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| library_name
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huggingtweets/wokal_distance | huggingtweets | 2021-08-28T16:30:35Z | 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://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
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/1334420408490057729/BoIR414f_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">Wokal Distance</div>
<div style="text-align: center; font-size: 14px;">@wokal_distance</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 Wokal Distance.
| Data | Wokal Distance |
| --- | --- |
| Tweets downloaded | 3242 |
| Retweets | 1382 |
| Short tweets | 145 |
| Tweets kept | 1715 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1udsr72i/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 @wokal_distance's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1pi9x5ai) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1pi9x5ai/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/wokal_distance')
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)
|
Flampt/DialoGPT-medium-Sheldon | Flampt | 2021-08-28T14:17:44Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:04Z | ---
tags:
- conversational
---
# Sheldon Cooper from The Big Bang Theory Show DialoGPT Model |
OsmyReal/Ayuda | OsmyReal | 2021-08-28T06:12:44Z | 0 | 0 | null | [
"region:us"
] | null | 2022-03-02T23:29:04Z | git lfs install
git clone https://huggingface.co/r3dhummingbird/DialoGPT-medium-joshua |
velociraptor/hugging-doge | velociraptor | 2021-08-28T06:01:46Z | 71 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2022-03-02T23:29:05Z | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: hugging-doge
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9375
---
# hugging-doge
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
#### corgi
#### golden retriever
#### husky
#### poodle
#### shiba inu
 |
cosmoquester/bart-ko-base | cosmoquester | 2021-08-28T05:12:02Z | 76 | 1 | transformers | [
"transformers",
"pytorch",
"tf",
"bart",
"text2text-generation",
"ko",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
language: ko
---
# Pretrained BART in Korean
This is pretrained BART model with multiple Korean Datasets.
I used multiple datasets for generalizing the model for both colloquial and written texts.
The training is supported by [TPU Research Cloud](https://sites.research.google/trc/) program.
The script which is used to pre-train model is [here](https://github.com/cosmoquester/transformers-bart-pretrain).
When you use the reference API, you must wrap the sentence with `[BOS]` and `[EOS]` like below example.
```
[BOS] 안녕하세요? 반가워요~~ [EOS]
```
You can also test mask filling performance using `[MASK]` token like this.
```
[BOS] [MASK] 먹었어? [EOS]
```
## Benchmark
<style>
table {
border-collapse: collapse;
border-style: hidden;
width: 100%;
}
td, th {
border: 1px solid #4d5562;
padding: 8px;
}
</style>
<table>
<tr>
<th>Dataset</th>
<td>KLUE NLI dev</th>
<td>NSMC test</td>
<td>QuestionPair test</td>
<td colspan="2">KLUE TC dev</td>
<td colspan="3">KLUE STS dev</td>
<td colspan="3">KorSTS dev</td>
<td colspan="2">HateSpeech dev</td>
</tr>
<tr>
<th>Metric</th>
<!-- KLUE NLI -->
<td>Acc</th>
<!-- NSMC -->
<td>Acc</td>
<!-- QuestionPair -->
<td>Acc</td>
<!-- KLUE TC -->
<td>Acc</td>
<td>F1</td>
<!-- KLUE STS -->
<td>F1</td>
<td>Pearson</td>
<td>Spearman</td>
<!-- KorSTS -->
<td>F1</td>
<td>Pearson</td>
<td>Spearman</td>
<!-- HateSpeech -->
<td>Bias Acc</td>
<td>Hate Acc</td>
</tr>
<tr>
<th>Score</th>
<!-- KLUE NLI -->
<td>0.7390</th>
<!-- NSMC -->
<td>0.8877</td>
<!-- QuestionPair -->
<td>0.9208</td>
<!-- KLUE TC -->
<td>0.8667</td>
<td>0.8637</td>
<!-- KLUE STS -->
<td>0.7654</td>
<td>0.8090</td>
<td>0.8040</td>
<!-- KorSTS -->
<td>0.8067</td>
<td>0.7909</td>
<td>0.7784</td>
<!-- HateSpeech -->
<td>0.8280</td>
<td>0.5669</td>
</tr>
</table>
- The performance was measured using [the notebooks here](https://github.com/cosmoquester/transformers-bart-finetune) with colab.
## Used Datasets
### [모두의 말뭉치](https://corpus.korean.go.kr/)
- 일상 대화 말뭉치 2020
- 구어 말뭉치
- 문어 말뭉치
- 신문 말뭉치
### AIhub
- [개방데이터 전문분야말뭉치](https://aihub.or.kr/aidata/30717)
- [개방데이터 한국어대화요약](https://aihub.or.kr/aidata/30714)
- [개방데이터 감성 대화 말뭉치](https://aihub.or.kr/aidata/7978)
- [개방데이터 한국어 음성](https://aihub.or.kr/aidata/105)
- [개방데이터 한국어 SNS](https://aihub.or.kr/aidata/30718)
### [세종 말뭉치](https://ithub.korean.go.kr/)
|
cosmoquester/bart-ko-small | cosmoquester | 2021-08-28T05:09:54Z | 48 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"bart",
"text2text-generation",
"ko",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
language: ko
---
# Pretrained BART in Korean
This is pretrained BART model with multiple Korean Datasets.
I used multiple datasets for generalizing the model for both colloquial and written texts.
The training is supported by [TPU Research Cloud](https://sites.research.google/trc/) program.
The script which is used to pre-train model is [here](https://github.com/cosmoquester/transformers-bart-pretrain).
When you use the reference API, you must wrap the sentence with `[BOS]` and `[EOS]` like below example.
```
[BOS] 안녕하세요? 반가워요~~ [EOS]
```
You can also test mask filling performance using `[MASK]` token like this.
```
[BOS] [MASK] 먹었어? [EOS]
```
## Benchmark
<style>
table {
border-collapse: collapse;
border-style: hidden;
width: 100%;
}
td, th {
border: 1px solid #4d5562;
padding: 8px;
}
</style>
<table>
<tr>
<th>Dataset</th>
<td>KLUE NLI dev</th>
<td>NSMC test</td>
<td>QuestionPair test</td>
<td colspan="2">KLUE TC dev</td>
<td colspan="3">KLUE STS dev</td>
<td colspan="3">KorSTS dev</td>
<td colspan="2">HateSpeech dev</td>
</tr>
<tr>
<th>Metric</th>
<!-- KLUE NLI -->
<td>Acc</th>
<!-- NSMC -->
<td>Acc</td>
<!-- QuestionPair -->
<td>Acc</td>
<!-- KLUE TC -->
<td>Acc</td>
<td>F1</td>
<!-- KLUE STS -->
<td>F1</td>
<td>Pearson</td>
<td>Spearman</td>
<!-- KorSTS -->
<td>F1</td>
<td>Pearson</td>
<td>Spearman</td>
<!-- HateSpeech -->
<td>Bias Acc</td>
<td>Hate Acc</td>
</tr>
<tr>
<th>Score</th>
<!-- KLUE NLI -->
<td>0.639</th>
<!-- NSMC -->
<td>0.8721</td>
<!-- QuestionPair -->
<td>0.905</td>
<!-- KLUE TC -->
<td>0.8551</td>
<td>0.8515</td>
<!-- KLUE STS -->
<td>0.7406</td>
<td>0.7593</td>
<td>0.7551</td>
<!-- KorSTS -->
<td>0.7897</td>
<td>0.7269</td>
<td>0.7037</td>
<!-- HateSpeech -->
<td>0.8068</td>
<td>0.5966</td>
</tr>
</table>
- The performance was measured using [the notebooks here](https://github.com/cosmoquester/transformers-bart-finetune) with colab.
## Used Datasets
### [모두의 말뭉치](https://corpus.korean.go.kr/)
- 일상 대화 말뭉치 2020
- 구어 말뭉치
- 문어 말뭉치
- 신문 말뭉치
### AIhub
- [개방데이터 전문분야말뭉치](https://aihub.or.kr/aidata/30717)
- [개방데이터 한국어대화요약](https://aihub.or.kr/aidata/30714)
- [개방데이터 감성 대화 말뭉치](https://aihub.or.kr/aidata/7978)
- [개방데이터 한국어 음성](https://aihub.or.kr/aidata/105)
- [개방데이터 한국어 SNS](https://aihub.or.kr/aidata/30718)
### [세종 말뭉치](https://ithub.korean.go.kr/)
|
SilentMyuth/sarcastic-model | SilentMyuth | 2021-08-27T21:10:27Z | 7 | 1 | transformers | [
"transformers",
"conversational",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z |
---
pipeline_tag: conversational
---
This model is a fine-tuned version of Microsoft/DialoGPT-medium trained to created sarcastic responses from the dataset "Sarcasm on Reddit" located [here](https://www.kaggle.com/danofer/sarcasm). |
nateraw/vit-base-beans-demo-v3 | nateraw | 2021-08-27T17:52:10Z | 71 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"other-image-classification",
"generated_from_trainer",
"dataset:beans",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- image-classification
- other-image-classification
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy
model-index:
- name: vit-base-beans-demo-v3
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: beans
type: beans
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9849624060150376
---
<!-- 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. -->
# vit-base-beans-demo-v3
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0645
- Accuracy: 0.9850
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0397 | 1.54 | 100 | 0.0645 | 0.9850 |
### Framework versions
- Transformers 4.10.0.dev0
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
nateraw/vit-base-beans-demo | nateraw | 2021-08-27T17:06:03Z | 74 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"other-image-classification",
"generated_from_trainer",
"dataset:beans",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- image-classification
- other-image-classification
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy
model-index:
- name: vit-base-beans-demo
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: beans
type: beans
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9774436090225563
---
<!-- 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. -->
# vit-base-beans-demo
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0853
- Accuracy: 0.9774
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0545 | 1.54 | 100 | 0.1436 | 0.9624 |
| 0.006 | 3.08 | 200 | 0.1058 | 0.9699 |
| 0.0038 | 4.62 | 300 | 0.0853 | 0.9774 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
zald/distilbert-base-uncased-finetuned-ner | zald | 2021-08-27T16:39:55Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"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
metric:
name: Accuracy
type: accuracy
value: 0.9835893688340985
---
<!-- 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: 0.9253
- Recall: 0.9350
- F1: 0.9301
- Accuracy: 0.9836
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.237 | 1.0 | 878 | 0.0701 | 0.9131 | 0.9228 | 0.9179 | 0.9809 |
| 0.0509 | 2.0 | 1756 | 0.0617 | 0.9182 | 0.9333 | 0.9257 | 0.9826 |
| 0.0299 | 3.0 | 2634 | 0.0607 | 0.9253 | 0.9350 | 0.9301 | 0.9836 |
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.11.0
- Tokenizers 0.10.3
|
HungVo/mt-dnn-ev-mrpc | HungVo | 2021-08-27T08:55:31Z | 0 | 0 | null | [
"region:us"
] | null | 2022-03-02T23:29:04Z | Model saved for Paraphrased Detection in English-Vietnamese cross-lingual based on XLM-R in MT-DNN
MT-DNN: github.com/namisan/mt-dnn |
KP2500/KPBot | KP2500 | 2021-08-27T06:53:22Z | 8 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:04Z | ---
tags:
- conversational
---
# RickBot built for [Chai](https://chai.ml/)
Make your own [here](https://colab.research.google.com/drive/1o5LxBspm-C28HQvXN-PRQavapDbm5WjG?usp=sharing)
|
hackertec/roberta-base-bne-finetuned-amazon_reviews_multi-taller | hackertec | 2021-08-26T18:26:27Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
license: cc-by-4.0
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
metrics:
- accuracy
model_index:
- name: roberta-base-bne-finetuned-amazon_reviews_multi-taller
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_reviews_multi
type: amazon_reviews_multi
args: es
metric:
name: Accuracy
type: accuracy
value: 0.91125
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-bne-finetuned-amazon_reviews_multi-taller
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2463
- Accuracy: 0.9113
## 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2474 | 1.0 | 125 | 0.2463 | 0.9113 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
imdhamu/DialoGPT-small-harrypotter | imdhamu | 2021-08-26T17:39:32Z | 0 | 0 | null | [
"region:us"
] | null | 2022-03-02T23:29:05Z | ---
tags:
- conversational
#Harry Potter DialoGPT Model |
uva-irlab/quretec | uva-irlab | 2021-08-26T14:06:47Z | 12 | 1 | transformers | [
"transformers",
"pytorch",
"bert",
"conversational-search",
"en",
"dataset:uva-irlab/canard_quretec",
"arxiv:2005.11723",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z | ---
language:
- en
tags:
- conversational-search # Example: audio
metrics:
- f1
datasets:
- uva-irlab/canard_quretec
model-index:
- name: QuReTec
results:
- task:
name: Conversational search # Example: Speech Recognition
type: conversational # Example: automatic-speech-recognition
dataset:
name: CANARD # Example: Common Voice zh-CN
type: canard # Example: common_voice
metrics:
- name: Micro F1 # Example: Test WER
type: f1 # Example: wer
value: 68.7 # Example: 20.90
- name: Micro Recall
type: recall
value: 66.1
- name: Micro Precision
type: precision
value: 71.5
---
# QuReTec: query resolution model
QuReTeC is a query resolution model. It finds the relevant terms in a question history.
It is based on **bert-large-uncased** with a max sequence length of 300.
# Config details
Training and evaluation was done using the following BertConfig:
```json
BertConfig {
"_name_or_path": "uva-irlab/quretec",
"architectures": ["BertForMaskedLM"],
"attention_probs_dropout_prob": 0.1,
"finetuning_task": "ner",
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.4,
"hidden_size": 1024,
"id2label": {
"0": "[PAD]",
"1": "O",
"2": "REL",
"3": "[CLS]",
"4": "[SEP]"
},
"initializer_range": 0.02,
"intermediate_size": 4096,
"label2id": {
"O": 1,
"REL": 2,
"[CLS]": 3,
"[PAD]": 0,
"[SEP]": 4
},
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 16,
"num_hidden_layers": 24,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"transformers_version": "4.6.1",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30522
}
```
# Original authors
QuReTeC model from the published SIGIR 2020 paper: Query Resolution for Conversational Search with Limited Supervision by N. Voskarides, D. Li, P. Ren, E. Kanoulas and M. de Rijke. [[pdf]](https://arxiv.org/abs/2005.11723).
# Contributions
Uploaded by G. Scheuer ([website](https://giguruscheuer.com)) |
huggingtweets/yourfavhwhw | huggingtweets | 2021-08-26T13:26:11Z | 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/yourfavhwhw/1629984367533/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/1423284698046865415/vfSSZ3t9_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">🥴</div>
<div style="text-align: center; font-size: 14px;">@yourfavhwhw</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 🥴.
| Data | 🥴 |
| --- | --- |
| Tweets downloaded | 3246 |
| Retweets | 57 |
| Short tweets | 525 |
| Tweets kept | 2664 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/18wxe7tu/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 @yourfavhwhw's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/imwcf0iy) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/imwcf0iy/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/yourfavhwhw')
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)
|
leonardvorbeck/wav2vec2-large-robust-LS960 | leonardvorbeck | 2021-08-26T12:22:00Z | 5 | 1 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"speech",
"CTC",
"Attention",
"en",
"dataset:libri_light",
"dataset:common_voice",
"dataset:switchboard",
"dataset:fisher",
"arxiv:2104.01027",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:05Z | ---
language: en
datasets:
- libri_light
- common_voice
- switchboard
- fisher
tags:
- speech
- automatic-speech-recognition
- CTC
- Attention
- wav2vec2
license: apache-2.0
---
# Wav2Vec2-Large-Robust - Finetuned on Librispeech (960 hours)
## Note : Model has not been initialized. If you want to use it without further finetuning, do a forward pass first to recalculate the normalized weights of the positional convolutional layer :
```ipython
with torch.no_grad():
model(torch.randn((1,300_000)))
```
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/)
The base model pretrained on 16kHz sampled speech audio.
Speech datasets from multiple domains were used to pretrain the model:
- [Libri-Light](https://github.com/facebookresearch/libri-light): open-source audio books from the LibriVox project; clean, read-out audio data
- [CommonVoice](https://huggingface.co/datasets/common_voice): crowd-source collected audio data; read-out text snippets
- [Switchboard](https://catalog.ldc.upenn.edu/LDC97S62): telephone speech corpus; noisy telephone data
- [Fisher](https://catalog.ldc.upenn.edu/LDC2004T19): conversational telephone speech; noisy telephone data
When using the model make sure that your speech input is also sampled at 16Khz.
Check out [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more information.
[Paper Robust Wav2Vec2](https://arxiv.org/abs/2104.01027)
Authors: Wei-Ning Hsu, Anuroop Sriram, Alexei Baevski, Tatiana Likhomanenko, Qiantong Xu, Vineel Pratap, Jacob Kahn, Ann Lee, Ronan Collobert, Gabriel Synnaeve, Michael Auli
**Abstract**
Self-supervised learning of speech representations has been a very active research area but most work is focused on a single domain such as read audio books for which there exist large quantities of labeled and unlabeled data. In this paper, we explore more general setups where the domain of the unlabeled data for pre-training data differs from the domain of the labeled data for fine-tuning, which in turn may differ from the test data domain. Our experiments show that using target domain data during pre-training leads to large performance improvements across a variety of setups. On a large-scale competitive setup, we show that pre-training on unlabeled in-domain data reduces the gap between models trained on in-domain and out-of-domain labeled data by 66%-73%. This has obvious practical implications since it is much easier to obtain unlabeled target domain data than labeled data. Moreover, we find that pre-training on multiple domains improves generalization performance on domains not seen during training. Code and models will be made available at this https URL.
The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20.
# Usage
See [this notebook](https://colab.research.google.com/drive/1FjTsqbYKphl9kL-eILgUc-bl4zVThL8F?usp=sharing) for more information on how to fine-tune the model. |
mervenoyan/PubMedBERT-QNLI | mervenoyan | 2021-08-26T10:27:15Z | 7 | 8 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2007.15779",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z |
# PubMedBERT Abstract + Full Text Fine-Tuned on QNLI Task
Use case: You can use it to search through a document for a given question, to see if your question is answered in that document.
LABEL0 is "not entailment" meaning your question is not answered by the context and LABEL1 is "entailment" meaning your question is answered.
> Example input: [CLS] Your question [SEP] The context to be searched in [SEP]
Link to the original model: https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext
Credits to the paper:
> @misc{pubmedbert, author = {Yu Gu and Robert Tinn and Hao Cheng and
> Michael Lucas and Naoto Usuyama and Xiaodong Liu and Tristan Naumann
> and Jianfeng Gao and Hoifung Poon}, title = {Domain-Specific
> Language Model Pretraining for Biomedical Natural Language
> Processing}, year = {2020}, eprint = {arXiv:2007.15779}, }
|
dragonSwing/viwav2vec2-base-100h | dragonSwing | 2021-08-26T03:25:02Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"speech",
"automatic-speech-recognition",
"vi",
"dataset:vlsp",
"arxiv:2006.11477",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:05Z | ---
language: vi
datasets:
- vlsp
tags:
- speech
- automatic-speech-recognition
license: apache-2.0
---
# Wav2Vec2-Base-Pretrain-Vietnamese
The base model is pre-trained on 16kHz sampled speech audio from 100h Vietnamese unlabelled data in [VLSP dataset](https://drive.google.com/file/d/1vUSxdORDxk-ePUt-bUVDahpoXiqKchMx/view?usp=sharing). When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Vietnamese Automatic Speech Recognition.
[Facebook's Wav2Vec2 blog](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/)
[Paper](https://arxiv.org/abs/2006.11477)
# Usage
See [this notebook](https://colab.research.google.com/drive/1FjTsqbYKphl9kL-eILgUc-bl4zVThL8F?usp=sharing) for more information on how to fine-tune the English pre-trained model. |
huggingartists/veggietales | huggingartists | 2021-08-26T03:09:19Z | 5 | 1 | transformers | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/veggietales",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
datasets:
- huggingartists/veggietales
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/d14c9e27b39f0e250784a2dce037a03d.720x720x1.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">VeggieTales</div>
<a href="https://genius.com/artists/veggietales">
<div style="text-align: center; font-size: 14px;">@veggietales</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 VeggieTales.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/veggietales).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/veggietales")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1r6205vr/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 VeggieTales's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/111uuafu) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/111uuafu/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/veggietales')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/veggietales")
model = AutoModelWithLMHead.from_pretrained("huggingartists/veggietales")
```
## 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/cocomelon | huggingartists | 2021-08-26T02:48:10Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/cocomelon",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
datasets:
- huggingartists/cocomelon
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/a6115c556163f271124bacf8a07db45d.499x499x1.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">Cocomelon</div>
<a href="https://genius.com/artists/cocomelon">
<div style="text-align: center; font-size: 14px;">@cocomelon</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 Cocomelon.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/cocomelon).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/cocomelon")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1avk18yc/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 Cocomelon's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/3s0b2uix) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/3s0b2uix/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/cocomelon')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/cocomelon")
model = AutoModelWithLMHead.from_pretrained("huggingartists/cocomelon")
```
## 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)
|
huggingtweets/beemoviescript | huggingtweets | 2021-08-26T01:52:42Z | 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://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
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/498860581072273408/q5v6iWVw_400x400.jpeg')">
</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">Bee Movie Script</div>
<div style="text-align: center; font-size: 14px;">@beemoviescript</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 Bee Movie Script.
| Data | Bee Movie Script |
| --- | --- |
| Tweets downloaded | 1427 |
| Retweets | 0 |
| Short tweets | 169 |
| Tweets kept | 1258 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/291me6fz/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 @beemoviescript's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3gtdvdf3) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3gtdvdf3/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/beemoviescript')
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/rikergoogling | huggingtweets | 2021-08-26T01:50:33Z | 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://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
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/499021253953347585/COG26p9r_400x400.jpeg')">
</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">Riker Googling</div>
<div style="text-align: center; font-size: 14px;">@rikergoogling</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 Riker Googling.
| Data | Riker Googling |
| --- | --- |
| Tweets downloaded | 3246 |
| Retweets | 100 |
| Short tweets | 342 |
| Tweets kept | 2804 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2489wq37/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 @rikergoogling's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/136vtf4e) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/136vtf4e/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/rikergoogling')
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)
|
mrm8488/bioclinicalBERT-finetuned-covid-papers | mrm8488 | 2021-08-25T22:05:46Z | 25 | 1 | transformers | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:05Z | ---
language:
- en
widget:
- text: "Masks are [MASK] for preventing"
---
# BioclinicalBERT fine-tuned for MLM on COVID Papers |
mrm8488/GPT-2-finetuned-covid-bio-medrxiv | mrm8488 | 2021-08-25T21:38:35Z | 90 | 1 | transformers | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail:
widget:
- text: "Old people with COVID-19 tends to suffer"
---
# GPT-2 + bio/medrxiv files from CORD19: 🦠 ✍ ⚕
**GPT-2** fine-tuned on **biorxiv_medrxiv** files from [CORD-19](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge) dataset.
## Datasets details:
| Dataset | # Files |
| ---------------------- | ----- |
| biorxiv_medrxiv | 885 |
## Model training:
The model was trained on a Tesla P100 GPU and 25GB of RAM with the following command:
```bash
export TRAIN_FILE=/path/to/dataset/train.txt
python run_language_modeling.py \\n --model_type gpt2 \\n --model_name_or_path gpt2 \\n --do_train \\n --train_data_file $TRAIN_FILE \\n --num_train_epochs 4 \\n --output_dir model_output \\n --overwrite_output_dir \\n --save_steps 2000 \\n --per_gpu_train_batch_size 3
```
## Model in action / Example of usage: ✒
You can get the following script [here](https://github.com/huggingface/transformers/blob/master/examples/text-generation/run_generation.py)
```bash
python run_generation.py \\n --model_type gpt2 \\n --model_name_or_path mrm8488/GPT-2-finetuned-CORD19 \\n --length 200
```
```txt
👵👴🦠
# Input: Old people with COVID-19 tends to suffer
# Output: === GENERATED SEQUENCE 1 ===
Old people with COVID-19 tends to suffer more symptom onset time and death. It is well known that many people with COVID-19 have high homozygous ZIKV infection in the face of severe symptoms in both severe and severe cases.
The origin of Wuhan Fever was investigated by Prof. Shen Jiang at the outbreak of Wuhan Fever [34]. As Huanan Province is the epicenter of this outbreak, Huanan, the epicenter of epidemic Wuhan Fever, is the most potential location for the direct transmission of infection (source: Zhongzhen et al., 2020). A negative risk ratio indicates more frequent underlying signs in the people in Huanan Province with COVID-19 patients. Further analysis of reported Huanan Fever onset data in the past two years indicated that the intensity of exposure is the key risk factor for developing MERS-CoV infection in this region, especially among children and elderly. To be continued to develop infected patients would be a very important area for
```
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/)
> Made with <span style="color: #e25555;">♥</span> in Spain
|
HeyLucasLeao/byt5-small-pt-product-reviews | HeyLucasLeao | 2021-08-25T17:02:07Z | 7 | 1 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"arxiv:2105.13626",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:04Z | Create README.md
## ByT5 Small Portuguese Product Reviews
#### Model Description
This is a finetuned version from ByT5 Small by Google for Sentimental Analysis from Product Reviews in Portuguese.
##### Paper: https://arxiv.org/abs/2105.13626
#### Training data
It was trained from products reviews from a Americanas.com. You can found the data here: https://github.com/HeyLucasLeao/finetuning-byt5-model.
#### Training Procedure
It was finetuned using the Trainer Class available on the Hugging Face library. For evaluation it was used accuracy, precision, recall and f1 score.
##### Learning Rate: **1e-4**
##### Epochs: **1**
##### Colab for Finetuning: https://colab.research.google.com/drive/1EChTeQkGeXi_52lClBNazHVuSNKEHN2f
##### Colab for Metrics: https://colab.research.google.com/drive/1o4tcsP3lpr1TobtE3Txhp9fllxPWXxlw#scrollTo=PXAoog5vQaTn
#### Score:
```python
Training Set:
'accuracy': 0.8974239585927603,
'f1': 0.927229848590765,
'precision': 0.9580290812115055,
'recall': 0.8983492356469835
Test Set:
'accuracy': 0.8957881282882026,
'f1': 0.9261366030421776,
'precision': 0.9559431131213848,
'recall': 0.8981326359661668
Validation Set:
'accuracy': 0.8925383190163382,
'f1': 0.9239208204149773,
'precision': 0.9525448733710351,
'recall': 0.8969668904839083
```
#### Goals
My true intention was totally educational, thus making available a this version of the model as a example for future proposes.
How to use
``` python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch
if torch.cuda.is_available():
device = torch.device('cuda')
else:
device = torch.device('cpu')
print(device)
tokenizer = AutoTokenizer.from_pretrained("HeyLucasLeao/byt5-small-pt-product-reviews")
model = AutoModelForSeq2SeqLM.from_pretrained("HeyLucasLeao/byt5-small-pt-product-reviews")
model.to(device)
def classificar_review(review):
inputs = tokenizer([review], padding='max_length', truncation=True, max_length=512, return_tensors='pt')
input_ids = inputs.input_ids.to(device)
attention_mask = inputs.attention_mask.to(device)
output = model.generate(input_ids, attention_mask=attention_mask)
pred = np.argmax(output.cpu(), axis=1)
dici = {0: 'Review Negativo', 1: 'Review Positivo'}
return dici[pred.item()]
classificar_review(review)
``` |
huggingtweets/urmomlolroasted | huggingtweets | 2021-08-25T14:06:07Z | 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/urmomlolroasted/1629900362212/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/1365680527307595778/V2TENQA-_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">anna!!!!!</div>
<div style="text-align: center; font-size: 14px;">@urmomlolroasted</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 anna!!!!!.
| Data | anna!!!!! |
| --- | --- |
| Tweets downloaded | 3192 |
| Retweets | 477 |
| Short tweets | 700 |
| Tweets kept | 2015 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1s1eoov7/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 @urmomlolroasted's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/36442rcs) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/36442rcs/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/urmomlolroasted')
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)
|
victoraavila/bert-base-uncased-finetuned-squad | victoraavila | 2021-08-25T12:44:54Z | 19 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model_index:
- name: bert-base-uncased-finetuned-squad
results:
- task:
name: Question Answering
type: question-answering
dataset:
name: squad
type: squad
args: plain_text
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-squad
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the SQuAD1.1 dataset. It was trained through Transformers' example Colab notebook on Question Answering, available [here](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb).
It achieves the following results on the evaluation set:
- Loss: 1.0780
## 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. They are equal to the ones used to fine-tune [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) for QA:
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.0706 | 1.0 | 5533 | 1.0250 |
| 0.7899 | 2.0 | 11066 | 1.0356 |
| 0.5991 | 3.0 | 16599 | 1.0780 |
### Validation results
| EM | F1 |
|:--------:|:-------:|
| 80.3690 | 88.0110 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
nielsr/dino_vitb16 | nielsr | 2021-08-25T11:57:11Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"vit",
"image-feature-extraction",
"endpoints_compatible",
"region:us"
] | image-feature-extraction | 2022-03-02T23:29:05Z | I've converted the DINO checkpoints from the [official repo](https://github.com/facebookresearch/dino):
You can use it as follows:
```python
from transformers import ViTModel
model = ViTModel.from_pretrained("nielsr/dino_vitb16", add_pooling_layer=False)
``` |
3koozy/gpt2-HxH | 3koozy | 2021-08-25T11:31:49Z | 26 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"feature-extraction",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2022-03-02T23:29:04Z | this is a fine tuned GPT2 text generation model on a Hunter x Hunter TV anime series dataset.\
you can find a link to the used dataset here : https://www.kaggle.com/bkoozy/hunter-x-hunter-subtitles
you can find a colab notebook for fine-tuning the gpt2 model here : https://github.com/3koozy/fine-tune-gpt2-HxH/ |
eugenesiow/pan | eugenesiow | 2021-08-25T08:38:00Z | 1,953 | 0 | transformers | [
"transformers",
"PAN",
"super-image",
"image-super-resolution",
"dataset:eugenesiow/Div2k",
"dataset:eugenesiow/Set5",
"dataset:eugenesiow/Set14",
"dataset:eugenesiow/BSD100",
"dataset:eugenesiow/Urban100",
"arxiv:2010.01073",
"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
---
# Pixel Attention Network (PAN)
PAN 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 [Efficient Image Super-Resolution Using Pixel Attention](https://arxiv.org/abs/2010.01073) by Zhao et al. (2020) and first released in [this repository](https://github.com/zhaohengyuan1/PAN).
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
The PAN model proposes a a lightweight convolutional neural network for image super resolution. Pixel attention (PA) is similar to channel attention and spatial attention in formulation. PA however produces 3D attention maps instead of a 1D attention vector or a 2D map. This attention scheme introduces fewer additional parameters but generates better SR results.
The model is very lightweight with the model being just 260k to 270k parameters (~1mb).
## 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 PanModel, 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 = PanModel.from_pretrained('eugenesiow/pan', 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, PanModel, PanConfig
training_args = TrainingArguments(
output_dir='./results', # output directory
num_train_epochs=1000, # total number of training epochs
)
config = PanConfig(
scale=4, # train a model to upscale 4x
)
model = PanModel(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 |pan |
|--- |--- |--- |--- |
|Set5 |2x |33.64/0.9292 |**37.77/0.9599** |
|Set5 |3x |30.39/0.8678 |**34.64/0.9376** |
|Set5 |4x |28.42/0.8101 |**31.92/0.8915** |
|Set14 |2x |30.22/0.8683 |**33.42/0.9162** |
|Set14 |3x |27.53/0.7737 |**30.8/0.8544** |
|Set14 |4x |25.99/0.7023 |**28.57/0.7802** |
|BSD100 |2x |29.55/0.8425 |**33.6/0.9235** |
|BSD100 |3x |27.20/0.7382 |**29.47/0.815** |
|BSD100 |4x |25.96/0.6672 |**28.35/0.7595** |
|Urban100 |2x |26.66/0.8408 |**31.31/0.9197** |
|Urban100 |3x | |**28.61/0.8603** |
|Urban100 |4x |23.14/0.6573 |**25.63/0.7692** |
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{zhao2020efficient,
title={Efficient Image Super-Resolution Using Pixel Attention},
author={Hengyuan Zhao and Xiangtao Kong and Jingwen He and Yu Qiao and Chao Dong},
year={2020},
eprint={2010.01073},
archivePrefix={arXiv},
primaryClass={eess.IV}
}
``` |
OthmaneJ/distil-wav2vec2 | OthmaneJ | 2021-08-25T07:59:39Z | 246 | 10 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"speech",
"audio",
"en",
"dataset:librispeech_asr",
"arxiv:2006.11477",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:04Z | ---
language: en
datasets:
- librispeech_asr
tags:
- speech
- audio
- automatic-speech-recognition
license: apache-2.0
---
# Distil-wav2vec2
This model is a distilled version of the wav2vec2 model (https://arxiv.org/pdf/2006.11477.pdf). This model is 45% times smaller and twice as fast as the original wav2vec2 base model.
# Evaluation results
This model achieves the following results (speed is mesured for a batch size of 64):
|Model| Size| WER Librispeech-test-clean |WER Librispeech-test-other|Speed on cpu|speed on gpu|
|----------| ------------- |-------------|-----------| ------|----|
|Distil-wav2vec2| 197.9 Mb | 0.0983 | 0.2266|0.4006s| 0.0046s|
|wav2vec2-base| 360 Mb | 0.0389 | 0.1047|0.4919s| 0.0082s|
# Usage
notebook (executes seamlessly on google colab) at https://github.com/OthmaneJ/distil-wav2vec2
|
Blaine-Mason/hackMIT-finetuned-sst2 | Blaine-Mason | 2021-08-25T00:31:45Z | 27 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:04Z | ---
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model_index:
- name: hackMIT-finetuned-sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: sst2
metric:
name: Accuracy
type: accuracy
value: 0.8027522935779816
---
<!-- 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. -->
# hackMIT-finetuned-sst2
This model is a fine-tuned version of [Blaine-Mason/hackMIT-finetuned-sst2](https://huggingface.co/Blaine-Mason/hackMIT-finetuned-sst2) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1086
- Accuracy: 0.8028
## 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: 2.033238621168611e-06
- train_batch_size: 16
- eval_batch_size: 8
- seed: 30
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0674 | 1.0 | 4210 | 1.1086 | 0.8028 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
huggingtweets/detseretninu-dumbricardo-illuminusnumb | huggingtweets | 2021-08-24T21:49:20Z | 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/detseretninu-dumbricardo-illuminusnumb/1629841756956/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/1412373998936027142/k2nY1nVc_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/1426046688263692288/RzlZFjIP_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/1312018147822759937/Z7XnZkhn_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">sad rico & follow me only if you're sad & ...</div>
<div style="text-align: center; font-size: 14px;">@detseretninu-dumbricardo-illuminusnumb</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 sad rico & follow me only if you're sad & ....
| Data | sad rico | follow me only if you're sad | ... |
| --- | --- | --- | --- |
| Tweets downloaded | 768 | 3233 | 677 |
| Retweets | 0 | 167 | 1 |
| Short tweets | 102 | 755 | 285 |
| Tweets kept | 666 | 2311 | 391 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/l42hthlz/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 @detseretninu-dumbricardo-illuminusnumb's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/c1hyp8lf) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/c1hyp8lf/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/detseretninu-dumbricardo-illuminusnumb')
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/itssixword | huggingtweets | 2021-08-24T19:25:30Z | 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/itssixword/1629833127428/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/628257137060229120/_3q_D4g2_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">Six words story</div>
<div style="text-align: center; font-size: 14px;">@itssixword</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 Six words story.
| Data | Six words story |
| --- | --- |
| Tweets downloaded | 282 |
| Retweets | 0 |
| Short tweets | 2 |
| Tweets kept | 280 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2dbtmbzz/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 @itssixword's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2wydugsv) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2wydugsv/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/itssixword')
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)
|
IsabellaKarabasz/roberta-base-bne-finetuned-amazon_reviews_multi | IsabellaKarabasz | 2021-08-24T14:16:29Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:04Z | ---
license: cc-by-4.0
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
model_index:
- name: roberta-base-bne-finetuned-amazon_reviews_multi
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_reviews_multi
type: amazon_reviews_multi
args: es
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-bne-finetuned-amazon_reviews_multi
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the amazon_reviews_multi 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: 2
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
anthony/tokenizers-test | anthony | 2021-08-24T08:17:27Z | 0 | 1 | null | [
"region:us"
] | null | 2022-03-02T23:29:05Z | This repository doesn't contain a model, but only a tokenizer that can be used with the
`tokenizers` library.
This tokenizer is just a copy of `bert-base-uncased`.
```python
from tokenizers import Tokenizer
tokenizer = Tokenizer.from_pretrained("anthony/tokenizers-test")
```
|
jacobduncan00/hackMIT-finetuned-sst2 | jacobduncan00 | 2021-08-24T04:05:25Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model_index:
- name: hackMIT-finetuned-sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: sst2
metric:
name: Accuracy
type: accuracy
value: 0.7970183486238532
---
<!-- 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. -->
# hackMIT-finetuned-sst2
This model is a fine-tuned version of [Blaine-Mason/hackMIT-finetuned-sst2](https://huggingface.co/Blaine-Mason/hackMIT-finetuned-sst2) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0046
- Accuracy: 0.7970
## 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: 1.7339491016138283e-05
- train_batch_size: 64
- eval_batch_size: 16
- seed: 23
- 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0652 | 1.0 | 1053 | 0.9837 | 0.7970 |
| 0.0586 | 2.0 | 2106 | 0.9927 | 0.7959 |
| 0.0549 | 3.0 | 3159 | 1.0046 | 0.7970 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
huggingartists/joji | huggingartists | 2021-08-23T21:47:22Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/joji",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
datasets:
- huggingartists/joji
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/d20ee1f900287060716f7594ccba7ea3.1000x1000x1.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">Joji</div>
<a href="https://genius.com/artists/joji">
<div style="text-align: center; font-size: 14px;">@joji</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 Joji.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/joji).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/joji")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/ns61e8zi/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 Joji's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/jz3ft48t) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/jz3ft48t/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/joji')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/joji")
model = AutoModelWithLMHead.from_pretrained("huggingartists/joji")
```
## 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)
|
nateraw/planes-trains-automobiles | nateraw | 2021-08-23T21:42:21Z | 404 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- huggingpics
- image-classification
- generated_from_trainer
metrics:
- accuracy
model_index:
- name: planes-trains-automobiles
results:
- task:
name: Image Classification
type: image-classification
metric:
name: Accuracy
type: accuracy
value: 0.9850746268656716
---
<!-- 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. -->
# planes-trains-automobiles
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the huggingpics dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0534
- Accuracy: 0.9851
## Model description
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
#### automobiles
#### planes
#### trains
## 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: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0283 | 1.0 | 48 | 0.0434 | 0.9851 |
| 0.0224 | 2.0 | 96 | 0.0548 | 0.9851 |
| 0.0203 | 3.0 | 144 | 0.0445 | 0.9851 |
| 0.0195 | 4.0 | 192 | 0.0534 | 0.9851 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
mrm8488/mT5-small-finetuned-tydiqa-for-xqa | mrm8488 | 2021-08-23T21:32:44Z | 75 | 2 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"multilingual",
"dataset:tydiqa",
"arxiv:2010.11934",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
language: multilingual
datasets:
- tydiqa
widget:
- text: "question: What won HuggingFace? context: HuggingFace won the best Demo paper at EMNLP2020."
---
# mT5-small fine-tuned on TyDiQA for multilingual QA 🗺📖❓
[Google's mT5-small](https://huggingface.co/google/mt5-small) fine-tuned on [TyDi QA](https://huggingface.co/nlp/viewer/?dataset=tydiqa&config=secondary_task) (secondary task) for **multingual Q&A** downstream task.
## Details of mT5
[Google's mT5](https://github.com/google-research/multilingual-t5)
mT5 is pretrained on the [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) corpus, covering 101 languages:
Afrikaans, Albanian, Amharic, Arabic, Armenian, Azerbaijani, Basque, Belarusian, Bengali, Bulgarian, Burmese, Catalan, Cebuano, Chichewa, Chinese, Corsican, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galician, Georgian, German, Greek, Gujarati, Haitian Creole, Hausa, Hawaiian, Hebrew, Hindi, Hmong, Hungarian, Icelandic, Igbo, Indonesian, Irish, Italian, Japanese, Javanese, Kannada, Kazakh, Khmer, Korean, Kurdish, Kyrgyz, Lao, Latin, Latvian, Lithuanian, Luxembourgish, Macedonian, Malagasy, Malay, Malayalam, Maltese, Maori, Marathi, Mongolian, Nepali, Norwegian, Pashto, Persian, Polish, Portuguese, Punjabi, Romanian, Russian, Samoan, Scottish Gaelic, Serbian, Shona, Sindhi, Sinhala, Slovak, Slovenian, Somali, Sotho, Spanish, Sundanese, Swahili, Swedish, Tajik, Tamil, Telugu, Thai, Turkish, Ukrainian, Urdu, Uzbek, Vietnamese, Welsh, West Frisian, Xhosa, Yiddish, Yoruba, Zulu.
**Note**: mT5 was only pre-trained on mC4 excluding any supervised training. Therefore, this model has to be fine-tuned before it is useable on a downstream task.
Pretraining Dataset: [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual)
Other Community Checkpoints: [here](https://huggingface.co/models?search=mt5)
Paper: [mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/abs/2010.11934)
Authors: *Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel*
## Details of the dataset 📚
**TyDi QA** is a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs. The languages of TyDi QA are diverse with regard to their typology -- the set of linguistic features that each language expresses -- such that we expect models performing well on this set to generalize across a large number of the languages in the world. It contains language phenomena that would not be found in English-only corpora. To provide a realistic information-seeking task and avoid priming effects, questions are written by people who want to know the answer, but don’t know the answer yet, (unlike SQuAD and its descendents) and the data is collected directly in each language without the use of translation (unlike MLQA and XQuAD).
| Dataset | Task | Split | # samples |
| -------- | ----- |------| --------- |
| TyDi QA | GoldP | train| 49881 |
| TyDi QA | GoldP | valid| 5077 |
## Results on validation dataset 📝
| Metric | # Value |
| ------ | --------- |
| **EM** | **41.65** |
## Model in Action 🚀
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
tokenizer = AutoTokenizer.from_pretrained("mrm8488/mT5-small-finetuned-tydiqa-for-xqa")
model = AutoModelForCausalLM.from_pretrained("mrm8488/mT5-small-finetuned-tydiqa-for-xqa").to(device)
def get_response(question, context, max_length=32):
input_text = 'question: %s context: %s' % (question, context)
features = tokenizer([input_text], return_tensors='pt')
output = model.generate(input_ids=features['input_ids'].to(device),
attention_mask=features['attention_mask'].to(device),
max_length=max_length)
return tokenizer.decode(output[0], skip_special_tokens=True)
# Some examples in different languages
context = 'HuggingFace won the best Demo paper at EMNLP2020.'
question = 'What won HuggingFace?'
get_response(question, context)
context = 'HuggingFace ganó la mejor demostración con su paper en la EMNLP2020.'
question = 'Qué ganó HuggingFace?'
get_response(question, context)
context = 'HuggingFace выиграл лучшую демонстрационную работу на EMNLP2020.'
question = 'Что победило в HuggingFace?'
get_response(question, context)
```
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/)
> Made with <span style="color: #e25555;">♥</span> in Spain
|
chandank/bart-base-finetuned-xsum | chandank | 2021-08-23T20:21:52Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
tags:
- generated_from_trainer
datasets:
- null
metrics:
- rouge
model_index:
- name: bart-base-finetuned-xsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
metric:
name: Rouge1
type: rouge
value: 27.887
---
<!-- 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. -->
# bart-base-finetuned-xsum
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5925
- Rouge1: 27.887
- Rouge2: 16.1414
- Rougel: 24.0525
- Rougelsum: 25.4029
- Gen Len: 19.9841
## 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: 4
- eval_batch_size: 4
- 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:-------:|:---------:|:-------:|
| 1.9826 | 1.0 | 879 | 1.5925 | 27.887 | 16.1414 | 24.0525 | 25.4029 | 19.9841 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
lewtun/roberta-base-bne-finetuned-amazon_reviews_multi | lewtun | 2021-08-23T17:13:32Z | 14 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
license: cc-by-4.0
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
metrics:
- accuracy
model_index:
- name: roberta-base-bne-finetuned-amazon_reviews_multi
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_reviews_multi
type: amazon_reviews_multi
args: es
metric:
name: Accuracy
type: accuracy
value: 0.93075
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-bne-finetuned-amazon_reviews_multi
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2306
- Accuracy: 0.9307
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1978 | 1.0 | 1250 | 0.1750 | 0.9325 |
| 0.0951 | 2.0 | 2500 | 0.2306 | 0.9307 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
hfeng/bert_base_uncased_conll2003 | hfeng | 2021-08-23T14:14:40Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-03-02T23:29:05Z | # BERT base model (uncased) fine-tuned on CoNLL-2003
This model was trained following the PyTorch token-classification example from Hugging Face: https://github.com/huggingface/transformers/tree/master/examples/pytorch/token-classification.
There were no tweaks to the model or dataset.
|
Narsil/deberta-large-mnli-zero-cls | Narsil | 2021-08-23T13:27:24Z | 943 | 14 | transformers | [
"transformers",
"pytorch",
"deberta",
"text-classification",
"deberta-v1",
"deberta-mnli",
"zero-shot-classification",
"en",
"arxiv:2006.03654",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | zero-shot-classification | 2022-03-02T23:29:04Z | ---
language: en
tags:
- deberta-v1
- deberta-mnli
tasks: mnli
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
pipeline_tag: zero-shot-classification
---
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check the [official repository](https://github.com/microsoft/DeBERTa) for more details and updates.
This is the DeBERTa large model fine-tuned with MNLI task.
#### Fine-tuning on NLU tasks
We present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks.
| Model | SQuAD 1.1 | SQuAD 2.0 | MNLI-m/mm | SST-2 | QNLI | CoLA | RTE | MRPC | QQP |STS-B |
|---------------------------|-----------|-----------|-------------|-------|------|------|--------|-------|-------|------|
| | F1/EM | F1/EM | Acc | Acc | Acc | MCC | Acc |Acc/F1 |Acc/F1 |P/S |
| BERT-Large | 90.9/84.1 | 81.8/79.0 | 86.6/- | 93.2 | 92.3 | 60.6 | 70.4 | 88.0/- | 91.3/- |90.0/- |
| RoBERTa-Large | 94.6/88.9 | 89.4/86.5 | 90.2/- | 96.4 | 93.9 | 68.0 | 86.6 | 90.9/- | 92.2/- |92.4/- |
| XLNet-Large | 95.1/89.7 | 90.6/87.9 | 90.8/- | 97.0 | 94.9 | 69.0 | 85.9 | 90.8/- | 92.3/- |92.5/- |
| [DeBERTa-Large](https://huggingface.co/microsoft/deberta-large)<sup>1</sup> | 95.5/90.1 | 90.7/88.0 | 91.3/91.1| 96.5|95.3| 69.5| 91.0| 92.6/94.6| 92.3/- |92.8/92.5 |
| [DeBERTa-XLarge](https://huggingface.co/microsoft/deberta-xlarge)<sup>1</sup> | -/- | -/- | 91.5/91.2| 97.0 | - | - | 93.1 | 92.1/94.3 | - |92.9/92.7|
| [DeBERTa-V2-XLarge](https://huggingface.co/microsoft/deberta-v2-xlarge)<sup>1</sup>|95.8/90.8| 91.4/88.9|91.7/91.6| **97.5**| 95.8|71.1|**93.9**|92.0/94.2|92.3/89.8|92.9/92.9|
|**[DeBERTa-V2-XXLarge](https://huggingface.co/microsoft/deberta-v2-xxlarge)<sup>1,2</sup>**|**96.1/91.4**|**92.2/89.7**|**91.7/91.9**|97.2|**96.0**|**72.0**| 93.5| **93.1/94.9**|**92.7/90.3** |**93.2/93.1** |
--------
#### Notes.
- <sup>1</sup> Following RoBERTa, for RTE, MRPC, STS-B, we fine-tune the tasks based on [DeBERTa-Large-MNLI](https://huggingface.co/microsoft/deberta-large-mnli), [DeBERTa-XLarge-MNLI](https://huggingface.co/microsoft/deberta-xlarge-mnli), [DeBERTa-V2-XLarge-MNLI](https://huggingface.co/microsoft/deberta-v2-xlarge-mnli), [DeBERTa-V2-XXLarge-MNLI](https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli). The results of SST-2/QQP/QNLI/SQuADv2 will also be slightly improved when start from MNLI fine-tuned models, however, we only report the numbers fine-tuned from pretrained base models for those 4 tasks.
- <sup>2</sup> To try the **XXLarge** model with **[HF transformers](https://huggingface.co/transformers/main_classes/trainer.html)**, you need to specify **--sharded_ddp**
```bash
cd transformers/examples/text-classification/
export TASK_NAME=mrpc
python -m torch.distributed.launch --nproc_per_node=8 run_glue.py --model_name_or_path microsoft/deberta-v2-xxlarge \\\n--task_name $TASK_NAME --do_train --do_eval --max_seq_length 128 --per_device_train_batch_size 4 \\\n--learning_rate 3e-6 --num_train_epochs 3 --output_dir /tmp/$TASK_NAME/ --overwrite_output_dir --sharded_ddp --fp16
```
### Citation
If you find DeBERTa useful for your work, please cite the following paper:
``` latex
@inproceedings{
he2021deberta,
title={DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION},
author={Pengcheng He and Xiaodong Liu and Jianfeng Gao and Weizhu Chen},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=XPZIaotutsD}
}
```
|
andi611/distilbert-base-uncased-ner-mit-restaurant | andi611 | 2021-08-23T08:11:51Z | 13 | 1 | transformers | [
"transformers",
"pytorch",
"distilbert",
"token-classification",
"generated_from_trainer",
"en",
"dataset:mit_restaurant",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-03-02T23:29:05Z | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- mit_restaurant
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: distilbert-base-uncased-ner-mit-restaurant
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: mit_restaurant
type: mit_restaurant
metric:
name: Accuracy
type: accuracy
value: 0.9118988661540467
---
<!-- 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-ner-mit-restaurant
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the mit_restaurant dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3097
- Precision: 0.7874
- Recall: 0.8104
- F1: 0.7988
- Accuracy: 0.9119
## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 431 | 0.4575 | 0.6220 | 0.6856 | 0.6523 | 0.8650 |
| 1.1705 | 2.0 | 862 | 0.3183 | 0.7747 | 0.7953 | 0.7848 | 0.9071 |
| 0.3254 | 3.0 | 1293 | 0.3163 | 0.7668 | 0.8021 | 0.7841 | 0.9058 |
| 0.2287 | 4.0 | 1724 | 0.3097 | 0.7874 | 0.8104 | 0.7988 | 0.9119 |
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
fadhilarkan/qa-indo-math-k | fadhilarkan | 2021-08-23T07:40:55Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
model-index:
- name: qa-indo-math-k
---
<!-- 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. -->
# qa-indo-math-k
This model was trained from scratch on an unkown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8801
## 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: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 127 | 0.7652 |
| No log | 2.0 | 254 | 0.7520 |
| No log | 3.0 | 381 | 0.7681 |
| 0.9618 | 4.0 | 508 | 0.7337 |
| 0.9618 | 5.0 | 635 | 0.7560 |
| 0.9618 | 6.0 | 762 | 0.7397 |
| 0.9618 | 7.0 | 889 | 0.7298 |
| 0.6652 | 8.0 | 1016 | 0.7891 |
| 0.6652 | 9.0 | 1143 | 0.7874 |
| 0.6652 | 10.0 | 1270 | 0.7759 |
| 0.6652 | 11.0 | 1397 | 0.7505 |
| 0.6174 | 12.0 | 1524 | 0.7838 |
| 0.6174 | 13.0 | 1651 | 0.7878 |
| 0.6174 | 14.0 | 1778 | 0.8028 |
| 0.6174 | 15.0 | 1905 | 0.8154 |
| 0.5733 | 16.0 | 2032 | 0.8131 |
| 0.5733 | 17.0 | 2159 | 0.8278 |
| 0.5733 | 18.0 | 2286 | 0.8308 |
| 0.5733 | 19.0 | 2413 | 0.8433 |
| 0.5378 | 20.0 | 2540 | 0.8303 |
| 0.5378 | 21.0 | 2667 | 0.8352 |
| 0.5378 | 22.0 | 2794 | 0.8369 |
| 0.5378 | 23.0 | 2921 | 0.8518 |
| 0.5095 | 24.0 | 3048 | 0.8749 |
| 0.5095 | 25.0 | 3175 | 0.8533 |
| 0.5095 | 26.0 | 3302 | 0.8547 |
| 0.5095 | 27.0 | 3429 | 0.8844 |
| 0.4856 | 28.0 | 3556 | 0.8752 |
| 0.4856 | 29.0 | 3683 | 0.8804 |
| 0.4856 | 30.0 | 3810 | 0.8801 |
### Framework versions
- Transformers 4.6.1
- Pytorch 1.7.0
- Datasets 1.11.0
- Tokenizers 0.10.3
|
andi611/distilbert-base-uncased-squad2-with-ner-mit-restaurant-with-neg-with-repeat | andi611 | 2021-08-23T05:38:50Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"en",
"dataset:squad_v2",
"dataset:mit_restaurant",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:05Z | ---
language:
- en
tags:
- generated_from_trainer
datasets:
- squad_v2
- mit_restaurant
model_index:
- name: distilbert-base-uncased-squad2-with-ner-mit-restaurant-with-neg-with-repeat
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: squad_v2
type: squad_v2
- task:
name: Token Classification
type: token-classification
dataset:
name: mit_restaurant
type: mit_restaurant
---
<!-- 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-squad2-with-ner-mit-restaurant-with-neg-with-repeat
This model is a fine-tuned version of [twmkn9/distilbert-base-uncased-squad2](https://huggingface.co/twmkn9/distilbert-base-uncased-squad2) on the squad_v2 and the mit_restaurant datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
eugenesiow/mdsr-bam | eugenesiow | 2021-08-23T01:37:09Z | 142 | 0 | transformers | [
"transformers",
"MDSR",
"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",
"arxiv:1803.08664",
"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
---
# Multi-Scale Deep Super-Resolution System (MDSR)
MDSR 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 and model upscaling.
## Model description
The MDSR 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 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 MdsrModel, 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 = MdsrModel.from_pretrained('eugenesiow/mdsr-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, MdsrModel, MdsrConfig
training_args = TrainingArguments(
output_dir='./results', # output directory
num_train_epochs=1000, # total number of training epochs
)
config = MdsrConfig(
scale=4, # train a model to upscale 4x
bam=True, # apply balanced attention to the network
)
model = MdsrModel(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 |mdsr-bam |
|--- |--- |--- |--- |
|Set5 |2x |33.64/0.9292 |**38/0.9607** |
|Set5 |3x |30.39/0.8678 |**35.07/0.9402** |
|Set5 |4x |28.42/0.8101 |**32.19/0.8949** |
|Set14 |2x |30.22/0.8683 |**33.68/0.9182** |
|Set14 |3x |27.53/0.7737 |**31.04/0.8582** |
|Set14 |4x |25.99/0.7023 |**28.73/0.7847** |
|BSD100 |2x |29.55/0.8425 |**33.77/0.9253** |
|BSD100 |3x |27.20/0.7382 |**29.62/0.8188** |
|BSD100 |4x |25.96/0.6672 |**28.5/0.7645** |
|Urban100 |2x |26.66/0.8408 |**32.04/0.9272** |
|Urban100 |3x | |**29.16/0.8717** |
|Urban100 |4x |23.14/0.6573 |**26.02/0.7834** |
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
@article{ahn2018fast,
title={Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network},
author={Ahn, Namhyuk and Kang, Byungkon and Sohn, Kyung-Ah},
journal={arXiv preprint arXiv:1803.08664},
year={2018}
}
``` |
eugenesiow/carn | eugenesiow | 2021-08-23T01:29:35Z | 28 | 1 | transformers | [
"transformers",
"CARN",
"super-image",
"image-super-resolution",
"dataset:eugenesiow/Div2k",
"dataset:eugenesiow/Set5",
"dataset:eugenesiow/Set14",
"dataset:eugenesiow/BSD100",
"dataset:eugenesiow/Urban100",
"arxiv:1803.08664",
"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
---
# Cascading Residual Network (CARN)
CARN 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 [Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network](https://arxiv.org/abs/1803.08664) by Ahn et al. (2018) and first released in [this repository](https://github.com/nmhkahn/CARN-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 and model upscaling.
## Model description
The CARN model proposes an architecture that implements a cascading mechanism upon a residual network for accurate and lightweight image super-resolution.
## 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 CarnModel, 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 = CarnModel.from_pretrained('eugenesiow/carn', 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, CarnModel, CarnConfig
training_args = TrainingArguments(
output_dir='./results', # output directory
num_train_epochs=1000, # total number of training epochs
)
config = CarnConfig(
scale=4, # train a model to upscale 4x
bam=True, # apply balanced attention to the network
)
model = CarnModel(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 |carn |
|--- |--- |--- |--- |
|Set5 |2x |33.64/0.9292 |**37.89/0.9602** |
|Set5 |3x |30.39/0.8678 |**34.88/0.9391** |
|Set5 |4x |28.42/0.8101 |**32.05/0.8931** |
|Set14 |2x |30.22/0.8683 |**33.53/0.9173** |
|Set14 |3x |27.53/0.7737 |**30.93/0.8566** |
|Set14 |4x |25.99/0.7023 |**28.67/0.7828** |
|BSD100 |2x |29.55/0.8425 |**33.66/0.9242** |
|BSD100 |3x |27.20/0.7382 |**29.56/0.8173** |
|BSD100 |4x |25.96/0.6672 |**28.44/0.7625** |
|Urban100 |2x |26.66/0.8408 |**31.62/0.9229** |
|Urban100 |3x | |**28.95/0.867** |
|Urban100 |4x |23.14/0.6573 |**25.85/0.7768** |
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
@article{ahn2018fast,
title={Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network},
author={Ahn, Namhyuk and Kang, Byungkon and Sohn, Kyung-Ah},
journal={arXiv preprint arXiv:1803.08664},
year={2018}
}
``` |
huggingartists/bruce-springsteen | huggingartists | 2021-08-22T22:20:09Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/bruce-springsteen",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
datasets:
- huggingartists/bruce-springsteen
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/6dfe4b89b895b331f09c6b136a0705e5.807x807x1.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">Bruce Springsteen</div>
<a href="https://genius.com/artists/bruce-springsteen">
<div style="text-align: center; font-size: 14px;">@bruce-springsteen</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 Bruce Springsteen.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/bruce-springsteen).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/bruce-springsteen")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/28yd4w57/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 Bruce Springsteen's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/6qq7wbab) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/6qq7wbab/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/bruce-springsteen')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/bruce-springsteen")
model = AutoModelWithLMHead.from_pretrained("huggingartists/bruce-springsteen")
```
## 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)
|
Kyoungmin/beauty-base-KLCP2 | Kyoungmin | 2021-08-22T19:24:49Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:04Z | **Second** BertForMaskedLM pretrained model in **KOREAN Beauty** domain.
About 120,000 reviews were used.
It was trained based on _beomi/kcbert-base_ .
Check out _Kyoungmin/beauty-base-KLCP_ for smaller model !! |
lewtun/roberta-base-bne-finetuned-amazon_reviews_multi-finetuned-amazon_reviews_multi | lewtun | 2021-08-22T18:59:30Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
metrics:
- accuracy
model_index:
- name: roberta-base-bne-finetuned-amazon_reviews_multi-finetuned-amazon_reviews_multi
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_reviews_multi
type: amazon_reviews_multi
args: es
metric:
name: Accuracy
type: accuracy
value: 0.9285
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-bne-finetuned-amazon_reviews_multi-finetuned-amazon_reviews_multi
This model was trained from scratch on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3595
- Accuracy: 0.9285
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.103 | 1.0 | 1250 | 0.2864 | 0.928 |
| 0.0407 | 2.0 | 2500 | 0.3595 | 0.9285 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
dvm1983/TinyBERT_General_4L_312D_de | dvm1983 | 2021-08-22T16:44:48Z | 13 | 2 | transformers | [
"transformers",
"pytorch",
"bert",
"tinybert",
"fill-mask",
"de",
"dataset:wiki",
"arxiv:1909.10351",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:05Z | ---
language:
- de
tags:
- tinybert
- fill-mask
datasets:
- wiki
---
Here is represented tinybert model for German language (de). The model was created by distilling of bert base cased model(https://huggingface.co/dbmdz/bert-base-german-cased) in the way described in https://arxiv.org/abs/1909.10351 (TinyBERT: Distilling BERT for Natural Language Understanding)
Dataset:
German Wikipedia Text Corpus - https://github.com/t-systems-on-site-services-gmbh/german-wikipedia-text-corpus
Versions:
torch==1.4.0
transformers==4.8.1
How to load model for LM(fill-mask) task:
tokenizer = transformers.BertTokenizer.from_pretrained(model_dir + '/vocab.txt', do_lower_case=False)
config = transformers.BertConfig.from_json_file(model_dir+'config.json')
model = transformers.BertModel(config=config)
model.pooler = nn.Sequential(nn.Linear(in_features=model.config.hidden_size, out_features=model.config.hidden_size, bias=True),
nn.LayerNorm((model.config.hidden_size,), eps=1e-12, elementwise_affine=True),
nn.Linear(in_features=model.config.hidden_size, out_features=len(tokenizer), bias=True))
model.resize_token_embeddings(len(tokenizer))
checkpoint = torch.load(model_dir+'/pytorch_model.bin', map_location=torch.device('cuda'))
model.load_state_dict(checkpoint)
In case of NER or Classification task we have to load model for LM task and change pooler:
model.pooler = nn.Sequential(nn.Dropout(p=config.hidden_dropout_prob, inplace=False),
nn.Linear(in_features=config.hidden_size, out_features=n_classes, bias=True))
|
EasthShin/Youth_Chatbot_Kogpt2-base | EasthShin | 2021-08-22T16:28:22Z | 107 | 1 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:04Z | ## Youth_Chatbot_KoGPT2-base
**Demo Web**: [Ainize Endpoint](https://main-youth-chatbot-ko-gpt2-base-east-h-shin.endpoint.ainize.ai/)
<br>
**Demo Web Code**: [Github](https://github.com/EastHShin/Youth_Chatbot_KoGPT2-base)
<br>
**Youth-Chatbot API**: [Ainize API](https://ainize.ai/EastHShin/Youth_Chatbot_KoGPT2-base_API?branch=main)
<br>
<br>
## Overview
**Language model**: KoGPT2
<br>
**Language**: Korean
<br>
**Training data**: [Aihub](https://aihub.or.kr/aidata/7978)
## Usage
```
from transformers import PreTrainedTokenizerFast, GPT2LMHeadModel
U_TKN = '<usr>'
S_TKN = '<sys>'
MASK = '<unused0>'
SENT = '<unused1>'
tokenizer = PreTrainedTokenizerFast.from_pretrained("EasthShin/Youth_Chatbot_Kogpt2-base",
bos_token='</s>', eos_token='</s>', unk_token='<unk>',
pad_token='<pad>', mask_token=MASK)
model = GPT2LMHeadModel.from_pretrained('EasthShin/Youth_Chatbot_Kogpt2-base')
input_ids = tokenizer.encode(U_TKN + {your text} + sent + S_TKN)
gen_ids = model.generate(torch.tensor([input_ids]),
max_length=128,
repetition_penalty= 2.0,
pad_token_id=tokenizer.pad_token_id,
eos_token_id=tokenizer.eos_token_id,
bos_token_id=tokenizer.bos_token_id,
use_cache=True)
generated = tokenizer.decode(gen_ids[0, :].tolist())
print(generated)
``` |
EasthShin/Android_Ios_Classification | EasthShin | 2021-08-22T16:18:37Z | 9 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:04Z | ## Bert-base-uncased for Android-Ios Question Classification
**Code**: See [Ainize Workspace](https://ainize.ai/workspace/create?imageId=hnj95592adzr02xPTqss&git=https://github.com/EastHShin/Android-Ios-Classification-Workspace)
<br>
**Android-Ios-Classification DEMO**: [Ainize Endpoint](https://main-android-ios-classification-east-h-shin.endpoint.ainize.ai/)
<br>
**Demo web Code**: [Github](https://github.com/EastHShin/Android-Ios-Classification)
<br>
**Android-Ios-Classification API**: [Ainize API](https://ainize.ai/EastHShin/Android-Ios-Classification)
<br>
<br>
## Overview
**Language model**: bert-base-cased
<br>
**Language**: English
<br>
**Training data**: Question classification Android-Ios dataset from [Kaggle](https://www.kaggle.com/xhlulu/question-classification-android-or-ios)
## Usage
```
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_path = "EasthShin/Android_Ios_Classification"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForSequenceClassification.from_pretrained(model_path)
classifier = pipeline('text-classification', model=model_path, tokenizer=tokenizer)
question = "I bought goodnote in Appstore"
result = dict()
result[0] = classifier(question)[0]
``` |
DeadBeast/emoBERTTamil | DeadBeast | 2021-08-22T15:46:05Z | 8 | 2 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:tamilmixsentiment",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:04Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tamilmixsentiment
metrics:
- accuracy
model_index:
- name: emoBERTTamil
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tamilmixsentiment
type: tamilmixsentiment
args: default
metric:
name: Accuracy
type: accuracy
value: 0.671
---
<!-- 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. -->
# emoBERTTamil
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the tamilmixsentiment dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9666
- Accuracy: 0.671
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1128 | 1.0 | 250 | 1.0290 | 0.672 |
| 1.0226 | 2.0 | 500 | 1.0172 | 0.686 |
| 0.9137 | 3.0 | 750 | 0.9666 | 0.671 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
huggingtweets/pepexbt | huggingtweets | 2021-08-22T13:00:37Z | 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/pepexbt/1629637214827/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/1428232830761455617/VC6_ALvV_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">pepe</div>
<div style="text-align: center; font-size: 14px;">@pepexbt</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 pepe.
| Data | pepe |
| --- | --- |
| Tweets downloaded | 3249 |
| Retweets | 56 |
| Short tweets | 809 |
| Tweets kept | 2384 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1jezukab/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 @pepexbt's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3isjrvll) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3isjrvll/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/pepexbt')
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)
|
oumeima/finetuned-bert-mrpc | oumeima | 2021-08-22T11:35:18Z | 9 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"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
datasets:
- glue
metrics:
- accuracy
- f1
model_index:
- name: finetuned-bert-mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: mrpc
metric:
name: F1
type: f1
value: 0.9003322259136212
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned-bert-mrpc
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5280
- Accuracy: 0.8529
- F1: 0.9003
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5704 | 1.0 | 230 | 0.4204 | 0.7917 | 0.8542 |
| 0.3391 | 2.0 | 460 | 0.4157 | 0.8456 | 0.8955 |
| 0.1923 | 3.0 | 690 | 0.5280 | 0.8529 | 0.9003 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
dadada/opus-mt-zh-en-ep1-renri-zh-to-en | dadada | 2021-08-22T06:54:09Z | 14 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- bleu
model_index:
- name: opus-mt-zh-en-ep1-renri-zh-to-en
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
metric:
name: Bleu
type: bleu
value: 18.2579
---
<!-- 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-zh-en-ep1-renri-zh-to-en
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-zh-en](https://huggingface.co/Helsinki-NLP/opus-mt-zh-en) on an unkown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2192
- Bleu: 18.2579
- Gen Len: 28.4817
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 2.2194 | 1.0 | 59472 | 2.2192 | 18.2579 | 28.4817 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
huggingtweets/williamblakebot | huggingtweets | 2021-08-21T20:17:06Z | 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/williamblakebot/1629577022887/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/975180581440053249/yaM9x-Lq_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">William Blake</div>
<div style="text-align: center; font-size: 14px;">@williamblakebot</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 William Blake.
| Data | William Blake |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 0 |
| Short tweets | 0 |
| Tweets kept | 3250 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2lyz5wo1/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 @williamblakebot's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3hz2kxqg) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3hz2kxqg/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/williamblakebot')
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/daddykratos1 | huggingtweets | 2021-08-21T20:04:36Z | 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/daddykratos1/1629576272636/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/1353401231729950721/EAfnSQDa_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">Tweets by Kratos🪓</div>
<div style="text-align: center; font-size: 14px;">@daddykratos1</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 Tweets by Kratos🪓.
| Data | Tweets by Kratos🪓 |
| --- | --- |
| Tweets downloaded | 626 |
| Retweets | 14 |
| Short tweets | 52 |
| Tweets kept | 560 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/12nz41n2/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 @daddykratos1's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/33zt2owy) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/33zt2owy/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/daddykratos1')
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)
|
shahukareem/wav2vec2-large-xlsr-53-dhivehi-v2 | shahukareem | 2021-08-21T18:31:59Z | 8 | 3 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"dv",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:05Z | ---
language: dv
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
license: apache-2.0
---
# Wav2Vec2-Large-XLSR-53-Dhivehi
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Dhivehi using the [Common Voice](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "dv", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("shahukareem/wav2vec2-large-xlsr-53-dhivehi-v2")
model = Wav2Vec2ForCTC.from_pretrained("shahukareem/wav2vec2-large-xlsr-53-dhivehi-v2")
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the Dhivehi test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "dv", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("shahukareem/wav2vec2-large-xlsr-53-dhivehi-v2")
model = Wav2Vec2ForCTC.from_pretrained("shahukareem/wav2vec2-large-xlsr-53-dhivehi-v2")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\،\.\؟\!\'\"\–\’]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
``` |
fadhilarkan/t5-small-finetuned-xsum-2 | fadhilarkan | 2021-08-21T13:51:05Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"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
datasets:
- squad
metrics:
- rouge
model_index:
- name: t5-small-finetuned-xsum-2
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: squad
type: squad
args: plain_text
metric:
name: Rouge1
type: rouge
value: 28.8137
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum-2
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9536
- Rouge1: 28.8137
- Rouge2: 9.1265
- Rougel: 26.0238
- Rougelsum: 26.0217
- Gen Len: 13.854
## 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: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.2142 | 1.0 | 8760 | 1.9994 | 29.007 | 9.2583 | 26.2377 | 26.2356 | 13.4546 |
| 2.1372 | 2.0 | 17520 | 1.9622 | 29.1077 | 9.445 | 26.3734 | 26.3687 | 13.6995 |
| 2.0755 | 3.0 | 26280 | 1.9536 | 28.8137 | 9.1265 | 26.0238 | 26.0217 | 13.854 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
baffo32/genji-python-6B-split | baffo32 | 2021-08-21T13:33:22Z | 5 | 0 | transformers | [
"transformers",
"gpt_neo",
"text-generation",
"pytorch",
"causal-lm",
"en",
"arxiv:2104.09864",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language:
- en
tags:
- pytorch
- causal-lm
license: apache-2.0
datasets:
- the Pile
---
# Genji-python 6B
For example usage or to easily use the model you can check our colab notebook:
[Notebook](https://colab.research.google.com/drive/1PnWpx02IEUkY8jhLKd_NewUGEXahAska?usp=sharing)
## Model Description
Genji is a transformer model finetuned on EleutherAI's GPT-J 6B model. This particular model is trained on python only code approaching 4GB in size.
Split model has the checkpoints splitted, which makes it use less system RAM while loading and makes it faster to load.
This model needs more effort to set up as you need to install git-lfs and pull the repo.
| Hyperparameter | Value |
|-------------------|--------|
| n_parameters | 6,053,381,344 |
| n_layers | 28* |
| d_model | 4,096 |
| d_ff | 16,384 |
| n_heads | 16 |
| d_head | 256 |
| n_ctx | 2,048 |
| n_vocab | 50,400 (same tokenizer as GPT-2/3) |
| position encoding | [Rotary position encodings (RoPE)](https://arxiv.org/abs/2104.09864) |
| RoPE dimensions | [64](https://github.com/kingoflolz/mesh-transformer-jax/blob/f2aa66e0925de6593dcbb70e72399b97b4130482/mesh_transformer/layers.py#L223) |
`*` each layer consists of one feedforward block and one self attention block
The model consists of 28 layers with a model dimension of 4096, and a feedforward dimension of 16384. The model
dimension is split into 16 heads, each with a dimension of 256. Rotary position encodings (RoPE) was applied to 64
dimensions of each head. The model is trained with a tokenization vocabulary of 50257, using the same set of BPEs as
GPT-2/GPT-3.
## Training data
GPT-J 6B was pretrained on the [Pile](pile.eleuther.ai), a large scale curated dataset created by EleutherAI for the purpose of training this model. After the pre-training, it's finetuned on the python code that was taken from the Pile.
## Training procedure
Genji-python-6B is trained for 20k steps on around 655 million tokens with learning rate of 2e-06
## Intended Use
This model is trained for assistence on writing python code and having fun trying weird stuff with it.
### How to use
This model is only usable with our fork because GPT-J is not merged to the main transformers repo yet. When it's merged, we will make this model easily loadable.
For now, you need to use this fork:
[Fork](https://github.com/finetuneanon/transformers)
to install with pip:
```bash
pip install git+https://github.com/finetuneanon/transformers@gpt-neo-localattention3-rp-b
```
**git-lfs** also needs to be installed, on ubuntu:
```bash
apt install git-lfs
```
after it's installed, initialize git-lfs:
```bash
git lfs install
```
then clone this repo:
```bash
git clone https://huggingface.co/NovelAI/genji-python-6B-split
```
Now we can load the model.
We recommend the usage of the model as FP16. That way, it fits in 16GB VRAM cards.
How to use:
```python
from transformers import (
AutoTokenizer,
AutoModelForCausalLM,
GPTNeoForCausalLM,
)
model = AutoModelForCausalLM.from_pretrained("genji-python-6B-split/model").half().eval().cuda()
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-2.7B")
text = '''def print_customer_name'''
tokens = tokenizer(text, return_tensors="pt").input_ids
generated_tokens = model.generate(tokens.long().cuda(), use_cache=True, do_sample=True, top_k=50, temperature=0.3, top_p=0.9, repetition_penalty=1.125, min_length=1, max_length=len(tokens[0]) + 400, pad_token_id=tokenizer.eos_token_id)
last_tokens = generated_tokens[0][len(tokens[0]):]
generated_text = tokenizer.decode(last_tokens)
print("Generation:\n" + generated_text)
```
When ran, this code generates:
```python
Prompt:
def print_customer_name
Generation:
(self, customer):
"""Print the name of a customer."""
if not self.is_valid():
return
print("Customer: {}".format(customer))
```
For example usage, you can see our colab notebook as well:
[Notebook](https://colab.research.google.com/drive/1PnWpx02IEUkY8jhLKd_NewUGEXahAska?usp=sharing)
## Eval results
TBD
## Acknowledgements
This project was possible because of the compute provided by the
[TPU Research Cloud](https://sites.research.google/trc/) and [EleutherAI](https://eleuther.ai/) for pretraining of the GPT-J 6B.
Thanks to everyone who contributed to this project:
- [Aero](https://github.com/AeroScripts)
- [Finetune](https://github.com/finetuneanon)
- [Kurumuz](https://github.com/kurumuz) |
huggingtweets/domonic_m | huggingtweets | 2021-08-21T03:49:49Z | 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/domonic_m/1629517784951/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/1146161910448054273/b1HpVczo_400x400.png')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Domonic</div>
<div style="text-align: center; font-size: 14px;">@domonic_m</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 Domonic.
| Data | Domonic |
| --- | --- |
| Tweets downloaded | 502 |
| Retweets | 70 |
| Short tweets | 69 |
| Tweets kept | 363 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1q7f1cu6/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 @domonic_m's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/no8iew6j) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/no8iew6j/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/domonic_m')
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)
|
ramybaly/ner_conll2003 | ramybaly | 2021-08-21T03:21:14Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"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: ner_conll2003
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metric:
name: Accuracy
type: accuracy
value: 0.9772880710440217
---
<!-- 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. -->
# ner_conll2003
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1495
- Precision: 0.8985
- Recall: 0.9130
- F1: 0.9057
- Accuracy: 0.9773
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.423 | 1.0 | 877 | 0.0656 | 0.9158 | 0.9268 | 0.9213 | 0.9818 |
| 0.0575 | 2.0 | 1754 | 0.0574 | 0.9285 | 0.9445 | 0.9364 | 0.9847 |
| 0.0295 | 3.0 | 2631 | 0.0631 | 0.9414 | 0.9456 | 0.9435 | 0.9859 |
| 0.0155 | 4.0 | 3508 | 0.0680 | 0.9395 | 0.9467 | 0.9431 | 0.9860 |
| 0.0097 | 5.0 | 4385 | 0.0694 | 0.9385 | 0.9513 | 0.9449 | 0.9863 |
| 0.0059 | 6.0 | 5262 | 0.0743 | 0.9363 | 0.9471 | 0.9416 | 0.9860 |
| 0.0041 | 7.0 | 6139 | 0.0803 | 0.9371 | 0.9518 | 0.9444 | 0.9862 |
### Framework versions
- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.2
|
templates/automatic-speech-recognition | templates | 2021-08-20T14:18:50Z | 0 | 3 | generic | [
"generic",
"automatic-speech-recognition",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:05Z | ---
tags:
- automatic-speech-recognition
library_name: generic
---
# Automatic Speech Recognition repository template
This is a template repository for Automatic Speech Recognition to support generic inference with Hugging Face Hub generic Inference API. There are two required steps:
1. Specify the requirements by defining a `requirements.txt` file.
2. Implement the `pipeline.py` `__init__` and `__call__` methods. These methods are called by the Inference API. The `__init__` method should load the model and preload all the elements needed for inference (model, processors, tokenizers, etc.). This is only called once. The `__call__` method performs the actual inference. Make sure to follow the same input/output specifications defined in the template for the pipeline to work.
Example repos
* https://huggingface.co/osanseviero/pyctcdecode_asr
## How to start
First create a repo in https://hf.co/new.
Then clone this template and push it to your repo.
```
git clone https://huggingface.co/templates/automatic-speech-recognition
cd automatic-speech-recognition
git remote set-url origin https://huggingface.co/$YOUR_USER/$YOUR_REPO_NAME
git push --force
``` |
templates/feature-extraction | templates | 2021-08-20T14:18:25Z | 0 | 1 | generic | [
"generic",
"feature-extraction",
"region:us"
] | feature-extraction | 2022-03-02T23:29:05Z | ---
tags:
- feature-extraction
library_name: generic
---
# Feature Extraction repository template
This is a template repository for feature extraction to support generic inference with Hugging Face Hub generic Inference API. There are two required steps
1. Specify the requirements by defining a `requirements.txt` file.
2. Implement the `pipeline.py` `__init__` and `__call__` methods. These methods are called by the Inference API. The `__init__` method should load the model and preload all the elements needed for inference (model, processors, tokenizers, etc.). This is only called once. The `__call__` method performs the actual inference. Make sure to follow the same input/output specifications defined in the template for the pipeline to work.
Example repos
* https://huggingface.co/osanseviero/fasttext_english
## How to start
First create a repo in https://hf.co/new.
Then clone this template and push it to your repo.
```
git clone https://huggingface.co/templates/feature-extraction
cd feature-extraction
git remote set-url origin https://huggingface.co/$YOUR_USER/$YOUR_REPO_NAME
git push --force
``` |
imNitin001/firstRepo | imNitin001 | 2021-08-20T14:18:02Z | 0 | 0 | generic | [
"generic",
"text-to-image",
"region:us"
] | text-to-image | 2022-11-19T08:23:09Z | ---
tags:
- text-to-image
library_name: generic
---
# Text To Image repository template
This is a template repository for text to image to support generic inference with Hugging Face Hub generic Inference API. There are two required steps
1. Specify the requirements by defining a `requirements.txt` file.
2. Implement the `pipeline.py` `__init__` and `__call__` methods. These methods are called by the Inference API. The `__init__` method should load the model and preload all the elements needed for inference (model, processors, tokenizers, etc.). This is only called once. The `__call__` method performs the actual inference. Make sure to follow the same input/output specifications defined in the template for the pipeline to work.
Example repos
* https://huggingface.co/osanseviero/BigGAN-deep-128/blob/main/pipeline.py
## How to start
First create a repo in https://hf.co/new.
Then clone this template and push it to your repo.
```
git clone https://huggingface.co/templates/text-to-image
cd text-to-image
git remote set-url origin https://huggingface.co/$YOUR_USER/$YOUR_REPO_NAME
git push --force
``` |
tonybingo/test | tonybingo | 2021-08-20T14:18:02Z | 0 | 0 | generic | [
"generic",
"text-to-image",
"region:us"
] | text-to-image | 2022-11-18T08:09:55Z | ---
tags:
- text-to-image
library_name: generic
---
# Text To Image repository template
This is a template repository for text to image to support generic inference with Hugging Face Hub generic Inference API. There are two required steps
1. Specify the requirements by defining a `requirements.txt` file.
2. Implement the `pipeline.py` `__init__` and `__call__` methods. These methods are called by the Inference API. The `__init__` method should load the model and preload all the elements needed for inference (model, processors, tokenizers, etc.). This is only called once. The `__call__` method performs the actual inference. Make sure to follow the same input/output specifications defined in the template for the pipeline to work.
Example repos
* https://huggingface.co/osanseviero/BigGAN-deep-128/blob/main/pipeline.py
## How to start
First create a repo in https://hf.co/new.
Then clone this template and push it to your repo.
```
git clone https://huggingface.co/templates/text-to-image
cd text-to-image
git remote set-url origin https://huggingface.co/$YOUR_USER/$YOUR_REPO_NAME
git push --force
``` |
Arkenbrien/text-to-image-Arkenbrien | Arkenbrien | 2021-08-20T14:18:02Z | 0 | 1 | generic | [
"generic",
"text-to-image",
"region:us"
] | text-to-image | 2022-08-24T14:06:40Z | ---
tags:
- text-to-image
library_name: generic
---
# Text To Image repository template
This is a template repository for text to image to support generic inference with Hugging Face Hub generic Inference API. There are two required steps
1. Specify the requirements by defining a `requirements.txt` file.
2. Implement the `pipeline.py` `__init__` and `__call__` methods. These methods are called by the Inference API. The `__init__` method should load the model and preload all the elements needed for inference (model, processors, tokenizers, etc.). This is only called once. The `__call__` method performs the actual inference. Make sure to follow the same input/output specifications defined in the template for the pipeline to work.
Example repos
* https://huggingface.co/osanseviero/BigGAN-deep-128/blob/main/pipeline.py
## How to start
First create a repo in https://hf.co/new.
Then clone this template and push it to your repo.
```
git clone https://huggingface.co/templates/text-to-image
cd text-to-image
git remote set-url origin https://huggingface.co/$YOUR_USER/$YOUR_REPO_NAME
git push --force
``` |
ericsali/painting | ericsali | 2021-08-20T14:18:02Z | 0 | 1 | generic | [
"generic",
"text-to-image",
"region:us"
] | text-to-image | 2023-04-18T03:45:13Z | ---
tags:
- text-to-image
library_name: generic
---
# Text To Image repository template
This is a template repository for text to image to support generic inference with Hugging Face Hub generic Inference API. There are two required steps
1. Specify the requirements by defining a `requirements.txt` file.
2. Implement the `pipeline.py` `__init__` and `__call__` methods. These methods are called by the Inference API. The `__init__` method should load the model and preload all the elements needed for inference (model, processors, tokenizers, etc.). This is only called once. The `__call__` method performs the actual inference. Make sure to follow the same input/output specifications defined in the template for the pipeline to work.
Example repos
* https://huggingface.co/osanseviero/BigGAN-deep-128/blob/main/pipeline.py
## How to start
First create a repo in https://hf.co/new.
Then clone this template and push it to your repo.
```
git clone https://huggingface.co/templates/text-to-image
cd text-to-image
git remote set-url origin https://huggingface.co/$YOUR_USER/$YOUR_REPO_NAME
git push --force
``` |
templates/token-classification | templates | 2021-08-20T14:17:42Z | 0 | 1 | generic | [
"generic",
"token-classification",
"region:us"
] | token-classification | 2022-03-02T23:29:05Z | ---
tags:
- token-classification
library_name: generic
---
# Token Classification repository template
This is a template repository for token classification to support generic inference with Hugging Face Hub generic Inference API. There are two required steps
1. Specify the requirements by defining a `requirements.txt` file.
2. Implement the `pipeline.py` `__init__` and `__call__` methods. These methods are called by the Inference API. The `__init__` method should load the model and preload all the elements needed for inference (model, processors, tokenizers, etc.). This is only called once. The `__call__` method performs the actual inference. Make sure to follow the same input/output specifications defined in the template for the pipeline to work.
Example repos
* https://huggingface.co/osanseviero/en_core_web_sm/blob/main/pipeline.py
## How to start
First create a repo in https://hf.co/new.
Then clone this template and push it to your repo.
```
git clone https://huggingface.co/templates/token-classification
cd token-classification
git remote set-url origin https://huggingface.co/$YOUR_USER/$YOUR_REPO_NAME
git push --force
``` |
pin/senda | pin | 2021-08-20T11:00:39Z | 11 | 4 | transformers | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"danish",
"sentiment",
"polarity",
"da",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
language: da
tags:
- danish
- bert
- sentiment
- polarity
license: cc-by-4.0
widget:
- text: "Sikke en dejlig dag det er i dag"
---
# Danish BERT fine-tuned for Sentiment Analysis with `senda`
This model detects polarity ('positive', 'neutral', 'negative') of Danish texts.
It is trained and tested on Tweets annotated by [Alexandra Institute](https://github.com/alexandrainst). The model is trained with the [`senda`](https://github.com/ebanalyse/senda) package.
Here is an example of how to load the model in PyTorch using the [🤗Transformers](https://github.com/huggingface/transformers) library:
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
tokenizer = AutoTokenizer.from_pretrained("pin/senda")
model = AutoModelForSequenceClassification.from_pretrained("pin/senda")
# create 'senda' sentiment analysis pipeline
senda_pipeline = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
text = "Sikke en dejlig dag det er i dag"
# in English: 'what a lovely day'
senda_pipeline(text)
```
## Performance
The `senda` model achieves an accuracy of 0.77 and a macro-averaged F1-score of 0.73 on a small test data set, that [Alexandra Institute](https://github.com/alexandrainst/danlp/blob/master/docs/docs/datasets.md#twitter-sentiment) provides. The model can most certainly be improved, and we encourage all NLP-enthusiasts to give it their best shot - you can use the [`senda`](https://github.com/ebanalyse/senda) package to do this.
#### Contact
Feel free to contact author Lars Kjeldgaard on [[email protected]](mailto:[email protected]).
#### Shout-outs
Props to [Malte Højmark-Berthelsen](mailto:[email protected]) for pretraining Danish BERT and helping out adding a TensorFlow backend for `senda`.
|
huggingtweets/scottadamssays | huggingtweets | 2021-08-20T04:19:05Z | 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/scottadamssays/1629433141180/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/1259614511859765248/uxqTchXo_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">Scott Adams</div>
<div style="text-align: center; font-size: 14px;">@scottadamssays</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 Scott Adams.
| Data | Scott Adams |
| --- | --- |
| Tweets downloaded | 3246 |
| Retweets | 863 |
| Short tweets | 177 |
| Tweets kept | 2206 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/28q4l0oa/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 @scottadamssays's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/va3cwft8) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/va3cwft8/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/scottadamssays')
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/conceptualjames | huggingtweets | 2021-08-20T04:09:07Z | 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/conceptualjames/1629432543025/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/1419411594572873733/bCBGq8T9_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">James Lindsay, manipulated media</div>
<div style="text-align: center; font-size: 14px;">@conceptualjames</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 James Lindsay, manipulated media.
| Data | James Lindsay, manipulated media |
| --- | --- |
| Tweets downloaded | 3226 |
| Retweets | 1436 |
| Short tweets | 520 |
| Tweets kept | 1270 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1sj5ihe6/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 @conceptualjames's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1jnu1ceq) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1jnu1ceq/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/conceptualjames')
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)
|
fidukm34/biobert_v1.1_pubmed-finetuned-ner-finetuned-ner | fidukm34 | 2021-08-20T01:06:53Z | 14 | 2 | transformers | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:ncbi_disease",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-03-02T23:29:05Z | ---
tags:
- generated_from_trainer
datasets:
- ncbi_disease
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: biobert_v1.1_pubmed-finetuned-ner-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: ncbi_disease
type: ncbi_disease
args: ncbi_disease
metric:
name: Accuracy
type: accuracy
value: 0.9829142288061745
---
<!-- 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. -->
# biobert_v1.1_pubmed-finetuned-ner-finetuned-ner
This model is a fine-tuned version of [fidukm34/biobert_v1.1_pubmed-finetuned-ner](https://huggingface.co/fidukm34/biobert_v1.1_pubmed-finetuned-ner) on the ncbi_disease dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0715
- Precision: 0.8464
- Recall: 0.8872
- F1: 0.8663
- Accuracy: 0.9829
## 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 340 | 0.0715 | 0.8464 | 0.8872 | 0.8663 | 0.9829 |
### Framework versions
- Transformers 4.8.1
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
k0t1k/test | k0t1k | 2021-08-19T17:31:25Z | 8 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"pretraining",
"russian",
"fill-mask",
"embeddings",
"masked-lm",
"tiny",
"ru",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:05Z | ---
language: ["ru", "en"]
tags:
- russian
- fill-mask
- pretraining
- embeddings
- masked-lm
- tiny
license: mit
widget:
- text: "Миниатюрная модель для [MASK] разных задач."
---
Копия модели https://huggingface.co/cointegrated/rubert-tiny. Чисто для теста!
|
supah-hakah/distilgpt2-finetuned-wikitext2 | supah-hakah | 2021-08-19T12:59:37Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- null
model_index:
- name: distilgpt2-finetuned-wikitext2
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. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6424
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 3.7598 | 1.0 | 2334 | 3.6654 |
| 3.6321 | 2.0 | 4668 | 3.6453 |
| 3.6076 | 3.0 | 7002 | 3.6424 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
vishnun/distilgpt2-finetuned-distilgpt2-med_articles | vishnun | 2021-08-19T10:23:17Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- null
model_index:
- name: distilgpt2-finetuned-distilgpt2-med_articles
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. -->
# distilgpt2-finetuned-distilgpt2-med_articles
This model is a fine-tuned version of [vishnun/distilgpt2-finetuned-distilgpt2-med_articles](https://huggingface.co/vishnun/distilgpt2-finetuned-distilgpt2-med_articles) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3171
## 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 | 65 | 3.3417 |
| No log | 2.0 | 130 | 3.3300 |
| No log | 3.0 | 195 | 3.3231 |
| No log | 4.0 | 260 | 3.3172 |
| No log | 5.0 | 325 | 3.3171 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
huggingtweets/nftmansa | huggingtweets | 2021-08-18T21:04:18Z | 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/nftmansa/1629320654994/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/1398377108007755781/nmudFxl3_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">NFT</div>
<div style="text-align: center; font-size: 14px;">@nftmansa</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 NFT.
| Data | NFT |
| --- | --- |
| Tweets downloaded | 3223 |
| Retweets | 3037 |
| Short tweets | 36 |
| Tweets kept | 150 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/wwiy7t0n/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 @nftmansa's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/b9rzi99p) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/b9rzi99p/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/nftmansa')
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)
|
akreal/tiny-random-xlnet | akreal | 2021-08-18T15:08:21Z | 2,120 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"xlnet",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z | This is a copy of: https://huggingface.co/hf-internal-testing/tiny-random-xlnet
Changes: use old format for `pytorch_model.bin`.
|
patrickvonplaten/bert2gpt2-cnn_dailymail-fp16 | patrickvonplaten | 2021-08-18T14:38:10Z | 603 | 6 | transformers | [
"transformers",
"pytorch",
"jax",
"encoder_decoder",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | # Bert2GPT2 Summarization with 🤗 EncoderDecoder Framework
This model is a Bert2Bert model fine-tuned on summarization.
Bert2GPT2 is a `EncoderDecoderModel`, meaning that the encoder is a `bert-base-uncased`
BERT model and the decoder is a `gpt2` GPT2 model. Leveraging the [EncoderDecoderFramework](https://huggingface.co/transformers/model_doc/encoderdecoder.html#encoder-decoder-models), the
two pretrained models can simply be loaded into the framework via:
```python
bert2gpt2 = EncoderDecoderModel.from_encoder_decoder_pretrained("bert-base-uncased", "gpt2")
```
The decoder of an `EncoderDecoder` model needs cross-attention layers and usually makes use of causal
masking for auto-regressiv generation.
Thus, ``bert2gpt2`` is consequently fined-tuned on the `CNN/Daily Mail`dataset and the resulting model
`bert2gpt2-cnn_dailymail-fp16` is uploaded here.
## Example
The model is by no means a state-of-the-art model, but nevertheless
produces reasonable summarization results. It was mainly fine-tuned
as a proof-of-concept for the 🤗 EncoderDecoder Framework.
The model can be used as follows:
```python
from transformers import BertTokenizer, GPT2Tokenizer, EncoderDecoderModel
model = EncoderDecoderModel.from_pretrained("patrickvonplaten/bert2gpt2-cnn_dailymail-fp16")
# reuse tokenizer from bert2bert encoder-decoder model
bert_tokenizer = BertTokenizer.from_pretrained("patrickvonplaten/bert2bert-cnn_dailymail-fp16")
article = """(CNN)Sigma Alpha Epsilon is under fire for a video showing party-bound fraternity members singing a racist chant. SAE's national chapter suspended the students, but University of Oklahoma President David B
oren took it a step further, saying the university's affiliation with the fraternity is permanently done. The news is shocking, but it's not the first time SAE has faced controversy. SAE was founded March 9, 185
6, at the University of Alabama, five years before the American Civil War, according to the fraternity website. When the war began, the group had fewer than 400 members, of which "369 went to war for the Confede
rate States and seven for the Union Army," the website says. The fraternity now boasts more than 200,000 living alumni, along with about 15,000 undergraduates populating 219 chapters and 20 "colonies" seeking fu
ll membership at universities. SAE has had to work hard to change recently after a string of member deaths, many blamed on the hazing of new recruits, SAE national President Bradley Cohen wrote in a message on t
he fraternity's website. The fraternity's website lists more than 130 chapters cited or suspended for "health and safety incidents" since 2010. At least 30 of the incidents involved hazing, and dozens more invol
ved alcohol. However, the list is missing numerous incidents from recent months. Among them, according to various media outlets: Yale University banned the SAEs from campus activities last month after members al
legedly tried to interfere with a sexual misconduct investigation connected to an initiation rite. Stanford University in December suspended SAE housing privileges after finding sorority members attending a frat
ernity function were subjected to graphic sexual content. And Johns Hopkins University in November suspended the fraternity for underage drinking. "The media has labeled us as the 'nation's deadliest fraternity,
' " Cohen said. In 2011, for example, a student died while being coerced into excessive alcohol consumption, according to a lawsuit. SAE's previous insurer dumped the fraternity. "As a result, we are paying Lloy
d's of London the highest insurance rates in the Greek-letter world," Cohen said. Universities have turned down SAE's attempts to open new chapters, and the fraternity had to close 12 in 18 months over hazing in
cidents."""
input_ids = bert_tokenizer(article, return_tensors="pt").input_ids
output_ids = model.generate(input_ids)
# we need a gpt2 tokenizer for the output word embeddings
gpt2_tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
print(gpt2_tokenizer.decode(output_ids[0], skip_special_tokens=True))
# should produce
# SAE's national chapter suspended the students, but university president says it's permanent.
# The fraternity has had to deal with a string of incidents since 2010.
# SAE has more than 200,000 members, many of whom are students.
# A student died while being coerced into drinking alcohol.
```
## Training script:
**IMPORTANT**: In order for this code to work, make sure you checkout to the branch
[more_general_trainer_metric](https://github.com/huggingface/transformers/tree/more_general_trainer_metric), which slightly adapts
the `Trainer` for `EncoderDecoderModels` according to this PR: https://github.com/huggingface/transformers/pull/5840.
The following code shows the complete training script that was used to fine-tune `bert2gpt2-cnn_dailymail-fp16
` for reproducability. The training last ~11h on a standard GPU.
```python
#!/usr/bin/env python3
import nlp
import logging
from transformers import BertTokenizer, GPT2Tokenizer, EncoderDecoderModel, Trainer, TrainingArguments
logging.basicConfig(level=logging.INFO)
model = EncoderDecoderModel.from_encoder_decoder_pretrained("bert-base-cased", "gpt2")
# cache is currently not supported by EncoderDecoder framework
model.decoder.config.use_cache = False
bert_tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
# CLS token will work as BOS token
bert_tokenizer.bos_token = bert_tokenizer.cls_token
# SEP token will work as EOS token
bert_tokenizer.eos_token = bert_tokenizer.sep_token
# make sure GPT2 appends EOS in begin and end
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
outputs = [self.bos_token_id] + token_ids_0 + [self.eos_token_id]
return outputs
GPT2Tokenizer.build_inputs_with_special_tokens = build_inputs_with_special_tokens
gpt2_tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
# set pad_token_id to unk_token_id -> be careful here as unk_token_id == eos_token_id == bos_token_id
gpt2_tokenizer.pad_token = gpt2_tokenizer.unk_token
# set decoding params
model.config.decoder_start_token_id = gpt2_tokenizer.bos_token_id
model.config.eos_token_id = gpt2_tokenizer.eos_token_id
model.config.max_length = 142
model.config.min_length = 56
model.config.no_repeat_ngram_size = 3
model.early_stopping = True
model.length_penalty = 2.0
model.num_beams = 4
# load train and validation data
train_dataset = nlp.load_dataset("cnn_dailymail", "3.0.0", split="train")
val_dataset = nlp.load_dataset("cnn_dailymail", "3.0.0", split="validation[:5%]")
# load rouge for validation
rouge = nlp.load_metric("rouge", experiment_id=1)
encoder_length = 512
decoder_length = 128
batch_size = 16
# map data correctly
def map_to_encoder_decoder_inputs(batch): # Tokenizer will automatically set [BOS] <text> [EOS]
# use bert tokenizer here for encoder
inputs = bert_tokenizer(batch["article"], padding="max_length", truncation=True, max_length=encoder_length)
# force summarization <= 128
outputs = gpt2_tokenizer(batch["highlights"], padding="max_length", truncation=True, max_length=decoder_length)
batch["input_ids"] = inputs.input_ids
batch["attention_mask"] = inputs.attention_mask
batch["decoder_input_ids"] = outputs.input_ids
batch["labels"] = outputs.input_ids.copy()
batch["decoder_attention_mask"] = outputs.attention_mask
# complicated list comprehension here because pad_token_id alone is not good enough to know whether label should be excluded or not
batch["labels"] = [
[-100 if mask == 0 else token for mask, token in mask_and_tokens] for mask_and_tokens in [zip(masks, labels) for masks, labels in zip(batch["decoder_attention_mask"], batch["labels"])]
]
assert all([len(x) == encoder_length for x in inputs.input_ids])
assert all([len(x) == decoder_length for x in outputs.input_ids])
return batch
def compute_metrics(pred):
labels_ids = pred.label_ids
pred_ids = pred.predictions
# all unnecessary tokens are removed
pred_str = gpt2_tokenizer.batch_decode(pred_ids, skip_special_tokens=True)
labels_ids[labels_ids == -100] = gpt2_tokenizer.eos_token_id
label_str = gpt2_tokenizer.batch_decode(labels_ids, skip_special_tokens=True)
rouge_output = rouge.compute(predictions=pred_str, references=label_str, rouge_types=["rouge2"])["rouge2"].mid
return {
"rouge2_precision": round(rouge_output.precision, 4),
"rouge2_recall": round(rouge_output.recall, 4),
"rouge2_fmeasure": round(rouge_output.fmeasure, 4),
}
# make train dataset ready
train_dataset = train_dataset.map(
map_to_encoder_decoder_inputs, batched=True, batch_size=batch_size, remove_columns=["article", "highlights"],
)
train_dataset.set_format(
type="torch", columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"],
)
# same for validation dataset
val_dataset = val_dataset.map(
map_to_encoder_decoder_inputs, batched=True, batch_size=batch_size, remove_columns=["article", "highlights"],
)
val_dataset.set_format(
type="torch", columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"],
)
# set training arguments - these params are not really tuned, feel free to change
training_args = TrainingArguments(
output_dir="./",
per_device_train_batch_size=batch_size,
per_device_eval_batch_size=batch_size,
predict_from_generate=True,
evaluate_during_training=True,
do_train=True,
do_eval=True,
logging_steps=1000,
save_steps=1000,
eval_steps=1000,
overwrite_output_dir=True,
warmup_steps=2000,
save_total_limit=10,
fp16=True,
)
# instantiate trainer
trainer = Trainer(
model=model,
args=training_args,
compute_metrics=compute_metrics,
train_dataset=train_dataset,
eval_dataset=val_dataset,
)
# start training
trainer.train()
```
## Evaluation
The following script evaluates the model on the test set of
CNN/Daily Mail.
```python
#!/usr/bin/env python3
import nlp
from transformers import BertTokenizer, GPT2Tokenizer, EncoderDecoderModel
model = EncoderDecoderModel.from_pretrained("patrickvonplaten/bert2gpt2-cnn_dailymail-fp16")
model.to("cuda")
bert_tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
# CLS token will work as BOS token
bert_tokenizer.bos_token = bert_tokenizer.cls_token
# SEP token will work as EOS token
bert_tokenizer.eos_token = bert_tokenizer.sep_token
# make sure GPT2 appends EOS in begin and end
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
outputs = [self.bos_token_id] + token_ids_0 + [self.eos_token_id]
return outputs
GPT2Tokenizer.build_inputs_with_special_tokens = build_inputs_with_special_tokens
gpt2_tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
# set pad_token_id to unk_token_id -> be careful here as unk_token_id == eos_token_id == bos_token_id
gpt2_tokenizer.pad_token = gpt2_tokenizer.unk_token
# set decoding params
model.config.decoder_start_token_id = gpt2_tokenizer.bos_token_id
model.config.eos_token_id = gpt2_tokenizer.eos_token_id
model.config.max_length = 142
model.config.min_length = 56
model.config.no_repeat_ngram_size = 3
model.early_stopping = True
model.length_penalty = 2.0
model.num_beams = 4
test_dataset = nlp.load_dataset("cnn_dailymail", "3.0.0", split="test")
batch_size = 64
# map data correctly
def generate_summary(batch):
# Tokenizer will automatically set [BOS] <text> [EOS]
# cut off at BERT max length 512
inputs = bert_tokenizer(batch["article"], padding="max_length", truncation=True, max_length=512, return_tensors="pt")
input_ids = inputs.input_ids.to("cuda")
attention_mask = inputs.attention_mask.to("cuda")
outputs = model.generate(input_ids, attention_mask=attention_mask)
# all special tokens including will be removed
output_str = gpt2_tokenizer.batch_decode(outputs, skip_special_tokens=True)
batch["pred"] = output_str
return batch
results = test_dataset.map(generate_summary, batched=True, batch_size=batch_size, remove_columns=["article"])
# load rouge for validation
rouge = nlp.load_metric("rouge")
pred_str = results["pred"]
label_str = results["highlights"]
rouge_output = rouge.compute(predictions=pred_str, references=label_str, rouge_types=["rouge2"])["rouge2"].mid
print(rouge_output)
```
The obtained results should be:
| - | Rouge2 - mid -precision | Rouge2 - mid - recall | Rouge2 - mid - fmeasure |
|----------|:-------------:|:------:|:------:|
| **CNN/Daily Mail** | 14.42 | 16.99 | **15.16** |
|
msakthiganesh/TabQGen-Large | msakthiganesh | 2021-08-18T14:37:35Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | > **TabQGen** model is released along with the dataset **Question Generation for Tables** in the paper - **Answer-Aware Question Generation from Tabular and Textual Data using T5**
|
ehdwns1516/klue-roberta-base_sae | ehdwns1516 | 2021-08-18T11:31:20Z | 11 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | # klue-roberta-base-sae
* This model trained with Korean dataset.
* Input sentence what you want to grasp intent.
* You can use English, but don't expect accuracy.
klue-roberta-base-kornli DEMO: [Ainize DEMO](https://main-klue-roberta-base-kornli-ehdwns1516.endpoint.ainize.ai/)
klue-roberta-base-kornli API: [Ainize API](https://ainize.web.app/redirect?git_repo=https://github.com/ehdwns1516/KLUE-RoBERTa-base_sae)
## Overview
Language model: [klue/roberta-base](https://huggingface.co/klue/roberta-base)
Language: Korean
Training data: [kor_sae](https://huggingface.co/datasets/kor_sae)
Eval data: [kor_sae](https://huggingface.co/datasets/kor_sae)
Code: See [Ainize Workspace](https://ainize.ai/workspace/create?imageId=hnj95592adzr02xPTqss&git=https://github.com/ehdwns1516/KLUE-RoBERTa-base_sae_notebook)
## Usage
## In Transformers
```
from transformers import AutoTokenizer, pipeline
tokenizer = AutoTokenizer.from_pretrained("ehdwns1516/klue-roberta-base-sae")
classifier = pipeline(
"text-classification",
model="ehdwns1516/klue-roberta-base-kornli",
return_all_scores=True,
)
context = "sentence what you want to grasp intent"
result = dict()
result[0] = classifier(context)[0]
```
|
fadhilarkan/t5-small-finetuned-xsum | fadhilarkan | 2021-08-18T10:37:43Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"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
datasets:
- squad
model_index:
- name: t5-small-finetuned-xsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: squad
type: squad
args: plain_text
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the squad 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: 10
- eval_batch_size: 10
- 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 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
mrm8488/t5-small-spanish-finetuned-squadv1 | mrm8488 | 2021-08-17T22:02:49Z | 16 | 1 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"es",
"dataset:squad_es",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
language: es
datasets:
- squad_es
widget:
- text: "pregunta: ¿Cuál es el mayor placer de la vida? contexto: El mayor placer de la vida es dormir"
---
# T5 small (Spanish) fine-tuned on SQUAD (ES) for Q&A |
huggingtweets/queenjennyxoxo | huggingtweets | 2021-08-17T19:26:25Z | 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/queenjennyxoxo/1629228381536/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/1252793011815288833/J9iuR7rW_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">Queen Jenny XoXo ♠️🐰</div>
<div style="text-align: center; font-size: 14px;">@queenjennyxoxo</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 Queen Jenny XoXo ♠️🐰.
| Data | Queen Jenny XoXo ♠️🐰 |
| --- | --- |
| Tweets downloaded | 1452 |
| Retweets | 34 |
| Short tweets | 248 |
| Tweets kept | 1170 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2rl5ylqw/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 @queenjennyxoxo's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/simhtmij) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/simhtmij/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/queenjennyxoxo')
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)
|
gagan3012/summarsiation | gagan3012 | 2021-08-17T17:17:30Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
Summarisation model summarsiation |
birgermoell/ner-swedish-wikiann | birgermoell | 2021-08-17T15:28:47Z | 30 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:wikiann",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- token-classification
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ner-swedish-wikiann
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wikiann
type: wikiann
metrics:
- name: Precision
type: precision
value: 0.8331921416757433
- name: Recall
type: recall
value: 0.84243586083126
- name: F1
type: f1
value: 0.8377885044416501
- name: Accuracy
type: accuracy
value: 0.91930707459758
---
<!-- 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. -->
# ner-swedish-wikiann
This model is a fine-tuned version of [nordic-roberta-wiki](hhttps://huggingface.co/flax-community/nordic-roberta-wiki) trained for NER on the wikiann dataset.
eval F1-Score: **83,78**
test F1-Score: **83,76**
## Model Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("birgermoell/ner-swedish-wikiann")
model = AutoModelForTokenClassification.from_pretrained("birgermoell/ner-swedish-wikiann")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "Jag heter Per och jag jobbar på KTH"
nlp(example)
```
<!--
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4.9086903597787154e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
- mixed_precision_training: Native AMP
### Training results
It achieves the following results on the evaluation set:
- Loss: 0.3156
- Precision: 0.8332
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("birgermoell/ner-swedish-wikiann")
model = AutoModelForTokenClassification.from_pretrained("birgermoell/ner-swedish-wikiann")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "Jag heter Per och jag jobbar på KTH"
nlp(example)
- F1: 0.8378
- Accuracy: 0.9193
It achieves the following results on the test set:
- Loss: 0.3023
- Precision: 0.8301
- Recall: 0.8452
- F1: 0.8376
- Accuracy: 0.92
### Framework versions
- Transformers 4.6.1
- Pytorch 1.8.1+cu101
- Datasets 1.6.2
- Tokenizers 0.10.2
-->
|
huggingtweets/factoport-lifedote-lifelywords | huggingtweets | 2021-08-17T13:47:21Z | 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/factoport-lifedote-lifelywords/1629208035773/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/1271838750209867776/AIzGDVfw_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/1272055508279664640/jgeplEoJ_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/1290232914135982080/1CpBaNOH_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">SweetyMe ❤️ & My World Baby 💖 & Magnificent Life 🦋</div>
<div style="text-align: center; font-size: 14px;">@factoport-lifedote-lifelywords</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 SweetyMe ❤️ & My World Baby 💖 & Magnificent Life 🦋.
| Data | SweetyMe ❤️ | My World Baby 💖 | Magnificent Life 🦋 |
| --- | --- | --- | --- |
| Tweets downloaded | 2607 | 1488 | 2419 |
| Retweets | 0 | 1 | 1 |
| Short tweets | 57 | 18 | 2 |
| Tweets kept | 2550 | 1469 | 2416 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/24g662kp/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 @factoport-lifedote-lifelywords's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1qsyqlji) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1qsyqlji/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/factoport-lifedote-lifelywords')
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/cuckoldresss-qobetty-ragamuffin197 | huggingtweets | 2021-08-17T12:17:54Z | 4 | 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://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
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/1399382014214737924/QsAw6oxP_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/755753205028577280/nwtLbTwy_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/1254593296455872513/Qdyli1JK_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">BettyBoopQoS & Ragamuffin1970 & Cuckoldress Scarlet</div>
<div style="text-align: center; font-size: 14px;">@cuckoldresss-qobetty-ragamuffin197</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 BettyBoopQoS & Ragamuffin1970 & Cuckoldress Scarlet.
| Data | BettyBoopQoS | Ragamuffin1970 | Cuckoldress Scarlet |
| --- | --- | --- | --- |
| Tweets downloaded | 129 | 3247 | 1005 |
| Retweets | 2 | 11 | 252 |
| Short tweets | 10 | 584 | 70 |
| Tweets kept | 117 | 2652 | 683 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/zfpi2vmm/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 @cuckoldresss-qobetty-ragamuffin197's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/172rz2sh) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/172rz2sh/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/cuckoldresss-qobetty-ragamuffin197')
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/cuckolddna | huggingtweets | 2021-08-17T11:19:37Z | 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/cuckolddna/1629199173022/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/1342468924496031745/GQXNyPSq_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">Cuckold DNA</div>
<div style="text-align: center; font-size: 14px;">@cuckolddna</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 Cuckold DNA.
| Data | Cuckold DNA |
| --- | --- |
| Tweets downloaded | 2868 |
| Retweets | 1537 |
| Short tweets | 107 |
| Tweets kept | 1224 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/39n7komh/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 @cuckolddna's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3tnket83) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3tnket83/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/cuckolddna')
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/bbcqos-fitslut63-kellyg_official | huggingtweets | 2021-08-17T11:06:20Z | 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/bbcqos-fitslut63-kellyg_official/1629198375751/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/1358510866371661830/rxzOoe9A_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/1073647682487410688/2yrbD4RY_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/1334065878917390338/V6Eh8ZJn_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">Miss Gbadamosi ♠ & ♠Jenny Summers♠ & ♠️MsWhite♠️</div>
<div style="text-align: center; font-size: 14px;">@bbcqos-fitslut63-kellyg_official</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 Miss Gbadamosi ♠ & ♠Jenny Summers♠ & ♠️MsWhite♠️.
| Data | Miss Gbadamosi ♠ | ♠Jenny Summers♠ | ♠️MsWhite♠️ |
| --- | --- | --- | --- |
| Tweets downloaded | 480 | 882 | 3063 |
| Retweets | 117 | 55 | 1391 |
| Short tweets | 154 | 483 | 230 |
| Tweets kept | 209 | 344 | 1442 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3rzzq99i/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 @bbcqos-fitslut63-kellyg_official's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/xd2e2hom) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/xd2e2hom/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/bbcqos-fitslut63-kellyg_official')
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/bbcqos | huggingtweets | 2021-08-17T10:52:33Z | 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/bbcqos/1629197549972/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/1073647682487410688/2yrbD4RY_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">♠Jenny Summers♠</div>
<div style="text-align: center; font-size: 14px;">@bbcqos</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 ♠Jenny Summers♠.
| Data | ♠Jenny Summers♠ |
| --- | --- |
| Tweets downloaded | 882 |
| Retweets | 55 |
| Short tweets | 483 |
| Tweets kept | 344 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2uwts9v5/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 @bbcqos's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1shy0ous) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1shy0ous/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/bbcqos')
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)
|
osanseviero/dalle-mini-fork | osanseviero | 2021-08-17T10:30:17Z | 7 | 5 | generic | [
"generic",
"jax",
"bart",
"text-to-image",
"en",
"region:us"
] | text-to-image | 2022-03-02T23:29:05Z | ---
library_name: generic
language:
- en
pipeline_tag: text-to-image
---
## Fork of DALL·E mini - Generate images from text
For the original repo, head to https://huggingface.co/flax-community/dalle-mini |
ricardo-filho/sbertimbau-base-quora-multitask | ricardo-filho | 2021-08-17T10:20:30Z | 5 | 1 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2022-03-02T23:29:05Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, max pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 3227 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 4333 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.OnlineContrastiveLoss.OnlineContrastiveLoss`
Parameters of the fit()-Method:
```
{
"callback": null,
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 75, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
cogito233/distilbert-base-uncased-finetuned-ner | cogito233 | 2021-08-17T10:12:35Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"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
metric:
name: Accuracy
type: accuracy
value: 0.9837323462595516
---
<!-- 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: 0.0605
- Precision: 0.9251
- Recall: 0.9357
- F1: 0.9304
- Accuracy: 0.9837
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2402 | 1.0 | 878 | 0.0694 | 0.9168 | 0.9215 | 0.9191 | 0.9814 |
| 0.051 | 2.0 | 1756 | 0.0595 | 0.9249 | 0.9330 | 0.9289 | 0.9833 |
| 0.0302 | 3.0 | 2634 | 0.0605 | 0.9251 | 0.9357 | 0.9304 | 0.9837 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
huggingtweets/thecoolestcool | huggingtweets | 2021-08-17T08:58:28Z | 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/thecoolestcool/1629190704554/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
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<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">Ross Simmonds</div>
<div style="text-align: center; font-size: 14px;">@thecoolestcool</div>
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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 Ross Simmonds.
| Data | Ross Simmonds |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 150 |
| Short tweets | 485 |
| Tweets kept | 2615 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3qv5owo5/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 @thecoolestcool's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/s2alparu) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/s2alparu/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/thecoolestcool')
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)
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