modelId
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| author
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-11 18:29:29
| downloads
int64 0
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| likes
int64 0
11.7k
| library_name
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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) |
|
Kuray107/librispeech-100h-supervised-aug
|
Kuray107
| 2022-04-05T12:57:44Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-31T03:24:38Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: librispeech-100h-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. -->
# librispeech-100h-supervised-aug
This model is a fine-tuned version of [Kuray107/librispeech-5h-supervised](https://huggingface.co/Kuray107/librispeech-5h-supervised) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0776
- Wer: 0.0327
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.3099 | 1.12 | 1000 | 0.0748 | 0.0521 |
| 0.1873 | 2.24 | 2000 | 0.0674 | 0.0440 |
| 0.146 | 3.36 | 3000 | 0.0671 | 0.0406 |
| 0.1233 | 4.48 | 4000 | 0.0619 | 0.0381 |
| 0.1098 | 5.61 | 5000 | 0.0618 | 0.0381 |
| 0.0985 | 6.73 | 6000 | 0.0590 | 0.0355 |
| 0.0907 | 7.85 | 7000 | 0.0659 | 0.0352 |
| 0.0837 | 8.97 | 8000 | 0.0679 | 0.0359 |
| 0.0762 | 10.09 | 9000 | 0.0701 | 0.0349 |
| 0.0707 | 11.21 | 10000 | 0.0715 | 0.0348 |
| 0.0666 | 12.33 | 11000 | 0.0719 | 0.0346 |
| 0.0631 | 13.45 | 12000 | 0.0746 | 0.0347 |
| 0.0593 | 14.57 | 13000 | 0.0757 | 0.0340 |
| 0.0554 | 15.7 | 14000 | 0.0746 | 0.0337 |
| 0.053 | 16.82 | 15000 | 0.0757 | 0.0331 |
| 0.0525 | 17.94 | 16000 | 0.0752 | 0.0327 |
| 0.0514 | 19.06 | 17000 | 0.0776 | 0.0327 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2
- Datasets 1.18.2
- Tokenizers 0.10.3
|
robvanderg/bert-base-multilingual-cased-segment1
|
robvanderg
| 2022-04-05T12:39:54Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"hack",
"multilingual",
"dataset:Wikipedia",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-04-05T12:27:21Z |
---
language:
- multilingual
tags:
- hack
datasets:
- Wikipedia
---
## bert-base-multilingual-cased-segment1
This is a version of multilingual bert (bert-base-multilingual-cased), where the segment embedding of the 1's is copied into the 0's. Yes, that's all there is to it. We have found that this improves performance substantially in low-resource setups for word-level tasks (e.g. average 2.5 LAS on a variety of UD treebanks). More details are to be released in our LREC2022 paper titled: Frustratingly Easy Performance Improvements for Cross-lingual Transfer: A Tale on BERT and Segment Embeddings.
These embeddings are generated by the following code
```
import AutoModel
baseEmbeddings = AutoModel.from_pretrained("bert-base-multilingual-cased")
tte = baseEmbeddings.embeddings.token_type_embeddings.weight.clone().detach()
baseEmbeddings.embeddings.token_type_embeddings.weight[0,:] = tte[1,:]
```
More details and other varieties can be found in the repo: https://bitbucket.org/robvanderg/segmentembeds/
Note that when using this model on a single sentence task (or word-level task), the results would be similar as just using `token_type_id=1` for all tokens.
|
ramnika003/autotrain-sentiment_analysis_project-705021428
|
ramnika003
| 2022-04-05T09:23:07Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"autotrain",
"unk",
"dataset:ramnika003/autotrain-data-sentiment_analysis_project",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-05T09:17:50Z |
---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- ramnika003/autotrain-data-sentiment_analysis_project
co2_eq_emissions: 10.03748863138583
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 705021428
- CO2 Emissions (in grams): 10.03748863138583
## Validation Metrics
- Loss: 0.5534441471099854
- Accuracy: 0.768964665184087
- Macro F1: 0.7629008163259284
- Micro F1: 0.768964665184087
- Weighted F1: 0.7685397042536148
- Macro Precision: 0.7658234531650739
- Micro Precision: 0.768964665184087
- Weighted Precision: 0.7684017544026074
- Macro Recall: 0.7603505092881394
- Micro Recall: 0.768964665184087
- Weighted Recall: 0.768964665184087
## 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/ramnika003/autotrain-sentiment_analysis_project-705021428
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("ramnika003/autotrain-sentiment_analysis_project-705021428", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("ramnika003/autotrain-sentiment_analysis_project-705021428", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
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_e1
|
johnowhitaker
| 2022-04-05T07:31:52Z | 0 | 1 | null |
[
"pytorch",
"lightweightgan",
"en",
"dataset:glid3_orbs",
"license:apache-2.0",
"region:us"
] | null | 2022-03-31T17:14:36Z |
---
language: en
tags:
- lightweightgan
license: apache-2.0
datasets:
- glid3_orbs
---
# orbgan
lightweight GAN trained on my glid-3 orbs (https://huggingface.co/datasets/johnowhitaker/glid3_orbs) for demo I'm working on.
Training notebook: https://colab.research.google.com/drive/16o1TdrxnQ54Msbr813XfPVsnEt2QTRAa?usp=sharing
Inference notebook: https://colab.research.google.com/drive/1e7dR2dptM8F1xhRcyy-Aqow9YSe0NE3z?usp=sharing
The lightwightgan code has an assert requiring a GPU. For inference on the CPU we ned to re-define the Generator class and some other functions - see minimal example here: https://colab.research.google.com/drive/1fnHLdJ7niPMGOOBjGkNsnc6iADpf1Ujd?usp=sharing . This approach was used to make the demo space here: https://huggingface.co/spaces/johnowhitaker/orbgan_demo
Please credit if you use this, and feedback on the code is welcomed :)
EDIT: you may need to use an older version of lightweightgan, eg from commit 708633205d60c99b1b9d4e6b47eb3722aa4159d6 since there have been some recent changes that happened after this model was trained.
## Demo:
```python
from lightweight_gan import Generator
import torch
from matplotlib import pyplot as plt
from huggingface_hub import PyTorchModelHubMixin
# Initialize a generator model
gan_new = Generator(latent_dim=256, image_size=256, attn_res_layers = [32])
# Load from local saved state dict
# gan_new.load_state_dict(torch.load('/content/orbgan_e3_state_dict.pt'))
# Load from model hub:
class GeneratorWithPyTorchModelHubMixin(gan_new.__class__, PyTorchModelHubMixin):
pass
gan_new.__class__ = GeneratorWithPyTorchModelHubMixin
gan_new = gan_new.from_pretrained('johnowhitaker/orbgan_e1', latent_dim=256, image_size=256, attn_res_layers = [32])
# View some examples
n_rows = 3
ims = gan_new(torch.randn(n_rows**2, 256)).clamp_(0., 1.)
fig, axs = plt.subplots(n_rows, n_rows, figsize=(9, 9))
for i, ax in enumerate(axs.flatten()):
ax.imshow(ims[i].permute(1, 2, 0).detach().cpu().numpy())
plt.tight_layout()
```
|
johnowhitaker/orbgan_dark
|
johnowhitaker
| 2022-04-05T07:31:24Z | 0 | 0 | null |
[
"pytorch",
"region:us"
] | null | 2022-04-03T14:54:33Z |
A version of https://huggingface.co/johnowhitaker/orbgan_e1 trained on only dark images
|
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
|
agi-css/distilbert-base-uncased-finetuned-truthful
|
agi-css
| 2022-04-05T07:23:56Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-05T07:09:11Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-truthful
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-truthful
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: 0.4660
- Accuracy: 0.87
- F1: 0.8697
## 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: 9.910294163459086e-05
- train_batch_size: 400
- eval_batch_size: 400
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 9
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 5 | 0.6509 | 0.59 | 0.5780 |
| No log | 2.0 | 10 | 0.4950 | 0.77 | 0.7701 |
| No log | 3.0 | 15 | 0.4787 | 0.81 | 0.8099 |
| No log | 4.0 | 20 | 0.4936 | 0.81 | 0.8096 |
| No log | 5.0 | 25 | 0.4443 | 0.82 | 0.82 |
| No log | 6.0 | 30 | 0.4547 | 0.85 | 0.8497 |
| No log | 7.0 | 35 | 0.4268 | 0.85 | 0.8500 |
| No log | 8.0 | 40 | 0.4790 | 0.87 | 0.8697 |
| No log | 9.0 | 45 | 0.4660 | 0.87 | 0.8697 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.1
- Datasets 2.0.0
- Tokenizers 0.11.0
|
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}
}
```
|
agi-css/distilbert-base-uncased-finetuned-toxicity
|
agi-css
| 2022-04-05T06:08:25Z | 2 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-05T05:38:55Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-toxicity
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-toxicity
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: 0.0086
- Accuracy: 0.999
- F1: 0.9990
## 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: 8.589778712669143e-05
- train_batch_size: 400
- eval_batch_size: 400
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 20 | 0.0142 | 0.998 | 0.998 |
| No log | 2.0 | 40 | 0.0112 | 0.997 | 0.9970 |
| No log | 3.0 | 60 | 0.0088 | 0.999 | 0.9990 |
| No log | 4.0 | 80 | 0.0091 | 0.998 | 0.998 |
| No log | 5.0 | 100 | 0.0086 | 0.999 | 0.9990 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.1
- Datasets 2.0.0
- Tokenizers 0.11.0
|
avialfont/dummy-translation-marian-kde4-en-to-fr
|
avialfont
| 2022-04-05T04:27:40Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"marian",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-04-04T19:57:33Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: avialfont/dummy-translation-marian-kde4-en-to-fr
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-translation-marian-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 an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.9807
- Validation Loss: 0.8658
- 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': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 17733, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.9807 | 0.8658 | 0 |
### Framework versions
- Transformers 4.16.2
- TensorFlow 2.8.0
- Datasets 1.18.3
- Tokenizers 0.11.6
|
agi-css/distilbert-base-uncased-finetuned-moral-ctx-action-conseq
|
agi-css
| 2022-04-05T02:48:15Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-05T01:58:12Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-moral-ctx-action-conseq
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-moral-ctx-action-conseq
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: 0.1111
- Accuracy: 0.9676
- F1: 0.9676
## 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: 9.989502318502869e-05
- train_batch_size: 2000
- eval_batch_size: 2000
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 10 | 0.1569 | 0.9472 | 0.9472 |
| No log | 2.0 | 20 | 0.1171 | 0.9636 | 0.9636 |
| No log | 3.0 | 30 | 0.1164 | 0.9664 | 0.9664 |
| No log | 4.0 | 40 | 0.1117 | 0.9672 | 0.9672 |
| No log | 5.0 | 50 | 0.1111 | 0.9676 | 0.9676 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.1
- Datasets 2.0.0
- Tokenizers 0.11.0
|
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/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.
|
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
|
bdunnette/derbynames-aitextgen-gpt2
|
bdunnette
| 2022-04-04T19:27:12Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-04-04T19:13:54Z |
---
license: cc-by-nc-sa-4.0
---
|
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
|
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.
|
huggingtweets/weirdokun
|
huggingtweets
| 2022-04-04T16:40:11Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-04-04T16:40:03Z |
---
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/1447886082163417093/l0n43HWC_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">#LetLeniLead</div>
<div style="text-align: center; font-size: 14px;">@weirdokun</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 #LetLeniLead.
| Data | #LetLeniLead |
| --- | --- |
| Tweets downloaded | 3114 |
| Retweets | 544 |
| Short tweets | 273 |
| Tweets kept | 2297 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/wraydb99/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 @weirdokun's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3lf5g2np) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3lf5g2np/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/weirdokun')
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)
|
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')`.
|
frahman/bert-base-uncased-issues-128
|
frahman
| 2022-04-04T15:11:09Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-04-04T13:00:28Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-issues-128
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-issues-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: 1.2551
## 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: 8
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0984 | 1.0 | 291 | 1.7081 |
| 1.6512 | 2.0 | 582 | 1.4289 |
| 1.4854 | 3.0 | 873 | 1.3845 |
| 1.3924 | 4.0 | 1164 | 1.3844 |
| 1.3375 | 5.0 | 1455 | 1.1944 |
| 1.2969 | 6.0 | 1746 | 1.2848 |
| 1.2443 | 7.0 | 2037 | 1.2678 |
| 1.1998 | 8.0 | 2328 | 1.2151 |
| 1.1805 | 9.0 | 2619 | 1.1638 |
| 1.1396 | 10.0 | 2910 | 1.2131 |
| 1.1333 | 11.0 | 3201 | 1.1966 |
| 1.0974 | 12.0 | 3492 | 1.1687 |
| 1.0822 | 13.0 | 3783 | 1.2283 |
| 1.0736 | 14.0 | 4074 | 1.1640 |
| 1.0595 | 15.0 | 4365 | 1.1207 |
| 1.0515 | 16.0 | 4656 | 1.2551 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
leixu/xlm-roberta-base-finetuned-panx-de
|
leixu
| 2022-04-04T14:38:14Z | 4 | 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
| 2022-04-04T14:32:44Z |
---
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: 0.8605061131646289
---
<!-- 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: 0.1377
- F1: 0.8605
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2573 | 1.0 | 525 | 0.1651 | 0.8199 |
| 0.1296 | 2.0 | 1050 | 0.1482 | 0.8413 |
| 0.081 | 3.0 | 1575 | 0.1377 | 0.8605 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.7.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
ramazan/fatima-cats
|
ramazan
| 2022-04-04T11:30:22Z | 0 | 0 | null |
[
"cifar",
"cats",
"upsidedown",
"dataset:cifar10_reduced",
"license:mit",
"model-index",
"region:us"
] | null | 2022-04-04T09:05:27Z |
---
language:
- Python
- PyTorch
tags:
- cifar
- cats
- upsidedown
license: mit
datasets:
- cifar10_reduced
metrics:
- Accuracy
- Precision
- Recall
model-index:
- name: CatsNet
results:
- task:
type: image-classification
name: Image Classification
dataset:
type: cifar10
name: CIFAR10_CATS
metrics:
- type: Accuracy
value: 0.83
name: Test Accuracy
- type: Precision
value: 0.83
name: Test Precision
- type: Recall
value: 0.82
name: Test Recall
---
Model for Fatima Fellowship code challenge. <br>
Full training and evaluation pipelines can be found at: https://colab.research.google.com/drive/1hjHn6EggRDsxOZz5fMo6ZdT-4aKcUCTt
|
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
|
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-mixed
|
nherve
| 2022-04-04T10:26:49Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"flaubert",
"bert",
"language-model",
"french",
"flaubert-base",
"uncased",
"asr",
"speech",
"oral",
"natural language understanding",
"NLU",
"spoken language understanding",
"SLU",
"understanding",
"fr",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-18T13:46:50Z |
---
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-qmc-finance-v1
|
DMetaSoul
| 2022-04-04T07:21:28Z | 5 | 2 |
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
| 2022-03-25T10:23:55Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- semantic-search
- chinese
---
# DMetaSoul/sbert-chinese-qmc-finance-v1
此模型基于 [bert-base-chinese](https://huggingface.co/bert-base-chinese) 版本 BERT 模型，在大规模银行问题匹配数据集（[BQCorpus](http://icrc.hitsz.edu.cn/info/1037/1162.htm)）上进行训练调优，适用于**金融领域的问题匹配**场景，比如：
- 8千日利息400元? VS 10000元日利息多少钱
- 提前还款是按全额计息 VS 还款扣款不成功怎么还款？
- 为什么我借钱交易失败 VS 刚申请的借款为什么会失败
注：此模型的[轻量化版本](https://huggingface.co/DMetaSoul/sbert-chinese-qmc-finance-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 = ["到期不能按时还款怎么办", "剩余欠款还有多少？"]
model = SentenceTransformer('DMetaSoul/sbert-chinese-qmc-finance-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 = ["到期不能按时还款怎么办", "剩余欠款还有多少？"]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('DMetaSoul/sbert-chinese-qmc-finance-v1')
model = AutoModel.from_pretrained('DMetaSoul/sbert-chinese-qmc-finance-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-finance-v1** | 77.40% | 74.55% | 36.01% | 75.75% | 73.25% | 11.58% | 54.76% |
## Citing & Authors
E-mail: xiaowenbin@dmetasoul.com
|
Yaxin/roberta-large-ernie2-skep-en
|
Yaxin
| 2022-04-04T07:18:20Z | 4 | 2 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"en",
"arxiv:2005.05635",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-04-04T06:27:48Z |
---
language: en
---
# SKEP-Roberta
## Introduction
SKEP (SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis) is proposed by Baidu in 2020,
SKEP propose Sentiment Knowledge Enhanced Pre-training for sentiment analysis. Sentiment masking and three sentiment pre-training objectives are designed to incorporate various types of knowledge for pre-training model.
More detail: https://aclanthology.org/2020.acl-main.374.pdf
## Released Model Info
|Model Name|Language|Model Structure|
|:---:|:---:|:---:|
|skep-roberta-large| English |Layer:24, Hidden:1024, Heads:24|
This released pytorch model is converted from the officially released PaddlePaddle SKEP model and
a series of experiments have been conducted to check the accuracy of the conversion.
- Official PaddlePaddle SKEP repo:
1. https://github.com/PaddlePaddle/PaddleNLP/blob/develop/paddlenlp/transformers/skep
2. https://github.com/baidu/Senta
- Pytorch Conversion repo: Not released yet
## How to use
```Python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("Yaxin/roberta-large-ernie2-skep-en")
model = AutoModel.from_pretrained("Yaxin/roberta-large-ernie2-skep-en")
```
```
#!/usr/bin/env python
#encoding: utf-8
import torch
from transformers import RobertaTokenizer, RobertaForMaskedLM
tokenizer = RobertaTokenizer.from_pretrained('Yaxin/roberta-large-ernie2-skep-en')
input_tx = "<s> He like play with student, so he became a <mask> after graduation </s>"
# input_tx = "<s> He is a <mask> and likes to get along with his students </s>"
tokenized_text = tokenizer.tokenize(input_tx)
indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
tokens_tensor = torch.tensor([indexed_tokens])
segments_tensors = torch.tensor([[0] * len(tokenized_text)])
model = RobertaForMaskedLM.from_pretrained('Yaxin/roberta-large-ernie2-skep-en')
model.eval()
with torch.no_grad():
outputs = model(tokens_tensor, token_type_ids=segments_tensors)
predictions = outputs[0]
predicted_index = [torch.argmax(predictions[0, i]).item() for i in range(0, (len(tokenized_text) - 1))]
predicted_token = [tokenizer.convert_ids_to_tokens([predicted_index[x]])[0] for x in
range(1, (len(tokenized_text) - 1))]
print('Predicted token is:', predicted_token)
```
## Citation
```bibtex
@article{tian2020skep,
title={SKEP: Sentiment knowledge enhanced pre-training for sentiment analysis},
author={Tian, Hao and Gao, Can and Xiao, Xinyan and Liu, Hao and He, Bolei and Wu, Hua and Wang, Haifeng and Wu, Feng},
journal={arXiv preprint arXiv:2005.05635},
year={2020}
}
```
```
reference:
https://github.com/nghuyong/ERNIE-Pytorch
```
|
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
|
jjeamin/ArcaneStyleTransfer
|
jjeamin
| 2022-04-04T01:57:26Z | 0 | 4 | null |
[
"pytorch",
"onnx",
"license:apache-2.0",
"region:us"
] | null | 2022-03-31T11:34:03Z |
---
license: apache-2.0
---
|
hamedkhaledi/persain-flair-ner
|
hamedkhaledi
| 2022-04-03T22:22:20Z | 31 | 1 |
flair
|
[
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"fa",
"region:us"
] |
token-classification
| 2022-03-25T10:16:41Z |
---
tags:
- flair
- token-classification
- sequence-tagger-model
language: fa
dataset:
- NSURL-2019
widget:
- text: "آخرین مقام برجسته ژاپنی که پس از انقلاب 57 تاکنون به ایران سفر کرده است شینتارو آبه است."
---
## Persian NER in Flair
This is the universal Named-entity recognition model for Persian that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **84.03** (NSURL-2019)
Predicts NER tags:
| **tag** | **meaning** |
|:---------------------------------:|:-----------:|
| PER | person name |
| LOC | location name |
| ORG | organization name |
| DAT | date |
| TIM | time |
| PCT | percent |
| MON | Money|
---
### Demo: How to use in Flair
Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
```python
from flair.data import Sentence
from flair.models import SequenceTagger
# load tagger
tagger = SequenceTagger.load("hamedkhaledi/persain-flair-ner")
# make example sentence
sentence = Sentence("آخرین مقام برجسته ژاپنی که پس از انقلاب 57 تاکنون به ایران سفر کرده است شینتارو آبه است.")
tagger.predict(sentence)
#print result
print(sentence.to_tagged_string())
```
This yields the following output:
```
آخرین مقام برجسته ژاپنی که پس از انقلاب 57 <B-DAT> تاکنون به ایران <B-LOC> سفر کرده است شینتارو <B-PER> آبه <I-PER> است .
```
---
### Results
- F-score (micro) 0.8403
- F-score (macro) 0.8656
- Accuracy 0.7357
```
By class:
precision recall f1-score support
LOC 0.8789 0.8589 0.8688 4083
ORG 0.8390 0.7653 0.8005 3166
PER 0.8395 0.8169 0.8280 2741
DAT 0.8648 0.7957 0.8288 1150
MON 0.9758 0.9020 0.9374 357
TIM 0.8500 0.8193 0.8344 166
PCT 0.9615 0.9615 0.9615 156
micro avg 0.8616 0.8200 0.8403 11819
macro avg 0.8871 0.8456 0.8656 11819
weighted avg 0.8613 0.8200 0.8400 11819
samples avg 0.7357 0.7357 0.7357 11819
Loss: 0.06893542408943176'
```
|
BigSalmon/InformalToFormalLincoln34
|
BigSalmon
| 2022-04-03T20:41:44Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-04-03T20:17:27Z |
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln34")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln34")
```
```
- 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.
|
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.
|
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/roberta-base-biomedical-es-squad2-es
|
somosnlp-hackathon-2022
| 2022-04-03T14:51:38Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"question-answering",
"es",
"dataset:squad_es",
"dataset:hackathon-pln-es/biomed_squad_es_v2",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-04-02T18:25:38Z |
---
language: es
datasets:
- squad_es
- hackathon-pln-es/biomed_squad_es_v2
metrics:
- "f1"
---
# roberta-base-biomedical-es 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 [PlanTL-GOB-ES/roberta-base-biomedical-es](https://huggingface.co/PlanTL-GOB-ES/roberta-base-biomedical-es) on the [squad_es (v2)](https://huggingface.co/datasets/squad_es) training dataset.
## Hyperparameters
The hyperparameters were chosen based on those used in [PlanTL-GOB-ES/roberta-base-bne-sqac](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne-sqac), a spanish-based QA model trained on a dataset with SQUAD v1 fromat.
```
--num_train_epochs 2
--learning_rate 3e-5
--weight_decay 0.01
--max_seq_length 386
--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
|
AnnaBabaie/ms-marco-MiniLM-L-12-v2-news
|
AnnaBabaie
| 2022-04-03T13:46:51Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-03T12:55:06Z |
This model is fined tuned for the Fake news classifier: Train a text classification model to detect fake news articles. Base on the Kaggle dataset(https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset).
|
fmmolina/bert-base-spanish-wwm-uncased-finetuned-NER-medical
|
fmmolina
| 2022-04-03T13:39:03Z | 17 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-29T21:37:37Z |
---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-spanish-wwm-uncased-finetuned-NER-medical
results: []
widget:
- text: "El útero o matriz es el lugar donde se desarrolla el bebé cuando una mujer está embarazada."
- text: "El síndrome de dolor regional complejo es un trastorno de dolor crónico."
---
# bert-base-spanish-wwm-uncased-finetuned-NER-medical
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) on an adaptation of [eHealth-KD Challenge 2020 dataset](https://knowledge-learning.github.io/ehealthkd-2020/), filtered only for the task of NER. The dataset annotations for NER are ['Concept', 'Action', 'Predicate', 'Reference'].
Before the training process, the dataset had processed to label it with the BIO annotation format. Some cleaning and adaptations were needed, for example, to work with overlapped entities.
It achieves the following results on the evaluation set:
- Loss: 0.6433
- Precision: 0.8297
- Recall: 0.8367
- F1: 0.8332
- Accuracy: 0.8876
## Model description
A BERT adaptation for Spanish medical NER. This type of models can be part of NLP pipelines created, for example, to analyse clinical documents, automatic labelling of clinical documents following standard classifications such as CIE-10 o SNOMED, etc.
## Training and evaluation data
The adapted dataset has this structure:
- Training: 800 labelled sentences
- Development: 199 labelled sentences
- Testing: 100 labelled sentences
## Training procedure
The chapter [“Token classification”]( https://huggingface.co/course/chapter7/2) in the Hugging Face online course was used as starting point for the training process. We made some adaptions because our dataset follows a slightly different structure. Moreover, a conversion between string labels and integers labels was needed.
### 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: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1139 | 1.0 | 50 | 0.3932 | 0.8671 | 0.8378 | 0.8522 | 0.9004 |
| 0.074 | 2.0 | 100 | 0.4334 | 0.8682 | 0.8367 | 0.8522 | 0.9004 |
| 0.0564 | 3.0 | 150 | 0.4498 | 0.8654 | 0.8353 | 0.8501 | 0.8993 |
| 0.0431 | 4.0 | 200 | 0.4683 | 0.8629 | 0.8425 | 0.8526 | 0.8985 |
| 0.0328 | 5.0 | 250 | 0.4850 | 0.8508 | 0.8454 | 0.8481 | 0.8964 |
| 0.027 | 6.0 | 300 | 0.4983 | 0.8608 | 0.8432 | 0.8519 | 0.8988 |
| 0.0253 | 7.0 | 350 | 0.5334 | 0.8618 | 0.8457 | 0.8537 | 0.9004 |
| 0.0242 | 8.0 | 400 | 0.5546 | 0.8636 | 0.8450 | 0.8542 | 0.9009 |
| 0.0233 | 9.0 | 450 | 0.5507 | 0.8543 | 0.8436 | 0.8489 | 0.8961 |
| 0.0203 | 10.0 | 500 | 0.5410 | 0.8605 | 0.8432 | 0.8518 | 0.9001 |
| 0.0179 | 11.0 | 550 | 0.5547 | 0.8603 | 0.8507 | 0.8555 | 0.9006 |
| 0.0149 | 12.0 | 600 | 0.5568 | 0.8616 | 0.8446 | 0.8531 | 0.9012 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
AykeeSalazar/violation-classification-bantai_vit
|
AykeeSalazar
| 2022-04-03T12:26:48Z | 62 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-04-03T03:01:22Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- image_folder
model-index:
- name: violation-classification-bantai_vit
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. -->
# violation-classification-bantai_vit
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:
- eval_loss: 0.2362
- eval_accuracy: 0.9478
- eval_runtime: 43.2567
- eval_samples_per_second: 85.42
- eval_steps_per_second: 2.682
- epoch: 87.0
- step: 10005
## 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: 500
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
maxhilsdorf/distilbert-base-uncased-finetuned-emotion
|
maxhilsdorf
| 2022-04-03T12:08:54Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-01T15:32:54Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
model-index:
- name: distilbert-base-uncased-finetuned-emotion
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-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:
- eval_loss: 0.2991
- eval_accuracy: 0.91
- eval_f1: 0.9083
- eval_runtime: 3.258
- eval_samples_per_second: 613.873
- eval_steps_per_second: 9.822
- epoch: 1.0
- step: 250
## 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
### Framework versions
- Transformers 4.14.1
- Pytorch 1.11.0
- Datasets 2.0.0
- Tokenizers 0.10.3
|
Awais/Audio_Source_Separation
|
Awais
| 2022-04-03T11:03:43Z | 11 | 21 |
asteroid
|
[
"asteroid",
"pytorch",
"audio",
"ConvTasNet",
"audio-to-audio",
"dataset:Libri2Mix",
"dataset:sep_clean",
"license:cc-by-sa-4.0",
"region:us"
] |
audio-to-audio
| 2022-04-02T13:01:03Z |
---
tags:
- asteroid
- audio
- ConvTasNet
- audio-to-audio
datasets:
- Libri2Mix
- sep_clean
license: cc-by-sa-4.0
---
## Asteroid model `Awais/Audio_Source_Separation`
Imported from [Zenodo](https://zenodo.org/record/3873572#.X9M69cLjJH4)
Description:
This model was trained by Joris Cosentino using the librimix recipe in [Asteroid](https://github.com/asteroid-team/asteroid).
It was trained on the `sep_clean` task of the Libri2Mix dataset.
Training config:
```yaml
data:
n_src: 2
sample_rate: 8000
segment: 3
task: sep_clean
train_dir: data/wav8k/min/train-360
valid_dir: data/wav8k/min/dev
filterbank:
kernel_size: 16
n_filters: 512
stride: 8
masknet:
bn_chan: 128
hid_chan: 512
mask_act: relu
n_blocks: 8
n_repeats: 3
skip_chan: 128
optim:
lr: 0.001
optimizer: adam
weight_decay: 0.0
training:
batch_size: 24
early_stop: True
epochs: 200
half_lr: True
num_workers: 2
```
Results :
On Libri2Mix min test set :
```yaml
si_sdr: 14.764543634468069
si_sdr_imp: 14.764029375607246
sdr: 15.29337970745095
sdr_imp: 15.114146605113111
sir: 24.092904661115366
sir_imp: 23.913669683141528
sar: 16.06055906916849
sar_imp: -51.980784441287454
stoi: 0.9311142440593033
stoi_imp: 0.21817376142710482
```
License notice:
This work "ConvTasNet_Libri2Mix_sepclean_8k"
is a derivative of [LibriSpeech ASR corpus](http://www.openslr.org/12) by Vassil Panayotov,
used under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). "ConvTasNet_Libri2Mix_sepclean_8k"
is licensed under [Attribution-ShareAlike 3.0 Unported](https://creativecommons.org/licenses/by-sa/3.0/) by Cosentino Joris.
|
abd-1999/autotrain-bbc-news-summarization-694821095
|
abd-1999
| 2022-04-03T09:25:08Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain",
"unk",
"dataset:abd-1999/autotrain-data-bbc-news-summarization",
"co2_eq_emissions",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-04-01T21:16:19Z |
---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- abd-1999/autotrain-data-bbc-news-summarization
co2_eq_emissions: 2313.4037079026934
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 694821095
- CO2 Emissions (in grams): 2313.4037079026934
## Validation Metrics
- Loss: 3.0294156074523926
- Rouge1: 2.1467
- Rouge2: 0.0853
- RougeL: 2.1524
- RougeLsum: 2.1534
- Gen Len: 18.5603
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/abd-1999/autotrain-bbc-news-summarization-694821095
```
|
Prinernian/distilbert-base-uncased-finetuned-emotion
|
Prinernian
| 2022-04-03T09:11:07Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-02T17:49:11Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
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-emotion
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: 0.2208
- Accuracy: 0.924
- F1: 0.9240
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8538 | 1.0 | 250 | 0.3317 | 0.904 | 0.8999 |
| 0.2599 | 2.0 | 500 | 0.2208 | 0.924 | 0.9240 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Tokenizers 0.11.6
|
AykeeSalazar/vit-base-patch16-224-in21k-bantai_vitv1
|
AykeeSalazar
| 2022-04-03T02:43:41Z | 63 | 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
| 2022-04-02T14:17:18Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-in21k-bantai_vitv1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: image_folder
type: image_folder
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8635994587280108
---
<!-- 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. -->
# vit-base-patch16-224-in21k-bantai_vitv1
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: 0.3961
- Accuracy: 0.8636
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5997 | 1.0 | 115 | 0.5401 | 0.7886 |
| 0.4696 | 2.0 | 230 | 0.4410 | 0.8482 |
| 0.4019 | 3.0 | 345 | 0.3961 | 0.8636 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
Asayaya/Upside_down_detector
|
Asayaya
| 2022-04-03T01:00:26Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2022-04-03T00:55:24Z |
---
license: apache-2.0
---
# -*- coding: utf-8 -*-
'''
Original file is located at
https://colab.research.google.com/drive/1HrNm5UMZr2Zjmze_HKW799p6LAHM8BTa
'''
from google.colab import files
files.upload()
!pip install kaggle
!cp kaggle.json ~/.kaggle/
!chmod 600 ~/.kaggle/kaggle.json
!kaggle datasets download 'shaunthesheep/microsoft-catsvsdogs-dataset'
!unzip microsoft-catsvsdogs-dataset
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
image_dir='/content/PetImages/Cat'
!mkdir train_folder
!mkdir test_folder
import os
path='/content/train_folder/'
dir='upside_down'
dir2='normal'
training_normal= os.path.join(path, dir2)
training_upside= os.path.join(path, dir)
os.mkdir(training_normal)
os.mkdir(training_upside)
#creating classes directories
path='/content/test_folder/'
dir='upside_down'
dir2='normal'
training_normal= os.path.join(path, dir2)
training_upside= os.path.join(path, dir)
os.mkdir(training_normal)
os.mkdir(training_upside)
#copying only the cat images to my train folder
fnames = ['{}.jpg'.format(i) for i in range(2000)]
for fname in fnames:
src = os.path.join('/content/PetImages/Cat', fname)
dst = os.path.join('/content/train_folder/normal', fname)
shutil.copyfile(src, dst)
import os
import shutil
fnames = ['{}.jpg'.format(i) for i in range(2000, 4000)]
for fname in fnames:
src = os.path.join('/content/PetImages/Cat', fname)
dst = os.path.join('/content/test_folder/normal', fname)
shutil.copyfile(src, dst)
from scipy import ndimage, misc
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
import imageio
import os
import cv2
#inverting Training Images
outPath = '/content/train_folder/upside_down'
path ='/content/train_folder/normal'
# iterate through the names of contents of the folder
for image_path in os.listdir(path):
# create the full input path and read the file
input_path = os.path.join(path, image_path)
image_to_rotate =plt.imread(input_path)
# rotate the image
rotated = np.flipud(image_to_rotate)
# create full output path, 'example.jpg'
# becomes 'rotate_example.jpg', save the file to disk
fullpath = os.path.join(outPath, 'rotated_'+image_path)
imageio.imwrite(fullpath, rotated)
#nverting images for Validation
outPath = '/content/test_folder/upside_down'
path ='/content/test_folder/normal'
# iterate through the names of contents of the folder
for image_path in os.listdir(path):
# create the full input path and read the file
input_path = os.path.join(path, image_path)
image_to_rotate =plt.imread(input_path)
# rotate the image
rotated = np.flipud(image_to_rotate)
# create full output path, 'example.jpg'
# becomes 'rotate_example.jpg', save the file to disk
fullpath = os.path.join(outPath, 'rotated_'+image_path)
imageio.imwrite(fullpath, rotated)
ima='/content/train_folder/inverted/rotated_1001.jpg'
image=plt.imread(ima)
plt.imshow(image)
# visualize the the figure
plt.show()
train_dir='/content/train_folder'
train_gen=ImageDataGenerator(rescale=1./255)
train_images= train_gen.flow_from_directory(
train_dir,
target_size=(250,250),
batch_size=50,
class_mode='binary'
)
validation_dir='/content/test_folder'
test_gen=ImageDataGenerator(rescale=1./255)
test_images= test_gen.flow_from_directory(
validation_dir,
target_size=(250,250),
batch_size=50,
class_mode='binary'
)
model=tf.keras.Sequential([
tf.keras.layers.Conv2D(16, (3,3), activation='relu', input_shape=(250,250,3)),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Conv2D(32, (3,3), activation='relu'),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Conv2D(128, (3,3), activation='relu'),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(512, activation='relu'),
tf.keras.layers.Dense(1, activation='sigmoid')
])
from tensorflow.keras.optimizers import RMSprop
model.compile(optimizer=RMSprop(learning_rate=0.001), loss=tf.keras.losses.BinaryCrossentropy(), metrics=['acc'])
history=model.fit(train_images, validation_data=test_images, epochs=5, steps_per_epoch=40 )
|
vicl/canine-s-finetuned-cola
|
vicl
| 2022-04-02T23:01:51Z | 3 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"canine",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-02T22:29:20Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: canine-s-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.059386434587477076
---
<!-- 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. -->
# canine-s-finetuned-cola
This model is a fine-tuned version of [google/canine-s](https://huggingface.co/google/canine-s) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6653
- Matthews Correlation: 0.0594
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.6132 | 1.0 | 535 | 0.6289 | 0.0 |
| 0.6062 | 2.0 | 1070 | 0.6179 | 0.0 |
| 0.6122 | 3.0 | 1605 | 0.6160 | 0.0 |
| 0.5939 | 4.0 | 2140 | 0.6159 | 0.0 |
| 0.5721 | 5.0 | 2675 | 0.6653 | 0.0594 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
vicl/distilbert-base-uncased-finetuned-mrpc
|
vicl
| 2022-04-02T21:56:07Z | 7 | 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
| 2022-04-02T21:45:00Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8480392156862745
- name: F1
type: f1
value: 0.89419795221843
---
<!-- 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-mrpc
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: 0.4044
- Accuracy: 0.8480
- F1: 0.8942
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 230 | 0.3830 | 0.8162 | 0.8673 |
| No log | 2.0 | 460 | 0.3957 | 0.8456 | 0.8952 |
| 0.4307 | 3.0 | 690 | 0.4044 | 0.8480 | 0.8942 |
| 0.4307 | 4.0 | 920 | 0.5649 | 0.8407 | 0.8915 |
| 0.1739 | 5.0 | 1150 | 0.5983 | 0.8480 | 0.8956 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
mp6kv/paper_feedback_intent
|
mp6kv
| 2022-04-02T21:42:38Z | 7 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-02T04:37:40Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: paper_feedback_intent
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. -->
# paper_feedback_intent
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3621
- Accuracy: 0.9302
- Precision: 0.9307
- Recall: 0.9302
- F1: 0.9297
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.9174 | 1.0 | 11 | 0.7054 | 0.7907 | 0.7903 | 0.7907 | 0.7861 |
| 0.6917 | 2.0 | 22 | 0.4665 | 0.8140 | 0.8134 | 0.8140 | 0.8118 |
| 0.4276 | 3.0 | 33 | 0.3326 | 0.9070 | 0.9065 | 0.9070 | 0.9041 |
| 0.2656 | 4.0 | 44 | 0.3286 | 0.9070 | 0.9065 | 0.9070 | 0.9041 |
| 0.1611 | 5.0 | 55 | 0.3044 | 0.9302 | 0.9307 | 0.9302 | 0.9297 |
| 0.1025 | 6.0 | 66 | 0.3227 | 0.9302 | 0.9307 | 0.9302 | 0.9297 |
| 0.0799 | 7.0 | 77 | 0.3216 | 0.9302 | 0.9307 | 0.9302 | 0.9297 |
| 0.0761 | 8.0 | 88 | 0.3529 | 0.9302 | 0.9307 | 0.9302 | 0.9297 |
| 0.0479 | 9.0 | 99 | 0.3605 | 0.9302 | 0.9307 | 0.9302 | 0.9297 |
| 0.0358 | 10.0 | 110 | 0.3621 | 0.9302 | 0.9307 | 0.9302 | 0.9297 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
vocab-transformers/distilbert-mlm-best
|
vocab-transformers
| 2022-04-02T21:18:53Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-04-02T21:18:48Z |
distilbert-base-uncased trained for 680K steps (lowest loss on dev dataset) with batch size 64 on C4, MSMARCO, Wikipedia, S2ORC, News
|
vocab-transformers/distilbert-mlm-1000k
|
vocab-transformers
| 2022-04-02T21:16:58Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-04-02T21:16:53Z |
distilbert-base-uncased trained for 1000K steps with batch size 64 on C4, MSMARCO, Wikipedia, S2ORC, News
|
vocab-transformers/distilbert-mlm-500k
|
vocab-transformers
| 2022-04-02T21:12:46Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-04-02T21:12:40Z |
distilbert-base-uncased trained for 500K steps with batch size 64 on C4, MSMARCO, Wikipedia, S2ORC, News
|
vocab-transformers/distilbert-mlm-250k
|
vocab-transformers
| 2022-04-02T21:10:59Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-04-02T21:07:10Z |
distilbert-base-uncased trained for 250K steps with batch size 64 on C4, MSMARCO, Wikipedia, S2ORC, News
|
vicl/distilbert-base-uncased-finetuned-cola
|
vicl
| 2022-04-02T20:16:28Z | 7 | 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
| 2022-04-02T18:29:35Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5598704865754364
---
<!-- 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-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8697
- Matthews Correlation: 0.5599
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5223 | 1.0 | 535 | 0.5444 | 0.4309 |
| 0.3457 | 2.0 | 1070 | 0.5213 | 0.5021 |
| 0.2351 | 3.0 | 1605 | 0.6793 | 0.5234 |
| 0.1693 | 4.0 | 2140 | 0.7587 | 0.5527 |
| 0.1301 | 5.0 | 2675 | 0.8697 | 0.5599 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
shwetha/distilbert-base-uncased-finetuned-squad
|
shwetha
| 2022-04-02T17:11:25Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-04-02T14:51:59Z |
---
license: apache-2.0
tags:
- generated_from_trainer
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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 5.5925
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 2 | 5.9198 |
| No log | 2.0 | 4 | 5.7019 |
| No log | 3.0 | 6 | 5.5925 |
### Framework versions
- Transformers 4.11.0
- Pytorch 1.10.2+cpu
- Datasets 2.0.0
- Tokenizers 0.10.3
|
JustAdvanceTechonology/medical_notes_mulitilingual
|
JustAdvanceTechonology
| 2022-04-02T16:37:24Z | 4 | 0 |
transformers
|
[
"transformers",
"tf",
"mt5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-04-02T11:06:15Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: JustAdvanceTechonology/medical_notes_mulitilingual
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. -->
# JustAdvanceTechonology/medical_notes_mulitilingual
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 8.7536
- Validation Loss: 6.1397
- Epoch: 7
## 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': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 1209, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 11.2097 | 6.1454 | 0 |
| 8.7069 | 6.1880 | 1 |
| 8.7350 | 6.1834 | 2 |
| 8.7021 | 6.1364 | 3 |
| 8.7385 | 6.2117 | 4 |
| 8.7318 | 6.2004 | 5 |
| 8.7487 | 6.1531 | 6 |
| 8.7536 | 6.1397 | 7 |
### Framework versions
- Transformers 4.16.2
- TensorFlow 2.5.0
- Datasets 2.0.0
- Tokenizers 0.10.1
|
jaygala24/distilroberta-base-finetuned-fake-news-english
|
jaygala24
| 2022-04-02T15:52:11Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-31T14:18:19Z |
---
license: apache-2.0
language: en
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: distilroberta-base-finetuned-fake-news-english
results: []
widget:
- text: "Wisconsin has not counted more votes than it has registered voters. This tweet is comparing the vote count from 2020 with the number of registered voters from 2018. When we take a look at Wisconsin’s current total of registered voters, we see that there is nothing fraudulent about the state’s count."
example_title: fake
- text: "Barack Hussein Obama II is an American politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic Party, Obama was the first African-American president of the United States."
example_title: real
---
# distilroberta-base-finetuned-fake-news-english
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the [fake-and-real news](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0020
- Accuracy: 0.9997
- F1: 0.9997
- Precision: 0.9994
- Recall: 1.0
- Auc: 0.9997
## Intended uses & limitations
The model may not work with the articles over 512 tokens after preprocessing as the model's context is restricted to a maximum of 512 tokens in the sequence.
## Training and evaluation data
The [fake-and-real news](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset) dataset contains a total of 44,898 annotated articles with 21,417 real and 23,481 fake. The dataset was stratified split into train, validation, and test subsets with a proportion of 60:20:20 respectively. The model was fine-tuned on the train subset and evaluated on validation and test subsets.
| Split | # examples |
|:----------:|:----------:|
| train | 17959 |
| validation | 13469 |
| test | 13470 |
## 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
- 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: 224
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|
| 0.251 | 0.36 | 200 | 0.0030 | 0.9996 | 0.9995 | 0.9995 | 0.9995 | 0.9996 |
| 0.0022 | 0.71 | 400 | 0.0012 | 0.9998 | 0.9998 | 0.9995 | 1.0 | 0.9998 |
| 0.0013 | 1.07 | 600 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0004 | 1.43 | 800 | 0.0015 | 0.9997 | 0.9997 | 0.9994 | 1.0 | 0.9997 |
| 0.0013 | 1.78 | 1000 | 0.0020 | 0.9997 | 0.9997 | 0.9994 | 1.0 | 0.9997 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.12.0
|
asimokby/fakeBert
|
asimokby
| 2022-04-02T15:22:27Z | 0 | 0 | null |
[
"text-classification",
"PyTorch",
"Transformers",
"license:mit",
"region:us"
] |
text-classification
| 2022-04-02T10:56:45Z |
---
license: mit
tags:
- text-classification
- PyTorch
- Transformers
---
# fakeBert
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on a [news dataset](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset) from Kaggle.
## Model description
Fine-tuning Bert for text classification.
## Training and evaluation data
Training & Validation: [Fake and real news dataset](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset)
Testing: [Fake News Detection Challenge KDD 2020](https://www.kaggle.com/competitions/fakenewskdd2020/overview)
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-5
- train_batch_size: 16
- eval_batch_size: 16
- optimizer: AdamW
|
hylee/DualStyleGAN
|
hylee
| 2022-04-02T14:44:30Z | 0 | 1 | null |
[
"region:us"
] | null | 2022-04-02T01:54:21Z |
---
title: DualStyleGAN
emoji: 👀
colorFrom: green
colorTo: gray
sdk: gradio
sdk_version: 2.8.13
app_file: app.py
pinned: false
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
|
Denzil/distilbert-base-uncased-finetuned-emotion
|
Denzil
| 2022-04-02T14:27:32Z | 3 | 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
| 2022-04-02T14:14:59Z |
---
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: 0.9239207626877816
---
<!-- 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: 0.2169
- Accuracy: 0.924
- F1: 0.9239
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8101 | 1.0 | 250 | 0.3068 | 0.905 | 0.9019 |
| 0.2456 | 2.0 | 500 | 0.2169 | 0.924 | 0.9239 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
rahulacj/mbart-large-cc25-finetuned-hi-to-en-v1
|
rahulacj
| 2022-04-02T14:18:26Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-04-01T13:41:48Z |
---
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: mbart-large-cc25-finetuned-hi-to-en-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. -->
# mbart-large-cc25-finetuned-hi-to-en-v1
This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4978
- Bleu: 33.3366
- Gen Len: 22.7806
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- 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 | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 1.6774 | 1.0 | 3955 | 1.5499 | 7.9551 | 73.7518 |
| 1.2296 | 2.0 | 7910 | 1.4846 | 32.8075 | 23.7341 |
| 0.9127 | 3.0 | 11865 | 1.5345 | 31.9747 | 23.6264 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
mnne/duck-and-cover-genre-encoder
|
mnne
| 2022-04-02T13:53:50Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-04-02T13:12:20Z |
# Duck and Cover - Genre Autoencoder
This model is part of the [duck_and_cover](https://github.com/mcschmitz/duck_and_cover) repository. Scope of this repository is to generate album covers based on several conditions like release year, artist & album name, and genre(s) using different types of GANs. The possible list of genres that this encoder covers can be found [here](https://github.com/mcschmitz/duck_and_cover/blob/master/data/genres.txt).
For training [prajjwal1/bert-mini](https://huggingface.co/prajjwal1/bert-mini) has been finetuned on a list of 466.045 albums with different genre combinations taken from the aforementioned list to embed genre information, while a simple Linear Layer was trained to decode and predict the given genre from the embeddings. The albums are real-world albums retrieved using the Spotify API. The intention behind this model is that Hard Rock is somehow related to Rock, while Pop Rock is related to Rock as well and a BERT Tokenizer can capture this information as a lot of music genres are described by using pre- and suffixes.
The model was validated on 133.155 during training and tested on 66.578. It yields a 98.29% Exact Match ratio on the testset and a 98.24% Exact Match Ratio on the validation set, which is extremely high given that the model can embed up to 3452 labels and most of the albums only had up to 5 labels.
## Usage
The model can be used to embed genres to a 256 dimensional space using the following input.
```python
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("mnne/duck-and-cover-genre-encoder")
tokenizer = AutoTokenizer.from_pretrained("mnne/duck-and-cover-genre-encoder")
genres = " , ".join(["classic soul", "memphis soul", "soul", "soul blues", "southern soul"])
x = tokenizer([genres], return_tensors="pt")
output = model(**x)
```
|
OmarAlasqa/RotNet_FatimaFellowship
|
OmarAlasqa
| 2022-04-02T12:45:33Z | 0 | 0 | null |
[
"tensorboard",
"region:us"
] | null | 2022-04-02T10:31:43Z |
**Upside down detector**: Train a model to detect if images are upside down
* Trained on Google Street View.
* Synthetically turn some of images upside down. Create a training and test set.
* Build a neural network using TensorFlow.
* Train it to classify image orientation until a reasonable accuracy is reached.
* Look at some of the images that were classified incorrectly. Please explain what you might do to improve your model's performance on these images in the future.
*The code is taken from: [RotNet](https://github.com/d4nst/RotNet), with minor changes.*
|
huggingtweets/sanjabh
|
huggingtweets
| 2022-04-02T12:14:56Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-04-02T12:13:25Z |
---
language: en
thumbnail: http://www.huggingtweets.com/sanjabh/1648901691950/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/1484080880222351360/FtDB2j4B_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">Lucid Dreams</div>
<div style="text-align: center; font-size: 14px;">@sanjabh</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 Lucid Dreams.
| Data | Lucid Dreams |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 373 |
| Short tweets | 137 |
| Tweets kept | 2740 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2s7tzf32/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 @sanjabh's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1cl1cjnx) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1cl1cjnx/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/sanjabh')
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)
|
DMetaSoul/sbert-chinese-qmc-domain-v1-distill
|
DMetaSoul
| 2022-04-02T10:03:06Z | 6 | 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
| 2022-04-02T10:02:53Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- semantic-search
- chinese
---
# DMetaSoul/sbert-chinese-qmc-domain-v1
此模型是基于之前开源[问题匹配模型](https://huggingface.co/DMetaSoul/sbert-chinese-qmc-domain-v1)的蒸馏轻量化版本（仅含4层 BERT），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？
离线训练好的大模型如果直接用于线上推理，对计算资源有苛刻的需求，而且难以满足业务环境对延迟、吞吐量等性能指标的要求，这里我们使用蒸馏手段来把大模型轻量化。从 12 层 BERT 蒸馏为 4 层后，模型参数量缩小到 44%，大概 latency 减半、throughput 翻倍、精度下降 4% 左右（具体结果详见下文评估小节）。
# 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
这里主要跟蒸馏前对应的 teacher 模型作了对比
*性能：*
| | Teacher | Student | Gap |
| ---------- | --------------------- | ------------------- | ----- |
| Model | BERT-12-layers (102M) | BERT-4-layers (45M) | 0.44x |
| Cost | 23s | 12s | -47% |
| Latency | 38ms | 20ms | -47% |
| Throughput | 421 sentence/s | 791 sentence/s | 1.9x |
*精度：*
| | **csts_dev** | **csts_test** | **afqmc** | **lcqmc** | **bqcorpus** | **pawsx** | **xiaobu** | **Avg** |
| -------------- | ------------ | ------------- | --------- | --------- | ------------ | --------- | ---------- | ------- |
| **Teacher** | 80.90% | 76.62% | 34.51% | 77.05% | 52.95% | 12.97% | 59.47% | 56.35% |
| **Student** | 79.89% | 76.34% | 27.59% | 69.26% | 49.40% | 9.06% | 53.52% | 52.15% |
| **Gap** (abs.) | - | - | - | - | - | - | - | -4.2% |
*基于1万条数据测试，GPU设备是V100，batch_size=16，max_seq_len=256*
## Citing & Authors
E-mail: xiaowenbin@dmetasoul.com
|
DMetaSoul/sbert-chinese-general-v2-distill
|
DMetaSoul
| 2022-04-02T09:58:33Z | 15 | 6 |
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
| 2022-04-02T09:58:18Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- semantic-search
- chinese
---
# DMetaSoul/sbert-chinese-general-v2-distill
此模型是之前[开源通用语义匹配模型](https://huggingface.co/DMetaSoul/sbert-chinese-general-v2)的蒸馏版本（仅4层 BERT），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**泛化能力更好且编码速度更快**。
离线训练好的大模型如果直接用于线上推理，对计算资源有苛刻的需求，而且难以满足业务环境对延迟、吞吐量等性能指标的要求，这里我们使用蒸馏手段来把大模型轻量化。从 12 层 BERT 蒸馏为 4 层后，模型参数量缩小到 44%，大概 latency 减半、throughput 翻倍、精度下降 6% 左右（具体结果详见下文评估小节）。
# 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-distill')
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-distill')
model = AutoModel.from_pretrained('DMetaSoul/sbert-chinese-general-v2-distill')
# 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
这里主要跟蒸馏前对应的 teacher 模型作了对比：
*性能：*
| | Teacher | Student | Gap |
| ---------- | --------------------- | ------------------- | ----- |
| Model | BERT-12-layers (102M) | BERT-4-layers (45M) | 0.44x |
| Cost | 23s | 12s | -47% |
| Latency | 38ms | 20ms | -47% |
| Throughput | 418 sentence/s | 791 sentence/s | 1.9x |
*精度：*
| | **csts_dev** | **csts_test** | **afqmc** | **lcqmc** | **bqcorpus** | **pawsx** | **xiaobu** | **Avg** |
| -------------- | ------------ | ------------- | --------- | --------- | ------------ | --------- | ---------- | ------- |
| **Teacher** | 77.19% | 72.59% | 36.79% | 76.91% | 49.62% | 16.24% | 63.15% | 56.07% |
| **Student** | 76.49% | 73.33% | 26.46% | 64.26% | 46.02% | 11.83% | 52.45% | 50.12% |
| **Gap** (abs.) | - | - | - | - | - | - | - | -5.95% |
*基于1万条数据测试，GPU设备是V100，batch_size=16，max_seq_len=256*
## Citing & Authors
E-mail: xiaowenbin@dmetasoul.com
|
junnyu/flash_small_wwm_cluecorpussmall
|
junnyu
| 2022-04-02T09:46:27Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"flash",
"fill-mask",
"license:mit",
"autotrain_compatible",
"region:us"
] |
fill-mask
| 2022-04-02T02:59:48Z |
---
license: mit
inference: False
---
# training logs
- https://wandb.ai/junyu/huggingface/runs/1jg2jlgt
# install
- https://github.com/JunnYu/FLASHQuad_pytorch
# usage
```python
import torch
from flash import FLASHForMaskedLM
from transformers import BertTokenizerFast
tokenizer = BertTokenizerFast.from_pretrained("junnyu/flash_small_wwm_cluecorpussmall")
model = FLASHForMaskedLM.from_pretrained("junnyu/flash_small_wwm_cluecorpussmall")
model.eval()
text = "天气预报说今天的天[MASK]很好，那么我[MASK]一起去公园玩吧！"
inputs = tokenizer(text, return_tensors="pt", padding="max_length", max_length=512, return_token_type_ids=False) #这里必须是512，不然结果可能不对。
with torch.no_grad():
pt_outputs = model(**inputs).logits[0]
pt_outputs_sentence = "pytorch: "
for i, id in enumerate(tokenizer.encode(text)):
if id == tokenizer.mask_token_id:
val,idx = pt_outputs[i].softmax(-1).topk(k=5)
tokens = tokenizer.convert_ids_to_tokens(idx)
new_tokens = []
for v,t in zip(val.cpu(),tokens):
new_tokens.append(f"{t}+{round(v.item(),4)}")
pt_outputs_sentence += "[" + "||".join(new_tokens) + "]"
else:
pt_outputs_sentence += "".join(
tokenizer.convert_ids_to_tokens([id], skip_special_tokens=True))
print(pt_outputs_sentence)
# pytorch: 天气预报说今天的天[气+0.994||天+0.0015||空+0.0014||晴+0.0005||阳+0.0003]很好，那么我[们+0.9563||就+0.0381||也+0.0032||俩+0.0004||来+0.0002]一起去公园玩吧！
```
|
satoshiz01/Flipped_CIFAR10_vision
|
satoshiz01
| 2022-04-02T05:09:07Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-04-02T03:30:55Z |
**Google Colab Notebook link:**
https://colab.research.google.com/drive/1iA8nvb93VLcrDfIt17AOIHnkVdLSNcW_?usp=sharing
This repo contains files for defining and creating a simple convolutional network for
classifying/detecting the orientation of CIFAR-10 images (either normal orientation or flipped upside down/180 degrees).
The following files are in this repo:
Coding_Challenge_for_Fatima_Fellowship.ipynb -- a copy of the Google Collab notebook with the code/output/writeup
best_model.pth -- dictionary of best model stats/weights found during training
cifar10flip_trn.pt -- saved training dataset of ~50% flipped CIFAR10 images
cifar10flip_tst.pt -- saved training dataset of ~50% flipped CIFAR10 images
image_examples.png -- an array of example imags from flipped CIFAR10 dataset
write-up -- write up of data processing, model results, and potential improvements (also in Google Colab)
wrong_predictions.zip -- a zip file of PNG images that were incorrectly classified by my model
(each file name provide information on the image's prediction, true label, and its class)
|
huggingtweets/clortown
|
huggingtweets
| 2022-04-02T04:51:29Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-04-02T02:36:56Z |
---
language: en
thumbnail: http://www.huggingtweets.com/clortown/1648875085007/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/1488574779351187458/RlIQNUFG_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">yeosang elf agenda</div>
<div style="text-align: center; font-size: 14px;">@clortown</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 yeosang elf agenda.
| Data | yeosang elf agenda |
| --- | --- |
| Tweets downloaded | 3140 |
| Retweets | 538 |
| Short tweets | 463 |
| Tweets kept | 2139 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3cupnlna/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 @clortown's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/uii743r9) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/uii743r9/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/clortown')
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)
|
vicl/canine-s-finetuned-stsb
|
vicl
| 2022-04-01T23:25:04Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"canine",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-01T19:47:18Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- spearmanr
model-index:
- name: canine-s-finetuned-stsb
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: stsb
metrics:
- name: Spearmanr
type: spearmanr
value: 0.8397182061195433
---
<!-- 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. -->
# canine-s-finetuned-stsb
This model is a fine-tuned version of [google/canine-s](https://huggingface.co/google/canine-s) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7223
- Pearson: 0.8397
- Spearmanr: 0.8397
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|
| No log | 1.0 | 360 | 0.7938 | 0.8083 | 0.8077 |
| 1.278 | 2.0 | 720 | 0.7349 | 0.8322 | 0.8305 |
| 0.6765 | 3.0 | 1080 | 0.7075 | 0.8374 | 0.8366 |
| 0.6765 | 4.0 | 1440 | 0.7586 | 0.8360 | 0.8376 |
| 0.4629 | 5.0 | 1800 | 0.7223 | 0.8397 | 0.8397 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
DrishtiSharma/poem-gen-spanish-t5-small-d2
|
DrishtiSharma
| 2022-04-01T22:38:26Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-04-01T17:08:12Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: poem-gen-spanish-t5-small-d2
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. -->
# poem-gen-spanish-t5-small-d2
This model is a fine-tuned version of [flax-community/spanish-t5-small](https://huggingface.co/flax-community/spanish-t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9027
## 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: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 3.223 | 0.73 | 30000 | 3.1479 |
| 3.0109 | 1.46 | 60000 | 3.0544 |
| 2.8649 | 2.19 | 90000 | 2.9730 |
| 2.7603 | 2.93 | 120000 | 2.9301 |
| 2.6343 | 3.66 | 150000 | 2.9188 |
| 2.5094 | 4.39 | 180000 | 2.9064 |
| 2.391 | 5.12 | 210000 | 2.9073 |
| 2.3592 | 5.85 | 240000 | 2.9022 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
lgris/bp500-base10k_voxpopuli
|
lgris
| 2022-04-01T20:34:35Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"pt",
"portuguese-speech-corpus",
"PyTorch",
"hf-asr-leaderboard",
"dataset:common_voice",
"dataset:mls",
"dataset:cetuc",
"dataset:lapsbm",
"dataset:voxforge",
"dataset:tedx",
"dataset:sid",
"arxiv:2012.03411",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: pt
datasets:
- common_voice
- mls
- cetuc
- lapsbm
- voxforge
- tedx
- sid
metrics:
- wer
tags:
- audio
- speech
- wav2vec2
- pt
- portuguese-speech-corpus
- automatic-speech-recognition
- speech
- PyTorch
- hf-asr-leaderboard
model-index:
- name: bp500-base10k_voxpopuli
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice
type: common_voice
args: pt
metrics:
- name: Test WER
type: wer
value: 24.9
license: apache-2.0
---
# bp500-base10k_voxpopuli: Wav2vec 2.0 with Brazilian Portuguese (BP) Dataset
This is a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the following datasets:
- [CETUC](http://www02.smt.ufrj.br/~igor.quintanilha/alcaim.tar.gz): contains approximately 145 hours of Brazilian Portuguese speech distributed among 50 male and 50 female speakers, each pronouncing approximately 1,000 phonetically balanced sentences selected from the [CETEN-Folha](https://www.linguateca.pt/cetenfolha/) corpus.
- [Common Voice 7.0](https://commonvoice.mozilla.org/pt): is a project proposed by Mozilla Foundation with the goal to create a wide open dataset in different languages. In this project, volunteers donate and validate speech using the [oficial site](https://commonvoice.mozilla.org/pt).
- [Lapsbm](https://github.com/falabrasil/gitlab-resources): "Falabrasil - UFPA" is a dataset used by the Fala Brasil group to benchmark ASR systems in Brazilian Portuguese. Contains 35 speakers (10 females), each one pronouncing 20 unique sentences, totalling 700 utterances in Brazilian Portuguese. The audios were recorded in 22.05 kHz without environment control.
- [Multilingual Librispeech (MLS)](https://arxiv.org/abs/2012.03411): a massive dataset available in many languages. The MLS is based on audiobook recordings in public domain like [LibriVox](https://librivox.org/). The dataset contains a total of 6k hours of transcribed data in many languages. The set in Portuguese [used in this work](http://www.openslr.org/94/) (mostly Brazilian variant) has approximately 284 hours of speech, obtained from 55 audiobooks read by 62 speakers.
- [Multilingual TEDx](http://www.openslr.org/100): a collection of audio recordings from TEDx talks in 8 source languages. The Portuguese set (mostly Brazilian Portuguese variant) contains 164 hours of transcribed speech.
- [Sidney](https://igormq.github.io/datasets/) (SID): contains 5,777 utterances recorded by 72 speakers (20 women) from 17 to 59 years old with fields such as place of birth, age, gender, education, and occupation;
- [VoxForge](http://www.voxforge.org/): is a project with the goal to build open datasets for acoustic models. The corpus contains approximately 100 speakers and 4,130 utterances of Brazilian Portuguese, with sample rates varying from 16kHz to 44.1kHz.
These datasets were combined to build a larger Brazilian Portuguese dataset. All data was used for training except Common Voice dev/test sets, that were used for validation/test respectively. We also made test sets for all the gathered datasets.
| Dataset | Train | Valid | Test |
|--------------------------------|-------:|------:|------:|
| CETUC | 94.0h | -- | 5.4h |
| Common Voice | 37.8h | 8.9h | 9.5h |
| LaPS BM | 0.8h | -- | 0.1h |
| MLS | 161.0h | -- | 3.7h |
| Multilingual TEDx (Portuguese) | 148.9h | -- | 1.8h |
| SID | 7.2h | -- | 1.0h |
| VoxForge | 3.9h | -- | 0.1h |
| Total | 453.6h | 8.9h | 21.6h |
The original model was fine-tuned using [fairseq](https://github.com/pytorch/fairseq). This notebook uses a converted version of the original one. The link to the original fairseq model is available [here](https://drive.google.com/file/d/19kkENi8uvczmw9OLSdqnjvKqBE53cl_W/view?usp=sharing).
#### Summary
| | CETUC | CV | LaPS | MLS | SID | TEDx | VF | AVG |
|----------------------|---------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|
| bp\_500-base10k_voxpopuli (demonstration below) | 0.120 | 0.249 | 0.039 | 0.227 | 0.169 | 0.349 | 0.116 | 0.181 |
| bp\_500-base10k_voxpopuli + 4-gram (demonstration below) | 0.074 | 0.174 | 0.032 | 0.182 | 0.181 | 0.349 | 0.111 | 0.157 |
#### Transcription examples
| Text | Transcription |
|------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------|
|suco de uva e água misturam bem|suco **deúva** e água **misturão** bem|
|culpa do dinheiro|**cupa** do dinheiro|
|eu amo shooters call of duty é o meu favorito|eu **omo** **shúters cofedete** é meu favorito|
|você pode explicar por que isso acontece|você pode explicar *por* que isso **ontece**|
|no futuro você desejará ter começado a investir hoje|no futuro você desejará **a** ter começado a investir hoje|
## Demonstration
```python
MODEL_NAME = "lgris/bp500-base10k_voxpopuli"
```
### Imports and dependencies
```python
%%capture
!pip install torch==1.8.2+cu111 torchvision==0.9.2+cu111 torchaudio===0.8.2 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html
!pip install datasets
!pip install jiwer
!pip install transformers
!pip install soundfile
!pip install pyctcdecode
!pip install https://github.com/kpu/kenlm/archive/master.zip
```
```python
import jiwer
import torchaudio
from datasets import load_dataset, load_metric
from transformers import (
Wav2Vec2ForCTC,
Wav2Vec2Processor,
)
from pyctcdecode import build_ctcdecoder
import torch
import re
import sys
```
### Helpers
```python
chars_to_ignore_regex = '[\,\?\.\!\;\:\"]' # noqa: W605
def map_to_array(batch):
speech, _ = torchaudio.load(batch["path"])
batch["speech"] = speech.squeeze(0).numpy()
batch["sampling_rate"] = 16_000
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'")
batch["target"] = batch["sentence"]
return batch
```
```python
def calc_metrics(truths, hypos):
wers = []
mers = []
wils = []
for t, h in zip(truths, hypos):
try:
wers.append(jiwer.wer(t, h))
mers.append(jiwer.mer(t, h))
wils.append(jiwer.wil(t, h))
except: # Empty string?
pass
wer = sum(wers)/len(wers)
mer = sum(mers)/len(mers)
wil = sum(wils)/len(wils)
return wer, mer, wil
```
```python
def load_data(dataset):
data_files = {'test': f'{dataset}/test.csv'}
dataset = load_dataset('csv', data_files=data_files)["test"]
return dataset.map(map_to_array)
```
### Model
```python
class STT:
def __init__(self,
model_name,
device='cuda' if torch.cuda.is_available() else 'cpu',
lm=None):
self.model_name = model_name
self.model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device)
self.processor = Wav2Vec2Processor.from_pretrained(model_name)
self.vocab_dict = self.processor.tokenizer.get_vocab()
self.sorted_dict = {
k.lower(): v for k, v in sorted(self.vocab_dict.items(),
key=lambda item: item[1])
}
self.device = device
self.lm = lm
if self.lm:
self.lm_decoder = build_ctcdecoder(
list(self.sorted_dict.keys()),
self.lm
)
def batch_predict(self, batch):
features = self.processor(batch["speech"],
sampling_rate=batch["sampling_rate"][0],
padding=True,
return_tensors="pt")
input_values = features.input_values.to(self.device)
with torch.no_grad():
logits = self.model(input_values).logits
if self.lm:
logits = logits.cpu().numpy()
batch["predicted"] = []
for sample_logits in logits:
batch["predicted"].append(self.lm_decoder.decode(sample_logits))
else:
pred_ids = torch.argmax(logits, dim=-1)
batch["predicted"] = self.processor.batch_decode(pred_ids)
return batch
```
### Download datasets
```python
%%capture
!gdown --id 1HFECzIizf-bmkQRLiQD0QVqcGtOG5upI
!mkdir bp_dataset
!unzip bp_dataset -d bp_dataset/
```
```python
%cd bp_dataset
```
/content/bp_dataset
### Tests
```python
stt = STT(MODEL_NAME)
```
#### CETUC
```python
ds = load_data('cetuc_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8)
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("CETUC WER:", wer)
```
CETUC WER: 0.12096759949218888
#### Common Voice
```python
ds = load_data('commonvoice_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8)
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("CV WER:", wer)
```
CV WER: 0.24977003159495725
#### LaPS
```python
ds = load_data('lapsbm_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8)
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("Laps WER:", wer)
```
Laps WER: 0.039769570707070705
#### MLS
```python
ds = load_data('mls_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8)
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("MLS WER:", wer)
```
MLS WER: 0.2269637077788063
#### SID
```python
ds = load_data('sid_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8)
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("Sid WER:", wer)
```
Sid WER: 0.1691680138494731
#### TEDx
```python
ds = load_data('tedx_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8)
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("TEDx WER:", wer)
```
TEDx WER: 0.34908555859018014
#### VoxForge
```python
ds = load_data('voxforge_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8)
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("VoxForge WER:", wer)
```
VoxForge WER: 0.11649350649350651
### Tests with LM
```python
!rm -rf ~/.cache
!gdown --id 1GJIKseP5ZkTbllQVgOL98R4yYAcIySFP # trained with wikipedia
stt = STT(MODEL_NAME, lm='pt-BR-wiki.word.4-gram.arpa')
# !gdown --id 1dLFldy7eguPtyJj5OAlI4Emnx0BpFywg # trained with bp
# stt = STT(MODEL_NAME, lm='pt-BR.word.4-gram.arpa')
```
### Cetuc
```python
ds = load_data('cetuc_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8)
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("CETUC WER:", wer)
```
CETUC WER: 0.07499558425787961
#### Common Voice
```python
ds = load_data('commonvoice_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8)
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("CV WER:", wer)
```
CV WER: 0.17442648452610307
#### LaPS
```python
ds = load_data('lapsbm_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8)
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("Laps WER:", wer)
```
Laps WER: 0.032774621212121206
#### MLS
```python
ds = load_data('mls_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8)
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("MLS WER:", wer)
```
MLS WER: 0.18213620321569274
#### SID
```python
ds = load_data('sid_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8)
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("Sid WER:", wer)
```
Sid WER: 0.18102544972868206
#### TEDx
```python
ds = load_data('tedx_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8)
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("TEDx WER:", wer)
```
TEDx WER: 0.3491402028105601
#### VoxForge
```python
ds = load_data('voxforge_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8)
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("VoxForge WER:", wer)
```
VoxForge WER: 0.11189529220779222
|
lgris/bp500-xlsr
|
lgris
| 2022-04-01T20:33:47Z | 15 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"pt",
"portuguese-speech-corpus",
"PyTorch",
"hf-asr-leaderboard",
"dataset:common_voice",
"dataset:mls",
"dataset:cetuc",
"dataset:lapsbm",
"dataset:voxforge",
"dataset:tedx",
"dataset:sid",
"arxiv:2012.03411",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: pt
datasets:
- common_voice
- mls
- cetuc
- lapsbm
- voxforge
- tedx
- sid
metrics:
- wer
tags:
- audio
- speech
- wav2vec2
- pt
- portuguese-speech-corpus
- automatic-speech-recognition
- speech
- PyTorch
- hf-asr-leaderboard
model-index:
- name: bp400-xlsr
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice
type: common_voice
args: pt
metrics:
- name: Test WER
type: wer
value: 13.6
license: apache-2.0
---
# bp500-xlsr: Wav2vec 2.0 with Brazilian Portuguese (BP) Dataset
This is a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the following datasets:
- [CETUC](http://www02.smt.ufrj.br/~igor.quintanilha/alcaim.tar.gz): contains approximately 145 hours of Brazilian Portuguese speech distributed among 50 male and 50 female speakers, each pronouncing approximately 1,000 phonetically balanced sentences selected from the [CETEN-Folha](https://www.linguateca.pt/cetenfolha/) corpus;
- [Common Voice 7.0](https://commonvoice.mozilla.org/pt): is a project proposed by Mozilla Foundation with the goal to create a wide open dataset in different languages. In this project, volunteers donate and validate speech using the [oficial site](https://commonvoice.mozilla.org/pt);
- [Lapsbm](https://github.com/falabrasil/gitlab-resources): "Falabrasil - UFPA" is a dataset used by the Fala Brasil group to benchmark ASR systems in Brazilian Portuguese. Contains 35 speakers (10 females), each one pronouncing 20 unique sentences, totalling 700 utterances in Brazilian Portuguese. The audios were recorded in 22.05 kHz without environment control;
- [Multilingual Librispeech (MLS)](https://arxiv.org/abs/2012.03411): a massive dataset available in many languages. The MLS is based on audiobook recordings in public domain like [LibriVox](https://librivox.org/). The dataset contains a total of 6k hours of transcribed data in many languages. The set in Portuguese [used in this work](http://www.openslr.org/94/) (mostly Brazilian variant) has approximately 284 hours of speech, obtained from 55 audiobooks read by 62 speakers;
- [VoxForge](http://www.voxforge.org/): is a project with the goal to build open datasets for acoustic models. The corpus contains approximately 100 speakers and 4,130 utterances of Brazilian Portuguese, with sample rates varying from 16kHz to 44.1kHz.
These datasets were combined to build a larger Brazilian Portuguese dataset. All data was used for training except Common Voice dev/test sets, that were used for validation/test respectively. We also made test sets for all the gathered datasets.
| Dataset | Train | Valid | Test |
|--------------------------------|-------:|------:|------:|
| CETUC | 93.9h | -- | 5.4h |
| Common Voice | 37.6h | 8.9h | 9.5h |
| LaPS BM | 0.8h | -- | 0.1h |
| MLS | 161.0h | -- | 3.7h |
| Multilingual TEDx (Portuguese) | 144.2h | -- | 1.8h |
| SID | 5.0h | -- | 1.0h |
| VoxForge | 2.8h | -- | 0.1h |
| Total | 437.2h | 8.9h | 21.6h |
The original model was fine-tuned using [fairseq](https://github.com/pytorch/fairseq). This notebook uses a converted version of the original one. The link to the original fairseq model is available [here](https://drive.google.com/file/d/1J8aR1ltDLQFe-dVrGuyxoRm2uyJjCWgf/view?usp=sharing).
#### Summary
| | CETUC | CV | LaPS | MLS | SID | TEDx | VF | AVG |
|----------------------|---------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|
| bp\_500 (demonstration below) | 0.051 | 0.136 | 0.032 | 0.118 | 0.095 | 0.248 | 0.082 | 0.108 |
| bp\_500 + 4-gram (demonstration below) | 0.032 | 0.097 | 0.022 | 0.114 | 0.125 | 0.246 | 0.065 | 0.100 |
#### Transcription examples
| Text | Transcription |
|------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------|
|não há um departamento de mediadores independente das federações e das agremiações|não há um **dearamento** de mediadores independente das federações e das **agrebiações**|
|mas que bodega|**masque** bodega|
|a cortina abriu o show começou|a cortina abriu o **chô** começou|
|por sorte havia uma passadeira|**busote avinhoa** **passadeiro**|
|estou maravilhada está tudo pronto|**stou** estou maravilhada está tudo pronto|
## Demonstration
```python
MODEL_NAME = "lgris/bp500-xlsr"
```
### Imports and dependencies
```python
%%capture
!pip install torch==1.8.2+cu111 torchvision==0.9.2+cu111 torchaudio===0.8.2 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html
!pip install datasets
!pip install jiwer
!pip install transformers
!pip install soundfile
!pip install pyctcdecode
!pip install https://github.com/kpu/kenlm/archive/master.zip
```
```python
import jiwer
import torchaudio
from datasets import load_dataset, load_metric
from transformers import (
Wav2Vec2ForCTC,
Wav2Vec2Processor,
)
from pyctcdecode import build_ctcdecoder
import torch
import re
import sys
```
### Helpers
```python
chars_to_ignore_regex = '[\,\?\.\!\;\:\"]' # noqa: W605
def map_to_array(batch):
speech, _ = torchaudio.load(batch["path"])
batch["speech"] = speech.squeeze(0).numpy()
batch["sampling_rate"] = 16_000
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'")
batch["target"] = batch["sentence"]
return batch
```
```python
def calc_metrics(truths, hypos):
wers = []
mers = []
wils = []
for t, h in zip(truths, hypos):
try:
wers.append(jiwer.wer(t, h))
mers.append(jiwer.mer(t, h))
wils.append(jiwer.wil(t, h))
except: # Empty string?
pass
wer = sum(wers)/len(wers)
mer = sum(mers)/len(mers)
wil = sum(wils)/len(wils)
return wer, mer, wil
```
```python
def load_data(dataset):
data_files = {'test': f'{dataset}/test.csv'}
dataset = load_dataset('csv', data_files=data_files)["test"]
return dataset.map(map_to_array)
```
### Model
```python
class STT:
def __init__(self,
model_name,
device='cuda' if torch.cuda.is_available() else 'cpu',
lm=None):
self.model_name = model_name
self.model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device)
self.processor = Wav2Vec2Processor.from_pretrained(model_name)
self.vocab_dict = self.processor.tokenizer.get_vocab()
self.sorted_dict = {
k.lower(): v for k, v in sorted(self.vocab_dict.items(),
key=lambda item: item[1])
}
self.device = device
self.lm = lm
if self.lm:
self.lm_decoder = build_ctcdecoder(
list(self.sorted_dict.keys()),
self.lm
)
def batch_predict(self, batch):
features = self.processor(batch["speech"],
sampling_rate=batch["sampling_rate"][0],
padding=True,
return_tensors="pt")
input_values = features.input_values.to(self.device)
attention_mask = features.attention_mask.to(self.device)
with torch.no_grad():
logits = self.model(input_values, attention_mask=attention_mask).logits
if self.lm:
logits = logits.cpu().numpy()
batch["predicted"] = []
for sample_logits in logits:
batch["predicted"].append(self.lm_decoder.decode(sample_logits))
else:
pred_ids = torch.argmax(logits, dim=-1)
batch["predicted"] = self.processor.batch_decode(pred_ids)
return batch
```
### Download datasets
```python
%%capture
!gdown --id 1HFECzIizf-bmkQRLiQD0QVqcGtOG5upI
!mkdir bp_dataset
!unzip bp_dataset -d bp_dataset/
```
```python
%cd bp_dataset
```
/content/bp_dataset
### Tests
```python
stt = STT(MODEL_NAME)
```
#### CETUC
```python
ds = load_data('cetuc_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8)
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("CETUC WER:", wer)
```
CETUC WER: 0.05159097808687998
#### Common Voice
```python
ds = load_data('commonvoice_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8)
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("CV WER:", wer)
```
CV WER: 0.13659981509705973
#### LaPS
```python
ds = load_data('lapsbm_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8)
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("Laps WER:", wer)
```
Laps WER: 0.03196969696969697
#### MLS
```python
ds = load_data('mls_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8)
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("MLS WER:", wer)
```
MLS WER: 0.1178481066463896
#### SID
```python
ds = load_data('sid_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8)
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("Sid WER:", wer)
```
Sid WER: 0.09544588416964224
#### TEDx
```python
ds = load_data('tedx_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8)
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("TEDx WER:", wer)
```
TEDx WER: 0.24868046340420813
#### VoxForge
```python
ds = load_data('voxforge_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8)
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("VoxForge WER:", wer)
```
VoxForge WER: 0.08246076839826841
### Tests with LM
```python
!rm -rf ~/.cache
!gdown --id 1GJIKseP5ZkTbllQVgOL98R4yYAcIySFP # trained with wikipedia
stt = STT(MODEL_NAME, lm='pt-BR-wiki.word.4-gram.arpa')
# !gdown --id 1dLFldy7eguPtyJj5OAlI4Emnx0BpFywg # trained with bp
# stt = STT(MODEL_NAME, lm='pt-BR.word.4-gram.arpa')
```
### Cetuc
```python
ds = load_data('cetuc_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8)
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("CETUC WER:", wer)
```
CETUC WER: 0.03222801788375573
#### Common Voice
```python
ds = load_data('commonvoice_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8)
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("CV WER:", wer)
```
CV WER: 0.09713866021093655
#### LaPS
```python
ds = load_data('lapsbm_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8)
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("Laps WER:", wer)
```
Laps WER: 0.022310606060606065
#### MLS
```python
ds = load_data('mls_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8)
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("MLS WER:", wer)
```
MLS WER: 0.11408590958696524
#### SID
```python
ds = load_data('sid_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8)
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("Sid WER:", wer)
```
Sid WER: 0.12502797252979136
#### TEDx
```python
ds = load_data('tedx_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8)
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("TEDx WER:", wer)
```
TEDx WER: 0.24603179403904793
#### VoxForge
```python
ds = load_data('voxforge_dataset')
result = ds.map(stt.batch_predict, batched=True, batch_size=8)
wer, mer, wil = calc_metrics(result["sentence"], result["predicted"])
print("VoxForge WER:", wer)
```
VoxForge WER: 0.06542207792207791
|
lgris/wav2vec2-large-xlsr-open-brazilian-portuguese
|
lgris
| 2022-04-01T20:32:58Z | 268 | 9 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"pt",
"portuguese-speech-corpus",
"PyTorch",
"hf-asr-leaderboard",
"dataset:common_voice",
"dataset:mls",
"dataset:cetuc",
"dataset:lapsbm",
"dataset:voxforge",
"arxiv:2012.03411",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: pt
datasets:
- common_voice
- mls
- cetuc
- lapsbm
- voxforge
metrics:
- wer
tags:
- audio
- speech
- wav2vec2
- pt
- portuguese-speech-corpus
- automatic-speech-recognition
- speech
- PyTorch
- hf-asr-leaderboard
license: apache-2.0
model-index:
- name: Lucas Gris XLSR Wav2Vec2 Large 53 Brazilian Portuguese
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
metrics:
- name: Test WER
type: wer
value: 12.905054857823264%
---
# Wav2vec 2.0 With Open Brazilian Portuguese Datasets
This a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the following datasets:
- [CETUC](http://www02.smt.ufrj.br/~igor.quintanilha/alcaim.tar.gz): contains approximately 145 hours of Brazilian Portuguese speech distributed among 50 male and 50 female speakers, each pronouncing approximately 1,000 phonetically balanced sentences selected from the [CETEN-Folha](https://www.linguateca.pt/cetenfolha/) corpus.
- [Multilingual Librispeech (MLS)](https://arxiv.org/abs/2012.03411): a massive dataset available in many languages. The MLS is based on audiobook recordings in public domain like [LibriVox](https://librivox.org/). The dataset contains a total of 6k hours of transcribed data in many languages. The set in Portuguese [used in this work](http://www.openslr.org/94/) (mostly Brazilian variant) has approximately 284 hours of speech, obtained from 55 audiobooks read by 62 speakers.
- [VoxForge](http://www.voxforge.org/): is a project with the goal to build open datasets for acoustic models. The corpus contains approximately 100 speakers and 4,130 utterances of Brazilian Portuguese, with sample rates varying from 16kHz to 44.1kHz.
- [Common Voice 6.1](https://commonvoice.mozilla.org/pt) (_only train_): is a project proposed by Mozilla Foundation with the goal to create a wide open dataset in different languages to train ASR models. In this project, volunteers donate and validate speech using the [oficial site](https://commonvoice.mozilla.org/pt). The set in Portuguese (mostly Brazilian variant) used in this work is the 6.1 version (pt_63h_2020-12-11) that contains about 50 validated hours and 1,120 unique speakers.
- [Lapsbm](https://github.com/falabrasil/gitlab-resources): "Falabrasil - UFPA" is a dataset used by the Fala Brasil group to benchmark ASR systems in Brazilian Portuguese. Contains 35 speakers (10 females), each one pronouncing 20 unique sentences, totalling 700 utterances in Brazilian Portuguese. The audios were recorded in 22.05 kHz without environment control.
These datasets were combined to build a larger Brazilian Portuguese dataset. All data was used for training except Common Voice dev/test sets, that were used for validation/test respectively.
The original model was fine-tuned using [fairseq](https://github.com/pytorch/fairseq). This notebook uses a converted version of the original one. The link to the original fairseq model is available [here](https://drive.google.com/drive/folders/1XTKIUB4kp3oYOavwH97wq8IPFsxP5sNz?usp=sharing).
This model was trained in 80k updates.
#### Datasets in number of instances and number of frames
The following image shows the overall distribution of the dataset:
#### Transcription examples
| Text | Transcription |
|------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------|
| É comum os usuários confundirem software livre com software livre | É comum os __usuares__ __confunder em__ __softwerlivr__ com __softwerlivre__ |
| Ele fez tanto ghostwriting que ele começa a se sentir como um fantasma também | Ele fez tanto __golstraitn__ que ele __começou__ a se sentir como um fantasma também |
| Arnold apresentou um gráfico mostrando quantas cegonhas ele havia contado nos últimos dez anos | Arnold apresentou um gráfico mostrando quantas __segonhas__ ele havia contado nos últimos dez anos |
| Mais cedo ou mais tarde eles descobrirão como ler esses hieróglifos | Mais __sedo__ ou mais tarde eles descobriram como __de__ esses __ierogrôficos__ |
| Viver juntos compartilhar objetivos e ter um bom relacionamento | __E ver__ juntos __signafica__ viver juntos ou __fartlhar__ objetivos ter um bom __relacionamentoo__ |
| Da mesma forma uma patente pode impedir que concorrentes desenvolvam produtos similares | Da mesma forma uma patente pode impedir que concorrentes __desenvolva__ produtos similares |
| Duas mulheres e uma menina levantam com troféus | Duas mulheres e uma menina levantam com __trofés__ |
| Esse acrobata de circo deve ter um sistema vestibular bem treinado pensou o espectador | Esse acrobata de __cirko__ deve ter um sistema vestibular __bemtreinado__ pensou o espectador |
| Durante a exposição o tribunal pode fazer quaisquer perguntas ou esclarecimentos que considere apropriados | Durante a exposição o tribunal pode fazer quaisquer perguntas ou esclarecimentos que considere __apropriado__ |
## Imports and dependencies
```python
%%capture
!pip install datasets
!pip install jiwer
!pip install torchaudio
!pip install transformers
!pip install soundfile
```
```python
import torchaudio
from datasets import load_dataset, load_metric
from transformers import (
Wav2Vec2ForCTC,
Wav2Vec2Processor,
)
import torch
import re
import sys
```
## Preparation
```python
chars_to_ignore_regex = '[\,\?\.\!\;\:\"]' # noqa: W605
wer = load_metric("wer")
device = "cuda"
```
```python
model_name = 'lgris/wav2vec2-large-xlsr-open-brazilian-portuguese'
model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device)
processor = Wav2Vec2Processor.from_pretrained(model_name)
```
```python
def map_to_pred(batch):
features = processor(batch["speech"], sampling_rate=batch["sampling_rate"][0], padding=True, return_tensors="pt")
input_values = features.input_values.to(device)
attention_mask = features.attention_mask.to(device)
with torch.no_grad():
logits = model(input_values, attention_mask=attention_mask).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["predicted"] = processor.batch_decode(pred_ids)
batch["predicted"] = [pred.lower() for pred in batch["predicted"]]
batch["target"] = batch["sentence"]
return batch
```
## Tests
### Test against Common Voice (In-domain)
```python
dataset = load_dataset("common_voice", "pt", split="test", data_dir="./cv-corpus-6.1-2020-12-11")
resampler = torchaudio.transforms.Resample(orig_freq=48_000, new_freq=16_000)
def map_to_array(batch):
speech, _ = torchaudio.load(batch["path"])
batch["speech"] = resampler.forward(speech.squeeze(0)).numpy()
batch["sampling_rate"] = resampler.new_freq
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'")
return batch
```
```python
ds = dataset.map(map_to_array)
result = ds.map(map_to_pred, batched=True, batch_size=1, remove_columns=list(ds.features.keys()))
print(wer.compute(predictions=result["predicted"], references=result["target"]))
for pred, target in zip(result["predicted"][:10], result["target"][:10]):
print(pred, "|", target)
```
0.12905054857823264
nem o varanin os altros influmindo os de teterno um bombederster | nem o radar nem os outros instrumentos detectaram o bombardeiro stealth
pedir dinheiro é emprestado das pessoas do aldeia | pedir dinheiro emprestado às pessoas da aldeia
oito | oito
teno calcos | trancá-los
realizaram a investigação para resolver o problema | realizar uma investigação para resolver o problema
iotube ainda é a melhor plataforma de vídeos | o youtube ainda é a melhor plataforma de vídeos
menina e menino beijando nas sombras | menina e menino beijando nas sombras
eu sou o senhor | eu sou o senhor
duas metcas sentam-se para baixo randes jornais | duas mulheres que sentam-se para baixo lendo jornais
eu originalmente esperava | eu originalmente esperava
**Result**: 12.90%
### Test against [TEDx](http://www.openslr.org/100/) (Out-of-domain)
```python
!gdown --id 1HJEnvthaGYwcV_whHEywgH2daIN4bQna
!tar -xf tedx.tar.gz
```
```python
dataset = load_dataset('csv', data_files={'test': 'tedx/test.csv'})['test']
def map_to_array(batch):
speech, _ = torchaudio.load(batch["path"])
batch["speech"] = speech.squeeze(0).numpy()
batch["sampling_rate"] = resampler.new_freq
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'")
return batch
```
```python
ds = dataset.map(map_to_array)
result = ds.map(map_to_pred, batched=True, batch_size=1, remove_columns=list(ds.features.keys()))
print(wer.compute(predictions=result["predicted"], references=result["target"]))
for pred, target in zip(result["predicted"][:10], result["target"][:10]):
print(pred, "|", target)
```
0.35215851987208774
com isso a gente vê que essa rede de pactuação de de deparcerias nos remete a um raciocínio lógico que ao que a gente crê que é a prevenção | com isso a gente vê que essa rede de pactuação de parcerias nos remete a um raciocínio lógico que é o que a gente crê que é a prevenção
ente vai para o resultado | e aí a gente vai pro resultado
curiosidade hé o que eu descobri desde que comecei a fazer pesquisa lá no ensino médio | e a curiosidade é algo que descobri desde que comecei a fazer pesquisa lá no ensino médio
val des quemesho | há vários caminhos
que é uma opcissão por comer soldado | que é uma obsessão por comer saudável
isso é tão é forte algoltão universal que existem dados que mostram que setenta e cinco por cento das reuniões são dominadas pela voz masculina | e isso é tão forte é algo tão universal que existem dados que mostram que das reuniões são dominadas pela voz masculina
não era exatamente isso não estávamos deveto | e não era exatamente isso que nós estávamos a ver
durante meci do médio ofiz pesquisa estudei numa escola que chamam a fundação liberate ficava relativamente próximo daqui | durante o ensino médio eu fiz pesquisa estudei numa escola que se chama fundação liberato que fica relativamente próxima daqui
oito anos atrás eu fui apresentado por uma doença que até então eu não conhecia e que é bem provável que a maior parte de nós todos aqui não conheçamos | oito anos atrás fui apresentado para uma doença que até então eu não conhecia e que é bem provável que a maior parte de nós todos aqui não conheçamos
o terceiro é o museu do ripiopeco | o terceiro é o museu do hip hop
**Result**: 35.21%
|
birgermoell/psst-libri960_big
|
birgermoell
| 2022-04-01T20:17:17Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-04-01T19:05:31Z |
pssteval INFO: ASR metrics for split `valid` FER: 9.8% PER: 20.9%
|
juaner/distilbert-base-uncased-finetuned-cola
|
juaner
| 2022-04-01T18:20:42Z | 5 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-01T17:59:52Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: juaner/distilbert-base-uncased-finetuned-cola
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# juaner/distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1909
- Validation Loss: 0.5553
- Train Matthews Correlation: 0.5279
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2670, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Matthews Correlation | Epoch |
|:----------:|:---------------:|:--------------------------:|:-----:|
| 0.5191 | 0.4491 | 0.4718 | 0 |
| 0.3270 | 0.4571 | 0.5196 | 1 |
| 0.1909 | 0.5553 | 0.5279 | 2 |
### Framework versions
- Transformers 4.16.2
- TensorFlow 2.8.0
- Datasets 1.18.3
- Tokenizers 0.11.0
|
FrankCorrigan/results
|
FrankCorrigan
| 2022-04-01T18:15:40Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-04-01T01:41:22Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: results
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. -->
# results
This model is a fine-tuned version of [linydub/bart-large-samsum](https://huggingface.co/linydub/bart-large-samsum) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0158
## 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
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 1 | 0.9563 |
| No log | 2.0 | 2 | 0.9877 |
| No log | 3.0 | 3 | 1.0158 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0
- Datasets 2.0.0
- Tokenizers 0.11.6
|
vicl/canine-c-finetuned-cola
|
vicl
| 2022-04-01T17:38:35Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"canine",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-01T17:13:12Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: canine-c-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.0990441507705203
---
<!-- 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. -->
# canine-c-finetuned-cola
This model is a fine-tuned version of [google/canine-c](https://huggingface.co/google/canine-c) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6246
- Matthews Correlation: 0.0990
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.6142 | 1.0 | 535 | 0.6268 | 0.0 |
| 0.607 | 2.0 | 1070 | 0.6234 | 0.0 |
| 0.6104 | 3.0 | 1605 | 0.6226 | 0.0 |
| 0.5725 | 4.0 | 2140 | 0.6246 | 0.0990 |
| 0.5426 | 5.0 | 2675 | 0.6866 | 0.0495 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
McGill-NLP/bart-qg-nq-checkpoint
|
McGill-NLP
| 2022-04-01T17:35:04Z | 26 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"arxiv:1910.13461",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-04-01T16:32:49Z |
---
license: cc-by-4.0
---
# BART-base fine-tuned on NaturalQuestions for **Question Generation**
[BART Model](https://arxiv.org/pdf/1910.13461.pdf) fine-tuned on [Google NaturalQuestions](https://ai.google.com/research/NaturalQuestions/) for **Question Generation** by treating long answer as input, and question as output.
## Details of BART
The **BART** model was presented in [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/pdf/1910.13461.pdf) by *Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, Luke Zettlemoyer* in Here the abstract:
We present BART, a denoising autoencoder for pretraining sequence-to-sequence models. BART is trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. It uses a standard Tranformer-based neural machine translation architecture which, despite its simplicity, can be seen as generalizing BERT (due to the bidirectional encoder), GPT (with the left-to-right decoder), and many other more recent pretraining schemes. We evaluate a number of noising approaches, finding the best performance by both randomly shuffling the order of the original sentences and using a novel in-filling scheme, where spans of text are replaced with a single mask token. BART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 6 ROUGE. BART also provides a 1.1 BLEU increase over a back-translation system for machine translation, with only target language pretraining. We also report ablation experiments that replicate other pretraining schemes within the BART framework, to better measure which factors most influence end-task performance.
## Details of the downstream task (QG) - Dataset 📚 🧐
Dataset: ```NaturalQuestions``` from Google (https://ai.google.com/research/NaturalQuestions/)
| Dataset | Split | # samples |
| -------- | ----- | --------- |
| NaturalQuestions | train | 97650 |
| NaturalQuestions | valid | 10850 |
## Model fine-tuning 🏋️
The training script can be found [here](https://github.com/McGill-NLP/MLQuestions/blob/main/QG/train.py)
## Model in Action 🚀
```python
from transformers import AutoModel, BartTokenizer
#Load the tokenizer
tokenizer = BartTokenizer.from_pretrained('facebook/bart-base')
#Load the model
model = AutoModelForSeq2SeqLM.from_pretrained("McGill-NLP/bart-qg-nq-checkpoint")
```
## Citation
If you want to cite this model you can use this:
```bibtex
@inproceedings{kulshreshtha-etal-2021-back,
title = "Back-Training excels Self-Training at Unsupervised Domain Adaptation of Question Generation and Passage Retrieval",
author = "Kulshreshtha, Devang and
Belfer, Robert and
Serban, Iulian Vlad and
Reddy, Siva",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.566",
pages = "7064--7078",
abstract = "In this work, we introduce back-training, an alternative to self-training for unsupervised domain adaptation (UDA). While self-training generates synthetic training data where natural inputs are aligned with noisy outputs, back-training results in natural outputs aligned with noisy inputs. This significantly reduces the gap between target domain and synthetic data distribution, and reduces model overfitting to source domain. We run UDA experiments on question generation and passage retrieval from the Natural Questions domain to machine learning and biomedical domains. We find that back-training vastly outperforms self-training by a mean improvement of 7.8 BLEU-4 points on generation, and 17.6{\%} top-20 retrieval accuracy across both domains. We further propose consistency filters to remove low-quality synthetic data before training. We also release a new domain-adaptation dataset - MLQuestions containing 35K unaligned questions, 50K unaligned passages, and 3K aligned question-passage pairs.",
}
```
> Created by [Devang Kulshreshtha](https://geekydevu.netlify.app/)
> Made with <span style="color: #e25555;">♥</span> in Spain
|
ahmedzaky91/Fatima-Fake_news_calssifier
|
ahmedzaky91
| 2022-04-01T16:54:24Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-04-01T00:00:39Z |
## This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on Fake and real dataset on kaggle
## The following hyperparameters were used during training:
learning_rate: 5e-05
train_batch_size: 8
num_epochs: 2
|
vicl/canine-c-finetuned-mrpc
|
vicl
| 2022-04-01T16:33:28Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"canine",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-01T16:05:44Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: canine-c-finetuned-mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8627450980392157
- name: F1
type: f1
value: 0.9014084507042254
---
<!-- 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. -->
# canine-c-finetuned-mrpc
This model is a fine-tuned version of [google/canine-c](https://huggingface.co/google/canine-c) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4066
- Accuracy: 0.8627
- F1: 0.9014
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 230 | 0.5014 | 0.7696 | 0.8479 |
| No log | 2.0 | 460 | 0.4755 | 0.7892 | 0.8622 |
| 0.5096 | 3.0 | 690 | 0.3645 | 0.8431 | 0.8869 |
| 0.5096 | 4.0 | 920 | 0.4066 | 0.8627 | 0.9014 |
| 0.2619 | 5.0 | 1150 | 0.4551 | 0.8431 | 0.8877 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
somosnlp-hackathon-2022/es_tweets_laboral
|
somosnlp-hackathon-2022
| 2022-04-01T14:50:40Z | 1 | 1 |
spacy
|
[
"spacy",
"text-classification",
"es",
"region:us"
] |
text-classification
| 2022-04-01T13:48:09Z |
---
tags:
- spacy
- text-classification
language: es
widget:
- text: "todos merecemos un salario justo"
---
## es_tweets_laboral ##
Modelo creado por @hucruz, @DanielaGarciaQuezada, @hylandude, @BloodBoy21
|
eren23/pneumonia_test_attempt
|
eren23
| 2022-04-01T14:41:01Z | 57 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-19T16:31:28Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: pneumonia_test_attempt
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9783163070678711
---
# pneumonia-bielefeld-dl-course
This registry contains the model for making pneumonia predictions and was prepared for Bielefeld University Deep Learning course homework.
The code used for this implementation mostly comes from here: https://github.com/nateraw/huggingpics it was a ready pipeline for model fine-tuning with huggingface and PyTorch Lightning for another dataset.
|
bmichele/poetry-generation-firstline-mbart-ws-fi-sorted
|
bmichele
| 2022-04-01T13:03:49Z | 0 | 0 | null |
[
"pytorch",
"region:us"
] | null | 2022-04-01T12:58:00Z |
TODO: This is still a demo model, the file does not match with the model card!!!
# poetry-generation-firstline-mbart-ws-fi-sorted
* `nextline`: generates the first poem line from keywords
* `mbart`: base model is [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25)
* `ws`: trained on Wikisource data
* `fi`: Finnish language
* `sorted`: the order of input keywords matter when generating candidates
|
bharatR/up_down
|
bharatR
| 2022-04-01T12:38:05Z | 0 | 0 | null |
[
"classification",
"en",
"dataset:cifar10-custom",
"region:us"
] | null | 2022-04-01T12:19:00Z |
---
language: en
tags:
- classification
datasets:
- cifar10-custom
metrics:
- accuracy
---
# Up-Down Classification
This repo has the weights of resnet-18 model training on cifar-10 custom data, where some images are made upside down, and the goal is to predict the orientation of the image(0/1 classification task).
|
bmichele/poetry-generation-nextline-mbart-ws-fi-single
|
bmichele
| 2022-04-01T11:51:32Z | 0 | 0 | null |
[
"pytorch",
"region:us"
] | null | 2022-04-01T11:35:07Z |
# poetry-generation-nextline-mbart-ws-fi-single
* `nextline`: generates a poem line from previous line(s)
* `mbart`: base model is [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25)
* `ws`: trained on Wikisource data
* `fi`: Finnish language
* `single`: uses only last poem line as input for generation
|
osanseviero/llama-alpaca-snake
|
osanseviero
| 2022-04-01T09:45:01Z | 62 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"llama-leaderboard",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-04-01T09:20:01Z |
---
tags:
- image-classification
- pytorch
- huggingpics
- llama-leaderboard
metrics:
- accuracy
model-index:
- name: llama-alpaca-snake
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.7910447716712952
---
# llama-alpaca-snake
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
#### alpaca
#### llamas
#### snake
|
abdusah/aradia-ctc-data2vec-ft
|
abdusah
| 2022-04-01T08:19:29Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"data2vec-audio",
"automatic-speech-recognition",
"abdusahmbzuai/arabic_speech_massive_300hrs",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-31T14:34:56Z |
---
tags:
- automatic-speech-recognition
- abdusahmbzuai/arabic_speech_massive_300hrs
- generated_from_trainer
model-index:
- name: aradia-ctc-data2vec-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-data2vec-ft
This model is a fine-tuned version of [/l/users/abdulwahab.sahyoun/aradia/aradia-ctc-data2vec-ft](https://huggingface.co//l/users/abdulwahab.sahyoun/aradia/aradia-ctc-data2vec-ft) on the ABDUSAHMBZUAI/ARABIC_SPEECH_MASSIVE_300HRS - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0464
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 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 | 3.3600 | 1.0 |
| No log | 0.87 | 200 | 3.0887 | 1.0 |
| No log | 1.3 | 300 | 3.0779 | 1.0 |
| No log | 1.74 | 400 | 3.0551 | 1.0 |
| 4.8553 | 2.17 | 500 | 3.0526 | 1.0 |
| 4.8553 | 2.61 | 600 | 3.0560 | 1.0 |
| 4.8553 | 3.04 | 700 | 3.1251 | 1.0 |
| 4.8553 | 3.48 | 800 | 3.0870 | 1.0 |
| 4.8553 | 3.91 | 900 | 3.0822 | 1.0 |
| 3.1133 | 4.35 | 1000 | 3.0484 | 1.0 |
| 3.1133 | 4.78 | 1100 | 3.0558 | 1.0 |
| 3.1133 | 5.22 | 1200 | 3.1019 | 1.0 |
| 3.1133 | 5.65 | 1300 | 3.0914 | 1.0 |
| 3.1133 | 6.09 | 1400 | 3.0691 | 1.0 |
| 3.109 | 6.52 | 1500 | 3.0589 | 1.0 |
| 3.109 | 6.95 | 1600 | 3.0508 | 1.0 |
| 3.109 | 7.39 | 1700 | 3.0540 | 1.0 |
| 3.109 | 7.82 | 1800 | 3.0546 | 1.0 |
| 3.109 | 8.26 | 1900 | 3.0524 | 1.0 |
| 3.1106 | 8.69 | 2000 | 3.0569 | 1.0 |
| 3.1106 | 9.13 | 2100 | 3.0622 | 1.0 |
| 3.1106 | 9.56 | 2200 | 3.0518 | 1.0 |
| 3.1106 | 10.0 | 2300 | 3.0749 | 1.0 |
| 3.1106 | 10.43 | 2400 | 3.0698 | 1.0 |
| 3.1058 | 10.87 | 2500 | 3.0665 | 1.0 |
| 3.1058 | 11.3 | 2600 | 3.0555 | 1.0 |
| 3.1058 | 11.74 | 2700 | 3.0589 | 1.0 |
| 3.1058 | 12.17 | 2800 | 3.0611 | 1.0 |
| 3.1058 | 12.61 | 2900 | 3.0561 | 1.0 |
| 3.1071 | 13.04 | 3000 | 3.0480 | 1.0 |
| 3.1071 | 13.48 | 3100 | 3.0492 | 1.0 |
| 3.1071 | 13.91 | 3200 | 3.0574 | 1.0 |
| 3.1071 | 14.35 | 3300 | 3.0538 | 1.0 |
| 3.1071 | 14.78 | 3400 | 3.0505 | 1.0 |
| 3.1061 | 15.22 | 3500 | 3.0600 | 1.0 |
| 3.1061 | 15.65 | 3600 | 3.0596 | 1.0 |
| 3.1061 | 16.09 | 3700 | 3.0623 | 1.0 |
| 3.1061 | 16.52 | 3800 | 3.0800 | 1.0 |
| 3.1061 | 16.95 | 3900 | 3.0583 | 1.0 |
| 3.1036 | 17.39 | 4000 | 3.0534 | 1.0 |
| 3.1036 | 17.82 | 4100 | 3.0563 | 1.0 |
| 3.1036 | 18.26 | 4200 | 3.0481 | 1.0 |
| 3.1036 | 18.69 | 4300 | 3.0477 | 1.0 |
| 3.1036 | 19.13 | 4400 | 3.0505 | 1.0 |
| 3.1086 | 19.56 | 4500 | 3.0485 | 1.0 |
| 3.1086 | 20.0 | 4600 | 3.0481 | 1.0 |
| 3.1086 | 20.43 | 4700 | 3.0615 | 1.0 |
| 3.1086 | 20.87 | 4800 | 3.0658 | 1.0 |
| 3.1086 | 21.3 | 4900 | 3.0505 | 1.0 |
| 3.1028 | 21.74 | 5000 | 3.0492 | 1.0 |
| 3.1028 | 22.17 | 5100 | 3.0485 | 1.0 |
| 3.1028 | 22.61 | 5200 | 3.0483 | 1.0 |
| 3.1028 | 23.04 | 5300 | 3.0479 | 1.0 |
| 3.1028 | 23.48 | 5400 | 3.0509 | 1.0 |
| 3.1087 | 23.91 | 5500 | 3.0530 | 1.0 |
| 3.1087 | 24.35 | 5600 | 3.0486 | 1.0 |
| 3.1087 | 24.78 | 5700 | 3.0514 | 1.0 |
| 3.1087 | 25.22 | 5800 | 3.0505 | 1.0 |
| 3.1087 | 25.65 | 5900 | 3.0508 | 1.0 |
| 3.1043 | 26.09 | 6000 | 3.0501 | 1.0 |
| 3.1043 | 26.52 | 6100 | 3.0467 | 1.0 |
| 3.1043 | 26.95 | 6200 | 3.0466 | 1.0 |
| 3.1043 | 27.39 | 6300 | 3.0465 | 1.0 |
| 3.1043 | 27.82 | 6400 | 3.0465 | 1.0 |
| 3.1175 | 28.26 | 6500 | 3.0466 | 1.0 |
| 3.1175 | 28.69 | 6600 | 3.0466 | 1.0 |
| 3.1175 | 29.13 | 6700 | 3.0465 | 1.0 |
| 3.1175 | 29.56 | 6800 | 3.0465 | 1.0 |
| 3.1175 | 30.0 | 6900 | 3.0464 | 1.0 |
### Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.10.2+cu113
- Datasets 1.18.4
- Tokenizers 0.11.6
|
Basedino/GPT-RO
|
Basedino
| 2022-04-01T07:47:41Z | 0 | 0 | null |
[
"license:gpl-3.0",
"region:us"
] | null | 2022-03-31T08:19:30Z |
---
license: gpl-3.0
---
So i made this model because i had nothing to do. it's gpt 2 124m finetuned to a bunch of italian recipes.
I made it using aitextgen, so you can use that to play with the model easily.
|
yy642/bert-base-uncased-finetuned-mnli-rte-wnli-10
|
yy642
| 2022-04-01T06:04:00Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-31T23:51:06Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-uncased-finetuned-mnli-rte-wnli-10
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-mnli-rte-wnli-10
This model is a fine-tuned version of [yy642/bert-base-uncased-finetuned-mnli-rte-wnli-5](https://huggingface.co/yy642/bert-base-uncased-finetuned-mnli-rte-wnli-5) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5876
- Accuracy: 0.9206
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0641 | 1.0 | 16558 | 0.4528 | 0.9138 |
| 0.0479 | 2.0 | 33116 | 0.5116 | 0.9153 |
| 0.0363 | 3.0 | 49674 | 0.5660 | 0.9138 |
| 0.0244 | 4.0 | 66232 | 0.5876 | 0.9206 |
| 0.0145 | 5.0 | 82790 | 0.6156 | 0.9192 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0a0+17540c5
- Datasets 2.0.0
- Tokenizers 0.11.6
|
Mr-Wick/xlnet-base-cased
|
Mr-Wick
| 2022-04-01T01:31:59Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"xlnet",
"question-answering",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-26T12:52:07Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: xlnet-base-cased
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. -->
# xlnet-base-cased
This model was trained from scratch 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': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 16530, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.17.0
- TensorFlow 2.8.0
- Datasets 2.0.0
- Tokenizers 0.12.0
|
arjundd/vortex-release
|
arjundd
| 2022-03-31T21:54:43Z | 0 | 0 | null |
[
"mri",
"reconstruction",
"artifact correction",
"en",
"arxiv:2111.02549",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: en
license: apache-2.0
tags:
- mri
- reconstruction
- artifact correction
---
# VORTEX
<div align="center">
<img src="https://drive.google.com/uc?export=view&id=1q0jAm6Kg5ZhRg3h0w0ZbtIgcRF3_-Vgb" alt="Vortex Schematic" width="700px" />
</div>
> **VORTEX: Physics-Driven Data Augmentations for Consistency Training for Robust Accelerated MRI Reconstruction**\
> Arjun Desai, Beliz Gunel, Batu Ozturkler, Harris Beg, Shreyas Vasanawala, Brian Hargreaves, Christopher Ré, John Pauly, Akshay Chaudhari\
> https://arxiv.org/abs/2111.02549
This repository contains the artifacts for the VORTEX paper. To use our code
and artifacts in your research, please use the [Meddlr](https://github.com/ad12/meddlr) package.
|
israel/fake-news-classification
|
israel
| 2022-03-31T21:03:49Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-31T16:35:48Z |
---
license: mit
---
# Fake and real news classification task
Model : [DistilRoBERTa base model](https://huggingface.co/distilroberta-base)
Dataset : [Fake and real news dataset](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset)
|
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