modelId
stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-31 00:44:29
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 530
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-08-31 00:43:54
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|---|---|---|---|---|---|---|---|---|---|
guillaumegg/wav2vec2-base-timit-demo-4
|
guillaumegg
| 2022-04-07T09:34:14Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-04-07T08:25:30Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-4
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-4
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53-french](https://huggingface.co/facebook/wav2vec2-large-xlsr-53-french) on the None 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: 0.0001
- 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: 4
### Training results
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 2.0.1.dev0
- Tokenizers 0.11.6
|
KoboldAI/fairseq-dense-13B-Shinen
|
KoboldAI
| 2022-04-07T09:10:04Z | 256 | 30 |
transformers
|
[
"transformers",
"pytorch",
"xglm",
"text-generation",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-04-07T08:05:12Z |
---
language: en
license: mit
---
# Fairseq-dense 13B - Shinen
## Model Description
Fairseq-dense 13B-Shinen is a finetune created using Fairseq's MoE dense model. Compared to GPT-Neo-2.7-Horni, this model is much heavier on the sexual content.
**Warning: THIS model is NOT suitable for use by minors. The model will output X-rated content.**
## Training data
The training data contains user-generated stories from sexstories.com. All stories are tagged using the following way:
```
[Theme: <theme1>, <theme2> ,<theme3>]
<Story goes here>
```
### How to use
You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run:
```py
>>> from transformers import pipeline
>>> generator = pipeline('text-generation', model='KoboldAI/fairseq-dense-13B-Shinen')
>>> generator("She was staring at me", do_sample=True, min_length=50)
[{'generated_text': 'She was staring at me with a look that said it all. She wanted me so badly tonight that I wanted'}]
```
### Limitations and Biases
Based on known problems with NLP technology, potential relevant factors include bias (gender, profession, race and religion).
### BibTeX entry and citation info
```
Artetxe et al. (2021): Efficient Large Scale Language Modeling with Mixtures of Experts
```
|
sndsabin/fake-news-classifier
|
sndsabin
| 2022-04-07T08:58:17Z | 0 | 0 | null |
[
"license:gpl-3.0",
"region:us"
] | null | 2022-03-31T08:53:49Z |
---
license: gpl-3.0
---
**Fake News Classifier**: Text classification model to detect fake news articles!
**Dataset**: [Kaggle Fake and real news dataset](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset)
|
abideen305/fatima_coding_NLP
|
abideen305
| 2022-04-07T08:35:41Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-04-07T08:35:09Z |
---
license: mit
---
https://colab.research.google.com/drive/10h5b_OIB16v5R3ywaX7Yse1RSvv94CHo?usp=sharing
|
shubh024/autotrain-intentclassificationfilipino-715021714
|
shubh024
| 2022-04-07T07:38:46Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain",
"unk",
"dataset:shubh024/autotrain-data-intentclassificationfilipino",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-07T07:37:58Z |
---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- shubh024/autotrain-data-intentclassificationfilipino
co2_eq_emissions: 0.003341516495672918
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 715021714
- CO2 Emissions (in grams): 0.003341516495672918
## Validation Metrics
- Loss: 0.5571377873420715
- Accuracy: 0.8
- Macro F1: 0.6709090909090909
- Micro F1: 0.8000000000000002
- Weighted F1: 0.7739393939393939
- Macro Precision: 0.7
- Micro Precision: 0.8
- Weighted Precision: 0.8
- Macro Recall: 0.7
- Micro Recall: 0.8
- Weighted Recall: 0.8
## 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 AutoTrain"}' https://api-inference.huggingface.co/models/shubh024/autotrain-intentclassificationfilipino-715021714
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("shubh024/autotrain-intentclassificationfilipino-715021714", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("shubh024/autotrain-intentclassificationfilipino-715021714", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
Sleoruiz/distilbert-base-uncased-finetuned-emotion
|
Sleoruiz
| 2022-04-07T06:34:58Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-07T05:28:24Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.927
- name: F1
type: f1
value: 0.9273201074587852
---
<!-- 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-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2176
- Accuracy: 0.927
- F1: 0.9273
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8252 | 1.0 | 250 | 0.3121 | 0.916 | 0.9140 |
| 0.2471 | 2.0 | 500 | 0.2176 | 0.927 | 0.9273 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
nickil/real-fake-news
|
nickil
| 2022-04-07T05:50:48Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"longformer",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-07T03:58:32Z |
---
license: mit
---
Data: [https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset)
|
abdusah/aradia-ctc-distilhubert-ft
|
abdusah
| 2022-04-07T02:06:55Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"hubert",
"automatic-speech-recognition",
"abdusahmbzuai/arabic_speech_massive_sm",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-04-06T18:40:14Z |
---
license: apache-2.0
tags:
- automatic-speech-recognition
- abdusahmbzuai/arabic_speech_massive_sm
- generated_from_trainer
model-index:
- name: aradia-ctc-distilhubert-ft
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. -->
# aradia-ctc-distilhubert-ft
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the ABDUSAHMBZUAI/ARABIC_SPEECH_MASSIVE_SM - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7114
- Wer: 0.8908
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 0.43 | 100 | 4.4129 | 1.0 |
| No log | 0.87 | 200 | 3.5927 | 1.0 |
| No log | 1.3 | 300 | 3.3780 | 1.0 |
| No log | 1.74 | 400 | 3.0830 | 1.0 |
| 5.3551 | 2.17 | 500 | 2.6278 | 0.9999 |
| 5.3551 | 2.61 | 600 | 1.8359 | 1.0000 |
| 5.3551 | 3.04 | 700 | 1.7878 | 0.9914 |
| 5.3551 | 3.48 | 800 | 1.5219 | 0.9875 |
| 5.3551 | 3.91 | 900 | 1.4348 | 0.9879 |
| 1.7199 | 4.35 | 1000 | 1.4354 | 0.9644 |
| 1.7199 | 4.78 | 1100 | 1.5210 | 0.9519 |
| 1.7199 | 5.22 | 1200 | 1.3607 | 0.9475 |
| 1.7199 | 5.65 | 1300 | 1.3839 | 0.9343 |
| 1.7199 | 6.09 | 1400 | 1.2806 | 0.8944 |
| 1.2342 | 6.52 | 1500 | 1.3036 | 0.9011 |
| 1.2342 | 6.95 | 1600 | 1.3704 | 0.9072 |
| 1.2342 | 7.39 | 1700 | 1.2981 | 0.8891 |
| 1.2342 | 7.82 | 1800 | 1.2786 | 0.8733 |
| 1.2342 | 8.26 | 1900 | 1.2897 | 0.8867 |
| 0.9831 | 8.69 | 2000 | 1.4436 | 0.8780 |
| 0.9831 | 9.13 | 2100 | 1.3680 | 0.8873 |
| 0.9831 | 9.56 | 2200 | 1.3471 | 0.8692 |
| 0.9831 | 10.0 | 2300 | 1.3725 | 0.8729 |
| 0.9831 | 10.43 | 2400 | 1.4439 | 0.8771 |
| 0.8071 | 10.87 | 2500 | 1.5114 | 0.8928 |
| 0.8071 | 11.3 | 2600 | 1.6156 | 0.8958 |
| 0.8071 | 11.74 | 2700 | 1.4381 | 0.8749 |
| 0.8071 | 12.17 | 2800 | 1.5088 | 0.8717 |
| 0.8071 | 12.61 | 2900 | 1.5486 | 0.8813 |
| 0.6321 | 13.04 | 3000 | 1.4536 | 0.8884 |
| 0.6321 | 13.48 | 3100 | 1.4679 | 0.8947 |
| 0.6321 | 13.91 | 3200 | 1.5628 | 0.9117 |
| 0.6321 | 14.35 | 3300 | 1.5831 | 0.8716 |
| 0.6321 | 14.78 | 3400 | 1.6733 | 0.8702 |
| 0.4998 | 15.22 | 3500 | 1.8225 | 0.8665 |
| 0.4998 | 15.65 | 3600 | 1.8558 | 0.8732 |
| 0.4998 | 16.09 | 3700 | 1.7513 | 0.8766 |
| 0.4998 | 16.52 | 3800 | 1.8562 | 0.8753 |
| 0.4998 | 16.95 | 3900 | 1.9018 | 0.8704 |
| 0.4421 | 17.39 | 4000 | 1.9341 | 0.8789 |
| 0.4421 | 17.82 | 4100 | 1.9582 | 0.8781 |
| 0.4421 | 18.26 | 4200 | 1.8863 | 0.8821 |
| 0.4421 | 18.69 | 4300 | 1.9366 | 0.8847 |
| 0.4421 | 19.13 | 4400 | 2.1902 | 0.8721 |
| 0.3712 | 19.56 | 4500 | 2.1641 | 0.8670 |
| 0.3712 | 20.0 | 4600 | 2.1639 | 0.8776 |
| 0.3712 | 20.43 | 4700 | 2.2695 | 0.9030 |
| 0.3712 | 20.87 | 4800 | 2.1909 | 0.8937 |
| 0.3712 | 21.3 | 4900 | 2.1606 | 0.8959 |
| 0.3067 | 21.74 | 5000 | 2.1756 | 0.8943 |
| 0.3067 | 22.17 | 5100 | 2.4092 | 0.8773 |
| 0.3067 | 22.61 | 5200 | 2.4991 | 0.8721 |
| 0.3067 | 23.04 | 5300 | 2.3340 | 0.8910 |
| 0.3067 | 23.48 | 5400 | 2.3567 | 0.8946 |
| 0.2764 | 23.91 | 5500 | 2.3215 | 0.8897 |
| 0.2764 | 24.35 | 5600 | 2.4824 | 0.9002 |
| 0.2764 | 24.78 | 5700 | 2.4585 | 0.8963 |
| 0.2764 | 25.22 | 5800 | 2.5804 | 0.8879 |
| 0.2764 | 25.65 | 5900 | 2.5814 | 0.8903 |
| 0.2593 | 26.09 | 6000 | 2.5374 | 0.8868 |
| 0.2593 | 26.52 | 6100 | 2.5346 | 0.8922 |
| 0.2593 | 26.95 | 6200 | 2.5465 | 0.8873 |
| 0.2593 | 27.39 | 6300 | 2.6002 | 0.8919 |
| 0.2593 | 27.82 | 6400 | 2.6102 | 0.8928 |
| 0.227 | 28.26 | 6500 | 2.6925 | 0.8914 |
| 0.227 | 28.69 | 6600 | 2.6981 | 0.8913 |
| 0.227 | 29.13 | 6700 | 2.6872 | 0.8891 |
| 0.227 | 29.56 | 6800 | 2.7015 | 0.8897 |
| 0.227 | 30.0 | 6900 | 2.7114 | 0.8908 |
### Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.10.2+cu113
- Datasets 1.18.4
- Tokenizers 0.11.6
|
thangcv/distilbert-base-uncased-finetuned-emotion
|
thangcv
| 2022-04-07T02:01:30Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-06T09:11:14Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.924
- name: F1
type: f1
value: 0.9242608108878096
---
<!-- 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-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2156
- Accuracy: 0.924
- F1: 0.9243
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8151 | 1.0 | 250 | 0.3062 | 0.9115 | 0.9089 |
| 0.2428 | 2.0 | 500 | 0.2156 | 0.924 | 0.9243 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
shamimtowhid/upside_down_detector
|
shamimtowhid
| 2022-04-06T23:45:05Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2022-04-06T22:32:45Z |
---
license: apache-2.0
---
## General Information:
Used Dataset: cats_vs_dogs (https://huggingface.co/datasets/cats_vs_dogs)
Used Label: Randomly Images are flipped and labels for the flipped images are 1, otherwise 0.
Used Library: Pytorch
Used Model: ResNet18 from torchvision
Number of classes: 2 (0 means No flip and 1 means Flipped image)
Train Test Split: 70-30
## Some sample Images and Labels from created dataset
## Specific information about the Dataset:
The following files from the dataset were removed during the training because those files are broken/ corrupted.
- ./kagglecatsanddogs_3367a/PetImages/Cat/666.jpg
- ./kagglecatsanddogs_3367a/PetImages/Cat/10404.jpg
- ./kagglecatsanddogs_3367a/PetImages/Dog/11702.jpg
## Training Information:
Total Epoch: 5
Pretrained: True (ImageNet weight) (Every layer is trainable)
Image Size: 224 x 224
Batch Size: 128
Optimizer: SGD
Learning Rate: 0.001 (Constant throughout the training)
Momentum: 0.9
Loss: CrossEntropy Loss
## Result:
Accuracy: 98.4266
F1: 98.4271
Recall: 98.4261
Precision: 98.4265
## Confusion Matrix:
## Some Misclassified Images (Randomly Selected):
## Some possible improvements:
- Most of the misclassified images are occluded by some other objects or partly visible. One possible improvement could be to improve this type of image in the training dataset.
- Hyperparameter tuning is another option, we could try to see whether the performance improves or not. For example, instead of using a constant learning rate, we could try a cyclical learning rate. This type of learning rate helps the model overcome local minima.
- If we consider the rightmost image in the above figure, we see that the pose of the cat is different than most of the images in the training set. I think Augmentation like CutMix will be helpful in this scenario.
|
tdrenis/finetuned-bot-detector
|
tdrenis
| 2022-04-06T20:31:05Z | 48 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-03T03:38:27Z |
Student project that fine-tuned the roberta-base-openai-detector model on the Twibot-20 dataset.
|
KrishnaAgarwal16/607-project-adversarial
|
KrishnaAgarwal16
| 2022-04-06T18:43:49Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-04-06T18:23:35Z |
Model trained for 1 epoch on 1000 examples from the `adversarial_qa` dataset
|
ankitkupadhyay/bert-finetuned-squad
|
ankitkupadhyay
| 2022-04-06T18:38:57Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-04-06T12:55:45Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-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-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 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
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
hafidber/rare-puppers
|
hafidber
| 2022-04-06T17:53:06Z | 60 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-04-06T17:52:54Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: rare-puppers
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9552238583564758
---
# rare-puppers
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### corgi
#### samoyed
#### shiba inu
|
Busayor/Fake_news_classifier_bert
|
Busayor
| 2022-04-06T17:19:59Z | 0 | 0 | null |
[
"license:afl-3.0",
"region:us"
] | null | 2022-04-06T15:03:39Z |
---
license: afl-3.0
---
# Fatima Fellowship Challenge
**This repo contains a trained keras model built to effectively classify between fake and real news**
|
btjiong/robbert-twitter-sentiment
|
btjiong
| 2022-04-06T17:18:23Z | 23 | 1 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:dutch_social",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-06T14:54:31Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- dutch_social
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: robbert-twitter-sentiment
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: dutch_social
type: dutch_social
args: dutch_social
metrics:
- name: Accuracy
type: accuracy
value: 0.749
- name: F1
type: f1
value: 0.7491844724992662
- name: Precision
type: precision
value: 0.7493911755249737
- name: Recall
type: recall
value: 0.749
---
<!-- 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. -->
# robbert-twitter-sentiment
This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) on the dutch_social dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6818
- Accuracy: 0.749
- F1: 0.7492
- Precision: 0.7494
- Recall: 0.749
## 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 | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.7485 | 1.0 | 188 | 0.7670 | 0.692 | 0.6915 | 0.6920 | 0.692 |
| 0.5202 | 2.0 | 376 | 0.6818 | 0.749 | 0.7492 | 0.7494 | 0.749 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cpu
- Datasets 2.0.0
- Tokenizers 0.12.0
|
mahendra/cifar-up-down-image-classification
|
mahendra
| 2022-04-06T15:59:42Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2022-04-05T15:01:23Z |
---
license: apache-2.0
---
### Dataset
* UpDown dataset is created using the CIFAR10 dataset
### Model Information
* Finetune the 'google/vit-base-patch16-224-in21k' pretrained model for 1 epoch
### Performance Measurement
* Binary Cross-Entropy loss is used to measure the training loss
* Accuracy is used to measure the overall model performance in the test set
|
moshew/distilbert-base-uncased-finetuned-clinc
|
moshew
| 2022-04-06T15:38:17Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-06T15:27:26Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9187096774193548
---
<!-- 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-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7703
- Accuracy: 0.9187
## 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: 48
- eval_batch_size: 48
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2896 | 1.0 | 318 | 3.2887 | 0.7419 |
| 2.6309 | 2.0 | 636 | 1.8797 | 0.8310 |
| 1.5443 | 3.0 | 954 | 1.1537 | 0.8974 |
| 1.0097 | 4.0 | 1272 | 0.8560 | 0.9135 |
| 0.7918 | 5.0 | 1590 | 0.7703 | 0.9187 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Kuray107/ls-timit-wsj0-100percent-supervised-aug
|
Kuray107
| 2022-04-06T14:26:52Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-04-06T04:04:41Z |
---
tags:
- generated_from_trainer
model-index:
- name: ls-timit-wsj0-100percent-supervised-aug
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. -->
# ls-timit-wsj0-100percent-supervised-aug
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0489
- Wer: 0.0275
## 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: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.3491 | 4.57 | 1000 | 0.0470 | 0.0416 |
| 0.1088 | 9.13 | 2000 | 0.0582 | 0.0343 |
| 0.0702 | 13.7 | 3000 | 0.0471 | 0.0271 |
| 0.0532 | 18.26 | 4000 | 0.0489 | 0.0275 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2
- Datasets 1.18.2
- Tokenizers 0.10.3
|
zigonk/Fetima_CodingChallenge_DL4Vision
|
zigonk
| 2022-04-06T14:08:39Z | 0 | 0 | null |
[
"tensorboard",
"region:us"
] | null | 2022-04-05T08:39:29Z |
# Fetima Coding Challenge (Task DL for Vision)
|
nielsr/segformer-test-sidewalk-v2
|
nielsr
| 2022-04-06T13:11:06Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"segformer",
"vision",
"image-segmentation",
"dataset:segments/sidewalk-semantic",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-segmentation
| 2022-04-06T12:59:02Z |
---
license: apache-2.0
tags:
- vision
- image-segmentation
datasets:
- segments/sidewalk-semantic
widget:
- src: https://segmentsai-prod.s3.eu-west-2.amazonaws.com/assets/admin-tobias/439f6843-80c5-47ce-9b17-0b2a1d54dbeb.jpg
example_title: Brugge
---
|
softcatala/fullstop-catalan-punctuation-prediction
|
softcatala
| 2022-04-06T12:45:54Z | 13 | 1 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"token-classification",
"punctuation prediction",
"punctuation",
"ca",
"dataset:softcatala/Europarl-catalan",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-04-04T09:31:12Z |
---
language:
- ca
tags:
- punctuation prediction
- punctuation
datasets: softcatala/Europarl-catalan
widget:
- text: "Els investigadors suggereixen que tot i que es tracta de la cua d'un dinosaure jove la mostra revela un plomatge adult i no pas plomissol"
example_title: "Catalan"
metrics:
- f1
---
This model predicts the punctuation of Catalan language.
The model restores the following punctuation markers: **"." "," "?" "-" ":"**
Based on the work https://github.com/oliverguhr/fullstop-deep-punctuation-prediction
## Results
The performance differs for the single punctuation markers as hyphens and colons, in many cases, are optional and can be substituted by either a comma or a full stop. The model achieves the following F1 scores for Catalan language:
| Label | CA |
| ------------- | ----- |
| 0 | 0.99 |
| . | 0.93 |
| , | 0.82 |
| ? | 0.76 |
| - | 0.89 |
| : | 0.64 |
| macro average | 0.84 |
## Contact
Jordi Mas <[email protected]>
|
xxr/bert-base-chinese-complaint-128
|
xxr
| 2022-04-06T11:06:31Z | 2 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-04-06T10:12:50Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- null
model_index:
- name: bert-base-chinese-complaint-128
results:
- task:
name: Masked Language Modeling
type: fill-mask
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-chinese-complaint-128
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: 1.3004
## 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: 16
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 3.3735 | 1.0 | 1250 | 2.4628 |
| 2.2412 | 2.0 | 2500 | 2.0378 |
| 1.9251 | 3.0 | 3750 | 1.8368 |
| 1.7407 | 4.0 | 5000 | 1.6972 |
| 1.6137 | 5.0 | 6250 | 1.5937 |
| 1.5365 | 6.0 | 7500 | 1.5315 |
| 1.4662 | 7.0 | 8750 | 1.4921 |
| 1.3985 | 8.0 | 10000 | 1.4517 |
| 1.3509 | 9.0 | 11250 | 1.4308 |
| 1.3047 | 10.0 | 12500 | 1.3906 |
| 1.2745 | 11.0 | 13750 | 1.3467 |
| 1.2377 | 12.0 | 15000 | 1.3306 |
| 1.2139 | 13.0 | 16250 | 1.3205 |
| 1.2027 | 14.0 | 17500 | 1.3098 |
| 1.1722 | 15.0 | 18750 | 1.2845 |
| 1.1697 | 16.0 | 20000 | 1.3004 |
### Framework versions
- Transformers 4.8.2
- Pytorch 1.7.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
edangx100/t5-small-finetuned-wikisql
|
edangx100
| 2022-04-06T10:23:39Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wiki_sql",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-04-06T09:15:32Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wiki_sql
model-index:
- name: t5-small-finetuned-wikisql
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-wikisql
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wiki_sql dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1246
- Rouge2 Precision: 0.8187
- Rouge2 Recall: 0.7269
- Rouge2 Fmeasure: 0.7629
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 0.1952 | 1.0 | 4049 | 0.1567 | 0.7948 | 0.7057 | 0.7406 |
| 0.167 | 2.0 | 8098 | 0.1382 | 0.8092 | 0.7171 | 0.7534 |
| 0.1517 | 3.0 | 12147 | 0.1296 | 0.8145 | 0.7228 | 0.7589 |
| 0.1433 | 4.0 | 16196 | 0.1260 | 0.8175 | 0.7254 | 0.7617 |
| 0.1414 | 5.0 | 20245 | 0.1246 | 0.8187 | 0.7269 | 0.7629 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
copenlu/citebert
|
copenlu
| 2022-04-06T08:33:47Z | 28 | 3 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
This is the SciBERT pretrained language model further fine-tuned on masked language modeling and cite-worthiness detection on the [CiteWorth](https://github.com/copenlu/cite-worth) dataset. Note that this model should be used for further fine-tuning on downstream scientific document understanding tasks.
|
luxiao/alilingjie
|
luxiao
| 2022-04-06T07:44:47Z | 0 | 1 |
transformers
|
[
"transformers",
"pytorch",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-06T07:21:55Z |
---
license: apache-2.0
---
|
Siddique/wav2vec2-large-xls-r-300m-turkish-colab
|
Siddique
| 2022-04-06T05:42:59Z | 3 | 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
| 2022-04-06T05:08:38Z |
---
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: 500
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
hemhemoh/FatimaFellowship_NLPtask
|
hemhemoh
| 2022-04-05T22:12:01Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-04-05T21:35:08Z |
This model was trained using the 'bert-base-uncased' from the transformer library and it was trained on the popular fake/real news dataset from Kaggle.
Pytorch is the framework used to train the model and it had an accuracy score of 93.5 % and here is what the classification report looks like.
precision recall f1-score support
0 0.96 0.92 0.94 2348
1 0.92 0.96 0.94 2142
accuracy 0.94 4490
macro avg 0.94 0.94 0.94 4490
weighted avg 0.94 0.94 0.94 4490
---
license: apache-2.0
---
|
huggingtweets/foxnews
|
huggingtweets
| 2022-04-05T21:06:29Z | 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/foxnews/1649192783021/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/1459143267673677853/xtIvtfZp_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">Fox News</div>
<div style="text-align: center; font-size: 14px;">@foxnews</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 Fox News.
| Data | Fox News |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 84 |
| Short tweets | 0 |
| Tweets kept | 3166 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3gz4o7tf/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 @foxnews's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/10czim3i) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/10czim3i/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/foxnews')
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)
|
RAJESHNEMANI/Chatbot_AI
|
RAJESHNEMANI
| 2022-04-05T21:04:18Z | 3 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-04-05T20:57:52Z |
---
tags:
- conversational
---
# RickBot built for [Chai](https://chai.ml/)
Make your own [here](https://colab.research.google.com/drive/1LtVm-VHvDnfNy7SsbZAqhh49ikBwh1un?usp=sharing)
|
miesnerjacob/marian-finetuned-kde4-en-to-fr
|
miesnerjacob
| 2022-04-05T20:28:41Z | 12 | 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
| 2022-04-05T18:34:17Z |
---
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: 52.94560734092563
---
<!-- 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: 0.8559
- Bleu: 52.9456
## 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: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
Kuray107/ls-timit-100percent-supervised-aug
|
Kuray107
| 2022-04-05T20:18:46Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-04-05T16:33:16Z |
---
tags:
- generated_from_trainer
model-index:
- name: ls-timit-100percent-supervised-aug
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. -->
# ls-timit-100percent-supervised-aug
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0519
- Wer: 0.0292
## 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: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2985 | 7.04 | 1000 | 0.0556 | 0.0380 |
| 0.1718 | 14.08 | 2000 | 0.0519 | 0.0292 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2
- Datasets 1.18.2
- Tokenizers 0.10.3
|
huggingtweets/vivchen_
|
huggingtweets
| 2022-04-05T20:13:38Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-04-05T20:12:26Z |
---
language: en
thumbnail: http://www.huggingtweets.com/vivchen_/1649189613639/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/1453748100594642948/BAASh9m3_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">Vivian</div>
<div style="text-align: center; font-size: 14px;">@vivchen_</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 Vivian.
| Data | Vivian |
| --- | --- |
| Tweets downloaded | 1616 |
| Retweets | 39 |
| Short tweets | 166 |
| Tweets kept | 1411 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/vqb4rpuh/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 @vivchen_'s tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1xzxtr20) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1xzxtr20/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/vivchen_')
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/benk14894427
|
huggingtweets
| 2022-04-05T19:26:24Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-04-05T19:25:50Z |
---
language: en
thumbnail: http://www.huggingtweets.com/benk14894427/1649186779847/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/1442847071829204995/C-gqdXsf_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">Benk</div>
<div style="text-align: center; font-size: 14px;">@benk14894427</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 Benk.
| Data | Benk |
| --- | --- |
| Tweets downloaded | 269 |
| Retweets | 6 |
| Short tweets | 34 |
| Tweets kept | 229 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1zhhq7f1/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 @benk14894427's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2ns3y5oi) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2ns3y5oi/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/benk14894427')
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)
|
itaihay/wav2vec_asr_swbd_10_epochs
|
itaihay
| 2022-04-05T19:02:43Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-04-02T10:53:01Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec_asr_swbd_10_epochs
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. -->
# wav2vec_asr_swbd_10_epochs
This model is a fine-tuned version of [facebook/wav2vec2-large-robust-ft-swbd-300h](https://huggingface.co/facebook/wav2vec2-large-robust-ft-swbd-300h) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Wer: 0.9627
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:------:|:---------------:|:------:|
| 1.0682 | 0.22 | 5000 | 0.7383 | 0.4431 |
| 0.9143 | 0.44 | 10000 | 0.7182 | 0.4058 |
| 0.8905 | 0.66 | 15000 | 0.6291 | 0.3987 |
| 0.8354 | 0.87 | 20000 | 0.5976 | 0.3954 |
| 0.7749 | 1.09 | 25000 | 0.5773 | 0.3901 |
| 0.7336 | 1.31 | 30000 | 0.5812 | 0.3871 |
| 0.7314 | 1.53 | 35000 | 0.5802 | 0.3895 |
| 0.0 | 1.75 | 40000 | nan | 0.9627 |
| 0.0 | 1.97 | 45000 | nan | 0.9627 |
| 0.0 | 2.19 | 50000 | nan | 0.9627 |
| 0.0 | 2.4 | 55000 | nan | 0.9627 |
| 0.0 | 2.62 | 60000 | nan | 0.9627 |
| 0.0 | 2.84 | 65000 | nan | 0.9627 |
| 0.0 | 3.06 | 70000 | nan | 0.9627 |
| 0.0 | 3.28 | 75000 | nan | 0.9627 |
| 0.0 | 3.5 | 80000 | nan | 0.9627 |
| 0.0 | 3.72 | 85000 | nan | 0.9627 |
| 0.0 | 3.93 | 90000 | nan | 0.9627 |
| 0.0 | 4.15 | 95000 | nan | 0.9627 |
| 0.0 | 4.37 | 100000 | nan | 0.9627 |
| 0.0 | 4.59 | 105000 | nan | 0.9627 |
| 0.0 | 4.81 | 110000 | nan | 0.9627 |
| 0.0 | 5.03 | 115000 | nan | 0.9627 |
| 0.0 | 5.25 | 120000 | nan | 0.9627 |
| 0.0 | 5.46 | 125000 | nan | 0.9627 |
| 0.0 | 5.68 | 130000 | nan | 0.9627 |
| 0.0 | 5.9 | 135000 | nan | 0.9627 |
| 0.0 | 6.12 | 140000 | nan | 0.9627 |
| 0.0 | 6.34 | 145000 | nan | 0.9627 |
| 0.0 | 6.56 | 150000 | nan | 0.9627 |
| 0.0 | 6.78 | 155000 | nan | 0.9627 |
| 0.0 | 7.0 | 160000 | nan | 0.9627 |
| 0.0 | 7.21 | 165000 | nan | 0.9627 |
| 0.0 | 7.43 | 170000 | nan | 0.9627 |
| 0.0 | 7.65 | 175000 | nan | 0.9627 |
| 0.0 | 7.87 | 180000 | nan | 0.9627 |
| 0.0 | 8.09 | 185000 | nan | 0.9627 |
| 0.0 | 8.31 | 190000 | nan | 0.9627 |
| 0.0 | 8.53 | 195000 | nan | 0.9627 |
| 0.0 | 8.74 | 200000 | nan | 0.9627 |
| 0.0 | 8.96 | 205000 | nan | 0.9627 |
| 0.0 | 9.18 | 210000 | nan | 0.9627 |
| 0.0 | 9.4 | 215000 | nan | 0.9627 |
| 0.0 | 9.62 | 220000 | nan | 0.9627 |
| 0.0 | 9.84 | 225000 | nan | 0.9627 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.4
- Tokenizers 0.11.6
|
novarac23/xlm-roberta-base-finetuned-panx-de
|
novarac23
| 2022-04-05T18:26:07Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-04-05T18:00:22Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.862669465085938
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1374
- F1: 0.8627
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2596 | 1.0 | 525 | 0.1571 | 0.8302 |
| 0.1292 | 2.0 | 1050 | 0.1416 | 0.8455 |
| 0.0809 | 3.0 | 1575 | 0.1374 | 0.8627 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Harsit/bert-finetuned-squad
|
Harsit
| 2022-04-05T17:57:01Z | 10 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-04-05T15:02:48Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-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-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 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
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
ViktorDo/distilbert-base-uncased-finetuned-imdb
|
ViktorDo
| 2022-04-05T17:17:10Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-04-05T12:28:13Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: distilbert-base-uncased-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. -->
# distilbert-base-uncased-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:
## 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': -875, '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: float32
### Training results
### Framework versions
- Transformers 4.17.0
- TensorFlow 2.8.0
- Datasets 2.0.0
- Tokenizers 0.11.6
|
akiyamasho/AnimeBackgroundGAN-Shinkai
|
akiyamasho
| 2022-04-05T17:11:49Z | 0 | 61 |
pytorch
|
[
"pytorch",
"gan",
"image-to-image",
"license:mit",
"region:us"
] |
image-to-image
| 2022-04-05T09:34:35Z |
---
license: mit
library_name: pytorch
tags:
- gan
- image-to-image
---
# AnimeBackgroundGAN (CartoonGAN by Chen et. al.)
<img src="https://m.media-amazon.com/images/M/MV5BZTExN2EwMmYtNDcwZS00ZmI1LTk1NGQtNTQ3YWFjMmY3YjQwXkEyXkFqcGdeQXVyNTU1OTUzNDg@._V1_.jpg" alt="5 Centimetres per Second directed by Makoto Shinkai" style="height: 300px;"/>
- [Makoto Shinkai （新海誠）](https://en.wikipedia.org/wiki/Makoto_Shinkai) pre-trained model from [CartoonGAN](http://openaccess.thecvf.com/content_cvpr_2018/CameraReady/2205.pdf) `[Chen et al., CVPR18]`.
- This model can transform real-life photos into Japanese-animation-like backgrounds, following the style of movies such as [Kimi no Na wa](https://en.wikipedia.org/wiki/Kimi_no_Na_wa) with a photorealistic painting style.
- The implementation is in PyTorch (see [source code here](https://huggingface.co/spaces/akiyamasho/AnimeBackgroundGAN/blob/main/network/Transformer.py)).
- Check out the demo here:
[](https://huggingface.co/spaces/akiyamasho/AnimeBackgroundGAN)
# Other pre-trained model versions
The other versions were also trained from movies of the different Japanese animation directors.
##### Mamoru Hosoda（細田守）
- director of [Wolf Children](https://en.wikipedia.org/wiki/Wolf_Children), with a distinct mild and cool background style
- [Director Profile](https://en.wikipedia.org/wiki/Mamoru_Hosoda)
- **Model Repository**: https://huggingface.co/akiyamasho/AnimeBackgroundGAN-Hosoda
##### Satoshi Kon（今敏）
- director of [Paprika](https://en.wikipedia.org/wiki/Paprika_(2006_film)) with a distinct high contrast, reddish hue style
- [Director Profile](https://en.wikipedia.org/wiki/Satoshi_Kon)
- **Model Repository**: https://huggingface.co/akiyamasho/AnimeBackgroundGAN-Kon
##### Hayao Miyazaki（宮崎駿）
- director of [Howl's Moving Castle](https://en.wikipedia.org/wiki/Howl%27s_Moving_Castle_(film)) with a relatively soft and painterly style
- [Director Profile](https://en.wikipedia.org/wiki/Hayao_Miyazaki)
- **Model Repository**: https://huggingface.co/akiyamasho/AnimeBackgroundGAN-Miyazaki
### Credits
- Paper at [CartoonGAN: Generative Adversarial Networks for Photo Cartoonization](http://openaccess.thecvf.com/content_cvpr_2018/CameraReady/2205.pdf) `[Chen et al., CVPR18]`
- Original PyTorch implementation was created by [Yijun Li](https://github.com/Yijunmaverick/)
- Spaces/Models re-packaging and implementation by [Shō Akiyama](https://github.com/Yijunmaverick/).
##### Special Thanks
- [Nima Boscarino](https://github.com/NimaBoscarino)
- [Omar Sanseviero](https://github.com/osanseviero)
|
akiyamasho/AnimeBackgroundGAN-Hosoda
|
akiyamasho
| 2022-04-05T17:11:29Z | 0 | 16 |
pytorch
|
[
"pytorch",
"gan",
"image-to-image",
"license:mit",
"region:us"
] |
image-to-image
| 2022-04-05T09:37:32Z |
---
license: mit
library_name: pytorch
tags:
- gan
- image-to-image
---
# AnimeBackgroundGAN-Hosoda (CartoonGAN by Chen et. al.)
<img src="https://m.media-amazon.com/images/M/MV5BYjgxYjk4OTktZjU3Ni00YzE5LTkyMmItMzI4YzY1YTlhNDg2XkEyXkFqcGdeQXVyNzEyMDQ1MDA@._V1_.jpg" alt="Mirai directed by Mamoru Hosoda" style="height: 300px;"/>
- [Mamoru Hosoda（細田守）](https://en.wikipedia.org/wiki/Mamoru_Hosoda) pre-trained model from [CartoonGAN](http://openaccess.thecvf.com/content_cvpr_2018/CameraReady/2205.pdf) `[Chen et al., CVPR18]`.
- This model can transform real-life photos into Japanese-animation-like backgrounds, following the style of movies such as [Wolf Children](https://en.wikipedia.org/wiki/Wolf_Children), with a distinct mild and cool background style.
- The implementation is in PyTorch (see [source code here](https://huggingface.co/spaces/akiyamasho/AnimeBackgroundGAN/blob/main/network/Transformer.py)).
- Check out the demo here:
[](https://huggingface.co/spaces/akiyamasho/AnimeBackgroundGAN)
# Other pre-trained model versions
The other versions were also trained from movies of the different Japanese animation directors.
##### Makoto Shinkai （新海誠）
- director of [Kimi no Na wa](https://en.wikipedia.org/wiki/Kimi_no_Na_wa) with a photorealistic painting style
- [Director Profile](https://en.wikipedia.org/wiki/Makoto_Shinkai)
- **Model Repository**: https://huggingface.co/akiyamasho/AnimeBackgroundGAN-Shinkai
##### Satoshi Kon（今敏）
- director of [Paprika](https://en.wikipedia.org/wiki/Paprika_(2006_film)) with a distinct high contrast, reddish hue style
- [Director Profile](https://en.wikipedia.org/wiki/Satoshi_Kon)
- **Model Repository**: https://huggingface.co/akiyamasho/AnimeBackgroundGAN-Kon
##### Hayao Miyazaki（宮崎駿）
- director of [Howl's Moving Castle](https://en.wikipedia.org/wiki/Howl%27s_Moving_Castle_(film)) with a relatively soft and painterly style
- [Director Profile](https://en.wikipedia.org/wiki/Hayao_Miyazaki)
- **Model Repository**: https://huggingface.co/akiyamasho/AnimeBackgroundGAN-Miyazaki
### Credits
- Paper at [CartoonGAN: Generative Adversarial Networks for Photo Cartoonization](http://openaccess.thecvf.com/content_cvpr_2018/CameraReady/2205.pdf) `[Chen et al., CVPR18]`
- Original PyTorch implementation was created by [Yijun Li](https://github.com/Yijunmaverick/)
- Spaces/Models re-packaging and implementation by [Shō Akiyama](https://github.com/Yijunmaverick/).
##### Special Thanks
- [Nima Boscarino](https://github.com/NimaBoscarino)
- [Omar Sanseviero](https://github.com/osanseviero)
|
alefiury/wav2vec2-large-xlsr-53-coraa-brazilian-portuguese-gain-normalization-sna
|
alefiury
| 2022-04-05T16:59:13Z | 7 | 2 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"pt",
"portuguese-speech-corpus",
"PyTorch",
"dataset:CORAA",
"dataset:common_voice",
"dataset:mls",
"dataset:cetuc",
"dataset:voxforge",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-26T18:58:07Z |
---
language: pt
datasets:
- CORAA
- common_voice
- mls
- cetuc
- voxforge
metrics:
- wer
tags:
- audio
- speech
- wav2vec2
- pt
- portuguese-speech-corpus
- automatic-speech-recognition
- speech
- PyTorch
license: apache-2.0
model-index:
- name: Alef Iury XLSR Wav2Vec2 Large 53 Portuguese
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
metrics:
- name: Test CORAA WER
type: wer
value: 24.89%
---
# Wav2vec 2.0 trained with CORAA Portuguese Dataset and Open Portuguese Datasets
This a the demonstration of a fine-tuned Wav2vec model for Portuguese using the following datasets:
- [CORAA dataset](https://github.com/nilc-nlp/CORAA)
- [CETUC](http://www02.smt.ufrj.br/~igor.quintanilha/alcaim.tar.gz).
- [Multilingual Librispeech (MLS)](http://www.openslr.org/94/).
- [VoxForge](http://www.voxforge.org/).
- [Common Voice 6.1](https://commonvoice.mozilla.org/pt).
## Repository
The repository that implements the model to be trained and tested is avaible [here](https://github.com/alefiury/SE-R_2022_Challenge_Wav2vec2).
|
alefiury/wav2vec2-large-xlsr-53-coraa-brazilian-portuguese-gain-normalization
|
alefiury
| 2022-04-05T16:58:36Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"pt",
"portuguese-speech-corpus",
"PyTorch",
"dataset:CORAA",
"dataset:common_voice",
"dataset:mls",
"dataset:cetuc",
"dataset:voxforge",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-27T16:34:54Z |
---
language: pt
datasets:
- CORAA
- common_voice
- mls
- cetuc
- voxforge
metrics:
- wer
tags:
- audio
- speech
- wav2vec2
- pt
- portuguese-speech-corpus
- automatic-speech-recognition
- speech
- PyTorch
license: apache-2.0
model-index:
- name: Alef Iury XLSR Wav2Vec2 Large 53 Portuguese
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
metrics:
- name: Test CORAA WER
type: wer
value: 24.89%
---
# Wav2vec 2.0 trained with CORAA Portuguese Dataset and Open Portuguese Datasets
This a the demonstration of a fine-tuned Wav2vec model for Portuguese using the following datasets:
- [CORAA dataset](https://github.com/nilc-nlp/CORAA)
- [CETUC](http://www02.smt.ufrj.br/~igor.quintanilha/alcaim.tar.gz).
- [Multilingual Librispeech (MLS)](http://www.openslr.org/94/).
- [VoxForge](http://www.voxforge.org/).
- [Common Voice 6.1](https://commonvoice.mozilla.org/pt).
## Repository
The repository that implements the model to be trained and tested is avaible [here](https://github.com/alefiury/SE-R_2022_Challenge_Wav2vec2).
|
facebook/wav2vec2-large-robust-ft-swbd-300h
|
facebook
| 2022-04-05T16:42:51Z | 2,724 | 18 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"speech",
"audio",
"en",
"dataset:libri_light",
"dataset:common_voice",
"dataset:switchboard",
"dataset:fisher",
"arxiv:2104.01027",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- libri_light
- common_voice
- switchboard
- fisher
tags:
- speech
- audio
- automatic-speech-recognition
widget:
- example_title: Librispeech sample 1
src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
- example_title: Librispeech sample 2
src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
license: apache-2.0
---
# Wav2Vec2-Large-Robust finetuned on Switchboard
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/).
This model is a fine-tuned version of the [wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) model.
It has been pretrained on:
- [Libri-Light](https://github.com/facebookresearch/libri-light): open-source audio books from the LibriVox project; clean, read-out audio data
- [CommonVoice](https://huggingface.co/datasets/common_voice): crowd-source collected audio data; read-out text snippets
- [Switchboard](https://catalog.ldc.upenn.edu/LDC97S62): telephone speech corpus; noisy telephone data
- [Fisher](https://catalog.ldc.upenn.edu/LDC2004T19): conversational telephone speech; noisy telephone data
and subsequently been finetuned on 300 hours of
- [Switchboard](https://catalog.ldc.upenn.edu/LDC97S62): telephone speech corpus; noisy telephone data
When using the model make sure that your speech input is also sampled at 16Khz.
[Paper Robust Wav2Vec2](https://arxiv.org/abs/2104.01027)
Authors: Wei-Ning Hsu, Anuroop Sriram, Alexei Baevski, Tatiana Likhomanenko, Qiantong Xu, Vineel Pratap, Jacob Kahn, Ann Lee, Ronan Collobert, Gabriel Synnaeve, Michael Auli
**Abstract**
Self-supervised learning of speech representations has been a very active research area but most work is focused on a single domain such as read audio books for which there exist large quantities of labeled and unlabeled data. In this paper, we explore more general setups where the domain of the unlabeled data for pre-training data differs from the domain of the labeled data for fine-tuning, which in turn may differ from the test data domain. Our experiments show that using target domain data during pre-training leads to large performance improvements across a variety of setups. On a large-scale competitive setup, we show that pre-training on unlabeled in-domain data reduces the gap between models trained on in-domain and out-of-domain labeled data by 66%-73%. This has obvious practical implications since it is much easier to obtain unlabeled target domain data than labeled data. Moreover, we find that pre-training on multiple domains improves generalization performance on domains not seen during training. Code and models will be made available at this https URL.
The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20.
# Usage
To transcribe audio files the model can be used as a standalone acoustic model as follows:
```python
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
from datasets import load_dataset
import torch
# load model and processor
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-robust-ft-swbd-300h")
model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-robust-ft-swbd-300h")
# load dummy dataset and read soundfiles
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
# tokenize
input_values = processor(ds[0]["audio"]["array"], return_tensors="pt", padding="longest").input_values # Batch size 1
# retrieve logits
logits = model(input_values).logits
# take argmax and decode
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
```
|
facebook/wav2vec2-large-960h
|
facebook
| 2022-04-05T16:40:42Z | 781,006 | 28 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"speech",
"en",
"dataset:librispeech_asr",
"arxiv:2006.11477",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- librispeech_asr
tags:
- speech
license: apache-2.0
---
# Wav2Vec2-Large-960h
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/)
The large model pretrained and fine-tuned on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model
make sure that your speech input is also sampled at 16Khz.
[Paper](https://arxiv.org/abs/2006.11477)
Authors: Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli
**Abstract**
We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks the speech input in the latent space and solves a contrastive task defined over a quantization of the latent representations which are jointly learned. Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of labeled data and pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech recognition with limited amounts of labeled data.
The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20.
# Usage
To transcribe audio files the model can be used as a standalone acoustic model as follows:
```python
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
from datasets import load_dataset
import torch
# load model and processor
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-960h")
model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h")
# load dummy dataset and read soundfiles
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
# tokenize
input_values = processor(ds[0]["audio"]["array"],, return_tensors="pt", padding="longest").input_values # Batch size 1
# retrieve logits
logits = model(input_values).logits
# take argmax and decode
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
```
## Evaluation
This code snippet shows how to evaluate **facebook/wav2vec2-large-960h** on LibriSpeech's "clean" and "other" test data.
```python
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import soundfile as sf
import torch
from jiwer import wer
librispeech_eval = load_dataset("librispeech_asr", "clean", split="test")
model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h").to("cuda")
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-960h")
def map_to_pred(batch):
input_values = processor(batch["audio"]["array"], return_tensors="pt", padding="longest").input_values
with torch.no_grad():
logits = model(input_values.to("cuda")).logits
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
batch["transcription"] = transcription
return batch
result = librispeech_eval.map(map_to_pred, batched=True, batch_size=1, remove_columns=["speech"])
print("WER:", wer(result["text"], result["transcription"]))
```
*Result (WER)*:
| "clean" | "other" |
|---|---|
| 2.8 | 6.3 |
|
HenryHXR/scibert_scivocab_uncased_epoch20-finetuned-ner
|
HenryHXR
| 2022-04-05T15:51:56Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-04-05T15:44:27Z |
---
tags:
- generated_from_trainer
model-index:
- name: scibert_scivocab_uncased_epoch20-finetuned-ner
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. -->
# scibert_scivocab_uncased_epoch20-finetuned-ner
This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
jeremykke/bert-base-uncased-finetuned-swag
|
jeremykke
| 2022-04-05T15:29:55Z | 2 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"multiple-choice",
"generated_from_trainer",
"dataset:swag",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
multiple-choice
| 2022-04-05T03:34:21Z |
---
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: 1.0087
- Accuracy: 0.7911
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7545 | 1.0 | 4597 | 0.5963 | 0.7695 |
| 0.3914 | 2.0 | 9194 | 0.6152 | 0.7879 |
| 0.1385 | 3.0 | 13791 | 1.0087 | 0.7911 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
gaetangate/bart-large_genrl_qald9
|
gaetangate
| 2022-04-05T15:10:33Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"arxiv:2108.07337",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
---
This model is used in the paper **Generative Relation Linking for Question Answering over Knowledge Bases**. [ArXiv](https://arxiv.org/abs/2108.07337), [GitHub](https://github.com/IBM/kbqa-relation-linking)
## Citation
```bibtex
@inproceedings{rossiello-genrl-2021,
title={Generative relation linking for question answering over knowledge bases},
author={Rossiello, Gaetano and Mihindukulasooriya, Nandana and Abdelaziz, Ibrahim and Bornea, Mihaela and Gliozzo, Alfio and Naseem, Tahira and Kapanipathi, Pavan},
booktitle={International Semantic Web Conference},
pages={321--337},
year={2021},
organization={Springer},
url = "https://link.springer.com/chapter/10.1007/978-3-030-88361-4_19",
doi = "10.1007/978-3-030-88361-4_19"
}
```
|
gaetangate/bart-large_genrl_lcquad2
|
gaetangate
| 2022-04-05T15:10:15Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"arxiv:2108.07337",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
---
This model is used in the paper **Generative Relation Linking for Question Answering over Knowledge Bases**. [ArXiv](https://arxiv.org/abs/2108.07337), [GitHub](https://github.com/IBM/kbqa-relation-linking)
## Citation
```bibtex
@inproceedings{rossiello-genrl-2021,
title={Generative relation linking for question answering over knowledge bases},
author={Rossiello, Gaetano and Mihindukulasooriya, Nandana and Abdelaziz, Ibrahim and Bornea, Mihaela and Gliozzo, Alfio and Naseem, Tahira and Kapanipathi, Pavan},
booktitle={International Semantic Web Conference},
pages={321--337},
year={2021},
organization={Springer},
url = "https://link.springer.com/chapter/10.1007/978-3-030-88361-4_19",
doi = "10.1007/978-3-030-88361-4_19"
}
```
|
tbosse/bert-base-german-cased-finetuned-subj_v3
|
tbosse
| 2022-04-05T15:03:58Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-04-05T13:32:50Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-german-cased-finetuned-subj_v3
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-german-cased-finetuned-subj_v3
This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1790
- Precision: 0.1875
- Recall: 0.0079
- F1: 0.0152
- Accuracy: 0.9472
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 136 | 0.1721 | 0.0 | 0.0 | 0.0 | 0.9488 |
| No log | 2.0 | 272 | 0.1731 | 0.0 | 0.0 | 0.0 | 0.9482 |
| No log | 3.0 | 408 | 0.1790 | 0.1875 | 0.0079 | 0.0152 | 0.9472 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
chibubu/Deeplearning_for_vision
|
chibubu
| 2022-04-05T14:36:36Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2022-04-05T14:29:18Z |
---
license: apache-2.0
---
|
medhabi/distilbert-base-uncased-mlm-ta-local
|
medhabi
| 2022-04-05T14:05:55Z | 2 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-04-05T11:20:38Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-mlm-ta-local
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-mlm-ta-local
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:
- Loss: 2.0658
## 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: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.4431 | 1.0 | 3125 | 2.1817 |
| 2.2197 | 2.0 | 6250 | 2.0929 |
| 2.1519 | 3.0 | 9375 | 2.0696 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.1
- Datasets 2.0.0
- Tokenizers 0.11.6
|
naver-clova-ocr/bros-base-uncased
|
naver-clova-ocr
| 2022-04-05T13:56:46Z | 40,502 | 18 |
transformers
|
[
"transformers",
"pytorch",
"bros",
"feature-extraction",
"arxiv:2108.04539",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
# BROS
GitHub: https://github.com/clovaai/bros
## Introduction
BROS (BERT Relying On Spatiality) is a pre-trained language model focusing on text and layout for better key information extraction from documents.<br>
Given the OCR results of the document image, which are text and bounding box pairs, it can perform various key information extraction tasks, such as extracting an ordered item list from receipts.<br>
For more details, please refer to our paper:
BROS: A Pre-trained Language Model Focusing on Text and Layout for Better Key Information Extraction from Documents<br>
Teakgyu Hong, Donghyun Kim, Mingi Ji, Wonseok Hwang, Daehyun Nam, Sungrae Park<br>
AAAI 2022 - Main Technical Track
[[arXiv]](https://arxiv.org/abs/2108.04539)
## Pre-trained models
| name | # params | Hugging Face - Models |
|---------------------|---------:|-------------------------------------------------------------------------------------------------|
| bros-base-uncased (**this**) | < 110M | [naver-clova-ocr/bros-base-uncased](https://huggingface.co/naver-clova-ocr/bros-base-uncased) |
| bros-large-uncased | < 340M | [naver-clova-ocr/bros-large-uncased](https://huggingface.co/naver-clova-ocr/bros-large-uncased) |
|
johnowhitaker/colorb_gan
|
johnowhitaker
| 2022-04-05T07:43:07Z | 0 | 1 | null |
[
"pytorch",
"region:us"
] | null | 2022-04-05T06:55:12Z |
A lightweightgan trained briefly on https://huggingface.co/datasets/johnowhitaker/colorbs
See https://huggingface.co/johnowhitaker/orbgan_e1 for training script and so on, since this was basically just copying that and running on a new dataset.
Note: lightweightgan code was updated between training orbgan_e1 and this one, so if you're trying to run the CPU inference notebook you'll get errors. See an updated version running this model on a CPU here: https://colab.research.google.com/drive/16XKJ7XZeSI0rvUf1GU6m9qrmwr1pMRWy?usp=sharing
See demo on spaces here: https://huggingface.co/spaces/huggan/Colorb_GAN
|
johnowhitaker/orbgan_light
|
johnowhitaker
| 2022-04-05T07:31:09Z | 0 | 0 | null |
[
"pytorch",
"region:us"
] | null | 2022-04-03T14:58:51Z |
A version of https://huggingface.co/johnowhitaker/orbgan_e1 trained on only light images
|
ukr-models/uk_core_news_trf
|
ukr-models
| 2022-04-05T06:57:34Z | 3 | 2 |
spacy
|
[
"spacy",
"token-classification",
"uk",
"license:mit",
"model-index",
"region:us"
] |
token-classification
| 2022-04-05T05:50:26Z |
---
tags:
- spacy
- token-classification
language:
- uk
license: mit
widget:
- text: "Могила Тараса Шевченка — місце поховання видатного українського поета Тараса Шевченка в місті Канів (Черкаська область) на Чернечій горі, над яким із 1939 року височіє бронзовий пам'ятник роботи скульптора Матвія Манізера."
model-index:
- name: uk_core_news_trf
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.8891135827
- name: NER Recall
type: recall
value: 0.8895133191
- name: NER F Score
type: f_score
value: 0.889313406
- task:
name: POS
type: token-classification
metrics:
- name: POS (UPOS) Accuracy
type: accuracy
value: 0.9833735418
- task:
name: MORPH
type: token-classification
metrics:
- name: Morph (UFeats) Accuracy
type: accuracy
value: 0.9611670877
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.9619342309
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0.9462333693
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 0.9300427148
---
Spacy transformer pipeline for Ukrainian language ([XLM-Roberta based](https://huggingface.co/ukr-models/xlm-roberta-base-uk)). Components: transformer, ner, morphologizer, parser.
[Training details](https://github.com/kurnosovv/ukr-spacy)
|
UWB-AIR/MQDD-duplicates
|
UWB-AIR
| 2022-04-05T06:24:29Z | 18 | 0 |
transformers
|
[
"transformers",
"pytorch",
"longformer",
"feature-extraction",
"arxiv:2203.14093",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-25T16:17:08Z |
---
license: cc-by-nc-sa-4.0
---
# MQDD - Multimodal Question Duplicity Detection
This repository publishes trained models and other supporting materials for the paper
[MQDD – Pre-training of Multimodal Question Duplicity Detection for Software Engineering Domain](https://arxiv.org/abs/2203.14093). For more information, see the paper.
The Stack Overflow Datasets (SOD) and Stack Overflow Duplicity Dataset (SODD) presented in the paper can be obtained from our [Stack Overflow Dataset repository](https://github.com/kiv-air/StackOverflowDataset).
To acquire the pre-trained model only, see the [UWB-AIR/MQDD-pretrained](https://huggingface.co/UWB-AIR/MQDD-pretrained).
## Fine-tuned MQDD
We release a fine-tuned version of our MQDD model for duplicate detection task. The model's architecture follows the architecture of a two-tower model as depicted in the figure below:
<img src="https://raw.githubusercontent.com/kiv-air/MQDD/master/img/architecture.png" width="700">
A self-standing encoder without a duplicate detection head can be loaded using the following source code snippet. Such a model can be used for building search systems based, for example, on [Faiss](https://github.com/facebookresearch/faiss) library.
```Python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("UWB-AIR/MQDD-duplicates")
model = AutoModel.from_pretrained("UWB-AIR/MQDD-duplicates")
```
A checkpoint of a full two-tower model can than be obtained from our [GoogleDrive folder](https://drive.google.com/drive/folders/1CYiqF2GJ2fSQzx_oM4-X_IhpObi4af5Q?usp=sharing). To load the model, one needs to use the model's implementation from `models/MQDD_model.py` in our [GitHub repository](https://github.com/kiv-air/MQDD). To construct the model and load it's checkpoint, use the following source code:
```Python
from MQDD_model import ClsHeadModelMQDD
model = ClsHeadModelMQDD("UWB-AIR/MQDD-duplicates")
ckpt = torch.load("model.pt", map_location="cpu")
model.load_state_dict(ckpt["model_state"])
```
## Licence
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. http://creativecommons.org/licenses/by-nc-sa/4.0/
## How should I cite the MQDD?
For now, please cite [the Arxiv paper](https://arxiv.org/abs/2203.14093):
```
@misc{https://doi.org/10.48550/arxiv.2203.14093,
doi = {10.48550/ARXIV.2203.14093},
url = {https://arxiv.org/abs/2203.14093},
author = {Pašek, Jan and Sido, Jakub and Konopík, Miloslav and Pražák, Ondřej},
title = {MQDD -- Pre-training of Multimodal Question Duplicity Detection for Software Engineering Domain},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
}
```
|
UWB-AIR/MQDD-pretrained
|
UWB-AIR
| 2022-04-05T06:14:47Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"longformer",
"feature-extraction",
"arxiv:2203.14093",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-25T16:16:40Z |
---
license: cc-by-nc-sa-4.0
---
# MQDD - Multimodal Question Duplicity Detection
This repository publishes pre-trained model for the paper
[MQDD – Pre-training of Multimodal Question Duplicity Detection for Software Engineering Domain](https://arxiv.org/abs/2203.14093). For more information, see the paper.
The Stack Overflow Datasets (SOD) and Stack Overflow Duplicity Dataset (SODD) presented in the paper can be obtained from our [Stack Overflow Dataset repository](https://github.com/kiv-air/StackOverflowDataset).
To acquire the fine-tuned model, see [UWB-AIR/MQDD-duplicate](https://huggingface.co/UWB-AIR/MQDD-duplicates).
The MQDD model, which is based on a Longformer architecture and is pre-trained on 218.5M training examples. The model was trained using MLM training objective accompanied with our novel Same Post (SP) and Question Answer (QA) learning objectives targeting specifically the duplicate detection task.
The model can be loaded using the following source code snippet:
```Python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("UWB-AIR/MQDD-pretrained")
model = AutoModel.from_pretrained("UWB-AIR/MQDD-pretrained")
```
## Licence
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. http://creativecommons.org/licenses/by-nc-sa/4.0/
## How should I cite the MQDD?
For now, please cite [the Arxiv paper](https://arxiv.org/abs/2203.14093):
```
@misc{https://doi.org/10.48550/arxiv.2203.14093,
doi = {10.48550/ARXIV.2203.14093},
url = {https://arxiv.org/abs/2203.14093},
author = {Pašek, Jan and Sido, Jakub and Konopík, Miloslav and Pražák, Ondřej},
title = {MQDD -- Pre-training of Multimodal Question Duplicity Detection for Software Engineering Domain},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
}
```
|
pinku/FatimaFellowship_fake_and_real_news
|
pinku
| 2022-04-05T03:22:45Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"license:bsd-3-clause",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-04T09:09:53Z |
---
license: bsd-3-clause
---
# Fatima Fellowship NLP Project
## Fake News Classifier
- BERT base model finetuned to classify fake news.
|
huggingtweets/hasanthehun
|
huggingtweets
| 2022-04-05T00:22:36Z | 3 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1207601173756174336/djTLQauA_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">hasanabi</div>
<div style="text-align: center; font-size: 14px;">@hasanthehun</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 hasanabi.
| Data | hasanabi |
| --- | --- |
| Tweets downloaded | 3231 |
| Retweets | 619 |
| Short tweets | 202 |
| Tweets kept | 2410 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/6atkn60d/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 @hasanthehun's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2a6l3ych) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2a6l3ych/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/hasanthehun')
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)
|
BigSalmon/GPTNeo350MInformalToFormalLincoln7
|
BigSalmon
| 2022-04-04T23:01:23Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-04-04T22:54:00Z |
Trained on this model: https://huggingface.co/xhyi/PT_GPTNEO350_ATG/tree/main
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/GPTNeo350MInformalToFormalLincoln7")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/GPTNeo350MInformalToFormalLincoln7")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
***
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
***
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
informal english:
```
```
- declining viewership facing the nba.
- does not have to be this way.
- in fact, many solutions exist.
- the four point line would surely draw in eyes.
Text: failing to draw in the masses, the NBA has fallen into disrepair. such does not have to be the case, however. in fact, a myriad of simple, relatively cheap solutions could revive the league. the addition of the much-hyped four-point line would surely juice viewership.
***
-
```
```
infill: chrome extensions [MASK] accomplish everyday tasks.
Translated into the Style of Abraham Lincoln: chrome extensions ( expedite the ability to / unlock the means to more readily ) accomplish everyday tasks.
infill: at a time when nintendo has become inflexible, [MASK] consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
Translated into the Style of Abraham Lincoln: at a time when nintendo has become inflexible, ( stubbornly [MASK] on / firmly set on / unyielding in its insistence on ) consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
infill:
```
```
Essay Intro (California High-Speed Rail): built with an eye on the future, california's high-speed rail service resolves to change the face of travel.
Essay Intro (YIMBY's Need To Win): home to the most expensive housing market in the united states, san francisco is the city in which the yimby and anti-yimby hordes wage an eternal battle.
Essay Intro (
```
```
Search: What is the definition of Checks and Balances?
https://en.wikipedia.org/wiki/Checks_and_balances
Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate.
https://www.harvard.edu/glossary/Checks_and_Balances
Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power
https://www.law.cornell.edu/library/constitution/Checks_and_Balances
Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power.
***
Search: What is the definition of Separation of Powers?
https://en.wikipedia.org/wiki/Separation_of_powers
The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power.
https://www.yale.edu/tcf/Separation_of_Powers.html
Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined.
***
Search: What is the definition of Connection of Powers?
https://en.wikipedia.org/wiki/Connection_of_powers
Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches.
https://simple.wikipedia.org/wiki/Connection_of_powers
The term Connection of Powers describes a system of government in which there is overlap between different parts of the government.
***
Search: What is the definition of
```
```
Search: What are phrase synonyms for "second-guess"?
https://www.powerthesaurus.org/second-guess/synonyms
Shortest to Longest:
- feel dubious about
- raise an eyebrow at
- wrinkle their noses at
- cast a jaundiced eye at
- teeter on the fence about
***
Search: What are phrase synonyms for "mean to newbies"?
https://www.powerthesaurus.org/mean_to_newbies/synonyms
Shortest to Longest:
- readiness to balk at rookies
- absence of tolerance for novices
- hostile attitude toward newcomers
***
Search: What are phrase synonyms for "make use of"?
https://www.powerthesaurus.org/make_use_of/synonyms
Shortest to Longest:
- call upon
- glean value from
- reap benefits from
- derive utility from
- seize on the merits of
- draw on the strength of
- tap into the potential of
***
Search: What are phrase synonyms for "hurting itself"?
https://www.powerthesaurus.org/hurting_itself/synonyms
Shortest to Longest:
- erring
- slighting itself
- forfeiting its integrity
- doing itself a disservice
- evincing a lack of backbone
***
Search: What are phrase synonyms for "
```
|
BigSalmon/MediumInformalToFormalLincoln
|
BigSalmon
| 2022-04-04T22:25:35Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-04-04T21:54:23Z |
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/MediumInformalToFormalLincoln")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/MediumInformalToFormalLincoln")
```
```
- moviepass to return
- this summer
- swooped up by
- original co-founder stacy spikes
text: the re-launch of moviepass is set to transpire this summer, ( rescued at the hands of / under the stewardship of / spearheaded by ) its founding father, stacy spikes.
***
- middle schools do not have recess
- should get back to doing it
- amazing for communication
- and getting kids to move around
text: a casualty of the education reform craze, recess has been excised from middle schools. this is tragic, for it is instrumental in honing children's communication skills and encouraging physical activity.
***
-
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
***
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
***
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
informal english:
```
```
infill: chrome extensions [MASK] accomplish everyday tasks.
Translated into the Style of Abraham Lincoln: chrome extensions ( expedite the ability to / unlock the means to more readily ) accomplish everyday tasks.
infill: at a time when nintendo has become inflexible, [MASK] consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
Translated into the Style of Abraham Lincoln: at a time when nintendo has become inflexible, ( stubbornly [MASK] on / firmly set on / unyielding in its insistence on ) consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
infill:
```
```
Essay Intro (Warriors vs. Rockets in Game 7):
text: eagerly anticipated by fans, game 7's are the highlight of the post-season.
text: ever-building in suspense, game 7's have the crowd captivated.
***
Essay Intro (South Korean TV Is Becoming Popular):
text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ).
text: increasingly held in critical esteem, south korean television continues to impress.
text: at the forefront of quality content, south korea is quickly achieving celebrity status.
***
Essay Intro (
```
```
Search: What is the definition of Checks and Balances?
https://en.wikipedia.org/wiki/Checks_and_balances
Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate.
https://www.harvard.edu/glossary/Checks_and_Balances
Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power
https://www.law.cornell.edu/library/constitution/Checks_and_Balances
Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power.
***
Search: What is the definition of Separation of Powers?
https://en.wikipedia.org/wiki/Separation_of_powers
The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power.
https://www.yale.edu/tcf/Separation_of_Powers.html
Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined.
***
Search: What is the definition of Connection of Powers?
https://en.wikipedia.org/wiki/Connection_of_powers
Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches.
https://simple.wikipedia.org/wiki/Connection_of_powers
The term Connection of Powers describes a system of government in which there is overlap between different parts of the government.
***
Search: What is the definition of
```
```
Search: What are phrase synonyms for "second-guess"?
https://www.powerthesaurus.org/second-guess/synonyms
Shortest to Longest:
- feel dubious about
- raise an eyebrow at
- wrinkle their noses at
- cast a jaundiced eye at
- teeter on the fence about
***
Search: What are phrase synonyms for "mean to newbies"?
https://www.powerthesaurus.org/mean_to_newbies/synonyms
Shortest to Longest:
- readiness to balk at rookies
- absence of tolerance for novices
- hostile attitude toward newcomers
***
Search: What are phrase synonyms for "make use of"?
https://www.powerthesaurus.org/make_use_of/synonyms
Shortest to Longest:
- call upon
- glean value from
- reap benefits from
- derive utility from
- seize on the merits of
- draw on the strength of
- tap into the potential of
***
Search: What are phrase synonyms for "hurting itself"?
https://www.powerthesaurus.org/hurting_itself/synonyms
Shortest to Longest:
- erring
- slighting itself
- forfeiting its integrity
- doing itself a disservice
- evincing a lack of backbone
***
Search: What are phrase synonyms for "
```
```
- declining viewership facing the nba.
- does not have to be this way.
- in fact, many solutions exist.
- the four point line would surely draw in eyes.
text: failing to draw in the masses, the nba has ( fallen into / succumb to / bowed to ) disrepair. such does not have to be the case, however. in fact, a myriad of simple, relatively cheap ( solutions / interventions / enhancements ) could revive the league. the addition of the much-hyped four-point line would surely juice viewership.
***
-
```
```
original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick.
infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick.
***
original:
```
```
wordy: classical music is becoming less popular more and more.
Translate into Concise Text: interest in classic music is fading.
***
wordy:
```
```
sweet: savvy voters ousted him.
longer: voters who were informed delivered his defeat.
***
sweet:
```
```
1: commercial space company spacex plans to launch a whopping 52 flights in 2022.
2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022.
3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights.
4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company.
5: a commercial space company, spacex aims to conduct 52 flights in 2022.
***
1:
```
Keywords to sentences or sentence.
|
wanyu/IteraTeR-ROBERTA-Intention-Classifier
|
wanyu
| 2022-04-04T20:13:42Z | 10 | 5 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"dataset:IteraTeR_full_sent",
"arxiv:2203.03802",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-13T19:10:06Z |
---
datasets:
- IteraTeR_full_sent
---
# IteraTeR RoBERTa model
This model was obtained by fine-tuning [roberta-large](https://huggingface.co/roberta-large) on [IteraTeR-human-sent](https://huggingface.co/datasets/wanyu/IteraTeR_human_sent) dataset.
Paper: [Understanding Iterative Revision from Human-Written Text](https://arxiv.org/abs/2203.03802) <br>
Authors: Wanyu Du, Vipul Raheja, Dhruv Kumar, Zae Myung Kim, Melissa Lopez, Dongyeop Kang
## Edit Intention Prediction Task
Given a pair of original sentence and revised sentence, our model can predict the edit intention for this revision pair.<br>
More specifically, the model will predict the probability of the following edit intentions:
<table>
<tr>
<th>Edit Intention</th>
<th>Definition</th>
<th>Example</th>
</tr>
<tr>
<td>clarity</td>
<td>Make the text more formal, concise, readable and understandable.</td>
<td>
Original: It's like a house which anyone can enter in it. <br>
Revised: It's like a house which anyone can enter.
</td>
</tr>
<tr>
<td>fluency</td>
<td>Fix grammatical errors in the text.</td>
<td>
Original: In the same year he became the Fellow of the Royal Society. <br>
Revised: In the same year, he became the Fellow of the Royal Society.
</td>
</tr>
<tr>
<td>coherence</td>
<td>Make the text more cohesive, logically linked and consistent as a whole.</td>
<td>
Original: Achievements and awards Among his other activities, he founded the Karachi Film Guild and Pakistan Film and TV Academy. <br>
Revised: Among his other activities, he founded the Karachi Film Guild and Pakistan Film and TV Academy.
</td>
</tr>
<tr>
<td>style</td>
<td>Convey the writer’s writing preferences, including emotions, tone, voice, etc..</td>
<td>
Original: She was last seen on 2005-10-22. <br>
Revised: She was last seen on October 22, 2005.
</td>
</tr>
<tr>
<td>meaning-changed</td>
<td>Update or add new information to the text.</td>
<td>
Original: This method improves the model accuracy from 64% to 78%. <br>
Revised: This method improves the model accuracy from 64% to 83%.
</td>
</tr>
</table>
## Usage
```python
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("wanyu/IteraTeR-ROBERTA-Intention-Classifier")
model = AutoModelForSequenceClassification.from_pretrained("wanyu/IteraTeR-ROBERTA-Intention-Classifier")
id2label = {0: "clarity", 1: "fluency", 2: "coherence", 3: "style", 4: "meaning-changed"}
before_text = 'I likes coffee.'
after_text = 'I like coffee.'
model_input = tokenizer(before_text, after_text, return_tensors='pt')
model_output = model(**model_input)
softmax_scores = torch.softmax(model_output.logits, dim=-1)
pred_id = torch.argmax(softmax_scores)
pred_label = id2label[pred_id.int()]
```
|
wanyu/IteraTeR-BART-Revision-Generator
|
wanyu
| 2022-04-04T20:09:49Z | 8 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"dataset:IteraTeR_full_sent",
"arxiv:2203.03802",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-15T01:21:43Z |
---
datasets:
- IteraTeR_full_sent
---
# IteraTeR BART model
This model was obtained by fine-tuning [facebook/bart-base](https://huggingface.co/facebook/bart-base) on [IteraTeR-full-sent](https://huggingface.co/datasets/wanyu/IteraTeR_full_sent) dataset.
Paper: [Understanding Iterative Revision from Human-Written Text](https://arxiv.org/abs/2203.03802) <br>
Authors: Wanyu Du, Vipul Raheja, Dhruv Kumar, Zae Myung Kim, Melissa Lopez, Dongyeop Kang
## Text Revision Task
Given an edit intention and an original sentence, our model can generate a revised sentence.<br>
The edit intentions are provided by [IteraTeR-full-sent](https://huggingface.co/datasets/wanyu/IteraTeR_full_sent) dataset, which are categorized as follows:
<table>
<tr>
<th>Edit Intention</th>
<th>Definition</th>
<th>Example</th>
</tr>
<tr>
<td>clarity</td>
<td>Make the text more formal, concise, readable and understandable.</td>
<td>
Original: It's like a house which anyone can enter in it. <br>
Revised: It's like a house which anyone can enter.
</td>
</tr>
<tr>
<td>fluency</td>
<td>Fix grammatical errors in the text.</td>
<td>
Original: In the same year he became the Fellow of the Royal Society. <br>
Revised: In the same year, he became the Fellow of the Royal Society.
</td>
</tr>
<tr>
<td>coherence</td>
<td>Make the text more cohesive, logically linked and consistent as a whole.</td>
<td>
Original: Achievements and awards Among his other activities, he founded the Karachi Film Guild and Pakistan Film and TV Academy. <br>
Revised: Among his other activities, he founded the Karachi Film Guild and Pakistan Film and TV Academy.
</td>
</tr>
<tr>
<td>style</td>
<td>Convey the writer’s writing preferences, including emotions, tone, voice, etc..</td>
<td>
Original: She was last seen on 2005-10-22. <br>
Revised: She was last seen on October 22, 2005.
</td>
</tr>
</table>
## Usage
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("wanyu/IteraTeR-BART-Revision-Generator")
model = AutoModelForSeq2SeqLM.from_pretrained("wanyu/IteraTeR-BART-Revision-Generator")
before_input = '<fluency> I likes coffee.'
model_input = tokenizer(before_input, return_tensors='pt')
model_outputs = model.generate(**model_input, num_beams=8, max_length=1024)
after_text = tokenizer.batch_decode(model_outputs, skip_special_tokens=True)[0]
```
|
wanyu/IteraTeR-PEGASUS-Revision-Generator
|
wanyu
| 2022-04-04T20:08:12Z | 12 | 2 |
transformers
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"dataset:IteraTeR_full_sent",
"arxiv:2203.03802",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-13T18:55:49Z |
---
datasets:
- IteraTeR_full_sent
---
# IteraTeR PEGASUS model
This model was obtained by fine-tuning [google/pegasus-large](https://huggingface.co/google/pegasus-large) on [IteraTeR-full-sent](https://huggingface.co/datasets/wanyu/IteraTeR_full_sent) dataset.
Paper: [Understanding Iterative Revision from Human-Written Text](https://arxiv.org/abs/2203.03802) <br>
Authors: Wanyu Du, Vipul Raheja, Dhruv Kumar, Zae Myung Kim, Melissa Lopez, Dongyeop Kang
## Text Revision Task
Given an edit intention and an original sentence, our model can generate a revised sentence.<br>
The edit intentions are provided by [IteraTeR-full-sent](https://huggingface.co/datasets/wanyu/IteraTeR_full_sent) dataset, which are categorized as follows:
<table>
<tr>
<th>Edit Intention</th>
<th>Definition</th>
<th>Example</th>
</tr>
<tr>
<td>clarity</td>
<td>Make the text more formal, concise, readable and understandable.</td>
<td>
Original: It's like a house which anyone can enter in it. <br>
Revised: It's like a house which anyone can enter.
</td>
</tr>
<tr>
<td>fluency</td>
<td>Fix grammatical errors in the text.</td>
<td>
Original: In the same year he became the Fellow of the Royal Society. <br>
Revised: In the same year, he became the Fellow of the Royal Society.
</td>
</tr>
<tr>
<td>coherence</td>
<td>Make the text more cohesive, logically linked and consistent as a whole.</td>
<td>
Original: Achievements and awards Among his other activities, he founded the Karachi Film Guild and Pakistan Film and TV Academy. <br>
Revised: Among his other activities, he founded the Karachi Film Guild and Pakistan Film and TV Academy.
</td>
</tr>
<tr>
<td>style</td>
<td>Convey the writer’s writing preferences, including emotions, tone, voice, etc..</td>
<td>
Original: She was last seen on 2005-10-22. <br>
Revised: She was last seen on October 22, 2005.
</td>
</tr>
</table>
## Usage
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("wanyu/IteraTeR-PEGASUS-Revision-Generator")
model = AutoModelForSeq2SeqLM.from_pretrained("wanyu/IteraTeR-PEGASUS-Revision-Generator")
before_input = '<fluency> I likes coffee.'
model_input = tokenizer(before_input, return_tensors='pt')
model_outputs = model.generate(**model_input, num_beams=8, max_length=1024)
after_text = tokenizer.batch_decode(model_outputs, skip_special_tokens=True)[0]
```
|
Sajib-006/fake_news_detection_xlmRoberta
|
Sajib-006
| 2022-04-04T20:06:58Z | 0 | 0 | null |
[
"NLP",
"Fake News Detection",
"XLM RoBERTa",
"region:us"
] | null | 2022-04-04T13:19:49Z |
---
language:
- Python
tags:
- NLP
- Fake News Detection
- XLM RoBERTa
datasets:
- https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset
metrics:
- Accuracy
- F1-score
---
# Write up:
## Link to hugging face model:
https://huggingface.co/Sajib-006/fake_news_detection_xlmRoberta
## Model Description:
* Used pretrained XLM-Roberta base model.
* Added classifier layer after bert model
* For tokenization, i used max length of text as 512(which is max bert can handle)
## Result:
* Using bert base uncased english model, the accuracy was near 85% (For all samples)
* Using XLM Roberta base model, the accuracy was almost 100% ( For only 2k samples)
## Limitations:
* Pretrained XLM Roberta is a heavy model. Training it with the full dataset(44k+ samples) was not possible using google colab free version. So i had to take small sample of 2k size for my experiment.
* As we can see, there is almost 100% accuracy and F1-score for 2000 dataset, so i haven't tried to find misclassified data.
* I couldn't run the model for the whole dataset as i used google colab free version, there was RAM and disk restrictions. XLMRoberta is a heavy model, so training it for the full dataset tends to take huge time. Colab doesn't provide GPU for long time.
* As one run for one epoch took huge time, i had to save checkpoint after 1 epoch and retrain the model loading weights for 2nd time. After 2 epoch it showed almost 100% accuracy, so i didn't continue to train again.
* A more clear picture could have been seen if it could be run for the full dataset. I thought of some ideas about better model but couldn't implement for hardware restriction as mentioned and time constraint. My ideas are given below.
## Ideas to imrove on full dataset:
* Using XLM Roberta large instead of base can improve
* Adding dense layer and dropout layer to reduce overfitting(Though in my result there is 100% accuracy on hold-out test set, so no overfitting seems to be there)
* Adding convolutional layer after the bert encoder work even better.
* Combination of different complex convolution layers can be added to check if accuracy increases further more.
* Hyperparameter tuning of the layers to ensure best result.
|
Sevil/t5-small-finetuned-wikihow_3epoch_v2
|
Sevil
| 2022-04-04T20:03:46Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wikihow",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-04-04T13:45:46Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wikihow
metrics:
- rouge
model-index:
- name: t5-small-finetuned-wikihow_3epoch_v2
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: wikihow
type: wikihow
args: all
metrics:
- name: Rouge1
type: rouge
value: 27.48
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-wikihow_3epoch_v2
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wikihow dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2758
- Rouge1: 27.48
- Rouge2: 10.7621
- Rougel: 23.4136
- Rougelsum: 26.7923
- Gen Len: 18.5424
## 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: 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: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 2.8423 | 0.13 | 5000 | 2.5715 | 25.2685 | 8.6964 | 21.229 | 24.5773 | 18.4479 |
| 2.7345 | 0.25 | 10000 | 2.5236 | 24.982 | 8.7823 | 21.1609 | 24.3066 | 18.3631 |
| 2.6811 | 0.38 | 15000 | 2.4911 | 25.7585 | 9.3372 | 21.8388 | 25.1052 | 18.3997 |
| 2.6611 | 0.51 | 20000 | 2.4510 | 26.022 | 9.4708 | 22.0899 | 25.3236 | 18.5472 |
| 2.6133 | 0.64 | 25000 | 2.4272 | 26.3481 | 9.6769 | 22.4484 | 25.7046 | 18.3863 |
| 2.6083 | 0.76 | 30000 | 2.4108 | 26.4131 | 9.6643 | 22.4021 | 25.6958 | 18.5585 |
| 2.5842 | 0.89 | 35000 | 2.3866 | 26.2852 | 9.7505 | 22.4525 | 25.5908 | 18.5485 |
| 2.5554 | 1.02 | 40000 | 2.3816 | 26.3018 | 9.7218 | 22.3673 | 25.6515 | 18.4912 |
| 2.4895 | 1.14 | 45000 | 2.3730 | 26.6439 | 9.9665 | 22.6593 | 25.9521 | 18.5635 |
| 2.4781 | 1.27 | 50000 | 2.3541 | 26.8488 | 10.0364 | 22.8202 | 26.1598 | 18.4254 |
| 2.4821 | 1.4 | 55000 | 2.3440 | 26.9511 | 10.2079 | 23.0133 | 26.2821 | 18.5712 |
| 2.4593 | 1.53 | 60000 | 2.3370 | 26.945 | 10.3123 | 22.9245 | 26.2493 | 18.5978 |
| 2.4521 | 1.65 | 65000 | 2.3309 | 26.9652 | 10.314 | 22.9657 | 26.298 | 18.4837 |
| 2.4523 | 1.78 | 70000 | 2.3249 | 27.0548 | 10.4204 | 23.1286 | 26.379 | 18.4717 |
| 2.4563 | 1.91 | 75000 | 2.3079 | 27.4563 | 10.6452 | 23.3985 | 26.7812 | 18.5642 |
| 2.4229 | 2.03 | 80000 | 2.3115 | 27.0538 | 10.44 | 22.9957 | 26.349 | 18.5914 |
| 2.3694 | 2.16 | 85000 | 2.3017 | 27.332 | 10.6556 | 23.3135 | 26.629 | 18.459 |
| 2.3749 | 2.29 | 90000 | 2.2941 | 27.3294 | 10.5967 | 23.2039 | 26.6411 | 18.5179 |
| 2.3779 | 2.42 | 95000 | 2.2891 | 27.3725 | 10.6539 | 23.3455 | 26.707 | 18.5367 |
| 2.3638 | 2.54 | 100000 | 2.2895 | 27.3487 | 10.6738 | 23.2894 | 26.681 | 18.6128 |
| 2.3549 | 2.67 | 105000 | 2.2833 | 27.408 | 10.6903 | 23.3575 | 26.7137 | 18.6035 |
| 2.3652 | 2.8 | 110000 | 2.2788 | 27.561 | 10.8202 | 23.4672 | 26.8584 | 18.5565 |
| 2.3553 | 2.93 | 115000 | 2.2758 | 27.48 | 10.7621 | 23.4136 | 26.7923 | 18.5424 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
nielsr/convnext-tiny-finetuned-eurostat
|
nielsr
| 2022-04-04T19:25:58Z | 61 | 0 |
transformers
|
[
"transformers",
"pytorch",
"convnext",
"image-classification",
"dataset:eurosat",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-04-04T18:59:04Z |
---
license: apache-2.0
datasets:
- eurosat
widget:
- src: forest.png
example_title: Forest
---
# ConvNext fine-tuned on Eurosat
This model is a `facebook/convnext-tiny-224` model fine-tuned on the [Eurosat dataset](https://github.com/phelber/EuroSAT).
|
okep/distilbert-base-uncased-finetuned-emotion
|
okep
| 2022-04-04T18:53:56Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-28T20:03:24Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9245
- name: F1
type: f1
value: 0.9245483619750937
---
<!-- 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-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2269
- Accuracy: 0.9245
- F1: 0.9245
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.853 | 1.0 | 250 | 0.3507 | 0.8925 | 0.8883 |
| 0.2667 | 2.0 | 500 | 0.2269 | 0.9245 | 0.9245 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
2NRC/Fake-New-Classifier
|
2NRC
| 2022-04-04T18:53:07Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2022-04-04T18:33:31Z |
---
license: other
---
Deep Learning for NLP: Training a text classification model to detect fake news articles!
Training and test dataset gotten from https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset
Dataset size = 44898 articles
Training set size = 35918 articles
Test set size = 8980 articles
Accuracy on the training set = 0.990394788128515
Accuracy on the test set = 0.983184855233853
|
aswinsson/fake_new_classifier
|
aswinsson
| 2022-04-04T18:50:02Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-04T18:35:15Z |
---
license: afl-3.0
---
The fake news classifer built using distillbert uncased. Created for the Fatima Fellowship coding challenge and trained on P100 instance for 3 epochs. The model is a binary classifier which predicts 1 in case of real news.
Library: transformers \
Language: English \
Dataset: https:\/\/www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset
|
johnowhitaker/butterfly-gan-10k
|
johnowhitaker
| 2022-04-04T18:12:07Z | 0 | 0 | null |
[
"pytorch",
"region:us"
] | null | 2022-04-04T16:23:33Z |
Badly trained lightweightgan - ignore
|
mgreenbe/607-demo-model
|
mgreenbe
| 2022-04-04T17:35:06Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"tag2",
"en",
"dataset:yelp_polarity",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-04T17:07:37Z |
---
language:
- en
tags:
- text-classification
- tag2
license: apache-2.0
datasets:
- yelp_polarity
metrics:
- accuracy
---
Demo model for predicting the polarity of Yelp reviews.
Trained for 1 epoch on 4096 reviews.
|
efederici/cross-encoder-bert-base-stsb
|
efederici
| 2022-04-04T17:09:02Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"cross-encoder",
"sentence-similarity",
"it",
"dataset:stsb_multi_mt",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-04T16:26:27Z |
---
pipeline_tag: text-classification
language:
- it
datasets:
- stsb_multi_mt
tags:
- cross-encoder
- sentence-similarity
- transformers
---
# Cross-Encoder
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
<p align="center">
<img src="https://upload.wikimedia.org/wikipedia/commons/f/f6/Edouard_Vuillard%2C_1920c_-_Sunlit_Interior.jpg" width="400"> </br>
Edouard Vuillard, Sunlit Interior
</p>
## Training Data
This model was trained on [stsb](https://huggingface.co/datasets/stsb_multi_mt/viewer/it/train). The model will predict a score between 0 and 1 how for the semantic similarity of two sentences.
## Usage and Performance
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('efederici/cross-encoder-umberto-stsb')
scores = model.predict([('Sentence 1', 'Sentence 2'), ('Sentence 3', 'Sentence 4')])
```
The model will predict scores for the pairs `('Sentence 1', 'Sentence 2')` and `('Sentence 3', 'Sentence 4')`.
|
efederici/cross-encoder-umberto-stsb
|
efederici
| 2022-04-04T16:09:44Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"camembert",
"text-classification",
"cross-encoder",
"sentence-similarity",
"it",
"dataset:stsb_multi_mt",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-04T15:48:58Z |
---
pipeline_tag: text-classification
language:
- it
datasets:
- stsb_multi_mt
tags:
- cross-encoder
- sentence-similarity
- transformers
---
# Cross-Encoder
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
<p align="center">
<img src="https://user-images.githubusercontent.com/7140210/72913702-d55a8480-3d3d-11ea-99fc-f2ef29af4e72.jpg" width="700"> </br>
Marco Lodola, Monument to Umberto Eco, Alessandria 2019
</p>
## Training Data
This model was trained on [stsb](https://huggingface.co/datasets/stsb_multi_mt/viewer/it/train). The model will predict a score between 0 and 1 how for the semantic similarity of two sentences.
## Usage and Performance
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('efederici/cross-encoder-umberto-stsb')
scores = model.predict([('Sentence 1', 'Sentence 2'), ('Sentence 3', 'Sentence 4')])
```
The model will predict scores for the pairs `('Sentence 1', 'Sentence 2')` and `('Sentence 3', 'Sentence 4')`.
|
Manimaran/pokemon_classifer
|
Manimaran
| 2022-04-04T15:59:25Z | 0 | 1 | null |
[
"license:wtfpl",
"region:us"
] | null | 2022-04-04T15:31:57Z |
---
license: wtfpl
---
# Pokemon Classifier
This repo is a part of my study in deep learning with [fast.ai](https://www.fast.ai), this app uses this template [repo](https://github.com/render-examples/fastai-v3). thanks to them for the starter code and the [fast ai MOOC](https://course.fast.ai/) for making it easy to build deep learning models, and also the creator of this <del>[dataset](https://kaggle.com/mrgravelord/complete-pokemon-image-dataset)</del> for putting up a curated dataset (removed from kaggle).
I have also hosted this web app on heroku, Check it out [here](https://pokemon-classifier.herokuapp.com).
Blog post explaining the model building process [here](https://mani2106.github.io/Blog-Posts/pokemon-classifer/image-classification/fastai/2019/06/01/Fast_ai_lesson_2_pokemon_classifier.html).
## Demo
|
enimai/OPUS-mt-en-fr-finetuned-MUST-C
|
enimai
| 2022-04-04T11:49:17Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"marian",
"text2text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-04-04T11:26:27Z |
---
license: apache-2.0
---
|
kleinay/qanom-seq2seq-model-baseline
|
kleinay
| 2022-04-04T11:05:47Z | 24 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"semantic-role-labeling",
"question-answer generation",
"en",
"dataset:kleinay/qanom",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
language:
- en
tags:
- semantic-role-labeling
- question-answer generation
- pytorch
datasets:
- kleinay/qanom
---
# A Seq2Seq model for QANom parsing
This is a `t5-small` pretrained model, fine-tuned on the task of generating QANom QAs.
"QANom" stands for "QASRL for Nominalizations", which is an adaptation of [QASRL (Question-Answer driven Semantic Role Labeling)](https://qasrl.org) for the nominal predicates domain. See the [QANom paper](https://aclanthology.org/2020.coling-main.274/) for details about the task. The QANom Dataset official site is a [Google drive](https://drive.google.com/drive/folders/15PHKVdPm65ysgdkV47z6J_73kETk7_of), but we also wrapped it into a [Huggingface Dataset](https://huggingface.co/datasets/biu-nlp/qanom), which is easier to plug-and-play with (check out our [HF profile](https://huggingface.co/biu-nlp) for other related datasets, such as QASRL, QAMR, QADiscourse, and QA-Align).
## Demo
Visit [our demo](https://huggingface.co/spaces/kleinay/qanom-seq2seq-demo) for interactively exploring our model!
## Usage
The model and tokenizer can be downloaded as simply as running:
```python
import transformers
model = transformers.AutoModelForSeq2SeqLM.from_pretrained("kleinay/qanom-seq2seq-model-baseline")
tokenizer = transformers.AutoTokenizer.from_pretrained("kleinay/qanom-seq2seq-model-baseline")
```
However, the model fine-tuning procedure involves input preprocessing (marking the predicate in the sentence, T5's "task prefix", incorporating the predicate type and/or the verbal for of the nominalization) and output postprocessing (parsing the sequence into a list of QASRL-formatted QAs).
In order to use the model for QANom parsing easily, we suggest downloading the [`pipeline.py`](https://huggingface.co/kleinay/qanom-seq2seq-model-baseline/blob/main/pipeline.py) file from this repository, and then use the `QASRL_Pipeline` class:
```python
from pipeline import QASRL_Pipeline
pipe = QASRL_Pipeline("kleinay/qanom-seq2seq-model-baseline")
pipe("The student was interested in Luke 's <predicate> research about see animals .", verb_form="research", predicate_type="nominal")
```
Which will output:
```json
[{'generated_text': 'who _ _ researched something _ _ ?<extra_id_7> Luke',
'QAs': [{'question': 'who researched something ?', 'answers': ['Luke']}]}]
```
You can learn more about using `transformers.pipelines` in the [official docs](https://huggingface.co/docs/transformers/main_classes/pipelines).
Notice that you need to specify which word in the sentence is the predicate, about which the question will interrogate. By default, you should precede the predicate with the `<predicate>` symbol, but you can also specify your own predicate marker:
```python
pipe("The student was interested in Luke 's <PRED> research about see animals .", verb_form="research", predicate_type="nominal", predicate_marker="<PRED>")
```
In addition, you can specify additional kwargs for controling the model's decoding algorithm:
```python
pipe("The student was interested in Luke 's <predicate> research about see animals .", verb_form="research", predicate_type="nominal", num_beams=3)
```
|
avialfont/dummy-finetuned-imdb
|
avialfont
| 2022-04-04T10:53:31Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-04-04T10:06:50Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: avialfont/dummy-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. -->
# avialfont/dummy-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: 2.8606
- Validation Loss: 2.5865
- 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 |
|:----------:|:---------------:|:-----:|
| 2.8606 | 2.5865 | 0 |
### Framework versions
- Transformers 4.16.2
- TensorFlow 2.8.0
- Datasets 1.18.3
- Tokenizers 0.11.6
|
blacktree/distilbert-base-uncased-finetuned-sst2
|
blacktree
| 2022-04-04T10:44:22Z | 15 | 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
| 2022-04-01T12:29:26Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: sst2
metrics:
- name: Accuracy
type: accuracy
value: 0.5091743119266054
---
<!-- 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-sst2
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: 0.7027
- Accuracy: 0.5092
## 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: 0.01
- 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6868 | 1.0 | 1053 | 0.7027 | 0.5092 |
| 0.6868 | 2.0 | 2106 | 0.7027 | 0.5092 |
| 0.6867 | 3.0 | 3159 | 0.6970 | 0.5092 |
| 0.687 | 4.0 | 4212 | 0.6992 | 0.5092 |
| 0.6866 | 5.0 | 5265 | 0.6983 | 0.5092 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
somosnlp-hackathon-2022/readability-es-sentences
|
somosnlp-hackathon-2022
| 2022-04-04T10:41:09Z | 21 | 5 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"spanish",
"bertin",
"es",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-30T12:30:08Z |
---
language: es
license: cc-by-4.0
tags:
- spanish
- roberta
- bertin
pipeline_tag: text-classification
widget:
- text: La ciencia nos enseña, en efecto, a someter nuestra razón a la verdad y a conocer y juzgar las cosas como son, es decir, como ellas mismas eligen ser y no como quisiéramos que fueran.
---
# Readability ES Sentences for two classes
Model based on the Roberta architecture finetuned on [BERTIN](https://huggingface.co/bertin-project/bertin-roberta-base-spanish) for readability assessment of Spanish texts.
## Description and performance
This version of the model was trained on a mix of datasets, using sentence-level granularity when possible. The model performs binary classification among the following classes:
- Simple.
- Complex.
It achieves a F1 macro average score of 0.8923, measured on the validation set.
## Model variants
- `readability-es-sentences` (this model). Two classes, sentence-based dataset.
- [`readability-es-paragraphs`](https://huggingface.co/hackathon-pln-es/readability-es-paragraphs). Two classes, paragraph-based dataset.
- [`readability-es-3class-sentences`](https://huggingface.co/hackathon-pln-es/readability-es-3class-sentences). Three classes, sentence-based dataset.
- [`readability-es-3class-paragraphs`](https://huggingface.co/hackathon-pln-es/readability-es-3class-paragraphs). Three classes, paragraph-based dataset.
## Datasets
- [`readability-es-hackathon-pln-public`](https://huggingface.co/datasets/hackathon-pln-es/readability-es-hackathon-pln-public), composed of:
* coh-metrix-esp corpus.
* Various text resources scraped from websites.
- Other non-public datasets: newsela-es, simplext.
## Training details
Please, refer to [this training run](https://wandb.ai/readability-es/readability-es/runs/3rgvwps0/overview) for full details on hyperparameters and training regime.
## Biases and Limitations
- Due to the scarcity of data and the lack of a reliable gold test set, performance metrics are reported on the validation set.
- One of the datasets involved is the Spanish version of newsela, which is frequently used as a reference. However, it was created by translating previous datasets, and therefore it may contain somewhat unnatural phrases.
- Some of the datasets used cannot be publicly disseminated, making it more difficult to assess the existence of biases or mistakes.
- Language might be biased towards the Spanish dialect spoken in Spain. Other regional variants might be sub-represented.
- No effort has been performed to alleviate the shortcomings and biases described in the [original implementation of BERTIN](https://huggingface.co/bertin-project/bertin-roberta-base-spanish#bias-examples-spanish).
## Authors
- [Laura Vásquez-Rodríguez](https://lmvasque.github.io/)
- [Pedro Cuenca](https://twitter.com/pcuenq)
- [Sergio Morales](https://www.fireblend.com/)
- [Fernando Alva-Manchego](https://feralvam.github.io/)
|
nherve/flaubert-oral-ft
|
nherve
| 2022-04-04T10:27:14Z | 3 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"language-model",
"flaubert",
"french",
"flaubert-base",
"uncased",
"asr",
"speech",
"oral",
"natural language understanding",
"NLU",
"spoken language understanding",
"SLU",
"understanding",
"fr",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-23T12:33:05Z |
---
language: fr
license: mit
tags:
- bert
- language-model
- flaubert
- french
- flaubert-base
- uncased
- asr
- speech
- oral
- natural language understanding
- NLU
- spoken language understanding
- SLU
- understanding
---
# FlauBERT-Oral models: Using ASR-Generated Text for Spoken Language Modeling
**FlauBERT-Oral** are French BERT models trained on a very large amount of automatically transcribed speech from 350,000 hours of diverse French TV shows. They were trained with the [**FlauBERT software**](https://github.com/getalp/Flaubert) using the same parameters as the [flaubert-base-uncased](https://huggingface.co/flaubert/flaubert_base_uncased) model (12 layers, 12 attention heads, 768 dims, 137M parameters, uncased).
## Available FlauBERT-Oral models
- `flaubert-oral-asr` : trained from scratch on ASR data, keeping the BPE tokenizer and vocabulary of flaubert-base-uncased
- `flaubert-oral-asr_nb` : trained from scratch on ASR data, BPE tokenizer is also trained on the same corpus
- `flaubert-oral-mixed` : trained from scratch on a mixed corpus of ASR and text data, BPE tokenizer is also trained on the same corpus
- `flaubert-oral-ft` : fine-tuning of flaubert-base-uncased for a few epochs on ASR data
## Usage for sequence classification
```python
flaubert_tokenizer = FlaubertTokenizer.from_pretrained("nherve/flaubert-oral-asr")
flaubert_classif = FlaubertForSequenceClassification.from_pretrained("nherve/flaubert-oral-asr", num_labels=14)
flaubert_classif.sequence_summary.summary_type = 'mean'
# Then, train your model
```
## References
If you use FlauBERT-Oral models for your scientific publication, or if you find the resources in this repository useful, please cite the following papers:
```
@InProceedings{herve2022flaubertoral,
author = {Herv\'{e}, Nicolas and Pelloin, Valentin and Favre, Benoit and Dary, Franck and Laurent, Antoine and Meignier, Sylvain and Besacier, Laurent},
title = {Using ASR-Generated Text for Spoken Language Modeling},
booktitle = {Proceedings of "Challenges & Perspectives in Creating Large Language Models" ACL 2022 Workshop},
month = {May},
year = {2022}
}
```
|
ikekobby/fake-real-news-classifier
|
ikekobby
| 2022-04-04T09:23:36Z | 5 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-03T17:57:15Z |
Model based trained on 30% of the kaggle public data on fake and reals news article. The model achieved an `auc` of 1.0, precision, recall and f1score all at score of 1.0.
* Task;- The predictor classifies news articles into either fake or real news.
* It is a transformer model trained using the `ktrain` library on 30% of dataset of size 194MB after preprocessing.
* Metrics used are recall,, precision, f1score and roc_auc_score.
|
DMetaSoul/sbert-chinese-dtm-domain-v1
|
DMetaSoul
| 2022-04-04T07:25:03Z | 17 | 5 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"semantic-search",
"chinese",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-25T10:18:38Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- semantic-search
- chinese
---
# DMetaSoul/sbert-chinese-dtm-domain-v1
此模型基于 [bert-base-chinese](https://huggingface.co/bert-base-chinese) 版本 BERT 模型，在 OPPO 手机助手小布对话匹配数据集（[BUSTM](https://github.com/xiaobu-coai/BUSTM)）上进行训练调优，适用于**开放领域的对话匹配**场景（偏口语化），比如：
- 哪有好玩的 VS. 这附近有什么好玩的地方
- 定时25分钟 VS. 计时半个小时
- 我要听王琦的歌 VS. 放一首王琦的歌
注：此模型的[轻量化版本](https://huggingface.co/DMetaSoul/sbert-chinese-dtm-domain-v1-distill)，也已经开源啦！
# Usage
## 1. Sentence-Transformers
通过 [sentence-transformers](https://www.SBERT.net) 框架来使用该模型，首先进行安装：
```
pip install -U sentence-transformers
```
然后使用下面的代码来载入该模型并进行文本表征向量的提取：
```python
from sentence_transformers import SentenceTransformer
sentences = ["我的儿子！他猛然间喊道，我的儿子在哪儿？", "我的儿子呢！他突然喊道，我的儿子在哪里？"]
model = SentenceTransformer('DMetaSoul/sbert-chinese-dtm-domain-v1')
embeddings = model.encode(sentences)
print(embeddings)
```
## 2. HuggingFace Transformers
如果不想使用 [sentence-transformers](https://www.SBERT.net) 的话，也可以通过 HuggingFace Transformers 来载入该模型并进行文本向量抽取：
```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 = ["我的儿子！他猛然间喊道，我的儿子在哪儿？", "我的儿子呢！他突然喊道，我的儿子在哪里？"]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('DMetaSoul/sbert-chinese-dtm-domain-v1')
model = AutoModel.from_pretrained('DMetaSoul/sbert-chinese-dtm-domain-v1')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation
该模型在公开的几个语义匹配数据集上进行了评测，计算了向量相似度跟真实标签之间的相关性系数：
| | **csts_dev** | **csts_test** | **afqmc** | **lcqmc** | **bqcorpus** | **pawsx** | **xiaobu** |
| ------------------------------- | ------------ | ------------- | --------- | --------- | ------------ | --------- | ---------- |
| **sbert-chinese-dtm-domain-v1** | 78.36% | 74.46% | 32.18% | 75.95% | 44.01% | 14.50% | 66.85% |
## Citing & Authors
E-mail: [email protected]
|
DMetaSoul/sbert-chinese-qmc-domain-v1
|
DMetaSoul
| 2022-04-04T07:24:17Z | 10 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"semantic-search",
"chinese",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-25T09:06:52Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- semantic-search
- chinese
---
# DMetaSoul/sbert-chinese-qmc-domain-v1
此模型基于 [bert-base-chinese](https://huggingface.co/bert-base-chinese) 版本 BERT 模型，在百度知道问题匹配数据集（[LCQMC](http://icrc.hitsz.edu.cn/Article/show/171.html)）上进行训练调优，适用于**开放领域的问题匹配**场景，比如：
- 洗澡用什么香皂好？vs. 洗澡用什么香皂好
- 大连哪里拍婚纱照好点？ vs. 大连哪里拍婚纱照比较好
- 银行卡怎样挂失？vs. 银行卡丢了怎么挂失啊？
注：此模型的[轻量化版本](https://huggingface.co/DMetaSoul/sbert-chinese-qmc-domain-v1-distill)，也已经开源啦！
# Usage
## 1. Sentence-Transformers
通过 [sentence-transformers](https://www.SBERT.net) 框架来使用该模型，首先进行安装：
```
pip install -U sentence-transformers
```
然后使用下面的代码来载入该模型并进行文本表征向量的提取：
```python
from sentence_transformers import SentenceTransformer
sentences = ["我的儿子！他猛然间喊道，我的儿子在哪儿？", "我的儿子呢！他突然喊道，我的儿子在哪里？"]
model = SentenceTransformer('DMetaSoul/sbert-chinese-qmc-domain-v1')
embeddings = model.encode(sentences)
print(embeddings)
```
## 2. HuggingFace Transformers
如果不想使用 [sentence-transformers](https://www.SBERT.net) 的话，也可以通过 HuggingFace Transformers 来载入该模型并进行文本向量抽取：
```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 = ["我的儿子！他猛然间喊道，我的儿子在哪儿？", "我的儿子呢！他突然喊道，我的儿子在哪里？"]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('DMetaSoul/sbert-chinese-qmc-domain-v1')
model = AutoModel.from_pretrained('DMetaSoul/sbert-chinese-qmc-domain-v1')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation
该模型在公开的几个语义匹配数据集上进行了评测，计算了向量相似度跟真实标签之间的相关性系数：
| | **csts_dev** | **csts_test** | **afqmc** | **lcqmc** | **bqcorpus** | **pawsx** | **xiaobu** |
| ------------------------------- | ------------ | ------------- | --------- | --------- | ------------ | --------- | ---------- |
| **sbert-chinese-qmc-domain-v1** | 80.90% | 76.63% | 34.51% | 77.06% | 52.96% | 12.98% | 59.48% |
## Citing & Authors
E-mail: [email protected]
|
DMetaSoul/sbert-chinese-general-v2
|
DMetaSoul
| 2022-04-04T07:22:23Z | 1,656 | 33 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"semantic-search",
"chinese",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-25T08:59:33Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- semantic-search
- chinese
---
# DMetaSoul/sbert-chinese-general-v2
此模型基于 [bert-base-chinese](https://huggingface.co/bert-base-chinese) 版本 BERT 模型，在百万级语义相似数据集 [SimCLUE](https://github.com/CLUEbenchmark/SimCLUE) 上进行训练，适用于**通用语义匹配**场景，从效果来看该模型在各种任务上**泛化能力更好**。
注：此模型的[轻量化版本](https://huggingface.co/DMetaSoul/sbert-chinese-general-v2-distill)，也已经开源啦！
# Usage
## 1. Sentence-Transformers
通过 [sentence-transformers](https://www.SBERT.net) 框架来使用该模型，首先进行安装：
```
pip install -U sentence-transformers
```
然后使用下面的代码来载入该模型并进行文本表征向量的提取：
```python
from sentence_transformers import SentenceTransformer
sentences = ["我的儿子！他猛然间喊道，我的儿子在哪儿？", "我的儿子呢！他突然喊道，我的儿子在哪里？"]
model = SentenceTransformer('DMetaSoul/sbert-chinese-general-v2')
embeddings = model.encode(sentences)
print(embeddings)
```
## 2. HuggingFace Transformers
如果不想使用 [sentence-transformers](https://www.SBERT.net) 的话，也可以通过 HuggingFace Transformers 来载入该模型并进行文本向量抽取：
```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 = ["我的儿子！他猛然间喊道，我的儿子在哪儿？", "我的儿子呢！他突然喊道，我的儿子在哪里？"]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('DMetaSoul/sbert-chinese-general-v2')
model = AutoModel.from_pretrained('DMetaSoul/sbert-chinese-general-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, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation
该模型在公开的几个语义匹配数据集上进行了评测，计算了向量相似度跟真实标签之间的相关性系数：
| | **csts_dev** | **csts_test** | **afqmc** | **lcqmc** | **bqcorpus** | **pawsx** | **xiaobu** |
| ---------------------------- | ------------ | ------------- | ---------- | ---------- | ------------ | ---------- | ---------- |
| **sbert-chinese-general-v1** | **84.54%** | **82.17%** | 23.80% | 65.94% | 45.52% | 11.52% | 48.51% |
| **sbert-chinese-general-v2** | 77.20% | 72.60% | **36.80%** | **76.92%** | **49.63%** | **16.24%** | **63.16%** |
这里对比了本模型跟之前我们发布 [sbert-chinese-general-v1](https://huggingface.co/DMetaSoul/sbert-chinese-general-v1) 之间的差异，可以看到本模型在多个任务上的泛化能力更好。
## Citing & Authors
E-mail: [email protected]
|
vkamthe/upside_down_detector
|
vkamthe
| 2022-04-04T07:01:28Z | 5 | 0 |
tf-keras
|
[
"tf-keras",
"tag1",
"tag2",
"dataset:dataset1",
"dataset:dataset2",
"license:cc",
"region:us"
] | null | 2022-04-04T06:16:41Z |
---
language:
- "List of ISO 639-1 code for your language"
- lang1
- lang2
thumbnail: "url to a thumbnail used in social sharing"
tags:
- tag1
- tag2
license: "cc"
datasets:
- dataset1
- dataset2
metrics:
- metric1
- metric2
---
This is Image Orientation Detector by Vikram Kamthe
Given an image, it will classify it into Original Image or Upside Down Image
|
BigSalmon/GPT2Neo1.3BPoints
|
BigSalmon
| 2022-04-04T05:14:11Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-04-04T04:17:46Z |
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/GPT2Neo1.3BPoints")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/GPT2Neo1.3BPoints")
```
```
- moviepass to return
- this summer
- swooped up by
- original co-founder stacy spikes
text: the re-launch of moviepass is set to transpire this summer, ( rescued at the hands of / under the stewardship of / spearheaded by ) its founding father, stacy spikes.
***
- middle schools do not have recess
- should get back to doing it
- amazing for communication
- and getting kids to move around
text: a casualty of the education reform craze, recess has been excised from middle schools. this is tragic, for it is instrumental in honing children's communication skills and encouraging physical activity.
***
-
```
|
foongminwong/dl-nlp
|
foongminwong
| 2022-04-04T05:09:39Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-04-04T02:54:33Z |
## Coding Challenge - Deep Learning for NLP (Foong)
### Description:
This repository contains a Jupyter notebook using scikit-learn SVM to classify real & fake news.
Dataset: https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset
Libraries used: Scikit-learn, NLTK, pandas, numpy, csv
### Write-up:
The accuracy of the model is 0.995.
There are a couple of misclassified news articles and to improve the model's performance on these news articles, here're some suggestions:
- Remove stop words: The news article title and text contain a lot of commonly used words which should be removed as features. Therefore, more data cleaning should be performed prior to model building.
- Try using the neural network by setting batch size, apply dropout & finetuning it
- Run cross-validation
|
somosnlp-hackathon-2022/electricidad-base-generator-fake-news
|
somosnlp-hackathon-2022
| 2022-04-04T04:04:01Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"electra",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-29T19:52:54Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: electricidad-base-generator-fake-news
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. -->
# electricidad-base-generator-fake-news
This model is a fine-tuned version of [mrm8488/electricidad-base-generator](https://huggingface.co/mrm8488/electricidad-base-generator) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0067
- Accuracy: 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: 2e-05
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1136 | 1.0 | 180 | 0.0852 | 1.0 |
| 0.0267 | 2.0 | 360 | 0.0219 | 1.0 |
| 0.0132 | 3.0 | 540 | 0.0108 | 1.0 |
| 0.0091 | 4.0 | 720 | 0.0075 | 1.0 |
| 0.0077 | 5.0 | 900 | 0.0067 | 1.0 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
somosnlp-hackathon-2022/unam_tesis_ROBERTA_GOB_finnetuning
|
somosnlp-hackathon-2022
| 2022-04-04T02:19:15Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2022-04-04T01:57:47Z |
---
license: apache-2.0
---
# Unam_tesis_ROBERTA_GOB_finnetuning: Unam's thesis classification with PlanTL-GOB-ES/roberta-large-bne
This model is created from the finetuning of the pre-model
for RoBERTa large trained with data from the National Library of Spain (BNE) [
PlanTL-GOB-ES] (https://huggingface.co/PlanTL-GOB-ES/roberta-large-bne), using PyTorch framework,
and trained with a set of theses of the National Autonomous University of Mexico [UNAM](https://tesiunam.dgb.unam.mx/F?func=find-b-0&local_base=TES01).
The model classifies for five (Psicología, Derecho, Química Farmacéutico Biológica, Actuaría, Economía)
possible careers at the University of Mexico.
List of careers from a text.
## Training Dataset
1000 documents (Thesis introduction, Author´s first name, Author´s last name, Thesis title, Year, Career )
| Careers | Size |
|--------------|----------------------|
| Actuaría | 200 |
| Derecho| 200 |
| Economía| 200 |
| Psicología| 200 |
| Química Farmacéutico Biológica| 200 |
## Example of use
For further details on how to use unam_tesis_ROBERTA_GOB_finnetuning you can visit the Huggingface Transformers library, starting with the Quickstart section. Unam_tesis models can be accessed simply as 'hackathon-pln-e/unam_tesis_beto_finnetuning' by using the Transformers library. An example of how to download and use the models on this page can be found in this colab notebook.
```python
tokenizer = AutoTokenizer.from_pretrained('PlanTL-GOB-ES/roberta-large-bne', use_fast=False)
model = AutoModelForSequenceClassification.from_pretrained(
'hackathon-pln-e/unam_tesis_ROBERTA_GOB_finnetuning', num_labels=5, output_attentions=False,
output_hidden_states=False)
pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True)
classificationResult = pipe("El objetivo de esta tesis es elaborar un estudio de las condiciones asociadas al aprendizaje desde casa")
```
To cite this resource in a publication please use the following:
## Citation
[UNAM's Tesis with PlanTL-GOB-ES/roberta-large-bne ](https://huggingface.co/hackathon-pln-es/unam_tesis_ROBERTA_GOB_finnetuning)
To cite this resource in a publication please use the following:
```
@inproceedings{SpanishNLPHackaton2022,
title={Unam's thesis with PlanTL-GOB-ES/roberta-large-bne classify },
author={López López, Isaac Isaías and López Ramos, Dionis and Clavel Quintero, Yisel and López López, Ximena Yeraldin },
booktitle={Somos NLP Hackaton 2022},
year={2022}
}
```
## Team members
- Isaac Isaías López López ([MajorIsaiah](https://huggingface.co/MajorIsaiah))
- Dionis López Ramos ([inoid](https://huggingface.co/inoid))
- Yisel Clavel Quintero ([clavel](https://huggingface.co/clavel))
- Ximyer Yeraldin López López ([Ximyer](https://huggingface.co/Ximyer))
|
somosnlp-hackathon-2022/bertin-roberta-base-finetuning-esnli
|
somosnlp-hackathon-2022
| 2022-04-04T01:45:21Z | 74 | 7 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"es",
"dataset:hackathon-pln-es/nli-es",
"arxiv:1908.10084",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-28T19:08:33Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
language:
- es
datasets:
- hackathon-pln-es/nli-es
widget:
- text: "A ver si nos tenemos que poner todos en huelga hasta cobrar lo que queramos."
- text: "La huelga es el método de lucha más eficaz para conseguir mejoras en el salario."
- text: "Tendremos que optar por hacer una huelga para cobrar lo que queremos."
- text: "Queda descartada la huelga aunque no cobremos lo que queramos."
---
# bertin-roberta-base-finetuning-esnli
This is a [sentence-transformers](https://www.SBERT.net) model trained on a
collection of NLI tasks for Spanish. It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
Based around the siamese networks approach from [this paper](https://arxiv.org/pdf/1908.10084.pdf).
<!--- Describe your model here -->
You can see a demo for this model [here](https://huggingface.co/spaces/hackathon-pln-es/Sentence-Embedding-Bertin).
You can find our other model, **paraphrase-spanish-distilroberta** [here](https://huggingface.co/hackathon-pln-es/paraphrase-spanish-distilroberta) and its demo [here](https://huggingface.co/spaces/hackathon-pln-es/Paraphrase-Bertin).
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["Este es un ejemplo", "Cada oración es transformada"]
model = SentenceTransformer('hackathon-pln-es/bertin-roberta-base-finetuning-esnli')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('hackathon-pln-es/bertin-roberta-base-finetuning-esnli')
model = AutoModel.from_pretrained('hackathon-pln-es/bertin-roberta-base-finetuning-esnli')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
Our model was evaluated on the task of Semantic Textual Similarity using the [SemEval-2015 Task](https://alt.qcri.org/semeval2015/task2/) for [Spanish](http://alt.qcri.org/semeval2015/task2/data/uploads/sts2015-es-test.zip). We measure
| | [BETO STS](https://huggingface.co/espejelomar/sentece-embeddings-BETO) | BERTIN STS (this model) | Relative improvement |
|-------------------:|---------:|-----------:|---------------------:|
| cosine_pearson | 0.609803 | 0.683188 | +12.03 |
| cosine_spearman | 0.528776 | 0.615916 | +16.48 |
| euclidean_pearson | 0.590613 | 0.672601 | +13.88 |
| euclidean_spearman | 0.526529 | 0.611539 | +16.15 |
| manhattan_pearson | 0.589108 | 0.672040 | +14.08 |
| manhattan_spearman | 0.525910 | 0.610517 | +16.09 |
| dot_pearson | 0.544078 | 0.600517 | +10.37 |
| dot_spearman | 0.460427 | 0.521260 | +13.21 |
## Training
The model was trained with the parameters:
**Dataset**
We used a collection of datasets of Natural Language Inference as training data:
- [ESXNLI](https://raw.githubusercontent.com/artetxem/esxnli/master/esxnli.tsv), only the part in spanish
- [SNLI](https://nlp.stanford.edu/projects/snli/), automatically translated
- [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/), automatically translated
The whole dataset used is available [here](https://huggingface.co/datasets/hackathon-pln-es/nli-es).
Here we leave the trick we used to increase the amount of data for training here:
```
for row in reader:
if row['language'] == 'es':
sent1 = row['sentence1'].strip()
sent2 = row['sentence2'].strip()
add_to_samples(sent1, sent2, row['gold_label'])
add_to_samples(sent2, sent1, row['gold_label']) #Also add the opposite
```
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader`
of length 1818 with parameters:
```
{'batch_size': 64}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 909,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 514, 'do_lower_case': False}) with Transformer model: RobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Authors
[Anibal Pérez](https://huggingface.co/Anarpego),
[Emilio Tomás Ariza](https://huggingface.co/medardodt),
[Lautaro Gesuelli](https://huggingface.co/Lgesuelli) y
[Mauricio Mazuecos](https://huggingface.co/mmazuecos).
|
abdelhalim/Rec_Business_Names
|
abdelhalim
| 2022-04-04T01:39:41Z | 15 | 4 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"Text2Text Generation",
"Business names",
"Recommendation system",
"dataset:BSD-1",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-23T13:25:14Z |
---
datasets:
- BSD-1
tags:
- Text2Text Generation
- Business names
- Recommendation system
metrics:
- Rouge
---
**Context**
Most of the business name generator systems based on Rule based approach and only take as input a name or keyword not context. The present trained model its aim is to take in a summary for a business idea (1-2 sentences, could be even keywords) and generate a viable business name for users.
**Introduction**
The goal is to create an AI service which is helpful to people and yet could turn into a small business. After fiddling around with T5, I have realized it has an immense creative potential that could prove useful in creative text generation. So, after scraping around 350.000 websites from different Domain list, I have fine-tuned T5 small parameter on this dataset. Results are much depends to the context and creative at the same time.
T5 small is already pre-trained language model which is capable of creating text with a near human quality. It's able to understand the context of a given prefix to generate text. When fine tuned based on the domain names and their meta context, it was able to understand the relation between domain name and the content of the website.
**Dataset**
t5 small needs lots of data to be trained properly. Quality of the data that we will use for fine tuning will have a direct effect on the model quality therefore we need to make sure the data we are scraping from the websites are as clean as possible. The dateset will be under request.
# Usage
In order to use the model in your Python script just copy the following code:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("abdelhalim/Rec_Business_Names")
model = AutoModelForSeq2SeqLM.from_pretrained("abdelhalim/Rec_Business_Names")
encoder_input_str = "fourniture and decor brand"
number_of_business_names = 10
input_ids = tokenizer(encoder_input_str, return_tensors="pt").input_ids
outputs = model.generate(
input_ids,
num_beams=number_of_business_names,
num_return_sequences=number_of_business_names,
no_repeat_ngram_size=1,
remove_invalid_values=True,
)
for i in range(len(outputs)):
print(tokenizer.decode(outputs[i], skip_special_tokens=True))
#Output
edgy.com
Furnace.com
Decorsy.com
Furnacea.com
Decorse.com
Furniture.com
edgys.com
Furnishing.com
Lavender.com
edgya.com
```
|
tbosse/bert-base-german-cased-finetuned-subj_v2_v1
|
tbosse
| 2022-04-03T19:15:50Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-04-03T17:49:37Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-german-cased-finetuned-subj_v2_v1
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-german-cased-finetuned-subj_v2_v1
This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1587
- Precision: 0.2222
- Recall: 0.0107
- F1: 0.0204
- Accuracy: 0.9511
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 136 | 0.1569 | 0.6667 | 0.0053 | 0.0106 | 0.9522 |
| No log | 2.0 | 272 | 0.1562 | 0.1667 | 0.0053 | 0.0103 | 0.9513 |
| No log | 3.0 | 408 | 0.1587 | 0.2222 | 0.0107 | 0.0204 | 0.9511 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
anton-l/xtreme_s_xlsr_300m_mls
|
anton-l
| 2022-04-03T18:54:35Z | 19 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"google/xtreme_s",
"generated_from_trainer",
"dataset:google/xtreme_s",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-14T20:25:22Z |
---
license: apache-2.0
tags:
- automatic-speech-recognition
- google/xtreme_s
- generated_from_trainer
datasets:
- google/xtreme_s
model-index:
- name: xtreme_s_xlsr_mls
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. -->
# xtreme_s_xlsr_300m_mls
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the GOOGLE/XTREME_S - MLS dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6215
- Wer: 0.3033
- Cer: 0.0951
## 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: 4
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 3000
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| 3.0446 | 1.91 | 500 | 2.9866 | 1.0 | 1.0 |
| 0.8789 | 3.82 | 1000 | 0.8574 | 0.7225 | 0.2355 |
| 0.4766 | 5.72 | 1500 | 0.4813 | 0.4624 | 0.1394 |
| 0.3779 | 7.63 | 2000 | 0.4465 | 0.4154 | 0.1309 |
| 0.3244 | 9.54 | 2500 | 0.4213 | 0.3683 | 0.1163 |
| 0.346 | 11.45 | 3000 | 0.4606 | 0.4033 | 0.1299 |
| 0.3092 | 13.36 | 3500 | 0.4160 | 0.3585 | 0.1115 |
| 0.3287 | 15.27 | 4000 | 0.4364 | 0.3631 | 0.1165 |
| 0.3165 | 17.18 | 4500 | 0.4218 | 0.3451 | 0.1056 |
| 0.2874 | 19.08 | 5000 | 0.4583 | 0.3650 | 0.1151 |
| 0.3089 | 20.99 | 5500 | 0.4424 | 0.3485 | 0.1137 |
| 0.2689 | 22.9 | 6000 | 0.4427 | 0.3542 | 0.1128 |
| 0.234 | 24.81 | 6500 | 0.4204 | 0.3431 | 0.1069 |
| 0.2363 | 26.72 | 7000 | 0.4792 | 0.3689 | 0.1191 |
| 0.2796 | 28.62 | 7500 | 0.4867 | 0.3662 | 0.1154 |
| 0.2447 | 30.53 | 8000 | 0.4908 | 0.3584 | 0.1160 |
| 0.22 | 32.44 | 8500 | 0.5315 | 0.3626 | 0.1240 |
| 0.1961 | 34.35 | 9000 | 0.5121 | 0.3610 | 0.1168 |
| 0.1959 | 36.26 | 9500 | 0.5140 | 0.3648 | 0.1179 |
| 0.1748 | 38.17 | 10000 | 0.5464 | 0.3763 | 0.1206 |
| 0.197 | 40.08 | 10500 | 0.5199 | 0.3515 | 0.1128 |
| 0.2166 | 41.98 | 11000 | 0.5336 | 0.3607 | 0.1191 |
| 0.2078 | 43.89 | 11500 | 0.5389 | 0.3518 | 0.1136 |
| 0.1827 | 45.8 | 12000 | 0.5014 | 0.3287 | 0.1053 |
| 0.1783 | 47.71 | 12500 | 0.5408 | 0.3545 | 0.1121 |
| 0.1489 | 49.62 | 13000 | 0.5292 | 0.3472 | 0.1098 |
| 0.1665 | 51.53 | 13500 | 0.5052 | 0.3300 | 0.1033 |
| 0.1631 | 53.43 | 14000 | 0.5241 | 0.3362 | 0.1081 |
| 0.1943 | 55.34 | 14500 | 0.5453 | 0.3373 | 0.1076 |
| 0.1504 | 57.25 | 15000 | 0.5958 | 0.3594 | 0.1149 |
| 0.136 | 59.16 | 15500 | 0.5645 | 0.3367 | 0.1082 |
| 0.1224 | 61.07 | 16000 | 0.5322 | 0.3302 | 0.1039 |
| 0.1156 | 62.98 | 16500 | 0.5728 | 0.3332 | 0.1061 |
| 0.114 | 64.88 | 17000 | 0.5994 | 0.3410 | 0.1125 |
| 0.1445 | 66.79 | 17500 | 0.6048 | 0.3471 | 0.1098 |
| 0.1281 | 68.7 | 18000 | 0.5747 | 0.3278 | 0.1042 |
| 0.1233 | 70.61 | 18500 | 0.6021 | 0.3375 | 0.1082 |
| 0.1109 | 72.52 | 19000 | 0.5851 | 0.3188 | 0.1021 |
| 0.0943 | 74.43 | 19500 | 0.5944 | 0.3238 | 0.1033 |
| 0.1418 | 76.34 | 20000 | 0.5904 | 0.3143 | 0.0997 |
| 0.1317 | 78.24 | 20500 | 0.6291 | 0.3283 | 0.1047 |
| 0.1177 | 80.15 | 21000 | 0.6114 | 0.3190 | 0.1000 |
| 0.1138 | 82.06 | 21500 | 0.6155 | 0.3245 | 0.1023 |
| 0.1074 | 83.97 | 22000 | 0.6094 | 0.3153 | 0.1004 |
| 0.11 | 85.88 | 22500 | 0.6041 | 0.3141 | 0.0988 |
| 0.1096 | 87.78 | 23000 | 0.6243 | 0.3110 | 0.0986 |
| 0.1017 | 89.69 | 23500 | 0.6110 | 0.3121 | 0.0984 |
| 0.1015 | 91.6 | 24000 | 0.6385 | 0.3093 | 0.0978 |
| 0.0952 | 93.51 | 24500 | 0.6155 | 0.3036 | 0.0953 |
| 0.0896 | 95.42 | 25000 | 0.6215 | 0.3033 | 0.0951 |
| 0.0953 | 97.33 | 25500 | 0.6293 | 0.3037 | 0.0953 |
| 0.0834 | 99.24 | 26000 | 0.6302 | 0.3036 | 0.0952 |
### Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 1.18.4.dev0
- Tokenizers 0.11.6
|
Suhail/Upside_down_detector
|
Suhail
| 2022-04-03T17:55:07Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-04-03T14:23:47Z |
This model uses images of cats to detect if an image of a cat is upside down or not.
<br>
I have used fastai library for this.
<br>
I have collected data on my google drive through colab by using duckduckgo search API
<br>
I used transfer learning by implementing resnet-18 architecture to solve this particular task.
|
morahil/wav2vec2-large-xls-r-300m-hindi
|
morahil
| 2022-04-03T17:28:16Z | 3 | 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
| 2022-04-03T16:45:22Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-hindi
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-hindi
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: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
Giyaseddin/distilbert-base-cased-finetuned-fake-and-real-news-dataset
|
Giyaseddin
| 2022-04-03T16:39:39Z | 93 | 1 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"en",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-03T14:52:37Z |
---
license: gpl-3.0
language: en
library: transformers
other: distilbert
datasets:
- Fake and real news dataset
---
# DistilBERT base cased model for Fake News Classification
## Model description
DistilBERT is a transformers model, smaller and faster than BERT, which was pretrained on the same corpus in a
self-supervised fashion, using the BERT base model as a teacher. This means it was pretrained on the raw texts only,
with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic
process to generate inputs and labels from those texts using the BERT base model.
This is a Fake News classification model finetuned [pretrained DistilBERT model](https://huggingface.co/distilbert-base-cased) on
[Fake and real news dataset](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset)
## Intended uses & limitations
This can only be used for the kind of news that are similar to the ones in the dataset,
please visit the [dataset's kaggle page](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset) to see the data.
### How to use
You can use this model directly with a :
```python
>>> from transformers import pipeline
>>> classifier = pipeline("text-classification", model="Giyaseddin/distilbert-base-cased-finetuned-fake-and-real-news-dataset", return_all_scores=True)
>>> examples = ["Yesterday, Speaker Paul Ryan tweeted a video of himself on the Mexican border flying in a helicopter and traveling on horseback with US border agents. RT if you agree It is time for The Wall. pic.twitter.com/s5MO8SG7SL Paul Ryan (@SpeakerRyan) August 1, 2017It makes for great theater to see Republican Speaker Ryan pleading the case for a border wall, but how sincere are the GOP about building the border wall? Even after posting a video that appears to show Ryan s support for the wall, he still seems unsure of himself. It s almost as though he s testing the political winds when he asks Twitter users to retweet if they agree that we need to start building the wall. How committed is the (formerly?) anti-Trump Paul Ryan to building the border wall that would fulfill one of President Trump s most popular campaign promises to the American people? Does he have the what it takes to defy the wishes of corporate donors and the US Chamber of Commerce, and do the right thing for the national security and well-being of our nation?The Last Refuge- Republicans are in control of the House of Representatives, Republicans are in control of the Senate, a Republican President is in the White House, and somehow there s negotiations on how to fund the #1 campaign promise of President Donald Trump, the border wall.Here s the rub.Here s what pundits never discuss.The Republican party doesn t need a single Democrat to fund the border wall.A single spending bill could come from the House of Representatives that fully funds 100% of the border wall. The spending bill then goes to the senate, where again, it doesn t need a single Democrat vote because spending legislation is specifically what reconciliation was designed to facilitate. That House bill can pass the Senate with 51 votes and proceed directly to the President s desk for signature.So, ask yourself: why is this even a point of discussion?The honest answer, for those who are no longer suffering from Battered Conservative Syndrome, is that Republicans don t want to fund or build an actual physical barrier known as the Southern Border Wall.It really is that simple.If one didn t know better, they d almost think Speaker Ryan was attempting to emulate the man he clearly despised during the 2016 presidential campaign."]
>>> classifier(examples)
[[{'label': 'LABEL_0', 'score': 1.0},
{'label': 'LABEL_1', 'score': 1.0119109106199176e-08}]]
```
### Limitations and bias
Even if the training data used for this model could be characterized as fairly neutral, this model can have biased
predictions. It also inherits some of
[the bias of its teacher model](https://huggingface.co/bert-base-uncased#limitations-and-bias).
This bias will also affect all fine-tuned versions of this model.
## Pre-training data
DistilBERT pretrained on the same data as BERT, which is [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset
consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia)
(excluding lists, tables and headers).
## Fine-tuning data
[Fake and real news dataset](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset)
## Training procedure
### Preprocessing
In the preprocessing phase, both the title and the text of the news are concatenated using a separator `[SEP]`.
This makes the full text as:
```
[CLS] Title Sentence [SEP] News text body [SEP]
```
The data are splitted according to the following ratio:
- Training set 60%.
- Validation set 20%.
- Test set 20%.
Lables are mapped as: `{fake: 0, true: 1}`
### Fine-tuning
The model was finetuned on GeForce GTX 960M for 5 hours. The parameters are:
| Parameter | Value |
|:-------------------:|:-----:|
| Learning rate | 5e-5 |
| Weight decay | 0.01 |
| Training batch size | 4 |
| Epochs | 3 |
Here is the scores during the training:
| Epoch | Training Loss | Validation Loss | Accuracy | F1 | Precision | Recall |
|:----------:|:-------------:|:-----------------:|:----------:|:---------:|:-----------:|:---------:|
| 1 | 0.008300 | 0.005783 | 0.998330 | 0.998252 | 0.996511 | 1.000000 |
| 2 | 0.000000 | 0.000161 | 0.999889 | 0.999883 | 0.999767 | 1.000000 |
| 3 | 0.000000 | 0.000122 | 0.999889 | 0.999883 | 0.999767 | 1.000000 |
## Evaluation results
When fine-tuned on downstream task of fake news binary classification, this model achieved the following results:
(scores are rounded to 2 floating points)
| | precision | recall | f1-score | support |
|:------------:|:---------:|:------:|:--------:|:-------:|
| Fake | 1.00 | 1.00 | 1.00 | 4697 |
| True | 1.00 | 1.00 | 1.00 | 4283 |
| accuracy | - | - | 1.00 | 8980 |
| macro avg | 1.00 | 1.00 | 1.00 | 8980 |
| weighted avg | 1.00 | 1.00 | 1.00 | 8980 |
Confision matrix:
| Actual\Predicted | Fake | True |
|:-----------------:|:----:|:----:|
| Fake | 4696 | 1 |
| True | 1 | 4282 |
The AUC score is 0.9997
|
AykeeSalazar/violation-classification-bantai-vit-v100ep
|
AykeeSalazar
| 2022-04-03T16:16:07Z | 64 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-04-03T14:05:38Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
model-index:
- name: violation-classification-bantai-vit-v100ep
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: image_folder
type: image_folder
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9157343919162757
---
<!-- 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. -->
# violation-classification-bantai-vit-v100ep
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2557
- Accuracy: 0.9157
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2811 | 1.0 | 101 | 0.2855 | 0.9027 |
| 0.2382 | 2.0 | 202 | 0.2763 | 0.9085 |
| 0.2361 | 3.0 | 303 | 0.2605 | 0.9109 |
| 0.196 | 4.0 | 404 | 0.2652 | 0.9110 |
| 0.1395 | 5.0 | 505 | 0.2648 | 0.9134 |
| 0.155 | 6.0 | 606 | 0.2656 | 0.9152 |
| 0.1422 | 7.0 | 707 | 0.2607 | 0.9141 |
| 0.1511 | 8.0 | 808 | 0.2557 | 0.9157 |
| 0.1938 | 9.0 | 909 | 0.2679 | 0.9049 |
| 0.2094 | 10.0 | 1010 | 0.2392 | 0.9137 |
| 0.1835 | 11.0 | 1111 | 0.2400 | 0.9156 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
somosnlp-hackathon-2022/biomedtra-small-es-squad2-es
|
somosnlp-hackathon-2022
| 2022-04-03T14:51:12Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"question-answering",
"es",
"dataset:squad_es",
"dataset:hackathon-pln-es/biomed_squad_es_v2",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-04-02T03:31:31Z |
---
language: es
datasets:
- squad_es
- hackathon-pln-es/biomed_squad_es_v2
metrics:
- "f1"
---
# biomedtra-small for QA
This model was trained as part of the "Extractive QA Biomedicine" project developed during the 2022 [Hackathon](https://somosnlp.org/hackathon) organized by SOMOS NLP.
## Motivation
Recent research has made available Spanish Language Models trained on Biomedical corpus. This project explores the use of these new models to generate extractive Question Answering models for Biomedicine, and compares their effectiveness with general masked language models.
The models trained during the [Hackathon](https://somosnlp.org/hackathon) were:
[hackathon-pln-es/roberta-base-bne-squad2-es](https://huggingface.co/hackathon-pln-es/roberta-base-bne-squad2-es)
[hackathon-pln-es/roberta-base-biomedical-clinical-es-squad2-es](https://huggingface.co/hackathon-pln-es/roberta-base-biomedical-clinical-es-squad2-es)
[hackathon-pln-es/roberta-base-biomedical-es-squad2-es](https://huggingface.co/hackathon-pln-es/roberta-base-biomedical-es-squad2-es)
[hackathon-pln-es/biomedtra-small-es-squad2-es](https://huggingface.co/hackathon-pln-es/biomedtra-small-es-squad2-es)
## Description
This model is a fine-tuned version of [mrm8488/biomedtra-small-es](https://huggingface.co/mrm8488/biomedtra-small-es) on the [squad_es (v2)](https://huggingface.co/datasets/squad_es) training dataset.
## Hyperparameters
The hyperparameters were chosen based on those used in [deepset/electra-base-squad2](https://huggingface.co/deepset/electra-base-squad2), an english-based model trained for similar purposes
```
--num_train_epochs 10 \
--learning_rate 1e-4 \
--max_seq_length 384 \
--doc_stride 128 \
```
## Performance
Evaluated on the [hackathon-pln-es/biomed_squad_es_v2](https://huggingface.co/datasets/hackathon-pln-es/biomed_squad_es_v2) dev set.
|Model |Base Model Domain|exact |f1 |HasAns_exact|HasAns_f1|NoAns_exact|NoAns_f1|
|--------------------------------------------------------------|-----------------|-------|-------|------------|---------|-----------|--------|
|hackathon-pln-es/roberta-base-bne-squad2-es |General |67.6341|75.6988|53.7367 |70.0526 |81.2174 |81.2174 |
|hackathon-pln-es/roberta-base-biomedical-clinical-es-squad2-es|Biomedical |66.8426|75.2346|53.0249 |70.0031 |80.3478 |80.3478 |
|hackathon-pln-es/roberta-base-biomedical-es-squad2-es |Biomedical |67.6341|74.5612|47.6868 |61.7012 |87.1304 | 87.1304|
|hackathon-pln-es/biomedtra-small-es-squad2-es |Biomedical |34.4767|44.3294|45.3737 |65.307 |23.8261 |23.8261 |
## Team
Santiago Maximo: [smaximo](https://huggingface.co/smaximo)
|
jsunster/distilbert-base-uncased-finetuned-squad
|
jsunster
| 2022-04-03T14:46:14Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-04-03T13:02:11Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-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. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1476
## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 1.2823 | 1.0 | 2767 | 1.1980 |
| 1.0336 | 2.0 | 5534 | 1.1334 |
| 0.8513 | 3.0 | 8301 | 1.1476 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0
- Datasets 2.0.0
- Tokenizers 0.11.6
|
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