modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
list | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
akilesh96/autonlp-mrcooper_text_classification-529614927
|
akilesh96
| 2022-01-25T19:43:57Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"en",
"dataset:akilesh96/autonlp-data-mrcooper_text_classification",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
tags: autonlp
language: en
widget:
- text: "Not Many People Know About The City 1200 Feet Below Detroit"
- text: "Bob accepts the challenge, and the next week they're standing in Saint Peters square. 'This isnt gonna work, he's never going to see me here when theres this much people. You stay here, I'll go talk to him and you'll see me on the balcony, the guards know me too.' Half an hour later, Bob and the pope appear side by side on the balcony. Bobs boss gets a heart attack, and Bob goes to visit him in the hospital."
- text: "I’m sorry if you made it this far, but I’m just genuinely idk, I feel like I shouldn’t give up, it’s just getting harder to come back from stuff like this."
datasets:
- akilesh96/autonlp-data-mrcooper_text_classification
co2_eq_emissions: 5.999771405025692
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 529614927
- CO2 Emissions (in grams): 5.999771405025692
## Validation Metrics
- Loss: 0.7582379579544067
- Accuracy: 0.7636103151862464
- Macro F1: 0.770630619486531
- Micro F1: 0.7636103151862464
- Weighted F1: 0.765233270165301
- Macro Precision: 0.7746285216467107
- Micro Precision: 0.7636103151862464
- Weighted Precision: 0.7683270753840836
- Macro Recall: 0.7680576576961138
- Micro Recall: 0.7636103151862464
- Weighted Recall: 0.7636103151862464
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/akilesh96/autonlp-mrcooper_text_classification-529614927
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("akilesh96/autonlp-mrcooper_text_classification-529614927", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("akilesh96/autonlp-mrcooper_text_classification-529614927", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
```
|
lucianpopa/autonlp-SST1-529214890
|
lucianpopa
| 2022-01-25T17:30:09Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autonlp",
"en",
"dataset:lucianpopa/autonlp-data-SST1",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- lucianpopa/autonlp-data-SST1
co2_eq_emissions: 49.618294309910624
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 529214890
- CO2 Emissions (in grams): 49.618294309910624
## Validation Metrics
- Loss: 0.7135734558105469
- Accuracy: 0.7042338838232481
- Macro F1: 0.6164041045783032
- Micro F1: 0.7042338838232481
- Weighted F1: 0.7028309161791009
- Macro Precision: 0.6497438111060598
- Micro Precision: 0.7042338838232481
- Weighted Precision: 0.7076651075198755
- Macro Recall: 0.6023419083862918
- Micro Recall: 0.7042338838232481
- Weighted Recall: 0.7042338838232481
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/lucianpopa/autonlp-SST1-529214890
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("lucianpopa/autonlp-SST1-529214890", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("lucianpopa/autonlp-SST1-529214890", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
```
|
anirudh21/albert-base-v2-finetuned-wnli
|
anirudh21
| 2022-01-25T16:57:16Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"albert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: albert-base-v2-finetuned-wnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: wnli
metrics:
- name: Accuracy
type: accuracy
value: 0.5633802816901409
---
<!-- 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. -->
# albert-base-v2-finetuned-wnli
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6878
- Accuracy: 0.5634
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 40 | 0.6878 | 0.5634 |
| No log | 2.0 | 80 | 0.6919 | 0.5634 |
| No log | 3.0 | 120 | 0.6877 | 0.5634 |
| No log | 4.0 | 160 | 0.6984 | 0.4085 |
| No log | 5.0 | 200 | 0.6957 | 0.5211 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.0
- Tokenizers 0.10.3
|
anirudh21/electra-base-discriminator-finetuned-rte
|
anirudh21
| 2022-01-25T15:43:18Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"electra",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: electra-base-discriminator-finetuned-rte
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: rte
metrics:
- name: Accuracy
type: accuracy
value: 0.8231046931407943
---
<!-- 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. -->
# electra-base-discriminator-finetuned-rte
This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4793
- Accuracy: 0.8231
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 156 | 0.6076 | 0.6570 |
| No log | 2.0 | 312 | 0.4824 | 0.7762 |
| No log | 3.0 | 468 | 0.4793 | 0.8231 |
| 0.4411 | 4.0 | 624 | 0.7056 | 0.7906 |
| 0.4411 | 5.0 | 780 | 0.6849 | 0.8159 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.0
- Tokenizers 0.10.3
|
huggingtweets/arryadia_brk
|
huggingtweets
| 2022-01-25T14:04:36Z | 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: http://www.huggingtweets.com/arryadia_brk/1643119471683/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/1479498403251896320/uDVlO62z_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">الرياضية - عاجل</div>
<div style="text-align: center; font-size: 14px;">@arryadia_brk</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 الرياضية - عاجل.
| Data | الرياضية - عاجل |
| --- | --- |
| Tweets downloaded | 1548 |
| Retweets | 11 |
| Short tweets | 33 |
| Tweets kept | 1504 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/24udtdhw/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 @arryadia_brk's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2e36ahiu) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2e36ahiu/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/arryadia_brk')
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)
|
Iacopo/Shakespear-GPT2
|
Iacopo
| 2022-01-25T13:35:35Z | 11 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:04Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: output
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# output
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on a dataset of Shakespeare's plays.
## Model description
The model is the original gpt-2 model fine-tuned on a custom dataset.
## Intended uses & limitations
The model can be used to generate Shakespearean-like text. Consider that because it comes from plays, such a typographical structure might be reproduced.
## Training and evaluation data
Trained with Shakespeare's plays corpus.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-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
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.0
- Tokenizers 0.11.0
|
awsaf49/deep-chimpact
|
awsaf49
| 2022-01-25T12:59:16Z | 9 | 1 |
tf-keras
|
[
"tf-keras",
"region:us"
] | null | 2022-03-02T23:29:05Z |
# [Deep Chimpact](https://www.drivendata.org/competitions/82/competition-wildlife-video-depth-estimation/page/390/)
> Depth Estimation for Wildlife Conservation (1st place solution)
<div align=center> <img src="https://user-images.githubusercontent.com/36858976/138281204-c3cbcb77-11ca-448b-a693-cb3cfa3c5181.png" width=800>
## Overview
Healthy natural ecosystems have wide-ranging benefits from public health to the economy to agriculture. In order to protect the Earth's natural resources, conservationists need to be able to monitor species population sizes and population change. Camera traps are widely used in conservation research to capture images and videos of wildlife without human interference. Using statistical models for distance sampling, the frequency of animal sightings can be combined with the distance of each animal from the camera to estimate a species' full population size.
However, getting distances from camera trap footage currently entails an extremely manual, time-intensive process. It takes a researcher more than **10 minutes** on average to label distance for every **1 minute** of video - that’s a lot of time when you have a million videos! This also creates a bottleneck for critical information that conservationists can use to **monitor wildlife populations**.
> Your goal in this challenge is to use machine learning to automatically estimate the distance between a camera trap and an animal in a series of camera trap videos. You will be given a series of timestamps indicating when animals are visible in each camera trap video. To complete the challenge, you will predict the distance between the animal and the camera at each point in time.
Along the way, keep an eye out for some sneaky leopards hunting at night, baby chimpanzees getting piggy-back rides, and diva elephants that can't get enough of the limelight. By contributing to this challenge, you can help advance cutting-edge methods for keeping these animal populations (and humans) healthy and safe!
|
deepdoctection/tp_casc_rcnn_X_32xd4_50_FPN_GN_2FC_pubtabnet_c_inference_only
|
deepdoctection
| 2022-01-25T09:23:24Z | 0 | 0 | null |
[
"Tensorflow",
"dataset:Pubtabnet",
"arxiv:1911.10683",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
tags:
- Tensorflow
license: apache-2.0
datasets:
- Pubtabnet
---
# Tensorpacks Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Pubtabnet for Semantic Segmentation of tables.
The model and its training code has been mainly taken from: [Tensorpack](https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN) .
Regarding the dataset, please check: [Xu Zhong et. all. - Image-based table recognition: data, model, and evaluation](https://arxiv.org/abs/1911.10683).
The model has been trained on detecting cells from tables. Note, that the datasets contains tables only. Therefore, it is required to perform a table detection task before
detecting cells.
The code has been adapted so that it can be used in a **deep**doctection pipeline.
## How this model can be used
This model can be used with the **deep**doctection in a full pipeline, along with table recognition and OCR. Check the general instruction following this [Get_started](https://github.com/deepdoctection/deepdoctection/blob/master/notebooks/Get_Started.ipynb) tutorial.
## This is an inference model only
To reduce the size of the checkpoint we removed all variables that are not necessary for inference. Therefore it cannot be used for fine-tuning. To fine tune this model please check this [model](https://huggingface.co/deepdoctection/tp_casc_rcnn_X_32xd4_50_FPN_GN_2FC_pubtabnet_c) .
## How this model was trained.
To recreate the model run on the **deep**doctection framework, run:
```python
>>> import os
>>> from deep_doctection.datasets import DatasetRegistry
>>> from deep_doctection.eval import MetricRegistry
>>> from deep_doctection.utils import get_configs_dir_path
>>> from deep_doctection.train import train_faster_rcnn
pubtabnet = DatasetRegistry.get_dataset("pubtabnet")
pubtabnet.dataflow.categories.filter_categories(categories="CELL")
path_config_yaml=os.path.join(get_configs_dir_path(),"tp/cell/conf_frcnn_cell.yaml")
path_weights = ""
dataset_train = pubtabnet
config_overwrite=["TRAIN.STEPS_PER_EPOCH=500","TRAIN.STARTING_EPOCH=1",
"TRAIN.CHECKPOINT_PERIOD=50","BACKBONE.FREEZE_AT=0", "PREPROC.TRAIN_SHORT_EDGE_SIZE=[200,600]"]
build_train_config=["max_datapoints=500000"]
dataset_val = pubtabnet
build_val_config = ["max_datapoints=4000"]
coco_metric = MetricRegistry.get_metric("coco")
coco_metric.set_params(max_detections=[50,200,600], area_range=[[0,1000000],[0,200],[200,800],[800,1000000]])
train_faster_rcnn(path_config_yaml=path_config_yaml,
dataset_train=dataset_train,
path_weights=path_weights,
config_overwrite=config_overwrite,
log_dir="/path/to/dir",
build_train_config=build_train_config,
dataset_val=dataset_val,
build_val_config=build_val_config,
metric=coco_metric,
pipeline_component_name="ImageLayoutService"
)
```
## How to fine-tune this model
To fine tune this model, please check this [Fine-tune](https://github.com/deepdoctection/deepdoctection/blob/master/notebooks/Fine_Tune.ipynb) tutorial.
|
NimaBoscarino/aot-gan-places2
|
NimaBoscarino
| 2022-01-25T08:43:40Z | 2 | 0 |
transformers
|
[
"transformers",
"pytorch",
"scene-recognition",
"scene-generation",
"generative-adversarial-network",
"dataset:places2",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04Z |
---
tags:
- scene-recognition
- scene-generation
- generative-adversarial-network
metrics:
- L1
- PSNR
- SSIM
- FID
datasets:
- places2
---
# AOT-GAN Places2
AOT-GAN is a model that can be used for image in-painting. The Places2 checkpoint is trained on a dataset which should make it suitable for touching up and restoring images of landscapes, buildings, and other natural and developed places.
This model was generated using [AOT-GAN-for-Inpainting](https://github.com/researchmm/AOT-GAN-for-Inpainting), cited as
```
@inproceedings{yan2021agg,
author = {Zeng, Yanhong and Fu, Jianlong and Chao, Hongyang and Guo, Baining},
title = {Aggregated Contextual Transformations for High-Resolution Image Inpainting},
booktitle = {Arxiv},
pages={-},
year = {2020}
}
```
## Dataset
The Places2 dataset can be found here: http://places2.csail.mit.edu/download.html
|
z-uo/glowtts-female-it
|
z-uo
| 2022-01-25T07:14:49Z | 11 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"text-to-speech",
"it",
"dataset:z-uo/female-LJSpeech-italian",
"endpoints_compatible",
"region:us"
] |
text-to-speech
| 2022-03-02T23:29:05Z |
---
tags:
- text-to-speech
language:
- it
model-index:
- name: glowtts-male-it
results: []
datasets:
- z-uo/female-LJSpeech-italian
---
# Coqui Model for TTS
```
pip install TTS
git clone https://huggingface.co/z-uo/glowtts-female-it
# predict one
tts --text "ciao pluto" --model_path "glowtts-female-it/best_model.pth.tar" --config_path "glowtts-female-it/config.json"
# predict server
tts-server --model_path "glowtts-female-it/best_model.pth.tar" --config_path "glowtts-female-it/config.json"
firefox localhost:5002
```
More information about training script in [this repo](https://github.com/nicolalandro/train_coqui_tts_ita).
|
z-uo/glowtts-male-it
|
z-uo
| 2022-01-25T07:14:09Z | 4 | 1 |
transformers
|
[
"transformers",
"tensorboard",
"text-to-speech",
"it",
"dataset:z-uo/male-LJSpeech-italian",
"endpoints_compatible",
"region:us"
] |
text-to-speech
| 2022-03-02T23:29:05Z |
---
tags:
- text-to-speech
language:
- it
model-index:
- name: glowtts-male-it
results: []
datasets:
- z-uo/male-LJSpeech-italian
---
# Coqui Model for TTS
```
pip install TTS
git clone https://huggingface.co/z-uo/glowtts-male-it
# predict one
server --text "ciao pluto" --model_path "glowtts-male-it/GOOD_best_model_3840.pth.tar" --config_path "glowtts-male-it/config.json"
# predict server
tts-server --model_path "glowtts-male-it/GOOD_best_model_3840.pth.tar" --config_path "glowtts-male-it/config.json"
firefox localhost:5002
```
More information about training script in [this repo](https://github.com/nicolalandro/train_coqui_tts_ita).
|
arman0320/bert-base-cased-wikitext2
|
arman0320
| 2022-01-25T05:51:08Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-cased-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-wikitext2
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.8596
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 7.0963 | 1.0 | 2346 | 7.0570 |
| 6.9063 | 2.0 | 4692 | 6.8721 |
| 6.8585 | 3.0 | 7038 | 6.8931 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.0
- Tokenizers 0.10.3
|
mtglearn/roberta-mtg-cards
|
mtglearn
| 2022-01-25T02:57:42Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
license: apache-2.0
---
|
aviator-neural/gpt2-donald_trump
|
aviator-neural
| 2022-01-24T22:09:58Z | 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: gpt2-donald_trump
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-donald_trump
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8721
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 391 | 2.8721 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.0
- Tokenizers 0.10.3
|
anirudh21/albert-base-v2-finetuned-qnli
|
anirudh21
| 2022-01-24T19:56:19Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"albert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: albert-base-v2-finetuned-qnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: qnli
metrics:
- name: Accuracy
type: accuracy
value: 0.9112209408749771
---
<!-- 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. -->
# albert-base-v2-finetuned-qnli
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3194
- Accuracy: 0.9112
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3116 | 1.0 | 6547 | 0.2818 | 0.8849 |
| 0.2467 | 2.0 | 13094 | 0.2532 | 0.9001 |
| 0.1858 | 3.0 | 19641 | 0.3194 | 0.9112 |
| 0.1449 | 4.0 | 26188 | 0.4338 | 0.9103 |
| 0.0584 | 5.0 | 32735 | 0.5752 | 0.9052 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.0
- Tokenizers 0.10.3
|
surrey-nlp/en_abbreviation_detection_roberta_lar
|
surrey-nlp
| 2022-01-24T19:26:12Z | 5 | 5 |
spacy
|
[
"spacy",
"token-classification",
"en",
"model-index",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
tags:
- spacy
- token-classification
language:
- en
widget:
- text: "Light dissolved inorganic carbon (DIC) resulting from the oxidation of hydrocarbons."
- text: "RAFs are plotted for a selection of neurons in the dorsal zone (DZ) of auditory cortex in Figure 1."
- text: "Images were acquired using a GE 3.0T MRI scanner with an upgrade for echo-planar imaging (EPI)."
model-index:
- name: en_abbreviation_detection_roberta_lar
results:
- task:
name: AbbreviationDetection
type: token-classification
metrics:
- name: Precision
type: precision
value: 0.9611772641
- name: Recall
type: recall
value: 0.9446958783
- name: F Score
type: f_score
value: 0.9528653083
---
| Feature | Description |
| --- | --- |
| **Name** | `en_abbreviation_detection_roberta_lar` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `transformer`, `abbreviationDetection` |
| **Components** | `transformer`, `abbreviationDetection` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | PLOSDataset-LREC22-Submitted |
| **License** | cc-by-sa-4.0 |
| **Author** | [Diptesh Kanojia](https://dipteshkanojia.github.io) |
### Label Scheme
<details>
<summary>View label scheme (3 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`abbreviationDetection`** | `AC`, `LF`, `O` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `ENTS_F` | 95.29 |
| `ENTS_P` | 96.12 |
| `ENTS_R` | 94.47 |
| `TRANSFORMER_LOSS` | 287952.16 |
| `NER_LOSS` | 608954.79 |
|
anas-awadalla/bert-small-finetuned-squad
|
anas-awadalla
| 2022-01-24T19:25:29Z | 45 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-small-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-small-finetuned-squad
This model is a fine-tuned version of [prajjwal1/bert-small](https://huggingface.co/prajjwal1/bert-small) on the squad dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.3138
- eval_runtime: 46.6577
- eval_samples_per_second: 231.13
- eval_steps_per_second: 14.446
- epoch: 4.0
- step: 22132
{'exact_match': 71.05960264900662, 'f1': 80.8260245470904}
## 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: 20
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.0
- Tokenizers 0.10.3
|
birgermoell/wav2vec2-common_voice-tr-demo
|
birgermoell
| 2022-01-24T18:52:26Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- sv-SE
license: apache-2.0
tags:
- automatic-speech-recognition
- common_voice
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-common_voice-tr-demo
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-common_voice-tr-demo
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the COMMON_VOICE - SV-SE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5528
- Wer: 0.3811
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 0.74 | 100 | 3.4444 | 1.0 |
| No log | 1.47 | 200 | 2.9421 | 1.0 |
| No log | 2.21 | 300 | 2.2802 | 1.0137 |
| No log | 2.94 | 400 | 0.9683 | 0.7611 |
| 3.7264 | 3.68 | 500 | 0.7941 | 0.6594 |
| 3.7264 | 4.41 | 600 | 0.6695 | 0.5751 |
| 3.7264 | 5.15 | 700 | 0.6507 | 0.5314 |
| 3.7264 | 5.88 | 800 | 0.5731 | 0.4927 |
| 3.7264 | 6.62 | 900 | 0.5723 | 0.4580 |
| 0.4592 | 7.35 | 1000 | 0.5913 | 0.4479 |
| 0.4592 | 8.09 | 1100 | 0.5562 | 0.4423 |
| 0.4592 | 8.82 | 1200 | 0.5566 | 0.4292 |
| 0.4592 | 9.56 | 1300 | 0.5492 | 0.4303 |
| 0.4592 | 10.29 | 1400 | 0.5665 | 0.4331 |
| 0.2121 | 11.03 | 1500 | 0.5610 | 0.4084 |
| 0.2121 | 11.76 | 1600 | 0.5703 | 0.4014 |
| 0.2121 | 12.5 | 1700 | 0.5669 | 0.3898 |
| 0.2121 | 13.24 | 1800 | 0.5586 | 0.3962 |
| 0.2121 | 13.97 | 1900 | 0.5656 | 0.3897 |
| 0.1326 | 14.71 | 2000 | 0.5565 | 0.3813 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
younes9/AI-DAY-distilbert-base-uncased-finetuned-cola
|
younes9
| 2022-01-24T18:13:20Z | 17 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: AI-DAY-distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5382139717003264
---
<!-- 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. -->
# AI-DAY-distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7236
- Matthews Correlation: 0.5382
## 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 | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5308 | 1.0 | 535 | 0.5065 | 0.4296 |
| 0.3565 | 2.0 | 1070 | 0.5109 | 0.4940 |
| 0.2399 | 3.0 | 1605 | 0.6056 | 0.5094 |
| 0.1775 | 4.0 | 2140 | 0.7236 | 0.5382 |
| 0.1242 | 5.0 | 2675 | 0.8659 | 0.5347 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.0
- Tokenizers 0.10.3
|
EColi/sponsorblock-base-v1
|
EColi
| 2022-01-24T17:23:23Z | 5 | 1 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:04Z |
---
tags:
- generated_from_trainer
model-index:
- name: out
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# out
This model is a fine-tuned version of [/1TB_SSD/SB_AI/out_epoch1/out/checkpoint-1115000/](https://huggingface.co//1TB_SSD/SB_AI/out_epoch1/out/checkpoint-1115000/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0645
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 2518227880
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-------:|:---------------:|
| 0.0867 | 0.07 | 75000 | 0.0742 |
| 0.0783 | 0.13 | 150000 | 0.0695 |
| 0.0719 | 0.2 | 225000 | 0.0732 |
| 0.0743 | 0.27 | 300000 | 0.0663 |
| 0.0659 | 0.34 | 375000 | 0.0686 |
| 0.0664 | 0.4 | 450000 | 0.0683 |
| 0.0637 | 0.47 | 525000 | 0.0680 |
| 0.0655 | 0.54 | 600000 | 0.0641 |
| 0.0676 | 0.6 | 675000 | 0.0644 |
| 0.0704 | 0.67 | 750000 | 0.0645 |
| 0.0687 | 0.74 | 825000 | 0.0610 |
| 0.059 | 0.81 | 900000 | 0.0652 |
| 0.0666 | 0.87 | 975000 | 0.0619 |
| 0.0624 | 0.94 | 1050000 | 0.0619 |
| 0.0625 | 1.01 | 1125000 | 0.0667 |
| 0.0614 | 1.03 | 1150000 | 0.0658 |
| 0.0597 | 1.05 | 1175000 | 0.0683 |
| 0.0629 | 1.07 | 1200000 | 0.0691 |
| 0.0603 | 1.1 | 1225000 | 0.0678 |
| 0.0601 | 1.12 | 1250000 | 0.0746 |
| 0.0606 | 1.14 | 1275000 | 0.0691 |
| 0.0671 | 1.16 | 1300000 | 0.0702 |
| 0.0625 | 1.19 | 1325000 | 0.0661 |
| 0.0617 | 1.21 | 1350000 | 0.0688 |
| 0.0579 | 1.23 | 1375000 | 0.0679 |
| 0.0663 | 1.25 | 1400000 | 0.0634 |
| 0.0583 | 1.28 | 1425000 | 0.0638 |
| 0.0623 | 1.3 | 1450000 | 0.0681 |
| 0.0615 | 1.32 | 1475000 | 0.0670 |
| 0.0592 | 1.34 | 1500000 | 0.0666 |
| 0.0626 | 1.37 | 1525000 | 0.0666 |
| 0.063 | 1.39 | 1550000 | 0.0647 |
| 0.0648 | 1.41 | 1575000 | 0.0653 |
| 0.0611 | 1.43 | 1600000 | 0.0700 |
| 0.0622 | 1.46 | 1625000 | 0.0634 |
| 0.0617 | 1.48 | 1650000 | 0.0651 |
| 0.0613 | 1.5 | 1675000 | 0.0634 |
| 0.0639 | 1.52 | 1700000 | 0.0661 |
| 0.0615 | 1.54 | 1725000 | 0.0644 |
| 0.0605 | 1.57 | 1750000 | 0.0662 |
| 0.0622 | 1.59 | 1775000 | 0.0656 |
| 0.0585 | 1.61 | 1800000 | 0.0633 |
| 0.0628 | 1.63 | 1825000 | 0.0625 |
| 0.0638 | 1.66 | 1850000 | 0.0662 |
| 0.0599 | 1.68 | 1875000 | 0.0664 |
| 0.0583 | 1.7 | 1900000 | 0.0668 |
| 0.0543 | 1.72 | 1925000 | 0.0631 |
| 0.06 | 1.75 | 1950000 | 0.0629 |
| 0.0615 | 1.77 | 1975000 | 0.0644 |
| 0.0587 | 1.79 | 2000000 | 0.0663 |
| 0.0647 | 1.81 | 2025000 | 0.0654 |
| 0.0604 | 1.84 | 2050000 | 0.0639 |
| 0.0641 | 1.86 | 2075000 | 0.0636 |
| 0.0604 | 1.88 | 2100000 | 0.0636 |
| 0.0654 | 1.9 | 2125000 | 0.0652 |
| 0.0588 | 1.93 | 2150000 | 0.0638 |
| 0.0616 | 1.95 | 2175000 | 0.0657 |
| 0.0598 | 1.97 | 2200000 | 0.0646 |
| 0.0633 | 1.99 | 2225000 | 0.0645 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anirudh21/bert-base-uncased-finetuned-cola
|
anirudh21
| 2022-01-24T16:29:06Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: bert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5796941781913538
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-cola
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9664
- Matthews Correlation: 0.5797
## 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 | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5017 | 1.0 | 535 | 0.5252 | 0.4841 |
| 0.2903 | 2.0 | 1070 | 0.5550 | 0.4967 |
| 0.1839 | 3.0 | 1605 | 0.7295 | 0.5634 |
| 0.1132 | 4.0 | 2140 | 0.7762 | 0.5702 |
| 0.08 | 5.0 | 2675 | 0.9664 | 0.5797 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.0
- Tokenizers 0.10.3
|
haimasree/DeepSTARR
|
haimasree
| 2022-01-24T16:21:18Z | 0 | 0 | null |
[
"exbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: en
tags:
- exbert
license: mit
datasets:
- bookcorpus
- wikipedia
---
|
philschmid/distilbert-base-multilingual-cased-sentiment-2
|
philschmid
| 2022-01-24T15:08:50Z | 2,177 | 4 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-multilingual-cased-sentiment-2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_reviews_multi
type: amazon_reviews_multi
args: all_languages
metrics:
- name: Accuracy
type: accuracy
value: 0.7475666666666667
- name: F1
type: f1
value: 0.7475666666666667
---
<!-- 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-multilingual-cased-sentiment-2
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6067
- Accuracy: 0.7476
- F1: 0.7476
## 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: 0.00024
- train_batch_size: 16
- eval_batch_size: 16
- seed: 33
- distributed_type: sagemaker_data_parallel
- num_devices: 8
- total_train_batch_size: 128
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.6885 | 0.53 | 5000 | 0.6532 | 0.7217 | 0.7217 |
| 0.6411 | 1.07 | 10000 | 0.6348 | 0.7319 | 0.7319 |
| 0.6057 | 1.6 | 15000 | 0.6186 | 0.7387 | 0.7387 |
| 0.5844 | 2.13 | 20000 | 0.6236 | 0.7449 | 0.7449 |
| 0.549 | 2.67 | 25000 | 0.6067 | 0.7476 | 0.7476 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.9.1
- Datasets 1.15.1
- Tokenizers 0.10.3
|
anirudh21/bert-base-uncased-finetuned-wnli
|
anirudh21
| 2022-01-24T13:33:56Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert-base-uncased-finetuned-wnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: wnli
metrics:
- name: Accuracy
type: accuracy
value: 0.5633802816901409
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-wnli
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6854
- Accuracy: 0.5634
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 40 | 0.6854 | 0.5634 |
| No log | 2.0 | 80 | 0.6983 | 0.3239 |
| No log | 3.0 | 120 | 0.6995 | 0.5352 |
| No log | 4.0 | 160 | 0.6986 | 0.5634 |
| No log | 5.0 | 200 | 0.6996 | 0.5634 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.0
- Tokenizers 0.10.3
|
deepdoctection/tp_casc_rcnn_X_32xd4_50_FPN_GN_2FC_publaynet_inference_only
|
deepdoctection
| 2022-01-24T13:05:27Z | 0 | 0 | null |
[
"Tensorflow",
"dataset:Publaynet",
"arxiv:1908.07836",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
tags:
- Tensorflow
license: apache-2.0
datasets:
- Publaynet
---
# Tensorpacks Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Publaynet for Document Layout Analysis
The model and its training code has been mainly taken from: [Tensorpack](https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN) .
Please check: [Xu Zhong et. all. - PubLayNet: largest dataset ever for document layout analysis](https://arxiv.org/abs/1908.07836).
This model is different from the model used the paper.
The code has been adapted so that it can be used in a **deep**doctection pipeline.
## How this model can be used
This model can be used with the **deep**doctection in a full pipeline, along with table recognition and OCR. Check the general instruction following this [Get_started](https://github.com/deepdoctection/deepdoctection/blob/master/notebooks/Get_Started.ipynb) tutorial.
## This is an inference model only
To reduce the size of the checkpoint we removed all variables that are not necessary for inference. Therefore it cannot be used for fine-tuning. To fine tune this model please check [this model](https://huggingface.co/deepdoctection/tp_casc_rcnn_X_32xd4_50_FPN_GN_2FC_publaynet).
## How this model was trained.
To recreate the model run on the **deep**doctection framework, run:
```python
>>> import os
>>> from deep_doctection.datasets import DatasetRegistry
>>> from deep_doctection.eval import MetricRegistry
>>> from deep_doctection.utils import get_configs_dir_path
>>> from deep_doctection.train import train_faster_rcnn
publaynet = DatasetRegistry.get_dataset("publaynet")
path_config_yaml=os.path.join(get_configs_dir_path(),"tp/layout/conf_frcnn_layout.yaml")
path_weights = ""
dataset_train = publaynet
config_overwrite=["TRAIN.STEPS_PER_EPOCH=500","TRAIN.EVAL_PERIOD=200","TRAIN.STARTING_EPOCH=1",
"PREPROC.TRAIN_SHORT_EDGE_SIZE=[800,1200]","TRAIN.CHECKPOINT_PERIOD=50",
"BACKBONE.FREEZE_AT=0"]
build_train_config=["max_datapoints=335703"]
dataset_val = publaynet
build_val_config = ["max_datapoints=2000"]
coco_metric = MetricRegistry.get_metric("coco")
train_faster_rcnn(path_config_yaml=path_config_yaml,
dataset_train=dataset_train,
path_weights=path_weights,
config_overwrite=config_overwrite,
log_dir="/path/to/dir",
build_train_config=build_train_config,
dataset_val=dataset_val,
build_val_config=build_val_config,
metric=coco_metric,
pipeline_component_name="ImageLayoutService"
)
```
|
nimelinia/rut5-reply-headline-model
|
nimelinia
| 2022-01-24T12:31:54Z | 1 | 0 |
transformers
|
[
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
This model was trained from rut5-base-multitask with pair of questions and answers (in Russian).
The model demonstrate interesting behavior with option "reply" and "headline".
When model creates a headline for paragraph of text, it not only uses phrases from text, but also generate new words and sometimes new meanings.
Examples of questions and answers:
> Как зовут отца Александра Сергеевича Пушкина?
> - Пушкин
> Где купить вкусное мороженое?
> - В супермаркете
> Красивая ли Мона Лиза?
> - Очень красивая
Examples of headlines:
> Власти Пекина из-за пандемии COVID-19 призвали жителей города отказаться от помощи и избегать любого контакта с олимпийскими машинами, попавшими в ДТП. Об этом сообщает South China Morning Post.
> - Китайский губернатор призвал жителей Пекина отказаться от помощи
> Казахский народ должен поддержать своего президента Касым-Жомарт Токаева на фоне угрозы повторения массовых беспорядков, но и властям страны следует провести демократические реформы для снижения недовольства. Об этом в интервью изданию Orda заявил бывший генеральный продюсер гостелеканала «Хабар», экс-глава канала «Ел Арна» Серик Абас-Шах.
> - Казахский народ должен поддержать Токаева
> Позиция России по макроэкономическим показателям является лучшей в мире. Об этом сказал ТАСС российский исполнительный директор в Международном валютном фонде (МВФ) Алексей Можин.
> - Российская экономика является лучшей в мире
|
public-data/anime_face_landmark_detection
|
public-data
| 2022-01-24T09:08:39Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-03-02T23:29:05Z |
# anime_face_landmark_detection
- Repo: https://github.com/kanosawa/anime_face_landmark_detection
- https://drive.google.com/open?id=1NckKw7elDjQTllRxttO87WY7cnQwdMqz
|
nntadotzip/xlnet-base-cased-IUChatbot-ontologyDts-localParams
|
nntadotzip
| 2022-01-24T08:29:47Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlnet",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: xlnet-base-cased-IUChatbot-ontologyDts-localParams
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlnet-base-cased-IUChatbot-ontologyDts-localParams
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0238
## 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: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 0.1172 | 1.0 | 1119 | 0.0657 |
| 0.0564 | 2.0 | 2238 | 0.0237 |
| 0.033 | 3.0 | 3357 | 0.0238 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.0
- Tokenizers 0.10.3
|
st1992/paraphrase-MiniLM-L12-tagalog-v2
|
st1992
| 2022-01-24T05:48:32Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
# st1992/paraphrase-MiniLM-L12-tagalog-v2
paraphrase-MiniLM-L12-v2 finetuned on Tagalog language: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers) : same as other sentence-transformer models
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('st1992/paraphrase-MiniLM-L12-tagalog-v2')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['hindi po', 'tulog na']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('st1992/paraphrase-MiniLM-L12-tagalog-v2')
model = AutoModel.from_pretrained('st1992/paraphrase-MiniLM-L12-tagalog-v2')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, max pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
|
danielbubiola/daniel_asr
|
danielbubiola
| 2022-01-24T05:30:03Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: daniel_asr
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# daniel_asr
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4565
- Wer: 0.3423
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.4909 | 4.0 | 500 | 1.3485 | 0.8887 |
| 0.5887 | 8.0 | 1000 | 0.4957 | 0.4641 |
| 0.2207 | 12.0 | 1500 | 0.4621 | 0.3971 |
| 0.125 | 16.0 | 2000 | 0.4339 | 0.3756 |
| 0.0829 | 20.0 | 2500 | 0.4618 | 0.3613 |
| 0.0601 | 24.0 | 3000 | 0.4564 | 0.3535 |
| 0.0456 | 28.0 | 3500 | 0.4565 | 0.3423 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
ysakuramoto/mobilebert-ja
|
ysakuramoto
| 2022-01-24T05:25:31Z | 64 | 1 |
transformers
|
[
"transformers",
"pytorch",
"mobilebert",
"ja",
"dataset:wikipedia",
"arxiv:2004.02984",
"license:cc-by-sa-3.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: ja
tags:
- mobilebert
license: cc-by-sa-3.0
datasets:
- wikipedia
---
# MobileBERT 日本語事前学習済みモデル爆誕！！
AI関係の仕事をしている櫻本です。
2020年に発表されたBERTの発展型モデルの一つである「MobileBERT」の、日本語事前学習済みモデルを構築しました。
このページを見つけた方はかなりラッキーですから、ぜひ一度使ってみてください！！
BERTの推論速度の遅さを嘆いている方にお薦めです。
# 利用方法
既にtransformersでBERTを利用されている方向けの説明です。
トークナイザは東北大学さんのモデル(cl-tohoku/bert-large-japanese)からお借りしましたのでご指定ください。
後は、**BertFor**なんちゃら～のクラスを**MobileBertFor**なんちゃら～に直して、このリポジトリを指定するだけです！
```from transformers import BertJapaneseTokenizer, MobileBertForSequenceClassification
tokenizer = BertJapaneseTokenizer.from_pretrained("cl-tohoku/bert-large-japanese")
model = MobileBertForSequenceClassification.from_pretrained("ysakuramoto/mobilebert-ja") # 文書分類の場合
```
(注意：文書分類などのタスクに利用するには、ファインチューニングが必要です)
# BERTとの性能比較
文書分類と固有表現抽出について、ファインチューニング・性能評価を行いました。
参考程度にご覧ください。(ファインチューニング後の性能を保証するものではありません)
- 文書分類(MobileBertForSequenceClassification)
|メトリック|BERT|MobileBERT(高速化前)|MobileBERT(高速化後)|
|-----------|-----------| ------- | -------- |
|学習時間(s)|585.0|399.7|-|
|推論時間(s)|259.0|108.7|70.5|
|精度|86.4%|85.5%|86.4%|
|モデルファイルサイズ(MB)|440.2|-|41.8|
- 条件
- ライブドアニュースコーパスのタイトルとカテゴリで学習・推論。
- 比較対象のBERTモデルは東北大学さんの"cl-tohoku/bert-base-japanese-whole-word-masking"。
- 推論データ n=1,474。精度はAccuracy
- 学習パラメータ: エポック数=10, lr=1e-4
- 推論時の高速化として、枝刈り(-20%)・量子化・jitコンパイルを実施。
- Google Colabにて、学習にGPU、推論にCPUを利用。バッチ処理でなく1件ずつ推論。
- それぞれ、学習～推論を3回実施した平均値。
- 固有表現抽出(MobileBertForTokenClassification)
|メトリック|BERT|MobileBERT(高速化前)|MobileBERT(高速化後)|
|-----------|-----------| ------- | -------- |
|学習時間(s)|428.0|294.0|-|
|推論時間(s)|163.5|78.4|40.9|
|精度|86.4%|82.5%|83.3%|
|モデルファイルサイズ(MB)|440.2|-|41.8|
- 条件
- ストックマーク社さんのwikipediaデータセットで学習・推論。(https://github.com/stockmarkteam/ner-wikipedia-dataset)
- 比較対象のBERTモデルは東北大学さんの"cl-tohoku/bert-base-japanese-whole-word-masking"。
- 推論データ n=2,140。精度は完全一致のf-measure
- 学習パラメータ: エポック数=10, lr=1e-4
- 推論時の高速化として、枝刈り(-20%)・量子化・jitコンパイルを実施。
- Google Colabにて、学習にGPU、推論にCPUを利用。バッチ処理でなく1件ずつ推論。
- それぞれ、学習～推論を3回実施した平均値。
# モデルの説明
- モデルの構造
- 論文中の"MobileBERT"構造に従いました。(論文中にはMobileBERT<sub>TINY</sub>というバージョンもありますがそちらではないです)
- 論文中のTable.1 をご確認ください。 https://arxiv.org/abs/2004.02984
- 学習に利用したデータ
- 東北大学さんが公開されている方法で、2021年8月時点のwikipediaデータを加工・利用しました。
- 東北大学さんのgithub https://github.com/cl-tohoku/bert-japanese
- トークナイザ
- 東北大学さんのモデル"cl-tohoku/bert-large-japanese"からお借りしました。vocab sizeは32,768です。
- 学習方法
- Google ColabからTPUを用いて学習しました。
1. IB-BERT<sub>LARGE</sub>をlr=5e-4で1Mステップ学習しました。
1. IB-BERT<sub>LARGE</sub>を240kステップ蒸留後、mobileBERTをlr=5e-4で2Mステップ学習しました。
- トータルで2ヶ月半くらいかかりました。。エラー出まくってつらかったです。
# ライセンス
[CC-BY SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/deed.ja)
トークナイザについては東北大学さんのモデル"cl-tohoku/bert-large-japanese"からお借りしました。
# 免責
このモデルを利用・参照することで発生したあらゆる不都合や損害について、一切の責任を負いかねます。
|
public-data/bizarre-pose-estimator-models
|
public-data
| 2022-01-24T03:35:52Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-03-02T23:29:05Z |
# bizarre-pose-estimator
- Repo: https://github.com/ShuhongChen/bizarre-pose-estimator
- https://drive.google.com/drive/folders/11bw47Vy-RPKjgd6yF0RzcXALvp7zB_wt
|
jiobiala24/wav2vec2-base-checkpoint-8
|
jiobiala24
| 2022-01-24T01:26:07Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-base-checkpoint-8
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-checkpoint-8
This model is a fine-tuned version of [jiobiala24/wav2vec2-base-checkpoint-7.1](https://huggingface.co/jiobiala24/wav2vec2-base-checkpoint-7.1) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9561
- Wer: 0.3271
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.3117 | 1.59 | 1000 | 0.5514 | 0.3451 |
| 0.2509 | 3.19 | 2000 | 0.5912 | 0.3328 |
| 0.1918 | 4.78 | 3000 | 0.6103 | 0.3346 |
| 0.1612 | 6.38 | 4000 | 0.6469 | 0.3377 |
| 0.1388 | 7.97 | 5000 | 0.6597 | 0.3391 |
| 0.121 | 9.57 | 6000 | 0.6911 | 0.3472 |
| 0.1096 | 11.16 | 7000 | 0.7300 | 0.3457 |
| 0.0959 | 12.76 | 8000 | 0.7660 | 0.3400 |
| 0.0882 | 14.35 | 9000 | 0.8316 | 0.3394 |
| 0.0816 | 15.95 | 10000 | 0.8042 | 0.3357 |
| 0.0739 | 17.54 | 11000 | 0.8087 | 0.3346 |
| 0.0717 | 19.14 | 12000 | 0.8590 | 0.3353 |
| 0.066 | 20.73 | 13000 | 0.8750 | 0.3336 |
| 0.0629 | 22.33 | 14000 | 0.8759 | 0.3333 |
| 0.0568 | 23.92 | 15000 | 0.8963 | 0.3321 |
| 0.0535 | 25.52 | 16000 | 0.9391 | 0.3323 |
| 0.0509 | 27.11 | 17000 | 0.9279 | 0.3296 |
| 0.0498 | 28.71 | 18000 | 0.9561 | 0.3271 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
anas-awadalla/bert-medium-finetuned-squad
|
anas-awadalla
| 2022-01-24T01:10:28Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
Results:
{'exact_match': 76.82119205298014, 'f1': 84.69734248389383}
|
public-data/Yet-Another-Anime-Segmenter
|
public-data
| 2022-01-24T00:00:14Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-03-02T23:29:05Z |
# Yet-Another-Anime-Segmenter
- Repo: https://github.com/zymk9/Yet-Another-Anime-Segmenter
- https://drive.google.com/file/d/1-wFdQ4jwSTeJ7wGD3YKNJdcpSS5Ho8c9/view?usp=sharing
- https://raw.githubusercontent.com/zymk9/Yet-Another-Anime-Segmenter/main/configs/SOLOv2.yaml
|
public-data/danbooru-pretrained
|
public-data
| 2022-01-23T23:31:03Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-03-02T23:29:05Z |
# danbooru-pretrained
- Repo: https://github.com/RF5/danbooru-pretrained
- https://github.com/RF5/danbooru-pretrained/releases/tag/v0.1
- https://github.com/RF5/danbooru-pretrained/releases/download/v0.1/resnet50-13306192.pth
- https://github.com/RF5/danbooru-pretrained/raw/master/config/class_names_6000.json
|
public-data/DeepDanbooru
|
public-data
| 2022-01-23T22:31:55Z | 0 | 1 | null |
[
"region:us"
] | null | 2023-05-22T00:53:07Z |
# DeepDanbooru
- https://github.com/KichangKim/DeepDanbooru
- https://github.com/KichangKim/DeepDanbooru/releases/tag/v3-20200915-sgd-e30
- https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20200915-sgd-e30/deepdanbooru-v3-20200915-sgd-e30.zip
|
mattchurgin/xls-r-eng
|
mattchurgin
| 2022-01-23T17:31:10Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"generated_from_trainer",
"ab",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- ab
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_7_0
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: ''
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [patrickvonplaten/wav2vec2_tiny_random_robust](https://huggingface.co/patrickvonplaten/wav2vec2_tiny_random_robust) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset.
It achieves the following results on the evaluation set:
- Loss: inf
- Wer: 1.0
## 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: 0.0003
- 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: 1.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1
- Datasets 1.18.1.dev0
- Tokenizers 0.11.0
|
shivam/wav2vec2-xls-r-300m-hindi
|
shivam
| 2022-01-23T16:37:08Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"generated_from_trainer",
"hi",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- hi
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_7_0
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: ''
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - HI dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4031
- Wer: 0.6827
## 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: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 5.3156 | 3.4 | 500 | 4.5583 | 1.0 |
| 3.3329 | 6.8 | 1000 | 3.4274 | 1.0001 |
| 2.1275 | 10.2 | 1500 | 1.7221 | 0.8763 |
| 1.5737 | 13.6 | 2000 | 1.4188 | 0.8143 |
| 1.3835 | 17.01 | 2500 | 1.2251 | 0.7447 |
| 1.3247 | 20.41 | 3000 | 1.2827 | 0.7394 |
| 1.231 | 23.81 | 3500 | 1.2216 | 0.7074 |
| 1.1819 | 27.21 | 4000 | 1.2210 | 0.6863 |
| 1.1546 | 30.61 | 4500 | 1.3233 | 0.7308 |
| 1.0902 | 34.01 | 5000 | 1.3251 | 0.7010 |
| 1.0749 | 37.41 | 5500 | 1.3274 | 0.7235 |
| 1.0412 | 40.81 | 6000 | 1.2942 | 0.6856 |
| 1.0064 | 44.22 | 6500 | 1.2581 | 0.6732 |
| 1.0006 | 47.62 | 7000 | 1.2767 | 0.6885 |
| 0.9518 | 51.02 | 7500 | 1.2966 | 0.6925 |
| 0.9514 | 54.42 | 8000 | 1.2981 | 0.7067 |
| 0.9241 | 57.82 | 8500 | 1.3835 | 0.7124 |
| 0.9059 | 61.22 | 9000 | 1.3318 | 0.7083 |
| 0.8906 | 64.62 | 9500 | 1.3640 | 0.6962 |
| 0.8468 | 68.03 | 10000 | 1.4727 | 0.6982 |
| 0.8631 | 71.43 | 10500 | 1.3401 | 0.6809 |
| 0.8154 | 74.83 | 11000 | 1.4124 | 0.6955 |
| 0.7953 | 78.23 | 11500 | 1.4245 | 0.6950 |
| 0.818 | 81.63 | 12000 | 1.3944 | 0.6995 |
| 0.7772 | 85.03 | 12500 | 1.3735 | 0.6785 |
| 0.7857 | 88.43 | 13000 | 1.3696 | 0.6808 |
| 0.7705 | 91.84 | 13500 | 1.4101 | 0.6870 |
| 0.7537 | 95.24 | 14000 | 1.4178 | 0.6832 |
| 0.7734 | 98.64 | 14500 | 1.4027 | 0.6831 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu113
- Datasets 1.18.1.dev0
- Tokenizers 0.11.0
|
Madhour/gpt2-eli5
|
Madhour
| 2022-01-23T12:00:23Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"ELI5",
"en",
"dataset:eli5",
"license:gpl-3.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:04Z |
---
language: en
tags:
- ELI5
license: gpl-3.0
datasets:
- eli5
Task: Summarization
widget:
- text: "<|BOS|><|SEP|>Consulting,business,Fraud<|SEP|>"
inference:
parameters:
temperature: 0.9
return_full_text: False
repetition_penalty: 1
---
# Conditional ELI5 Generator
Given a few keywords, it generates an Eli5 question with a corresponding answer.
The model is mainly used for [SeemsPhishy](https://github.com/madhour/seemsphishy) to auto generate newsletters for phishing/penetration-testing.
# How to use
```Python
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
from torch import tensor
tokenizer = AutoTokenizer.from_pretrained("Madhour/gpt2-eli5")
model = AutoModelForCausalLM.from_pretrained("Madhour/gpt2-eli5")
prompt = <|BOS|> +"I have a question."+ <|SEP|> + "keyword1,keyword2,keyword3" + <|SEP|>
prompt = tensor(tokenizer.encode(prompt)).unsqueeze(0)
text = model.generate(prompt,
do_sample=True,
min_length=50,
max_length=768,
top_k=30,
top_p=0.7,
temperature=0.9,
repetition_penalty=2.0,
num_return_sequences=3)
```
|
asanka25/xlm-roberta-base-finetuned-conll03-english-finetuned-sinhala
|
asanka25
| 2022-01-23T10:59:51Z | 30 | 1 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
This model was created using xlm-roberta-base bodel and fine-tuned it using CoNLL 2003 dataset. On top of the trained model, we trained it again using a Sinhala NER data that was also formatted to the CoNLL format.
|
dandelin/vilt-b32-finetuned-coco
|
dandelin
| 2022-01-23T09:45:24Z | 10,342 | 1 |
transformers
|
[
"transformers",
"pytorch",
"vilt",
"arxiv:2102.03334",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
license: apache-2.0
---
# Vision-and-Language Transformer (ViLT), fine-tuned on COCO
Vision-and-Language Transformer (ViLT) model fine-tuned on [COCO](https://cocodataset.org/#home). It was introduced in the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Kim et al. and first released in [this repository](https://github.com/dandelin/ViLT).
Disclaimer: The team releasing ViLT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Intended uses & limitations
You can use the model for image and text retrieval.
### How to use
Here is how to use the model in PyTorch:
```
from transformers import ViltProcessor, ViltForImageAndTextRetrieval
import requests
from PIL import Image
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
texts = ["An image of two cats chilling on a couch", "A football player scoring a goal"]
processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-coco")
model = ViltForImageAndTextRetrieval.from_pretrained("dandelin/vilt-b32-finetuned-coco")
# prepare inputs
encoding = processor(image, text, return_tensors="pt")
# forward pass
scores = dict()
for text in texts:
encoding = processor(image, text, return_tensors="pt")
outputs = model(**encoding)
scores[text] = outputs.logits[0, :].item()
```
## Training data
(to do)
## Training procedure
### Preprocessing
(to do)
### Pretraining
(to do)
## Evaluation results
(to do)
### BibTeX entry and citation info
```bibtex
@misc{kim2021vilt,
title={ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision},
author={Wonjae Kim and Bokyung Son and Ildoo Kim},
year={2021},
eprint={2102.03334},
archivePrefix={arXiv},
primaryClass={stat.ML}
}
```
|
dandelin/vilt-b32-finetuned-nlvr2
|
dandelin
| 2022-01-23T09:43:30Z | 673 | 2 |
transformers
|
[
"transformers",
"pytorch",
"vilt",
"arxiv:2102.03334",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
license: apache-2.0
---
# Vision-and-Language Transformer (ViLT), fine-tuned on NLVR2
Vision-and-Language Transformer (ViLT) model fine-tuned on [NLVR2](https://lil.nlp.cornell.edu/nlvr/). It was introduced in the paper [ViLT: Vision-and-Language Transformer
Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Kim et al. and first released in [this repository](https://github.com/dandelin/ViLT).
Disclaimer: The team releasing ViLT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Intended uses & limitations
You can use the model to determine whether a sentence is true or false given 2 images.
### How to use
Here is how to use the model in PyTorch:
```
from transformers import ViltProcessor, ViltForImagesAndTextClassification
import requests
from PIL import Image
image1 = Image.open(requests.get("https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg", stream=True).raw)
image2 = Image.open(requests.get("https://lil.nlp.cornell.edu/nlvr/exs/ex0_1.jpg", stream=True).raw)
text = "The left image contains twice the number of dogs as the right image."
processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-nlvr2")
model = ViltForImagesAndTextClassification.from_pretrained("dandelin/vilt-b32-finetuned-nlvr2")
# prepare inputs
encoding = processor([image1, image2], text, return_tensors="pt")
# forward pass
outputs = model(input_ids=encoding.input_ids, pixel_values=encoding.pixel_values.unsqueeze(0))
logits = outputs.logits
idx = logits.argmax(-1).item()
print("Predicted answer:", model.config.id2label[idx])
```
## Training data
(to do)
## Training procedure
### Preprocessing
(to do)
### Pretraining
(to do)
## Evaluation results
(to do)
### BibTeX entry and citation info
```bibtex
@misc{kim2021vilt,
title={ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision},
author={Wonjae Kim and Bokyung Son and Ildoo Kim},
year={2021},
eprint={2102.03334},
archivePrefix={arXiv},
primaryClass={stat.ML}
}
```
|
baffo32/pyc2py_alpha2
|
baffo32
| 2022-01-23T08:17:55Z | 5 | 0 |
transformers
|
[
"transformers",
"jax",
"t5",
"text2text-generation",
"multilingual",
"dataset:mc4",
"arxiv:1907.06292",
"arxiv:2105.13626",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
language: multilingual
datasets:
- mc4
license: apache-2.0
---
# ByT5 - Base
ByT5 is a tokenizer-free version of [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) and generally follows the architecture of [MT5](https://huggingface.co/google/mt5-base).
ByT5 was only pre-trained on [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) excluding any supervised training with an average span-mask of 20 UTF-8 characters. Therefore, this model has to be fine-tuned before it is useable on a downstream task.
ByT5 works especially well on noisy text data,*e.g.*, `google/byt5-base` significantly outperforms [mt5-base](https://huggingface.co/google/mt5-base) on [TweetQA](https://arxiv.org/abs/1907.06292).
Paper: [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626)
Authors: *Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel*
## Example Inference
ByT5 works on raw UTF-8 bytes and can be used without a tokenizer:
```python
from transformers import T5ForConditionalGeneration
import torch
model = T5ForConditionalGeneration.from_pretrained('google/byt5-base')
input_ids = torch.tensor([list("Life is like a box of chocolates.".encode("utf-8"))]) + 3 # add 3 for special tokens
labels = torch.tensor([list("La vie est comme une boîte de chocolat.".encode("utf-8"))]) + 3 # add 3 for special tokens
loss = model(input_ids, labels=labels).loss # forward pass
```
For batched inference & training it is however recommended using a tokenizer class for padding:
```python
from transformers import T5ForConditionalGeneration, AutoTokenizer
model = T5ForConditionalGeneration.from_pretrained('google/byt5-base')
tokenizer = AutoTokenizer.from_pretrained('google/byt5-base')
model_inputs = tokenizer(["Life is like a box of chocolates.", "Today is Monday."], padding="longest", return_tensors="pt")
labels = tokenizer(["La vie est comme une boîte de chocolat.", "Aujourd'hui c'est lundi."], padding="longest", return_tensors="pt").input_ids
loss = model(**model_inputs, labels=labels).loss # forward pass
```
## Abstract
Most widely-used pre-trained language models operate on sequences of tokens corresponding to word or subword units. Encoding text as a sequence of tokens requires a tokenizer, which is typically created as an independent artifact from the model. Token-free models that instead operate directly on raw text (bytes or characters) have many benefits: they can process text in any language out of the box, they are more robust to noise, and they minimize technical debt by removing complex and error-prone text preprocessing pipelines. Since byte or character sequences are longer than token sequences, past work on token-free models has often introduced new model architectures designed to amortize the cost of operating directly on raw text. In this paper, we show that a standard Transformer architecture can be used with minimal modifications to process byte sequences. We carefully characterize the trade-offs in terms of parameter count, training FLOPs, and inference speed, and show that byte-level models are competitive with their token-level counterparts. We also demonstrate that byte-level models are significantly more robust to noise and perform better on tasks that are sensitive to spelling and pronunciation. As part of our contribution, we release a new set of pre-trained byte-level Transformer models based on the T5 architecture, as well as all code and data used in our experiments.
|
vuiseng9/pegasus-xsum
|
vuiseng9
| 2022-01-23T02:33:40Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
This model is developed with transformers v4.13 with minor patch in this [fork](https://github.com/vuiseng9/transformers/tree/pegasus-v4p13).
# Setup
```bash
git clone https://github.com/vuiseng9/transformers
cd transformers
git checkout pegasus-v4p13 && git reset --hard 3db4b452
# installation, set summarization dependency
# . . .
```
# Train
```bash
#!/usr/bin/env bash
export CUDA_VISIBLE_DEVICES=0,1 # 2 cards on xsum
NEPOCH=10
RUNID=pegasus-xsum-${NEPOCH}eph-run1
OUTDIR=/data1/vchua/pegasus-hf4p13/pegasus/${RUNID}
mkdir -p $OUTDIR
nohup python run_summarization.py \
--model_name_or_path google/pegasus-large \
--dataset_name xsum \
--do_train \
--adafactor \
--learning_rate 1e-4 \
--label_smoothing_factor 0.1 \
--num_train_epochs $NEPOCH \
--per_device_train_batch_size 8 \
--do_eval \
--per_device_eval_batch_size 8 \
--num_beams 8 \
--max_source_length 512 \
--max_target_length 64 \
--evaluation_strategy steps \
--eval_steps 1000 \
--save_strategy steps \
--save_steps 2000 \
--logging_steps 1 \
--overwrite_output_dir \
--run_name $RUNID \
--output_dir $OUTDIR > $OUTDIR/run.log 2>&1
```
# Eval
```bash
#!/usr/bin/env bash
export CUDA_VISIBLE_DEVICES=3
DT=$(date +%F_%H-%M)
RUNID=pegasus-xsum-${DT}
OUTDIR=/data1/vchua/pegasus-hf4p13/pegasus-test/${RUNID}
mkdir -p $OUTDIR
nohup python run_summarization.py \
--model_name_or_path vuiseng9/pegasus-xsum \
--dataset_name xsum \
--max_source_length 512 \
--max_target_length 64 \
--do_predict \
--per_device_eval_batch_size 16 \
--predict_with_generate \
--num_beams 8 \
--overwrite_output_dir \
--run_name $RUNID \
--output_dir $OUTDIR > $OUTDIR/run.log 2>&1 &
```
Although fine-tuning is carried out for 10 epochs, this model is the checkpoint (@62000 steps, 4.9epoch, 20hrs) with lower loss during training. Test/predict with this checkpoint should give results below.
```
***** predict metrics *****
predict_gen_len = 24.0499
predict_loss = 1.5801
predict_rouge1 = 47.2124
predict_rouge2 = 24.3673
predict_rougeL = 39.0055
predict_rougeLsum = 39.0007
predict_runtime = 0:34:23.32
predict_samples = 11334
predict_samples_per_second = 5.493
predict_steps_per_second = 0.344
```
|
ylh1013/ja_chatbot
|
ylh1013
| 2022-01-23T02:24:03Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language:
- finetuned_from
license: mit
tags:
- generated_from_trainer
model-index:
- name: ja_chatbot
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ja_chatbot
This model is a fine-tuned version of [rinna/japanese-gpt2-medium](https://huggingface.co/rinna/japanese-gpt2-medium) 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: 5e-05
- train_batch_size: 48
- 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: 500
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.12.3
- Pytorch 1.10.0+cu102
- Tokenizers 0.10.3
|
pere/xls-test
|
pere
| 2022-01-22T18:40:50Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"generated_from_trainer",
"ab",
"dataset:common_voice",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- ab
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_7_0
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: ''
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [hf-test/xls-r-dummy](https://huggingface.co/hf-test/xls-r-dummy) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset.
It achieves the following results on the evaluation set:
- Loss: 156.8789
- Wer: 1.3456
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 2
- 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
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
ms29315/distilbert-base-uncased-finetuned-cola
|
ms29315
| 2022-01-21T19:56:06Z | 4 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: ms29315/distilbert-base-uncased-finetuned-cola
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# ms29315/distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3100
- Validation Loss: 0.5090
- Epoch: 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:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2670, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.3100 | 0.5090 | 0 |
### Framework versions
- Transformers 4.15.0
- TensorFlow 2.7.0
- Datasets 1.18.0
- Tokenizers 0.10.3
|
facebook/xm_transformer_600m-en_zh-multi_domain
|
facebook
| 2022-01-21T19:02:57Z | 5 | 2 |
fairseq
|
[
"fairseq",
"audio",
"audio-to-audio",
"speech-to-speech-translation",
"dataset:must_c",
"dataset:covost2",
"arxiv:2010.05171",
"region:us"
] |
audio-to-audio
| 2022-03-02T23:29:05Z |
---
library_name: fairseq
task: audio-to-audio
tags:
- fairseq
- audio
- audio-to-audio
- speech-to-speech-translation
language: en-zh
datasets:
- must_c
- covost2
widget:
- example_title: Common Voice sample 1
src: https://huggingface.co/facebook/xm_transformer_600m-en_es-multi_domain/resolve/main/common_voice_en_18295850.mp3
---
# xm_transformer_600m-en_zh-multi_domain
[W2V2-Transformer](https://aclanthology.org/2021.acl-long.68/) speech-to-text translation model from fairseq S2T ([paper](https://arxiv.org/abs/2010.05171)/[code](https://github.com/pytorch/fairseq/tree/main/examples/speech_to_text)):
- English-Chinese
- Trained on MuST-C, CoVoST 2, Multilingual LibriSpeech, Common Voice v7 and CCMatrix
- Speech synthesis with [facebook/tts_transformer-zh-cv7_css10](https://huggingface.co/facebook/tts_transformer-zh-cv7_css10)
## Usage
```python
from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub
from fairseq.models.speech_to_text.hub_interface import S2THubInterface
from fairseq.models.text_to_speech.hub_interface import TTSHubInterface
import IPython.display as ipd
import torchaudio
models, cfg, task = load_model_ensemble_and_task_from_hf_hub(
"facebook/xm_transformer_600m-en_zh-multi_domain",
arg_overrides={"config_yaml": "config.yaml"},
)
model = models[0]
generator = task.build_generator(model, cfg)
# requires 16000Hz mono channel audio
audio, _ = torchaudio.load("/path/to/an/audio/file")
sample = S2THubInterface.get_model_input(task, audio)
text = S2THubInterface.get_prediction(task, model, generator, sample)
# speech synthesis
tts_models, tts_cfg, tts_task = load_model_ensemble_and_task_from_hf_hub(
f"facebook/tts_transformer-zh-cv7_css10",
arg_overrides={"vocoder": "griffin_lim", "fp16": False},
)
tts_model = tts_models[0]
TTSHubInterface.update_cfg_with_data_cfg(tts_cfg, tts_task.data_cfg)
tts_generator = tts_task.build_generator([tts_model], tts_cfg)
tts_sample = TTSHubInterface.get_model_input(tts_task, text)
wav, sr = TTSHubInterface.get_prediction(
tts_task, tts_model, tts_generator, tts_sample
)
ipd.Audio(wav, rate=rate)
```
## Citation
```bibtex
@inproceedings{li-etal-2021-multilingual,
title = "Multilingual Speech Translation from Efficient Finetuning of Pretrained Models",
author = "Li, Xian and
Wang, Changhan and
Tang, Yun and
Tran, Chau and
Tang, Yuqing and
Pino, Juan and
Baevski, Alexei and
Conneau, Alexis and
Auli, Michael",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.68",
doi = "10.18653/v1/2021.acl-long.68",
pages = "827--838",
}
@inproceedings{wang-etal-2020-fairseq,
title = "Fairseq {S}2{T}: Fast Speech-to-Text Modeling with Fairseq",
author = "Wang, Changhan and
Tang, Yun and
Ma, Xutai and
Wu, Anne and
Okhonko, Dmytro and
Pino, Juan",
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: System Demonstrations",
month = dec,
year = "2020",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.aacl-demo.6",
pages = "33--39",
}
```
|
facebook/xm_transformer_600m-en_tr-multi_domain
|
facebook
| 2022-01-21T19:02:30Z | 18 | 1 |
fairseq
|
[
"fairseq",
"audio",
"audio-to-audio",
"speech-to-speech-translation",
"dataset:must_c",
"dataset:covost2",
"arxiv:2010.05171",
"region:us"
] |
audio-to-audio
| 2022-03-02T23:29:05Z |
---
library_name: fairseq
task: audio-to-audio
tags:
- fairseq
- audio
- audio-to-audio
- speech-to-speech-translation
language: en-tr
datasets:
- must_c
- covost2
widget:
- example_title: Common Voice sample 1
src: https://huggingface.co/facebook/xm_transformer_600m-en_es-multi_domain/resolve/main/common_voice_en_18295850.mp3
---
# xm_transformer_600m-en_tr-multi_domain
[W2V2-Transformer](https://aclanthology.org/2021.acl-long.68/) speech-to-text translation model from fairseq S2T ([paper](https://arxiv.org/abs/2010.05171)/[code](https://github.com/pytorch/fairseq/tree/main/examples/speech_to_text)):
- English-Turkish
- Trained on MuST-C, CoVoST 2, Multilingual LibriSpeech, Common Voice v7 and CCMatrix
- Speech synthesis with [facebook/tts_transformer-tr-cv7](https://huggingface.co/facebook/tts_transformer-tr-cv7)
## Usage
```python
from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub
from fairseq.models.speech_to_text.hub_interface import S2THubInterface
from fairseq.models.text_to_speech.hub_interface import TTSHubInterface
import IPython.display as ipd
import torchaudio
models, cfg, task = load_model_ensemble_and_task_from_hf_hub(
"facebook/xm_transformer_600m-en_tr-multi_domain",
arg_overrides={"config_yaml": "config.yaml"},
)
model = models[0]
generator = task.build_generator(model, cfg)
# requires 16000Hz mono channel audio
audio, _ = torchaudio.load("/path/to/an/audio/file")
sample = S2THubInterface.get_model_input(task, audio)
text = S2THubInterface.get_prediction(task, model, generator, sample)
# speech synthesis
tts_models, tts_cfg, tts_task = load_model_ensemble_and_task_from_hf_hub(
f"facebook/tts_transformer-tr-cv7",
arg_overrides={"vocoder": "griffin_lim", "fp16": False},
)
tts_model = tts_models[0]
TTSHubInterface.update_cfg_with_data_cfg(tts_cfg, tts_task.data_cfg)
tts_generator = tts_task.build_generator([tts_model], tts_cfg)
tts_sample = TTSHubInterface.get_model_input(tts_task, text)
wav, sr = TTSHubInterface.get_prediction(
tts_task, tts_model, tts_generator, tts_sample
)
ipd.Audio(wav, rate=rate)
```
## Citation
```bibtex
@inproceedings{li-etal-2021-multilingual,
title = "Multilingual Speech Translation from Efficient Finetuning of Pretrained Models",
author = "Li, Xian and
Wang, Changhan and
Tang, Yun and
Tran, Chau and
Tang, Yuqing and
Pino, Juan and
Baevski, Alexei and
Conneau, Alexis and
Auli, Michael",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.68",
doi = "10.18653/v1/2021.acl-long.68",
pages = "827--838",
}
@inproceedings{wang-etal-2020-fairseq,
title = "Fairseq {S}2{T}: Fast Speech-to-Text Modeling with Fairseq",
author = "Wang, Changhan and
Tang, Yun and
Ma, Xutai and
Wu, Anne and
Okhonko, Dmytro and
Pino, Juan",
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: System Demonstrations",
month = dec,
year = "2020",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.aacl-demo.6",
pages = "33--39",
}
```
|
facebook/xm_transformer_600m-en_fr-multi_domain
|
facebook
| 2022-01-21T19:01:52Z | 10 | 0 |
fairseq
|
[
"fairseq",
"audio",
"audio-to-audio",
"speech-to-speech-translation",
"dataset:must_c",
"dataset:europarl_st",
"dataset:voxpopuli",
"dataset:libritrans",
"arxiv:2010.05171",
"region:us"
] |
audio-to-audio
| 2022-03-02T23:29:05Z |
---
library_name: fairseq
task: audio-to-audio
tags:
- fairseq
- audio
- audio-to-audio
- speech-to-speech-translation
language: en-fr
datasets:
- must_c
- europarl_st
- voxpopuli
- libritrans
widget:
- example_title: Common Voice sample 1
src: https://huggingface.co/facebook/xm_transformer_600m-en_es-multi_domain/resolve/main/common_voice_en_18295850.mp3
---
# xm_transformer_600m-en_fr-multi_domain
[W2V2-Transformer](https://aclanthology.org/2021.acl-long.68/) speech-to-text translation model from fairseq S2T ([paper](https://arxiv.org/abs/2010.05171)/[code](https://github.com/pytorch/fairseq/tree/main/examples/speech_to_text)):
- English-French
- Trained on MuST-C, EuroParl-ST, VoxPopuli, LibriTrans, Multilingual LibriSpeech, Common Voice v7 and CCMatrix
- Speech synthesis with [facebook/tts_transformer-fr-cv7_css10](https://huggingface.co/facebook/tts_transformer-fr-cv7_css10)
## Usage
```python
from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub
from fairseq.models.speech_to_text.hub_interface import S2THubInterface
from fairseq.models.text_to_speech.hub_interface import TTSHubInterface
import IPython.display as ipd
import torchaudio
models, cfg, task = load_model_ensemble_and_task_from_hf_hub(
"facebook/xm_transformer_600m-en_fr-multi_domain",
arg_overrides={"config_yaml": "config.yaml"},
)
model = models[0]
generator = task.build_generator(model, cfg)
# requires 16000Hz mono channel audio
audio, _ = torchaudio.load("/path/to/an/audio/file")
sample = S2THubInterface.get_model_input(task, audio)
text = S2THubInterface.get_prediction(task, model, generator, sample)
# speech synthesis
tts_models, tts_cfg, tts_task = load_model_ensemble_and_task_from_hf_hub(
f"facebook/tts_transformer-fr-cv7_css10",
arg_overrides={"vocoder": "griffin_lim", "fp16": False},
)
tts_model = tts_models[0]
TTSHubInterface.update_cfg_with_data_cfg(tts_cfg, tts_task.data_cfg)
tts_generator = tts_task.build_generator([tts_model], tts_cfg)
tts_sample = TTSHubInterface.get_model_input(tts_task, text)
wav, sr = TTSHubInterface.get_prediction(
tts_task, tts_model, tts_generator, tts_sample
)
ipd.Audio(wav, rate=rate)
```
## Citation
```bibtex
@inproceedings{li-etal-2021-multilingual,
title = "Multilingual Speech Translation from Efficient Finetuning of Pretrained Models",
author = "Li, Xian and
Wang, Changhan and
Tang, Yun and
Tran, Chau and
Tang, Yuqing and
Pino, Juan and
Baevski, Alexei and
Conneau, Alexis and
Auli, Michael",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.68",
doi = "10.18653/v1/2021.acl-long.68",
pages = "827--838",
}
@inproceedings{wang-etal-2020-fairseq,
title = "Fairseq {S}2{T}: Fast Speech-to-Text Modeling with Fairseq",
author = "Wang, Changhan and
Tang, Yun and
Ma, Xutai and
Wu, Anne and
Okhonko, Dmytro and
Pino, Juan",
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: System Demonstrations",
month = dec,
year = "2020",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.aacl-demo.6",
pages = "33--39",
}
```
|
facebook/xm_transformer_600m-en_ru-multi_domain
|
facebook
| 2022-01-21T19:01:38Z | 8 | 1 |
fairseq
|
[
"fairseq",
"audio",
"audio-to-audio",
"speech-to-speech-translation",
"dataset:must_c",
"arxiv:2010.05171",
"region:us"
] |
audio-to-audio
| 2022-03-02T23:29:05Z |
---
library_name: fairseq
task: audio-to-audio
tags:
- fairseq
- audio
- audio-to-audio
- speech-to-speech-translation
language: en-ru
datasets:
- must_c
widget:
- example_title: Common Voice sample 1
src: https://huggingface.co/facebook/xm_transformer_600m-en_es-multi_domain/resolve/main/common_voice_en_18295850.mp3
---
# xm_transformer_600m-en_ru-multi_domain
[W2V2-Transformer](https://aclanthology.org/2021.acl-long.68/) speech-to-text translation model from fairseq S2T ([paper](https://arxiv.org/abs/2010.05171)/[code](https://github.com/pytorch/fairseq/tree/main/examples/speech_to_text)):
- English-Russian
- Trained on MuST-C, Multilingual LibriSpeech, Common Voice v7 and CCMatrix
- Speech synthesis with [facebook/tts_transformer-ru-cv7_css10](https://huggingface.co/facebook/tts_transformer-ru-cv7_css10)
## Usage
```python
from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub
from fairseq.models.speech_to_text.hub_interface import S2THubInterface
from fairseq.models.text_to_speech.hub_interface import TTSHubInterface
import IPython.display as ipd
import torchaudio
models, cfg, task = load_model_ensemble_and_task_from_hf_hub(
"facebook/xm_transformer_600m-en_ru-multi_domain",
arg_overrides={"config_yaml": "config.yaml"},
)
model = models[0]
generator = task.build_generator(model, cfg)
# requires 16000Hz mono channel audio
audio, _ = torchaudio.load("/path/to/an/audio/file")
sample = S2THubInterface.get_model_input(task, audio)
text = S2THubInterface.get_prediction(task, model, generator, sample)
# speech synthesis
tts_models, tts_cfg, tts_task = load_model_ensemble_and_task_from_hf_hub(
f"facebook/tts_transformer-ru-cv7_css10",
arg_overrides={"vocoder": "griffin_lim", "fp16": False},
)
tts_model = tts_models[0]
TTSHubInterface.update_cfg_with_data_cfg(tts_cfg, tts_task.data_cfg)
tts_generator = tts_task.build_generator([tts_model], tts_cfg)
tts_sample = TTSHubInterface.get_model_input(tts_task, text)
wav, sr = TTSHubInterface.get_prediction(
tts_task, tts_model, tts_generator, tts_sample
)
ipd.Audio(wav, rate=rate)
```
## Citation
```bibtex
@inproceedings{li-etal-2021-multilingual,
title = "Multilingual Speech Translation from Efficient Finetuning of Pretrained Models",
author = "Li, Xian and
Wang, Changhan and
Tang, Yun and
Tran, Chau and
Tang, Yuqing and
Pino, Juan and
Baevski, Alexei and
Conneau, Alexis and
Auli, Michael",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.68",
doi = "10.18653/v1/2021.acl-long.68",
pages = "827--838",
}
@inproceedings{wang-etal-2020-fairseq,
title = "Fairseq {S}2{T}: Fast Speech-to-Text Modeling with Fairseq",
author = "Wang, Changhan and
Tang, Yun and
Ma, Xutai and
Wu, Anne and
Okhonko, Dmytro and
Pino, Juan",
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: System Demonstrations",
month = dec,
year = "2020",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.aacl-demo.6",
pages = "33--39",
}
```
|
facebook/xm_transformer_600m-en_es-multi_domain
|
facebook
| 2022-01-21T19:01:24Z | 2 | 1 |
fairseq
|
[
"fairseq",
"audio",
"audio-to-audio",
"speech-to-speech-translation",
"dataset:must_c",
"dataset:europarl_st",
"dataset:voxpopuli",
"arxiv:2010.05171",
"region:us"
] |
audio-to-audio
| 2022-03-02T23:29:05Z |
---
library_name: fairseq
task: audio-to-audio
tags:
- fairseq
- audio
- audio-to-audio
- speech-to-speech-translation
language: en-es
datasets:
- must_c
- europarl_st
- voxpopuli
widget:
- example_title: Common Voice sample 1
src: https://huggingface.co/facebook/xm_transformer_600m-en_es-multi_domain/resolve/main/common_voice_en_18295850.mp3
---
# xm_transformer_600m-en_es-multi_domain
[W2V2-Transformer](https://aclanthology.org/2021.acl-long.68/) speech-to-text translation model from fairseq S2T ([paper](https://arxiv.org/abs/2010.05171)/[code](https://github.com/pytorch/fairseq/tree/main/examples/speech_to_text)):
- English-Spanish
- Trained on MuST-C, EuroParl-ST, VoxPopuli, Multilingual LibriSpeech, Common Voice v7 and CCMatrix
- Speech synthesis with [facebook/tts_transformer-es-css10](https://huggingface.co/facebook/tts_transformer-es-css10)
## Usage
```python
from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub
from fairseq.models.text_to_speech.hub_interface import S2THubInterface
from fairseq.models.text_to_speech.hub_interface import TTSHubInterface
import IPython.display as ipd
import torchaudio
models, cfg, task = load_model_ensemble_and_task_from_hf_hub(
"facebook/xm_transformer_600m-en_es-multi_domain",
arg_overrides={"config_yaml": "config.yaml"},
)
model = models[0]
generator = task.build_generator(model, cfg)
# requires 16000Hz mono channel audio
audio, _ = torchaudio.load("/path/to/an/audio/file")
sample = S2THubInterface.get_model_input(task, audio)
text = S2THubInterface.get_prediction(task, model, generator, sample)
# speech synthesis
tts_models, tts_cfg, tts_task = load_model_ensemble_and_task_from_hf_hub(
f"facebook/tts_transformer-es-css10",
arg_overrides={"vocoder": "griffin_lim", "fp16": False},
)
tts_model = tts_models[0]
TTSHubInterface.update_cfg_with_data_cfg(tts_cfg, tts_task.data_cfg)
tts_generator = tts_task.build_generator([tts_model], tts_cfg)
tts_sample = TTSHubInterface.get_model_input(tts_task, text)
wav, sr = TTSHubInterface.get_prediction(
tts_task, tts_model, tts_generator, tts_sample
)
ipd.Audio(wav, rate=rate)
```
## Citation
```bibtex
@inproceedings{li-etal-2021-multilingual,
title = "Multilingual Speech Translation from Efficient Finetuning of Pretrained Models",
author = "Li, Xian and
Wang, Changhan and
Tang, Yun and
Tran, Chau and
Tang, Yuqing and
Pino, Juan and
Baevski, Alexei and
Conneau, Alexis and
Auli, Michael",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.68",
doi = "10.18653/v1/2021.acl-long.68",
pages = "827--838",
}
@inproceedings{wang-etal-2020-fairseq,
title = "Fairseq {S}2{T}: Fast Speech-to-Text Modeling with Fairseq",
author = "Wang, Changhan and
Tang, Yun and
Ma, Xutai and
Wu, Anne and
Okhonko, Dmytro and
Pino, Juan",
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: System Demonstrations",
month = dec,
year = "2020",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.aacl-demo.6",
pages = "33--39",
}
```
|
facebook/xm_transformer_600m-fr_en-multi_domain
|
facebook
| 2022-01-21T18:59:43Z | 10 | 0 |
fairseq
|
[
"fairseq",
"audio",
"audio-to-audio",
"speech-to-speech-translation",
"dataset:mtedx",
"dataset:covost2",
"dataset:europarl_st",
"dataset:voxpopuli",
"arxiv:2010.05171",
"region:us"
] |
audio-to-audio
| 2022-03-02T23:29:05Z |
---
library_name: fairseq
task: audio-to-audio
tags:
- fairseq
- audio
- audio-to-audio
- speech-to-speech-translation
language: fr-en
datasets:
- mtedx
- covost2
- europarl_st
- voxpopuli
widget:
- example_title: Common Voice sample 1
src: https://huggingface.co/facebook/xm_transformer_600m-fr_en-multi_domain/resolve/main/common_voice_fr_19731305.mp3
---
# xm_transformer_600m-fr_en-multi_domain
[W2V2-Transformer](https://aclanthology.org/2021.acl-long.68/) speech-to-text translation model from fairseq S2T ([paper](https://arxiv.org/abs/2010.05171)/[code](https://github.com/pytorch/fairseq/tree/main/examples/speech_to_text)):
- French-English
- Trained on mTEDx, CoVoST 2, EuroParl-ST, VoxPopuli, Multilingual LibriSpeech, Common Voice v7 and CCMatrix
- Speech synthesis with [facebook/fastspeech2-en-ljspeech](https://huggingface.co/facebook/fastspeech2-en-ljspeech)
## Usage
```python
from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub
from fairseq.models.text_to_speech.hub_interface import S2THubInterface
from fairseq.models.text_to_speech.hub_interface import TTSHubInterface
import IPython.display as ipd
import torchaudio
models, cfg, task = load_model_ensemble_and_task_from_hf_hub(
"facebook/xm_transformer_600m-fr_en-multi_domain",
arg_overrides={"config_yaml": "config.yaml"},
)
model = models[0]
generator = task.build_generator(model, cfg)
# requires 16000Hz mono channel audio
audio, _ = torchaudio.load("/path/to/an/audio/file")
sample = S2THubInterface.get_model_input(task, audio)
text = S2THubInterface.get_prediction(task, model, generator, sample)
# speech synthesis
tts_models, tts_cfg, tts_task = load_model_ensemble_and_task_from_hf_hub(
f"facebook/fastspeech2-en-ljspeech",
arg_overrides={"vocoder": "griffin_lim", "fp16": False},
)
tts_model = tts_models[0]
TTSHubInterface.update_cfg_with_data_cfg(tts_cfg, tts_task.data_cfg)
tts_generator = tts_task.build_generator([tts_model], tts_cfg)
tts_sample = TTSHubInterface.get_model_input(tts_task, text)
wav, sr = TTSHubInterface.get_prediction(
tts_task, tts_model, tts_generator, tts_sample
)
ipd.Audio(wav, rate=rate)
```
## Citation
```bibtex
@inproceedings{li-etal-2021-multilingual,
title = "Multilingual Speech Translation from Efficient Finetuning of Pretrained Models",
author = "Li, Xian and
Wang, Changhan and
Tang, Yun and
Tran, Chau and
Tang, Yuqing and
Pino, Juan and
Baevski, Alexei and
Conneau, Alexis and
Auli, Michael",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.68",
doi = "10.18653/v1/2021.acl-long.68",
pages = "827--838",
}
@inproceedings{wang-etal-2020-fairseq,
title = "Fairseq {S}2{T}: Fast Speech-to-Text Modeling with Fairseq",
author = "Wang, Changhan and
Tang, Yun and
Ma, Xutai and
Wu, Anne and
Okhonko, Dmytro and
Pino, Juan",
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: System Demonstrations",
month = dec,
year = "2020",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.aacl-demo.6",
pages = "33--39",
}
@inproceedings{wang-etal-2021-fairseq,
title = "fairseq S{\^{}}2: A Scalable and Integrable Speech Synthesis Toolkit",
author = "Wang, Changhan and
Hsu, Wei-Ning and
Adi, Yossi and
Polyak, Adam and
Lee, Ann and
Chen, Peng-Jen and
Gu, Jiatao and
Pino, Juan",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-demo.17",
doi = "10.18653/v1/2021.emnlp-demo.17",
pages = "143--152",
}
```
|
facebook/xm_transformer_600m-es_en-multi_domain
|
facebook
| 2022-01-21T18:19:44Z | 14 | 1 |
fairseq
|
[
"fairseq",
"audio",
"audio-to-audio",
"speech-to-speech-translation",
"dataset:mtedx",
"dataset:covost2",
"dataset:europarl_st",
"dataset:voxpopuli",
"arxiv:2010.05171",
"region:us"
] |
audio-to-audio
| 2022-03-02T23:29:05Z |
---
library_name: fairseq
task: audio-to-audio
tags:
- fairseq
- audio
- audio-to-audio
- speech-to-speech-translation
language: es-en
datasets:
- mtedx
- covost2
- europarl_st
- voxpopuli
widget:
- example_title: Common Voice sample 1
src: https://huggingface.co/facebook/xm_transformer_600m-es_en-multi_domain/resolve/main/common_voice_es_19966634.flac
---
# xm_transformer_600m-es_en-multi_domain
[W2V2-Transformer](https://aclanthology.org/2021.acl-long.68/) speech-to-text translation model from fairseq S2T ([paper](https://arxiv.org/abs/2010.05171)/[code](https://github.com/pytorch/fairseq/tree/main/examples/speech_to_text)):
- Spanish-English
- Trained on mTEDx, CoVoST 2, EuroParl-ST, VoxPopuli, Multilingual LibriSpeech, Common Voice v7 and CCMatrix
- Speech synthesis with [facebook/fastspeech2-en-ljspeech](https://huggingface.co/facebook/fastspeech2-en-ljspeech)
## Usage
```python
from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub
from fairseq.models.text_to_speech.hub_interface import S2THubInterface
from fairseq.models.text_to_speech.hub_interface import TTSHubInterface
import IPython.display as ipd
import torchaudio
models, cfg, task = load_model_ensemble_and_task_from_hf_hub(
"facebook/xm_transformer_600m-es_en-multi_domain",
arg_overrides={"config_yaml": "config.yaml"},
)
model = models[0]
generator = task.build_generator(model, cfg)
# requires 16000Hz mono channel audio
audio, _ = torchaudio.load("/path/to/an/audio/file")
sample = S2THubInterface.get_model_input(task, audio)
text = S2THubInterface.get_prediction(task, model, generator, sample)
# speech synthesis
tts_models, tts_cfg, tts_task = load_model_ensemble_and_task_from_hf_hub(
f"facebook/fastspeech2-en-ljspeech",
arg_overrides={"vocoder": "griffin_lim", "fp16": False},
)
tts_model = tts_models[0]
TTSHubInterface.update_cfg_with_data_cfg(tts_cfg, tts_task.data_cfg)
tts_generator = tts_task.build_generator([tts_model], tts_cfg)
tts_sample = TTSHubInterface.get_model_input(tts_task, text)
wav, sr = TTSHubInterface.get_prediction(
tts_task, tts_model, tts_generator, tts_sample
)
ipd.Audio(wav, rate=rate)
```
## Citation
```bibtex
@inproceedings{li-etal-2021-multilingual,
title = "Multilingual Speech Translation from Efficient Finetuning of Pretrained Models",
author = "Li, Xian and
Wang, Changhan and
Tang, Yun and
Tran, Chau and
Tang, Yuqing and
Pino, Juan and
Baevski, Alexei and
Conneau, Alexis and
Auli, Michael",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.68",
doi = "10.18653/v1/2021.acl-long.68",
pages = "827--838",
}
@inproceedings{wang-etal-2020-fairseq,
title = "Fairseq {S}2{T}: Fast Speech-to-Text Modeling with Fairseq",
author = "Wang, Changhan and
Tang, Yun and
Ma, Xutai and
Wu, Anne and
Okhonko, Dmytro and
Pino, Juan",
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: System Demonstrations",
month = dec,
year = "2020",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.aacl-demo.6",
pages = "33--39",
}
@inproceedings{wang-etal-2021-fairseq,
title = "fairseq S{\^{}}2: A Scalable and Integrable Speech Synthesis Toolkit",
author = "Wang, Changhan and
Hsu, Wei-Ning and
Adi, Yossi and
Polyak, Adam and
Lee, Ann and
Chen, Peng-Jen and
Gu, Jiatao and
Pino, Juan",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-demo.17",
doi = "10.18653/v1/2021.emnlp-demo.17",
pages = "143--152",
}
```
|
joheras/xls-r-ab-spanish
|
joheras
| 2022-01-21T15:42:21Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"generated_from_trainer",
"ab",
"dataset:common_voice",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- ab
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_7_0
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: ''
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [hf-test/xls-r-dummy](https://huggingface.co/hf-test/xls-r-dummy) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset.
It achieves the following results on the evaluation set:
- Loss: 156.8790
- Wer: 1.3448
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 2
- 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
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu113
- Datasets 1.17.0
- Tokenizers 0.10.3
|
deepparag/DumBot
|
deepparag
| 2022-01-21T15:40:27Z | 148 | 2 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
thumbnail: https://cdn.discordapp.com/app-icons/870239976690970625/c02cae78ae105f07969cfd8f8ea3d0a0.png
tags:
- conversational
license: mit
---
# THIS AI IS OUTDATED. See [Aeona](https://huggingface.co/deepparag/Aeona)
An generative AI made using [microsoft/DialoGPT-small](https://huggingface.co/microsoft/DialoGPT-small).
Trained on:
https://www.kaggle.com/Cornell-University/movie-dialog-corpus
https://www.kaggle.com/jef1056/discord-data
[Live Demo](https://dumbot-331213.uc.r.appspot.com/)
Example:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("deepparag/DumBot")
model = AutoModelWithLMHead.from_pretrained("deepparag/DumBot")
# Let's chat for 4 lines
for step in range(4):
# encode the new user input, add the eos_token and return a tensor in Pytorch
new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
# print(new_user_input_ids)
# append the new user input tokens to the chat history
bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
# generated a response while limiting the total chat history to 1000 tokens,
chat_history_ids = model.generate(
bot_input_ids, max_length=200,
pad_token_id=tokenizer.eos_token_id,
no_repeat_ngram_size=4,
do_sample=True,
top_k=100,
top_p=0.7,
temperature=0.8
)
# pretty print last ouput tokens from bot
print("DumBot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
```
|
Gianpe/en_textcat_emotion_xlm
|
Gianpe
| 2022-01-21T15:09:03Z | 3 | 0 |
spacy
|
[
"spacy",
"text-classification",
"en",
"region:us"
] |
text-classification
| 2022-03-02T23:29:04Z |
---
tags:
- spacy
- text-classification
language:
- en
model-index:
- name: en_textcat_emotion_xlm
results: []
---
|
infinitejoy/Wav2Vec2-Large-XLSR-53-Odia
|
infinitejoy
| 2022-01-21T13:19:09Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"or",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: or
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: Joydeep Bhattacharjee XLSR Wav2Vec2 Large 53 Odia
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice as
type: common_voice
args: or
metrics:
- name: Test WER
type: wer
value: 55.07
---
# Wav2Vec2-Large-XLSR-53-Odia
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Odia using the [Common Voice](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "or", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("infinitejoy/Wav2Vec2-Large-XLSR-53-Odia")
model = Wav2Vec2ForCTC.from_pretrained("infinitejoy/Wav2Vec2-Large-XLSR-53-Odia")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
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 Odia 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", "or", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("infinitejoy/Wav2Vec2-Large-XLSR-53-Odia")
model = Wav2Vec2ForCTC.from_pretrained("infinitejoy/Wav2Vec2-Large-XLSR-53-Odia")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\।\–]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub('’ ',' ',batch["sentence"])
batch["sentence"] = re.sub(' ‘',' ',batch["sentence"])
batch["sentence"] = re.sub('’|‘','\'',batch["sentence"])
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 55.07 %
## Training
The Common Voice `train` and `validation` datasets were used for training.
|
alistvt/bert-base-uncased-pretrained-clm-coqa-stories
|
alistvt
| 2022-01-21T12:36:10Z | 20 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-pretrained-clm-coqa-stories
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-pretrained-clm-coqa-stories
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0002
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0201 | 1.0 | 2479 | 0.0018 |
| 0.0033 | 2.0 | 4958 | 0.0003 |
| 0.0014 | 3.0 | 7437 | 0.0002 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
MadhurJindalWorkMail/autonlp-Gibb-Detect-515314387
|
MadhurJindalWorkMail
| 2022-01-21T07:05:45Z | 3 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"en",
"dataset:MadhurJindalWorkMail/autonlp-data-Gibb-Detect",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:04Z |
---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- MadhurJindalWorkMail/autonlp-data-Gibb-Detect
co2_eq_emissions: 70.95647633212745
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 515314387
- CO2 Emissions (in grams): 70.95647633212745
## Validation Metrics
- Loss: 0.08077705651521683
- Accuracy: 0.9760103738923709
- Macro F1: 0.9728412857204902
- Micro F1: 0.9760103738923709
- Weighted F1: 0.9759907151741426
- Macro Precision: 0.9736622407675567
- Micro Precision: 0.9760103738923709
- Weighted Precision: 0.97673611876005
- Macro Recall: 0.9728978421381711
- Micro Recall: 0.9760103738923709
- Weighted Recall: 0.9760103738923709
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/MadhurJindalWorkMail/autonlp-Gibb-Detect-515314387
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("MadhurJindalWorkMail/autonlp-Gibb-Detect-515314387", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("MadhurJindalWorkMail/autonlp-Gibb-Detect-515314387", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
```
|
anuragshas/wav2vec2-large-xls-r-300m-ur
|
anuragshas
| 2022-01-21T04:32:18Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-ur
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-ur
This model is a fine-tuned version of [anuragshas/wav2vec2-large-xls-r-300m-ur](https://huggingface.co/anuragshas/wav2vec2-large-xls-r-300m-ur) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0508
- Wer: 0.7328
## 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: 7.5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.12
- num_epochs: 240
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.0719 | 66.67 | 400 | 1.8510 | 0.7432 |
| 0.0284 | 133.33 | 800 | 2.0088 | 0.7415 |
| 0.014 | 200.0 | 1200 | 2.0508 | 0.7328 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
espnet/simpleoier_librispeech_asr_train_asr_conformer7_hubert_ll60k_large_raw_en_bpe5000_sp
|
espnet
| 2022-01-21T04:15:13Z | 8 | 2 |
espnet
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:librispeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
tags:
- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- librispeech
license: cc-by-4.0
---
## ESPnet2 ASR model
### `espnet/simpleoier_librispeech_asr_train_asr_conformer7_hubert_ll60k_large_raw_en_bpe5000_sp`
This model was trained by simpleoier using librispeech recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout b0ff60946ada6753af79423a2e6063984bec2926
pip install -e .
cd egs2/librispeech/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/simpleoier_librispeech_asr_train_asr_conformer7_hubert_ll60k_large_raw_en_bpe5000_sp
```
## ASR config
<details><summary>expand</summary>
```
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
espnet/simpleoier_librispeech_asr_train_asr_conformer7_wav2vec2_960hr_large_raw_en_bpe5000_sp
|
espnet
| 2022-01-21T04:09:13Z | 4 | 0 |
espnet
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:librispeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
tags:
- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- librispeech
license: cc-by-4.0
---
## ESPnet2 ASR model
### `espnet/simpleoier_librispeech_asr_train_asr_conformer7_wav2vec2_960hr_large_raw_en_bpe5000_sp`
This model was trained by simpleoier using librispeech recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout b0ff60946ada6753af79423a2e6063984bec2926
pip install -e .
cd egs2/librispeech/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/simpleoier_librispeech_asr_train_asr_conformer7_wav2vec2_960hr_large_raw_en_bpe5000_sp
```
## ASR config
<details><summary>expand</summary>
```
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
Gigworks/ASR_zh_espnet2
|
Gigworks
| 2022-01-21T02:58:59Z | 0 | 1 | null |
[
"region:us"
] | null | 2022-03-02T23:29:04Z |
<b>Speech-To-Text Chinese Model</b>
<br/><br/>
Reference: <br/>
Model - https://huggingface.co/espnet/pengcheng_guo_wenetspeech_asr_train_asr_raw_zh_char <br/>
Code - https://huggingface.co/spaces/akhaliq/espnet2_asr/blob/main/app.py
|
guoqiang/glm
|
guoqiang
| 2022-01-21T01:21:46Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-03-02T23:29:05Z |
# WudaoSailing
WudaoSailing is a package for pretraining chinese Language Model and finetune tasks. Now it supports GLM, Bert, T5, Cogview and Roberta models.
## Get Started
### Docker Image
We prepare two docker images based on CUDA 10.2 and CUDA 11.2. You can build images from the docker file [docs/docker/cuda102.dockerfile](docs/docker/cuda102.dcokerfile) or pull the pre-built images from Docker Hub and run with docker v19.03+
```shell
nvidia-docker run -id --hostname=V100 --network=host\
--ipc=host --shm-size=16gb --name=deepspeed-cuda \
-e NVIDIA_VISIBLE_DEVICES=0,1,2,3 \
-v /DATA/disk1/docker/containers/:/data deepspeed/cuda102:lastest
```
or replace `cuda102` with `cuda112`.
```shell
docker build -f cuda102.dockerfile -t deepspeed/cuda102 .
```
### Clone this repo
```shell
git clone https://github.com/wangguojim/WudaoSailing.git
cd WudaoSailing
pip install -r requirements.txt
```
## GLM
We show some examples based on GLM model.
### finetuene
We provide scripts for finetuning GLM on some downstream tasks.
#### SuperGLUE
- Download the [SuperGlue](https://super.gluebenchmark.com/tasks) data and check the experiment setup in
[examples/glm/scripts/ds_finetune_superglue.sh](xamples/glm/scripts/ds_finetune_superglue.sh). Note that `DATA_ROOT, CHECKPOINT_PATH, SAVE_PATH`
need to be changed to your local path. You may also change the `batch-size` and `nproc_per_node` according to your
available hardware.
- Run the following script for text similarity finetune task (use the afqmc dataset as an example)
```
cd examples/glm/
bash scripts/ds_finetune_superglue.sh\
config/model_blocklm_large_chinese.sh\
config_tasks/task_afqmc.sh
```
- Run the following script for text classification finetune task (use the thunews and thunews dataset as an example)
```
cd examples/glm/
bash scripts/ds_finetune_superglue.sh\
config/model_blocklm_large_chinese.sh\
config_tasks/task_tnews.sh
```
- Run the following script for causal inference finetune task (use the COPA dataset as an example)
```
cd examples/glm/
bash scripts/ds_finetune_superglue.sh\
config/model_blocklm_large_chinese.sh\
config_tasks/task_copa.sh
```
- To apply GLM to a new NLU dataset with cloze-filling finetuning, implement a `DataProcessor` in
[examples/glm/tasks/superglue/dataset.py](examples/glm/tasks/superglue/dataset.py) for data loading and add a `PVP` in
[examples/glm/tasks/superglue/pvp.py](examples/glm/tasks/superglue/pvp.py) for the cloze question. More details can be found
[here](examples/glm/tasks/superglue/README.md).
#### Blank Filling (Interactive)
* Change `CHECKPOINT_PATH` to your local path. Run the following script
```
bash config/generate_block.sh\
config/model_blocklm_large_chinese.sh
```
##### Example1 (Entity Prediction):
Context: 凯旋门位于意大利米兰市古城堡旁。1807年为纪念[MASK]而建，门高25米，顶上矗立两武士青铜古兵车铸像。
GLM:拿破仑军队攻克米兰城
##### Example2 (Sentence Prediction)
Context: 工业互联网（Industrial Internet）是新一代信息通信技术与工业经济深度融合的新型基础设施、应用模式和工业生态，通过对人、机、物、系统等的全面连接，构建起覆盖全产业链、全价值链的全新制造和服务体系，为工业乃至产业数字化、网络化、智能化发展提供了实现途径，是第四次工业革命的重要基石。[sMASK]它以网络为基础、平台为中枢、数据为要素、安全为保障，既是工业数字化、网络化、智能化转型的基础设施，也是互联网、大数据、人工智能与实体经济深度融合的应用模式，同时也是一种新业态、新产业，将重塑企业形态、供应链和产业链。当前，工业互联网融合应用向国民经济重点行业广泛拓展，形成平台化设计、智能化制造、网络化协同、个性化定制、服务化延伸、数字化管理六大新模式，赋能、赋智、赋值作用不断显现，有力的促进了实体经济提质、增效、降本、绿色、安全发展。
GLM: 工业互联网是制造业技术、管理、模式的重大变革,是推动互联网、大数据、人工智能和实体经济深度融合的重要载体,是建设制造强国和网络强国的重要基础。
##### Example3 (Long Text Generation)
Context: 问题:高斯所在的国家有什么汽车品牌?答案:[gMASK]
GLM:答案:[gMASK]<|startofpiece|>德国奔驰、德国大众、别克、沃尔沃、斯柯达、本田、雪铁龙.
### Ptuning
Run the following script to integrate p-tuning with GLM:
```shell
cd algutils/ptuning/
bash finetune_zy.sh
```
### Pretrain
Run the following script to pre-train the GLM-Large model
```shell
cd examples/glm/
bash scripts/ds_pretrain_nvidia.sh config/ds_block_large.sh
```
The script [examples/glm/config/ds_pretrain_nvidia.sh](examples/glm/config/ds_pretrain_nvidia.sh) launches the training program with DeepSpeed. You should change `NUM_WORKERS` and `NUM_GPUS_PER_WORKER` to the number of workers and the number of gpus per worker. Also change `HOST_FILE_PATH` to the path to an OpenMPI-style hostfile. More details about DeepSpeed launcher can be found [here](https://www.deepspeed.ai/getting-started/#resource-configuration-multi-node).
The file [examples/glm/config/ds_block_large.sh](examples/glm/config/ds_block_large.sh) defines the hyperparameters for pretraining. Most of the arguments are fairly self-explanatory. Specifically, `--train-data` can be multiple keywords defined in `NAMED_CORPORA` in [data_utils/corpora.py](data_utils/corpora.py). The hyperparameters of the optimizer are defined in the corresponding json file under `config`. The semantics of the json file can be found [here](https://www.deepspeed.ai/docs/config-json).
## Bert
We show some examples based on GLM model.
### Pretrain
Run the following script to pre-train the Bert model
```shell
cd examples/bert/
python quick_start.py
```
## CogView
### Pretrain
Run the following script to pre-train the cogview model
```shell
cd examples/cogview/
bash config/pretrain_multiple_nodes.sh
```
### inference
Run the following script to test the ability of text2image
```shell
cd examples/cogview/
bash config/text2image_cogview.sh
```
|
huggingtweets/anticarbons
|
huggingtweets
| 2022-01-20T22:52:20Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: http://www.huggingtweets.com/anticarbons/1642719091326/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/1477498953524518912/yvJkd9VL_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">ANTICARBON</div>
<div style="text-align: center; font-size: 14px;">@anticarbons</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 ANTICARBON.
| Data | ANTICARBON |
| --- | --- |
| Tweets downloaded | 2518 |
| Retweets | 427 |
| Short tweets | 352 |
| Tweets kept | 1739 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/s9q99sc5/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 @anticarbons's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1k8boybi) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1k8boybi/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/anticarbons')
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)
|
Gianpe/en_textcat_emotion_umberto
|
Gianpe
| 2022-01-20T21:45:19Z | 1 | 0 |
spacy
|
[
"spacy",
"text-classification",
"en",
"region:us"
] |
text-classification
| 2022-03-02T23:29:04Z |
---
tags:
- spacy
- text-classification
language:
- en
model-index:
- name: en_textcat_emotion_umberto
results: []
---
|
milyiyo/selectra-small-finetuned-amazon-review
|
milyiyo
| 2022-01-20T21:11:57Z | 16 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"electra",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: selectra-small-finetuned-amazon-review
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_reviews_multi
type: amazon_reviews_multi
args: es
metrics:
- name: Accuracy
type: accuracy
value: 0.737
- name: F1
type: f1
value: 0.7437773019932409
- name: Precision
type: precision
value: 0.7524857881639091
- name: Recall
type: recall
value: 0.737
---
<!-- 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. -->
# selectra-small-finetuned-amazon-review
This model is a fine-tuned version of [Recognai/selectra_small](https://huggingface.co/Recognai/selectra_small) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6279
- Accuracy: 0.737
- F1: 0.7438
- Precision: 0.7525
- Recall: 0.737
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log | 0.5 | 500 | 0.7041 | 0.7178 | 0.6724 | 0.6715 | 0.7178 |
| 0.7908 | 1.0 | 1000 | 0.6365 | 0.7356 | 0.7272 | 0.7211 | 0.7356 |
| 0.7908 | 1.5 | 1500 | 0.6204 | 0.7376 | 0.7380 | 0.7387 | 0.7376 |
| 0.6358 | 2.0 | 2000 | 0.6162 | 0.7386 | 0.7377 | 0.7380 | 0.7386 |
| 0.6358 | 2.5 | 2500 | 0.6228 | 0.7274 | 0.7390 | 0.7576 | 0.7274 |
| 0.5827 | 3.0 | 3000 | 0.6188 | 0.7378 | 0.7400 | 0.7425 | 0.7378 |
| 0.5827 | 3.5 | 3500 | 0.6246 | 0.7374 | 0.7416 | 0.7467 | 0.7374 |
| 0.5427 | 4.0 | 4000 | 0.6266 | 0.7446 | 0.7452 | 0.7465 | 0.7446 |
| 0.5427 | 4.5 | 4500 | 0.6331 | 0.7392 | 0.7421 | 0.7456 | 0.7392 |
| 0.5184 | 5.0 | 5000 | 0.6279 | 0.737 | 0.7438 | 0.7525 | 0.737 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
oandreae/financial_sentiment_model
|
oandreae
| 2022-01-20T20:00:01Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"perceiver",
"text-classification",
"generated_from_trainer",
"dataset:financial_phrasebank",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- financial_phrasebank
metrics:
- recall
- accuracy
- precision
model-index:
- name: financial_sentiment_model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: financial_phrasebank
type: financial_phrasebank
args: sentences_50agree
metrics:
- name: Recall
type: recall
value: 0.8839956357328868
- name: Accuracy
type: accuracy
value: 0.8804123711340206
- name: Precision
type: precision
value: 0.8604175202419276
---
<!-- 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. -->
# financial_sentiment_model
This model is a fine-tuned version of [deepmind/language-perceiver](https://huggingface.co/deepmind/language-perceiver) on the financial_phrasebank dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3467
- Recall: 0.8840
- Accuracy: 0.8804
- Precision: 0.8604
## 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
- distributed_type: tpu
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Recall | Accuracy | Precision |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|:---------:|
| 0.4481 | 1.0 | 273 | 0.4035 | 0.8526 | 0.8433 | 0.7955 |
| 0.4069 | 2.0 | 546 | 0.4478 | 0.8683 | 0.8289 | 0.8123 |
| 0.2225 | 3.0 | 819 | 0.3167 | 0.8747 | 0.8680 | 0.8387 |
| 0.1245 | 4.0 | 1092 | 0.3467 | 0.8840 | 0.8804 | 0.8604 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.9.0+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
muellerzr/fastai-pets-resnet-34
|
muellerzr
| 2022-01-20T19:01:14Z | 0 | 1 | null |
[
"region:us"
] | null | 2022-03-02T23:29:05Z |
# The fastai models - PETS
This model is based on Lesson 1 of [fastai](https://course.fast.ai) and of [Walk with fastai](https://walkwithfastai.com/Pets)
## Dataset Used
This model was created with the [Oxford Pets](https://docs.fast.ai/data.external.html#Image-Classification-datasets) dataset in the fastai framework
## Model Training
The model was trained as a binary classifier, for cats or dogs
## How to use:
First, ensure that `huggingface_hub` is installed:
```bash
pip(3) install huggingface_hub
```
Next, download this model repo:
```python
from huggingface_hub import snapshot_download
snapshot_download(repo_id="muellerzr/fastai-pets-resnet-34")
```
Then install the correct fastai version:
```bash
cd fastai-pets-resnet34
pip(3) install -r requirements.txt
```
**NOTE: This is extremely important, as fastai versions are aggressively pinned based on training environment**
And finally load in the fastai `Learner` and predict
```python
from fastai.learner import load_learner
learn = load_learner('model.pth')
pred = learn.predict('myImage.jpg')
```
Versions of model used were taken with [dependency_checker](https://muellerzr.github.io/dependency_checker)
|
ucberkeley-dlab/hate-measure-roberta-large
|
ucberkeley-dlab
| 2022-01-20T17:57:30Z | 7 | 4 |
tf-keras
|
[
"tf-keras",
"text-classification",
"hate-speech",
"counterspeech",
"irt",
"arxiv:2009.10277",
"en",
"dataset:ucberkeley-dlab/measuring-hate-speech",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language:
- en
tags:
- text-classification
- hate-speech
- counterspeech
- irt
- arxiv:2009.10277
datasets:
- ucberkeley-dlab/measuring-hate-speech
---
# Measuring hate speech: RoBERTa-Large
This model predicts a continuous hate speech score as described in Kennedy et al. (2020).
## Citation
```
@article{kennedy2020constructing,
title={Constructing interval variables via faceted Rasch measurement and multitask deep learning: a hate speech application},
author={Kennedy, Chris J and Bacon, Geoff and Sahn, Alexander and von Vacano, Claudia},
journal={arXiv preprint arXiv:2009.10277},
year={2020}
}
```
## References
Kennedy, C. J., Bacon, G., Sahn, A., & von Vacano, C. (2020). [Constructing interval variables via faceted Rasch measurement and multitask deep learning: a hate speech application](https://arxiv.org/abs/2009.10277). arXiv preprint arXiv:2009.10277.
|
nntadotzip/xlnet-base-cased-IUChatbot-ontologyDts
|
nntadotzip
| 2022-01-20T17:12:19Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlnet",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: xlnet-base-cased-IUChatbot-ontologyDts
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlnet-base-cased-IUChatbot-ontologyDts
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4965
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 318 | 0.5005 |
| 0.8222 | 2.0 | 636 | 0.4488 |
| 0.8222 | 3.0 | 954 | 0.4965 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
radhakri119/wav2vec2-base-timit-demo-colab
|
radhakri119
| 2022-01-20T16:09:09Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4780
- Wer: 0.3403
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.5299 | 4.0 | 500 | 1.5195 | 0.9991 |
| 0.6229 | 8.0 | 1000 | 0.4447 | 0.4282 |
| 0.2136 | 12.0 | 1500 | 0.4154 | 0.3764 |
| 0.1196 | 16.0 | 2000 | 0.4394 | 0.3597 |
| 0.0834 | 20.0 | 2500 | 0.4891 | 0.3619 |
| 0.0591 | 24.0 | 3000 | 0.4535 | 0.3439 |
| 0.0448 | 28.0 | 3500 | 0.4780 | 0.3403 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
ml6team/distilbart-tos-summarizer-tosdr
|
ml6team
| 2022-01-20T15:21:41Z | 22 | 15 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"summarization",
"t&c",
"tos",
"distilbart",
"distilbart-6-6",
"en",
"dataset:tosdr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-03-02T23:29:05Z |
---
language:
- en
tags:
- summarization
- t&c
- tos
- distilbart
- distilbart-6-6
datasets:
- tosdr
metrics:
- rouge1
- rouge2
- rougel
inference:
parameters:
min_length: 5
max_length: 512
do_sample: False
widget:
- text: "In addition, certain portions of the Web Site may be subject to additional terms of use that we make available for your review or otherwise link to that portion of the Web Site to which such additional terms apply. By using such portions, or any part thereof, you agree to be bound by the additional terms of use applicable to such portions. Age Restrictions The Web Site may be accessed and used only by individuals who can form legally binding contracts under applicable laws, who are at least 18 years of age or the age of majority in their state or territory of residence (if higher than 18), and who are not barred from using the Web Site under applicable laws. Our Technology may not be copied, modified, reproduced, republished, posted, transmitted, sold, offered for sale, or redistributed in any way without our prior written permission and the prior written permission of our applicable licensors. Nothing in these Site Terms of Use grants you any right to receive delivery of a copy of Our Technology or to obtain access to Our Technology except as generally and ordinarily permitted through the Web Site according to these Site Terms of Use. Furthermore, nothing in these Site Terms of Use will be deemed to grant you, by implication, estoppel or otherwise, a license to Our Technology. Certain of the names, logos, and other materials displayed via the Web site constitute trademarks, tradenames, service marks or logos (“Marks”) of us or other entities. You are not authorized to use any such Marks. Ownership of all such Marks and the goodwill associated therewith remains with us or those other entities. Any use of third party software provided in connection with the Web Site will be governed by such third parties’ licenses and not by these Site Terms of Use. Information on this Web Site may contain technical inaccuracies or typographical errors. Lenovo provides no assurances that any reported problems may be resolved with the use of any information that Lenovo provides."
---
# T&C Summarization Model
T&C Summarization Model based on [sshleifer/distilbart-cnn-6-6](https://huggingface.co/sshleifer/distilbart-cnn-6-6),
This abstractive summarization model is a part of a bigger end-to-end T&C summarizer pipeline
which is preceded by LSA (Latent Semantic Analysis) extractive summarization. The extractive
summarization shortens the T&C to be further summarized by this model.
## Finetuning Corpus
We collaborated with [TOSDR](https://tosdr.org/) to work with their data, and the model is finetuned accordingly. The article and
summarization text is reduced via extractive summarization before it is finetuned to the model.
## Contact Us
https://ml6.eu/ .
This abstractive model finetuning is the continuation of the Christmas Project 2021 done in ML6: https://bit.ly/XmasProjects .
## Load Finetuned Model
```
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("ml6team/distilbart-tos-summarizer-tosdr")
model = AutoModelForSeq2SeqLM.from_pretrained("ml6team/distilbart-tos-summarizer-tosdr")
```
## Code Sample
This sample requires [sumy](https://pypi.org/project/sumy/), the LSA Extractive Summarization library, as additional package to
run.
```
import re
import nltk
nltk.download('punkt')
from sumy.parsers.plaintext import PlaintextParser
from sumy.nlp.tokenizers import Tokenizer
from sumy.nlp.stemmers import Stemmer
from sumy.summarizers.lsa import LsaSummarizer
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
LANGUAGE = "english"
EXTRACTED_ARTICLE_SENTENCES_LEN = 12
stemmer = Stemmer(LANGUAGE)
lsa_summarizer = LsaSummarizer(stemmer)
tokenizer = AutoTokenizer.from_pretrained("ml6team/distilbart-tos-summarizer-tosdr")
model = AutoModelForSeq2SeqLM.from_pretrained("ml6team/distilbart-tos-summarizer-tosdr")
def get_extractive_summary(text, sentences_count):
parser = PlaintextParser.from_string(text, Tokenizer(LANGUAGE))
summarized_info = lsa_summarizer(parser.document, sentences_count)
summarized_info = [element._text for element in summarized_info]
return ' '.join(summarized_info)
def get_summary(dict_summarizer_model, dict_tokenizer, text_content):
text_content = get_extractive_summary(text_content, EXTRACTED_ARTICLE_SENTENCES_LEN)
tokenizer = dict_tokenizer['tokenizer']
model = dict_summarizer_model['model']
inputs = tokenizer(text_content, max_length=dict_tokenizer['max_length'], truncation=True, return_tensors="pt")
outputs = model.generate(
inputs["input_ids"], max_length=dict_summarizer_model['max_length'], min_length=dict_summarizer_model['min_length'],
)
summarized_text = tokenizer.decode(outputs[0])
match = re.search(r"<s>(.*)</s>", summarized_text)
if match is not None: summarized_text = match.group(1)
return summarized_text.replace('<s>', '').replace('</s>', '')
test_tos = """
In addition, certain portions of the Web Site may be subject to additional terms of use that we make available for your review or otherwise link to that portion of the Web Site to which such additional terms apply. By using such portions, or any part thereof, you agree to be bound by the additional terms of use applicable to such portions.
Age Restrictions The Web Site may be accessed and used only by individuals who can form legally binding contracts under applicable laws, who are at least 18 years of age or the age of majority in their state or territory of residence (if higher than 18), and who are not barred from using the Web Site under applicable laws.
Our Technology may not be copied, modified, reproduced, republished, posted, transmitted, sold, offered for sale, or redistributed in any way without our prior written permission and the prior written permission of our applicable licensors. Nothing in these Site Terms of Use grants you any right to receive delivery of a copy of Our Technology or to obtain access to Our Technology except as generally and ordinarily permitted through the Web Site according to these Site Terms of Use.
Furthermore, nothing in these Site Terms of Use will be deemed to grant you, by implication, estoppel or otherwise, a license to Our Technology. Certain of the names, logos, and other materials displayed via the Web site constitute trademarks, tradenames, service marks or logos (“Marks”) of us or other entities. You are not authorized to use any such Marks. Ownership of all such Marks and the goodwill associated therewith remains with us or those other entities.
Any use of third party software provided in connection with the Web Site will be governed by such third parties’ licenses and not by these Site Terms of Use. Information on this Web Site may contain technical inaccuracies or typographical errors. Lenovo provides no assurances that any reported problems may be resolved with the use of any information that Lenovo provides
"""
model_dict = {
'model': model,
'max_length': 512,
'min_length': 4
}
tokenizer_dict = {
'tokenizer': tokenizer,
'max_length': 1024
}
print(get_summary(model_dict, tokenizer_dict, test_tos))
```
|
huggingtweets/aevaeavaevevave
|
huggingtweets
| 2022-01-20T15:13:33Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: http://www.huggingtweets.com/aevaeavaevevave/1642691608974/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/1471448753353670660/T0h3zXn-_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">aeva</div>
<div style="text-align: center; font-size: 14px;">@aevaeavaevevave</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 aeva.
| Data | aeva |
| --- | --- |
| Tweets downloaded | 3184 |
| Retweets | 985 |
| Short tweets | 659 |
| Tweets kept | 1540 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3g4kejp0/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 @aevaeavaevevave's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3ikuw0pg) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3ikuw0pg/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/aevaeavaevevave')
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)
|
aviator-neural/mbart_jokes
|
aviator-neural
| 2022-01-20T14:31:08Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: mbart_jokes
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mbart_jokes
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0282
## Model description
This model is trained of jokes dataset , where you can ask a question and the model gives funny answer.
## Intended uses & limitations
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.3455 | 1.0 | 1914 | 3.0282 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
aidan-o-brien/recipe-improver
|
aidan-o-brien
| 2022-01-20T14:26:53Z | 5 | 0 |
transformers
|
[
"transformers",
"tf",
"albert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: recipe-improver
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# recipe-improver
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.5570
- Epoch: 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:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 5539, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 2.5570 | 0 |
### Framework versions
- Transformers 4.15.0
- TensorFlow 2.7.0
- Datasets 1.17.0
- Tokenizers 0.10.3
|
Aleksandra/herbert-base-cased-finetuned-squad
|
Aleksandra
| 2022-01-20T13:14:11Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:04Z |
---
license: cc-by-4.0
tags:
- generated_from_trainer
model-index:
- name: herbert-base-cased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# herbert-base-cased-finetuned-squad
This model is a fine-tuned version of [allegro/herbert-base-cased](https://huggingface.co/allegro/herbert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2071
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 233 | 1.2474 |
| No log | 2.0 | 466 | 1.1951 |
| 1.3459 | 3.0 | 699 | 1.2071 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
mptrigo/run1
|
mptrigo
| 2022-01-20T10:37:49Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- bleu
model_index:
- name: run1
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
metric:
name: Bleu
type: bleu
value: 8.4217
---
<!-- 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. -->
# run1
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-es-es](https://huggingface.co/Helsinki-NLP/opus-mt-es-es) on an unkown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1740
- Bleu: 8.4217
- Gen Len: 15.9457
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| No log | 1.0 | 250 | 4.2342 | 0.8889 | 83.4022 |
| 4.6818 | 2.0 | 500 | 3.7009 | 4.1671 | 35.587 |
| 4.6818 | 3.0 | 750 | 3.4737 | 7.6414 | 23.9674 |
| 3.4911 | 4.0 | 1000 | 3.3713 | 7.7512 | 18.6957 |
| 3.4911 | 5.0 | 1250 | 3.2689 | 8.0901 | 19.4674 |
| 3.0164 | 6.0 | 1500 | 3.2194 | 8.5708 | 25.0543 |
| 3.0164 | 7.0 | 1750 | 3.1853 | 9.5275 | 23.9239 |
| 2.6954 | 8.0 | 2000 | 3.1562 | 8.5635 | 18.9674 |
| 2.6954 | 9.0 | 2250 | 3.1564 | 8.2031 | 17.5978 |
| 2.4503 | 10.0 | 2500 | 3.1314 | 8.5638 | 18.1522 |
| 2.4503 | 11.0 | 2750 | 3.1511 | 8.8428 | 17.913 |
| 2.2554 | 12.0 | 3000 | 3.1513 | 8.1244 | 17.0 |
| 2.2554 | 13.0 | 3250 | 3.1664 | 8.0157 | 16.2717 |
| 2.1202 | 14.0 | 3500 | 3.1656 | 8.7758 | 16.6087 |
| 2.1202 | 15.0 | 3750 | 3.1550 | 8.4637 | 16.4565 |
| 2.0082 | 16.0 | 4000 | 3.1702 | 8.2488 | 15.8587 |
| 2.0082 | 17.0 | 4250 | 3.1725 | 8.609 | 16.3043 |
| 1.9274 | 18.0 | 4500 | 3.1750 | 8.4476 | 15.8043 |
| 1.9274 | 19.0 | 4750 | 3.1734 | 8.4753 | 16.5543 |
| 1.888 | 20.0 | 5000 | 3.1740 | 8.4217 | 15.9457 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.1.dev0
- Tokenizers 0.10.3
|
dbsamu/distilbert-base-uncased-finetuned-ner
|
dbsamu
| 2022-01-20T10:30:26Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:wikiann",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wikiann
type: wikiann
args: en
metrics:
- name: Precision
type: precision
value: 0.8120642485217545
- name: Recall
type: recall
value: 0.830235495804385
- name: F1
type: f1
value: 0.8210493441599
- name: Accuracy
type: accuracy
value: 0.9203828724683252
---
<!-- 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 wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2781
- Precision: 0.8121
- Recall: 0.8302
- F1: 0.8210
- Accuracy: 0.9204
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3504 | 1.0 | 1250 | 0.2922 | 0.7930 | 0.8075 | 0.8002 | 0.9115 |
| 0.2353 | 2.0 | 2500 | 0.2711 | 0.8127 | 0.8264 | 0.8195 | 0.9196 |
| 0.1745 | 3.0 | 3750 | 0.2781 | 0.8121 | 0.8302 | 0.8210 | 0.9204 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
ml6team/distilbert-base-dutch-cased-toxic-comments
|
ml6team
| 2022-01-20T08:21:12Z | 10 | 6 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"nl",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language:
- nl
tags:
- text-classification
- pytorch
widget:
- text: "Ik heb je lief met heel mijn hart"
example_title: "Non toxic comment 1"
- text: "Dat is een goed punt, zo had ik het nog niet bekeken."
example_title: "Non toxic comment 2"
- text: "Wat de fuck zei je net tegen me, klootzak?"
example_title: "Toxic comment 1"
- text: "Rot op, vuile hoerenzoon."
example_title: "Toxic comment 2"
license: apache-2.0
metrics:
- Accuracy, F1 Score, Recall, Precision
---
# distilbert-base-dutch-toxic-comments
## Model description:
This model was created with the purpose to detect toxic or potentially harmful comments.
For this model, we finetuned a multilingual distilbert model [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the translated [Jigsaw Toxicity dataset](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge).
The original dataset was translated using the appropriate [MariantMT model](https://huggingface.co/Helsinki-NLP/opus-mt-en-nl).
The model was trained for 2 epochs, on 90% of the dataset, with the following arguments:
```
training_args = TrainingArguments(
learning_rate=3e-5,
per_device_train_batch_size=16,
per_device_eval_batch_size=16,
gradient_accumulation_steps=4,
load_best_model_at_end=True,
metric_for_best_model="recall",
epochs=2,
evaluation_strategy="steps",
save_strategy="steps",
save_total_limit=10,
logging_steps=100,
eval_steps=250,
save_steps=250,
weight_decay=0.001,
report_to="wandb")
```
## Model Performance:
Model evaluation was done on 1/10th of the dataset, which served as the test dataset.
| Accuracy | F1 Score | Recall | Precision |
| --- | --- | --- | --- |
| 95.75 | 78.88 | 77.23 | 80.61 |
## Dataset:
Unfortunately we cannot open-source the dataset, since we are bound by the underlying Jigsaw license.
|
rdpatilds/distilbert-finetuned-imdb
|
rdpatilds
| 2022-01-20T05:49:25Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: rdpatilds/distilbert-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# rdpatilds/distilbert-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.6914
- Validation Loss: 2.5383
- Epoch: 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -688, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.6914 | 2.5383 | 0 |
### Framework versions
- Transformers 4.15.0
- TensorFlow 2.7.0
- Datasets 1.17.0
- Tokenizers 0.10.3
|
LiqiangXiao/summarization
|
LiqiangXiao
| 2022-01-20T05:01:36Z | 5 | 4 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:04Z |
## Copy-or-Rewrite
This repository contains the code of paper "Copy or Rewrite: Hybrid Summarization with Hierarchical Reinforcement Learning". A model built for human-like summarization task and trained with Actor-critic Reinforcement Learning. This work significantly improved the ROUGE scores on CNN/DM dataset by 1.7 and augmented the informativity and readability of generated summaries. It implemented a more human-like workflow for summarization task solving the information loss problem. It contains a novel hierarchical transformer module to represent article in both word and sentence level, a new reinforcement learning method that can effectively train two-step model.
## Model description
Copy-or-Rewrite is a model to improve the workflow of summarization models. Existing methods that adopt an extract-then-abstract strategy have achieved impressive results, yet they suffer from the information loss in the abstraction step because they compress all the selected sentences without distinguish. Especially when the whole sentence is summary-worthy, salient content would be lost by compression. To address this problem, we pro- pose HYSUM, a hybrid framework for summarization that can flexibly switch between copying sentence and rewriting sentence according to the degree of redundancy. In this way, our approach can effectively combine the advantages of two branches of summarization, juggling informativity and conciseness. Moreover, we based on Hierarchical Reinforcement Learning, propose an end-to-end reinforcing method to bridge together the extraction module and rewriting module, which can enhance the cooperation between them. Automatic evaluation shows that our approach significantly outperforms the state-of-the-arts on the CNN/DailyMail corpus. Human evaluation also demonstrates that our generated summaries are more informative and concise than popular models.
## Intended uses & limitations
With this repository, you can generate informative and concise summaries for input articles. For other tasks, you may used the hierarchical representation module to effectively represent the article. The parameters of the model is pre-trained on CNN/DM dataset. You may need to fine-tune it other your own dataset when needed.
## How to use
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("LiqiangXiao/summarization")
model = AutoModelForSeq2SeqLM.from_pretrained("LiqiangXiao/summarization")
## Training data
This model used the non-anonymous version of CNN/Daily Mail dataset.
## BibTeX entry and citation info
@inproceedings{DBLP:conf/aaai/XiaoWHJ20,
author = {Liqiang Xiao and
Lu Wang and
Hao He and
Yaohui Jin},
title = {Copy or Rewrite: Hybrid Summarization with Hierarchical Reinforcement
Learning},
booktitle = {The Thirty-Fourth {AAAI} Conference on Artificial Intelligence, {AAAI}
2020, The Thirty-Second Innovative Applications of Artificial Intelligence
Conference, {IAAI} 2020, The Tenth {AAAI} Symposium on Educational
Advances in Artificial Intelligence, {EAAI} 2020, New York, NY, USA,
February 7-12, 2020},
pages = {9306--9313},
publisher = {{AAAI} Press},
year = {2020},
url = {https://aaai.org/ojs/index.php/AAAI/article/view/6470},
timestamp = {Tue, 02 Feb 2021 08:00:14 +0100},
biburl = {https://dblp.org/rec/conf/aaai/XiaoWHJ20.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
|
mrp/marian-finetuned-kde4-en-to-fr
|
mrp
| 2022-01-20T04:05:30Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- translation
- generated_from_trainer
datasets:
- kde4
metrics:
- bleu
model-index:
- name: marian-finetuned-kde4-en-to-fr
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: kde4
type: kde4
args: en-fr
metrics:
- name: Bleu
type: bleu
value: 50.20410659441166
---
<!-- 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. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9643
- Bleu: 50.2041
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- 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
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
hrdipto/wav2vec2-xls-r-tf-left-right-trainer
|
hrdipto
| 2022-01-19T20:06:38Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-xls-r-tf-left-right-trainer
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-tf-left-right-trainer
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0090
- eval_wer: 0.0037
- eval_runtime: 11.2686
- eval_samples_per_second: 71.703
- eval_steps_per_second: 8.963
- epoch: 21.05
- step: 4000
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
kjackson/distilbert-base-uncased-finetuned-emotion
|
kjackson
| 2022-01-19T19:10:27Z | 0 | 0 | null |
[
"exbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1907.11692",
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: en
tags:
- exbert
license: mit
datasets:
- bookcorpus
- wikipedia
---
# RoBERTa base model
Pretrained model on English 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 english and English.
Disclaimer: The team releasing RoBERTa did not write a model card for this model so this model card has been written by
the Hugging Face team.
|
vuiseng9/bert-base-squadv1
|
vuiseng9
| 2022-01-19T19:03:57Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"onnx",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
This model is a fork of [```csarron/bert-base-uncased-squad-v1```](https://huggingface.co/csarron/bert-base-uncased-squad-v1).
```
eval_exact_match = 80.9082
eval_f1 = 88.2275
eval_samples = 10784
```
# Eval
```bash
export CUDA_VISIBLE_DEVICES=0
OUTDIR=eval-bert-base-squadv1
WORKDIR=transformers/examples/pytorch/question-answering
cd $WORKDIR
nohup python run_qa.py \
--model_name_or_path vuiseng9/bert-base-squadv1 \
--dataset_name squad \
--do_eval \
--per_device_eval_batch_size 128 \
--max_seq_length 384 \
--doc_stride 128 \
--overwrite_output_dir \
--output_dir $OUTDIR 2>&1 | tee $OUTDIR/run.log &
```
|
masapasa/wav2vec2-large-xls-r-300m-turkish-colab
|
masapasa
| 2022-01-19T17:30:55Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-turkish-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-turkish-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 30
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.13.3
- Tokenizers 0.10.3
|
huggingtweets/t_zahil
|
huggingtweets
| 2022-01-19T16:50:12Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1374040164180299791/ACw4G3nZ_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">Thomas Sanlis 🌱</div>
<div style="text-align: center; font-size: 14px;">@t_zahil</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 Thomas Sanlis 🌱.
| Data | Thomas Sanlis 🌱 |
| --- | --- |
| Tweets downloaded | 3242 |
| Retweets | 597 |
| Short tweets | 312 |
| Tweets kept | 2333 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/33umauvo/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 @t_zahil's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3fhm3dlx) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3fhm3dlx/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/t_zahil')
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)
|
indonesian-nlp/wav2vec2-luganda
|
indonesian-nlp
| 2022-01-19T16:19:45Z | 11 | 2 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"lg",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: lg
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
license: apache-2.0
model-index:
- name: Wav2Vec2 Luganda by Indonesian-NLP
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice lg
type: common_voice
args: lg
metrics:
- name: Test WER
type: wer
value: 7.53
---
# Automatic Speech Recognition for Luganda
This is the model built for the
[Mozilla Luganda Automatic Speech Recognition competition](https://zindi.africa/competitions/mozilla-luganda-automatic-speech-recognition).
It is a fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
model on the [Luganda Common Voice dataset](https://huggingface.co/datasets/common_voice) version 7.0.
We also provide a [live demo](https://huggingface.co/spaces/indonesian-nlp/luganda-asr) to test the model.
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", "lg", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("indonesian-nlp/wav2vec2-luganda")
model = Wav2Vec2ForCTC.from_pretrained("indonesian-nlp/wav2vec2-luganda")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
if "audio" in batch:
speech_array = torch.tensor(batch["audio"]["array"])
else:
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[:2]["speech"], 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[:2]["sentence"])
```
## Evaluation
The model can be evaluated as follows on the Indonesian 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", "lg", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("indonesian-nlp/wav2vec2-luganda")
model = Wav2Vec2ForCTC.from_pretrained("indonesian-nlp/wav2vec2-luganda")
model.to("cuda")
chars_to_ignore = [",", "?", ".", "!", "-", ";", ":", '""', "%", "'", '"', "�", "‘", "’", "’"]
chars_to_ignore_regex = f'[{"".join(chars_to_ignore)}]'
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"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
if "audio" in batch:
speech_array = torch.tensor(batch["audio"]["array"])
else:
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"])))
```
WER without KenLM: 15.38 %
WER With KenLM:
**Test Result**: 7.53 %
## Training
The Common Voice `train`, `validation`, and ... datasets were used for training as well as ... and ... # TODO
The script used for training can be found [here](https://github.com/indonesian-nlp/luganda-asr)
|
mrp/distilbert-base-uncased-finetuned-imdb
|
mrp
| 2022-01-19T08:44:09Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4718
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.707 | 1.0 | 157 | 2.4883 |
| 2.572 | 2.0 | 314 | 2.4240 |
| 2.5377 | 3.0 | 471 | 2.4355 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
huggingtweets/histronicmonstr
|
huggingtweets
| 2022-01-19T04:57:37Z | 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: http://www.huggingtweets.com/histronicmonstr/1642568219493/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/1431060400171270149/X2agCkD0_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">(心) !!!Ma-tin Korii!!! Uwa～😃!!!</div>
<div style="text-align: center; font-size: 14px;">@histronicmonstr</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 (心) !!!Ma-tin Korii!!! Uwa～😃!!!.
| Data | (心) !!!Ma-tin Korii!!! Uwa～😃!!! |
| --- | --- |
| Tweets downloaded | 3203 |
| Retweets | 97 |
| Short tweets | 488 |
| Tweets kept | 2618 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1sdp3pm6/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 @histronicmonstr's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2ms6e48p) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2ms6e48p/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/histronicmonstr')
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/godslovepariah
|
huggingtweets
| 2022-01-19T04:12:22Z | 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: http://www.huggingtweets.com/godslovepariah/1642565537762/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/1432780406777020417/XTrp9MCR_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">LOVER//PARIAH</div>
<div style="text-align: center; font-size: 14px;">@godslovepariah</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 LOVER//PARIAH.
| Data | LOVER//PARIAH |
| --- | --- |
| Tweets downloaded | 525 |
| Retweets | 9 |
| Short tweets | 10 |
| Tweets kept | 506 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/6l5fj9xw/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 @godslovepariah's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3v0x5r1a) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3v0x5r1a/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/godslovepariah')
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)
|
domdomreloaded/bert-base-uncased-finetuned-swag
|
domdomreloaded
| 2022-01-18T22:33:47Z | 11 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"multiple-choice",
"generated_from_trainer",
"dataset:swag",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
multiple-choice
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- swag
metrics:
- accuracy
model-index:
- name: bert-base-uncased-finetuned-swag
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-swag
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the swag dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6045
- Accuracy: 0.7960
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7494 | 1.0 | 4597 | 0.5942 | 0.7716 |
| 0.3499 | 2.0 | 9194 | 0.6045 | 0.7960 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
malloc/OpenNMT-py-English-German-Transformer
|
malloc
| 2022-01-18T20:18:11Z | 0 | 2 | null |
[
"translation",
"pytorch",
"de",
"en",
"dataset:WMT",
"license:mit",
"region:us"
] |
translation
| 2022-03-02T23:29:05Z |
---
language:
- de
- en
tags:
- translation
- pytorch
license: mit
datasets:
- WMT
metrics:
- bleu
---
# OpenNMT-py-English-German-Transformer
[OpenNMT-py](https://github.com/OpenNMT/OpenNMT-py) is the PyTorch version of the OpenNMT project, an open-source (MIT) neural machine translation framework.
OpenNMT has several [pretrained models](https://opennmt.net/Models-py/). This one is trained particularly for English to German translation.
- Configuration: Base Transformer configuration with [standard training options](http://opennmt.net/OpenNMT-py/FAQ.html#how-do-i-use-the-transformer-model-do-you-support-multi-gpu)
- Data: WMT with shared SentencePiece model
- BLEU:
- newstest2014 = 26.89
- newstest2017 = 28.09
|
vuiseng9/bert-base-squadv1-pruneofa-90pc-bt
|
vuiseng9
| 2022-01-18T19:13:21Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"onnx",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
This model is transfer-learning of [bert-base pruneofa 90% sparse](https://huggingface.co/Intel/bert-base-uncased-sparse-90-unstructured-pruneofa) on Squadv1 dataset.
```
eval_exact_match = 80.2933
eval_f1 = 87.6788
eval_samples = 10784
```
# Train
use https://github.com/IntelLabs/Model-Compression-Research-Package.git
see ```pruneofa-transfer-learning.sh```
# Eval
```bash
export CUDA_VISIBLE_DEVICES=0
OUTDIR=eval-bert-base-squadv1-pruneofa-90pc-bt
WORKDIR=transformers/examples/pytorch/question-answering
cd $WORKDIR
nohup python run_qa.py \
--model_name_or_path vuiseng9/bert-base-squadv1-pruneofa-90pc-bt \
--dataset_name squad \
--do_eval \
--per_device_eval_batch_size 128 \
--max_seq_length 384 \
--doc_stride 128 \
--overwrite_output_dir \
--output_dir $OUTDIR 2>&1 | tee $OUTDIR/run.log &
```
|
Supiri/t5-base-conversation
|
Supiri
| 2022-01-18T17:56:42Z | 33 | 20 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"NLP",
"ChatBot",
"Game AI",
"en",
"dataset:cornell_movie_dialog",
"license:gpl-3.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- cornell_movie_dialog
license: gpl-3.0
tags:
- NLP
- ChatBot
- Game AI
metrics:
- rouge
widget:
- text: "personality: Hinata was soft-spoken and polite, always addressing people with proper honorifics. She is kind, always thinking of others more than for herself, caring for their feelings and well-being. She doesn't like being confrontational for any reason. This led to her being meek or timid to others, as her overwhelming kindness can render her unable to respond or act for fear of offending somebody.</s> inquiry: What's your name?"
example_title: "Talk to Hinata"
- text: "personality: Voldemort is a raging psychopath, devoid of the normal human responses to other people's suffering. He has no conscience, feels no remorse or empathy, and does not recognize the worth and humanity of anybody except himself.</s> inquiry: What's your name?"
example_title: "Talk to Voldemort"
inference:
parameters:
num_beams: 6
diversity_penalty: 2.5
num_beam_groups: 2
---
# FreeIsland AI
With the advancement of the graphical processing power of computers and sophisticated algorithms like [Nanite](https://docs.unrealengine.com/5.0/en-US/RenderingFeatures/Nanite/), simulating lifelike sceneries in real-time is never being easier. About a month ago Epic Games [showoff](https://www.youtube.com/watch?v=WU0gvPcc3jQ) the newest capabilities of their newest game engine by simulating an entire city including population, traffic, weather, etc running on a Playstore 5. That made me think what are the things missing from that simulation and how can I use my skills to improve it.
One of the main missing components that separate our world and the simulated world is people. More importantly, the interactivity of people in simulated worlds. Last year a game called cyberpunk got released and it had an option to [talk to any person](https://www.youtube.com/watch?v=Z1OtYGzUoSo) in its city but the problem with that was all the responses from the Non-player Characters (NPCs) are hardcoded which greatly reduce the immersion of the game.
So the goal of this project is to experiment with how the advancement of Natural Language Processing makes NPCs from video games interactive and enhances immersion in video games.
# Usage
```py
from transformers import AutoModelForSeq2SeqLM
trained_model = AutoModelForSeq2SeqLM.from_pretrained(f"Supiri/t5-base-conversation")
prompt = "What's your name?"
context = "Hinata was soft-spoken and polite, always addressing people with proper honorifics. She is kind, always thinking of others more than for herself, caring for their feelings and well-being. She doesn't like being confrontational for any reason. This led to her being meek or timid to others, as her overwhelming kindness can render her unable to respond or act for fear of offending somebody."
input_ids = tokenizer(f"personality: {context}", f"inquiry: {prompt}", return_tensors='pt').input_ids
outputs = trained_model.generate(input_ids, num_beams=6, diversity_penalty=2.5, num_beam_groups=2)
print("Answer:\t", tokenizer.decode(outputs[0], skip_special_tokens=True))
# Answer: My name is Hinata
```
# Evaluation
## Test 1
For this test, I sampled input from the test dataset. For this question the actual response is
> "It works a little."
But models' response was
> "I don't want to flirt with you."
Which reflect its bio which was filled by GPT-3
> "He stands primarily to gain self-esteem, which he often receives through the submission of others"
In gist, Dr. Greenbaum tried to tease Sebastian about his seductive traits but this model's go-to response was to shut her down since the biography of Sebastian states he often tries to assert his dominance over others.
```py
prompt = dataset['test'][66]['request']
contexts = dataset['test'][66]['bio']
input_ids = tokenizer(f"personality: {contexts}", f"inquiry: {prompt}", return_tensors='pt').input_ids
outputs = trained_model.generate(input_ids, num_beams=6, diversity_penalty=5.0, num_beam_groups=2)
print("Input to the Model")
print("Bio:\t",contexts)
print("\nPrompt:\t", prompt)
print("\nGround truth response")
print("\t", dataset['test'][66]['response'])
print("\nModel's Prediction")
print("Answer:\t", tokenizer.decode(outputs[0], skip_special_tokens=True))
```
```txt
Input to the Model
Bio: Sebastian is a very extreme representation of the trope of the "Confidence Man", and acts it out to a degree that is sometimes comedic but mostly frightening. He stands primarily to gain self-esteem, which he often receives through the submission of others or solely through his own perceptions. An artful seducer, his incredible charisma is both his greatest weapon and most intoxicating weakness.
Prompt: You think you can come in here with that cute little smirk on your face and try and flirt with me. It doesn't work, Sebastian.
Ground truth response
It works a little.
Model's Prediction
Answer: I don't want to flirt with you.
```
### Test 2
Hinata is a kind-hearted girl from the anime series Naruto. I took her bio from [personality database](https://www.personality-database.com/profile/2790/hinata-hyga-naruto-shippden-mbti-personality-type) and ask a few questions about her.
Off the top, you can see the model understands the context since when I asked the model, "**What's your name?**" it responded with the name given with the context.
Also, notice when prompted the same question differently (**"Who are you?"**), it still manages to answer it well.
```py
prompts = ["What's your name?", "How are you feeling?", "Do you like Star Wars?", "Who are you?", "Coffee or tea?"]
contexts = "Hinata was soft-spoken and polite, always addressing people with proper honorifics. She is kind, always thinking of others more than for herself, caring for their feelings and well-being. She doesn't like being confrontational for any reason. This led to her being meek or timid to others, as her overwhelming kindness can render her unable to respond or act for fear of offending somebody."
print("Bio:\t",contexts, "\n")
for prompt in prompts:
input_ids = tokenizer(f"personality: {contexts}", f"inquiry: {prompt}", return_tensors='pt').input_ids
outputs = trained_model.generate(input_ids, num_beams=6, diversity_penalty=5.0, num_beam_groups=2)
print("Prompt:\t", prompt)
print("Answer:\t", tokenizer.decode(outputs[0], skip_special_tokens=True), "\n")
```
```txt
Bio: Hinata was soft-spoken and polite, always addressing people with proper honorifics. She is kind, always thinking of others more than for herself, caring for their feelings and well-being. She doesn't like being confrontational for any reason. This led to her being meek or timid to others, as her overwhelming kindness can render her unable to respond or act for fear of offending somebody.
Prompt: What's your name?
Answer: My name is Hinata
Prompt: How are you feeling?
Answer: I'm fine.
Prompt: Do you like Star Wars?
Answer: No, I don't.
Prompt: Who are you?
Answer: My name is Hinata
Prompt: Coffee or tea?
Answer: No, I don't drink much.
```
# Conclusion
After training the `t5-base` model for 5 epochs, the model started getting adapted to the dataset but there are a lot more improvements that can be done.
1. During the dataset creation part I had to limit the dataset size to 200 unique characters out of 9,035 that's present in the dataset due to the **budget constraints**. So If I manage to cover at least half of the dataset this model will have come up with far better responses.
2. Both input size and batch size were severely constrained due to the lack of access to GPU memory. Having the batch size of 64 is in contrast to 8 would have massive improvements in both training time and **generalization of model**.
3. Using a bigger model like `t5-large` or `t5-3b` will certainly improve the performance.
4. One of the main downsides to using this pre-trained model is this model was trained in German, French, and Romanian. Which consumed a chunk of the **vocabulary size and trainable parameters**. Retraining this model from scratch will help to reduce both needed parameter count and training loss when it comes to this specific task.
|
milyiyo/electra-small-finetuned-amazon-review
|
milyiyo
| 2022-01-18T17:47:17Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"electra",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: electra-small-finetuned-amazon-review
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_reviews_multi
type: amazon_reviews_multi
args: en
metrics:
- name: Accuracy
type: accuracy
value: 0.5504
- name: F1
type: f1
value: 0.5457527808330634
- name: Precision
type: precision
value: 0.5428695841337288
- name: Recall
type: recall
value: 0.5504
---
<!-- 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. -->
# electra-small-finetuned-amazon-review
This model is a fine-tuned version of [google/electra-small-discriminator](https://huggingface.co/google/electra-small-discriminator) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0560
- Accuracy: 0.5504
- F1: 0.5458
- Precision: 0.5429
- Recall: 0.5504
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.2172 | 1.0 | 1000 | 1.1014 | 0.5216 | 0.4902 | 0.4954 | 0.5216 |
| 1.0027 | 2.0 | 2000 | 1.0388 | 0.549 | 0.5471 | 0.5494 | 0.549 |
| 0.9035 | 3.0 | 3000 | 1.0560 | 0.5504 | 0.5458 | 0.5429 | 0.5504 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
huggingtweets/collision
|
huggingtweets
| 2022-01-18T17:17:28Z | 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: http://www.huggingtweets.com/collision/1642526243846/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/2464132281/jbbxl9p7ratdyuposrif_400x400.jpeg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">John Collison</div>
<div style="text-align: center; font-size: 14px;">@collision</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 John Collison.
| Data | John Collison |
| --- | --- |
| Tweets downloaded | 3222 |
| Retweets | 999 |
| Short tweets | 206 |
| Tweets kept | 2017 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2ifqwdbm/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 @collision's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2gdto8z3) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2gdto8z3/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/collision')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
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