modelId
stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-01 06:29:04
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 530
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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|---|---|---|---|---|---|---|---|---|---|
Pyjay/bert-base-dutch-cased-finetuned-gv
|
Pyjay
| 2021-07-23T08:54:10Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
---
tags:
- generated_from_trainer
model_index:
- name: bert-base-dutch-cased-finetuned-gv
results:
- task:
name: Masked Language Modeling
type: fill-mask
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-dutch-cased-finetuned-gv
This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/bert-base-dutch-cased) on an unkown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7837
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 1.4741 | 1.0 | 2603 | 1.8404 |
| 1.2384 | 2.0 | 5206 | 1.8457 |
| 1.2121 | 3.0 | 7809 | 1.7837 |
### Framework versions
- Transformers 4.9.0
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3
|
vasudevgupta/gsoc-wav2vec2
|
vasudevgupta
| 2021-07-23T08:25:58Z | 6 | 0 |
transformers
|
[
"transformers",
"tf",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
Wav2Vec2 Model (initialized from [`facebook/wav2vec2-base`](https://huggingface.co/facebook/wav2vec2-base)) with **no** LM head.
Model weights are converted into TensorFlow using following script:
```shell
python3 convert_torch_to_tf.py --hf_model_id "facebook/wav2vec2-base"
```
**TF SavedModel** is obtained by running following commands:
```shell
git clone https://huggingface.co/vasudevgupta/gsoc-wav2vec2
python3 export2hub.py \
--hf_model_id facebook/wav2vec2-base \
--saved_model_dir gsoc-wav2vec2/saved-model \
--seqlen 246000
cd gsoc-wav2vec2 && tar -czf saved-model.tar.gz saved-model
```
Project Link: https://github.com/vasudevgupta7/gsoc-wav2vec2
|
TransQuest/siamesetransquest-da-ru_en-reddit_wikiquotes
|
TransQuest
| 2021-07-23T08:16:47Z | 30 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"Quality Estimation",
"siamesetransquest",
"da",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
---
language: ru-en
tags:
- Quality Estimation
- siamesetransquest
- da
license: apache-2.0
---
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commercial translation workflows as they have numerous potential uses. They can be employed to select the best translation when several translation engines are available or can inform the end user about the reliability of automatically translated content. In addition, QE systems can be used to decide whether a translation can be published as it is in a given context, or whether it requires human post-editing before publishing or translation from scratch by a human. The quality estimation can be done at different levels: document level, sentence level and word level.
With TransQuest, we have opensourced our research in translation quality estimation which also won the sentence-level direct assessment quality estimation shared task in [WMT 2020](http://www.statmt.org/wmt20/quality-estimation-task.html). TransQuest outperforms current open-source quality estimation frameworks such as [OpenKiwi](https://github.com/Unbabel/OpenKiwi) and [DeepQuest](https://github.com/sheffieldnlp/deepQuest).
## Features
- Sentence-level translation quality estimation on both aspects: predicting post editing efforts and direct assessment.
- Word-level translation quality estimation capable of predicting quality of source words, target words and target gaps.
- Outperform current state-of-the-art quality estimation methods like DeepQuest and OpenKiwi in all the languages experimented.
- Pre-trained quality estimation models for fifteen language pairs are available in [HuggingFace.](https://huggingface.co/TransQuest)
## Installation
### From pip
```bash
pip install transquest
```
### From Source
```bash
git clone https://github.com/TharinduDR/TransQuest.git
cd TransQuest
pip install -r requirements.txt
```
## Using Pre-trained Models
```python
import torch
from transquest.algo.sentence_level.siamesetransquest.run_model import SiameseTransQuestModel
model = SiameseTransQuestModel("TransQuest/siamesetransquest-da-ru_en-reddit_wikiquotes")
predictions = model.predict([["Reducerea acestor conflicte este importantă pentru conservare.", "Reducing these conflicts is not important for preservation."]])
print(predictions)
```
## Documentation
For more details follow the documentation.
1. **[Installation](https://tharindudr.github.io/TransQuest/install/)** - Install TransQuest locally using pip.
2. **Architectures** - Checkout the architectures implemented in TransQuest
1. [Sentence-level Architectures](https://tharindudr.github.io/TransQuest/architectures/sentence_level_architectures/) - We have released two architectures; MonoTransQuest and SiameseTransQuest to perform sentence level quality estimation.
2. [Word-level Architecture](https://tharindudr.github.io/TransQuest/architectures/word_level_architecture/) - We have released MicroTransQuest to perform word level quality estimation.
3. **Examples** - We have provided several examples on how to use TransQuest in recent WMT quality estimation shared tasks.
1. [Sentence-level Examples](https://tharindudr.github.io/TransQuest/examples/sentence_level_examples/)
2. [Word-level Examples](https://tharindudr.github.io/TransQuest/examples/word_level_examples/)
4. **Pre-trained Models** - We have provided pretrained quality estimation models for fifteen language pairs covering both sentence-level and word-level
1. [Sentence-level Models](https://tharindudr.github.io/TransQuest/models/sentence_level_pretrained/)
2. [Word-level Models](https://tharindudr.github.io/TransQuest/models/word_level_pretrained/)
5. **[Contact](https://tharindudr.github.io/TransQuest/contact/)** - Contact us for any issues with TransQuest
## Citations
If you are using the word-level architecture, please consider citing this paper which is accepted to [ACL 2021](https://2021.aclweb.org/).
```bash
@InProceedings{ranasinghe2021,
author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
title = {An Exploratory Analysis of Multilingual Word Level Quality Estimation with Cross-Lingual Transformers},
booktitle = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics},
year = {2021}
}
```
If you are using the sentence-level architectures, please consider citing these papers which were presented in [COLING 2020](https://coling2020.org/) and in [WMT 2020](http://www.statmt.org/wmt20/) at EMNLP 2020.
```bash
@InProceedings{transquest:2020a,
author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
title = {TransQuest: Translation Quality Estimation with Cross-lingual Transformers},
booktitle = {Proceedings of the 28th International Conference on Computational Linguistics},
year = {2020}
}
```
```bash
@InProceedings{transquest:2020b,
author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
title = {TransQuest at WMT2020: Sentence-Level Direct Assessment},
booktitle = {Proceedings of the Fifth Conference on Machine Translation},
year = {2020}
}
```
|
ehdwns1516/gpt3-kor-based_gpt2_review_SR3
|
ehdwns1516
| 2021-07-23T01:18:13Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
# ehdwns1516/gpt3-kor-based_gpt2_review_SR3
* This model has been trained Korean dataset as a star of 3 in the [naver shopping reivew dataset](https://github.com/bab2min/corpus/tree/master/sentiment).
* Input text what you want to generate review.
* If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well.
review generator DEMO: [Ainize DEMO](https://main-review-generator-ehdwns1516.endpoint.ainize.ai/)
review generator API: [Ainize API](https://ainize.web.app/redirect?git_repo=https://github.com/ehdwns1516/review_generator)
## Model links for each 1 to 5 star
* [ehdwns1516/gpt3-kor-based_gpt2_review_SR1](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR1)
* [ehdwns1516/gpt3-kor-based_gpt2_review_SR2](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR2)
* [ehdwns1516/gpt3-kor-based_gpt2_review_SR3](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR3)
* [ehdwns1516/gpt3-kor-based_gpt2_review_SR4](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR4)
* [ehdwns1516/gpt3-kor-based_gpt2_review_SR5](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR5)
## Overview
Language model: [gpt3-kor-small_based_on_gpt2](https://huggingface.co/kykim/gpt3-kor-small_based_on_gpt2)
Language: Korean
Training data: review_body dataset with a star of 3 in the [naver shopping reivew dataset](https://github.com/bab2min/corpus/tree/master/sentiment).
Code: See [Ainize Workspace](https://ainize.ai/workspace/create?imageId=hnj95592adzr02xPTqss&git=https://github.com/ehdwns1516/gpt2_review_fine-tunning_note)
## Usage
## In Transformers
```
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("ehdwns1516/gpt3-kor-based_gpt2_review_SR3")
model = AutoModelWithLMHead.from_pretrained("ehdwns1516/gpt3-kor-based_gpt2_review_SR3")
generator = pipeline(
"text-generation",
model="ehdwns1516/gpt3-kor-based_gpt2_review_SR3",
tokenizer=tokenizer
)
context = "your context"
result = dict()
result[0] = generator(context)[0]
```
|
ehdwns1516/gpt2_review_star5
|
ehdwns1516
| 2021-07-23T01:07:44Z | 5 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
# gpt2_review_star5
* This model has been trained as a review_body dataset with a star of 5 in the [amazon_review dataset](https://huggingface.co/datasets/amazon_reviews_multi).
* Input text what you want to generate review.
* If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well.
review generator DEMO: [Ainize DEMO](https://main-review-generator-ehdwns1516.endpoint.ainize.ai/)
review generator API: [Ainize API](https://ainize.web.app/redirect?git_repo=https://github.com/ehdwns1516/review_generator)
## Model links for each 1 to 5 star
* [ehdwns1516/gpt2_review_star1](https://huggingface.co/ehdwns1516/gpt2_review_star1)
* [ehdwns1516/gpt2_review_star2](https://huggingface.co/ehdwns1516/gpt2_review_star2)
* [ehdwns1516/gpt2_review_star3](https://huggingface.co/ehdwns1516/gpt2_review_star3)
* [ehdwns1516/gpt2_review_star4](https://huggingface.co/ehdwns1516/gpt2_review_star4)
* [ehdwns1516/gpt2_review_star5](https://huggingface.co/ehdwns1516/gpt2_review_star5)
## Overview
Language model: [gpt2](https://huggingface.co/gpt2)
Language: English
Training data: review_body dataset with a star of 5 in the [amazon_review dataset](https://huggingface.co/datasets/amazon_reviews_multi).
Code: See [Ainize Workspace](https://ainize.ai/workspace/create?imageId=hnj95592adzr02xPTqss&git=https://github.com/ehdwns1516/gpt2_review_fine-tunning_note)
## Usage
## In Transformers
```
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("ehdwns1516/gpt2_review_star5")
model = AutoModelWithLMHead.from_pretrained("ehdwns1516/gpt2_review_star5")
generator = pipeline(
"text-generation",
model="ehdwns1516/gpt2_review_star5",
tokenizer=tokenizer
)
context = "your context"
result = dict()
result[0] = generator(context)[0]
```
|
ehdwns1516/gpt2_review_star1
|
ehdwns1516
| 2021-07-23T01:06:16Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
# gpt2_review_star1
* This model has been trained as a review_body dataset with a star of 1 in the [amazon_review dataset](https://huggingface.co/datasets/amazon_reviews_multi).
* Input text what you want to generate review.
* If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well.
review generator DEMO: [Ainize DEMO](https://main-review-generator-ehdwns1516.endpoint.ainize.ai/)
review generator API: [Ainize API](https://ainize.web.app/redirect?git_repo=https://github.com/ehdwns1516/review_generator)
## Model links for each 1 to 5 star
* [ehdwns1516/gpt2_review_star1](https://huggingface.co/ehdwns1516/gpt2_review_star1)
* [ehdwns1516/gpt2_review_star2](https://huggingface.co/ehdwns1516/gpt2_review_star2)
* [ehdwns1516/gpt2_review_star3](https://huggingface.co/ehdwns1516/gpt2_review_star3)
* [ehdwns1516/gpt2_review_star4](https://huggingface.co/ehdwns1516/gpt2_review_star4)
* [ehdwns1516/gpt2_review_star5](https://huggingface.co/ehdwns1516/gpt2_review_star5)
## Overview
Language model: [gpt2](https://huggingface.co/gpt2)
Language: English
Training data: review_body dataset with a star of 1 in the [amazon_review dataset](https://huggingface.co/datasets/amazon_reviews_multi).
Code: See [Ainize Workspace](https://ainize.ai/workspace/create?imageId=hnj95592adzr02xPTqss&git=https://github.com/ehdwns1516/gpt2_review_fine-tunning_note)
## Usage
## In Transformers
```
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("ehdwns1516/gpt2_review_star1")
model = AutoModelWithLMHead.from_pretrained("ehdwns1516/gpt2_review_star1")
generator = pipeline(
"text-generation",
model="ehdwns1516/gpt2_review_star1",
tokenizer=tokenizer
)
context = "your context"
result = dict()
result[0] = generator(context)[0]
```
|
huggingtweets/alampaydavis
|
huggingtweets
| 2021-07-22T23:20:00Z | 6 | 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/alampaydavis/1626995945354/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/1410861223273549825/HwwcW6y2_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">Matthew</div>
<div style="text-align: center; font-size: 14px;">@alampaydavis</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 Matthew.
| Data | Matthew |
| --- | --- |
| Tweets downloaded | 3219 |
| Retweets | 1067 |
| Short tweets | 228 |
| Tweets kept | 1924 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1qawxu8m/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 @alampaydavis's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1ub62hd1) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1ub62hd1/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/alampaydavis')
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)
|
Fraser/wiki-vae
|
Fraser
| 2021-07-22T19:16:20Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-03-02T23:29:04Z |
# Wiki-VAE
A Transformer-VAE trained on all the sentences in wikipedia.
Training is done on AWS SageMaker.
|
suhnylla/planes_airlines
|
suhnylla
| 2021-07-22T02:21:24Z | 69 | 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: planes_airlines
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.32307693362236023
---
# planes_airlines
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
#### planes cathay pacific
#### planes delta airlines
#### planes malaysia airlines
#### planes singapore airlines
#### planes virgin airlines
|
aristotletan/t5-small-finetuned-xsum
|
aristotletan
| 2021-07-22T00:18:39Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wsj_markets",
"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:
- wsj_markets
metrics:
- rouge
model_index:
- name: t5-small-finetuned-xsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: wsj_markets
type: wsj_markets
args: default
metric:
name: Rouge1
type: rouge
value: 10.4492
---
<!-- 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 wsj_markets dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1447
- Rouge1: 10.4492
- Rouge2: 3.9563
- Rougel: 9.3368
- Rougelsum: 9.9828
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 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: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:------:|:---------:|:-------:|
| 2.2742 | 1.0 | 868 | 1.3135 | 9.4644 | 2.618 | 8.4048 | 8.9764 | 19.0 |
| 1.4607 | 2.0 | 1736 | 1.2134 | 9.6327 | 3.8535 | 9.0703 | 9.2466 | 19.0 |
| 1.3579 | 3.0 | 2604 | 1.1684 | 10.1616 | 3.5498 | 9.2294 | 9.4507 | 19.0 |
| 1.3314 | 4.0 | 3472 | 1.1514 | 10.0621 | 3.6907 | 9.1635 | 9.4955 | 19.0 |
| 1.3084 | 5.0 | 4340 | 1.1447 | 10.4492 | 3.9563 | 9.3368 | 9.9828 | 19.0 |
### Framework versions
- Transformers 4.8.2
- Pytorch 1.9.0+cu102
- Datasets 1.10.0
- Tokenizers 0.10.3
|
bgfruna/double-bart-ensemble-squad2
|
bgfruna
| 2021-07-21T22:47:12Z | 0 | 0 | null |
[
"pytorch",
"question-answering",
"en",
"dataset:squad_v2",
"dataset:squad2",
"license:cc-by-4.0",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
language: en
tags:
- pytorch
- question-answering
datasets:
- squad_v2
- squad2
license: cc-by-4.0
metrics:
- squad_v2
- exact
- f1
widget:
- text: "By what main attribute are computational problems classified utilizing computational complexity theory?"
context: "Computational complexity theory is a branch of the theory of computation in theoretical computer science that focuses on classifying computational problems according to their inherent difficulty, and relating those classes to each other. A computational problem is understood to be a task that is in principle amenable to being solved by a computer, which is equivalent to stating that the problem may be solved by mechanical application of mathematical steps, such as an algorithm."
---
# Performance
This ensemble was evaluated on [SQuAD 2.0](https://huggingface.co/datasets/squad_v2) with the following results:
```
{'HasAns_exact': 52.5472334682861,
'HasAns_f1': 67.94939813758602,
'HasAns_total': 5928,
'NoAns_exact': 91.75777964676199,
'NoAns_f1': 91.75777964676199,
'NoAns_total': 5945,
'best_exact': 72.16373283921503,
'best_exact_thresh': 0.0,
'best_f1': 79.85378860941708,
'best_f1_thresh': 0.0,
'exact': 72.1805777815211,
'f1': 79.87063355172326,
'total': 11873
}
```
|
huggingtweets/nigelthurlow
|
huggingtweets
| 2021-07-21T22:34:57Z | 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/nigelthurlow/1626906893945/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/1163117736140124160/u23u5DU4_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">Nigel Thurlow</div>
<div style="text-align: center; font-size: 14px;">@nigelthurlow</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 Nigel Thurlow.
| Data | Nigel Thurlow |
| --- | --- |
| Tweets downloaded | 1264 |
| Retweets | 648 |
| Short tweets | 27 |
| Tweets kept | 589 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/n4jwj2tf/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 @nigelthurlow's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2r5nb7zp) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2r5nb7zp/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/nigelthurlow')
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/glownigga
|
huggingtweets
| 2021-07-21T22:15:19Z | 8 | 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/glownigga/1626905715267/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/1292227674539208704/uNcnG4c3_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">gl0w</div>
<div style="text-align: center; font-size: 14px;">@glownigga</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 gl0w.
| Data | gl0w |
| --- | --- |
| Tweets downloaded | 3132 |
| Retweets | 157 |
| Short tweets | 776 |
| Tweets kept | 2199 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3t0rqzrr/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 @glownigga's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3qjksoiw) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3qjksoiw/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/glownigga')
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)
|
a1noack/bart-large-gigaword
|
a1noack
| 2021-07-21T21:26:04Z | 24 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"summarization",
"dataset:gigaword",
"license:mit",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-03-02T23:29:05Z |
---
tags:
- summarization
datasets:
- gigaword
license: mit
thumbnail: https://en.wikipedia.org/wiki/Bart_Simpson#/media/File:Bart_Simpson_200px.png
---
# BART for Gigaword
- This model was created by fine-tuning the `facebook/bart-large-cnn` weights (also on HuggingFace) for the Gigaword dataset. The model was fine-tuned on the Gigaword training set for 3 epochs, and the model with the highest ROUGE-1 score on the training set batches was kept.
- The BART Tokenizer for CNN-Dailymail was used in the fine-tuning process and that is the tokenizer that will be loaded automatically when doing:
```
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("a1noack/bart-large-gigaword")
```
# Summary generation
- This model achieves ROUGE-1 / ROUGE-2 / ROUGE-L of 37.28 / 18.58 / 34.53 on the Gigaword test set; this is pretty good when compared to PEGASUS, `google/pegasus-gigaword`, which achieves 39.12 / 19.86 / 36.24.
- To achieve these results, generate text using the code below. `text_list` is a list of input text string.
```
input_ids_list = tokenizer(text_list, truncation=True, max_length=128,
return_tensors='pt', padding=True)['input_ids']
output_ids_list = model.generate(input_ids_list, min_length=0)
outputs_list = tokenizer.batch_decode(output_ids_list, skip_special_tokens=True,
clean_up_tokenization_spaces=False)
```
|
ktangri/gpt-neo-demo
|
ktangri
| 2021-07-21T15:20:09Z | 10 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"text generation",
"the Pile",
"causal-lm",
"en",
"arxiv:2101.00027",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language:
- en
tags:
- text generation
- pytorch
- the Pile
- causal-lm
license: apache-2.0
datasets:
- the Pile
---
# GPT-Neo 2.7B (By EleutherAI)
## Model Description
GPT-Neo 2.7B is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 2.7B represents the number of parameters of this particular pre-trained model.
## Training data
GPT-Neo 2.7B was trained on the Pile, a large scale curated dataset created by EleutherAI for the purpose of training this model.
## Training procedure
This model was trained for 420 billion tokens over 400,000 steps. It was trained as a masked autoregressive language model, using cross-entropy loss.
## Intended Use and Limitations
This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a prompt.
### How to use
You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run:
```py
>>> from transformers import pipeline
>>> generator = pipeline('text-generation', model='EleutherAI/gpt-neo-2.7B')
>>> generator("EleutherAI has", do_sample=True, min_length=50)
[{'generated_text': 'EleutherAI has made a commitment to create new software packages for each of its major clients and has'}]
```
### Limitations and Biases
GPT-Neo was trained as an autoregressive language model. This means that its core functionality is taking a string of text and predicting the next token. While language models are widely used for tasks other than this, there are a lot of unknowns with this work.
GPT-Neo was trained on the Pile, a dataset known to contain profanity, lewd, and otherwise abrasive language. Depending on your usecase GPT-Neo may produce socially unacceptable text. See Sections 5 and 6 of the Pile paper for a more detailed analysis of the biases in the Pile.
As with all language models, it is hard to predict in advance how GPT-Neo will respond to particular prompts and offensive content may occur without warning. We recommend having a human curate or filter the outputs before releasing them, both to censor undesirable content and to improve the quality of the results.
## Eval results
All evaluations were done using our [evaluation harness](https://github.com/EleutherAI/lm-evaluation-harness). Some results for GPT-2 and GPT-3 are inconsistent with the values reported in the respective papers. We are currently looking into why, and would greatly appreciate feedback and further testing of our eval harness. If you would like to contribute evaluations you have done, please reach out on our [Discord](https://discord.gg/vtRgjbM).
### Linguistic Reasoning
| Model and Size | Pile BPB | Pile PPL | Wikitext PPL | Lambada PPL | Lambada Acc | Winogrande | Hellaswag |
| ---------------- | ---------- | ---------- | ------------- | ----------- | ----------- | ---------- | ----------- |
| GPT-Neo 1.3B | 0.7527 | 6.159 | 13.10 | 7.498 | 57.23% | 55.01% | 38.66% |
| GPT-2 1.5B | 1.0468 | ----- | 17.48 | 10.634 | 51.21% | 59.40% | 40.03% |
| **GPT-Neo 2.7B** | **0.7165** | **5.646** | **11.39** | **5.626** | **62.22%** | **56.50%** | **42.73%** |
| GPT-3 Ada | 0.9631 | ----- | ----- | 9.954 | 51.60% | 52.90% | 35.93% |
### Physical and Scientific Reasoning
| Model and Size | MathQA | PubMedQA | Piqa |
| ---------------- | ---------- | ---------- | ----------- |
| GPT-Neo 1.3B | 24.05% | 54.40% | 71.11% |
| GPT-2 1.5B | 23.64% | 58.33% | 70.78% |
| **GPT-Neo 2.7B** | **24.72%** | **57.54%** | **72.14%** |
| GPT-3 Ada | 24.29% | 52.80% | 68.88% |
### Down-Stream Applications
TBD
### BibTeX entry and citation info
To cite this model, use
```bibtex
@article{gao2020pile,
title={The Pile: An 800GB Dataset of Diverse Text for Language Modeling},
author={Gao, Leo and Biderman, Stella and Black, Sid and Golding, Laurence and Hoppe, Travis and Foster, Charles and Phang, Jason and He, Horace and Thite, Anish and Nabeshima, Noa and others},
journal={arXiv preprint arXiv:2101.00027},
year={2020}
}
```
To cite the codebase that this model was trained with, use
```bibtex
@software{gpt-neo,
author = {Black, Sid and Gao, Leo and Wang, Phil and Leahy, Connor and Biderman, Stella},
title = {{GPT-Neo}: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow},
url = {http://github.com/eleutherai/gpt-neo},
version = {1.0},
year = {2021},
}
```
|
bipin/malayalam-news-classifier
|
bipin
| 2021-07-21T13:40:25Z | 9 | 3 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"malayalam",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- text-classification
- roberta
- malayalam
- pytorch
widget:
- text: "2032 ഒളിമ്പിക്സിന് ബ്രിസ്ബെയ്ന് വേദിയാകും; ഗെയിംസിന് വേദിയാകുന്ന മൂന്നാമത്തെ ഓസ്ട്രേലിയന് നഗരം"
---
## Malayalam news classifier
### Overview
This model is trained on top of [MalayalamBert](https://huggingface.co/eliasedwin7/MalayalamBERT) for the task of classifying malayalam news headlines. Presently, the following news categories are supported:
* Business
* Sports
* Entertainment
### Dataset
The dataset used for training this model can be found [here](https://www.kaggle.com/disisbig/malyalam-news-dataset).
### Using the model with HF pipeline
```python
from transformers import pipeline
news_headline = "ക്രിപ്റ്റോ ഇടപാടുകളുടെ വിവരങ്ങൾ ആവശ്യപ്പെട്ട് ആദായനികുതി വകുപ്പ് നോട്ടീസയച്ചു"
model = pipeline(task="text-classification", model="bipin/malayalam-news-classifier")
model(news_headline)
# Output
# [{'label': 'business', 'score': 0.9979357123374939}]
```
### Contact
For feedback and questions, feel free to contact via twitter [@bkrish_](https://twitter.com/bkrish_)
|
ifis-zork/ZORK_AI_SCI_FI_TEMP
|
ifis-zork
| 2021-07-21T13:06:26Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
model_index:
- name: ZORK_AI_SCI_FI_TEMP
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. -->
# ZORK_AI_SCI_FI_TEMP
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unkown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.9.0+cu102
- Tokenizers 0.10.3
|
mshamrai/bert-base-ukr-eng-rus-uncased
|
mshamrai
| 2021-07-21T12:05:26Z | 38 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:04Z |
This repository shares smaller version of bert-base-multilingual-uncased that keeps only Ukrainian, English, and Russian tokens in the vocabulary.
| Model | Num parameters | Size |
| ----------------------------------------- | -------------- | --------- |
| bert-base-multilingual-uncased | 167 million | ~650 MB |
| MaxVortman/bert-base-ukr-eng-rus-uncased | 110 million | ~423 MB |
|
lemon234071/t5-base-Chinese
|
lemon234071
| 2021-07-21T09:49:44Z | 78 | 16 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
A mt5-base model that the vocab and word embedding are truncated, only Chinese and English characters are retained.
https://github.com/lemon234071/TransformerBaselines
|
flax-community/clip-vision-bert-vqa-ft-6k
|
flax-community
| 2021-07-21T09:21:58Z | 4 | 4 |
transformers
|
[
"transformers",
"jax",
"clip-vision-bert",
"text-classification",
"arxiv:1908.03557",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
# CLIP-Vision-BERT Multilingual VQA Model
Fine-tuned CLIP-Vision-BERT on translated [VQAv2](https://visualqa.org/challenge.html) image-text pairs using sequence classification objective. We translate the dataset to three other languages other than English: French, German, and Spanish using the [MarianMT Models](https://huggingface.co/transformers/model_doc/marian.html). This model is based on the VisualBERT which was introduced in
[this paper](https://arxiv.org/abs/1908.03557) and first released in
[this repository](https://github.com/uclanlp/visualbert). The output is 3129 class logits, the same classes as used by VisualBERT authors.
The initial weights are loaded from the Conceptual-12M 60k [checkpoints](https://huggingface.co/flax-community/clip-vision-bert-cc12m-60k).
We trained the CLIP-Vision-BERT VQA model during community week hosted by Huggingface 🤗 using JAX/Flax.
## Model description
CLIP-Vision-BERT is a modified BERT model which takes in visual embeddings from the CLIP-Vision transformer and concatenates them with BERT textual embeddings before passing them to the self-attention layers of BERT. This is done for deep cross-modal interaction between the two modes.
## Intended uses & limitations❗️
This model is fine-tuned on a multi-translated version of the visual question answering task - [VQA v2](https://visualqa.org/challenge.html). Since VQAv2 is a dataset scraped from the internet, it will involve some biases which will also affect all fine-tuned versions of this model.
### How to use❓
You can use this model directly on visual question answering. You will need to clone the model from [here](https://github.com/gchhablani/multilingual-vqa). An example of usage is shown below:
```python
>>> from torchvision.io import read_image
>>> import numpy as np
>>> import os
>>> from transformers import CLIPProcessor, BertTokenizerFast
>>> from model.flax_clip_vision_bert.modeling_clip_vision_bert import FlaxCLIPVisionBertForSequenceClassification
>>> image_path = os.path.join('images/val2014', os.listdir('images/val2014')[0])
>>> img = read_image(image_path)
>>> clip_processor = CLIPProcessor.from_pretrained('openai/clip-vit-base-patch32')
ftfy or spacy is not installed using BERT BasicTokenizer instead of ftfy.
>>> clip_outputs = clip_processor(images=img)
>>> clip_outputs['pixel_values'][0] = clip_outputs['pixel_values'][0].transpose(1,2,0) # Need to transpose images as model expected channel last images.
>>> tokenizer = BertTokenizerFast.from_pretrained('bert-base-multilingual-uncased')
>>> model = FlaxCLIPVisionBertForSequenceClassification.from_pretrained('flax-community/clip-vision-bert-vqa-ft-6k')
>>> text = "Are there teddy bears in the image?"
>>> tokens = tokenizer([text], return_tensors="np")
>>> pixel_values = np.concatenate([clip_outputs['pixel_values']])
>>> outputs = model(pixel_values=pixel_values, **tokens)
>>> preds = outputs.logits[0]
>>> sorted_indices = np.argsort(preds)[::-1] # Get reverse sorted scores
>>> top_5_indices = sorted_indices[:5]
>>> top_5_tokens = list(map(model.config.id2label.get,top_5_indices))
>>> top_5_scores = preds[top_5_indices]
>>> print(dict(zip(top_5_tokens, top_5_scores)))
{'yes': 15.809224, 'no': 7.8785815, '<unk>': 4.622649, 'very': 4.511462, 'neither': 3.600822}
```
## Training data 🏋🏻♂️
The CLIP-Vision-BERT model was fine-tuned on the translated version of the VQAv2 dataset in four languages using Marian: English, French, German and Spanish. Hence, the dataset is four times the original English questions.
The dataset questions and image URLs/paths can be downloaded from [flax-community/multilingual-vqa](https://huggingface.co/datasets/flax-community/multilingual-vqa).
## Data Cleaning 🧹
Though the original dataset contains 443,757 train and 214,354 validation image-question pairs. We only use the `multiple_choice_answer`. The answers which are not present in the 3129 classes are mapped to the `<unk>` label.
**Splits**
We use the original train-val splits from the VQAv2 dataset. After translation, we get 1,775,028 train image-text pairs, and 857,416 validation image-text pairs.
## Training procedure 👨🏻💻
### Preprocessing
The texts are lowercased and tokenized using WordPiece and a shared vocabulary size of approximately 110,000. The beginning of a new document is marked with `[CLS]` and the end of one by `[SEP]`.
### Fine-tuning
The checkpoint of the model was trained on Google Cloud Engine TPUv3-8 machine (with 335 GB of RAM, 1000 GB of hard drive, 96 CPU cores) **8 v3 TPU cores** for 6k steps with a per device batch size of 128 and a max sequence length of 128. The optimizer used is AdamW with a learning rate of 5e-5, learning rate warmup for 1600 steps, and linear decay of the learning rate after.
We tracked experiments using TensorBoard. Here is link to main dashboard: [CLIP Vision BERT VQAv2 Fine-tuning Dashboard](https://huggingface.co/flax-community/multilingual-vqa-pt-60k-ft/tensorboard)
#### **Fine-tuning Results 📊**
The model at this checkpoint reached **eval accuracy of 0.49** on our multilingual VQAv2 dataset.
## Team Members
- Gunjan Chhablani [@gchhablani](https://hf.co/gchhablani)
- Bhavitvya Malik[@bhavitvyamalik](https://hf.co/bhavitvyamalik)
## Acknowledgements
We thank [Nilakshan Kunananthaseelan](https://huggingface.co/knilakshan20) for helping us whenever he could get a chance. We also thank [Abheesht Sharma](https://huggingface.co/abheesht) for helping in the discussions in the initial phases. [Luke Melas](https://github.com/lukemelas) helped us get the CC-12M data on our TPU-VMs and we are very grateful to him.
This project would not be possible without the help of [Patrick](https://huggingface.co/patrickvonplaten) and [Suraj](https://huggingface.co/valhalla) who met with us frequently and helped review our approach and guided us throughout the project.
Huge thanks to Huggingface 🤗 & Google Jax/Flax team for such a wonderful community week and for answering our queries on the Slack channel, and for providing us with the TPU-VMs.
<img src=https://pbs.twimg.com/media/E443fPjX0AY1BsR.jpg:large>
|
flax-community/clip-vision-bert-cc12m-60k
|
flax-community
| 2021-07-21T09:17:15Z | 9 | 2 |
transformers
|
[
"transformers",
"jax",
"clip-vision-bert",
"fill-mask",
"arxiv:1908.03557",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
# CLIP-Vision-BERT Multilingual Pre-trained Model
Pretrained CLIP-Vision-BERT pre-trained on translated [Conceptual-12M](https://github.com/google-research-datasets/conceptual-12m) image-text pairs using a masked language modeling (MLM) objective. 10M cleaned image-text pairs are translated using [mBART-50 one-to-many model](https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt) to 2.5M examples each in English, French, German and Spanish. This model is based on the VisualBERT which was introduced in
[this paper](https://arxiv.org/abs/1908.03557) and first released in
[this repository](https://github.com/uclanlp/visualbert). We trained CLIP-Vision-BERT model during community week hosted by Huggingface 🤗 using JAX/Flax.
This checkpoint is pre-trained for 60k steps.
## Model description
CLIP-Vision-BERT is a modified BERT model which takes in visual embeddings from CLIP-Vision transformer and concatenates them with BERT textual embeddings before passing them to the self-attention layers of BERT. This is done for deep cross-modal interaction between the two modes.
## Intended uses & limitations❗️
You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.
Note that this model is primarily aimed at being fine-tuned on tasks such as visuo-linguistic sequence classification or visual question answering. We used this model to fine-tuned on a multi-translated version of the visual question answering task - [VQA v2](https://visualqa.org/challenge.html). Since Conceptual-12M is a dataset scraped from the internet, it will involve some biases which will also affect all fine-tuned versions of this model.
### How to use❓
You can use this model directly with a pipeline for masked language modeling. You will need to clone the model from [here](https://github.com/gchhablani/multilingual-vqa). An example of usage is shown below:
```python
>>> from torchvision.io import read_image
>>> import numpy as np
>>> import os
>>> from transformers import CLIPProcessor, BertTokenizerFast
>>> from model.flax_clip_vision_bert.modeling_clip_vision_bert import FlaxCLIPVisionBertForMaskedLM
>>> image_path = os.path.join('images/val2014', os.listdir('images/val2014')[0])
>>> img = read_image(image_path)
>>> clip_processor = CLIPProcessor.from_pretrained('openai/clip-vit-base-patch32')
ftfy or spacy is not installed using BERT BasicTokenizer instead of ftfy.
>>> clip_outputs = clip_processor(images=img)
>>> clip_outputs['pixel_values'][0] = clip_outputs['pixel_values'][0].transpose(1,2,0) # Need to transpose images as model expected channel last images.
>>> tokenizer = BertTokenizerFast.from_pretrained('bert-base-multilingual-uncased')
>>> model = FlaxCLIPVisionBertForMaskedLM.from_pretrained('flax-community/clip-vision-bert-cc12m-60k')
>>> text = "Three teddy [MASK] in a showcase."
>>> tokens = tokenizer([text], return_tensors="np")
>>> pixel_values = np.concatenate([clip_outputs['pixel_values']])
>>> outputs = model(pixel_values=pixel_values, **tokens)
>>> indices = np.where(tokens['input_ids']==tokenizer.mask_token_id)
>>> preds = outputs.logits[indices][0]
>>> sorted_indices = np.argsort(preds)[::-1] # Get reverse sorted scores
/home/crocoder/anaconda3/lib/python3.8/site-packages/jax/_src/numpy/lax_numpy.py:4615: UserWarning: 'kind' argument to argsort is ignored.
warnings.warn("'kind' argument to argsort is ignored.")
>>> top_5_indices = sorted_indices[:5]
>>> top_5_tokens = tokenizer.convert_ids_to_tokens(top_5_indices)
>>> top_5_scores = preds[top_5_indices]
>>> print(dict(zip(top_5_tokens, top_5_scores)))
{'bears': 19.241959, 'bear': 17.700356, 'animals': 14.368396, 'girls': 14.343797, 'dolls': 14.274415}
```
## Training data 🏋🏻♂️
The CLIP-Vision-BERT model was pre-trained on a translated version of the Conceptual-12m dataset in four languages using mBART-50: English, French, German and Spanish, with 2.5M image-text pairs in each.
The dataset captions and image urls can be downloaded from [flax-community/conceptual-12m-mbart-50-translated](https://huggingface.co/datasets/flax-community/conceptual-12m-mbart-50-multilingual).
## Data Cleaning 🧹
Though the original dataset contains 12M image-text pairs, a lot of the URLs are invalid now, and in some cases, images are corrupt or broken. We remove such examples from our data, which leaves us with approximately 10M image-text pairs.
**Splits**
We used 99% of the 10M examples as a train set, and the remaining ~ 100K examples as our validation set.
## Training procedure 👨🏻💻
### Preprocessing
The texts are lowercased and tokenized using WordPiece and a shared vocabulary size of approximately 110,000. The beginning of a new document is marked with `[CLS]` and the end of one by `[SEP]`
The details of the masking procedure for each sentence are the following:
- 15% of the tokens are masked.
- In 80% of the cases, the masked tokens are replaced by `[MASK]`.
- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
- In the 10% remaining cases, the masked tokens are left as is.
The visual embeddings are taken from the CLIP-Vision model and combined with the textual embeddings inside the BERT embedding layer. The padding is done in the middle. Here is an example of what the embeddings look like:
```
[CLS Emb] [Textual Embs] [SEP Emb] [Pad Embs] [Visual Embs]
```
A total length of 128 tokens, including the visual embeddings, is used. The texts are truncated or padded accordingly.
### Pretraining
The checkpoint of the model was trained on Google Cloud Engine TPUv3-8 machine (with 335 GB of RAM, 1000 GB of hard drive, 96 CPU cores) **8 v3 TPU cores** for 60k steps with a per device batch size of 64 and a max sequence length of 128. The optimizer used is Adafactor with a learning rate of 1e-4, learning rate warmup for 5,000 steps, and linear decay of the learning rate after.
We tracked experiments using TensorBoard. Here is the link to the main dashboard: [CLIP Vision BERT CC12M Pre-training Dashboard](https://huggingface.co/flax-community/multilingual-vqa-pt-ckpts/tensorboard)
#### **Pretraining Results 📊**
The model at this checkpoint reached **eval accuracy of 67.53%** and **with train loss at 1.793 and eval loss at 1.724**.
## Fine Tuning on downstream tasks
We performed fine-tuning on downstream tasks. We used the following datasets for visual question answering:
1. Multilingual of [Visual Question Answering (VQA) v2](https://visualqa.org/challenge.html) - We translated this dataset to the four languages using `Helsinki-NLP` Marian models. The translated data can be found at [flax-community/multilingual-vqa](https://huggingface.co/datasets/flax-community/multilingual-vqa).
The checkpoints for the fine-tuned model on this pre-trained checkpoint can be found [here](https://huggingface.co/flax-community/multilingual-vqa-pt-60k-ft/tensorboard).
The fine-tuned model achieves eval accuracy of 49% on our validation dataset.
## Team Members
- Gunjan Chhablani [@gchhablani](https://hf.co/gchhablani)
- Bhavitvya Malik[@bhavitvyamalik](https://hf.co/bhavitvyamalik)
## Acknowledgements
We thank [Nilakshan Kunananthaseelan](https://huggingface.co/knilakshan20) for helping us whenever he could get a chance. We also thank [Abheesht Sharma](https://huggingface.co/abheesht) for helping in the discussions in the initial phases. [Luke Melas](https://github.com/lukemelas) helped us get the CC-12M data on our TPU-VMs and we are very grateful to him.
This project would not be possible without the help of [Patrick](https://huggingface.co/patrickvonplaten) and [Suraj](https://huggingface.co/valhalla) who met with us frequently and helped review our approach and guided us throughout the project.
Huge thanks to Huggingface 🤗 & Google Jax/Flax team for such a wonderful community week and for answering our queries on the Slack channel, and for providing us with the TPU-VMs.
<img src=https://pbs.twimg.com/media/E443fPjX0AY1BsR.jpg:large>
|
huggingtweets/grapefried
|
huggingtweets
| 2021-07-21T08:54:37Z | 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/grapefried/1626857673378/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/1392696284549632008/QOl3l-zh_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">ju1ce💎</div>
<div style="text-align: center; font-size: 14px;">@grapefried</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 ju1ce💎.
| Data | ju1ce💎 |
| --- | --- |
| Tweets downloaded | 2034 |
| Retweets | 504 |
| Short tweets | 403 |
| Tweets kept | 1127 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1actx5cl/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 @grapefried's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1a1nwhd0) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1a1nwhd0/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/grapefried')
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)
|
flax-community/clip-vision-bert-cc12m-70k
|
flax-community
| 2021-07-21T08:48:04Z | 7 | 1 |
transformers
|
[
"transformers",
"jax",
"clip-vision-bert",
"fill-mask",
"arxiv:1908.03557",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
# CLIP-Vision-BERT Multilingual Pre-trained Model
Pretrained CLIP-Vision-BERT pre-trained on translated [Conceptual-12M](https://github.com/google-research-datasets/conceptual-12m) image-text pairs using a masked language modeling (MLM) objective. 10M cleaned image-text pairs are translated using [mBART-50 one-to-many model](https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt) to 2.5M examples each in English, French, German and Spanish. This model is based on the VisualBERT which was introduced in
[this paper](https://arxiv.org/abs/1908.03557) and first released in
[this repository](https://github.com/uclanlp/visualbert). We trained CLIP-Vision-BERT model during community week hosted by Huggingface 🤗 using JAX/Flax.
This checkpoint is pre-trained for 70k steps.
## Model description
CLIP-Vision-BERT is a modified BERT model which takes in visual embeddings from CLIP-Vision transformer and concatenates them with BERT textual embeddings before passing them to the self-attention layers of BERT. This is done for deep cross-modal interaction between the two modes.
## Intended uses & limitations❗️
You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.
Note that this model is primarily aimed at being fine-tuned on tasks such as visuo-linguistic sequence classification or visual question answering. We used this model to fine-tuned on a multi-translated version of the visual question answering task - [VQA v2](https://visualqa.org/challenge.html). Since Conceptual-12M is a dataset scraped from the internet, it will involve some biases which will also affect all fine-tuned versions of this model.
### How to use❓
You can use this model directly with a pipeline for masked language modeling. You will need to clone the model from [here](https://github.com/gchhablani/multilingual-vqa). An example of usage is shown below:
```python
>>> from torchvision.io import read_image
>>> import numpy as np
>>> import os
>>> from transformers import CLIPProcessor, BertTokenizerFast
>>> from model.flax_clip_vision_bert.modeling_clip_vision_bert import FlaxCLIPVisionBertForMaskedLM
>>> image_path = os.path.join('images/val2014', os.listdir('images/val2014')[0])
>>> img = read_image(image_path)
>>> clip_processor = CLIPProcessor.from_pretrained('openai/clip-vit-base-patch32')
ftfy or spacy is not installed using BERT BasicTokenizer instead of ftfy.
>>> clip_outputs = clip_processor(images=img)
>>> clip_outputs['pixel_values'][0] = clip_outputs['pixel_values'][0].transpose(1,2,0) # Need to transpose images as model expected channel last images.
>>> tokenizer = BertTokenizerFast.from_pretrained('bert-base-multilingual-uncased')
>>> model = FlaxCLIPVisionBertForMaskedLM.from_pretrained('flax-community/clip-vision-bert-cc12m-70k')
>>> text = "Three teddy [MASK] in a showcase."
>>> tokens = tokenizer([text], return_tensors="np")
>>> pixel_values = np.concatenate([clip_outputs['pixel_values']])
>>> outputs = model(pixel_values=pixel_values, **tokens)
>>> indices = np.where(tokens['input_ids']==tokenizer.mask_token_id)
>>> preds = outputs.logits[indices][0]
>>> sorted_indices = np.argsort(preds)[::-1] # Get reverse sorted scores
>>> top_5_indices = sorted_indices[:5]
>>> top_5_tokens = tokenizer.convert_ids_to_tokens(top_5_indices)
>>> top_5_scores = preds[top_5_indices]
>>> print(dict(zip(top_5_tokens, top_5_scores)))
{'bears': 19.400345, 'bear': 17.866995, 'animals': 14.453735, 'dogs': 14.427426, 'girls': 14.097499}
```
## Training data 🏋🏻♂️
The CLIP-Vision-BERT model was pre-trained on a translated version of the Conceptual-12m dataset in four languages using mBART-50: English, French, German and Spanish, with 2.5M image-text pairs in each.
The dataset captions and image urls can be downloaded from [flax-community/conceptual-12m-mbart-50-translated](https://huggingface.co/datasets/flax-community/conceptual-12m-mbart-50-multilingual).
## Data Cleaning 🧹
Though the original dataset contains 12M image-text pairs, a lot of the URLs are invalid now, and in some cases, images are corrupt or broken. We remove such examples from our data, which leaves us with approximately 10M image-text pairs.
**Splits**
We used 99% of the 10M examples as a train set, and the remaining ~ 100K examples as our validation set.
## Training procedure 👨🏻💻
### Preprocessing
The texts are lowercased and tokenized using WordPiece and a shared vocabulary size of approximately 110,000. The beginning of a new document is marked with `[CLS]` and the end of one by `[CLS]`
The details of the masking procedure for each sentence are the following:
- 15% of the tokens are masked.
- In 80% of the cases, the masked tokens are replaced by `[MASK]`.
- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
- In the 10% remaining cases, the masked tokens are left as is.
The visual embeddings are taken from the CLIP-Vision model and combined with the textual embeddings inside the BERT embedding layer. The padding is done in the middle. Here is an example of what the embeddings look like:
```
[CLS Emb] [Textual Embs] [SEP Emb] [Pad Embs] [Visual Embs]
```
A total length of 128 tokens, including the visual embeddings, is used. The texts are truncated or padded accordingly.
### Pretraining
The checkpoint of the model was trained on Google Cloud Engine TPUv3-8 machine (with 335 GB of RAM, 1000 GB of hard drive, 96 CPU cores) **8 v3 TPU cores** for 70k steps with a per device batch size of 64 and a max sequence length of 128. The optimizer used is Adafactor with a learning rate of 1e-4, learning rate warmup for 1,000 steps, and linear decay of the learning rate after.
We tracked experiments using TensorBoard. Here is the link to the main dashboard: [CLIP Vision BERT CC12M Pre-training Dashboard](https://huggingface.co/flax-community/multilingual-vqa-pt-ckpts/tensorboard)
#### **Pretraining Results 📊**
The model at this checkpoint reached **eval accuracy of 67.85%** and **with train loss at 1.756 and eval loss at 1.706**.
## Team Members
- Gunjan Chhablani [@gchhablani](https://hf.co/gchhablani)
- Bhavitvya Malik[@bhavitvyamalik](https://hf.co/bhavitvyamalik)
## Acknowledgements
We thank [Nilakshan Kunananthaseelan](https://huggingface.co/knilakshan20) for helping us whenever he could get a chance. We also thank [Abheesht Sharma](https://huggingface.co/abheesht) for helping in the discussions in the initial phases. [Luke Melas](https://github.com/lukemelas) helped us get the CC-12M data on our TPU-VMs and we are very grateful to him.
This project would not be possible without the help of [Patrick](https://huggingface.co/patrickvonplaten) and [Suraj](https://huggingface.co/valhalla) who met with us frequently and helped review our approach and guided us throughout the project.
Huge thanks to Huggingface 🤗 & Google Jax/Flax team for such a wonderful community week and for answering our queries on the Slack channel, and for providing us with the TPU-VMs.
<img src=https://pbs.twimg.com/media/E443fPjX0AY1BsR.jpg:large>
|
huggingtweets/plesmasquerade
|
huggingtweets
| 2021-07-21T02:40:45Z | 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/plesmasquerade/1626834982015/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/1415803411002314752/X0K3MR1R_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">lovely lovely aerie, 🍭👑🪞🕯️🌙💫🪶🧣🗑️🔪</div>
<div style="text-align: center; font-size: 14px;">@plesmasquerade</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 lovely lovely aerie, 🍭👑🪞🕯️🌙💫🪶🧣🗑️🔪.
| Data | lovely lovely aerie, 🍭👑🪞🕯️🌙💫🪶🧣🗑️🔪 |
| --- | --- |
| Tweets downloaded | 3235 |
| Retweets | 1376 |
| Short tweets | 330 |
| Tweets kept | 1529 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/39gtjjjo/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 @plesmasquerade's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/6jt0gb2r) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/6jt0gb2r/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/plesmasquerade')
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)
|
lg/ghpy_20k
|
lg
| 2021-07-20T23:55:56Z | 10 | 2 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
**This model is provided with no guarantees whatsoever; use at your own risk.**
This is a Neo2.7B model fine tuned on github data scraped by an EleutherAI member (filtered for python-only) for 20k steps. A better code model is coming soon™ (hopefully, maybe); this model was created mostly as a test of infrastructure code.
|
ifis-zork/IFIS_ZORK_AI_MEDIUM_HORROR
|
ifis-zork
| 2021-07-20T23:14:58Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
model_index:
- name: IFIS_ZORK_AI_MEDIUM_HORROR
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. -->
# IFIS_ZORK_AI_MEDIUM_HORROR
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unkown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.9.0+cu102
- Tokenizers 0.10.3
|
espnet/kan-bayashi_csmsc_conformer_fastspeech2
|
espnet
| 2021-07-20T21:31:29Z | 8 | 1 |
espnet
|
[
"espnet",
"audio",
"text-to-speech",
"zh",
"dataset:csmsc",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
text-to-speech
| 2022-03-02T23:29:05Z |
---
tags:
- espnet
- audio
- text-to-speech
language: zh
datasets:
- csmsc
license: cc-by-4.0
---
## ESPnet2 TTS pretrained model
### `kan-bayashi/csmsc_conformer_fastspeech2`
♻️ Imported from https://zenodo.org/record/4031955/
This model was trained by kan-bayashi using csmsc/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
ifis-zork/ZORK_AI_MODERN
|
ifis-zork
| 2021-07-20T20:47:22Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
model_index:
- name: ZORK_AI_MODERN
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. -->
# ZORK_AI_MODERN
This model is a fine-tuned version of [ifis-zork/ZORK_AI_MODERN_A](https://huggingface.co/ifis-zork/ZORK_AI_MODERN_A) on an unkown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.9.0+cu102
- Tokenizers 0.10.3
|
ifis-zork/ZORK_AI_MODERN_A
|
ifis-zork
| 2021-07-20T19:37:56Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
model_index:
- name: ZORK_AI_MODERN_A
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. -->
# ZORK_AI_MODERN_A
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unkown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.9.0+cu102
- Tokenizers 0.10.3
|
Amrrs/south-indian-foods
|
Amrrs
| 2021-07-20T18:22:24Z | 76 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-02T23:29:04Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: south-indian-foods
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.875
---
# south-indian-foods
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
#### dosai
#### idiyappam
#### idli
#### puttu
#### vadai
|
ritog/robi-kobi
|
ritog
| 2021-07-20T15:25:11Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"bn",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: bn
tags:
- text-generation
widget:
- text: তোমাকে দেখেছি আমার হৃদয় মাঝে
---
# Robi Kobi
### Created by [Ritobrata Ghosh](https://ghosh-r.github.io)
A model that writes Bengali poems in the style of Nobel Laureate poet Rabindranath Tagore.
This model is fine-tuned on 1,400+ poems written by Rabindranath Tagore. This model leverages the [Bangla GPT-2](https://huggingface.co/ghosh-r/bangla-gpt2) pretrained model, trained on mc4-Bengali dataset.
|
flax-community/roberta-hindi
|
flax-community
| 2021-07-20T12:50:29Z | 105 | 2 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
widget:
- text: "मुझे उनसे बात करना <mask> अच्छा लगा"
- text: "हम आपके सुखद <mask> की कामना करते हैं"
- text: "सभी अच्छी चीजों का एक <mask> होता है"
---
# RoBERTa base model for Hindi language
Pretrained model on Hindi language using a masked language modeling (MLM) objective. [A more interactive & comparison demo is available here](https://huggingface.co/spaces/flax-community/roberta-hindi).
> This is part of the
[Flax/Jax Community Week](https://discuss.huggingface.co/t/pretrain-roberta-from-scratch-in-hindi/7091), organized by [Hugging Face](https://huggingface.co/) and TPU usage sponsored by Google.
## Model description
RoBERTa Hindi is a transformers model pretrained on a large corpus of Hindi data(a combination of **mc4, oscar and indic-nlp** datasets)
### How to use
You can use this model directly with a pipeline for masked language modeling:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='flax-community/roberta-hindi')
>>> unmasker("हम आपके सुखद <mask> की कामना करते हैं")
[{'score': 0.3310680091381073,
'sequence': 'हम आपके सुखद सफर की कामना करते हैं',
'token': 1349,
'token_str': ' सफर'},
{'score': 0.15317578613758087,
'sequence': 'हम आपके सुखद पल की कामना करते हैं',
'token': 848,
'token_str': ' पल'},
{'score': 0.07826550304889679,
'sequence': 'हम आपके सुखद समय की कामना करते हैं',
'token': 453,
'token_str': ' समय'},
{'score': 0.06304813921451569,
'sequence': 'हम आपके सुखद पहल की कामना करते हैं',
'token': 404,
'token_str': ' पहल'},
{'score': 0.058322224766016006,
'sequence': 'हम आपके सुखद अवसर की कामना करते हैं',
'token': 857,
'token_str': ' अवसर'}]
```
## Training data
The RoBERTa Hindi model was pretrained on the reunion of the following datasets:
- [OSCAR](https://huggingface.co/datasets/oscar) is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture.
- [mC4](https://huggingface.co/datasets/mc4) is a multilingual colossal, cleaned version of Common Crawl's web crawl corpus.
- [IndicGLUE](https://indicnlp.ai4bharat.org/indic-glue/) is a natural language understanding benchmark.
- [Samanantar](https://indicnlp.ai4bharat.org/samanantar/) is a parallel corpora collection for Indic language.
- [Hindi Text Short and Large Summarization Corpus](https://www.kaggle.com/disisbig/hindi-text-short-and-large-summarization-corpus) is a collection of ~180k articles with their headlines and summary collected from Hindi News Websites.
- [Hindi Text Short Summarization Corpus](https://www.kaggle.com/disisbig/hindi-text-short-summarization-corpus) is a collection of ~330k articles with their headlines collected from Hindi News Websites.
- [Old Newspapers Hindi](https://www.kaggle.com/crazydiv/oldnewspapershindi) is a cleaned subset of HC Corpora newspapers.
## Training procedure
### Preprocessing
The texts are tokenized using a byte version of Byte-Pair Encoding (BPE) and a vocabulary size of 50265. The inputs of
the model take pieces of 512 contiguous token that may span over documents. The beginning of a new document is marked
with `<s>` and the end of one by `</s>`.
- We had to perform cleanup of **mC4** and **oscar** datasets by removing all non hindi (non Devanagari) characters from the datasets.
- We tried to filter out evaluation set of WikiNER of [IndicGlue](https://indicnlp.ai4bharat.org/indic-glue/) benchmark by [manual labelling](https://github.com/amankhandelia/roberta_hindi/blob/master/wikiner_incorrect_eval_set.csv) where the actual labels were not correct and modifying the [downstream evaluation dataset](https://github.com/amankhandelia/roberta_hindi/blob/master/utils.py).
The details of the masking procedure for each sentence are the following:
- 15% of the tokens are masked.
- In 80% of the cases, the masked tokens are replaced by `<mask>`.
- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
- In the 10% remaining cases, the masked tokens are left as is.
Contrary to BERT, the masking is done dynamically during pretraining (e.g., it changes at each epoch and is not fixed).
### Pretraining
The model was trained on Google Cloud Engine TPUv3-8 machine (with 335 GB of RAM, 1000 GB of hard drive, 96 CPU cores).A randomized shuffle of combined dataset of **mC4, oscar** and other datasets listed above was used to train the model. Training logs are present in [wandb](https://wandb.ai/wandb/hf-flax-roberta-hindi).
## Evaluation Results
RoBERTa Hindi is evaluated on various downstream tasks. The results are summarized below.
| Task | Task Type | IndicBERT | HindiBERTa | Indic Transformers Hindi BERT | RoBERTa Hindi Guj San | RoBERTa Hindi |
|-------------------------|----------------------|-----------|------------|-------------------------------|-----------------------|---------------|
| BBC News Classification | Genre Classification | **76.44** | 66.86 | **77.6** | 64.9 | 73.67 |
| WikiNER | Token Classification | - | 90.68 | **95.09** | 89.61 | **92.76** |
| IITP Product Reviews | Sentiment Analysis | **78.01** | 73.23 | **78.39** | 66.16 | 75.53 |
| IITP Movie Reviews | Sentiment Analysis | 60.97 | 52.26 | **70.65** | 49.35 | **61.29** |
## Team Members
- Aman K ([amankhandelia](https://huggingface.co/amankhandelia))
- Haswanth Aekula ([hassiahk](https://huggingface.co/hassiahk))
- Kartik Godawat ([dk-crazydiv](https://huggingface.co/dk-crazydiv))
- Prateek Agrawal ([prateekagrawal](https://huggingface.co/prateekagrawal))
- Rahul Dev ([mlkorra](https://huggingface.co/mlkorra))
## Credits
Huge thanks to Hugging Face 🤗 & Google Jax/Flax team for such a wonderful community week, especially for providing such massive computing resources. Big thanks to [Suraj Patil](https://huggingface.co/valhalla) & [Patrick von Platen](https://huggingface.co/patrickvonplaten) for mentoring during the whole week.
<img src=https://pbs.twimg.com/media/E443fPjX0AY1BsR.jpg:medium>
|
huggingtweets/yigitckahyaoglu
|
huggingtweets
| 2021-07-20T03:00:16Z | 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/yigitckahyaoglu/1626750011426/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/1407084026507182089/ywRe7M0Z_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">yiğit</div>
<div style="text-align: center; font-size: 14px;">@yigitckahyaoglu</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 yiğit.
| Data | yiğit |
| --- | --- |
| Tweets downloaded | 1671 |
| Retweets | 165 |
| Short tweets | 64 |
| Tweets kept | 1442 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2cqhj21l/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 @yigitckahyaoglu's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2k3eal89) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2k3eal89/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/yigitckahyaoglu')
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)
|
mnaylor/base-bert-finetuned-mtsamples
|
mnaylor
| 2021-07-19T15:53:35Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
# BERT Base Fine-tuned on MTSamples
This model is [BERT-base](https://huggingface.co/bert-base-uncased) fine-tuned on the MTSamples dataset, with a classification task defined in [this repo](https://github.com/socd06/medical-nlp).
|
mnaylor/bioclinical-bert-finetuned-mtsamples
|
mnaylor
| 2021-07-19T15:52:36Z | 12 | 3 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
# BioClinical BERT Fine-tuned on MTSamples
This model is simply [Alsentzer's Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) fine-tuned on the MTSamples dataset, with a classification task defined in [this repo](https://github.com/socd06/medical-nlp).
|
flax-community/mr-indicnlp-classifier
|
flax-community
| 2021-07-19T12:53:33Z | 10 | 1 |
transformers
|
[
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
# IndicNLP Marathi News Classifier
This model was fine-tuned using [Marathi RoBERTa](https://huggingface.co/flax-community/roberta-base-mr) on [IndicNLP Marathi News Dataset](https://github.com/AI4Bharat/indicnlp_corpus#indicnlp-news-article-classification-dataset)
## Dataset
IndicNLP Marathi news dataset consists 3 classes - `['lifestyle', 'entertainment', 'sports']` - with following docs distribution as per classes:
| train | eval | test |
| ----- | ---- | ---- |
| 9672 | 477 | 478 |
💯 Our **`mr-indicnlp-classifier`** model fine tuned from **roberta-base-mr** Pretrained Marathi RoBERTa model outperformed both classifier mentioned in [Arora, G. (2020). iNLTK](https://www.semanticscholar.org/paper/iNLTK%3A-Natural-Language-Toolkit-for-Indic-Languages-Arora/5039ed9e100d3a1cbbc25a02c82f6ee181609e83/figure/3) and [Kunchukuttan, Anoop et al. AI4Bharat-IndicNLP.](https://www.semanticscholar.org/paper/AI4Bharat-IndicNLP-Corpus%3A-Monolingual-Corpora-and-Kunchukuttan-Kakwani/7997d432925aff0ba05497d2893c09918298ca55/figure/4)
| Dataset | FT-W | FT-WC | INLP | iNLTK | **roberta-base-mr 🏆** |
| --------------- | ----- | ----- | ----- | ----- | --------------------- |
| iNLTK Headlines | 83.06 | 81.65 | 89.92 | 92.4 | **97.48** |
|
flax-community/Sinhala-gpt2
|
flax-community
| 2021-07-19T11:20:34Z | 10 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"gpt2",
"feature-extraction",
"Sinhala",
"text-generation",
"si",
"dataset:mc4",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: si
tags:
- Sinhala
- text-generation
- gpt2
datasets:
- mc4
---
# Sinhala GPT2 trained on MC4 (manually cleaned)
### Overview
This is a smaller GPT2 model trained on [MC4](https://github.com/allenai/allennlp/discussions/5056) Sinhala dataset. As Sinhala is one of those low resource languages, there are only a handful of models been trained. So, this would be a great place to start training for more downstream tasks.
This model uses a manually cleaned version of MC4 dataset which can be found [here](https://huggingface.co/datasets/keshan/clean-si-mc4). Although the dataset is relatively small ~3GB. The finetuned model on [news articles](https://huggingface.co/keshan/sinhala-gpt2-newswire) generates good and acceptable results.
## Model Specification
The model chosen for training is GPT2 with the following specifications:
1. vocab_size=50257
2. n_embd=768
3. n_head=12
4. n_layer=12
5. n_positions=1024
## How to Use
You can use this model directly with a pipeline for causal language modeling:
```py
from transformers import pipeline
generator = pipeline('text-generation', model='flax-community/Sinhala-gpt2')
generator("මම", max_length=50, num_return_sequences=5)
```
|
huggingtweets/deathbattlebot
|
huggingtweets
| 2021-07-19T06:42:58Z | 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/deathbattlebot/1626676974616/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/1295317265257238529/8q3IptgS_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">Death Battle Bot</div>
<div style="text-align: center; font-size: 14px;">@deathbattlebot</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 Death Battle Bot.
| Data | Death Battle Bot |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 1 |
| Short tweets | 20 |
| Tweets kept | 3229 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1hcf8oqg/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 @deathbattlebot's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2d0vuhj5) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2d0vuhj5/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/deathbattlebot')
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)
|
flax-community/wav2vec2-spanish
|
flax-community
| 2021-07-19T05:02:39Z | 4 | 2 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"es",
"dataset:common_voice",
"arxiv:2006.11477",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: es
tags:
- audio
- automatic-speech-recognition
datasets:
- common_voice
---
# Wav2Vec2 Spanish
Wav2Vec2 model pre-trained using the Spanish portion of the Common Voice dataset. The model is trained with Flax and using TPUs sponsored by Google since this is part of the [Flax/Jax Community Week](https://discss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104) organised by HuggingFace.
## Model description
The model used for training is [Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) by FacebookAI. It was introduced in the paper
"wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations" by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, and Michael Auli (https://arxiv.org/abs/2006.11477).
This model is available in the 🤗 [Model Hub](https://huggingface.co/facebook/wav2vec2-base-960h).
## Training data
Spanish portion of [Common Voice](https://commonvoice.mozilla.org/en/datasets). Common Voice is an open source, multi-language dataset of voices part of Mozilla's initiative to help teach machines how real people speak.
The dataset is also available in the 🤗 [Datasets](https://huggingface.co/datasets/common_voice) library.
## Team members
- María Grandury ([@mariagrandury](https://github.com/mariagrandury))
- Manuel Romero ([@mrm8488](https://huggingface.co/mrm8488))
- Eduardo González Ponferrada ([@edugp](https://huggingface.co/edugp))
- pcuenq ([@pcuenq](https://huggingface.co/pcuenq))
|
andi611/distilbert-base-uncased-qa-with-ner
|
andi611
| 2021-07-19T01:20:54Z | 31 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
model_index:
- name: distilbert-base-uncased-qa-with-ner
results:
- task:
name: Question Answering
type: question-answering
dataset:
name: conll2003
type: conll2003
args: conll2003
---
<!-- 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-qa-with-ner
This model is a fine-tuned version of [andi611/distilbert-base-uncased-qa](https://huggingface.co/andi611/distilbert-base-uncased-qa) on the conll2003 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: 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
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
huggingtweets/ellis_hughes
|
huggingtweets
| 2021-07-18T18:42:16Z | 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/ellis_hughes/1626633732954/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/1004536007012651008/ZWJUeJ2W_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">Ellis Hughes</div>
<div style="text-align: center; font-size: 14px;">@ellis_hughes</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 Ellis Hughes.
| Data | Ellis Hughes |
| --- | --- |
| Tweets downloaded | 2170 |
| Retweets | 396 |
| Short tweets | 91 |
| Tweets kept | 1683 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3rqrdlum/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 @ellis_hughes's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3n17xu9k) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3n17xu9k/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/ellis_hughes')
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)
|
flax-community/gpt-2-tamil
|
flax-community
| 2021-07-18T16:03:33Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"ta",
"dataset:oscar",
"dataset:IndicNLP",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: ta
datasets:
- oscar
- IndicNLP
widget:
- text: 'ஒரு ஊரிலே ஒரு காக்கைக்கு'
---
# GPT2-Tamil
This repository is created as part of the Flax/Jax community week by Huggingface. The aim of this project is to pretrain a language model using GPT-2 specifically for Tamil language.
## Setup:
To setup the project, run the following command,
```python
pip install -r requirements.txt
```
## Model:
Pretrained model on Tamil language using a causal language modeling (CLM) objective.
## Dataset Used:
The GTP-2 model is trained on [oscar dataset - ta](https://huggingface.co/datasets/oscar)
## Intended uses & limitations:
You can use the raw model for next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=gpt) to look for fine-tuned versions on a task that interests you.
## How to pretrain the model:
To perform training, do the following steps,
- Export the model directory (where you want to store the model artifacts like config, tokenizer, etc.)
```python
>>> export MODEL_DIR=<model_dir>
```
- Create the config.json by running the following command,
```python
>>> python src/create_config.py
```
- Create the tokenizer by running the following command,
```python
>>> python src/train_tokenizer.py
```
- Once the config and tokenizer is created, run the following script to start training the flax model
```python
>>> python scripts/train_gpt2-oscar-tamil.sh
```
## How to use:
To perform language generation using the model, pipeline can be used directly.
- First convert the flax model to pytorch using the following command,
```python
python src/convert_flax_to_pytorch.py
```
- Use the following snippet to perform language generation,
```python
>>> from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline
>>> model_name = 'abinayam/gpt-2-tamil'
>>> model = AutoModelWithLMHead.from_pretrained(model_name)
>>> tokenizer = AutoTokenizer.from_pretrained(model_name)
>>> set_seed(42)
>>> input_text = "ஒரு ஊரிலே ஒரு காக்கைக்கு"
>>> max_len = 300
>>> no_seq = 5
>>> generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
>>> sequence = generator(input_text, max_length=max_len, num_return_sequences=no_seq)
```
|
imvladikon/bert-base-uncased-jigsaw
|
imvladikon
| 2021-07-18T15:46:05Z | 1 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"generated_from_trainer",
"en",
"dataset:jigsaw",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language:
- en
license:
tags:
- generated_from_trainer
datasets:
- jigsaw
model_index:
- name: bert-base-uncased
results:
- {}
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased
This model is a fine-tuned version of [](https://huggingface.co/) on the jigsaw dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0393
- Precision Micro: 0.7758
- Recall Micro: 0.7858
- F1 Micro: 0.7808
- F2 Micro: 0.7838
- Precision Macro: 0.6349
- Recall Macro: 0.5972
- F1 Macro: 0.6105
- F2 Macro: 0.6015
- Overall Precision: 0.9841
- Overall Recall: 0.9841
- Overall F1: 0.9841
- Overall F2: 0.9841
- Overall Accuracy: 0.9841
- Matthews Corrcoef: 0.7725
- Aucroc Macro: 0.9897
- Aucroc Micro: 0.9920
- Accuracy Toxic: 0.9678
- F1 Toxic: 0.8295
- Accuracy Severe Toxic: 0.9899
- F1 Severe Toxic: 0.3313
- Accuracy Obscene: 0.9816
- F1 Obscene: 0.8338
- Accuracy Threat: 0.9974
- F1 Threat: 0.4545
- Accuracy Insult: 0.9763
- F1 Insult: 0.7662
- Accuracy Identity Hate: 0.9914
- F1 Identity Hate: 0.4480
## 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: 24
- eval_batch_size: 12
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 48
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision Micro | Recall Micro | F1 Micro | F2 Micro | Precision Macro | Recall Macro | F1 Macro | F2 Macro | Overall Precision | Overall Recall | Overall F1 | Overall F2 | Overall Accuracy | Matthews Corrcoef | Aucroc Macro | Aucroc Micro | Accuracy Toxic | F1 Toxic | Accuracy Severe Toxic | F1 Severe Toxic | Accuracy Obscene | F1 Obscene | Accuracy Threat | F1 Threat | Accuracy Insult | F1 Insult | Accuracy Identity Hate | F1 Identity Hate |
|:-------------:|:-----:|:-----:|:---------------:|:---------------:|:------------:|:--------:|:--------:|:---------------:|:------------:|:--------:|:--------:|:-----------------:|:--------------:|:----------:|:----------:|:----------------:|:-----------------:|:------------:|:------------:|:--------------:|:--------:|:---------------------:|:---------------:|:----------------:|:----------:|:---------------:|:---------:|:---------------:|:---------:|:----------------------:|:----------------:|
| 0.0433 | 1.0 | 2659 | 0.0423 | 0.7607 | 0.7798 | 0.7702 | 0.7759 | 0.6398 | 0.5561 | 0.5585 | 0.5535 | 0.9832 | 0.9832 | 0.9832 | 0.9832 | 0.9832 | 0.7615 | 0.9887 | 0.9908 | 0.9671 | 0.8211 | 0.9878 | 0.4354 | 0.9805 | 0.8265 | 0.9974 | 0.2243 | 0.9746 | 0.7602 | 0.9918 | 0.2834 |
| 0.0366 | 2.0 | 5318 | 0.0393 | 0.7758 | 0.7858 | 0.7808 | 0.7838 | 0.6349 | 0.5972 | 0.6105 | 0.6015 | 0.9841 | 0.9841 | 0.9841 | 0.9841 | 0.9841 | 0.7725 | 0.9897 | 0.9920 | 0.9678 | 0.8295 | 0.9899 | 0.3313 | 0.9816 | 0.8338 | 0.9974 | 0.4545 | 0.9763 | 0.7662 | 0.9914 | 0.4480 |
| 0.0305 | 3.0 | 7977 | 0.0399 | 0.7608 | 0.8186 | 0.7887 | 0.8064 | 0.6621 | 0.6856 | 0.6715 | 0.6794 | 0.9842 | 0.9842 | 0.9842 | 0.9842 | 0.9842 | 0.7810 | 0.9897 | 0.9919 | 0.9662 | 0.8272 | 0.9892 | 0.4772 | 0.9815 | 0.8347 | 0.9977 | 0.5629 | 0.9772 | 0.7740 | 0.9931 | 0.5528 |
| 0.0263 | 4.0 | 10636 | 0.0435 | 0.7333 | 0.8336 | 0.7803 | 0.8114 | 0.6395 | 0.7039 | 0.6687 | 0.6890 | 0.9830 | 0.9830 | 0.9830 | 0.9830 | 0.9830 | 0.7732 | 0.9897 | 0.9912 | 0.9608 | 0.8083 | 0.9898 | 0.4791 | 0.9812 | 0.8319 | 0.9972 | 0.5368 | 0.9756 | 0.7700 | 0.9935 | 0.5861 |
| 0.0218 | 5.0 | 13295 | 0.0456 | 0.7480 | 0.8108 | 0.7781 | 0.7974 | 0.6661 | 0.6720 | 0.6662 | 0.6691 | 0.9833 | 0.9833 | 0.9833 | 0.9833 | 0.9833 | 0.7701 | 0.9890 | 0.9907 | 0.9612 | 0.8071 | 0.9894 | 0.4642 | 0.9823 | 0.8354 | 0.9977 | 0.5325 | 0.9754 | 0.7613 | 0.9936 | 0.5968 |
### Framework versions
- Transformers 4.8.2
- Pytorch 1.9.0+cu102
- Datasets 1.9.0
- Tokenizers 0.10.3
|
flax-community/wav2vec2-base-persian
|
flax-community
| 2021-07-18T05:44:28Z | 24 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"tensorboard",
"wav2vec2",
"pretraining",
"speech",
"fa",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: fa
datasets:
- common_voice
tags:
- speech
license: apache-2.0
---
# Wav2Vec2 4 Persian
> This is part of the
[Flax/Jax Community Week](https://discuss.huggingface.co/t/pretrain-wav2vec2-in-persian/8180), organized by [HuggingFace](https://huggingface.co/) and TPU usage sponsored by Google.
## Team Members
- Mehrdad Farahani ([m3hrdadfi](https://huggingface.co/m3hrdadfi))
## Dataset TODO: Update
## How To Use TODO: Update
## Demo TODO: Update
## Evaluation TODO: Update
|
johnpaulbin/gpt2-skript-80-v3
|
johnpaulbin
| 2021-07-18T04:53:22Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
GPT-2 Skript 80k lines. v3
Training loss: `0.594200`
1.5 GB
Inferencing colab: https://colab.research.google.com/drive/1uTAPLa1tuNXFpG0qVLSseMro6iU9-xNc
|
huggingtweets/percyvader
|
huggingtweets
| 2021-07-17T22:54:48Z | 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/percyvader/1626562484510/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/848218865528078336/OTr3Lo3N_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">trades cowboy hat for fedora</div>
<div style="text-align: center; font-size: 14px;">@percyvader</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 trades cowboy hat for fedora.
| Data | trades cowboy hat for fedora |
| --- | --- |
| Tweets downloaded | 2818 |
| Retweets | 628 |
| Short tweets | 746 |
| Tweets kept | 1444 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2vmsj6nk/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 @percyvader's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1euqbqf4) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1euqbqf4/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/percyvader')
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)
|
birgermoell/roberta-swedish
|
birgermoell
| 2021-07-17T07:52:59Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
widget:
- text: "Var kan jag hitta någon <mask> talar engelska?"
---
Swedish RoBERTa
## Model series
This model is part of a series of models training on TPU with Flax Jax during Huggingface Flax/Jax challenge.
## Gpt models
## Swedish Gpt
https://huggingface.co/birgermoell/swedish-gpt/
## Swedish gpt wiki
https://huggingface.co/flax-community/swe-gpt-wiki
# Nordic gpt wiki
https://huggingface.co/flax-community/nordic-gpt-wiki
## Dansk gpt wiki
https://huggingface.co/flax-community/dansk-gpt-wiki
## Norsk gpt wiki
https://huggingface.co/flax-community/norsk-gpt-wiki
## Roberta models
## Nordic Roberta Wiki
https://huggingface.co/flax-community/nordic-roberta-wiki
## Swe Roberta Wiki Oscar
https://huggingface.co/flax-community/swe-roberta-wiki-oscar
## Roberta Swedish Scandi
https://huggingface.co/birgermoell/roberta-swedish-scandi
## Roberta Swedish
https://huggingface.co/birgermoell/roberta-swedish
## Swedish T5 model
https://huggingface.co/birgermoell/t5-base-swedish
|
birgermoell/t5-base-swedish
|
birgermoell
| 2021-07-17T07:52:39Z | 11 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"tensorboard",
"t5",
"feature-extraction",
"summarization",
"translation",
"sv",
"dataset:oscar",
"arxiv:1910.10683",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-03-02T23:29:05Z |
---
language:
- sv
datasets:
- oscar
tags:
- summarization
- translation
license: apache-2.0
---
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html)
Pretraining Dataset: [C4](https://huggingface.co/datasets/oscar)
Paper: [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf)
Authors: *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu*
## Abstract
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpus”, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.
## Model series
This model is part of a series of models training on TPU with Flax Jax during Huggingface Flax/Jax challenge.
## Gpt models
## Swedish Gpt
https://huggingface.co/birgermoell/swedish-gpt/
## Swedish gpt wiki
https://huggingface.co/flax-community/swe-gpt-wiki
# Nordic gpt wiki
https://huggingface.co/flax-community/nordic-gpt-wiki
## Dansk gpt wiki
https://huggingface.co/flax-community/dansk-gpt-wiki
## Norsk gpt wiki
https://huggingface.co/flax-community/norsk-gpt-wiki
## Roberta models
## Nordic Roberta Wiki
https://huggingface.co/flax-community/nordic-roberta-wiki
## Swe Roberta Wiki Oscar
https://huggingface.co/flax-community/swe-roberta-wiki-oscar
## Roberta Swedish Scandi
https://huggingface.co/birgermoell/roberta-swedish-scandi
## Roberta Swedish
https://huggingface.co/birgermoell/roberta-swedish
## Swedish T5 model
https://huggingface.co/birgermoell/t5-base-swedish
|
birgermoell/swedish-gpt
|
birgermoell
| 2021-07-17T07:45:52Z | 30 | 2 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"tensorboard",
"gpt2",
"text-generation",
"sv",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: sv
widget:
- text: "Jag är en svensk språkmodell."
---
## Model series
This model is part of a series of models training on TPU with Flax Jax during Huggingface Flax/Jax challenge.
## Gpt models
## Swedish Gpt
https://huggingface.co/birgermoell/swedish-gpt/
## Swedish gpt wiki
https://huggingface.co/flax-community/swe-gpt-wiki
# Nordic gpt wiki
https://huggingface.co/flax-community/nordic-gpt-wiki
## Dansk gpt wiki
https://huggingface.co/flax-community/dansk-gpt-wiki
## Norsk gpt wiki
https://huggingface.co/flax-community/norsk-gpt-wiki
## Roberta models
## Nordic Roberta Wiki
https://huggingface.co/flax-community/nordic-roberta-wiki
## Swe Roberta Wiki Oscar
https://huggingface.co/flax-community/swe-roberta-wiki-oscar
## Roberta Swedish Scandi
https://huggingface.co/birgermoell/roberta-swedish-scandi
## Roberta Swedish
https://huggingface.co/birgermoell/roberta-swedish
## Swedish T5 model
https://huggingface.co/birgermoell/t5-base-swedish
# GPT-svenska-wikipedia
A swedish GPT2 style model trained using Flax CLM pipeline on the Swedish
part of the wiki40b dataset and the Oscar dataset.
https://huggingface.co/datasets/wiki40b
The model was trained for around 22600 steps (42 hours) as part of the Huggingface Jax/Flax challenge with the following loss and learning rate
Loss: 3.1715331077575684, Learning Rate: 0.0024816440418362617)
The model could likely be trained for longer.
## Data cleaning and preprocessing
The data was cleaned and preprocessed using the following script. Make sure to install depencies for beam_runner to make the dataset work.
```python
from datasets import load_dataset
def load_and_clean_wiki():
dataset = load_dataset('wiki40b', 'sv', beam_runner='DirectRunner', split="train")
#dataset = load_dataset('wiki40b', 'sv', beam_runner='DirectRunner')
dataset = dataset.remove_columns(['wikidata_id', 'version_id'])
filtered_dataset = dataset.map(filter_wikipedia)
# filtered_dataset[:3]
# print(filtered_dataset[:3])
return filtered_dataset
def filter_wikipedia(batch):
batch["text"] = " ".join(batch["text"].split("\
_START_SECTION_\
"))
batch["text"] = " ".join(batch["text"].split("\
_START_ARTICLE_\
"))
batch["text"] = " ".join(batch["text"].split("\
_START_ARTICLE_\
"))
batch["text"] = " ".join(batch["text"].split("\
_START_PARAGRAPH_\
"))
batch["text"] = " ".join(batch["text"].split("_NEWLINE_"))
batch["text"] = " ".join(batch["text"].split("\xa0"))
return batch
```
## Training script
The following training script was used to train the model.
```bash
./run_clm_flax.py --output_dir="${MODEL_DIR}" --model_type="gpt2" --config_name="${MODEL_DIR}" --tokenizer_name="${MODEL_DIR}" --dataset_name="wiki40b" --dataset_config_name="sv" --do_train --do_eval --block_size="512" --per_device_train_batch_size="64" --per_device_eval_batch_size="64" --learning_rate="5e-3" --warmup_steps="1000" --adam_beta1="0.9" --adam_beta2="0.98" --weight_decay="0.01" --overwrite_output_dir --num_train_epochs="20" --logging_steps="500" --save_steps="1000" --eval_steps="2500" --push_to_hub
```
|
flax-community/koclip
|
flax-community
| 2021-07-17T05:08:38Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-03-02T23:29:05Z |
# KoCLIP
This repository includes
## Installation
Create a virtual env and install `requirements.txt`.
```
pip install -r requirements.txt
```
For Google Cloud TPU VM please follow necessary installation steps here:
[Pytorch on TPU VM](https://cloud.google.com/tpu/docs/pytorch-xla-ug-tpu-vm)
[JAX/Flax on TPU VM](https://cloud.google.com/tpu/docs/jax-quickstart-tpu-vm)
|
huggingtweets/enderdev_
|
huggingtweets
| 2021-07-16T20:30:38Z | 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/enderdev_/1626467434270/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/1415445991503839234/RSxcTJiJ_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">Kieran</div>
<div style="text-align: center; font-size: 14px;">@enderdev_</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 Kieran.
| Data | Kieran |
| --- | --- |
| Tweets downloaded | 2518 |
| Retweets | 388 |
| Short tweets | 691 |
| Tweets kept | 1439 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2qz7ps6o/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 @enderdev_'s tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3aqdw40t) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3aqdw40t/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/enderdev_')
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)
|
BumBelDumBel/ZORK-AI-TEST
|
BumBelDumBel
| 2021-07-16T17:12:42Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:04Z |
---
license: mit
tags:
- generated_from_trainer
model_index:
- name: ZORK-AI-TEST
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. -->
# ZORK-AI-TEST
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unkown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.9.0+cu102
- Tokenizers 0.10.3
|
huggingtweets/benchestnut
|
huggingtweets
| 2021-07-16T16:34:14Z | 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/benchestnut/1626453250687/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/1045023385816686592/7wIqU8ZY_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">Ben Chestnut</div>
<div style="text-align: center; font-size: 14px;">@benchestnut</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 Ben Chestnut.
| Data | Ben Chestnut |
| --- | --- |
| Tweets downloaded | 3229 |
| Retweets | 943 |
| Short tweets | 124 |
| Tweets kept | 2162 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/lyrugs4q/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 @benchestnut's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2u96gtbs) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2u96gtbs/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/benchestnut')
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)
|
keshan/sinhala-gpt2-newswire
|
keshan
| 2021-07-16T15:46:36Z | 5 | 2 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"sinhala",
"si",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: si
tags:
- sinhala
- gpt2
pipeline_tag: text-generation
widget:
- text: "මම"
---
This is a finetunes version of keshan/sinhala-gpt2 with newswire articles. This was finetuned on ~12MB of data
- Num examples=8395
- Batch size =8
It got a Perplexity of 3.15
|
clarin-pl/fastText-kgr10
|
clarin-pl
| 2021-07-16T13:09:29Z | 0 | 2 | null |
[
"fastText",
"pl",
"dataset:kgr10",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: pl
tags:
- fastText
datasets:
- kgr10
---
# KGR10 FastText Polish word embeddings
Distributional language model (both textual and binary) for Polish (word embeddings) trained on KGR10 corpus (over 4 billion of words) using Fasttext with the following variants (all possible combinations):
- dimension: 100, 300
- method: skipgram, cbow
- tool: FastText, Magnitude
- source text: plain, plain.lower, plain.lemma, plain.lemma.lower
## Models
In the repository you can find 4 selected models, that were examined in the paper (see Citation).
A model that performed the best is the default model/config (see `default_config.json`).
## Usage
To use these embedding models easily, it is required to install [embeddings](https://github.com/CLARIN-PL/embeddings).
```bash
pip install clarinpl-embeddings
```
### Utilising the default model (the easiest way)
Word embedding:
```python
from embeddings.embedding.auto_flair import AutoFlairWordEmbedding
from flair.data import Sentence
sentence = Sentence("Myśl z duszy leci bystro, Nim się w słowach złamie.")
embedding = AutoFlairWordEmbedding.from_hub("clarin-pl/fastText-kgr10")
embedding.embed([sentence])
for token in sentence:
print(token)
print(token.embedding)
```
Document embedding (averaged over words):
```python
from embeddings.embedding.auto_flair import AutoFlairDocumentEmbedding
from flair.data import Sentence
sentence = Sentence("Myśl z duszy leci bystro, Nim się w słowach złamie.")
embedding = AutoFlairDocumentEmbedding.from_hub("clarin-pl/fastText-kgr10")
embedding.embed([sentence])
print(sentence.embedding)
```
### Customisable way
Word embedding:
```python
from embeddings.embedding.static.embedding import AutoStaticWordEmbedding
from embeddings.embedding.static.fasttext import KGR10FastTextConfig
from flair.data import Sentence
config = KGR10FastTextConfig(method='cbow', dimension=100)
embedding = AutoStaticWordEmbedding.from_config(config)
sentence = Sentence("Myśl z duszy leci bystro, Nim się w słowach złamie.")
embedding.embed([sentence])
for token in sentence:
print(token)
print(token.embedding)
```
Document embedding (averaged over words):
```python
from embeddings.embedding.static.embedding import AutoStaticDocumentEmbedding
from embeddings.embedding.static.fasttext import KGR10FastTextConfig
from flair.data import Sentence
config = KGR10FastTextConfig(method='cbow', dimension=100)
embedding = AutoStaticDocumentEmbedding.from_config(config)
sentence = Sentence("Myśl z duszy leci bystro, Nim się w słowach złamie.")
embedding.embed([sentence])
print(sentence.embedding)
```
## Citation
The link below leads to the NextCloud directory with all variants of embeddings. If you use it, please cite the following article:
```
@article{kocon2018embeddings,
author = {Koco\'{n}, Jan and Gawor, Micha{\l}},
title = {Evaluating {KGR10} {P}olish word embeddings in the recognition of temporal
expressions using {BiLSTM-CRF}},
journal = {Schedae Informaticae},
volume = {27},
year = {2018},
url = {http://www.ejournals.eu/Schedae-Informaticae/2018/Volume-27/art/13931/},
doi = {10.4467/20838476SI.18.008.10413}
}
```
|
johnpaulbin/gpt2-skript-80
|
johnpaulbin
| 2021-07-16T05:43:37Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
GPT-2 for the Minecraft Plugin: Skript (80,000 Lines, 3< GB: GPT-2 Large model finetune)
Inferencing Colab: https://colab.research.google.com/drive/1uTAPLa1tuNXFpG0qVLSseMro6iU9-xNc
|
huggingtweets/gambsvns
|
huggingtweets
| 2021-07-15T21:50:46Z | 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/gambsvns/1626385842515/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/1415310065960198148/w9Yr9mLK_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">gãmbs</div>
<div style="text-align: center; font-size: 14px;">@gambsvns</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 gãmbs.
| Data | gãmbs |
| --- | --- |
| Tweets downloaded | 3246 |
| Retweets | 86 |
| Short tweets | 308 |
| Tweets kept | 2852 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2wahjzcj/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 @gambsvns's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1td3tcaf) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1td3tcaf/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/gambsvns')
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)
|
flax-community/t5-vae-python
|
flax-community
| 2021-07-15T21:11:45Z | 4 | 1 |
transformers
|
[
"transformers",
"jax",
"transformer_vae",
"vae",
"dataset:Fraser/python-lines",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: python
tags: vae
license: apache-2.0
datasets: Fraser/python-lines
---
# T5-VAE-Python (flax)
A Transformer-VAE made using flax.
Try the [demo](https://huggingface.co/spaces/flax-community/t5-vae)!
It has been trained to interpolate on lines of Python code from the [python-lines dataset](https://huggingface.co/datasets/Fraser/python-lines).
Done as part of Huggingface community training ([see forum post](https://discuss.huggingface.co/t/train-a-vae-to-interpolate-on-english-sentences/7548)).
Builds on T5, using an autoencoder to convert it into an MMD-VAE ([more info](http://fras.uk/ml/large%20prior-free%20models/transformer-vae/2020/08/13/Transformers-as-Variational-Autoencoders.html)).
## How to use from the 🤗/transformers library
Add model repo as a submodule:
```bash
git submodule add https://github.com/Fraser-Greenlee/t5-vae-flax.git t5_vae_flax
```
```python
from transformers import AutoTokenizer
from t5_vae_flax.src.t5_vae import FlaxT5VaeForAutoencoding
tokenizer = AutoTokenizer.from_pretrained("t5-base")
model = FlaxT5VaeForAutoencoding.from_pretrained("flax-community/t5-vae-python")
```
## Setup
Run `setup_tpu_vm_venv.sh` to setup a virtual enviroment on a TPU VM for training.
|
Vivek/gpt2-common-sense-reasoning
|
Vivek
| 2021-07-15T09:29:46Z | 11 | 1 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"gpt2",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
This is to test the common sense reasoning of a GPT-2 model.To assess how intelligent or it is adapted to this datasets which requires not only big models but also a little common sense also.
|
huggingtweets/theisaiahw
|
huggingtweets
| 2021-07-14T21:05:53Z | 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/theisaiahw/1626296749614/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/1388820869762322434/v3h5S7mu_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">Isaiah Williams</div>
<div style="text-align: center; font-size: 14px;">@theisaiahw</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 Isaiah Williams.
| Data | Isaiah Williams |
| --- | --- |
| Tweets downloaded | 620 |
| Retweets | 65 |
| Short tweets | 72 |
| Tweets kept | 483 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/336gn9be/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 @theisaiahw's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ohqpafvm) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ohqpafvm/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/theisaiahw')
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)
|
ehdwns1516/klue-roberta-base-kornli
|
ehdwns1516
| 2021-07-14T08:11:08Z | 19 | 1 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
# klue-roberta-base-kornli
* This model trained with Korean dataset.
* Input premise sentence and hypothesis sentence.
* You can use English, but don't expect accuracy.
* If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well.
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_kornli)
## Overview
Language model: [klue/roberta-base](https://huggingface.co/klue/roberta-base)
Language: Korean
Training data: [kakaobrain KorNLI](https://github.com/kakaobrain/KorNLUDatasets/tree/master/KorNLI)
Eval data: [kakaobrain KorNLI](https://github.com/kakaobrain/KorNLUDatasets/tree/master/KorNLI)
Code: See [Ainize Workspace](https://ainize.ai/workspace/create?imageId=hnj95592adzr02xPTqss&git=https://github.com/ehdwns1516/klue-roberta-base_finetunning_ex)
## Usage
## In Transformers
```
from transformers import AutoTokenizer, pipeline
tokenizer = AutoTokenizer.from_pretrained("ehdwns1516/klue-roberta-base-kornli")
classifier = pipeline(
"text-classification",
model="ehdwns1516/klue-roberta-base-kornli",
return_all_scores=True,
)
premise = "your premise"
hypothesis = "your hypothesis"
result = dict()
result[0] = classifier(premise + tokenizer.sep_token + hypothesis)[0]
```
|
cstorm125/wangchanberta-base-wiki-20210520-news-spm-finetune-qa
|
cstorm125
| 2021-07-14T07:35:27Z | 20 | 0 |
transformers
|
[
"transformers",
"pytorch",
"camembert",
"question-answering",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
widget:
- text: "สวนกุหลาบเป็นโรงเรียนอะไร"
context: "โรงเรียนสวนกุหลาบวิทยาลัย (Suankularb Wittayalai School) (อักษรย่อ : ส.ก. / S.K.) เป็นโรงเรียนชายล้วน ระดับชั้นมัธยมศึกษาขนาดใหญ่พิเศษ สังกัดสำนักงานเขตพื้นที่การศึกษามัธยมศึกษาเขต 1 สำนักงานคณะกรรมการการศึกษาขั้นพื้นฐาน (ชื่อเดิม: กรมสามัญศึกษา) กระทรวงศึกษาธิการ ก่อตั้งโดย พระบาทสมเด็จพระจุลจอมเกล้าเจ้าอยู่หัว ได้รับการสถาปนาขึ้นในวันที่ 8 มีนาคม พ.ศ. 2424 (ขณะนั้นนับวันที่ 1 เมษายน เป็นวันขึ้นปีใหม่ เมื่อนับอย่างสากลถือเป็น พ.ศ. 2425) โดยเป็นโรงเรียนรัฐบาลแห่งแรกของประเทศไทย"
---
# wangchanberta-base-wiki-20210520-news-spm-finetune-qa
Finetuning `airesearchth/wangchanberta-base-wiki-20210520-news-spm` with the training set of `iapp_wiki_qa_squad`, `thaiqa_squad`, and `nsc_qa` (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the latter two are trimmed to be around 300 `newmm` words). Benchmarks shared on [wandb](https://wandb.ai/cstorm125/wangchanberta-qa) using validation and test sets of `iapp_wiki_qa_squad`.
Trained with [thai2transformers](https://github.com/vistec-AI/thai2transformers/blob/dev/scripts/downstream/train_question_answering_lm_finetuning.py).
Run with:
```
export MODEL_NAME=airesearchth/wangchanberta-base-wiki-20210520-news-spm
CUDA_LAUNCH_BLOCKING=1 python train_question_answering_lm_finetuning.py \
--model_name $MODEL_NAME \
--dataset_name chimera_qa \
--output_dir $MODEL_NAME-finetune-chimera_qa-model \
--log_dir $MODEL_NAME-finetune-chimera_qa-log \
--model_max_length 400 \
--pad_on_right \
--fp16
```
|
cstorm125/wangchanberta-base-att-spm-uncased-finetune-qa
|
cstorm125
| 2021-07-14T07:24:50Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"camembert",
"question-answering",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
widget:
- text: "สวนกุหลาบเป็นโรงเรียนอะไร"
context: "โรงเรียนสวนกุหลาบวิทยาลัย (Suankularb Wittayalai School) (อักษรย่อ : ส.ก. / S.K.) เป็นโรงเรียนชายล้วน ระดับชั้นมัธยมศึกษาขนาดใหญ่พิเศษ สังกัดสำนักงานเขตพื้นที่การศึกษามัธยมศึกษาเขต 1 สำนักงานคณะกรรมการการศึกษาขั้นพื้นฐาน (ชื่อเดิม: กรมสามัญศึกษา) กระทรวงศึกษาธิการ ก่อตั้งโดย พระบาทสมเด็จพระจุลจอมเกล้าเจ้าอยู่หัว ได้รับการสถาปนาขึ้นในวันที่ 8 มีนาคม พ.ศ. 2424 (ขณะนั้นนับวันที่ 1 เมษายน เป็นวันขึ้นปีใหม่ เมื่อนับอย่างสากลถือเป็น พ.ศ. 2425) โดยเป็นโรงเรียนรัฐบาลแห่งแรกของประเทศไทย"
---
# airesearch/wangchanberta-base-att-spm-uncased
Finetuning `airesearch/wangchanberta-base-att-spm-uncased` with the training set of `iapp_wiki_qa_squad`, `thaiqa_squad`, and `nsc_qa` (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the latter two are trimmed to be around 300 `newmm` words). Benchmarks shared on [wandb](https://wandb.ai/cstorm125/wangchanberta-qa) using validation and test sets of `iapp_wiki_qa_squad`.
Trained with [thai2transformers](https://github.com/vistec-AI/thai2transformers/blob/dev/scripts/downstream/train_question_answering_lm_finetuning.py).
Run with:
```
export MODEL_NAME=airesearch/wangchanberta-base-att-spm-uncased
python train_question_answering_lm_finetuning.py \
--model_name $MODEL_NAME \
--dataset_name chimera_qa \
--output_dir $MODEL_NAME-finetune-chimera_qa-model \
--log_dir $MODEL_NAME-finetune-chimera_qa-log \
--lowercase \
--pad_on_right \
--fp16
```
|
keshan/sinhala-roberta-oscar
|
keshan
| 2021-07-14T06:28:47Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"oscar",
"Sinhala",
"si",
"dataset:oscar",
"arxiv:1907.11692",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language: si
tags:
- oscar
- Sinhala
- roberta
- fill-mask
widget:
- text: "මම සිංහල භාෂාව <mask>"
datasets:
- oscar
---
### Overview
This is a slightly smaller model trained on [OSCAR](https://oscar-corpus.com/) Sinhala dedup dataset. As Sinhala is one of those low resource languages, there are only a handful of models been trained. So, this would be a great place to start training for more downstream tasks.
## Model Specification
The model chosen for training is [Roberta](https://arxiv.org/abs/1907.11692) with the following specifications:
1. vocab_size=50265
2. max_position_embeddings=514
3. num_attention_heads=12
4. num_hidden_layers=12
5. type_vocab_size=1
## How to Use
You can use this model directly with a pipeline for masked language modeling:
```py
from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline
model = AutoModelWithLMHead.from_pretrained("keshan/sinhala-roberta-oscar")
tokenizer = AutoTokenizer.from_pretrained("keshan/sinhala-roberta-oscar")
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)
fill_mask("මම ගෙදර <mask>.")
```
|
flax-community/wav2vec2-german
|
flax-community
| 2021-07-14T00:41:39Z | 4 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"wav2vec2",
"pretraining",
"speech",
"de",
"dataset:librispeech_asr",
"arxiv:2006.11477",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: de
datasets:
- librispeech_asr
tags:
- speech
license: apache-2.0
---
# Wav2Vec2-german model
[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. 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 Automatic Speech Recognition. Check out [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more information.
[Paper](https://arxiv.org/abs/2006.11477)
Authors: Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli
**Abstract**
We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks the speech input in the latent space and solves a contrastive task defined over a quantization of the latent representations which are jointly learned. Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of labeled data and pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech recognition with limited amounts of labeled data.
The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20.
## Necessary installations:
- sndfile library: `sudo apt-get install libsndfile1-dev`
- ffmpeg: `sudo apt install ffmpeg` & `pip install ffmpeg`
## Model description `TODO: Update`
## How to use `TODO: Update`
```python
from transformers import FlaxWav2Vec2Processor, TFWav2Vec2Model
model_id = "flax-community/wav2vec2-german"
from datasets import load_dataset
import soundfile as sf
processor = Wav2Vec2Processor.from_pretrained(model_id)
model = TFWav2Vec2Model.from_pretrained(model_id)
def map_to_array(batch):
speech, _ = sf.read(batch["file"])
batch["speech"] = speech
return batch
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
ds = ds.map(map_to_array)
input_values = processor(ds["speech"][0], return_tensors="flax").input_values # Batch size 1
hidden_states = model(input_values).last_hidden_state
```
## Training Data `TODO: Update`
## Training Procedure `TODO: Update`
|
YusufSahin99/Zork_AI_SciFi
|
YusufSahin99
| 2021-07-13T14:58:01Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
model_index:
- name: Zork_AI_SciFi
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. -->
# Zork_AI_SciFi
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unkown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.9.0+cu102
- Tokenizers 0.10.3
|
AIDA-UPM/mstsb-paraphrase-multilingual-mpnet-base-v2
|
AIDA-UPM
| 2021-07-13T14:12:45Z | 292 | 12 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"multilingual",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:04Z |
---
pipeline_tag: sentence-similarity
language: "multilingual"
tags:
- feature-extraction
- sentence-similarity
- transformers
- multilingual
---
# mstsb-paraphrase-multilingual-mpnet-base-v2
This is a fine-tuned version of `paraphrase-multilingual-mpnet-base-v2` from [sentence-transformers](https://www.SBERT.net) model with [Semantic Textual Similarity Benchmark](http://ixa2.si.ehu.eus/stswiki/index.php/Main_Page) extended to 15 languages: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering, semantic search and measuring the similarity between two sentences.
<!--- Describe your model here -->
This model is fine-tuned version of `paraphrase-multilingual-mpnet-base-v2` for semantic textual similarity with multilingual data. The dataset used for this fine-tuning is STSb extended to 15 languages with Google Translator. For mantaining data quality the sentence pairs with a confidence value below 0.7 were dropped. The extended dataset is available at [GitHub](https://github.com/Huertas97/Multilingual-STSB). The languages included in the extended version are: ar, cs, de, en, es, fr, hi, it, ja, nl, pl, pt, ru, tr, zh-CN, zh-TW. The pooling operation used to condense the word embeddings into a sentence embedding is mean pooling (more info below).
<!-- ## 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
# It support several languages
sentences = ["This is an example sentence", "Esta es otra frase de ejemplo", "最後の例文"]
# The pooling technique is automatically detected (mean pooling)
model = SentenceTransformer('mstsb-paraphrase-multilingual-mpnet-base-v2')
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
# We should define the proper pooling function: Mean pooling
# 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", "Esta es otra frase de ejemplo", "最後の例文"]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('AIDA-UPM/mstsb-paraphrase-multilingual-mpnet-base-v2')
model = AutoModel.from_pretrained('AIDA-UPM/mstsb-paraphrase-multilingual-mpnet-base-v2')
# 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 -->
Check the test results in the Semantic Textual Similarity Tasks. The 15 languages available at the [Multilingual STSB](https://github.com/Huertas97/Multilingual-STSB) have been combined into monolingual and cross-lingual tasks, giving a total of 31 tasks. Monolingual tasks have both sentences from the same language source (e.g., Ar-Ar, Es-Es), while cross-lingual tasks have two sentences, each in a different language being one of them English (e.g., en-ar, en-es).
Here we compare the average multilingual semantic textual similairty capabilities between the `paraphrase-multilingual-mpnet-base-v2` based model and the `mstsb-paraphrase-multilingual-mpnet-base-v2` fine-tuned model across the 31 tasks. It is worth noting that both models are multilingual, but the second model is adjusted with multilingual data for semantic similarity. The average of correlation coefficients is computed by transforming each correlation coefficient to a Fisher's z value, averaging them, and then back-transforming to a correlation coefficient.
| Model | Average Spearman Cosine Test |
|:---------------------------------------------:|:------------------------------:|
| mstsb-paraphrase-multilingual-mpnet-base-v2 | 0.835890 |
| paraphrase-multilingual-mpnet-base-v2 | 0.818896 |
<br>
The following tables breakdown the performance of `mstsb-paraphrase-multilingual-mpnet-base-v2` according to the different tasks. For the sake of readability tasks have been splitted into monolingual and cross-lingual tasks.
| Monolingual Task | Pearson Cosine test | Spearman Cosine test |
|:------------------:|:---------------------:|:-----------------------:|
| en;en | 0.868048310692506 | 0.8740170943535747 |
| ar;ar | 0.8267139454193487 | 0.8284459741532022 |
| cs;cs | 0.8466821720942157 | 0.8485417688803879 |
| de;de | 0.8517285961812183 | 0.8557680051557893 |
| es;es | 0.8519185309064691 | 0.8552243211580456 |
| fr;fr | 0.8430951067985064 | 0.8466614534379704 |
| hi;hi | 0.8178258630578092 | 0.8176462079184331 |
| it;it | 0.8475909574305637 | 0.8494216064459076 |
| ja;ja | 0.8435588859386477 | 0.8456031494178619 |
| nl;nl | 0.8486765104527032 | 0.8520856765262531 |
| pl;pl | 0.8407840177883407 | 0.8443070467300299 |
| pt;pt | 0.8534880178249296 | 0.8578544068829622 |
| ru;ru | 0.8390897585455678 | 0.8423041443534423 |
| tr;tr | 0.8382125451820572 | 0.8421587450058385 |
| zh-CN;zh-CN | 0.826233678946644 | 0.8248515460782744 |
| zh-TW;zh-TW | 0.8242683809675422 | 0.8235506799952028 |
<br>
| Cross-lingual Task | Pearson Cosine test | Spearman Cosine test |
|:--------------------:|:---------------------:|:-----------------------:|
| en;ar | 0.7990830340462535 | 0.7956792016468148 |
| en;cs | 0.8381274879061265 | 0.8388713450024455 |
| en;de | 0.8414439600928739 | 0.8441971698649943 |
| en;es | 0.8442337511356952 | 0.8445035292903559 |
| en;fr | 0.8378437644605063 | 0.8387903367907733 |
| en;hi | 0.7951955086055527 | 0.7905052217683244 |
| en;it | 0.8415686372978766 | 0.8419480899107785 |
| en;ja | 0.8094306665283388 | 0.8032512280936449 |
| en;nl | 0.8389526140129767 | 0.8409310421803277 |
| en;pl | 0.8261309163979578 | 0.825976253023656 |
| en;pt | 0.8475546209070765 | 0.8506606391790897 |
| en;ru | 0.8248514914263723 | 0.8224871183202255 |
| en;tr | 0.8191803661207868 | 0.8194200775744044 |
| en;zh-CN | 0.8147678083378249 | 0.8102089470690433 |
| en;zh-TW | 0.8107272160374955 | 0.8056129680510944 |
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 687 with parameters:
```
{'batch_size': 132, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"callback": null,
"epochs": 2,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 140,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
marefa-nlp/summarization-arabic-english-news
|
marefa-nlp
| 2021-07-13T13:06:31Z | 62 | 4 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
------------
## Arabic and English News Summarization NLP Model
### About
This model is for summarizing news stories in short highlights for both Arabic and English tasks.
نموذج معرفي متخصص في تلخيص الأخبار العربية و الإنجليزية الى مجموعة من أهم النقاط
### Fine-Tuning
The model was finetuned using the [Arabic T5 Model](https://huggingface.co/bakrianoo/t5-arabic-large) which developed by [Abu Bakr Soliman](http://github.com/bakrianoo).
The primary summarization model also developed by the same developer.
### How to Use
- You can use this [Colab Notebook](https://colab.research.google.com/drive/1DWND1CAfCXD4OxrfmLBEaKeXhjGmYkod?usp=sharing) to test the model
1. Install [PyTorch](https://pytorch.org/)
2. Install the following Python packages
`$ pip3 install transformers==4.7.0 nltk==3.5 protobuf==3.15.3 sentencepiece==0.1.96`
3. Run this code
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
import torch
import nltk
nltk.download('punkt')
from nltk.tokenize import sent_tokenize
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
m_name = "marefa-nlp/summarization-arabic-english-news"
tokenizer = AutoTokenizer.from_pretrained(m_name)
model = AutoModelWithLMHead.from_pretrained(m_name).to(device)
def get_summary(text, tokenizer, model, device="cpu", num_beams=2):
if len(text.strip()) < 50:
return ["Please provide more longer text"]
text = "summarize: <paragraph> " + " <paragraph> ".join([ s.strip() for s in sent_tokenize(text) if s.strip() != ""]) + " </s>"
text = text.strip().replace("\n","")
tokenized_text = tokenizer.encode(text, return_tensors="pt").to(device)
summary_ids = model.generate(
tokenized_text,
max_length=512,
num_beams=num_beams,
repetition_penalty=1.5,
length_penalty=1.0,
early_stopping=True
)
output = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
return [ s.strip() for s in output.split("<hl>") if s.strip() != "" ]
## Prepare Samples
samples = [
"""
قال المدافع الإيطالي ليوناردو بونوتشي إن منتخب بلاده ليس خائفا من مواجهة نظيره الانجليزي على أرضه في المباراة النهائية في بطولة يورو 2020 لكرة القدم، في حين وصف المدافع الانجليزي جون ستونز المباراة المرتقبة بأنها ستكون "أكثر تميزا".
وسوف تقام المباراة في استاد ويمبلي، شمال غربي لندن، يوم الأحد.
وتسعى إيطاليا لإحراز اللقب الأوروبي للمرة الثانية بعد فوزها به أول مرة عام 1968.
ولم يفز الفريق الانجليزي بهذا اللقب القاري من قبل. والبطولة الرئيسية الوحيدة التي فازت بها انجلترا هي كأس العالم عام 1966 الذي أقيمت مباراته النهائية في استاد ويمبلي.
""",
"""
On a night fraught with tension, Italy clinched its first major title for 15 years with a penalty shootout win over England in the Euro 2020 final.
Luke Shaw's goal inside the opening two minutes gave England a lead it looked like it would hold onto all night, before a goalmouth scramble midway through the second half allowed Leonardo Bonucci to poke home an equalizer for Italy.
For the remainder of the match, it felt as though extra-time and penalties were inevitable, as neither side seemed willing or brave enough to commit enough men forward to really trouble the opposing defenders.
England had suffered innumerable heartbreaks on penalties over the years and this time it was Italy's turn to inflict yet more pain on beleaguered English fans as Marcus Rashford, Jadon Sancho and Bukayo Saka all missed from the spot.
""",
]
## Get summaries
print("Original Article:", samples[0])
print("\n===========\nSummary: \n")
hls = get_summary(samples[0], tokenizer, model, device)
for hl in hls:
print("\t-", hl)
print("Original Article:", samples[1])
print("\n=========== \nSummary: \n")
hls = get_summary(samples[1], tokenizer, model, device)
for hl in hls:
print("\t-", hl)
```
Results
```
Original Article:
قال المدافع الإيطالي ليوناردو بونوتشي إن منتخب بلاده ليس خائفا من مواجهة نظيره الانجليزي على أرضه في المباراة النهائية في بطولة يورو 2020 لكرة القدم، في حين وصف المدافع الانجليزي جون ستونز المباراة المرتقبة بأنها ستكون "أكثر تميزا".
وسوف تقام المباراة في استاد ويمبلي، شمال غربي لندن، يوم الأحد.
وتسعى إيطاليا لإحراز اللقب الأوروبي للمرة الثانية بعد فوزها به أول مرة عام 1968.
ولم يفز الفريق الانجليزي بهذا اللقب القاري من قبل. والبطولة الرئيسية الوحيدة التي فازت بها انجلترا هي كأس العالم عام 1966 الذي أقيمت مباراته النهائية في استاد ويمبلي.
===========
Summary:
- وسوف تواجه إيطاليا إنجلترا في بطولة يورو 2020 لكرة القدم يوم الأحد.
- ستقام المباراة في استاد ويمبلي، شمال غربي لندن، يوم الأحد.
- ولم يفز الفريق الانجليزي بهذا اللقب القاري قبل.
```
```
Original Article:
On a night fraught with tension, Italy clinched its first major title for 15 years with a penalty shootout win over England in the Euro 2020 final.
Luke Shaw's goal inside the opening two minutes gave England a lead it looked like it would hold onto all night, before a goalmouth scramble midway through the second half allowed Leonardo Bonucci to poke home an equalizer for Italy.
For the remainder of the match it felt as though extra-time and penalties were inevitable, as neither side seemed willing or brave enough to commit enough men forward to really trouble the opposing defenders.
England had suffered innumerable heartbreaks on penalties over the years and this time it was Italy's turn to inflict yet more pain on beleaguered English fans as Marcus Rashford, Jadon Sancho and Bukayo Saka all missed from the spot.
===========
Summary:
- Luke Shaw's goal gave England a lead it looked like it would hold onto all night.
- Leonardo Bonucci scored the equalizer for Italy.
- Marcus Rashford, Jadon Sancho and Bukayo Saka all missed.
```
|
hetpandya/t5-small-quora
|
hetpandya
| 2021-07-13T12:37:51Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:quora",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- quora
---
# T5-small for paraphrase generation
Google's T5-small fine-tuned on [Quora Question Pairs](https://huggingface.co/datasets/quora) dataset for paraphrasing.
## Model in Action 🚀
```python
from transformers import T5ForConditionalGeneration, T5Tokenizer
tokenizer = T5Tokenizer.from_pretrained("hetpandya/t5-small-quora")
model = T5ForConditionalGeneration.from_pretrained("hetpandya/t5-small-quora")
def get_paraphrases(sentence, prefix="paraphrase: ", n_predictions=5, top_k=120, max_length=256,device="cpu"):
text = prefix + sentence + " </s>"
encoding = tokenizer.encode_plus(
text, pad_to_max_length=True, return_tensors="pt"
)
input_ids, attention_masks = encoding["input_ids"].to(device), encoding[
"attention_mask"
].to(device)
model_output = model.generate(
input_ids=input_ids,
attention_mask=attention_masks,
do_sample=True,
max_length=max_length,
top_k=top_k,
top_p=0.98,
early_stopping=True,
num_return_sequences=n_predictions,
)
outputs = []
for output in model_output:
generated_sent = tokenizer.decode(
output, skip_special_tokens=True, clean_up_tokenization_spaces=True
)
if (
generated_sent.lower() != sentence.lower()
and generated_sent not in outputs
):
outputs.append(generated_sent)
return outputs
paraphrases = get_paraphrases("The house will be cleaned by me every Saturday.")
for sent in paraphrases:
print(sent)
```
## Output
```
My house is up clean on Saturday morning. Thank you for this email. I'm introducing a new name and name. I'm running my house at home. I'm a taller myself. I'm gonna go with it on Monday. (the house will be up cleaned).
Is there anything that will be cleaned every Saturday morning?
The house is clean and will be cleaned each Saturday by my wife.
I will clean the house for almost a week. I have to clean it all the weekend. I will be able to do it. My house is new.
If I clean my house every Monday, I can call it clean.
```
Created by [Het Pandya/@hetpandya](https://github.com/hetpandya) | [LinkedIn](https://www.linkedin.com/in/het-pandya)
Made with <span style="color: red;">♥</span> in India
|
huggingtweets/onlinepete-recyrb
|
huggingtweets
| 2021-07-13T03:03:22Z | 6 | 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/onlinepete-recyrb/1626145398544/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/1305948384524730369/FoDgA8gh_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/456958582731603969/QZKpv6eI_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>
<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">bryce & im pete online</div>
<div style="text-align: center; font-size: 14px;">@onlinepete-recyrb</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 bryce & im pete online.
| Data | bryce | im pete online |
| --- | --- | --- |
| Tweets downloaded | 3176 | 3190 |
| Retweets | 183 | 94 |
| Short tweets | 406 | 1003 |
| Tweets kept | 2587 | 2093 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1y0uormq/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 @onlinepete-recyrb's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/16lwsen4) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/16lwsen4/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/onlinepete-recyrb')
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)
|
jaimin/Gujarati-Model
|
jaimin
| 2021-07-12T13:23:21Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
tokenizer = AutoTokenizer.from_pretrained("jaimin/Gujarati-Model")
model = AutoModel.from_pretrained("jaimin/Gujarati-Model")
|
Andrija/RobertaFastBPE
|
Andrija
| 2021-07-12T11:11:21Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-03-02T23:29:04Z |
from transformers import RobertaTokenizerFast
tokenizer = RobertaTokenizerFast.from_pretrained('Andrija/RobertaFastBPE', bos_token="<s>", eos_token="</s>")
encoded = tokenizer('Stručnjaci te bolnice, predvođeni dr Alisom Lim')
# {'input_ids': [0, 47541, 34632, 603, 24817, 16, 27540, 6768, 2350, 2803, 3991, 2733, 81, 1], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
tokenizer.decode(encoded['input_ids'])
# <s>Stručnjaci te bolnice, predvođeni dr Alisom Lim</s>
|
huggingtweets/pontifex_es
|
huggingtweets
| 2021-07-12T08:26:39Z | 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/pontifex_es/1626078305422/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/507819548834148352/jyx1JOS-_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">Papa Francisco</div>
<div style="text-align: center; font-size: 14px;">@pontifex_es</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 Papa Francisco.
| Data | Papa Francisco |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 0 |
| Short tweets | 45 |
| Tweets kept | 3205 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2a8o5bwd/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 @pontifex_es's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3183nmsb) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3183nmsb/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/pontifex_es')
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)
|
Littlejohn/analisis_sentimientos
|
Littlejohn
| 2021-07-12T00:22:27Z | 11 | 0 |
transformers
|
[
"transformers",
"text-classification",
"en",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:04Z |
---
language:
- en
pipeline_tag: text-classification
---
# bert-base-cased-sentiment
Es un modelo de BERT (bert-base-cased) afinado para el analisis de sentimientos para dos clases.
El sentimiento solo se define como positivo negativo según sea el caso de la oración suministrada.
## Training data
El set de datos utilizado para el entrenamiento del modelo fue a traves de una recopilación de reseñas de amazón, el cual se puede descargar desde el autor original en Kaggle [Adam Bittlingmayer](https://www.kaggle.com/bittlingmayer/amazonreviews) Amazon Reviews for Sentiment Analysis.
El numero de datos fue solo de 40 000 oraciones de las cuales solo se tomaron las primeras 100 palabras para conformar cada una de las oraciones.
## Accuaracy
El modelo afinado fue sometido a 3 pruebas para conocer su precisión.
- La primera prueba fue en un set de datos de Reseñas de hoteles
| Accuracy (Precisión) |
| -------- |
| 95% |
- La segunda prueba fue en un set de datos de Reseñas de comida
| Accuracy (Precisión) |
| -------- |
| 88% |
- La tercera prueba fue en un set de datos de Sentimientos generales
| Accuracy (Precisión) |
| -------- |
| 65% |
## Contact
Contacto a traves de github [Murdoocc7](https://github.com/murdoocc)
|
srosy/distilbert-base-uncased-finetuned-ner
|
srosy
| 2021-07-11T15:29:20Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"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: 0.9844313470062116
---
<!-- 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: 0.0590
- Precision: 0.9266
- Recall: 0.9381
- F1: 0.9323
- Accuracy: 0.9844
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0616 | 1.0 | 878 | 0.0604 | 0.9195 | 0.9370 | 0.9282 | 0.9833 |
| 0.0328 | 2.0 | 1756 | 0.0588 | 0.9258 | 0.9375 | 0.9316 | 0.9841 |
| 0.0246 | 3.0 | 2634 | 0.0590 | 0.9266 | 0.9381 | 0.9323 | 0.9844 |
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1
- Datasets 1.9.0
- Tokenizers 0.10.3
|
nateraw/donut-or-bagel
|
nateraw
| 2021-07-10T19:54:49Z | 71 | 1 |
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: donut-or-bagel
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9375
---
# donut-or-bagel
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
#### bagel
#### donut
|
Unbabel/xlm-roberta-comet-small
|
Unbabel
| 2021-07-10T17:32:40Z | 17,293 | 2 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"arxiv:2012.15828",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
# Model
mMiniLM-L12xH384 XLM-R model proposed in [MiniLMv2: Multi-Head Self-Attention Relation Distillation for Compressing Pretrained Transformers](https://arxiv.org/abs/2012.15828) that we fine-tune using the direct assessment annotations collected in the Workshop on Statistical Machine Translation (WMT) 2015 to 2020.
This model is much more light weight than the traditional XLM-RoBERTa base and large.
|
rafio/argentina
|
rafio
| 2021-07-10T17:29:18Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-03-02T23:29:05Z |
https://twitter.com/i/events/1413870919320104965
https://peatix.com/group/11420372/
https://cmdt-guyane.fr/advert/argentina-vs-brazil-live-stream-final-2021/
https://www.quisqueyapeach.com/advert/argentina-vs-brazil-live-stream-final-2021/
https://www.beauvaissubaquatique.fr/advert/argentina-vs-brazil-live-stream-final-2021/
https://www.dmcityview.com/event/argentina-vs-brazil-live-stream-final-2021/
|
sebastian-hofstaetter/prettr-distilbert-split_at_3-margin_mse-T2-msmarco
|
sebastian-hofstaetter
| 2021-07-10T10:14:14Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"knowledge-distillation",
"en",
"dataset:ms_marco",
"arxiv:2004.14255",
"arxiv:2010.02666",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: "en"
tags:
- knowledge-distillation
datasets:
- ms_marco
---
# Margin-MSE Trained PreTTR
We provide a retrieval trained DistilBert-based PreTTR model (https://arxiv.org/abs/2004.14255). Our model is trained with Margin-MSE using a 3 teacher BERT_Cat (concatenated BERT scoring) ensemble on MSMARCO-Passage.
This instance can be used to **re-rank a candidate set**. The architecture is a 6-layer DistilBERT, split at layer 3, with an additional single linear layer at the end for scoring the CLS token.
If you want to know more about our simple, yet effective knowledge distillation method for efficient information retrieval models for a variety of student architectures that is used for this model instance check out our paper: https://arxiv.org/abs/2010.02666 🎉
For more information, training data, source code, and a minimal usage example please visit: https://github.com/sebastian-hofstaetter/neural-ranking-kd
## Configuration
- We split the DistilBERT in half at layer 3
## Model Code
````python
from transformers import DistilBertModel,AutoTokenizer
from transformers.models.distilbert.modeling_distilbert import *
import math
import torch
from torch import nn as nn
class PreTTRConfig(DistilBertConfig):
join_layer_idx = 3
class PreTTR(DistilBertModel):
'''
PreTTR changes the distilbert model from huggingface to be able to split query and document until a set layer,
we skipped compression present in the original
from: Efficient Document Re-Ranking for Transformers by Precomputing Term Representations
MacAvaney, et al. https://arxiv.org/abs/2004.14255
'''
config_class = PreTTRConfig
def __init__(self, config):
super().__init__(config)
self.transformer = SplitTransformer(config) # Encoder, we override the classes, but the names stay the same -> so it gets properly initialized
self.embeddings = PosOffsetEmbeddings(config) # Embeddings
self._classification_layer = torch.nn.Linear(self.config.hidden_size, 1, bias=False)
self.join_layer_idx = config.join_layer_idx
def forward(
self,
query,
document,
use_fp16: bool = False) -> torch.Tensor:
with torch.cuda.amp.autocast(enabled=use_fp16):
query_input_ids = query["input_ids"]
query_attention_mask = query["attention_mask"]
document_input_ids = document["input_ids"][:, 1:]
document_attention_mask = document["attention_mask"][:, 1:]
query_embs = self.embeddings(query_input_ids) # (bs, seq_length, dim)
document_embs = self.embeddings(document_input_ids, query_input_ids.shape[-1]) # (bs, seq_length, dim)
tfmr_output = self.transformer(
query_embs=query_embs,
query_mask=query_attention_mask,
doc_embs=document_embs,
doc_mask=document_attention_mask,
join_layer_idx=self.join_layer_idx
)
hidden_state = tfmr_output[0]
score = self._classification_layer(hidden_state[:, 0, :]).squeeze()
return score
class PosOffsetEmbeddings(nn.Module):
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.dim, padding_idx=config.pad_token_id)
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.dim)
if config.sinusoidal_pos_embds:
create_sinusoidal_embeddings(
n_pos=config.max_position_embeddings, dim=config.dim, out=self.position_embeddings.weight
)
self.LayerNorm = nn.LayerNorm(config.dim, eps=1e-12)
self.dropout = nn.Dropout(config.dropout)
def forward(self, input_ids, pos_offset=0):
"""
Parameters
----------
input_ids: torch.tensor(bs, max_seq_length)
The token ids to embed.
Outputs
-------
embeddings: torch.tensor(bs, max_seq_length, dim)
The embedded tokens (plus position embeddings, no token_type embeddings)
"""
seq_length = input_ids.size(1)
position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device) # (max_seq_length)
position_ids = position_ids.unsqueeze(0).expand_as(input_ids) + pos_offset # (bs, max_seq_length)
word_embeddings = self.word_embeddings(input_ids) # (bs, max_seq_length, dim)
position_embeddings = self.position_embeddings(position_ids) # (bs, max_seq_length, dim)
embeddings = word_embeddings + position_embeddings # (bs, max_seq_length, dim)
embeddings = self.LayerNorm(embeddings) # (bs, max_seq_length, dim)
embeddings = self.dropout(embeddings) # (bs, max_seq_length, dim)
return embeddings
class SplitTransformer(nn.Module):
def __init__(self, config):
super().__init__()
self.n_layers = config.n_layers
layer = TransformerBlock(config)
self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(config.n_layers)])
def forward(self, query_embs, query_mask, doc_embs, doc_mask, join_layer_idx, output_attentions=False, output_hidden_states=False):
"""
Parameters
----------
x: torch.tensor(bs, seq_length, dim)
Input sequence embedded.
attn_mask: torch.tensor(bs, seq_length)
Attention mask on the sequence.
Outputs
-------
hidden_state: torch.tensor(bs, seq_length, dim)
Sequence of hiddens states in the last (top) layer
all_hidden_states: Tuple[torch.tensor(bs, seq_length, dim)]
Tuple of length n_layers with the hidden states from each layer.
Optional: only if output_hidden_states=True
all_attentions: Tuple[torch.tensor(bs, n_heads, seq_length, seq_length)]
Tuple of length n_layers with the attention weights from each layer
Optional: only if output_attentions=True
"""
all_hidden_states = ()
all_attentions = ()
#
# query / doc sep.
#
hidden_state_q = query_embs
hidden_state_d = doc_embs
for layer_module in self.layer[:join_layer_idx]:
layer_outputs_q = layer_module(
x=hidden_state_q, attn_mask=query_mask, head_mask=None, output_attentions=output_attentions
)
hidden_state_q = layer_outputs_q[-1]
layer_outputs_d = layer_module(
x=hidden_state_d, attn_mask=doc_mask, head_mask=None, output_attentions=output_attentions
)
hidden_state_d = layer_outputs_d[-1]
#
# combine
#
x = torch.cat([hidden_state_q, hidden_state_d], dim=1)
attn_mask = torch.cat([query_mask, doc_mask], dim=1)
#
# combined
#
hidden_state = x
for layer_module in self.layer[join_layer_idx:]:
layer_outputs = layer_module(
x=hidden_state, attn_mask=attn_mask, head_mask=None, output_attentions=output_attentions
)
hidden_state = layer_outputs[-1]
# Add last layer
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_state,)
outputs = (hidden_state,)
if output_hidden_states:
outputs = outputs + (all_hidden_states,)
if output_attentions:
outputs = outputs + (all_attentions,)
return outputs # last-layer hidden state, (all hidden states), (all attentions)
#
# init the model & tokenizer (using the distilbert tokenizer)
#
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased") # honestly not sure if that is the best way to go, but it works :)
model = PreTTR.from_pretrained("sebastian-hofstaetter/prettr-distilbert-split_at_3-margin_mse-T2-msmarco")
````
## Effectiveness on MSMARCO Passage
We trained our model on the MSMARCO standard ("small"-400K query) training triples with knowledge distillation with a batch size of 32 on a single consumer-grade GPU (11GB memory).
For re-ranking we used the top-1000 BM25 results.
### MSMARCO-DEV
Here, we use the larger 49K query DEV set (same range as the smaller 7K DEV set, minimal changes possible)
| | MRR@10 | NDCG@10 |
|----------------------------------|--------|---------|
| BM25 | .194 | .241 |
| **Margin-MSE PreTTR** (Re-ranking) | .386 | .447 |
For more metrics, baselines, info and analysis, please see the paper: https://arxiv.org/abs/2010.02666
## Limitations & Bias
- The model inherits social biases from both DistilBERT and MSMARCO.
- The model is only trained on relatively short passages of MSMARCO (avg. 60 words length), so it might struggle with longer text.
## Citation
If you use our model checkpoint please cite our work as:
```
@misc{hofstaetter2020_crossarchitecture_kd,
title={Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation},
author={Sebastian Hofst{\"a}tter and Sophia Althammer and Michael Schr{\"o}der and Mete Sertkan and Allan Hanbury},
year={2020},
eprint={2010.02666},
archivePrefix={arXiv},
primaryClass={cs.IR}
}
```
|
huggingtweets/cassandraautumn
|
huggingtweets
| 2021-07-10T03:53:34Z | 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/cassandraautumn/1625889209816/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/1368065495816151041/PHixetcc_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">Cassandra Autumn</div>
<div style="text-align: center; font-size: 14px;">@cassandraautumn</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 Cassandra Autumn.
| Data | Cassandra Autumn |
| --- | --- |
| Tweets downloaded | 583 |
| Retweets | 283 |
| Short tweets | 76 |
| Tweets kept | 224 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1d6zyhom/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 @cassandraautumn's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2c2uc7mv) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2c2uc7mv/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/cassandraautumn')
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/i_apx_86
|
huggingtweets
| 2021-07-10T03:25:37Z | 7 | 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/i_apx_86/1625887532973/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/1404915017703575558/05H2noyT_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">🍮CC🍮</div>
<div style="text-align: center; font-size: 14px;">@i_apx_86</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 🍮CC🍮.
| Data | 🍮CC🍮 |
| --- | --- |
| Tweets downloaded | 701 |
| Retweets | 391 |
| Short tweets | 22 |
| Tweets kept | 288 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2xwg9l0v/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 @i_apx_86's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/11srzptq) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/11srzptq/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/i_apx_86')
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/dhivehi-roberta-base
|
shahukareem
| 2021-07-10T00:19:12Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"dv",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language: dv
tags:
- dv
- roberta
widget:
- text: "<mask> މާލެ އަކީ ދިވެހިރާއްޖޭގެ"
---
# Dhivehi Roberta Base - Oscar
## Description
RoBERTA pretrained from scratch using Jax/Flax backend and with the Dhivehi Oscar Corpus only.
|
huggingtweets/marxhaunting
|
huggingtweets
| 2021-07-09T22:04:38Z | 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/marxhaunting/1625868274804/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/1323823559182045184/Vqrrga8t_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">Karl Marx</div>
<div style="text-align: center; font-size: 14px;">@marxhaunting</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 Karl Marx.
| Data | Karl Marx |
| --- | --- |
| Tweets downloaded | 1287 |
| Retweets | 16 |
| Short tweets | 25 |
| Tweets kept | 1246 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1zcjng5j/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 @marxhaunting's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1nimlh0s) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1nimlh0s/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/marxhaunting')
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/hochimeme1
|
huggingtweets
| 2021-07-09T20:06:55Z | 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/hochimeme1/1625861211819/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/1408277423498698752/aUTHbyW2_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">Moe Chi Minh</div>
<div style="text-align: center; font-size: 14px;">@hochimeme1</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 Moe Chi Minh.
| Data | Moe Chi Minh |
| --- | --- |
| Tweets downloaded | 3242 |
| Retweets | 55 |
| Short tweets | 484 |
| Tweets kept | 2703 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/21ljhxlm/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 @hochimeme1's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2vctf4ad) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2vctf4ad/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/hochimeme1')
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)
|
Prim9000/try
|
Prim9000
| 2021-07-09T14:57:22Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-03-02T23:29:04Z |
https://github.com/Prim9000/Thai_TTS
|
jephthah/dfjgidfhj
|
jephthah
| 2021-07-09T12:39:42Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-03-02T23:29:05Z |
https://natureecoevocommunity.nature.com/users/123movies-hd-watch-hitman-s-wife-s-bodyguard-2021-full-movie-online
https://natureecoevocommunity.nature.com/users/123movies-hd-watch-a-quiet-place-part-2-2021-full-movie-online-free-1d44a4a0-bbe0-4b52-a56c-c86e7ce72c1c
https://natureecoevocommunity.nature.com/users/123movies-hd-watch-a-quiet-place-part-2-2021-full-movie-online-free-reddit
https://natureecoevocommunity.nature.com/users/123movies-hd-watch-the-conjuring-3-2021-full-movie-online-free-reddit
https://natureecoevocommunity.nature.com/users/123movies-hd-watch-luca-2021-full-movie-online-free-reddit
https://natureecoevocommunity.nature.com/users/123movies-hd-watch-space-jam-2-a-new-legacy-2021-full-movie-online-free-reddit
https://natureecoevocommunity.nature.com/users/123movies-hd-watch-cruella-2021-full-movie-online-free-reddit
https://natureecoevocommunity.nature.com/users/123movies-hd-watch-the-forever-purge-2021-full-movie-online-free-reddit
https://natureecoevocommunity.nature.com/users/123movies-hd-watch-the-boss-baby-2-family-business-2021-full-movie-online-free-reddit
https://natureecoevocommunity.nature.com/users/123movies-hd-watch-fast-and-furious-9-2021-online-full-movie-free-reddit
https://natureecoevocommunity.nature.com/users/123movies-hd-watch-black-widow-2021-online-full-movie-free-reddit
|
flax-community/roberta-base-als
|
flax-community
| 2021-07-09T12:18:18Z | 4 | 0 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
This project pretrains a [`roberta-base`](https://huggingface.co/roberta-base) on the *Alemannic* (`als`) data subset of the [OSCAR](https://oscar-corpus.com/) corpus in JAX/Flax.
We will be using the masked-language modeling loss for pretraining.
|
Alireza1044/bert_classification_lm
|
Alireza1044
| 2021-07-09T08:50:58Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:04Z |
A simple model trained on dialogues of characters in NBC series, `The Office`. The model can do a binary classification between `Michael Scott` and `Dwight Shrute`'s dialogues.
<style type="text/css">
.tg {border-collapse:collapse;border-spacing:0;}
.tg td{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;
overflow:hidden;padding:10px 5px;word-break:normal;}
.tg th{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;
font-weight:normal;overflow:hidden;padding:10px 5px;word-break:normal;}
.tg .tg-c3ow{border-color:inherit;text-align:center;vertical-align:top}
</style>
<table class="tg">
<thead>
<tr>
<th class="tg-c3ow" colspan="2">Label Definitions</th>
</tr>
</thead>
<tbody>
<tr>
<td class="tg-c3ow">Label 0</td>
<td class="tg-c3ow">Michael</td>
</tr>
<tr>
<td class="tg-c3ow">Label 1</td>
<td class="tg-c3ow">Dwight</td>
</tr>
</tbody>
</table>
|
ThomasNLG/t5-qg_squad1-en
|
ThomasNLG
| 2021-07-09T07:45:35Z | 886 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"qg",
"question",
"generation",
"SQuAD",
"metric",
"nlg",
"t5-small",
"en",
"dataset:squad",
"arxiv:2103.12693",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
language: en
tags:
- qg
- question
- generation
- SQuAD
- metric
- nlg
- t5-small
license: mit
datasets:
- squad
model-index:
- name: t5-qg_squad1-en
results:
- task:
name: Question Generation
type: Text2Text-Generation
widget:
- text: "sv1 </s> Louis 14 </s> Louis 14 was a French King."
---
# t5-qg_squad1-en
## Model description
This model is a *Question Generation* model based on T5-small.
It is actually a component of [QuestEval](https://github.com/ThomasScialom/QuestEval) metric but can be used independently as it is, for QG only.
## How to use
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("ThomasNLG/t5-qg_squad1-en")
model = T5ForConditionalGeneration.from_pretrained("ThomasNLG/t5-qg_squad1-en")
```
You can play with the model using the inference API, the text input format should follow this template (accordingly to the training stage of the model):
`text_input = "sv1 </s> {ANSWER} </s> {CONTEXT}"`
## Training data
The model was trained on SQuAD.
### Citation info
```bibtex
@article{scialom2020QuestEval,
title={QuestEval: Summarization Asks for Fact-based Evaluation},
author={Scialom, Thomas and Dray, Paul-Alexis and Gallinari, Patrick and Lamprier, Sylvain and Piwowarski, Benjamin and Staiano, Jacopo and Wang, Alex},
journal={arXiv preprint arXiv:2103.12693},
year={2021}
}
```
|
ThomasNLG/t5-qa_webnlg_synth-en
|
ThomasNLG
| 2021-07-09T07:45:27Z | 260 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"qa",
"question",
"answering",
"SQuAD",
"data2text",
"metric",
"nlg",
"t5-small",
"en",
"dataset:squad_v2",
"arxiv:2104.07555",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
language: en
tags:
- qa
- question
- answering
- SQuAD
- data2text
- metric
- nlg
- t5-small
license: mit
datasets:
- squad_v2
model-index:
- name: t5-qa_webnlg_synth-en
results:
- task:
name: Data Question Answering
type: extractive-qa
widget:
- text: "What is the food type at The Eagle? </s> name [ The Eagle ] , eatType [ coffee shop ] , food [ French ] , priceRange [ £ 2 0 - 2 5 ]"
---
# t5-qa_webnlg_synth-en
## Model description
This model is a *Data Question Answering* model based on T5-small, that answers questions given a structured table as input.
It is actually a component of [QuestEval](https://github.com/ThomasScialom/QuestEval) metric but can be used independently as it is, for QA only.
## How to use
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("ThomasNLG/t5-qa_webnlg_synth-en")
model = T5ForConditionalGeneration.from_pretrained("ThomasNLG/t5-qa_webnlg_synth-en")
```
You can play with the model using the inference API, the text input format should follow this template (accordingly to the training stage of the model):
`text_input = "{QUESTION} </s> {CONTEXT}"`
where `CONTEXT` is a structured table that is linearised this way:
`CONTEXT = "name [ The Eagle ] , eatType [ coffee shop ] , food [ French ] , priceRange [ £ 2 0 - 2 5 ]"`
## Training data
The model was trained on synthetic data as described in [Data-QuestEval: A Referenceless Metric for Data to Text Semantic Evaluation](https://arxiv.org/abs/2104.07555).
### Citation info
```bibtex
@article{rebuffel2021data,
title={Data-QuestEval: A Referenceless Metric for Data to Text Semantic Evaluation},
author={Rebuffel, Cl{\'e}ment and Scialom, Thomas and Soulier, Laure and Piwowarski, Benjamin and Lamprier, Sylvain and Staiano, Jacopo and Scoutheeten, Geoffrey and Gallinari, Patrick},
journal={arXiv preprint arXiv:2104.07555},
year={2021}
}
```
|
ThomasNLG/t5-qa_squad2neg-en
|
ThomasNLG
| 2021-07-09T07:44:39Z | 797 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"qa",
"question",
"answering",
"SQuAD",
"metric",
"nlg",
"t5-small",
"en",
"dataset:squad_v2",
"arxiv:2103.12693",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
language: en
tags:
- qa
- question
- answering
- SQuAD
- metric
- nlg
- t5-small
license: mit
datasets:
- squad_v2
model-index:
- name: t5-qa_squad2neg-en
results:
- task:
name: Question Answering
type: extractive-qa
widget:
- text: "Who was Louis 14? </s> Louis 14 was a French King."
---
# t5-qa_squad2neg-en
## Model description
This model is a *Question Answering* model based on T5-small.
It is actually a component of [QuestEval](https://github.com/ThomasScialom/QuestEval) metric but can be used independently as it is, for QA only.
## How to use
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("ThomasNLG/t5-qa_squad2neg-en")
model = T5ForConditionalGeneration.from_pretrained("ThomasNLG/t5-qa_squad2neg-en")
```
You can play with the model using the inference API, the text input format should follow this template (accordingly to the training stage of the model):
`text_input = "{QUESTION} </s> {CONTEXT}"`
## Training data
The model was trained on:
- SQuAD-v2
- SQuAD-v2 neg: in addition to the training data of SQuAD-v2, for each answerable example, a negative sampled example has been added with the label *unanswerable* to help the model learning when the question is not answerable given the context. For more details, see the [paper](https://arxiv.org/abs/2103.12693).
### Citation info
```bibtex
@article{scialom2020QuestEval,
title={QuestEval: Summarization Asks for Fact-based Evaluation},
author={Scialom, Thomas and Dray, Paul-Alexis and Gallinari, Patrick and Lamprier, Sylvain and Piwowarski, Benjamin and Staiano, Jacopo and Wang, Alex},
journal={arXiv preprint arXiv:2103.12693},
year={2021}
}
```
|
mboth/distil-eng-quora-sentence
|
mboth
| 2021-07-09T06:00:21Z | 130 | 1 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"distilbert",
"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
---
# mboth/distil-eng-quora-sentence
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('mboth/distil-eng-quora-sentence')
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('mboth/distil-eng-quora-sentence')
model = AutoModel.from_pretrained('mboth/distil-eng-quora-sentence')
# 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=mboth/distil-eng-quora-sentence)
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel
(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 -->
|
franklu/pubmed_bert_squadv2
|
franklu
| 2021-07-09T05:25:26Z | 42 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
**[`microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext`](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext)** fine-tuned on **[`SQuAD V2`](https://rajpurkar.github.io/SQuAD-explorer/)** using **[`run_qa.py`](https://github.com/huggingface/transformers/blob/master/examples/pytorch/question-answering/run_qa.py)**
Tunning script:
```bash
BASE_MODEL=microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext
OUTPUT_DIR=~/Documents/projects/tunned_models/ms_pubmed_bert_squadv2/
python run_qa.py \
--model_name_or_path $BASE_MODEL\
--dataset_name squad_v2 \
--do_train \
--do_eval \
--version_2_with_negative \
--per_device_train_batch_size 12 \
--learning_rate 3e-5 \
--num_train_epochs 2 \
--max_seq_length 384 \
--doc_stride 128 \
--output_dir $OUTPUT_DIR
```
|
scikit-learn-examples/example
|
scikit-learn-examples
| 2021-07-08T15:04:55Z | 5 | 0 |
sklearn
|
[
"sklearn",
"joblib",
"structured-data-classification",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
tags:
- structured-data-classification
- sklearn
dataset:
- wine-quality
---
|
osanseviero/keras-dog-or-cat
|
osanseviero
| 2021-07-08T13:36:49Z | 39 | 0 |
keras
|
[
"keras",
"tf",
"image-classification",
"license:apache-2.0",
"region:us"
] |
image-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- image-classification
- keras
library_name: keras
---
Keras Dog vs Cat based on the [official Keras documentation](https://keras.io/examples/vision/image_classification_from_scratch/)
|
NakHyun/electra_kr_v1
|
NakHyun
| 2021-07-08T12:20:40Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"feature-extraction",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:04Z |
Test
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("NakHyun/electra_kr_v1")
model = AutoModel.from_pretrained("NakHyun/electra_kr_v1")
|
henryu-lin/t5-large-samsum-deepspeed
|
henryu-lin
| 2021-07-08T09:13:46Z | 16 | 1 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"azureml",
"summarization",
"deepspeed",
"en",
"dataset:samsum",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-03-02T23:29:05Z |
---
language: en
tags:
- azureml
- t5
- summarization
- deepspeed
license: apache-2.0
datasets:
- samsum
model-index:
- name: t5-large-samsum-deepspeed
results:
- task:
name: Abstractive Text Summarization
type: abstractive-text-summarization
dataset:
name: "SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization"
type: samsum
widget:
- text: |
Kevin: Hey man, are you excited to watch Finding Nemo tonight?
Henry: Yea, I can't wait to watch that same movie for the 89th time. Is Nate coming over to watch it with us tonight?
Kevin: Yep, he said he'll be arriving a bit later at around 7 since he gets off of work at 6. Have you taken out the garbage yet? It's starting to make the kitchen really smell.
Henry: Oh I forgot. I'll do that once I'm finished with my assignment for my math class. I didn't get to start on it until an hour ago, and it's due in 30 minutes.
Kevin: Okay dude, you should take it out as soon as possible. By the way, Nate is bringing his girlfriend and their cat too.
Henry: Nice, I'm really looking forward to seeing them again.
---
## `t5-large-samsum-deepspeed`
This model was trained using Microsoft's `AzureML` and `DeepSpeed`'s ZeRO 2 optimization. It was fine-tuned on the `SAMSum` corpus from `t5-large` checkpoint.
More information on the fine-tuning process (includes samples and benchmarks):
*(currently still WIP, major updates coming soon: 7/6/21~7/9/21)*
## Resource Usage
These results are retrieved from AzureML Studio's resource monitoring module. All experiments were ran on AzureML's low priority clusters.
| key | value |
| --- | ----- |
| AzureML SKU | ND40rs_v2 (8 X V100 32GB) |
| Region | US West 2 |
| Run Duration | 12m 47.13s |
| Compute Cost (LowPriority/Dedicated) | $0.94/$4.69 (USD) |
| Average CPU Utilization | 51.2% |
| Average GPU Utilization | 42.0% |
| GPU Memory Usage (Avg/Peak) | 24.85/28.79 (GB) |
| Total GPU Energy Usage | 670.38 (kJ) |
*Compute cost is calculated from run duration and SKU's price per hour. Updated SKU pricing could be found here: https://azure.microsoft.com/en-us/pricing/details/machine-learning/
*Peak memory usage is calculated from average peak across all utilized GPUs.
### Carbon Emissions
These results are obtained using `codecarbon`. The carbon emission is estimated from training runtime only (excluding setup and evaluation runtime).
CodeCarbon: https://github.com/mlco2/codecarbon
| key | value |
| --- | ----- |
| timestamp | 2021-07-08T06:29:27 |
| duration | 515.5018835067749 |
| emissions | 0.043562840982919106 |
| energy_consumed | 0.14638051405550773 |
| country_name | USA |
| region | Washington |
| cloud_provider | azure |
| cloud_region | westus2 |
## Hyperparameters
```yaml
fp16: True
per device batch size: 8
effective batch size: 64
epoch: 3.0
learning rate: 1e-4
weight decay: 0.1
seed: 1
```
*Same `per device batch size` for evaluations
### DeepSpeed
Optimizer = `AdamW`, Scheduler = `WarmupDecayLR`, Offload = `none`
```json
"zero_optimization": {
"stage": 2,
"allgather_partitions": true,
"allgather_bucket_size": 1300000000,
"overlap_comm": true,
"reduce_scatter": true,
"reduce_bucket_size": 1300000000,
"contiguous_gradients": true
}
```
## Usage
```python
from transformers import pipeline
summarizer = pipeline("summarization", model="henryu-lin/t5-large-samsum-deepspeed")
conversation = '''Kevin: Hey man, are you excited to watch Finding Nemo tonight?
Henry: Yea, I can't wait to watch that same movie for the 89th time. Is Nate coming over to watch it with us tonight?
Kevin: Yep, he said he'll be arriving a bit later at around 7 since he gets off of work at 6. Have you taken out the garbage yet? It's starting to make the kitchen really smell.
Henry: Oh I forgot. I'll do that once I'm finished with my assignment for my math class. I didn't get to start on it until an hour ago, and it's due in 30 minutes.
Kevin: Okay dude, you should take it out as soon as possible. By the way, Nate is bringing his girlfriend and their cat too.
Henry: Nice, I'm really looking forward to seeing them again.
'''
summarizer(conversation)
```
## Results
| ROUGE | Score |
| ----- | ----- |
| eval_rouge1 | 53.0823 |
| eval_rouge2 | 28.7097 |
| eval_rougeL | 43.939 |
| eval_rougeLsum | 49.067 |
| predict_rouge1 | 51.6716 |
| predict_rouge2 | 26.5372 |
| predict_rougeL | 42.9681 |
| predict_rougeLsum | 47.4084 |
| Metric | Value |
| ------ | ----- |
| eval_gen_len | 26.4071 |
| predict_gen_len | 25.9451 |
| train_loss | 1.3212629926497115 |
| eval_loss | 1.23828125 |
| predict_loss | 1.2333984375 |
| train_runtime | 515.2198 |
| train_samples | 14732 |
| train_samples_per_second | 85.781 |
| train_steps_per_second | 1.345 |
| eval_runtime | 61.275 |
| eval_samples | 818 |
| eval_samples_per_second | 13.35 |
| eval_steps_per_second | 0.212 |
| predict_runtime | 63.3732 |
| predict_samples | 819 |
| predict_samples_per_second | 12.923 |
| predict_steps_per_second | 0.205 |
| total_steps | 693 |
| total_flos | 7.20140924616704e+16 |
|
henryu-lin/t5-3b-samsum-deepspeed
|
henryu-lin
| 2021-07-08T06:45:41Z | 56 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"azureml",
"summarization",
"deepspeed",
"en",
"dataset:samsum",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-03-02T23:29:05Z |
---
language: en
tags:
- azureml
- t5
- summarization
- deepspeed
license: apache-2.0
datasets:
- samsum
model-index:
- name: t5-3b-samsum-deepspeed
results:
- task:
name: Abstractive Text Summarization
type: abstractive-text-summarization
dataset:
name: "SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization"
type: samsum
widget:
- text: |
Henry: Hey, is Nate coming over to watch the movie tonight?
Kevin: Yea, he said he'll be arriving a bit later at around 7 since he gets off of work at 6. Have you taken out the garbage yet? It's starting to make the kitchen really smell.
Henry: Oh I forgot. I'll do that once I'm finished with my assignment for my math class.
Kevin: Yea, you should take it out as soon as possible. And also, Nate is bringing his girlfriend too.
Henry: Nice, I'm really looking forward to seeing them again.
---
## `t5-3b-samsum-deepspeed`
This model was trained using Microsoft's `AzureML` and `DeepSpeed`'s ZeRO 2 optimization. It was fine-tuned on the `SAMSum` corpus from `t5-3b` checkpoint.
More information on the fine-tuning process (includes samples and benchmarks):
*(currently still WIP, updates coming soon: 7/6/21~7/9/21)*
## Resource Usage
These results are retrieved from AzureML Studio's resource monitoring module. All experiments were ran on AzureML's low priority clusters.
| key | value |
| --- | ----- |
| AzureML SKU | ND40rs_v2 (8 X V100 32GB) |
| Region | US West 2 |
| Run Duration | 43m 51.05s |
| Compute Cost (LowPriority/Dedicated) | $3.22/$16.10 (USD) |
| Average CPU Utilization | 46.0% |
| Average GPU Utilization | 56.9% |
| GPU Memory Usage (Avg/Peak) | 26.77/30.49 (GB) |
| Total GPU Energy Usage | 2448.69 (kJ) |
*Compute cost is calculated from run duration and SKU's price per hour. Updated SKU pricing could be found here: https://azure.microsoft.com/en-us/pricing/details/machine-learning/
*Peak memory usage is calculated from average peak across all utilized GPUs.
### Carbon Emissions
These results are obtained using `codecarbon`. The carbon emission is estimated from training runtime only (excluding setup and evaluation runtime).
CodeCarbon: https://github.com/mlco2/codecarbon
| key | value |
| --- | ----- |
| timestamp | 2021-07-06T21:57:39 |
| duration | 1841.4621863365173 |
| emissions | 0.17802492531467784 |
| energy_consumed | 0.5982020339874927 |
| country_name | USA |
| region | Washington |
| cloud_provider | azure |
| cloud_region | westus2 |
## Hyperparameters
```yaml
fp16: True
per device batch size: 2
effective batch size: 16
epoch: 3.0
learning rate: 3e-5
weight decay: 0.0
seed: 1
```
*Same `per device batch size` for evaluations
### DeepSpeed
Optimizer = `AdamW`, Scheduler = `WarmupDecayLR`, Offload = `none`
```json
"zero_optimization": {
"stage": 2,
"allgather_partitions": true,
"allgather_bucket_size": 1000000000,
"overlap_comm": true,
"reduce_scatter": true,
"reduce_bucket_size": 1000000000,
"contiguous_gradients": true
}
```
## Usage
```python
from transformers import pipeline
summarizer = pipeline("summarization", model="henryu-lin/t5-3b-samsum-deepspeed")
conversation = '''Henry: Hey, is Nate coming over to watch the movie tonight?
Kevin: Yea, he said he'll be arriving a bit later at around 7 since he gets off of work at 6. Have you taken out the garbage yet? It's starting to make the kitchen really smell.
Henry: Oh I forgot. I'll do that once I'm finished with my assignment for my math class.
Kevin: Yea, you should take it out as soon as possible. And also, Nate is bringing his girlfriend too.
Henry: Nice, I'm really looking forward to seeing them again.
'''
summarizer(conversation)
```
## Results
| ROUGE | Score |
| ----- | ----- |
| eval_rouge1 | 54.7875 |
| eval_rouge2 | 30.565 |
| eval_rougeL | 45.7625 |
| eval_rougeLsum | 50.3915 |
| predict_rouge1 | 53.6628 |
| predict_rouge2 | 29.0196 |
| predict_rougeL | 45.1257 |
| predict_rougeLsum | 49.171 |
| Metric | Value |
| ------ | ----- |
| eval_gen_len | 25.3399 |
| predict_gen_len | 24.9133 |
| train_loss | 1.1206104169494209 |
| eval_loss | 1.0732421875 |
| predict_loss | 1.087890625 |
| train_runtime | 1841.3751 |
| train_samples | 14732 |
| train_samples_per_second | 24.002 |
| train_steps_per_second | 1.501 |
| eval_runtime | 163.8357 |
| eval_samples | 818 |
| eval_samples_per_second | 4.993 |
| eval_steps_per_second | 0.317 |
| predict_runtime | 168.8245 |
| predict_samples | 819 |
| predict_samples_per_second | 4.851 |
| predict_steps_per_second | 0.308 |
| total_steps | 2763 |
| total_flos | 1.84452086400811e+17 |
|
liam168/c2-roberta-base-finetuned-dianping-chinese
|
liam168
| 2021-07-08T01:50:53Z | 174 | 23 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"zh",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language: zh
widget:
- text: "我喜欢下雨。"
- text: "我讨厌他。"
---
# liam168/c2-roberta-base-finetuned-dianping-chinese
## Model description
用中文对话情绪语料训练的模型，2分类：乐观和悲观。
## Overview
- **Language model**: BertForSequenceClassification
- **Model size**: 410M
- **Language**: Chinese
## Example
```python
>>> from transformers import AutoModelForSequenceClassification , AutoTokenizer, pipeline
>>> model_name = "liam168/c2-roberta-base-finetuned-dianping-chinese"
>>> class_num = 2
>>> ts_texts = ["我喜欢下雨。", "我讨厌他."]
>>> model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=class_num)
>>> tokenizer = AutoTokenizer.from_pretrained(model_name)
>>> classifier = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
>>> classifier(ts_texts[0])
>>> classifier(ts_texts[1])
[{'label': 'positive', 'score': 0.9973447918891907}]
[{'label': 'negative', 'score': 0.9972558617591858}]
```
|
tdopierre/ProtAugment-ParaphraseGenerator
|
tdopierre
| 2021-07-07T14:15:07Z | 4 | 5 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"Paraphase Generation",
"Data Augmentation",
"en",
"dataset:Quora",
"dataset:MSR",
"dataset:Google-PAWS",
"arxiv:2105.12995",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
language: "en"
tags:
- Paraphase Generation
- Data Augmentation
datasets:
- Quora
- MSR
- Google-PAWS
---
[](https://arxiv.org/abs/2105.12995)
This model is used to generate paraphrases. It has been trained on a mix of 3 different paraphrase detection datasets: MSR, Quora, Google-PAWS.
We use this model in our ACL'21 Paper ["PROTAUGMENT: Unsupervised diverse short-texts paraphrasing for intent detection meta-learning"](https://arxiv.org/abs/2105.12995)
Jointly used with generation constraints, this model allows to generate diverse paraphrases. We use those paraphrases as a data augmentation technique to further boosts a classification model's generalization capability. Feel free to play with the [code](https://github.com/tdopierre/ProtAugment)!
If you use this model, please consider citing our paper.
```
@article{Dopierre2021ProtAugmentUD,
title={ProtAugment: Unsupervised diverse short-texts paraphrasing for intent detection meta-learning},
author={Thomas Dopierre and C. Gravier and Wilfried Logerais},
journal={ArXiv},
year={2021},
volume={abs/2105.12995}
}
```
|
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