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81
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int64 0
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ConstellationBoi/Oop | [] | null | {
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 261.06 +/- 28.61
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
|
Contrastive-Tension/BERT-Base-CT-STSb | [
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"tf",
"jax",
"bert",
"feature-extraction",
"transformers"
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} | 5 | null | ---
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: electricidad-base-finetuned-parmex
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# electricidad-base-finetuned-parmex
This model is a fine-tuned version of [mrm8488/electricidad-base-discriminator](https://huggingface.co/mrm8488/electricidad-base-discriminator) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0372
- F1: 0.9764
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 8.309269976237555e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 208 | 0.0377 | 0.9801 |
| No log | 2.0 | 416 | 0.0372 | 0.9764 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Contrastive-Tension/BERT-Base-CT | [
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"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
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} | 16 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
metrics:
- accuracy
model-index:
- name: roberta-base-bne-finetuned-amazon_reviews_multi
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_reviews_multi
type: amazon_reviews_multi
args: es
metrics:
- name: Accuracy
type: accuracy
value: 0.93425
---
<!-- 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-amazon_reviews_multi
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2291
- Accuracy: 0.9343
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1909 | 1.0 | 1250 | 0.1784 | 0.9295 |
| 0.1013 | 2.0 | 2500 | 0.2291 | 0.9343 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Contrastive-Tension/BERT-Distil-CT-STSb | [
"pytorch",
"tf",
"distilbert",
"feature-extraction",
"transformers"
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} | 1 | null | ---
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: 0.8620945214069894
---
<!-- 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: 0.1372
- F1: 0.8621
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2575 | 1.0 | 525 | 0.1621 | 0.8292 |
| 0.1287 | 2.0 | 1050 | 0.1378 | 0.8526 |
| 0.0831 | 3.0 | 1575 | 0.1372 | 0.8621 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Contrastive-Tension/BERT-Distil-CT | [
"pytorch",
"tf",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
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} | 9 | null | ---
language:
- de
tags:
- Text Classification
- sentiment
- Simpletransformers
- deepset/gbert-base
---
This gBert-base model was finetuned on a sentiment prediction task with tweets from German politician during the German Federal Election in 2021.
## Model Description:
This model was trained on ~30.000 annotated tweets in German language on its sentiment. It can predict tweets as negative, positive or neutral. It achieved an accuracy of 93% on the specific dataset.
## Model Implementation
You can implement this model for example with Simpletransformers. First you have to unpack the file.
def unpack_model(model_name=''):
tar = tarfile.open(f"{model_name}.tar.gz", "r:gz")
tar.extractall()
tar.close()
The hyperparameter were defined as follows:
train_args ={"reprocess_input_data": True,
"fp16":False,
"num_train_epochs": 4,
"overwrite_output_dir":True,
"train_batch_size": 32,
"eval_batch_size": 32}
Now create the model:
unpack_model(YOUR_DOWNLOADED_FILE_HERE)
model = ClassificationModel(
"bert", "content/outputs/",
num_labels= 3,
args=train_args
)
In this case for the output:
- 0 = positive
- 1 = negative
- 2 = neutral
Example for a positive prediction:
model.predict(["Das ist gut! Wir danken dir."])
([0], array([[ 2.06561327, -3.57908797, 1.5340755 ]]))
Example for a negative prediction:
model.predict(["Ich hasse dich!"])
([1], array([[-3.50486898, 4.29590368, -0.9000684 ]]))
Example for a neutral prediction:
model.predict(["Heute ist Sonntag."])
([2], array([[-2.94458342, -2.91875601, 4.94414234]]))
This model was created by Maximilian Weissenbacher for a project at the University of Regensburg. |
Contrastive-Tension/BERT-Distil-NLI-CT | [
"pytorch",
"tf",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
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} | 6 | null | ---
language: ja
license: cc-by-sa-4.0
datasets:
- wikipedia
- cc100
mask_token: "[MASK]"
widget:
- text: "早稲田 大学 で 自然 言語 処理 を [MASK] する 。"
---
# nlp-waseda/roberta-large-japanese
## Model description
This is a Japanese RoBERTa large model pretrained on Japanese Wikipedia and the Japanese portion of CC-100.
## How to use
You can use this model for masked language modeling as follows:
```python
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("nlp-waseda/roberta-large-japanese")
model = AutoModelForMaskedLM.from_pretrained("nlp-waseda/roberta-large-japanese")
sentence = '早稲田 大学 で 自然 言語 処理 を [MASK] する 。' # input should be segmented into words by Juman++ in advance
encoding = tokenizer(sentence, return_tensors='pt')
...
```
You can fine-tune this model on downstream tasks.
## Tokenization
The input text should be segmented into words by [Juman++](https://github.com/ku-nlp/jumanpp) in advance. Juman++ 2.0.0-rc3 was used for pretraining. Each word is tokenized into tokens by [sentencepiece](https://github.com/google/sentencepiece).
`BertJapaneseTokenizer` now supports automatic `JumanppTokenizer` and `SentencepieceTokenizer`. You can use [this model](https://huggingface.co/nlp-waseda/roberta-large-japanese-with-auto-jumanpp) without any data preprocessing.
## Vocabulary
The vocabulary consists of 32000 tokens including words ([JumanDIC](https://github.com/ku-nlp/JumanDIC)) and subwords induced by the unigram language model of [sentencepiece](https://github.com/google/sentencepiece).
## Training procedure
This model was trained on Japanese Wikipedia (as of 20210920) and the Japanese portion of CC-100. It took two weeks using eight NVIDIA A100 GPUs.
The following hyperparameters were used during pretraining:
- learning_rate: 6e-5
- per_device_train_batch_size: 103
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 5
- total_train_batch_size: 4120
- max_seq_length: 128
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-6
- lr_scheduler_type: linear
- training_steps: 670000
- warmup_steps: 10000
- mixed_precision_training: Native AMP
## Performance on JGLUE
See the [Baseline Scores](https://github.com/yahoojapan/JGLUE#baseline-scores) of JGLUE.
|
Contrastive-Tension/BERT-Large-CT-STSb | [
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"tf",
"jax",
"bert",
"feature-extraction",
"transformers"
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} | 7 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 208.07 +/- 67.39
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
|
Contrastive-Tension/RoBerta-Large-CT-STSb | [
"pytorch",
"tf",
"jax",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
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} | 5 | null | ---
datasets: SberDevices/Golos
---
# **Acoustic and language models**
Acoustic model built using [QuartzNet15x5](https://arxiv.org/pdf/1910.10261.pdf) architecture and trained using [NeMo toolkit](https://github.com/NVIDIA/NeMo/tree/r1.0.0b4)
Three n-gram language models created using [KenLM Language Model Toolkit](https://kheafield.com/code/kenlm)
* LM built on [Common Crawl](https://commoncrawl.org) Russian dataset
* LM built on [Golos](https://huggingface.co/datasets/SberDevices/Golos) train set
* LM built on [Common Crawl](https://commoncrawl.org) and [Golos](https://huggingface.co/datasets/SberDevices/Golos) datasets together (50/50)
| Archives | Size | Links |
|--------------------------|------------|-----------------|
| QuartzNet15x5_golos.nemo | 68 MB | https://sc.link/ZMv |
| KenLMs.tar | 4.8 GB | https://sc.link/YL0 |
Golos data and models are also available in the hub of pre-trained models, datasets, and containers - DataHub ML Space. You can train the model and deploy it on the high-performance SberCloud infrastructure in [ML Space](https://sbercloud.ru/ru/aicloud/mlspace) - full-cycle machine learning development platform for DS-teams collaboration based on the Christofari Supercomputer.
## **Evaluation**
Percents of Word Error Rate for different test sets
| Decoder \ Test set | Crowd test | Farfield test | MCV<sup>1</sup> dev | MCV<sup>1</sup> test |
|-------------------------------------|-----------|----------|-----------|----------|
| Greedy decoder | 4.389 % | 14.949 % | 9.314 % | 11.278 % |
| Beam Search with Common Crawl LM | 4.709 % | 12.503 % | 6.341 % | 7.976 % |
| Beam Search with Golos train set LM | 3.548 % | 12.384 % | - | - |
| Beam Search with Common Crawl and Golos LM | 3.318 % | 11.488 % | 6.4 % | 8.06 % |
<sup>1</sup> [Common Voice](https://commonvoice.mozilla.org) - Mozilla's initiative to help teach machines how real people speak.
## **Resources**
[[arxiv.org] Golos: Russian Dataset for Speech Research](https://arxiv.org/abs/2106.10161)
[[habr.com] Golos — самый большой русскоязычный речевой датасет, размеченный вручную, теперь в открытом доступе](https://habr.com/ru/company/sberdevices/blog/559496/)
[[habr.com] Как улучшить распознавание русской речи до 3% WER с помощью открытых данных](https://habr.com/ru/company/sberdevices/blog/569082/) |
CouchCat/ma_ner_v7_distil | [
"pytorch",
"distilbert",
"token-classification",
"en",
"transformers",
"ner",
"license:mit",
"autotrain_compatible"
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} | 13 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 186.57 +/- 75.05
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
|
CouchCat/ma_sa_v7_distil | [
"pytorch",
"distilbert",
"text-classification",
"en",
"transformers",
"sentiment-analysis",
"license:mit"
] | text-classification | {
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} | 38 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 243.43 +/- 22.55
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
|
Cyrell/Cyrell | [] | null | {
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}
} | 0 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/marcfriedrich7/1652179164370/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/1418445526375223297/XdAgs-rW_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">marc friedrich</div>
<div style="text-align: center; font-size: 14px;">@marcfriedrich7</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 marc friedrich.
| Data | marc friedrich |
| --- | --- |
| Tweets downloaded | 3249 |
| Retweets | 705 |
| Short tweets | 672 |
| Tweets kept | 1872 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2p2smtko/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 @marcfriedrich7's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2ly8l45f) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2ly8l45f/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/marcfriedrich7')
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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} | 0 | null | ---
language:
- en
library_name: k2
tags:
- automatic-speech-recognition
- k2
widget:
- example_title: Librispeech sample 1
src: https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless3-2022-05-13/resolve/main/test_wavs/1089-134686-0001.wav
- example_title: Librispeech sample 2
src: https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless3-2022-05-13/resolve/main/test_wavs/1221-135766-0001.wav
- example_title: Librispeech sample 3
src: https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless3-2022-05-13/resolve/main/test_wavs/1221-135766-0002.wav
---
# Introduction
See https://github.com/k2-fsa/icefall/pull/363 |
D3xter1922/distilbert-base-uncased-finetuned-cola | [] | null | {
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} | 0 | null | ---
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: 0.5185
- Wer: 0.3370
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.5137 | 1.0 | 500 | 1.6719 | 0.9580 |
| 0.8324 | 2.01 | 1000 | 0.5546 | 0.5341 |
| 0.4365 | 3.01 | 1500 | 0.4567 | 0.4635 |
| 0.3058 | 4.02 | 2000 | 0.4429 | 0.4454 |
| 0.2284 | 5.02 | 2500 | 0.4734 | 0.4186 |
| 0.1892 | 6.02 | 3000 | 0.4191 | 0.4030 |
| 0.1542 | 7.03 | 3500 | 0.4522 | 0.3985 |
| 0.1364 | 8.03 | 4000 | 0.4749 | 0.3922 |
| 0.1239 | 9.04 | 4500 | 0.4950 | 0.3977 |
| 0.1092 | 10.04 | 5000 | 0.4468 | 0.3779 |
| 0.0956 | 11.04 | 5500 | 0.4897 | 0.3789 |
| 0.0897 | 12.05 | 6000 | 0.4927 | 0.3718 |
| 0.0792 | 13.05 | 6500 | 0.5242 | 0.3699 |
| 0.0731 | 14.06 | 7000 | 0.5202 | 0.3772 |
| 0.0681 | 15.06 | 7500 | 0.5046 | 0.3637 |
| 0.062 | 16.06 | 8000 | 0.5336 | 0.3664 |
| 0.0556 | 17.07 | 8500 | 0.5017 | 0.3633 |
| 0.0556 | 18.07 | 9000 | 0.5466 | 0.3736 |
| 0.0461 | 19.08 | 9500 | 0.5489 | 0.3566 |
| 0.0439 | 20.08 | 10000 | 0.5399 | 0.3559 |
| 0.0397 | 21.08 | 10500 | 0.5154 | 0.3539 |
| 0.0346 | 22.09 | 11000 | 0.5170 | 0.3513 |
| 0.0338 | 23.09 | 11500 | 0.5236 | 0.3492 |
| 0.0342 | 24.1 | 12000 | 0.5288 | 0.3493 |
| 0.0282 | 25.1 | 12500 | 0.5147 | 0.3449 |
| 0.0251 | 26.1 | 13000 | 0.5092 | 0.3442 |
| 0.0268 | 27.11 | 13500 | 0.5093 | 0.3413 |
| 0.021 | 28.11 | 14000 | 0.5310 | 0.3399 |
| 0.022 | 29.12 | 14500 | 0.5185 | 0.3370 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
|
D3xter1922/electra-base-discriminator-finetuned-mnli | [] | null | {
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}
} | 0 | null | ---
tags:
- generated_from_keras_callback
model-index:
- name: madatnlp/ket5-config-scratch
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. -->
# madatnlp/ket5-config-scratch
This model is a fine-tuned version of [madatnlp/ket5-config-scratch](https://huggingface.co/madatnlp/ket5-config-scratch) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.3667
- Validation Loss: 1.4931
- Epoch: 18
## 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': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.4117 | 1.5494 | 0 |
| 1.4012 | 1.5838 | 1 |
| 1.4056 | 1.5710 | 2 |
| 1.4133 | 1.5190 | 3 |
| 1.3887 | 1.4858 | 4 |
| 1.3951 | 1.4920 | 5 |
| 1.3889 | 1.5038 | 6 |
| 1.3804 | 1.5079 | 7 |
| 1.3991 | 1.4368 | 8 |
| 1.3852 | 1.4974 | 9 |
| 1.3971 | 1.5164 | 10 |
| 1.3853 | 1.5632 | 11 |
| 1.3719 | 1.5163 | 12 |
| 1.3805 | 1.4271 | 13 |
| 1.3818 | 1.5078 | 14 |
| 1.3741 | 1.4687 | 15 |
| 1.3796 | 1.4623 | 16 |
| 1.3701 | 1.5326 | 17 |
| 1.3667 | 1.4931 | 18 |
### Framework versions
- Transformers 4.18.0
- TensorFlow 2.8.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
D4RL1NG/yes | [] | null | {
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 254.09 +/- 18.39
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
|
DCU-NLP/electra-base-irish-cased-generator-v1 | [
"pytorch",
"electra",
"fill-mask",
"ga",
"transformers",
"irish",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
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"ElectraForMaskedLM"
],
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} | 7 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/broductmanager/1652182609331/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/1522425562895044608/H93gVhPH_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">rahul</div>
<div style="text-align: center; font-size: 14px;">@broductmanager</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 rahul.
| Data | rahul |
| --- | --- |
| Tweets downloaded | 3244 |
| Retweets | 85 |
| Short tweets | 1164 |
| Tweets kept | 1995 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1r967jne/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 @broductmanager's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2zx676ih) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2zx676ih/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/broductmanager')
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)
|
DJSammy/bert-base-danish-uncased_BotXO-ai | [
"pytorch",
"jax",
"da",
"dataset:common_crawl",
"dataset:wikipedia",
"transformers",
"bert",
"masked-lm",
"license:cc-by-4.0",
"fill-mask"
] | fill-mask | {
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} | 14 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.924327912379688
- name: Recall
type: recall
value: 0.9346683074169371
- name: F1
type: f1
value: 0.9294693514295249
- name: Accuracy
type: accuracy
value: 0.9836529143565221
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0615
- Precision: 0.9243
- Recall: 0.9347
- F1: 0.9295
- Accuracy: 0.9837
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2396 | 1.0 | 878 | 0.0715 | 0.9135 | 0.9228 | 0.9181 | 0.9805 |
| 0.051 | 2.0 | 1756 | 0.0617 | 0.9192 | 0.9334 | 0.9263 | 0.9826 |
| 0.0295 | 3.0 | 2634 | 0.0615 | 0.9243 | 0.9347 | 0.9295 | 0.9837 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
DJStomp/TestingSalvoNET | [
"transformers"
] | null | {
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} | 1 | null | ---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: dummy-model
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. -->
# dummy-model
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.18.0
- TensorFlow 2.8.0
- Tokenizers 0.12.1
|
DKpro000/DialoGPT-medium-harrypotter | [] | null | {
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} | 0 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/_avichalp_/1652183801632/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/1472922431396331520/eqT17_QF_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">avi</div>
<div style="text-align: center; font-size: 14px;">@_avichalp_</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 avi.
| Data | avi |
| --- | --- |
| Tweets downloaded | 2625 |
| Retweets | 259 |
| Short tweets | 596 |
| Tweets kept | 1770 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2wg7ysai/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 @_avichalp_'s tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3ae6t1qq) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3ae6t1qq/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/_avichalp_')
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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} | 0 | 2022-05-10T11:59:05Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 237.90 +/- 21.51
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
|
DSI/TweetBasedSA | [
"pytorch",
"bert",
"text-classification",
"transformers"
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} | 29 | null | ---
language: en
license: mit
---
# MultiCite: Multi-label Citation Intent Analysis as paper-level Q&A (NAACL 2022)
This model has been trained on the data available here: https://github.com/allenai/multicite. |
Daivakai/DialoGPT-small-saitama | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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} | 9 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 256.23 +/- 14.87
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
|
Danbi/distilgpt2-finetuned-wikitext2 | [] | null | {
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} | 0 | null | ---
license: apache-2.0
tags:
- text2text-generation
- generated_from_trainer
metrics:
- rouge
- bleu
datasets:
- domenicrosati/QA2D
model-index:
- name: QA2D-t5-small
results:
- task:
name: Question to Declarative Sentence
type: text2text-generation
dataset:
name: domenicrosati/QA2D
type: domenicrosati/QA2D
args: plain_text
metrics:
- name: Rouge1
type: rouge
value: 89.8753
- name: Rouge2
type: rouge
value: 81.8104
- name: Rougel
type: rouge
value: 85.4253
- name: Rougelsum
type: rouge
value: 85.4236
- name: Bleu
type: bleu
value: 72.1080
widget:
- text: "where in the world is carmen sandiego. she is in abruzzo"
example_title: "Where is Carmen Sandiego?"
- text: "which province is halifax in. nova scotia"
example_title: "A Halifact"
---
# QA2D-t5-small
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on [QA2D](https://huggingface.co/datasets/domenicrosati/QA2D).
It achieves the following results on the evaluation set:
- Loss: 0.3236
- Rouge1: 89.8753
- Rouge2: 81.8104
- Rougel: 85.4253
- Rougelsum: 85.4236
- Bleu: 72.1080
See: [https://wandb.ai/domenicrosati/huggingface/runs/n1yallpe](https://wandb.ai/domenicrosati/huggingface/runs/n1yallpe) for training and eval stats and [https://github.com/domenicrosati/qa2d-models](https://github.com/domenicrosati/qa2d-models) for the code!
## Model description
A t5-model model to convert questions, answer pairs into statements.
Due to the way it's been trained the input should be all lower case and punctuation removed.
Use with `. ` as the seperator between question and answer.
> "where in the world is carmen. abruzzo"
> Output: "carmen is in abruzzo"
Thought punctation and upper case works.
```
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained('domenicrosati/QA2D-t5-small')
model = AutoModelForSeq2SeqLM.from_pretrained('domenicrosati/QA2D-t5-small')
question = "where in the world is carmen sandiego"
answer = "she is in abruzzo"
SEP = ". "
prompt = f'{question}{SEP}{answer}'
input_ids = tokenizer(prompt, return_tensors='pt').input_ids
output_ids = model.generate(input_ids)
responses = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
# ['carmen sandiego is in abruzzo']
```
## Intended uses & limitations
To convert questions, answer pairs into statements.
## Training and evaluation data
Uses [QA2D](https://huggingface.co/datasets/domenicrosati/QA2D).
See [https://github.com/domenicrosati/qa2d-models](https://github.com/domenicrosati/qa2d-models)
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 12
- eval_batch_size: 12
- 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu |
|:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 0.3177 | 1.0 | 5060 | 0.3144 | 89.6379 | 81.3168 | 85.2036 | 85.1904 | 71.4255 |
| 0.2479 | 2.0 | 10120 | 0.3035 | 89.7816 | 81.6556 | 85.3541 | 85.3406 | 71.7248 |
| 0.2268 | 3.0 | 15180 | 0.3015 | 89.8287 | 81.698 | 85.3434 | 85.3387 | 71.8344 |
| 0.2111 | 4.0 | 20240 | 0.3014 | 89.8082 | 81.7192 | 85.4094 | 85.406 | 71.9172 |
| 0.1991 | 5.0 | 25300 | 0.3023 | 89.8776 | 81.7607 | 85.3912 | 85.3842 | 71.9417 |
| 0.1886 | 6.0 | 30360 | 0.3012 | 89.901 | 81.7614 | 85.3345 | 85.3315 | 72.0218 |
| 0.1803 | 7.0 | 35420 | 0.3010 | 89.8776 | 81.8189 | 85.4154 | 85.4097 | 72.0533 |
| 0.1724 | 8.0 | 40480 | 0.3041 | 89.9168 | 81.8663 | 85.4457 | 85.4447 | 72.1470 |
| 0.1654 | 9.0 | 45540 | 0.3076 | 89.8901 | 81.8536 | 85.4857 | 85.4863 | 72.0830 |
| 0.1601 | 10.0 | 50600 | 0.3083 | 89.9186 | 81.881 | 85.4653 | 85.4594 | 72.1048 |
| 0.1546 | 11.0 | 55660 | 0.3136 | 89.8958 | 81.8533 | 85.4217 | 85.4238 | 72.0752 |
| 0.1502 | 12.0 | 60720 | 0.3138 | 89.903 | 81.8604 | 85.4301 | 85.4267 | 72.1373 |
| 0.1461 | 13.0 | 65780 | 0.3140 | 89.8867 | 81.7945 | 85.3698 | 85.3662 | 72.0718 |
| 0.1423 | 14.0 | 70840 | 0.3171 | 89.8985 | 81.8221 | 85.4348 | 85.4331 | 72.1168 |
| 0.1392 | 15.0 | 75900 | 0.3186 | 89.8938 | 81.8246 | 85.402 | 85.3991 | 72.0858 |
| 0.1366 | 16.0 | 80960 | 0.3208 | 89.859 | 81.8133 | 85.4194 | 85.4182 | 72.1014 |
| 0.1344 | 17.0 | 86020 | 0.3222 | 89.8909 | 81.828 | 85.4392 | 85.435 | 72.1380 |
| 0.1324 | 18.0 | 91080 | 0.3226 | 89.8906 | 81.8351 | 85.4506 | 85.4441 | 72.1622 |
| 0.1309 | 19.0 | 96140 | 0.3231 | 89.8925 | 81.8369 | 85.4375 | 85.4366 | 72.1552 |
| 0.1305 | 20.0 | 101200 | 0.3236 | 89.8753 | 81.8104 | 85.4253 | 85.4236 | 72.1080 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Dandara/bertimbau-socioambiental | [
"pytorch",
"bert",
"text-classification",
"transformers"
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} | 27 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# florentgbelidji/setfit_emotion
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('florentgbelidji/setfit_emotion')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=florentgbelidji/setfit_emotion)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 203 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.dataloader._InfiniteConstantSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.BatchHardTripletLoss.BatchHardTripletLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 4060,
"warmup_steps": 406,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
DannyMichael/ECU911 | [] | null | {
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} | 0 | null | ---
tags:
- generated_from_trainer
model-index:
- name: con_gpt_med_model
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. -->
# con_gpt_med_model
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
hello
|
DarkestSky/distilbert-base-uncased-finetuned-ner | [] | null | {
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: vanichandna/bert-base-multilingual-cased-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. -->
# vanichandna/bert-base-multilingual-cased-finetuned-squad
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5313
- Epoch: 3
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 43880, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 1.2336 | 0 |
| 0.8301 | 1 |
| 0.6456 | 2 |
| 0.5313 | 3 |
### Framework versions
- Transformers 4.18.0
- TensorFlow 2.8.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Davlan/bert-base-multilingual-cased-finetuned-naija | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
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} | 13 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-peyma-fa
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-peyma-fa
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: 0.0937
- F1: 0.9249
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1562 | 1.0 | 998 | 0.0691 | 0.8777 |
| 0.0638 | 2.0 | 1996 | 0.0703 | 0.8908 |
| 0.0457 | 3.0 | 2994 | 0.0645 | 0.8975 |
| 0.0281 | 4.0 | 3992 | 0.0842 | 0.8994 |
| 0.0206 | 5.0 | 4990 | 0.0651 | 0.9164 |
| 0.0139 | 6.0 | 5988 | 0.0787 | 0.9148 |
| 0.0083 | 7.0 | 6986 | 0.0838 | 0.9253 |
| 0.0052 | 8.0 | 7984 | 0.0833 | 0.9221 |
| 0.0031 | 9.0 | 8982 | 0.0947 | 0.9230 |
| 0.0028 | 10.0 | 9980 | 0.0937 | 0.9249 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.9.1
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Davlan/bert-base-multilingual-cased-finetuned-swahili | [
"pytorch",
"tf",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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} | 67 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-base-finetuned-scrambled-squad-5
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-finetuned-scrambled-squad-5
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7078
## 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: 7e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.7695 | 1.0 | 5532 | 1.7078 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Davlan/distilbert-base-multilingual-cased-masakhaner | [
"pytorch",
"tf",
"distilbert",
"token-classification",
"arxiv:2103.11811",
"transformers",
"autotrain_compatible"
] | token-classification | {
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} | 16 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 285.62 +/- 20.33
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
|
Davlan/mbart50-large-eng-yor-mt | [
"pytorch",
"mbart",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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"MBartForConditionalGeneration"
],
"model_type": "mbart",
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} | 5 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-osdg
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-osdg
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8193
- F1 Score: 0.7962
- Accuracy: 0.8434
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.3769 | 1.0 | 1017 | 0.8258 | 0.7729 | 0.8257 |
| 0.2759 | 2.0 | 2034 | 0.8364 | 0.7773 | 0.8262 |
| 0.1412 | 3.0 | 3051 | 1.0203 | 0.7833 | 0.8379 |
| 0.1423 | 4.0 | 4068 | 1.1603 | 0.7683 | 0.8224 |
| 0.0939 | 5.0 | 5085 | 1.3029 | 0.7843 | 0.8329 |
| 0.0757 | 6.0 | 6102 | 1.3562 | 0.7931 | 0.8379 |
| 0.0801 | 7.0 | 7119 | 1.2925 | 0.7840 | 0.8395 |
| 0.0311 | 8.0 | 8136 | 1.4632 | 0.7750 | 0.8318 |
| 0.0263 | 9.0 | 9153 | 1.5760 | 0.7843 | 0.8312 |
| 0.0196 | 10.0 | 10170 | 1.5689 | 0.7890 | 0.8417 |
| 0.0313 | 11.0 | 11187 | 1.6034 | 0.7909 | 0.8417 |
| 0.0007 | 12.0 | 12204 | 1.6725 | 0.7889 | 0.8406 |
| 0.0081 | 13.0 | 13221 | 1.6463 | 0.7911 | 0.8395 |
| 0.0061 | 14.0 | 14238 | 1.7730 | 0.7861 | 0.8345 |
| 0.003 | 15.0 | 15255 | 1.8001 | 0.7847 | 0.8379 |
| 0.0002 | 16.0 | 16272 | 1.7328 | 0.7912 | 0.8434 |
| 0.0 | 17.0 | 17289 | 1.7914 | 0.8011 | 0.8489 |
| 0.0009 | 18.0 | 18306 | 1.7772 | 0.7958 | 0.8456 |
| 0.0 | 19.0 | 19323 | 1.8028 | 0.7958 | 0.8434 |
| 0.0 | 20.0 | 20340 | 1.8193 | 0.7962 | 0.8434 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Davlan/mt5_base_eng_yor_mt | [
"pytorch",
"mt5",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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"MT5ForConditionalGeneration"
],
"model_type": "mt5",
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} | 2 | 2022-05-10T14:48:32Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 273.23 +/- 17.77
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
|
Davlan/xlm-roberta-base-finetuned-wolof | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
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} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-rater
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-rater
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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: 3
### Training results
### Framework versions
- Transformers 4.16.2
- Pytorch 1.9.1
- Datasets 1.18.4
- Tokenizers 0.11.6
|
Dazai/Ok | [] | null | {
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} | 0 | 2022-05-10T17:31:43Z | ---
tags:
- opt_metasq
---
# This repo let's you run the following checkpoint using facebookresearch/metaseq.
Do the following:
## 1. Install PyTorch
```
pip3 install torch==1.10.1+cu113 torchvision==0.11.2+cu113 torchaudio==0.10.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
```
## 2. Install Megatron
```
git clone https://github.com/patrickvonplaten/Megatron-LM.git
cd Megatron-LM
pip3 install six regex
pip3 install -e .
```
## 3. Install fairscale
```
git clone https://github.com/facebookresearch/fairscale.git
cd fairscale
git checkout prefetch_fsdp_params_simple
pip3 install -e .
```
## 4. Install metaseq
```
git clone https://github.com/patrickvonplaten/metaseq.git
cd metaseq
pip3 install -e .
```
## 5. Clone this repo (click top right on "How to clone")
## 6. Run the following:
```bash
cd <path/to/cloned/repo>
bash run.sh
``` |
Dbluciferm3737/Idk | [] | null | {
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} | 0 | 2022-05-10T17:31:55Z | ---
tags:
- opt_metasq
---
# This repo let's you run the following checkpoint using facebookresearch/metaseq.
Do the following:
## 1. Install PyTorch
```
pip3 install torch==1.10.1+cu113 torchvision==0.11.2+cu113 torchaudio==0.10.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
```
## 2. Install Megatron
```
git clone https://github.com/patrickvonplaten/Megatron-LM.git
cd Megatron-LM
pip3 install six regex
pip3 install -e .
```
## 3. Install fairscale
```
git clone https://github.com/facebookresearch/fairscale.git
cd fairscale
git checkout prefetch_fsdp_params_simple
pip3 install -e .
```
## 4. Install metaseq
```
git clone https://github.com/patrickvonplaten/metaseq.git
cd metaseq
pip3 install -e .
```
## 5. Clone this repo (click top right on "How to clone")
## 6. Run the following:
```bash
cd <path/to/cloned/repo>
bash run.sh
``` |
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}
} | 0 | null | ---
tags:
- opt_metasq
---
# This repo let's you run the following checkpoint using facebookresearch/metaseq.
Do the following:
## 1. Install PyTorch
```
pip3 install torch==1.10.1+cu113 torchvision==0.11.2+cu113 torchaudio==0.10.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
```
## 2. Install Megatron
```
git clone https://github.com/patrickvonplaten/Megatron-LM.git
cd Megatron-LM
pip3 install six regex
pip3 install -e .
```
## 3. Install fairscale
```
git clone https://github.com/facebookresearch/fairscale.git
cd fairscale
git checkout prefetch_fsdp_params_simple
pip3 install -e .
```
## 4. Install metaseq
```
git clone https://github.com/patrickvonplaten/metaseq.git
cd metaseq
pip3 install -e .
```
## 5. Clone this repo (click top right on "How to clone")
## 6. Run the following:
```bash
cd <path/to/cloned/repo>
bash run.sh
``` |
Ddarkros/Test | [] | null | {
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} | 0 | 2022-05-10T17:32:24Z | ---
tags:
- opt_metasq
---
# This repo let's you run the following checkpoint using facebookresearch/metaseq.
Do the following:
## 1. Install PyTorch
```
pip3 install torch==1.10.1+cu113 torchvision==0.11.2+cu113 torchaudio==0.10.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
```
## 2. Install Megatron
```
git clone https://github.com/patrickvonplaten/Megatron-LM.git
cd Megatron-LM
pip3 install six regex
pip3 install -e .
```
## 3. Install fairscale
```
git clone https://github.com/facebookresearch/fairscale.git
cd fairscale
git checkout prefetch_fsdp_params_simple
pip3 install -e .
```
## 4. Install metaseq
```
git clone https://github.com/patrickvonplaten/metaseq.git
cd metaseq
pip3 install -e .
```
## 5. Clone this repo (click top right on "How to clone")
## 6. Run the following:
```bash
cd <path/to/cloned/repo>
bash run.sh
``` |
DeadBeast/emoBERTTamil | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"dataset:tamilmixsentiment",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
} | 35 | 2022-05-10T17:38:23Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-turkish-colab_common_voice-8_6
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_common_voice-8_6
This model is a fine-tuned version of [husnu/wav2vec2-large-xls-r-300m-turkish-colab_common_voice-8_5](https://huggingface.co/husnu/wav2vec2-large-xls-r-300m-turkish-colab_common_voice-8_5) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3646
- Wer: 0.3478
## 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: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1024 | 0.51 | 400 | 0.4030 | 0.4171 |
| 0.1533 | 1.02 | 800 | 0.4733 | 0.4570 |
| 0.1584 | 1.53 | 1200 | 0.4150 | 0.4371 |
| 0.1538 | 2.04 | 1600 | 0.4104 | 0.4390 |
| 0.1395 | 2.55 | 2000 | 0.3891 | 0.4133 |
| 0.1415 | 3.07 | 2400 | 0.3877 | 0.4015 |
| 0.1261 | 3.58 | 2800 | 0.3685 | 0.3899 |
| 0.1149 | 4.09 | 3200 | 0.3791 | 0.3881 |
| 0.1003 | 4.6 | 3600 | 0.3642 | 0.3626 |
| 0.0934 | 5.11 | 4000 | 0.3755 | 0.3516 |
| 0.0805 | 5.62 | 4400 | 0.3646 | 0.3478 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 2.1.0
- Tokenizers 0.10.3
|
DeadBeast/korscm-mBERT | [
"pytorch",
"bert",
"text-classification",
"korean",
"dataset:Korean-Sarcasm",
"transformers",
"license:apache-2.0"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
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}
}
} | 43 | 2022-05-10T17:47:22Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-rater
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-rater
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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: 3
### Training results
### Framework versions
- Transformers 4.16.2
- Pytorch 1.9.1
- Datasets 1.18.4
- Tokenizers 0.11.6
|
DeadBeast/mbert-base-cased-finetuned-bengali-fakenews | [
"pytorch",
"bert",
"text-classification",
"bengali",
"dataset:BanFakeNews",
"transformers",
"license:apache-2.0"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
} | 37 | 2022-05-10T18:05:51Z | ---
tags:
- espnet
- audio
- text-to-speech
language: en
datasets:
- ryanspeech
license: cc-by-nc-4.0
widget:
- text: "This seems a very pleasant place, and I think I shall enjoy myself very much."
---
## RyanSpeech model (based on ESPnet2)
### `espnet/english_male_ryanspeech_tacotron`
This model was trained by [Rohola Zandie](https://scholar.google.com/citations?user=xv0jIe0AAAAJ&hl=en) using ryanspeech recipe in [espnet](https://github.com/espnet/espnet/). For the best results you need to download the vocoder separately from [here](https://drive.google.com/file/d/10GYvB_mIKzXzSjD67tSnBhknZRoBjsNb/view?usp=sharing) and then use the following code:
```
from espnet2.bin.tts_inference import Text2Speech
from scipy.io.wavfile import write
model = Text2Speech.from_pretrained(
model_file="espnet/english_male_ryanspeech_tacotron",
vocoder_file="path_to_vocoder/train_nodev_parallel_wavegan.v1.long/checkpoint-1000000steps.pkl"
)
output = model("This is a simple test.")
write("x.wav", 22050, output['wav'].numpy())
```
## Download the dataset
You can download RyanSpeech dataset from [here](https://www.kaggle.com/datasets/roholazandie/ryanspeech) or here.
## TTS config
<details><summary>expand</summary>
```
config: conf/train.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/tts_train_raw_phn_tacotron_g2p_en_no_space
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 200
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- loss
- min
- - train
- loss
- min
keep_nbest_models: 5
grad_clip: 1.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
pretrain_path: []
pretrain_key: []
num_iters_per_epoch: 500
batch_size: 20
valid_batch_size: null
batch_bins: 5120000
valid_batch_bins: null
train_shape_file:
- exp/tts_stats_raw_phn_tacotron_g2p_en_no_space/train/text_shape.phn
- exp/tts_stats_raw_phn_tacotron_g2p_en_no_space/train/speech_shape
valid_shape_file:
- exp/tts_stats_raw_phn_tacotron_g2p_en_no_space/valid/text_shape.phn
- exp/tts_stats_raw_phn_tacotron_g2p_en_no_space/valid/speech_shape
batch_type: numel
valid_batch_type: null
fold_length:
- 150
- 204800
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/tr_no_dev/text
- text
- text
- - dump/raw/tr_no_dev/wav.scp
- speech
- sound
valid_data_path_and_name_and_type:
- - dump/raw/dev/text
- text
- text
- - dump/raw/dev/wav.scp
- speech
- sound
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.001
eps: 1.0e-06
weight_decay: 0.0
scheduler: null
scheduler_conf: {}
token_list:
- <blank>
- <unk>
- AH0
- T
- N
- S
- R
- D
- L
- K
- IH1
- M
- EH1
- Z
- DH
- UW1
- AE1
- IH0
- AY1
- AH1
- W
- .
- P
- F
- IY1
- V
- ER0
- AA1
- B
- AO1
- HH
- EY1
- IY0
- ','
- Y
- NG
- OW1
- G
- AW1
- TH
- SH
- UH1
- '?'
- ER1
- JH
- CH
- OW0
- OW2
- EH2
- IH2
- EY2
- AA2
- AE2
- AY2
- ''''
- OY1
- UW0
- '!'
- AO2
- EH0
- ZH
- AH2
- AE0
- UW2
- AA0
- AY0
- IY2
- AW2
- AO0
- EY0
- ER2
- UH2
- '...'
- AW0
- UH0
- OY2
- <sos/eos>
odim: null
model_conf: {}
use_preprocessor: true
token_type: phn
bpemodel: null
non_linguistic_symbols: null
cleaner: tacotron
g2p: g2p_en_no_space
feats_extract: fbank
feats_extract_conf:
fs: 22050
fmin: 80
fmax: 7600
n_mels: 80
hop_length: 256
n_fft: 1024
win_length: null
normalize: global_mvn
normalize_conf:
stats_file: exp/tts_stats_raw_phn_tacotron_g2p_en_no_space/train/feats_stats.npz
tts: tacotron2
tts_conf:
embed_dim: 512
elayers: 1
eunits: 512
econv_layers: 3
econv_chans: 512
econv_filts: 5
atype: location
adim: 512
aconv_chans: 32
aconv_filts: 15
cumulate_att_w: true
dlayers: 2
dunits: 1024
prenet_layers: 2
prenet_units: 256
postnet_layers: 5
postnet_chans: 512
postnet_filts: 5
output_activation: null
use_batch_norm: true
use_concate: true
use_residual: false
dropout_rate: 0.5
zoneout_rate: 0.1
reduction_factor: 1
spk_embed_dim: null
use_masking: true
bce_pos_weight: 5.0
use_guided_attn_loss: true
guided_attn_loss_sigma: 0.4
guided_attn_loss_lambda: 1.0
pitch_extract: null
pitch_extract_conf: {}
pitch_normalize: null
pitch_normalize_conf: {}
energy_extract: null
energy_extract_conf: {}
energy_normalize: null
energy_normalize_conf: {}
required:
- output_dir
- token_list
distributed: false
```
</details>
### Citing RyanSpeech
```BibTex
@inproceedings{Zandie2021RyanSpeechAC,
title={RyanSpeech: A Corpus for Conversational Text-to-Speech Synthesis},
author={Rohola Zandie and Mohammad H. Mahoor and Julia Madsen and Eshrat S. Emamian},
booktitle={Interspeech},
year={2021}
}
``` |
DeadBeast/roberta-base-pretrained-mr-2 | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
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}
} | 5 | 2022-05-10T18:13:25Z | ---
tags:
- espnet
- audio
- text-to-speech
language: en
datasets:
- ryanspeech
license: cc-by-nc-4.0
widget:
- text: "This seems a very pleasant place, and I think I shall enjoy myself very much."
---
## RyanSpeech model (based on ESPnet2)
### `espnet/english_male_ryanspeech_fastspeech2`
This model was trained by [Rohola Zandie](https://scholar.google.com/citations?user=xv0jIe0AAAAJ&hl=en) using ryanspeech recipe in [espnet](https://github.com/espnet/espnet/). For the best results you need to download the vocoder separately from [here](https://drive.google.com/file/d/10GYvB_mIKzXzSjD67tSnBhknZRoBjsNb/view?usp=sharing) and then use the following code:
```
from espnet2.bin.tts_inference import Text2Speech
from scipy.io.wavfile import write
model = Text2Speech.from_pretrained(
model_file="espnet/english_male_ryanspeech_fastspeech2",
vocoder_file="path_to_vocoder/train_nodev_parallel_wavegan.v1.long/checkpoint-1000000steps.pkl"
)
output = model("This is a simple test.")
write("x.wav", 22050, output['wav'].numpy())
```
## Download the dataset
You can download RyanSpeech dataset from [here](https://www.kaggle.com/datasets/roholazandie/ryanspeech) or here.
## TTS config
<details><summary>expand</summary>
```
config: conf/tuning/train_fastspeech.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/tts_train_fastspeech2_raw_phn_tacotron_g2p_en_no_space
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 1000
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- loss
- min
- - train
- loss
- min
keep_nbest_models: 5
grad_clip: 1.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 6
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
pretrain_path: []
pretrain_key: []
num_iters_per_epoch: 500
batch_size: 20
valid_batch_size: null
batch_bins: 800000
valid_batch_bins: null
train_shape_file:
- exp/tts_train_raw_phn_tacotron_g2p_en_no_space/decode_use_teacher_forcingtrue_train.loss.ave/stats/train/text_shape.phn
- exp/tts_train_raw_phn_tacotron_g2p_en_no_space/decode_use_teacher_forcingtrue_train.loss.ave/stats/train/speech_shape
valid_shape_file:
- exp/tts_train_raw_phn_tacotron_g2p_en_no_space/decode_use_teacher_forcingtrue_train.loss.ave/stats/valid/text_shape.phn
- exp/tts_train_raw_phn_tacotron_g2p_en_no_space/decode_use_teacher_forcingtrue_train.loss.ave/stats/valid/speech_shape
batch_type: numel
valid_batch_type: null
fold_length:
- 150
- 204800
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/tr_no_dev/text
- text
- text
- - exp/tts_train_raw_phn_tacotron_g2p_en_no_space/decode_use_teacher_forcingtrue_train.loss.ave/tr_no_dev/durations
- durations
- text_int
- - dump/raw/tr_no_dev/wav.scp
- speech
- sound
valid_data_path_and_name_and_type:
- - dump/raw/dev/text
- text
- text
- - exp/tts_train_raw_phn_tacotron_g2p_en_no_space/decode_use_teacher_forcingtrue_train.loss.ave/dev/durations
- durations
- text_int
- - dump/raw/dev/wav.scp
- speech
- sound
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 1.0
scheduler: noamlr
scheduler_conf:
model_size: 384
warmup_steps: 4000
token_list:
- <blank>
- <unk>
- AH0
- T
- N
- S
- R
- D
- L
- K
- IH1
- M
- EH1
- Z
- DH
- UW1
- AE1
- IH0
- AY1
- AH1
- W
- .
- P
- F
- IY1
- V
- ER0
- AA1
- B
- AO1
- HH
- EY1
- IY0
- ','
- Y
- NG
- OW1
- G
- AW1
- TH
- SH
- UH1
- '?'
- ER1
- JH
- CH
- OW0
- OW2
- EH2
- IH2
- EY2
- AA2
- AE2
- AY2
- ''''
- OY1
- UW0
- '!'
- AO2
- EH0
- ZH
- AH2
- AE0
- UW2
- AA0
- AY0
- IY2
- AW2
- AO0
- EY0
- ER2
- UH2
- '...'
- AW0
- UH0
- OY2
- <sos/eos>
odim: null
model_conf: {}
use_preprocessor: true
token_type: phn
bpemodel: null
non_linguistic_symbols: null
cleaner: tacotron
g2p: g2p_en_no_space
feats_extract: fbank
feats_extract_conf:
fs: 22050
fmin: 80
fmax: 7600
n_mels: 80
hop_length: 256
n_fft: 1024
win_length: null
normalize: global_mvn
normalize_conf:
stats_file: exp/tts_train_raw_phn_tacotron_g2p_en_no_space/decode_use_teacher_forcingtrue_train.loss.ave/stats/train/feats_stats.npz
tts: fastspeech
tts_conf:
adim: 384
aheads: 2
elayers: 6
eunits: 1536
dlayers: 6
dunits: 1536
positionwise_layer_type: conv1d
positionwise_conv_kernel_size: 3
duration_predictor_layers: 2
duration_predictor_chans: 384
duration_predictor_kernel_size: 3
postnet_layers: 5
postnet_filts: 5
postnet_chans: 256
use_masking: true
use_scaled_pos_enc: true
encoder_normalize_before: true
decoder_normalize_before: true
reduction_factor: 1
init_type: xavier_uniform
init_enc_alpha: 1.0
init_dec_alpha: 1.0
transformer_enc_dropout_rate: 0.1
transformer_enc_positional_dropout_rate: 0.1
transformer_enc_attn_dropout_rate: 0.1
transformer_dec_dropout_rate: 0.1
transformer_dec_positional_dropout_rate: 0.1
transformer_dec_attn_dropout_rate: 0.1
pitch_extract: null
pitch_extract_conf: {}
pitch_normalize: null
pitch_normalize_conf: {}
energy_extract: null
energy_extract_conf: {}
energy_normalize: null
energy_normalize_conf: {}
required:
- output_dir
- token_list
distributed: false
```
</details>
### Citing RyanSpeech
```BibTex
@inproceedings{Zandie2021RyanSpeechAC,
title={RyanSpeech: A Corpus for Conversational Text-to-Speech Synthesis},
author={Rohola Zandie and Mohammad H. Mahoor and Julia Madsen and Eshrat S. Emamian},
booktitle={Interspeech},
year={2021}
}
``` |
DeadBeast/roberta-base-pretrained-mr | [
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
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}
} | 6 | 2022-05-10T18:15:33Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: distilbart-cnn-arxiv-pubmed-pubmed-v3-e16
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. -->
# distilbart-cnn-arxiv-pubmed-pubmed-v3-e16
This model is a fine-tuned version of [theojolliffe/distilbart-cnn-arxiv-pubmed-pubmed](https://huggingface.co/theojolliffe/distilbart-cnn-arxiv-pubmed-pubmed) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8306
- Rouge1: 56.4519
- Rouge2: 41.6818
- Rougel: 44.7833
- Rougelsum: 54.6359
- Gen Len: 141.9815
## 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: 16
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| No log | 1.0 | 398 | 1.1157 | 50.9487 | 31.3005 | 34.0145 | 48.6057 | 141.8519 |
| 1.3569 | 2.0 | 796 | 0.9688 | 53.0653 | 34.1855 | 37.0759 | 50.5942 | 141.2963 |
| 0.8704 | 3.0 | 1194 | 0.9053 | 53.9684 | 36.0388 | 38.6674 | 51.9604 | 142.0 |
| 0.6287 | 4.0 | 1592 | 0.8515 | 54.2379 | 36.4915 | 39.1393 | 51.6991 | 141.4074 |
| 0.6287 | 5.0 | 1990 | 0.8274 | 53.6806 | 34.8373 | 37.7369 | 51.239 | 141.6481 |
| 0.465 | 6.0 | 2388 | 0.8486 | 55.2534 | 39.1757 | 41.6366 | 53.2989 | 141.9259 |
| 0.3432 | 7.0 | 2786 | 0.8116 | 54.539 | 37.6314 | 40.5531 | 52.1997 | 141.3889 |
| 0.2577 | 8.0 | 3184 | 0.7976 | 54.8212 | 36.8347 | 40.6768 | 52.7785 | 142.0 |
| 0.204 | 9.0 | 3582 | 0.8010 | 53.9302 | 37.3523 | 40.135 | 52.139 | 141.7778 |
| 0.204 | 10.0 | 3980 | 0.8168 | 54.3151 | 38.0665 | 42.4112 | 52.4682 | 142.0 |
| 0.1663 | 11.0 | 4378 | 0.8171 | 54.7027 | 38.3117 | 42.0196 | 52.8821 | 142.0 |
| 0.135 | 12.0 | 4776 | 0.8202 | 54.1035 | 37.9154 | 40.7676 | 52.2509 | 142.0 |
| 0.1102 | 13.0 | 5174 | 0.8204 | 56.223 | 41.0947 | 44.0131 | 54.3353 | 142.0 |
| 0.0928 | 14.0 | 5572 | 0.8280 | 56.1637 | 41.0408 | 44.2931 | 54.5488 | 142.0 |
| 0.0928 | 15.0 | 5970 | 0.8273 | 56.2608 | 41.3855 | 44.4432 | 54.5778 | 142.0 |
| 0.0847 | 16.0 | 6368 | 0.8306 | 56.4519 | 41.6818 | 44.7833 | 54.6359 | 141.9815 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.2.0
- Tokenizers 0.12.1
|
Dean/summarsiation | [] | null | {
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}
} | 0 | null | ---
language:
- et
widget:
- text: "te olete ka noh, noh, päris korralikult ka Rahvusringhäälingu teatud mõttes sellisesse keerulisse olukorda pannud,"
- text: "Et, et, et miks mitte olla siis tasakaalus, ma noh, hüpoteetiliselt viskan selle palli üles,"
---
Model usage:
```
tokenizer = MBart50Tokenizer.from_pretrained("IljaSamoilov/MBART-estonian-subtitles", src_lang="et_EE", tgt_lang="et_EE")
model = MBartForConditionalGeneration.from_pretrained("IljaSamoilov/MBART-estonian-subtitles")
``` |
Declan/Breitbart_model_v2 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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}
} | 7 | 2022-05-10T18:23:41Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 289.14 +/- 17.41
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
|
Declan/Breitbart_model_v6 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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}
} | 3 | 2022-05-10T18:46:07Z | # Basic Information
This is the Dr. Decr model used in XOR-TyDi leaderboard task 1 whitebox submission.
https://nlp.cs.washington.edu/xorqa/
The detailed implementation of the model can be found in:
https://arxiv.org/pdf/2112.08185.pdf
Source code to train the model can be found via PrimeQA's IR component:
https://github.com/primeqa/primeqa/tree/main/examples/drdecr
It is a Neural IR model built on top of the ColBERTv1 api and not directly compatible with Huggingface API. The inference result on XOR Dev dataset is:
```
R@2kt R@5kt
te 66.67 70.88
bn 70.23 75.08
fi 82.24 86.18
ja 65.92 72.93
ko 67.93 71.73
ru 63.07 69.71
ar 78.15 82.77
Avg 70.60 75.61
```
# Limitations and Bias
This model used pre-trained XLM-R base model and fine tuned on 7 languages in XOR-TyDi leaderboard. The performance of other languages was not tested.
Since the model was fine-tuned on a large pre-trained language model XLM-Roberta, biases associated with the pre-existing XLM-Roberta model may be present in our fine-tuned model, Dr. Decr
# Citation
```
@article{Li2021_DrDecr,
doi = {10.48550/ARXIV.2112.08185},
url = {https://arxiv.org/abs/2112.08185},
author = {Li, Yulong and Franz, Martin and Sultan, Md Arafat and Iyer, Bhavani and Lee, Young-Suk and Sil, Avirup},
keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Learning Cross-Lingual IR from an English Retriever},
publisher = {arXiv},
year = {2021}
}
```
|
Declan/CNN_model_v1 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
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}
} | 7 | 2022-05-10T19:03:07Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 238.37 +/- 65.78
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
|
Declan/CNN_model_v2 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
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}
} | 5 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-base-finetuned-scrambled-squad-15
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-finetuned-scrambled-squad-15
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8722
## 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: 7e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.8944 | 1.0 | 5590 | 1.8722 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Declan/CNN_model_v4 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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}
} | 3 | 2022-05-10T19:16:46Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 224.96 +/- 73.06
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
|
Declan/CNN_model_v7 | [] | null | {
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} | 0 | 2022-05-10T19:23:18Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 238.83 +/- 22.61
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
|
Declan/ChicagoTribune_model_v2 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
} | 7 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: mlp
results:
- metrics:
- type: mean_reward
value: 274.83 +/- 24.24
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **mlp** Agent playing **LunarLander-v2**
This is a trained model of a **mlp** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
Declan/ChicagoTribune_model_v3 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
],
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} | 3 | 2022-05-10T19:34:58Z | ---
tags:
- generated_from_trainer
model-index:
- name: tests-finetuned-squad-full
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. -->
# tests-finetuned-squad-full
This model is a fine-tuned version of [sberbank-ai/sbert_large_nlu_ru](https://huggingface.co/sberbank-ai/sbert_large_nlu_ru) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5672
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.0601 | 1.0 | 11307 | 1.0849 |
| 0.6918 | 2.0 | 22614 | 1.1588 |
| 0.4071 | 3.0 | 33921 | 1.5672 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.2.2.dev0
- Tokenizers 0.12.1
|
Declan/ChicagoTribune_model_v4 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
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} | 7 | 2022-05-10T19:37:10Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- keyword_pubmed_dataset
metrics:
- accuracy
model-index:
- name: kw_pubmed_1000_0.0003
results:
- task:
name: Masked Language Modeling
type: fill-mask
dataset:
name: keyword_pubmed_dataset
type: keyword_pubmed_dataset
args: sentence
metrics:
- name: Accuracy
type: accuracy
value: 0.33938523162661094
---
<!-- 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. -->
# kw_pubmed_1000_0.0003
This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the keyword_pubmed_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 4.7086
- Accuracy: 0.3394
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 250
- total_train_batch_size: 8000
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.09 | 4 | 4.3723 | 0.3436 |
| 6.0386 | 0.17 | 8 | 4.2113 | 0.3442 |
| 3.7573 | 0.26 | 12 | 4.2079 | 0.3634 |
| 2.9944 | 0.35 | 16 | 4.3370 | 0.3513 |
| 2.7048 | 0.44 | 20 | 4.8594 | 0.3067 |
| 2.7048 | 0.52 | 24 | 4.4929 | 0.3383 |
| 2.9458 | 0.61 | 28 | 4.5146 | 0.3408 |
| 2.3783 | 0.7 | 32 | 4.5680 | 0.3430 |
| 2.2485 | 0.78 | 36 | 4.5095 | 0.3477 |
| 2.1701 | 0.87 | 40 | 4.4971 | 0.3449 |
| 2.1701 | 0.96 | 44 | 4.7051 | 0.3321 |
| 2.0861 | 1.07 | 48 | 4.7615 | 0.3310 |
| 2.4168 | 1.15 | 52 | 4.7086 | 0.3394 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Declan/ChicagoTribune_model_v8 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
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} | 7 | 2022-05-10T19:52:23Z | ---
tags:
- generated_from_trainer
model-index:
- name: zh-finetuned-squad-qa-minilmv2-32
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. -->
# zh-finetuned-squad-qa-minilmv2-32
This model is a fine-tuned version of [subhasisj/zh-TAPT-MLM-MiniLM](https://huggingface.co/subhasisj/zh-TAPT-MLM-MiniLM) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4311
## 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: 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: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 338 | 2.3706 |
| 3.1254 | 2.0 | 676 | 1.7422 |
| 1.6449 | 3.0 | 1014 | 1.5323 |
| 1.6449 | 4.0 | 1352 | 1.4375 |
| 1.3122 | 5.0 | 1690 | 1.4311 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.2.0
- Tokenizers 0.12.1
|
Declan/FoxNews_model_v2 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
],
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}
} | 3 | 2022-05-10T20:05:10Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 281.46 +/- 17.95
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
|
Declan/FoxNews_model_v3 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
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}
} | 7 | 2022-05-10T20:06:22Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 228.05 +/- 22.63
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
|
Declan/FoxNews_model_v4 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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} | 7 | 2022-05-10T20:11:36Z | ---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: vanichandna/indic-bert-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. -->
# vanichandna/indic-bert-finetuned-squad
This model is a fine-tuned version of [ai4bharat/indic-bert](https://huggingface.co/ai4bharat/indic-bert) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.0802
- Epoch: 3
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 21984, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 1.8468 | 0 |
| 1.4510 | 1 |
| 1.2435 | 2 |
| 1.0802 | 3 |
### Framework versions
- Transformers 4.18.0
- TensorFlow 2.8.0
- Datasets 2.2.0
- Tokenizers 0.12.1
|
Declan/FoxNews_model_v5 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
],
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}
} | 7 | 2022-05-10T20:13:04Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 266.98 +/- 12.23
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
|
Declan/HuffPost_model_v2 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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}
} | 3 | 2022-05-10T20:34:51Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 283.12 +/- 15.88
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
|
Declan/HuffPost_model_v5 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
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} | 3 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 287.12 +/- 20.40
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
|
Declan/HuffPost_model_v6 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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} | 9 | null | ---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: KenP/codeparrot-ds
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. -->
# KenP/codeparrot-ds
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 10.3900
- Validation Loss: 9.6171
- 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': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': -922, '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 |
|:----------:|:---------------:|:-----:|
| 10.3900 | 9.6171 | 0 |
### Framework versions
- Transformers 4.18.0
- TensorFlow 2.8.0
- Datasets 2.2.0
- Tokenizers 0.12.1
|
Declan/HuffPost_model_v8 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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} | 7 | 2022-05-10T20:49:01Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 289.86 +/- 15.74
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
|
Declan/NPR_model_v1 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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} | 3 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 252.23 +/- 33.93
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
|
Declan/NPR_model_v3 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
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} | 9 | 2022-05-10T21:03:08Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 250.14 +/- 16.42
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
|
Declan/NPR_model_v4 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
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} | 3 | 2022-05-10T21:27:07Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: distilbart-cnn-arxiv-pubmed-pubmed-v3-e8
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. -->
# distilbart-cnn-arxiv-pubmed-pubmed-v3-e8
This model is a fine-tuned version of [theojolliffe/distilbart-cnn-arxiv-pubmed-pubmed](https://huggingface.co/theojolliffe/distilbart-cnn-arxiv-pubmed-pubmed) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8422
- Rouge1: 54.9328
- Rouge2: 36.7154
- Rougel: 39.5674
- Rougelsum: 52.4889
- Gen Len: 142.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 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: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| No log | 1.0 | 398 | 1.1158 | 50.9754 | 30.9416 | 33.9908 | 48.4925 | 142.0 |
| 1.3585 | 2.0 | 796 | 0.9733 | 52.7954 | 33.8196 | 36.7836 | 50.4929 | 141.9259 |
| 0.8785 | 3.0 | 1194 | 0.9142 | 53.5548 | 35.3954 | 37.4787 | 51.1024 | 142.0 |
| 0.6485 | 4.0 | 1592 | 0.8666 | 52.6449 | 34.0018 | 37.5391 | 50.428 | 141.4074 |
| 0.6485 | 5.0 | 1990 | 0.8458 | 53.8913 | 35.4481 | 38.1552 | 51.3737 | 141.8889 |
| 0.4993 | 6.0 | 2388 | 0.8571 | 54.7333 | 36.8173 | 40.228 | 52.5574 | 141.9444 |
| 0.3957 | 7.0 | 2786 | 0.8455 | 54.9826 | 37.9674 | 40.5786 | 52.5968 | 141.9815 |
| 0.328 | 8.0 | 3184 | 0.8422 | 54.9328 | 36.7154 | 39.5674 | 52.4889 | 142.0 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.2.0
- Tokenizers 0.12.1
|
Declan/NPR_model_v5 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
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} | 7 | 2022-05-10T21:40:24Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 159.15 +/- 61.12
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
|
Declan/NPR_model_v6 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
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} | 3 | 2022-05-10T21:44:09Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 267.52 +/- 11.83
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
|
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} | 0 | 2022-05-10T21:52:11Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 245.12 +/- 56.29
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
|
Declan/Politico_model_v1 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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} | 3 | null | ---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
Declan/Politico_model_v5 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
],
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} | 7 | 2022-05-10T23:04:00Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 219.43 +/- 72.12
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
|
Declan/Politico_model_v6 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
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} | 3 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 285.06 +/- 23.34
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
|
Declan/Politico_model_v8 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
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} | 7 | 2022-05-10T23:17:10Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 299.86 +/- 20.60
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
|
Declan/Reuters_model_v1 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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} | 3 | 2022-05-10T23:19:28Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 293.97 +/- 11.26
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
|
Declan/Reuters_model_v4 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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} | 3 | 2022-05-10T23:23:03Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 299.81 +/- 18.80
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
|
Declan/Reuters_model_v5 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
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} | 3 | 2022-05-10T23:25:05Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 293.91 +/- 13.82
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
|
Declan/WallStreetJournal_model_v3 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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} | 3 | 2022-05-10T23:33:17Z | precision recall f1-score support
negative 0.733238 0.778788 0.755327 660
neutral 0.757962 0.779963 0.768805 1068
positive 0.722793 0.728778 0.725773 483
skip 0.660714 0.501355 0.570108 369
speech 0.767857 0.868687 0.815166 99
accuracy 0.735349 2679
macro avg 0.728513 0.731514 0.727036 2679
weighted avg 0.732501 0.735349 0.732071 2679
Avg macro Precision 0.7297744200895245
Avg macro Recall 0.7248163039465004
Avg macro F1 0.7229310729744304
Avg weighted F1 0.7281243075011377 |
Declan/test_push | [] | null | {
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} | 0 | 2022-05-10T23:46:11Z | ---
library_name: stable-baselines3
tags:
- BipedalWalker-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 185.82 +/- 92.04
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: BipedalWalker-v3
type: BipedalWalker-v3
---
# **PPO** Agent playing **BipedalWalker-v3**
This is a trained model of a **PPO** agent playing **BipedalWalker-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
DeepPavlov/bert-base-multilingual-cased-sentence | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"multilingual",
"arxiv:1704.05426",
"arxiv:1809.05053",
"arxiv:1908.10084",
"transformers"
] | feature-extraction | {
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} | 140 | 2022-05-11T00:35:40Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 143.30 +/- 118.69
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
|
Denver/distilbert-base-uncased-finetuned-squad | [] | null | {
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: -3.67 +/- 28.18
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
|
DeskDown/MarianMixFT_en-my | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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} | 7 | null | ---
license: apache-2.0
---
[千言—AdvertiseGen广告文案生成数据集](https://www.luge.ai/#/luge/dataDetail?id=9)
> 仅支持.bin(pytorch)
在该千言数据集微调了5个epoch，
```python
input_text = '类型#裙*材质#针织*风格#简约*风格#青春*风格#清新*风格#性感*图案#条纹*图案#撞色*裙下摆#开叉*裙长#连衣裙*裙款式#拼接*裙款式#吊带'
output_text = gen_ads(input_text)
output_text = output_text.replace(' ', '')
output_text = output_text[len(input_text):]
output_text
```
输出(实际中注意控制max_length)
```python
output_text='夏天穿的针织衫，搭配简约上衣+牛仔裙，一下子就活泼起来了好吧，就这么简约的蓝色衬托出女性优雅的气质，搭出一派优雅女人味，让人印象深刻哦~好了，今天是秋天来了，天气凉了，是不是该穿上针织呢，秋天会是一个充满阳光的日子呢？让我们一起去看看今天的穿搭吧！首先是白色风衣，其次是棉质风衣。在秋天我们应该穿丝缎或者花边，这种比较清新的风格一定不会让人觉得很成熟，而且又是简约款式，显得自然、有气质。再就是皮草风衣啦，一件白皮草+一件牛仔+两件棉纱的搭配就很潮'
```
|
DeskDown/MarianMix_en-ja-10 | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
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} | 1 | 2022-05-11T02:17:54Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: flat_N_max
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. -->
# flat_N_max
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8536
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 1.2462 | 1.0 | 2213 | 1.7958 |
| 0.9293 | 2.0 | 4426 | 1.8093 |
| 0.7249 | 3.0 | 6639 | 1.8536 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.2.0
- Tokenizers 0.12.1
|
DeskDown/MarianMix_en-zh-10 | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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} | 3 | null | ---
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: 0.8620945214069894
---
<!-- 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: 0.1372
- F1: 0.8621
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2575 | 1.0 | 525 | 0.1621 | 0.8292 |
| 0.1287 | 2.0 | 1050 | 0.1378 | 0.8526 |
| 0.0831 | 3.0 | 1575 | 0.1372 | 0.8621 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Despin89/test | [] | null | {
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: -126.43 +/- 27.04
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
|
DevsIA/Devs_IA | [] | null | {
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 296.42 +/- 11.35
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
|
Dhruva/Interstellar | [] | null | {
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 105.84 +/- 83.18
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
|
DicoTiar/wisdomfiy | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: NER_EHR_Spanish_model_Mulitlingual_BERT
results: []
widget:
- text: 'Presentamos el caso de una mujer de 30 años, fumadora de 20 cigarrillos/día y sin otros antecedentes personales de interés. La paciente refiere infecciones urinarias de repetición. Se indica realización de ecografía abdominal, observándose una lesión nodular intravesical, por lo que es derivada a consulta de urología.
En cistoscopia se visualiza tumoración exofítica de 3x3 cms. en cara lateral derecha con mucosa vesical íntegra, no encontrándose alteraciones en el resto de la vejiga. Se realiza exploración bajo anestesia (EBA) y resección transuretral de dicha lesión (RTU).
En el informe de anatomía patológica macroscópicamente se describen fragmentos de pared vesical con urotelio conservado sin displasia, destacando en la capa muscular propia y en continuidad con el tejido muscular de la misma, una tumoración fusocelular con células que muestran unos núcleos de gran tamaño, pleomórficos, de aspecto vesiculoso y unos citoplasmas amplios eosinófilos. Esta celularidad se dispone en formas de fascículos mal definidos y entre la misma se reconoce abundante celularidad constituida fundamentalmente por numerosas células plasmáticas y leucocitos polimorfonucleares eosinófilos. No se observa un índice mitótico elevado, aunque el índice de proliferación medido como positividad nuclear con anticuerpos frente a MIB-1 se encuentra entre el 10 y el 25% de la celularidad tumoral. No se han objetivado áreas de necrosis. En estudio inmunohistoquímico se observa marcada positividad frente a citoqueratinas (AE1/AE3) y CAM5.2 a nivel citoplasmático, así como una marcada positividad citoplasmática con anticuerpos frente a p80 (proteína ALK). La celularidad descrita ha resultado negativa con anticuerpos frente a músculo liso (actina de músculo liso, MyO D1 y Calretinina), así como para CEA y citoqueratinas de alto peso molecular, observándose tan sólo positividad focal y aislada frente a EMA. Tras realización de FISH sobre material parafinado no se evidencia traslocación en el gen de la ALK.
El diagnóstico anatomopatológico definitivo es tumor miofibroblástico inflamatorio vesical.'
---
<!-- 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. -->
# NER_EHR_Spanish_model_Mulitlingual_BERT
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the DisTEMIST shared task 2022 dataset. It is available at: https://temu.bsc.es/distemist/category/data/
It achieves the following results on the evaluation set:
- Loss: 0.2603
- Precision: 0.5637
- Recall: 0.5801
- F1: 0.5718
- Accuracy: 0.9534
## Model description
For a complete description of our system, please go to: https://ceur-ws.org/Vol-3180/paper-26.pdf
## Training and evaluation data
Dataset provided by DisTEMIST shared task, it is available at: https://temu.bsc.es/distemist/category/data/
## 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: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 71 | 0.2060 | 0.5017 | 0.5540 | 0.5266 | 0.9496 |
| No log | 2.0 | 142 | 0.2163 | 0.5363 | 0.5433 | 0.5398 | 0.9495 |
| No log | 3.0 | 213 | 0.2245 | 0.5521 | 0.5356 | 0.5438 | 0.9514 |
| No log | 4.0 | 284 | 0.2453 | 0.5668 | 0.5985 | 0.5822 | 0.9522 |
| No log | 5.0 | 355 | 0.2433 | 0.5657 | 0.5579 | 0.5617 | 0.9530 |
| No log | 6.0 | 426 | 0.2553 | 0.5762 | 0.5762 | 0.5762 | 0.9536 |
| No log | 7.0 | 497 | 0.2603 | 0.5637 | 0.5801 | 0.5718 | 0.9534 |
### How to cite this work:
Tamayo, A., Burgos, D. A., & Gelbukh, A. (2022). mbert and simple post-processing: A baseline for disease mention detection in spanish. In Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings.
@inproceedings{tamayo2022mbert,
title={mbert and simple post-processing: A baseline for disease mention detection in spanish},
author={Tamayo, Antonio and Burgos, Diego A and Gelbukh, Alexander},
booktitle={Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings},
year={2022}
}
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.2.0
- Tokenizers 0.12.1
|
Doiman/DialoGPT-medium-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
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} | 13 | 2022-05-11T04:00:17Z | https://twitter.com/itsnewkknpenari
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DongHyoungLee/distilbert-base-uncased-finetuned-cola | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | {
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"DistilBertForSequenceClassification"
],
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} | 27 | 2022-05-11T04:19:42Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: single_label_N_max
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. -->
# single_label_N_max
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9326
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.9746 | 1.0 | 674 | 2.0265 |
| 1.6756 | 2.0 | 1348 | 1.9134 |
| 1.1333 | 3.0 | 2022 | 1.9326 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.2.0
- Tokenizers 0.12.1
|
DongHyoungLee/kogpt2-base-v2-finetuned-kogpt2_nsmc_single_sentence_classification | [] | null | {
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 215.32 +/- 46.32
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
|
Dongmin/testmodel | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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"T5ForConditionalGeneration"
],
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"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
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"prefix": "translate English to Romanian: "
}
}
} | 11 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 173.71 +/- 111.75
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
|
Waynehillsdev/Wayne_NLP_mT5 | [
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
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"MT5ForConditionalGeneration"
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}
} | 11 | 2022-05-11T05:31:27Z | ---
license: afl-3.0
---
SalamaThanks Transformer for English-to-Filipino Text Translation version 1.
Based on the Helsinki-NLP/opus-mt-en-tl transformer model. |
Doogie/Waynehills-KE-T5-doogie | [] | null | {
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} | 0 | null | ---
license: afl-3.0
---
SalamaThanks Transformer for Filipino-to-English Text Translation version 1.
Based on the Helsinki-NLP/opus-mt-tl-en transformer model. |
Waynehillsdev/Waynehills-STT-doogie-server | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | automatic-speech-recognition | {
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"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
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} | 61 | null | ---
license: afl-3.0
---
SalamaThanks Transformer for English-to-Filipino Text Translation version 2.
A finetuned transformer model based on the Helsinki-NLP/opus-mt-en-tl transformer model. |
Waynehillsdev/waynehills_sentimental_kor | [
"pytorch",
"electra",
"text-classification",
"transformers"
] | text-classification | {
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"ElectraForSequenceClassification"
],
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} | 33 | 2022-05-11T05:42:28Z | ---
license: afl-3.0
---
SalamaThanks Transformer for Filipino-to-English Text Translation version 2.
A finetuned model based on the Helsinki-NLP/opus-mt-en-tl transformer model. |
Doohae/p_encoder | [
"pytorch"
] | null | {
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} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: unique_N_max
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. -->
# unique_N_max
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7409
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0901 | 1.0 | 1162 | 1.8326 |
| 1.5479 | 2.0 | 2324 | 1.7201 |
| 1.2903 | 3.0 | 3486 | 1.7409 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.2.0
- Tokenizers 0.12.1
|
Doohae/roberta | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
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} | 3 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 39289 with parameters:
```
{'batch_size': 8}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 3928,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
Doquey/DialoGPT-small-Luisbot1 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
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}
} | 7 | 2022-05-11T06:24:56Z | ---
license: mit
language: de
pipeline_tag: text-generation
widget:
- text: "In einer schockierenden Entdeckung fanden Wissenschaftler eine Herde Einhörner, die in "
example_title: "Einhörner ..."
- text: |-
Definiere folgende Wörter
Wort: Einhorn
Definition: Das Einhorn ist ein Fabelwesen von Pferde- oder Ziegengestalt mit einem geraden Horn auf der Stirnmitte.
Wort: Regierungschef
Definition: Der Regierungschef ist der Leiter der Regierung eines Staates (z. B. National- oder Gliedstaat).
Wort: Waffendrill
Definition:
example_title: "Definiere ..."
---
# German GPT2-XL (1.5B)
- trained with [BigScience's DeepSpeed-Megatron-LM code base](https://github.com/bigscience-workshop/Megatron-DeepSpeed)
- word embedding initialized with [WECHSEL](https://arxiv.org/abs/2112.06598) and all other weights taken from English [gpt2-xl](https://huggingface.co/gpt2-xl)
- ~ 3 days on 16xA100 GPUs (~ 80 TFLOPs / GPU)
- stopped after 100k steps
- 26.2B tokens
- less than a single epoch on `oscar_unshuffled_deduplicated_de` (excluding validation set; original model was trained for 75 epochs on less data)
- bf16
- zero stage 0
- tp/pp = 1
### How to use
You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we
set a seed for reproducibility:
```python
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='malteos/gpt2-xl-wechsel-german')
>>> set_seed(42)
>>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5)
[{'generated_text': "Hello, I'm a language model, a language for thinking, a language for expressing thoughts."},
{'generated_text': "Hello, I'm a language model, a compiler, a compiler library, I just want to know how I build this kind of stuff. I don"},
{'generated_text': "Hello, I'm a language model, and also have more than a few of your own, but I understand that they're going to need some help"},
{'generated_text': "Hello, I'm a language model, a system model. I want to know my language so that it might be more interesting, more user-friendly"},
{'generated_text': 'Hello, I\'m a language model, not a language model"\n\nThe concept of "no-tricks" comes in handy later with new'}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import GPT2Tokenizer, GPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('malteos/gpt2-xl-wechsel-german')
model = GPT2Model.from_pretrained('malteos/gpt2-xl-wechsel-german')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
## Evaluation
| Model (size) | PPL |
|---|---|
| `gpt2-xl-wechsel-german` (1.5B) | **14.5** |
| `gpt2-wechsel-german-ds-meg` (117M) | 26.4 |
| `gpt2-wechsel-german` (117M) | 26.8 |
| `gpt2` (retrained from scratch) (117M) | 27.63 |
## License
MIT
|
DoyyingFace/bert-COVID-HATE-finetuned-test | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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"BertForSequenceClassification"
],
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}
} | 29 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 117759 with parameters:
```
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.SoftmaxLoss.SoftmaxLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 11775,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
DoyyingFace/bert-asian-hate-tweets-asian-unclean-freeze-12 | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
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}
} | 29 | null | ---
language:
- vi
tags:
- capitalization
- punctuation
- token-classification
license: cc-by-sa-4.0
datasets:
- oscar-corpus/OSCAR-2109
metrics:
- accuracy
- precision
- recall
- f1
---
# ✨ vibert-capitalization-punctuation
This a [viBERT](https://huggingface.co/FPTAI/vibert-base-cased) model finetuned for punctuation restoration on the [OSCAR-2109](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109) dataset.
The model predicts the punctuation and upper-casing of plain, lower-cased text. An example use case can be ASR output. Or other cases when text has lost punctuation.
This model is intended for direct use as a punctuation restoration model for the general Vietnamese language. Alternatively, you can use this for further fine-tuning on domain-specific texts for punctuation restoration tasks.
Model restores the following punctuations -- **[. , : ? ]**
The model also restores the complex upper-casing of words like *YouTube*, *MobiFone*.
-----------------------------------------------
## 🚋 Usage
**Below is a quick way to get up and running with the model.**
1. Download files from hub
```python
import os
import shutil
import sys
from huggingface_hub import snapshot_download
cache_dir = "./capu"
def download_files(repo_id, cache_dir=None, ignore_regex=None):
download_dir = snapshot_download(repo_id=repo_id, cache_dir=cache_dir, ignore_regex=ignore_regex)
if cache_dir is None or download_dir == cache_dir:
return download_dir
file_names = os.listdir(download_dir)
for file_name in file_names:
shutil.move(os.path.join(download_dir, file_name), cache_dir)
os.rmdir(download_dir)
return cache_dir
cache_dir = download_files(repo_id="dragonSwing/vibert-capu", cache_dir=cache_dir, ignore_regex=["*.json", "*.bin"])
sys.path.append(cache_dir)
```
2. Sample python code
```python
import os
from gec_model import GecBERTModel
model = GecBERTModel(
vocab_path=os.path.join(cache_dir, "vocabulary"),
model_paths="dragonSwing/vibert-capu",
split_chunk=True
)
model("theo đó thủ tướng dự kiến tiếp bộ trưởng nông nghiệp mỹ tom wilsack bộ trưởng thương mại mỹ gina raimondo bộ trưởng tài chính janet yellen gặp gỡ thượng nghị sĩ patrick leahy và một số nghị sĩ mỹ khác")
# Always return list of outputs.
# ['Theo đó, Thủ tướng dự kiến tiếp Bộ trưởng Nông nghiệp Mỹ Tom Wilsack, Bộ trưởng Thương mại Mỹ Gina Raimondo, Bộ trưởng Tài chính Janet Yellen, gặp gỡ Thượng nghị sĩ Patrick Leahy và một số nghị sĩ Mỹ khác.']
model("những gói cước năm g mobifone sẽ mang đến cho bạn những trải nghiệm mới lạ trên cả tuyệt vời so với mạng bốn g thì tốc độ truy cập mạng 5 g mobifone được nhận định là siêu đỉnh với mức truy cập nhanh gấp 10 lần")
# ['Những gói cước 5G MobiFone sẽ mang đến cho bạn những trải nghiệm mới lạ trên cả tuyệt vời. So với mạng 4G thì tốc độ truy cập mạng 5G MobiFone được nhận định là siêu đỉnh với mức truy cập nhanh gấp 10 lần.']
```
**This model can work on arbitrarily large text in Vietnamese language.**
-----------------------------------------------
## 📡 Training data
Here is the number of product reviews we used for fine-tuning the model:
| Language | Number of text samples |
| --- | --- |
| Vietnamese | 5,600,000 |
-----------------------------------------------
## 🎯 Accuracy
Below is a breakdown of the performance of the model by each label on 10,000 held-out text samples:
| label | precision | recall | f1-score | support |
| --- | --- | --- | --- | --- |
| **Upper** | 0.88 | 0.89 | 0.89 | 56497 |
| **Complex-Upper** | 0.92 | 0.83 | 0.88 | 480 |
| **.** | 0.81 | 0.82 | 0.82 | 18139 |
| **,** | 0.73 | 0.70 | 0.71 | 22961 |
| **:** | 0.74 | 0.56 | 0.64 | 1432 |
| **?** | 0.80 | 0.76 | 0.78 | 1730 |
| **none** | 0.99 | 0.99 | 0.99 |475611 |
-----------------------------------------------
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-freeze-4 | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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}
} | 44 | null | ---
language:
- vi
tags:
- capitalization
- punctuation
- token-classification
license: cc-by-sa-4.0
datasets:
- oscar-corpus/OSCAR-2109
metrics:
- accuracy
- precision
- recall
- f1
---
# ✨ xlm-roberta-capitalization-punctuation
This a [XLM-RoBERTa](https://huggingface.co/xlm-roberta-base) model finetuned for Vietnamese punctuation restoration on the [OSCAR-2109](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109) dataset.
The model predicts the punctuation and upper-casing of plain, lower-cased text. An example use case can be ASR output. Or other cases when text has lost punctuation.
This model is intended for direct use as a punctuation restoration model for the general Vietnamese language. Alternatively, you can use this for further fine-tuning on domain-specific texts for punctuation restoration tasks.
Model restores the following punctuations -- **[. , : ? ]**
The model also restores the complex upper-casing of words like *YouTube*, *MobiFone*.
-----------------------------------------------
## 🚋 Usage
**Below is a quick way to get up and running with the model.**
1. Download files from hub
```python
import os
import shutil
import sys
from huggingface_hub import snapshot_download
cache_dir = "./capu"
def download_files(repo_id, cache_dir=None, ignore_regex=None):
download_dir = snapshot_download(repo_id=repo_id, cache_dir=cache_dir, ignore_regex=ignore_regex)
if cache_dir is None or download_dir == cache_dir:
return download_dir
file_names = os.listdir(download_dir)
for file_name in file_names:
shutil.move(os.path.join(download_dir, file_name), cache_dir)
os.rmdir(download_dir)
return cache_dir
cache_dir = download_files(repo_id="dragonSwing/xlm-roberta-capu", cache_dir=cache_dir, ignore_regex=["*.json", "*.bin"])
sys.path.append(cache_dir)
```
2. Sample python code
```python
import os
from gec_model import GecBERTModel
model = GecBERTModel(
vocab_path=os.path.join(cache_dir, "vocabulary"),
model_paths="dragonSwing/xlm-roberta-capu",
split_chunk=True
)
model("theo đó thủ tướng dự kiến tiếp bộ trưởng nông nghiệp mỹ tom wilsack bộ trưởng thương mại mỹ gina raimondo bộ trưởng tài chính janet yellen gặp gỡ thượng nghị sĩ patrick leahy và một số nghị sĩ mỹ khác")
# Always return list of outputs.
# ['Theo đó, Thủ tướng dự kiến tiếp Bộ trưởng Nông nghiệp Mỹ Tom Wilsack, Bộ trưởng Thương mại Mỹ Gina Raimondo, Bộ trưởng Tài chính Janet Yellen, gặp gỡ Thượng nghị sĩ Patrick Leahy và một số nghị sĩ Mỹ khác.']
model("những gói cước năm g mobifone sẽ mang đến cho bạn những trải nghiệm mới lạ trên cả tuyệt vời so với mạng bốn g thì tốc độ truy cập mạng 5 g mobifone được nhận định là siêu đỉnh với mức truy cập nhanh gấp 10 lần")
# ['Những gói cước 5G MobiFone sẽ mang đến cho bạn những trải nghiệm mới lạ trên cả tuyệt vời. So với mạng 4G thì tốc độ truy cập mạng 5G MobiFone được Nhận định là siêu đỉnh với mức truy cập nhanh gấp 10 lần.']
```
**This model can work on arbitrarily large text in Vietnamese language.**
-----------------------------------------------
## 📡 Training data
Here is the number of product reviews we used for fine-tuning the model:
| Language | Number of text samples |
| --- | --- |
| Vietnamese | 5,600,000 |
-----------------------------------------------
## 🎯 Accuracy
Below is a breakdown of the performance of the model by each label on 10,000 held-out text samples:
| label | precision | recall | f1-score | support |
| --- | --- | --- | --- | --- |
| **Upper** | 0.89 | 0.90 | 0.89 | 56497 |
| **Complex-Upper** | 0.93 | 0.83 | 0.88 | 480 |
| **.** | 0.81 | 0.84 | 0.82 | 18139 |
| **,** | 0.69 | 0.75 | 0.72 | 22961 |
| **:** | 0.76 | 0.60 | 0.67 | 1432 |
| **?** | 0.82 | 0.75 | 0.78 | 1730 |
| **none** | 0.99 | 0.99 | 0.99 |475611 |
-----------------------------------------------
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-25 | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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"BertForSequenceClassification"
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}
} | 30 | null | ---
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/1521957986335297536/itVSA7l0_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/1446623190252343301/qIJAwo9I_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 & Kim Kardashian</div>
<div style="text-align: center; font-size: 14px;">@elonmusk-kimkardashian</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 & Kim Kardashian.
| Data | Elon Musk | Kim Kardashian |
| --- | --- | --- |
| Tweets downloaded | 222 | 3241 |
| Retweets | 16 | 715 |
| Short tweets | 47 | 667 |
| Tweets kept | 159 | 1859 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/17bd0o7t/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-kimkardashian's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2g9hft2n) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2g9hft2n/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-kimkardashian')
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)
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-75 | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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"BertForSequenceClassification"
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}
} | 37 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 291.52 +/- 22.96
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
|
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