | 24.7% | 17.3% | 20.1% | 24.9% | 24.6% | 24.6% | 24.9% | 23.8% |
| Poetry | APCD | CA | 79.8% | 80.9% | 79.6% | 79.7% | 79.9% | 80.0% | 79.7% | 79.8% |
### Results (Average)
| | Variant | Mix | CA | DA | MSA | MSA-1/2 | MSA-1/4 | MSA-1/8 | MSA-1/16 |
| -------------------- | ------- | ----- | ----- | ----- | ----- | ------- | ------- | ------- | -------- |
| Variant-wise-average<sup>[[1]](#footnote-1)</sup> | MSA | 82.1% | 75.7% | 80.1% | 83.4% | 83.0% | 83.3% | 83.2% | 82.3% |
| | DA | 74.4% | 72.1% | 72.9% | 74.2% | 74.0% | 74.3% | 74.1% | 73.9% |
| | CA | 79.8% | 80.9% | 79.6% | 79.7% | 79.9% | 80.0% | 79.7% | 79.8% |
| Macro-Average | ALL | 78.7% | 74.7% | 77.1% | 79.2% | 79.0% | 79.2% | 79.1% | 78.6% |
<a name="footnote-1">[1]</a>: Variant-wise-average refers to average over a group of tasks in the same language variant.
## Acknowledgements
This research was supported with Cloud TPUs from Google’s TensorFlow Research Cloud (TFRC).
## Citation
```bibtex
@inproceedings{inoue-etal-2021-interplay,
title = "The Interplay of Variant, Size, and Task Type in {A}rabic Pre-trained Language Models",
author = "Inoue, Go and
Alhafni, Bashar and
Baimukan, Nurpeiis and
Bouamor, Houda and
Habash, Nizar",
booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop",
month = apr,
year = "2021",
address = "Kyiv, Ukraine (Online)",
publisher = "Association for Computational Linguistics",
abstract = "In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models. To do so, we build three pre-trained language models across three variants of Arabic: Modern Standard Arabic (MSA), dialectal Arabic, and classical Arabic, in addition to a fourth language model which is pre-trained on a mix of the three. We also examine the importance of pre-training data size by building additional models that are pre-trained on a scaled-down set of the MSA variant. We compare our different models to each other, as well as to eight publicly available models by fine-tuning them on five NLP tasks spanning 12 datasets. Our results suggest that the variant proximity of pre-training data to fine-tuning data is more important than the pre-training data size. We exploit this insight in defining an optimized system selection model for the studied tasks.",
}
```
|
lewtun/perceriver-test-01
|
lewtun
| 2021-09-14T14:07:26Z | 2 | 0 |
transformers
|
[
"transformers",
"pytorch",
"satflow",
"forecasting",
"timeseries",
"remote-sensing",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
license: mit
tags:
- satflow
- forecasting
- timeseries
- remote-sensing
---
# Perceiver
## Model description
[More information needed]
## Intended uses & limitations
[More information needed]
## How to use
[More information needed]
## Limitations and bias
[More information needed]
## Training data
[More information needed]
## Training procedure
[More information needed]
## Evaluation results
[More information needed]
|
mdhugol/indonesia-bert-sentiment-classification
|
mdhugol
| 2021-09-14T08:24:28Z | 11,943 | 21 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
Indonesian BERT Base Sentiment Classifier is a sentiment-text-classification model. The model was originally the pre-trained [IndoBERT Base Model (phase1 - uncased)](https://huggingface.co/indobenchmark/indobert-base-p1) model using [Prosa sentiment dataset](https://github.com/indobenchmark/indonlu/tree/master/dataset/smsa_doc-sentiment-prosa)
## How to Use
### As Text Classifier
```python
from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForSequenceClassification
pretrained= "mdhugol/indonesia-bert-sentiment-classification"
model = AutoModelForSequenceClassification.from_pretrained(pretrained)
tokenizer = AutoTokenizer.from_pretrained(pretrained)
sentiment_analysis = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
label_index = {'LABEL_0': 'positive', 'LABEL_1': 'neutral', 'LABEL_2': 'negative'}
pos_text = "Sangat bahagia hari ini"
neg_text = "Dasar anak sialan!! Kurang ajar!!"
result = sentiment_analysis(pos_text)
status = label_index[result[0]['label']]
score = result[0]['score']
print(f'Text: {pos_text} | Label : {status} ({score * 100:.3f}%)')
result = sentiment_analysis(neg_text)
status = label_index[result[0]['label']]
score = result[0]['score']
print(f'Text: {neg_text} | Label : {status} ({score * 100:.3f}%)')
```
|
huggingtweets/4by3animetits
|
huggingtweets
| 2021-09-14T06:15:43Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/4by3animetits/1631600106043/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/1437436917201637376/YMXf838Y_400x400.jpg')">
</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">Numb</div>
<div style="text-align: center; font-size: 14px;">@4by3animetits</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 Numb.
| Data | Numb |
| --- | --- |
| Tweets downloaded | 3206 |
| Retweets | 1497 |
| Short tweets | 491 |
| Tweets kept | 1218 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3pdw5mgr/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 @4by3animetits's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/5yrdnbzr) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/5yrdnbzr/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/4by3animetits')
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)
|
huggingtweets/jamescharles-loganpaul-tanamongeau
|
huggingtweets
| 2021-09-14T05:53:11Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/jamescharles-loganpaul-tanamongeau/1631598787303/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/1420806762408464385/10y3M0iO_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/1324782032124215296/HMG6-q8g_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/1401837042934468611/okzqIoMb_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">CANCELLED & James Charles & Logan Paul</div>
<div style="text-align: center; font-size: 14px;">@jamescharles-loganpaul-tanamongeau</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 CANCELLED & James Charles & Logan Paul.
| Data | CANCELLED | James Charles | Logan Paul |
| --- | --- | --- | --- |
| Tweets downloaded | 3167 | 3182 | 3246 |
| Retweets | 938 | 480 | 98 |
| Short tweets | 522 | 496 | 287 |
| Tweets kept | 1707 | 2206 | 2861 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2avr905u/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 @jamescharles-loganpaul-tanamongeau's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2at101p1) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2at101p1/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/jamescharles-loganpaul-tanamongeau')
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)
|
dbmdz/bert-base-french-europeana-cased
|
dbmdz
| 2021-09-13T21:03:24Z | 44,865 | 4 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"historic french",
"fr",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: fr
license: mit
tags:
- "historic french"
---
# 🤗 + 📚 dbmdz BERT model
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources French Europeana BERT models 🎉
# French Europeana BERT
We extracted all French texts using the `language` metadata attribute from the Europeana corpus.
The resulting corpus has a size of 63GB and consists of 11,052,528,456 tokens.
Based on the metadata information, texts from the 18th - 20th century are mainly included in the
training corpus.
Detailed information about the data and pretraining steps can be found in
[this repository](https://github.com/stefan-it/europeana-bert).
## Model weights
BERT model weights for PyTorch and TensorFlow are available.
* French Europeana BERT: `dbmdz/bert-base-french-europeana-cased` - [model hub page](https://huggingface.co/dbmdz/bert-base-french-europeana-cased/tree/main)
## Results
For results on Historic NER, please refer to [this repository](https://github.com/stefan-it/europeana-bert).
## Usage
With Transformers >= 2.3 our French Europeana BERT model can be loaded like:
```python
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-french-europeana-cased")
model = AutoModel.from_pretrained("dbmdz/bert-base-french-europeana-cased")
```
# Huggingface model hub
All models are available on the [Huggingface model hub](https://huggingface.co/dbmdz).
# Contact (Bugs, Feedback, Contribution and more)
For questions about our BERT model just open an issue
[here](https://github.com/dbmdz/berts/issues/new) 🤗
# Acknowledgments
Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC).
Thanks for providing access to the TFRC ❤️
Thanks to the generous support from the [Hugging Face](https://huggingface.co/) team,
it is possible to download our model from their S3 storage 🤗
|
shashank2123/t5-base-fine-tuned-for-Punctuation-Restoration
|
shashank2123
| 2021-09-13T14:42:51Z | 34 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: t5-base-fine-tuned-for-Punctuation-Restoration
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
---
<!-- 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-base-fine-tuned-for-Punctuation-Restoration
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1097
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 0.1796 | 1.0 | 1431 | 0.1097 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
nielsr/beit-base-patch16-224
|
nielsr
| 2021-09-13T13:36:43Z | 73 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"beit",
"image-classification",
"dataset:imagenet",
"dataset:imagenet-21k",
"arxiv:2106.08254",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- image-classification
datasets:
- imagenet
- imagenet-21k
---
# BEiT (base-sized model, fine-tuned on ImageNet-1k after being intermediately fine-tuned on ImageNet-22k)
BEiT (BERT pre-training of Image Transformers) model pre-trained in a self-supervised way on ImageNet-22k (14 million images, 21,841 classes) at resolution 224x224, and also fine-tuned on the same dataset at the same resolution. It was introduced in the paper [BEiT: BERT Pre-Training of Image Transformers](https://arxiv.org/abs/2106.08254) by Hangbo Bao, Li Dong and Furu Wei and first released in [this repository](https://github.com/microsoft/unilm/tree/master/beit).
Disclaimer: The team releasing BEiT did not write a model card for this model so this model card has been written by the Hugging Face team.
|
SIKU-BERT/sikubert
|
SIKU-BERT
| 2021-09-13T13:34:40Z | 419 | 10 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"chinese",
"classical chinese",
"literary chinese",
"ancient chinese",
"roberta",
"zh",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
---
language:
- "zh"
thumbnail: "https://raw.githubusercontent.com/SIKU-BERT/SikuBERT/main/appendix/sikubert.png"
tags:
- "chinese"
- "classical chinese"
- "literary chinese"
- "ancient chinese"
- "bert"
- "roberta"
- "pytorch"
inference: false
license: "apache-2.0"
---
# SikuBERT
## Model description
Digital humanities research needs the support of large-scale corpus and high-performance ancient Chinese natural language processing tools. The pre-training language model has greatly improved the accuracy of text mining in English and modern Chinese texts. At present, there is an urgent need for a pre-training model specifically for the automatic processing of ancient texts. We used the verified high-quality “Siku Quanshu” full-text corpus as the training set, based on the BERT deep language model architecture, we constructed the SikuBERT and SikuRoBERTa pre-training language models for intelligent processing tasks of ancient Chinese.
## How to use
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("SIKU-BERT/sikubert")
model = AutoModel.from_pretrained("SIKU-BERT/sikubert")
```
## About Us
We are from Nanjing Agricultural University.
> Created with by SIKU-BERT [](https://github.com/SIKU-BERT/SikuBERT-for-digital-humanities-and-classical-Chinese-information-processing)
|
Gregor/xlm-roberta-large-wmt21-qe
|
Gregor
| 2021-09-13T11:22:14Z | 2 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"xlm-roberta",
"adapterhub:quality_estimation/wmt21",
"region:us"
] | null | 2022-03-02T23:29:04Z |
---
tags:
- adapter-transformers
- xlm-roberta
- adapterhub:quality_estimation/wmt21
---
# Adapter `Gregor/xlm-roberta-large-wmt21-qe` for xlm-roberta-large
An [adapter](https://adapterhub.ml) for the xlm-roberta-large model that was trained on the [quality_estimation/wmt21](https://adapterhub.ml/explore/quality_estimation/wmt21/) 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 AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("xlm-roberta-large")
adapter_name = model.load_adapter("Gregor/xlm-roberta-large-wmt21-qe")
model.active_adapters = adapter_name
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
huggingtweets/lux_capital
|
huggingtweets
| 2021-09-13T09:48:37Z | 3 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/lux_capital/1631526513457/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/728194457632395264/rwtxA-v4_400x400.jpg')">
</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">Lux Capital</div>
<div style="text-align: center; font-size: 14px;">@lux_capital</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 Lux Capital.
| Data | Lux Capital |
| --- | --- |
| Tweets downloaded | 2329 |
| Retweets | 597 |
| Short tweets | 22 |
| Tweets kept | 1710 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3khqan1v/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 @lux_capital's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1gfkbn7u) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1gfkbn7u/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/lux_capital')
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)
|
eugenesiow/drln-bam
|
eugenesiow
| 2021-09-13T08:34:40Z | 69,548 | 1 |
transformers
|
[
"transformers",
"DRLN",
"super-image",
"image-super-resolution",
"dataset:eugenesiow/Div2k",
"dataset:eugenesiow/Set5",
"dataset:eugenesiow/Set14",
"dataset:eugenesiow/BSD100",
"dataset:eugenesiow/Urban100",
"arxiv:1906.12021",
"arxiv:2104.07566",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- super-image
- image-super-resolution
datasets:
- eugenesiow/Div2k
- eugenesiow/Set5
- eugenesiow/Set14
- eugenesiow/BSD100
- eugenesiow/Urban100
metrics:
- pnsr
- ssim
---
# Densely Residual Laplacian Super-Resolution (DRLN)
DRLN model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper [Densely Residual Laplacian Super-resolution](https://arxiv.org/abs/1906.12021) by Anwar et al. (2020) and first released in [this repository](https://github.com/saeed-anwar/DRLN).
The goal of image super resolution is to restore a high resolution (HR) image from a single low resolution (LR) image. The image below shows the ground truth (HR), the bicubic upscaling and model upscaling.
## Model description
Super-Resolution convolutional neural networks have recently demonstrated high-quality restoration for single images. However, existing algorithms often require very deep architectures and long training times. Furthermore, current convolutional neural networks for super-resolution are unable to exploit features at multiple scales and weigh them equally, limiting their learning capability. In this exposition, we present a compact and accurate super-resolution algorithm namely, Densely Residual Laplacian Network (DRLN). The proposed network employs cascading residual on the residual structure to allow the flow of low-frequency information to focus on learning high and mid-level features. In addition, deep supervision is achieved via the densely concatenated residual blocks settings, which also helps in learning from high-level complex features. Moreover, we propose Laplacian attention to model the crucial features to learn the inter and intra-level dependencies between the feature maps. Furthermore, comprehensive quantitative and qualitative evaluations on low-resolution, noisy low-resolution, and real historical image benchmark datasets illustrate that our DRLN algorithm performs favorably against the state-of-the-art methods visually and accurately.
This model also applies the balanced attention (BAM) method invented by [Wang et al. (2021)](https://arxiv.org/abs/2104.07566) to further improve the results.
## Intended uses & limitations
You can use the pre-trained models for upscaling your images 2x, 3x and 4x. You can also use the trainer to train a model on your own dataset.
### How to use
The model can be used with the [super_image](https://github.com/eugenesiow/super-image) library:
```bash
pip install super-image
```
Here is how to use a pre-trained model to upscale your image:
```python
from super_image import DrlnModel, ImageLoader
from PIL import Image
import requests
url = 'https://paperswithcode.com/media/datasets/Set5-0000002728-07a9793f_zA3bDjj.jpg'
image = Image.open(requests.get(url, stream=True).raw)
model = DrlnModel.from_pretrained('eugenesiow/drln-bam', scale=2) # scale 2, 3 and 4 models available
inputs = ImageLoader.load_image(image)
preds = model(inputs)
ImageLoader.save_image(preds, './scaled_2x.png') # save the output 2x scaled image to `./scaled_2x.png`
ImageLoader.save_compare(inputs, preds, './scaled_2x_compare.png') # save an output comparing the super-image with a bicubic scaling
```
[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Upscale_Images_with_Pretrained_super_image_Models.ipynb "Open in Colab")
## Training data
The models for 2x, 3x and 4x image super resolution were pretrained on [DIV2K](https://huggingface.co/datasets/eugenesiow/Div2k), a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).
## Training procedure
### Preprocessing
We follow the pre-processing and training method of [Wang et al.](https://arxiv.org/abs/2104.07566).
Low Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.
During training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.
Data augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.
We need the huggingface [datasets](https://huggingface.co/datasets?filter=task_ids:other-other-image-super-resolution) library to download the data:
```bash
pip install datasets
```
The following code gets the data and preprocesses/augments the data.
```python
from datasets import load_dataset
from super_image.data import EvalDataset, TrainDataset, augment_five_crop
augmented_dataset = load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='train')\
.map(augment_five_crop, batched=True, desc="Augmenting Dataset") # download and augment the data with the five_crop method
train_dataset = TrainDataset(augmented_dataset) # prepare the train dataset for loading PyTorch DataLoader
eval_dataset = EvalDataset(load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='validation')) # prepare the eval dataset for the PyTorch DataLoader
```
### Pretraining
The model was trained on GPU. The training code is provided below:
```python
from super_image import Trainer, TrainingArguments, DrlnModel, DrlnConfig
training_args = TrainingArguments(
output_dir='./results', # output directory
num_train_epochs=1000, # total number of training epochs
)
config = DrlnConfig(
scale=4, # train a model to upscale 4x
bam=True, # apply balanced attention to the network
)
model = DrlnModel(config)
trainer = Trainer(
model=model, # the instantiated model to be trained
args=training_args, # training arguments, defined above
train_dataset=train_dataset, # training dataset
eval_dataset=eval_dataset # evaluation dataset
)
trainer.train()
```
[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Train_super_image_Models.ipynb "Open in Colab")
## Evaluation results
The evaluation metrics include [PSNR](https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio#Quality_estimation_with_PSNR) and [SSIM](https://en.wikipedia.org/wiki/Structural_similarity#Algorithm).
Evaluation datasets include:
- Set5 - [Bevilacqua et al. (2012)](https://huggingface.co/datasets/eugenesiow/Set5)
- Set14 - [Zeyde et al. (2010)](https://huggingface.co/datasets/eugenesiow/Set14)
- BSD100 - [Martin et al. (2001)](https://huggingface.co/datasets/eugenesiow/BSD100)
- Urban100 - [Huang et al. (2015)](https://huggingface.co/datasets/eugenesiow/Urban100)
The results columns below are represented below as `PSNR/SSIM`. They are compared against a Bicubic baseline.
|Dataset |Scale |Bicubic |drln-bam |
|--- |--- |--- |--- |
|Set5 |2x |33.64/0.9292 |**38.23/0.9614** |
|Set5 |3x |30.39/0.8678 |**35.3/0.9422** |
|Set5 |4x |28.42/0.8101 |**32.49/0.8986** |
|Set14 |2x |30.22/0.8683 |**33.95/0.9206** |
|Set14 |3x |27.53/0.7737 |**31.27/0.8624** |
|Set14 |4x |25.99/0.7023 |**28.94/0.7899** |
|BSD100 |2x |29.55/0.8425 |**33.95/0.9269** |
|BSD100 |3x |27.20/0.7382 |**29.78/0.8224** |
|BSD100 |4x |25.96/0.6672 |**28.63/0.7686** |
|Urban100 |2x |26.66/0.8408 |**32.81/0.9339** |
|Urban100 |3x | |**29.82/0.8828** |
|Urban100 |4x |23.14/0.6573 |**26.53/0.7991** |
You can find a notebook to easily run evaluation on pretrained models below:
[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Evaluate_Pretrained_super_image_Models.ipynb "Open in Colab")
## BibTeX entry and citation info
```bibtex
@misc{wang2021bam,
title={BAM: A Lightweight and Efficient Balanced Attention Mechanism for Single Image Super Resolution},
author={Fanyi Wang and Haotian Hu and Cheng Shen},
year={2021},
eprint={2104.07566},
archivePrefix={arXiv},
primaryClass={eess.IV}
}
```
```bibtex
@misc{anwar2019densely,
title={Densely Residual Laplacian Super-Resolution},
author={Saeed Anwar and Nick Barnes},
year={2019},
eprint={1906.12021},
archivePrefix={arXiv},
primaryClass={eess.IV}
}
```
|
pkufool/icefall_asr_aishell_tdnn_lstm_ctc
|
pkufool
| 2021-09-13T06:42:32Z | 0 | 1 | null |
[
"region:us"
] | null | 2022-03-02T23:29:05Z |
# TDNN-LSTM model for aishell with icefall
|
Aftabhussain/Tomato_Leaf_Classifier
|
Aftabhussain
| 2021-09-13T04:14:44Z | 75 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-02T23:29:04Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: Tomato_Leaf_Classifier
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 1.0
---
# Tomato_Leaf_Classifier
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### Bacterial_spot
#### Healthy
|
arogyaGurkha/kobert-finetuned-squad_kor_v1
|
arogyaGurkha
| 2021-09-13T03:59:34Z | 44 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad_kor_v1",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- squad_kor_v1
model-index:
- name: kobert-finetuned-squad_kor_v1
results:
- task:
name: Question Answering
type: question-answering
dataset:
name: squad_kor_v1
type: squad_kor_v1
args: squad_kor_v1
---
<!-- 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. -->
# kobert-finetuned-squad_kor_v1
This model is a fine-tuned version of [monologg/kobert](https://huggingface.co/monologg/kobert) on the squad_kor_v1 dataset.
It achieves the following results on the evaluation set:
- Loss: 4.0928
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 4.0155 | 1.0 | 3808 | 4.0928 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
eugenesiow/edsr
|
eugenesiow
| 2021-09-13T03:46:42Z | 4,608 | 4 |
transformers
|
[
"transformers",
"EDSR",
"super-image",
"image-super-resolution",
"dataset:eugenesiow/Div2k",
"dataset:eugenesiow/Set5",
"dataset:eugenesiow/Set14",
"dataset:eugenesiow/BSD100",
"dataset:eugenesiow/Urban100",
"arxiv:1707.02921",
"arxiv:2104.07566",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- super-image
- image-super-resolution
datasets:
- eugenesiow/Div2k
- eugenesiow/Set5
- eugenesiow/Set14
- eugenesiow/BSD100
- eugenesiow/Urban100
metrics:
- pnsr
- ssim
---
# Enhanced Deep Residual Networks for Single Image Super-Resolution (EDSR)
EDSR model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://arxiv.org/abs/1707.02921) by Lim et al. (2017) and first released in [this repository](https://github.com/sanghyun-son/EDSR-PyTorch).
The goal of image super resolution is to restore a high resolution (HR) image from a single low resolution (LR) image. The image below shows the ground truth (HR), the bicubic upscaling x2 and EDSR upscaling x2.
## Model description
EDSR is a model that uses both deeper and wider architecture (32 ResBlocks and 256 channels) to improve performance. It uses both global and local skip connections, and up-scaling is done at the end of the network. It doesn't use batch normalization layers (input and output have similar distributions, normalizing intermediate features may not be desirable) instead it uses constant scaling layers to ensure stable training. An L1 loss function (absolute error) is used instead of L2 (MSE), the authors showed better performance empirically and it requires less computation.
This is a base model (~5mb vs ~100mb) that includes just 16 ResBlocks and 64 channels.
## Intended uses & limitations
You can use the pre-trained models for upscaling your images 2x, 3x and 4x. You can also use the trainer to train a model on your own dataset.
### How to use
The model can be used with the [super_image](https://github.com/eugenesiow/super-image) library:
```bash
pip install super-image
```
Here is how to use a pre-trained model to upscale your image:
```python
from super_image import EdsrModel, ImageLoader
from PIL import Image
import requests
url = 'https://paperswithcode.com/media/datasets/Set5-0000002728-07a9793f_zA3bDjj.jpg'
image = Image.open(requests.get(url, stream=True).raw)
model = EdsrModel.from_pretrained('eugenesiow/edsr', scale=2) # scale 2, 3 and 4 models available
inputs = ImageLoader.load_image(image)
preds = model(inputs)
ImageLoader.save_image(preds, './scaled_2x.png') # save the output 2x scaled image to `./scaled_2x.png`
ImageLoader.save_compare(inputs, preds, './scaled_2x_compare.png') # save an output comparing the super-image with a bicubic scaling
```
[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Upscale_Images_with_Pretrained_super_image_Models.ipynb "Open in Colab")
## Training data
The models for 2x, 3x and 4x image super resolution were pretrained on [DIV2K](https://huggingface.co/datasets/eugenesiow/Div2k), a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).
## Training procedure
### Preprocessing
We follow the pre-processing and training method of [Wang et al.](https://arxiv.org/abs/2104.07566).
Low Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.
During training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.
Data augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.
We need the huggingface [datasets](https://huggingface.co/datasets?filter=task_ids:other-other-image-super-resolution) library to download the data:
```bash
pip install datasets
```
The following code gets the data and preprocesses/augments the data.
```python
from datasets import load_dataset
from super_image.data import EvalDataset, TrainDataset, augment_five_crop
augmented_dataset = load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='train')\
.map(augment_five_crop, batched=True, desc="Augmenting Dataset") # download and augment the data with the five_crop method
train_dataset = TrainDataset(augmented_dataset) # prepare the train dataset for loading PyTorch DataLoader
eval_dataset = EvalDataset(load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='validation')) # prepare the eval dataset for the PyTorch DataLoader
```
### Pretraining
The model was trained on GPU. The training code is provided below:
```python
from super_image import Trainer, TrainingArguments, EdsrModel, EdsrConfig
training_args = TrainingArguments(
output_dir='./results', # output directory
num_train_epochs=1000, # total number of training epochs
)
config = EdsrConfig(
scale=4, # train a model to upscale 4x
)
model = EdsrModel(config)
trainer = Trainer(
model=model, # the instantiated model to be trained
args=training_args, # training arguments, defined above
train_dataset=train_dataset, # training dataset
eval_dataset=eval_dataset # evaluation dataset
)
trainer.train()
```
[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Train_super_image_Models.ipynb "Open in Colab")
## Evaluation results
The evaluation metrics include [PSNR](https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio#Quality_estimation_with_PSNR) and [SSIM](https://en.wikipedia.org/wiki/Structural_similarity#Algorithm).
Evaluation datasets include:
- Set5 - [Bevilacqua et al. (2012)](https://huggingface.co/datasets/eugenesiow/Set5)
- Set14 - [Zeyde et al. (2010)](https://huggingface.co/datasets/eugenesiow/Set14)
- BSD100 - [Martin et al. (2001)](https://huggingface.co/datasets/eugenesiow/BSD100)
- Urban100 - [Huang et al. (2015)](https://huggingface.co/datasets/eugenesiow/Urban100)
The results columns below are represented below as `PSNR/SSIM`. They are compared against a Bicubic baseline.
|Dataset |Scale |Bicubic |edsr |
|--- |--- |--- |--- |
|Set5 |2x |33.64/0.9292 |**38.19/0.9612** |
|Set5 |3x |30.39/0.8678 |**35.31/0.9421** |
|Set5 |4x |28.42/0.8101 |**32.5/0.8986** |
|Set14 |2x |30.22/0.8683 |**33.99/0.9215** |
|Set14 |3x |27.53/0.7737 |**31.18/0.862** |
|Set14 |4x |25.99/0.7023 |**28.92/0.7899** |
|BSD100 |2x |29.55/0.8425 |**33.89/0.9266** |
|BSD100 |3x |27.20/0.7382 |**29.77/0.8224** |
|BSD100 |4x |25.96/0.6672 |**28.62/0.7689** |
|Urban100 |2x |26.66/0.8408 |**32.68/0.9331** |
|Urban100 |3x | |**29.75/0.8825** |
|Urban100 |4x |23.14/0.6573 |**26.53/0.7995** |
You can find a notebook to easily run evaluation on pretrained models below:
[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Evaluate_Pretrained_super_image_Models.ipynb "Open in Colab")
## BibTeX entry and citation info
```bibtex
@InProceedings{Lim_2017_CVPR_Workshops,
author = {Lim, Bee and Son, Sanghyun and Kim, Heewon and Nah, Seungjun and Lee, Kyoung Mu},
title = {Enhanced Deep Residual Networks for Single Image Super-Resolution},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {July},
year = {2017}
}
```
|
huggingtweets/coinburnm
|
huggingtweets
| 2021-09-13T02:25:49Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/coinburnm/1631499945178/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/1396939691870535682/062raFlk_400x400.jpg')">
</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">Coinburn</div>
<div style="text-align: center; font-size: 14px;">@coinburnm</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 Coinburn.
| Data | Coinburn |
| --- | --- |
| Tweets downloaded | 837 |
| Retweets | 72 |
| Short tweets | 141 |
| Tweets kept | 624 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/38wldrmx/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 @coinburnm's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2z4rh9o1) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2z4rh9o1/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/coinburnm')
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)
|
pkufool/icefall_asr_librispeech_tdnn-lstm_ctc
|
pkufool
| 2021-09-13T02:05:58Z | 0 | 1 | null |
[
"region:us"
] | null | 2022-03-02T23:29:05Z |
# Pre-trained TDNN-LSTM-CTC models for the librispeech dataset with icefall.
The model was trained on full [LibriSpeech](http://openslr.org/12/) with the scripts in [icefall](https://github.com/k2-fsa/icefall).
See (https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/tdnn_lstm_ctc) for more details of this model.
## How to use
See (https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/tdnn_lstm_ctc/Pre-trained.md)
## Training procedure
The version of the mainly repositories are list below.
k2: https://github.com/k2-fsa/k2/commit/81cec9ec736d2c603ad75d933bb3e3a3706fb0dd
icefall: https://github.com/k2-fsa/icefall/commit/7a647a13780cf011f9cfe3067e87a6ebb3bb8411
lhotse: https://github.com/lhotse-speech/lhotse/commit/5dfe0f4c02b1334ebb7db6d67e1141fe406ca76b
* Install k2 and lhotse, k2 installation guide refers to https://k2.readthedocs.io/en/latest/installation/index.html, lhotse refers to https://lhotse.readthedocs.io/en/latest/getting-started.html#installation. It is better to use the given version above, but I think the latest version would be ok. And also install the requirements listed in icefall.
* Clone icefall(https://github.com/k2-fsa/icefall) and check to the commit showed above.
```
git clone https://github.com/k2-fsa/icefall
cd icefall
git checkout 7a647a13780cf011f9cfe3067e87a6ebb3bb8411
```
* Preparing data.
```
cd egs/librispeech/ASR
bash ./prepare.sh
```
* Training
```
export CUDA_VISIBLE_DEVICES="0,1,2,3"
python tdnn_lstm_ctc/train.py --bucketing-sampler True \
--concatenate-cuts False \
--max-duration 200 \
--full-libri True \
--world-size 4
```
## Evaluation results
The best decoding results (WERs) on LibriSpeech test-clean and test-other are listed below, we got this results by averaging models from epoch 14 to 19, the decoding method is `whole-lattice-rescoring`.
||test-clean|test-other|
|--|--|--|
|WER|6.59%|17.69%|
|
huggingartists/peter-paul-and-mary
|
huggingartists
| 2021-09-13T00:35:13Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/peter-paul-and-mary",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- huggingartists/peter-paul-and-mary
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/02fe78bca7c47dc6869673e7552c7978.500x338x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Peter, Paul and Mary</div>
<a href="https://genius.com/artists/peter-paul-and-mary">
<div style="text-align: center; font-size: 14px;">@peter-paul-and-mary</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from Peter, Paul and Mary.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/peter-paul-and-mary).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/peter-paul-and-mary")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/svwa6bev/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 Peter, Paul and Mary's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1s4mkr9x) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1s4mkr9x/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='huggingartists/peter-paul-and-mary')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/peter-paul-and-mary")
model = AutoModelWithLMHead.from_pretrained("huggingartists/peter-paul-and-mary")
```
## 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 Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
doyoungkim/bert-base-uncased-sst2-membership-attack
|
doyoungkim
| 2021-09-12T15:14:11Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model_index:
name: bert-base-uncased-sst2-membership-attack
---
<!-- 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-sst2-membership-attack
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unkown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6296
- Accuracy: 0.8681
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6921 | 1.0 | 3813 | 0.6263 | 0.8360 |
| 0.6916 | 2.0 | 7626 | 0.6296 | 0.8681 |
| 0.6904 | 3.0 | 11439 | 0.6105 | 0.8406 |
| 0.6886 | 4.0 | 15252 | 0.6192 | 0.8200 |
| 0.6845 | 5.0 | 19065 | 0.6250 | 0.7798 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.8.1
- Datasets 1.11.0
- Tokenizers 0.10.1
|
huggingartists/aikko
|
huggingartists
| 2021-09-12T14:10:49Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/aikko",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- huggingartists/aikko
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/a1a40316d1405fa83df2a21923d64168.1000x1000x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">aikko</div>
<a href="https://genius.com/artists/aikko">
<div style="text-align: center; font-size: 14px;">@aikko</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from aikko.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/aikko).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/aikko")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1cfdpsrg/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 aikko's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/oesyn53g) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/oesyn53g/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='huggingartists/aikko')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/aikko")
model = AutoModelWithLMHead.from_pretrained("huggingartists/aikko")
```
## 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 Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
huggingartists/krechet
|
huggingartists
| 2021-09-12T14:06:03Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/krechet",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- huggingartists/krechet
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/61181ccb60b6a0e1e7f8fb8ae2a2ab0a.1000x1000x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Krechet</div>
<a href="https://genius.com/artists/krechet">
<div style="text-align: center; font-size: 14px;">@krechet</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from Krechet.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/krechet).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/krechet")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1c2yk38s/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 Krechet's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/39bxkroc) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/39bxkroc/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='huggingartists/krechet')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/krechet")
model = AutoModelWithLMHead.from_pretrained("huggingartists/krechet")
```
## 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 Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
huggingartists/mashina-vremeni
|
huggingartists
| 2021-09-12T12:25:03Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/mashina-vremeni",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- huggingartists/mashina-vremeni
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/b780335021ab0e732601f25bd7a3d319.380x380x1.png')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Машина Времени (Mashina Vremeni)</div>
<a href="https://genius.com/artists/mashina-vremeni">
<div style="text-align: center; font-size: 14px;">@mashina-vremeni</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from Машина Времени (Mashina Vremeni).
Dataset is available [here](https://huggingface.co/datasets/huggingartists/mashina-vremeni).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/mashina-vremeni")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3r1yxrx7/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 Машина Времени (Mashina Vremeni)'s lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1cgaltpc) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1cgaltpc/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='huggingartists/mashina-vremeni')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/mashina-vremeni")
model = AutoModelWithLMHead.from_pretrained("huggingartists/mashina-vremeni")
```
## 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 Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
huggingartists/system-of-a-down
|
huggingartists
| 2021-09-12T12:08:09Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/system-of-a-down",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- huggingartists/system-of-a-down
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/5688d59e74bfc07b0531636114f56c1e.1000x1000x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">System of a Down</div>
<a href="https://genius.com/artists/system-of-a-down">
<div style="text-align: center; font-size: 14px;">@system-of-a-down</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from System of a Down.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/system-of-a-down).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/system-of-a-down")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3m1sikv8/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 System of a Down's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/wf3qe4yi) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/wf3qe4yi/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='huggingartists/system-of-a-down')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/system-of-a-down")
model = AutoModelWithLMHead.from_pretrained("huggingartists/system-of-a-down")
```
## 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 Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
pritoms/gpt-neo-125M-Byethon
|
pritoms
| 2021-09-12T11:14:38Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt_neo",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- null
model-index:
- name: gpt-neo-125M-Byethon
results:
- task:
name: Causal Language Modeling
type: text-generation
---
<!-- 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. -->
# gpt-neo-125M-Byethon
This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6609
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 237 | 0.8348 |
| No log | 2.0 | 474 | 0.6931 |
| 0.8151 | 3.0 | 711 | 0.6609 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
huggingartists/post-malone
|
huggingartists
| 2021-09-12T03:17:01Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/post-malone",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- huggingartists/post-malone
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/1010194fa644be099aa2d1329de0b230.448x448x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Post Malone</div>
<a href="https://genius.com/artists/post-malone">
<div style="text-align: center; font-size: 14px;">@post-malone</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from Post Malone.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/post-malone).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/post-malone")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/5ig21wpy/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 Post Malone's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/2ih9ntzv) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/2ih9ntzv/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='huggingartists/post-malone')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/post-malone")
model = AutoModelWithLMHead.from_pretrained("huggingartists/post-malone")
```
## 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 Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
huggingartists/armin-van-buuren
|
huggingartists
| 2021-09-12T03:06:42Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/armin-van-buuren",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- huggingartists/armin-van-buuren
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/b1a35069a1a44927425ef26c0bbda4a4.1000x1000x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Armin van Buuren</div>
<a href="https://genius.com/artists/armin-van-buuren">
<div style="text-align: center; font-size: 14px;">@armin-van-buuren</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from Armin van Buuren.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/armin-van-buuren).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/armin-van-buuren")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/hrrfc55y/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 Armin van Buuren's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/3q93rwo8) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/3q93rwo8/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='huggingartists/armin-van-buuren')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/armin-van-buuren")
model = AutoModelWithLMHead.from_pretrained("huggingartists/armin-van-buuren")
```
## 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 Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
blizrys/distilbert-base-uncased-finetuned-mnli
|
blizrys
| 2021-09-11T19:31:42Z | 5 | 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
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-mnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: mnli
metrics:
- name: Accuracy
type: accuracy
value: 0.8205807437595517
---
<!-- 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-mnli
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: 0.6753
- Accuracy: 0.8206
## 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 | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.5146 | 1.0 | 24544 | 0.4925 | 0.8049 |
| 0.4093 | 2.0 | 49088 | 0.5090 | 0.8164 |
| 0.3122 | 3.0 | 73632 | 0.5299 | 0.8185 |
| 0.2286 | 4.0 | 98176 | 0.6753 | 0.8206 |
| 0.182 | 5.0 | 122720 | 0.8372 | 0.8195 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
huggingartists/imagine-dragons
|
huggingartists
| 2021-09-11T13:36:33Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/imagine-dragons",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- huggingartists/imagine-dragons
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/ec1df125fd46ec3ef56f228df021a8cd.1000x1000x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Imagine Dragons</div>
<a href="https://genius.com/artists/imagine-dragons">
<div style="text-align: center; font-size: 14px;">@imagine-dragons</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from Imagine Dragons.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/imagine-dragons).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/imagine-dragons")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/dln6ixis/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 Imagine Dragons's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/3cj3c8z1) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/3cj3c8z1/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='huggingartists/imagine-dragons')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/imagine-dragons")
model = AutoModelWithLMHead.from_pretrained("huggingartists/imagine-dragons")
```
## 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 Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
huggingartists/sergei-letov
|
huggingartists
| 2021-09-11T12:13:08Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/sergei-letov",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- huggingartists/sergei-letov
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/a5717aec4301e2adfb464d3b85701f74.300x300x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Сергей Летов (Sergei Letov)</div>
<a href="https://genius.com/artists/sergei-letov">
<div style="text-align: center; font-size: 14px;">@sergei-letov</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from Сергей Летов (Sergei Letov).
Dataset is available [here](https://huggingface.co/datasets/huggingartists/sergei-letov).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/sergei-letov")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1chw67j7/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 Сергей Летов (Sergei Letov)'s lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/my7m2jp6) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/my7m2jp6/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='huggingartists/sergei-letov')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/sergei-letov")
model = AutoModelWithLMHead.from_pretrained("huggingartists/sergei-letov")
```
## 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 Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
huggingartists/v-x-v-prince
|
huggingartists
| 2021-09-11T11:37:19Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/v-x-v-prince",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- huggingartists/v-x-v-prince
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/08ad78acc3e91c45a426390e7524d4e9.853x853x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">V $ X V PRiNCE</div>
<a href="https://genius.com/artists/v-x-v-prince">
<div style="text-align: center; font-size: 14px;">@v-x-v-prince</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from V $ X V PRiNCE.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/v-x-v-prince).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/v-x-v-prince")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/a6qdzbfe/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 V $ X V PRiNCE's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1rv03n56) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1rv03n56/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='huggingartists/v-x-v-prince')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/v-x-v-prince")
model = AutoModelWithLMHead.from_pretrained("huggingartists/v-x-v-prince")
```
## 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 Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
airesearch/wangchanberta-base-wiki-newmm
|
airesearch
| 2021-09-11T09:39:18Z | 636 | 2 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"th",
"arxiv:1907.11692",
"arxiv:2101.09635",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language: th
---
# WangchanBERTa base model: `wangchanberta-base-wiki-newmm`
<br>
Pretrained RoBERTa BASE model on Thai Wikipedia corpus.
The script and documentation can be found at [this reposiryory](https://github.com/vistec-AI/thai2transformers).
<br>
## Model description
<br>
The architecture of the pretrained model is based on RoBERTa [[Liu et al., 2019]](https://arxiv.org/abs/1907.11692).
<br>
## Intended uses & limitations
<br>
You can use the pretrained model for masked language modeling (i.e. predicting a mask token in the input text). In addition, we also provide finetuned models for multiclass/multilabel text classification and token classification task.
<br>
**Multiclass text classification**
- `wisesight_sentiment`
4-class text classification task (`positive`, `neutral`, `negative`, and `question`) based on social media posts and tweets.
- `wongnai_reivews`
Users' review rating classification task (scale is ranging from 1 to 5)
- `generated_reviews_enth` : (`review_star` as label)
Generated users' review rating classification task (scale is ranging from 1 to 5).
**Multilabel text classification**
- `prachathai67k`
Thai topic classification with 12 labels based on news article corpus from prachathai.com. The detail is described in this [page](https://huggingface.co/datasets/prachathai67k).
**Token classification**
- `thainer`
Named-entity recognition tagging with 13 named-entities as descibed in this [page](https://huggingface.co/datasets/thainer).
- `lst20` : NER NER and POS tagging
Named-entity recognition tagging with 10 named-entities and Part-of-Speech tagging with 16 tags as descibed in this [page](https://huggingface.co/datasets/lst20).
<br>
## How to use
<br>
The getting started notebook of WangchanBERTa model can be found at this [Colab notebook](https://colab.research.google.com/drive/1Kbk6sBspZLwcnOE61adAQo30xxqOQ9ko)
<br>
## Training data
`wangchanberta-base-wiki-newmm` model was pretrained on Thai Wikipedia. Specifically, we use the Wikipedia dump articles on 20 August 2020 (dumps.wikimedia.org/thwiki/20200820/). We opt out lists, and tables.
### Preprocessing
Texts are preprocessed with the following rules:
- Replace non-breaking space, zero-width non-breaking space, and soft hyphen with spaces.
- Remove an empty parenthesis that occur right after the title of the first paragraph.
- Replace spaces wtth <_>.
<br>
Regarding the vocabulary, we use wordl-level token from [PyThaiNLP](https://github.com/PyThaiNLP/pythainlp)'s dictionary-based tokenizer namedly `newmm`. The total number of word-level tokens in the vocabulary is 97,982.
We sample sentences contigously to have the length of at most 512 tokens. For some sentences that overlap the boundary of 512 tokens, we split such sentence with an additional token as document separator. This is the same approach as proposed by [[Liu et al., 2019]](https://arxiv.org/abs/1907.11692) (called "FULL-SENTENCES").
Regarding the masking procedure, for each sequence, we sampled 15% of the tokens and replace them with<mask>token.Out of the 15%, 80% is replaced with a<mask>token, 10% is left unchanged and 10% is replaced with a random token.
<br>
**Train/Val/Test splits**
We split sequencially 944,782 sentences for training set, 24,863 sentences for validation set and 24,862 sentences for test set.
<br>
**Pretraining**
The model was trained on 32 V100 GPUs for 31,250 steps with the batch size of 8,192 (16 sequences per device with 16 accumulation steps) and a sequence length of 512 tokens. The optimizer we used is Adam with the learning rate of $7e-4$, $\beta_1 = 0.9$, $\beta_2= 0.98$ and $\epsilon = 1e-6$. The learning rate is warmed up for the first 1250 steps and linearly decayed to zero. The model checkpoint with minimum validation loss will be selected as the best model checkpoint.
<br>
**BibTeX entry and citation info**
```
@misc{lowphansirikul2021wangchanberta,
title={WangchanBERTa: Pretraining transformer-based Thai Language Models},
author={Lalita Lowphansirikul and Charin Polpanumas and Nawat Jantrakulchai and Sarana Nutanong},
year={2021},
eprint={2101.09635},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
airesearch/wangchanberta-base-wiki-20210520-spm-finetune-qa
|
airesearch
| 2021-09-11T09:28:19Z | 111 | 0 |
transformers
|
[
"transformers",
"pytorch",
"camembert",
"question-answering",
"th",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
language: th
widget:
- text: "สวนกุหลาบเป็นโรงเรียนอะไร"
context: "โรงเรียนสวนกุหลาบวิทยาลัย (Suankularb Wittayalai School) (อักษรย่อ : ส.ก. / S.K.) เป็นโรงเรียนชายล้วน ระดับชั้นมัธยมศึกษาขนาดใหญ่พิเศษ สังกัดสำนักงานเขตพื้นที่การศึกษามัธยมศึกษาเขต 1 สำนักงานคณะกรรมการการศึกษาขั้นพื้นฐาน (ชื่อเดิม: กรมสามัญศึกษา) กระทรวงศึกษาธิการ ก่อตั้งโดย พระบาทสมเด็จพระจุลจอมเกล้าเจ้าอยู่หัว ได้รับการสถาปนาขึ้นในวันที่ 8 มีนาคม พ.ศ. 2424 (ขณะนั้นนับวันที่ 1 เมษายน เป็นวันขึ้นปีใหม่ เมื่อนับอย่างสากลถือเป็น พ.ศ. 2425) โดยเป็นโรงเรียนรัฐบาลแห่งแรกของประเทศไทย"
---
# wangchanberta-base-wiki-20210520-spm-finetune-qa
Finetuning `airesearchth/wangchanberta-base-wiki-20210520-spmd` with the training set of `iapp_wiki_qa_squad`, `thaiqa_squad`, and `nsc_qa` (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the latter two are trimmed to be around 300 `newmm` words). Benchmarks shared on [wandb](https://wandb.ai/cstorm125/wangchanberta-qa) using validation and test sets of `iapp_wiki_qa_squad`.
Trained with [thai2transformers](https://github.com/vistec-AI/thai2transformers/blob/dev/scripts/downstream/train_question_answering_lm_finetuning.py).
Run with:
```
export MODEL_NAME=airesearchth/wangchanberta-base-wiki-20210520-news-spm
CUDA_LAUNCH_BLOCKING=1 python train_question_answering_lm_finetuning.py \\n --model_name $MODEL_NAME \\n --dataset_name chimera_qa \\n --output_dir $MODEL_NAME-finetune-chimera_qa-model \\n --log_dir $MODEL_NAME-finetune-chimera_qa-log \\n --model_max_length 400 \\n --pad_on_right \\n --fp16
```
|
Narrativa/spanish-gpt2-finetuned-rap-lyrics
|
Narrativa
| 2021-09-11T08:46:33Z | 11 | 5 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"GPT-2",
"Rap",
"Lyrics",
"Songs",
"es",
"dataset:large_spanish_corpus",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:04Z |
---
language: es
tags:
- GPT-2
- Rap
- Lyrics
- Songs
datasets:
- large_spanish_corpus
widget:
- text: "Déjame contarte lo importante que es buscarte un plan\nNo para golpearles o ganarles, sino para darles paz\n"
license: mit
---
# Spanish GPT-2 trained on [Spanish RAP Lyrics](https://www.kaggle.com/smunoz3801/9325-letras-de-rap-en-espaol)
Created by: [Narrativa](https://www.narrativa.com/)
About Narrativa: Natural Language Generation (NLG) | Gabriele, our machine learning-based platform, builds and deploys natural language solutions. #NLG #AI
|
arogyaGurkha/koelectra-base-discriminator-finetuned-squad_kor_v1
|
arogyaGurkha
| 2021-09-11T08:34:39Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"electra",
"question-answering",
"generated_from_trainer",
"dataset:squad_kor_v1",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- squad_kor_v1
model-index:
- name: koelectra-base-discriminator-finetuned-squad_kor_v1
results:
- task:
name: Question Answering
type: question-answering
dataset:
name: squad_kor_v1
type: squad_kor_v1
args: squad_kor_v1
---
<!-- 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. -->
# koelectra-base-discriminator-finetuned-squad_kor_v1
This model is a fine-tuned version of [monologg/koelectra-base-discriminator](https://huggingface.co/monologg/koelectra-base-discriminator) on the squad_kor_v1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5589
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.5774 | 1.0 | 4025 | 0.5589 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
huggingartists/rocket
|
huggingartists
| 2021-09-11T07:31:59Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/rocket",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- huggingartists/rocket
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/0fb709925134799103886db5e722ef73.1000x1000x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">ROCKET</div>
<a href="https://genius.com/artists/rocket">
<div style="text-align: center; font-size: 14px;">@rocket</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from ROCKET.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/rocket).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/rocket")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3ceqmb05/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 ROCKET's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/37kckftd) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/37kckftd/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='huggingartists/rocket')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/rocket")
model = AutoModelWithLMHead.from_pretrained("huggingartists/rocket")
```
## 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 Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
tobiaslee/bert-2l-768h-uncased
|
tobiaslee
| 2021-09-11T03:10:34Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
# BERT-uncased-2L-768H
This is a converted pytorch checkpoint for bert with 2L trained from scratch.
See [Google BERT](https://github.com/google-research/bert) for details.
|
megagonlabs/optimus-amzn
|
megagonlabs
| 2021-09-11T00:16:57Z | 12 | 1 |
transformers
|
[
"transformers",
"pytorch",
"summarization",
"en",
"license:bsd-3-clause",
"region:us"
] |
summarization
| 2022-03-02T23:29:05Z |
---
language: en
tags:
- summarization
inference: false
license: bsd-3-clause
---
## Optimus model
See original GitHub repo for more details [here](https://github.com/megagonlabs/coop)
|
megagonlabs/optimus-yelp
|
megagonlabs
| 2021-09-11T00:16:32Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"summarization",
"en",
"license:bsd-3-clause",
"region:us"
] |
summarization
| 2022-03-02T23:29:05Z |
---
language: en
tags:
- summarization
inference: false
license: bsd-3-clause
---
## Optimus model
See original GitHub repo for more details [here](https://github.com/megagonlabs/coop)
|
megagonlabs/bimeanvae-yelp
|
megagonlabs
| 2021-09-11T00:12:51Z | 22 | 1 |
transformers
|
[
"transformers",
"pytorch",
"summarization",
"en",
"license:bsd-3-clause",
"region:us"
] |
summarization
| 2022-03-02T23:29:05Z |
---
language: en
tags:
- summarization
inference: false
license: bsd-3-clause
---
## BiMeanVAE model
See original GitHub repo for more details [here](https://github.com/megagonlabs/coop)
|
megagonlabs/bimeanvae-amzn
|
megagonlabs
| 2021-09-11T00:10:54Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"summarization",
"en",
"license:bsd-3-clause",
"region:us"
] |
summarization
| 2022-03-02T23:29:05Z |
---
language: en
tags:
- summarization
inference: false
license: bsd-3-clause
---
## BiMeanVAE model
See original GitHub repo for more details [here](https://github.com/megagonlabs/coop)
|
huggingartists/aaron-watson
|
huggingartists
| 2021-09-10T15:49:57Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/aaron-watson",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- huggingartists/aaron-watson
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/894021d09a748eef8c6d63ad898b814b.650x430x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Aaron Watson</div>
<a href="https://genius.com/artists/aaron-watson">
<div style="text-align: center; font-size: 14px;">@aaron-watson</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from Aaron Watson.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/aaron-watson).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/aaron-watson")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/14ha1tnc/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 Aaron Watson's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/34e4zb2v) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/34e4zb2v/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='huggingartists/aaron-watson')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/aaron-watson")
model = AutoModelWithLMHead.from_pretrained("huggingartists/aaron-watson")
```
## 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 Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
huggingartists/skillet
|
huggingartists
| 2021-09-10T14:51:47Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/skillet",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- huggingartists/skillet
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/c42b7baa88dae01013eebc53c0aed177.1000x1000x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Skillet</div>
<a href="https://genius.com/artists/skillet">
<div style="text-align: center; font-size: 14px;">@skillet</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from Skillet.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/skillet).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/skillet")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1wmbkzn8/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 Skillet's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/3jke6b6i) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/3jke6b6i/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='huggingartists/skillet')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/skillet")
model = AutoModelWithLMHead.from_pretrained("huggingartists/skillet")
```
## 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 Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
huggingartists/scriptonite
|
huggingartists
| 2021-09-10T13:10:06Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/scriptonite",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- huggingartists/scriptonite
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/411d50392aef867fe0e9dd55a074ecfb.1000x1000x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Скриптонит (Scriptonite)</div>
<a href="https://genius.com/artists/scriptonite">
<div style="text-align: center; font-size: 14px;">@scriptonite</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from Скриптонит (Scriptonite).
Dataset is available [here](https://huggingface.co/datasets/huggingartists/scriptonite).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/scriptonite")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/13pxeww0/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 Скриптонит (Scriptonite)'s lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1itfp830) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1itfp830/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='huggingartists/scriptonite')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/scriptonite")
model = AutoModelWithLMHead.from_pretrained("huggingartists/scriptonite")
```
## 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 Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
huggingartists/25-17
|
huggingartists
| 2021-09-10T12:55:59Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/25-17",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- huggingartists/25-17
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/4fedc5dd2830a874a5274bf1cac62002.1000x1000x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">25/17</div>
<a href="https://genius.com/artists/25-17">
<div style="text-align: center; font-size: 14px;">@25-17</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from 25/17.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/25-17).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/25-17")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1iuytbjp/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 25/17's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/knv4l4gw) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/knv4l4gw/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='huggingartists/25-17')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/25-17")
model = AutoModelWithLMHead.from_pretrained("huggingartists/25-17")
```
## 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 Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
Riser/YOLOP
|
Riser
| 2021-09-10T09:08:34Z | 0 | 9 | null |
[
"object-detection",
"arxiv:2108.11250",
"arxiv:1612.07695",
"arxiv:1606.02147",
"region:us"
] |
object-detection
| 2022-03-02T23:29:04Z |
---
tags:
- object-detection
---
<div align="left">
## You Only Look Once for Panoptic Driving Perception
> [**You Only Look at Once for Panoptic driving Perception**](https://arxiv.org/abs/2108.11250)
>
> by Dong Wu, Manwen Liao, Weitian Zhang, [Xinggang Wang](https://xinggangw.info/) [*School of EIC, HUST*](http://eic.hust.edu.cn/English/Home.htm)
>
> *arXiv technical report ([arXiv 2108.11250](https://arxiv.org/abs/2108.11250))*
---
### The Illustration of YOLOP
### Contributions
* We put forward an efficient multi-task network that can jointly handle three crucial tasks in autonomous driving: object detection, drivable area segmentation and lane detection to save computational costs, reduce inference time as well as improve the performance of each task. Our work is the first to reach real-time on embedded devices while maintaining state-of-the-art level performance on the `BDD100K `dataset.
* We design the ablative experiments to verify the effectiveness of our multi-tasking scheme. It is proved that the three tasks can be learned jointly without tedious alternating optimization.
### Results
#### Traffic Object Detection Result
| Model | Recall(%) | mAP50(%) | Speed(fps) |
| -------------- | --------- | -------- | ---------- |
| `Multinet` | 81.3 | 60.2 | 8.6 |
| `DLT-Net` | 89.4 | 68.4 | 9.3 |
| `Faster R-CNN` | 77.2 | 55.6 | 5.3 |
| `YOLOv5s` | 86.8 | 77.2 | 82 |
| `YOLOP(ours)` | 89.2 | 76.5 | 41 |
#### Drivable Area Segmentation Result
| Model | mIOU(%) | Speed(fps) |
| ------------- | ------- | ---------- |
| `Multinet` | 71.6 | 8.6 |
| `DLT-Net` | 71.3 | 9.3 |
| `PSPNet` | 89.6 | 11.1 |
| `YOLOP(ours)` | 91.5 | 41 |
#### Lane Detection Result:
| Model | mIOU(%) | IOU(%) |
| ------------- | ------- | ------ |
| `ENet` | 34.12 | 14.64 |
| `SCNN` | 35.79 | 15.84 |
| `ENet-SAD` | 36.56 | 16.02 |
| `YOLOP(ours)` | 70.50 | 26.20 |
#### Ablation Studies 1: End-to-end v.s. Step-by-step:
| Training_method | Recall(%) | AP(%) | mIoU(%) | Accuracy(%) | IoU(%) |
| --------------- | --------- | ----- | ------- | ----------- | ------ |
| `ES-W` | 87.0 | 75.3 | 90.4 | 66.8 | 26.2 |
| `ED-W` | 87.3 | 76.0 | 91.6 | 71.2 | 26.1 |
| `ES-D-W` | 87.0 | 75.1 | 91.7 | 68.6 | 27.0 |
| `ED-S-W` | 87.5 | 76.1 | 91.6 | 68.0 | 26.8 |
| `End-to-end` | 89.2 | 76.5 | 91.5 | 70.5 | 26.2 |
#### Ablation Studies 2: Multi-task v.s. Single task:
| Training_method | Recall(%) | AP(%) | mIoU(%) | Accuracy(%) | IoU(%) | Speed(ms/frame) |
| --------------- | --------- | ----- | ------- | ----------- | ------ | --------------- |
| `Det(only)` | 88.2 | 76.9 | - | - | - | 15.7 |
| `Da-Seg(only)` | - | - | 92.0 | - | - | 14.8 |
| `Ll-Seg(only)` | - | - | - | 79.6 | 27.9 | 14.8 |
| `Multitask` | 89.2 | 76.5 | 91.5 | 70.5 | 26.2 | 24.4 |
**Notes**:
- The works we has use for reference including `Multinet` ([paper](https://arxiv.org/pdf/1612.07695.pdf?utm_campaign=affiliate-ir-Optimise%20media%28%20South%20East%20Asia%29%20Pte.%20ltd._156_-99_national_R_all_ACQ_cpa_en&utm_content=&utm_source=%20388939),[code](https://github.com/MarvinTeichmann/MultiNet)）,`DLT-Net` ([paper](https://ieeexplore.ieee.org/abstract/document/8937825)）,`Faster R-CNN` ([paper](https://proceedings.neurips.cc/paper/2015/file/14bfa6bb14875e45bba028a21ed38046-Paper.pdf),[code](https://github.com/ShaoqingRen/faster_rcnn)）,`YOLOv5s`（[code](https://github.com/ultralytics/yolov5)) ,`PSPNet`([paper](https://openaccess.thecvf.com/content_cvpr_2017/papers/Zhao_Pyramid_Scene_Parsing_CVPR_2017_paper.pdf),[code](https://github.com/hszhao/PSPNet)) ,`ENet`([paper](https://arxiv.org/pdf/1606.02147.pdf),[code](https://github.com/osmr/imgclsmob)) `SCNN`([paper](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/download/16802/16322),[code](https://github.com/XingangPan/SCNN)) `SAD-ENet`([paper](https://openaccess.thecvf.com/content_ICCV_2019/papers/Hou_Learning_Lightweight_Lane_Detection_CNNs_by_Self_Attention_Distillation_ICCV_2019_paper.pdf),[code](https://github.com/cardwing/Codes-for-Lane-Detection)). Thanks for their wonderful works.
- In table 4, E, D, S and W refer to Encoder, Detect head, two Segment heads and whole network. So the Algorithm (First, we only train Encoder and Detect head. Then we freeze the Encoder and Detect head as well as train two Segmentation heads. Finally, the entire network is trained jointly for all three tasks.) can be marked as ED-S-W, and the same for others.
---
### Visualization
#### Traffic Object Detection Result
#### Drivable Area Segmentation Result
#### Lane Detection Result
**Notes**:
- The visualization of lane detection result has been post processed by quadratic fitting.
---
### Project Structure
```python
├─inference
│ ├─images # inference images
│ ├─output # inference result
├─lib
│ ├─config/default # configuration of training and validation
│ ├─core
│ │ ├─activations.py # activation function
│ │ ├─evaluate.py # calculation of metric
│ │ ├─function.py # training and validation of model
│ │ ├─general.py #calculation of metric、nms、conversion of data-format、visualization
│ │ ├─loss.py # loss function
│ │ ├─postprocess.py # postprocess(refine da-seg and ll-seg, unrelated to paper)
│ ├─dataset
│ │ ├─AutoDriveDataset.py # Superclass dataset，general function
│ │ ├─bdd.py # Subclass dataset，specific function
│ │ ├─hust.py # Subclass dataset(Campus scene, unrelated to paper)
│ │ ├─convect.py
│ │ ├─DemoDataset.py # demo dataset(image, video and stream)
│ ├─models
│ │ ├─YOLOP.py # Setup and Configuration of model
│ │ ├─light.py # Model lightweight（unrelated to paper, zwt)
│ │ ├─commom.py # calculation module
│ ├─utils
│ │ ├─augmentations.py # data augumentation
│ │ ├─autoanchor.py # auto anchor(k-means)
│ │ ├─split_dataset.py # (Campus scene, unrelated to paper)
│ │ ├─utils.py # logging、device_select、time_measure、optimizer_select、model_save&initialize 、Distributed training
│ ├─run
│ │ ├─dataset/training time # Visualization, logging and model_save
├─tools
│ │ ├─demo.py # demo(folder、camera)
│ │ ├─test.py
│ │ ├─train.py
├─toolkits
│ │ ├─depoly # Deployment of model
├─weights # Pretraining model
```
---
### Requirement
This codebase has been developed with python version 3.7, PyTorch 1.7+ and torchvision 0.8+:
```
conda install pytorch==1.7.0 torchvision==0.8.0 cudatoolkit=10.2 -c pytorch
```
See `requirements.txt` for additional dependencies and version requirements.
```setup
pip install -r requirements.txt
```
### Data preparation
#### Download
- Download the images from [images](https://bdd-data.berkeley.edu/).
- Download the annotations of detection from [det_annotations](https://drive.google.com/file/d/1Ge-R8NTxG1eqd4zbryFo-1Uonuh0Nxyl/view?usp=sharing).
- Download the annotations of drivable area segmentation from [da_seg_annotations](https://drive.google.com/file/d/1xy_DhUZRHR8yrZG3OwTQAHhYTnXn7URv/view?usp=sharing).
- Download the annotations of lane line segmentation from [ll_seg_annotations](https://drive.google.com/file/d/1lDNTPIQj_YLNZVkksKM25CvCHuquJ8AP/view?usp=sharing).
We recommend the dataset directory structure to be the following:
```
# The id represent the correspondence relation
├─dataset root
│ ├─images
│ │ ├─train
│ │ ├─val
│ ├─det_annotations
│ │ ├─train
│ │ ├─val
│ ├─da_seg_annotations
│ │ ├─train
│ │ ├─val
│ ├─ll_seg_annotations
│ │ ├─train
│ │ ├─val
```
Update the your dataset path in the `./lib/config/default.py`.
### Training
You can set the training configuration in the `./lib/config/default.py`. (Including: the loading of preliminary model, loss, data augmentation, optimizer, warm-up and cosine annealing, auto-anchor, training epochs, batch_size).
If you want try alternating optimization or train model for single task, please modify the corresponding configuration in `./lib/config/default.py` to `True`. (As following, all configurations is `False`, which means training multiple tasks end to end).
```python
# Alternating optimization
_C.TRAIN.SEG_ONLY = False # Only train two segmentation branchs
_C.TRAIN.DET_ONLY = False # Only train detection branch
_C.TRAIN.ENC_SEG_ONLY = False # Only train encoder and two segmentation branchs
_C.TRAIN.ENC_DET_ONLY = False # Only train encoder and detection branch
# Single task
_C.TRAIN.DRIVABLE_ONLY = False # Only train da_segmentation task
_C.TRAIN.LANE_ONLY = False # Only train ll_segmentation task
_C.TRAIN.DET_ONLY = False # Only train detection task
```
Start training:
```shell
python tools/train.py
```
### Evaluation
You can set the evaluation configuration in the `./lib/config/default.py`. (Including： batch_size and threshold value for nms).
Start evaluating:
```shell
python tools/test.py --weights weights/End-to-end.pth
```
### Demo Test
We provide two testing method.
#### Folder
You can store the image or video in `--source`, and then save the reasoning result to `--save-dir`
```shell
python tools/demo --source inference/images
```
#### Camera
If there are any camera connected to your computer, you can set the `source` as the camera number(The default is 0).
```shell
python tools/demo --source 0
```
### Deployment
Our model can reason in real-time on `Jetson Tx2`, with `Zed Camera` to capture image. We use `TensorRT` tool for speeding up. We provide code for deployment and reasoning of model in `./toolkits/deploy`.
## Citation
If you find our paper and code useful for your research, please consider giving a star and citation:
```BibTeX
@misc{2108.11250,
Author = {Dong Wu and Manwen Liao and Weitian Zhang and Xinggang Wang},
Title = {YOLOP: You Only Look Once for Panoptic Driving Perception},
Year = {2021},
Eprint = {arXiv:2108.11250},
}
```
|
huggingartists/agata-christie
|
huggingartists
| 2021-09-10T09:07:11Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/agata-christie",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- huggingartists/agata-christie
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/61b6b0a0b7f6587d1b33542d5c18ad3c.489x489x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Агата Кристи (Agata Christie)</div>
<a href="https://genius.com/artists/agata-christie">
<div style="text-align: center; font-size: 14px;">@agata-christie</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from Агата Кристи (Agata Christie).
Dataset is available [here](https://huggingface.co/datasets/huggingartists/agata-christie).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/agata-christie")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1dtf6ia5/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 Агата Кристи (Agata Christie)'s lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/q27fvz1h) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/q27fvz1h/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='huggingartists/agata-christie')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/agata-christie")
model = AutoModelWithLMHead.from_pretrained("huggingartists/agata-christie")
```
## 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 Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
huggingartists/the-velvet-underground
|
huggingartists
| 2021-09-10T09:04:08Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/the-velvet-underground",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- huggingartists/the-velvet-underground
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://s3.amazonaws.com/rapgenius/vu.jpeg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">The Velvet Underground</div>
<a href="https://genius.com/artists/the-velvet-underground">
<div style="text-align: center; font-size: 14px;">@the-velvet-underground</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from The Velvet Underground.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/the-velvet-underground).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/the-velvet-underground")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/lbkqy84q/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 The Velvet Underground's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1e4s74q4) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1e4s74q4/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='huggingartists/the-velvet-underground')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/the-velvet-underground")
model = AutoModelWithLMHead.from_pretrained("huggingartists/the-velvet-underground")
```
## 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 Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
huggingartists/enigma
|
huggingartists
| 2021-09-10T08:57:05Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/enigma",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- huggingartists/enigma
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/4b5472082f220eb9c2ca6b22f4d12f45.1000x1000x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Enigma</div>
<a href="https://genius.com/artists/enigma">
<div style="text-align: center; font-size: 14px;">@enigma</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from Enigma.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/enigma).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/enigma")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/8bx90lw6/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 Enigma's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1c1t20ji) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1c1t20ji/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='huggingartists/enigma')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/enigma")
model = AutoModelWithLMHead.from_pretrained("huggingartists/enigma")
```
## 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 Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
huggingartists/kipelov
|
huggingartists
| 2021-09-10T08:40:56Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/kipelov",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- huggingartists/kipelov
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/d4ae6ad73ca63bc97b2a10dfefc47b63.1000x1000x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Кипелов (Kipelov)</div>
<a href="https://genius.com/artists/kipelov">
<div style="text-align: center; font-size: 14px;">@kipelov</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from Кипелов (Kipelov).
Dataset is available [here](https://huggingface.co/datasets/huggingartists/kipelov).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/kipelov")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/225m5y65/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 Кипелов (Kipelov)'s lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/38es269x) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/38es269x/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='huggingartists/kipelov')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/kipelov")
model = AutoModelWithLMHead.from_pretrained("huggingartists/kipelov")
```
## 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 Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
huggingartists/grigory-leps
|
huggingartists
| 2021-09-10T08:13:40Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/grigory-leps",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- huggingartists/grigory-leps
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/f30e8944a06a196868ee4b077a7926a6.1000x1000x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Григорий Лепс (Grigory Leps)</div>
<a href="https://genius.com/artists/grigory-leps">
<div style="text-align: center; font-size: 14px;">@grigory-leps</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from Григорий Лепс (Grigory Leps).
Dataset is available [here](https://huggingface.co/datasets/huggingartists/grigory-leps).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/grigory-leps")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/32wqexib/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 Григорий Лепс (Grigory Leps)'s lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1j0f6nwb) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1j0f6nwb/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='huggingartists/grigory-leps')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/grigory-leps")
model = AutoModelWithLMHead.from_pretrained("huggingartists/grigory-leps")
```
## 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 Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
huggingartists/vladimir-vysotsky
|
huggingartists
| 2021-09-10T07:47:12Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/vladimir-vysotsky",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- huggingartists/vladimir-vysotsky
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/18735fe10bace7b3f615b2da9c95ac73.938x938x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Владимир Высоцкий (Vladimir Vysotsky)</div>
<a href="https://genius.com/artists/vladimir-vysotsky">
<div style="text-align: center; font-size: 14px;">@vladimir-vysotsky</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from Владимир Высоцкий (Vladimir Vysotsky).
Dataset is available [here](https://huggingface.co/datasets/huggingartists/vladimir-vysotsky).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/vladimir-vysotsky")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1w1qc649/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 Владимир Высоцкий (Vladimir Vysotsky)'s lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1inrl5qe) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1inrl5qe/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='huggingartists/vladimir-vysotsky')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/vladimir-vysotsky")
model = AutoModelWithLMHead.from_pretrained("huggingartists/vladimir-vysotsky")
```
## 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 Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
flooptherocket/DialogGPT-small-rick
|
flooptherocket
| 2021-09-10T01:17:41Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
tags: conversational
---
@Rick from Rick and Morty GPT-2 Conversation Model
---
|
bshlgrs/autonlp-old-data-trained-10022181
|
bshlgrs
| 2021-09-09T21:46:53Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"en",
"dataset:bshlgrs/autonlp-data-old-data-trained",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- bshlgrs/autonlp-data-old-data-trained
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 10022181
## Validation Metrics
- Loss: 0.369505375623703
- Accuracy: 0.8706206896551724
- Macro F1: 0.5410226656476808
- Micro F1: 0.8706206896551724
- Weighted F1: 0.8515634683886795
- Macro Precision: 0.5159711665622992
- Micro Precision: 0.8706206896551724
- Weighted Precision: 0.8346991124101657
- Macro Recall: 0.5711653346601209
- Micro Recall: 0.8706206896551724
- Weighted Recall: 0.8706206896551724
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/bshlgrs/autonlp-old-data-trained-10022181
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("bshlgrs/autonlp-old-data-trained-10022181", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("bshlgrs/autonlp-old-data-trained-10022181", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
```
|
sevbqewre/hyou
|
sevbqewre
| 2021-09-09T17:57:48Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-03-02T23:29:05Z |
https://maccaboard.paulmccartney.com/users/watch-shang-chi-2021-full-movie-watch-online-download-hdrip
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https://maccaboard.paulmccartney.com/users/shang-chi-hindi-dubbed-movie-download-full-hd-720p-telegram
https://maccaboard.paulmccartney.com/users/watch-shang-chi-hindi-dubbed-movie-download-full-hd-720p-0
https://maccaboard.paulmccartney.com/users/hindi-dubbed-watch-shang-chi-2021-full-hd-movie-online-free
https://maccaboard.paulmccartney.com/users/download-shang-chi-2021-torrent-movie-free-hd-yts
https://maccaboard.paulmccartney.com/users/download-720p-shang-chi-2021-full-movie-watch-free
https://maccaboard.paulmccartney.com/users/watch-shang-chi-2021-full-movie-hd-online-free-download
https://maccaboard.paulmccartney.com/users/download-shang-chi-full-movie-and-watch-online-free-hd-720p
https://maccaboard.paulmccartney.com/users/123movies-free-shang-chi-2021-online-download-full-hd
|
Aleksandar/distilbert-srb-ner
|
Aleksandar
| 2021-09-09T06:27:16Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"token-classification",
"generated_from_trainer",
"sr",
"dataset:wikiann",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:04Z |
---
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
language:
- sr
model_index:
- name: distilbert-srb-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wikiann
type: wikiann
args: sr
metric:
name: Accuracy
type: accuracy
value: 0.9576561462374611
---
<!-- 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-srb-ner
This model was trained from scratch on the wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2972
- Precision: 0.8871
- Recall: 0.9100
- F1: 0.8984
- Accuracy: 0.9577
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3818 | 1.0 | 625 | 0.2175 | 0.8175 | 0.8370 | 0.8272 | 0.9306 |
| 0.198 | 2.0 | 1250 | 0.1766 | 0.8551 | 0.8732 | 0.8640 | 0.9458 |
| 0.1423 | 3.0 | 1875 | 0.1702 | 0.8597 | 0.8763 | 0.8679 | 0.9473 |
| 0.079 | 4.0 | 2500 | 0.1774 | 0.8674 | 0.8875 | 0.8773 | 0.9515 |
| 0.0531 | 5.0 | 3125 | 0.2011 | 0.8688 | 0.8965 | 0.8825 | 0.9522 |
| 0.0429 | 6.0 | 3750 | 0.2082 | 0.8769 | 0.8970 | 0.8868 | 0.9538 |
| 0.032 | 7.0 | 4375 | 0.2268 | 0.8764 | 0.8916 | 0.8839 | 0.9528 |
| 0.0204 | 8.0 | 5000 | 0.2423 | 0.8726 | 0.8959 | 0.8841 | 0.9529 |
| 0.0148 | 9.0 | 5625 | 0.2522 | 0.8774 | 0.8991 | 0.8881 | 0.9538 |
| 0.0125 | 10.0 | 6250 | 0.2544 | 0.8823 | 0.9024 | 0.8922 | 0.9559 |
| 0.0108 | 11.0 | 6875 | 0.2592 | 0.8780 | 0.9041 | 0.8909 | 0.9553 |
| 0.007 | 12.0 | 7500 | 0.2672 | 0.8877 | 0.9056 | 0.8965 | 0.9571 |
| 0.0048 | 13.0 | 8125 | 0.2714 | 0.8879 | 0.9089 | 0.8982 | 0.9583 |
| 0.0049 | 14.0 | 8750 | 0.2872 | 0.8873 | 0.9068 | 0.8970 | 0.9573 |
| 0.0034 | 15.0 | 9375 | 0.2915 | 0.8883 | 0.9114 | 0.8997 | 0.9577 |
| 0.0027 | 16.0 | 10000 | 0.2890 | 0.8865 | 0.9103 | 0.8983 | 0.9581 |
| 0.0028 | 17.0 | 10625 | 0.2885 | 0.8877 | 0.9085 | 0.8980 | 0.9576 |
| 0.0014 | 18.0 | 11250 | 0.2928 | 0.8860 | 0.9073 | 0.8965 | 0.9577 |
| 0.0013 | 19.0 | 11875 | 0.2963 | 0.8856 | 0.9099 | 0.8976 | 0.9576 |
| 0.001 | 20.0 | 12500 | 0.2972 | 0.8871 | 0.9100 | 0.8984 | 0.9577 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0
- Datasets 1.11.0
- Tokenizers 0.10.1
|
rizky22/IndoBERT
|
rizky22
| 2021-09-09T05:33:05Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-03-02T23:29:05Z |
https://sites.google.com/view/watchonline-full-hd-we-need-to/
https://sites.google.com/view/watch-hdthegateway2021fullmovi/
https://sites.google.com/view/downloadwatch-hdwildindian2021/
https://sites.google.com/view/putlocker123movieswatchkaren20/
https://sites.google.com/view/full-hdzone4142021moviewatchon/
https://sites.google.com/view/watch-hdmalignant2021onlinemov/
https://sites.google.com/view/watch-the-card-counter-2021-fu/
https://sites.google.com/view/queenpins2021onlinemoviefullhd/
https://sites.google.com/view/watch-hdsmallenginerepair2021f/
https://sites.google.com/view/shang-chi-watch/
https://sites.google.com/view/watch-vivo2021-online-free/
https://sites.google.com/view/watch-free-guy-download/
https://sites.google.com/view/hd-yakuza-princess-20/
https://www.metooo.io/e/watch-free-blue-bayou-2021-hd-movies-full-online-4k-uhd
https://www.metooo.io/e/123movies-hd-watch-the-card-counter-online-movie-2021-full-free-download0
https://www.peacefirst.org/user-profile/cry-macho-2021-movie-online-full-hd-1
https://ok.ru/group/63840774127847/topic/153545931483367
https://medium.com/@arbor.hooper/123movies-watch-the-card-counter-2021-movie-online-full-free-download-1382366cc20a
http://perencanaan.setjen.pertanian.go.id/index.php/forum/baca/123movies-watch-we-need-to-do-something-2021-movie-online-full-free-download-in-hd
|
eugenesiow/han
|
eugenesiow
| 2021-09-09T01:59:04Z | 150 | 0 |
transformers
|
[
"transformers",
"HAN",
"super-image",
"image-super-resolution",
"dataset:eugenesiow/Div2k",
"dataset:eugenesiow/Set5",
"dataset:eugenesiow/Set14",
"dataset:eugenesiow/BSD100",
"dataset:eugenesiow/Urban100",
"arxiv:2008.08767",
"arxiv:2104.07566",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- super-image
- image-super-resolution
datasets:
- eugenesiow/Div2k
- eugenesiow/Set5
- eugenesiow/Set14
- eugenesiow/BSD100
- eugenesiow/Urban100
metrics:
- pnsr
- ssim
---
# Holistic Attention Network (HAN)
HAN model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper [Single Image Super-Resolution via a Holistic Attention Network](https://arxiv.org/abs/2008.08767) by Niu et al. (2020) and first released in [this repository](https://github.com/wwlCape/HAN).
The goal of image super resolution is to restore a high resolution (HR) image from a single low resolution (LR) image. The image below shows the ground truth (HR), the bicubic upscaling and model upscaling.
## Model description
Informative features play a crucial role in the single image super-resolution task. Channel attention has been demonstrated to be effective for preserving information-rich features in each layer. However, channel attention treats each convolution layer as a separate process that misses the correlation among different layers. To address this problem, we propose a new holistic attention network (HAN), which consists of a layer attention module (LAM) and a channel-spatial attention module (CSAM), to model the holistic interdependencies among layers, channels, and positions. Specifically, the proposed LAM adaptively emphasizes hierarchical features by considering correlations among layers. Meanwhile, CSAM learns the confidence at all the positions of each channel to selectively capture more informative features. Extensive experiments demonstrate that the proposed HAN performs favorably against the state-of-the-art single image super- resolution approaches.
## Intended uses & limitations
You can use the pre-trained models for upscaling your images 2x, 3x and 4x. You can also use the trainer to train a model on your own dataset.
### How to use
The model can be used with the [super_image](https://github.com/eugenesiow/super-image) library:
```bash
pip install super-image
```
Here is how to use a pre-trained model to upscale your image:
```python
from super_image import HanModel, ImageLoader
from PIL import Image
import requests
url = 'https://paperswithcode.com/media/datasets/Set5-0000002728-07a9793f_zA3bDjj.jpg'
image = Image.open(requests.get(url, stream=True).raw)
model = HanModel.from_pretrained('eugenesiow/han', scale=2) # scale 2, 3 and 4 models available
inputs = ImageLoader.load_image(image)
preds = model(inputs)
ImageLoader.save_image(preds, './scaled_2x.png') # save the output 2x scaled image to `./scaled_2x.png`
ImageLoader.save_compare(inputs, preds, './scaled_2x_compare.png') # save an output comparing the super-image with a bicubic scaling
```
[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Upscale_Images_with_Pretrained_super_image_Models.ipynb "Open in Colab")
## Training data
The models for 2x, 3x and 4x image super resolution were pretrained on [DIV2K](https://huggingface.co/datasets/eugenesiow/Div2k), a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).
## Training procedure
### Preprocessing
We follow the pre-processing and training method of [Wang et al.](https://arxiv.org/abs/2104.07566).
Low Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.
During training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.
Data augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.
We need the huggingface [datasets](https://huggingface.co/datasets?filter=task_ids:other-other-image-super-resolution) library to download the data:
```bash
pip install datasets
```
The following code gets the data and preprocesses/augments the data.
```python
from datasets import load_dataset
from super_image.data import EvalDataset, TrainDataset, augment_five_crop
augmented_dataset = load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='train')\
.map(augment_five_crop, batched=True, desc="Augmenting Dataset") # download and augment the data with the five_crop method
train_dataset = TrainDataset(augmented_dataset) # prepare the train dataset for loading PyTorch DataLoader
eval_dataset = EvalDataset(load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='validation')) # prepare the eval dataset for the PyTorch DataLoader
```
### Pretraining
The model was trained on GPU. The training code is provided below:
```python
from super_image import Trainer, TrainingArguments, HanModel, HanConfig
training_args = TrainingArguments(
output_dir='./results', # output directory
num_train_epochs=1000, # total number of training epochs
)
config = HanConfig(
scale=4, # train a model to upscale 4x
)
model = HanModel(config)
trainer = Trainer(
model=model, # the instantiated model to be trained
args=training_args, # training arguments, defined above
train_dataset=train_dataset, # training dataset
eval_dataset=eval_dataset # evaluation dataset
)
trainer.train()
```
[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Train_super_image_Models.ipynb "Open in Colab")
## Evaluation results
The evaluation metrics include [PSNR](https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio#Quality_estimation_with_PSNR) and [SSIM](https://en.wikipedia.org/wiki/Structural_similarity#Algorithm).
Evaluation datasets include:
- Set5 - [Bevilacqua et al. (2012)](https://huggingface.co/datasets/eugenesiow/Set5)
- Set14 - [Zeyde et al. (2010)](https://huggingface.co/datasets/eugenesiow/Set14)
- BSD100 - [Martin et al. (2001)](https://huggingface.co/datasets/eugenesiow/BSD100)
- Urban100 - [Huang et al. (2015)](https://huggingface.co/datasets/eugenesiow/Urban100)
The results columns below are represented below as `PSNR/SSIM`. They are compared against a Bicubic baseline.
|Dataset |Scale |Bicubic |han |
|--- |--- |--- |--- |
|Set5 |2x |33.64/0.9292 |**** |
|Set5 |3x |30.39/0.8678 |**** |
|Set5 |4x |28.42/0.8101 |**31.21/0.8778** |
|Set14 |2x |30.22/0.8683 |**** |
|Set14 |3x |27.53/0.7737 |**** |
|Set14 |4x |25.99/0.7023 |**28.18/0.7712** |
|BSD100 |2x |29.55/0.8425 |**** |
|BSD100 |3x |27.20/0.7382 |**** |
|BSD100 |4x |25.96/0.6672 |**28.09/0.7533** |
|Urban100 |2x |26.66/0.8408 |**** |
|Urban100 |3x | |**** |
|Urban100 |4x |23.14/0.6573 |**25.1/0.7497** |
You can find a notebook to easily run evaluation on pretrained models below:
[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Evaluate_Pretrained_super_image_Models.ipynb "Open in Colab")
## BibTeX entry and citation info
```bibtex
@misc{niu2020single,
title={Single Image Super-Resolution via a Holistic Attention Network},
author={Ben Niu and Weilei Wen and Wenqi Ren and Xiangde Zhang and Lianping Yang and Shuzhen Wang and Kaihao Zhang and Xiaochun Cao and Haifeng Shen},
year={2020},
eprint={2008.08767},
archivePrefix={arXiv},
primaryClass={eess.IV}
}
```
|
elisno/is_core_web_trf
|
elisno
| 2021-09-08T21:19:54Z | 4 | 0 |
spacy
|
[
"spacy",
"token-classification",
"is",
"model-index",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
tags:
- spacy
- token-classification
language:
- is
model-index:
- name: is_core_web_trf
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.9193318395
- name: NER Recall
type: recall
value: 0.9217728758
- name: NER F Score
type: f_score
value: 0.9205507394
---
| Feature | Description |
| --- | --- |
| **Name** | `is_core_web_trf` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.1.1,<3.2.0` |
| **Default Pipeline** | `transformer`, `ner`, `tagger`, `parser` |
| **Components** | `transformer`, `ner`, `tagger`, `parser` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |
### Label Scheme
<details>
<summary>View label scheme (591 labels for 3 components)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `Date`, `Location`, `Miscellaneous`, `Money`, `Organization`, `Percent`, `Person`, `Time` |
| **`tagger`** | `aa`, `aae`, `aam`, `af`, `afe`, `afm`, `au`, `c`, `cn`, `ct`, `e`, `fahee`, `fahen`, `faheo`, `faheþ`, `fahfe`, `fahfn`, `fahfo`, `fahfþ`, `fakee`, `faken`, `fakeo`, `fakeþ`, `fakfe`, `fakfn`, `fakfo`, `fakfþ`, `favee`, `faven`, `faveo`, `faveþ`, `favfe`, `favfn`, `favfo`, `favfþ`, `fbhee`, `fbhen`, `fbheo`, `fbheþ`, `fbhfe`, `fbhfn`, `fbhfo`, `fbhfþ`, `fbkee`, `fbken`, `fbkeo`, `fbkeþ`, `fbkfe`, `fbkfn`, `fbkfo`, `fbkfþ`, `fbvee`, `fbven`, `fbveo`, `fbveþ`, `fbvfe`, `fbvfn`, `fbvfo`, `fbvfþ`, `fehee`, `fehen`, `feheo`, `feheþ`, `fehfe`, `fehfn`, `fehfo`, `fehfþ`, `fekee`, `feken`, `fekeo`, `fekeþ`, `fekfe`, `fekfn`, `fekfo`, `fekfþ`, `fevee`, `feven`, `feveo`, `feveþ`, `fevfe`, `fevfn`, `fevfo`, `fevfþ`, `fohee`, `fohen`, `foheo`, `foheþ`, `fohfe`, `fohfn`, `fohfo`, `fohfþ`, `fokee`, `foken`, `fokeo`, `fokeþ`, `fokfe`, `fokfn`, `fokfo`, `fokfþ`, `fovee`, `foven`, `foveo`, `foveþ`, `fovfe`, `fovfn`, `fovfo`, `fovfþ`, `fp1ee`, `fp1en`, `fp1eo`, `fp1eþ`, `fp1fe`, `fp1fn`, `fp1fo`, `fp1fþ`, `fp2ee`, `fp2en`, `fp2eo`, `fp2eþ`, `fp2fe`, `fp2fn`, `fp2fo`, `fp2fþ`, `fphee`, `fphen`, `fpheo`, `fpheþ`, `fphfe`, `fphfn`, `fphfo`, `fphfþ`, `fpkee`, `fpken`, `fpkeo`, `fpkeþ`, `fpkfe`, `fpkfn`, `fpkfo`, `fpkfþ`, `fpvee`, `fpven`, `fpveo`, `fpveþ`, `fpvfe`, `fpvfn`, `fpvfo`, `fpvfþ`, `fshee`, `fshen`, `fsheo`, `fsheþ`, `fshfe`, `fshfn`, `fshfo`, `fshfþ`, `fskee`, `fsken`, `fskeo`, `fskeþ`, `fskfe`, `fskfn`, `fskfo`, `fskfþ`, `fsvee`, `fsven`, `fsveo`, `fsveþ`, `fsvfe`, `fsvfn`, `fsvfo`, `fsvfþ`, `ghee`, `ghen`, `gheo`, `gheþ`, `ghfe`, `ghfn`, `ghfo`, `ghfþ`, `gkee`, `gken`, `gkeo`, `gkeþ`, `gkfe`, `gkfn`, `gkfo`, `gkfþ`, `gvee`, `gven`, `gveo`, `gveþ`, `gvfe`, `gvfn`, `gvfo`, `gvfþ`, `ks`, `kt`, `lheeof`, `lheesf`, `lheeve`, `lheevf`, `lheevm`, `lhenof`, `lhense`, `lhensf`, `lhenve`, `lhenvf`, `lhenvm`, `lheoof`, `lheose`, `lheosf`, `lheosm`, `lheove`, `lheovf`, `lheovm`, `lheþof`, `lheþse`, `lheþsf`, `lheþve`, `lheþvf`, `lheþvm`, `lhfeof`, `lhfese`, `lhfesf`, `lhfeve`, `lhfevf`, `lhfevm`, `lhfnof`, `lhfnse`, `lhfnsf`, `lhfnve`, `lhfnvf`, `lhfnvm`, `lhfoof`, `lhfose`, `lhfosf`, `lhfove`, `lhfovf`, `lhfovm`, `lhfþof`, `lhfþse`, `lhfþsf`, `lhfþve`, `lhfþvf`, `lhfþvm`, `lkeeof`, `lkeesf`, `lkeeve`, `lkeevf`, `lkeevm`, `lkenof`, `lkense`, `lkensf`, `lkenve`, `lkenvf`, `lkenvm`, `lkeoof`, `lkeose`, `lkeosf`, `lkeove`, `lkeovf`, `lkeovm`, `lkeþof`, `lkeþse`, `lkeþsf`, `lkeþve`, `lkeþvf`, `lkeþvm`, `lkfeof`, `lkfese`, `lkfesf`, `lkfeve`, `lkfevf`, `lkfevm`, `lkfnof`, `lkfnse`, `lkfnsf`, `lkfnve`, `lkfnvf`, `lkfnvm`, `lkfoof`, `lkfose`, `lkfosf`, `lkfove`, `lkfovf`, `lkfovm`, `lkfþof`, `lkfþse`, `lkfþsf`, `lkfþsm`, `lkfþve`, `lkfþvf`, `lkfþvm`, `lveeof`, `lveese`, `lveesf`, `lveeve`, `lveevf`, `lveevm`, `lvenof`, `lvense`, `lvensf`, `lvenve`, `lvenvf`, `lvenvm`, `lveoof`, `lveose`, `lveosf`, `lveove`, `lveovf`, `lveovm`, `lveþof`, `lveþse`, `lveþsf`, `lveþve`, `lveþvf`, `lveþvm`, `lvfeof`, `lvfese`, `lvfesf`, `lvfeve`, `lvfevf`, `lvfevm`, `lvfnof`, `lvfnse`, `lvfnsf`, `lvfnve`, `lvfnvf`, `lvfnvm`, `lvfoof`, `lvfose`, `lvfosf`, `lvfove`, `lvfovf`, `lvfovm`, `lvfþof`, `lvfþse`, `lvfþsf`, `lvfþsm`, `lvfþve`, `lvfþvf`, `lvfþvm`, `m`, `n----s`, `n-ee`, `n-ee-s`, `n-en`, `n-en-s`, `n-eng`, `n-eo`, `n-eo-s`, `n-eþ`, `n-eþ-s`, `n-fn`, `nhee`, `nhee-s`, `nheeg`, `nheegs`, `nhen`, `nhen-s`, `nheng`, `nhengs`, `nheo`, `nheo-s`, `nheog`, `nheogs`, `nheþ`, `nheþ-s`, `nheþg`, `nheþgs`, `nhfe`, `nhfe-s`, `nhfeg`, `nhfegs`, `nhfn`, `nhfn-s`, `nhfng`, `nhfngs`, `nhfo`, `nhfo-s`, `nhfog`, `nhfogs`, `nhfþ`, `nhfþ-s`, `nhfþg`, `nhfþgs`, `nkee`, `nkee-s`, `nkeeg`, `nkeegs`, `nken`, `nken-s`, `nkeng`, `nkengs`, `nkeo`, `nkeo-s`, `nkeog`, `nkeogs`, `nkeþ`, `nkeþ-s`, `nkeþg`, `nkeþgs`, `nkfe`, `nkfe-s`, `nkfeg`, `nkfegs`, `nkfn`, `nkfn-s`, `nkfng`, `nkfngs`, `nkfo`, `nkfo-s`, `nkfog`, `nkfogs`, `nkfþ`, `nkfþ-s`, `nkfþg`, `nkfþgs`, `nvee`, `nvee-s`, `nveeg`, `nveegs`, `nven`, `nven-s`, `nveng`, `nvengs`, `nveo`, `nveo-s`, `nveog`, `nveogs`, `nveþ`, `nveþ-s`, `nveþg`, `nveþgs`, `nvfe`, `nvfe-s`, `nvfeg`, `nvfegs`, `nvfn`, `nvfn-s`, `nvfng`, `nvfngs`, `nvfo`, `nvfo-s`, `nvfog`, `nvfogs`, `nvfþ`, `nvfþ-s`, `nvfþg`, `nvfþgs`, `pa`, `pg`, `pk`, `pl`, `sbg2en`, `sbg2fn`, `sbm2en`, `sbm2fn`, `sfg1en`, `sfg1eþ`, `sfg1fn`, `sfg1fþ`, `sfg2en`, `sfg2eþ`, `sfg2fn`, `sfg2fþ`, `sfg3en`, `sfg3eþ`, `sfg3fn`, `sfg3fþ`, `sfm1en`, `sfm1eþ`, `sfm1fn`, `sfm1fþ`, `sfm2en`, `sfm2eþ`, `sfm2fn`, `sfm2fþ`, `sfm3en`, `sfm3eþ`, `sfm3fn`, `sfm3fþ`, `slg`, `sng`, `snm`, `svg1en`, `svg1eþ`, `svg1fn`, `svg1fþ`, `svg2en`, `svg2eþ`, `svg2fn`, `svg2fþ`, `svg3en`, `svg3eþ`, `svg3fn`, `svg3fþ`, `svm1en`, `svm1eþ`, `svm1fn`, `svm1fþ`, `svm2en`, `svm2eþ`, `svm2fn`, `svm3en`, `svm3eþ`, `svm3fn`, `svm3fþ`, `sþghen`, `sþgheo`, `sþghfn`, `sþghfo`, `sþgken`, `sþgkeo`, `sþgkfn`, `sþgkfo`, `sþgven`, `sþgveo`, `sþgvfn`, `sþgvfo`, `sþgvfþ`, `sþmhen`, `sþmheo`, `sþmken`, `sþmven`, `ta`, `tfhee`, `tfhen`, `tfheo`, `tfheþ`, `tfhfe`, `tfhfn`, `tfhfo`, `tfhfþ`, `tfkee`, `tfken`, `tfkeo`, `tfkeþ`, `tfkfe`, `tfkfn`, `tfkfo`, `tfkfþ`, `tfvee`, `tfven`, `tfveo`, `tfveþ`, `tfvfe`, `tfvfn`, `tfvfo`, `tfvfþ`, `to`, `tp`, `v`, `x` |
| **`parser`** | `ROOT`, `acl`, `acl:relcl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `case`, `cc`, `ccomp`, `compound:prt`, `conj`, `cop`, `dep`, `det`, `fixed`, `flat:name`, `mark`, `nmod`, `nmod:poss`, `nsubj`, `nummod`, `obj`, `obl`, `obl:arg`, `parataxis`, `punct`, `xcomp` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `ENTS_F` | 92.06 |
| `ENTS_P` | 91.93 |
| `ENTS_R` | 92.18 |
| `TRANSFORMER_LOSS` | 248325.98 |
| `NER_LOSS` | 120059.07 |
|
LeoCordoba/mt5-small-cc-news-es-titles
|
LeoCordoba
| 2021-09-08T17:03:30Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"spanish",
"es",
"dataset:LeoCordoba/CC-NEWS-ES-titles",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-03-02T23:29:04Z |
---
language: es
tags:
- summarization
- mt5
- spanish
license: apache-2.0
datasets:
- LeoCordoba/CC-NEWS-ES-titles
model-index:
- name: mt5-small-ccnews-titles-es
results:
- task:
name: Abstractive Text Summarization
type: abstractive-text-summarization
dataset:
name: "CCNEWS-ES-titles"
type: LeoCordoba/CC-NEWS-ES-titles
metrics:
- name: Validation ROGUE-1
type: rogue-1
value: 22.6623
- name: Validation ROGUE-2
type: rogue-2
value: 7.7894
- name: Validation ROGUE-L
type: rogue-l
value: 19.8015
- name: Validation ROGUE-Lsum
type: rogue-lsum
value: 19.8092
- name: Test ROGUE-1
type: rogue-1
value: 22.9263
- name: Test ROGUE-2
type: rogue-2
value: 7.9146
- name: Test ROGUE-L
type: rogue-l
value: 20.0272
- name: Test ROGUE-Lsum
type: rogue-lsum
value: 20.0387
widget:
- text: "La chocotorta, el tradicional y práctico antojo dulce de los argentinos, fue elegida como el mejor postre del mundo por críticos de restaurants internacionales, a casi 40 años de su creación. El ránking Taste Atlas ubicó primero en su lista al postre insignia local de galletitas, queso crema y dulce de leche, por delante del helado de pistacho italiano y la tarta alemana de manzana. “Este postre argentino sin hornear fue influenciado por la cocina italiana y se inspiró en el famoso tiramisú italiano. Está elaborado con tres ingredientes básicos argentinos: galletas de chocolate, dulce de leche y queso crema”, explica la página web que exhorta a los turistas de todo el mundo a que prueben la chocotorta. En la votación, superó también a los waffles belgas y el zserbó húngaro. A nivel local le sigue el alfajor, con 4,2 puntos contra los 4,7 de la torta. En el texto que acompaña al listón dorado de “postre número uno“, los expertos enseñan además cómo se hacen las chocotortas, paso por paso. “Las galletas se ablandan en leche y se cubren con una combinación de queso crema y dulce de leche. Las formas de la chocotorta pueden variar, mientras que las galletas se pueden remojar con leche con chocolate, café o incluso licor de café”, detallan. Por último, adjudican su creación a una “campaña de márketing” diseñada para promover las galletitas icónicas que le dan su nombre. La chocotorta, infaltable en los cumpleaños argentinos, fue creada en 1982 por una creativa de las agencias más importantes del país, Marité Mabragaña."
---
## Hyperparameters
{
"max_target_length": 64,
"model_name_or_path": "google/mt5-small",
"num_train_epochs": 3,
"seed": 7,
"summary_column": "output_text",
"text_column": "text",
"encoder_max_length" : 512,
"decoder_max_length" :36,
"batch_size" : 128
}
## Usage
```
article = """ La chocotorta, el tradicional y práctico antojo dulce de los argentinos, fue elegida como el mejor postre del mundo por críticos de restaurants internacionales, a casi 40 años de su creación. El ránking Taste Atlas ubicó primero en su lista al postre insignia local de galletitas, queso crema y dulce de leche, por delante del helado de pistacho italiano y la tarta alemana de manzana. “Este postre argentino sin hornear fue influenciado por la cocina italiana y se inspiró en el famoso tiramisú italiano. Está elaborado con tres ingredientes básicos argentinos: galletas de chocolate, dulce de leche y queso crema”, explica la página web que exhorta a los turistas de todo el mundo a que prueben la chocotorta. En la votación, superó también a los waffles belgas y el zserbó húngaro. A nivel local le sigue el alfajor, con 4,2 puntos contra los 4,7 de la torta. En el texto que acompaña al listón dorado de “postre número uno", los expertos enseñan además cómo se hacen las chocotortas, paso por paso. “Las galletas se ablandan en leche y se cubren con una combinación de queso crema y dulce de leche. Las formas de la chocotorta pueden variar, mientras que las galletas se pueden remojar con leche con chocolate, café o incluso licor de café”, detallan. Por último, adjudican su creación a una “campaña de márketing” diseñada para promover las galletitas icónicas que le dan su nombre. La chocotorta, infaltable en los cumpleaños argentinos, fue creada en 1982 por una creativa de las agencias más importantes del país, Marité Mabragaña. """
from transformers import pipeline
summarizer = pipeline("summarization", model="LeoCordoba/mt5-small-ccnews-titles-es")
summarizer(article, min_length=5, max_length=64)
```
## Results
| metric | score |
| --- | ----- |
| eval_loss | 2.879085063934326 |
| eval_rouge1 | 22.6623 |
| eval_rouge2 | 7.7894 |
| eval_rougeL | 19.8015, |
| eval_rougeLsum | 19.8092 |
| eval_gen_len | 17.1839 |
| test_loss | 2.878429412841797 |
| test_rouge1 | 22.9263 |
| test_rouge2 | 7.9146 |
| test_rougeL | 20.0272 |
| test_rougeLsum | 20.0387 |
| test_gen_len | 17.1696 |
|
sv/gpt2-nft-poetry
|
sv
| 2021-09-08T16:15:47Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- null
model-index:
- name: gpt2-nft-poetry
results:
- task:
name: Causal Language Modeling
type: text-generation
---
<!-- 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. -->
# gpt2-nft-poetry
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.0243
## 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: 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 282 | 4.3092 |
| 4.5403 | 2.0 | 564 | 4.1283 |
| 4.5403 | 3.0 | 846 | 4.0605 |
| 4.039 | 4.0 | 1128 | 4.0321 |
| 4.039 | 5.0 | 1410 | 4.0243 |
### Framework versions
- Transformers 4.10.0
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
charlecheng/distilbert-base-uncased-finetuned-ner
|
charlecheng
| 2021-09-08T03:51:22Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9276454293628809
- name: Recall
type: recall
value: 0.9365700861393892
- name: F1
type: f1
value: 0.9320863950122468
- name: Accuracy
type: accuracy
value: 0.9840500738716699
---
<!-- 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-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0607
- Precision: 0.9276
- Recall: 0.9366
- F1: 0.9321
- Accuracy: 0.9841
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.246 | 1.0 | 878 | 0.0696 | 0.9152 | 0.9215 | 0.9183 | 0.9812 |
| 0.0518 | 2.0 | 1756 | 0.0606 | 0.9196 | 0.9342 | 0.9269 | 0.9831 |
| 0.0309 | 3.0 | 2634 | 0.0607 | 0.9276 | 0.9366 | 0.9321 | 0.9841 |
### Framework versions
- Transformers 4.10.0
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
fihtrotuld/123
|
fihtrotuld
| 2021-09-08T01:35:59Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-03-02T23:29:05Z |
import requests
API_URL = "https://api-inference.huggingface.co/models/huggingface/prunebert-base-uncased-6-finepruned-w-distil-squad"
headers = {"Authorization": "Bearer api_UXqrzQBiZKXaWxstVwEKcYvHQpGSGiQGbr"}
def query(payload):
response = requests.post(API_URL, headers=headers, json=payload)
return response.json()
output = query({
"inputs": {
"question": "What's my name?",
"context": "My name is Clara and I live in Berkeley.",
},
})
|
nateraw/timm-resnet50-beans
|
nateraw
| 2021-09-07T17:21:50Z | 14 | 1 |
timm
|
[
"timm",
"pytorch",
"image-classification",
"region:us"
] |
image-classification
| 2022-03-02T23:29:05Z |
---
tags:
- image-classification
- timm
library_tag: timm
---
# Model card for `timm-resnet50-beans`
**TODO**
**For now, try dragging and dropping this image into the inference widget. It should classify as angular_leaf_spot.**
|
kamalkraj/bioelectra-base-discriminator-pubmed
|
kamalkraj
| 2021-09-07T13:52:16Z | 810 | 6 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"pretraining",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
## BioELECTRA:Pretrained Biomedical text Encoder using Discriminators
Recent advancements in pretraining strategies in NLP have shown a significant improvement in the performance of models on various text mining tasks. In this paper, we introduce BioELECTRA, a biomedical domain-specific language encoder model that adapts ELECTRA (Clark et al., 2020) for the Biomedical domain. BioELECTRA outperforms the previous models and achieves state of the art (SOTA) on all the 13 datasets in BLURB benchmark and on all the 4 Clinical datasets from BLUE Benchmark across 7 NLP tasks. BioELECTRA pretrained on PubMed and PMC full text articles performs very well on Clinical datasets as well. BioELECTRA achieves new SOTA 86.34%(1.39% accuracy improvement) on MedNLI and 64% (2.98% accuracy improvement) on PubMedQA dataset.
For a detailed description and experimental results, please refer to our paper [BioELECTRA:Pretrained Biomedical text Encoder using Discriminators](https://www.aclweb.org/anthology/2021.bionlp-1.16/).
Cite our paper using below citation
```
@inproceedings{kanakarajan-etal-2021-bioelectra,
title = "{B}io{ELECTRA}:Pretrained Biomedical text Encoder using Discriminators",
author = "Kanakarajan, Kamal raj and
Kundumani, Bhuvana and
Sankarasubbu, Malaikannan",
booktitle = "Proceedings of the 20th Workshop on Biomedical Language Processing",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.bionlp-1.16",
doi = "10.18653/v1/2021.bionlp-1.16",
pages = "143--154",
abstract = "Recent advancements in pretraining strategies in NLP have shown a significant improvement in the performance of models on various text mining tasks. We apply {`}replaced token detection{'} pretraining technique proposed by ELECTRA and pretrain a biomedical language model from scratch using biomedical text and vocabulary. We introduce BioELECTRA, a biomedical domain-specific language encoder model that adapts ELECTRA for the Biomedical domain. WE evaluate our model on the BLURB and BLUE biomedical NLP benchmarks. BioELECTRA outperforms the previous models and achieves state of the art (SOTA) on all the 13 datasets in BLURB benchmark and on all the 4 Clinical datasets from BLUE Benchmark across 7 different NLP tasks. BioELECTRA pretrained on PubMed and PMC full text articles performs very well on Clinical datasets as well. BioELECTRA achieves new SOTA 86.34{\%}(1.39{\%} accuracy improvement) on MedNLI and 64{\%} (2.98{\%} accuracy improvement) on PubMedQA dataset.",
}
```
## How to use the discriminator in `transformers`
```python
from transformers import ElectraForPreTraining, ElectraTokenizerFast
import torch
discriminator = ElectraForPreTraining.from_pretrained("kamalkraj/bioelectra-base-discriminator-pubmed")
tokenizer = ElectraTokenizerFast.from_pretrained("kamalkraj/bioelectra-base-discriminator-pubmed")
sentence = "The quick brown fox jumps over the lazy dog"
fake_sentence = "The quick brown fox fake over the lazy dog"
fake_tokens = tokenizer.tokenize(fake_sentence)
fake_inputs = tokenizer.encode(fake_sentence, return_tensors="pt")
discriminator_outputs = discriminator(fake_inputs)
predictions = torch.round((torch.sign(discriminator_outputs[0]) + 1) / 2)
[print("%7s" % token, end="") for token in fake_tokens]
[print("%7s" % int(prediction), end="") for prediction in predictions[0].tolist()]
```
|
M47Labs/spanish_news_classification_headlines
|
M47Labs
| 2021-09-07T11:56:58Z | 106 | 3 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:04Z |
---
widget:
- text: "El dólar se dispara tras la reunión de la Fed"
---
# Spanish News Classification Headlines
SNCH: this model was develop by [M47Labs](https://www.m47labs.com/es/) the goal is text classification, the base model use was [BETO](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased), it was fine-tuned on 1000 example dataset.
## Dataset Sample
Dataset size : 1000
Columns: idTask,task content 1,idTag,tag.
|idTask|task content 1|idTag|tag|
|------|------|------|------|
|3637d9ac-119c-4a8f-899c-339cf5b42ae0|Alcalá de Guadaíra celebra la IV Semana de la Diversidad Sexual con acciones de sensibilización|81b36360-6cbf-4ffa-b558-9ef95c136714|sociedad|
|d56bab52-0029-45dd-ad90-5c17d4ed4c88|El Archipiélago Chinijo Graciplus se impone en el Trofeo Centro Comercial Rubicón|ed198b6d-a5b9-4557-91ff-c0be51707dec|deportes|
|dec70bc5-4932-4fa2-aeac-31a52377be02|Un total de 39 personas padecen ELA actualmente en la provincia|81b36360-6cbf-4ffa-b558-9ef95c136714|sociedad|
|fb396ba9-fbf1-4495-84d9-5314eb731405|Eurocopa 2021 : Italia vence a Gales y pasa a octavos con su candidatura reforzada|ed198b6d-a5b9-4557-91ff-c0be51707dec|deportes|
|bc5a36ca-4e0a-422e-9167-766b41008c01|Resolución de 10 de junio de 2021, del Ayuntamiento de Tarazona de La Mancha (Albacete), referente a la convocatoria para proveer una plaza.|81b36360-6cbf-4ffa-b558-9ef95c136714|sociedad|
|a87f8703-ce34-47a5-9c1b-e992c7fe60f6|El primer ministro sueco pierde una moción de censura|209ae89e-55b4-41fd-aac0-5400feab479e|politica|
|d80bdaad-0ad5-43a0-850e-c473fd612526|El dólar se dispara tras la reunión de la Fed|11925830-148e-4890-a2bc-da9dc059dc17|economia|
## Labels:
* ciencia_tecnologia
* clickbait
* cultura
* deportes
* economia
* educacion
* medio_ambiente
* opinion
* politica
* sociedad
## Example of Use
### Pipeline
```{python}
import torch
from transformers import AutoTokenizer, BertForSequenceClassification,TextClassificationPipeline
review_text = 'los vehiculos que esten esperando pasajaeros deberan estar apagados para reducir emisiones'
path = "M47Labs/spanish_news_classification_headlines"
tokenizer = AutoTokenizer.from_pretrained(path)
model = BertForSequenceClassification.from_pretrained(path)
nlp = TextClassificationPipeline(task = "text-classification",
model = model,
tokenizer = tokenizer)
print(nlp(review_text))
```
```[{'label': 'medio_ambiente', 'score': 0.5648820996284485}]```
### Pytorch
```{python}
import torch
from transformers import AutoTokenizer, BertForSequenceClassification,TextClassificationPipeline
from numpy import np
model_name = 'M47Labs/spanish_news_classification_headlines'
MAX_LEN = 32
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
texto = "las emisiones estan bajando, debido a las medidas ambientales tomadas por el gobierno"
encoded_review = tokenizer.encode_plus(
texto,
max_length=MAX_LEN,
add_special_tokens=True,
#return_token_type_ids=False,
pad_to_max_length=True,
return_attention_mask=True,
return_tensors='pt',
)
input_ids = encoded_review['input_ids']
attention_mask = encoded_review['attention_mask']
output = model(input_ids, attention_mask)
_, prediction = torch.max(output['logits'], dim=1)
print(f'Review text: {texto}')
print(f'Sentiment : {model.config.id2label[prediction.detach().cpu().numpy()[0]]}')
```
```Review text: las emisiones estan bajando, debido a las medidas ambientales tomadas por el gobierno```
```Sentiment : medio_ambiente```
A more in depth example on how to use the model can be found in this colab notebook: https://colab.research.google.com/drive/1XsKea6oMyEckye2FePW_XN7Rf8v41Cw_?usp=sharing
## Finetune Hyperparameters
* MAX_LEN = 32
* TRAIN_BATCH_SIZE = 8
* VALID_BATCH_SIZE = 4
* EPOCHS = 5
* LEARNING_RATE = 1e-05
## Train Results
|n_example|epoch|loss|acc|
|------|------|------|------|
|100|0|2.286327266693115|12.5|
|100|1|2.018876111507416|40.0|
|100|2|1.8016730904579163|43.75|
|100|3|1.6121837735176086|46.25|
|100|4|1.41565443277359|68.75|
|n_example|epoch|loss|acc|
|------|------|------|------|
|500|0|2.0770938420295715|24.5|
|500|1|1.6953029704093934|50.25|
|500|2|1.258900796175003|64.25|
|500|3|0.8342628020048142|78.25|
|500|4|0.5135736921429634|90.25|
|n_example|epoch|loss|acc|
|------|------|------|------|
|1000|0|1.916002897115854|36.1997226074896|
|1000|1|1.2941598492664295|62.2746185852982|
|1000|2|0.8201534710415117|76.97642163661581|
|1000|3|0.524806430051615|86.9625520110957|
|1000|4|0.30662027455784463|92.64909847434119|
## Validation Results
|n_examples|100|
|------|------|
|Accuracy Score|0.35|
|Precision (Macro)|0.35|
|Recall (Macro)|0.16|
|n_examples|500|
|------|------|
|Accuracy Score|0.62|
|Precision (Macro)|0.60|
|Recall (Macro)|0.47|
|n_examples|1000|
|------|------|
|Accuracy Score|0.68|
|Precision(Macro)|0.68|
|Recall (Macro)|0.64|
|
pritoms/gpt-neo-125M-finetuned-pgt
|
pritoms
| 2021-09-07T08:20:52Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt_neo",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- null
model-index:
- name: gpt-neo-125M-finetuned-pgt
results:
- task:
name: Causal Language Modeling
type: text-generation
---
<!-- 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. -->
# gpt-neo-125M-finetuned-pgt
This model is a fine-tuned version of [pritoms/gpt-neo-125M-finetuned-pgt](https://huggingface.co/pritoms/gpt-neo-125M-finetuned-pgt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6026
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 26 | 1.5947 |
| No log | 2.0 | 52 | 1.5963 |
| No log | 3.0 | 78 | 1.6026 |
### Framework versions
- Transformers 4.10.0
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
MaryaAI/opus-mt-ar-en-finetuned-ar-to-en
|
MaryaAI
| 2021-09-07T07:26:24Z | 251 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:opus_wikipedia",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:04Z |
---
tags:
- generated_from_trainer
datasets:
- opus_wikipedia
model-index:
- name: opus-mt-ar-en-finetuned-ar-to-en
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: opus_wikipedia
type: opus_wikipedia
args: ar-en
---
<!-- 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. -->
# opus-mt-ar-en-finetuned-ar-to-en
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ar-en](https://huggingface.co/Helsinki-NLP/opus-mt-ar-en) on the opus_wikipedia 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: 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
### Framework versions
- Transformers 4.10.0
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
espnet/xuankai_chang_librispeech_asr_train_asr_conformer7_wav2vec2_960hr_larg-truncated-5b94d9
|
espnet
| 2021-09-07T03:11:55Z | 2 | 0 |
espnet
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:librispeech",
"license:cc-by-4.0",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
tags:
- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- librispeech
license: cc-by-4.0
inference: false
---
# ESPnet2 ASR pretrained model
## `Xuankai Chang/xuankai_chang_librispeech_asr_train_asr_conformer7_wav2vec2_960hr_large_raw_en_bpe5000_sp_25epoch, fs=16k, lang=en`
This model was trained by Takashi Maekaku using librispeech recipe in [espnet](https://github.com/espnet/espnet/).
### Python API
```text
See https://github.com/espnet/espnet_model_zoo
```
### Evaluate in the recipe
```python
# coming soon
```
### Results
```bash
# RESULTS
## Environments
- date: `Sat Jul 3 23:10:19 JST 2021`
- python version: `3.7.9 (default, Apr 23 2021, 13:48:31) [GCC 5.5.0 20171010]`
- espnet version: `espnet 0.9.9`
- pytorch version: `pytorch 1.7.0`
- Git hash: `0f7558a716ab830d0c29da8785840124f358d47b`
- Commit date: `Tue Jun 8 15:33:49 2021 -0400`
## asr_train_asr_conformer7_wav2vec2_960hr_large_raw_en_bpe5000_sp
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_clean|2703|54402|98.3|1.6|0.2|0.2|1.9|24.9|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_other|2864|50948|95.1|4.3|0.6|0.4|5.4|42.8|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/test_clean|2620|52576|98.1|1.7|0.2|0.2|2.2|26.8|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/test_other|2939|52343|95.3|4.1|0.6|0.5|5.2|45.8|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_clean|2703|288456|99.5|0.2|0.2|0.2|0.6|24.9|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_other|2864|265951|98.1|1.0|0.9|0.5|2.4|42.8|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/test_clean|2620|281530|99.5|0.2|0.3|0.2|0.7|26.8|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/test_other|2939|272758|98.3|0.8|0.9|0.5|2.3|45.8|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_clean|2703|68010|97.8|1.6|0.6|0.4|2.6|24.9|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_other|2864|63110|94.1|4.3|1.6|1.1|7.0|42.8|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/test_clean|2620|65818|97.6|1.6|0.8|0.4|2.8|26.8|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/test_other|2939|65101|94.3|4.0|1.8|1.0|6.7|45.8|
```
### Training config
See full config in [`config.yaml`](./exp/asr_train_asr_conformer7_hubert_960hr_large_raw_en_bpe5000_sp/config.yaml)
```yaml
config: conf/tuning/train_asr_conformer7_hubert_960hr_large.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_conformer7_hubert_960hr_large_raw_en_bpe5000_sp
ngpu: 3
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: 4
dist_rank: 3
local_rank: 3
dist_master_addr: localhost
dist_master_port: 33643
dist_launcher: null
multiprocessing_distributed: true
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
```
|
espnet/xuankai_chang_librispeech_asr_train_asr_conformer7_hubert_960hr_large_raw_en_bpe5000_sp_26epoch
|
espnet
| 2021-09-07T03:05:41Z | 2 | 0 |
espnet
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:librispeech",
"license:cc-by-4.0",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
tags:
- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- librispeech
license: cc-by-4.0
inference: false
---
# ESPnet2 ASR pretrained model
## `Xuankai Chang/xuankai_chang_librispeech_asr_train_asr_conformer7_hubert_960hr_large_raw_en_bpe5000_sp_26epoch, fs=16k, lang=en`
This model was trained by Takashi Maekaku using librispeech recipe in [espnet](https://github.com/espnet/espnet/).
### Python API
```text
See https://github.com/espnet/espnet_model_zoo
```
### Evaluate in the recipe
```python
# coming soon
```
### Results
```bash
# RESULTS
## Environments
- date: `Fri Aug 6 11:44:39 JST 2021`
- python version: `3.7.9 (default, Apr 23 2021, 13:48:31) [GCC 5.5.0 20171010]`
- espnet version: `espnet 0.9.9`
- pytorch version: `pytorch 1.7.0`
- Git hash: `0f7558a716ab830d0c29da8785840124f358d47b`
- Commit date: `Tue Jun 8 15:33:49 2021 -0400`
## asr_train_asr_conformer7_hubert_960hr_large_raw_en_bpe5000_sp
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_clean|2703|54402|98.5|1.3|0.2|0.2|1.7|22.1|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_other|2864|50948|96.8|2.8|0.4|0.3|3.4|33.7|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/test_clean|2620|52576|98.4|1.4|0.2|0.2|1.8|22.1|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/test_other|2939|52343|96.8|2.8|0.4|0.4|3.6|36.0|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_clean|2703|288456|99.6|0.2|0.2|0.2|0.6|22.1|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_other|2864|265951|98.8|0.6|0.6|0.3|1.5|33.7|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/test_clean|2620|281530|99.6|0.2|0.2|0.2|0.6|22.1|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/test_other|2939|272758|98.9|0.5|0.5|0.4|1.4|36.0|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_clean|2703|68010|98.2|1.3|0.5|0.4|2.2|22.1|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/dev_other|2864|63110|96.0|2.8|1.2|0.6|4.6|33.7|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/test_clean|2620|65818|98.1|1.3|0.6|0.4|2.3|22.1|
|decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.acc.best/test_other|2939|65101|96.0|2.7|1.3|0.6|4.6|36.0|
```
### Training config
See full config in [`config.yaml`](./exp/asr_train_asr_conformer7_hubert_960hr_large_raw_en_bpe5000_sp/config.yaml)
```yaml
config: conf/tuning/train_asr_conformer7_hubert_960hr_large.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_conformer7_hubert_960hr_large_raw_en_bpe5000_sp
ngpu: 3
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: 4
dist_rank: 3
local_rank: 3
dist_master_addr: localhost
dist_master_port: 33643
dist_launcher: null
multiprocessing_distributed: true
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
```
|
huggingtweets/discountpicasso-dril-liam_100000
|
huggingtweets
| 2021-09-07T00:14:05Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/discountpicasso-dril-liam_100000/1630973640579/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/1426930394297819137/-zzMnfJo_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/847818629840228354/VXyQHfn0_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/980964012170121217/U6FjPH4H_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">LIAM & wint & Picasso</div>
<div style="text-align: center; font-size: 14px;">@discountpicasso-dril-liam_100000</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 LIAM & wint & Picasso.
| Data | LIAM | wint | Picasso |
| --- | --- | --- | --- |
| Tweets downloaded | 1962 | 3226 | 3216 |
| Retweets | 135 | 472 | 427 |
| Short tweets | 435 | 313 | 421 |
| Tweets kept | 1392 | 2441 | 2368 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1w4ekve8/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 @discountpicasso-dril-liam_100000's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2s4a755y) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2s4a755y/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/discountpicasso-dril-liam_100000')
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)
|
huggingtweets/itskillerdog
|
huggingtweets
| 2021-09-06T23:46:38Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/itskillerdog/1630971994166/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/1355537154538000391/0mOGv6Mw_400x400.jpg')">
</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">june party corner</div>
<div style="text-align: center; font-size: 14px;">@itskillerdog</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 june party corner.
| Data | june party corner |
| --- | --- |
| Tweets downloaded | 196 |
| Retweets | 20 |
| Short tweets | 30 |
| Tweets kept | 146 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1u7twx27/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 @itskillerdog's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1vg0bbs8) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1vg0bbs8/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/itskillerdog')
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)
|
julien-c/dummy-for-flat
|
julien-c
| 2021-09-06T21:02:55Z | 0 | 1 | null |
[
"region:us"
] | null | 2022-03-02T23:29:05Z |
in the editor i only change this line
Example of a hf.co repo containing signed commits.
hello tabs
|
yseop/FNP_T5_D2T_simple
|
yseop
| 2021-09-06T20:54:48Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
# T5-base data to text model specialized for Finance NLG
__simple version__
This model was trained on a limited number of indicators, values and dates
----
## Usage (HuggingFace Transformers)
#### Call the model
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("yseop/FNP_T5_D2T_simple")
model = AutoModelForSeq2SeqLM.from_pretrained("yseop/FNP_T5_D2T_simple")
text = ["Group profit | valIs | $ 10 && € $10 | dTime | in 2019"]
```
#### Choose a generation method
```python
input_ids = tokenizer.encode(": {}".format(text), return_tensors="pt")
p=0.72
k=40
outputs = model.generate(input_ids,
do_sample=True,
top_p=p,
top_k=k,
early_stopping=True)
print(tokenizer.decode(outputs[0]))
```
```python
input_ids = tokenizer.encode(": {}".format(text), return_tensors="pt")
outputs = model.generate(input_ids,
max_length=200,
num_beams=2, repetition_penalty=2.5,
top_k=50, top_p=0.98,
length_penalty=1.0,
early_stopping=True)
print(tokenizer.decode(outputs[0]))
```
**Created by:** [Yseop](https://www.yseop.com/) | Pioneer in Natural Language Generation (NLG) technology. Scaling human expertise through Natural Language Generation.
|
sv/gpt2-finetuned-nft-shakes
|
sv
| 2021-09-06T16:59:11Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- null
model-index:
- name: gpt2-finetuned-nft-shakes
results:
- task:
name: Causal Language Modeling
type: text-generation
---
<!-- 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. -->
# gpt2-finetuned-nft-shakes
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7566
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 306 | 3.9679 |
| 4.2957 | 2.0 | 612 | 3.7979 |
| 4.2957 | 3.0 | 918 | 3.7566 |
### Framework versions
- Transformers 4.10.0
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
huggingtweets/beesforbo-cafe_orbitinnit-weebbutt
|
huggingtweets
| 2021-09-06T15:26:27Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/beesforbo-cafe_orbitinnit-weebbutt/1630941920455/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/1429115399975497731/JZdA725e_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/1434240567001636864/BkVzkg7C_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/1434228331315187712/IrO7AP6L_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">✨たち Tommy’s an Orbit 🌙 たち✨ & Goose & c!tubbo + glatt</div>
<div style="text-align: center; font-size: 14px;">@beesforbo-cafe_orbitinnit-weebbutt</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 ✨たち Tommy’s an Orbit 🌙 たち✨ & Goose & c!tubbo + glatt.
| Data | ✨たち Tommy’s an Orbit 🌙 たち✨ | Goose | c!tubbo + glatt |
| --- | --- | --- | --- |
| Tweets downloaded | 2241 | 3243 | 3242 |
| Retweets | 1335 | 511 | 108 |
| Short tweets | 323 | 512 | 1198 |
| Tweets kept | 583 | 2220 | 1936 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/p0uk28zi/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 @beesforbo-cafe_orbitinnit-weebbutt's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/310986pt) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/310986pt/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/beesforbo-cafe_orbitinnit-weebbutt')
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)
|
BSC-LT/gpt2-large-bne
|
BSC-LT
| 2021-09-06T14:13:06Z | 26 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"national library of spain",
"spanish",
"bne",
"es",
"dataset:bne",
"arxiv:2107.07253",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:04Z |
---
language:
- es
license: apache-2.0
tags:
- "national library of spain"
- "spanish"
- "bne"
datasets:
- "bne"
metrics:
- "ppl"
---
# GPT2-large trained with data from National Library of Spain (BNE)
## Model Description
GPT2-large-bne is a transformer-based model for the Spanish language. It is based on the [GPT-2](http://www.persagen.com/files/misc/radford2019language.pdf) model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019.
## Training corpora and preprocessing
The [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) crawls all .es domains once a year. The training corpus consists of 59TB of WARC files from these crawls, carried out from 2009 to 2019.
To obtain a high-quality training corpus, the corpus has been preprocessed with a pipeline of operations, including among the others, sentence splitting, language detection, filtering of bad-formed sentences and deduplication of repetitive contents. During the process document boundaries are kept. This resulted into 2TB of Spanish clean corpus. Further global deduplication among the corpus is applied, resulting into 570GB of text.
Some of the statistics of the corpus:
| Corpora | Number of documents | Number of tokens | Size (GB) |
|---------|---------------------|------------------|-----------|
| BNE | 201,080,084 | 135,733,450,668 | 570GB |
## Tokenization and pre-training
The training corpus has been tokenized using a byte version of Byte-Pair Encoding (BPE) used in the original [GPT-2](http://www.persagen.com/files/misc/radford2019language.pdf) model with a vocabulary size of 50,262 tokens. The GPT2-large-bne pre-training consists of an autoregressive language model training that follows the approach of the GPT-2. The training lasted a total of 10 days with 32 computing nodes each one with 4 NVIDIA V100 GPUs of 16GB VRAM.
## Evaluation and results
For evaluation details visit our [GitHub repository](https://github.com/PlanTL-SANIDAD/lm-spanish).
## Citing
Check out our paper for all the details: https://arxiv.org/abs/2107.07253
```
@misc{gutierrezfandino2021spanish,
title={Spanish Language Models},
author={Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Marc Pàmies and Joan Llop-Palao and Joaquín Silveira-Ocampo and Casimiro Pio Carrino and Aitor Gonzalez-Agirre and Carme Armentano-Oller and Carlos Rodriguez-Penagos and Marta Villegas},
year={2021},
eprint={2107.07253},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
lewtun/metnet-test-5
|
lewtun
| 2021-09-06T11:01:50Z | 2 | 0 |
transformers
|
[
"transformers",
"pytorch",
"satflow",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
license: mit
tags:
- satflow
---
# MetNet
## Model description
[More information needed]
## Intended uses & limitations
[More information needed]
## How to use
[More information needed]
## Limitations and bias
[More information needed]
## Training data
[More information needed]
## Training procedure
[More information needed]
## Evaluation results
[More information needed]
|
lewtun/metnet-test-4
|
lewtun
| 2021-09-06T11:00:39Z | 1 | 0 |
transformers
|
[
"transformers",
"pytorch",
"satflow",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
license: mit
tags:
- satflow
---
# Model Card for MetNet
## Model description
[More information needed]
## Intended uses & limitations
[More information needed]
## How to use
[More information needed]
## Limitations and bias
[More information needed]
## Training data
[More information needed]
## Training procedure
[More information needed]
## Evaluation results
[More information needed]
|
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