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timestamp[us, tz=UTC]date 2020-02-15 11:33:14
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| library_name
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fabianmmueller/deep-haiku-gpt-j-6b-8bit
|
fabianmmueller
| 2022-06-13T15:40:47Z | 7 | 3 |
transformers
|
[
"transformers",
"pytorch",
"gptj",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-10T10:10:18Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: deep-haiku-gpt-j-6b-8bit
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. -->
# deep-haiku-gpt-j-6b-8bit
This model is a fine-tuned version of [gpt-j-6B-8bit](https://huggingface.co/hivemind/gpt-j-6B-8bit) on the [haiku](https://huggingface.co/datasets/statworx/haiku) dataset.
## Model description
The model is a fine-tuned version of GPT-J-6B-8Bit for generation of [Haikus](https://en.wikipedia.org/wiki/Haiku). The model, data and training procedure is inspired by a [blog post by Robert A. Gonsalves](https://towardsdatascience.com/deep-haiku-teaching-gpt-j-to-compose-with-syllable-patterns-5234bca9701).
We used the same multitask training approach as in der post, but significantly extended the dataset (almost double the size of the original one). A prepared version of the dataset can be found [here](https://huggingface.co/datasets/statworx/haiku).
## Intended uses & limitations
The model is intended to generate Haikus. To do so, it was trained using a multitask learning approach (see [Caruana 1997](http://www.cs.cornell.edu/~caruana/mlj97.pdf)) with the following four different tasks: :
- topic2graphemes `(keywords = text)`
- topic2phonemes `<keyword_phonemes = text_phonemes>`
- graphemes2phonemes `[text = text_phonemes]`
- phonemes2graphemes `{text_phonemes = text}`
To use the model, use an appropriate prompt like `"(dog rain ="` and let the model generate a Haiku given the keyword.
## Training and evaluation data
We used a collection of existing haikus for training. Furthermore, all haikus were used in their graphemes version as well as a phonemes version. In addition, we extracted key word for all haikus using [KeyBERT](https://github.com/MaartenGr/KeyBERT) and sorted out haikus with a low text quality according to the [GRUEN score](https://github.com/WanzhengZhu/GRUEN).
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 10
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu102
- Datasets 2.2.1
- Tokenizers 0.12.1
|
huggingtweets/salgotrader
|
huggingtweets
| 2022-06-13T14:46:27Z | 107 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-13T14:45:34Z |
---
language: en
thumbnail: http://www.huggingtweets.com/salgotrader/1655131582645/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/1521075169611112448/S_w82Ewg_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">0xPatrician.eth</div>
<div style="text-align: center; font-size: 14px;">@salgotrader</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 0xPatrician.eth.
| Data | 0xPatrician.eth |
| --- | --- |
| Tweets downloaded | 910 |
| Retweets | 250 |
| Short tweets | 84 |
| Tweets kept | 576 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2f275xqv/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 @salgotrader's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ljt0uhcw) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ljt0uhcw/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/salgotrader')
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)
|
Andrey1989/xlmr-finetuned-ner
|
Andrey1989
| 2022-06-13T14:03:57Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:wikiann",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-13T13:23:58Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlmr-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wikiann
type: wikiann
args: et
metrics:
- name: Precision
type: precision
value: 0.9044097027481772
- name: Recall
type: recall
value: 0.9136978539556626
- name: F1
type: f1
value: 0.9090300532008596
- name: Accuracy
type: accuracy
value: 0.9649304793632428
---
<!-- 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. -->
# xlmr-finetuned-ner
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1395
- Precision: 0.9044
- Recall: 0.9137
- F1: 0.9090
- Accuracy: 0.9649
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4215 | 1.0 | 938 | 0.1650 | 0.8822 | 0.8781 | 0.8802 | 0.9529 |
| 0.1559 | 2.0 | 1876 | 0.1412 | 0.9018 | 0.9071 | 0.9045 | 0.9631 |
| 0.1051 | 3.0 | 2814 | 0.1395 | 0.9044 | 0.9137 | 0.9090 | 0.9649 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Shenghao1993/xlm-roberta-base-finetuned-panx-all
|
Shenghao1993
| 2022-06-13T13:45:41Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-13T13:21:03Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-all
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. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1719
- F1: 0.8544
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2951 | 1.0 | 835 | 0.1882 | 0.8171 |
| 0.1547 | 2.0 | 1670 | 0.1707 | 0.8454 |
| 0.1018 | 3.0 | 2505 | 0.1719 | 0.8544 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
optimum/distilbert-base-uncased-finetuned-sst-2-english
|
optimum
| 2022-06-13T13:43:16Z | 28,187 | 2 |
transformers
|
[
"transformers",
"onnx",
"text-classification",
"en",
"dataset:sst2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-24T16:06:17Z |
---
language: en
license: apache-2.0
datasets:
- sst2
---
# ONNX convert DistilBERT base uncased finetuned SST-2
## Conversion of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english)
This model is a fine-tune checkpoint of [DistilBERT-base-uncased](https://huggingface.co/distilbert-base-uncased), fine-tuned on SST-2.
This model reaches an accuracy of 91.3 on the dev set (for comparison, Bert bert-base-uncased version reaches an accuracy of 92.7).
For more details about DistilBERT, we encourage users to check out [this model card](https://huggingface.co/distilbert-base-uncased).
# Fine-tuning hyper-parameters
- learning_rate = 1e-5
- batch_size = 32
- warmup = 600
- max_seq_length = 128
- num_train_epochs = 3.0
# Bias
Based on a few experimentations, we observed that this model could produce biased predictions that target underrepresented populations.
For instance, for sentences like `This film was filmed in COUNTRY`, this binary classification model will give radically different probabilities for the positive label depending on the country (0.89 if the country is France, but 0.08 if the country is Afghanistan) when nothing in the input indicates such a strong semantic shift. In this [colab](https://colab.research.google.com/gist/ageron/fb2f64fb145b4bc7c49efc97e5f114d3/biasmap.ipynb), [Aurélien Géron](https://twitter.com/aureliengeron) made an interesting map plotting these probabilities for each country.
<img src="https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/map.jpeg" alt="Map of positive probabilities per country." width="500"/>
We strongly advise users to thoroughly probe these aspects on their use-cases in order to evaluate the risks of this model. We recommend looking at the following bias evaluation datasets as a place to start: [WinoBias](https://huggingface.co/datasets/wino_bias), [WinoGender](https://huggingface.co/datasets/super_glue), [Stereoset](https://huggingface.co/datasets/stereoset).
|
zoha/wav2vec2-base-librispeech100h-google-colab
|
zoha
| 2022-06-13T13:39:58Z | 77 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-06-11T17:07:29Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-librispeech100h-google-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-librispeech100h-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1156
- Wer: 0.0756
## 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: 16
- 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: 500
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.6033 | 0.9 | 1600 | 0.4802 | 0.2728 |
| 0.1912 | 1.79 | 3200 | 0.1601 | 0.1140 |
| 0.1409 | 2.69 | 4800 | 0.1423 | 0.0932 |
| 0.108 | 3.59 | 6400 | 0.1260 | 0.0806 |
| 0.1045 | 4.48 | 8000 | 0.1156 | 0.0756 |
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.2.3.dev0
- Tokenizers 0.12.1
|
Shenghao1993/xlm-roberta-base-finetuned-panx-en
|
Shenghao1993
| 2022-06-13T13:20:02Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-13T13:02:56Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-en
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.en
metrics:
- name: F1
type: f1
value: 0.7032474804031354
---
<!-- 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-en
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: 0.3859
- F1: 0.7032
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.0494 | 1.0 | 50 | 0.5464 | 0.5507 |
| 0.5329 | 2.0 | 100 | 0.4217 | 0.6715 |
| 0.3799 | 3.0 | 150 | 0.3859 | 0.7032 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Shenghao1993/xlm-roberta-base-finetuned-panx-it
|
Shenghao1993
| 2022-06-13T13:02:39Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-13T12:44:36Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-it
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.it
metrics:
- name: F1
type: f1
value: 0.8358085808580858
---
<!-- 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-it
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: 0.2403
- F1: 0.8358
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.7053 | 1.0 | 70 | 0.3077 | 0.7587 |
| 0.2839 | 2.0 | 140 | 0.2692 | 0.8007 |
| 0.1894 | 3.0 | 210 | 0.2403 | 0.8358 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Shenghao1993/xlm-roberta-base-finetuned-panx-fr
|
Shenghao1993
| 2022-06-13T12:44:17Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-13T12:25:56Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-fr
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.fr
metrics:
- name: F1
type: f1
value: 0.8438448566610455
---
<!-- 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-fr
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: 0.2675
- F1: 0.8438
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5606 | 1.0 | 191 | 0.3157 | 0.7967 |
| 0.2755 | 2.0 | 382 | 0.2684 | 0.8288 |
| 0.1811 | 3.0 | 573 | 0.2675 | 0.8438 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Shenghao1993/xlm-roberta-base-finetuned-panx-de-fr
|
Shenghao1993
| 2022-06-13T11:59:43Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-13T11:52:07Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de-fr
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. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1623
- F1: 0.8596
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2865 | 1.0 | 715 | 0.1981 | 0.8167 |
| 0.1484 | 2.0 | 1430 | 0.1595 | 0.8486 |
| 0.0949 | 3.0 | 2145 | 0.1623 | 0.8596 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Priya9/wav2vec2-large-xls-r-300m-turkish-colab
|
Priya9
| 2022-06-13T11:18:47Z | 104 | 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
| 2022-06-13T04:46:15Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-turkish-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-turkish-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3859
- Wer: 0.4680
## 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: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.8707 | 3.67 | 400 | 0.6588 | 0.7110 |
| 0.3955 | 7.34 | 800 | 0.3859 | 0.4680 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
Satyamatury/wav2vec2-large-xls-r-300m-hindi-colab
|
Satyamatury
| 2022-06-13T11:08:04Z | 103 | 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
| 2022-05-27T16:29:37Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-hindi-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-hindi-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7529
- Wer: 0.9130
## 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: 60
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.2923 | 44.42 | 400 | 1.7529 | 0.9130 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
|
zoha/wav2vec2-base-timit-google-colab
|
zoha
| 2022-06-13T10:50:12Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-06-13T06:02:13Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-google-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4659
- Wer: 0.3080
## 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: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.5787 | 0.87 | 500 | 1.7648 | 1.0305 |
| 0.8692 | 1.73 | 1000 | 0.5136 | 0.5103 |
| 0.4346 | 2.6 | 1500 | 0.4364 | 0.4515 |
| 0.31 | 3.46 | 2000 | 0.3889 | 0.4070 |
| 0.234 | 4.33 | 2500 | 0.4161 | 0.3863 |
| 0.2054 | 5.19 | 3000 | 0.3845 | 0.3722 |
| 0.165 | 6.06 | 3500 | 0.4035 | 0.3643 |
| 0.1436 | 6.92 | 4000 | 0.4090 | 0.3623 |
| 0.1381 | 7.79 | 4500 | 0.4007 | 0.3673 |
| 0.1175 | 8.65 | 5000 | 0.4588 | 0.3632 |
| 0.1052 | 9.52 | 5500 | 0.4441 | 0.3588 |
| 0.0988 | 10.38 | 6000 | 0.4133 | 0.3489 |
| 0.0877 | 11.25 | 6500 | 0.4758 | 0.3510 |
| 0.0856 | 12.11 | 7000 | 0.4454 | 0.3425 |
| 0.0731 | 12.98 | 7500 | 0.4252 | 0.3351 |
| 0.0712 | 13.84 | 8000 | 0.4163 | 0.3370 |
| 0.0711 | 14.71 | 8500 | 0.4166 | 0.3367 |
| 0.06 | 15.57 | 9000 | 0.4195 | 0.3347 |
| 0.0588 | 16.44 | 9500 | 0.4697 | 0.3367 |
| 0.0497 | 17.3 | 10000 | 0.4255 | 0.3314 |
| 0.0523 | 18.17 | 10500 | 0.4676 | 0.3307 |
| 0.0444 | 19.03 | 11000 | 0.4570 | 0.3244 |
| 0.0435 | 19.9 | 11500 | 0.4307 | 0.3243 |
| 0.0348 | 20.76 | 12000 | 0.4763 | 0.3245 |
| 0.036 | 21.63 | 12500 | 0.4635 | 0.3238 |
| 0.0347 | 22.49 | 13000 | 0.4602 | 0.3212 |
| 0.0333 | 23.36 | 13500 | 0.4472 | 0.3195 |
| 0.0311 | 24.22 | 14000 | 0.4449 | 0.3183 |
| 0.0294 | 25.09 | 14500 | 0.4631 | 0.3175 |
| 0.025 | 25.95 | 15000 | 0.4466 | 0.3164 |
| 0.023 | 26.82 | 15500 | 0.4581 | 0.3138 |
| 0.0216 | 27.68 | 16000 | 0.4665 | 0.3114 |
| 0.0198 | 28.55 | 16500 | 0.4590 | 0.3092 |
| 0.0181 | 29.41 | 17000 | 0.4659 | 0.3080 |
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
|
0xrushi/dqn-SpaceInvadersNoFrameskip-v4
|
0xrushi
| 2022-06-13T10:39:05Z | 7 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-13T10:38:33Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 892.00 +/- 340.52
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga rushic24 -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga rushic24
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
antonioricciardi/q-FrozenLake-v1-4x4-noSlippery
|
antonioricciardi
| 2022-06-13T10:13:44Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-13T10:13:37Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="antonioricciardi/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
huggingtweets/ruinsman
|
huggingtweets
| 2022-06-13T09:33:18Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-13T09:13:47Z |
---
language: en
thumbnail: http://www.huggingtweets.com/ruinsman/1655112758889/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/1428391928110911499/qWeZuRbL_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">ManAmongTheRuins</div>
<div style="text-align: center; font-size: 14px;">@ruinsman</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 ManAmongTheRuins.
| Data | ManAmongTheRuins |
| --- | --- |
| Tweets downloaded | 3184 |
| Retweets | 424 |
| Short tweets | 213 |
| Tweets kept | 2547 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3evn1l2w/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 @ruinsman's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/apc372yb) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/apc372yb/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/ruinsman')
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)
|
anvayS/reddit-aita-classifier
|
anvayS
| 2022-06-13T09:08:26Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-13T07:47:08Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: reddit-aita-classifier
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. -->
# reddit-aita-classifier
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1667
- Accuracy: 0.9497
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5866 | 1.0 | 1250 | 0.5692 | 0.7247 |
| 0.5638 | 2.0 | 2500 | 0.4841 | 0.7813 |
| 0.4652 | 3.0 | 3750 | 0.2712 | 0.9077 |
| 0.3088 | 4.0 | 5000 | 0.1667 | 0.9497 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
masifayub/autotrain-PAN-976832386
|
masifayub
| 2022-06-13T08:05:59Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain",
"en",
"dataset:masifayub/autotrain-data-PAN",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-13T08:02:19Z |
---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- masifayub/autotrain-data-PAN
co2_eq_emissions: 7.17945527948844
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 976832386
- CO2 Emissions (in grams): 7.17945527948844
## Validation Metrics
- Loss: 0.4892439842224121
- Accuracy: 0.7591666666666667
- Precision: 0.8088978766430738
- Recall: 0.8888888888888888
- AUC: 0.7550212962962962
- F1: 0.8470089994706194
## 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/masifayub/autotrain-PAN-976832386
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("masifayub/autotrain-PAN-976832386", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("masifayub/autotrain-PAN-976832386", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
huggingtweets/demondicekaren
|
huggingtweets
| 2022-06-13T07:19:24Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-13T07:18:24Z |
---
language: en
thumbnail: http://www.huggingtweets.com/demondicekaren/1655104759793/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/1488027988075507712/FTIBkQRn_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">😈🎲 || DEMONDICE</div>
<div style="text-align: center; font-size: 14px;">@demondicekaren</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 😈🎲 || DEMONDICE.
| Data | 😈🎲 || DEMONDICE |
| --- | --- |
| Tweets downloaded | 3246 |
| Retweets | 371 |
| Short tweets | 617 |
| Tweets kept | 2258 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3fxxzewl/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 @demondicekaren's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2ow01rap) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2ow01rap/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/demondicekaren')
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)
|
zoha/wav2vec2-base-timit-demo-google-colab
|
zoha
| 2022-06-13T06:01:58Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-05-18T13:11:37Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-google-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5035
- Wer: 0.3346
## 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: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.1411 | 1.0 | 500 | 0.6675 | 0.6001 |
| 0.5668 | 2.01 | 1000 | 0.4699 | 0.4973 |
| 0.3773 | 3.01 | 1500 | 0.4475 | 0.4403 |
| 0.2696 | 4.02 | 2000 | 0.4162 | 0.4166 |
| 0.2165 | 5.02 | 2500 | 0.3809 | 0.4011 |
| 0.1849 | 6.02 | 3000 | 0.4120 | 0.3849 |
| 0.1542 | 7.03 | 3500 | 0.4436 | 0.3770 |
| 0.1385 | 8.03 | 4000 | 0.3977 | 0.3732 |
| 0.1224 | 9.04 | 4500 | 0.4530 | 0.3672 |
| 0.1233 | 10.04 | 5000 | 0.3949 | 0.3596 |
| 0.1078 | 11.04 | 5500 | 0.4616 | 0.3539 |
| 0.097 | 12.05 | 6000 | 0.4354 | 0.3697 |
| 0.0821 | 13.05 | 6500 | 0.4370 | 0.3643 |
| 0.0724 | 14.06 | 7000 | 0.4729 | 0.3587 |
| 0.0678 | 15.06 | 7500 | 0.5822 | 0.3742 |
| 0.0632 | 16.06 | 8000 | 0.4460 | 0.3513 |
| 0.0627 | 17.07 | 8500 | 0.5531 | 0.3537 |
| 0.0574 | 18.07 | 9000 | 0.5262 | 0.3575 |
| 0.0515 | 19.08 | 9500 | 0.4794 | 0.3488 |
| 0.0475 | 20.08 | 10000 | 0.4941 | 0.3458 |
| 0.0463 | 21.08 | 10500 | 0.4741 | 0.3377 |
| 0.0392 | 22.09 | 11000 | 0.5390 | 0.3381 |
| 0.0401 | 23.09 | 11500 | 0.4984 | 0.3413 |
| 0.0371 | 24.1 | 12000 | 0.5112 | 0.3460 |
| 0.0305 | 25.1 | 12500 | 0.5255 | 0.3418 |
| 0.0278 | 26.1 | 13000 | 0.5045 | 0.3389 |
| 0.0265 | 27.11 | 13500 | 0.4990 | 0.3371 |
| 0.0248 | 28.11 | 14000 | 0.5242 | 0.3362 |
| 0.0249 | 29.12 | 14500 | 0.5035 | 0.3346 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
|
huggingtweets/elonmusk-jack
|
huggingtweets
| 2022-06-13T04:16:05Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-13T04:15:18Z |
---
language: en
thumbnail: http://www.huggingtweets.com/elonmusk-jack/1655093760817/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/1529956155937759233/Nyn1HZWF_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1115644092329758721/AFjOr-K8_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Elon Musk & jack</div>
<div style="text-align: center; font-size: 14px;">@elonmusk-jack</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 Elon Musk & jack.
| Data | Elon Musk | jack |
| --- | --- | --- |
| Tweets downloaded | 3200 | 3232 |
| Retweets | 147 | 1137 |
| Short tweets | 959 | 832 |
| Tweets kept | 2094 | 1263 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2zwk8y4o/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 @elonmusk-jack's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/16z5871k) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/16z5871k/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/elonmusk-jack')
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)
|
rx-gkit/teset
|
rx-gkit
| 2022-06-13T00:41:05Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2022-06-13T00:31:05Z |
---
license: apache-2.0
---
# Convert Text to Binary
Open **bin.js** and run it with main HTML file.
```html
<script src="./bin.js"></script>
```
|
TencentMedicalNet/MedicalNet-Resnet101
|
TencentMedicalNet
| 2022-06-12T23:17:54Z | 0 | 2 | null |
[
"MedicalNet",
"medical images",
"medical",
"3D",
"Med3D",
"en",
"dataset:MRBrainS18",
"arxiv:1904.00625",
"license:mit",
"region:us"
] | null | 2022-06-12T00:52:14Z |
---
license: mit
datasets:
- MRBrainS18
language:
- en
metrics:
-
tags:
- MedicalNet
- medical images
- medical
- 3D
- Med3D
thumbnail: "https://github.com/Tencent/MedicalNet/blob/master/images/logo.png?raw=true"
---
# MedicalNet
This repository contains a Pytorch implementation of [Med3D: Transfer Learning for 3D Medical Image Analysis](https://arxiv.org/abs/1904.00625).
Many studies have shown that the performance on deep learning is significantly affected by volume of training data. The MedicalNet project aggregated the dataset with diverse modalities, target organs, and pathologies to to build relatively large datasets. Based on this dataset, a series of 3D-ResNet pre-trained models and corresponding transfer-learning training code are provided.
### License
MedicalNet is released under the MIT License (refer to the LICENSE file for detailso).
### Citing MedicalNet
If you use this code or pre-trained models, please cite the following:
```
@article{chen2019med3d,
title={Med3D: Transfer Learning for 3D Medical Image Analysis},
author={Chen, Sihong and Ma, Kai and Zheng, Yefeng},
journal={arXiv preprint arXiv:1904.00625},
year={2019}
}
```
### Update(2019/07/30)
We uploaded 4 pre-trained models based on more datasets (23 datasets).
```
Model name : parameters settings
resnet_10_23dataset.pth: --model resnet --model_depth 10 --resnet_shortcut B
resnet_18_23dataset.pth: --model resnet --model_depth 18 --resnet_shortcut A
resnet_34_23dataset.pth: --model resnet --model_depth 34 --resnet_shortcut A
resnet_50_23dataset.pth: --model resnet --model_depth 50 --resnet_shortcut B
```
Hugging Face repository contribution by:
[Rafael Zimmer](https://www.github.com/rzimmerdev)
|
TencentMedicalNet/MedicalNet-Resnet50
|
TencentMedicalNet
| 2022-06-12T23:17:39Z | 0 | 0 | null |
[
"MedicalNet",
"medical images",
"medical",
"3D",
"Med3D",
"en",
"dataset:MRBrainS18",
"arxiv:1904.00625",
"license:mit",
"region:us"
] | null | 2022-06-12T00:35:55Z |
---
license: mit
datasets:
- MRBrainS18
language:
- en
metrics:
-
tags:
- MedicalNet
- medical images
- medical
- 3D
- Med3D
thumbnail: "https://github.com/Tencent/MedicalNet/blob/master/images/logo.png?raw=true"
---
# MedicalNet
This repository contains a Pytorch implementation of [Med3D: Transfer Learning for 3D Medical Image Analysis](https://arxiv.org/abs/1904.00625).
Many studies have shown that the performance on deep learning is significantly affected by volume of training data. The MedicalNet project aggregated the dataset with diverse modalities, target organs, and pathologies to to build relatively large datasets. Based on this dataset, a series of 3D-ResNet pre-trained models and corresponding transfer-learning training code are provided.
### License
MedicalNet is released under the MIT License (refer to the LICENSE file for detailso).
### Citing MedicalNet
If you use this code or pre-trained models, please cite the following:
```
@article{chen2019med3d,
title={Med3D: Transfer Learning for 3D Medical Image Analysis},
author={Chen, Sihong and Ma, Kai and Zheng, Yefeng},
journal={arXiv preprint arXiv:1904.00625},
year={2019}
}
```
### Update(2019/07/30)
We uploaded 4 pre-trained models based on more datasets (23 datasets).
```
Model name : parameters settings
resnet_10_23dataset.pth: --model resnet --model_depth 10 --resnet_shortcut B
resnet_18_23dataset.pth: --model resnet --model_depth 18 --resnet_shortcut A
resnet_34_23dataset.pth: --model resnet --model_depth 34 --resnet_shortcut A
resnet_50_23dataset.pth: --model resnet --model_depth 50 --resnet_shortcut B
```
Hugging Face repository contribution by:
[Rafael Zimmer](https://www.github.com/rzimmerdev)
|
C5i/SEAD-L-6_H-384_A-12-mnli
|
C5i
| 2022-06-12T22:59:51Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"SEAD",
"en",
"dataset:glue",
"dataset:mnli",
"arxiv:1910.01108",
"arxiv:1909.10351",
"arxiv:2002.10957",
"arxiv:1810.04805",
"arxiv:1804.07461",
"arxiv:1905.00537",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-12T22:59:29Z |
---
language:
- en
license: apache-2.0
tags:
- SEAD
datasets:
- glue
- mnli
---
## Paper
## [SEAD: SIMPLE ENSEMBLE AND KNOWLEDGE DISTILLATION FRAMEWORK FOR NATURAL LANGUAGE UNDERSTANDING](https://www.adasci.org/journals/lattice-35309407/?volumes=true&open=621a3b18edc4364e8a96cb63)
Aurthors: *Moyan Mei*, *Rohit Sroch*
## Abstract
With the widespread use of pre-trained language models (PLM), there has been increased research on how to make them applicable, especially in limited-resource or low latency high throughput scenarios. One of the dominant approaches is knowledge distillation (KD), where a smaller model is trained by receiving guidance from a large PLM. While there are many successful designs for learning knowledge from teachers, it remains unclear how students can learn better. Inspired by real university teaching processes, in this work we further explore knowledge distillation and propose a very simple yet effective framework, SEAD, to further improve task-specific generalization by utilizing multiple teachers. Our experiments show that SEAD leads to better performance compared to other popular KD methods [[1](https://arxiv.org/abs/1910.01108)] [[2](https://arxiv.org/abs/1909.10351)] [[3](https://arxiv.org/abs/2002.10957)] and achieves comparable or superior performance to its teacher model such as BERT [[4](https://arxiv.org/abs/1810.04805)] on total 13 tasks for the GLUE [[5](https://arxiv.org/abs/1804.07461)] and SuperGLUE [[6](https://arxiv.org/abs/1905.00537)] benchmarks.
*Moyan Mei and Rohit Sroch. 2022. [SEAD: Simple ensemble and knowledge distillation framework for natural language understanding](https://www.adasci.org/journals/lattice-35309407/?volumes=true&open=621a3b18edc4364e8a96cb63).
Lattice, THE MACHINE LEARNING JOURNAL by Association of Data Scientists, 3(1).*
## SEAD-L-6_H-384_A-12-mnli
This is a student model distilled from [**BERT base**](https://huggingface.co/bert-base-uncased) as teacher by using SEAD framework on **mnli** task. For weights initialization, we used [microsoft/xtremedistil-l6-h384-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h384-uncased)
## All SEAD Checkpoints
Other Community Checkpoints: [here](https://huggingface.co/models?search=SEAD)
## Intended uses & limitations
More information needed
### Training hyperparameters
Please take a look at the `training_args.bin` file
```python
$ import torch
$ hyperparameters = torch.load(os.path.join('training_args.bin'))
```
### Evaluation results
| eval_m-accuracy | eval_m-runtime | eval_m-samples_per_second | eval_m-steps_per_second | eval_m-loss | eval_m-samples | eval_mm-accuracy | eval_mm-runtime | eval_mm-samples_per_second | eval_mm-steps_per_second | eval_mm-loss | eval_mm-samples |
|:---------------:|:--------------:|:-------------------------:|:-----------------------:|:-----------:|:--------------:|:----------------:|:---------------:|:--------------------------:|:------------------------:|:------------:|:---------------:|
| 0.8495 | 6.5443 | 1499.776 | 46.911 | 0.4366 | 9815 | 0.8508 | 5.6975 | 1725.678 | 54.059 | 0.4252 | 9832 |
### Framework versions
- Transformers >=4.8.0
- Pytorch >=1.6.0
- TensorFlow >=2.5.0
- Flax >=0.3.5
- Datasets >=1.10.2
- Tokenizers >=0.11.6
If you use these models, please cite the following paper:
```
@article{article,
author={Mei, Moyan and Sroch, Rohit},
title={SEAD: Simple Ensemble and Knowledge Distillation Framework for Natural Language Understanding},
volume={3},
number={1},
journal={Lattice, The Machine Learning Journal by Association of Data Scientists},
day={26},
year={2022},
month={Feb},
url = {www.adasci.org/journals/lattice-35309407/?volumes=true&open=621a3b18edc4364e8a96cb63}
}
```
|
C5i/SEAD-L-6_H-256_A-8-mnli
|
C5i
| 2022-06-12T22:43:38Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"SEAD",
"en",
"dataset:glue",
"dataset:mnli",
"arxiv:1910.01108",
"arxiv:1909.10351",
"arxiv:2002.10957",
"arxiv:1810.04805",
"arxiv:1804.07461",
"arxiv:1905.00537",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-12T22:43:18Z |
---
language:
- en
license: apache-2.0
tags:
- SEAD
datasets:
- glue
- mnli
---
## Paper
## [SEAD: SIMPLE ENSEMBLE AND KNOWLEDGE DISTILLATION FRAMEWORK FOR NATURAL LANGUAGE UNDERSTANDING](https://www.adasci.org/journals/lattice-35309407/?volumes=true&open=621a3b18edc4364e8a96cb63)
Aurthors: *Moyan Mei*, *Rohit Sroch*
## Abstract
With the widespread use of pre-trained language models (PLM), there has been increased research on how to make them applicable, especially in limited-resource or low latency high throughput scenarios. One of the dominant approaches is knowledge distillation (KD), where a smaller model is trained by receiving guidance from a large PLM. While there are many successful designs for learning knowledge from teachers, it remains unclear how students can learn better. Inspired by real university teaching processes, in this work we further explore knowledge distillation and propose a very simple yet effective framework, SEAD, to further improve task-specific generalization by utilizing multiple teachers. Our experiments show that SEAD leads to better performance compared to other popular KD methods [[1](https://arxiv.org/abs/1910.01108)] [[2](https://arxiv.org/abs/1909.10351)] [[3](https://arxiv.org/abs/2002.10957)] and achieves comparable or superior performance to its teacher model such as BERT [[4](https://arxiv.org/abs/1810.04805)] on total 13 tasks for the GLUE [[5](https://arxiv.org/abs/1804.07461)] and SuperGLUE [[6](https://arxiv.org/abs/1905.00537)] benchmarks.
*Moyan Mei and Rohit Sroch. 2022. [SEAD: Simple ensemble and knowledge distillation framework for natural language understanding](https://www.adasci.org/journals/lattice-35309407/?volumes=true&open=621a3b18edc4364e8a96cb63).
Lattice, THE MACHINE LEARNING JOURNAL by Association of Data Scientists, 3(1).*
## SEAD-L-6_H-256_A-8-mnli
This is a student model distilled from [**BERT base**](https://huggingface.co/bert-base-uncased) as teacher by using SEAD framework on **mnli** task. For weights initialization, we used [microsoft/xtremedistil-l6-h256-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased)
## All SEAD Checkpoints
Other Community Checkpoints: [here](https://huggingface.co/models?search=SEAD)
## Intended uses & limitations
More information needed
### Training hyperparameters
Please take a look at the `training_args.bin` file
```python
$ import torch
$ hyperparameters = torch.load(os.path.join('training_args.bin'))
```
### Evaluation results
| eval_m-accuracy | eval_m-runtime | eval_m-samples_per_second | eval_m-steps_per_second | eval_m-loss | eval_m-samples | eval_mm-accuracy | eval_mm-runtime | eval_mm-samples_per_second | eval_mm-steps_per_second | eval_mm-loss | eval_mm-samples |
|:---------------:|:--------------:|:-------------------------:|:-----------------------:|:-----------:|:--------------:|:----------------:|:---------------:|:--------------------------:|:------------------------:|:------------:|:---------------:|
| 0.8277 | 6.4665 | 1517.828 | 47.476 | 0.6014 | 9815 | 0.8310 | 5.3528 | 1836.786 | 57.54 | 0.5724 | 9832 |
### Framework versions
- Transformers >=4.8.0
- Pytorch >=1.6.0
- TensorFlow >=2.5.0
- Flax >=0.3.5
- Datasets >=1.10.2
- Tokenizers >=0.11.6
If you use these models, please cite the following paper:
```
@article{article,
author={Mei, Moyan and Sroch, Rohit},
title={SEAD: Simple Ensemble and Knowledge Distillation Framework for Natural Language Understanding},
volume={3},
number={1},
journal={Lattice, The Machine Learning Journal by Association of Data Scientists},
day={26},
year={2022},
month={Feb},
url = {www.adasci.org/journals/lattice-35309407/?volumes=true&open=621a3b18edc4364e8a96cb63}
}
```
|
C5i/SEAD-L-6_H-384_A-12-qqp
|
C5i
| 2022-06-12T22:24:04Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"SEAD",
"en",
"dataset:glue",
"dataset:qqp",
"arxiv:1910.01108",
"arxiv:1909.10351",
"arxiv:2002.10957",
"arxiv:1810.04805",
"arxiv:1804.07461",
"arxiv:1905.00537",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-12T22:23:41Z |
---
language:
- en
license: apache-2.0
tags:
- SEAD
datasets:
- glue
- qqp
---
## Paper
## [SEAD: SIMPLE ENSEMBLE AND KNOWLEDGE DISTILLATION FRAMEWORK FOR NATURAL LANGUAGE UNDERSTANDING](https://www.adasci.org/journals/lattice-35309407/?volumes=true&open=621a3b18edc4364e8a96cb63)
Aurthors: *Moyan Mei*, *Rohit Sroch*
## Abstract
With the widespread use of pre-trained language models (PLM), there has been increased research on how to make them applicable, especially in limited-resource or low latency high throughput scenarios. One of the dominant approaches is knowledge distillation (KD), where a smaller model is trained by receiving guidance from a large PLM. While there are many successful designs for learning knowledge from teachers, it remains unclear how students can learn better. Inspired by real university teaching processes, in this work we further explore knowledge distillation and propose a very simple yet effective framework, SEAD, to further improve task-specific generalization by utilizing multiple teachers. Our experiments show that SEAD leads to better performance compared to other popular KD methods [[1](https://arxiv.org/abs/1910.01108)] [[2](https://arxiv.org/abs/1909.10351)] [[3](https://arxiv.org/abs/2002.10957)] and achieves comparable or superior performance to its teacher model such as BERT [[4](https://arxiv.org/abs/1810.04805)] on total 13 tasks for the GLUE [[5](https://arxiv.org/abs/1804.07461)] and SuperGLUE [[6](https://arxiv.org/abs/1905.00537)] benchmarks.
*Moyan Mei and Rohit Sroch. 2022. [SEAD: Simple ensemble and knowledge distillation framework for natural language understanding](https://www.adasci.org/journals/lattice-35309407/?volumes=true&open=621a3b18edc4364e8a96cb63).
Lattice, THE MACHINE LEARNING JOURNAL by Association of Data Scientists, 3(1).*
## SEAD-L-6_H-384_A-12-qqp
This is a student model distilled from [**BERT base**](https://huggingface.co/bert-base-uncased) as teacher by using SEAD framework on **qqp** task. For weights initialization, we used [microsoft/xtremedistil-l6-h384-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h384-uncased)
## All SEAD Checkpoints
Other Community Checkpoints: [here](https://huggingface.co/models?search=SEAD)
## Intended uses & limitations
More information needed
### Training hyperparameters
Please take a look at the `training_args.bin` file
```python
$ import torch
$ hyperparameters = torch.load(os.path.join('training_args.bin'))
```
### Evaluation results
| eval_accuracy | eval_f1 | eval_runtime | eval_samples_per_second | eval_steps_per_second | eval_loss | eval_samples |
|:-------------:|:-------:|:------------:|:-----------------------:|:---------------------:|:---------:|:------------:|
| 0.9126 | 0.8822 | 23.0122 | 1756.896 | 54.927 | 0.3389 | 40430 |
### Framework versions
- Transformers >=4.8.0
- Pytorch >=1.6.0
- TensorFlow >=2.5.0
- Flax >=0.3.5
- Datasets >=1.10.2
- Tokenizers >=0.11.6
If you use these models, please cite the following paper:
```
@article{article,
author={Mei, Moyan and Sroch, Rohit},
title={SEAD: Simple Ensemble and Knowledge Distillation Framework for Natural Language Understanding},
volume={3},
number={1},
journal={Lattice, The Machine Learning Journal by Association of Data Scientists},
day={26},
year={2022},
month={Feb},
url = {www.adasci.org/journals/lattice-35309407/?volumes=true&open=621a3b18edc4364e8a96cb63}
}
```
|
spuun/kekbot-beta-4-medium
|
spuun
| 2022-06-12T21:36:45Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"en",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-12T21:21:05Z |
---
language:
- en
tags:
- conversational
co2_eq_emissions:
emissions: "840"
source: "mlco2.github.io"
training_type: "fine-tuning"
geographical_location: "West Java, Indonesia"
hardware_used: "1 Tesla P100"
license: cc-by-nc-sa-4.0
widget:
- text: "Hey kekbot! What's up?"
example_title: "Asking what's up"
- text: "Hey kekbot! How r u?"
example_title: "Asking how he is"
---
> THIS MODEL IS IN PUBLIC BETA, PLEASE DO NOT EXPECT ANY FORM OF STABILITY IN ITS CURRENT STATE.
# Art Union server chatbot
Based on a DialoGPT-medium (`kekbot-beta-3-medium`) model, fine-tuned to a select subset (65k<= messages) of Art Union's general-chat channel chat history.
### Current issues
(Which hopefully will be fixed in future iterations) Include, but not limited to:
- Limited turns, after ~20 turns output may break for no apparent reason.
- Inconsistent variance, acts like an overfitted model from time to time for no reason whatsoever.
|
C5i/SEAD-L-6_H-256_A-8-stsb
|
C5i
| 2022-06-12T21:12:01Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"SEAD",
"en",
"dataset:glue",
"dataset:stsb",
"arxiv:1910.01108",
"arxiv:1909.10351",
"arxiv:2002.10957",
"arxiv:1810.04805",
"arxiv:1804.07461",
"arxiv:1905.00537",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-12T21:11:42Z |
---
language:
- en
license: apache-2.0
tags:
- SEAD
datasets:
- glue
- stsb
---
## Paper
## [SEAD: SIMPLE ENSEMBLE AND KNOWLEDGE DISTILLATION FRAMEWORK FOR NATURAL LANGUAGE UNDERSTANDING](https://www.adasci.org/journals/lattice-35309407/?volumes=true&open=621a3b18edc4364e8a96cb63)
Aurthors: *Moyan Mei*, *Rohit Sroch*
## Abstract
With the widespread use of pre-trained language models (PLM), there has been increased research on how to make them applicable, especially in limited-resource or low latency high throughput scenarios. One of the dominant approaches is knowledge distillation (KD), where a smaller model is trained by receiving guidance from a large PLM. While there are many successful designs for learning knowledge from teachers, it remains unclear how students can learn better. Inspired by real university teaching processes, in this work we further explore knowledge distillation and propose a very simple yet effective framework, SEAD, to further improve task-specific generalization by utilizing multiple teachers. Our experiments show that SEAD leads to better performance compared to other popular KD methods [[1](https://arxiv.org/abs/1910.01108)] [[2](https://arxiv.org/abs/1909.10351)] [[3](https://arxiv.org/abs/2002.10957)] and achieves comparable or superior performance to its teacher model such as BERT [[4](https://arxiv.org/abs/1810.04805)] on total 13 tasks for the GLUE [[5](https://arxiv.org/abs/1804.07461)] and SuperGLUE [[6](https://arxiv.org/abs/1905.00537)] benchmarks.
*Moyan Mei and Rohit Sroch. 2022. [SEAD: Simple ensemble and knowledge distillation framework for natural language understanding](https://www.adasci.org/journals/lattice-35309407/?volumes=true&open=621a3b18edc4364e8a96cb63).
Lattice, THE MACHINE LEARNING JOURNAL by Association of Data Scientists, 3(1).*
## SEAD-L-6_H-256_A-8-stsb
This is a student model distilled from [**BERT base**](https://huggingface.co/bert-base-uncased) as teacher by using SEAD framework on **stsb** task. For weights initialization, we used [microsoft/xtremedistil-l6-h256-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased)
## All SEAD Checkpoints
Other Community Checkpoints: [here](https://huggingface.co/models?search=SEAD)
## Intended uses & limitations
More information needed
### Training hyperparameters
Please take a look at the `training_args.bin` file
```python
$ import torch
$ hyperparameters = torch.load(os.path.join('training_args.bin'))
```
### Evaluation results
| eval_pearson | eval_spearmanr | eval_runtime | eval_samples_per_second | eval_steps_per_second | eval_loss | eval_samples |
|:------------:|:--------------:|:------------:|:-----------------------:|:---------------------:|:---------:|:------------:|
| 0.8962 | 0.8978 | 2.1978 | 682.498 | 21.385 | 0.4679 | 1500 |
### Framework versions
- Transformers >=4.8.0
- Pytorch >=1.6.0
- TensorFlow >=2.5.0
- Flax >=0.3.5
- Datasets >=1.10.2
- Tokenizers >=0.11.6
If you use these models, please cite the following paper:
```
@article{article,
author={Mei, Moyan and Sroch, Rohit},
title={SEAD: Simple Ensemble and Knowledge Distillation Framework for Natural Language Understanding},
volume={3},
number={1},
journal={Lattice, The Machine Learning Journal by Association of Data Scientists},
day={26},
year={2022},
month={Feb},
url = {www.adasci.org/journals/lattice-35309407/?volumes=true&open=621a3b18edc4364e8a96cb63}
}
```
|
C5i/SEAD-L-6_H-256_A-8-rte
|
C5i
| 2022-06-12T21:02:01Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"SEAD",
"en",
"dataset:glue",
"dataset:rte",
"arxiv:1910.01108",
"arxiv:1909.10351",
"arxiv:2002.10957",
"arxiv:1810.04805",
"arxiv:1804.07461",
"arxiv:1905.00537",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-12T21:01:41Z |
---
language:
- en
license: apache-2.0
tags:
- SEAD
datasets:
- glue
- rte
---
## Paper
## [SEAD: SIMPLE ENSEMBLE AND KNOWLEDGE DISTILLATION FRAMEWORK FOR NATURAL LANGUAGE UNDERSTANDING](https://www.adasci.org/journals/lattice-35309407/?volumes=true&open=621a3b18edc4364e8a96cb63)
Aurthors: *Moyan Mei*, *Rohit Sroch*
## Abstract
With the widespread use of pre-trained language models (PLM), there has been increased research on how to make them applicable, especially in limited-resource or low latency high throughput scenarios. One of the dominant approaches is knowledge distillation (KD), where a smaller model is trained by receiving guidance from a large PLM. While there are many successful designs for learning knowledge from teachers, it remains unclear how students can learn better. Inspired by real university teaching processes, in this work we further explore knowledge distillation and propose a very simple yet effective framework, SEAD, to further improve task-specific generalization by utilizing multiple teachers. Our experiments show that SEAD leads to better performance compared to other popular KD methods [[1](https://arxiv.org/abs/1910.01108)] [[2](https://arxiv.org/abs/1909.10351)] [[3](https://arxiv.org/abs/2002.10957)] and achieves comparable or superior performance to its teacher model such as BERT [[4](https://arxiv.org/abs/1810.04805)] on total 13 tasks for the GLUE [[5](https://arxiv.org/abs/1804.07461)] and SuperGLUE [[6](https://arxiv.org/abs/1905.00537)] benchmarks.
*Moyan Mei and Rohit Sroch. 2022. [SEAD: Simple ensemble and knowledge distillation framework for natural language understanding](https://www.adasci.org/journals/lattice-35309407/?volumes=true&open=621a3b18edc4364e8a96cb63).
Lattice, THE MACHINE LEARNING JOURNAL by Association of Data Scientists, 3(1).*
## SEAD-L-6_H-256_A-8-rte
This is a student model distilled from [**BERT base**](https://huggingface.co/bert-base-uncased) as teacher by using SEAD framework on **rte** task. For weights initialization, we used [microsoft/xtremedistil-l6-h256-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased)
## All SEAD Checkpoints
Other Community Checkpoints: [here](https://huggingface.co/models?search=SEAD)
## Intended uses & limitations
More information needed
### Training hyperparameters
Please take a look at the `training_args.bin` file
```python
$ import torch
$ hyperparameters = torch.load(os.path.join('training_args.bin'))
```
### Evaluation results
| eval_accuracy | eval_runtime | eval_samples_per_second | eval_steps_per_second | eval_loss | eval_samples |
|:-------------:|:------------:|:-----------------------:|:---------------------:|:---------:|:------------:|
| 0.7906 | 1.5528 | 178.391 | 5.796 | 0.6934 | 277 |
### Framework versions
- Transformers >=4.8.0
- Pytorch >=1.6.0
- TensorFlow >=2.5.0
- Flax >=0.3.5
- Datasets >=1.10.2
- Tokenizers >=0.11.6
If you use these models, please cite the following paper:
```
@article{article,
author={Mei, Moyan and Sroch, Rohit},
title={SEAD: Simple Ensemble and Knowledge Distillation Framework for Natural Language Understanding},
volume={3},
number={1},
journal={Lattice, The Machine Learning Journal by Association of Data Scientists},
day={26},
year={2022},
month={Feb},
url = {www.adasci.org/journals/lattice-35309407/?volumes=true&open=621a3b18edc4364e8a96cb63}
}
```
|
84rry/84rry-xls-r-300M-AR
|
84rry
| 2022-06-12T20:54:28Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-06-09T00:08:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: 84rry-xls-r-300M-AR
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. -->
# 84rry-xls-r-300M-AR
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.
It achieves the following results on the evaluation set:
- Loss: 1.0647
- Wer: 0.5078
## 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: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1428 | 9.01 | 1000 | 0.9233 | 0.7477 |
| 0.4941 | 18.02 | 2000 | 0.7661 | 0.5633 |
| 0.3609 | 27.03 | 3000 | 0.8757 | 0.5480 |
| 0.2395 | 36.04 | 4000 | 1.0097 | 0.5275 |
| 0.1671 | 45.04 | 5000 | 1.0647 | 0.5078 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
tauseefr84/distilbert-base-uncased-finetuned-emotion
|
tauseefr84
| 2022-06-12T20:52:51Z | 5 | 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
| 2022-06-12T12:23:32Z |
---
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: 0.838
- name: F1
type: f1
value: 0.822753081351476
---
<!-- 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: 0.5268
- Accuracy: 0.838
- F1: 0.8228
## 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.9225 | 1.0 | 250 | 0.5268 | 0.838 | 0.8228 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
C5i/SEAD-L-6_H-256_A-8-mrpc
|
C5i
| 2022-06-12T20:35:41Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"SEAD",
"en",
"dataset:glue",
"dataset:mrpc",
"arxiv:1910.01108",
"arxiv:1909.10351",
"arxiv:2002.10957",
"arxiv:1810.04805",
"arxiv:1804.07461",
"arxiv:1905.00537",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-12T20:35:21Z |
---
language:
- en
license: apache-2.0
tags:
- SEAD
datasets:
- glue
- mrpc
---
## Paper
## [SEAD: SIMPLE ENSEMBLE AND KNOWLEDGE DISTILLATION FRAMEWORK FOR NATURAL LANGUAGE UNDERSTANDING](https://www.adasci.org/journals/lattice-35309407/?volumes=true&open=621a3b18edc4364e8a96cb63)
Aurthors: *Moyan Mei*, *Rohit Sroch*
## Abstract
With the widespread use of pre-trained language models (PLM), there has been increased research on how to make them applicable, especially in limited-resource or low latency high throughput scenarios. One of the dominant approaches is knowledge distillation (KD), where a smaller model is trained by receiving guidance from a large PLM. While there are many successful designs for learning knowledge from teachers, it remains unclear how students can learn better. Inspired by real university teaching processes, in this work we further explore knowledge distillation and propose a very simple yet effective framework, SEAD, to further improve task-specific generalization by utilizing multiple teachers. Our experiments show that SEAD leads to better performance compared to other popular KD methods [[1](https://arxiv.org/abs/1910.01108)] [[2](https://arxiv.org/abs/1909.10351)] [[3](https://arxiv.org/abs/2002.10957)] and achieves comparable or superior performance to its teacher model such as BERT [[4](https://arxiv.org/abs/1810.04805)] on total 13 tasks for the GLUE [[5](https://arxiv.org/abs/1804.07461)] and SuperGLUE [[6](https://arxiv.org/abs/1905.00537)] benchmarks.
*Moyan Mei and Rohit Sroch. 2022. [SEAD: Simple ensemble and knowledge distillation framework for natural language understanding](https://www.adasci.org/journals/lattice-35309407/?volumes=true&open=621a3b18edc4364e8a96cb63).
Lattice, THE MACHINE LEARNING JOURNAL by Association of Data Scientists, 3(1).*
## SEAD-L-6_H-256_A-8-mrpc
This is a student model distilled from [**BERT base**](https://huggingface.co/bert-base-uncased) as teacher by using SEAD framework on **mrpc** task. For weights initialization, we used [microsoft/xtremedistil-l6-h256-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased)
## All SEAD Checkpoints
Other Community Checkpoints: [here](https://huggingface.co/models?search=SEAD)
## Intended uses & limitations
More information needed
### Training hyperparameters
Please take a look at the `training_args.bin` file
```python
$ import torch
$ hyperparameters = torch.load(os.path.join('training_args.bin'))
```
### Evaluation results
| eval_accuracy | eval_f1 | eval_runtime | eval_samples_per_second | eval_steps_per_second | eval_loss | eval_samples |
|:-------------:|:-------:|:------------:|:-----------------------:|:---------------------:|:---------:|:------------:|
| 0.8897 | 0.9206 | 1.4486 | 281.643 | 8.974 | 0.4399 | 408 |
### Framework versions
- Transformers >=4.8.0
- Pytorch >=1.6.0
- TensorFlow >=2.5.0
- Flax >=0.3.5
- Datasets >=1.10.2
- Tokenizers >=0.11.6
If you use these models, please cite the following paper:
```
@article{article,
author={Mei, Moyan and Sroch, Rohit},
title={SEAD: Simple Ensemble and Knowledge Distillation Framework for Natural Language Understanding},
volume={3},
number={1},
journal={Lattice, The Machine Learning Journal by Association of Data Scientists},
day={26},
year={2022},
month={Feb},
url = {www.adasci.org/journals/lattice-35309407/?volumes=true&open=621a3b18edc4364e8a96cb63}
}
```
|
C5i/SEAD-L-6_H-384_A-12-mrpc
|
C5i
| 2022-06-12T20:21:42Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"SEAD",
"en",
"dataset:glue",
"dataset:mrpc",
"arxiv:1910.01108",
"arxiv:1909.10351",
"arxiv:2002.10957",
"arxiv:1810.04805",
"arxiv:1804.07461",
"arxiv:1905.00537",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-12T20:21:20Z |
---
language:
- en
license: apache-2.0
tags:
- SEAD
datasets:
- glue
- mrpc
---
## Paper
## [SEAD: SIMPLE ENSEMBLE AND KNOWLEDGE DISTILLATION FRAMEWORK FOR NATURAL LANGUAGE UNDERSTANDING](https://www.adasci.org/journals/lattice-35309407/?volumes=true&open=621a3b18edc4364e8a96cb63)
Aurthors: *Moyan Mei*, *Rohit Sroch*
## Abstract
With the widespread use of pre-trained language models (PLM), there has been increased research on how to make them applicable, especially in limited-resource or low latency high throughput scenarios. One of the dominant approaches is knowledge distillation (KD), where a smaller model is trained by receiving guidance from a large PLM. While there are many successful designs for learning knowledge from teachers, it remains unclear how students can learn better. Inspired by real university teaching processes, in this work we further explore knowledge distillation and propose a very simple yet effective framework, SEAD, to further improve task-specific generalization by utilizing multiple teachers. Our experiments show that SEAD leads to better performance compared to other popular KD methods [[1](https://arxiv.org/abs/1910.01108)] [[2](https://arxiv.org/abs/1909.10351)] [[3](https://arxiv.org/abs/2002.10957)] and achieves comparable or superior performance to its teacher model such as BERT [[4](https://arxiv.org/abs/1810.04805)] on total 13 tasks for the GLUE [[5](https://arxiv.org/abs/1804.07461)] and SuperGLUE [[6](https://arxiv.org/abs/1905.00537)] benchmarks.
*Moyan Mei and Rohit Sroch. 2022. [SEAD: Simple ensemble and knowledge distillation framework for natural language understanding](https://www.adasci.org/journals/lattice-35309407/?volumes=true&open=621a3b18edc4364e8a96cb63).
Lattice, THE MACHINE LEARNING JOURNAL by Association of Data Scientists, 3(1).*
## SEAD-L-6_H-384_A-12-mrpc
This is a student model distilled from [**BERT base**](https://huggingface.co/bert-base-uncased) as teacher by using SEAD framework on **mrpc** task. For weights initialization, we used [microsoft/xtremedistil-l6-h384-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h384-uncased)
## All SEAD Checkpoints
Other Community Checkpoints: [here](https://huggingface.co/models?search=SEAD)
## Intended uses & limitations
More information needed
### Training hyperparameters
Please take a look at the `training_args.bin` file
```python
$ import torch
$ hyperparameters = torch.load(os.path.join('training_args.bin'))
```
### Evaluation results
| eval_accuracy | eval_f1 | eval_runtime | eval_samples_per_second | eval_steps_per_second | eval_loss | eval_samples |
|:-------------:|:-------:|:------------:|:-----------------------:|:---------------------:|:---------:|:------------:|
| 0.9093 | 0.9345 | 1.1947 | 341.494 | 10.881 | 0.4309 | 408 |
### Framework versions
- Transformers >=4.8.0
- Pytorch >=1.6.0
- TensorFlow >=2.5.0
- Flax >=0.3.5
- Datasets >=1.10.2
- Tokenizers >=0.11.6
If you use these models, please cite the following paper:
```
@article{article,
author={Mei, Moyan and Sroch, Rohit},
title={SEAD: Simple Ensemble and Knowledge Distillation Framework for Natural Language Understanding},
volume={3},
number={1},
journal={Lattice, The Machine Learning Journal by Association of Data Scientists},
day={26},
year={2022},
month={Feb},
url = {www.adasci.org/journals/lattice-35309407/?volumes=true&open=621a3b18edc4364e8a96cb63}
}
```
|
huggingtweets/pandershirts
|
huggingtweets
| 2022-06-12T20:14:03Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-12T20:12:46Z |
---
language: en
thumbnail: http://www.huggingtweets.com/pandershirts/1655064824816/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/1535688698993512449/903NKFWz_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">Hellvetika</div>
<div style="text-align: center; font-size: 14px;">@pandershirts</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 Hellvetika.
| Data | Hellvetika |
| --- | --- |
| Tweets downloaded | 3246 |
| Retweets | 119 |
| Short tweets | 360 |
| Tweets kept | 2767 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/kyjr0nr8/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 @pandershirts's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/k8rb7z0d) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/k8rb7z0d/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/pandershirts')
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)
|
rhuang/PPO-LunarLander-v2-baseline
|
rhuang
| 2022-06-12T19:59:39Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-12T19:59:00Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 174.18 +/- 29.85
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
eslamxm/mbert2mbert-finetuned-ar-wikilingua
|
eslamxm
| 2022-06-12T19:37:00Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"summarization",
"ar",
"mbert",
"mbert2mbert",
"Abstractive Summarization",
"generated_from_trainer",
"dataset:wiki_lingua",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-06-12T06:43:14Z |
---
tags:
- summarization
- ar
- encoder-decoder
- mbert
- mbert2mbert
- Abstractive Summarization
- generated_from_trainer
datasets:
- wiki_lingua
model-index:
- name: mbert2mbert-finetuned-ar-wikilingua
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. -->
# mbert2mbert-finetuned-ar-wikilingua
This model is a fine-tuned version of [](https://huggingface.co/) on the wiki_lingua dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6753
- Rouge-1: 15.19
- Rouge-2: 5.45
- Rouge-l: 14.64
- Gen Len: 20.0
- Bertscore: 67.86
## 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: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- num_epochs: 8
- label_smoothing_factor: 0.1
### Training results
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
ilhami/Tr_En_AcademicTranslation
|
ilhami
| 2022-06-12T19:05:53Z | 26 | 2 |
transformers
|
[
"transformers",
"pytorch",
"marian",
"text2text-generation",
"translation",
"tr",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-06-12T09:19:05Z |
---
language:
- tr
- en
tags:
- translation
license: apache-2.0
datasets:
- Parallel Corpora for Turkish-English Academic Translations
metrics:
- bleu
- sacrebleu
---
## Model Details
- **Developed by:** İlhami SEL
- **Model type:** Turkish-English Machine Translation -- Transformer Based(6 Layer)
- **Language:** Turkish - English
- **Resources for more information:** Sel, İ. , Üzen, H. & Hanbay, D. (2021). Creating a Parallel Corpora for Turkish-English Academic Translations . Computer Science , 5th International Artificial Intelligence and Data Processing symposium , 335-340 . DOI: 10.53070/bbd.990959
```python
checkpoint = "ilhami/Tr_En_AcademicTranslation"
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint).to("cuda")
tr= ["Sohbet robotları son yıllarda yaygın bir şekilde kullanılmaya başlanmıştır. ",
"İnsanları taklit eden ve daha iyi müşteri memnuniyeti sağlayan sohbet robotları en gelişkin doğal dil işleme tekniklerine ihtiyaç duymaktadır. ",
"Bu çalışma sohbet robotu konuşmalarının niyet tahminini geliştirmeye odaklanmıştır." ,
"Kelime gösterimi için TF-IDF, Doc2vec ve BERT gibi geleneksel ve gelişmiş doğal dil işleme yöntemleri, çoklu sınıf ve çoklu etiket tahmini için ise lojistik regresyon, rastgele orman ve yapay sinir ağları kullanılmıştır." ,
"Sohbet robotu konuşma veri kümeleri, sinema bileti rezervasyonu, restoran rezervasyonu ve taksi çağırma olmak üzere üç farklı alandan alınmıştır. ",
"Bu çalışmanın sonunda, BERT ve BERT ile TF-IDF birleşimi modellerin diğer kombinasyonlardan daha iyi sonuç verdiği görülmüştür. ",
"BERT gibi ön eğitimli modellerden faydalanmanın daha iyi bağlamsal anlama sağladığı ortaya çıkmıştır. ",
"TF-IDF yerleştirmeleri, BERT gösterimi ile birleştirilerek niyet kategorisi tahmininin iyileştirilmesi amaçlanmıştır."]
encoded_text = tokenizer(tr, return_tensors="pt", padding = True).to("cuda")
generated_tokens = model.generate(**encoded_text)
en = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
```
|
abdoutony207/mbart-large-cc25-en-ar-evaluated-en-to-ar-1000instancesopus-leaningRate2e-05-batchSize2
|
abdoutony207
| 2022-06-12T18:25:47Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"generated_from_trainer",
"dataset:opus100",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-12T17:51:43Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- opus100
metrics:
- bleu
model-index:
- name: mbart-large-cc25-en-ar-evaluated-en-to-ar-1000instancesopus-leaningRate2e-05-batchSize2
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: opus100
type: opus100
args: ar-en
metrics:
- name: Bleu
type: bleu
value: 10.5645
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mbart-large-cc25-en-ar-evaluated-en-to-ar-1000instancesopus-leaningRate2e-05-batchSize2
This model is a fine-tuned version of [akhooli/mbart-large-cc25-en-ar](https://huggingface.co/akhooli/mbart-large-cc25-en-ar) on the opus100 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4673
- Bleu: 10.5645
- Meteor: 0.0783
- Gen Len: 10.23
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 11
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Meteor | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|
| 8.1731 | 0.25 | 100 | 2.8417 | 0.9599 | 0.028 | 230.885 |
| 0.6743 | 0.5 | 200 | 0.4726 | 6.4055 | 0.0692 | 14.81 |
| 0.3028 | 0.75 | 300 | 0.4572 | 6.7544 | 0.0822 | 23.92 |
| 0.2555 | 1.0 | 400 | 0.4172 | 8.4078 | 0.0742 | 13.655 |
| 0.1644 | 1.25 | 500 | 0.4236 | 9.284 | 0.071 | 13.03 |
| 0.1916 | 1.5 | 600 | 0.4222 | 4.8976 | 0.0779 | 32.225 |
| 0.2011 | 1.75 | 700 | 0.4305 | 7.6909 | 0.0738 | 16.675 |
| 0.1612 | 2.0 | 800 | 0.4416 | 10.8622 | 0.0855 | 10.91 |
| 0.116 | 2.25 | 900 | 0.4673 | 10.5645 | 0.0783 | 10.23 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
nlokam/adanimals_V1
|
nlokam
| 2022-06-12T18:02:30Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-12T17:43:58Z |
---
thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png
tags:
- conversational
license: mit
---
|
nlokam99/ada_sample_3
|
nlokam99
| 2022-06-12T17:43:04Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-12T17:40:59Z |
---
thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png
tags:
- conversational
license: mit
---
|
huggingtweets/dodecahedra
|
huggingtweets
| 2022-06-12T17:42:15Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-12T17:37:18Z |
---
language: en
thumbnail: http://www.huggingtweets.com/dodecahedra/1655055731499/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/3232494514/760c72bca0af20fac2cd61bcec557e7a_400x400.jpeg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">William Rose</div>
<div style="text-align: center; font-size: 14px;">@dodecahedra</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 William Rose.
| Data | William Rose |
| --- | --- |
| Tweets downloaded | 3241 |
| Retweets | 1115 |
| Short tweets | 158 |
| Tweets kept | 1968 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1geru0ac/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 @dodecahedra's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1uy1zk82) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1uy1zk82/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/dodecahedra')
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)
|
vasudevgupta/speech_jax_wav2vec2-large-lv60_960h
|
vasudevgupta
| 2022-06-12T16:10:32Z | 7 | 0 |
transformers
|
[
"transformers",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-05-29T20:52:47Z |
* Evaluation Notebook: https://colab.research.google.com/drive/1dV1Z3WajMCYMjNZab98CEEcg3FTbtONO?usp=sharing
* Training Code: https://github.com/vasudevgupta7/speech-jax/blob/main/projects/finetune_wav2vec2.py
* Weights & Biases: https://wandb.ai/7vasudevgupta/speech-JAX?workspace=user-7vasudevgupta
Following results are obtained with `23ffe236840b7f75c9f01a9c347b01485a2bf9f6` & `95c3bc1b83c74452df29f792e0b5651c09fdaeb9`
| dataset | WER |
|------------------------|-------|
| Librispeech-test-clean | 3.3 % |
|
kravchenko/uk-mt5-large
|
kravchenko
| 2022-06-12T15:00:46Z | 8 | 1 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"t5",
"uk",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-12T14:23:52Z |
---
language:
- uk
- en
tags:
- t5
---
The aim is to compress the mT5-large model to leave only the Ukrainian language and some basic English.
Reproduced the similar result (but with another language) from [this](https://towardsdatascience.com/how-to-adapt-a-multilingual-t5-model-for-a-single-language-b9f94f3d9c90) medium article.
Results:
- 1.2B params -> 779M params (37%)
- 250K tokens -> 8900 tokens
- 4.6GB size model -> 2.9GB size model
|
kravchenko/uk-mt5-base
|
kravchenko
| 2022-06-12T14:57:59Z | 14 | 4 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"t5",
"uk",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-03T09:41:33Z |
---
language:
- uk
- en
tags:
- t5
---
The aim is to compress the mT5-base model to leave only the Ukrainian language and some basic English.
Reproduced the similar result (but with another language) from [this](https://towardsdatascience.com/how-to-adapt-a-multilingual-t5-model-for-a-single-language-b9f94f3d9c90) medium article.
Results:
- 582M params -> 244M params (58%)
- 250K tokens -> 30K tokens
- 2.2GB size model -> 0.95GB size model
|
Doohae/msmarco-query-encoder-v0
|
Doohae
| 2022-06-12T14:52:52Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-06-12T14:42:45Z |
Query Encoder trained on Tevatron small sample dataset(3epochs)
|
ahmeddbahaa/mt5-base-finetune-ar-xlsum
|
ahmeddbahaa
| 2022-06-12T13:55:10Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"mT5_multilingual_XLSum",
"abstractive summarization",
"ar",
"xlsum",
"generated_from_trainer",
"dataset:xlsum",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-06-11T20:41:00Z |
---
license: apache-2.0
tags:
- summarization
- mT5_multilingual_XLSum
- mt5
- abstractive summarization
- ar
- xlsum
- generated_from_trainer
datasets:
- xlsum
model-index:
- name: mt5-base-finetune-ar-xlsum
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. -->
# mt5-base-finetune-ar-xlsum
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the xlsum dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2546
- Rouge-1: 22.2
- Rouge-2: 9.57
- Rouge-l: 20.26
- Gen Len: 19.0
- Bertscore: 71.43
## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- num_epochs: 10
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|
| 4.9261 | 1.0 | 585 | 3.6314 | 18.19 | 6.49 | 16.37 | 19.0 | 70.17 |
| 3.8429 | 2.0 | 1170 | 3.4253 | 19.45 | 7.58 | 17.73 | 19.0 | 70.35 |
| 3.6311 | 3.0 | 1755 | 3.3569 | 20.83 | 8.54 | 18.9 | 19.0 | 70.89 |
| 3.4917 | 4.0 | 2340 | 3.3101 | 20.77 | 8.53 | 18.89 | 19.0 | 70.98 |
| 3.3873 | 5.0 | 2925 | 3.2867 | 21.47 | 9.0 | 19.54 | 19.0 | 71.23 |
| 3.3037 | 6.0 | 3510 | 3.2693 | 21.41 | 9.0 | 19.5 | 19.0 | 71.21 |
| 3.2357 | 7.0 | 4095 | 3.2581 | 22.05 | 9.36 | 20.04 | 19.0 | 71.43 |
| 3.1798 | 8.0 | 4680 | 3.2522 | 22.21 | 9.56 | 20.23 | 19.0 | 71.41 |
| 3.1359 | 9.0 | 5265 | 3.2546 | 22.27 | 9.58 | 20.23 | 19.0 | 71.46 |
| 3.0997 | 10.0 | 5850 | 3.2546 | 22.2 | 9.57 | 20.26 | 19.0 | 71.43 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
keras-io/ProbabalisticBayesianModel-Wine
|
keras-io
| 2022-06-12T13:54:27Z | 0 | 2 |
keras
|
[
"keras",
"tensorboard",
"probabilistic-models",
"regression",
"region:us"
] | null | 2022-06-06T15:36:50Z |
---
library_name: keras
tags:
- probabilistic-models
- regression
---
## Model description
This repo contains model weights for the the probabilistic model from [Probabilistic Bayesian Neural Networks](https://keras.io/examples/keras_recipes/bayesian_neural_networks/). This example demonstrates how to build basic probabilistic Bayesian neural networks to account for these two types of uncertainty. We use TensorFlow Probability library, which is compatible with Keras API.
Taking a probabilistic approach to deep learning allows to account for uncertainty, so that models can assign less levels of confidence to incorrect predictions. Sources of uncertainty can be found in the data, due to measurement error or noise in the labels, or the model, due to insufficient data availability for the model to learn effectively.
**Full credits go to [Khalid Salama](https://www.linkedin.com/in/khalid-salama-24403144/)**
## Using this model
This repo contains model weights only. To use this model, refer to the following code contained in load_bnn_model.py.
## Training and evaluation data 🍷
We use the wine quality dataset found [here](https://www.tensorflow.org/datasets/catalog/wine_quality). Each wine was scored from 0-10 by wine experts, and includes 11 physicochemical features about the wine.
## Versioning
The training was done using TensorFlow 2.8.0 and TensorFlow Probability 0.16.0. When working with TensorFlow Probability, it is encouraged to check out the [releases](https://github.com/tensorflow/probability/releases/tag/v0.17.0) to make sure you are using a stable TensorFlow counterpart.
### Training hyperparameters
| Optimizer | learning_rate | decay | rho | momentum | epsilon | centered | training_precision |
|----|-------------|-----|------|------|-------|-------|------------------|
|RMSprop|0.001|0.0|0.9|0.0|1e-07|False|float32|
|
nestoralvaro/mt5-base-finetuned-xsum-RAW_data_prep_2021_12_26___t55_403.csv__google_mt5_base
|
nestoralvaro
| 2022-06-12T12:25:16Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-12T10:01:09Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-base-finetuned-xsum-RAW_data_prep_2021_12_26___t55_403.csv__google_mt5_base
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. -->
# mt5-base-finetuned-xsum-RAW_data_prep_2021_12_26___t55_403.csv__google_mt5_base
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Rouge1: 0.9712
- Rouge2: 0.1329
- Rougel: 0.9638
- Rougelsum: 0.9675
- Gen Len: 6.4489
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.0 | 1.0 | 36479 | nan | 0.9712 | 0.1329 | 0.9638 | 0.9675 | 6.4489 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
FabianWillner/distilbert-base-uncased-finetuned-squad
|
FabianWillner
| 2022-06-12T12:09:32Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-05-09T10:41:12Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
metrics:
- 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 [FabianWillner/distilbert-base-uncased-finetuned-squad](https://huggingface.co/FabianWillner/distilbert-base-uncased-finetuned-squad) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
huggingnft/hedgies
|
huggingnft
| 2022-06-12T12:08:25Z | 7 | 0 |
transformers
|
[
"transformers",
"huggingnft",
"nft",
"huggan",
"gan",
"image",
"images",
"unconditional-image-generation",
"dataset:huggingnft/hedgies",
"license:mit",
"endpoints_compatible",
"region:us"
] |
unconditional-image-generation
| 2022-05-24T18:12:29Z |
---
tags:
- huggingnft
- nft
- huggan
- gan
- image
- images
- unconditional-image-generation
datasets:
- huggingnft/hedgies
license: mit
---
# Hugging NFT: hedgies
## Disclaimer
All rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright
holder.
## Model description
LightWeight GAN model for unconditional generation.
NFT collection available [here](https://opensea.io/collection/hedgies).
Dataset is available [here](https://huggingface.co/datasets/huggingnft/hedgies).
Check Space: [link](https://huggingface.co/spaces/AlekseyKorshuk/huggingnft).
Project repository: [link](https://github.com/AlekseyKorshuk/huggingnft).
[](https://github.com/AlekseyKorshuk/huggingnft)
## Intended uses & limitations
#### How to use
Check project repository: [link](https://github.com/AlekseyKorshuk/huggingnft).
#### Limitations and bias
Check project repository: [link](https://github.com/AlekseyKorshuk/huggingnft).
## Training data
Dataset is available [here](https://huggingface.co/datasets/huggingnft/hedgies).
## Training procedure
Training script is available [here](https://github.com/AlekseyKorshuk/huggingnft).
## Generated Images
Check results with Space: [link](https://huggingface.co/spaces/AlekseyKorshuk/huggingnft).
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingnft)
### BibTeX entry and citation info
```bibtex
@InProceedings{huggingnft,
author={Aleksey Korshuk}
year=2022
}
```
|
mgfrantz/dql-SpaceInvadersNoFrameskip-v4
|
mgfrantz
| 2022-06-12T11:13:41Z | 7 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-12T11:12:58Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 1003.50 +/- 404.22
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga mgfrantz -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga mgfrantz
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
eunbeee/ainize-kobart-news-eb-finetuned-meetings-papers
|
eunbeee
| 2022-06-12T11:02:29Z | 105 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-12T08:37:23Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: ainize-kobart-news-eb-finetuned-meetings-papers
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. -->
# ainize-kobart-news-eb-finetuned-meetings-papers
This model is a fine-tuned version of [ainize/kobart-news](https://huggingface.co/ainize/kobart-news) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3289
- Rouge1: 17.3988
- Rouge2: 7.0454
- Rougel: 17.3877
- Rougelsum: 17.42
- Gen Len: 19.9473
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 0.1402 | 1.0 | 7588 | 0.2930 | 17.1421 | 7.0141 | 17.1211 | 17.1473 | 19.9374 |
| 0.0997 | 2.0 | 15176 | 0.2842 | 17.1692 | 6.8824 | 17.1557 | 17.1985 | 19.9435 |
| 0.0692 | 3.0 | 22764 | 0.3052 | 17.4241 | 7.1083 | 17.4028 | 17.4472 | 19.9453 |
| 0.0556 | 4.0 | 30352 | 0.3289 | 17.3988 | 7.0454 | 17.3877 | 17.42 | 19.9473 |
| 0.0533 | 5.0 | 37940 | 0.3289 | 17.3988 | 7.0454 | 17.3877 | 17.42 | 19.9473 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
vishvamahadevan/distilbert-base-uncased-finetuned-squad
|
vishvamahadevan
| 2022-06-12T10:34:52Z | 6 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-06-12T08:07:48Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: vishvamahadevan/distilbert-base-uncased-finetuned-squad
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. -->
# vishvamahadevan/distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.9560
- Validation Loss: 1.1174
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 11064, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.3862 | 1.1639 | 0 |
| 0.9560 | 1.1174 | 1 |
### Framework versions
- Transformers 4.19.4
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1
|
z-uo/vits-commonvoice9.0
|
z-uo
| 2022-06-12T09:46:23Z | 1 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"text-to-speech",
"it",
"dataset:mozilla-foundation/common_voice_9_0",
"endpoints_compatible",
"region:us"
] |
text-to-speech
| 2022-06-12T07:07:07Z |
---
tags:
- text-to-speech
language:
- it
model-index:
- name: vits-commonvoice9.0
results: []
datasets:
- mozilla-foundation/common_voice_9_0
---
# Common Voice it Vits
Train on [Mozzila Common voice](https://commonvoice.mozilla.org/) v9.0 it with [Coqui VITS](https://github.com/coqui-ai/TTS)
```
# Coqui tts sha commit coquitts: 0cf3265a4686d7e856bd472cdaf1572d61cab2b8
PYTORCH_CUDA_ALLOC_CONF="max_split_size_mb:25" CUDA_VISIBLE_DEVICES=1 python recipes/common_voice/vits/train_vits.py
CUDA_VISIBLE_DEVICES=0 tts-server --model_path "/run/media/opensuse/Barracuda/Models/TTS_new/trained_common_voice/vits_vctk-June-05-2022_03+45PM-0cf3265a/best_model.pth" --config_path "/run/media/opensuse/Barracuda/Models/TTS_new/trained_common_voice/vits_vctk-June-05-2022_03+45PM-0cf3265a/config.json"
```
|
huggingtweets/bosstjanz
|
huggingtweets
| 2022-06-12T09:27:34Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-12T09:26:54Z |
---
language: en
thumbnail: http://www.huggingtweets.com/bosstjanz/1655026050127/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/1342130927737176064/SiNG_CxQ_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">Zrimškow</div>
<div style="text-align: center; font-size: 14px;">@bosstjanz</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 Zrimškow.
| Data | Zrimškow |
| --- | --- |
| Tweets downloaded | 3225 |
| Retweets | 368 |
| Short tweets | 279 |
| Tweets kept | 2578 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/23nemiqj/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 @bosstjanz's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2pjrymzt) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2pjrymzt/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/bosstjanz')
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)
|
ironbar/dqn-SpaceInvadersNoFrameskip-v4-1M-steps
|
ironbar
| 2022-06-12T08:16:08Z | 11 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-12T08:15:30Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 629.50 +/- 140.06
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga ironbar -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga ironbar
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
ahmeddbahaa/arabert2arabert-finetuned-ar-wikilingua
|
ahmeddbahaa
| 2022-06-12T05:51:47Z | 21 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"summarization",
"ar",
"arabert",
"arabert2arabert",
"Abstractive Summarization",
"generated_from_trainer",
"dataset:wiki_lingua",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-06-12T01:03:07Z |
---
tags:
- summarization
- ar
- encoder-decoder
- arabert
- arabert2arabert
- Abstractive Summarization
- generated_from_trainer
datasets:
- wiki_lingua
model-index:
- name: arabert2arabert-finetuned-ar-wikilingua
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. -->
# arabert2arabert-finetuned-ar-wikilingua
This model is a fine-tuned version of [](https://huggingface.co/) on the wiki_lingua dataset.
It achieves the following results on the evaluation set:
- Loss: 4.6877
- Rouge-1: 13.2
- Rouge-2: 3.43
- Rouge-l: 12.45
- Gen Len: 20.0
- Bertscore: 64.88
## 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: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- num_epochs: 8
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|
| 6.7667 | 1.0 | 156 | 5.3846 | 3.36 | 0.56 | 3.27 | 20.0 | 60.6 |
| 5.257 | 2.0 | 312 | 5.0424 | 5.44 | 0.88 | 5.35 | 20.0 | 60.56 |
| 4.743 | 3.0 | 468 | 4.8294 | 9.21 | 1.8 | 8.93 | 20.0 | 62.91 |
| 4.3832 | 4.0 | 624 | 4.7240 | 9.88 | 2.19 | 9.6 | 20.0 | 62.65 |
| 4.1166 | 5.0 | 780 | 4.6861 | 11.61 | 2.86 | 11.13 | 20.0 | 63.71 |
| 3.91 | 6.0 | 936 | 4.6692 | 12.27 | 3.11 | 11.76 | 20.0 | 64.07 |
| 3.7569 | 7.0 | 1092 | 4.6805 | 12.93 | 3.38 | 12.28 | 20.0 | 64.61 |
| 3.6454 | 8.0 | 1248 | 4.6877 | 13.2 | 3.43 | 12.45 | 20.0 | 64.88 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
xdai/mimic_roberta_base
|
xdai
| 2022-06-12T04:51:26Z | 5 | 1 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"Clinical notes",
"Discharge summaries",
"RoBERTa",
"dataset:MIMIC-III",
"arxiv:2204.06683",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-12T04:12:20Z |
---
language:
- English
tags:
- Clinical notes
- Discharge summaries
- RoBERTa
license: "cc-by-4.0"
datasets:
- MIMIC-III
---
* Continue pre-training RoBERTa-base using discharge summaries from MIMIC-III datasets.
* Details can be found in the following paper
> Xiang Dai and Ilias Chalkidis and Sune Darkner and Desmond Elliott. 2022. Revisiting Transformer-based Models for Long Document Classification. (https://arxiv.org/abs/2204.06683)
* Important hyper-parameters
| | |
|---|---|
| Max sequence | 128 |
| Batch size | 128 |
| Learning rate | 5e-5 |
| Training epochs | 15 |
| Training time | 40 GPU-hours |
|
huggingtweets/tayplaysgaymes
|
huggingtweets
| 2022-06-12T03:56:41Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-12T03:55:39Z |
---
language: en
thumbnail: http://www.huggingtweets.com/tayplaysgaymes/1655006196516/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/1144053838459969536/lv3yBmoX_400x400.png')">
</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">Tay</div>
<div style="text-align: center; font-size: 14px;">@tayplaysgaymes</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 Tay.
| Data | Tay |
| --- | --- |
| Tweets downloaded | 3212 |
| Retweets | 693 |
| Short tweets | 367 |
| Tweets kept | 2152 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1hmextiq/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 @tayplaysgaymes's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3r0cse8x) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3r0cse8x/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/tayplaysgaymes')
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)
|
Zengwei/icefall-asr-librispeech-conv-emformer-transducer-stateless-2022-06-11
|
Zengwei
| 2022-06-12T02:51:06Z | 0 | 0 | null |
[
"tensorboard",
"region:us"
] | null | 2022-06-11T15:22:15Z |
See <https://github.com/k2-fsa/icefall/pull/389>
|
bguan/SpaceInvadersNoFrameskip-v4
|
bguan
| 2022-06-12T01:05:09Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-12T01:04:38Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 255.00 +/- 93.83
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga bguan -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga bguan
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 500000),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
meghazisofiane/opus-mt-en-ar-evaluated-en-to-ar-2000instances-un_multi-leaningRate2e-05-batchSize8-11-action-1
|
meghazisofiane
| 2022-06-12T00:44:37Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:un_multi",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-12T00:34:12Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- un_multi
metrics:
- bleu
model-index:
- name: opus-mt-en-ar-evaluated-en-to-ar-2000instances-un_multi-leaningRate2e-05-batchSize8-11-action-1
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: un_multi
type: un_multi
args: ar-en
metrics:
- name: Bleu
type: bleu
value: 53.0137
---
<!-- 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. -->
# opus-mt-en-ar-evaluated-en-to-ar-2000instances-un_multi-leaningRate2e-05-batchSize8-11-action-1
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ar](https://huggingface.co/Helsinki-NLP/opus-mt-en-ar) on the un_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1873
- Bleu: 53.0137
- Meteor: 0.5005
- Gen Len: 25.845
## 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: 11
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Meteor | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|
| 0.6585 | 0.5 | 100 | 0.2085 | 52.5874 | 0.4969 | 25.485 |
| 0.1802 | 1.0 | 200 | 0.1788 | 52.9434 | 0.4982 | 25.1725 |
| 0.1501 | 1.5 | 300 | 0.1683 | 53.6994 | 0.5033 | 25.625 |
| 0.1454 | 2.0 | 400 | 0.1706 | 53.3946 | 0.5005 | 25.6675 |
| 0.1193 | 2.5 | 500 | 0.1774 | 53.2011 | 0.4982 | 25.58 |
| 0.1194 | 3.0 | 600 | 0.1741 | 53.8651 | 0.5026 | 25.5775 |
| 0.1002 | 3.5 | 700 | 0.1878 | 53.1332 | 0.5005 | 25.8975 |
| 0.0979 | 4.0 | 800 | 0.1881 | 52.5989 | 0.4974 | 25.485 |
| 0.0807 | 4.5 | 900 | 0.1873 | 53.0137 | 0.5005 | 25.845 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
TencentMedicalNet/MedicalNet-Resnet10
|
TencentMedicalNet
| 2022-06-12T00:26:42Z | 0 | 4 | null |
[
"MedicalNet",
"medical images",
"medical",
"3D",
"Med3D",
"en",
"dataset:MRBrainS18",
"arxiv:1904.00625",
"license:mit",
"region:us"
] | null | 2022-06-11T23:12:06Z |
---
license: mit
datasets:
- MRBrainS18
language:
- en
metrics:
-
tags:
- MedicalNet
- medical images
- medical
- 3D
- Med3D
thumbnail: "https://github.com/Tencent/MedicalNet/blob/master/images/logo.png?raw=true"
---
# MedicalNet
This repository contains a Pytorch implementation of [Med3D: Transfer Learning for 3D Medical Image Analysis](https://arxiv.org/abs/1904.00625).
Many studies have shown that the performance on deep learning is significantly affected by volume of training data. The MedicalNet project aggregated the dataset with diverse modalities, target organs, and pathologies to to build relatively large datasets. Based on this dataset, a series of 3D-ResNet pre-trained models and corresponding transfer-learning training code are provided.
### License
MedicalNet is released under the MIT License (refer to the LICENSE file for detailso).
### Citing MedicalNet
If you use this code or pre-trained models, please cite the following:
```
@article{chen2019med3d,
title={Med3D: Transfer Learning for 3D Medical Image Analysis},
author={Chen, Sihong and Ma, Kai and Zheng, Yefeng},
journal={arXiv preprint arXiv:1904.00625},
year={2019}
}
```
### Update(2019/07/30)
We uploaded 4 pre-trained models based on more datasets (23 datasets).
```
Model name : parameters settings
resnet_10_23dataset.pth: --model resnet --model_depth 10 --resnet_shortcut B
resnet_18_23dataset.pth: --model resnet --model_depth 18 --resnet_shortcut A
resnet_34_23dataset.pth: --model resnet --model_depth 34 --resnet_shortcut A
resnet_50_23dataset.pth: --model resnet --model_depth 50 --resnet_shortcut B
```
Hugging Face repository contribution by:
[Rafael Zimmer](https://www.github.com/rzimmerdev)
|
huggingtweets/laserboat999
|
huggingtweets
| 2022-06-11T23:53:52Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-11T23:49:07Z |
---
language: en
thumbnail: http://www.huggingtweets.com/laserboat999/1654991516445/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/1500274766195793921/bA4siut7_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">donald boat</div>
<div style="text-align: center; font-size: 14px;">@laserboat999</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 donald boat.
| Data | donald boat |
| --- | --- |
| Tweets downloaded | 3233 |
| Retweets | 75 |
| Short tweets | 516 |
| Tweets kept | 2642 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/38v40fpf/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 @laserboat999's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/pk1xum9h) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/pk1xum9h/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/laserboat999')
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)
|
DLWCMD/TEST2ppo-LunarLander-v2
|
DLWCMD
| 2022-06-11T23:39:16Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-11T23:38:43Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 263.13 +/- 22.16
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
745H1N/LunarLander-v2-DQN-optuna
|
745H1N
| 2022-06-11T23:36:51Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-11T23:36:25Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: -140.18 +/- 41.67
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **DQN** Agent playing **LunarLander-v2**
This is a trained model of a **DQN** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
745H1N/LunarLander-v2-PPO-optuna
|
745H1N
| 2022-06-11T23:30:59Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-11T23:30:32Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: -170.80 +/- 74.73
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
aprischa/bart-large-cnn-aprischa2
|
aprischa
| 2022-06-11T23:27:38Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-11T17:40:18Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-large-cnn-aprischa2
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. -->
# bart-large-cnn-aprischa2
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3425
- Rouge1: 65.7088
- Rouge2: 56.6701
- Rougel: 62.1926
- Rougelsum: 64.7727
- Gen Len: 140.8469
## 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: 3e-05
- train_batch_size: 2
- eval_batch_size: 2
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| 0.3772 | 1.0 | 5403 | 0.3586 | 65.7702 | 56.7968 | 62.264 | 64.8605 | 140.268 |
| 0.316 | 2.0 | 10806 | 0.3421 | 64.8238 | 55.8837 | 61.3245 | 63.8894 | 140.7472 |
| 0.2397 | 3.0 | 16209 | 0.3425 | 65.7088 | 56.6701 | 62.1926 | 64.7727 | 140.8469 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
twieland/SCRATCH_ja-en_helsinki
|
twieland
| 2022-06-11T23:01:52Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-11T01:05:11Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: SCRATCH_ja-en_helsinki
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. -->
# SCRATCH_ja-en_helsinki
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ja-en](https://huggingface.co/Helsinki-NLP/opus-mt-ja-en) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5583
- Otaku Benchmark VN BLEU: 19.12
- Otaku Benchmark LN BLEU: 11.55
- Otaku Benchmark MANGA BLEU: 12.98
## 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: 96
- 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 3.0252 | 0.02 | 2000 | 2.4140 |
| 2.8406 | 0.03 | 4000 | 2.2819 |
| 2.7505 | 0.05 | 6000 | 2.3018 |
| 2.6948 | 0.06 | 8000 | 2.1931 |
| 2.6408 | 0.08 | 10000 | 2.1724 |
| 2.6004 | 0.09 | 12000 | 2.1583 |
| 2.5685 | 0.11 | 14000 | 2.1203 |
| 2.5432 | 0.12 | 16000 | 2.1593 |
| 2.5153 | 0.14 | 18000 | 2.1009 |
| 2.4906 | 0.15 | 20000 | 2.0899 |
| 2.4709 | 0.17 | 22000 | 2.0512 |
| 2.4471 | 0.18 | 24000 | 2.0208 |
| 2.4295 | 0.2 | 26000 | 2.0773 |
| 2.4154 | 0.21 | 28000 | 2.0441 |
| 2.4008 | 0.23 | 30000 | 2.0235 |
| 2.3834 | 0.24 | 32000 | 2.0190 |
| 2.3709 | 0.26 | 34000 | 1.9831 |
| 2.3537 | 0.27 | 36000 | 1.9870 |
| 2.3486 | 0.29 | 38000 | 1.9692 |
| 2.3346 | 0.3 | 40000 | 1.9517 |
| 2.3195 | 0.32 | 42000 | 1.9800 |
| 2.3104 | 0.33 | 44000 | 1.9676 |
| 2.298 | 0.35 | 46000 | 1.9563 |
| 2.2905 | 0.36 | 48000 | 1.9217 |
| 2.2792 | 0.38 | 50000 | 1.9195 |
| 2.2714 | 0.39 | 52000 | 1.9109 |
| 2.2593 | 0.41 | 54000 | 1.9044 |
| 2.2582 | 0.42 | 56000 | 1.8876 |
| 2.2482 | 0.44 | 58000 | 1.8860 |
| 2.2394 | 0.45 | 60000 | 1.8887 |
| 2.2273 | 0.47 | 62000 | 1.8862 |
| 2.2255 | 0.48 | 64000 | 1.8705 |
| 2.2166 | 0.5 | 66000 | 1.8696 |
| 2.2075 | 0.51 | 68000 | 1.8657 |
| 2.1992 | 0.53 | 70000 | 1.8585 |
| 2.1969 | 0.54 | 72000 | 1.8526 |
| 2.1894 | 0.56 | 74000 | 1.8493 |
| 2.1817 | 0.57 | 76000 | 1.8480 |
| 2.1771 | 0.59 | 78000 | 1.8333 |
| 2.1683 | 0.6 | 80000 | 1.8342 |
| 2.1667 | 0.62 | 82000 | 1.8537 |
| 2.1546 | 0.63 | 84000 | 1.8261 |
| 2.1467 | 0.65 | 86000 | 1.8092 |
| 2.1421 | 0.66 | 88000 | 1.8137 |
| 2.1395 | 0.68 | 90000 | 1.8286 |
| 2.1313 | 0.69 | 92000 | 1.8042 |
| 2.1241 | 0.71 | 94000 | 1.7934 |
| 2.1214 | 0.72 | 96000 | 1.7940 |
| 2.12 | 0.74 | 98000 | 1.8064 |
| 2.1096 | 0.75 | 100000 | 1.7983 |
| 2.1035 | 0.77 | 102000 | 1.8089 |
| 2.0937 | 0.78 | 104000 | 1.7941 |
| 2.0893 | 0.8 | 106000 | 1.7791 |
| 2.0869 | 0.81 | 108000 | 1.7807 |
| 2.0845 | 0.83 | 110000 | 1.7852 |
| 2.0782 | 0.84 | 112000 | 1.7675 |
| 2.0755 | 0.86 | 114000 | 1.7756 |
| 2.0657 | 0.87 | 116000 | 1.7604 |
| 2.0614 | 0.89 | 118000 | 1.7447 |
| 2.0591 | 0.9 | 120000 | 1.7489 |
| 2.0586 | 0.92 | 122000 | 1.7550 |
| 2.0498 | 0.93 | 124000 | 1.7543 |
| 2.0455 | 0.95 | 126000 | 1.7510 |
| 2.04 | 0.96 | 128000 | 1.7439 |
| 2.0385 | 0.98 | 130000 | 1.7407 |
| 2.0267 | 0.99 | 132000 | 1.7467 |
| 2.0088 | 1.01 | 134000 | 1.7455 |
| 1.9826 | 1.02 | 136000 | 1.7210 |
| 1.9785 | 1.04 | 138000 | 1.7524 |
| 1.9777 | 1.05 | 140000 | 1.7272 |
| 1.9763 | 1.07 | 142000 | 1.7283 |
| 1.9736 | 1.08 | 144000 | 1.7210 |
| 1.9704 | 1.1 | 146000 | 1.7001 |
| 1.9625 | 1.11 | 148000 | 1.7112 |
| 1.9665 | 1.13 | 150000 | 1.7236 |
| 1.9592 | 1.14 | 152000 | 1.7169 |
| 1.9606 | 1.16 | 154000 | 1.6962 |
| 1.9571 | 1.17 | 156000 | 1.7064 |
| 1.9532 | 1.19 | 158000 | 1.6898 |
| 1.9465 | 1.2 | 160000 | 1.7004 |
| 1.9438 | 1.22 | 162000 | 1.7092 |
| 1.9435 | 1.23 | 164000 | 1.6927 |
| 1.9361 | 1.25 | 166000 | 1.6838 |
| 1.9369 | 1.26 | 168000 | 1.6784 |
| 1.9287 | 1.28 | 170000 | 1.6709 |
| 1.928 | 1.29 | 172000 | 1.6735 |
| 1.9227 | 1.31 | 174000 | 1.6689 |
| 1.9213 | 1.32 | 176000 | 1.6685 |
| 1.9152 | 1.34 | 178000 | 1.6635 |
| 1.9092 | 1.35 | 180000 | 1.6561 |
| 1.9059 | 1.37 | 182000 | 1.6673 |
| 1.9094 | 1.38 | 184000 | 1.6717 |
| 1.9006 | 1.4 | 186000 | 1.6593 |
| 1.8956 | 1.41 | 188000 | 1.6483 |
| 1.8972 | 1.43 | 190000 | 1.6635 |
| 1.8907 | 1.44 | 192000 | 1.6604 |
| 1.8885 | 1.46 | 194000 | 1.6465 |
| 1.8844 | 1.47 | 196000 | 1.6444 |
| 1.8799 | 1.49 | 198000 | 1.6307 |
| 1.8813 | 1.5 | 200000 | 1.6240 |
| 1.8693 | 1.52 | 202000 | 1.6102 |
| 1.8768 | 1.53 | 204000 | 1.6197 |
| 1.8678 | 1.55 | 206000 | 1.6275 |
| 1.8588 | 1.56 | 208000 | 1.6183 |
| 1.8585 | 1.58 | 210000 | 1.6197 |
| 1.8564 | 1.59 | 212000 | 1.6004 |
| 1.8493 | 1.61 | 214000 | 1.6078 |
| 1.85 | 1.62 | 216000 | 1.6001 |
| 1.8428 | 1.64 | 218000 | 1.6106 |
| 1.8428 | 1.65 | 220000 | 1.5866 |
| 1.8423 | 1.67 | 222000 | 1.5993 |
| 1.8352 | 1.68 | 224000 | 1.6052 |
| 1.8385 | 1.7 | 226000 | 1.5959 |
| 1.8307 | 1.71 | 228000 | 1.6024 |
| 1.8248 | 1.73 | 230000 | 1.5969 |
| 1.82 | 1.74 | 232000 | 1.5878 |
| 1.8254 | 1.76 | 234000 | 1.5934 |
| 1.8188 | 1.77 | 236000 | 1.5827 |
| 1.813 | 1.79 | 238000 | 1.5797 |
| 1.8128 | 1.8 | 240000 | 1.5758 |
| 1.8044 | 1.82 | 242000 | 1.5752 |
| 1.808 | 1.83 | 244000 | 1.5818 |
| 1.8025 | 1.85 | 246000 | 1.5772 |
| 1.7992 | 1.86 | 248000 | 1.5738 |
| 1.8021 | 1.88 | 250000 | 1.5752 |
| 1.7988 | 1.89 | 252000 | 1.5717 |
| 1.7967 | 1.91 | 254000 | 1.5690 |
| 1.7909 | 1.92 | 256000 | 1.5607 |
| 1.7942 | 1.94 | 258000 | 1.5618 |
| 1.7897 | 1.95 | 260000 | 1.5585 |
| 1.7871 | 1.97 | 262000 | 1.5576 |
| 1.7843 | 1.98 | 264000 | 1.5577 |
| 1.7888 | 2.0 | 266000 | 1.5583 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
meghazisofiane/opus-mt-en-ar-evaluated-en-to-ar-4000instances-opus-leaningRate2e-05-batchSize8-11-action-1
|
meghazisofiane
| 2022-06-11T21:50:40Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:opus100",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-11T21:33:30Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- opus100
metrics:
- bleu
model-index:
- name: opus-mt-en-ar-evaluated-en-to-ar-4000instances-opus-leaningRate2e-05-batchSize8-11-action-1
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: opus100
type: opus100
args: ar-en
metrics:
- name: Bleu
type: bleu
value: 26.8232
---
<!-- 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. -->
# opus-mt-en-ar-evaluated-en-to-ar-4000instances-opus-leaningRate2e-05-batchSize8-11-action-1
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ar](https://huggingface.co/Helsinki-NLP/opus-mt-en-ar) on the opus100 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1717
- Bleu: 26.8232
- Meteor: 0.172
- Gen Len: 12.1288
## 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: 11
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Meteor | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|
| 0.7364 | 0.25 | 100 | 0.1731 | 27.2753 | 0.1729 | 12.0887 |
| 0.2175 | 0.5 | 200 | 0.1731 | 27.2055 | 0.1722 | 11.5675 |
| 0.2193 | 0.75 | 300 | 0.1722 | 27.3277 | 0.1798 | 12.1325 |
| 0.2321 | 1.0 | 400 | 0.1750 | 27.5152 | 0.1762 | 11.925 |
| 0.1915 | 1.25 | 500 | 0.1690 | 27.5043 | 0.1751 | 11.9038 |
| 0.1794 | 1.5 | 600 | 0.1719 | 26.8607 | 0.1713 | 11.8138 |
| 0.1741 | 1.75 | 700 | 0.1725 | 26.974 | 0.1724 | 11.8462 |
| 0.1732 | 2.0 | 800 | 0.1717 | 26.8232 | 0.172 | 12.1288 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
tjscollins/q-Taxi-v3
|
tjscollins
| 2022-06-11T21:37:49Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-11T21:00:50Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 12.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="tjscollins/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
meghazisofiane/opus-mt-en-ar-evaluated-en-to-ar-2000instancesopus-leaningRate2e-05-batchSize8-11epoch-3
|
meghazisofiane
| 2022-06-11T21:27:25Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:opus100",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-11T19:41:31Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- opus100
metrics:
- bleu
model-index:
- name: opus-mt-en-ar-evaluated-en-to-ar-2000instancesopus-leaningRate2e-05-batchSize8-11epoch-3
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: opus100
type: opus100
args: ar-en
metrics:
- name: Bleu
type: bleu
value: 26.2629
---
<!-- 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. -->
# opus-mt-en-ar-evaluated-en-to-ar-2000instancesopus-leaningRate2e-05-batchSize8-11epoch-3
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ar](https://huggingface.co/Helsinki-NLP/opus-mt-en-ar) on the opus100 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1959
- Bleu: 26.2629
- Meteor: 0.1703
- Gen Len: 11.0925
## 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: 11
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Meteor | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|
| 1.0519 | 0.5 | 100 | 0.1985 | 27.3525 | 0.1815 | 11.0725 |
| 0.1947 | 1.0 | 200 | 0.1902 | 26.9728 | 0.1789 | 10.82 |
| 0.1489 | 1.5 | 300 | 0.1910 | 27.7003 | 0.1811 | 10.975 |
| 0.1665 | 2.0 | 400 | 0.1905 | 26.3739 | 0.1772 | 11.1075 |
| 0.1321 | 2.5 | 500 | 0.1926 | 26.752 | 0.1772 | 10.975 |
| 0.1271 | 3.0 | 600 | 0.1927 | 27.3663 | 0.1751 | 10.9725 |
| 0.1105 | 3.5 | 700 | 0.1952 | 27.134 | 0.1738 | 10.9975 |
| 0.109 | 4.0 | 800 | 0.1959 | 26.2629 | 0.1703 | 11.0925 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
MyMild/bert-finetuned-squad
|
MyMild
| 2022-06-11T21:24:26Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-06-11T20:26:57Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.15.0
- Pytorch 1.11.0+cu113
- Datasets 1.17.0
- Tokenizers 0.10.3
|
tjscollins/q-FrozenLake-v1-4x4-slippery
|
tjscollins
| 2022-06-11T20:58:40Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-11T20:32:22Z |
---
tags:
- FrozenLake-v1-4x4-4x4
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-slippery
results:
- metrics:
- type: mean_reward
value: 0.75 +/- 0.43
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-4x4
type: FrozenLake-v1-4x4-4x4
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="tjscollins/q-FrozenLake-v1-4x4-slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
huggingtweets/elonmusk-rshowerthoughts-stephenking
|
huggingtweets
| 2022-06-11T20:15:51Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-11T20:04:06Z |
---
language: en
thumbnail: http://www.huggingtweets.com/elonmusk-rshowerthoughts-stephenking/1654978546952/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/1529956155937759233/Nyn1HZWF_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/378800000836981162/b683f7509ec792c3e481ead332940cdc_400x400.jpeg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/641699738224455680/L_ji6ClT_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Elon Musk & Stephen King & Showerthoughts</div>
<div style="text-align: center; font-size: 14px;">@elonmusk-rshowerthoughts-stephenking</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 Elon Musk & Stephen King & Showerthoughts.
| Data | Elon Musk | Stephen King | Showerthoughts |
| --- | --- | --- | --- |
| Tweets downloaded | 3200 | 3230 | 3200 |
| Retweets | 147 | 780 | 0 |
| Short tweets | 954 | 202 | 0 |
| Tweets kept | 2099 | 2248 | 3200 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1fvudd5c/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 @elonmusk-rshowerthoughts-stephenking's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/39f9xftz) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/39f9xftz/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/elonmusk-rshowerthoughts-stephenking')
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)
|
meln1k/qrdqn-SpaceInvadersNoFrameskip-v4
|
meln1k
| 2022-06-11T19:51:36Z | 5 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-11T09:29:19Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: QRDQN
results:
- metrics:
- type: mean_reward
value: 2581.50 +/- 1151.96
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **QRDQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **QRDQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -orga meln1k -f logs/
python enjoy.py --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga meln1k
```
## Hyperparameters
```python
OrderedDict([('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_fraction', 0.025),
('frame_stack', 4),
('n_timesteps', 10000000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('normalize', False)])
```
|
huggingtweets/rshowerthoughts-stephenking
|
huggingtweets
| 2022-06-11T19:50:01Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-11T19:42:08Z |
---
language: en
thumbnail: http://www.huggingtweets.com/rshowerthoughts-stephenking/1654976942704/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/378800000836981162/b683f7509ec792c3e481ead332940cdc_400x400.jpeg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/641699738224455680/L_ji6ClT_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Stephen King & Showerthoughts</div>
<div style="text-align: center; font-size: 14px;">@rshowerthoughts-stephenking</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 King & Showerthoughts.
| Data | Stephen King | Showerthoughts |
| --- | --- | --- |
| Tweets downloaded | 3230 | 3200 |
| Retweets | 780 | 0 |
| Short tweets | 202 | 0 |
| Tweets kept | 2248 | 3200 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2bn3s9yg/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 @rshowerthoughts-stephenking's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2waq2b3w) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2waq2b3w/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/rshowerthoughts-stephenking')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/mdoukmas
|
huggingtweets
| 2022-06-11T19:35:54Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-11T19:34:24Z |
---
language: en
thumbnail: http://www.huggingtweets.com/mdoukmas/1654976150184/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/1098660288193269762/n5v9daol_400x400.png')">
</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">Maya Dukmasova</div>
<div style="text-align: center; font-size: 14px;">@mdoukmas</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 Maya Dukmasova.
| Data | Maya Dukmasova |
| --- | --- |
| Tweets downloaded | 3241 |
| Retweets | 896 |
| Short tweets | 158 |
| Tweets kept | 2187 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2jwhv7l5/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 @mdoukmas's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/25v3pmsy) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/25v3pmsy/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/mdoukmas')
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)
|
meghazisofiane/opus-mt-en-ar-evaluated-en-to-ar-1000instancesopus-leaningRate2e-05-batchSize8-11epoch-3
|
meghazisofiane
| 2022-06-11T19:25:04Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:opus100",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-11T19:16:47Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- opus100
metrics:
- bleu
model-index:
- name: opus-mt-en-ar-evaluated-en-to-ar-1000instancesopus-leaningRate2e-05-batchSize8-11epoch-3
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: opus100
type: opus100
args: ar-en
metrics:
- name: Bleu
type: bleu
value: 21.3028
---
<!-- 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. -->
# opus-mt-en-ar-evaluated-en-to-ar-1000instancesopus-leaningRate2e-05-batchSize8-11epoch-3
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ar](https://huggingface.co/Helsinki-NLP/opus-mt-en-ar) on the opus100 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1421
- Bleu: 21.3028
- Meteor: 0.1285
- Gen Len: 9.975
## 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: 11
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Meteor | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|
| 1.0508 | 1.0 | 100 | 0.1413 | 27.9009 | 0.1416 | 8.85 |
| 0.1253 | 2.0 | 200 | 0.1372 | 23.11 | 0.1345 | 9.855 |
| 0.1017 | 3.0 | 300 | 0.1390 | 21.7885 | 0.1364 | 9.97 |
| 0.0868 | 4.0 | 400 | 0.1378 | 21.3889 | 0.1314 | 9.835 |
| 0.0754 | 5.0 | 500 | 0.1398 | 22.198 | 0.132 | 9.675 |
| 0.0667 | 6.0 | 600 | 0.1396 | 20.8645 | 0.1308 | 10.055 |
| 0.0604 | 7.0 | 700 | 0.1408 | 20.289 | 0.1303 | 10.53 |
| 0.0553 | 8.0 | 800 | 0.1414 | 21.7023 | 0.1293 | 10.005 |
| 0.0518 | 9.0 | 900 | 0.1421 | 21.3028 | 0.1285 | 9.975 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
huggingtweets/elonmusk-iamjohnoliver-neiltyson
|
huggingtweets
| 2022-06-11T19:00:50Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-11T18:54:15Z |
---
language: en
thumbnail: http://www.huggingtweets.com/elonmusk-iamjohnoliver-neiltyson/1654974044761/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/1529956155937759233/Nyn1HZWF_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1393958859/main_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/74188698/NeilTysonOriginsA-Crop_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Elon Musk & John Oliver & Neil deGrasse Tyson</div>
<div style="text-align: center; font-size: 14px;">@elonmusk-iamjohnoliver-neiltyson</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 Elon Musk & John Oliver & Neil deGrasse Tyson.
| Data | Elon Musk | John Oliver | Neil deGrasse Tyson |
| --- | --- | --- | --- |
| Tweets downloaded | 3200 | 636 | 3237 |
| Retweets | 147 | 122 | 10 |
| Short tweets | 954 | 9 | 87 |
| Tweets kept | 2099 | 505 | 3140 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/14h905cr/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 @elonmusk-iamjohnoliver-neiltyson's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3gcc5ko3) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3gcc5ko3/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/elonmusk-iamjohnoliver-neiltyson')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/rterdogan
|
huggingtweets
| 2022-06-11T18:56:47Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1151410974240444416/yVvaD7hU_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">Recep Tayyip Erdoğan</div>
<div style="text-align: center; font-size: 14px;">@rterdogan</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 Recep Tayyip Erdoğan.
| Data | Recep Tayyip Erdoğan |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 418 |
| Short tweets | 54 |
| Tweets kept | 2778 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/wf1dbaih/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 @rterdogan's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1a3w2qxa) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1a3w2qxa/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/rterdogan')
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)
|
Galeros/dqn-mountaincar-v0-local
|
Galeros
| 2022-06-11T18:38:27Z | 3 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"MountainCar-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-11T18:38:19Z |
---
library_name: stable-baselines3
tags:
- MountainCar-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: -98.80 +/- 21.88
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: MountainCar-v0
type: MountainCar-v0
---
# **DQN** Agent playing **MountainCar-v0**
This is a trained model of a **DQN** agent playing **MountainCar-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
lllFaNToMlll/wac2vec-lllfantomlll
|
lllFaNToMlll
| 2022-06-11T18:07:44Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-06-11T11:42:50Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wac2vec-lllfantomlll
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. -->
# wac2vec-lllfantomlll
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5560
- Wer: 0.3417
## 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: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.5768 | 1.0 | 500 | 2.0283 | 1.0238 |
| 0.9219 | 2.01 | 1000 | 0.5103 | 0.5022 |
| 0.4497 | 3.01 | 1500 | 0.4746 | 0.4669 |
| 0.3163 | 4.02 | 2000 | 0.4144 | 0.4229 |
| 0.2374 | 5.02 | 2500 | 0.4186 | 0.4161 |
| 0.2033 | 6.02 | 3000 | 0.4115 | 0.3975 |
| 0.1603 | 7.03 | 3500 | 0.4424 | 0.3817 |
| 0.1455 | 8.03 | 4000 | 0.4151 | 0.3918 |
| 0.1276 | 9.04 | 4500 | 0.4940 | 0.3798 |
| 0.108 | 10.04 | 5000 | 0.4580 | 0.3688 |
| 0.1053 | 11.04 | 5500 | 0.4243 | 0.3700 |
| 0.0929 | 12.05 | 6000 | 0.4999 | 0.3727 |
| 0.0896 | 13.05 | 6500 | 0.4991 | 0.3624 |
| 0.0748 | 14.06 | 7000 | 0.4924 | 0.3602 |
| 0.0681 | 15.06 | 7500 | 0.4908 | 0.3544 |
| 0.0619 | 16.06 | 8000 | 0.5021 | 0.3559 |
| 0.0569 | 17.07 | 8500 | 0.5448 | 0.3518 |
| 0.0549 | 18.07 | 9000 | 0.4919 | 0.3508 |
| 0.0478 | 19.08 | 9500 | 0.4704 | 0.3513 |
| 0.0437 | 20.08 | 10000 | 0.5058 | 0.3555 |
| 0.0421 | 21.08 | 10500 | 0.5127 | 0.3489 |
| 0.0362 | 22.09 | 11000 | 0.5439 | 0.3527 |
| 0.0322 | 23.09 | 11500 | 0.5418 | 0.3469 |
| 0.0327 | 24.1 | 12000 | 0.5298 | 0.3422 |
| 0.0292 | 25.1 | 12500 | 0.5511 | 0.3426 |
| 0.0246 | 26.1 | 13000 | 0.5349 | 0.3472 |
| 0.0251 | 27.11 | 13500 | 0.5646 | 0.3391 |
| 0.0214 | 28.11 | 14000 | 0.5821 | 0.3424 |
| 0.0217 | 29.12 | 14500 | 0.5560 | 0.3417 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
|
huggingnft/frames
|
huggingnft
| 2022-06-11T17:38:03Z | 5 | 0 |
transformers
|
[
"transformers",
"huggingnft",
"nft",
"huggan",
"gan",
"image",
"images",
"unconditional-image-generation",
"dataset:huggingnft/frames",
"license:mit",
"endpoints_compatible",
"region:us"
] |
unconditional-image-generation
| 2022-06-11T14:58:47Z |
---
tags:
- huggingnft
- nft
- huggan
- gan
- image
- images
- unconditional-image-generation
datasets:
- huggingnft/frames
license: mit
---
# Hugging NFT: frames
## Disclaimer
All rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright
holder.
## Model description
LightWeight GAN model for unconditional generation.
NFT collection available [here](https://opensea.io/collection/frames).
Dataset is available [here](https://huggingface.co/datasets/huggingnft/frames).
Check Space: [link](https://huggingface.co/spaces/AlekseyKorshuk/huggingnft).
Project repository: [link](https://github.com/AlekseyKorshuk/huggingnft).
[](https://github.com/AlekseyKorshuk/huggingnft)
## Intended uses & limitations
#### How to use
Check project repository: [link](https://github.com/AlekseyKorshuk/huggingnft).
#### Limitations and bias
Check project repository: [link](https://github.com/AlekseyKorshuk/huggingnft).
## Training data
Dataset is available [here](https://huggingface.co/datasets/huggingnft/frames).
## Training procedure
Training script is available [here](https://github.com/AlekseyKorshuk/huggingnft).
## Generated Images
Check results with Space: [link](https://huggingface.co/spaces/AlekseyKorshuk/huggingnft).
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingnft)
### BibTeX entry and citation info
```bibtex
@InProceedings{huggingnft,
author={Aleksey Korshuk}
year=2022
}
```
|
DancingIguana/codeparrot-ds
|
DancingIguana
| 2022-06-11T16:58:04Z | 107 | 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
| 2022-06-08T21:56:49Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: codeparrot-ds
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
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) 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: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
YeRyeongLee/bertweet-base-finetuned-filtered-0609
|
YeRyeongLee
| 2022-06-11T16:50:19Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-11T15:37:42Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: bertweet-base-finetuned-filtered-0609
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. -->
# bertweet-base-finetuned-filtered-0609
This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5397
- Accuracy: 0.9299
- Precision: 0.9297
- Recall: 0.9299
- F1: 0.9298
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.331 | 1.0 | 3180 | 0.3687 | 0.9069 | 0.9147 | 0.9069 | 0.9081 |
| 0.2611 | 2.0 | 6360 | 0.3725 | 0.9223 | 0.9227 | 0.9223 | 0.9224 |
| 0.1993 | 3.0 | 9540 | 0.2948 | 0.9336 | 0.9350 | 0.9336 | 0.9339 |
| 0.1648 | 4.0 | 12720 | 0.3563 | 0.9296 | 0.9303 | 0.9296 | 0.9298 |
| 0.1324 | 5.0 | 15900 | 0.4136 | 0.9267 | 0.9279 | 0.9267 | 0.9270 |
| 0.1102 | 6.0 | 19080 | 0.4060 | 0.9352 | 0.9357 | 0.9352 | 0.9353 |
| 0.0568 | 7.0 | 22260 | 0.4653 | 0.9321 | 0.9328 | 0.9321 | 0.9322 |
| 0.0292 | 8.0 | 25440 | 0.4818 | 0.9311 | 0.9310 | 0.9311 | 0.9310 |
| 0.0155 | 9.0 | 28620 | 0.5405 | 0.9286 | 0.9288 | 0.9286 | 0.9286 |
| 0.0095 | 10.0 | 31800 | 0.5397 | 0.9299 | 0.9297 | 0.9299 | 0.9298 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.9.1+cu111
- Datasets 1.16.1
- Tokenizers 0.12.1
|
bubblecookie/t5-small-finetuned-cnndm_trained
|
bubblecookie
| 2022-06-11T16:48:45Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-10T06:21:02Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
model-index:
- name: t5-small-finetuned-cnndm_trained
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-cnndm_trained
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dailymail 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: 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
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
robingeibel/longformer-base-finetuned-big_patent
|
robingeibel
| 2022-06-11T16:33:49Z | 62 | 1 |
transformers
|
[
"transformers",
"tf",
"longformer",
"fill-mask",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-05T17:24:27Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: robingeibel/longformer-base-finetuned-big_patent
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. -->
# robingeibel/longformer-base-finetuned-big_patent
This model is a fine-tuned version of [robingeibel/longformer-base-finetuned-big_patent](https://huggingface.co/robingeibel/longformer-base-finetuned-big_patent) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.1860
- Validation Loss: 1.0692
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 152946, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.1860 | 1.0692 | 0 |
### Framework versions
- Transformers 4.19.4
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1
|
tuni/distilbert-base-uncased-finetuned-cola
|
tuni
| 2022-06-11T15:12:53Z | 17 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-11T13:50:38Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5324115893962171
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7035
- Matthews Correlation: 0.5324
## 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: 3.785228097724678e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 28
- 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 | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5227 | 1.0 | 535 | 0.5005 | 0.4121 |
| 0.318 | 2.0 | 1070 | 0.5265 | 0.4977 |
| 0.1887 | 3.0 | 1605 | 0.7035 | 0.5324 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
antonioricciardi/CarRacing-v0
|
antonioricciardi
| 2022-06-11T14:26:51Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"CarRacing-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-11T14:26:00Z |
---
library_name: stable-baselines3
tags:
- CarRacing-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: -75.94 +/- 1.29
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CarRacing-v0
type: CarRacing-v0
---
# **PPO** Agent playing **CarRacing-v0**
This is a trained model of a **PPO** agent playing **CarRacing-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
neeenway/ppo-LunarLander-v2
|
neeenway
| 2022-06-11T13:43:31Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-11T13:43:03Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: ppo
results:
- metrics:
- type: mean_reward
value: 240.31 +/- 12.46
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **ppo** Agent playing **LunarLander-v2**
This is a trained model of a **ppo** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Akshat/xlm-roberta-base-finetuned-panx-de
|
Akshat
| 2022-06-11T13:35:25Z | 104 | 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
| 2022-06-11T12:19:48Z |
---
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: 0.8611443210930829
---
<!-- 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: 0.1405
- F1: 0.8611
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2542 | 1.0 | 787 | 0.1788 | 0.8083 |
| 0.1307 | 2.0 | 1574 | 0.1371 | 0.8488 |
| 0.0784 | 3.0 | 2361 | 0.1405 | 0.8611 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
YeRyeongLee/albert-base-v2-finetuned-filtered-0609
|
YeRyeongLee
| 2022-06-11T13:33:02Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-11T11:46:52Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: albert-base-v2-finetuned-filtered-0609
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. -->
# albert-base-v2-finetuned-filtered-0609
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2062
- Accuracy: 0.9723
- Precision: 0.9724
- Recall: 0.9723
- F1: 0.9723
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.2688 | 1.0 | 3180 | 0.2282 | 0.9560 | 0.9577 | 0.9560 | 0.9562 |
| 0.2268 | 2.0 | 6360 | 0.1909 | 0.9638 | 0.9640 | 0.9638 | 0.9638 |
| 0.1831 | 3.0 | 9540 | 0.2590 | 0.9572 | 0.9584 | 0.9572 | 0.9572 |
| 0.1588 | 4.0 | 12720 | 0.1752 | 0.9673 | 0.9678 | 0.9673 | 0.9673 |
| 0.0972 | 5.0 | 15900 | 0.1868 | 0.9695 | 0.9696 | 0.9695 | 0.9695 |
| 0.0854 | 6.0 | 19080 | 0.2042 | 0.9701 | 0.9707 | 0.9701 | 0.9702 |
| 0.0599 | 7.0 | 22260 | 0.1793 | 0.9748 | 0.9749 | 0.9748 | 0.9749 |
| 0.0389 | 8.0 | 25440 | 0.1996 | 0.9742 | 0.9743 | 0.9742 | 0.9742 |
| 0.0202 | 9.0 | 28620 | 0.2188 | 0.9723 | 0.9726 | 0.9723 | 0.9724 |
| 0.0152 | 10.0 | 31800 | 0.2062 | 0.9723 | 0.9724 | 0.9723 | 0.9723 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.9.1+cu111
- Datasets 1.16.1
- Tokenizers 0.12.1
|
marieke93/BERT-evidence-types
|
marieke93
| 2022-06-11T13:32:10Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-08T11:54:50Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: BERT-evidence-types
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-evidence-types
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the evidence types dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8008
- Macro f1: 0.4227
- Weighted f1: 0.6976
- Accuracy: 0.7154
- Balanced accuracy: 0.3876
## Training and evaluation data
The data set, as well as the code that was used to fine tune this model can be found in the GitHub repository [BA-Thesis-Information-Science-Persuasion-Strategies](https://github.com/mariekevdh/BA-Thesis-Information-Science-Persuasion-Strategies)
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-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: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Macro f1 | Weighted f1 | Accuracy | Balanced accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:-----------------:|
| 1.1148 | 1.0 | 125 | 1.0531 | 0.2566 | 0.6570 | 0.6705 | 0.2753 |
| 0.7546 | 2.0 | 250 | 0.9725 | 0.3424 | 0.6947 | 0.7002 | 0.3334 |
| 0.4757 | 3.0 | 375 | 1.1375 | 0.3727 | 0.7113 | 0.7184 | 0.3680 |
| 0.2637 | 4.0 | 500 | 1.3585 | 0.3807 | 0.6836 | 0.6910 | 0.3805 |
| 0.1408 | 5.0 | 625 | 1.6605 | 0.3785 | 0.6765 | 0.6872 | 0.3635 |
| 0.0856 | 6.0 | 750 | 1.9703 | 0.3802 | 0.6890 | 0.7047 | 0.3704 |
| 0.0502 | 7.0 | 875 | 2.1245 | 0.4067 | 0.6995 | 0.7169 | 0.3751 |
| 0.0265 | 8.0 | 1000 | 2.2676 | 0.3756 | 0.6816 | 0.6925 | 0.3647 |
| 0.0147 | 9.0 | 1125 | 2.4286 | 0.4052 | 0.6887 | 0.7062 | 0.3803 |
| 0.0124 | 10.0 | 1250 | 2.5773 | 0.4084 | 0.6853 | 0.7040 | 0.3695 |
| 0.0111 | 11.0 | 1375 | 2.5941 | 0.4146 | 0.6915 | 0.7085 | 0.3834 |
| 0.0076 | 12.0 | 1500 | 2.6124 | 0.4157 | 0.6936 | 0.7078 | 0.3863 |
| 0.0067 | 13.0 | 1625 | 2.7050 | 0.4139 | 0.6925 | 0.7108 | 0.3798 |
| 0.0087 | 14.0 | 1750 | 2.6695 | 0.4252 | 0.7009 | 0.7169 | 0.3920 |
| 0.0056 | 15.0 | 1875 | 2.7357 | 0.4257 | 0.6985 | 0.7161 | 0.3868 |
| 0.0054 | 16.0 | 2000 | 2.7389 | 0.4249 | 0.6955 | 0.7116 | 0.3890 |
| 0.0051 | 17.0 | 2125 | 2.7767 | 0.4197 | 0.6967 | 0.7146 | 0.3863 |
| 0.004 | 18.0 | 2250 | 2.7947 | 0.4211 | 0.6977 | 0.7154 | 0.3876 |
| 0.0041 | 19.0 | 2375 | 2.8030 | 0.4204 | 0.6953 | 0.7131 | 0.3855 |
| 0.0042 | 20.0 | 2500 | 2.8008 | 0.4227 | 0.6976 | 0.7154 | 0.3876 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
send-it/dqn-SpaceInvadersNoFrameskip-v4
|
send-it
| 2022-06-11T13:31:04Z | 7 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-11T13:30:29Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 558.50 +/- 102.18
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga send-it -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga send-it
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
titi7242229/roberta-base-bne-finetuned_personality_multi_3
|
titi7242229
| 2022-06-11T13:13:47Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-11T07:10:27Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-base-bne-finetuned_personality_multi_3
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. -->
# roberta-base-bne-finetuned_personality_multi_3
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1145
- Accuracy: 0.4847
## 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: 1e-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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2498 | 1.0 | 63 | 2.2799 | 0.2236 |
| 2.3044 | 2.0 | 126 | 2.1644 | 0.2980 |
| 1.9017 | 3.0 | 189 | 1.9934 | 0.4127 |
| 2.2281 | 4.0 | 252 | 1.8517 | 0.4501 |
| 1.2955 | 5.0 | 315 | 1.7588 | 0.4870 |
| 1.221 | 6.0 | 378 | 1.7269 | 0.4888 |
| 1.1381 | 7.0 | 441 | 1.7617 | 0.4888 |
| 0.8415 | 8.0 | 504 | 1.8101 | 0.4853 |
| 0.6696 | 9.0 | 567 | 1.8325 | 0.4928 |
| 0.6646 | 10.0 | 630 | 1.8707 | 0.4841 |
| 0.3758 | 11.0 | 693 | 1.8766 | 0.4876 |
| 0.3477 | 12.0 | 756 | 1.9171 | 0.4905 |
| 0.2854 | 13.0 | 819 | 1.9203 | 0.4980 |
| 0.2713 | 14.0 | 882 | 2.0089 | 0.4813 |
| 0.3434 | 15.0 | 945 | 2.0130 | 0.4905 |
| 0.0758 | 16.0 | 1008 | 2.0230 | 0.4922 |
| 0.2518 | 17.0 | 1071 | 2.0793 | 0.4824 |
| 0.0783 | 18.0 | 1134 | 2.0920 | 0.4830 |
| 0.0933 | 19.0 | 1197 | 2.1067 | 0.4836 |
| 0.184 | 20.0 | 1260 | 2.1145 | 0.4847 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
antonioricciardi/FrozenLake-v1
|
antonioricciardi
| 2022-06-11T13:06:56Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"FrozenLake-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-11T13:06:48Z |
---
library_name: stable-baselines3
tags:
- FrozenLake-v1
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1
type: FrozenLake-v1
---
# **PPO** Agent playing **FrozenLake-v1**
This is a trained model of a **PPO** agent playing **FrozenLake-v1**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
DavidCollier/SpaceInvader
|
DavidCollier
| 2022-06-11T12:40:06Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-11T12:39:28Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 15.50 +/- 12.54
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga DavidCollier -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga DavidCollier
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
huggingtweets/adrianramy
|
huggingtweets
| 2022-06-11T12:12:59Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-11T12:12:20Z |
---
language: en
thumbnail: http://www.huggingtweets.com/adrianramy/1654949574810/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/1192394634305134593/kWwF0YSv_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">Adri</div>
<div style="text-align: center; font-size: 14px;">@adrianramy</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 Adri.
| Data | Adri |
| --- | --- |
| Tweets downloaded | 3050 |
| Retweets | 1585 |
| Short tweets | 275 |
| Tweets kept | 1190 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/30dqbz5d/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 @adrianramy's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/16tp54yl) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/16tp54yl/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/adrianramy')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
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Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.