modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-11 06:30:11
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 555
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-09-11 06:29:58
| card
stringlengths 11
1.01M
|
|---|---|---|---|---|---|---|---|---|---|
adityavithaldas/distilbert-base-uncased-finetuned-ner
|
adityavithaldas
| 2021-09-22T19:33:37Z | 6 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
model-index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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
### Framework versions
- Transformers 4.10.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
|
LysandreJik/testing
|
LysandreJik
| 2021-09-22T19:19:12Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:04Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: testing
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.6813725490196079
- name: F1
type: f1
value: 0.8104956268221574
---
<!-- 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. -->
# testing
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6644
- Accuracy: 0.6814
- F1: 0.8105
- Combined Score: 0.7459
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10
### Training results
### Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.11.0
- Tokenizers 0.10.3
|
huggingtweets/rishiosaur
|
huggingtweets
| 2021-09-22T18:19:38Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/rishiosaur/1632334774825/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/1429632040673103878/I5Xe_evK_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">rishi ⠕</div>
<div style="text-align: center; font-size: 14px;">@rishiosaur</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 rishi ⠕.
| Data | rishi ⠕ |
| --- | --- |
| Tweets downloaded | 1333 |
| Retweets | 523 |
| Short tweets | 162 |
| Tweets kept | 648 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2d049rbc/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 @rishiosaur's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2n4pe9ce) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2n4pe9ce/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/rishiosaur')
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)
|
nateraw/resnet50-beans-dummy-sagemaker
|
nateraw
| 2021-09-22T18:01:58Z | 10 | 0 |
timm
|
[
"timm",
"pytorch",
"tensorboard",
"image-classification",
"generated_from_trainer",
"dataset:beans",
"region:us"
] |
image-classification
| 2022-03-02T23:29:05Z |
---
tags:
- image-classification
- timm
- generated_from_trainer
datasets:
- beans
model-index:
- name: model
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: beans
type: beans
args: default
library_tag: timm
---
<!-- 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. -->
# model
This model is a fine-tuned version of [resnet18](https://huggingface.co/resnet18) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0219
- Acc1: 56.3910
- Acc5: 100.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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
- training_steps: 20
### Training results
### Framework versions
- Transformers 4.10.2
- Pytorch 1.7.1
- Datasets 1.12.1
- Tokenizers 0.10.3
|
flax-community/RoBERTa-large-finnish
|
flax-community
| 2021-09-22T17:31:14Z | 11 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"finnish",
"fi",
"dataset:mc4",
"arxiv:1907.11692",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language:
- fi
license: apache-2.0
tags:
- finnish
- roberta
datasets:
- mc4
widget:
- text: "Moikka olen <mask> kielimalli."
---
# NOTE: We have trained newer and better Finnish RoBERTa large model which can be found from different repository: [https://huggingface.co/Finnish-NLP/roberta-large-finnish](https://huggingface.co/Finnish-NLP/roberta-large-finnish). Our future Finnish models will be available at the [Finnish-NLP](https://huggingface.co/Finnish-NLP) Hugging Face organization
# RoBERTa large model for Finnish
Pretrained model on Finnish language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1907.11692) and first released in
[this repository](https://github.com/pytorch/fairseq/tree/master/examples/roberta). This model is case-sensitive: it
makes a difference between finnish and Finnish.
## Model description
RoBERTa is a transformers model pretrained on a large corpus of Finnish data in a self-supervised fashion. This means
it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts.
More precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model
randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict
the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one
after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to
learn a bidirectional representation of the sentence.
This way, the model learns an inner representation of the Finnish language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
classifier using the features produced by the RoBERTa model as inputs.
## Intended uses & limitations
You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.
### How to use
You can use this model directly with a pipeline for masked language modeling:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='flax-community/RoBERTa-large-finnish')
>>> unmasker("Moikka olen <mask> kielimalli.")
[{'sequence': 'Moikka olen uusi kielimalli.',
'score': 0.05129234120249748,
'token': 1825,
'token_str': ' uusi'},
{'sequence': 'Moikka olen toinen kielimalli.',
'score': 0.03112379088997841,
'token': 2194,
'token_str': ' toinen'},
{'sequence': 'Moikka olen myös kielimalli.',
'score': 0.025534993037581444,
'token': 491,
'token_str': ' myös'},
{'sequence': 'Moikka olen ensimmäinen kielimalli.',
'score': 0.020146571099758148,
'token': 2832,
'token_str': ' ensimmäinen'},
{'sequence': 'Moikka olen vapaa kielimalli.',
'score': 0.018089469522237778,
'token': 2257,
'token_str': ' vapaa'}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('flax-community/RoBERTa-large-finnish')
model = RobertaModel.from_pretrained('flax-community/RoBERTa-large-finnish')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('flax-community/RoBERTa-large-finnish')
model = TFRobertaModel.from_pretrained('flax-community/RoBERTa-large-finnish', from_pt=True)
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
### Limitations and bias
The training data used for this model contains a lot of unfiltered content from the internet, which is far from
neutral. Therefore, the model can have biased predictions.
## Training data
This Finnish RoBERTa model was pretrained on the combination of two datasets:
- [mc4](https://huggingface.co/datasets/mc4), the dataset mC4 is a multilingual colossal, cleaned version of Common Crawl's web crawl corpus. We used the Finnish subset of the mC4 dataset
- [Yle Finnish News Archive](http://urn.fi/urn:nbn:fi:lb-2017070501)
Raw datasets were cleaned to filter out bad quality and non-Finnish examples. Together these cleaned datasets were around 51GB of text.
## Training procedure
### Preprocessing
The texts are tokenized using a byte version of Byte-Pair Encoding (BPE) and a vocabulary size of 50265. The inputs of
the model take pieces of 512 contiguous token that may span over documents. The beginning of a new document is marked
with `<s>` and the end of one by `</s>`
The details of the masking procedure for each sentence are the following:
- 15% of the tokens are masked.
- In 80% of the cases, the masked tokens are replaced by `<mask>`.
- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
- In the 10% remaining cases, the masked tokens are left as is.
Contrary to BERT, the masking is done dynamically during pretraining (e.g., it changes at each epoch and is not fixed).
### Pretraining
The model was trained on TPUv3-8 VM, sponsored by the Hugging Face JAX/Flax community week event, for 2 epochs with a sequence length of 128 and continuing for one more epoch with a sequence length of 512. The optimizer used is Adafactor with a learning rate of 2e-4, \\(\beta_{1} = 0.9\\), \\(\beta_{2} = 0.98\\) and \\(\epsilon = 1e-6\\), learning rate warmup for 1500 steps and linear decay of the learning rate after.
## Evaluation results
Evaluation was done by fine-tuning the model on downstream text classification task with two different labeled datasets: [Yle News](https://github.com/spyysalo/yle-corpus) and [Eduskunta](https://github.com/aajanki/eduskunta-vkk). Yle News classification fine-tuning was done with two different sequence lengths: 128 and 512 but Eduskunta only with 128 sequence length.
When fine-tuned on those datasets, this model (the first row of the table) achieves the following accuracy results compared to the [FinBERT (Finnish BERT)](https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1) and to our newer [Finnish RoBERTa-large](https://huggingface.co/Finnish-NLP/roberta-large-finnish) trained with larger dataset:
| | Average | Yle News 128 length | Yle News 512 length | Eduskunta 128 length |
|----------------------------------------|----------|---------------------|---------------------|----------------------|
|flax-community/RoBERTa-large-finnish |87.72 |94.42 |95.06 |73.67 |
|Finnish-NLP/roberta-large-finnish |88.02 |94.53 |95.23 |74.30 |
|TurkuNLP/bert-base-finnish-cased-v1 |**88.82** |**94.90** |**95.49** |**76.07** |
To conclude, this model slightly loses to our newer [Finnish RoBERTa-large](https://huggingface.co/Finnish-NLP/roberta-large-finnish) model trained with larger dataset and also slightly loses to the [FinBERT (Finnish BERT)](https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1) model.
## Team Members
- Aapo Tanskanen, [Hugging Face profile](https://huggingface.co/aapot), [LinkedIn profile](https://www.linkedin.com/in/aapotanskanen/)
- Rasmus Toivanen [Hugging Face profile](https://huggingface.co/RASMUS), [LinkedIn profile](https://www.linkedin.com/in/rasmustoivanen/)
- Tommi Vehviläinen [Hugging Face profile](https://huggingface.co/Tommi)
Feel free to contact us for more details 🤗
|
Haotian/distilgpt2-finetuned-wikitext2
|
Haotian
| 2021-09-22T12:24:29Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-wikitext2
results:
- task:
name: Causal Language Modeling
type: text-generation
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6424
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.7608 | 1.0 | 2334 | 3.6655 |
| 3.6335 | 2.0 | 4668 | 3.6455 |
| 3.6066 | 3.0 | 7002 | 3.6424 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.0
- Tokenizers 0.10.3
|
cfisicaro/distilbert-base-uncased-finetuned-ner
|
cfisicaro
| 2021-09-22T10:25:03Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9281908990011098
- name: Recall
type: recall
value: 0.9355632621098557
- name: F1
type: f1
value: 0.9318624993035824
- name: Accuracy
type: accuracy
value: 0.9837641190207635
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0629
- Precision: 0.9282
- Recall: 0.9356
- F1: 0.9319
- Accuracy: 0.9838
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2406 | 1.0 | 878 | 0.0721 | 0.9072 | 0.9172 | 0.9122 | 0.9801 |
| 0.0529 | 2.0 | 1756 | 0.0637 | 0.9166 | 0.9318 | 0.9241 | 0.9826 |
| 0.0315 | 3.0 | 2634 | 0.0629 | 0.9282 | 0.9356 | 0.9319 | 0.9838 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
|
imthanhlv/t5vi
|
imthanhlv
| 2021-09-22T09:57:47Z | 13 | 1 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
# T5 Vietnamese pretrain on news corpus
|
huggingartists/zemfira
|
huggingartists
| 2021-09-22T09:43:44Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/zemfira",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
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)
|
castorini/ance-dpr-context-multi
|
castorini
| 2021-09-22T09:41:18Z | 110 | 2 |
transformers
|
[
"transformers",
"pytorch",
"dpr",
"arxiv:2007.00808",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
This model is converted from the original ANCE [repo](https://github.com/microsoft/ANCE) and fitted into Pyserini:
> Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul Bennett, Junaid Ahmed, Arnold Overwijk. [Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval](https://arxiv.org/pdf/2007.00808.pdf)
For more details on how to use it, check our experiments in [Pyserini](https://github.com/castorini/pyserini/blob/master/docs/experiments-ance.md)
|
petabyte/unang_mang_bert
|
petabyte
| 2021-09-22T09:33:40Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"Tagalog",
"Mang Bert",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
---
language:
- Tagalog
thumbnail:
tags:
- Tagalog
- Mang Bert
license: apache-2.0
datasets:
- OSCAR tl
---
# Mang Bert
## Model description
Fine-Tuned Roberta Model using RobertaForMaskedLM
Tagalog Dataset from OSCAR tl
## Training data
458206 text dataset from OSCAR
|
ozcangundes/mt5-small-turkish-squad
|
ozcangundes
| 2021-09-22T09:31:24Z | 33 | 3 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"mt5",
"text2text-generation",
"question-answering",
"tr",
"dataset:TQUAD",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
language: tr
datasets:
- TQUAD
pipeline_tag: question-answering
license: mit
---
# mT5-small based Turkish Question Answering System
[Google's Multilingual T5-small](https://github.com/google-research/multilingual-t5) is fine-tuned on [Turkish Question Answering dataset](https://github.com/TQuad/turkish-nlp-qa-dataset) for **Q&A** downstream task by using Pytorch Lightning.⚡
The notebook that includes all fine tuning process will be shared on my Github page later. mT5 small model has 300 million parameters and model size is about 1.2GB. Therefore, it takes significant amount of time to fine tune it.
**Important Note**: mT5 was only pre-trained on [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual)
excluding any supervised training. Therefore, the mT5 model has to be fine-tuned before it is useable on a downstream task.
## Usage 🚀
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("ozcangundes/mt5-small-turkish-squad")
model = AutoModelForSeq2SeqLM.from_pretrained("ozcangundes/mt5-small-turkish-squad")
def get_answer(question,context):
source_encoding=tokenizer(
question,
context,
max_length=512,
padding="max_length",
truncation="only_second",
return_attention_mask=True,
add_special_tokens=True,
return_tensors="pt")
generated_ids=model.generate(
input_ids=source_encoding["input_ids"],
attention_mask=source_encoding["attention_mask"],
max_length=120)
preds=[tokenizer.decode(gen_id, skip_special_tokens=True, clean_up_tokenization_spaces=True) for gen_id in generated_ids]
return "".join(preds)
```
### Example 1
```python
question={
"context":"Pardus, Google'ın öğrencilerle staj ve kendini geliştirme imkânı ile \
tasarılara geliştirici ve katkı sağlamayı amaçladığı açık kaynak tasarısı \
Google Summer of Code'a 2008 ve 2009 olmak üzere iki kere katılmıştır. Bu organizasyona \
ilk katılan Türk tasarısı Pardus olmuştur. Bazı dönemlerde Pardus hakkındaki gelişmeleri \
halka duyurmak ve tasarıya olan ilgiyi arttırmak amacıyla CeBIT Eurasia Bilişim Fuarı'na \
katılım sağlanmaktadır. 2006, 2008, 2009, 2010, 2011,2013 ve 2014 bu fuarlarda Pardus \
standı kurulmuştur.2014 yılında ICT SummitT Now Bilişim Zirvesi'nde yer alınmıştır. \
BİLİŞİM’2014 TBD 31. Ulusal Bilişim Kurultayı ve CITEX’2014 Ankara Bilişim Fuarı’na \
Gümüş sponsorluk ile katkıda bulunulmuş ve Pardus standı kurulmuştur.",
"question":"Pardus’un Google Summer of Code'a katıldığı yıllar nelerdir?"
}
get_answer(question["question"],question["context"])
```
> 2008 ve 2009
### Example 2
```python
question2={
"context":"II. Bayezid ve I. Selim devrinde yaşadı ve iki defa hekimbaşılık yaptı. \
Böbrek ve idrar kesesindeki taş oluşumunun nedenlerini ve tedavisini incelediği \
eseriyle tanınır. Adı kaynaklarda Ahmed ve Mahmud olarak da geçer. Ahi Çelebi \
olarak ün yapmıştır. Babası Tabib Mevlana Kemal ile birlikte 1463’te İstanbul’a yerleşti. \
Mevlana Kemal, devrin ünlü hekimlerindendir. Tebriz ya da Şirvan asıllı olduğu çeşitli \
kaynaklarda belirtilir. Ahi Mehmet Çelebi, hekimliği daha çok babasından öğrendi. Onun \
ölümünden sonra devrin önemli hekimleri Kutbüddin ile Altunîzâde’den ders alıp kısa zamanda \
mesleğini ilerletti. Hekimlik becerisinin yanı sıra kuramsal bilgisiyle de kendisini \
kabul ettirerek önce Fâtih Darüşşifasına hekim, sonra da başhekim oldu. II. Bayezid’in \
güvenini kazanarak mutfak eminliğine, ardından da Hekimbaşılığa getirildi. Dört buçuk \
yıl bu görevde kalan Ahî Çelebi, II. Bayezid’in ölümü üzerine geleneğe uyularak azledildi. \
Bir müddet sonra Yavuz onu tekrar Hekimbaşılığa getirdi ve Mısır seferine beraberinde \
götürdü. I. Selim'in ölümünden sonra Hekimbaşılık tan tekrar azledildi. Kaynakların \
belirttiğine göre, yaşı doksanı geçmiş olduğu halde, hacdan dönerken Kahire’de \
ölmüş ve İmam Şafi'nin kabri civarına defnedilmiştir.",
"question":"Ahi Mehmet Çelebi hangi eseri ile tanınır?"
}
get_answer(question2["question"],question2["context"])
```
> Böbrek ve idrar kesesindeki taş oluşumunun nedenlerini ve tedavisini incelediği eseriyle
Created by Özcan Gündeş ✌️
---
Twitter: <a href="https://twitter.com/ozcangundes" target="blank"><img align="center" src="https://cdn.jsdelivr.net/npm/simple-icons@3.0.1/icons/twitter.svg" alt="ozcangundes" height="30" width="30" /></a>
Linkedin: <a href="https://www.linkedin.com/in/%C3%B6zcan-g%C3%BCnde%C5%9F-7693055b/" target="blank"><img align="center" src="https://cdn.jsdelivr.net/npm/simple-icons@3.0.1/icons/linkedin.svg" alt="13198517" height="30" width="30" /></a>
Medium: <a href="https://medium.com/@ozcangundes" target="blank"><img align="center" src="https://cdn.jsdelivr.net/npm/simple-icons@3.0.1/icons/medium.svg" alt="@ozcangundes" height="30" width="30" /></a>
Github: <a href="https://github.com/ozcangundes" target="blank"><img align="center" src="https://cdn.jsdelivr.net/npm/simple-icons@3.0.1/icons/github.svg" alt="@ozcangundes" height="30" width="30" /></a>
|
ozcangundes/T5-base-for-BioQA
|
ozcangundes
| 2021-09-22T09:31:21Z | 25 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"question-answering",
"dataset:bioASQ",
"arxiv:1910.10683",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
language: english
datasets:
- bioASQ
pipeline_tag: question-answering
license: mit
---
# T5-base model fine-tuned on BioASQ for Biological Question Answering 👩⚕️👨⚕️
[Google's T5-base](https://huggingface.co/t5-base) fine-tuned on [BioASQ](https://github.com/dmis-lab/biobert) (secondary task) for **Q&A** downstream task.
## Details of T5
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html)
Pretraining Dataset: [C4](https://huggingface.co/datasets/c4)
Paper: [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf)
Authors: *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu*
## Dependencies
transformers == 4.3.3
sentencepiece >= 0.1.94
## Usage 🚀
```python
import torch
from transformers import T5ForConditionalGeneration, T5Tokenizer
tokenizer = T5Tokenizer.from_pretrained("ozcangundes/T5-base-for-BioQA")
model = T5ForConditionalGeneration.from_pretrained("ozcangundes/T5-base-for-BioQA")
def get_answer(question,context):
source_encoding=tokenizer(
question,
context,
max_length=512,
padding="max_length",
truncation="only_second",
return_attention_mask=True,
add_special_tokens=True,
return_tensors="pt")
generated_ids=model.generate(
input_ids=source_encoding["input_ids"],
attention_mask=source_encoding["attention_mask"])
preds=[tokenizer.decode(gen_id, skip_special_tokens=True, clean_up_tokenization_spaces=True) for gen_id in generated_ids]
return "".join(preds)
```
### Example 1
```python
question={
"context":"Effect of food on the pharmacokinetics of empagliflozin, a sodium glucose cotransporter 2 (SGLT2) inhibitor, and assessment of dose proportionality in healthy volunteers. OBJECTIVES: Empagliflozin is an orally available, potent and highly selective inhibitor of the sodium glucose cotransporter 2 (SGLT2). This study was undertaken to investigate the effect of food on the pharmacokinetics of 25 mg empagliflozin and to assess dose proportionality between 10 mg and 25 mg empagliflozin under fasted conditions. MATERIALS AND METHODS: In this open-label, 3-way, cross-over study, 18 healthy volunteers received 3 single doses of empagliflozin in a randomized sequence (25 mg empagliflozin under fasted conditions, 25 mg empagliflozin after a high-fat, high-calorie breakfast and 10 mg empagliflozin under fasted conditions), each separated by a washout period of at least 7 days. Serial plasma samples were collected at selected time points over a period of 72 hours. RESULTS: Administration with food had no clinically relevant effect on the area under the plasma concentration-time curve (AUC0-∞) of empagliflozin (geometric mean ratio (GMR): 84.04, 90% confidence interval (CI): 80.86 - 87.34). The decrease observed in the maximum plasma concentrations (Cmax) of empagliflozin (GMR: 63.22, 90% CI: 56.74 - 70.44) when administered with food was not considered clinically meaningful. The increases in AUC0-∞ and Cmax for 10 mg vs. 25 mg empagliflozin administered under fasting conditions were roughly dose-proportional, as demonstrated by the slope β of the regression lines being slightly less than 1 (slope β for AUC0-∞: 0.94, 95% CI: 0.90 - 0.97; slope β for Cmax: 0.91, 95% CI: 0.80 - 1.01). Empagliflozin was well tolerated under fed and fasting conditions. CONCLUSIONS: The results support administration of empagliflozin tablets independently of food. Increases in empagliflozin exposure under fasting conditions were roughly dose-proportional between 10 mg and 25 mg empagliflozin.",
"question":"Which protein does empagliflozin inhibit?"
}
get_answer(question["question"],question["context"])
```
> SGLT2
### Example 2
```python
question2={
"context":"Dermatitis herpetiformis: jejunal findings and skin response to gluten free diet. Fifty seven children with dermatitis herpetiformis, 18 from Finland and 39 from Hungary, were studied. Diagnostic criteria included the finding of granular IgA deposits in the skin of all patients. The mean age at onset of the rash was 7 X 2 years and favoured sites were the elbows, knees, and buttocks. Symptoms suggesting small intestinal disease were rare but in 35 (61%) of the children subtotal villous atrophy and in 16 (28%) partial villous atrophy were found on jejunal biopsy. Eighteen children underwent a second biopsy after a mean of 21 months on a gluten free diet; villous height was found to be increased and the intraepithelial lymphocyte count decreased in all these patients. Gluten challenge caused a reversal in the two children who underwent a third biopsy. The effect of the gluten free diet on the rash was examined in Finnish children by observing the daily requirements of dapsone, a drug used to control the rash at the beginning of the diet. Eight (67%) of the 12 children were able to stop taking dapsone after a mean of 11 months on the diet and all three patients treated with diet alone became asymptomatic after three to 6 months on the diet. These results confirm that most children with dermatitis herpetiformis have jejunal villous atrophy, though they rarely have gastrointestinal symptoms. The central role of gluten in childhood dermatitis herpetiformis is evidenced by the fact that a gluten free diet helps the damaged jejunal mucosa to recover and controls the rash even in those children who do not have an abnormal jejunal biopsy.",
"question":"What is the typical rash associated with gluten?"
}
get_answer(question2["question"],question2["context"])
```
> dermatitis herpetiformis
Created by Özcan Gündeş ✌️
---
Twitter: <a href="https://twitter.com/ozcangundes" target="blank"><img align="center" src="https://cdn.jsdelivr.net/npm/simple-icons@3.0.1/icons/twitter.svg" alt="ozcangundes" height="30" width="30" /></a>
Linkedin: <a href="https://www.linkedin.com/in/%C3%B6zcan-g%C3%BCnde%C5%9F-7693055b/" target="blank"><img align="center" src="https://cdn.jsdelivr.net/npm/simple-icons@3.0.1/icons/linkedin.svg" alt="13198517" height="30" width="30" /></a>
Medium: <a href="https://medium.com/@ozcangundes" target="blank"><img align="center" src="https://cdn.jsdelivr.net/npm/simple-icons@3.0.1/icons/medium.svg" alt="@ozcangundes" height="30" width="30" /></a>
Github: <a href="https://github.com/ozcangundes" target="blank"><img align="center" src="https://cdn.jsdelivr.net/npm/simple-icons@3.0.1/icons/github.svg" alt="@ozcangundes" height="30" width="30" /></a>
|
nlp4good/psych-search
|
nlp4good
| 2021-09-22T09:29:47Z | 46 | 5 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"mental-health",
"en",
"dataset:PubMed",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language:
- en
tags:
- mental-health
license: apache-2.0
datasets:
- PubMed
---
# Psych-Search
Psych-Search is a work in progress to bring cutting edge NLP to mental health practitioners. The model detailed here serves as a foundation for traditional classification models as well as NLU models for a Psych-Search application. The goal of the Psych-Search Application is to use a combination of traditional text classification models to expand the scope of the MESH taxonomy with the inclusion of relevant categories for mental health pracitioners designing suicide prevention programs for adolescent communities within the United States, as well as the automatic extraction and standardization of entities such as risk factors and protective factors.
Our first expansion efforts to the MESH taxonomy include categories:
- Prevention Strategies
- Protective Factors
We are actively looking for partners on this work and would love to hear from you! Please ping us at nlp4good@gmail.com.
## Model description
This model is an extension of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased). Continued pretraining was done using SciBERT as the base model using abstract text only from Pyschology and Psychiatry PubMed research. Training was done on approximately 3.5 million papers for 10 epochs and evaluated on a task similar to BioASQ Task A.
## Intended uses & limitations
#### How to use
```python
from transformers import AutoTokenizer, AutoModel
mname = "nlp4good/psych-search"
tokenizer = AutoTokenizer.from_pretrained(mname)
model = AutoModel.from_pretrained(mname)
```
### Limitations and bias
This model was trained on all PubMed abstracts categorized under [Psychology and Psychiatry](https://meshb.nlm.nih.gov/treeView). As of March 1, this corresponds to approximately 3.2 million papers that contains abstract text. Of these 3.2 million papers, relevant sparse mental health categories were back translated to increase the representation of certain mental health categories.
There are several limitation with this dataset including large discrepancies in the number of papers associated with [Sexual and Gender Minorities](https://meshb.nlm.nih.gov/record/ui?ui=D000072339). The training data consisted of the following breakdown across gender groups:
Female | Male | Sexual and Gender Minorities
-------|---------|----------
1,896,301 | 1,945,279 | 4,529
Similar discrepancies are present within [Ethnic Groups](https://meshb.nlm.nih.gov/record/ui?ui=D005006) as defined within the MESH taxonomy:
| African Americans | Arabs | Asian Americans | Hispanic Americans | Indians, Central American | Indians, North American | Indians, South American | Indigenous Peoples | Mexican Americans |
|-------------------|-------|-----------------|--------------------|---------------------------|-------------------------|-------------------------|--------------------|-------------------|
| 31,027 | 2,437 | 5,612 | 18,893 | 124 | 5,657 | 633 | 174 | 3,234 |
These discrepancies can have a significant impact on information retrieval systems, downstream machine learning models, and other forms of NLP that leverage these pretrained models.
## Training data
This model was trained on all PubMed abstracts categorized under [Psychology and Psychiatry](https://meshb.nlm.nih.gov/treeView). As of March 1, this corresponds to approximately 3.2 million papers that contains abstract text. Of these 3.2 million papers, relevant sparse categories were back translated from english to french and from french to english to increase the representation of sparser mental health categories. This included backtranslating the following papers with the following categories:
- Depressive Disorder
- Risk Factors
- Mental Disorders
- Child, Preschool
- Mental Health
In aggregate, this process added 557,980 additional papers to our training data.
## Training procedure
Continued pretraining was done on Psychology and Psychiatry PubMed papers for 10 epochs. Default parameters were used with the exception of gradient accumulation steps which was set at 4, with a per device train batch size of 32. 2 x Nvidia 3090's were used in the development of this model.
## Evaluation results
To evaluate the effectiveness of psych-search within the mental health domain, an evaluation task was constructed by finetuning psych-search for a task similar to [BioASQ Task A](http://bioasq.org/). Here we perform large scale biomedical indexing using the MESH taxonomy associated with each paper underneath Psychology and Psychiatry. The evaluation metric is the micro F1 score across all second level descriptors within Psychology and Psychiatry. This corresponds to 38 different MESH categories used during evaluation.
bert-base-uncased | SciBERT Scivocab Uncased | Psych-Search
-------|---------|----------
0.7348 | 0.7394 | 0.7415
## Next Steps
If you are interested in continuing to build on this work or have other ideas on how we can build on others work, please let us know! We can be reached at nlp4good@gmail.com. Our goal is to bring state of the art NLP capabilities to underserved areas of research, with mental health being our top priority.
|
neuraly/bert-base-italian-cased-sentiment
|
neuraly
| 2021-09-22T09:29:18Z | 4,144 | 8 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"sentiment",
"Italian",
"it",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language: it
thumbnail: https://neuraly.ai/static/assets/images/huggingface/thumbnail.png
tags:
- sentiment
- Italian
license: mit
widget:
- text: Huggingface è un team fantastico!
---
# 🤗 + neuraly - Italian BERT Sentiment model
## Model description
This model performs sentiment analysis on Italian sentences. It was trained starting from an instance of [bert-base-italian-cased](https://huggingface.co/dbmdz/bert-base-italian-cased), and fine-tuned on an Italian dataset of tweets, reaching 82% of accuracy on the latter one.
## Intended uses & limitations
#### How to use
```python
import torch
from torch import nn
from transformers import AutoTokenizer, AutoModelForSequenceClassification
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained("neuraly/bert-base-italian-cased-sentiment")
# Load the model, use .cuda() to load it on the GPU
model = AutoModelForSequenceClassification.from_pretrained("neuraly/bert-base-italian-cased-sentiment")
sentence = 'Huggingface è un team fantastico!'
input_ids = tokenizer.encode(sentence, add_special_tokens=True)
# Create tensor, use .cuda() to transfer the tensor to GPU
tensor = torch.tensor(input_ids).long()
# Fake batch dimension
tensor = tensor.unsqueeze(0)
# Call the model and get the logits
logits, = model(tensor)
# Remove the fake batch dimension
logits = logits.squeeze(0)
# The model was trained with a Log Likelyhood + Softmax combined loss, hence to extract probabilities we need a softmax on top of the logits tensor
proba = nn.functional.softmax(logits, dim=0)
# Unpack the tensor to obtain negative, neutral and positive probabilities
negative, neutral, positive = proba
```
#### Limitations and bias
A possible drawback (or bias) of this model is related to the fact that it was trained on a tweet dataset, with all the limitations that come with it. The domain is strongly related to football players and teams, but it works surprisingly well even on other topics.
## Training data
We trained the model by combining the two tweet datasets taken from [Sentipolc EVALITA 2016](http://www.di.unito.it/~tutreeb/sentipolc-evalita16/data.html). Overall the dataset consists of 45K pre-processed tweets.
The model weights come from a pre-trained instance of [bert-base-italian-cased](https://huggingface.co/dbmdz/bert-base-italian-cased). A huge "thank you" goes to that team, brilliant work!
## Training procedure
#### Preprocessing
We tried to save as much information as possible, since BERT captures extremely well the semantic of complex text sequences. Overall we removed only **@mentions**, **urls** and **emails** from every tweet and kept pretty much everything else.
#### Hardware
- **GPU**: Nvidia GTX1080ti
- **CPU**: AMD Ryzen7 3700x 8c/16t
- **RAM**: 64GB DDR4
#### Hyperparameters
- Optimizer: **AdamW** with learning rate of **2e-5**, epsilon of **1e-8**
- Max epochs: **5**
- Batch size: **32**
- Early Stopping: **enabled** with patience = 1
Early stopping was triggered after 3 epochs.
## Eval results
The model achieves an overall accuracy on the test set equal to 82%
The test set is a 20% split of the whole dataset.
## About us
[Neuraly](https://neuraly.ai) is a young and dynamic startup committed to designing AI-driven solutions and services through the most advanced Machine Learning and Data Science technologies. You can find out more about who we are and what we do on our [website](https://neuraly.ai).
## Acknowledgments
Thanks to the generous support from the [Hugging Face](https://huggingface.co/) team,
it is possible to download the model from their S3 storage and live test it from their inference API 🤗.
|
gchhablani/fnet-large-finetuned-mrpc
|
gchhablani
| 2021-09-22T09:06:01Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"fnet",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: fnet-large-finetuned-mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8259803921568627
- name: F1
type: f1
value: 0.8798646362098139
---
<!-- 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. -->
# fnet-large-finetuned-mrpc
This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-large) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0872
- Accuracy: 0.8260
- F1: 0.8799
- Combined Score: 0.8529
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
| 0.5656 | 1.0 | 917 | 0.6999 | 0.7843 | 0.8581 | 0.8212 |
| 0.3874 | 2.0 | 1834 | 0.7280 | 0.8088 | 0.8691 | 0.8390 |
| 0.1627 | 3.0 | 2751 | 1.1274 | 0.8162 | 0.8780 | 0.8471 |
| 0.0751 | 4.0 | 3668 | 1.0289 | 0.8333 | 0.8870 | 0.8602 |
| 0.0339 | 5.0 | 4585 | 1.0872 | 0.8260 | 0.8799 | 0.8529 |
### Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.0
- Datasets 1.12.1
- Tokenizers 0.10.3
|
microsoft/unihanlm-base
|
microsoft
| 2021-09-22T09:00:56Z | 18 | 3 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"xlm",
"feature-extraction",
"crosslingual",
"zh",
"ja",
"dataset:Wikipedia",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
---
language:
- zh
- ja
tags:
- crosslingual
license: apache-2.0
datasets:
- Wikipedia
---
# Unihan LM: Coarse-to-Fine Chinese-Japanese Language Model Pretraining with the Unihan Database
## Model description
Chinese and Japanese share many characters with similar surface morphology. To better utilize the shared knowledge across the languages, we propose UnihanLM, a self-supervised Chinese-Japanese pretrained masked language model (MLM) with a novel two-stage coarse-to-fine training approach. We exploit Unihan, a ready-made database constructed by linguistic experts to first merge morphologically similar characters into clusters. The resulting clusters are used to replace the original characters in sentences for the coarse-grained pretraining of the MLM. Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters. We conduct extensive experiments on a variety of Chinese and Japanese NLP benchmarks, showing that our proposed UnihanLM is effective on both mono- and cross-lingual Chinese and Japanese tasks, shedding light on a new path to exploit the homology of languages. [Paper](https://www.aclweb.org/anthology/2020.aacl-main.24/)
## Intended uses & limitations
#### How to use
Use it like how you use XLM :)
#### Limitations and bias
The training corpus is solely from Wikipedia so the model may perform worse on informal text data. Be careful with English words! The tokenizer would cut it to characters.
## Training data
We use Chinese and Japanese Wikipedia to train the model.
## Training procedure
Please refer to our paper: https://www.aclweb.org/anthology/2020.aacl-main.24/
## Eval results
Please refer to our paper: https://www.aclweb.org/anthology/2020.aacl-main.24/
### BibTeX entry and citation info
```bibtex
@inproceedings{xu-etal-2020-unihanlm,
title = "{U}nihan{LM}: Coarse-to-Fine {C}hinese-{J}apanese Language Model Pretraining with the Unihan Database",
author = "Xu, Canwen and
Ge, Tao and
Li, Chenliang and
Wei, Furu",
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",
month = dec,
year = "2020",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.aacl-main.24",
pages = "201--211"
}
```
|
megagonlabs/transformers-ud-japanese-electra-base-ginza
|
megagonlabs
| 2021-09-22T09:00:17Z | 9 | 1 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"pretraining",
"ja",
"arxiv:1910.10683",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: ja
license: mit
datasets:
- mC4 Japanese
---
# transformers-ud-japanese-electra-ginza (sudachitra-wordpiece, mC4 Japanese)
This is an [ELECTRA](https://github.com/google-research/electra) model pretrained on approximately 200M Japanese sentences extracted from the [mC4](https://huggingface.co/datasets/mc4) and finetuned by [spaCy v3](https://spacy.io/usage/v3) on [UD\_Japanese\_BCCWJ r2.8](https://universaldependencies.org/treebanks/ja_bccwj/index.html).
The base pretrain model is [megagonlabs/transformers-ud-japanese-electra-base-discrimininator](https://huggingface.co/megagonlabs/transformers-ud-japanese-electra-base-discriminator), which requires [SudachiTra](https://github.com/WorksApplications/SudachiTra) for tokenization.
The entire spaCy v3 model is distributed as a python package named [`ja_ginza_electra`](https://pypi.org/project/ja-ginza-electra/) from PyPI along with [`GiNZA v5`](https://github.com/megagonlabs/ginza) which provides some custom pipeline components to recognize the Japanese bunsetu-phrase structures.
Try running it as follows:
```console
$ pip install ginza ja-ginza-electra
$ ginza
```
## Licenses
The models are distributed under the terms of the [MIT License](https://opensource.org/licenses/mit-license.php).
## Acknowledgments
This model is permitted to be published under the `MIT License` under a joint research agreement between `NINJAL` (National Institute for Japanese Language and Linguistics) and `Megagon Labs Tokyo`.
## Citations
- [mC4](https://huggingface.co/datasets/mc4)
Contains information from `mC4` which is made available under the [ODC Attribution License](https://opendatacommons.org/licenses/by/1-0/).
```
@article{2019t5,
author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu},
title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
journal = {arXiv e-prints},
year = {2019},
archivePrefix = {arXiv},
eprint = {1910.10683},
}
```
- [UD\_Japanese\_BCCWJ r2.8](https://universaldependencies.org/treebanks/ja_bccwj/index.html)
```
Asahara, M., Kanayama, H., Tanaka, T., Miyao, Y., Uematsu, S., Mori, S.,
Matsumoto, Y., Omura, M., & Murawaki, Y. (2018).
Universal Dependencies Version 2 for Japanese.
In LREC-2018.
```
|
megagonlabs/transformers-ud-japanese-electra-base-discriminator
|
megagonlabs
| 2021-09-22T09:00:15Z | 6 | 6 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"pretraining",
"ja",
"arxiv:1910.10683",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: ja
license: mit
datasets:
- mC4 Japanese
---
# transformers-ud-japanese-electra-ginza (sudachitra-wordpiece, mC4 Japanese) - [MIYAGINO](https://www.ntj.jac.go.jp/assets/images/member/pertopics/image/per100510_3.jpg)
This is an [ELECTRA](https://github.com/google-research/electra) model pretrained on approximately 200M Japanese sentences.
The input text is tokenized by [SudachiTra](https://github.com/WorksApplications/SudachiTra) with the WordPiece subword tokenizer.
See `tokenizer_config.json` for the setting details.
## How to use
```python
from transformers import ElectraModel
from sudachitra import ElectraSudachipyTokenizer
model = ElectraModel.from_pretrained("megagonlabs/transformers-ud-japanese-electra-base-discriminator")
tokenizer = ElectraSudachipyTokenizer.from_pretrained("megagonlabs/transformers-ud-japanese-electra-base-discriminator")
model(**tokenizer("まさにオールマイティーな商品だ。", return_tensors="pt")).last_hidden_state
tensor([[[-0.0498, -0.0285, 0.1042, ..., 0.0062, -0.1253, 0.0338],
[-0.0686, 0.0071, 0.0087, ..., -0.0210, -0.1042, -0.0320],
[-0.0636, 0.1465, 0.0263, ..., 0.0309, -0.1841, 0.0182],
...,
[-0.1500, -0.0368, -0.0816, ..., -0.0303, -0.1653, 0.0650],
[-0.0457, 0.0770, -0.0183, ..., -0.0108, -0.1903, 0.0694],
[-0.0981, -0.0387, 0.1009, ..., -0.0150, -0.0702, 0.0455]]],
grad_fn=<NativeLayerNormBackward>)
```
## Model architecture
The model architecture is the same as the original ELECTRA base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads.
## Training data and libraries
This model is trained on the Japanese texts extracted from the [mC4](https://huggingface.co/datasets/mc4) Common Crawl's multilingual web crawl corpus.
We used the [Sudachi](https://github.com/WorksApplications/Sudachi) to split texts into sentences, and also applied a simple rule-based filter to remove nonlinguistic segments of mC4 multilingual corpus.
The extracted texts contains over 600M sentences in total, and we used approximately 200M sentences for pretraining.
We used [NVIDIA's TensorFlow2-based ELECTRA implementation](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow2/LanguageModeling/ELECTRA) for pretraining. The time required for the pretrainig was about 110 hours using GCP DGX A100 8gpu instance with enabling Automatic Mixed Precision.
## Licenses
The pretrained models are distributed under the terms of the [MIT License](https://opensource.org/licenses/mit-license.php).
## Citations
- mC4
Contains information from `mC4` which is made available under the [ODC Attribution License](https://opendatacommons.org/licenses/by/1-0/).
```
@article{2019t5,
author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu},
title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
journal = {arXiv e-prints},
year = {2019},
archivePrefix = {arXiv},
eprint = {1910.10683},
}
```
|
marefa-nlp/marefa-mt-en-ar
|
marefa-nlp
| 2021-09-22T08:59:51Z | 391 | 13 |
transformers
|
[
"transformers",
"pytorch",
"marian",
"text2text-generation",
"translation",
"Arabic Abjad Characters",
"Arabic",
"en",
"ar",
"dataset:marefa-mt",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-03-02T23:29:05Z |
---
language:
- en
- ar
tags:
- translation
- Arabic Abjad Characters
- Arabic
license: apache-2.0
datasets:
- marefa-mt
---
# Marefa-Mt-En-Ar
# نموذج المعرفة للترجمة الآلية من الإنجليزية للعربية
## Model description
This is a model for translating English to Arabic. The special about this model that is take into considration the
using of additional Arabic characters like `پ` or `گ`.
## عن النموذج
هذا النموذج للترجمة الآلية من اللغة الإنجليزية إلى اللغة العربية, هو أول نماذج الترجمة الآلية التي تصدر تحت رعاية
[موسوعة المعرفة](https://www.marefa.org)
يتميز هذا النموذج عن غيره من النماذج بدعمه لحروف الأبجدية العربية الإضافية لتمييز الصوتيات الخاصة في اللغة الإنجليزية مثل `پ` , `گ`.
يمكنك زيارة
[هذه الصفحة](https://www.marefa.org/%D8%A7%D9%84%D9%85%D8%B9%D8%B1%D9%81%D8%A9:%D8%AF%D9%84%D9%8A%D9%84_%D8%A7%D9%84%D8%A3%D8%B3%D9%84%D9%88%D8%A8#.D8.AD.D8.B1.D9.88.D9.81_.D8.A5.D8.B6.D8.A7.D9.81.D9.8A.D8.A9_.D9.84.D9.84.D9.86.D8.B7.D9.82_.D8.A7.D9.84.D8.B3.D9.84.D9.8A.D9.85)
لمعرفة أكثر عن أسلوب إستخدام هذه الحروف الأبجدية العربية
### How to use كيفية الإستخدام
Install transformers and sentencepiece (python >= 3.6)
`$ pip3 install transformers==4.3.0 sentencepiece==0.1.95 nltk==3.5 protobuf==3.15.3 torch==1.7.1`
> If you are using `Google Colab`, please restart your runtime after installing the packages.
-----------
```python
from transformers import MarianTokenizer, MarianMTModel
mname = "marefa-nlp/marefa-mt-en-ar"
tokenizer = MarianTokenizer.from_pretrained(mname)
model = MarianMTModel.from_pretrained(mname)
# English Sample Text
input = "President Putin went to the presidential palace in the capital, Kiev"
translated_tokens = model.generate(**tokenizer.prepare_seq2seq_batch([input], return_tensors="pt"))
translated_text = [tokenizer.decode(t, skip_special_tokens=True) for t in translated_tokens]
# translated Arabic Text
print(translated_text)
# ذهب الرئيس پوتن إلى القصر الرئاسي في العاصمة كييڤ
```
|
macedonizer/sr-roberta-base
|
macedonizer
| 2021-09-22T08:59:00Z | 8 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"masked-lm",
"sr",
"dataset:wiki-sr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language:
- sr
thumbnail: https://huggingface.co/macedonizer/sr-roberta-base/lets-talk-about-nlp-sr.jpg
tags:
- masked-lm
license: apache-2.0
datasets:
- wiki-sr
---
# SR-RoBERTa base model
Pretrained model on Serbian language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is case-sensitive: it makes a difference between скопје and Скопје.
# Model description
RoBERTa is a transformers model pre-trained on a large corpus of мацед data in a self-supervised fashion. This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts.
More precisely, it was pre-trained with the Masked language modeling (MLM) objective. Taking a sentence, the model randomly masks 15% of the words in the input then runs the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence.
This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences, for instance, you can train a standard classifier using the features produced by the BERT model as inputs.
# Intended uses & limitations
You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. See the model hub to look for fine-tuned versions of a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification, or question answering. For tasks such as text generation, you should look at models like GPT2.
# How to use
You can use this model directly with a pipeline for masked language modeling: \
from transformers import pipeline \
unmasker = pipeline('fill-mask', model='macedonizer/sr-roberta-base') \
unmasker("Београд је <mask> град Србије.") \
[{'score': 0.7834128141403198,
'sequence': 'Београд је главни град Србије',
'token': 3087,
'token_str': ' главни'},
{'score': 0.15424974262714386,
'sequence': 'Београд је највећи град Србије',
'token': 3916,
'token_str': ' највећи'},
{'score': 0.0035441946238279343,
'sequence': 'Београд је најважнији град Србије',
'token': 18577,
'token_str': ' најважнији'},
{'score': 0.003132033161818981,
'sequence': 'Београд је велики град Србије',
'token': 2063,
'token_str': ' велики'},
{'score': 0.0030417360831052065,
'sequence': 'Београд је важан град Србије',
'token': 9463,
'token_str': ' важан'}]
Here is how to use this model to get the features of a given text in PyTorch:
from transformers import RobertaTokenizer, RobertaModel \
tokenizer = RobertaTokenizer.from_pretrained('macedonizer/mk-roberta-base') \
model = RobertaModel.from_pretrained('macedonizer/sr-roberta-base') \
text = "Replace me by any text you'd like." \
encoded_input = tokenizer(text, return_tensors='pt') \
output = model(**encoded_input)
|
macedonizer/sr-gpt2
|
macedonizer
| 2021-09-22T08:58:57Z | 54 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"sr",
"dataset:wiki-sr",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language:
- sr
thumbnail: https://huggingface.co/macedonizer/sr-gpt2/desanka-maksimovic.jpeg
license: apache-2.0
datasets:
- wiki-sr
---
# sr-gpt2
Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
and first released at [this page](https://openai.com/blog/better-language-models/).
## Model description
sr-gpt2 is a transformers model pretrained on a very large corpus of Serbian data in a self-supervised fashion. This
means it was pretrained on the raw texts only, with no humans labeling them in any way (which is why it can use lots
of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely,
it was trained to guess the next word in sentences.
More precisely, inputs are sequences of the continuous text of a certain length and the targets are the same sequence,
shifted one token (word or piece of the word) to the right. The model uses internally a mask-mechanism to make sure the
predictions for the token `i` only uses the inputs from `1` to `i` but not the future tokens.
This way, the model learns an inner representation of the Macedonian language that can then be used to extract features
useful for downstream tasks. The model is best at what it was pretrained for, however, which is generating texts from a
prompt.
### How to use
Here is how to use this model to get the features of a given text in PyTorch:
import random \
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained('macedonizer/sr-gpt2') \
model = AutoModelWithLMHead.from_pretrained('macedonizer/sr-gpt2')
input_text = 'Ја сам био '
if len(input_text) == 0: \
encoded_input = tokenizer(input_text, return_tensors="pt") \
output = model.generate( \
bos_token_id=random.randint(1, 50000), \
do_sample=True, \
top_k=50, \
max_length=1024, \
top_p=0.95, \
num_return_sequences=1, \
) \
else: \
encoded_input = tokenizer(input_text, return_tensors="pt") \
output = model.generate( \
**encoded_input, \
bos_token_id=random.randint(1, 50000), \
do_sample=True, \
top_k=50, \
max_length=1024, \
top_p=0.95, \
num_return_sequences=1, \
)
decoded_output = [] \
for sample in output: \
decoded_output.append(tokenizer.decode(sample, skip_special_tokens=True))
print(decoded_output)
|
macedonizer/sl-gpt2
|
macedonizer
| 2021-09-22T08:58:51Z | 5 | 2 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"sl",
"dataset:wiki-sl",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language:
- sl
thumbnail: https://huggingface.co/macedonizer/mkgpt2/lets-talk-about-nlp.jpg
license: apache-2.0
datasets:
- wiki-sl
---
# sl-gpt2
Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
and first released at [this page](https://openai.com/blog/better-language-models/).
## Model description
sl-gpt2 is a transformers model pretrained on a very large corpus of Slovenian data in a self-supervised fashion. This
means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots
of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely,
it was trained to guess the next word in sentences.
More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence,
shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the
predictions for the token `i` only uses the inputs from `1` to `i` but not the future tokens.
This way, the model learns an inner representation of the Macedonian language that can then be used to extract features
useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a
prompt.
### How to use
Here is how to use this model to get the features of a given text in PyTorch:
import random
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained('macedonizer/sl-gpt2') \
model = AutoModelWithLMHead.from_pretrained('macedonizer/sl-gpt2')
input_text = 'Ljubljana '
if len(input_text) == 0: \
encoded_input = tokenizer(input_text, return_tensors="pt") \
output = model.generate( \
bos_token_id=random.randint(1, 50000), \
do_sample=True, \
top_k=50, \
max_length=1024, \
top_p=0.95, \
num_return_sequences=1, \
) \
else: \
encoded_input = tokenizer(input_text, return_tensors="pt") \
output = model.generate( \
**encoded_input, \
bos_token_id=random.randint(1, 50000), \
do_sample=True, \
top_k=50, \
max_length=1024, \
top_p=0.95, \
num_return_sequences=1, \
)
decoded_output = [] \\nfor sample in output: \
decoded_output.append(tokenizer.decode(sample, skip_special_tokens=True))
print(decoded_output)
|
macedonizer/gr-roberta-base
|
macedonizer
| 2021-09-22T08:58:38Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"masked-lm",
"gr",
"dataset:wiki-gr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language:
- gr
thumbnail: https://huggingface.co/macedonizer/gr-roberta-base/lets-talk-about-nlp-gr.jpg
tags:
- masked-lm
license: apache-2.0
datasets:
- wiki-gr
---
# GR-RoBERTa base model
Pretrained model on Macedonian language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is case-sensitive: it makes a difference between Athens and athens.
# Model description
RoBERTa is a transformers model pre-trained on a large corpus of мацед data in a self-supervised fashion. This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts.
More precisely, it was pre-trained with the Masked language modeling (MLM) objective. Taking a sentence, the model randomly masks 15% of the words in the input then runs the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence.
This way, the model learns an inner representation of the Greek language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences, for instance, you can train a standard classifier using the features produced by the BERT model as inputs.
# Intended uses & limitations
You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. See the model hub to look for fine-tuned versions of a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification, or question answering. For tasks such as text generation, you should look at models like GPT2.
# How to use
You can use this model directly with a pipeline for masked language modeling:
from transformers import pipeline \
unmasker = pipeline('fill-mask', model='macedonizer/gr-roberta-base') \
unmasker("Η Αθήνα είναι η \<mask\> της Ελλάδας") \
[{'score': 0.8832866549491882, \
'sequence': 'Η Αθήνα είναι η πρωτεύουσα της Ελλάδας', \
'token': 2788, \
'token_str': ' πρωτεύουσα'}, \
{'score': 0.018105432391166687, \
'sequence': 'Η Αθήνα είναι η μεγαλύτερη της Ελλάδας', \
'token': 2363, \
'token_str': ' μεγαλύτερη'}, \
{'score': 0.015836946666240692, \
'sequence': 'Η Αθήνα είναι η έδρα της Ελλάδας', \
'token': 1950, \
'token_str': ' έδρα'}, \
{'score': 0.015673324465751648, \
'sequence': 'Η Αθήνα είναι η μόνη της Ελλάδας', \
'token': 6548, \
'token_str': ' μόνη'}, \
{'score': 0.01375910360366106, \
'sequence': 'Η Αθήνα είναι η πόλη της Ελλάδας', \
'token': 825, \
'token_str': ' πόλη'}]
Here is how to use this model to get the features of a given text in PyTorch:
from transformers import RobertaTokenizer, RobertaModel \
tokenizer = RobertaTokenizer.from_pretrained('macedonizer/gr-roberta-base') \
model = RobertaModel.from_pretrained('macedonizer/gr-roberta-base') \
text = "Replace me by any text you'd like." \
encoded_input = tokenizer(text, return_tensors='pt') \
output = model(**encoded_input)
|
macedonizer/blaze-koneski
|
macedonizer
| 2021-09-22T08:58:34Z | 11 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"mk",
"dataset:wiki-mk",
"dataset:blaze-koneski-poetry",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language:
- mk
thumbnail: https://huggingface.co/macedonizer/blaze-koneski/blaze-koneski.jpg
license: apache-2.0
datasets:
- wiki-mk
- blaze-koneski-poetry
---
# blaze-koneski
GPT-2 type of model. We finetuned macedonizer/mk-gpt-2 with Blaze Koneski's poetry.
## About Blaze Koneski
Born in a village near Prilep in 1921. Studied philology at Skopje University and worked there as a professor. Was the first chairman of the Macedonian Academy of Sciences and Arts, corresponding member of the Yugoslav Academy of Sciences and Arts, as well as of the Serbian and Slovene Academies, and honorary doctor of the Universities of Chicago and Krakow.
Wrote poetry, short stories, and essays, as well as scholarly works, many of them on the Macedonian language. Editor of the Dictionarv of the Macedonian Language, translator of Heine and Shakespeare. His works have been translated into Serbian, Croatian, Slovene, Albanian, Turkish, Hungarian, French, Russian, Italian, Greek, Polish, Romanian, German, and English.
Winner of numerous prizes, including the Golden Wreath of the Struga Poetry Evenings.
### How to use
Here is how to use this model to get the features of a given text in PyTorch:
import random
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained('macedonizer/blaze-koneski')
nmodel = AutoModelWithLMHead.from_pretrained('macedonizer/blaze-koneski')
input_text = 'Москва '
if len(input_text) == 0: \
encoded_input = tokenizer(input_text, return_tensors="pt") \
output = model.generate( \
bos_token_id=random.randint(1, 50000), \
do_sample=True, \
top_k=50, \
max_length=1024, \
top_p=0.95, \
num_return_sequences=1, \
) \
else: \
encoded_input = tokenizer(input_text, return_tensors="pt") \
output = model.generate( \
**encoded_input, \
bos_token_id=random.randint(1, 50000), \
do_sample=True, \
top_k=50, \
max_length=1024, \
top_p=0.95, \
num_return_sequences=1, \
)
decoded_output = [] \
for sample in output: \
decoded_output.append(tokenizer.decode(sample, skip_special_tokens=True))
print(decoded_output)
|
macedonizer/ba-roberta-base
|
macedonizer
| 2021-09-22T08:58:31Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"masked-lm",
"ba",
"dataset:wiki-bs",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language:
- ba
thumbnail: https://huggingface.co/macedonizer/ba-roberta-base/abdulah-sidran.jpg
tags:
- masked-lm
license: apache-2.0
datasets:
- wiki-bs
---
# BA-RoBERTa base model
Pretrained model on Bosnian language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is case-sensitive: it makes a difference between sarajevo and Sarajevo.
# Model description
RoBERTa is a transformers model pre-trained on a large corpus of Bosnian texts in a self-supervised fashion. This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts.
More precisely, it was pre-trained with the Masked language modeling (MLM) objective. Taking a sentence, the model randomly masks 15% of the words in the input then runs the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence.
This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences, for instance, you can train a standard classifier using the features produced by the BERT model as inputs.
# Intended uses & limitations
You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. See the model hub to look for fine-tuned versions of a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification, or question answering. For tasks such as text generation, you should look at models like GPT2.
# How to use
You can use this model directly with a pipeline for masked language modeling: \
from transformers import pipeline \
unmasker = pipeline('fill-mask', model='macedonizer/ba-roberta-base') \
unmasker("Sarajevo je \\<mask\\> grad Bosne i Hercegovine.") \
[{'score': 0.6210788488388062, \
'sequence': 'Sarajevo je glavni grad Bosne i Hercegovine', \
'token': 2006, \
'token_str': ' glavni'}, \
{'score': 0.19640550017356873, \
'sequence': 'Sarajevo je najveći grad Bosne i Hercegovine', \
'token': 1707, \
'token_str': ' najveći'}, \
{'score': 0.0210184995085001, \
'sequence': 'Sarajevo je srednjovjekovni grad Bosne i Hercegovine', \
'token': 22596, \
'token_str': ' srednjovjekovni'}, \
{'score': 0.010822420939803123, \
'sequence': 'Sarajevo je najmnogoljudniji grad Bosne i Hercegovine', \
'token': 40186, \
'token_str': ' najmnogoljudniji'}, \
{'score': 0.006114463787525892, \
'sequence': 'Sarajevo je službeni grad Bosne i Hercegovine', \
'token': 8546, \
'token_str': ' službeni'}] \
Here is how to use this model to get the features of a given text in PyTorch:
from transformers import RobertaTokenizer, RobertaModel \
tokenizer = RobertaTokenizer.from_pretrained('macedonizer/ba-roberta-base') \
model = RobertaModel.from_pretrained('macedonizer/ba-roberta-base') \
text = "Replace me by any text you'd like." \
encoded_input = tokenizer(text, return_tensors='pt') \
output = model(**encoded_input)
|
macedonizer/al-roberta-base
|
macedonizer
| 2021-09-22T08:58:28Z | 14 | 1 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"masked-lm",
"al",
"dataset:wiki-sh",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language:
- al
thumbnail: https://huggingface.co/macedonizer/al-roberta-base/lets-talk-about-nlp-al.jpg
tags:
- masked-lm
license: apache-2.0
datasets:
- wiki-sh
---
# AL-RoBERTa base model
Pretrained model on Albanian language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is case-sensitive: it makes a difference between tirana and Tirana.
# Model description
RoBERTa is a transformers model pre-trained on a large corpus of text data in a self-supervised fashion. This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts.
More precisely, it was pre-trained with the Masked language modeling (MLM) objective. Taking a sentence, the model randomly masks 15% of the words in the input then runs the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence.
This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences, for instance, you can train a standard classifier using the features produced by the BERT model as inputs.
# Intended uses & limitations
You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. See the model hub to look for fine-tuned versions of a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification, or question answering. For tasks such as text generation, you should look at models like GPT2.
# How to use
You can use this model directly with a pipeline for masked language modeling: \
from transformers import pipeline \
unmasker = pipeline('fill-mask', model='macedonizer/al-roberta-base') \
unmasker("Tirana është \\<mask\\> i Shqipërisë.") \
[{'score': 0.9426872134208679,
'sequence': 'Tirana është kryeqyteti i Shqipërisë',
'token': 7901,
'token_str': ' kryeqyteti'},
{'score': 0.03112833760678768,
'sequence': 'Tirana është kryeqytet i Shqipërisë',
'token': 7439,
'token_str': ' kryeqytet'},
{'score': 0.0022084848023951054,
'sequence': 'Tirana është qytet i Shqipërisë',
'token': 2246,
'token_str': ' qytet'},
{'score': 0.0016222079284489155,
'sequence': 'Tirana është qyteti i Shqipërisë',
'token': 2784,
'token_str': ' qyteti'},
{'score': 0.0008979254635050893,
'sequence': 'Tirana është Kryeqytet i Shqipërisë',
'token': 37653,
'token_str': ' Kryeqytet'}]
Here is how to use this model to get the features of a given text in PyTorch:
from transformers import RobertaTokenizer, RobertaModel \
tokenizer = RobertaTokenizer.from_pretrained('macedonizer/al-roberta-base') \
model = RobertaModel.from_pretrained('macedonizer/al-roberta-base') \
text = "Replace me by any text you'd like." \
encoded_input = tokenizer(text, return_tensors='pt') \
output = model(**encoded_input)
|
liaad/ud_srl-pt_bertimbau-large
|
liaad
| 2021-09-22T08:56:43Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"bert-large-portuguese-cased",
"semantic role labeling",
"finetuned",
"dependency parsing",
"multilingual",
"pt",
"arxiv:2101.01213",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
---
language:
- multilingual
- pt
tags:
- bert-large-portuguese-cased
- semantic role labeling
- finetuned
- dependency parsing
license: apache-2.0
datasets:
- PropBank.Br
- CoNLL-2012
- Universal Dependencies
metrics:
- F1 Measure
---
# BERTimbau large fine-tune in Portuguese Universal Dependencies and semantic role labeling
## Model description
This model is the [`neuralmind/bert-large-portuguese-cased`](https://huggingface.co/neuralmind/bert-large-portuguese-cased) fine-tuned first on the Universal Dependencies Portuguese dataset and then fine-tuned on the PropBank.Br data. This is part of a project from which resulted the following models:
* [liaad/srl-pt_bertimbau-base](https://huggingface.co/liaad/srl-pt_bertimbau-base)
* [liaad/srl-pt_bertimbau-large](https://huggingface.co/liaad/srl-pt_bertimbau-large)
* [liaad/srl-pt_xlmr-base](https://huggingface.co/liaad/srl-pt_xlmr-base)
* [liaad/srl-pt_xlmr-large](https://huggingface.co/liaad/srl-pt_xlmr-large)
* [liaad/srl-pt_mbert-base](https://huggingface.co/liaad/srl-pt_mbert-base)
* [liaad/srl-en_xlmr-base](https://huggingface.co/liaad/srl-en_xlmr-base)
* [liaad/srl-en_xlmr-large](https://huggingface.co/liaad/srl-en_xlmr-large)
* [liaad/srl-en_mbert-base](https://huggingface.co/liaad/srl-en_mbert-base)
* [liaad/srl-enpt_xlmr-base](https://huggingface.co/liaad/srl-enpt_xlmr-base)
* [liaad/srl-enpt_xlmr-large](https://huggingface.co/liaad/srl-enpt_xlmr-large)
* [liaad/srl-enpt_mbert-base](https://huggingface.co/liaad/srl-enpt_mbert-base)
* [liaad/ud_srl-pt_bertimbau-large](https://huggingface.co/liaad/ud_srl-pt_bertimbau-large)
* [liaad/ud_srl-pt_xlmr-large](https://huggingface.co/liaad/ud_srl-pt_xlmr-large)
* [liaad/ud_srl-enpt_xlmr-large](https://huggingface.co/liaad/ud_srl-enpt_xlmr-large)
For more information, please see the accompanying article (See BibTeX entry and citation info below) and the [project's github](https://github.com/asofiaoliveira/srl_bert_pt).
## Intended uses & limitations
#### How to use
To use the transformers portion of this model:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("liaad/ud_srl-pt_bertimbau-large")
model = AutoModel.from_pretrained("liaad/ud_srl-pt_bertimbau-large")
```
To use the full SRL model (transformers portion + a decoding layer), refer to the [project's github](https://github.com/asofiaoliveira/srl_bert_pt).
#### Limitations and bias
- The model was trained only for 10 epochs in the Universal Dependencies dataset.
## Training procedure
The model was trained on the Universal Dependencies Portuguese dataset; then on the CoNLL formatted OntoNotes v5.0; then on Portuguese semantic role labeling data (PropBank.Br) using 10-fold Cross-Validation. The 10 resulting models were tested on the folds as well as on a smaller opinion dataset "Buscapé". For more information, please see the accompanying article (See BibTeX entry and citation info below) and the [project's github](https://github.com/asofiaoliveira/srl_bert_pt).
## Eval results
| Model Name | F<sub>1</sub> CV PropBank.Br (in domain) | F<sub>1</sub> Buscapé (out of domain) |
| --------------- | ------ | ----- |
| `srl-pt_bertimbau-base` | 76.30 | 73.33 |
| `srl-pt_bertimbau-large` | 77.42 | 74.85 |
| `srl-pt_xlmr-base` | 75.22 | 72.82 |
| `srl-pt_xlmr-large` | 77.59 | 73.84 |
| `srl-pt_mbert-base` | 72.76 | 66.89 |
| `srl-en_xlmr-base` | 66.59 | 65.24 |
| `srl-en_xlmr-large` | 67.60 | 64.94 |
| `srl-en_mbert-base` | 63.07 | 58.56 |
| `srl-enpt_xlmr-base` | 76.50 | 73.74 |
| `srl-enpt_xlmr-large` | **78.22** | 74.55 |
| `srl-enpt_mbert-base` | 74.88 | 69.19 |
| `ud_srl-pt_bertimbau-large` | 77.53 | 74.49 |
| `ud_srl-pt_xlmr-large` | 77.69 | 74.91 |
| `ud_srl-enpt_xlmr-large` | 77.97 | **75.05** |
### BibTeX entry and citation info
```bibtex
@misc{oliveira2021transformers,
title={Transformers and Transfer Learning for Improving Portuguese Semantic Role Labeling},
author={Sofia Oliveira and Daniel Loureiro and Alípio Jorge},
year={2021},
eprint={2101.01213},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
liaad/srl-pt_xlmr-base
|
liaad
| 2021-09-22T08:56:34Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"xlm-roberta-base",
"semantic role labeling",
"finetuned",
"multilingual",
"pt",
"dataset:PropBank.Br",
"arxiv:2101.01213",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
---
language:
- multilingual
- pt
tags:
- xlm-roberta-base
- semantic role labeling
- finetuned
license: apache-2.0
datasets:
- PropBank.Br
metrics:
- F1 Measure
---
# XLM-R base fine-tuned on Portuguese semantic role labeling
## Model description
This model is the [`xlm-roberta-base`](https://huggingface.co/xlm-roberta-base) fine-tuned on Portuguese semantic role labeling data. This is part of a project from which resulted the following models:
* [liaad/srl-pt_bertimbau-base](https://huggingface.co/liaad/srl-pt_bertimbau-base)
* [liaad/srl-pt_bertimbau-large](https://huggingface.co/liaad/srl-pt_bertimbau-large)
* [liaad/srl-pt_xlmr-base](https://huggingface.co/liaad/srl-pt_xlmr-base)
* [liaad/srl-pt_xlmr-large](https://huggingface.co/liaad/srl-pt_xlmr-large)
* [liaad/srl-pt_mbert-base](https://huggingface.co/liaad/srl-pt_mbert-base)
* [liaad/srl-en_xlmr-base](https://huggingface.co/liaad/srl-en_xlmr-base)
* [liaad/srl-en_xlmr-large](https://huggingface.co/liaad/srl-en_xlmr-large)
* [liaad/srl-en_mbert-base](https://huggingface.co/liaad/srl-en_mbert-base)
* [liaad/srl-enpt_xlmr-base](https://huggingface.co/liaad/srl-enpt_xlmr-base)
* [liaad/srl-enpt_xlmr-large](https://huggingface.co/liaad/srl-enpt_xlmr-large)
* [liaad/srl-enpt_mbert-base](https://huggingface.co/liaad/srl-enpt_mbert-base)
* [liaad/ud_srl-pt_bertimbau-large](https://huggingface.co/liaad/ud_srl-pt_bertimbau-large)
* [liaad/ud_srl-pt_xlmr-large](https://huggingface.co/liaad/ud_srl-pt_xlmr-large)
* [liaad/ud_srl-enpt_xlmr-large](https://huggingface.co/liaad/ud_srl-enpt_xlmr-large)
For more information, please see the accompanying article (See BibTeX entry and citation info below) and the [project's github](https://github.com/asofiaoliveira/srl_bert_pt).
## Intended uses & limitations
#### How to use
To use the transformers portion of this model:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("liaad/srl-pt_xlmr-base")
model = AutoModel.from_pretrained("liaad/srl-pt_xlmr-base")
```
To use the full SRL model (transformers portion + a decoding layer), refer to the [project's github](https://github.com/asofiaoliveira/srl_bert_pt).
#### Limitations and bias
- This model does not include a Tensorflow version. This is because the "type_vocab_size" in this model was changed (from 1 to 2) and, therefore, it cannot be easily converted to Tensorflow.
## Training procedure
The model was trained on the PropBank.Br datasets, using 10-fold Cross-Validation. The 10 resulting models were tested on the folds as well as on a smaller opinion dataset "Buscapé". For more information, please see the accompanying article (See BibTeX entry and citation info below) and the [project's github](https://github.com/asofiaoliveira/srl_bert_pt).
## Eval results
| Model Name | F<sub>1</sub> CV PropBank.Br (in domain) | F<sub>1</sub> Buscapé (out of domain) |
| --------------- | ------ | ----- |
| `srl-pt_bertimbau-base` | 76.30 | 73.33 |
| `srl-pt_bertimbau-large` | 77.42 | 74.85 |
| `srl-pt_xlmr-base` | 75.22 | 72.82 |
| `srl-pt_xlmr-large` | 77.59 | 73.84 |
| `srl-pt_mbert-base` | 72.76 | 66.89 |
| `srl-en_xlmr-base` | 66.59 | 65.24 |
| `srl-en_xlmr-large` | 67.60 | 64.94 |
| `srl-en_mbert-base` | 63.07 | 58.56 |
| `srl-enpt_xlmr-base` | 76.50 | 73.74 |
| `srl-enpt_xlmr-large` | **78.22** | 74.55 |
| `srl-enpt_mbert-base` | 74.88 | 69.19 |
| `ud_srl-pt_bertimbau-large` | 77.53 | 74.49 |
| `ud_srl-pt_xlmr-large` | 77.69 | 74.91 |
| `ud_srl-enpt_xlmr-large` | 77.97 | **75.05** |
### BibTeX entry and citation info
```bibtex
@misc{oliveira2021transformers,
title={Transformers and Transfer Learning for Improving Portuguese Semantic Role Labeling},
author={Sofia Oliveira and Daniel Loureiro and Alípio Jorge},
year={2021},
eprint={2101.01213},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
liaad/srl-pt_mbert-base
|
liaad
| 2021-09-22T08:56:31Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"bert-base-multilingual-cased",
"semantic role labeling",
"finetuned",
"multilingual",
"pt",
"dataset:PropBank.Br",
"arxiv:2101.01213",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
---
language:
- multilingual
- pt
tags:
- bert-base-multilingual-cased
- semantic role labeling
- finetuned
license: apache-2.0
datasets:
- PropBank.Br
metrics:
- F1 Measure
---
# mBERT fine-tuned on Portuguese semantic role labeling
## Model description
This model is the [`bert-base-multilingual-cased`](https://huggingface.co/bert-base-multilingual-cased) fine-tuned on Portuguese semantic role labeling data. This is part of a project from which resulted the following models:
* [liaad/srl-pt_bertimbau-base](https://huggingface.co/liaad/srl-pt_bertimbau-base)
* [liaad/srl-pt_bertimbau-large](https://huggingface.co/liaad/srl-pt_bertimbau-large)
* [liaad/srl-pt_xlmr-base](https://huggingface.co/liaad/srl-pt_xlmr-base)
* [liaad/srl-pt_xlmr-large](https://huggingface.co/liaad/srl-pt_xlmr-large)
* [liaad/srl-pt_mbert-base](https://huggingface.co/liaad/srl-pt_mbert-base)
* [liaad/srl-en_xlmr-base](https://huggingface.co/liaad/srl-en_xlmr-base)
* [liaad/srl-en_xlmr-large](https://huggingface.co/liaad/srl-en_xlmr-large)
* [liaad/srl-en_mbert-base](https://huggingface.co/liaad/srl-en_mbert-base)
* [liaad/srl-enpt_xlmr-base](https://huggingface.co/liaad/srl-enpt_xlmr-base)
* [liaad/srl-enpt_xlmr-large](https://huggingface.co/liaad/srl-enpt_xlmr-large)
* [liaad/srl-enpt_mbert-base](https://huggingface.co/liaad/srl-enpt_mbert-base)
* [liaad/ud_srl-pt_bertimbau-large](https://huggingface.co/liaad/ud_srl-pt_bertimbau-large)
* [liaad/ud_srl-pt_xlmr-large](https://huggingface.co/liaad/ud_srl-pt_xlmr-large)
* [liaad/ud_srl-enpt_xlmr-large](https://huggingface.co/liaad/ud_srl-enpt_xlmr-large)
For more information, please see the accompanying article (See BibTeX entry and citation info below) and the [project's github](https://github.com/asofiaoliveira/srl_bert_pt).
## Intended uses & limitations
#### How to use
To use the transformers portion of this model:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("liaad/srl-pt_mbert-base")
model = AutoModel.from_pretrained("liaad/srl-pt_mbert-base")
```
To use the full SRL model (transformers portion + a decoding layer), refer to the [project's github](https://github.com/asofiaoliveira/srl_bert_pt).
## Training procedure
The model was trained on the PropBank.Br datasets, using 10-fold Cross-Validation. The 10 resulting models were tested on the folds as well as on a smaller opinion dataset "Buscapé". For more information, please see the accompanying article (See BibTeX entry and citation info below) and the [project's github](https://github.com/asofiaoliveira/srl_bert_pt).
## Eval results
| Model Name | F<sub>1</sub> CV PropBank.Br (in domain) | F<sub>1</sub> Buscapé (out of domain) |
| --------------- | ------ | ----- |
| `srl-pt_bertimbau-base` | 76.30 | 73.33 |
| `srl-pt_bertimbau-large` | 77.42 | 74.85 |
| `srl-pt_xlmr-base` | 75.22 | 72.82 |
| `srl-pt_xlmr-large` | 77.59 | 73.84 |
| `srl-pt_mbert-base` | 72.76 | 66.89 |
| `srl-en_xlmr-base` | 66.59 | 65.24 |
| `srl-en_xlmr-large` | 67.60 | 64.94 |
| `srl-en_mbert-base` | 63.07 | 58.56 |
| `srl-enpt_xlmr-base` | 76.50 | 73.74 |
| `srl-enpt_xlmr-large` | **78.22** | 74.55 |
| `srl-enpt_mbert-base` | 74.88 | 69.19 |
| `ud_srl-pt_bertimbau-large` | 77.53 | 74.49 |
| `ud_srl-pt_xlmr-large` | 77.69 | 74.91 |
| `ud_srl-enpt_xlmr-large` | 77.97 | **75.05** |
### BibTeX entry and citation info
```bibtex
@misc{oliveira2021transformers,
title={Transformers and Transfer Learning for Improving Portuguese Semantic Role Labeling},
author={Sofia Oliveira and Daniel Loureiro and Alípio Jorge},
year={2021},
eprint={2101.01213},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
liaad/srl-pt_bertimbau-base
|
liaad
| 2021-09-22T08:56:26Z | 8 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"bert-base-portuguese-cased",
"semantic role labeling",
"finetuned",
"multilingual",
"pt",
"dataset:PropBank.Br",
"arxiv:2101.01213",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
---
language:
- multilingual
- pt
tags:
- bert-base-portuguese-cased
- semantic role labeling
- finetuned
license: apache-2.0
datasets:
- PropBank.Br
metrics:
- F1 Measure
---
# BERTimbau base fine-tuned on Portuguese semantic role labeling
## Model description
This model is the [`neuralmind/bert-base-portuguese-cased`](https://huggingface.co/neuralmind/bert-base-portuguese-cased) fine-tuned on Portuguese semantic role labeling data. This is part of a project from which resulted the following models:
* [liaad/srl-pt_bertimbau-base](https://huggingface.co/liaad/srl-pt_bertimbau-base)
* [liaad/srl-pt_bertimbau-large](https://huggingface.co/liaad/srl-pt_bertimbau-large)
* [liaad/srl-pt_xlmr-base](https://huggingface.co/liaad/srl-pt_xlmr-base)
* [liaad/srl-pt_xlmr-large](https://huggingface.co/liaad/srl-pt_xlmr-large)
* [liaad/srl-pt_mbert-base](https://huggingface.co/liaad/srl-pt_mbert-base)
* [liaad/srl-en_xlmr-base](https://huggingface.co/liaad/srl-en_xlmr-base)
* [liaad/srl-en_xlmr-large](https://huggingface.co/liaad/srl-en_xlmr-large)
* [liaad/srl-en_mbert-base](https://huggingface.co/liaad/srl-en_mbert-base)
* [liaad/srl-enpt_xlmr-base](https://huggingface.co/liaad/srl-enpt_xlmr-base)
* [liaad/srl-enpt_xlmr-large](https://huggingface.co/liaad/srl-enpt_xlmr-large)
* [liaad/srl-enpt_mbert-base](https://huggingface.co/liaad/srl-enpt_mbert-base)
* [liaad/ud_srl-pt_bertimbau-large](https://huggingface.co/liaad/ud_srl-pt_bertimbau-large)
* [liaad/ud_srl-pt_xlmr-large](https://huggingface.co/liaad/ud_srl-pt_xlmr-large)
* [liaad/ud_srl-enpt_xlmr-large](https://huggingface.co/liaad/ud_srl-enpt_xlmr-large)
For more information, please see the accompanying article (See BibTeX entry and citation info below) and the [project's github](https://github.com/asofiaoliveira/srl_bert_pt).
## Intended uses & limitations
#### How to use
To use the transformers portion of this model:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("liaad/srl-pt_bertimbau-base")
model = AutoModel.from_pretrained("liaad/srl-pt_bertimbau-base")
```
To use the full SRL model (transformers portion + a decoding layer), refer to the [project's github](https://github.com/asofiaoliveira/srl_bert_pt).
## Training procedure
The model was trained on the PropBank.Br datasets, using 10-fold Cross-Validation. The 10 resulting models were tested on the folds as well as on a smaller opinion dataset "Buscapé". For more information, please see the accompanying article (See BibTeX entry and citation info below) and the [project's github](https://github.com/asofiaoliveira/srl_bert_pt).
## Eval results
| Model Name | F<sub>1</sub> CV PropBank.Br (in domain) | F<sub>1</sub> Buscapé (out of domain) |
| --------------- | ------ | ----- |
| `srl-pt_bertimbau-base` | 76.30 | 73.33 |
| `srl-pt_bertimbau-large` | 77.42 | 74.85 |
| `srl-pt_xlmr-base` | 75.22 | 72.82 |
| `srl-pt_xlmr-large` | 77.59 | 73.84 |
| `srl-pt_mbert-base` | 72.76 | 66.89 |
| `srl-en_xlmr-base` | 66.59 | 65.24 |
| `srl-en_xlmr-large` | 67.60 | 64.94 |
| `srl-en_mbert-base` | 63.07 | 58.56 |
| `srl-enpt_xlmr-base` | 76.50 | 73.74 |
| `srl-enpt_xlmr-large` | **78.22** | 74.55 |
| `srl-enpt_mbert-base` | 74.88 | 69.19 |
| `ud_srl-pt_bertimbau-large` | 77.53 | 74.49 |
| `ud_srl-pt_xlmr-large` | 77.69 | 74.91 |
| `ud_srl-enpt_xlmr-large` | 77.97 | **75.05** |
### BibTeX entry and citation info
```bibtex
@misc{oliveira2021transformers,
title={Transformers and Transfer Learning for Improving Portuguese Semantic Role Labeling},
author={Sofia Oliveira and Daniel Loureiro and Alípio Jorge},
year={2021},
eprint={2101.01213},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
liaad/srl-enpt_xlmr-large
|
liaad
| 2021-09-22T08:56:23Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"xlm-roberta-large",
"semantic role labeling",
"finetuned",
"multilingual",
"pt",
"en",
"dataset:PropBank.Br",
"dataset:CoNLL-2012",
"arxiv:2101.01213",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
---
language:
- multilingual
- pt
- en
tags:
- xlm-roberta-large
- semantic role labeling
- finetuned
license: apache-2.0
datasets:
- PropBank.Br
- CoNLL-2012
metrics:
- F1 Measure
---
# XLM-R large fine-tuned in English and Portuguese semantic role labeling
## Model description
This model is the [`xlm-roberta-large`](https://huggingface.co/xlm-roberta-large) fine-tuned first on the English CoNLL formatted OntoNotes v5.0 semantic role labeling data and then fine-tuned on the PropBank.Br data. This is part of a project from which resulted the following models:
* [liaad/srl-pt_bertimbau-base](https://huggingface.co/liaad/srl-pt_bertimbau-base)
* [liaad/srl-pt_bertimbau-large](https://huggingface.co/liaad/srl-pt_bertimbau-large)
* [liaad/srl-pt_xlmr-base](https://huggingface.co/liaad/srl-pt_xlmr-base)
* [liaad/srl-pt_xlmr-large](https://huggingface.co/liaad/srl-pt_xlmr-large)
* [liaad/srl-pt_mbert-base](https://huggingface.co/liaad/srl-pt_mbert-base)
* [liaad/srl-en_xlmr-base](https://huggingface.co/liaad/srl-en_xlmr-base)
* [liaad/srl-en_xlmr-large](https://huggingface.co/liaad/srl-en_xlmr-large)
* [liaad/srl-en_mbert-base](https://huggingface.co/liaad/srl-en_mbert-base)
* [liaad/srl-enpt_xlmr-base](https://huggingface.co/liaad/srl-enpt_xlmr-base)
* [liaad/srl-enpt_xlmr-large](https://huggingface.co/liaad/srl-enpt_xlmr-large)
* [liaad/srl-enpt_mbert-base](https://huggingface.co/liaad/srl-enpt_mbert-base)
* [liaad/ud_srl-pt_bertimbau-large](https://huggingface.co/liaad/ud_srl-pt_bertimbau-large)
* [liaad/ud_srl-pt_xlmr-large](https://huggingface.co/liaad/ud_srl-pt_xlmr-large)
* [liaad/ud_srl-enpt_xlmr-large](https://huggingface.co/liaad/ud_srl-enpt_xlmr-large)
For more information, please see the accompanying article (See BibTeX entry and citation info below) and the [project's github](https://github.com/asofiaoliveira/srl_bert_pt).
## Intended uses & limitations
#### How to use
To use the transformers portion of this model:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("liaad/srl-enpt_xlmr-large")
model = AutoModel.from_pretrained("liaad/srl-enpt_xlmr-large")
```
To use the full SRL model (transformers portion + a decoding layer), refer to the [project's github](https://github.com/asofiaoliveira/srl_bert_pt).
#### Limitations and bias
- This model does not include a Tensorflow version. This is because the "type_vocab_size" in this model was changed (from 1 to 2) and, therefore, it cannot be easily converted to Tensorflow.
- The English data was preprocessed to match the Portuguese data, so there are some differences in role attributions and some roles were removed from the data.
## Training procedure
The model was first fine-tuned on the CoNLL-2012 dataset, preprocessed to match the Portuguese PropBank.Br data; then it was fine-tuned in the PropBank.Br dataset using 10-fold Cross-Validation. The resulting models were tested on the folds as well as on a smaller opinion dataset "Buscapé". For more information, please see the accompanying article (See BibTeX entry and citation info below) and the [project's github](https://github.com/asofiaoliveira/srl_bert_pt).
## Eval results
| Model Name | F<sub>1</sub> CV PropBank.Br (in domain) | F<sub>1</sub> Buscapé (out of domain) |
| --------------- | ------ | ----- |
| `srl-pt_bertimbau-base` | 76.30 | 73.33 |
| `srl-pt_bertimbau-large` | 77.42 | 74.85 |
| `srl-pt_xlmr-base` | 75.22 | 72.82 |
| `srl-pt_xlmr-large` | 77.59 | 73.84 |
| `srl-pt_mbert-base` | 72.76 | 66.89 |
| `srl-en_xlmr-base` | 66.59 | 65.24 |
| `srl-en_xlmr-large` | 67.60 | 64.94 |
| `srl-en_mbert-base` | 63.07 | 58.56 |
| `srl-enpt_xlmr-base` | 76.50 | 73.74 |
| `srl-enpt_xlmr-large` | **78.22** | 74.55 |
| `srl-enpt_mbert-base` | 74.88 | 69.19 |
| `ud_srl-pt_bertimbau-large` | 77.53 | 74.49 |
| `ud_srl-pt_xlmr-large` | 77.69 | 74.91 |
| `ud_srl-enpt_xlmr-large` | 77.97 | **75.05** |
### BibTeX entry and citation info
```bibtex
@misc{oliveira2021transformers,
title={Transformers and Transfer Learning for Improving Portuguese Semantic Role Labeling},
author={Sofia Oliveira and Daniel Loureiro and Alípio Jorge},
year={2021},
eprint={2101.01213},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
liaad/srl-enpt_mbert-base
|
liaad
| 2021-09-22T08:56:17Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"bert-base-multilingual-cased",
"semantic role labeling",
"finetuned",
"multilingual",
"pt",
"en",
"dataset:PropBank.Br",
"dataset:CoNLL-2012",
"arxiv:2101.01213",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
---
language:
- multilingual
- pt
- en
tags:
- bert-base-multilingual-cased
- semantic role labeling
- finetuned
license: apache-2.0
datasets:
- PropBank.Br
- CoNLL-2012
metrics:
- F1 Measure
---
# mBERT base fine-tune in English and Portuguese semantic role labeling
## Model description
This model is the [`bert-base-multilingual-cased`](https://huggingface.co/bert-base-multilingual-cased) fine-tuned first on the English CoNLL formatted OntoNotes v5.0 semantic role labeling data and then fine-tuned on the PropBank.Br data. This is part of a project from which resulted the following models:
* [liaad/srl-pt_bertimbau-base](https://huggingface.co/liaad/srl-pt_bertimbau-base)
* [liaad/srl-pt_bertimbau-large](https://huggingface.co/liaad/srl-pt_bertimbau-large)
* [liaad/srl-pt_xlmr-base](https://huggingface.co/liaad/srl-pt_xlmr-base)
* [liaad/srl-pt_xlmr-large](https://huggingface.co/liaad/srl-pt_xlmr-large)
* [liaad/srl-pt_mbert-base](https://huggingface.co/liaad/srl-pt_mbert-base)
* [liaad/srl-en_xlmr-base](https://huggingface.co/liaad/srl-en_xlmr-base)
* [liaad/srl-en_xlmr-large](https://huggingface.co/liaad/srl-en_xlmr-large)
* [liaad/srl-en_mbert-base](https://huggingface.co/liaad/srl-en_mbert-base)
* [liaad/srl-enpt_xlmr-base](https://huggingface.co/liaad/srl-enpt_xlmr-base)
* [liaad/srl-enpt_xlmr-large](https://huggingface.co/liaad/srl-enpt_xlmr-large)
* [liaad/srl-enpt_mbert-base](https://huggingface.co/liaad/srl-enpt_mbert-base)
* [liaad/ud_srl-pt_bertimbau-large](https://huggingface.co/liaad/ud_srl-pt_bertimbau-large)
* [liaad/ud_srl-pt_xlmr-large](https://huggingface.co/liaad/ud_srl-pt_xlmr-large)
* [liaad/ud_srl-enpt_xlmr-large](https://huggingface.co/liaad/ud_srl-enpt_xlmr-large)
For more information, please see the accompanying article (See BibTeX entry and citation info below) and the [project's github](https://github.com/asofiaoliveira/srl_bert_pt).
## Intended uses & limitations
#### How to use
To use the transformers portion of this model:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("liaad/srl-enpt_mbert-base")
model = AutoModel.from_pretrained("liaad/srl-enpt_mbert-base")
```
To use the full SRL model (transformers portion + a decoding layer), refer to the [project's github](https://github.com/asofiaoliveira/srl_bert_pt).
#### Limitations and bias
- The English data was preprocessed to match the Portuguese data, so there are some differences in role attributions and some roles were removed from the data.
## Training procedure
The model was first fine-tuned on the CoNLL-2012 dataset, preprocessed to match the Portuguese PropBank.Br data; then it was fine-tuned in the PropBank.Br dataset using 10-fold Cross-Validation. The resulting models were tested on the folds as well as on a smaller opinion dataset "Buscapé". For more information, please see the accompanying article (See BibTeX entry and citation info below) and the [project's github](https://github.com/asofiaoliveira/srl_bert_pt).
## Eval results
| Model Name | F<sub>1</sub> CV PropBank.Br (in domain) | F<sub>1</sub> Buscapé (out of domain) |
| --------------- | ------ | ----- |
| `srl-pt_bertimbau-base` | 76.30 | 73.33 |
| `srl-pt_bertimbau-large` | 77.42 | 74.85 |
| `srl-pt_xlmr-base` | 75.22 | 72.82 |
| `srl-pt_xlmr-large` | 77.59 | 73.84 |
| `srl-pt_mbert-base` | 72.76 | 66.89 |
| `srl-en_xlmr-base` | 66.59 | 65.24 |
| `srl-en_xlmr-large` | 67.60 | 64.94 |
| `srl-en_mbert-base` | 63.07 | 58.56 |
| `srl-enpt_xlmr-base` | 76.50 | 73.74 |
| `srl-enpt_xlmr-large` | **78.22** | 74.55 |
| `srl-enpt_mbert-base` | 74.88 | 69.19 |
| `ud_srl-pt_bertimbau-large` | 77.53 | 74.49 |
| `ud_srl-pt_xlmr-large` | 77.69 | 74.91 |
| `ud_srl-enpt_xlmr-large` | 77.97 | **75.05** |
### BibTeX entry and citation info
```bibtex
@misc{oliveira2021transformers,
title={Transformers and Transfer Learning for Improving Portuguese Semantic Role Labeling},
author={Sofia Oliveira and Daniel Loureiro and Alípio Jorge},
year={2021},
eprint={2101.01213},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
liaad/srl-en_xlmr-large
|
liaad
| 2021-09-22T08:56:14Z | 1,786 | 2 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"xlm-roberta-large",
"semantic role labeling",
"finetuned",
"multilingual",
"pt",
"en",
"dataset:PropBank.Br",
"dataset:CoNLL-2012",
"arxiv:2101.01213",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
---
language:
- multilingual
- pt
- en
tags:
- xlm-roberta-large
- semantic role labeling
- finetuned
license: apache-2.0
datasets:
- PropBank.Br
- CoNLL-2012
metrics:
- F1 Measure
---
# XLM-R large fine-tuned on English semantic role labeling
## Model description
This model is the [`xlm-roberta-large`](https://huggingface.co/xlm-roberta-large) fine-tuned on the English CoNLL formatted OntoNotes v5.0 semantic role labeling data. This is part of a project from which resulted the following models:
* [liaad/srl-pt_bertimbau-base](https://huggingface.co/liaad/srl-pt_bertimbau-base)
* [liaad/srl-pt_bertimbau-large](https://huggingface.co/liaad/srl-pt_bertimbau-large)
* [liaad/srl-pt_xlmr-base](https://huggingface.co/liaad/srl-pt_xlmr-base)
* [liaad/srl-pt_xlmr-large](https://huggingface.co/liaad/srl-pt_xlmr-large)
* [liaad/srl-pt_mbert-base](https://huggingface.co/liaad/srl-pt_mbert-base)
* [liaad/srl-en_xlmr-base](https://huggingface.co/liaad/srl-en_xlmr-base)
* [liaad/srl-en_xlmr-large](https://huggingface.co/liaad/srl-en_xlmr-large)
* [liaad/srl-en_mbert-base](https://huggingface.co/liaad/srl-en_mbert-base)
* [liaad/srl-enpt_xlmr-base](https://huggingface.co/liaad/srl-enpt_xlmr-base)
* [liaad/srl-enpt_xlmr-large](https://huggingface.co/liaad/srl-enpt_xlmr-large)
* [liaad/srl-enpt_mbert-base](https://huggingface.co/liaad/srl-enpt_mbert-base)
* [liaad/ud_srl-pt_bertimbau-large](https://huggingface.co/liaad/ud_srl-pt_bertimbau-large)
* [liaad/ud_srl-pt_xlmr-large](https://huggingface.co/liaad/ud_srl-pt_xlmr-large)
* [liaad/ud_srl-enpt_xlmr-large](https://huggingface.co/liaad/ud_srl-enpt_xlmr-large)
For more information, please see the accompanying article (See BibTeX entry and citation info below) and the [project's github](https://github.com/asofiaoliveira/srl_bert_pt).
## Intended uses & limitations
#### How to use
To use the transformers portion of this model:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("liaad/srl-en_xlmr-large")
model = AutoModel.from_pretrained("liaad/srl-en_xlmr-large")
```
To use the full SRL model (transformers portion + a decoding layer), refer to the [project's github](https://github.com/asofiaoliveira/srl_bert_pt).
#### Limitations and bias
- This model does not include a Tensorflow version. This is because the "type_vocab_size" in this model was changed (from 1 to 2) and, therefore, it cannot be easily converted to Tensorflow.
- The models were trained only for 5 epochs.
- The English data was preprocessed to match the Portuguese data, so there are some differences in role attributions and some roles were removed from the data.
## Training procedure
The models were trained on the CoNLL-2012 dataset, preprocessed to match the Portuguese PropBank.Br data. They were tested on the PropBank.Br data set as well as on a smaller opinion dataset "Buscapé". For more information, please see the accompanying article (See BibTeX entry and citation info below) and the [project's github](https://github.com/asofiaoliveira/srl_bert_pt).
## Eval results
| Model Name | F<sub>1</sub> CV PropBank.Br (in domain) | F<sub>1</sub> Buscapé (out of domain) |
| --------------- | ------ | ----- |
| `srl-pt_bertimbau-base` | 76.30 | 73.33 |
| `srl-pt_bertimbau-large` | 77.42 | 74.85 |
| `srl-pt_xlmr-base` | 75.22 | 72.82 |
| `srl-pt_xlmr-large` | 77.59 | 73.84 |
| `srl-pt_mbert-base` | 72.76 | 66.89 |
| `srl-en_xlmr-base` | 66.59 | 65.24 |
| `srl-en_xlmr-large` | 67.60 | 64.94 |
| `srl-en_mbert-base` | 63.07 | 58.56 |
| `srl-enpt_xlmr-base` | 76.50 | 73.74 |
| `srl-enpt_xlmr-large` | **78.22** | 74.55 |
| `srl-enpt_mbert-base` | 74.88 | 69.19 |
| `ud_srl-pt_bertimbau-large` | 77.53 | 74.49 |
| `ud_srl-pt_xlmr-large` | 77.69 | 74.91 |
| `ud_srl-enpt_xlmr-large` | 77.97 | **75.05** |
### BibTeX entry and citation info
```bibtex
@misc{oliveira2021transformers,
title={Transformers and Transfer Learning for Improving Portuguese Semantic Role Labeling},
author={Sofia Oliveira and Daniel Loureiro and Alípio Jorge},
year={2021},
eprint={2101.01213},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
kiri-ai/t5-base-qa-summary-emotion
|
kiri-ai
| 2021-09-22T08:55:00Z | 294 | 8 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question-answering",
"emotion-detection",
"summarisation",
"en",
"dataset:coqa",
"dataset:squad_v2",
"dataset:go_emotions",
"dataset:cnn_dailymail",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
language:
- en
tags:
- question-answering
- emotion-detection
- summarisation
license: apache-2.0
datasets:
- coqa
- squad_v2
- go_emotions
- cnn_dailymail
metrics:
- f1
pipeline_tag: text2text-generation
widget:
- text: 'q: Who is Elon Musk? a: an entrepreneur q: When was he born? c: Elon Musk
is an entrepreneur born in 1971. </s>'
- text: 'emotion: I hope this works! </s>'
---
# T5 Base with QA + Summary + Emotion
## Dependencies
Requires transformers>=4.0.0
## Description
This model was finetuned on the CoQa, Squad 2, GoEmotions and CNN/DailyMail.
It achieves a score of **F1 79.5** on the Squad 2 dev set and a score of **F1 70.6** on the CoQa dev set.
Summarisation and emotion detection has not been evaluated yet.
## Usage
### Question answering
#### With Transformers
```python
from transformers import T5ForConditionalGeneration, T5Tokenizer
model = T5ForConditionalGeneration.from_pretrained("kiri-ai/t5-base-qa-summary-emotion")
tokenizer = T5Tokenizer.from_pretrained("kiri-ai/t5-base-qa-summary-emotion")
def get_answer(question, prev_qa, context):
input_text = [f"q: {qa[0]} a: {qa[1]}" for qa in prev_qa]
input_text.append(f"q: {question}")
input_text.append(f"c: {context}")
input_text = " ".join(input_text)
features = tokenizer([input_text], return_tensors='pt')
tokens = model.generate(input_ids=features['input_ids'],
attention_mask=features['attention_mask'], max_length=64)
return tokenizer.decode(tokens[0], skip_special_tokens=True)
print(get_answer("Why is the moon yellow?", "I'm not entirely sure why the moon is yellow.")) # unknown
context = "Elon Musk left OpenAI to avoid possible future conflicts with his role as CEO of Tesla."
print(get_answer("Why not?", [("Does Elon Musk still work with OpenAI", "No")], context)) # to avoid possible future conflicts with his role as CEO of Tesla
```
#### With Kiri
```python
from kiri.models import T5QASummaryEmotion
context = "Elon Musk left OpenAI to avoid possible future conflicts with his role as CEO of Tesla."
prev_qa = [("Does Elon Musk still work with OpenAI", "No")]
model = T5QASummaryEmotion()
# Leave prev_qa blank for non conversational question-answering
model.qa("Why not?", context, prev_qa=prev_qa)
> "to avoid possible future conflicts with his role as CEO of Tesla"
```
### Summarisation
#### With Transformers
```python
from transformers import T5ForConditionalGeneration, T5Tokenizer
model = T5ForConditionalGeneration.from_pretrained("kiri-ai/t5-base-qa-summary-emotion")
tokenizer = T5Tokenizer.from_pretrained("kiri-ai/t5-base-qa-summary-emotion")
def summary(context):
input_text = f"summarize: {context}"
features = tokenizer([input_text], return_tensors='pt')
tokens = model.generate(input_ids=features['input_ids'],
attention_mask=features['attention_mask'], max_length=64)
return tokenizer.decode(tokens[0], skip_special_tokens=True)
```
#### With Kiri
```python
from kiri.models import T5QASummaryEmotion
model = T5QASummaryEmotion()
model.summarise("Long text to summarise")
> "Short summary of long text"
```
### Emotion detection
#### With Transformers
```python
from transformers import T5ForConditionalGeneration, T5Tokenizer
model = T5ForConditionalGeneration.from_pretrained("kiri-ai/t5-base-qa-summary-emotion")
tokenizer = T5Tokenizer.from_pretrained("kiri-ai/t5-base-qa-summary-emotion")
def emotion(context):
input_text = f"emotion: {context}"
features = tokenizer([input_text], return_tensors='pt')
tokens = model.generate(input_ids=features['input_ids'],
attention_mask=features['attention_mask'], max_length=64)
return tokenizer.decode(tokens[0], skip_special_tokens=True)
```
#### With Kiri
```python
from kiri.models import T5QASummaryEmotion
model = T5QASummaryEmotion()
model.emotion("I hope this works!")
> "optimism"
```
## About us
Kiri makes using state-of-the-art models easy, accessible and scalable.
[Website](https://kiri.ai) | [Natural Language Engine](https://github.com/kiri-ai/kiri)
|
kanishka/GlossBERT
|
kanishka
| 2021-09-22T08:54:41Z | 134 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"glossbert",
"en",
"dataset:SemCor3.0",
"arxiv:1908.07245",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: en
tags:
- glossbert
license: mit
datasets:
- SemCor3.0
---
## GlossBERT
A BERT-based model fine-tuned on SemCor 3.0 to perform word-sense-disambiguation by leveraging gloss information. This model is the research output of the paper titled: '[GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge](https://arxiv.org/pdf/1908.07245.pdf)'
Disclaimer: This model was built and trained by a group of researchers different than the repository's author. The original model code can be found on github: https://github.com/HSLCY/GlossBERT
## Usage
The following code loads GlossBERT:
```py
from transformers import AutoTokenizer, BertForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained('kanishka/GlossBERT')
model = BertForSequenceClassification.from_pretrained('kanishka/GlossBERT')
```
## Citation
If you use this model in any of your projects, please cite the original authors using the following bibtex:
```
@inproceedings{huang-etal-2019-glossbert,
title = "{G}loss{BERT}: {BERT} for Word Sense Disambiguation with Gloss Knowledge",
author = "Huang, Luyao and
Sun, Chi and
Qiu, Xipeng and
Huang, Xuanjing",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D19-1355",
doi = "10.18653/v1/D19-1355",
pages = "3507--3512"
}
```
|
junnyu/roformer_small_discriminator
|
junnyu
| 2021-09-22T08:54:23Z | 32 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roformer",
"feature-extraction",
"electra",
"rotary position embedding",
"en",
"dataset:openwebtext",
"license:mit",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://github.com/junnyu
tags:
- pytorch
- electra
- roformer
- rotary position embedding
license: mit
datasets:
- openwebtext
---
# 一、 个人在openwebtext数据集上添加rotary-position-embedding，训练得到的electra-small模型
# 二、 复现结果(dev dataset)
|Model|CoLA|SST|MRPC|STS|QQP|MNLI|QNLI|RTE|Avg.|
|---|---|---|---|---|---|---|---|---|---|
|ELECTRA-Small-OWT(original)|56.8|88.3|87.4|86.8|88.3|78.9|87.9|68.5|80.36|
|**ELECTRA-RoFormer-Small-OWT (this)**|55.76|90.45|87.3|86.64|89.61|81.17|88.85|62.71|80.31|
# 三、 训练细节
- 数据集 openwebtext
- 训练batch_size 256
- 学习率lr 5e-4
- 最大句子长度max_seqlen 128
- 训练total step 50W
- GPU RTX3090
- 训练时间总共耗费55h
# 四、wandb日志
- [**预训练日志**](https://wandb.ai/junyu/electra_rotary_small_pretrain?workspace=user-junyu)
- [**GLUE微调日志**](https://wandb.ai/junyu/electra_rotary_glue_100?workspace=user-junyu)
# 五、 使用
```python
import torch
from transformers import ElectraTokenizer,RoFormerModel
tokenizer = ElectraTokenizer.from_pretrained("junnyu/roformer_small_discriminator")
model = RoFormerModel.from_pretrained("junnyu/roformer_small_discriminator")
inputs = tokenizer("Beijing is the capital of China.", return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
print(outputs[0].shape)
```
|
junnyu/electra_small_discriminator
|
junnyu
| 2021-09-22T08:54:15Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"pretraining",
"en",
"dataset:openwebtext",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://github.com/junnyu
tags:
- pytorch
- electra
license: mit
datasets:
- openwebtext
---
# 一、 个人在openwebtext数据集上训练得到的electra-small模型
# 二、 复现结果(dev dataset)
|Model|CoLA|SST|MRPC|STS|QQP|MNLI|QNLI|RTE|Avg.|
|---|---|---|---|---|---|---|---|---|---|
|Metrics|MCC|Acc|Acc|Spearman|Acc|Acc|Acc|Acc||
|ELECTRA-Small-OWT(original)|56.8|88.3|87.4|86.8|88.3|78.9|87.9|68.5|80.36|
|**ELECTRA-Small-OWT (this)**| 55.82 |89.67|87.0|86.96|89.28|80.08|87.50|66.07|80.30|
# 三、 训练细节
- 数据集 openwebtext
- 训练batch_size 256
- 学习率lr 5e-4
- 最大句子长度max_seqlen 128
- 训练total step 62.5W
- GPU RTX3090
- 训练时间总共耗费2.5天
# 四、 使用
```python
import torch
from transformers.models.electra import ElectraModel, ElectraTokenizer
tokenizer = ElectraTokenizer.from_pretrained("junnyu/electra_small_discriminator")
model = ElectraModel.from_pretrained("junnyu/electra_small_discriminator")
inputs = tokenizer("Beijing is the capital of China.", return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
print(outputs[0].shape)
```
|
jimregan/wav2vec2-large-xlsr-irish-basic
|
jimregan
| 2021-09-22T08:52:55Z | 20 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"ga",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: ga
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Irish by Jim O'Regan
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice ga-IE
type: common_voice
args: ga-IE
metrics:
- name: Test WER
type: wer
value: 47.4
---
# Wav2Vec2-Large-XLSR-Irish
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
on the [Irish Common Voice dataset](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "ga-IE", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("jimregan/wav2vec2-large-xlsr-irish-basic")
model = Wav2Vec2ForCTC.from_pretrained("jimregan/wav2vec2-large-xlsr-irish-basic")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the Irish test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "ga-IE", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("jimregan/wav2vec2-large-xlsr-irish-basic")
model = Wav2Vec2ForCTC.from_pretrained("jimregan/wav2vec2-large-xlsr-irish-basic")
model.to("cuda")
# So, tolower() for Irish is a bit complicated: tAthar -> t-athair
# toupper() is non-deterministic :)
def is_upper_vowel(letter):
if letter in ['A', 'E', 'I', 'O', 'U', 'Á', 'É', 'Í', 'Ó', 'Ú']:
return True
else:
return False
def irish_lower(word):
if len(word) > 1 and word[0] in ['n', 't'] and is_upper_vowel(word[1]):
return word[0] + '-' + word[1:].lower()
else:
return word.lower()
def irish_lower_sentence(sentence):
return " ".join([irish_lower(w) for w in sentence.split(" ")])
chars_to_ignore_regex = '[,\?\.\!\;\:\"\“\%\‘\”\(\)\*]'
def remove_special_characters(sentence):
tmp = re.sub('’ ', ' ', sentence)
tmp = re.sub("’$", '', tmp)
tmp = re.sub('’', '\'', tmp)
tmp = re.sub(chars_to_ignore_regex, '', tmp)
sentence = irish_lower_sentence(tmp) + ' '
return sentence
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = remove_special_characters(batch["sentence"])
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 43.7 %
## Training
The Common Voice `train` and `validation` datasets were used for training.
The script used for training can be found [here](https://github.com/jimregan/wav2vec2-sprint/blob/main/irish/fine-tune-xlsr-wav2vec2-on-irish-asr-with-transformers.ipynb)
|
jannesg/takalane_xho_roberta
|
jannesg
| 2021-09-22T08:52:19Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"xho",
"masked-lm",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language:
- xho
thumbnail: https://pbs.twimg.com/media/EVjR6BsWoAAFaq5.jpg
tags:
- xho
- fill-mask
- pytorch
- roberta
- masked-lm
license: mit
---
# Takalani Sesame - Xhosa 🇿🇦
<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_xho_roberta")
model = AutoModelWithLMHead.from_pretrained("jannesg/takalane_xho_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:** 100000
## Training procedure
No preprocessing. Standard Huggingface hyperparameters.
## Author
Jannes Germishuys [website](http://jannesgg.github.io)
|
jannesg/takalane_ven_roberta
|
jannesg
| 2021-09-22T08:52:16Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"ven",
"masked-lm",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
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)
|
jannesg/takalane_tso_roberta
|
jannesg
| 2021-09-22T08:52:13Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"ts",
"masked-lm",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language:
- ts
thumbnail: https://pbs.twimg.com/media/EVjR6BsWoAAFaq5.jpg
tags:
- ts
- fill-mask
- pytorch
- roberta
- masked-lm
license: mit
---
# Takalani Sesame - Tsonga 🇿🇦
<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_tso_roberta")
model = AutoModelWithLMHead.from_pretrained("jannesg/takalane_tso_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_sot_roberta
|
jannesg
| 2021-09-22T08:52:06Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"sot",
"masked-lm",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
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)
|
indigo-ai/BERTino
|
indigo-ai
| 2021-09-22T08:51:24Z | 298 | 16 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"distilbert",
"fill-mask",
"DISTILbert",
"Italian",
"it",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language: it
tags:
- DISTILbert
- Italian
license: mit
widget:
- text: Vado al [MASK] a fare la spesa
- text: Vado al parco a guardare le [MASK]
- text: Il cielo è [MASK] di stelle.
---
# BERTino: an Italian DistilBERT model
This repository hosts BERTino, an Italian DistilBERT model pre-trained by
[indigo.ai](https://indigo.ai/en/)
on a large general-domain Italian corpus. BERTino is task-agnostic and can be
fine-tuned for every downstream task.
### Corpus
The pre-training corpus that we used is the union of the
[Paisa](https://www.corpusitaliano.it/) and
[ItWaC](https://corpora.dipintra.it/public/run.cgi/corp_info?corpname=itwac_full)
corpora. The final corpus counts 14 millions of sentences for a total of 12 GB
of text.
### Downstream Results
To validate the pre-training that we conducted, we evaluated BERTino on the
[Italian ParTUT](https://universaldependencies.org/treebanks/it_partut/index.html),
[Italian ISDT](https://universaldependencies.org/treebanks/it_isdt/index.html),
[Italian WikiNER](https://figshare.com/articles/Learning_multilingual_named_entity_recognition_from_Wikipedia/5462500)
and multi-class sentence classification tasks. We report for comparison results
obtained by the [teacher model](https://huggingface.co/dbmdz/bert-base-italian-xxl-uncased)
fine-tuned in the same tasks and for the same number of epochs.
**Italian ISDT:**
| Model | F1 score | Fine-tuning time | Evaluation time |
|--------------|----------|------------------|-----------------|
| BERTino | 0,9801 | 9m, 4s | 3s |
| Teacher | 0,983 | 16m, 28s | 5s |
**Italian ParTUT:**
| Model | F1 score | Fine-tuning time | Evaluation time |
|--------------|----------|------------------|-----------------|
| BERTino | 0,9268 | 1m, 18s | 1s |
| Teacher | 0,9688 | 2m, 18s | 1s |
**Italian WikiNER:**
| Model | F1 score | Fine-tuning time | Evaluation time |
|--------------|----------|------------------|-----------------|
| BERTino | 0,9038 | 35m, 35s | 3m, 1s |
| Teacher | 0,9178 | 67m, 8s | 5m, 16s |
**Multi-class sentence classification:**
| Model | F1 score | Fine-tuning time | Evaluation time |
|--------------|----------|------------------|-----------------|
| BERTino | 0,7788 | 4m, 40s | 6s |
| Teacher | 0,7986 | 8m, 52s | 9s |
|
gagan3012/k2t
|
gagan3012
| 2021-09-22T08:27:36Z | 312 | 3 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"keytotext",
"k2t",
"Keywords to Sentences",
"en",
"dataset:WebNLG",
"dataset:Dart",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
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)
|
gagan3012/k2t-new
|
gagan3012
| 2021-09-22T08:27:25Z | 149 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"keytotext",
"k2t",
"Keywords to Sentences",
"en",
"dataset:common_gen",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: Keywords to Sentences
tags:
- keytotext
- k2t
- Keywords to Sentences
license: mit
datasets:
- common_gen
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)
|
flax-community/gpt-neo-125M-apps-all
|
flax-community
| 2021-09-22T08:25:32Z | 5 | 2 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt_neo",
"text-generation",
"code_synthesis",
"dataset:apps",
"arxiv:2107.03374",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language:
- en
- python
license: mit
tags:
- gpt_neo
- code_synthesis
datasets:
- apps
---
# GPT-Neo-125M-APPS-all
> **Please refer to our new [GitHub Wiki](https://github.com/ncoop57/gpt-code-clippy/wiki) which documents our efforts in detail in creating the open source version of GitHub Copilot**
## Model Description
GPT-Neo-125M-APPS-all is a GPT-Neo-125M finetuned on APPS dataset. This model is specialized to solve programming tasks.
## Training data
The model is trained on the [Automated Programming Progress Standard (APPS) dataset](https://github.com/hendrycks/apps). The dataset consists of 10,000 coding problems in total, with 131,836 test cases for checking solutions and 232,444 ground-truth solutions written by humans. Problems can be complicated, as the average length of a problem is 293.2 words. The data are split evenly into training and test sets, with 5,000 problems each.
This model is fine-tuned using most of the APPS dataset including both train and test split to explore the impact of this training task on model performance on other code synthesis evaluation metrics. A model fine-tuned on train set only can be found [here](https://huggingface.co/flax-community/gpt-neo-125M-apps).
## Training procedure
The training script used to train this model can be found [here](https://github.com/ncoop57/gpt-code-clippy/blob/camera-ready/training/run_clm_apps.py).
Training is done for 5 epochs using AdamW optimizer and leaner decay learning rate schedule with 800 warmup steps. To reproduce the training one can use this command with the above script:
```bash
python run_clm_apps.py \
--output_dir $HOME/gpt-neo-125M-apps \
--model_name_or_path EleutherAI/gpt-neo-125B \
--dataset_name $HOME/gpt-code-clippy/data_processing/apps.py \
--dataset_config_name formatted \
--do_train --do_eval \
--block_size="1024" \
--per_device_train_batch_size="16" \
--per_device_eval_batch_size="16" \
--preprocessing_num_workers="16" \
--learning_rate="8e-5" \
--warmup_steps="800" \
--adam_beta1="0.9" \
--adam_beta2="0.98" \
--weight_decay="0.1" \
--overwrite_output_dir \
--num_train_epochs="5" \
--logging_steps="50" \
--eval_steps="2000" \
--report_to="wandb" \
--dtype="bfloat16" \
--save_strategy epoch \
--gradient_accumulation_steps 2 \
--all_data true \
```
## Intended Use and Limitations
The model is finetuned to solve programming problems given a text description and optional starter code.
### How to use
You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run:
```py
from transformers import AutoModelForCausalLM, AutoTokenizer, FlaxAutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("flax-community/gpt-code-clippy-125M-apps-alldata")
tokenizer = AutoTokenizer.from_pretrained("flax-community/gpt-code-clippy-125M-apps-alldata")
prompt = """
A function to greet user. Given a user name it should say hello
def greet(name):
ANSWER:
"""
input_ids = tokenizer(prompt, return_tensors='pt').input_ids.to(device)
start = input_ids.size(1)
out = model.generate(input_ids, do_sample=True, max_length=50, num_beams=2,
early_stopping=True, eos_token_id=tokenizer.eos_token_id, )
print(tokenizer.decode(out[0][start:]))
```
### Limitations and Biases
The model is intended to be used for research purposes and comes with no guarantees of quality of generated code.
The paper ["Evaluating Large Language Models Trained on Code"](https://arxiv.org/abs/2107.03374) from OpenAI has a good discussion on what the impact of a large language model trained on code could be. Therefore, some parts of their discuss are highlighted here as it pertains to this dataset and models that may be trained from it. **As well as some differences in views from the paper, particularly around legal implications**.
1. **Over-reliance:** This model may generate plausible solutions that may appear correct, but are not necessarily the correct solution. Not properly evaluating the generated code may cause have negative consequences such as the introduction of bugs, or the introduction of security vulnerabilities. Therefore, it is important that users are aware of the limitations and potential negative consequences of using this language model.
2. **Economic and labor market impacts:** Large language models trained on large code datasets such as this one that are capable of generating high-quality code have the potential to automate part of the software development process. This may negatively impact software developers. However, as discussed in the paper, as shown in the Summary Report of software developers from [O*NET OnLine](https://www.onetonline.org/link/summary/15-1252.00), developers don't just write software.
5. **Biases:** The model is trained on data containing prompt questions formatted in specific way. The performance of the model can be worse if the prompt
formatting is different from that used in APPS dataset.
GPT-CC is finetuned GPT-Neo and might have inhereted biases and limitations from it. See [GPT-Neo model card](https://huggingface.co/EleutherAI/gpt-neo-125M#limitations-and-biases) for details.
## Eval results
Coming soon...
|
digitalepidemiologylab/covid-twitter-bert-v2-mnli
|
digitalepidemiologylab
| 2021-09-22T08:20:04Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"Twitter",
"COVID-19",
"tensorflow",
"zero-shot-classification",
"en",
"dataset:mnli",
"arxiv:1909.00161",
"arxiv:2005.07503",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
zero-shot-classification
| 2022-03-02T23:29:05Z |
---
language:
- en
thumbnail: https://raw.githubusercontent.com/digitalepidemiologylab/covid-twitter-bert/master/images/COVID-Twitter-BERT_small.png
tags:
- Twitter
- COVID-19
- text-classification
- pytorch
- tensorflow
- bert
license: mit
datasets:
- mnli
pipeline_tag: zero-shot-classification
widget:
- text: To stop the pandemic it is important that everyone turns up for their shots.
candidate_labels: health, sport, vaccine, guns
---
# COVID-Twitter-BERT v2 MNLI
## Model description
This model provides a zero-shot classifier to be used in cases where it is not possible to finetune CT-BERT on a specific task, due to lack of labelled data.
The technique is based on [Yin et al.](https://arxiv.org/abs/1909.00161).
The article describes a very clever way of using pre-trained MNLI models as zero-shot sequence classifiers.
The model is already finetuned on 400'000 generaic logical tasks.
We can then use it as a zero-shot classifier by reformulating the classification task as a question.
Let's say we want to classify COVID-tweets as vaccine-related and not vaccine-related.
The typical way would be to collect a few hunder pre-annotated tweets and organise them in two classes.
Then you would finetune the model on this.
With the zero-shot mnli-classifier, you can instead reformulate your question as "This text is about vaccines", and use this directly on inference - without any training.
Find more info about the model on our [GitHub page](https://github.com/digitalepidemiologylab/covid-twitter-bert).
## Usage
Please note that how you formulate the question can give slightly different results.
Collecting a training set and finetuning on this, will most likely give you better accuracy.
The easiest way to try this out is by using the Hugging Face pipeline.
This uses the default Enlish template where it puts the text "This example is " in front of the text.
```python
from transformers import pipeline
classifier = pipeline("zero-shot-classification", model="digitalepidemiologylab/covid-twitter-bert-v2-mnli")
```
You can then use this pipeline to classify sequences into any of the class names you specify.
```python
sequence_to_classify = 'To stop the pandemic it is important that everyone turns up for their shots.'
candidate_labels = ['health', 'sport', 'vaccine','guns']
hypothesis_template = 'This example is {}.'
classifier(sequence_to_classify, candidate_labels, hypothesis_template=hypothesis_template, multi_class=True)
```
## Training procedure
The model is finetuned on the 400k large [MNLI-task](https://cims.nyu.edu/~sbowman/multinli/).
## References
```bibtex
@article{muller2020covid,
title={COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter},
author={M{\"u}ller, Martin and Salath{\'e}, Marcel and Kummervold, Per E},
journal={arXiv preprint arXiv:2005.07503},
year={2020}
}
```
or
```
Martin Müller, Marcel Salathé, and Per E. Kummervold.
COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter.
arXiv preprint arXiv:2005.07503 (2020).
```
|
Luyu/bert-base-mdoc-bm25
|
Luyu
| 2021-09-22T08:11:56Z | 3,789 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"text reranking",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:04Z |
---
language:
- en
tags:
- text reranking
license: apache-2.0
datasets:
- MS MARCO document ranking
---
# BERT Reranker for MS-MARCO Document Ranking
## Model description
A text reranker trained for BM25 retriever on MS MARCO document dataset.
## Intended uses & limitations
It is possible to work with other retrievers like but using aligned BM25 works the best.
We used anserini toolkit's BM25 implementation and indexed with tuned parameters (k1=3.8, b=0.87) following [this instruction](https://github.com/castorini/anserini/blob/master/docs/experiments-msmarco-doc.md).
#### How to use
See our [project repo page](https://github.com/luyug/Reranker).
## Eval results
MRR @10: 0.423 on Dev.
### BibTeX entry and citation info
```bibtex
@inproceedings{gao2021lce,
title={Rethink Training of BERT Rerankers in Multi-Stage Retrieval Pipeline},
author={Luyu Gao and Zhuyun Dai and Jamie Callan},
year={2021},
booktitle={The 43rd European Conference On Information Retrieval (ECIR)},
}
```
|
Harveenchadha/vakyansh-wav2vec2-tamil-tam-250
|
Harveenchadha
| 2021-09-22T07:55:33Z | 4,600 | 2 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"ta",
"arxiv:2107.07402",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language: ta
#datasets:
#- Interspeech 2021
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
license: mit
model-index:
- name: Wav2Vec2 Vakyansh Tamil Model by Harveen Chadha
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice ta
type: common_voice
args: ta
metrics:
- name: Test WER
type: wer
value: 53.64
---
## Pretrained Model
Fine-tuned on Multilingual Pretrained Model [CLSRIL-23](https://arxiv.org/abs/2107.07402). The original fairseq checkpoint is present [here](https://github.com/Open-Speech-EkStep/vakyansh-models). When using this model, make sure that your speech input is sampled at 16kHz.
**Note: The result from this model is without a language model so you may witness a higher WER in some cases.**
## Dataset
This model was trained on 4200 hours of Hindi Labelled Data. The labelled data is not present in public domain as of now.
## Training Script
Models were trained using experimental platform setup by Vakyansh team at Ekstep. Here is the [training repository](https://github.com/Open-Speech-EkStep/vakyansh-wav2vec2-experimentation).
In case you want to explore training logs on wandb they are [here](https://wandb.ai/harveenchadha/tamil-finetuning-multilingual).
## [Colab Demo](https://github.com/harveenchadha/bol/blob/main/demos/hf/tamil/hf_tamil_tnm_4200_demo.ipynb)
## Usage
The model can be used directly (without a language model) as follows:
```python
import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import argparse
def parse_transcription(wav_file):
# load pretrained model
processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-tamil-tam-250")
model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-tamil-tam-250")
# load audio
audio_input, sample_rate = sf.read(wav_file)
# pad input values and return pt tensor
input_values = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values
# INFERENCE
# retrieve logits & take argmax
logits = model(input_values).logits
predicted_ids = torch.argmax(logits, dim=-1)
# transcribe
transcription = processor.decode(predicted_ids[0], skip_special_tokens=True)
print(transcription)
```
## Evaluation
The model can be evaluated as follows on the hindi test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "ta", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-tamil-tam-250")
model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-tamil-tam-250")
model.to("cuda")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“]'
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids, skip_special_tokens=True)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 53.64 %
[**Colab Evaluation**](https://github.com/harveenchadha/bol/blob/main/demos/hf/tamil/hf_vakyansh_tamil_tnm_4200_evaluation_common_voice.ipynb)
## Credits
Thanks to Ekstep Foundation for making this possible. The vakyansh team will be open sourcing speech models in all the Indic Languages.
|
Guscode/DKbert-hatespeech-detection
|
Guscode
| 2021-09-22T07:55:16Z | 9 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"bert",
"text-classification",
"Hatespeech",
"Danish",
"BERT",
"da",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:04Z |
---
language:
- da
tags:
- Hatespeech
- Danish
- BERT
license: mit
datasets:
- DKHate - OffensEval2020
Classes:
- Hateful
- Not Hateful
---
# DKbert-hatespeech-classification
Use this model to detect hatespeech in Danish. For details, guide and command line tool see [DK hate github](https://github.com/Guscode/DKbert-hatespeech-detection)
## Training data
Training data is from OffensEval2020 which can be found [here]( https://figshare.com/articles/dataset/Danish_Hate_Speech_Abusive_Language_data/12220805)
## Performance
The model achieves a macro F1-score of 0.78
Precision hateful: 0.77
Recall hateful: 0.49
See more on [DK hate github](https://github.com/Guscode/DKbert-hatespeech-detection)
## Training procedure
- [BOTXO Nordic Bert](https://huggingface.co/DJSammy/bert-base-danish-uncased_BotXO,ai)
- Learning rate: 1e-5,
- Batch size: 16
- Max sequence length: 128
## Project information
This model was made in collaboration between [Johan Horsmans](https://github.com/JohanHorsmans) and [Gustav Aarup Lauridsen](https://github.com/Guscode) for their Cultural Data Science Exam.
|
gchhablani/bert-large-cased-finetuned-mrpc
|
gchhablani
| 2021-09-22T07:00:38Z | 5 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: bert-large-cased-finetuned-mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.6838235294117647
- name: F1
type: f1
value: 0.8122270742358079
---
<!-- 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-large-cased-finetuned-mrpc
This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6274
- Accuracy: 0.6838
- F1: 0.8122
- Combined Score: 0.7480
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
| 0.6441 | 1.0 | 917 | 0.6370 | 0.6838 | 0.8122 | 0.7480 |
| 0.6451 | 2.0 | 1834 | 0.6553 | 0.6838 | 0.8122 | 0.7480 |
| 0.6428 | 3.0 | 2751 | 0.6332 | 0.6838 | 0.8122 | 0.7480 |
| 0.6476 | 4.0 | 3668 | 0.6248 | 0.6838 | 0.8122 | 0.7480 |
| 0.6499 | 5.0 | 4585 | 0.6274 | 0.6838 | 0.8122 | 0.7480 |
### Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.0
- Datasets 1.12.1
- Tokenizers 0.10.3
|
csukuangfj/icefall_asr_yesno_tdnn
|
csukuangfj
| 2021-09-22T02:33:22Z | 0 | 1 | null |
[
"region:us"
] | null | 2022-03-02T23:29:05Z |
## Pre-trained TDNN models for the yesno dataset with icefall.
Refer to <https://github.com/k2-fsa/icefall/tree/master/egs/yesno/ASR>
for more information about this pre-trained model.
You can find usage instructions there.
## Sound files for testing the pre-trained model
The folder `test_waves` contains test sound files. They
are downloaded from <https://www.openslr.org/1/>.
There are 60 files in the dataset, 30 are used for training.
The remaining 30 files, contained in `test_waves` are kept for testing.
The code for splitting the dataset can be found at
<https://github.com/lhotse-speech/lhotse/blob/master/lhotse/recipes/yesno.py#L138>
```python
wave_files = list(corpus_dir.glob("*.wav"))
assert len(wave_files) == 60
wave_files.sort()
train_set = wave_files[::2]
test_set = wave_files[1::2]
assert len(train_set) == 30
assert len(test_set) == 30
```
|
wzkariampuzha/EpiExtract4GARD
|
wzkariampuzha
| 2021-09-21T20:01:37Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
This is the model that can extract epidemiological information from rare disease abstracts.
|
huggingtweets/boss_lady_fenja-ladyfenja_promo
|
huggingtweets
| 2021-09-21T16:19:05Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/boss_lady_fenja-ladyfenja_promo/1632241140819/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/1424482960749776907/NL5l0P9Q_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/1432371607977275395/j60VC-cp_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">✨Boss Lady Fenja✨ 9.6% 🦋 & Boss_Lady_Fenja_promo</div>
<div style="text-align: center; font-size: 14px;">@boss_lady_fenja-ladyfenja_promo</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 ✨Boss Lady Fenja✨ 9.6% 🦋 & Boss_Lady_Fenja_promo.
| Data | ✨Boss Lady Fenja✨ 9.6% 🦋 | Boss_Lady_Fenja_promo |
| --- | --- | --- |
| Tweets downloaded | 3153 | 654 |
| Retweets | 380 | 240 |
| Short tweets | 646 | 160 |
| Tweets kept | 2127 | 254 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1jpqrjjb/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 @boss_lady_fenja-ladyfenja_promo's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/10coew7p) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/10coew7p/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/boss_lady_fenja-ladyfenja_promo')
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)
|
alexanderfalk/danbert-small-cased
|
alexanderfalk
| 2021-09-21T15:57:39Z | 14 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"named entity recognition",
"token criticality",
"da",
"en",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language:
- da
- en
thumbnail:
tags:
- named entity recognition
- token criticality
license: apache-2.0
datasets:
- custom danish dataset
inference: false
metrics:
- array of metric identifiers
---
# DanBERT
## Model description
DanBERT is a danish pre-trained model based on BERT-Base. The pre-trained model has been trained on more than 2 million sentences and 40 millions, danish words. The training has been conducted as part of a thesis.
The model can be found at:
* [danbert-da](https://huggingface.co/alexanderfalk/danbert-small-cased)
## Intended uses & limitations
#### How to use
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("alexanderfalk/danbert-small-cased")
model = AutoModel.from_pretrained("alexanderfalk/danbert-small-cased")
```
### BibTeX entry and citation info
```bibtex
@inproceedings{...,
year={2020},
title={Anonymization of Danish, Real-Time Data, and Personalized Modelling},
author={Alexander Falk},
}
```
|
AkshaySg/LanguageIdentification
|
AkshaySg
| 2021-09-21T15:45:47Z | 2 | 0 | null |
[
"LID",
"spoken language recognition",
"multilingual",
"dataset:VoxLingua107",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:04Z |
---
language: multilingual
tags:
- LID
- spoken language recognition
license: apache-2.0
datasets:
- VoxLingua107
metrics:
- ER
inference: false
---
# Spoken Language Identification Model
## Model description
The model can classify a speech utterance according to the language spoken.
It covers following different languages (
English,
Indonesian,
Japanese,
Korean,
Thai,
Vietnamese,
Mandarin Chinese).
|
gniemiec/mt5-small-finetuned-xsum
|
gniemiec
| 2021-09-21T13:22:57Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-xsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xsum
type: xsum
args: default
metrics:
- name: Rouge1
type: rouge
value: 2.8351
---
<!-- 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. -->
# mt5-small-finetuned-xsum
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Rouge1: 2.8351
- Rouge2: 0.3143
- Rougel: 2.6488
- Rougelsum: 2.6463
- Gen Len: 4.9416
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| nan | 1.0 | 12753 | nan | 2.8351 | 0.3143 | 2.6488 | 2.6463 | 4.9416 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
|
satyaalmasian/temporal_tagger_roberta2roberta
|
satyaalmasian
| 2021-09-21T11:11:22Z | 5 | 6 |
transformers
|
[
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
# RoBERTa2RoBERTa temporal tagger
Seq2seq model for temporal tagging of plain text using RoBERTa language model. The model is introduced in the paper BERT got a Date: Introducing Transformers to Temporal Tagging and release in this [repository](https://github.com/satya77/Transformer_Temporal_Tagger).
# Model description
RoBERTa is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. We use RoBERTa in an encoder-decoder architecture for text generation, where the input is raw text and the output is the temporally annotated text. The model is pre-trained on a weakly annotated dataset from a rule-based system (HeidelTime) and fine-tuned on the temporal benchmark datasets (Wikiwars, Tweets, Tempeval-3).
# Intended uses & limitations
This model is best used accompanied with code from the [repository](https://github.com/satya77/Transformer_Temporal_Tagger). Especially for inference, the direct output might be noisy and hard to decipher, in the repository we provide cleaning functions for the output and insert the temporal tags from the generated text in the input text. If you have temporally annotated data you can fine-tune this model.
# How to use
you can load the model as follows:
```
tokenizer = AutoTokenizer.from_pretrained("satyaalmasian/temporal_tagger_roberta2roberta")
model = EncoderDecoderModel.from_pretrained("satyaalmasian/temporal_tagger_roberta2roberta")
```
for inference use:
```
model_inputs = tokenizer(input_text, truncation=True, return_tensors="pt")
out = model.generate(**model_inputs)
decoded_preds = tokenizer.batch_decode(out, skip_special_tokens=True)
```
for an example with post-processing, refer to the [repository](https://github.com/satya77/Transformer_Temporal_Tagger).
to further fine-tune, use the `Seq2SeqTrainer` from hugginface. An example of a similar fine-tuning can be found [here](https://github.com/satya77/Transformer_Temporal_Tagger/blob/master/run_seq2seq_bert_roberta.py).
```
trainer = Seq2SeqTrainer(
model=model2model,
tokenizer=tokenizer,
args=training_args,
compute_metrics=metrics.compute_metrics,
train_dataset=train_data,
eval_dataset=val_data,
)
train_result=trainer.train()
```
where the `training_args` is an instance of `Seq2SeqTrainingArguments`.
#Training data
We use four data sources:
For Pretraining :1 million weakly annotated samples from heideltime. The samples are from news articles between the 1st January 2019 and the 30th July.
Fine-tunning: [Tempeval-3](https://www.cs.york.ac.uk/semeval-2013/task1/index.php%3Fid=data.html), Wikiwars, Tweets datasets. For the correct data versions please refer to our [repository](https://github.com/satya77/Transformer_Temporal_Tagger).
#Training procedure
The model is pre-trained on the weakly labeled data for $3$ epochs on the train set, from publicly available checkpoints on huggingface (`roberta-base`), with a batch size of 12. We use a learning rate of 5e-05 with an Adam optimizer and linear weight decay.
Additionally, we use 2000 warmup steps.
We fine-tune the 3 benchmark data for 8 epochs with 5 different random seeds, this version of the model is the only seed=4.
The batch size and the learning rate is the same as the pre-training setup, but the warm-up steps are reduced to 100.
For training, we use 2 NVIDIA A100 GPUs with 40GB of memory.
For inference in seq2seq models, we use Greedy decoding, since beam search had sub-optimal results.
|
jogonba2/POCTS
|
jogonba2
| 2021-09-21T09:35:25Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"summarization",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- summarization
metrics:
- rouge
model-index:
- name: POCTS
results:
- task:
name: Summarization
type: summarization
metrics:
- name: Rouge1
type: rouge
value: 26.1391
---
<!-- 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. -->
# POCTS
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0970
- Rouge1: 26.1391
- Rouge2: 7.3101
- Rougel: 19.1217
- Rougelsum: 21.9706
- Gen Len: 46.2245
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.15
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:------:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 3.3259 | 1.0 | 33875 | 3.2535 | 17.942 | 4.5143 | 14.2766 | 15.582 | 19.3901 |
| 2.9764 | 2.0 | 67750 | 3.1278 | 18.6558 | 5.1844 | 15.0939 | 16.3367 | 19.9174 |
| 2.5889 | 3.0 | 101625 | 3.0970 | 19.1763 | 5.4517 | 15.5342 | 16.7186 | 19.8855 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.7.1+cu110
- Datasets 1.11.0
- Tokenizers 0.10.3
|
Frederick0291/t5-small-finetuned-billsum
|
Frederick0291
| 2021-09-21T08:33:18Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:billsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- billsum
metrics:
- rouge
model-index:
- name: t5-small-finetuned-billsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: billsum
type: billsum
args: default
metrics:
- name: Rouge1
type: rouge
value: 16.6044
---
<!-- 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-billsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0972
- Rouge1: 16.6044
- Rouge2: 12.8656
- Rougel: 15.7876
- Rougelsum: 15.9784
- Gen Len: 18.9948
## 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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 2.3854 | 1.0 | 2369 | 2.0972 | 16.6044 | 12.8656 | 15.7876 | 15.9784 | 18.9948 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
|
ramsrigouthamg/t5-large-paraphraser-diverse-high-quality
|
ramsrigouthamg
| 2021-09-21T05:21:49Z | 602 | 26 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
Blog post with more details as well as easy to use Google Colab link: https://towardsdatascience.com/high-quality-sentence-paraphraser-using-transformers-in-nlp-c33f4482856f
!pip install transformers==4.10.2
!pip install sentencepiece==0.1.96
```
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("ramsrigouthamg/t5-large-paraphraser-diverse-high-quality")
tokenizer = AutoTokenizer.from_pretrained("ramsrigouthamg/t5-large-paraphraser-diverse-high-quality")
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print ("device ",device)
model = model.to(device)
# Beam Search
context = "Once, a group of frogs were roaming around the forest in search of water."
text = "paraphrase: "+context + " </s>"
encoding = tokenizer.encode_plus(text,max_length =128, padding=True, return_tensors="pt")
input_ids,attention_mask = encoding["input_ids"].to(device), encoding["attention_mask"].to(device)
model.eval()
beam_outputs = model.generate(
input_ids=input_ids,attention_mask=attention_mask,
max_length=128,
early_stopping=True,
num_beams=15,
num_return_sequences=3
)
print ("\n\n")
print ("Original: ",context)
for beam_output in beam_outputs:
sent = tokenizer.decode(beam_output, skip_special_tokens=True,clean_up_tokenization_spaces=True)
print (sent)
```
**Output from the above code**
```
Original: Once, a group of frogs were roaming around the forest in search of water.
paraphrasedoutput: A herd of frogs were wandering around the woods in search of water.
paraphrasedoutput: A herd of frogs was wandering around the woods in search of water.
paraphrasedoutput: A herd of frogs were wandering around the forest in search of water at one time.
```
|
gchhablani/bert-large-cased-finetuned-cola
|
gchhablani
| 2021-09-21T04:06:19Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: bert-large-cased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE COLA
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5957317644481708
---
<!-- 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-large-cased-finetuned-cola
This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8385
- Matthews Correlation: 0.5957
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 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 | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5533 | 1.0 | 2138 | 0.7943 | 0.4439 |
| 0.5004 | 2.0 | 4276 | 0.7272 | 0.5678 |
| 0.2865 | 3.0 | 6414 | 0.8385 | 0.5957 |
### Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.0
- Datasets 1.12.1
- Tokenizers 0.10.3
|
fabriceyhc/bert-base-uncased-dbpedia_14
|
fabriceyhc
| 2021-09-21T00:56:12Z | 52 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"sibyl",
"dataset:dbpedia_14",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
- sibyl
datasets:
- dbpedia_14
metrics:
- accuracy
model-index:
- name: bert-base-uncased-dbpedia_14
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: dbpedia_14
type: dbpedia_14
args: dbpedia_14
metrics:
- name: Accuracy
type: accuracy
value: 0.9902857142857143
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-dbpedia_14
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the dbpedia_14 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0547
- Accuracy: 0.9903
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 34650
- training_steps: 346500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.7757 | 0.03 | 2000 | 0.2732 | 0.9880 |
| 0.1002 | 0.06 | 4000 | 0.0620 | 0.9891 |
| 0.0547 | 0.09 | 6000 | 0.0723 | 0.9879 |
| 0.0558 | 0.12 | 8000 | 0.0678 | 0.9875 |
| 0.0534 | 0.14 | 10000 | 0.0554 | 0.9896 |
| 0.0632 | 0.17 | 12000 | 0.0670 | 0.9888 |
| 0.0612 | 0.2 | 14000 | 0.0733 | 0.9873 |
| 0.0667 | 0.23 | 16000 | 0.0623 | 0.9896 |
| 0.0636 | 0.26 | 18000 | 0.0836 | 0.9868 |
| 0.0705 | 0.29 | 20000 | 0.0776 | 0.9855 |
| 0.0726 | 0.32 | 22000 | 0.0805 | 0.9861 |
| 0.0778 | 0.35 | 24000 | 0.0713 | 0.9870 |
| 0.0713 | 0.38 | 26000 | 0.1277 | 0.9805 |
| 0.0965 | 0.4 | 28000 | 0.0810 | 0.9855 |
| 0.0881 | 0.43 | 30000 | 0.0910 | 0.985 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.7.1
- Datasets 1.6.1
- Tokenizers 0.10.3
|
fabriceyhc/bert-base-uncased-ag_news
|
fabriceyhc
| 2021-09-21T00:54:07Z | 541 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"sibyl",
"dataset:ag_news",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
- sibyl
datasets:
- ag_news
metrics:
- accuracy
model-index:
- name: bert-base-uncased-ag_news
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: ag_news
type: ag_news
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9375
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-ag_news
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the ag_news dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3284
- Accuracy: 0.9375
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 7425
- training_steps: 74250
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5773 | 0.13 | 2000 | 0.3627 | 0.8875 |
| 0.3101 | 0.27 | 4000 | 0.2938 | 0.9208 |
| 0.3076 | 0.4 | 6000 | 0.3114 | 0.9092 |
| 0.3114 | 0.54 | 8000 | 0.4545 | 0.9008 |
| 0.3154 | 0.67 | 10000 | 0.3875 | 0.9083 |
| 0.3095 | 0.81 | 12000 | 0.3390 | 0.9142 |
| 0.2948 | 0.94 | 14000 | 0.3341 | 0.9133 |
| 0.2557 | 1.08 | 16000 | 0.4573 | 0.9092 |
| 0.258 | 1.21 | 18000 | 0.3356 | 0.9217 |
| 0.2455 | 1.35 | 20000 | 0.3348 | 0.9283 |
| 0.2361 | 1.48 | 22000 | 0.3218 | 0.93 |
| 0.254 | 1.62 | 24000 | 0.3814 | 0.9033 |
| 0.2528 | 1.75 | 26000 | 0.3628 | 0.9158 |
| 0.2282 | 1.89 | 28000 | 0.3302 | 0.9308 |
| 0.224 | 2.02 | 30000 | 0.3967 | 0.9225 |
| 0.174 | 2.15 | 32000 | 0.3669 | 0.9333 |
| 0.1848 | 2.29 | 34000 | 0.3435 | 0.9283 |
| 0.19 | 2.42 | 36000 | 0.3552 | 0.93 |
| 0.1865 | 2.56 | 38000 | 0.3996 | 0.9258 |
| 0.1877 | 2.69 | 40000 | 0.3749 | 0.9258 |
| 0.1951 | 2.83 | 42000 | 0.3963 | 0.9258 |
| 0.1702 | 2.96 | 44000 | 0.3655 | 0.9317 |
| 0.1488 | 3.1 | 46000 | 0.3942 | 0.9292 |
| 0.1231 | 3.23 | 48000 | 0.3998 | 0.9267 |
| 0.1319 | 3.37 | 50000 | 0.4292 | 0.9242 |
| 0.1334 | 3.5 | 52000 | 0.4904 | 0.9192 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.7.1
- Datasets 1.6.1
- Tokenizers 0.10.3
|
deval/bert-base-NER-finetuned-ner
|
deval
| 2021-09-20T16:15:04Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:x_glue",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- x_glue
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-NER-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: x_glue
type: x_glue
args: ner
metrics:
- name: Precision
type: precision
value: 0.2273838630806846
- name: Recall
type: recall
value: 0.11185727172496743
- name: F1
type: f1
value: 0.14994961370507223
- name: Accuracy
type: accuracy
value: 0.8485324947589099
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-NER-finetuned-ner
This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) on the x_glue dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4380
- Precision: 0.2274
- Recall: 0.1119
- F1: 0.1499
- Accuracy: 0.8485
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0822 | 1.0 | 878 | 1.1648 | 0.2068 | 0.1101 | 0.1437 | 0.8471 |
| 0.0102 | 2.0 | 1756 | 1.2697 | 0.2073 | 0.1110 | 0.1445 | 0.8447 |
| 0.0049 | 3.0 | 2634 | 1.3945 | 0.2006 | 0.1073 | 0.1399 | 0.8368 |
| 0.0025 | 4.0 | 3512 | 1.3994 | 0.2243 | 0.1126 | 0.1499 | 0.8501 |
| 0.0011 | 5.0 | 4390 | 1.4380 | 0.2274 | 0.1119 | 0.1499 | 0.8485 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
|
dominiqueblok/roberta-base-finetuned-ner
|
dominiqueblok
| 2021-09-20T16:02:48Z | 187 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-base-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9529566113766282
- name: Recall
type: recall
value: 0.9604268983755194
- name: F1
type: f1
value: 0.9566771720212616
- name: Accuracy
type: accuracy
value: 0.988938664048357
---
<!-- 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 conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0492
- Precision: 0.9530
- Recall: 0.9604
- F1: 0.9567
- Accuracy: 0.9889
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2031 | 1.0 | 878 | 0.0560 | 0.9381 | 0.9445 | 0.9413 | 0.9858 |
| 0.0446 | 2.0 | 1756 | 0.0480 | 0.9510 | 0.9578 | 0.9544 | 0.9887 |
| 0.0263 | 3.0 | 2634 | 0.0492 | 0.9530 | 0.9604 | 0.9567 | 0.9889 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.0
- Tokenizers 0.10.3
|
huggingartists/machine-gun-kelly
|
huggingartists
| 2021-09-20T12:50:31Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/machine-gun-kelly",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
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)
|
Frederick0291/t5-small-finetuned-xsum
|
Frederick0291
| 2021-09-20T12:01:37Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: t5-small-finetuned-xsum-finetuned-billsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum-finetuned-billsum
This model is a fine-tuned version of [Frederick0291/t5-small-finetuned-xsum](https://huggingface.co/Frederick0291/t5-small-finetuned-xsum) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 330 | 1.8540 | 32.9258 | 14.9104 | 27.1067 | 27.208 | 18.8437 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
|
gniemiec/t5-small-finetuned-xsum
|
gniemiec
| 2021-09-20T11:36:55Z | 35 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: t5-small-finetuned-xsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xsum
type: xsum
args: default
metrics:
- name: Rouge1
type: rouge
value: 23.0533
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7967
- Rouge1: 23.0533
- Rouge2: 3.912
- Rougel: 17.8534
- Rougelsum: 17.8581
- Gen Len: 18.6878
## 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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 3.0574 | 1.0 | 1276 | 2.7967 | 23.0533 | 3.912 | 17.8534 | 17.8581 | 18.6878 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
|
gchhablani/fnet-base-finetuned-rte
|
gchhablani
| 2021-09-20T09:08:50Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"fnet",
"text-classification",
"generated_from_trainer",
"fnet-bert-base-comparison",
"en",
"dataset:glue",
"arxiv:2105.03824",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
- fnet-bert-base-comparison
datasets:
- glue
metrics:
- accuracy
model-index:
- name: fnet-base-finetuned-rte
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE RTE
type: glue
args: rte
metrics:
- name: Accuracy
type: accuracy
value: 0.628158844765343
---
<!-- 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. -->
# fnet-base-finetuned-rte
This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on the GLUE RTE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6978
- Accuracy: 0.6282
The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased).
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used:
```bash
#!/usr/bin/bash
python ../run_glue.py \\n --model_name_or_path google/fnet-base \\n --task_name rte \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir fnet-base-finetuned-rte \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6829 | 1.0 | 156 | 0.6657 | 0.5704 |
| 0.6174 | 2.0 | 312 | 0.6784 | 0.6101 |
| 0.5141 | 3.0 | 468 | 0.6978 | 0.6282 |
### Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.0
- Datasets 1.12.1
- Tokenizers 0.10.3
|
gchhablani/fnet-base-finetuned-qqp
|
gchhablani
| 2021-09-20T09:08:34Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"fnet",
"text-classification",
"generated_from_trainer",
"fnet-bert-base-comparison",
"en",
"dataset:glue",
"arxiv:2105.03824",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
- fnet-bert-base-comparison
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: fnet-base-finetuned-qqp
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE QQP
type: glue
args: qqp
metrics:
- name: Accuracy
type: accuracy
value: 0.8847390551570616
- name: F1
type: f1
value: 0.8466197090382463
---
<!-- 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. -->
# fnet-base-finetuned-qqp
This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3686
- Accuracy: 0.8847
- F1: 0.8466
- Combined Score: 0.8657
The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased).
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used:
```bash
#!/usr/bin/bash
#!/usr/bin/bash
python ../run_glue.py \\n --model_name_or_path google/fnet-base \\n --task_name qqp \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir fnet-base-finetuned-qqp \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.3484 | 1.0 | 22741 | 0.3014 | 0.8676 | 0.8297 | 0.8487 |
| 0.2387 | 2.0 | 45482 | 0.3011 | 0.8801 | 0.8429 | 0.8615 |
| 0.1739 | 3.0 | 68223 | 0.3686 | 0.8847 | 0.8466 | 0.8657 |
### Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.0
- Datasets 1.12.1
- Tokenizers 0.10.3
|
gchhablani/bert-base-cased-finetuned-qnli
|
gchhablani
| 2021-09-20T09:08:27Z | 1,726 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"fnet-bert-base-comparison",
"en",
"dataset:glue",
"arxiv:2105.03824",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
- fnet-bert-base-comparison
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert-base-cased-finetuned-qnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE QNLI
type: glue
args: qnli
metrics:
- name: Accuracy
type: accuracy
value: 0.9099395936298736
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-finetuned-qnli
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE QNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3986
- Accuracy: 0.9099
The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased).
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used:
```bash
#!/usr/bin/bash
python ../run_glue.py \\n --model_name_or_path bert-base-cased \\n --task_name qnli \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir bert-base-cased-finetuned-qnli \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:-----:|:--------:|:---------------:|
| 0.337 | 1.0 | 6547 | 0.9013 | 0.2448 |
| 0.1971 | 2.0 | 13094 | 0.9143 | 0.2839 |
| 0.1175 | 3.0 | 19641 | 0.9099 | 0.3986 |
### Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.0
- Datasets 1.12.1
- Tokenizers 0.10.3
|
gchhablani/fnet-base-finetuned-qnli
|
gchhablani
| 2021-09-20T09:08:18Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"fnet",
"text-classification",
"generated_from_trainer",
"fnet-bert-base-comparison",
"en",
"dataset:glue",
"arxiv:2105.03824",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
- fnet-bert-base-comparison
datasets:
- glue
metrics:
- accuracy
model-index:
- name: fnet-base-finetuned-qnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE QNLI
type: glue
args: qnli
metrics:
- name: Accuracy
type: accuracy
value: 0.8438586857038257
---
<!-- 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. -->
# fnet-base-finetuned-qnli
This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on the GLUE QNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4746
- Accuracy: 0.8439
The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased).
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used:
```bash
#!/usr/bin/bash
python ../run_glue.py \\n --model_name_or_path google/fnet-base \\n --task_name qnli \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir fnet-base-finetuned-qnli \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4597 | 1.0 | 6547 | 0.3713 | 0.8411 |
| 0.3252 | 2.0 | 13094 | 0.3781 | 0.8420 |
| 0.2243 | 3.0 | 19641 | 0.4746 | 0.8439 |
### Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.0
- Datasets 1.12.1
- Tokenizers 0.10.3
|
gchhablani/fnet-base-finetuned-wnli
|
gchhablani
| 2021-09-20T09:07:59Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"fnet",
"text-classification",
"generated_from_trainer",
"fnet-bert-base-comparison",
"en",
"dataset:glue",
"arxiv:2105.03824",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
- fnet-bert-base-comparison
datasets:
- glue
metrics:
- accuracy
model-index:
- name: fnet-base-finetuned-wnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE WNLI
type: glue
args: wnli
metrics:
- name: Accuracy
type: accuracy
value: 0.5492957746478874
---
<!-- 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. -->
# fnet-base-finetuned-wnli
This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on the GLUE WNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6887
- Accuracy: 0.5493
The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased).
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used:
```bash
#!/usr/bin/bash
python ../run_glue.py \\n --model_name_or_path google/fnet-base \\n --task_name wnli \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 5 \\n --output_dir fnet-base-finetuned-wnli \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7052 | 1.0 | 40 | 0.6902 | 0.5634 |
| 0.6957 | 2.0 | 80 | 0.7013 | 0.4366 |
| 0.6898 | 3.0 | 120 | 0.6898 | 0.5352 |
| 0.6958 | 4.0 | 160 | 0.6874 | 0.5634 |
| 0.6982 | 5.0 | 200 | 0.6887 | 0.5493 |
### Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.0
- Datasets 1.12.1
- Tokenizers 0.10.3
|
gchhablani/bert-base-cased-finetuned-mrpc
|
gchhablani
| 2021-09-20T09:07:44Z | 25 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"fnet-bert-base-comparison",
"en",
"dataset:glue",
"arxiv:2105.03824",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
- fnet-bert-base-comparison
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: bert-base-cased-finetuned-mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8602941176470589
- name: F1
type: f1
value: 0.9025641025641027
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-finetuned-mrpc
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7132
- Accuracy: 0.8603
- F1: 0.9026
- Combined Score: 0.8814
The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased).
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used:
```bash
#!/usr/bin/bash
python ../run_glue.py \\n --model_name_or_path bert-base-cased \\n --task_name mrpc \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 5 \\n --output_dir bert-base-cased-finetuned-mrpc \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
| 0.5981 | 1.0 | 230 | 0.4580 | 0.7892 | 0.8562 | 0.8227 |
| 0.3739 | 2.0 | 460 | 0.3806 | 0.8480 | 0.8942 | 0.8711 |
| 0.1991 | 3.0 | 690 | 0.4879 | 0.8529 | 0.8958 | 0.8744 |
| 0.1286 | 4.0 | 920 | 0.6342 | 0.8529 | 0.8986 | 0.8758 |
| 0.0812 | 5.0 | 1150 | 0.7132 | 0.8603 | 0.9026 | 0.8814 |
### Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.0
- Datasets 1.12.1
- Tokenizers 0.10.3
|
gchhablani/bert-base-cased-finetuned-mnli
|
gchhablani
| 2021-09-20T09:07:21Z | 12 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"fnet-bert-base-comparison",
"en",
"dataset:glue",
"arxiv:2105.03824",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
- fnet-bert-base-comparison
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert-base-cased-finetuned-mnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MNLI
type: glue
args: mnli
metrics:
- name: Accuracy
type: accuracy
value: 0.8410292921074044
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-finetuned-mnli
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5721
- Accuracy: 0.8410
The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased).
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used:
```bash
#!/usr/bin/bash
python ../run_glue.py \\n --model_name_or_path bert-base-cased \\n --task_name mnli \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir bert-base-cased-finetuned-mnli \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5323 | 1.0 | 24544 | 0.4431 | 0.8302 |
| 0.3447 | 2.0 | 49088 | 0.4725 | 0.8353 |
| 0.2267 | 3.0 | 73632 | 0.5887 | 0.8368 |
### Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.0
- Datasets 1.12.1
- Tokenizers 0.10.3
|
JorgeSarry/est5base-simplify
|
JorgeSarry
| 2021-09-20T08:42:39Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"es",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:04Z |
---
language: es
---
This is a smaller version of the google/mt5-base model with only Spanish and some English embeddings trained on 60k Spanish WikiEdits for sentence simplification.
You can use it with the command "simplify:"
|
huggingartists/i-dont-know-how-but-they-found-me
|
huggingartists
| 2021-09-20T07:59:50Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/i-dont-know-how-but-they-found-me",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- huggingartists/i-dont-know-how-but-they-found-me
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/4683327bb3a8906b18e9af8207c36dc9.645x645x1.png')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">I DONT KNOW HOW BUT THEY FOUND ME</div>
<a href="https://genius.com/artists/i-dont-know-how-but-they-found-me">
<div style="text-align: center; font-size: 14px;">@i-dont-know-how-but-they-found-me</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 I DONT KNOW HOW BUT THEY FOUND ME.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/i-dont-know-how-but-they-found-me).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/i-dont-know-how-but-they-found-me")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1j7uofwh/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on I DONT KNOW HOW BUT THEY FOUND ME's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1abhthz2) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1abhthz2/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/i-dont-know-how-but-they-found-me')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/i-dont-know-how-but-they-found-me")
model = AutoModelWithLMHead.from_pretrained("huggingartists/i-dont-know-how-but-they-found-me")
```
## 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/upsahl
|
huggingartists
| 2021-09-20T07:35:35Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/upsahl",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- huggingartists/upsahl
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/e0fa9b5bdd037ab75031dd7372d05cd6.1000x1000x1.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">UPSAHL</div>
<a href="https://genius.com/artists/upsahl">
<div style="text-align: center; font-size: 14px;">@upsahl</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 UPSAHL.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/upsahl).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/upsahl")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/2o3af3ts/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 UPSAHL's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/2lr9eqkt) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/2lr9eqkt/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/upsahl')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/upsahl")
model = AutoModelWithLMHead.from_pretrained("huggingartists/upsahl")
```
## 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)
|
slauw87/bart_summarisation
|
slauw87
| 2021-09-20T05:27:36Z | 5,860 | 59 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"sagemaker",
"summarization",
"en",
"dataset:samsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-03-02T23:29:05Z |
---
language: en
tags:
- sagemaker
- bart
- summarization
license: apache-2.0
datasets:
- samsum
model-index:
- name: bart-large-cnn-samsum
results:
- task:
name: Abstractive Text Summarization
type: abstractive-text-summarization
dataset:
name: "SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization"
type: samsum
metrics:
- name: Validation ROGUE-1
type: rogue-1
value: 43.2111
- name: Validation ROGUE-2
type: rogue-2
value: 22.3519
- name: Validation ROGUE-L
type: rogue-l
value: 33.315
- name: Test ROGUE-1
type: rogue-1
value: 41.8283
- name: Test ROGUE-2
type: rogue-2
value: 20.9857
- name: Test ROGUE-L
type: rogue-l
value: 32.3602
widget:
- text: |
Sugi: I am tired of everything in my life.
Tommy: What? How happy you life is! I do envy you.
Sugi: You don't know that I have been over-protected by my mother these years. I am really about to leave the family and spread my wings.
Tommy: Maybe you are right.
---
## `bart-large-cnn-samsum`
This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container.
For more information look at:
- [🤗 Transformers Documentation: Amazon SageMaker](https://huggingface.co/transformers/sagemaker.html)
- [Example Notebooks](https://github.com/huggingface/notebooks/tree/master/sagemaker)
- [Amazon SageMaker documentation for Hugging Face](https://docs.aws.amazon.com/sagemaker/latest/dg/hugging-face.html)
- [Python SDK SageMaker documentation for Hugging Face](https://sagemaker.readthedocs.io/en/stable/frameworks/huggingface/index.html)
- [Deep Learning Container](https://github.com/aws/deep-learning-containers/blob/master/available_images.md#huggingface-training-containers)
## Hyperparameters
{
"dataset_name": "samsum",
"do_eval": true,
"do_predict": true,
"do_train": true,
"fp16": true,
"learning_rate": 5e-05,
"model_name_or_path": "facebook/bart-large-cnn",
"num_train_epochs": 3,
"output_dir": "/opt/ml/model",
"per_device_eval_batch_size": 4,
"per_device_train_batch_size": 4,
"predict_with_generate": true,
"seed": 7
}
## Usage
from transformers import pipeline
summarizer = pipeline("summarization", model="slauw87/bart-large-cnn-samsum")
conversation = '''Sugi: I am tired of everything in my life.
Tommy: What? How happy you life is! I do envy you.
Sugi: You don't know that I have been over-protected by my mother these years. I am really about to leave the family and spread my wings.
Tommy: Maybe you are right.
'''
nlp(conversation)
## Results
| key | value |
| --- | ----- |
| eval_rouge1 | 43.2111 |
| eval_rouge2 | 22.3519 |
| eval_rougeL | 33.3153 |
| eval_rougeLsum | 40.0527 |
| predict_rouge1 | 41.8283 |
| predict_rouge2 | 20.9857 |
| predict_rougeL | 32.3602 |
| predict_rougeLsum | 38.7316 |
|
huggingartists/joni-mitchell
|
huggingartists
| 2021-09-20T04:37:42Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/joni-mitchell",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- huggingartists/joni-mitchell
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/ed9a330b2539058076e0c48398599b09.1000x1000x1.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">Joni Mitchell</div>
<a href="https://genius.com/artists/joni-mitchell">
<div style="text-align: center; font-size: 14px;">@joni-mitchell</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 Joni Mitchell.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/joni-mitchell).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/joni-mitchell")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1m5n59kk/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 Joni Mitchell's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/34saoh5x) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/34saoh5x/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/joni-mitchell')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/joni-mitchell")
model = AutoModelWithLMHead.from_pretrained("huggingartists/joni-mitchell")
```
## 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)
|
cambridgeltl/mirror-roberta-base-sentence
|
cambridgeltl
| 2021-09-19T22:48:01Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"arxiv:2104.08027",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
---
language: en
tags:
- sentence-embeddings
- sentence-similarity
### cambridgeltl/mirror-roberta-base-sentence
An unsupervised sentence encoder proposed by [Liu et al. (2021)](https://arxiv.org/pdf/2104.08027.pdf). The model is trained with unlabelled raw sentences, using [roberta-base](https://huggingface.co/roberta-base) as the base model. Please use `[CLS]` (before pooler) as the representation of the input.
Note the model does not replicate the exact numbers in the paper since the reported numbers in the paper are average of three runs.
### Citation
```bibtex
@inproceedings{
liu2021fast,
title={Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders},
author={Liu, Fangyu and Vuli{\'c}, Ivan and Korhonen, Anna and Collier, Nigel},
booktitle={EMNLP 2021},
year={2021}
}
```
|
cambridgeltl/mirror-bert-base-uncased-sentence
|
cambridgeltl
| 2021-09-19T22:47:28Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"arxiv:2104.08027",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
---
language: en
tags:
- sentence-embeddings
- sentence-similarity
### cambridgeltl/mirror-bert-base-uncased-sentence
An unsupervised sentence encoder proposed by [Liu et al. (2021)](https://arxiv.org/pdf/2104.08027.pdf). Trained with unlabelled raw sentences, using [bert-base-uncased](https://huggingface.co/bert-base-uncased) as the base model. Please use mean-pooling over *all tokens* (including padded ones) as the representation of the input.
Note the model does not replicate the exact numbers in the paper since the reported numbers in the paper are average of three runs.
### Citation
```bibtex
@inproceedings{
liu2021fast,
title={Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders},
author={Liu, Fangyu and Vuli{\'c}, Ivan and Korhonen, Anna and Collier, Nigel},
booktitle={EMNLP 2021},
year={2021}
}
```
|
matthias-wright/resnet
|
matthias-wright
| 2021-09-19T18:53:14Z | 0 | 0 | null |
[
"arxiv:1512.03385",
"region:us"
] | null | 2022-03-02T23:29:05Z |
# Deep Residual Learning for Image Recognition
<b>Paper:</b> <a href="https://arxiv.org/abs/1512.03385">https://arxiv.org/abs/1512.03385</a>
# About
These are the pretrained weights for [this](https://github.com/matthias-wright/flaxmodels/tree/main/flaxmodels/resnet) ResNet implementation in Jax/Flax. The weights are taken from [this](https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py) repository.
# Documentation
[Here](https://github.com/matthias-wright/flaxmodels/blob/main/docs/Documentation.md#1-checkpoints) is a documentation that explains the preprocessing steps as well as the format of the pretrained weights.
|
huggingartists/melanie-martinez
|
huggingartists
| 2021-09-19T17:22:10Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/melanie-martinez",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- huggingartists/melanie-martinez
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/917de5970c2afbbf03a7705f18eb6951.811x811x1.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">Melanie Martinez</div>
<a href="https://genius.com/artists/melanie-martinez">
<div style="text-align: center; font-size: 14px;">@melanie-martinez</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 Melanie Martinez.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/melanie-martinez).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/melanie-martinez")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/lb3ks0y5/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 Melanie Martinez's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/2rvs9wvc) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/2rvs9wvc/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/melanie-martinez')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/melanie-martinez")
model = AutoModelWithLMHead.from_pretrained("huggingartists/melanie-martinez")
```
## 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)
|
matthias-wright/vgg
|
matthias-wright
| 2021-09-19T14:41:32Z | 0 | 1 | null |
[
"arxiv:1409.1556",
"region:us"
] | null | 2022-03-02T23:29:05Z |
# Very Deep Convolutional Networks for Large-Scale Image Recognition
<b>Paper:</b> <a href="https://arxiv.org/abs/1409.1556">https://arxiv.org/abs/1409.1556</a>
# About
These are the pretrained weights for [this](https://github.com/matthias-wright/flaxmodels/tree/main/flaxmodels/vgg) VGG implementation in Jax/Flax. The weights are taken from [this](https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py) repository.
# Documentation
[Here](https://github.com/matthias-wright/flaxmodels/blob/main/docs/Documentation.md#1-checkpoints) is a documentation that explains the preprocessing steps as well as the format of the pretrained weights.
|
formermagic/pyt5-base
|
formermagic
| 2021-09-19T12:41:20Z | 7 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"arxiv:1910.10683",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
# Python T5 base model
Pre-trained model on CodeSearchNet Python dataset using a span-masking objective. The training objective and model were introduced in [this paper](https://arxiv.org/pdf/1910.10683.pdf) and first released in [this repository](https://github.com/google-research/text-to-text-transfer-transformer). PyT5 model used [git-t5](https://github.com/formermagic/git-t5) framework built on top of JAX/Flax to pre-train the model on a TPU v3-8 node.
# How to use
You can use this model to denoise span-masked sequences.
First, install the [git-t5](https://github.com/formermagic/git-t5) pip package:
```shell
> pip install git-t5
```
Next, download the model and tokenizer:
```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer,
model = AutoModelForSeq2SeqLM.from_pretrained("formermagic/pyt5-base")
tokenizer = AutoTokenizer.from_pretrained("formermagic/pyt5-base")
```
Finally, encode your input and generate the output sequence:
```python
from git_t5.utils import encode_input
text = """
def alias(self, annotationtype, set, fallback=False):
if inspect.isclass(annotationtype): annotationtype = annotationtype.ANNOTATIONTYPE
if annotationtype in self.set_alias and set in self.set_alias[annotationtype]:
return self.set_alias[annotationtype][set]
elif fallback:
return set
else:
raise KeyError("No alias for set " + set)
"""
batch, max_length = encode_input(tokenizer, text, seed=22)
outputs = model.generate(batch["input_ids"], max_length=max_length, num_beams=1)
print(tokenizer.batch_decode(outputs[..., 1:]))
print(tokenizer.batch_decode(batch["labels"]))
```
You should see the following output:
```shell
['<extra_id_0>, fallback=<extra_id_1> inspect<extra_id_2>.set_alias<extra_id_3> return self.set<extra_id_4>) def fallback']
['<extra_id_0>, fallback=<extra_id_1> inspect<extra_id_2>.set_alias<extra_id_3> return self.set<extra_id_4>) </s></s>']
```
As you can see, the predicted result is very close to the target sequence.
|
huggingartists/maroon-5
|
huggingartists
| 2021-09-19T12:07:21Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/maroon-5",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- huggingartists/maroon-5
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/6780ce1add3af75c73929a8f6630e099.900x900x1.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">Maroon 5</div>
<a href="https://genius.com/artists/maroon-5">
<div style="text-align: center; font-size: 14px;">@maroon-5</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 Maroon 5.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/maroon-5).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/maroon-5")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/38629b22/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 Maroon 5's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/2ylk8pym) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/2ylk8pym/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/maroon-5')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/maroon-5")
model = AutoModelWithLMHead.from_pretrained("huggingartists/maroon-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)
[](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/ariana-grande
|
huggingartists
| 2021-09-19T02:10:10Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/ariana-grande",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- huggingartists/ariana-grande
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/d36a47955ac0ddb12748c5e7c2bd4b4b.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">Ariana Grande</div>
<a href="https://genius.com/artists/ariana-grande">
<div style="text-align: center; font-size: 14px;">@ariana-grande</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 Ariana Grande.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/ariana-grande).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/ariana-grande")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/2nfg7v7i/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 Ariana Grande's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/3u3sn1bx) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/3u3sn1bx/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/ariana-grande')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/ariana-grande")
model = AutoModelWithLMHead.from_pretrained("huggingartists/ariana-grande")
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
huggingtweets/ai_hexcrawl-dailyartprompts
|
huggingtweets
| 2021-09-18T22:34:01Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/ai_hexcrawl-dailyartprompts/1632004437614/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/1250356895199760384/fOxe1Ymd_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/1391882949650440200/lmEKl2ZQ_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">Art Prompts & AI Hexcrawl</div>
<div style="text-align: center; font-size: 14px;">@ai_hexcrawl-dailyartprompts</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 Art Prompts & AI Hexcrawl.
| Data | Art Prompts | AI Hexcrawl |
| --- | --- | --- |
| Tweets downloaded | 726 | 741 |
| Retweets | 16 | 27 |
| Short tweets | 1 | 1 |
| Tweets kept | 709 | 713 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/prw4k5r4/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 @ai_hexcrawl-dailyartprompts's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1kxaov1u) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1kxaov1u/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/ai_hexcrawl-dailyartprompts')
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/dailyartprompts
|
huggingtweets
| 2021-09-18T21:32:41Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/dailyartprompts/1632000660527/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/1250356895199760384/fOxe1Ymd_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">Art Prompts</div>
<div style="text-align: center; font-size: 14px;">@dailyartprompts</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 Art Prompts.
| Data | Art Prompts |
| --- | --- |
| Tweets downloaded | 726 |
| Retweets | 16 |
| Short tweets | 1 |
| Tweets kept | 709 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1z29i666/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 @dailyartprompts's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3rrp1b3e) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3rrp1b3e/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/dailyartprompts')
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)
|
huggingartists/doja-cat
|
huggingartists
| 2021-09-18T17:16:11Z | 6 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/doja-cat",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- huggingartists/doja-cat
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/49b33cfa0bdb3ed97058a10960f2af8d.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">Doja Cat</div>
<a href="https://genius.com/artists/doja-cat">
<div style="text-align: center; font-size: 14px;">@doja-cat</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 Doja Cat.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/doja-cat).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/doja-cat")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1qxclk1g/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 Doja Cat's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/2lqvdntl) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/2lqvdntl/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/doja-cat')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/doja-cat")
model = AutoModelWithLMHead.from_pretrained("huggingartists/doja-cat")
```
## 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)
|
shuqi/seed-encoder
|
shuqi
| 2021-09-18T11:24:50Z | 2 | 0 |
transformers
|
[
"transformers",
"pytorch",
"seed_encoder",
"arxiv:2102.09206",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
# Less is More: Pre-train a Strong Text Encoder for Dense Retrieval Using a Weak Decoder
Please check the [official repository](https://github.com/microsoft/SEED-Encoder) for more details and updates.
# Fine-tuning on Marco passage/doc ranking tasks and NQ tasks
| MSMARCO Dev Passage Retrieval | MRR@10 | Recall@1k |
|------------------------------|---------------|--------------------- |
| BM25 warmup checkpoint | 0.329 | 0.953 |
| ANCE Passage checkpoint | 0.334 | 0.961 |
| MSMARCO Document Retrieval | MRR@10 (Dev) | MRR@10 (Eval) |
|---------------- | -------------- | -------------- |
| ANCE Document (FirstP) checkpoint | 0.394 | 0.362 |
| NQ Task | Top-1 | Top-5 | Top-20 | Top-100 | MRR@20 | P@20 |
|---------------- | -------------- | -------------- |-------------- | -------------- | -------------- |-------------- |
| DPR checkpoint | 46.1 | 68.8 | 80.4 | 87.1 | 56.2 | 20.1 |
| ANCE NQ checkpoint | 52.5 | 73.1 | 83.1 | 88.7 | 61.5 | 22.5
# Citation
If you find SEED-Encoder useful for your work, please cite the following paper:
```
@article{lu2021less,
title={Less is More: Pre-training a Strong Siamese Encoder Using a Weak Decoder},
author={Lu, Shuqi and Xiong, Chenyan and He, Di and Ke, Guolin and Malik, Waleed and Dou, Zhicheng and Bennett, Paul and Liu, Tieyan and Overwijk, Arnold},
journal={arXiv preprint arXiv:2102.09206},
year={2021}
}
```
|
huggingtweets/avgmeat-dril-methwaffles
|
huggingtweets
| 2021-09-18T11:05:56Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/avgmeat-dril-methwaffles/1631963152302/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/847818629840228354/VXyQHfn0_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1427457256958930948/J2FGNejT_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/1354274870264266753/9D_FgIsC_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">wint & Chet & ac</div>
<div style="text-align: center; font-size: 14px;">@avgmeat-dril-methwaffles</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 wint & Chet & ac.
| Data | wint | Chet | ac |
| --- | --- | --- | --- |
| Tweets downloaded | 3189 | 2471 | 3167 |
| Retweets | 468 | 748 | 209 |
| Short tweets | 310 | 299 | 816 |
| Tweets kept | 2411 | 1424 | 2142 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1gv4gxjf/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 @avgmeat-dril-methwaffles's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3dg2j508) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3dg2j508/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/avgmeat-dril-methwaffles')
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/ai_hexcrawl-gptmicrofic
|
huggingtweets
| 2021-09-18T03:18:36Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/ai_hexcrawl-gptmicrofic/1631934945678/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/1391882949650440200/lmEKl2ZQ_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/1261895681561804800/r6vOZGoH_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">AI Hexcrawl & GPT2-Microfic</div>
<div style="text-align: center; font-size: 14px;">@ai_hexcrawl-gptmicrofic</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 AI Hexcrawl & GPT2-Microfic.
| Data | AI Hexcrawl | GPT2-Microfic |
| --- | --- | --- |
| Tweets downloaded | 737 | 1127 |
| Retweets | 26 | 9 |
| Short tweets | 1 | 9 |
| Tweets kept | 710 | 1109 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2cmbpada/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 @ai_hexcrawl-gptmicrofic's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/5g9tts1o) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/5g9tts1o/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/ai_hexcrawl-gptmicrofic')
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/ai_hexcrawl-dril_gpt2-drilbot_neo
|
huggingtweets
| 2021-09-18T02:30:19Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://www.huggingtweets.com/ai_hexcrawl-dril_gpt2-drilbot_neo/1631932214962/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/1374924360780242944/-Q8NfgEr_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/1386749605216407555/QIJeyWfE_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/1391882949650440200/lmEKl2ZQ_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">wintbot_neo & wint but Al & AI Hexcrawl</div>
<div style="text-align: center; font-size: 14px;">@ai_hexcrawl-dril_gpt2-drilbot_neo</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 wintbot_neo & wint but Al & AI Hexcrawl.
| Data | wintbot_neo | wint but Al | AI Hexcrawl |
| --- | --- | --- | --- |
| Tweets downloaded | 3207 | 3198 | 737 |
| Retweets | 268 | 41 | 26 |
| Short tweets | 272 | 49 | 1 |
| Tweets kept | 2667 | 3108 | 710 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2g9pfbo8/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 @ai_hexcrawl-dril_gpt2-drilbot_neo's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/226pt34g) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/226pt34g/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/ai_hexcrawl-dril_gpt2-drilbot_neo')
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)
|
muirkat/tolkien-mythopoeic-gen
|
muirkat
| 2021-09-17T21:28:53Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: tolkien-mythopoeic-gen
results:
- task:
name: Causal Language Modeling
type: text-generation
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tolkien-mythopoeic-gen
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on Tolkien's mythopoeic works, namely The Silmarillion and Unfinished Tales of Numenor and Middle Earth.
It achieves the following results on the evaluation set:
- Loss: 3.5110
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.5732 | 1.0 | 145 | 3.5110 |
| 3.5713 | 2.0 | 290 | 3.5110 |
| 3.5718 | 3.0 | 435 | 3.5110 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.9.0+cu102
- Tokenizers 0.10.3
|
blizrys/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-pubmedqa-2
|
blizrys
| 2021-09-17T10:08:32Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- null
metrics:
- accuracy
model-index:
- name: BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-pubmedqa-2
results:
- task:
name: Text Classification
type: text-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.54
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-pubmedqa-2
This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0005
- Accuracy: 0.54
## 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: 0.003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 57 | 1.3510 | 0.54 |
| No log | 2.0 | 114 | 0.9606 | 0.54 |
| No log | 3.0 | 171 | 0.9693 | 0.54 |
| No log | 4.0 | 228 | 1.0445 | 0.54 |
| No log | 5.0 | 285 | 1.0005 | 0.54 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
|
tunib/electra-ko-small
|
tunib
| 2021-09-17T08:59:08Z | 8 | 3 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"pretraining",
"arxiv:2003.10555",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
# TUNiB-Electra
We release several new versions of the [ELECTRA](https://arxiv.org/abs/2003.10555) model, which we name TUNiB-Electra. There are two motivations. First, all the existing pre-trained Korean encoder models are monolingual, that is, they have knowledge about Korean only. Our bilingual models are based on the balanced corpora of Korean and English. Second, we want new off-the-shelf models trained on much more texts. To this end, we collected a large amount of Korean text from various sources such as blog posts, comments, news, web novels, etc., which sum up to 100 GB in total.
## How to use
You can use this model directly with [transformers](https://github.com/huggingface/transformers) library:
```python
from transformers import AutoModel, AutoTokenizer
# Small Model (Korean-only model)
tokenizer = AutoTokenizer.from_pretrained('tunib/electra-ko-small')
model = AutoModel.from_pretrained('tunib/electra-ko-small')
```
### Tokenizer example
```python
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained('tunib/electra-ko-small')
>>> tokenizer.tokenize("tunib is a natural language processing tech startup.")
['tun', '##ib', 'is', 'a', 'natural', 'language', 'processing', 'tech', 'startup', '.']
>>> tokenizer.tokenize("튜닙은 자연어처리 테크 스타트업입니다.")
['튜', '##닙', '##은', '자연', '##어', '##처리', '테크', '스타트업', '##입니다', '.']
```
## Results on Korean downstream tasks
| |**# Params** |**Avg.**| **NSMC**<br/>(acc) | **Naver NER**<br/>(F1) | **PAWS**<br/>(acc) | **KorNLI**<br/>(acc) | **KorSTS**<br/>(spearman) | **Question Pair**<br/>(acc) | **KorQuaD (Dev)**<br/>(EM/F1) |**Korean-Hate-Speech (Dev)**<br/>(F1)|
| :----------------:| :----------------: | :--------------------: | :----------------: | :------------------: | :-----------------------: | :-------------------------: | :---------------------------: | :---------------------------: | :---------------------------: | :----------------: |
|***TUNiB-Electra-ko-small*** | 14M | 81.29| **89.56** | 84.98 | 72.85 | 77.08 | 78.76 | **94.98** | 61.17 / 87.64 | **64.50** |
|***TUNiB-Electra-ko-en-small*** | 18M | 81.44 | 89.28 | 85.15 | 75.75 | 77.06 | 77.61 | 93.79 | 80.55 / 89.77 |63.13 |
| [KoELECTRA-small-v3](https://github.com/monologg/KoELECTRA) | 14M | **82.58** | 89.36 | **85.40** | **77.45** | **78.60** | **80.79** | 94.85 | **82.11 / 91.13** | 63.07 |
|
Subsets and Splits
Filtered Qwen2.5 Distill Models
Identifies specific configurations of models by filtering cards that contain 'distill', 'qwen2.5', '7b' while excluding certain base models and incorrect model ID patterns, uncovering unique model variants.
Filtered Model Cards Count
Finds the count of entries with specific card details that include 'distill', 'qwen2.5', '7b' but exclude certain base models, revealing valuable insights about the dataset's content distribution.
Filtered Distill Qwen 7B Models
Filters for specific card entries containing 'distill', 'qwen', and '7b', excluding certain strings and patterns, to identify relevant model configurations.
Filtered Qwen-7b Model Cards
The query performs a detailed filtering based on specific keywords and excludes certain entries, which could be useful for identifying a specific subset of cards but does not provide deeper insights or trends.
Filtered Qwen 7B Model Cards
The query filters for specific terms related to "distilled" or "distill", "qwen", and "7b" in the 'card' column but excludes certain base models, providing a limited set of entries for further inspection.
Qwen 7B Distilled Models
The query provides a basic filtering of records to find specific card names that include keywords related to distilled Qwen 7b models, excluding a particular base model, which gives limited insight but helps in focusing on relevant entries.
Qwen 7B Distilled Model Cards
The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.
Qwen 7B Distilled Models
Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.
Distilled Qwen 7B Models
The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.
Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.