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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
harish/PT-mbert-train-from-test-and-dev-FalseTrue-0_0_BEST | c9b1ed94f2d181eec89f1286f191ea7e6426a363 | 2021-05-19T18:38:31.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | harish | null | harish/PT-mbert-train-from-test-and-dev-FalseTrue-0_0_BEST | 5 | null | transformers | 16,600 | Entry not found |
hf-internal-testing/tiny-random-ctrl | 42f6ee0f45b523f232f876349517964a1b1e9fd4 | 2021-09-17T19:26:29.000Z | [
"pytorch",
"tf",
"ctrl",
"transformers"
]
| null | false | hf-internal-testing | null | hf-internal-testing/tiny-random-ctrl | 5 | null | transformers | 16,601 | Entry not found |
hf-internal-testing/tiny-random-fsmt | 8ffcd27c62c60221b14212157ae174fde75c22ed | 2021-09-15T21:26:37.000Z | [
"pytorch",
"fsmt",
"transformers"
]
| null | false | hf-internal-testing | null | hf-internal-testing/tiny-random-fsmt | 5 | null | transformers | 16,602 | Entry not found |
hf-internal-testing/tiny-random-layoutlm | d5318a7f70ceaec327ae059b40448103743855cd | 2021-09-17T19:24:07.000Z | [
"pytorch",
"tf",
"layoutlm",
"transformers"
]
| null | false | hf-internal-testing | null | hf-internal-testing/tiny-random-layoutlm | 5 | null | transformers | 16,603 | Entry not found |
hf-internal-testing/tiny-random-marian | 328fbe295c8dbc6ae4ec2c3c297046ce2e4b36b4 | 2021-09-17T19:25:22.000Z | [
"pytorch",
"tf",
"marian",
"transformers"
]
| null | false | hf-internal-testing | null | hf-internal-testing/tiny-random-marian | 5 | null | transformers | 16,604 | Entry not found |
hf-internal-testing/tiny-random-xlm | 272d5cdf26464126f163bb2116f356ade1a45908 | 2021-09-17T19:23:00.000Z | [
"pytorch",
"tf",
"xlm",
"transformers"
]
| null | false | hf-internal-testing | null | hf-internal-testing/tiny-random-xlm | 5 | null | transformers | 16,605 | Entry not found |
hf-internal-testing/tiny-random-xlnet | 34c842ab0324d89ef2d77daad5645308145836b2 | 2021-09-17T19:26:00.000Z | [
"pytorch",
"tf",
"xlnet",
"transformers"
]
| null | false | hf-internal-testing | null | hf-internal-testing/tiny-random-xlnet | 5 | null | transformers | 16,606 | Entry not found |
hfl/chinese-legal-electra-base-discriminator | d935c3bade27f64e1ec696959a576df7e2e437bc | 2021-01-22T05:19:42.000Z | [
"pytorch",
"tf",
"electra",
"zh",
"arxiv:2004.13922",
"transformers",
"license:apache-2.0"
]
| null | false | hfl | null | hfl/chinese-legal-electra-base-discriminator | 5 | null | transformers | 16,607 | ---
language:
- zh
license: "apache-2.0"
---
# This model is specifically designed for legal domain.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: [https://github.com/google-research/electra](https://github.com/google-research/electra)
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
``` |
hiiamsid/est5-base | 36adca992fc9c5dca03e811cd35dce8dfe332cc4 | 2021-10-05T07:35:26.000Z | [
"pytorch",
"t5",
"text2text-generation",
"es",
"transformers",
"spanish",
"license:mit",
"autotrain_compatible"
]
| text2text-generation | false | hiiamsid | null | hiiamsid/est5-base | 5 | null | transformers | 16,608 | ---
language: ["es"]
tags:
- spanish
license: mit
---
This is a smaller version of the [google/mt5-base](https://huggingface.co/google/mt5-base) model with only Spanish embeddings left.
* The original model has 582M parameters, with 237M of them being input and output embeddings.
* After shrinking the `sentencepiece` vocabulary from 250K to 25K (top 25K Spanish tokens) the number of model parameters reduced to 237M parameters, and model size reduced from 2.2GB to 0.9GB - 42% of the original one.
## Citing & Authors
- Datasets : [cleaned corpora](https://github.com/crscardellino/sbwce)
- Model : [google/mt5-base](https://huggingface.co/google/mt5-base)
- Reference: [cointegrated/rut5-base](https://huggingface.co/cointegrated/rut5-base)
|
hoangbinhmta99/wav2vec-demo | b7ff0022e4601cd8c223491d1a9134e29556f405 | 2022-03-30T17:18:48.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
]
| automatic-speech-recognition | false | hoangbinhmta99 | null | hoangbinhmta99/wav2vec-demo | 5 | 1 | transformers | 16,609 | Convert from model .pt to transformer
Link: https://huggingface.co/tommy19970714/wav2vec2-base-960h
Bash:
```bash
pip install transformers[sentencepiece]
pip install fairseq -U
git clone https://github.com/huggingface/transformers.git
cp transformers/src/transformers/models/wav2vec2/convert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py .
wget https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_small.pt -O ./wav2vec_small.pt
mkdir dict
wget https://dl.fbaipublicfiles.com/fairseq/wav2vec/dict.ltr.txt
mkdir outputs
python convert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py
--pytorch_dump_folder_path ./outputs --checkpoint_path ./finetuned/wav2vec_small.pt
--dict_path ./dict/dict.ltr.txt --not_finetuned
```
# install and upload model
```
curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash
git lfs install
sudo apt-get install git-lfs
git lfs install
git clone https://huggingface.co/hoangbinhmta99/wav2vec-demo
ls
cd wav2vec-demo/
git status
git add .
git commit -m "First model version"
git config --global user.email [yourname]
git config --global user.name [yourpass]
git commit -m "First model version"
git push
```
|
hoanhkhoa/roberta-base-finetuned-ner | bab456a6d790f10c97fb02d3ff4c4b235ea956d8 | 2021-08-18T03:55:19.000Z | [
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"autotrain_compatible"
]
| token-classification | false | hoanhkhoa | null | hoanhkhoa/roberta-base-finetuned-ner | 5 | 1 | transformers | 16,610 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- null
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: roberta-base-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
metric:
name: Accuracy
type: accuracy
value: 0.9914674251177673
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-ner
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0381
- Precision: 0.9469
- Recall: 0.9530
- F1: 0.9500
- Accuracy: 0.9915
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1328 | 1.0 | 753 | 0.0492 | 0.9143 | 0.9308 | 0.9225 | 0.9884 |
| 0.0301 | 2.0 | 1506 | 0.0378 | 0.9421 | 0.9474 | 0.9448 | 0.9910 |
| 0.0185 | 3.0 | 2259 | 0.0381 | 0.9469 | 0.9530 | 0.9500 | 0.9915 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
hoonst/distilbert-base-uncased-finetuned-cola | 0a5a4c15e76200e8705829d83983e92143effbaa | 2021-11-01T06:49:11.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers"
]
| text-classification | false | hoonst | null | hoonst/distilbert-base-uncased-finetuned-cola | 5 | null | transformers | 16,611 | Entry not found |
howey/roberta-large-squad2 | d1ac0d13d67464b17627742593245c9e0b28d7b4 | 2021-06-15T04:50:41.000Z | [
"pytorch"
]
| null | false | howey | null | howey/roberta-large-squad2 | 5 | null | null | 16,612 | Entry not found |
huawei-noah/DynaBERT_MNLI | 3875b92138f435a9c1d0c604375682e3d4514e8f | 2021-05-19T20:02:03.000Z | [
"pytorch",
"jax",
"bert",
"arxiv:2004.04037",
"transformers"
]
| null | false | huawei-noah | null | huawei-noah/DynaBERT_MNLI | 5 | null | transformers | 16,613 | ## DynaBERT: Dynamic BERT with Adaptive Width and Depth
* DynaBERT can flexibly adjust the size and latency by selecting adaptive width and depth, and
the subnetworks of it have competitive performances as other similar-sized compressed models.
The training process of DynaBERT includes first training a width-adaptive BERT and then
allowing both adaptive width and depth using knowledge distillation.
* This code is modified based on the repository developed by Hugging Face: [Transformers v2.1.1](https://github.com/huggingface/transformers/tree/v2.1.1), and is released in [GitHub](https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/DynaBERT).
### Reference
Lu Hou, Zhiqi Huang, Lifeng Shang, Xin Jiang, Xiao Chen, Qun Liu.
[DynaBERT: Dynamic BERT with Adaptive Width and Depth](https://arxiv.org/abs/2004.04037).
```
@inproceedings{hou2020dynabert,
title = {DynaBERT: Dynamic BERT with Adaptive Width and Depth},
author = {Lu Hou, Zhiqi Huang, Lifeng Shang, Xin Jiang, Xiao Chen, Qun Liu},
booktitle = {Advances in Neural Information Processing Systems},
year = {2020}
}
```
|
huawei-noah/TernaryBERT_SST-2 | 5be28da9080d3b7ce81491c0fdf701fce3d29518 | 2020-10-16T03:16:54.000Z | [
"pytorch",
"transformers"
]
| null | false | huawei-noah | null | huawei-noah/TernaryBERT_SST-2 | 5 | null | transformers | 16,614 | Entry not found |
huggingartists/billie-eilish | 2ec1c0b95cbf7f6a907c882faaa8d9afbf2fabb9 | 2021-07-30T11:57:08.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"dataset:huggingartists/billie-eilish",
"transformers",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm"
]
| text-generation | false | huggingartists | null | huggingartists/billie-eilish | 5 | null | transformers | 16,615 | ---
language: en
datasets:
- huggingartists/billie-eilish
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/1aa6c04aad3652556046bb3aabe96498.900x900x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Billie Eilish</div>
<a href="https://genius.com/artists/billie-eilish">
<div style="text-align: center; font-size: 14px;">@billie-eilish</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from Billie Eilish.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/billie-eilish).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/billie-eilish")
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/billie-eilish")
model = AutoModelWithLMHead.from_pretrained("huggingartists/billie-eilish")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3l1r2mnu/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 Billie Eilish's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/209kskmi) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/209kskmi/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingartists/billie-eilish')
generator("I am", 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 Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
huggingartists/machine-gun-kelly | c6580ad4170e7892fb26541c24ada97ad2ec3aa8 | 2021-09-20T12:50:31.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"dataset:huggingartists/machine-gun-kelly",
"transformers",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm"
]
| text-generation | false | huggingartists | null | huggingartists/machine-gun-kelly | 5 | null | transformers | 16,616 | ---
language: en
datasets:
- huggingartists/machine-gun-kelly
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/bee1868cba78bf4b170886b3368c4ae8.640x640x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Machine Gun Kelly</div>
<a href="https://genius.com/artists/machine-gun-kelly">
<div style="text-align: center; font-size: 14px;">@machine-gun-kelly</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from Machine Gun Kelly.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/machine-gun-kelly).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/machine-gun-kelly")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/33f2ce6m/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 Machine Gun Kelly's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/2bbn6fvb) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/2bbn6fvb/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingartists/machine-gun-kelly')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/machine-gun-kelly")
model = AutoModelWithLMHead.from_pretrained("huggingartists/machine-gun-kelly")
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
huggingartists/placebo | a4e8f3eaf7df461217336a48100712f158cc0e6b | 2021-08-10T17:26:47.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"dataset:huggingartists/placebo",
"transformers",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm"
]
| text-generation | false | huggingartists | null | huggingartists/placebo | 5 | null | transformers | 16,617 | ---
language: en
datasets:
- huggingartists/placebo
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/c7e467de49cab7cdcc1d52c9c95ccd47.931x931x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Placebo</div>
<a href="https://genius.com/artists/placebo">
<div style="text-align: center; font-size: 14px;">@placebo</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from Placebo.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/placebo).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/placebo")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3jfcdfc1/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 Placebo's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/jx3r5x9o) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/jx3r5x9o/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingartists/placebo')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/placebo")
model = AutoModelWithLMHead.from_pretrained("huggingartists/placebo")
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
huggingartists/tom-waits | 253253ec20657da67acf63c332b1da4e198f5d8a | 2021-08-02T07:29:08.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"dataset:huggingartists/tom-waits",
"transformers",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm"
]
| text-generation | false | huggingartists | null | huggingartists/tom-waits | 5 | null | transformers | 16,618 | ---
language: en
datasets:
- huggingartists/tom-waits
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/505d2d5d1d43304dca446fd2e788a0f8.750x750x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Tom Waits</div>
<a href="https://genius.com/artists/tom-waits">
<div style="text-align: center; font-size: 14px;">@tom-waits</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from Tom Waits.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/tom-waits).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/tom-waits")
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/tom-waits")
model = AutoModelWithLMHead.from_pretrained("huggingartists/tom-waits")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/216zw2jw/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 Tom Waits's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/16iei9vt) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/16iei9vt/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingartists/tom-waits')
generator("I am", 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 Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
huggingartists/xxxtentacion | 00326fa2a8ffb27680f2b81e03ef8d9765d07258 | 2021-09-10T19:22:45.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"dataset:huggingartists/xxxtentacion",
"transformers",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm"
]
| text-generation | false | huggingartists | null | huggingartists/xxxtentacion | 5 | null | transformers | 16,619 | ---
language: en
datasets:
- huggingartists/xxxtentacion
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/f72572986d8187cf35f0fc9f9d06afb2.900x900x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">XXXTENTACION</div>
<a href="https://genius.com/artists/xxxtentacion">
<div style="text-align: center; font-size: 14px;">@xxxtentacion</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from XXXTENTACION.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/xxxtentacion).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/xxxtentacion")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/12xi0jh5/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 XXXTENTACION's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/2l2qvy4j) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/2l2qvy4j/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingartists/xxxtentacion')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/xxxtentacion")
model = AutoModelWithLMHead.from_pretrained("huggingartists/xxxtentacion")
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
huggingartists/zemfira | f17b1c6b2a283d29b6b316205e0571072c557c19 | 2021-09-22T09:43:44.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"dataset:huggingartists/zemfira",
"transformers",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm"
]
| text-generation | false | huggingartists | null | huggingartists/zemfira | 5 | null | transformers | 16,620 | ---
language: en
datasets:
- huggingartists/zemfira
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/df440220b2dd0a34a119db791da90e59.1000x1000x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Земфира (Zemfira)</div>
<a href="https://genius.com/artists/zemfira">
<div style="text-align: center; font-size: 14px;">@zemfira</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from Земфира (Zemfira).
Dataset is available [here](https://huggingface.co/datasets/huggingartists/zemfira).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/zemfira")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3hj4sma8/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 Земфира (Zemfira)'s lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1v74giz2) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1v74giz2/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingartists/zemfira')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/zemfira")
model = AutoModelWithLMHead.from_pretrained("huggingartists/zemfira")
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
huggingfans/bert-mini | 769c226eaf4a530041f6891ad4db3688ff637a31 | 2020-08-19T06:55:55.000Z | [
"pytorch",
"transformers"
]
| null | false | huggingfans | null | huggingfans/bert-mini | 5 | null | transformers | 16,621 | Entry not found |
huggingtweets/_me_you_coward | b54099429c82000be4f606e6cc1bcfc1173dd00c | 2021-05-21T17:09:40.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
]
| text-generation | false | huggingtweets | null | huggingtweets/_me_you_coward | 5 | null | transformers | 16,622 | ---
language: en
thumbnail: https://www.huggingtweets.com/_me_you_coward/1616717688936/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1146178675991334912/OC_eRIcT_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Phlogiston International 😇🌷🇵🇸 ☭🏴 🤖 AI Bot </div>
<div style="font-size: 15px">@_me_you_coward bot</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 [@_me_you_coward's tweets](https://twitter.com/_me_you_coward).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3214 |
| Retweets | 358 |
| Short tweets | 629 |
| Tweets kept | 2227 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/362p183s/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 @_me_you_coward's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/clfmnc4u) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/clfmnc4u/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/_me_you_coward')
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/ambivalegenic | 6b6a5e2980efb5975a9719ffde9fb685e8f88949 | 2021-05-21T18:35:50.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
]
| text-generation | false | huggingtweets | null | huggingtweets/ambivalegenic | 5 | null | transformers | 16,623 | ---
language: en
thumbnail: https://www.huggingtweets.com/ambivalegenic/1616659230833/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1364898993998680066/stqI7iN8_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">not the kind of princess that is princess-cis 🤖 AI Bot </div>
<div style="font-size: 15px">@ambivalegenic bot</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 [@ambivalegenic's tweets](https://twitter.com/ambivalegenic).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 2614 |
| Retweets | 664 |
| Short tweets | 228 |
| Tweets kept | 1722 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1mvt2owy/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 @ambivalegenic's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/25yttpuo) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/25yttpuo/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/ambivalegenic')
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/d_greetest | 411274742456461457de61d57af0fa8e3365098b | 2021-12-15T02:04:34.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
]
| text-generation | false | huggingtweets | null | huggingtweets/d_greetest | 5 | null | transformers | 16,624 | ---
language: en
thumbnail: http://www.huggingtweets.com/d_greetest/1639533869820/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/1387092178753687567/43vkVfBK_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">Greetest</div>
<div style="text-align: center; font-size: 14px;">@d_greetest</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 Greetest.
| Data | Greetest |
| --- | --- |
| Tweets downloaded | 629 |
| Retweets | 265 |
| Short tweets | 34 |
| Tweets kept | 330 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3kz7im60/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 @d_greetest's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1h67ju9y) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1h67ju9y/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/d_greetest')
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/drilbot_neo-rusticgendarme | 93a0630cde1c3335277541768b1bfdfb79f42d49 | 2021-07-28T19:24:06.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
]
| text-generation | false | huggingtweets | null | huggingtweets/drilbot_neo-rusticgendarme | 5 | null | transformers | 16,625 | ---
language: en
thumbnail: https://www.huggingtweets.com/drilbot_neo-rusticgendarme/1627500242288/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/1405236436144508932/5bN_yThT_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/1374924360780242944/-Q8NfgEr_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>
<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">merzy & wintbot_neo</div>
<div style="text-align: center; font-size: 14px;">@drilbot_neo-rusticgendarme</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 merzy & wintbot_neo.
| Data | merzy | wintbot_neo |
| --- | --- | --- |
| Tweets downloaded | 2598 | 3244 |
| Retweets | 449 | 218 |
| Short tweets | 440 | 271 |
| Tweets kept | 1709 | 2755 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/33n6vv8i/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 @drilbot_neo-rusticgendarme's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3ti3qa9s) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3ti3qa9s/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/drilbot_neo-rusticgendarme')
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/fifteenai | fd6fc2395912db834527d6162196473849e78e09 | 2022-07-23T04:16:18.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
]
| text-generation | false | huggingtweets | null | huggingtweets/fifteenai | 5 | null | transformers | 16,626 | ---
language: en
thumbnail: http://www.huggingtweets.com/fifteenai/1658549683215/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/1513191641921765388/rToX3RpX_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">15</div>
<div style="text-align: center; font-size: 14px;">@fifteenai</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 15.
| Data | 15 |
| --- | --- |
| Tweets downloaded | 111 |
| Retweets | 9 |
| Short tweets | 10 |
| Tweets kept | 92 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/169wgrhk/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 @fifteenai's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/390dyi5s) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/390dyi5s/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/fifteenai')
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/hirox246 | b27e39fd85bef9b3fd76a37b9146a3b07a6c522b | 2022-03-28T13:12:56.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
]
| text-generation | false | huggingtweets | null | huggingtweets/hirox246 | 5 | null | transformers | 16,627 | ---
language: en
thumbnail: http://www.huggingtweets.com/hirox246/1648473171015/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/646595746905620480/oeKI14gB_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">ひろゆき, Hiroyuki Nishimura</div>
<div style="text-align: center; font-size: 14px;">@hirox246</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 ひろゆき, Hiroyuki Nishimura.
| Data | ひろゆき, Hiroyuki Nishimura |
| --- | --- |
| Tweets downloaded | 3246 |
| Retweets | 288 |
| Short tweets | 2002 |
| Tweets kept | 956 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1fs862rv/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 @hirox246's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ktc28kc0) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ktc28kc0/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/hirox246')
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/islamphobiacow-praisegodbarbon | 6965ff26b8b6a2a5f2006b6e8035081d5bb16d1f | 2021-07-23T16:06:26.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
]
| text-generation | false | huggingtweets | null | huggingtweets/islamphobiacow-praisegodbarbon | 5 | null | transformers | 16,628 | ---
language: en
thumbnail: https://www.huggingtweets.com/islamphobiacow-praisegodbarbon/1627056382131/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/1381764452098437120/74IgKP07_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/1368077075127603200/Z08slO2P_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>
<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">Boston Psychology PhD & keyvan</div>
<div style="text-align: center; font-size: 14px;">@islamphobiacow-praisegodbarbon</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 Boston Psychology PhD & keyvan.
| Data | Boston Psychology PhD | keyvan |
| --- | --- | --- |
| Tweets downloaded | 3224 | 3242 |
| Retweets | 858 | 179 |
| Short tweets | 251 | 223 |
| Tweets kept | 2115 | 2840 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3egvdux4/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 @islamphobiacow-praisegodbarbon's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/34hmjrwi) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/34hmjrwi/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/islamphobiacow-praisegodbarbon')
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/kanyewest | 2ae5ab8942439189a85dcc19cb6a493371a9d2d1 | 2021-10-20T04:02:20.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
]
| text-generation | false | huggingtweets | null | huggingtweets/kanyewest | 5 | null | transformers | 16,629 | ---
language: en
thumbnail: https://www.huggingtweets.com/kanyewest/1634702536209/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/1276461929934942210/cqNhNk6v_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">ye</div>
<div style="text-align: center; font-size: 14px;">@kanyewest</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 ye.
| Data | ye |
| --- | --- |
| Tweets downloaded | 1856 |
| Retweets | 186 |
| Short tweets | 573 |
| Tweets kept | 1097 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/bvnrjbxn/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 @kanyewest's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2nxhg2su) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2nxhg2su/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/kanyewest')
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/nntaleb | d9b023127429a477e4561ff95c9cc1619e3b4cf8 | 2022-06-16T09:52:06.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
]
| text-generation | false | huggingtweets | null | huggingtweets/nntaleb | 5 | null | transformers | 16,630 | ---
language: en
thumbnail: http://www.huggingtweets.com/nntaleb/1655373122161/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/1200820286964346880/Zz6YSFus_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">Nassim Nicholas Taleb</div>
<div style="text-align: center; font-size: 14px;">@nntaleb</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 Nassim Nicholas Taleb.
| Data | Nassim Nicholas Taleb |
| --- | --- |
| Tweets downloaded | 3247 |
| Retweets | 138 |
| Short tweets | 364 |
| Tweets kept | 2745 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3p17kwd2/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 @nntaleb's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/278506vz) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/278506vz/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/nntaleb')
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/postgohst | 08d3d001cc8a06bd5c4c3b8bd70a19d2f4f3d294 | 2021-09-24T22:10:56.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
]
| text-generation | false | huggingtweets | null | huggingtweets/postgohst | 5 | null | transformers | 16,631 | ---
language: en
thumbnail: https://www.huggingtweets.com/postgohst/1632521452929/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/1151812292889047040/BHktVZLN_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">Connoise: FALSE GOD SYSTEMS @ONLINE🖤</div>
<div style="text-align: center; font-size: 14px;">@postgohst</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 Connoise: FALSE GOD SYSTEMS @ONLINE🖤.
| Data | Connoise: FALSE GOD SYSTEMS @ONLINE🖤 |
| --- | --- |
| Tweets downloaded | 3191 |
| Retweets | 319 |
| Short tweets | 387 |
| Tweets kept | 2485 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1shgunpl/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 @postgohst's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3dybkr8z) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3dybkr8z/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/postgohst')
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/stephenking | 0aafb74048e7764da1bd8b2fe99f56b846428ee7 | 2022-07-27T06:45:12.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
]
| text-generation | false | huggingtweets | null | huggingtweets/stephenking | 5 | null | transformers | 16,632 | ---
language: en
thumbnail: http://www.huggingtweets.com/stephenking/1658904308336/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/378800000836981162/b683f7509ec792c3e481ead332940cdc_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">Stephen King</div>
<div style="text-align: center; font-size: 14px;">@stephenking</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 Stephen King.
| Data | Stephen King |
| --- | --- |
| Tweets downloaded | 3230 |
| Retweets | 770 |
| Short tweets | 205 |
| Tweets kept | 2255 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3c83ql6r/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 @stephenking's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/llolipvn) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/llolipvn/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/stephenking')
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/uberfacts | 7e425e777676f0a8ffa65e9cead08c3bd8a67048 | 2021-08-13T06:58:05.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
]
| text-generation | false | huggingtweets | null | huggingtweets/uberfacts | 5 | null | transformers | 16,633 | ---
language: en
thumbnail: https://www.huggingtweets.com/uberfacts/1628837881576/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/1340165521992372226/zJ0Zo4rD_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">UberFacts</div>
<div style="text-align: center; font-size: 14px;">@uberfacts</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 UberFacts.
| Data | UberFacts |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 1356 |
| Short tweets | 148 |
| Tweets kept | 1746 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2wg9sgv5/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 @uberfacts's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1bl0xgi3) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1bl0xgi3/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/uberfacts')
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)
|
husnu/xtremedistil-l6-h256-uncased-finetuned_lr-2e-05_epochs-3 | fb64003fc6fc93e4e1de208352d0fc8f773ce83d | 2022-01-14T00:17:31.000Z | [
"pytorch",
"tensorboard",
"bert",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
]
| question-answering | false | husnu | null | husnu/xtremedistil-l6-h256-uncased-finetuned_lr-2e-05_epochs-3 | 5 | null | transformers | 16,634 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: xtremedistil-l6-h256-uncased-finetuned_lr-2e-05_epochs-3
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. -->
# xtremedistil-l6-h256-uncased-finetuned_lr-2e-05_epochs-3
This model is a fine-tuned version of [microsoft/xtremedistil-l6-h256-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2864
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.6088 | 1.0 | 5533 | 1.4429 |
| 1.3928 | 2.0 | 11066 | 1.3183 |
| 1.3059 | 3.0 | 16599 | 1.2864 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
hyunwoongko/ctrlsum-bigpatent | 29c6b331fb305ad833bcecf7d866eaebc139565d | 2021-03-21T15:56:21.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | hyunwoongko | null | hyunwoongko/ctrlsum-bigpatent | 5 | null | transformers | 16,635 | Entry not found |
hyunwoongko/reddit-9B | 5add95bbf47daec949c2c3b3e4d417df14104a5e | 2021-06-22T16:09:14.000Z | [
"pytorch",
"blenderbot",
"text2text-generation",
"en",
"dataset:blended_skill_talk",
"arxiv:1907.06616",
"transformers",
"convAI",
"conversational",
"facebook",
"license:apache-2.0",
"autotrain_compatible"
]
| conversational | false | hyunwoongko | null | hyunwoongko/reddit-9B | 5 | null | transformers | 16,636 | ---
language:
- en
thumbnail:
tags:
- convAI
- conversational
- facebook
license: apache-2.0
datasets:
- blended_skill_talk
metrics:
- perplexity
---
## Model description
+ Paper: [Recipes for building an open-domain chatbot](https://arxiv.org/abs/1907.06616)
+ [Original PARLAI Code](https://parl.ai/projects/recipes/)
### Abstract
Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.
|
imjeffhi/syllables_generator | 67818d21695745e343766957ae3c06e19d4d4bf3 | 2021-12-18T19:44:23.000Z | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers"
]
| text-generation | false | imjeffhi | null | imjeffhi/syllables_generator | 5 | null | transformers | 16,637 | # Syllabizer Card
A fine-tuned version of GPT-neo (small, 125M parameters) on a syllables dataset collected via web scraping.
The model has two additional special tokens:
1. \<SPELLED\>: Is followed by the word you wish to syllabize spelled out (used to work around some aspects of tokenization).
2. \<SYLLABLES\>: Is where the model outputs syllables in a spaced format
A sample code can be seen here:
```python
word = "syllabizer"
characters = " ".join(word)
input_string = f"{word} <SPELLED> {characters} <SYLLABLES>"
```
Output:
```python
syllabizer <SPELLED> s y l l a b i z e r <SYLLABLES> syl lab iz er <|endoftext|>
``` |
imvladikon/charbert-roberta-wiki | f0c90bcf7b3fc535fd9c63ed86b8fd6054076dc8 | 2022-01-30T11:37:26.000Z | [
"pytorch",
"en",
"dataset:wikipedia",
"arxiv:2011.01513",
"transformers",
"language model"
]
| null | false | imvladikon | null | imvladikon/charbert-roberta-wiki | 5 | null | transformers | 16,638 | ---
language:
- en
tags:
- language model
datasets:
- wikipedia
---
pre-trained model from [CharBERT: Character-aware Pre-trained Language Model](https://github.com/wtma/CharBERT)
```
@misc{ma2020charbert,
title={CharBERT: Character-aware Pre-trained Language Model},
author={Wentao Ma and Yiming Cui and Chenglei Si and Ting Liu and Shijin Wang and Guoping Hu},
year={2020},
eprint={2011.01513},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
indobenchmark/indobert-lite-large-p1 | 94a271102d510db9d1ba8b603e5dc1f3227d2929 | 2020-12-11T21:45:56.000Z | [
"pytorch",
"tf",
"albert",
"feature-extraction",
"id",
"dataset:Indo4B",
"arxiv:2009.05387",
"transformers",
"indobert",
"indobenchmark",
"indonlu",
"license:mit"
]
| feature-extraction | false | indobenchmark | null | indobenchmark/indobert-lite-large-p1 | 5 | null | transformers | 16,639 | ---
language: id
tags:
- indobert
- indobenchmark
- indonlu
license: mit
inference: false
datasets:
- Indo4B
---
# IndoBERT-Lite Large Model (phase1 - uncased)
[IndoBERT](https://arxiv.org/abs/2009.05387) is a state-of-the-art language model for Indonesian based on the BERT model. The pretrained model is trained using a masked language modeling (MLM) objective and next sentence prediction (NSP) objective.
## All Pre-trained Models
| Model | #params | Arch. | Training data |
|--------------------------------|--------------------------------|-------|-----------------------------------|
| `indobenchmark/indobert-base-p1` | 124.5M | Base | Indo4B (23.43 GB of text) |
| `indobenchmark/indobert-base-p2` | 124.5M | Base | Indo4B (23.43 GB of text) |
| `indobenchmark/indobert-large-p1` | 335.2M | Large | Indo4B (23.43 GB of text) |
| `indobenchmark/indobert-large-p2` | 335.2M | Large | Indo4B (23.43 GB of text) |
| `indobenchmark/indobert-lite-base-p1` | 11.7M | Base | Indo4B (23.43 GB of text) |
| `indobenchmark/indobert-lite-base-p2` | 11.7M | Base | Indo4B (23.43 GB of text) |
| `indobenchmark/indobert-lite-large-p1` | 17.7M | Large | Indo4B (23.43 GB of text) |
| `indobenchmark/indobert-lite-large-p2` | 17.7M | Large | Indo4B (23.43 GB of text) |
## How to use
### Load model and tokenizer
```python
from transformers import BertTokenizer, AutoModel
tokenizer = BertTokenizer.from_pretrained("indobenchmark/indobert-lite-large-p1")
model = AutoModel.from_pretrained("indobenchmark/indobert-lite-large-p1")
```
### Extract contextual representation
```python
x = torch.LongTensor(tokenizer.encode('aku adalah anak [MASK]')).view(1,-1)
print(x, model(x)[0].sum())
```
## Authors
<b>IndoBERT</b> was trained and evaluated by Bryan Wilie\*, Karissa Vincentio\*, Genta Indra Winata\*, Samuel Cahyawijaya\*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti.
## Citation
If you use our work, please cite:
```bibtex
@inproceedings{wilie2020indonlu,
title={IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding},
author={Bryan Wilie and Karissa Vincentio and Genta Indra Winata and Samuel Cahyawijaya and X. Li and Zhi Yuan Lim and S. Soleman and R. Mahendra and Pascale Fung and Syafri Bahar and A. Purwarianti},
booktitle={Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing},
year={2020}
}
```
|
infinitejoy/wav2vec2-large-xls-r-300m-finnish | b7fd96c49c0eff74f2af2fdd5b59024b381168f3 | 2022-03-23T18:34:46.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fi",
"dataset:mozilla-foundation/common_voice_7_0",
"transformers",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"license:apache-2.0",
"model-index"
]
| automatic-speech-recognition | false | infinitejoy | null | infinitejoy/wav2vec2-large-xls-r-300m-finnish | 5 | null | transformers | 16,640 | ---
language:
- fi
license: apache-2.0
tags:
- automatic-speech-recognition
- fi
- generated_from_trainer
- hf-asr-leaderboard
- model_for_talk
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M - Finnish
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: fi
metrics:
- name: Test WER
type: wer
value: 29.97
- name: Test CER
type: cer
value: NA
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-finnish
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - FI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2307
- Wer: 0.2984
## 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: 7e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 70.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.9032 | 4.39 | 500 | 2.8768 | 1.0 |
| 1.5724 | 8.77 | 1000 | 0.5638 | 0.6438 |
| 1.1818 | 13.16 | 1500 | 0.3338 | 0.4759 |
| 1.0798 | 17.54 | 2000 | 0.2876 | 0.4086 |
| 1.0296 | 21.93 | 2500 | 0.2694 | 0.4248 |
| 1.0014 | 26.32 | 3000 | 0.2626 | 0.3733 |
| 0.9616 | 30.7 | 3500 | 0.2391 | 0.3294 |
| 0.9303 | 35.09 | 4000 | 0.2352 | 0.3218 |
| 0.9248 | 39.47 | 4500 | 0.2351 | 0.3207 |
| 0.8837 | 43.86 | 5000 | 0.2341 | 0.3103 |
| 0.8887 | 48.25 | 5500 | 0.2311 | 0.3115 |
| 0.8529 | 52.63 | 6000 | 0.2230 | 0.3001 |
| 0.8404 | 57.02 | 6500 | 0.2279 | 0.3054 |
| 0.8242 | 61.4 | 7000 | 0.2298 | 0.3006 |
| 0.8288 | 65.79 | 7500 | 0.2333 | 0.2997 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
infinitejoy/wav2vec2-large-xls-r-300m-romanian | 2dbe318e8470c6756dbd67f0bc2fa08737898df5 | 2022-03-23T18:33:55.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ro",
"dataset:mozilla-foundation/common_voice_7_0",
"transformers",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"license:apache-2.0",
"model-index"
]
| automatic-speech-recognition | false | infinitejoy | null | infinitejoy/wav2vec2-large-xls-r-300m-romanian | 5 | null | transformers | 16,641 | ---
language:
- ro
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- model_for_talk
- mozilla-foundation/common_voice_7_0
- ro
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M - Romanian
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: ro
metrics:
- name: Test WER
type: wer
value: 14.194
- name: Test CER
type: cer
value: 3.288
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: ro
metrics:
- name: Test WER
type: wer
value: 40.869
- name: Test CER
type: cer
value: 12.049
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: ro
metrics:
- name: Test WER
type: wer
value: 47.2
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-romanian
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - RO dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1167
- Wer: 0.1421
## 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: 7e-05
- train_batch_size: 32
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.1973 | 8.89 | 2000 | 0.4481 | 0.4849 |
| 0.6005 | 17.78 | 4000 | 0.1420 | 0.1777 |
| 0.5248 | 26.67 | 6000 | 0.1303 | 0.1651 |
| 0.4871 | 35.56 | 8000 | 0.1207 | 0.1523 |
| 0.4428 | 44.44 | 10000 | 0.1143 | 0.1425 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
it5/it5-large-repubblica-to-ilgiornale | a49eb55635437bb88b779379524ee0a7bc80079d | 2022-03-09T08:01:50.000Z | [
"pytorch",
"tf",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"it",
"dataset:gsarti/change_it",
"arxiv:2203.03759",
"transformers",
"italian",
"sequence-to-sequence",
"newspaper",
"ilgiornale",
"repubblica",
"style-transfer",
"license:apache-2.0",
"model-index",
"co2_eq_emissions",
"autotrain_compatible"
]
| text2text-generation | false | it5 | null | it5/it5-large-repubblica-to-ilgiornale | 5 | null | transformers | 16,642 | ---
language:
- it
license: apache-2.0
datasets:
- gsarti/change_it
tags:
- italian
- sequence-to-sequence
- newspaper
- ilgiornale
- repubblica
- style-transfer
widget:
- text: "WASHINGTON - La Corea del Nord torna dopo nove anni nella blacklist Usa degli Stati considerati sponsor del terrorismo. Come Iran, Siria e Sudan. Lo ha deciso Donald Trump , che ha preferito dare l'annuncio non durante il suo recente viaggio in Asia ma ieri, in una riunione del governo alla Casa Bianca. 'Oggi gli Stati Uniti designeranno la Corea del nord come uno stato sponsor del terrorismo', ha tuonato il tycoon, anticipando che sarà formalizzata oggi dal dipartimento di stato e sarà accompagnata da nuove e più severe sanzioni. 'Il livello più alto' mai imposto a Pyongyang, ha promesso. 'Avrebbe dovuto succedere molto tempo fa', ha aggiunto, scaricando per l'ennesima volta la responsabilità dell'attuale crisi sull'amministrazione Obama. Poi si è scagliato contro un 'regime assassino' che 'deve mettere fine allo sviluppo del suo programma illegale nucleare e balistico'. Per giustificare la svolta, Trump ha accusato Pyongyang non solo di 'minacciare il mondo con una devastazione nucleare' ma anche di aver 'ripetutamente sostenuto atti di terrorismo internazionale', compreso omicidi in suolo straniero. Il riferimento è all' uccisione all'aeroporto della capitale malese di Kim Jong Nam , il fratellastro del leader nordcoreano Kim Jong Un , ma non ci sono altri episodi noti. Tanto che alcuni esperti, come pure dirigenti Usa coperti dall'anonimato, dubitano che Pyongyang risponda ai criteri per una tale designazione. La mossa appare altamente simbolica, dato che la Corea del Nord è già pesantemente sanzionata a livello internazionale. Per il segretario di stato Rex Tillerson è solo l'ultima di una serie di passi per rafforzare la pressione su Pyongyang e costringerla a sedersi ad un tavolo perché gli Usa hanno sempre 'speranza nella diplomazia'. Ma nello stesso tempo è un monito per 'fermare e dissuadere' altri Paesi dal sostenere la Corea del Nord, finita nella blacklist 'anche per l'uso di armi chimiche'. Ma la mossa potrebbe anche essere controproducente, provocando una risposta di Kim o minando gli sforzi per sollecitare Pechino ad una maggiore pressione su Pyongyang. In ogni caso non aiuta il dialogo diretto tra Usa e Corea del Nord, che sembrava essere stato avviato in modo riservato. Come non aiutano gli scambi di insulti fra Trump e Kim. Nord Corea, Trump: 'Cerco di essere amico di Kim, sarebbe una bella cosa per il mondo'. Pyongyang era stata messa nella lista Usa degli Stati sponsor del terrorismo per aver fatto esplodere nel 1987 un volo della Korean Air uccidendo tutti i 115 passeggeri a bordo. Ma l'amministrazione di George W. Bush l'aveva rimossa sperando di far avanzare i negoziati sulla denuclearizzazione della penisola coreana. Il governo giapponese sostiene la decisione degli Stati Uniti di inserire la Corea del Nord nella lista degli stati che sponsorizzano il terrorismo, pur riconoscendo che l'annuncio potrebbe provocare una reazione immediata del regime di Pyongyang. Il premier Shinzo Abe ha accolto con consenso il comunicato Usa e ha detto alla stampa che servirà a incrementare la pressione sulla Corea del Nord. Il ministro della Difesa Itsunori Onodera , pur valutando positivamente la notifica, ha spiegato che si attendono azioni provocatorie dallo stato eremita, ribadendo che è vitale rimanere vigili. Secondo la stampa nipponica Abe aveva richiesto al dipartimento di Stato Usa di mettere la Corea del Nord sulla lista durante l'incontro col presidente Usa Donald Trump a Tokyo a inizio mese. L'ultimo lancio di missile balistico condotto da Pyongyang nell'oceano Pacifico, sorvolando il mare del Giappone, risale allo scorso settembre."
- text: "ROMA - Una nuova droga killer è stata sequestrata per la prima volta in Europa dagli investigatori del Nas. Si tratta di una nuova \"miscela psicoattiva altamente tossica\" per la prima volta individuata da forze di polizia, simile all'eroina sintetica, ma molto più economica e letale. Tanto che i 20 grammi scoperti sarebbero stati sufficienti per fabbricare ben 20.000 dosi e lo stesso contatto attraverso la pelle può provocare intossicazione. Individuata per la prima volta, la nuova droga presenta una struttura simile al farmaco sedativo Fentanyl ma con effetti molto più devastanti per l'organismo. Proveniva dell'estero ed era contenuta in un plico postale indirizzato in una città del centro Italia: è stata intercettata tramite accertamenti sul web grazie a un'operazione di intelligence che ha visto come protagonisti i militari della Sezione operativa centrale del Comando carabinieri per la Tutela della salute (Nas). Economica e letale, secondo gli investigatori \"in confronto l'eroina è quasi 'acqua fresca', anzi, proprio per la sua economicità, in alcuni casi viene venduta dai pusher a giovani conviti di comprare eroina\". La diffusione di nuove droghe sintetiche che continuamente appaiono sui mercati necessita di un'attività investigativa costante e complessa. Si tratta infatti di sostanze dalla struttura molecolare molto simile a quella del Fentanyl ma ogni volta leggermente diversa. Di qui la difficoltà di individuarle e l'importanza del nuovo sequestro. \"La chiamano impropriamente 'eroina sintetica' - spiega il comandante dei Nas, generale Adelmo Lusi - per il tipo di effetto psicotropo simile, ma dal punto di vista della tossicità è molto peggio: con 25 milligrammi di eroina ci si sballa, con 25mg di simil-fentanyl, come quello appena sequestrato, si muore\". Le indagini sono partite da ricoveri per overdose in ospedale, in cui arrivavano ragazzi che non rispondevano al trattamento disintossicante per l'eroina. La nuova sostanza verrà ora segnalata per l'inserimento tra le tabelle ministeriali degli stupefacenti prevista dal Dpr 309/1990."
- text: "Fragile come il burro. Il nostro territorio è precario. Ne sanno qualcosa i comuni che sono stati investititi dal maltempo . Il dissesto idrogeologico imperversa su tutto il territorio. Infatti, oltre 6.600 comuni , pari all’82% del totale, sono in aree ad elevato rischio idrogeologico, pari al 10% della sua superficie. La popolazione potenzialmente esposta è stimata in 5,8 milioni di persone. I dati emergono dalle recenti analisi fatte da Legambiente e Protezione civile, che mettono in evidenza come in 10 anni in Italia sia raddoppiata l’area dei territori colpiti da alluvioni e frane , passando da una media di quattro regioni all’anno a otto regioni. Nella classifica delle regioni a maggior rischio idrogeologico prima è la Calabria con il 100% dei comuni esposti; al 100% ci sono anche la provincia di Trento, il Molise, la Basilicata, l’Umbria, la Valle d’Aosta. Poi Marche, Liguria al 99%; Lazio, Toscana al 98%; Abruzzo (96%), Emilia-Romagna (95%), Campania e Friuli Venezia Giulia al 92%, Piemonte (87%), Sardegna (81%), Puglia (78%), Sicilia (71%), Lombardia (60%), provincia di Bolzano (59%), Veneto (56%). Tra le cause che condizionano ed amplificano il rischio idrogeologico c’è l’azione dell’uomo (abbandono e degrado, cementificazione, consumo di suolo, abusivismo, disboscamento e incendi). Ma anche e soprattutto la mancanza di una seria manutenzione ordinaria e non ad una organica politica di prevenzione."
- text: "Arriva dal Partito nazionalista basco (Pnv) la conferma che i cinque deputati che siedono in parlamento voteranno la sfiducia al governo guidato da Mariano Rajoy. Pochi voti, ma significativi quelli della formazione politica di Aitor Esteban, che interverrà nel pomeriggio. Pur con dimensioni molto ridotte, il partito basco si è trovato a fare da ago della bilancia in aula. E il sostegno alla mozione presentata dai Socialisti potrebbe significare per il primo ministro non trovare quei 176 voti che gli servono per continuare a governare. \" Perché dovrei dimettermi io che per il momento ho la fiducia della Camera e quella che mi è stato data alle urne \", ha detto oggi Rajoy nel suo intervento in aula, mentre procedeva la discussione sulla mozione di sfiducia. Il voto dei baschi ora cambia le carte in tavola e fa crescere ulteriormente la pressione sul premier perché rassegni le sue dimissioni. La sfiducia al premier, o un'eventuale scelta di dimettersi, porterebbe alle estreme conseguenze lo scandalo per corruzione che ha investito il Partito popolare. Ma per ora sembra pensare a tutt'altro. \"Non ha intenzione di dimettersi - ha detto il segretario generale del Partito popolare , María Dolores de Cospedal - Non gioverebbe all'interesse generale o agli interessi del Pp\"."
metrics:
- rouge
- bertscore
- headline-headline-consistency-classifier
- headline-article-consistency-classifier
model-index:
- name: it5-large-repubblica-to-ilgiornale
results:
- task:
type: headline-style-transfer-repubblica-to-ilgiornale
name: "Headline style transfer (Repubblica to Il Giornale)"
dataset:
type: gsarti/change_it
name: "CHANGE-IT"
metrics:
- type: rouge1
value: 0.270
name: "Test Rouge1"
- type: rouge2
value: 0.089
name: "Test Rouge2"
- type: rougeL
value: 0.237
name: "Test RougeL"
- type: bertscore
value: 0.400
name: "Test BERTScore"
args:
- model_type: "dbmdz/bert-base-italian-xxl-uncased"
- lang: "it"
- num_layers: 10
- rescale_with_baseline: True
- baseline_path: "bertscore_baseline_ita.tsv"
- type: headline-headline-consistency-classifier
value: 0.883
name: "Test Headline-Headline Consistency Accuracy"
- type: headline-article-consistency-classifier
value: 0.880
name: "Test Headline-Article Consistency Accuracy"
co2_eq_emissions:
emissions: "51g"
source: "Google Cloud Platform Carbon Footprint"
training_type: "fine-tuning"
geographical_location: "Eemshaven, Netherlands, Europe"
hardware_used: "1 TPU v3-8 VM"
thumbnail: https://gsarti.com/publication/it5/featured.png
---
# IT5 Large for News Headline Style Transfer (Repubblica to Il Giornale) 🗞️➡️🗞️ 🇮🇹
This repository contains the checkpoint for the [IT5 Large](https://huggingface.co/gsarti/it5-large) model fine-tuned on news headline style transfer in the Repubblica to Il Giornale direction on the Italian CHANGE-IT dataset as part of the experiments of the paper [IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation](https://arxiv.org/abs/2203.03759) by [Gabriele Sarti](https://gsarti.com) and [Malvina Nissim](https://malvinanissim.github.io).
A comprehensive overview of other released materials is provided in the [gsarti/it5](https://github.com/gsarti/it5) repository. Refer to the paper for additional details concerning the reported scores and the evaluation approach.
## Using the model
The model is trained to generate an headline in the style of Il Giornale from the full body of an article written in the style of Repubblica. Model checkpoints are available for usage in Tensorflow, Pytorch and JAX. They can be used directly with pipelines as:
```python
from transformers import pipelines
r2g = pipeline("text2text-generation", model='it5/it5-large-repubblica-to-ilgiornale')
r2g("Arriva dal Partito nazionalista basco (Pnv) la conferma che i cinque deputati che siedono in parlamento voteranno la sfiducia al governo guidato da Mariano Rajoy. Pochi voti, ma significativi quelli della formazione politica di Aitor Esteban, che interverrà nel pomeriggio. Pur con dimensioni molto ridotte, il partito basco si è trovato a fare da ago della bilancia in aula. E il sostegno alla mozione presentata dai Socialisti potrebbe significare per il primo ministro non trovare quei 176 voti che gli servono per continuare a governare. \" Perché dovrei dimettermi io che per il momento ho la fiducia della Camera e quella che mi è stato data alle urne \", ha detto oggi Rajoy nel suo intervento in aula, mentre procedeva la discussione sulla mozione di sfiducia. Il voto dei baschi ora cambia le carte in tavola e fa crescere ulteriormente la pressione sul premier perché rassegni le sue dimissioni. La sfiducia al premier, o un'eventuale scelta di dimettersi, porterebbe alle estreme conseguenze lo scandalo per corruzione che ha investito il Partito popolare. Ma per ora sembra pensare a tutt'altro. \"Non ha intenzione di dimettersi - ha detto il segretario generale del Partito popolare , María Dolores de Cospedal - Non gioverebbe all'interesse generale o agli interessi del Pp\".")
>>> [{"generated_text": "il nazionalista rajoy: 'voteremo la sfiducia'"}]
```
or loaded using autoclasses:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("it5/it5-large-repubblica-to-ilgiornale")
model = AutoModelForSeq2SeqLM.from_pretrained("it5/it5-large-repubblica-to-ilgiornale")
```
If you use this model in your research, please cite our work as:
```bibtex
@article{sarti-nissim-2022-it5,
title={{IT5}: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation},
author={Sarti, Gabriele and Nissim, Malvina},
journal={ArXiv preprint 2203.03759},
url={https://arxiv.org/abs/2203.03759},
year={2022},
month={mar}
}
``` |
it5/mt5-small-headline-generation | 2245c36e93dbc00b44a36aeeb5956f89472f6b91 | 2022-03-09T07:59:17.000Z | [
"pytorch",
"tf",
"jax",
"tensorboard",
"mt5",
"text2text-generation",
"it",
"dataset:gsarti/change_it",
"arxiv:2203.03759",
"transformers",
"italian",
"sequence-to-sequence",
"newspaper",
"ilgiornale",
"repubblica",
"headline-generation",
"license:apache-2.0",
"model-index",
"co2_eq_emissions",
"autotrain_compatible"
]
| text2text-generation | false | it5 | null | it5/mt5-small-headline-generation | 5 | null | transformers | 16,643 | ---
language:
- it
license: apache-2.0
datasets:
- gsarti/change_it
tags:
- italian
- sequence-to-sequence
- newspaper
- ilgiornale
- repubblica
- headline-generation
widget:
- text: "WASHINGTON - La Corea del Nord torna dopo nove anni nella blacklist Usa degli Stati considerati sponsor del terrorismo. Come Iran, Siria e Sudan. Lo ha deciso Donald Trump , che ha preferito dare l'annuncio non durante il suo recente viaggio in Asia ma ieri, in una riunione del governo alla Casa Bianca. 'Oggi gli Stati Uniti designeranno la Corea del nord come uno stato sponsor del terrorismo', ha tuonato il tycoon, anticipando che sarà formalizzata oggi dal dipartimento di stato e sarà accompagnata da nuove e più severe sanzioni. 'Il livello più alto' mai imposto a Pyongyang, ha promesso. 'Avrebbe dovuto succedere molto tempo fa', ha aggiunto, scaricando per l'ennesima volta la responsabilità dell'attuale crisi sull'amministrazione Obama. Poi si è scagliato contro un 'regime assassino' che 'deve mettere fine allo sviluppo del suo programma illegale nucleare e balistico'. Per giustificare la svolta, Trump ha accusato Pyongyang non solo di 'minacciare il mondo con una devastazione nucleare' ma anche di aver 'ripetutamente sostenuto atti di terrorismo internazionale', compreso omicidi in suolo straniero. Il riferimento è all' uccisione all'aeroporto della capitale malese di Kim Jong Nam , il fratellastro del leader nordcoreano Kim Jong Un , ma non ci sono altri episodi noti. Tanto che alcuni esperti, come pure dirigenti Usa coperti dall'anonimato, dubitano che Pyongyang risponda ai criteri per una tale designazione. La mossa appare altamente simbolica, dato che la Corea del Nord è già pesantemente sanzionata a livello internazionale. Per il segretario di stato Rex Tillerson è solo l'ultima di una serie di passi per rafforzare la pressione su Pyongyang e costringerla a sedersi ad un tavolo perché gli Usa hanno sempre 'speranza nella diplomazia'. Ma nello stesso tempo è un monito per 'fermare e dissuadere' altri Paesi dal sostenere la Corea del Nord, finita nella blacklist 'anche per l'uso di armi chimiche'. Ma la mossa potrebbe anche essere controproducente, provocando una risposta di Kim o minando gli sforzi per sollecitare Pechino ad una maggiore pressione su Pyongyang. In ogni caso non aiuta il dialogo diretto tra Usa e Corea del Nord, che sembrava essere stato avviato in modo riservato. Come non aiutano gli scambi di insulti fra Trump e Kim. Nord Corea, Trump: 'Cerco di essere amico di Kim, sarebbe una bella cosa per il mondo'. Pyongyang era stata messa nella lista Usa degli Stati sponsor del terrorismo per aver fatto esplodere nel 1987 un volo della Korean Air uccidendo tutti i 115 passeggeri a bordo. Ma l'amministrazione di George W. Bush l'aveva rimossa sperando di far avanzare i negoziati sulla denuclearizzazione della penisola coreana. Il governo giapponese sostiene la decisione degli Stati Uniti di inserire la Corea del Nord nella lista degli stati che sponsorizzano il terrorismo, pur riconoscendo che l'annuncio potrebbe provocare una reazione immediata del regime di Pyongyang. Il premier Shinzo Abe ha accolto con consenso il comunicato Usa e ha detto alla stampa che servirà a incrementare la pressione sulla Corea del Nord. Il ministro della Difesa Itsunori Onodera , pur valutando positivamente la notifica, ha spiegato che si attendono azioni provocatorie dallo stato eremita, ribadendo che è vitale rimanere vigili. Secondo la stampa nipponica Abe aveva richiesto al dipartimento di Stato Usa di mettere la Corea del Nord sulla lista durante l'incontro col presidente Usa Donald Trump a Tokyo a inizio mese. L'ultimo lancio di missile balistico condotto da Pyongyang nell'oceano Pacifico, sorvolando il mare del Giappone, risale allo scorso settembre."
- text: "ROMA - Una nuova droga killer è stata sequestrata per la prima volta in Europa dagli investigatori del Nas. Si tratta di una nuova \"miscela psicoattiva altamente tossica\" per la prima volta individuata da forze di polizia, simile all'eroina sintetica, ma molto più economica e letale. Tanto che i 20 grammi scoperti sarebbero stati sufficienti per fabbricare ben 20.000 dosi e lo stesso contatto attraverso la pelle può provocare intossicazione. Individuata per la prima volta, la nuova droga presenta una struttura simile al farmaco sedativo Fentanyl ma con effetti molto più devastanti per l'organismo. Proveniva dell'estero ed era contenuta in un plico postale indirizzato in una città del centro Italia: è stata intercettata tramite accertamenti sul web grazie a un'operazione di intelligence che ha visto come protagonisti i militari della Sezione operativa centrale del Comando carabinieri per la Tutela della salute (Nas). Economica e letale, secondo gli investigatori \"in confronto l'eroina è quasi 'acqua fresca', anzi, proprio per la sua economicità, in alcuni casi viene venduta dai pusher a giovani conviti di comprare eroina\". La diffusione di nuove droghe sintetiche che continuamente appaiono sui mercati necessita di un'attività investigativa costante e complessa. Si tratta infatti di sostanze dalla struttura molecolare molto simile a quella del Fentanyl ma ogni volta leggermente diversa. Di qui la difficoltà di individuarle e l'importanza del nuovo sequestro. \"La chiamano impropriamente 'eroina sintetica' - spiega il comandante dei Nas, generale Adelmo Lusi - per il tipo di effetto psicotropo simile, ma dal punto di vista della tossicità è molto peggio: con 25 milligrammi di eroina ci si sballa, con 25mg di simil-fentanyl, come quello appena sequestrato, si muore\". Le indagini sono partite da ricoveri per overdose in ospedale, in cui arrivavano ragazzi che non rispondevano al trattamento disintossicante per l'eroina. La nuova sostanza verrà ora segnalata per l'inserimento tra le tabelle ministeriali degli stupefacenti prevista dal Dpr 309/1990."
- text: "Fragile come il burro. Il nostro territorio è precario. Ne sanno qualcosa i comuni che sono stati investititi dal maltempo . Il dissesto idrogeologico imperversa su tutto il territorio. Infatti, oltre 6.600 comuni , pari all’82% del totale, sono in aree ad elevato rischio idrogeologico, pari al 10% della sua superficie. La popolazione potenzialmente esposta è stimata in 5,8 milioni di persone. I dati emergono dalle recenti analisi fatte da Legambiente e Protezione civile, che mettono in evidenza come in 10 anni in Italia sia raddoppiata l’area dei territori colpiti da alluvioni e frane , passando da una media di quattro regioni all’anno a otto regioni. Nella classifica delle regioni a maggior rischio idrogeologico prima è la Calabria con il 100% dei comuni esposti; al 100% ci sono anche la provincia di Trento, il Molise, la Basilicata, l’Umbria, la Valle d’Aosta. Poi Marche, Liguria al 99%; Lazio, Toscana al 98%; Abruzzo (96%), Emilia-Romagna (95%), Campania e Friuli Venezia Giulia al 92%, Piemonte (87%), Sardegna (81%), Puglia (78%), Sicilia (71%), Lombardia (60%), provincia di Bolzano (59%), Veneto (56%). Tra le cause che condizionano ed amplificano il rischio idrogeologico c’è l’azione dell’uomo (abbandono e degrado, cementificazione, consumo di suolo, abusivismo, disboscamento e incendi). Ma anche e soprattutto la mancanza di una seria manutenzione ordinaria e non ad una organica politica di prevenzione."
- text: "Arriva dal Partito nazionalista basco (Pnv) la conferma che i cinque deputati che siedono in parlamento voteranno la sfiducia al governo guidato da Mariano Rajoy. Pochi voti, ma significativi quelli della formazione politica di Aitor Esteban, che interverrà nel pomeriggio. Pur con dimensioni molto ridotte, il partito basco si è trovato a fare da ago della bilancia in aula. E il sostegno alla mozione presentata dai Socialisti potrebbe significare per il primo ministro non trovare quei 176 voti che gli servono per continuare a governare. \" Perché dovrei dimettermi io che per il momento ho la fiducia della Camera e quella che mi è stato data alle urne \", ha detto oggi Rajoy nel suo intervento in aula, mentre procedeva la discussione sulla mozione di sfiducia. Il voto dei baschi ora cambia le carte in tavola e fa crescere ulteriormente la pressione sul premier perché rassegni le sue dimissioni. La sfiducia al premier, o un'eventuale scelta di dimettersi, porterebbe alle estreme conseguenze lo scandalo per corruzione che ha investito il Partito popolare. Ma per ora sembra pensare a tutt'altro. \"Non ha intenzione di dimettersi - ha detto il segretario generale del Partito popolare , María Dolores de Cospedal - Non gioverebbe all'interesse generale o agli interessi del Pp\"."
metrics:
- rouge
- bertscore
model-index:
- name: mt5-small-headline-generation
results:
- task:
type: headline-generation
name: "Headline generation"
dataset:
type: headgen_it
name: "HeadGen-IT"
metrics:
- type: rouge1
value: 0.277
name: "Test Rouge1"
- type: rouge2
value: 0.094
name: "Test Rouge2"
- type: rougeL
value: 0.244
name: "Test RougeL"
- type: bertscore
value: 0.408
name: "Test BERTScore"
args:
- model_type: "dbmdz/bert-base-italian-xxl-uncased"
- lang: "it"
- num_layers: 10
- rescale_with_baseline: True
- baseline_path: "bertscore_baseline_ita.tsv"
co2_eq_emissions:
emissions: "17g"
source: "Google Cloud Platform Carbon Footprint"
training_type: "fine-tuning"
geographical_location: "Eemshaven, Netherlands, Europe"
hardware_used: "1 TPU v3-8 VM"
thumbnail: https://gsarti.com/publication/it5/featured.png
---
# mT5 Small for News Headline Generation 📣 🇮🇹
This repository contains the checkpoint for the [mT5 Small](https://huggingface.co/google/mt5-small) model fine-tuned on news headline generation on the Italian HeadGen-IT dataset as part of the experiments of the paper [IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation](https://arxiv.org/abs/2203.03759) by [Gabriele Sarti](https://gsarti.com) and [Malvina Nissim](https://malvinanissim.github.io).
A comprehensive overview of other released materials is provided in the [gsarti/it5](https://github.com/gsarti/it5) repository. Refer to the paper for additional details concerning the reported scores and the evaluation approach.
## Using the model
Model checkpoints are available for usage in Tensorflow, Pytorch and JAX. They can be used directly with pipelines as:
```python
from transformers import pipelines
hg = pipeline("text2text-generation", model='it5/mt5-small-headline-generation')
hg("Arriva dal Partito nazionalista basco (Pnv) la conferma che i cinque deputati che siedono in parlamento voteranno la sfiducia al governo guidato da Mariano Rajoy. Pochi voti, ma significativi quelli della formazione politica di Aitor Esteban, che interverrà nel pomeriggio. Pur con dimensioni molto ridotte, il partito basco si è trovato a fare da ago della bilancia in aula. E il sostegno alla mozione presentata dai Socialisti potrebbe significare per il primo ministro non trovare quei 176 voti che gli servono per continuare a governare. \" Perché dovrei dimettermi io che per il momento ho la fiducia della Camera e quella che mi è stato data alle urne \", ha detto oggi Rajoy nel suo intervento in aula, mentre procedeva la discussione sulla mozione di sfiducia. Il voto dei baschi ora cambia le carte in tavola e fa crescere ulteriormente la pressione sul premier perché rassegni le sue dimissioni. La sfiducia al premier, o un'eventuale scelta di dimettersi, porterebbe alle estreme conseguenze lo scandalo per corruzione che ha investito il Partito popolare. Ma per ora sembra pensare a tutt'altro. \"Non ha intenzione di dimettersi - ha detto il segretario generale del Partito popolare , María Dolores de Cospedal - Non gioverebbe all'interesse generale o agli interessi del Pp\".")
>>> [{"generated_text": "il nazionalista rajoy: 'voteremo la sfiducia'"}]
```
or loaded using autoclasses:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("it5/mt5-small-headline-generation")
model = AutoModelForSeq2SeqLM.from_pretrained("it5/mt5-small-headline-generation")
```
If you use this model in your research, please cite our work as:
```bibtex
@article{sarti-nissim-2022-it5,
title={{IT5}: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation},
author={Sarti, Gabriele and Nissim, Malvina},
journal={ArXiv preprint 2203.03759},
url={https://arxiv.org/abs/2203.03759},
year={2022},
month={mar}
}
``` |
it5/mt5-small-question-generation | a48144da3cd4d293ca4baf273e3e204f7c02464e | 2022-03-09T07:55:07.000Z | [
"pytorch",
"tf",
"jax",
"tensorboard",
"mt5",
"text2text-generation",
"it",
"dataset:squad_it",
"arxiv:2203.03759",
"transformers",
"italian",
"sequence-to-sequence",
"question-generation",
"squad_it",
"license:apache-2.0",
"model-index",
"co2_eq_emissions",
"autotrain_compatible"
]
| text2text-generation | false | it5 | null | it5/mt5-small-question-generation | 5 | null | transformers | 16,644 | ---
language:
- it
license: apache-2.0
datasets:
- squad_it
tags:
- italian
- sequence-to-sequence
- question-generation
- squad_it
- text2text-generation
widget:
- text: "Le conoscenze mediche erano stagnanti durante il Medioevo. Il resoconto più autorevole di allora è venuto dalla facoltà di medicina di Parigi in un rapporto al re di Francia che ha incolpato i cieli, sotto forma di una congiunzione di tre pianeti nel 1345 che causò una \"grande pestilenza nell' aria\". Questa relazione è diventata la prima e più diffusa di una serie di casi di peste che cercava di dare consigli ai malati. Che la peste fosse causata dalla cattiva aria divenne la teoria più accettata. Oggi, questo è conosciuto come la teoria di Miasma. La parola \"peste\" non aveva un significato particolare in questo momento, e solo la ricorrenza dei focolai durante il Medioevo gli diede il nome che è diventato il termine medico. Risposta: re di Francia"
- text: "Il 14 aprile 2011, ABC ha annullato le lunghe opere di sapone All My Children e One Life to Live dopo 41 e 43 anni in onda, rispettivamente (in seguito al contraccolpo dei tifosi, ABC ha venduto i diritti ad entrambi gli spettacoli a Prospect Park, che alla fine ha rilanciato i saponi su Hulu per un' ulteriore stagione nel 2013 e con entrambe le società che si citano in giudizio per accuse di interferenza con il processo di rilancio degli spettacoli, mancato pagamento delle tasse di licenza. Il talk/lifestyle show che ha sostituito One Life to Live, The Revolution, non è riuscito a generare giudizi soddisfacenti ed è stato a sua volta annullato dopo soli sette mesi. La stagione 2011-12 ha visto l' ABC cadere al quarto posto nel 18-49 demografico nonostante rinnovando una manciata di nuovi spettacoli (compresi i drammi matricole Scandal, Revenge e Once Upon a Time) per la seconda stagione. Risposta: Hulu"
- text: "L' American Broadcasting Company (ABC) (stlized nel suo logo come abc dal 1957) è una rete televisiva commerciale americana trasmissione televisiva che è di proprietà del Disney-ABC Television Group, una controllata della divisione Disney Media Networks di The Walt Disney Company. La rete fa parte delle grandi reti televisive Big Three. La rete ha sede a Columbus Avenue e West 66th Street a Manhattan, con ulteriori uffici e stabilimenti di produzione a New York City, Los Angeles e Burbank, California. Risposta: Manhattan"
- text: "La disobbedienza civile non rivoluzionaria è una semplice disobbedienza delle leggi sulla base del fatto che sono giudicate \"sbagliate\" da una coscienza individuale, o come parte di uno sforzo per rendere alcune leggi inefficaci, per causarne l' abrogazione, o per esercitare pressioni per ottenere i propri desideri politici su qualche altra questione. La disobbedienza civile rivoluzionaria è più che altro un tentativo attivo di rovesciare un governo (o di cambiare le tradizioni culturali, i costumi sociali, le credenze religiose, ecc. La rivoluzione non deve necessariamente essere politica, cioè \"rivoluzione culturale\", implica semplicemente un cambiamento radicale e diffuso in una sezione del tessuto sociale). Gli atti di Gandhi sono stati descritti come disobbedienza civile rivoluzionaria. È stato affermato che gli ungheresi sotto Ferenc Deák hanno diretto una disobbedienza civile rivoluzionaria contro il governo austriaco. Thoreau ha anche scritto di disobbedienza civile realizzando \"rivoluzione pacifica\". Howard Zinn, Harvey Wheeler e altri hanno identificato il diritto sposato nella Dichiarazione d' Indipendenza di \"alterare o abolire\" un governo ingiusto come principio di disobbedienza civile. Risposta: Ferenc Deák"
metrics:
- rouge
- bertscore
model-index:
- name: mt5-small-question-generation
results:
- task:
type: question-generation
name: "Question generation"
dataset:
type: squad_it
name: "SQuAD-IT"
metrics:
- type: rouge1
value: 0.306
name: "Test Rouge1"
- type: rouge2
value: 0.143
name: "Test Rouge2"
- type: rougeL
value: 0.286
name: "Test RougeL"
- type: bertscore
value: 0.463
name: "Test BERTScore"
args:
- model_type: "dbmdz/bert-base-italian-xxl-uncased"
- lang: "it"
- num_layers: 10
- rescale_with_baseline: True
- baseline_path: "bertscore_baseline_ita.tsv"
co2_eq_emissions:
emissions: "17g"
source: "Google Cloud Platform Carbon Footprint"
training_type: "fine-tuning"
geographical_location: "Eemshaven, Netherlands, Europe"
hardware_used: "1 TPU v3-8 VM"
thumbnail: https://gsarti.com/publication/it5/featured.png
---
# mT5 Small for Question Generation 💭 🇮🇹
This repository contains the checkpoint for the [mT5 Small](https://huggingface.co/google/mt5-small) model fine-tuned on question generation on the [SQuAD-IT corpus](https://huggingface.co/datasets/squad_it) as part of the experiments of the paper [IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation](https://arxiv.org/abs/2203.03759) by [Gabriele Sarti](https://gsarti.com) and [Malvina Nissim](https://malvinanissim.github.io).
A comprehensive overview of other released materials is provided in the [gsarti/it5](https://github.com/gsarti/it5) repository. Refer to the paper for additional details concerning the reported scores and the evaluation approach.
## Using the model
Model checkpoints are available for usage in Tensorflow, Pytorch and JAX. They can be used directly with pipelines as:
```python
from transformers import pipelines
qg = pipeline("text2text-generation", model='it5/mt5-small-question-generation')
qg("Le conoscenze mediche erano stagnanti durante il Medioevo. Il resoconto più autorevole di allora è venuto dalla facoltà di medicina di Parigi in un rapporto al re di Francia che ha incolpato i cieli, sotto forma di una congiunzione di tre pianeti nel 1345 che causò una "grande pestilenza nell\' aria". Questa relazione è diventata la prima e più diffusa di una serie di casi di peste che cercava di dare consigli ai malati. Che la peste fosse causata dalla cattiva aria divenne la teoria più accettata. Oggi, questo è conosciuto come la teoria di Miasma. La parola "peste" non aveva un significato particolare in questo momento, e solo la ricorrenza dei focolai durante il Medioevo gli diede il nome che è diventato il termine medico. Risposta: re di Francia")
>>> [{"generated_text": "Per chi è stato redatto il referto medico?"}]
```
or loaded using autoclasses:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("it5/mt5-small-question-generation")
model = AutoModelForSeq2SeqLM.from_pretrained("it5/mt5-small-question-generation")
```
If you use this model in your research, please cite our work as:
```bibtex
@article{sarti-nissim-2022-it5,
title={{IT5}: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation},
author={Sarti, Gabriele and Nissim, Malvina},
journal={ArXiv preprint 2203.03759},
url={https://arxiv.org/abs/2203.03759},
year={2022},
month={mar}
}
``` |
jacksonargo/music-mlm | 0ca3ca99e9f8562ec3f1b1e06c8bab7cd25e8c10 | 2021-12-03T16:31:53.000Z | [
"pytorch",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | false | jacksonargo | null | jacksonargo/music-mlm | 5 | null | transformers | 16,645 | Entry not found |
jacksonargo/music-production-qa | f6b57cdc6c326cf1c8cd9c74f3c8bc6dad08594d | 2021-12-03T03:27:29.000Z | [
"pytorch",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | false | jacksonargo | null | jacksonargo/music-production-qa | 5 | null | transformers | 16,646 | Entry not found |
jaesun/kogpt2-base-v2-finetuned-nsmc | 9b13e0b7004ce01cc933a7ccfe0f5dd075ee5e17 | 2021-10-21T07:33:48.000Z | [
"pytorch",
"gpt2",
"text-classification",
"transformers"
]
| text-classification | false | jaesun | null | jaesun/kogpt2-base-v2-finetuned-nsmc | 5 | null | transformers | 16,647 | Entry not found |
jambo/marker-associations-binary-base | 3b1b4989f4b8f8943778b8f6bc9564032fd74c40 | 2021-11-02T12:52:24.000Z | [
"pytorch",
"bert",
"text-classification",
"dataset:marker-associations-binary-base",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
]
| text-classification | false | jambo | null | jambo/marker-associations-binary-base | 5 | null | transformers | 16,648 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- marker-associations-binary-base
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: marker-associations-binary-base
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: marker-associations-binary-base
type: marker-associations-binary-base
metrics:
- name: Precision
type: precision
value: 0.7981651376146789
- name: Recall
type: recall
value: 0.9560439560439561
- name: F1
type: f1
value: 0.87
- name: Accuracy
type: accuracy
value: 0.8884120171673819
---
<!-- 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. -->
# marker-associations-binary-base
This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the marker-associations-binary-base dataset.
It achieves the following results on the evaluation set:
### Gene Results
- Precision = 0.808
- Recall = 0.940
- F1 = 0.869
- Accuracy = 0.862
- AUC = 0.944
### Chemical Results
- Precision = 0.774
- Recall = 1.0
- F1 = 0.873
- Accuracy = 0.926
- AUC = 0.964
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 1
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | Auc |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:------:|
| No log | 1.0 | 88 | 0.3266 | 0.8191 | 0.8462 | 0.8324 | 0.8670 | 0.9313 |
| No log | 2.0 | 176 | 0.3335 | 0.7870 | 0.9341 | 0.8543 | 0.8755 | 0.9465 |
| No log | 3.0 | 264 | 0.4243 | 0.7982 | 0.9560 | 0.87 | 0.8884 | 0.9516 |
| No log | 4.0 | 352 | 0.5388 | 0.825 | 0.7253 | 0.7719 | 0.8326 | 0.9384 |
| No log | 5.0 | 440 | 0.7101 | 0.8537 | 0.7692 | 0.8092 | 0.8584 | 0.9416 |
| 0.1824 | 6.0 | 528 | 0.6175 | 0.8242 | 0.8242 | 0.8242 | 0.8627 | 0.9478 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Tokenizers 0.10.3
|
jamesmullenbach/CLIP_DNote_BERT_Context | 035ae2434cf496bf47a8f65bb98e5f1f60b62d6d | 2021-08-03T19:03:54.000Z | [
"pytorch",
"bert",
"transformers"
]
| null | false | jamesmullenbach | null | jamesmullenbach/CLIP_DNote_BERT_Context | 5 | 1 | transformers | 16,649 | Entry not found |
jannesg/takalane_sot_roberta | b0cb9af0cf3488f46bc41b8a5b3f37729c30f9f3 | 2021-09-22T08:52:06.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"sot",
"transformers",
"masked-lm",
"license:mit",
"autotrain_compatible"
]
| fill-mask | false | jannesg | null | jannesg/takalane_sot_roberta | 5 | null | transformers | 16,650 | ---
language:
- sot
thumbnail: https://pbs.twimg.com/media/EVjR6BsWoAAFaq5.jpg
tags:
- sot
- fill-mask
- pytorch
- roberta
- masked-lm
license: mit
---
# Takalani Sesame - Southern Sotho 🇿🇦
<img src="https://pbs.twimg.com/media/EVjR6BsWoAAFaq5.jpg" width="600"/>
## Model description
Takalani Sesame (named after the South African version of Sesame Street) is a project that aims to promote the use of South African languages in NLP, and in particular look at techniques for low-resource languages to equalise performance with larger languages around the world.
## Intended uses & limitations
#### How to use
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("jannesg/takalane_sot_roberta")
model = AutoModelWithLMHead.from_pretrained("jannesg/takalane_sot_roberta")
```
#### Limitations and bias
Updates will be added continously to improve performance.
## Training data
Data collected from [https://wortschatz.uni-leipzig.de/en](https://wortschatz.uni-leipzig.de/en) <br/>
**Sentences:** 20000
## Training procedure
No preprocessing. Standard Huggingface hyperparameters.
## Author
Jannes Germishuys [website](http://jannesgg.github.io)
|
jannesg/takalane_ven_roberta | fb206bc483c748e5d77022d1b2ed2682f80d779a | 2021-09-22T08:52:16.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"ven",
"transformers",
"masked-lm",
"license:mit",
"autotrain_compatible"
]
| fill-mask | false | jannesg | null | jannesg/takalane_ven_roberta | 5 | null | transformers | 16,651 | ---
language:
- ven
thumbnail: https://pbs.twimg.com/media/EVjR6BsWoAAFaq5.jpg
tags:
- ven
- fill-mask
- pytorch
- roberta
- masked-lm
license: mit
---
# Takalani Sesame - Venda 🇿🇦
<img src="https://pbs.twimg.com/media/EVjR6BsWoAAFaq5.jpg" width="600"/>
## Model description
Takalani Sesame (named after the South African version of Sesame Street) is a project that aims to promote the use of South African languages in NLP, and in particular look at techniques for low-resource languages to equalise performance with larger languages around the world.
## Intended uses & limitations
#### How to use
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("jannesg/takalane_ven_roberta")
model = AutoModelWithLMHead.from_pretrained("jannesg/takalane_ven_roberta")
```
#### Limitations and bias
Updates will be added continously to improve performance.
## Training data
Data collected from [https://wortschatz.uni-leipzig.de/en](https://wortschatz.uni-leipzig.de/en) <br/>
**Sentences:** 9279
## Training procedure
No preprocessing. Standard Huggingface hyperparameters.
## Author
Jannes Germishuys [website](http://jannesgg.github.io)
|
jatinshah/bert-finetuned-ner | 4563e7bcaaf28ab9a6700d967ee40529121a56a6 | 2022-02-16T03:50:43.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"dataset:conll2003",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
]
| token-classification | false | jatinshah | null | jatinshah/bert-finetuned-ner | 5 | null | transformers | 16,652 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9330024813895782
- name: Recall
type: recall
value: 0.9491753618310333
- name: F1
type: f1
value: 0.9410194377242012
- name: Accuracy
type: accuracy
value: 0.9861511744275033
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0599
- Precision: 0.9330
- Recall: 0.9492
- F1: 0.9410
- Accuracy: 0.9862
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0852 | 1.0 | 1756 | 0.0647 | 0.9147 | 0.9345 | 0.9245 | 0.9826 |
| 0.0305 | 2.0 | 3512 | 0.0599 | 0.9333 | 0.9463 | 0.9398 | 0.9858 |
| 0.0212 | 3.0 | 5268 | 0.0599 | 0.9330 | 0.9492 | 0.9410 | 0.9862 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.9.1
- Datasets 1.18.3
- Tokenizers 0.10.3
|
jcblaise/bert-tagalog-base-uncased-WWM | 9123041ea7e4189dcff6b30bd141e340563be792 | 2021-11-12T03:21:09.000Z | [
"pytorch",
"jax",
"bert",
"fill-mask",
"tl",
"transformers",
"tagalog",
"filipino",
"license:gpl-3.0",
"autotrain_compatible"
]
| fill-mask | false | jcblaise | null | jcblaise/bert-tagalog-base-uncased-WWM | 5 | null | transformers | 16,653 | ---
language: tl
tags:
- bert
- tagalog
- filipino
license: gpl-3.0
inference: false
---
**Deprecation Notice**
This model is deprecated. New Filipino Transformer models trained with a much larger corpora are available.
Use [`jcblaise/roberta-tagalog-base`](https://huggingface.co/jcblaise/roberta-tagalog-base) or [`jcblaise/roberta-tagalog-large`](https://huggingface.co/jcblaise/roberta-tagalog-large) instead for better performance.
---
# BERT Tagalog Base Uncased (Whole Word Masking)
Tagalog version of BERT trained on a large preprocessed text corpus scraped and sourced from the internet. This model is part of a larger research project. We open-source the model to allow greater usage within the Filipino NLP community. This particular version uses whole word masking.
## Citations
All model details and training setups can be found in our papers. If you use our model or find it useful in your projects, please cite our work:
```
@article{cruz2020establishing,
title={Establishing Baselines for Text Classification in Low-Resource Languages},
author={Cruz, Jan Christian Blaise and Cheng, Charibeth},
journal={arXiv preprint arXiv:2005.02068},
year={2020}
}
@article{cruz2019evaluating,
title={Evaluating Language Model Finetuning Techniques for Low-resource Languages},
author={Cruz, Jan Christian Blaise and Cheng, Charibeth},
journal={arXiv preprint arXiv:1907.00409},
year={2019}
}
```
## Data and Other Resources
Data used to train this model as well as other benchmark datasets in Filipino can be found in my website at https://blaisecruz.com
## Contact
If you have questions, concerns, or if you just want to chat about NLP and low-resource languages in general, you may reach me through my work email at [email protected]
|
jhgan/ko-sroberta-nli | 493196adb7916f877b1cf9a5e836e8a3b10a2efc | 2022-03-02T07:37:35.000Z | [
"pytorch",
"roberta",
"feature-extraction",
"ko",
"sentence-transformers",
"sentence-similarity",
"transformers"
]
| sentence-similarity | false | jhgan | null | jhgan/ko-sroberta-nli | 5 | null | sentence-transformers | 16,654 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
language: ko
---
# ko-sroberta-nli
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 = ["안녕하세요?", "한국어 문장 임베딩을 위한 버트 모델입니다."]
model = SentenceTransformer('jhgan/ko-sroberta-nli')
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('jhgan/ko-sroberta-nli')
model = AutoModel.from_pretrained('jhgan/ko-sroberta-nli')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
KorNLI 학습 데이터셋으로 학습한 후 KorSTS 평가 데이터셋으로 평가한 결과입니다.
- Cosine Pearson: 82.83
- Cosine Spearman: 83.85
- Euclidean Pearson: 82.87
- Euclidean Spearman: 83.29
- Manhattan Pearson: 82.88
- Manhattan Spearman: 83.28
- Dot Pearson: 80.34
- Dot Spearman: 79.69
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 8885 with parameters:
```
{'batch_size': 64}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 1,
"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": 889,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: RobertaModel
(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 -->
- Ham, J., Choe, Y. J., Park, K., Choi, I., & Soh, H. (2020). Kornli and korsts: New benchmark datasets for korean natural language understanding. arXiv preprint arXiv:2004.03289
- Reimers, Nils and Iryna Gurevych. “Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.” ArXiv abs/1908.10084 (2019)
- Reimers, Nils and Iryna Gurevych. “Making Monolingual Sentence Embeddings Multilingual Using Knowledge Distillation.” EMNLP (2020).
|
jirmauritz/robbert-v2-dutch-base | 7fa77da1fb2452f6e3af8c725c2a1a7e31c9ffcf | 2021-06-23T09:16:10.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"nl",
"dataset:oscar",
"dataset:oscar (NL)",
"dataset:dbrd",
"dataset:lassy-ud",
"dataset:europarl-mono",
"dataset:conll2002",
"arxiv:2001.06286",
"arxiv:2004.02814",
"arxiv:2010.13652",
"arxiv:2101.05716",
"arxiv:1907.11692",
"arxiv:2001.02943",
"arxiv:1909.11942",
"transformers",
"Dutch",
"Flemish",
"RoBERTa",
"RobBERT",
"license:mit",
"autotrain_compatible"
]
| fill-mask | false | jirmauritz | null | jirmauritz/robbert-v2-dutch-base | 5 | null | transformers | 16,655 | ---
language: "nl"
thumbnail: "https://github.com/iPieter/RobBERT/raw/master/res/robbert_logo.png"
tags:
- Dutch
- Flemish
- RoBERTa
- RobBERT
license: mit
datasets:
- oscar
- oscar (NL)
- dbrd
- lassy-ud
- europarl-mono
- conll2002
widget:
- text: "Hallo, ik ben RobBERT, een <mask> taalmodel van de KU Leuven."
---
<p align="center">
<img src="https://github.com/iPieter/RobBERT/raw/master/res/robbert_logo_with_name.png" alt="RobBERT: A Dutch RoBERTa-based Language Model" width="75%">
</p>
# RobBERT: Dutch RoBERTa-based Language Model.
[RobBERT](https://github.com/iPieter/RobBERT) is the state-of-the-art Dutch BERT model. It is a large pre-trained general Dutch language model that can be fine-tuned on a given dataset to perform any text classification, regression or token-tagging task. As such, it has been successfully used by many [researchers](https://scholar.google.com/scholar?oi=bibs&hl=en&cites=7180110604335112086) and [practitioners](https://huggingface.co/models?search=robbert) for achieving state-of-the-art performance for a wide range of Dutch natural language processing tasks, including:
- [Emotion detection](https://www.aclweb.org/anthology/2021.wassa-1.27/)
- Sentiment analysis ([book reviews](https://arxiv.org/pdf/2001.06286.pdf), [news articles](https://biblio.ugent.be/publication/8704637/file/8704638.pdf)*)
- [Coreference resolution](https://arxiv.org/pdf/2001.06286.pdf)
- Named entity recognition ([CoNLL](https://arxiv.org/pdf/2001.06286.pdf), [job titles](https://arxiv.org/pdf/2004.02814.pdf)*, [SoNaR](https://github.com/proycon/deepfrog))
- Part-of-speech tagging ([Small UD Lassy](https://arxiv.org/pdf/2001.06286.pdf), [CGN](https://github.com/proycon/deepfrog))
- [Zero-shot word prediction](https://arxiv.org/pdf/2001.06286.pdf)
- [Humor detection](https://arxiv.org/pdf/2010.13652.pdf)
- [Cyberbulling detection](https://www.cambridge.org/core/journals/natural-language-engineering/article/abs/automatic-classification-of-participant-roles-in-cyberbullying-can-we-detect-victims-bullies-and-bystanders-in-social-media-text/A2079C2C738C29428E666810B8903342)
- [Correcting dt-spelling mistakes](https://gitlab.com/spelfouten/dutch-simpletransformers/)*
and also achieved outstanding, near-sota results for:
- [Natural language inference](https://arxiv.org/pdf/2101.05716.pdf)*
- [Review classification](https://medium.com/broadhorizon-cmotions/nlp-with-r-part-5-state-of-the-art-in-nlp-transformers-bert-3449e3cd7494)*
\\* *Note that several evaluations use RobBERT-v1, and that the second and improved RobBERT-v2 outperforms this first model on everything we tested*
*(Also note that this list is not exhaustive. If you used RobBERT for your application, we are happy to know about it! Send us a mail, or add it yourself to this list by sending a pull request with the edit!)*
More in-depth information about RobBERT can be found in our [blog post](https://people.cs.kuleuven.be/~pieter.delobelle/robbert/), [our paper](https://arxiv.org/abs/2001.06286) and [the RobBERT Github repository](https://github.com/iPieter/RobBERT)
## How to use
RobBERT uses the [RoBERTa](https://arxiv.org/abs/1907.11692) architecture and pre-training but with a Dutch tokenizer and training data. RoBERTa is the robustly optimized English BERT model, making it even more powerful than the original BERT model. Given this same architecture, RobBERT can easily be finetuned and inferenced using [code to finetune RoBERTa](https://huggingface.co/transformers/model_doc/roberta.html) models and most code used for BERT models, e.g. as provided by [HuggingFace Transformers](https://huggingface.co/transformers/) library.
By default, RobBERT has the masked language model head used in training. This can be used as a zero-shot way to fill masks in sentences. It can be tested out for free on [RobBERT's Hosted infererence API of Huggingface](https://huggingface.co/pdelobelle/robbert-v2-dutch-base?text=De+hoofdstad+van+Belgi%C3%AB+is+%3Cmask%3E.). You can also create a new prediction head for your own task by using any of HuggingFace's [RoBERTa-runners](https://huggingface.co/transformers/v2.7.0/examples.html#language-model-training), [their fine-tuning notebooks](https://huggingface.co/transformers/v4.1.1/notebooks.html) by changing the model name to `pdelobelle/robbert-v2-dutch-base`, or use the original fairseq [RoBERTa](https://github.com/pytorch/fairseq/tree/master/examples/roberta) training regimes.
Use the following code to download the base model and finetune it yourself, or use one of our finetuned models (documented on [our project site](https://people.cs.kuleuven.be/~pieter.delobelle/robbert/)).
```python
from transformers import RobertaTokenizer, RobertaForSequenceClassification
tokenizer = RobertaTokenizer.from_pretrained("pdelobelle/robbert-v2-dutch-base")
model = RobertaForSequenceClassification.from_pretrained("pdelobelle/robbert-v2-dutch-base")
```
Starting with `transformers v2.4.0` (or installing from source), you can use AutoTokenizer and AutoModel.
You can then use most of [HuggingFace's BERT-based notebooks](https://huggingface.co/transformers/v4.1.1/notebooks.html) for finetuning RobBERT on your type of Dutch language dataset.
## Technical Details From The Paper
### Our Performance Evaluation Results
All experiments are described in more detail in our [paper](https://arxiv.org/abs/2001.06286), with the code in [our GitHub repository](https://github.com/iPieter/RobBERT).
### Sentiment analysis
Predicting whether a review is positive or negative using the [Dutch Book Reviews Dataset](https://github.com/benjaminvdb/110kDBRD).
| Model | Accuracy [%] |
|-------------------|--------------------------|
| ULMFiT | 93.8 |
| BERTje | 93.0 |
| RobBERT v2 | **95.1** |
### Die/Dat (coreference resolution)
We measured how well the models are able to do coreference resolution by predicting whether "die" or "dat" should be filled into a sentence.
For this, we used the [EuroParl corpus](https://www.statmt.org/europarl/).
#### Finetuning on whole dataset
| Model | Accuracy [%] | F1 [%] |
|-------------------|--------------------------|--------------|
| [Baseline](https://arxiv.org/abs/2001.02943) (LSTM) | | 75.03 |
| mBERT | 98.285 | 98.033 |
| BERTje | 98.268 | 98.014 |
| RobBERT v2 | **99.232** | **99.121** |
#### Finetuning on 10K examples
We also measured the performance using only 10K training examples.
This experiment clearly illustrates that RobBERT outperforms other models when there is little data available.
| Model | Accuracy [%] | F1 [%] |
|-------------------|--------------------------|--------------|
| mBERT | 92.157 | 90.898 |
| BERTje | 93.096 | 91.279 |
| RobBERT v2 | **97.816** | **97.514** |
#### Using zero-shot word masking task
Since BERT models are pre-trained using the word masking task, we can use this to predict whether "die" or "dat" is more likely.
This experiment shows that RobBERT has internalised more information about Dutch than other models.
| Model | Accuracy [%] |
|-------------------|--------------------------|
| ZeroR | 66.70 |
| mBERT | 90.21 |
| BERTje | 94.94 |
| RobBERT v2 | **98.75** |
### Part-of-Speech Tagging.
Using the [Lassy UD dataset](https://universaldependencies.org/treebanks/nl_lassysmall/index.html).
| Model | Accuracy [%] |
|-------------------|--------------------------|
| Frog | 91.7 |
| mBERT | **96.5** |
| BERTje | 96.3 |
| RobBERT v2 | 96.4 |
Interestingly, we found that when dealing with **small data sets**, RobBERT v2 **significantly outperforms** other models.
<p align="center">
<img src="https://github.com/iPieter/RobBERT/raw/master/res/robbert_pos_accuracy.png" alt="RobBERT's performance on smaller datasets">
</p>
### Named Entity Recognition
Using the [CoNLL 2002 evaluation script](https://www.clips.uantwerpen.be/conll2002/ner/).
| Model | Accuracy [%] |
|-------------------|--------------------------|
| Frog | 57.31 |
| mBERT | **90.94** |
| BERT-NL | 89.7 |
| BERTje | 88.3 |
| RobBERT v2 | 89.08 |
## Pre-Training Procedure Details
We pre-trained RobBERT using the RoBERTa training regime.
We pre-trained our model on the Dutch section of the [OSCAR corpus](https://oscar-corpus.com/), a large multilingual corpus which was obtained by language classification in the Common Crawl corpus.
This Dutch corpus is 39GB large, with 6.6 billion words spread over 126 million lines of text, where each line could contain multiple sentences, thus using more data than concurrently developed Dutch BERT models.
RobBERT shares its architecture with [RoBERTa's base model](https://github.com/pytorch/fairseq/tree/master/examples/roberta), which itself is a replication and improvement over BERT.
Like BERT, it's architecture consists of 12 self-attention layers with 12 heads with 117M trainable parameters.
One difference with the original BERT model is due to the different pre-training task specified by RoBERTa, using only the MLM task and not the NSP task.
During pre-training, it thus only predicts which words are masked in certain positions of given sentences.
The training process uses the Adam optimizer with polynomial decay of the learning rate l_r=10^-6 and a ramp-up period of 1000 iterations, with hyperparameters beta_1=0.9
and RoBERTa's default beta_2=0.98.
Additionally, a weight decay of 0.1 and a small dropout of 0.1 helps prevent the model from overfitting.
RobBERT was trained on a computing cluster with 4 Nvidia P100 GPUs per node, where the number of nodes was dynamically adjusted while keeping a fixed batch size of 8192 sentences.
At most 20 nodes were used (i.e. 80 GPUs), and the median was 5 nodes.
By using gradient accumulation, the batch size could be set independently of the number of GPUs available, in order to maximally utilize the cluster.
Using the [Fairseq library](https://github.com/pytorch/fairseq/tree/master/examples/roberta), the model trained for two epochs, which equals over 16k batches in total, which took about three days on the computing cluster.
In between training jobs on the computing cluster, 2 Nvidia 1080 Ti's also covered some parameter updates for RobBERT v2.
## Investigating Limitations and Bias
In the [RobBERT paper](https://arxiv.org/abs/2001.06286), we also investigated potential sources of bias in RobBERT.
We found that the zeroshot model estimates the probability of *hij* (he) to be higher than *zij* (she) for most occupations in bleached template sentences, regardless of their actual job gender ratio in reality.
<p align="center">
<img src="https://github.com/iPieter/RobBERT/raw/master/res/gender_diff.png" alt="RobBERT's performance on smaller datasets">
</p>
By augmenting the DBRB Dutch Book sentiment analysis dataset with the stated gender of the author of the review, we found that highly positive reviews written by women were generally more accurately detected by RobBERT as being positive than those written by men.
<p align="center">
<img src="https://github.com/iPieter/RobBERT/raw/master/res/dbrd.png" alt="RobBERT's performance on smaller datasets">
</p>
## How to Replicate Our Paper Experiments
Replicating our paper experiments is [described in detail on teh RobBERT repository README](https://github.com/iPieter/RobBERT#how-to-replicate-our-paper-experiments).
## Name Origin of RobBERT
Most BERT-like models have the word *BERT* in their name (e.g. [RoBERTa](https://huggingface.co/transformers/model_doc/roberta.html), [ALBERT](https://arxiv.org/abs/1909.11942), [CamemBERT](https://camembert-model.fr/), and [many, many others](https://huggingface.co/models?search=bert)).
As such, we queried our newly trained model using its masked language model to name itself *\\<mask\\>bert* using [all](https://huggingface.co/pdelobelle/robbert-v2-dutch-base?text=Mijn+naam+is+%3Cmask%3Ebert.) [kinds](https://huggingface.co/pdelobelle/robbert-v2-dutch-base?text=Hallo%2C+ik+ben+%3Cmask%3Ebert.) [of](https://huggingface.co/pdelobelle/robbert-v2-dutch-base?text=Leuk+je+te+ontmoeten%2C+ik+heet+%3Cmask%3Ebert.) [prompts](https://huggingface.co/pdelobelle/robbert-v2-dutch-base?text=Niemand+weet%2C+niemand+weet%2C+dat+ik+%3Cmask%3Ebert+heet.), and it consistently called itself RobBERT.
We thought it was really quite fitting, given that RobBERT is a [*very* Dutch name](https://en.wikipedia.org/wiki/Robbert) *(and thus clearly a Dutch language model)*, and additionally has a high similarity to its root architecture, namely [RoBERTa](https://huggingface.co/transformers/model_doc/roberta.html).
Since *"rob"* is a Dutch words to denote a seal, we decided to draw a seal and dress it up like [Bert from Sesame Street](https://muppet.fandom.com/wiki/Bert) for the [RobBERT logo](https://github.com/iPieter/RobBERT/blob/master/res/robbert_logo.png).
## Credits and citation
This project is created by [Pieter Delobelle](https://people.cs.kuleuven.be/~pieter.delobelle), [Thomas Winters](https://thomaswinters.be) and [Bettina Berendt](https://people.cs.kuleuven.be/~bettina.berendt/).
If you would like to cite our paper or model, you can use the following BibTeX:
```
@inproceedings{delobelle2020robbert,
title = "{R}ob{BERT}: a {D}utch {R}o{BERT}a-based {L}anguage {M}odel",
author = "Delobelle, Pieter and
Winters, Thomas and
Berendt, Bettina",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.292",
doi = "10.18653/v1/2020.findings-emnlp.292",
pages = "3255--3265"
}
``` |
jkgrad/xlnet-base-cased-qqp | 1a7032ddd08046bfb09faaee3d1c6693b92aa278 | 2021-02-05T07:32:36.000Z | [
"pytorch",
"xlnet",
"text-classification",
"transformers"
]
| text-classification | false | jkgrad | null | jkgrad/xlnet-base-cased-qqp | 5 | null | transformers | 16,656 | Entry not found |
jnz/electra-ka | 020e4e2e53dff554a219bf8356b89436fb48c0d2 | 2020-12-12T21:53:36.000Z | [
"pytorch",
"electra",
"transformers"
]
| null | false | jnz | null | jnz/electra-ka | 5 | null | transformers | 16,657 | ### electra-ka is first of its kind, Transformer based, open source Georgian language model.
The model is trained on 33GB of Georgian text collected from 4854621 pages in commoncrowl archive.
|
jogonba2/mbarthez-copy_mechanism-hal_articles | 119236a8a1e6ee4a26b979bef9d0e91f31dc90ed | 2022-01-30T03:52:27.000Z | [
"pytorch",
"mbart",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| null | false | jogonba2 | null | jogonba2/mbarthez-copy_mechanism-hal_articles | 5 | null | transformers | 16,658 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mbarthez-copy_mechanism-hal_articles
results:
- task:
name: Summarization
type: summarization
metrics:
- name: Rouge1
type: rouge
value: 36.548
---
<!-- 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. -->
# mbarthez-davide_articles-copy_enhanced
This model is a fine-tuned version of [moussaKam/mbarthez](https://huggingface.co/moussaKam/mbarthez) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4905
- Rouge1: 36.548
- Rouge2: 19.6282
- Rougel: 30.2513
- Rougelsum: 30.2765
- Gen Len: 25.7238
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.6706 | 1.0 | 33552 | 1.5690 | 31.2477 | 16.5455 | 26.9855 | 26.9754 | 18.6217 |
| 1.3446 | 2.0 | 67104 | 1.5060 | 32.1108 | 17.1408 | 27.7833 | 27.7703 | 18.9115 |
| 1.3245 | 3.0 | 100656 | 1.4905 | 32.9084 | 17.7027 | 28.2912 | 28.2975 | 18.9801 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.7.1+cu110
- Datasets 1.11.0
- Tokenizers 0.10.3
|
jonfd/convbert-small-igc-is | d10ea3ad303262ff9f732d5d193f716287c384e2 | 2021-10-01T03:56:50.000Z | [
"pytorch",
"tf",
"convbert",
"feature-extraction",
"is",
"dataset:igc",
"transformers",
"license:cc-by-4.0"
]
| feature-extraction | false | jonfd | null | jonfd/convbert-small-igc-is | 5 | null | transformers | 16,659 | ---
language:
- is
license: cc-by-4.0
datasets:
- igc
---
# Icelandic ConvBERT-Small
This model was pretrained on the [Icelandic Gigaword Corpus](http://igc.arnastofnun.is/), which contains approximately 1.69B tokens, using default settings. The model uses a Unigram tokenizer with a vocabulary size of 96,000.
# Acknowledgments
This research was supported with Cloud TPUs from Google's TPU Research Cloud (TRC).
This project was funded by the Language Technology Programme for Icelandic 2019-2023. The programme, which is managed and coordinated by [Almannarómur](https://almannaromur.is/), is funded by the Icelandic Ministry of Education, Science and Culture. |
joniponi/bert-finetuned-sem_eval-english | 83e57d98fe5885b25a9d074238ecdd5c2586b73c | 2022-02-20T04:45:15.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | joniponi | null | joniponi/bert-finetuned-sem_eval-english | 5 | null | transformers | 16,660 | ---
Epoch Training Loss Validation Loss F1 Roc Auc Accuracy
1 0.115400 0.099458 0.888763 0.920410 0.731760
2 0.070400 0.080343 0.911700 0.943234 0.781116 |
jorgemariocalvo/roberta-base-bne-finetuned-amazon_reviews_multi | e9a144d6b57bb03268a1a5b2b9a9f1233985e799 | 2021-11-13T15:14:14.000Z | [
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"transformers"
]
| text-classification | false | jorgemariocalvo | null | jorgemariocalvo/roberta-base-bne-finetuned-amazon_reviews_multi | 5 | null | transformers | 16,661 | Entry not found |
jpreilly123/emojify_mvp | 40f381545a12c7ca399250f05aa949b4be541d03 | 2022-02-11T07:17:55.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | jpreilly123 | null | jpreilly123/emojify_mvp | 5 | null | transformers | 16,662 | Entry not found |
juliensimon/autonlp-reuters-summarization-31447312 | 2eacb8b061ca37e642178e3e0e2087f7e4512da6 | 2021-11-18T11:50:52.000Z | [
"pytorch",
"pegasus",
"text2text-generation",
"en",
"dataset:juliensimon/autonlp-data-reuters-summarization",
"transformers",
"autonlp",
"co2_eq_emissions",
"autotrain_compatible"
]
| text2text-generation | false | juliensimon | null | juliensimon/autonlp-reuters-summarization-31447312 | 5 | null | transformers | 16,663 | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- juliensimon/autonlp-data-reuters-summarization
co2_eq_emissions: 206.46626351359515
---
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 31447312
- CO2 Emissions (in grams): 206.46626351359515
## Validation Metrics
- Loss: 1.1907752752304077
- Rouge1: 55.9215
- Rouge2: 30.7724
- RougeL: 53.185
- RougeLsum: 53.3353
- Gen Len: 15.1236
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/juliensimon/autonlp-reuters-summarization-31447312
``` |
juliensimon/autonlp-song-lyrics-18753423 | 030289c3d3dd935ef699d923ccd227170ec71e50 | 2021-10-15T09:55:11.000Z | [
"pytorch",
"distilbert",
"text-classification",
"en",
"dataset:juliensimon/autonlp-data-song-lyrics",
"transformers",
"autonlp",
"co2_eq_emissions"
]
| text-classification | false | juliensimon | null | juliensimon/autonlp-song-lyrics-18753423 | 5 | null | transformers | 16,664 | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- juliensimon/autonlp-data-song-lyrics
co2_eq_emissions: 55.552987716859484
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 18753423
- CO2 Emissions (in grams): 55.552987716859484
## Validation Metrics
- Loss: 0.913820743560791
- Accuracy: 0.654110224531453
- Macro F1: 0.5327761649415296
- Micro F1: 0.654110224531453
- Weighted F1: 0.6339481529454227
- Macro Precision: 0.6799297267808116
- Micro Precision: 0.654110224531453
- Weighted Precision: 0.6533459269990771
- Macro Recall: 0.49907494605289154
- Micro Recall: 0.654110224531453
- Weighted Recall: 0.654110224531453
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/juliensimon/autonlp-song-lyrics-18753423
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("juliensimon/autonlp-song-lyrics-18753423", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("juliensimon/autonlp-song-lyrics-18753423", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` |
junnyu/ernie_gram | b17165bf5eac097ca7edb0fd94e916cad269cc82 | 2022-02-22T02:47:07.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
| feature-extraction | false | junnyu | null | junnyu/ernie_gram | 5 | null | transformers | 16,665 | Entry not found |
jwouts/belabBERT_115k | 2ef8a43c5f36861b61deb49e44f3a578936cf65f | 2021-05-20T17:32:05.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | false | jwouts | null | jwouts/belabBERT_115k | 5 | null | transformers | 16,666 | Entry not found |
kapilkd13/xls-r-300m-hi-prod | 18a022bf6257729540272725cc77567dfff4765b | 2022-03-23T18:27:33.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"hi",
"dataset:mozilla-foundation/common_voice_7_0",
"transformers",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"license:apache-2.0",
"model-index"
]
| automatic-speech-recognition | false | kapilkd13 | null | kapilkd13/xls-r-300m-hi-prod | 5 | null | transformers | 16,667 | ---
language:
- hi
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: ''
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7.0
type: mozilla-foundation/common_voice_7_0
args: hi
metrics:
- name: Test WER
type: wer
value: 39.21
---
<!-- 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. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - HI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7805
- Wer: 0.4340
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 8000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.36 | 400 | 1.9130 | 0.9244 |
| 5.0013 | 2.71 | 800 | 0.7789 | 0.5944 |
| 0.6544 | 4.07 | 1200 | 0.7298 | 0.5852 |
| 0.4021 | 5.42 | 1600 | 0.6978 | 0.5667 |
| 0.3003 | 6.78 | 2000 | 0.6764 | 0.5382 |
| 0.3003 | 8.14 | 2400 | 0.7249 | 0.5463 |
| 0.2345 | 9.49 | 2800 | 0.7280 | 0.5124 |
| 0.1993 | 10.85 | 3200 | 0.7289 | 0.4690 |
| 0.1617 | 12.2 | 3600 | 0.7431 | 0.4733 |
| 0.1432 | 13.56 | 4000 | 0.7448 | 0.4733 |
| 0.1432 | 14.92 | 4400 | 0.7746 | 0.4485 |
| 0.1172 | 16.27 | 4800 | 0.7589 | 0.4742 |
| 0.1035 | 17.63 | 5200 | 0.7539 | 0.4353 |
| 0.0956 | 18.98 | 5600 | 0.7648 | 0.4495 |
| 0.0845 | 20.34 | 6000 | 0.7877 | 0.4719 |
| 0.0845 | 21.69 | 6400 | 0.7884 | 0.4434 |
| 0.0761 | 23.05 | 6800 | 0.7796 | 0.4386 |
| 0.0634 | 24.41 | 7200 | 0.7729 | 0.4306 |
| 0.0571 | 25.76 | 7600 | 0.7826 | 0.4298 |
| 0.0508 | 27.12 | 8000 | 0.7805 | 0.4340 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.1+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
khanglam7012/t5-small | 66b06a9260277146b7590e277602e09470ca2f03 | 2022-01-10T13:32:38.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:WebNLG",
"dataset:Dart",
"transformers",
"keytotext",
"k2t",
"Keywords to Sentences",
"license:mit",
"autotrain_compatible"
]
| text2text-generation | false | khanglam7012 | null | khanglam7012/t5-small | 5 | null | transformers | 16,668 | ---
language: en
thumbnail: Keywords to Sentences
tags:
- keytotext
- k2t
- Keywords to Sentences
license: mit
datasets:
- WebNLG
- Dart
metrics:
- NLG
---
# keytotext
Idea is to build a model which will take keywords as inputs and generate sentences as outputs.
### Keytotext is powered by Huggingface 🤗
[](https://pypi.org/project/keytotext/)
[](https://pepy.tech/project/keytotext)
[](https://colab.research.google.com/github/gagan3012/keytotext/blob/master/Examples/K2T.ipynb)
[](https://share.streamlit.io/gagan3012/keytotext/UI/app.py)
## Model:
Keytotext is based on the Amazing T5 Model:
- `k2t`: [Model](https://huggingface.co/gagan3012/k2t)
- `k2t-tiny`: [Model](https://huggingface.co/gagan3012/k2t-tiny)
- `k2t-base`: [Model](https://huggingface.co/gagan3012/k2t-base)
Training Notebooks can be found in the [`Training Notebooks`](https://github.com/gagan3012/keytotext/tree/master/Training%20Notebooks) Folder
## Usage:
Example usage: [](https://colab.research.google.com/github/gagan3012/keytotext/blob/master/Examples/K2T.ipynb)
Example Notebooks can be found in the [`Notebooks`](https://github.com/gagan3012/keytotext/tree/master/Examples) Folder
```
pip install keytotext
```
## UI:
UI: [](https://share.streamlit.io/gagan3012/keytotext/UI/app.py)
```
pip install streamlit-tags
```
This uses a custom streamlit component built by me: [GitHub](https://github.com/gagan3012/streamlit-tags)
 |
khizon/distilbert-unreliable-news-eng-6L | 47eb057d757cff8551fffa8ad8c4558a4a15c032 | 2022-01-12T11:59:39.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
]
| text-classification | false | khizon | null | khizon/distilbert-unreliable-news-eng-6L | 5 | null | transformers | 16,669 | Entry not found |
kingabzpro/wav2vec2-large-xls-r-300m-Indonesian | 001d73c81037b690976bb64f8fab8f5e60cd3a34 | 2022-03-23T18:29:19.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"id",
"dataset:mozilla-foundation/common_voice_7_0",
"transformers",
"hf-asr-leaderboard",
"robust-speech-event",
"license:apache-2.0",
"model-index"
]
| automatic-speech-recognition | false | kingabzpro | null | kingabzpro/wav2vec2-large-xls-r-300m-Indonesian | 5 | null | transformers | 16,670 | ---
language:
- id
license: apache-2.0
tags:
- automatic-speech-recognition
- hf-asr-leaderboard
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
metrics:
- wer
- cer
model-index:
- name: wav2vec2-large-xls-r-300m-Indonesian
results:
- task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
type: mozilla-foundation/common_voice_7_0
name: Common Voice id
args: id
metrics:
- type: wer
value: 25.06
name: Test WER With LM
- type: cer
value: 6.5
name: Test CER With LM
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: id
metrics:
- name: Test WER
type: wer
value: 99.61
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: id
metrics:
- name: Test WER
type: wer
value: 106.39
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-Indonesian
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4087
- Wer: 0.2461
- Cer: 0.0666
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 400
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 5.0788 | 4.26 | 200 | 2.9389 | 1.0 | 1.0 |
| 2.8288 | 8.51 | 400 | 2.2535 | 1.0 | 0.8004 |
| 0.907 | 12.77 | 600 | 0.4558 | 0.4243 | 0.1095 |
| 0.4071 | 17.02 | 800 | 0.4013 | 0.3468 | 0.0913 |
| 0.3 | 21.28 | 1000 | 0.4167 | 0.3075 | 0.0816 |
| 0.2544 | 25.53 | 1200 | 0.4132 | 0.2835 | 0.0762 |
| 0.2145 | 29.79 | 1400 | 0.3878 | 0.2693 | 0.0729 |
| 0.1923 | 34.04 | 1600 | 0.4023 | 0.2623 | 0.0702 |
| 0.1681 | 38.3 | 1800 | 0.3984 | 0.2581 | 0.0686 |
| 0.1598 | 42.55 | 2000 | 0.3982 | 0.2493 | 0.0663 |
| 0.1464 | 46.81 | 2200 | 0.4087 | 0.2461 | 0.0666 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
|
kingla6/distilbert-magazine-classifier | d70f97f54c1ecfd8f1ccf6d11324727bbadf4dc6 | 2022-01-07T16:14:25.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | kingla6 | null | kingla6/distilbert-magazine-classifier | 5 | null | transformers | 16,671 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
model-index:
- name: distilbert-magazine-classifier
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-magazine-classifier
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8377
- Precision: 0.25
- Recall: 0.125
- Fscore: 0.1667
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Fscore |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.1779 | 1.0 | 2 | 1.7584 | 0.2222 | 0.3333 | 0.2667 |
| 0.1635 | 2.0 | 4 | 1.7585 | 0.25 | 0.125 | 0.1667 |
| 0.1405 | 3.0 | 6 | 1.8377 | 0.25 | 0.125 | 0.1667 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
kizunasunhy/distilbert-base-uncased-finetuned-squad | c1fb3e8581040460df2d2eb1f2c39e982221498e | 2021-10-13T08:37:15.000Z | [
"pytorch",
"distilbert",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | false | kizunasunhy | null | kizunasunhy/distilbert-base-uncased-finetuned-squad | 5 | null | transformers | 16,672 | Entry not found |
korca/textfooler-roberta-base-boolq | 1f3f63676802157cdde53bd04c5c89b94bdad79e | 2022-01-31T15:46:35.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers"
]
| text-classification | false | korca | null | korca/textfooler-roberta-base-boolq | 5 | null | transformers | 16,673 | Entry not found |
kornosk/bert-election2020-twitter-stance-trump | 36311a4ad7200ac54d3e3aff37daee69d6472888 | 2022-05-02T22:59:13.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"en",
"transformers",
"twitter",
"stance-detection",
"election2020",
"politics",
"license:gpl-3.0"
]
| text-classification | false | kornosk | null | kornosk/bert-election2020-twitter-stance-trump | 5 | 1 | transformers | 16,674 | ---
language: "en"
tags:
- twitter
- stance-detection
- election2020
- politics
license: "gpl-3.0"
---
# Pre-trained BERT on Twitter US Election 2020 for Stance Detection towards Donald Trump (f-BERT)
Pre-trained weights for **f-BERT** in [Knowledge Enhance Masked Language Model for Stance Detection](https://www.aclweb.org/anthology/2021.naacl-main.376), NAACL 2021.
# Training Data
This model is pre-trained on over 5 million English tweets about the 2020 US Presidential Election. Then fine-tuned using our [stance-labeled data](https://github.com/GU-DataLab/stance-detection-KE-MLM) for stance detection towards Donald Trump.
# Training Objective
This model is initialized with BERT-base and trained with normal MLM objective with classification layer fine-tuned for stance detection towards Donald Trump.
# Usage
This pre-trained language model is fine-tuned to the stance detection task specifically for Donald Trump.
Please see the [official repository](https://github.com/GU-DataLab/stance-detection-KE-MLM) for more detail.
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import numpy as np
# choose GPU if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# select mode path here
pretrained_LM_path = "kornosk/bert-election2020-twitter-stance-trump"
# load model
tokenizer = AutoTokenizer.from_pretrained(pretrained_LM_path)
model = AutoModelForSequenceClassification.from_pretrained(pretrained_LM_path)
id2label = {
0: "AGAINST",
1: "FAVOR",
2: "NONE"
}
##### Prediction Neutral #####
sentence = "Hello World."
inputs = tokenizer(sentence.lower(), return_tensors="pt")
outputs = model(**inputs)
predicted_probability = torch.softmax(outputs[0], dim=1)[0].tolist()
print("Sentence:", sentence)
print("Prediction:", id2label[np.argmax(predicted_probability)])
print("Against:", predicted_probability[0])
print("Favor:", predicted_probability[1])
print("Neutral:", predicted_probability[2])
##### Prediction Favor #####
sentence = "Go Go Trump!!!"
inputs = tokenizer(sentence.lower(), return_tensors="pt")
outputs = model(**inputs)
predicted_probability = torch.softmax(outputs[0], dim=1)[0].tolist()
print("Sentence:", sentence)
print("Prediction:", id2label[np.argmax(predicted_probability)])
print("Against:", predicted_probability[0])
print("Favor:", predicted_probability[1])
print("Neutral:", predicted_probability[2])
##### Prediction Against #####
sentence = "Trump is the worst."
inputs = tokenizer(sentence.lower(), return_tensors="pt")
outputs = model(**inputs)
predicted_probability = torch.softmax(outputs[0], dim=1)[0].tolist()
print("Sentence:", sentence)
print("Prediction:", id2label[np.argmax(predicted_probability)])
print("Against:", predicted_probability[0])
print("Favor:", predicted_probability[1])
print("Neutral:", predicted_probability[2])
# please consider citing our paper if you feel this is useful :)
```
# Reference
- [Knowledge Enhance Masked Language Model for Stance Detection](https://www.aclweb.org/anthology/2021.naacl-main.376), NAACL 2021.
# Citation
```bibtex
@inproceedings{kawintiranon2021knowledge,
title={Knowledge Enhanced Masked Language Model for Stance Detection},
author={Kawintiranon, Kornraphop and Singh, Lisa},
booktitle={Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
year={2021},
publisher={Association for Computational Linguistics},
url={https://www.aclweb.org/anthology/2021.naacl-main.376}
}
``` |
LACAI/DialoGPT-small-PFG | 905128cbb27d2375d85d11398bf468331ab9decc | 2022-01-14T01:36:36.000Z | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | false | LACAI | null | LACAI/DialoGPT-small-PFG | 5 | null | transformers | 16,675 | Base model: [microsoft/DialoGPT-small](https://huggingface.co/microsoft/DialoGPT-small)
Fine tuned for dialogue response generation on the [Persuasion For Good Dataset](https://gitlab.com/ucdavisnlp/persuasionforgood) (Wang et al., 2019)
Three additional special tokens were added during the fine-tuning process:
- <|pad|> padding token
- <|user|> speaker control token to prompt user responses
- <|system|> speaker control token to prompt system responses
The following Dialogues were excluded:
- Those with donation amounts outside of the task range of [$0, $2].
- Those where a donation of 0 was made at the end of the task but a non-zero amount was pledged in the dialogue.
- Those with more than 800 words.
Stats:
- Training set: 519 dialogues
- Validation set: 58 dialogues
- ~20 utterances per dialogue |
lalopey/pearkes | bfcb1e4db3eacbe482a9d18c9caf15145b23fb3c | 2021-05-23T06:26:23.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | false | lalopey | null | lalopey/pearkes | 5 | null | transformers | 16,676 | Entry not found |
laxya007/gpt2_TS_DM_AS_CC_TM_HCU | 9c3035d5ba3f4ea627bfabe9e47d911b3129c8b6 | 2022-01-03T13:02:48.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | false | laxya007 | null | laxya007/gpt2_TS_DM_AS_CC_TM_HCU | 5 | null | transformers | 16,677 | Entry not found |
ldacunto/distilbert-base-uncased-finetuned-cola | cb437f688b9670a1c2e7a2047b9517b8b787346f | 2022-01-14T13:56:25.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
]
| text-classification | false | ldacunto | null | ldacunto/distilbert-base-uncased-finetuned-cola | 5 | null | transformers | 16,678 | Entry not found |
leemeng/core-term-ner-v1 | ab933f86880e37afeded6fe1e4a2de8665f26567 | 2021-05-19T21:21:42.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | leemeng | null | leemeng/core-term-ner-v1 | 5 | null | transformers | 16,679 | Entry not found |
leolin12345/fine-tune-lr | faf831b94bc9cf92a0c31870bd45630fed50364e | 2022-02-25T00:51:08.000Z | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | false | leolin12345 | null | leolin12345/fine-tune-lr | 5 | null | transformers | 16,680 | Entry not found |
leslie/bert_finetuning_test | 9f9f87e39a98bd84c3c324895b701584fae9544a | 2021-05-19T21:22:40.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | leslie | null | leslie/bert_finetuning_test | 5 | null | transformers | 16,681 | Entry not found |
lewtun/xlm-roberta-base-finetuned-marc-19964-samples | 70b8186e43391135ddd6d5f9ea0cc7d7c88a6b52 | 2021-10-15T14:41:57.000Z | [
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"transformers"
]
| text-classification | false | lewtun | null | lewtun/xlm-roberta-base-finetuned-marc-19964-samples | 5 | null | transformers | 16,682 | Entry not found |
lewtun/xlm-roberta-base-finetuned-marc-en-hslu | 1c482ee39eacbfc8caebe68f65fc8957a569e35e | 2021-12-16T14:55:28.000Z | [
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"dataset:amazon_reviews_multi",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
]
| text-classification | false | lewtun | null | lewtun/xlm-roberta-base-finetuned-marc-en-hslu | 5 | null | transformers | 16,683 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
model-index:
- name: xlm-roberta-base-finetuned-marc-en-hslu
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-marc-en-hslu
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8826
- Mae: 0.5
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1121 | 1.0 | 235 | 0.9400 | 0.5732 |
| 0.9487 | 2.0 | 470 | 0.8826 | 0.5 |
### Framework versions
- Transformers 4.14.1
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
lgris/wav2vec2_base_10k_8khz_pt_cv7_2 | 76671014338f0ba0da780f1367df636c0d3947e7 | 2022-03-23T18:34:03.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"transformers",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"license:apache-2.0",
"model-index"
]
| automatic-speech-recognition | false | lgris | null | lgris/wav2vec2_base_10k_8khz_pt_cv7_2 | 5 | null | transformers | 16,684 | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_7_0
- pt
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: wav2vec2_base_10k_8khz_pt_cv7_2
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: pt
metrics:
- name: Test WER
type: wer
value: 36.9
- name: Test CER
type: cer
value: 14.82
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: sv
metrics:
- name: Test WER
type: wer
value: 40.53
- name: Test CER
type: cer
value: 16.95
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: pt
metrics:
- name: Test WER
type: wer
value: 37.15
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: pt
metrics:
- name: Test WER
type: wer
value: 38.95
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2_base_10k_8khz_pt_cv7_2
This model is a fine-tuned version of [lgris/seasr_2022_base_10k_8khz_pt](https://huggingface.co/lgris/seasr_2022_base_10k_8khz_pt) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 76.3426
- Wer: 0.1979
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 189.1362 | 0.65 | 500 | 80.6347 | 0.2139 |
| 174.2587 | 1.3 | 1000 | 80.2062 | 0.2116 |
| 164.676 | 1.95 | 1500 | 78.2161 | 0.2073 |
| 176.5856 | 2.6 | 2000 | 78.8920 | 0.2074 |
| 164.3583 | 3.25 | 2500 | 77.2865 | 0.2066 |
| 161.414 | 3.9 | 3000 | 77.8888 | 0.2048 |
| 158.283 | 4.55 | 3500 | 77.3472 | 0.2033 |
| 159.2265 | 5.19 | 4000 | 79.0953 | 0.2036 |
| 156.3967 | 5.84 | 4500 | 76.6855 | 0.2029 |
| 154.2743 | 6.49 | 5000 | 77.7785 | 0.2015 |
| 156.6497 | 7.14 | 5500 | 77.1220 | 0.2033 |
| 157.3038 | 7.79 | 6000 | 76.2926 | 0.2027 |
| 162.8151 | 8.44 | 6500 | 76.7602 | 0.2013 |
| 151.8613 | 9.09 | 7000 | 77.4777 | 0.2011 |
| 153.0225 | 9.74 | 7500 | 76.5206 | 0.2001 |
| 157.52 | 10.39 | 8000 | 76.1061 | 0.2006 |
| 145.0592 | 11.04 | 8500 | 76.7855 | 0.1992 |
| 150.0066 | 11.69 | 9000 | 76.0058 | 0.1988 |
| 146.8128 | 12.34 | 9500 | 76.2853 | 0.1987 |
| 146.9148 | 12.99 | 10000 | 76.3426 | 0.1979 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
lhoestq/test-albert-upload | 2ad0f93d116fda07b07a40db384db2099790bc2a | 2021-05-11T15:41:35.000Z | [
"pytorch",
"albert",
"feature-extraction",
"transformers"
]
| feature-extraction | false | lhoestq | null | lhoestq/test-albert-upload | 5 | null | transformers | 16,685 | Entry not found |
liamliang/demographics_race | e5e08bf7d7f52c166c414f714138a3eae91171ea | 2021-05-19T21:57:27.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | liamliang | null | liamliang/demographics_race | 5 | null | transformers | 16,686 | Entry not found |
liangxiaoxiao/bert_finetuning_test | 55c8f5bd23e87ce42ff502931cfa59e48436be56 | 2021-05-19T22:01:38.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | liangxiaoxiao | null | liangxiaoxiao/bert_finetuning_test | 5 | null | transformers | 16,687 | Entry not found |
llange/xlm-roberta-large-spanish | 87965ab2356326c1db48f670ee449f05424d1c9f | 2021-12-16T11:24:16.000Z | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | false | llange | null | llange/xlm-roberta-large-spanish | 5 | null | transformers | 16,688 | # Spanish XLM-R (from NLNDE-MEDDOPROF)
This Spanish language model was created for the MEDDOPROF shared task as part of the **NLNDE** team submission and outperformed all other participants in both sequence labeling tasks.
Details on the model, the pre-training corpus and the downstream task performance are given in the paper: "Boosting Transformers for Job Expression Extraction and Classification in a Low-Resource Setting" by Lukas Lange, Heike Adel and Jannik Strötgen.
The paper can be found [here](http://ceur-ws.org/Vol-2943/meddoprof_paper1.pdf).
In case of questions, please contact the authors as listed on the paper.
Please cite the above paper when reporting, reproducing or extending the results.
@inproceedings{lange-etal-2021-meddoprof,
author = {Lukas Lange and
Heike Adel and
Jannik Str{\"{o}}tgen},
title = {Boosting Transformers for Job Expression Extraction and Classification in a Low-Resource Setting},
year={2021},
booktitle= {{Proceedings of The Iberian Languages Evaluation Forum (IberLEF 2021)}},
series = {{CEUR} Workshop Proceedings},
url = {http://ceur-ws.org/Vol-2943/meddoprof_paper1.pdf},
}
## Training details
We use XLM-R (`xlm-roberta-large`, Conneau et al. 2020) as the main component of our models. XLM-R is a pretrained multilingual transformer model for 100 languages, including Spanish. It shows superior performance in different tasks across languages, and can even outperform
monolingual models in certain settings. It was pretrained on a large-scale corpus,
and Spanish documents made up only 2% of this data.
Thus, we explore further pretraining of this model and tune it towards Spanish
documents by pretraining a medium-size Spanish corpus with general
domain documents. For this, we use the [spanish corpus](https://github.com/josecannete/spanish-corpora) used to train the BETO model.
We use masked language modeling for pretraining and trained for three epochs
over the corpus, which roughly corresponds to 685k steps using a batch-size of 4.
## Performance
This model was trained in the context of the Meddoprof shared tasks and outperformed all other participants in both sequence labeling tasks. Our results (F1) in comparison with the standard XLM-R and the second-best system of the shared task are given in the Table.
More information on the shared task and other participants is given in this paper [here](http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6393/3813).
The code for our NER models can be found [here](https://github.com/boschresearch/nlnde-meddoprof).
| | Meddoprof Task 1 (NER) | Meddoprof Task 2 (CLASS) |
|---------------------------------|------------------------|--------------------------|
| Second-best System | 80.0 | 76.4 |
| XLM-R (our baseline) | 79.2 | 77.6 |
| Our Spanish XLM-R (best System) | **83.2** | **79.1** |
## Purpose of the project
This software is a research prototype, solely developed for and published as part of the publication cited above. It will neither be maintained nor monitored in any way.
## License
The CLIN-X models are open-sourced under the CC-BY 4.0 license.
See the [LICENSE](LICENSE) file for details. |
longcld/t5-small-e2e-qa-full | 03f4e3d6cc8052325f171739e7115af4be4fd6fe | 2021-09-20T07:45:02.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | longcld | null | longcld/t5-small-e2e-qa-full | 5 | null | transformers | 16,689 | Entry not found |
longcld/t5_small_qg_ae_hl | 3a9499c8548c4bacfa03542d49223856e1c37fba | 2021-07-26T00:56:13.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | longcld | null | longcld/t5_small_qg_ae_hl | 5 | null | transformers | 16,690 | Entry not found |
luffycodes/bb_narataka_roberta_large_nli_bsz_16_bb_bsz_16_nli_lr_3e5_bb_lr_3e5_wu_7k_grad_adam | 295acf00689a4eae2feb24a4d7cc900e20d2cb52 | 2021-10-30T03:15:24.000Z | [
"pytorch",
"roberta",
"transformers"
]
| null | false | luffycodes | null | luffycodes/bb_narataka_roberta_large_nli_bsz_16_bb_bsz_16_nli_lr_3e5_bb_lr_3e5_wu_7k_grad_adam | 5 | null | transformers | 16,691 | Entry not found |
lysandre/dummy | 771d33611eeded8a62de6bfae47e089ee76bfb10 | 2021-11-05T15:09:39.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | lysandre | null | lysandre/dummy | 5 | null | transformers | 16,692 | # My dummy model
Welcome to my model page!
Central definition, reproducibility tips, code samples below! |
m3hrdadfi/wili2018-roberta-base | 01a5dafa0e043fb77cda29d4affe5deeac2d618b | 2021-06-25T05:09:51.000Z | [
"pytorch",
"tf",
"roberta",
"fill-mask",
"multilingual",
"dataset:wili_2018",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | false | m3hrdadfi | null | m3hrdadfi/wili2018-roberta-base | 5 | null | transformers | 16,693 | ---
language: multilingual
license: apache-2.0
datasets:
- wili_2018
---
|
madlag/bert-base-uncased-squad-v1-sparse0.25 | 23d9c7d50179c9b62549d51cf501319281307e7a | 2021-05-19T22:31:23.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"question-answering",
"en",
"dataset:squad",
"arxiv:2005.07683",
"transformers",
"bert-base",
"license:mit",
"autotrain_compatible"
]
| question-answering | false | madlag | null | madlag/bert-base-uncased-squad-v1-sparse0.25 | 5 | null | transformers | 16,694 | ---
language: en
thumbnail:
license: mit
tags:
- question-answering
- bert
- bert-base
datasets:
- squad
metrics:
- squad
widget:
- text: "Where is located the Eiffel Tower ?"
context: "The Eiffel Tower is a wrought-iron lattice tower on the Champ de Mars in Paris, France. It is named after the engineer Gustave Eiffel, whose company designed and built the tower."
- text: "Who is Frederic Chopin?"
context: "Frédéric François Chopin, born Fryderyk Franciszek Chopin (1 March 1810 – 17 October 1849), was a Polish composer and virtuoso pianist of the Romantic era who wrote primarily for solo piano."
---
## BERT-base uncased model fine-tuned on SQuAD v1
This model is [block-sparse](https://github.com/huggingface/pytorch_block_sparse).
That means that with the right runtime it can run roughly 3x faster than an dense network, with 25% of the original weights.
This of course has some impact on the accuracy (see below).
It uses a modified version of Victor Sanh [Movement Pruning](https://arxiv.org/abs/2005.07683) method.
This model was fine-tuned from the HuggingFace [BERT](https://www.aclweb.org/anthology/N19-1423/) base uncased checkpoint on [SQuAD1.1](https://rajpurkar.github.io/SQuAD-explorer).
This model is case-insensitive: it does not make a difference between english and English.
## Details
| Dataset | Split | # samples |
| -------- | ----- | --------- |
| SQuAD1.1 | train | 90.6K |
| SQuAD1.1 | eval | 11.1k |
### Fine-tuning
- Python: `3.8.5`
- Machine specs:
`CPU: Intel(R) Core(TM) i7-6700K CPU`
`Memory: 64 GiB`
`GPUs: 1 GeForce GTX 3090, with 24GiB memory`
`GPU driver: 455.23.05, CUDA: 11.1`
### Results
**Model size**: `418M`
| Metric | # Value | # Original ([Table 2](https://www.aclweb.org/anthology/N19-1423.pdf))|
| ------ | --------- | --------- |
| **EM** | **74.82** | **80.8** |
| **F1** | **83.7** | **88.5** |
Note that the above results didn't involve any hyperparameter search.
## Example Usage
```python
from transformers import pipeline
qa_pipeline = pipeline(
"question-answering",
model="madlag/bert-base-uncased-squad-v1-sparse0.25",
tokenizer="madlag/bert-base-uncased-squad-v1-sparse0.25"
)
predictions = qa_pipeline({
'context': "Frédéric François Chopin, born Fryderyk Franciszek Chopin (1 March 1810 – 17 October 1849), was a Polish composer and virtuoso pianist of the Romantic era who wrote primarily for solo piano.",
'question': "Who is Frederic Chopin?",
})
print(predictions)
|
mamlong34/t5_base_race_cosmos_qa | b3698d2855d1f2244c4678c131def2e223eef8a6 | 2021-10-12T07:17:10.000Z | [
"pytorch",
"t5",
"text2text-generation",
"dataset:race",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
]
| text2text-generation | false | mamlong34 | null | mamlong34/t5_base_race_cosmos_qa | 5 | null | transformers | 16,695 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- race
metrics:
- accuracy
model-index:
- name: t5_base_race_cosmos_qa
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5_base_race_cosmos_qa
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the race dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4414
- Accuracy: 0.7424
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 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: 100
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4355 | 1.0 | 10984 | 0.3910 | 0.7072 |
| 0.3233 | 2.0 | 21968 | 0.3833 | 0.7321 |
| 0.229 | 3.0 | 32952 | 0.4414 | 0.7424 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0
- Datasets 1.12.1
- Tokenizers 0.10.3
|
marcelcastrobr/sagemaker-distilbert-emotion | 6ce99f6e59e55d279745f109349d9ffe4dd388c9 | 2021-11-19T12:56:45.000Z | [
"pytorch",
"distilbert",
"text-classification",
"dataset:emotion",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | marcelcastrobr | null | marcelcastrobr/sagemaker-distilbert-emotion | 5 | null | transformers | 16,696 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: sagemaker-distilbert-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.928
---
<!-- 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. -->
# sagemaker-distilbert-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1477
- Accuracy: 0.928
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9308 | 1.0 | 500 | 0.2632 | 0.916 |
| 0.1871 | 2.0 | 1000 | 0.1651 | 0.926 |
| 0.1025 | 3.0 | 1500 | 0.1477 | 0.928 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.9.1
- Datasets 1.15.1
- Tokenizers 0.10.3
|
marciovbarbosa/t5-small-finetuned-de-to-en-lr1e-4 | a25a8c33704278ef3a3f08830029f75c66fff1db | 2021-12-04T02:55:33.000Z | [
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"dataset:wmt16",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
]
| text2text-generation | false | marciovbarbosa | null | marciovbarbosa/t5-small-finetuned-de-to-en-lr1e-4 | 5 | null | transformers | 16,697 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wmt16
metrics:
- bleu
model-index:
- name: t5-small-finetuned-de-to-en-lr1e-4
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: wmt16
type: wmt16
args: de-en
metrics:
- name: Bleu
type: bleu
value: 11.427
---
<!-- 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-de-to-en-lr1e-4
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt16 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8228
- Bleu: 11.427
- Gen Len: 17.2674
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| No log | 1.0 | 272 | 1.9605 | 9.0786 | 17.3148 |
| 2.3992 | 2.0 | 544 | 1.8884 | 10.1443 | 17.3301 |
| 2.3992 | 3.0 | 816 | 1.8647 | 10.4816 | 17.3258 |
| 2.0832 | 4.0 | 1088 | 1.8473 | 10.7396 | 17.3231 |
| 2.0832 | 5.0 | 1360 | 1.8343 | 11.0937 | 17.2621 |
| 1.9193 | 6.0 | 1632 | 1.8282 | 11.1303 | 17.3098 |
| 1.9193 | 7.0 | 1904 | 1.8234 | 11.2971 | 17.2991 |
| 1.8351 | 8.0 | 2176 | 1.8241 | 11.3433 | 17.2621 |
| 1.8351 | 9.0 | 2448 | 1.8224 | 11.394 | 17.2691 |
| 1.7747 | 10.0 | 2720 | 1.8228 | 11.427 | 17.2674 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
marcolatella/irony_trained | a7d3a3fdd645fc24aa59914cd1976b23d8d59d75 | 2021-12-10T23:03:44.000Z | [
"pytorch",
"distilbert",
"text-classification",
"dataset:tweet_eval",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | marcolatella | null | marcolatella/irony_trained | 5 | null | transformers | 16,698 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: irony_trained
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: irony
metrics:
- name: F1
type: f1
value: 0.6946397550129713
---
<!-- 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. -->
# irony_trained
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6720
- F1: 0.6946
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2.6375567293432486e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.6643 | 1.0 | 716 | 0.5958 | 0.6776 |
| 0.5633 | 2.0 | 1432 | 0.8863 | 0.6759 |
| 0.348 | 3.0 | 2148 | 1.4215 | 0.6817 |
| 0.2192 | 4.0 | 2864 | 1.6720 | 0.6946 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
markg/swda-test | 05edf7a27c19ceef1818711844b753eb3ff646e4 | 2021-08-23T18:38:38.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | false | markg | null | markg/swda-test | 5 | null | transformers | 16,699 | Entry not found |
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