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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-07 06:34:03
| downloads
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| likes
int64 0
11.7k
| library_name
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21iridescent/RoBERTa-base-finetuned-squad2-lwt
|
21iridescent
| 2022-04-06T10:42:07Z | 17 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-28T17:24:17Z |
---
--license: mit
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: roberta-base-finetuned-squad2-lwt
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
## Model description
#### Finetuned on SQUAD2.0 Dataset
#### F1: 83.738696142672
Trained on single V100 GPU
Everyone is welcome to use~
Hope you have a nice day
## Performance
- HasAns_exact': 77.1255060728745, 'HasAns_f1': 83.87812741260885, 'HasAns_total': 5928,
- 'NoAns_exact': 83.59966358284272, 'NoAns_f1': 83.59966358284272, 'NoAns_total': 5945,
- 'best_exact': 80.36721974227238, 'best_exact_thresh': 0.0,
- 'best_f1': 83.7386961426719, 'best_f1_thresh': 0.0,
- 'exact': 80.36721974227238,
- 'f1': 83.738696142672,
- 'total': 11873
# roberta-base-finetuned-squad2-lwt
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9441
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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.871 | 1.0 | 8239 | 0.8156 |
| 0.6787 | 2.0 | 16478 | 0.8494 |
| 0.4867 | 3.0 | 24717 | 0.9441 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
luxiao/alilingjie
|
luxiao
| 2022-04-06T07:44:47Z | 0 | 1 |
transformers
|
[
"transformers",
"pytorch",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-06T07:21:55Z |
---
license: apache-2.0
---
|
rkoushikroy2/upside_down_efficientnet
|
rkoushikroy2
| 2022-04-06T04:48:05Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-04-06T04:24:50Z |
**Model Name:** EfficientNet B3
**Classification Type:** Binary classification of normal vs upside-down images
**Created For:** Coding Challenge for Fatima Fellowship
|
ABEMark45/upside-down-classifier
|
ABEMark45
| 2022-04-06T03:42:24Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-04-06T03:28:18Z |
# Upside Down Classifier
The model was trained for the task of orientation classification. The model was trained on `CIFAR-100` dataset which contains 60000 images covering 600 classes of 32x32 RGB images.
# Data
Data was split to `50000` train samples and `10000` test samples.
# Results
The training of the model on this dataset using `Adam` optimizer resulted in `100%` validation accuracy.
# Discussion
This model can be considered as a "toy" model as `CIFAR-100` image size is a huge disadvantage since everyday images are usually much larger. Models should be trained on better datasets with better resources.
# Future Work
The bottleneck of this model is that images of a class to be classified should be added to the training loop if we need to correctly classify their orientation. Although the model was not tested on classes different that those used in the dataset, the model should encode what it means to be "upright" and "upside-down". I think variants of the few-shots learning methods may be adapted for this type of problems; however, I do not know of the specifics of such approach.
|
SeifMosaad/mnist-upsidedowndetector
|
SeifMosaad
| 2022-04-06T03:28:40Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-04-06T03:26:40Z |
This is a Upside down detector on MNIST dateset using tensorflow/keras.
|
imessam/OrientationClassifier
|
imessam
| 2022-04-06T01:17:11Z | 4 | 0 |
transformers
|
[
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-04-05T23:35:25Z |
# Orientation Classifier
---
language:
- en
tags:
- image-classification
license: apache-2.0
datasets:
- cifar10
metrics:
- accuracy
- f1
---
|
anton-l/xtreme_s_xlsr_300m_fleurs_langid_quicker_warmup
|
anton-l
| 2022-04-05T23:16:38Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:xtreme_s",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2022-04-04T12:39:10Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xtreme_s
metrics:
- accuracy
model-index:
- name: xtreme_s_xlsr_300m_fleurs_langid_quicker_warmup
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_fleurs_langid_quicker_warmup
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the xtreme_s dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9765
- Accuracy: 0.6199
## 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: 1000
- num_epochs: 10.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:-----:|:--------:|:---------------:|
| 0.6644 | 0.26 | 1000 | 0.3071 | 3.2482 |
| 0.394 | 0.52 | 2000 | 0.5948 | 1.8833 |
| 0.1034 | 0.78 | 3000 | 0.6297 | 1.5852 |
| 0.1088 | 1.04 | 4000 | 0.5992 | 1.7903 |
| 0.0032 | 1.3 | 5000 | 0.6356 | 1.6219 |
| 0.1813 | 1.56 | 6000 | 0.5788 | 1.8168 |
| 0.0654 | 1.82 | 7000 | 0.6234 | 1.6089 |
| 0.0144 | 2.08 | 8000 | 0.6424 | 1.6071 |
| 0.0019 | 2.34 | 9000 | 0.5822 | 1.7820 |
| 0.0159 | 2.6 | 10000 | 0.6043 | 1.8407 |
| 0.0029 | 2.86 | 11000 | 0.5845 | 1.8600 |
| 0.0458 | 3.12 | 12000 | 0.6299 | 1.6591 |
| 0.013 | 3.38 | 13000 | 0.5903 | 2.0788 |
| 0.003 | 3.64 | 14000 | 0.6188 | 1.7645 |
| 0.0015 | 3.9 | 15000 | 0.6328 | 1.7739 |
| 0.0003 | 4.16 | 16000 | 0.6072 | 1.8742 |
| 0.0005 | 4.42 | 17000 | 0.6231 | 1.7102 |
| 0.006 | 4.68 | 18000 | 0.6122 | 1.6909 |
| 0.2367 | 4.93 | 19000 | 0.6029 | 1.9891 |
| 0.005 | 5.19 | 20000 | 0.6220 | 1.7245 |
| 0.0813 | 5.45 | 21000 | 0.5739 | 2.0495 |
| 0.1233 | 5.71 | 22000 | 0.6104 | 1.9601 |
| 0.0003 | 5.97 | 23000 | 0.5924 | 1.8881 |
| 0.0003 | 6.23 | 24000 | 0.6055 | 1.9568 |
| 0.0001 | 6.49 | 25000 | 0.6086 | 1.8489 |
| 0.2198 | 6.75 | 26000 | 0.6292 | 1.8048 |
| 0.0261 | 7.01 | 27000 | 2.0284 | 0.5989 |
| 0.0001 | 7.27 | 28000 | 1.7323 | 0.6431 |
| 0.0001 | 7.53 | 29000 | 1.9329 | 0.6310 |
| 0.0011 | 7.79 | 30000 | 1.9256 | 0.6107 |
| 0.0933 | 8.05 | 31000 | 2.3915 | 0.5896 |
| 0.0001 | 8.31 | 32000 | 1.9948 | 0.6021 |
| 0.0003 | 8.57 | 33000 | 1.9518 | 0.6126 |
| 0.0005 | 8.83 | 34000 | 1.8935 | 0.6243 |
| 0.0 | 9.09 | 35000 | 2.0177 | 0.6144 |
| 0.0002 | 9.35 | 36000 | 2.0234 | 0.6174 |
| 0.0 | 9.61 | 37000 | 1.9568 | 0.6216 |
| 0.0 | 9.87 | 38000 | 1.9765 | 0.6199 |
### Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 1.18.4.dev0
- Tokenizers 0.11.6
|
hemhemoh/FatimaFellowship_NLPtask
|
hemhemoh
| 2022-04-05T22:12:01Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-04-05T21:35:08Z |
This model was trained using the 'bert-base-uncased' from the transformer library and it was trained on the popular fake/real news dataset from Kaggle.
Pytorch is the framework used to train the model and it had an accuracy score of 93.5 % and here is what the classification report looks like.
precision recall f1-score support
0 0.96 0.92 0.94 2348
1 0.92 0.96 0.94 2142
accuracy 0.94 4490
macro avg 0.94 0.94 0.94 4490
weighted avg 0.94 0.94 0.94 4490
---
license: apache-2.0
---
|
vladimir-lomonosov/gpt2-wikitext2
|
vladimir-lomonosov
| 2022-04-05T21:45:58Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-04-05T20:05:35Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gpt2-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-wikitext2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.1153
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.5574 | 1.0 | 2249 | 6.4738 |
| 6.1911 | 2.0 | 4498 | 6.1998 |
| 6.0051 | 3.0 | 6747 | 6.1153 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.5.1+cu92
- Datasets 2.0.0
- Tokenizers 0.11.6
|
vocab-transformers/dense_encoder-distilbert-frozen_emb
|
vocab-transformers
| 2022-04-05T21:13:38Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"feature-extraction",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-04-05T20:57:09Z |
# Dense Encoder - Distilbert - Frozen Token Embeddings
This model is a distilbert-base-uncased model trained for 30 epochs (235k steps), 64 batch size with MarginMSE Loss on MS MARCO dataset.
The token embeddings were frozen.
| Dataset | Model with updated token embeddings | Model with frozen embeddings |
| --- | :---: | :---: |
| TREC-DL 19 | 70.68 | 68.60 |
| TREC-DL 20 | 67.69 | 70.21 |
| FiQA | 28.89 | 28.60 |
| Robust04 | 39.56 | 39.08 |
| TREC-COVID v2 | 69.80 | 69.84 |
| TREC-NEWS | 37.97 | 38.27 |
| Avg. 4 BEIR tasks | 44.06 | 43.95 |
|
miesnerjacob/marian-finetuned-kde4-en-to-fr
|
miesnerjacob
| 2022-04-05T20:28:41Z | 12 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-04-05T18:34:17Z |
---
license: apache-2.0
tags:
- translation
- generated_from_trainer
datasets:
- kde4
metrics:
- bleu
model-index:
- name: marian-finetuned-kde4-en-to-fr
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: kde4
type: kde4
args: en-fr
metrics:
- name: Bleu
type: bleu
value: 52.94560734092563
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8559
- Bleu: 52.9456
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
Kuray107/ls-timit-100percent-supervised-aug
|
Kuray107
| 2022-04-05T20:18:46Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-04-05T16:33:16Z |
---
tags:
- generated_from_trainer
model-index:
- name: ls-timit-100percent-supervised-aug
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ls-timit-100percent-supervised-aug
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0519
- Wer: 0.0292
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2985 | 7.04 | 1000 | 0.0556 | 0.0380 |
| 0.1718 | 14.08 | 2000 | 0.0519 | 0.0292 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2
- Datasets 1.18.2
- Tokenizers 0.10.3
|
huggingtweets/vivchen_
|
huggingtweets
| 2022-04-05T20:13:38Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-04-05T20:12:26Z |
---
language: en
thumbnail: http://www.huggingtweets.com/vivchen_/1649189613639/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1453748100594642948/BAASh9m3_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Vivian</div>
<div style="text-align: center; font-size: 14px;">@vivchen_</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Vivian.
| Data | Vivian |
| --- | --- |
| Tweets downloaded | 1616 |
| Retweets | 39 |
| Short tweets | 166 |
| Tweets kept | 1411 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/vqb4rpuh/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @vivchen_'s tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1xzxtr20) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1xzxtr20/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/vivchen_')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
Pavithra/codeparrot-ds-sample-gpt-small-neo
|
Pavithra
| 2022-04-05T20:04:16Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt_neo",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-30T05:57:53Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: codeparrot-ds-sample-gpt-small-neo
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. -->
# codeparrot-ds-sample-gpt-small-neo
This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.11.6
|
itaihay/wav2vec_asr_swbd_10_epochs
|
itaihay
| 2022-04-05T19:02:43Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-04-02T10:53:01Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec_asr_swbd_10_epochs
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec_asr_swbd_10_epochs
This model is a fine-tuned version of [facebook/wav2vec2-large-robust-ft-swbd-300h](https://huggingface.co/facebook/wav2vec2-large-robust-ft-swbd-300h) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Wer: 0.9627
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:------:|:---------------:|:------:|
| 1.0682 | 0.22 | 5000 | 0.7383 | 0.4431 |
| 0.9143 | 0.44 | 10000 | 0.7182 | 0.4058 |
| 0.8905 | 0.66 | 15000 | 0.6291 | 0.3987 |
| 0.8354 | 0.87 | 20000 | 0.5976 | 0.3954 |
| 0.7749 | 1.09 | 25000 | 0.5773 | 0.3901 |
| 0.7336 | 1.31 | 30000 | 0.5812 | 0.3871 |
| 0.7314 | 1.53 | 35000 | 0.5802 | 0.3895 |
| 0.0 | 1.75 | 40000 | nan | 0.9627 |
| 0.0 | 1.97 | 45000 | nan | 0.9627 |
| 0.0 | 2.19 | 50000 | nan | 0.9627 |
| 0.0 | 2.4 | 55000 | nan | 0.9627 |
| 0.0 | 2.62 | 60000 | nan | 0.9627 |
| 0.0 | 2.84 | 65000 | nan | 0.9627 |
| 0.0 | 3.06 | 70000 | nan | 0.9627 |
| 0.0 | 3.28 | 75000 | nan | 0.9627 |
| 0.0 | 3.5 | 80000 | nan | 0.9627 |
| 0.0 | 3.72 | 85000 | nan | 0.9627 |
| 0.0 | 3.93 | 90000 | nan | 0.9627 |
| 0.0 | 4.15 | 95000 | nan | 0.9627 |
| 0.0 | 4.37 | 100000 | nan | 0.9627 |
| 0.0 | 4.59 | 105000 | nan | 0.9627 |
| 0.0 | 4.81 | 110000 | nan | 0.9627 |
| 0.0 | 5.03 | 115000 | nan | 0.9627 |
| 0.0 | 5.25 | 120000 | nan | 0.9627 |
| 0.0 | 5.46 | 125000 | nan | 0.9627 |
| 0.0 | 5.68 | 130000 | nan | 0.9627 |
| 0.0 | 5.9 | 135000 | nan | 0.9627 |
| 0.0 | 6.12 | 140000 | nan | 0.9627 |
| 0.0 | 6.34 | 145000 | nan | 0.9627 |
| 0.0 | 6.56 | 150000 | nan | 0.9627 |
| 0.0 | 6.78 | 155000 | nan | 0.9627 |
| 0.0 | 7.0 | 160000 | nan | 0.9627 |
| 0.0 | 7.21 | 165000 | nan | 0.9627 |
| 0.0 | 7.43 | 170000 | nan | 0.9627 |
| 0.0 | 7.65 | 175000 | nan | 0.9627 |
| 0.0 | 7.87 | 180000 | nan | 0.9627 |
| 0.0 | 8.09 | 185000 | nan | 0.9627 |
| 0.0 | 8.31 | 190000 | nan | 0.9627 |
| 0.0 | 8.53 | 195000 | nan | 0.9627 |
| 0.0 | 8.74 | 200000 | nan | 0.9627 |
| 0.0 | 8.96 | 205000 | nan | 0.9627 |
| 0.0 | 9.18 | 210000 | nan | 0.9627 |
| 0.0 | 9.4 | 215000 | nan | 0.9627 |
| 0.0 | 9.62 | 220000 | nan | 0.9627 |
| 0.0 | 9.84 | 225000 | nan | 0.9627 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.4
- Tokenizers 0.11.6
|
ViktorDo/distilbert-base-uncased-finetuned-imdb
|
ViktorDo
| 2022-04-05T17:17:10Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-04-05T12:28:13Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -875, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.17.0
- TensorFlow 2.8.0
- Datasets 2.0.0
- Tokenizers 0.11.6
|
Sevil/t5-small-finetuned-cnndm_3epoch_v2
|
Sevil
| 2022-04-05T17:13:07Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-04-04T23:07:29Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
metrics:
- rouge
model-index:
- name: t5-small-finetuned-cnndm_3epoch_v2
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: cnn_dailymail
type: cnn_dailymail
args: 3.0.0
metrics:
- name: Rouge1
type: rouge
value: 24.7696
---
<!-- 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-cnndm_3epoch_v2
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dailymail dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6070
- Rouge1: 24.7696
- Rouge2: 11.9467
- Rougel: 20.4495
- Rougelsum: 23.3341
- Gen Len: 18.9999
## 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 |
|:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.9695 | 0.07 | 5000 | 1.7781 | 24.2253 | 11.472 | 20.0367 | 22.8469 | 18.9962 |
| 1.9536 | 0.14 | 10000 | 1.7575 | 24.2983 | 11.469 | 20.0054 | 22.9144 | 18.9995 |
| 1.9452 | 0.21 | 15000 | 1.7406 | 24.2068 | 11.4601 | 20.0021 | 22.8375 | 19.0 |
| 1.931 | 0.28 | 20000 | 1.7302 | 24.1589 | 11.4183 | 19.9736 | 22.7804 | 18.9996 |
| 1.9182 | 0.35 | 25000 | 1.7381 | 24.1634 | 11.5435 | 19.9643 | 22.7371 | 18.9999 |
| 1.9072 | 0.42 | 30000 | 1.7239 | 24.4401 | 11.6323 | 20.1243 | 22.9468 | 19.0 |
| 1.9027 | 0.49 | 35000 | 1.7162 | 24.1801 | 11.4788 | 20.0011 | 22.832 | 18.9996 |
| 1.8962 | 0.56 | 40000 | 1.7060 | 24.4153 | 11.6275 | 20.1742 | 23.0865 | 18.9998 |
| 1.8905 | 0.63 | 45000 | 1.7004 | 24.1446 | 11.5402 | 19.9986 | 22.7949 | 18.9983 |
| 1.8764 | 0.7 | 50000 | 1.6876 | 24.342 | 11.5448 | 20.0993 | 22.9509 | 18.9993 |
| 1.8772 | 0.77 | 55000 | 1.6879 | 24.3596 | 11.6063 | 20.1592 | 22.9966 | 19.0 |
| 1.8669 | 0.84 | 60000 | 1.6776 | 24.6201 | 11.6668 | 20.2639 | 23.201 | 18.9994 |
| 1.8692 | 0.91 | 65000 | 1.6838 | 24.2924 | 11.6129 | 20.1071 | 22.9112 | 18.9997 |
| 1.8552 | 0.98 | 70000 | 1.6885 | 24.2878 | 11.6773 | 20.1272 | 22.8797 | 18.9992 |
| 1.8205 | 1.04 | 75000 | 1.6717 | 24.5579 | 11.6421 | 20.2593 | 23.1442 | 19.0 |
| 1.8074 | 1.11 | 80000 | 1.6604 | 24.495 | 11.6542 | 20.1854 | 23.1091 | 18.9996 |
| 1.7951 | 1.18 | 85000 | 1.6705 | 24.4504 | 11.6601 | 20.2185 | 23.0597 | 18.9999 |
| 1.7937 | 1.25 | 90000 | 1.6645 | 24.5535 | 11.6921 | 20.2087 | 23.1099 | 18.9999 |
| 1.8017 | 1.32 | 95000 | 1.6647 | 24.5179 | 11.8005 | 20.2903 | 23.13 | 18.9993 |
| 1.7918 | 1.39 | 100000 | 1.6568 | 24.518 | 11.7528 | 20.222 | 23.0767 | 18.9991 |
| 1.7985 | 1.46 | 105000 | 1.6588 | 24.4636 | 11.636 | 20.1038 | 23.032 | 19.0 |
| 1.7944 | 1.53 | 110000 | 1.6498 | 24.6611 | 11.78 | 20.3059 | 23.2404 | 18.9999 |
| 1.7934 | 1.6 | 115000 | 1.6551 | 24.7267 | 11.823 | 20.3377 | 23.273 | 18.9997 |
| 1.7764 | 1.67 | 120000 | 1.6467 | 24.5052 | 11.8052 | 20.2617 | 23.1228 | 18.9996 |
| 1.7846 | 1.74 | 125000 | 1.6489 | 24.5423 | 11.8407 | 20.3464 | 23.1433 | 18.9999 |
| 1.7799 | 1.81 | 130000 | 1.6438 | 24.4915 | 11.7827 | 20.2592 | 23.1299 | 18.9999 |
| 1.7806 | 1.88 | 135000 | 1.6353 | 24.7804 | 11.9212 | 20.4678 | 23.359 | 19.0 |
| 1.7784 | 1.95 | 140000 | 1.6338 | 24.7892 | 11.8836 | 20.4227 | 23.373 | 18.9997 |
| 1.7551 | 2.02 | 145000 | 1.6341 | 24.6828 | 11.8257 | 20.3862 | 23.2536 | 18.9997 |
| 1.728 | 2.09 | 150000 | 1.6328 | 24.6697 | 11.851 | 20.3943 | 23.2738 | 18.9993 |
| 1.7201 | 2.16 | 155000 | 1.6309 | 24.7364 | 11.8505 | 20.365 | 23.2885 | 18.9992 |
| 1.7233 | 2.23 | 160000 | 1.6346 | 24.7298 | 12.0026 | 20.4444 | 23.3156 | 18.9999 |
| 1.7096 | 2.3 | 165000 | 1.6253 | 24.6443 | 11.9004 | 20.4138 | 23.2583 | 18.9999 |
| 1.7084 | 2.37 | 170000 | 1.6233 | 24.6688 | 11.8885 | 20.3623 | 23.2608 | 18.9996 |
| 1.7236 | 2.44 | 175000 | 1.6243 | 24.7174 | 11.8924 | 20.4012 | 23.2948 | 18.9996 |
| 1.7108 | 2.51 | 180000 | 1.6188 | 24.6013 | 11.8153 | 20.2969 | 23.1867 | 18.9997 |
| 1.711 | 2.58 | 185000 | 1.6125 | 24.7673 | 11.8646 | 20.3805 | 23.3114 | 18.9997 |
| 1.7108 | 2.65 | 190000 | 1.6101 | 24.8047 | 11.9763 | 20.494 | 23.3873 | 18.9998 |
| 1.7114 | 2.72 | 195000 | 1.6123 | 24.7019 | 11.9201 | 20.414 | 23.2823 | 18.9999 |
| 1.7004 | 2.79 | 200000 | 1.6083 | 24.7525 | 11.9197 | 20.4581 | 23.3371 | 18.9999 |
| 1.7104 | 2.86 | 205000 | 1.6061 | 24.7057 | 11.8818 | 20.4017 | 23.286 | 18.9999 |
| 1.7063 | 2.93 | 210000 | 1.6063 | 24.7707 | 11.934 | 20.4473 | 23.3316 | 18.9999 |
| 1.7039 | 3.0 | 215000 | 1.6070 | 24.7696 | 11.9467 | 20.4495 | 23.3341 | 18.9999 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
alefiury/wav2vec2-large-xlsr-53-coraa-brazilian-portuguese-gain-normalization
|
alefiury
| 2022-04-05T16:58:36Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"pt",
"portuguese-speech-corpus",
"PyTorch",
"dataset:CORAA",
"dataset:common_voice",
"dataset:mls",
"dataset:cetuc",
"dataset:voxforge",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-27T16:34:54Z |
---
language: pt
datasets:
- CORAA
- common_voice
- mls
- cetuc
- voxforge
metrics:
- wer
tags:
- audio
- speech
- wav2vec2
- pt
- portuguese-speech-corpus
- automatic-speech-recognition
- speech
- PyTorch
license: apache-2.0
model-index:
- name: Alef Iury XLSR Wav2Vec2 Large 53 Portuguese
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
metrics:
- name: Test CORAA WER
type: wer
value: 24.89%
---
# Wav2vec 2.0 trained with CORAA Portuguese Dataset and Open Portuguese Datasets
This a the demonstration of a fine-tuned Wav2vec model for Portuguese using the following datasets:
- [CORAA dataset](https://github.com/nilc-nlp/CORAA)
- [CETUC](http://www02.smt.ufrj.br/~igor.quintanilha/alcaim.tar.gz).
- [Multilingual Librispeech (MLS)](http://www.openslr.org/94/).
- [VoxForge](http://www.voxforge.org/).
- [Common Voice 6.1](https://commonvoice.mozilla.org/pt).
## Repository
The repository that implements the model to be trained and tested is avaible [here](https://github.com/alefiury/SE-R_2022_Challenge_Wav2vec2).
|
HenryHXR/scibert_scivocab_uncased_epoch20-finetuned-ner
|
HenryHXR
| 2022-04-05T15:51:56Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-04-05T15:44:27Z |
---
tags:
- generated_from_trainer
model-index:
- name: scibert_scivocab_uncased_epoch20-finetuned-ner
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# scibert_scivocab_uncased_epoch20-finetuned-ner
This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
erikacardenas300/StartupClassifier
|
erikacardenas300
| 2022-04-05T15:23:05Z | 16 | 2 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"en",
"dataset:Crunchbase",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-01T20:53:16Z |
---
language: en
datasets:
- Crunchbase
---
# Company Classifier
This fine-tuned Distilbert model is using company descriptions for classification. The model is tasked to classify the company as either finance or biotech. The demo can be found on my profile under Spaces (https://huggingface.co/erikacardenas300).
I hope you enjoy it!
|
BigSalmon/InformalToFormalLincolnConciseWordy
|
BigSalmon
| 2022-04-05T15:21:32Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-04-05T15:17:33Z |
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincolnConciseWordy")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincolnConciseWordy")
```
```
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:
```
Keywords to sentences or sentence.
|
gaetangate/bart-large_genrl_lcquad2
|
gaetangate
| 2022-04-05T15:10:15Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"arxiv:2108.07337",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
---
This model is used in the paper **Generative Relation Linking for Question Answering over Knowledge Bases**. [ArXiv](https://arxiv.org/abs/2108.07337), [GitHub](https://github.com/IBM/kbqa-relation-linking)
## Citation
```bibtex
@inproceedings{rossiello-genrl-2021,
title={Generative relation linking for question answering over knowledge bases},
author={Rossiello, Gaetano and Mihindukulasooriya, Nandana and Abdelaziz, Ibrahim and Bornea, Mihaela and Gliozzo, Alfio and Naseem, Tahira and Kapanipathi, Pavan},
booktitle={International Semantic Web Conference},
pages={321--337},
year={2021},
organization={Springer},
url = "https://link.springer.com/chapter/10.1007/978-3-030-88361-4_19",
doi = "10.1007/978-3-030-88361-4_19"
}
```
|
gaetangate/bart-large_genrl_lcquad1
|
gaetangate
| 2022-04-05T15:09:52Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"arxiv:2108.07337",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
---
This model is used in the paper **Generative Relation Linking for Question Answering over Knowledge Bases**. [ArXiv](https://arxiv.org/abs/2108.07337), [GitHub](https://github.com/IBM/kbqa-relation-linking)
## Citation
```bibtex
@inproceedings{rossiello-genrl-2021,
title={Generative relation linking for question answering over knowledge bases},
author={Rossiello, Gaetano and Mihindukulasooriya, Nandana and Abdelaziz, Ibrahim and Bornea, Mihaela and Gliozzo, Alfio and Naseem, Tahira and Kapanipathi, Pavan},
booktitle={International Semantic Web Conference},
pages={321--337},
year={2021},
organization={Springer},
url = "https://link.springer.com/chapter/10.1007/978-3-030-88361-4_19",
doi = "10.1007/978-3-030-88361-4_19"
}
```
|
gaetangate/bart-large_genrl_simpleq
|
gaetangate
| 2022-04-05T15:09:05Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"arxiv:2108.07337",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
---
This model is used in the paper **Generative Relation Linking for Question Answering over Knowledge Bases**. [ArXiv](https://arxiv.org/abs/2108.07337), [GitHub](https://github.com/IBM/kbqa-relation-linking)
## Citation
```bibtex
@inproceedings{rossiello-genrl-2021,
title={Generative relation linking for question answering over knowledge bases},
author={Rossiello, Gaetano and Mihindukulasooriya, Nandana and Abdelaziz, Ibrahim and Bornea, Mihaela and Gliozzo, Alfio and Naseem, Tahira and Kapanipathi, Pavan},
booktitle={International Semantic Web Conference},
pages={321--337},
year={2021},
organization={Springer},
url = "https://link.springer.com/chapter/10.1007/978-3-030-88361-4_19",
doi = "10.1007/978-3-030-88361-4_19"
}
```
|
tbosse/bert-base-german-cased-finetuned-subj_v3
|
tbosse
| 2022-04-05T15:03:58Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-04-05T13:32:50Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-german-cased-finetuned-subj_v3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-german-cased-finetuned-subj_v3
This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1790
- Precision: 0.1875
- Recall: 0.0079
- F1: 0.0152
- Accuracy: 0.9472
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 136 | 0.1721 | 0.0 | 0.0 | 0.0 | 0.9488 |
| No log | 2.0 | 272 | 0.1731 | 0.0 | 0.0 | 0.0 | 0.9482 |
| No log | 3.0 | 408 | 0.1790 | 0.1875 | 0.0079 | 0.0152 | 0.9472 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
chibubu/Deeplearning_for_vision
|
chibubu
| 2022-04-05T14:36:36Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2022-04-05T14:29:18Z |
---
license: apache-2.0
---
|
onecat1/1
|
onecat1
| 2022-04-05T14:30:12Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2022-04-05T14:30:12Z |
---
license: apache-2.0
---
|
medhabi/distilbert-base-uncased-mlm-ta-local
|
medhabi
| 2022-04-05T14:05:55Z | 2 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-04-05T11:20:38Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-mlm-ta-local
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-mlm-ta-local
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0658
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.4431 | 1.0 | 3125 | 2.1817 |
| 2.2197 | 2.0 | 6250 | 2.0929 |
| 2.1519 | 3.0 | 9375 | 2.0696 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.1
- Datasets 2.0.0
- Tokenizers 0.11.6
|
naver-clova-ocr/bros-large-uncased
|
naver-clova-ocr
| 2022-04-05T13:57:07Z | 406 | 6 |
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 | < 110M | [naver-clova-ocr/bros-base-uncased](https://huggingface.co/naver-clova-ocr/bros-base-uncased) |
| bros-large-uncased (**this**) | < 340M | [naver-clova-ocr/bros-large-uncased](https://huggingface.co/naver-clova-ocr/bros-large-uncased) |
|
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.
|
urielnguefack/Fake_News_Classification_with_Distilbert
|
urielnguefack
| 2022-04-05T12:32:27Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-04-05T12:25:15Z |
Fake news classifier:
The project is about building an NLP algorithm to detect fake News Articles
We use a pretrained model namely Distilbert
|
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/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()
```
|
ukr-models/uk_core_news_trf
|
ukr-models
| 2022-04-05T06:57:34Z | 3 | 2 |
spacy
|
[
"spacy",
"token-classification",
"uk",
"license:mit",
"model-index",
"region:us"
] |
token-classification
| 2022-04-05T05:50:26Z |
---
tags:
- spacy
- token-classification
language:
- uk
license: mit
widget:
- text: "Могила Тараса Шевченка — місце поховання видатного українського поета Тараса Шевченка в місті Канів (Черкаська область) на Чернечій горі, над яким із 1939 року височіє бронзовий пам'ятник роботи скульптора Матвія Манізера."
model-index:
- name: uk_core_news_trf
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.8891135827
- name: NER Recall
type: recall
value: 0.8895133191
- name: NER F Score
type: f_score
value: 0.889313406
- task:
name: POS
type: token-classification
metrics:
- name: POS (UPOS) Accuracy
type: accuracy
value: 0.9833735418
- task:
name: MORPH
type: token-classification
metrics:
- name: Morph (UFeats) Accuracy
type: accuracy
value: 0.9611670877
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.9619342309
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0.9462333693
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 0.9300427148
---
Spacy transformer pipeline for Ukrainian language ([XLM-Roberta based](https://huggingface.co/ukr-models/xlm-roberta-base-uk)). Components: transformer, ner, morphologizer, parser.
[Training details](https://github.com/kurnosovv/ukr-spacy)
|
UWB-AIR/MQDD-duplicates
|
UWB-AIR
| 2022-04-05T06:24:29Z | 18 | 0 |
transformers
|
[
"transformers",
"pytorch",
"longformer",
"feature-extraction",
"arxiv:2203.14093",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-25T16:17:08Z |
---
license: cc-by-nc-sa-4.0
---
# MQDD - Multimodal Question Duplicity Detection
This repository publishes trained models and other supporting materials for the paper
[MQDD – Pre-training of Multimodal Question Duplicity Detection for Software Engineering Domain](https://arxiv.org/abs/2203.14093). For more information, see the paper.
The Stack Overflow Datasets (SOD) and Stack Overflow Duplicity Dataset (SODD) presented in the paper can be obtained from our [Stack Overflow Dataset repository](https://github.com/kiv-air/StackOverflowDataset).
To acquire the pre-trained model only, see the [UWB-AIR/MQDD-pretrained](https://huggingface.co/UWB-AIR/MQDD-pretrained).
## Fine-tuned MQDD
We release a fine-tuned version of our MQDD model for duplicate detection task. The model's architecture follows the architecture of a two-tower model as depicted in the figure below:
<img src="https://raw.githubusercontent.com/kiv-air/MQDD/master/img/architecture.png" width="700">
A self-standing encoder without a duplicate detection head can be loaded using the following source code snippet. Such a model can be used for building search systems based, for example, on [Faiss](https://github.com/facebookresearch/faiss) library.
```Python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("UWB-AIR/MQDD-duplicates")
model = AutoModel.from_pretrained("UWB-AIR/MQDD-duplicates")
```
A checkpoint of a full two-tower model can than be obtained from our [GoogleDrive folder](https://drive.google.com/drive/folders/1CYiqF2GJ2fSQzx_oM4-X_IhpObi4af5Q?usp=sharing). To load the model, one needs to use the model's implementation from `models/MQDD_model.py` in our [GitHub repository](https://github.com/kiv-air/MQDD). To construct the model and load it's checkpoint, use the following source code:
```Python
from MQDD_model import ClsHeadModelMQDD
model = ClsHeadModelMQDD("UWB-AIR/MQDD-duplicates")
ckpt = torch.load("model.pt", map_location="cpu")
model.load_state_dict(ckpt["model_state"])
```
## Licence
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. http://creativecommons.org/licenses/by-nc-sa/4.0/
## How should I cite the MQDD?
For now, please cite [the Arxiv paper](https://arxiv.org/abs/2203.14093):
```
@misc{https://doi.org/10.48550/arxiv.2203.14093,
doi = {10.48550/ARXIV.2203.14093},
url = {https://arxiv.org/abs/2203.14093},
author = {Pašek, Jan and Sido, Jakub and Konopík, Miloslav and Pražák, Ondřej},
title = {MQDD -- Pre-training of Multimodal Question Duplicity Detection for Software Engineering Domain},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
}
```
|
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
|
gagan3012/fake-news-fatima-fellowship
|
gagan3012
| 2022-04-05T04:04:39Z | 16 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-04T21:45:24Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: fake-news-fatima-fellowship
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. -->
# fake-news-fatima-fellowship
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Accuracy: 1.0
- F1: 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.008 | 1.0 | 2514 | 0.0011 | 0.9996 | 0.9996 |
| 0.0004 | 2.0 | 5028 | 0.0000 | 1.0 | 1.0 |
| 0.0003 | 3.0 | 7542 | 0.0000 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.9.1
- Datasets 1.18.4
- Tokenizers 0.11.6
|
pinku/FatimaFellowship_fake_and_real_news
|
pinku
| 2022-04-05T03:22:45Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"license:bsd-3-clause",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-04T09:09:53Z |
---
license: bsd-3-clause
---
# Fatima Fellowship NLP Project
## Fake News Classifier
- BERT base model finetuned to classify fake news.
|
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
|
mgreenbe/607-live-demo-yelp-polarity
|
mgreenbe
| 2022-04-05T00:30:47Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-05T00:21:06Z |
Demo model trained for 1 epoch on 4096 examples from the `yelp_polarity` dataset.
|
BigSalmon/GPTNeo350MInformalToFormalLincoln7
|
BigSalmon
| 2022-04-04T23:01:23Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-04-04T22:54:00Z |
Trained on this model: https://huggingface.co/xhyi/PT_GPTNEO350_ATG/tree/main
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/GPTNeo350MInformalToFormalLincoln7")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/GPTNeo350MInformalToFormalLincoln7")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
***
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
***
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
informal english:
```
```
- declining viewership facing the nba.
- does not have to be this way.
- in fact, many solutions exist.
- the four point line would surely draw in eyes.
Text: failing to draw in the masses, the NBA has fallen into disrepair. such does not have to be the case, however. in fact, a myriad of simple, relatively cheap solutions could revive the league. the addition of the much-hyped four-point line would surely juice viewership.
***
-
```
```
infill: chrome extensions [MASK] accomplish everyday tasks.
Translated into the Style of Abraham Lincoln: chrome extensions ( expedite the ability to / unlock the means to more readily ) accomplish everyday tasks.
infill: at a time when nintendo has become inflexible, [MASK] consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
Translated into the Style of Abraham Lincoln: at a time when nintendo has become inflexible, ( stubbornly [MASK] on / firmly set on / unyielding in its insistence on ) consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
infill:
```
```
Essay Intro (California High-Speed Rail): built with an eye on the future, california's high-speed rail service resolves to change the face of travel.
Essay Intro (YIMBY's Need To Win): home to the most expensive housing market in the united states, san francisco is the city in which the yimby and anti-yimby hordes wage an eternal battle.
Essay Intro (
```
```
Search: What is the definition of Checks and Balances?
https://en.wikipedia.org/wiki/Checks_and_balances
Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate.
https://www.harvard.edu/glossary/Checks_and_Balances
Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power
https://www.law.cornell.edu/library/constitution/Checks_and_Balances
Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power.
***
Search: What is the definition of Separation of Powers?
https://en.wikipedia.org/wiki/Separation_of_powers
The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power.
https://www.yale.edu/tcf/Separation_of_Powers.html
Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined.
***
Search: What is the definition of Connection of Powers?
https://en.wikipedia.org/wiki/Connection_of_powers
Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches.
https://simple.wikipedia.org/wiki/Connection_of_powers
The term Connection of Powers describes a system of government in which there is overlap between different parts of the government.
***
Search: What is the definition of
```
```
Search: What are phrase synonyms for "second-guess"?
https://www.powerthesaurus.org/second-guess/synonyms
Shortest to Longest:
- feel dubious about
- raise an eyebrow at
- wrinkle their noses at
- cast a jaundiced eye at
- teeter on the fence about
***
Search: What are phrase synonyms for "mean to newbies"?
https://www.powerthesaurus.org/mean_to_newbies/synonyms
Shortest to Longest:
- readiness to balk at rookies
- absence of tolerance for novices
- hostile attitude toward newcomers
***
Search: What are phrase synonyms for "make use of"?
https://www.powerthesaurus.org/make_use_of/synonyms
Shortest to Longest:
- call upon
- glean value from
- reap benefits from
- derive utility from
- seize on the merits of
- draw on the strength of
- tap into the potential of
***
Search: What are phrase synonyms for "hurting itself"?
https://www.powerthesaurus.org/hurting_itself/synonyms
Shortest to Longest:
- erring
- slighting itself
- forfeiting its integrity
- doing itself a disservice
- evincing a lack of backbone
***
Search: What are phrase synonyms for "
```
|
wanyu/IteraTeR-ROBERTA-Intention-Classifier
|
wanyu
| 2022-04-04T20:13:42Z | 10 | 5 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"dataset:IteraTeR_full_sent",
"arxiv:2203.03802",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-13T19:10:06Z |
---
datasets:
- IteraTeR_full_sent
---
# IteraTeR RoBERTa model
This model was obtained by fine-tuning [roberta-large](https://huggingface.co/roberta-large) on [IteraTeR-human-sent](https://huggingface.co/datasets/wanyu/IteraTeR_human_sent) dataset.
Paper: [Understanding Iterative Revision from Human-Written Text](https://arxiv.org/abs/2203.03802) <br>
Authors: Wanyu Du, Vipul Raheja, Dhruv Kumar, Zae Myung Kim, Melissa Lopez, Dongyeop Kang
## Edit Intention Prediction Task
Given a pair of original sentence and revised sentence, our model can predict the edit intention for this revision pair.<br>
More specifically, the model will predict the probability of the following edit intentions:
<table>
<tr>
<th>Edit Intention</th>
<th>Definition</th>
<th>Example</th>
</tr>
<tr>
<td>clarity</td>
<td>Make the text more formal, concise, readable and understandable.</td>
<td>
Original: It's like a house which anyone can enter in it. <br>
Revised: It's like a house which anyone can enter.
</td>
</tr>
<tr>
<td>fluency</td>
<td>Fix grammatical errors in the text.</td>
<td>
Original: In the same year he became the Fellow of the Royal Society. <br>
Revised: In the same year, he became the Fellow of the Royal Society.
</td>
</tr>
<tr>
<td>coherence</td>
<td>Make the text more cohesive, logically linked and consistent as a whole.</td>
<td>
Original: Achievements and awards Among his other activities, he founded the Karachi Film Guild and Pakistan Film and TV Academy. <br>
Revised: Among his other activities, he founded the Karachi Film Guild and Pakistan Film and TV Academy.
</td>
</tr>
<tr>
<td>style</td>
<td>Convey the writer’s writing preferences, including emotions, tone, voice, etc..</td>
<td>
Original: She was last seen on 2005-10-22. <br>
Revised: She was last seen on October 22, 2005.
</td>
</tr>
<tr>
<td>meaning-changed</td>
<td>Update or add new information to the text.</td>
<td>
Original: This method improves the model accuracy from 64% to 78%. <br>
Revised: This method improves the model accuracy from 64% to 83%.
</td>
</tr>
</table>
## Usage
```python
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("wanyu/IteraTeR-ROBERTA-Intention-Classifier")
model = AutoModelForSequenceClassification.from_pretrained("wanyu/IteraTeR-ROBERTA-Intention-Classifier")
id2label = {0: "clarity", 1: "fluency", 2: "coherence", 3: "style", 4: "meaning-changed"}
before_text = 'I likes coffee.'
after_text = 'I like coffee.'
model_input = tokenizer(before_text, after_text, return_tensors='pt')
model_output = model(**model_input)
softmax_scores = torch.softmax(model_output.logits, dim=-1)
pred_id = torch.argmax(softmax_scores)
pred_label = id2label[pred_id.int()]
```
|
wanyu/IteraTeR-BART-Revision-Generator
|
wanyu
| 2022-04-04T20:09:49Z | 8 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"dataset:IteraTeR_full_sent",
"arxiv:2203.03802",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-15T01:21:43Z |
---
datasets:
- IteraTeR_full_sent
---
# IteraTeR BART model
This model was obtained by fine-tuning [facebook/bart-base](https://huggingface.co/facebook/bart-base) on [IteraTeR-full-sent](https://huggingface.co/datasets/wanyu/IteraTeR_full_sent) dataset.
Paper: [Understanding Iterative Revision from Human-Written Text](https://arxiv.org/abs/2203.03802) <br>
Authors: Wanyu Du, Vipul Raheja, Dhruv Kumar, Zae Myung Kim, Melissa Lopez, Dongyeop Kang
## Text Revision Task
Given an edit intention and an original sentence, our model can generate a revised sentence.<br>
The edit intentions are provided by [IteraTeR-full-sent](https://huggingface.co/datasets/wanyu/IteraTeR_full_sent) dataset, which are categorized as follows:
<table>
<tr>
<th>Edit Intention</th>
<th>Definition</th>
<th>Example</th>
</tr>
<tr>
<td>clarity</td>
<td>Make the text more formal, concise, readable and understandable.</td>
<td>
Original: It's like a house which anyone can enter in it. <br>
Revised: It's like a house which anyone can enter.
</td>
</tr>
<tr>
<td>fluency</td>
<td>Fix grammatical errors in the text.</td>
<td>
Original: In the same year he became the Fellow of the Royal Society. <br>
Revised: In the same year, he became the Fellow of the Royal Society.
</td>
</tr>
<tr>
<td>coherence</td>
<td>Make the text more cohesive, logically linked and consistent as a whole.</td>
<td>
Original: Achievements and awards Among his other activities, he founded the Karachi Film Guild and Pakistan Film and TV Academy. <br>
Revised: Among his other activities, he founded the Karachi Film Guild and Pakistan Film and TV Academy.
</td>
</tr>
<tr>
<td>style</td>
<td>Convey the writer’s writing preferences, including emotions, tone, voice, etc..</td>
<td>
Original: She was last seen on 2005-10-22. <br>
Revised: She was last seen on October 22, 2005.
</td>
</tr>
</table>
## Usage
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("wanyu/IteraTeR-BART-Revision-Generator")
model = AutoModelForSeq2SeqLM.from_pretrained("wanyu/IteraTeR-BART-Revision-Generator")
before_input = '<fluency> I likes coffee.'
model_input = tokenizer(before_input, return_tensors='pt')
model_outputs = model.generate(**model_input, num_beams=8, max_length=1024)
after_text = tokenizer.batch_decode(model_outputs, skip_special_tokens=True)[0]
```
|
wanyu/IteraTeR-PEGASUS-Revision-Generator
|
wanyu
| 2022-04-04T20:08:12Z | 12 | 2 |
transformers
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"dataset:IteraTeR_full_sent",
"arxiv:2203.03802",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-13T18:55:49Z |
---
datasets:
- IteraTeR_full_sent
---
# IteraTeR PEGASUS model
This model was obtained by fine-tuning [google/pegasus-large](https://huggingface.co/google/pegasus-large) on [IteraTeR-full-sent](https://huggingface.co/datasets/wanyu/IteraTeR_full_sent) dataset.
Paper: [Understanding Iterative Revision from Human-Written Text](https://arxiv.org/abs/2203.03802) <br>
Authors: Wanyu Du, Vipul Raheja, Dhruv Kumar, Zae Myung Kim, Melissa Lopez, Dongyeop Kang
## Text Revision Task
Given an edit intention and an original sentence, our model can generate a revised sentence.<br>
The edit intentions are provided by [IteraTeR-full-sent](https://huggingface.co/datasets/wanyu/IteraTeR_full_sent) dataset, which are categorized as follows:
<table>
<tr>
<th>Edit Intention</th>
<th>Definition</th>
<th>Example</th>
</tr>
<tr>
<td>clarity</td>
<td>Make the text more formal, concise, readable and understandable.</td>
<td>
Original: It's like a house which anyone can enter in it. <br>
Revised: It's like a house which anyone can enter.
</td>
</tr>
<tr>
<td>fluency</td>
<td>Fix grammatical errors in the text.</td>
<td>
Original: In the same year he became the Fellow of the Royal Society. <br>
Revised: In the same year, he became the Fellow of the Royal Society.
</td>
</tr>
<tr>
<td>coherence</td>
<td>Make the text more cohesive, logically linked and consistent as a whole.</td>
<td>
Original: Achievements and awards Among his other activities, he founded the Karachi Film Guild and Pakistan Film and TV Academy. <br>
Revised: Among his other activities, he founded the Karachi Film Guild and Pakistan Film and TV Academy.
</td>
</tr>
<tr>
<td>style</td>
<td>Convey the writer’s writing preferences, including emotions, tone, voice, etc..</td>
<td>
Original: She was last seen on 2005-10-22. <br>
Revised: She was last seen on October 22, 2005.
</td>
</tr>
</table>
## Usage
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("wanyu/IteraTeR-PEGASUS-Revision-Generator")
model = AutoModelForSeq2SeqLM.from_pretrained("wanyu/IteraTeR-PEGASUS-Revision-Generator")
before_input = '<fluency> I likes coffee.'
model_input = tokenizer(before_input, return_tensors='pt')
model_outputs = model.generate(**model_input, num_beams=8, max_length=1024)
after_text = tokenizer.batch_decode(model_outputs, skip_special_tokens=True)[0]
```
|
Sevil/t5-small-finetuned-wikihow_3epoch_v2
|
Sevil
| 2022-04-04T20:03:46Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wikihow",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-04-04T13:45:46Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wikihow
metrics:
- rouge
model-index:
- name: t5-small-finetuned-wikihow_3epoch_v2
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: wikihow
type: wikihow
args: all
metrics:
- name: Rouge1
type: rouge
value: 27.48
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-wikihow_3epoch_v2
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wikihow dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2758
- Rouge1: 27.48
- Rouge2: 10.7621
- Rougel: 23.4136
- Rougelsum: 26.7923
- Gen Len: 18.5424
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 2.8423 | 0.13 | 5000 | 2.5715 | 25.2685 | 8.6964 | 21.229 | 24.5773 | 18.4479 |
| 2.7345 | 0.25 | 10000 | 2.5236 | 24.982 | 8.7823 | 21.1609 | 24.3066 | 18.3631 |
| 2.6811 | 0.38 | 15000 | 2.4911 | 25.7585 | 9.3372 | 21.8388 | 25.1052 | 18.3997 |
| 2.6611 | 0.51 | 20000 | 2.4510 | 26.022 | 9.4708 | 22.0899 | 25.3236 | 18.5472 |
| 2.6133 | 0.64 | 25000 | 2.4272 | 26.3481 | 9.6769 | 22.4484 | 25.7046 | 18.3863 |
| 2.6083 | 0.76 | 30000 | 2.4108 | 26.4131 | 9.6643 | 22.4021 | 25.6958 | 18.5585 |
| 2.5842 | 0.89 | 35000 | 2.3866 | 26.2852 | 9.7505 | 22.4525 | 25.5908 | 18.5485 |
| 2.5554 | 1.02 | 40000 | 2.3816 | 26.3018 | 9.7218 | 22.3673 | 25.6515 | 18.4912 |
| 2.4895 | 1.14 | 45000 | 2.3730 | 26.6439 | 9.9665 | 22.6593 | 25.9521 | 18.5635 |
| 2.4781 | 1.27 | 50000 | 2.3541 | 26.8488 | 10.0364 | 22.8202 | 26.1598 | 18.4254 |
| 2.4821 | 1.4 | 55000 | 2.3440 | 26.9511 | 10.2079 | 23.0133 | 26.2821 | 18.5712 |
| 2.4593 | 1.53 | 60000 | 2.3370 | 26.945 | 10.3123 | 22.9245 | 26.2493 | 18.5978 |
| 2.4521 | 1.65 | 65000 | 2.3309 | 26.9652 | 10.314 | 22.9657 | 26.298 | 18.4837 |
| 2.4523 | 1.78 | 70000 | 2.3249 | 27.0548 | 10.4204 | 23.1286 | 26.379 | 18.4717 |
| 2.4563 | 1.91 | 75000 | 2.3079 | 27.4563 | 10.6452 | 23.3985 | 26.7812 | 18.5642 |
| 2.4229 | 2.03 | 80000 | 2.3115 | 27.0538 | 10.44 | 22.9957 | 26.349 | 18.5914 |
| 2.3694 | 2.16 | 85000 | 2.3017 | 27.332 | 10.6556 | 23.3135 | 26.629 | 18.459 |
| 2.3749 | 2.29 | 90000 | 2.2941 | 27.3294 | 10.5967 | 23.2039 | 26.6411 | 18.5179 |
| 2.3779 | 2.42 | 95000 | 2.2891 | 27.3725 | 10.6539 | 23.3455 | 26.707 | 18.5367 |
| 2.3638 | 2.54 | 100000 | 2.2895 | 27.3487 | 10.6738 | 23.2894 | 26.681 | 18.6128 |
| 2.3549 | 2.67 | 105000 | 2.2833 | 27.408 | 10.6903 | 23.3575 | 26.7137 | 18.6035 |
| 2.3652 | 2.8 | 110000 | 2.2788 | 27.561 | 10.8202 | 23.4672 | 26.8584 | 18.5565 |
| 2.3553 | 2.93 | 115000 | 2.2758 | 27.48 | 10.7621 | 23.4136 | 26.7923 | 18.5424 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
nielsr/convnext-tiny-finetuned-eurostat
|
nielsr
| 2022-04-04T19:25:58Z | 61 | 0 |
transformers
|
[
"transformers",
"pytorch",
"convnext",
"image-classification",
"dataset:eurosat",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-04-04T18:59:04Z |
---
license: apache-2.0
datasets:
- eurosat
widget:
- src: forest.png
example_title: Forest
---
# ConvNext fine-tuned on Eurosat
This model is a `facebook/convnext-tiny-224` model fine-tuned on the [Eurosat dataset](https://github.com/phelber/EuroSAT).
|
okep/distilbert-base-uncased-finetuned-emotion
|
okep
| 2022-04-04T18:53:56Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-28T20:03:24Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9245
- name: F1
type: f1
value: 0.9245483619750937
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2269
- Accuracy: 0.9245
- F1: 0.9245
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.853 | 1.0 | 250 | 0.3507 | 0.8925 | 0.8883 |
| 0.2667 | 2.0 | 500 | 0.2269 | 0.9245 | 0.9245 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
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
|
efederici/cross-encoder-bert-base-stsb
|
efederici
| 2022-04-04T17:09:02Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"cross-encoder",
"sentence-similarity",
"it",
"dataset:stsb_multi_mt",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-04T16:26:27Z |
---
pipeline_tag: text-classification
language:
- it
datasets:
- stsb_multi_mt
tags:
- cross-encoder
- sentence-similarity
- transformers
---
# Cross-Encoder
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
<p align="center">
<img src="https://upload.wikimedia.org/wikipedia/commons/f/f6/Edouard_Vuillard%2C_1920c_-_Sunlit_Interior.jpg" width="400"> </br>
Edouard Vuillard, Sunlit Interior
</p>
## Training Data
This model was trained on [stsb](https://huggingface.co/datasets/stsb_multi_mt/viewer/it/train). The model will predict a score between 0 and 1 how for the semantic similarity of two sentences.
## Usage and Performance
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('efederici/cross-encoder-umberto-stsb')
scores = model.predict([('Sentence 1', 'Sentence 2'), ('Sentence 3', 'Sentence 4')])
```
The model will predict scores for the pairs `('Sentence 1', 'Sentence 2')` and `('Sentence 3', 'Sentence 4')`.
|
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')`.
|
Manimaran/pokemon_classifer
|
Manimaran
| 2022-04-04T15:59:25Z | 0 | 1 | null |
[
"license:wtfpl",
"region:us"
] | null | 2022-04-04T15:31:57Z |
---
license: wtfpl
---
# Pokemon Classifier
This repo is a part of my study in deep learning with [fast.ai](https://www.fast.ai), this app uses this template [repo](https://github.com/render-examples/fastai-v3). thanks to them for the starter code and the [fast ai MOOC](https://course.fast.ai/) for making it easy to build deep learning models, and also the creator of this <del>[dataset](https://kaggle.com/mrgravelord/complete-pokemon-image-dataset)</del> for putting up a curated dataset (removed from kaggle).
I have also hosted this web app on heroku, Check it out [here](https://pokemon-classifier.herokuapp.com).
Blog post explaining the model building process [here](https://mani2106.github.io/Blog-Posts/pokemon-classifer/image-classification/fastai/2019/06/01/Fast_ai_lesson_2_pokemon_classifier.html).
## Demo
|
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
|
elena-soare/docu-t5-base-FK
|
elena-soare
| 2022-04-04T14:34:49Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-21T21:16:08Z |
# Text2SQL Task T5-Base + Foreign Keys
This is our T5 model fine-tuned on Spider using a schema serialization which includes foreign keys
## Running the model
Inspired by the work done by [Picard](https://github.com/ElementAI/picard/) by adding foreign keys relations.
|
enimai/OPUS-mt-en-fr-finetuned-MUST-C
|
enimai
| 2022-04-04T11:49:17Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"marian",
"text2text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-04-04T11:26:27Z |
---
license: apache-2.0
---
|
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
|
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}
}
```
|
tanlq/vit-base-patch16-224-in21k-finetuned-cifar10
|
tanlq
| 2022-04-04T08:20:16Z | 76 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:cifar10",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-31T03:09:09Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cifar10
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-in21k-finetuned-cifar10
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: cifar10
type: cifar10
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.9875
---
<!-- 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-finetuned-cifar10
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 cifar10 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0503
- Accuracy: 0.9875
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3118 | 1.0 | 1562 | 0.1135 | 0.9778 |
| 0.2717 | 2.0 | 3124 | 0.0619 | 0.9867 |
| 0.1964 | 3.0 | 4686 | 0.0503 | 0.9875 |
### Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.11.0
- Datasets 2.0.0
- Tokenizers 0.11.6
|
ydshieh/bert-base-cased-squad2
|
ydshieh
| 2022-04-04T08:06:26Z | 93 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"question-answering",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-04-01T15:23:10Z |
---
license: cc-by-4.0
---
This is a BERT base cased model trained on SQuAD v2
|
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
```
|
vkamthe/upside_down_detector
|
vkamthe
| 2022-04-04T07:01:28Z | 5 | 0 |
tf-keras
|
[
"tf-keras",
"tag1",
"tag2",
"dataset:dataset1",
"dataset:dataset2",
"license:cc",
"region:us"
] | null | 2022-04-04T06:16:41Z |
---
language:
- "List of ISO 639-1 code for your language"
- lang1
- lang2
thumbnail: "url to a thumbnail used in social sharing"
tags:
- tag1
- tag2
license: "cc"
datasets:
- dataset1
- dataset2
metrics:
- metric1
- metric2
---
This is Image Orientation Detector by Vikram Kamthe
Given an image, it will classify it into Original Image or Upside Down Image
|
BigSalmon/GPT2Neo1.3BPoints
|
BigSalmon
| 2022-04-04T05:14:11Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-04-04T04:17:46Z |
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/GPT2Neo1.3BPoints")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/GPT2Neo1.3BPoints")
```
```
- moviepass to return
- this summer
- swooped up by
- original co-founder stacy spikes
text: the re-launch of moviepass is set to transpire this summer, ( rescued at the hands of / under the stewardship of / spearheaded by ) its founding father, stacy spikes.
***
- middle schools do not have recess
- should get back to doing it
- amazing for communication
- and getting kids to move around
text: a casualty of the education reform craze, recess has been excised from middle schools. this is tragic, for it is instrumental in honing children's communication skills and encouraging physical activity.
***
-
```
|
somosnlp-hackathon-2022/electricidad-base-generator-fake-news
|
somosnlp-hackathon-2022
| 2022-04-04T04:04:01Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"electra",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-29T19:52:54Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: electricidad-base-generator-fake-news
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# electricidad-base-generator-fake-news
This model is a fine-tuned version of [mrm8488/electricidad-base-generator](https://huggingface.co/mrm8488/electricidad-base-generator) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0067
- Accuracy: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1136 | 1.0 | 180 | 0.0852 | 1.0 |
| 0.0267 | 2.0 | 360 | 0.0219 | 1.0 |
| 0.0132 | 3.0 | 540 | 0.0108 | 1.0 |
| 0.0091 | 4.0 | 720 | 0.0075 | 1.0 |
| 0.0077 | 5.0 | 900 | 0.0067 | 1.0 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
somosnlp-hackathon-2022/bertin-roberta-base-finetuning-esnli
|
somosnlp-hackathon-2022
| 2022-04-04T01:45:21Z | 74 | 7 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"es",
"dataset:hackathon-pln-es/nli-es",
"arxiv:1908.10084",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-28T19:08:33Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
language:
- es
datasets:
- hackathon-pln-es/nli-es
widget:
- text: "A ver si nos tenemos que poner todos en huelga hasta cobrar lo que queramos."
- text: "La huelga es el método de lucha más eficaz para conseguir mejoras en el salario."
- text: "Tendremos que optar por hacer una huelga para cobrar lo que queremos."
- text: "Queda descartada la huelga aunque no cobremos lo que queramos."
---
# bertin-roberta-base-finetuning-esnli
This is a [sentence-transformers](https://www.SBERT.net) model trained on a
collection of NLI tasks for Spanish. It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
Based around the siamese networks approach from [this paper](https://arxiv.org/pdf/1908.10084.pdf).
<!--- Describe your model here -->
You can see a demo for this model [here](https://huggingface.co/spaces/hackathon-pln-es/Sentence-Embedding-Bertin).
You can find our other model, **paraphrase-spanish-distilroberta** [here](https://huggingface.co/hackathon-pln-es/paraphrase-spanish-distilroberta) and its demo [here](https://huggingface.co/spaces/hackathon-pln-es/Paraphrase-Bertin).
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["Este es un ejemplo", "Cada oración es transformada"]
model = SentenceTransformer('hackathon-pln-es/bertin-roberta-base-finetuning-esnli')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('hackathon-pln-es/bertin-roberta-base-finetuning-esnli')
model = AutoModel.from_pretrained('hackathon-pln-es/bertin-roberta-base-finetuning-esnli')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
Our model was evaluated on the task of Semantic Textual Similarity using the [SemEval-2015 Task](https://alt.qcri.org/semeval2015/task2/) for [Spanish](http://alt.qcri.org/semeval2015/task2/data/uploads/sts2015-es-test.zip). We measure
| | [BETO STS](https://huggingface.co/espejelomar/sentece-embeddings-BETO) | BERTIN STS (this model) | Relative improvement |
|-------------------:|---------:|-----------:|---------------------:|
| cosine_pearson | 0.609803 | 0.683188 | +12.03 |
| cosine_spearman | 0.528776 | 0.615916 | +16.48 |
| euclidean_pearson | 0.590613 | 0.672601 | +13.88 |
| euclidean_spearman | 0.526529 | 0.611539 | +16.15 |
| manhattan_pearson | 0.589108 | 0.672040 | +14.08 |
| manhattan_spearman | 0.525910 | 0.610517 | +16.09 |
| dot_pearson | 0.544078 | 0.600517 | +10.37 |
| dot_spearman | 0.460427 | 0.521260 | +13.21 |
## Training
The model was trained with the parameters:
**Dataset**
We used a collection of datasets of Natural Language Inference as training data:
- [ESXNLI](https://raw.githubusercontent.com/artetxem/esxnli/master/esxnli.tsv), only the part in spanish
- [SNLI](https://nlp.stanford.edu/projects/snli/), automatically translated
- [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/), automatically translated
The whole dataset used is available [here](https://huggingface.co/datasets/hackathon-pln-es/nli-es).
Here we leave the trick we used to increase the amount of data for training here:
```
for row in reader:
if row['language'] == 'es':
sent1 = row['sentence1'].strip()
sent2 = row['sentence2'].strip()
add_to_samples(sent1, sent2, row['gold_label'])
add_to_samples(sent2, sent1, row['gold_label']) #Also add the opposite
```
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader`
of length 1818 with parameters:
```
{'batch_size': 64}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 909,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 514, 'do_lower_case': False}) with Transformer model: RobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Authors
[Anibal Pérez](https://huggingface.co/Anarpego),
[Emilio Tomás Ariza](https://huggingface.co/medardodt),
[Lautaro Gesuelli](https://huggingface.co/Lgesuelli) y
[Mauricio Mazuecos](https://huggingface.co/mmazuecos).
|
AykeeSalazar/violation-classification-bantai-vit-v80ep
|
AykeeSalazar
| 2022-04-03T23:45:50Z | 60 | 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
| 2022-04-03T16:46:23Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
model-index:
- name: violation-classification-bantai-vit-v80ep
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: image_folder
type: image_folder
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9559725730783111
---
<!-- 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-v80ep
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: 0.1974
- Accuracy: 0.9560
## 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: 80
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.797 | 4.95 | 500 | 0.3926 | 0.8715 |
| 0.3095 | 9.9 | 1000 | 0.2597 | 0.9107 |
| 0.1726 | 14.85 | 1500 | 0.2157 | 0.9253 |
| 0.1259 | 19.8 | 2000 | 0.1870 | 0.9392 |
| 0.0959 | 24.75 | 2500 | 0.1797 | 0.9444 |
| 0.0835 | 29.7 | 3000 | 0.2293 | 0.9354 |
| 0.0722 | 34.65 | 3500 | 0.1921 | 0.9441 |
| 0.0628 | 39.6 | 4000 | 0.1897 | 0.9491 |
| 0.059 | 44.55 | 4500 | 0.1719 | 0.9520 |
| 0.0531 | 49.5 | 5000 | 0.1987 | 0.9513 |
| 0.046 | 54.45 | 5500 | 0.1713 | 0.9556 |
| 0.0444 | 59.4 | 6000 | 0.2016 | 0.9525 |
| 0.042 | 64.36 | 6500 | 0.1950 | 0.9525 |
| 0.0363 | 69.31 | 7000 | 0.2017 | 0.9549 |
| 0.037 | 74.26 | 7500 | 0.1943 | 0.9551 |
| 0.0343 | 79.21 | 8000 | 0.1974 | 0.9560 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
hamedkhaledi/persain-flair-upos
|
hamedkhaledi
| 2022-04-03T22:15:00Z | 29 | 0 |
flair
|
[
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"fa",
"dataset:ontonotes",
"region:us"
] |
token-classification
| 2022-03-25T07:27:51Z |
---
tags:
- flair
- token-classification
- sequence-tagger-model
language:
- fa
datasets:
- ontonotes
widget:
- text: "مقامات مصری به خاطر حفظ ثبات کشور در منطقهای پرآشوب بر خود میبالند ، در حالی که این کشور در طول ۱۶ سال گذشته تنها هشت سال آنرا بدون اعلام وضعیت اضطراری سپری کرده است ."
---
## Persian Universal Part-of-Speech Tagging in Flair
This is the universal part-of-speech tagging model for Persian that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **97,73** (UD_PERSIAN)
Predicts Universal POS tags:
| **tag** | **meaning** |
|:---------------------------------:|:-----------:|
|ADJ | adjective |
| ADP | adposition |
| ADV | adverb |
| AUX | auxiliary |
| CCONJ | coordinating conjunction |
| DET | determiner |
| INTJ | interjection |
| NOUN | noun |
| NUM | numeral |
| PART | particle |
| PRON | pronoun |
| PUNCT | punctuation |
| SCONJ | subordinating conjunction |
| VERB | verb |
| X | other |
---
### 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-upos")
# make example sentence
sentence = Sentence("مقامات مصری به خاطر حفظ ثبات کشور در منطقهای پرآشوب بر خود میبالند .")
tagger.predict(sentence)
#print result
print(sentence.to_tagged_string())
```
This yields the following output:
```
مقامات <NOUN> مصری <ADJ> به <ADP> خاطر <NOUN> حفظ <NOUN> ثبات <NOUN> کشور <NOUN> در <ADP> منطقهای <NOUN> پرآشوب <ADJ> بر <ADP> خود <PRON> میبالند <VERB> . <PUNCT>
```
---
### Results
- F-score (micro) 0.9773
- F-score (macro) 0.9461
- Accuracy 0.9773
```
By class:
precision recall f1-score support
NOUN 0.9770 0.9849 0.9809 6420
ADP 0.9947 0.9916 0.9932 1909
ADJ 0.9342 0.9128 0.9234 1525
PUNCT 1.0000 1.0000 1.0000 1365
VERB 0.9840 0.9711 0.9775 1141
CCONJ 0.9912 0.9937 0.9925 794
AUX 0.9622 0.9799 0.9710 546
PRON 0.9751 0.9865 0.9808 517
SCONJ 0.9797 0.9757 0.9777 494
NUM 0.9948 1.0000 0.9974 385
ADV 0.9343 0.9033 0.9185 362
DET 0.9773 0.9711 0.9742 311
PART 0.9916 1.0000 0.9958 237
INTJ 0.8889 0.8000 0.8421 10
X 0.7143 0.6250 0.6667 8
micro avg 0.9773 0.9773 0.9773 16024
macro avg 0.9533 0.9397 0.9461 16024
weighted avg 0.9772 0.9773 0.9772 16024
samples avg 0.9773 0.9773 0.9773 16024
Loss: 0.12471389770507812
```
|
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.
|
tbosse/bert-base-german-cased-finetuned-subj_v2_v1
|
tbosse
| 2022-04-03T19:15:50Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-04-03T17:49:37Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-german-cased-finetuned-subj_v2_v1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-german-cased-finetuned-subj_v2_v1
This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1587
- Precision: 0.2222
- Recall: 0.0107
- F1: 0.0204
- Accuracy: 0.9511
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 136 | 0.1569 | 0.6667 | 0.0053 | 0.0106 | 0.9522 |
| No log | 2.0 | 272 | 0.1562 | 0.1667 | 0.0053 | 0.0103 | 0.9513 |
| No log | 3.0 | 408 | 0.1587 | 0.2222 | 0.0107 | 0.0204 | 0.9511 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
anton-l/xtreme_s_xlsr_300m_minds14
|
anton-l
| 2022-04-03T18:54:43Z | 557 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"minds14",
"google/xtreme_s",
"generated_from_trainer",
"all",
"dataset:google/xtreme_s",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2022-03-17T17:24:20Z |
---
language:
- all
license: apache-2.0
tags:
- minds14
- google/xtreme_s
- generated_from_trainer
datasets:
- google/xtreme_s
metrics:
- f1
- accuracy
model-index:
- name: xtreme_s_xlsr_300m_minds14
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_minds14
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 - MINDS14.ALL dataset.
It achieves the following results on the evaluation set:
- Accuracy: 0.9033
- Accuracy Cs-cz: 0.9164
- Accuracy De-de: 0.9477
- Accuracy En-au: 0.9235
- Accuracy En-gb: 0.9324
- Accuracy En-us: 0.9326
- Accuracy Es-es: 0.9177
- Accuracy Fr-fr: 0.9444
- Accuracy It-it: 0.9167
- Accuracy Ko-kr: 0.8649
- Accuracy Nl-nl: 0.9450
- Accuracy Pl-pl: 0.9146
- Accuracy Pt-pt: 0.8940
- Accuracy Ru-ru: 0.8667
- Accuracy Zh-cn: 0.7291
- F1: 0.9015
- F1 Cs-cz: 0.9154
- F1 De-de: 0.9467
- F1 En-au: 0.9199
- F1 En-gb: 0.9334
- F1 En-us: 0.9308
- F1 Es-es: 0.9158
- F1 Fr-fr: 0.9436
- F1 It-it: 0.9135
- F1 Ko-kr: 0.8642
- F1 Nl-nl: 0.9440
- F1 Pl-pl: 0.9159
- F1 Pt-pt: 0.8883
- F1 Ru-ru: 0.8646
- F1 Zh-cn: 0.7249
- Loss: 0.4119
- Loss Cs-cz: 0.3790
- Loss De-de: 0.2649
- Loss En-au: 0.3459
- Loss En-gb: 0.2853
- Loss En-us: 0.2203
- Loss Es-es: 0.2731
- Loss Fr-fr: 0.1909
- Loss It-it: 0.3520
- Loss Ko-kr: 0.5431
- Loss Nl-nl: 0.2515
- Loss Pl-pl: 0.4113
- Loss Pt-pt: 0.4798
- Loss Ru-ru: 0.6470
- Loss Zh-cn: 1.1216
- Predict Samples: 4086
## 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: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1500
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
| 2.6739 | 5.41 | 200 | 2.5687 | 0.0430 | 0.1190 |
| 1.4953 | 10.81 | 400 | 1.6052 | 0.5550 | 0.5692 |
| 0.6177 | 16.22 | 600 | 0.7927 | 0.8052 | 0.8011 |
| 0.3609 | 21.62 | 800 | 0.5679 | 0.8609 | 0.8609 |
| 0.4972 | 27.03 | 1000 | 0.5944 | 0.8509 | 0.8523 |
| 0.1799 | 32.43 | 1200 | 0.6194 | 0.8623 | 0.8621 |
| 0.1308 | 37.84 | 1400 | 0.5956 | 0.8569 | 0.8548 |
| 0.2298 | 43.24 | 1600 | 0.5201 | 0.8732 | 0.8743 |
| 0.0052 | 48.65 | 1800 | 0.3826 | 0.9106 | 0.9103 |
### Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.10.2+cu113
- Datasets 2.0.1.dev0
- Tokenizers 0.11.6
|
morahil/wav2vec2-large-xls-r-300m-hindi
|
morahil
| 2022-04-03T17:28:16Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-04-03T16:45:22Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-hindi
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-hindi
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
Giyaseddin/distilbert-base-cased-finetuned-fake-and-real-news-dataset
|
Giyaseddin
| 2022-04-03T16:39:39Z | 93 | 1 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"en",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-03T14:52:37Z |
---
license: gpl-3.0
language: en
library: transformers
other: distilbert
datasets:
- Fake and real news dataset
---
# DistilBERT base cased model for Fake News Classification
## Model description
DistilBERT is a transformers model, smaller and faster than BERT, which was pretrained on the same corpus in a
self-supervised fashion, using the BERT base model as a teacher. This means it was pretrained on the raw texts only,
with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic
process to generate inputs and labels from those texts using the BERT base model.
This is a Fake News classification model finetuned [pretrained DistilBERT model](https://huggingface.co/distilbert-base-cased) on
[Fake and real news dataset](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset)
## Intended uses & limitations
This can only be used for the kind of news that are similar to the ones in the dataset,
please visit the [dataset's kaggle page](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset) to see the data.
### How to use
You can use this model directly with a :
```python
>>> from transformers import pipeline
>>> classifier = pipeline("text-classification", model="Giyaseddin/distilbert-base-cased-finetuned-fake-and-real-news-dataset", return_all_scores=True)
>>> examples = ["Yesterday, Speaker Paul Ryan tweeted a video of himself on the Mexican border flying in a helicopter and traveling on horseback with US border agents. RT if you agree It is time for The Wall. pic.twitter.com/s5MO8SG7SL Paul Ryan (@SpeakerRyan) August 1, 2017It makes for great theater to see Republican Speaker Ryan pleading the case for a border wall, but how sincere are the GOP about building the border wall? Even after posting a video that appears to show Ryan s support for the wall, he still seems unsure of himself. It s almost as though he s testing the political winds when he asks Twitter users to retweet if they agree that we need to start building the wall. How committed is the (formerly?) anti-Trump Paul Ryan to building the border wall that would fulfill one of President Trump s most popular campaign promises to the American people? Does he have the what it takes to defy the wishes of corporate donors and the US Chamber of Commerce, and do the right thing for the national security and well-being of our nation?The Last Refuge- Republicans are in control of the House of Representatives, Republicans are in control of the Senate, a Republican President is in the White House, and somehow there s negotiations on how to fund the #1 campaign promise of President Donald Trump, the border wall.Here s the rub.Here s what pundits never discuss.The Republican party doesn t need a single Democrat to fund the border wall.A single spending bill could come from the House of Representatives that fully funds 100% of the border wall. The spending bill then goes to the senate, where again, it doesn t need a single Democrat vote because spending legislation is specifically what reconciliation was designed to facilitate. That House bill can pass the Senate with 51 votes and proceed directly to the President s desk for signature.So, ask yourself: why is this even a point of discussion?The honest answer, for those who are no longer suffering from Battered Conservative Syndrome, is that Republicans don t want to fund or build an actual physical barrier known as the Southern Border Wall.It really is that simple.If one didn t know better, they d almost think Speaker Ryan was attempting to emulate the man he clearly despised during the 2016 presidential campaign."]
>>> classifier(examples)
[[{'label': 'LABEL_0', 'score': 1.0},
{'label': 'LABEL_1', 'score': 1.0119109106199176e-08}]]
```
### Limitations and bias
Even if the training data used for this model could be characterized as fairly neutral, this model can have biased
predictions. It also inherits some of
[the bias of its teacher model](https://huggingface.co/bert-base-uncased#limitations-and-bias).
This bias will also affect all fine-tuned versions of this model.
## Pre-training data
DistilBERT pretrained on the same data as BERT, which is [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset
consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia)
(excluding lists, tables and headers).
## Fine-tuning data
[Fake and real news dataset](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset)
## Training procedure
### Preprocessing
In the preprocessing phase, both the title and the text of the news are concatenated using a separator `[SEP]`.
This makes the full text as:
```
[CLS] Title Sentence [SEP] News text body [SEP]
```
The data are splitted according to the following ratio:
- Training set 60%.
- Validation set 20%.
- Test set 20%.
Lables are mapped as: `{fake: 0, true: 1}`
### Fine-tuning
The model was finetuned on GeForce GTX 960M for 5 hours. The parameters are:
| Parameter | Value |
|:-------------------:|:-----:|
| Learning rate | 5e-5 |
| Weight decay | 0.01 |
| Training batch size | 4 |
| Epochs | 3 |
Here is the scores during the training:
| Epoch | Training Loss | Validation Loss | Accuracy | F1 | Precision | Recall |
|:----------:|:-------------:|:-----------------:|:----------:|:---------:|:-----------:|:---------:|
| 1 | 0.008300 | 0.005783 | 0.998330 | 0.998252 | 0.996511 | 1.000000 |
| 2 | 0.000000 | 0.000161 | 0.999889 | 0.999883 | 0.999767 | 1.000000 |
| 3 | 0.000000 | 0.000122 | 0.999889 | 0.999883 | 0.999767 | 1.000000 |
## Evaluation results
When fine-tuned on downstream task of fake news binary classification, this model achieved the following results:
(scores are rounded to 2 floating points)
| | precision | recall | f1-score | support |
|:------------:|:---------:|:------:|:--------:|:-------:|
| Fake | 1.00 | 1.00 | 1.00 | 4697 |
| True | 1.00 | 1.00 | 1.00 | 4283 |
| accuracy | - | - | 1.00 | 8980 |
| macro avg | 1.00 | 1.00 | 1.00 | 8980 |
| weighted avg | 1.00 | 1.00 | 1.00 | 8980 |
Confision matrix:
| Actual\Predicted | Fake | True |
|:-----------------:|:----:|:----:|
| Fake | 4696 | 1 |
| True | 1 | 4282 |
The AUC score is 0.9997
|
AykeeSalazar/violation-classification-bantai-vit-v100ep
|
AykeeSalazar
| 2022-04-03T16:16:07Z | 64 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-04-03T14:05:38Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
model-index:
- name: violation-classification-bantai-vit-v100ep
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: image_folder
type: image_folder
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9157343919162757
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# violation-classification-bantai-vit-v100ep
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2557
- Accuracy: 0.9157
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2811 | 1.0 | 101 | 0.2855 | 0.9027 |
| 0.2382 | 2.0 | 202 | 0.2763 | 0.9085 |
| 0.2361 | 3.0 | 303 | 0.2605 | 0.9109 |
| 0.196 | 4.0 | 404 | 0.2652 | 0.9110 |
| 0.1395 | 5.0 | 505 | 0.2648 | 0.9134 |
| 0.155 | 6.0 | 606 | 0.2656 | 0.9152 |
| 0.1422 | 7.0 | 707 | 0.2607 | 0.9141 |
| 0.1511 | 8.0 | 808 | 0.2557 | 0.9157 |
| 0.1938 | 9.0 | 909 | 0.2679 | 0.9049 |
| 0.2094 | 10.0 | 1010 | 0.2392 | 0.9137 |
| 0.1835 | 11.0 | 1111 | 0.2400 | 0.9156 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
somosnlp-hackathon-2022/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)
|
somosnlp-hackathon-2022/biomedtra-small-es-squad2-es
|
somosnlp-hackathon-2022
| 2022-04-03T14:51:12Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"question-answering",
"es",
"dataset:squad_es",
"dataset:hackathon-pln-es/biomed_squad_es_v2",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-04-02T03:31:31Z |
---
language: es
datasets:
- squad_es
- hackathon-pln-es/biomed_squad_es_v2
metrics:
- "f1"
---
# biomedtra-small for QA
This model was trained as part of the "Extractive QA Biomedicine" project developed during the 2022 [Hackathon](https://somosnlp.org/hackathon) organized by SOMOS NLP.
## Motivation
Recent research has made available Spanish Language Models trained on Biomedical corpus. This project explores the use of these new models to generate extractive Question Answering models for Biomedicine, and compares their effectiveness with general masked language models.
The models trained during the [Hackathon](https://somosnlp.org/hackathon) were:
[hackathon-pln-es/roberta-base-bne-squad2-es](https://huggingface.co/hackathon-pln-es/roberta-base-bne-squad2-es)
[hackathon-pln-es/roberta-base-biomedical-clinical-es-squad2-es](https://huggingface.co/hackathon-pln-es/roberta-base-biomedical-clinical-es-squad2-es)
[hackathon-pln-es/roberta-base-biomedical-es-squad2-es](https://huggingface.co/hackathon-pln-es/roberta-base-biomedical-es-squad2-es)
[hackathon-pln-es/biomedtra-small-es-squad2-es](https://huggingface.co/hackathon-pln-es/biomedtra-small-es-squad2-es)
## Description
This model is a fine-tuned version of [mrm8488/biomedtra-small-es](https://huggingface.co/mrm8488/biomedtra-small-es) on the [squad_es (v2)](https://huggingface.co/datasets/squad_es) training dataset.
## Hyperparameters
The hyperparameters were chosen based on those used in [deepset/electra-base-squad2](https://huggingface.co/deepset/electra-base-squad2), an english-based model trained for similar purposes
```
--num_train_epochs 10 \
--learning_rate 1e-4 \
--max_seq_length 384 \
--doc_stride 128 \
```
## Performance
Evaluated on the [hackathon-pln-es/biomed_squad_es_v2](https://huggingface.co/datasets/hackathon-pln-es/biomed_squad_es_v2) dev set.
|Model |Base Model Domain|exact |f1 |HasAns_exact|HasAns_f1|NoAns_exact|NoAns_f1|
|--------------------------------------------------------------|-----------------|-------|-------|------------|---------|-----------|--------|
|hackathon-pln-es/roberta-base-bne-squad2-es |General |67.6341|75.6988|53.7367 |70.0526 |81.2174 |81.2174 |
|hackathon-pln-es/roberta-base-biomedical-clinical-es-squad2-es|Biomedical |66.8426|75.2346|53.0249 |70.0031 |80.3478 |80.3478 |
|hackathon-pln-es/roberta-base-biomedical-es-squad2-es |Biomedical |67.6341|74.5612|47.6868 |61.7012 |87.1304 | 87.1304|
|hackathon-pln-es/biomedtra-small-es-squad2-es |Biomedical |34.4767|44.3294|45.3737 |65.307 |23.8261 |23.8261 |
## Team
Santiago Maximo: [smaximo](https://huggingface.co/smaximo)
|
jsunster/distilbert-base-uncased-finetuned-squad
|
jsunster
| 2022-04-03T14:46:14Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-04-03T13:02:11Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1476
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.2823 | 1.0 | 2767 | 1.1980 |
| 1.0336 | 2.0 | 5534 | 1.1334 |
| 0.8513 | 3.0 | 8301 | 1.1476 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0
- Datasets 2.0.0
- Tokenizers 0.11.6
|
alefiury/wav2vec2-xls-r-300m-pt-br-spontaneous-speech-emotion-recognition
|
alefiury
| 2022-04-03T12:38:09Z | 66 | 6 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"audio-classification",
"audio",
"speech",
"pt",
"portuguese-speech-corpus",
"italian-speech-corpus",
"english-speech-corpus",
"arabic-speech-corpus",
"spontaneous",
"PyTorch",
"dataset:coraa_ser",
"dataset:emovo",
"dataset:ravdess",
"dataset:baved",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2022-03-23T15:29:36Z |
---
language: pt
datasets:
- coraa_ser
- emovo
- ravdess
- baved
metrics:
- f1
tags:
- audio
- speech
- wav2vec2
- pt
- portuguese-speech-corpus
- italian-speech-corpus
- english-speech-corpus
- arabic-speech-corpus
- spontaneous
- speech
- PyTorch
license: apache-2.0
model_index:
name: wav2vec2-xls-r-300m-pt-br-spontaneous-speech-emotion-recognition
results:
metrics:
- name: Test Macro F1-Score
type: f1
value: 81.87%
---
# Wav2vec 2.0 XLS-R For Spontaneous Speech Emotion Recognition
This is the model that got first place in the SER track of the Automatic Speech Recognition for spontaneous and prepared speech & Speech Emotion Recognition in Portuguese (SE&R 2022) Workshop.
The following datasets were used in the training:
- [CORAA SER v1.0](https://github.com/rmarcacini/ser-coraa-pt-br/): a dataset composed of spontaneous portuguese speech and approximately 40 minutes of audio segments labeled in three classes: neutral, non-neutral female, and non-neutral male.
- [EMOVO Corpus](https://aclanthology.org/L14-1478/): a database of emotional speech for the Italian language, built from the voices of up to 6 actors who played 14 sentences simulating 6 emotional states (disgust, fear, anger, joy, surprise, sadness) plus the neutral state.
- [RAVDESS](https://zenodo.org/record/1188976#.YO6yI-gzaUk): a dataset that provides 1440 samples of recordings from actors performing on 8 different emotions in English, which are: angry, calm, disgust, fearful, happy, neutral, sad and surprised.
- [BAVED](https://github.com/40uf411/Basic-Arabic-Vocal-Emotions-Dataset): a collection of audio recordings of Arabic words spoken with varying degrees of emotion. The dataset contains seven words: like, unlike, this, file, good, neutral, and bad, which are spoken at three emotional levels: low emotion (tired or feeling down), neutral emotion (the way the speaker speaks daily), and high emotion (positive or negative emotions such as happiness, joy, sadness, anger).
The test set used is a part of the CORAA SER v1.0 that has been set aside for this purpose.
It achieves the following results on the test set:
- Accuracy: 0.9090
- Macro Precision: 0.8171
- Macro Recall: 0.8397
- Macro F1-Score: 0.8187
## Datasets Details
The following image shows the overall distribution of the datasets:
The following image shows the number of instances by label:
## Repository
The repository that implements the model to be trained and tested is avaible [here](https://github.com/alefiury/SE-R-2022-SER-Track).
|
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
|
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.
|
Zohar/distilgpt2-finetuned-restaurant-reviews-clean
|
Zohar
| 2022-04-03T10:29:27Z | 3 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-04-03T07:25:35Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-restaurant-reviews-clean
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. -->
# distilgpt2-finetuned-restaurant-reviews-clean
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5371
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.7221 | 1.0 | 2447 | 3.5979 |
| 3.6413 | 2.0 | 4894 | 3.5505 |
| 3.6076 | 3.0 | 7341 | 3.5371 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.11.0
|
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 )
|
huggingtweets/stephencurry30
|
huggingtweets
| 2022-04-02T22:43:52Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: http://www.huggingtweets.com/stephencurry30/1648939428122/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/1484233608793518081/tOID8aXq_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">Stephen Curry</div>
<div style="text-align: center; font-size: 14px;">@stephencurry30</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Stephen Curry.
| Data | Stephen Curry |
| --- | --- |
| Tweets downloaded | 3190 |
| Retweets | 384 |
| Short tweets | 698 |
| Tweets kept | 2108 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2n8n86da/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 @stephencurry30's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/24mjh4p6) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/24mjh4p6/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/stephencurry30')
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)
|
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-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
|
celine98/canine-c-finetuned-sst2
|
celine98
| 2022-04-02T19:11:13Z | 10 | 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
| 2022-03-24T14:40:39Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: canine-c-finetuned-sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: sst2
metrics:
- name: Accuracy
type: accuracy
value: 0.8486238532110092
---
<!-- 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-sst2
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: 0.6025
- Accuracy: 0.8486
## 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: 4.9121586874695155e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3415 | 1.0 | 2105 | 0.4196 | 0.8280 |
| 0.2265 | 2.0 | 4210 | 0.4924 | 0.8211 |
| 0.1439 | 3.0 | 6315 | 0.5726 | 0.8337 |
| 0.0974 | 4.0 | 8420 | 0.6025 | 0.8486 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
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/finetuned-vit-base-patch16-224-upside-down-detector
|
jaygala24
| 2022-04-02T15:24:57Z | 79 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"accelerator",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-04-02T08:42:45Z |
---
license: apache-2.0
tags:
- accelerator
metrics:
- accuracy
model-index:
- name: finetuned-vit-base-patch16-224-upside-down-detector
results: []
widget:
- src: https://huggingface.co/jaygala24/finetuned-vit-base-patch16-224-upside-down-detector/resolve/main/original.jpg
example_title: original
- src: https://huggingface.co/jaygala24/finetuned-vit-base-patch16-224-upside-down-detector/resolve/main/upside_down.jpg
example_title: upside_down
---
# finetuned-vit-base-patch16-224-upside-down-detector
This model is a fine-tuned version of [vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the custom image orientation dataset adapted from the [beans](https://huggingface.co/datasets/beans) dataset. It achieves the following results on the evaluation set:
- Accuracy: 0.8947
## Training and evaluation data
The custom dataset for image orientation adapted from [beans](https://huggingface.co/datasets/beans) dataset contains a total of 2,590 image samples with 1,295 original and 1,295 upside down. The model was fine-tuned on the train subset and evaluated on validation and test subsets. The dataset splits are listed below:
| Split | # examples |
|:----------:|:----------:|
| train | 2068 |
| validation | 133 |
| test | 128 |
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-04
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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: 32
- num_epochs: 5
### Training results
| Epoch | Accuracy |
|:----------:|:----------:|
| 0 | 0.8609 |
| 1 | 0.8835 |
| 2 | 0.8571 |
| 3 | 0.8941 |
| 4 | 0.8941 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.9.0+cu111
- Pytorch/XLA 1.9
- 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
|
notexist/ttt2
|
notexist
| 2022-04-02T15:09:26Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-04-02T14:54:08Z |
---
license: apache-2.0
---
|
jingwei001/distilgpt2-finetuned-wikitext2
|
jingwei001
| 2022-04-02T14:40:16Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-04-02T04:36:03Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6432
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.7607 | 1.0 | 2334 | 3.6664 |
| 3.6323 | 2.0 | 4668 | 3.6461 |
| 3.6075 | 3.0 | 7002 | 3.6432 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
ntt123/hifigan_ljs_24k
|
ntt123
| 2022-04-02T14:20:55Z | 0 | 0 | null |
[
"tensorboard",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-03-28T16:32:11Z |
---
license: cc-by-nc-4.0
---
|
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)
```
|
unjustify/autotrain-commonsense_1-696121179
|
unjustify
| 2022-04-02T13:49:28Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain",
"en",
"dataset:unjustify/autotrain-data-commonsense_1",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-02T13:45:27Z |
---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- unjustify/autotrain-data-commonsense_1
co2_eq_emissions: 4.355285184457145
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 696121179
- CO2 Emissions (in grams): 4.355285184457145
## Validation Metrics
- Loss: 0.34467628598213196
- Accuracy: 0.8544333807491702
- Precision: 0.9014251781472684
- Recall: 0.7721261444557477
- AUC: 0.9422766967397805
- F1: 0.8317808219178082
## 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/unjustify/autotrain-commonsense_1-696121179
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("unjustify/autotrain-commonsense_1-696121179", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("unjustify/autotrain-commonsense_1-696121179", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
yaswanth/distilbert-base-uncased_fakenews_identification
|
yaswanth
| 2022-04-02T13:18:07Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-31T06:10:15Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased_fakenews_identification
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_fakenews_identification
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the below dataset.
https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset
It achieves the following results on the evaluation set:
- Loss: 0.0059
- Accuracy: 0.999
- F1: 0.9990
## Label Description
LABEL_0 - Fake News
LABEL_1 - Real News
## 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.0014 | 1.0 | 1000 | 0.0208 | 0.9965 | 0.9965 |
| 0.0006 | 2.0 | 2000 | 0.0041 | 0.9994 | 0.9994 |
| 0.0006 | 3.0 | 3000 | 0.0044 | 0.9992 | 0.9993 |
| 0.0 | 4.0 | 4000 | 0.0059 | 0.999 | 0.9990 |
### Framework versions
- Transformers 4.16.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
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.*
|
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