modelId
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-30 00:39:23
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
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| pipeline_tag
stringclasses 55
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edwinhung/bird_classifier
|
edwinhung
| 2022-06-04T20:52:15Z | 0 | 0 |
fastai
|
[
"fastai",
"region:us"
] | null | 2022-06-04T19:43:58Z |
---
tags:
- fastai
---
# Model card
## Model description
A neural network model trained with fastai and timm to classify 400 bird species in an image.
## Intended uses & limitations
This bird classifier is used to predict bird species in a given image. The Image fed should have only one bird. This is a multi-class classification which will output a class even if there is no bird in the image.
## Training and evaluation data
Pre-trained model used is Efficient net from timm library, specifically *efficientnet_b3a*. The dataset trained is from Kaggle [BIRDS 400 - SPECIES IMAGE CLASSIFICATION](https://www.kaggle.com/datasets/gpiosenka/100-bird-species). Evaluation accuracy score on the given test set from Kaggle is 99.4%. Please note this is likely not representative of real world performance, as mentioned by dataset provider that the test set is hand picked as the best images.
|
yanekyuk/bert-cased-keyword-discriminator
|
yanekyuk
| 2022-06-04T20:24:14Z | 13 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-04T18:20:06Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
- f1
language:
- en
widget:
- text: "Broadcom agreed to acquire cloud computing company VMware in a $61 billion (€57bn) cash-and stock deal, massively diversifying the chipmaker’s business and almost tripling its software-related revenue to about 45% of its total sales. By the numbers: VMware shareholders will receive either $142.50 in cash or 0.2520 of a Broadcom share for each VMware stock. Broadcom will also assume $8 billion of VMware's net debt."
- text: "Canadian Natural Resources Minister Jonathan Wilkinson told Bloomberg that the country could start supplying Europe with liquefied natural gas (LNG) in as soon as three years by converting an existing LNG import facility on Canada’s Atlantic coast into an export terminal. Bottom line: Wilkinson said what Canada cares about is that the new LNG facility uses a low-emission process for the gas and is capable of transitioning to exporting hydrogen later on."
- text: "Google is being investigated by the UK’s antitrust watchdog for its dominance in the \"ad tech stack,\" the set of services that facilitate the sale of online advertising space between advertisers and sellers. Google has strong positions at various levels of the ad tech stack and charges fees to both publishers and advertisers. A step back: UK Competition and Markets Authority has also been investigating whether Google and Meta colluded over ads, probing into the advertising agreement between the two companies, codenamed Jedi Blue."
- text: "Shares in Twitter closed 6.35% up after an SEC 13D filing revealed that Elon Musk pledged to put up an additional $6.25 billion of his own wealth to fund the $44 billion takeover deal, lifting the total to $33.5 billion from an initial $27.25 billion. In other news: Former Twitter CEO Jack Dorsey announced he's stepping down, but would stay on Twitter’s board \\“until his term expires at the 2022 meeting of stockholders.\""
model-index:
- name: bert-keyword-discriminator
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-keyword-discriminator
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1310
- Precision: 0.8522
- Recall: 0.8868
- Accuracy: 0.9732
- F1: 0.8692
- Ent/precision: 0.8874
- Ent/accuracy: 0.9246
- Ent/f1: 0.9056
- Con/precision: 0.8011
- Con/accuracy: 0.8320
- Con/f1: 0.8163
## 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: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1 | Ent/precision | Ent/accuracy | Ent/f1 | Con/precision | Con/accuracy | Con/f1 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:--------:|:------:|:-------------:|:------------:|:------:|:-------------:|:------------:|:------:|
| 0.1744 | 1.0 | 1875 | 0.1261 | 0.7176 | 0.7710 | 0.9494 | 0.7433 | 0.7586 | 0.8503 | 0.8018 | 0.6514 | 0.6561 | 0.6537 |
| 0.1261 | 2.0 | 3750 | 0.1041 | 0.7742 | 0.8057 | 0.9600 | 0.7896 | 0.8083 | 0.8816 | 0.8433 | 0.7185 | 0.6957 | 0.7070 |
| 0.0878 | 3.0 | 5625 | 0.0979 | 0.8176 | 0.8140 | 0.9655 | 0.8158 | 0.8518 | 0.8789 | 0.8651 | 0.7634 | 0.7199 | 0.7410 |
| 0.0625 | 4.0 | 7500 | 0.0976 | 0.8228 | 0.8643 | 0.9696 | 0.8430 | 0.8515 | 0.9182 | 0.8836 | 0.7784 | 0.7862 | 0.7823 |
| 0.0456 | 5.0 | 9375 | 0.1047 | 0.8304 | 0.8758 | 0.9704 | 0.8525 | 0.8758 | 0.9189 | 0.8968 | 0.7655 | 0.8133 | 0.7887 |
| 0.0342 | 6.0 | 11250 | 0.1207 | 0.8363 | 0.8887 | 0.9719 | 0.8617 | 0.8719 | 0.9274 | 0.8988 | 0.7846 | 0.8327 | 0.8080 |
| 0.0256 | 7.0 | 13125 | 0.1241 | 0.848 | 0.8892 | 0.9731 | 0.8681 | 0.8791 | 0.9299 | 0.9038 | 0.8019 | 0.8302 | 0.8158 |
| 0.0205 | 8.0 | 15000 | 0.1310 | 0.8522 | 0.8868 | 0.9732 | 0.8692 | 0.8874 | 0.9246 | 0.9056 | 0.8011 | 0.8320 | 0.8163 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
kingabzpro/q-Taxi-v3
|
kingabzpro
| 2022-06-04T20:04:07Z | 0 | 1 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-04T18:53:45Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="kingabzpro/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
kingabzpro/q-FrozenLake-v1-4x4-noSlippery
|
kingabzpro
| 2022-06-04T18:51:21Z | 0 | 1 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-04T18:51:10Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="kingabzpro/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
format37/BipedalWalker-v3
|
format37
| 2022-06-04T18:28:06Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"BipedalWalker-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-04T18:27:31Z |
---
library_name: stable-baselines3
tags:
- BipedalWalker-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: SAC
results:
- metrics:
- type: mean_reward
value: 305.56 +/- 0.59
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: BipedalWalker-v3
type: BipedalWalker-v3
---
# **SAC** Agent playing **BipedalWalker-v3**
This is a trained model of a **SAC** agent playing **BipedalWalker-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
huggingtweets/centraldamiku
|
huggingtweets
| 2022-06-04T18:14:42Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-04T18:13:58Z |
---
language: en
thumbnail: http://www.huggingtweets.com/centraldamiku/1654366478559/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/1532142310741495808/VWMuTyjo_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">Central da Miku</div>
<div style="text-align: center; font-size: 14px;">@centraldamiku</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 Central da Miku.
| Data | Central da Miku |
| --- | --- |
| Tweets downloaded | 3242 |
| Retweets | 348 |
| Short tweets | 801 |
| Tweets kept | 2093 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/m8jk5mo9/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 @centraldamiku's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/rp6i3tpo) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/rp6i3tpo/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/centraldamiku')
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)
|
mcditoos/q-FrozenLake-v1-4x4-noSlippery
|
mcditoos
| 2022-06-04T17:09:47Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-04T17:09:40Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
tclong/wav2vec2-base-vios
|
tclong
| 2022-06-04T16:09:51Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:vivos_dataset",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-05-30T12:00:45Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- vivos_dataset
model-index:
- name: wav2vec2-base-vios
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-vios
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the vivos_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3729
- Wer: 0.2427
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.4755 | 1.37 | 500 | 0.7991 | 0.5957 |
| 0.5424 | 2.75 | 1000 | 0.4290 | 0.3653 |
| 0.3586 | 4.12 | 1500 | 0.3809 | 0.2890 |
| 0.2824 | 5.49 | 2000 | 0.3808 | 0.2749 |
| 0.2249 | 6.87 | 2500 | 0.3467 | 0.2389 |
| 0.1745 | 8.24 | 3000 | 0.3688 | 0.2384 |
| 0.1459 | 9.61 | 3500 | 0.3729 | 0.2427 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
yanekyuk/convberturk-keyword-extractor
|
yanekyuk
| 2022-06-04T11:19:51Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"convbert",
"token-classification",
"generated_from_trainer",
"tr",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-04T09:32:23Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
- f1
language:
- tr
widget:
- text: "İngiltere'de düzenlenen Avrupa Tekvando ve Para Tekvando Şampiyonası’nda millî tekvandocular 5 altın, 2 gümüş ve 4 bronz olmak üzere 11, millî para tekvandocular ise 4 altın, 3 gümüş ve 1 bronz olmak üzere 8 madalya kazanarak takım halinde Avrupa şampiyonu oldu."
- text: "Füme somon dedik ama aslında lox salamuralanmış somon anlamına geliyor, füme etme opsiyonel. Lox bagel, 1930'larda Eggs Benedict furyasında New Yorklu Yahudi cemaati tarafından koşer bir alternatif olarak çıkan bir lezzet. Günümüzde benim hangover yüreğim dâhil dünyanın birçok yerinde enfes bir kahvaltı sandviçi."
- text: "Türkiye'de son aylarda sıklıkla tartışılan konut satışı karşılığında yabancılara vatandaşlık verilmesi konusunu beyin göçü kapsamında ele almak mümkün. Daha önce 250 bin dolar olan vatandaşlık bedeli yükselen tepkiler üzerine 400 bin dolara çıkarılmıştı. Türkiye'den göç eden iyi eğitimli kişilerin , gittikleri ülkelerde 250 bin dolar tutarında yabancı yatırıma denk olduğu göz önüne alındığında nitelikli insan gücünün yabancılara konut karşılığında satılan vatandaşlık bedelin eş olduğunu görüyoruz. Yurt dışına giden her bir vatandaşın yüksek teknolojili katma değer üreten sektörlere yapacağı katkılar göz önünde bulundurulduğunda bu açığın inşaat sektörüyle kapatıldığını da görüyoruz. Beyin göçü konusunda sadece ekonomik perspektiften bakıldığında bile kısa vadeli döviz kaynağı yaratmak için kullanılan vatandaşlık satışı yerine beyin göçünü önleyecek önlemler alınmasının ülkemize çok daha faydalı olacağı sonucunu çıkarıyoruz."
- text: "Türkiye’de resmî verilere göre, 15 ve daha yukarı yaştaki kişilerde mevsim etkisinden arındırılmış işsiz sayısı, bu yılın ilk çeyreğinde bir önceki çeyreğe göre 50 bin kişi artarak 3 milyon 845 bin kişi oldu. Mevsim etkisinden arındırılmış işsizlik oranı ise 0,1 puanlık artışla %11,4 seviyesinde gerçekleşti. İşsizlik oranı, ilk çeyrekte geçen yılın aynı çeyreğine göre 1,7 puan azaldı."
- text: "Boeing’in insansız uzay aracı Starliner, birtakım sorunlara rağmen Uluslararası Uzay İstasyonuna (ISS) ulaşarak ilk kez başarılı bir şekilde kenetlendi. Aracın ISS’te beş gün kalmasını takiben sorunsuz bir şekilde New Mexico’ya inmesi halinde Boeing, sonbaharda astronotları yörüngeye göndermek için Starliner’ı kullanabilir.\n\nNeden önemli? NASA’nın personal aracı üretmeyi durdurmasından kaynaklı olarak görevli astronotlar ve kozmonotlar, ISS’te Rusya’nın ürettiği uzay araçları ile taşınıyordu. Starliner’ın kendini kanıtlaması ise bu konuda Rusya’ya olan bağımlılığın potansiyel olarak ortadan kalkabileceği anlamına geliyor."
model-index:
- name: convberturk-keyword-extractor
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. -->
# convberturk-keyword-extractor
This model is a fine-tuned version of [dbmdz/convbert-base-turkish-cased](https://huggingface.co/dbmdz/convbert-base-turkish-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4098
- Precision: 0.6742
- Recall: 0.7035
- Accuracy: 0.9175
- F1: 0.6886
## 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: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:--------:|:------:|
| 0.174 | 1.0 | 1875 | 0.1920 | 0.6546 | 0.6869 | 0.9184 | 0.6704 |
| 0.1253 | 2.0 | 3750 | 0.2030 | 0.6527 | 0.7317 | 0.9179 | 0.6900 |
| 0.091 | 3.0 | 5625 | 0.2517 | 0.6499 | 0.7473 | 0.9163 | 0.6952 |
| 0.0684 | 4.0 | 7500 | 0.2828 | 0.6633 | 0.7270 | 0.9167 | 0.6937 |
| 0.0536 | 5.0 | 9375 | 0.3307 | 0.6706 | 0.7194 | 0.9180 | 0.6942 |
| 0.0384 | 6.0 | 11250 | 0.3669 | 0.6655 | 0.7161 | 0.9157 | 0.6898 |
| 0.0316 | 7.0 | 13125 | 0.3870 | 0.6792 | 0.7002 | 0.9176 | 0.6895 |
| 0.0261 | 8.0 | 15000 | 0.4098 | 0.6742 | 0.7035 | 0.9175 | 0.6886 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
jg/xlm-roberta-base-finetuned-panx-de
|
jg
| 2022-06-04T10:59:50Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-05-29T17:32:47Z |
---
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 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
yanekyuk/camembert-keyword-extractor
|
yanekyuk
| 2022-06-04T10:28:45Z | 108,995 | 13 |
transformers
|
[
"transformers",
"pytorch",
"camembert",
"token-classification",
"generated_from_trainer",
"fr",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-04T02:03:03Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
- f1
language:
- fr
widget:
- text: "Le président de la République appelle en outre les Français à faire le choix d'une \"majorité stable et sérieuse pour les protéger face aux crises et pour agir pour l'avenir\". \"Je vois dans le projet de Jean-Luc Mélenchon ou de Madame Le Pen un projet de désordre et de soumission. Ils expliquent qu'il faut sortir de nos alliances, de l'Europe, et bâtir des alliances stratégiques avec la Russie. C'est la soumission à la Russie\", assure-t-il."
- text: "Top départ à l’ouverture des bureaux de vote. La Polynésie et les Français résidant à l'étranger, dont certains ont déjà pu voter en ligne, sont invités aux urnes ce week-end pour le premier tour des législatives, samedi 4 juin pour le continent américain et les Caraïbes, et dimanche 5 juin pour le reste du monde. En France métropolitaine, les premier et second tours auront lieu les 12 et 19 juin."
- text: "Le ministère a aussi indiqué que des missiles russes ont frappé un centre d'entraînement d'artillerie dans la région de Soumy où travaillaient des instructeurs étrangers. Il a jouté qu'une autre frappe avait détruit une position de \"mercenaires étrangers\" dans la région d'Odessa."
- text: "Le malaise est profond et ressemble à une crise existentielle. Fait rarissime au Quai d’Orsay, six syndicats et un collectif de 500 jeunes diplomates du ministère des Affaires étrangères ont appelé à la grève, jeudi 2 juin, pour protester contre la réforme de la haute fonction publique qui, à terme, entraînera la disparition des deux corps historiques de la diplomatie française : celui de ministre plénipotentiaire (ambassadeur) et celui de conseiller des affaires étrangères."
- text: "Ils se font passer pour des recruteurs de Lockheed Martin ou du géant britannique de la défense et de l’aérospatial BAE Systems. Ces soi-disant chasseurs de tête font miroiter des perspectives lucratives de carrière et des postes à responsabilité. Mais ce n’est que du vent. En réalité, il s’agit de cyberespions nord-coréens cherchant à voler des secrets industriels de groupes de défense ou du secteur de l’aérospatial, révèle Eset, une société slovaque de sécurité informatique, dans un rapport publié mardi 31 mai."
model-index:
- name: camembert-keyword-extractor
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. -->
# camembert-keyword-extractor
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:
- Loss: 0.2199
- Precision: 0.6743
- Recall: 0.6979
- Accuracy: 0.9346
- F1: 0.6859
## 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: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:--------:|:------:|
| 0.1747 | 1.0 | 1875 | 0.1780 | 0.5935 | 0.7116 | 0.9258 | 0.6472 |
| 0.1375 | 2.0 | 3750 | 0.1588 | 0.6505 | 0.7032 | 0.9334 | 0.6759 |
| 0.1147 | 3.0 | 5625 | 0.1727 | 0.6825 | 0.6689 | 0.9355 | 0.6756 |
| 0.0969 | 4.0 | 7500 | 0.1759 | 0.6886 | 0.6621 | 0.9350 | 0.6751 |
| 0.0837 | 5.0 | 9375 | 0.1967 | 0.6688 | 0.7112 | 0.9348 | 0.6893 |
| 0.0746 | 6.0 | 11250 | 0.2088 | 0.6646 | 0.7114 | 0.9334 | 0.6872 |
| 0.0666 | 7.0 | 13125 | 0.2169 | 0.6713 | 0.7054 | 0.9347 | 0.6879 |
| 0.0634 | 8.0 | 15000 | 0.2199 | 0.6743 | 0.6979 | 0.9346 | 0.6859 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
awalmeida/q-Taxi-v3
|
awalmeida
| 2022-06-04T08:45:06Z | 0 | 0 | null |
[
"Taxi-v3-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-04T07:04:21Z |
---
tags:
- Taxi-v3-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 0.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3-4x4-no_slippery
type: Taxi-v3-4x4-no_slippery
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="awalmeida/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
awalmeida/q-FrozenLake-v1-4x4-noSlippery
|
awalmeida
| 2022-06-04T06:28:17Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-04T06:23:51Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
madatnlp/rob-large-krmath2
|
madatnlp
| 2022-06-04T06:08:04Z | 9 | 0 |
transformers
|
[
"transformers",
"tf",
"roberta",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-04T03:47:50Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: madatnlp/rob-large-krmath2
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/rob-large-krmath2
This model is a fine-tuned version of [klue/roberta-large](https://huggingface.co/klue/roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0707
- Validation Loss: 0.2571
- Epoch: 17
## 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': 'SGD', 'learning_rate': 0.01, 'decay': 0.0, 'momentum': 0.9, 'nesterov': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.7479 | 1.5215 | 0 |
| 1.1286 | 0.7513 | 1 |
| 1.2498 | 0.9242 | 2 |
| 1.0213 | 0.7140 | 3 |
| 0.8002 | 0.6080 | 4 |
| 0.5895 | 0.3721 | 5 |
| 0.4699 | 0.3392 | 6 |
| 0.3064 | 0.2599 | 7 |
| 0.2803 | 0.2360 | 8 |
| 0.2162 | 0.3075 | 9 |
| 0.1878 | 0.2652 | 10 |
| 0.1635 | 0.1618 | 11 |
| 0.1342 | 0.1061 | 12 |
| 0.1058 | 0.2906 | 13 |
| 0.0869 | 0.3535 | 14 |
| 0.0704 | 0.2090 | 15 |
| 0.0608 | 0.1777 | 16 |
| 0.0707 | 0.2571 | 17 |
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Zengwei/icefall-asr-librispeech-pruned-transducer-stateless4-2022-06-03
|
Zengwei
| 2022-06-04T05:36:57Z | 0 | 0 | null |
[
"tensorboard",
"region:us"
] | null | 2022-06-03T14:36:27Z |
See <https://github.com/k2-fsa/icefall/pull/344>
Note: In the uploaded files, the epoch number counts from 0.
|
LinaR/Prediccion_titulos
|
LinaR
| 2022-06-04T04:44:50Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-04T03:33:36Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: Prediccion_titulos
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. -->
# Prediccion_titulos
Este modelo predice los encabezados de las noticias
## Model description
Este modelo fue entrenado con un Transformador T5 y una base de datos en español
## Intended uses & limitations
More information needed
## Training and evaluation data
Los datos fueron tomado del siguiente dataset de Kaggle : https://www.kaggle.com/datasets/josemamuiz/noticias-laraznpblico, el cual es un conjunto de datos se extrajo de las webs de periódicos españoles
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1
|
nbroad/splinter-base-squad2
|
nbroad
| 2022-06-04T03:47:06Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"splinter",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-06-04T01:30:38Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: splinter-base-squad2_3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# splinter-base-squad2_3
This model is a fine-tuned version of [tau/splinter-base-qass](https://huggingface.co/tau/splinter-base-qass) on the squad_v2 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: 64
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0a0+17540c5
- Datasets 2.2.2
- Tokenizers 0.12.1
|
send-it/q-FrozenLake-v1-4x4-noSlippery
|
send-it
| 2022-06-04T03:07:59Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-04T03:07:51Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="send-it/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
baru98/bert-base-cased-finetuned-squad
|
baru98
| 2022-06-04T02:53:28Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-06-04T01:42:22Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-base-cased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 5.4212
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 7 | 5.7012 |
| No log | 2.0 | 14 | 5.5021 |
| No log | 3.0 | 21 | 5.4212 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
yanekyuk/berturk-keyword-extractor
|
yanekyuk
| 2022-06-04T01:57:03Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"tr",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-04T01:02:48Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
- f1
language:
- tr
widget:
- text: "İngiltere'de düzenlenen Avrupa Tekvando ve Para Tekvando Şampiyonası’nda millî tekvandocular 5 altın, 2 gümüş ve 4 bronz olmak üzere 11, millî para tekvandocular ise 4 altın, 3 gümüş ve 1 bronz olmak üzere 8 madalya kazanarak takım halinde Avrupa şampiyonu oldu."
- text: "Füme somon dedik ama aslında lox salamuralanmış somon anlamına geliyor, füme etme opsiyonel. Lox bagel, 1930'larda Eggs Benedict furyasında New Yorklu Yahudi cemaati tarafından koşer bir alternatif olarak çıkan bir lezzet. Günümüzde benim hangover yüreğim dâhil dünyanın birçok yerinde enfes bir kahvaltı sandviçi."
- text: "Türkiye'de son aylarda sıklıkla tartışılan konut satışı karşılığında yabancılara vatandaşlık verilmesi konusunu beyin göçü kapsamında ele almak mümkün. Daha önce 250 bin dolar olan vatandaşlık bedeli yükselen tepkiler üzerine 400 bin dolara çıkarılmıştı. Türkiye'den göç eden iyi eğitimli kişilerin , gittikleri ülkelerde 250 bin dolar tutarında yabancı yatırıma denk olduğu göz önüne alındığında nitelikli insan gücünün yabancılara konut karşılığında satılan vatandaşlık bedelin eş olduğunu görüyoruz. Yurt dışına giden her bir vatandaşın yüksek teknolojili katma değer üreten sektörlere yapacağı katkılar göz önünde bulundurulduğunda bu açığın inşaat sektörüyle kapatıldığını da görüyoruz. Beyin göçü konusunda sadece ekonomik perspektiften bakıldığında bile kısa vadeli döviz kaynağı yaratmak için kullanılan vatandaşlık satışı yerine beyin göçünü önleyecek önlemler alınmasının ülkemize çok daha faydalı olacağı sonucunu çıkarıyoruz."
- text: "Türkiye’de resmî verilere göre, 15 ve daha yukarı yaştaki kişilerde mevsim etkisinden arındırılmış işsiz sayısı, bu yılın ilk çeyreğinde bir önceki çeyreğe göre 50 bin kişi artarak 3 milyon 845 bin kişi oldu. Mevsim etkisinden arındırılmış işsizlik oranı ise 0,1 puanlık artışla %11,4 seviyesinde gerçekleşti. İşsizlik oranı, ilk çeyrekte geçen yılın aynı çeyreğine göre 1,7 puan azaldı."
- text: "Boeing’in insansız uzay aracı Starliner, birtakım sorunlara rağmen Uluslararası Uzay İstasyonuna (ISS) ulaşarak ilk kez başarılı bir şekilde kenetlendi. Aracın ISS’te beş gün kalmasını takiben sorunsuz bir şekilde New Mexico’ya inmesi halinde Boeing, sonbaharda astronotları yörüngeye göndermek için Starliner’ı kullanabilir.\n\nNeden önemli? NASA’nın personal aracı üretmeyi durdurmasından kaynaklı olarak görevli astronotlar ve kozmonotlar, ISS’te Rusya’nın ürettiği uzay araçları ile taşınıyordu. Starliner’ın kendini kanıtlaması ise bu konuda Rusya’ya olan bağımlılığın potansiyel olarak ortadan kalkabileceği anlamına geliyor."
model-index:
- name: berturk-keyword-extractor
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. -->
# berturk-keyword-extractor
This model is a fine-tuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4306
- Precision: 0.6770
- Recall: 0.6899
- Accuracy: 0.9169
- F1: 0.6834
## 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: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:--------:|:------:|
| 0.1845 | 1.0 | 1875 | 0.1964 | 0.6380 | 0.6743 | 0.9164 | 0.6557 |
| 0.1338 | 2.0 | 3750 | 0.2023 | 0.6407 | 0.7081 | 0.9169 | 0.6727 |
| 0.0978 | 3.0 | 5625 | 0.2315 | 0.6434 | 0.7309 | 0.9159 | 0.6844 |
| 0.0742 | 4.0 | 7500 | 0.2746 | 0.6592 | 0.7144 | 0.9158 | 0.6857 |
| 0.0541 | 5.0 | 9375 | 0.3290 | 0.6700 | 0.6880 | 0.9161 | 0.6789 |
| 0.0426 | 6.0 | 11250 | 0.3608 | 0.6789 | 0.6860 | 0.9171 | 0.6824 |
| 0.0332 | 7.0 | 13125 | 0.4075 | 0.6769 | 0.6924 | 0.9168 | 0.6845 |
| 0.027 | 8.0 | 15000 | 0.4306 | 0.6770 | 0.6899 | 0.9169 | 0.6834 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
huggingtweets/katieoneuro
|
huggingtweets
| 2022-06-04T01:31:59Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-04T01:26:38Z |
---
language: en
thumbnail: http://www.huggingtweets.com/katieoneuro/1654306303616/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/1000482851853340672/LhUdoFyk_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">Katie O'Nell 🧠💻</div>
<div style="text-align: center; font-size: 14px;">@katieoneuro</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 Katie O'Nell 🧠💻.
| Data | Katie O'Nell 🧠💻 |
| --- | --- |
| Tweets downloaded | 552 |
| Retweets | 323 |
| Short tweets | 17 |
| Tweets kept | 212 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2umesznv/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 @katieoneuro's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2evfy6ho) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2evfy6ho/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/katieoneuro')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/ww_bokudyo
|
huggingtweets
| 2022-06-04T01:05:21Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-04T01:05:14Z |
---
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/1527089805955301377/vNsxxIZ5_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">✨wuwu🌟</div>
<div style="text-align: center; font-size: 14px;">@ww_bokudyo</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 ✨wuwu🌟.
| Data | ✨wuwu🌟 |
| --- | --- |
| Tweets downloaded | 785 |
| Retweets | 172 |
| Short tweets | 274 |
| Tweets kept | 339 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1hf6kghs/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 @ww_bokudyo's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3hbh0tk2) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3hbh0tk2/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/ww_bokudyo')
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)
|
yanekyuk/bert-keyword-extractor
|
yanekyuk
| 2022-06-04T00:51:39Z | 1,008 | 41 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-03T23:06:00Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
- f1
language:
- en
widget:
- text: "Broadcom agreed to acquire cloud computing company VMware in a $61 billion (€57bn) cash-and stock deal, massively diversifying the chipmaker’s business and almost tripling its software-related revenue to about 45% of its total sales. By the numbers: VMware shareholders will receive either $142.50 in cash or 0.2520 of a Broadcom share for each VMware stock. Broadcom will also assume $8 billion of VMware's net debt."
- text: "Canadian Natural Resources Minister Jonathan Wilkinson told Bloomberg that the country could start supplying Europe with liquefied natural gas (LNG) in as soon as three years by converting an existing LNG import facility on Canada’s Atlantic coast into an export terminal. Bottom line: Wilkinson said what Canada cares about is that the new LNG facility uses a low-emission process for the gas and is capable of transitioning to exporting hydrogen later on."
- text: "Google is being investigated by the UK’s antitrust watchdog for its dominance in the \"ad tech stack,\" the set of services that facilitate the sale of online advertising space between advertisers and sellers. Google has strong positions at various levels of the ad tech stack and charges fees to both publishers and advertisers. A step back: UK Competition and Markets Authority has also been investigating whether Google and Meta colluded over ads, probing into the advertising agreement between the two companies, codenamed Jedi Blue."
- text: "Shares in Twitter closed 6.35% up after an SEC 13D filing revealed that Elon Musk pledged to put up an additional $6.25 billion of his own wealth to fund the $44 billion takeover deal, lifting the total to $33.5 billion from an initial $27.25 billion. In other news: Former Twitter CEO Jack Dorsey announced he's stepping down, but would stay on Twitter’s board \\“until his term expires at the 2022 meeting of stockholders.\""
model-index:
- name: bert-keyword-extractor
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-keyword-extractor
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1341
- Precision: 0.8565
- Recall: 0.8874
- Accuracy: 0.9738
- F1: 0.8717
## 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: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:--------:|:------:|
| 0.1688 | 1.0 | 1875 | 0.1233 | 0.7194 | 0.7738 | 0.9501 | 0.7456 |
| 0.1219 | 2.0 | 3750 | 0.1014 | 0.7724 | 0.8166 | 0.9606 | 0.7939 |
| 0.0834 | 3.0 | 5625 | 0.0977 | 0.8280 | 0.8263 | 0.9672 | 0.8272 |
| 0.0597 | 4.0 | 7500 | 0.0984 | 0.8304 | 0.8680 | 0.9704 | 0.8488 |
| 0.0419 | 5.0 | 9375 | 0.1042 | 0.8417 | 0.8687 | 0.9717 | 0.8550 |
| 0.0315 | 6.0 | 11250 | 0.1161 | 0.8520 | 0.8839 | 0.9729 | 0.8677 |
| 0.0229 | 7.0 | 13125 | 0.1282 | 0.8469 | 0.8939 | 0.9734 | 0.8698 |
| 0.0182 | 8.0 | 15000 | 0.1341 | 0.8565 | 0.8874 | 0.9738 | 0.8717 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
jgriffi/xlm-roberta-base-finetuned-panx-it
|
jgriffi
| 2022-06-04T00:32:43Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-04T00:16:55Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-it
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.it
metrics:
- name: F1
type: f1
value: 0.8374017376913528
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2556
- F1: 0.8374
## 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: 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.6559 | 1.0 | 140 | 0.2821 | 0.7862 |
| 0.251 | 2.0 | 280 | 0.2658 | 0.8179 |
| 0.1457 | 3.0 | 420 | 0.2556 | 0.8374 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
StanKarz/q-Taxi-v3
|
StanKarz
| 2022-06-03T22:17:14Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-03T22:17:03Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Sicko-Code/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
thamaine/distilbert-base-cased
|
thamaine
| 2022-06-03T22:11:35Z | 0 | 0 |
keras
|
[
"keras",
"tf-keras",
"region:us"
] | null | 2022-05-23T06:07:23Z |
---
library_name: keras
---
## 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': 0.01, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: mixed_float16
## Training Metrics
| Epochs | Train Loss | Validation Loss |
|--- |--- |--- |
| 1| 5.965| 5.951|
## Model Plot
<details>
<summary>View Model Plot</summary>
</details>
|
nboudad/Maghriberta
|
nboudad
| 2022-06-03T21:52:55Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-03T18:51:51Z |
---
widget:
- text: "جاب ليا <mask> ."
example_title: "example1"
- text: "مشيت نجيب <mask> فالفرماسيان ."
example_title: "example2"
---
|
mmillet/rubert-tiny2_finetuned_emotion_experiment
|
mmillet
| 2022-06-03T20:03:37Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-19T16:22:22Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: rubert-tiny2_finetuned_emotion_experiment
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. -->
# rubert-tiny2_finetuned_emotion_experiment
This model is a fine-tuned version of [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3947
- Accuracy: 0.8616
- F1: 0.8577
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.651 | 1.0 | 54 | 0.5689 | 0.8172 | 0.8008 |
| 0.5355 | 2.0 | 108 | 0.4842 | 0.8486 | 0.8349 |
| 0.4561 | 3.0 | 162 | 0.4436 | 0.8590 | 0.8509 |
| 0.4133 | 4.0 | 216 | 0.4203 | 0.8590 | 0.8528 |
| 0.3709 | 5.0 | 270 | 0.4071 | 0.8564 | 0.8515 |
| 0.3346 | 6.0 | 324 | 0.3980 | 0.8564 | 0.8529 |
| 0.3153 | 7.0 | 378 | 0.3985 | 0.8590 | 0.8565 |
| 0.302 | 8.0 | 432 | 0.3967 | 0.8642 | 0.8619 |
| 0.2774 | 9.0 | 486 | 0.3958 | 0.8616 | 0.8575 |
| 0.2728 | 10.0 | 540 | 0.3959 | 0.8668 | 0.8644 |
| 0.2427 | 11.0 | 594 | 0.3962 | 0.8590 | 0.8550 |
| 0.2425 | 12.0 | 648 | 0.3959 | 0.8642 | 0.8611 |
| 0.2414 | 13.0 | 702 | 0.3959 | 0.8642 | 0.8611 |
| 0.2249 | 14.0 | 756 | 0.3949 | 0.8616 | 0.8582 |
| 0.2391 | 15.0 | 810 | 0.3947 | 0.8616 | 0.8577 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
|
Worldman/t5_70_articles
|
Worldman
| 2022-06-03T18:50:22Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-03T15:29:17Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: t5_70_articles
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5_70_articles
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
hananajiyya/mt5-small-summarization
|
hananajiyya
| 2022-06-03T18:09:47Z | 5 | 0 |
transformers
|
[
"transformers",
"tf",
"mt5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-03T00:27:50Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: mt5-small-summarization
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. -->
# mt5-small-summarization
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.9665
- Validation Loss: 2.4241
- Train Rouge1: 23.5645
- Train Rouge2: 8.2413
- Train Rougel: 19.7515
- Train Rougelsum: 19.9204
- Train Gen Len: 19.0
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch |
|:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:|
| 4.7187 | 2.6627 | 19.5921 | 5.9723 | 16.6769 | 16.8456 | 18.955 | 0 |
| 3.1929 | 2.4941 | 21.2334 | 6.9784 | 18.2158 | 18.2062 | 18.99 | 1 |
| 2.9665 | 2.4241 | 23.5645 | 8.2413 | 19.7515 | 19.9204 | 19.0 | 2 |
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1
|
sinhprous/q-Taxi-v3
|
sinhprous
| 2022-06-03T17:40:36Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-03T14:12:06Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="sinhprous/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
meghazisofiane/opus-mt-en-ar-finetuned-en-to-ar
|
meghazisofiane
| 2022-06-03T17:27:04Z | 16 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:un_multi",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-31T18:13:33Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- un_multi
metrics:
- bleu
model-index:
- name: opus-mt-en-ar-finetuned-en-to-ar
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: un_multi
type: un_multi
args: ar-en
metrics:
- name: Bleu
type: bleu
value: 64.6767
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opus-mt-en-ar-finetuned-en-to-ar
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ar](https://huggingface.co/Helsinki-NLP/opus-mt-en-ar) on the un_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8133
- Bleu: 64.6767
- Gen Len: 17.595
## 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: 16
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| No log | 1.0 | 50 | 0.7710 | 64.3416 | 17.4 |
| No log | 2.0 | 100 | 0.7569 | 63.9546 | 17.465 |
| No log | 3.0 | 150 | 0.7570 | 64.7484 | 17.385 |
| No log | 4.0 | 200 | 0.7579 | 65.4073 | 17.305 |
| No log | 5.0 | 250 | 0.7624 | 64.8939 | 17.325 |
| No log | 6.0 | 300 | 0.7696 | 65.1257 | 17.45 |
| No log | 7.0 | 350 | 0.7747 | 65.527 | 17.395 |
| No log | 8.0 | 400 | 0.7791 | 65.1357 | 17.52 |
| No log | 9.0 | 450 | 0.7900 | 65.3812 | 17.415 |
| 0.3982 | 10.0 | 500 | 0.7925 | 65.7346 | 17.39 |
| 0.3982 | 11.0 | 550 | 0.7951 | 65.1267 | 17.62 |
| 0.3982 | 12.0 | 600 | 0.8040 | 64.6874 | 17.495 |
| 0.3982 | 13.0 | 650 | 0.8069 | 64.7788 | 17.52 |
| 0.3982 | 14.0 | 700 | 0.8105 | 64.6701 | 17.585 |
| 0.3982 | 15.0 | 750 | 0.8120 | 64.7111 | 17.58 |
| 0.3982 | 16.0 | 800 | 0.8133 | 64.6767 | 17.595 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
VictorZhu/results
|
VictorZhu
| 2022-06-03T17:17:57Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-03T17:10:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: results
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1194
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.1428 | 1.0 | 510 | 0.1347 |
| 0.0985 | 2.0 | 1020 | 0.1189 |
| 0.0763 | 3.0 | 1530 | 0.1172 |
| 0.0646 | 4.0 | 2040 | 0.1194 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu102
- Datasets 2.2.2
- Tokenizers 0.12.1
|
kaouther/distilbert-base-uncased-finetuned-squad
|
kaouther
| 2022-06-03T15:29:20Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-06-03T13:51:59Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1703
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2166 | 1.0 | 5533 | 1.1583 |
| 0.9572 | 2.0 | 11066 | 1.1387 |
| 0.7377 | 3.0 | 16599 | 1.1703 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
baru98/distilbert-base-uncased-finetuned-squad
|
baru98
| 2022-06-03T13:54:01Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-06-03T11:00:56Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1274
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2393 | 1.0 | 5475 | 1.1570 |
| 0.9651 | 2.0 | 10950 | 1.0903 |
| 0.7513 | 3.0 | 16425 | 1.1274 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
huggingtweets/washirerpadvice
|
huggingtweets
| 2022-06-03T13:29:32Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-03T13:23:45Z |
---
language: en
thumbnail: http://www.huggingtweets.com/washirerpadvice/1654262967962/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/1381256890542387204/zaT8DfFD_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">Washire RP Tips</div>
<div style="text-align: center; font-size: 14px;">@washirerpadvice</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 Washire RP Tips.
| Data | Washire RP Tips |
| --- | --- |
| Tweets downloaded | 243 |
| Retweets | 4 |
| Short tweets | 5 |
| Tweets kept | 234 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/gq82nlvl/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 @washirerpadvice's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/325ay6n9) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/325ay6n9/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/washirerpadvice')
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)
|
Worldman/pega_70_articles
|
Worldman
| 2022-06-03T13:13:37Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-01T23:16:02Z |
---
tags:
- generated_from_trainer
model-index:
- name: pega_70_articles
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. -->
# pega_70_articles
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Splend1dchan/xtreme_s_xlsr_t5lephone-small_minds14.en-all
|
Splend1dchan
| 2022-06-03T12:19:36Z | 2 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"minds14",
"google/xtreme_s",
"generated_from_trainer",
"all",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-06-02T23:38:15Z |
---
language:
- all
license: apache-2.0
tags:
- minds14
- google/xtreme_s
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: xtreme_s_xlsr_t5lephone-small_minds14.en-all
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xtreme_s_xlsr_t5lephone-small_minds14.en-all
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the GOOGLE/XTREME_S - MINDS14.ALL dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5979
- F1: 0.8918
- Accuracy: 0.8921
## 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: 2
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1500
- num_epochs: 150.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:------:|:--------:|
| 2.3561 | 2.98 | 200 | 2.5464 | 0.0681 | 0.1334 |
| 1.1851 | 5.97 | 400 | 1.5056 | 0.5583 | 0.5861 |
| 1.2805 | 8.95 | 600 | 1.1397 | 0.7106 | 0.7044 |
| 1.0801 | 11.94 | 800 | 0.9863 | 0.7132 | 0.7198 |
| 0.9285 | 14.92 | 1000 | 0.9912 | 0.7037 | 0.7139 |
| 0.4164 | 17.91 | 1200 | 0.8226 | 0.7743 | 0.7741 |
| 0.7669 | 20.89 | 1400 | 0.8131 | 0.7783 | 0.7788 |
| 0.4606 | 23.88 | 1600 | 0.8314 | 0.7879 | 0.7792 |
| 0.6975 | 26.86 | 1800 | 0.7667 | 0.7927 | 0.7939 |
| 0.9913 | 29.85 | 2000 | 0.9207 | 0.7734 | 0.7707 |
| 0.2307 | 32.83 | 2200 | 0.7651 | 0.8072 | 0.8086 |
| 0.1412 | 35.82 | 2400 | 0.7132 | 0.8352 | 0.8311 |
| 0.2141 | 38.8 | 2600 | 0.7551 | 0.8276 | 0.8262 |
| 0.2169 | 41.79 | 2800 | 0.7900 | 0.8148 | 0.8160 |
| 0.3942 | 44.77 | 3000 | 0.8621 | 0.8130 | 0.8042 |
| 0.2306 | 47.76 | 3200 | 0.6788 | 0.8264 | 0.8253 |
| 0.0975 | 50.74 | 3400 | 0.7236 | 0.8295 | 0.8289 |
| 0.0062 | 53.73 | 3600 | 0.6872 | 0.8286 | 0.8277 |
| 0.1781 | 56.71 | 3800 | 0.6990 | 0.8393 | 0.8390 |
| 0.0309 | 59.7 | 4000 | 0.6348 | 0.8496 | 0.8500 |
| 0.0026 | 62.68 | 4200 | 0.6737 | 0.8585 | 0.8566 |
| 0.0043 | 65.67 | 4400 | 0.7780 | 0.8416 | 0.8387 |
| 0.0032 | 68.65 | 4600 | 0.6899 | 0.8482 | 0.8461 |
| 0.0302 | 71.64 | 4800 | 0.6813 | 0.8515 | 0.8495 |
| 0.0027 | 74.62 | 5000 | 0.7163 | 0.8530 | 0.8529 |
| 0.1165 | 77.61 | 5200 | 0.6249 | 0.8603 | 0.8595 |
| 0.0021 | 80.59 | 5400 | 0.6747 | 0.8588 | 0.8578 |
| 0.2558 | 83.58 | 5600 | 0.7514 | 0.8581 | 0.8581 |
| 0.0162 | 86.57 | 5800 | 0.6782 | 0.8667 | 0.8664 |
| 0.1929 | 89.55 | 6000 | 0.6371 | 0.8615 | 0.8600 |
| 0.0621 | 92.54 | 6200 | 0.8079 | 0.8600 | 0.8607 |
| 0.0017 | 95.52 | 6400 | 0.7072 | 0.8678 | 0.8669 |
| 0.0008 | 98.51 | 6600 | 0.7323 | 0.8572 | 0.8541 |
| 0.1655 | 101.49 | 6800 | 0.6953 | 0.8521 | 0.8505 |
| 0.01 | 104.48 | 7000 | 0.7149 | 0.8665 | 0.8674 |
| 0.0135 | 107.46 | 7200 | 0.8990 | 0.8523 | 0.8488 |
| 0.0056 | 110.45 | 7400 | 0.7320 | 0.8673 | 0.8664 |
| 0.0023 | 113.43 | 7600 | 0.7108 | 0.8700 | 0.8705 |
| 0.0025 | 116.42 | 7800 | 0.6464 | 0.8818 | 0.8820 |
| 0.0003 | 119.4 | 8000 | 0.6985 | 0.8706 | 0.8713 |
| 0.0048 | 122.39 | 8200 | 0.6620 | 0.8765 | 0.8740 |
| 0.2335 | 125.37 | 8400 | 0.6515 | 0.8832 | 0.8828 |
| 0.0005 | 128.36 | 8600 | 0.6961 | 0.8776 | 0.8762 |
| 0.0003 | 131.34 | 8800 | 0.5990 | 0.8878 | 0.8882 |
| 0.0002 | 134.33 | 9000 | 0.6236 | 0.8887 | 0.8889 |
| 0.002 | 137.31 | 9200 | 0.6671 | 0.8847 | 0.8845 |
| 0.0002 | 140.3 | 9400 | 0.5970 | 0.8931 | 0.8935 |
| 0.0002 | 143.28 | 9600 | 0.6095 | 0.8906 | 0.8913 |
| 0.0002 | 146.27 | 9800 | 0.6056 | 0.8910 | 0.8913 |
| 0.0002 | 149.25 | 10000 | 0.5979 | 0.8918 | 0.8921 |
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
huggingtweets/quora-reddit
|
huggingtweets
| 2022-06-03T12:09:44Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-02T13:30:18Z |
---
language: en
thumbnail: http://www.huggingtweets.com/quora-reddit/1654258179125/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/1532031893318737920/N4nwSAZv_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/1333471260483801089/OtTAJXEZ_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">Quora & Reddit</div>
<div style="text-align: center; font-size: 14px;">@quora-reddit</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 Quora & Reddit.
| Data | Quora | Reddit |
| --- | --- | --- |
| Tweets downloaded | 3244 | 3248 |
| Retweets | 181 | 331 |
| Short tweets | 22 | 392 |
| Tweets kept | 3041 | 2525 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/12sw605d/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 @quora-reddit's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2g51clcs) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2g51clcs/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/quora-reddit')
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)
|
jcastanyo/q-FrozenLake-v1-8x8-Slippery-v3-v2
|
jcastanyo
| 2022-06-03T10:41:57Z | 0 | 0 | null |
[
"FrozenLake-v1-8x8",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-03T10:41:48Z |
---
tags:
- FrozenLake-v1-8x8
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-Slippery-v3-v2
results:
- metrics:
- type: mean_reward
value: 0.48 +/- 0.50
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-8x8
type: FrozenLake-v1-8x8
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="jcastanyo/q-FrozenLake-v1-8x8-Slippery-v3-v2", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
huggingtweets/mundodeportivo
|
huggingtweets
| 2022-06-03T09:09:48Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-03T08:51:01Z |
---
language: en
thumbnail: http://www.huggingtweets.com/mundodeportivo/1654247301367/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/1277369340275437570/R-AXlYNT_400x400.png')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Mundo Deportivo</div>
<div style="text-align: center; font-size: 14px;">@mundodeportivo</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 Mundo Deportivo.
| Data | Mundo Deportivo |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 195 |
| Short tweets | 26 |
| Tweets kept | 3029 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/17m7lnrt/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 @mundodeportivo's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2mndpk3u) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2mndpk3u/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/mundodeportivo')
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)
|
lewtun/t5-small-finetuned-arxiv
|
lewtun
| 2022-06-03T08:23:12Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-03T07:36:30Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-small-finetuned-arxiv
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-arxiv
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1556
- Rouge1: 37.8405
- Rouge2: 20.4483
- Rougel: 33.996
- Rougelsum: 34.0071
- Gen Len: 15.8214
## 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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:------:|:---------:|:-------:|
| 2.3825 | 1.0 | 3564 | 2.1556 | 37.8405 | 20.4483 | 33.996 | 34.0071 | 15.8214 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu102
- Datasets 2.2.1
- Tokenizers 0.12.1
|
chans/q-Taxi-v3
|
chans
| 2022-06-03T07:57:28Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-03T07:57:22Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="chans/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
sriiikar/wav2vec2-hindi-bhoj-3
|
sriiikar
| 2022-06-03T07:11:46Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-06-03T04:23:42Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-hindi-bhoj-3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-hindi-bhoj-3
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 5.7033
- Wer: 1.1477
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- 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: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 8.6136 | 6.45 | 400 | 3.6017 | 1.0 |
| 2.6692 | 12.9 | 800 | 4.5408 | 1.0872 |
| 0.5639 | 19.35 | 1200 | 5.2302 | 1.2282 |
| 0.2296 | 25.8 | 1600 | 5.3323 | 1.0872 |
| 0.1496 | 32.26 | 2000 | 5.7219 | 1.1342 |
| 0.1098 | 38.7 | 2400 | 5.7033 | 1.1477 |
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.2.3.dev0
- Tokenizers 0.12.1
|
huggingtweets/tojibaceo
|
huggingtweets
| 2022-06-03T04:08:57Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-30T15:11:36Z |
---
language: en
thumbnail: http://www.huggingtweets.com/tojibaceo/1654229333065/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/1508824472924659725/267f4Lkm_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">Tojiba CPU Corp (🏭,🏭)</div>
<div style="text-align: center; font-size: 14px;">@tojibaceo</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 Tojiba CPU Corp (🏭,🏭).
| Data | Tojiba CPU Corp (🏭,🏭) |
| --- | --- |
| Tweets downloaded | 1401 |
| Retweets | 706 |
| Short tweets | 239 |
| Tweets kept | 456 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/32gtdln5/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 @tojibaceo's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/19scebmc) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/19scebmc/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/tojibaceo')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/marazack26
|
huggingtweets
| 2022-06-02T22:56:49Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-02T22:54:27Z |
---
language: en
thumbnail: http://www.huggingtweets.com/marazack26/1654210546142/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/1239803946643927041/AHuDYsfL_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">Mohammed Abd Al-Razack / محمد عبد الرزاق</div>
<div style="text-align: center; font-size: 14px;">@marazack26</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 Mohammed Abd Al-Razack / محمد عبد الرزاق.
| Data | Mohammed Abd Al-Razack / محمد عبد الرزاق |
| --- | --- |
| Tweets downloaded | 3060 |
| Retweets | 1619 |
| Short tweets | 167 |
| Tweets kept | 1274 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/264mzr04/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 @marazack26's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3p7448r6) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3p7448r6/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/marazack26')
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)
|
UBC-NLP/prags2
|
UBC-NLP
| 2022-06-02T22:52:49Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"license:cc-by-nc-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-05-30T22:47:15Z |
---
license: cc-by-nc-3.0
---
PragS2: Pragmatic Masked Language Modeling with Emoji_any dataset followed by Hashtag-Based Surrogate Fine-Tuning
You can load this model and use for downstream fine-tuning. For example:
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained('UBC-NLP/prags2', use_fast = True)
model = AutoModelForSequenceClassification.from_pretrained('UBC-NLP/prags2',num_labels=lable_size)
```
More details are in our paper:
```
@inproceedings{zhang-abdul-mageed-2022-improving,
title = "Improving Social Meaning Detection with Pragmatic Masking and Surrogate Fine-Tuning",
author = "Zhang, Chiyu and
Abdul-Mageed, Muhammad",
booktitle = "Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment {\&} Social Media Analysis",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.wassa-1.14",
pages = "141--156",
}
```
|
meetyildiz/TurQA-bert-base-turkish-cased-finetuned-toqad
|
meetyildiz
| 2022-06-02T22:41:29Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"feature-extraction",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-05-25T17:49:21Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: TurQA-bert-base-turkish-cased-finetuned-toqad
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. -->
# TurQA-bert-base-turkish-cased-finetuned-toqad
This model is a fine-tuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9711
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7191 | 1.0 | 800 | 3.0920 |
| 1.6875 | 2.0 | 1600 | 2.9778 |
| 1.4582 | 3.0 | 2400 | 2.9711 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
benwri/GaryOut
|
benwri
| 2022-06-02T21:19:11Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-06-02T21:18:55Z |
git lfs install
git clone https://huggingface.co/etmckinley/BERFALTER
# if you want to clone without large files – just their pointers
# prepend your git clone with the following env var:
GIT_LFS_SKIP_SMUDGE=1
|
bilalahmed15/Urdu_repo
|
bilalahmed15
| 2022-06-02T21:01:04Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-06-01T17:21:23Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-turkish-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-turkish-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7532
- Wer: 0.4020
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.9542 | 1.96 | 400 | 1.5737 | 0.8827 |
| 0.8596 | 3.92 | 800 | 0.7296 | 0.5696 |
| 0.4729 | 5.88 | 1200 | 0.6004 | 0.4934 |
| 0.3364 | 7.84 | 1600 | 0.5776 | 0.4656 |
| 0.2684 | 9.8 | 2000 | 0.6178 | 0.4563 |
| 0.2143 | 11.76 | 2400 | 0.6408 | 0.4690 |
| 0.1744 | 13.72 | 2800 | 0.6704 | 0.4573 |
| 0.1458 | 15.68 | 3200 | 0.7015 | 0.4484 |
| 0.1201 | 17.65 | 3600 | 0.7151 | 0.4228 |
| 0.104 | 19.61 | 4000 | 0.7123 | 0.4195 |
| 0.0887 | 21.57 | 4400 | 0.7102 | 0.4234 |
| 0.0807 | 23.53 | 4800 | 0.7561 | 0.4132 |
| 0.0697 | 25.49 | 5200 | 0.7435 | 0.4075 |
| 0.0611 | 27.45 | 5600 | 0.7465 | 0.4034 |
| 0.0556 | 29.41 | 6000 | 0.7532 | 0.4020 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
ponci/ppo-lunar-ponci-test
|
ponci
| 2022-06-02T20:32:45Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-02T20:32:15Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 155.69 +/- 124.06
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
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
erickfm/t5-large-finetuned-bias
|
erickfm
| 2022-06-02T20:32:44Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:WNC",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-02T20:07:14Z |
---
language:
- en
license: apache-2.0
datasets:
- WNC
metrics:
- accuracy
---
This model is a fine-tune checkpoint of [T5-large](https://huggingface.co/t5-large), fine-tuned on the [Wiki Neutrality Corpus (WNC)](https://github.com/rpryzant/neutralizing-bias), a labeled dataset composed of 180,000 biased and neutralized sentence pairs that are generated from Wikipedia edits tagged for “neutral point of view”. This model reaches an accuracy of [?] on a dev split of the WNC.
For more details about T5, check out this [model card](https://huggingface.co/t5-large).
|
roshnir/bert-multi-uncased-trained-squadv2
|
roshnir
| 2022-06-02T20:15:14Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-05-30T23:00:51Z |
mBERT base uncased model trained on 50% SQUAD data. This model can further be used to fine-tune using dev data for QA system on a specific language. The process is similar to what was followed in MLQA paper[https://aclanthology.org/2020.acl-main.421.pdf].
|
SEBIS/legal_t5_small_summ_es
|
SEBIS
| 2022-06-02T19:52:52Z | 66 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"summarization Spanish model",
"dataset:jrc-acquis",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:04Z |
---
language: Spanish
tags:
- summarization Spanish model
datasets:
- jrc-acquis
widget:
- text: "[notificada con el número C(2006) 166] (El texto en lengua portuguesa es el único auténtico) (2006/78/CE) LA COMISIÓN DE LAS COMUNIDADES EUROPEAS, Visto el Tratado constitutivo de la Comunidad Europea, Vista la Decisión 90/424/CEE del Consejo, de 26 de junio de 1990, relativa a determinados gastos en el sector veterinario [1], y, en particular, su artículo 3, apartado 2 bis, Considerando lo siguiente: (1) El 24 de noviembre de 2004 se declararon brotes de fiebre catarral ovina en Portugal. La aparición de esta enfermedad puede representar un grave riesgo para la cabaña ganadera de la Comunidad. (2) Para atajar la propagación de la enfermedad en el plazo más breve, la Comunidad debe participar en los gastos subvencionables que suponen para Portugal la adopción de medidas de urgencia contra la enfermedad, en las condiciones previstas en la Decisión 90/424/CEE. Por ello, el 15 de septiembre de 2005 se adoptó la Decisión 2005/660/CE de la Comisión relativa a una ayuda financiera de la Comunidad para medidas de urgencia contra la fiebre catarral ovina adoptadas en Portugal en 2004 y 2005 [2]. (3) La Comisión ha adoptado varias decisiones para delimitar las zonas de protección y vigilancia y fijar las condiciones que deben cumplir los animales que vayan a salir de esas zonas; la última de ellas es la Decisión 2005/393/CE, de 23 de mayo de 2005, sobre las zonas de protección y vigilancia en relación con la fiebre catarral ovina y las condiciones que se aplican a los traslados de animales desde estas zonas o a través de ellas [3]. (4) Desde el otoño de 2004, la excepcional escasez de lluvias en Portugal ha afectado gravemente al suministro de forraje y, en consecuencia, a las posibilidades de alimentación animal, lo que ha conllevado costes adicionales para los ganaderos. La situación tiene consecuencias particulares en Portugal, pues las explotaciones especializadas en reproducción de bovinos y de ovinos están ubicadas en las zonas afectadas por las restricciones aplicadas a los traslados de animales, mientras que las especializadas en engorde, que constituyen la salida lógica de los animales criados en aquéllas, están localizadas fuera de dichas zonas. (5) Portugal, en colaboración con España, puso en marcha otras medidas para controlar la epidemia, como la realización de estudios epidemiológicos y la aplicación de medidas de vigilancia de la enfermedad, incluidas las pruebas de laboratorio para el control serológico y virológico en el marco de las pruebas realizadas a los animales antes de su traslado y en el de la vigilancia entomológica. (6) Portugal y España han presentado pruebas de su cooperación para evitar la propagación de la enfermedad tomando medidas de vigilancia de la misma. (7) De conformidad con el artículo 3, apartado 2, del Reglamento (CE) no 1258/1999 del Consejo, de 17 de mayo de 1999, sobre la financiación de la política agrícola común [4], las medidas veterinarias y fitosanitarias ejecutadas según las normas comunitarias son financiadas por la sección Garantía del Fondo Europeo de Orientación y de Garantía Agrícola. El control financiero de estas acciones debe efectuarse de conformidad con lo dispuesto en los artículos 8 y 9 de dicho Reglamento. (8) El pago de la contribución financiera de la Comunidad se supedita a la realización efectiva de las acciones programadas y a la presentación por parte de las autoridades de toda la información necesaria en los plazos establecidos. (9) El 25 de febrero de 2005, Portugal presentó un primer cálculo de los costes de las demás medidas de urgencia, como las de vigilancia epidemiológica, tomadas para luchar contra la enfermedad. El importe estimado de las medidas de vigilancia epidemiológica se eleva a 4303336 EUR. (10) A la espera de que se efectúen los controles in situ de la Comisión, procede fijar desde ahora el importe de un primer pago de la ayuda financiera de la Comunidad. Este primer pago ha de ser igual al 50 % de la contribución de la Comunidad, establecida sobre la base del gasto subvencionable calculado para las medidas de vigilancia epidemiológica. Procede asimismo determinar los importes máximos que se reembolsarán en concepto de pruebas realizadas y de trampas utilizadas en el marco de dichas medidas. (11) Las autoridades portuguesas han cumplido íntegramente sus obligaciones técnicas y administrativas relacionadas con las medidas previstas en el artículo 3 de la Decisión 90/424/CEE. (12) Las medidas previstas en la presente Decisión se ajustan al dictamen del Comité permanente de la cadena alimentaria y de sanidad animal. HA ADOPTADO LA PRESENTE DECISIÓN: Artículo 1 Concesión de una ayuda financiera de la Comunidad a Portugal 1. En el marco de las medidas de urgencia contra la fiebre catarral ovina adoptadas en Portugal en 2004 y 2005, Portugal tendrá derecho a una contribución comunitaria del 50 % de los importes desembolsados en concepto de pruebas de laboratorio para la vigilancia serológica y virológica, así como en concepto de vigilancia entomológica, incluida la adquisición de trampas. 2. El importe máximo de los gastos que se reembolsarán a Portugal en concepto de las pruebas y las trampas mencionadas en el apartado 1 no excederá de: a) vigilancia serológica, prueba ELISA: 2,5 EUR por prueba; b) vigilancia virológica, reacción en cadena de la polimerasa retrotranscriptásica (RT.PCR): 15 EUR por prueba; c) vigilancia entomológica, trampa: 160 EUR por trampa. 3. El impuesto sobre el valor añadido se excluirá de la participación financiera de la Comunidad. Artículo 2 Modalidades de pago A reserva del resultado de los controles in situ llevados a cabo de conformidad con el artículo 9, apartado 1, de la Decisión 90/424/CEE, se efectuará un primer pago de 600000 EUR como parte de la ayuda financiera de la Comunidad prevista en el artículo 1. El pago se llevará a cabo previa presentación por parte de Portugal de justificantes de las pruebas de laboratorio y de la adquisición de las trampas mencionadas en el artículo 1, apartado 1. Artículo 3 Condiciones de pago y documentación justificativa 1. La ayuda financiera de la Comunidad contemplada en el artículo 1 se pagará atendiendo a los siguientes elementos: a) una solicitud que contenga los datos especificados en el anexo, presentada en el plazo establecido en el apartado 2 del presente artículo; b) la documentación justificativa mencionada en el artículo 2, que incluirá un informe epidemiológico y un informe financiero; c) el resultado de cualquiera de los controles in situ llevados a cabo de conformidad con el artículo 9, apartado 1, de la Decisión 90/424/CEE. Los documentos mencionados en la letra b) deberán estar disponibles para los controles in situ mencionados en la letra c). 2. La solicitud mencionada en el apartado 1, letra a), se presentará en formato electrónico en un plazo de 60 días naturales a partir de la fecha de notificación de la presente Decisión. Si no se respeta este plazo, la ayuda financiera comunitaria se reducirá un 25 % por cada mes de retraso. Artículo 4 Destinatario El destinatario de la presente Decisión es la República Portuguesa. Hecho en Bruselas, el 31 de enero de 2006. Por la Comisión Markos Kyprianou Miembro de la Comisión [1] DO L 224 de 18.8.1990, p. 19. Decisión modificada en último lugar por el Reglamento (CE) no 806/2003 (DO L 122 de 16.5.2003, p. 1). [2] DO L 244 de 20.9.2005, p. 28. [3] DO L 130 de 24.5.2005, p. 22. Decisión modificada en último lugar por la Decisión 2005/828/CE (DO L 311 de 26.11.2005, p. 37). [4] DO L 160 de 26.6.1999, p. 103. -------------------------------------------------- ANEXO Datos mencionados en el artículo 3, apartado 1, letra a) Gastos | Naturaleza de los costes | Número | Importe (sin IVA) | Pruebas ELISA | | | Pruebas RT.PCR | | | Otras pruebas virológicas | | | Trampas | | | Total | | -------------------------------------------------- "
---
# legal_t5_small_summ_es model
Model for Summarization of legal text written in Spanish. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). This model is trained on three parallel corpus from jrc-acquis.
## Model description
legal_t5_small_summ_es is based on the `t5-small` model and was trained on a large corpus of parallel text. This is a smaller model, which scales the baseline model of t5 down by using `dmodel = 512`, `dff = 2,048`, 8-headed attention, and only 6 layers each in the encoder and decoder. This variant has about 60 million parameters.
## Intended uses & limitations
The model could be used for summarization of legal texts written in Spanish.
### How to use
Here is how to use this model to summarize legal text written in Spanish in PyTorch:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead, TranslationPipeline
pipeline = TranslationPipeline(
model=AutoModelWithLMHead.from_pretrained("SEBIS/legal_t5_small_summ_es"),
tokenizer=AutoTokenizer.from_pretrained(pretrained_model_name_or_path = "SEBIS/legal_t5_small_summ_es", do_lower_case=False,
skip_special_tokens=True),
device=0
)
es_text = "[notificada con el número C(2006) 166] (El texto en lengua portuguesa es el único auténtico) (2006/78/CE) LA COMISIÓN DE LAS COMUNIDADES EUROPEAS, Visto el Tratado constitutivo de la Comunidad Europea, Vista la Decisión 90/424/CEE del Consejo, de 26 de junio de 1990, relativa a determinados gastos en el sector veterinario [1], y, en particular, su artículo 3, apartado 2 bis, Considerando lo siguiente: (1) El 24 de noviembre de 2004 se declararon brotes de fiebre catarral ovina en Portugal. La aparición de esta enfermedad puede representar un grave riesgo para la cabaña ganadera de la Comunidad. (2) Para atajar la propagación de la enfermedad en el plazo más breve, la Comunidad debe participar en los gastos subvencionables que suponen para Portugal la adopción de medidas de urgencia contra la enfermedad, en las condiciones previstas en la Decisión 90/424/CEE. Por ello, el 15 de septiembre de 2005 se adoptó la Decisión 2005/660/CE de la Comisión relativa a una ayuda financiera de la Comunidad para medidas de urgencia contra la fiebre catarral ovina adoptadas en Portugal en 2004 y 2005 [2]. (3) La Comisión ha adoptado varias decisiones para delimitar las zonas de protección y vigilancia y fijar las condiciones que deben cumplir los animales que vayan a salir de esas zonas; la última de ellas es la Decisión 2005/393/CE, de 23 de mayo de 2005, sobre las zonas de protección y vigilancia en relación con la fiebre catarral ovina y las condiciones que se aplican a los traslados de animales desde estas zonas o a través de ellas [3]. (4) Desde el otoño de 2004, la excepcional escasez de lluvias en Portugal ha afectado gravemente al suministro de forraje y, en consecuencia, a las posibilidades de alimentación animal, lo que ha conllevado costes adicionales para los ganaderos. La situación tiene consecuencias particulares en Portugal, pues las explotaciones especializadas en reproducción de bovinos y de ovinos están ubicadas en las zonas afectadas por las restricciones aplicadas a los traslados de animales, mientras que las especializadas en engorde, que constituyen la salida lógica de los animales criados en aquéllas, están localizadas fuera de dichas zonas. (5) Portugal, en colaboración con España, puso en marcha otras medidas para controlar la epidemia, como la realización de estudios epidemiológicos y la aplicación de medidas de vigilancia de la enfermedad, incluidas las pruebas de laboratorio para el control serológico y virológico en el marco de las pruebas realizadas a los animales antes de su traslado y en el de la vigilancia entomológica. (6) Portugal y España han presentado pruebas de su cooperación para evitar la propagación de la enfermedad tomando medidas de vigilancia de la misma. (7) De conformidad con el artículo 3, apartado 2, del Reglamento (CE) no 1258/1999 del Consejo, de 17 de mayo de 1999, sobre la financiación de la política agrícola común [4], las medidas veterinarias y fitosanitarias ejecutadas según las normas comunitarias son financiadas por la sección Garantía del Fondo Europeo de Orientación y de Garantía Agrícola. El control financiero de estas acciones debe efectuarse de conformidad con lo dispuesto en los artículos 8 y 9 de dicho Reglamento. (8) El pago de la contribución financiera de la Comunidad se supedita a la realización efectiva de las acciones programadas y a la presentación por parte de las autoridades de toda la información necesaria en los plazos establecidos. (9) El 25 de febrero de 2005, Portugal presentó un primer cálculo de los costes de las demás medidas de urgencia, como las de vigilancia epidemiológica, tomadas para luchar contra la enfermedad. El importe estimado de las medidas de vigilancia epidemiológica se eleva a 4303336 EUR. (10) A la espera de que se efectúen los controles in situ de la Comisión, procede fijar desde ahora el importe de un primer pago de la ayuda financiera de la Comunidad. Este primer pago ha de ser igual al 50 % de la contribución de la Comunidad, establecida sobre la base del gasto subvencionable calculado para las medidas de vigilancia epidemiológica. Procede asimismo determinar los importes máximos que se reembolsarán en concepto de pruebas realizadas y de trampas utilizadas en el marco de dichas medidas. (11) Las autoridades portuguesas han cumplido íntegramente sus obligaciones técnicas y administrativas relacionadas con las medidas previstas en el artículo 3 de la Decisión 90/424/CEE. (12) Las medidas previstas en la presente Decisión se ajustan al dictamen del Comité permanente de la cadena alimentaria y de sanidad animal. HA ADOPTADO LA PRESENTE DECISIÓN: Artículo 1 Concesión de una ayuda financiera de la Comunidad a Portugal 1. En el marco de las medidas de urgencia contra la fiebre catarral ovina adoptadas en Portugal en 2004 y 2005, Portugal tendrá derecho a una contribución comunitaria del 50 % de los importes desembolsados en concepto de pruebas de laboratorio para la vigilancia serológica y virológica, así como en concepto de vigilancia entomológica, incluida la adquisición de trampas. 2. El importe máximo de los gastos que se reembolsarán a Portugal en concepto de las pruebas y las trampas mencionadas en el apartado 1 no excederá de: a) vigilancia serológica, prueba ELISA: 2,5 EUR por prueba; b) vigilancia virológica, reacción en cadena de la polimerasa retrotranscriptásica (RT.PCR): 15 EUR por prueba; c) vigilancia entomológica, trampa: 160 EUR por trampa. 3. El impuesto sobre el valor añadido se excluirá de la participación financiera de la Comunidad. Artículo 2 Modalidades de pago A reserva del resultado de los controles in situ llevados a cabo de conformidad con el artículo 9, apartado 1, de la Decisión 90/424/CEE, se efectuará un primer pago de 600000 EUR como parte de la ayuda financiera de la Comunidad prevista en el artículo 1. El pago se llevará a cabo previa presentación por parte de Portugal de justificantes de las pruebas de laboratorio y de la adquisición de las trampas mencionadas en el artículo 1, apartado 1. Artículo 3 Condiciones de pago y documentación justificativa 1. La ayuda financiera de la Comunidad contemplada en el artículo 1 se pagará atendiendo a los siguientes elementos: a) una solicitud que contenga los datos especificados en el anexo, presentada en el plazo establecido en el apartado 2 del presente artículo; b) la documentación justificativa mencionada en el artículo 2, que incluirá un informe epidemiológico y un informe financiero; c) el resultado de cualquiera de los controles in situ llevados a cabo de conformidad con el artículo 9, apartado 1, de la Decisión 90/424/CEE. Los documentos mencionados en la letra b) deberán estar disponibles para los controles in situ mencionados en la letra c). 2. La solicitud mencionada en el apartado 1, letra a), se presentará en formato electrónico en un plazo de 60 días naturales a partir de la fecha de notificación de la presente Decisión. Si no se respeta este plazo, la ayuda financiera comunitaria se reducirá un 25 % por cada mes de retraso. Artículo 4 Destinatario El destinatario de la presente Decisión es la República Portuguesa. Hecho en Bruselas, el 31 de enero de 2006. Por la Comisión Markos Kyprianou Miembro de la Comisión [1] DO L 224 de 18.8.1990, p. 19. Decisión modificada en último lugar por el Reglamento (CE) no 806/2003 (DO L 122 de 16.5.2003, p. 1). [2] DO L 244 de 20.9.2005, p. 28. [3] DO L 130 de 24.5.2005, p. 22. Decisión modificada en último lugar por la Decisión 2005/828/CE (DO L 311 de 26.11.2005, p. 37). [4] DO L 160 de 26.6.1999, p. 103. -------------------------------------------------- ANEXO Datos mencionados en el artículo 3, apartado 1, letra a) Gastos | Naturaleza de los costes | Número | Importe (sin IVA) | Pruebas ELISA | | | Pruebas RT.PCR | | | Otras pruebas virológicas | | | Trampas | | | Total | | -------------------------------------------------- "
pipeline([es_text], max_length=512)
```
## Training data
The legal_t5_small_summ_es model was trained on [JRC-ACQUIS](https://wt-public.emm4u.eu/Acquis/index_2.2.html) dataset consisting of 23 Thousand texts.
## Training procedure
The model was trained on a single TPU Pod V3-8 for 250K steps in total, using sequence length 512 (batch size 64). It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture. The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training.
### Preprocessing
An unigram model trained with 88M lines of text from the parallel corpus (of all possible language pairs) to get the vocabulary (with byte pair encoding), which is used with this model.
### Pretraining
## Evaluation results
When the model is used for classification test dataset, achieves the following results:
Test results :
| Model | Rouge1 | Rouge2 | Rouge Lsum |
|:-----:|:-----:|:-----:|:-----:|
| legal_t5_small_summ_es | 80.23|70.16 |78.69|
### BibTeX entry and citation info
> Created by [Ahmed Elnaggar/@Elnaggar_AI](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/)
|
SEBIS/code_trans_t5_small_program_synthese_multitask
|
SEBIS
| 2022-06-02T19:50:32Z | 27 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"t5",
"feature-extraction",
"summarization",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-03-02T23:29:04Z |
---
tags:
- summarization
widget:
- text: "you are given an array of numbers a and a number b , compute the difference of elements in a and b"
---
# CodeTrans model for program synthesis
Pretrained model on programming language lisp inspired DSL using the t5 small model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans).
## Model description
This CodeTrans model is based on the `t5-small` model. It has its own SentencePiece vocabulary model. It used multi-task training on 13 supervised tasks in the software development domain and 7 unsupervised datasets.
## Intended uses & limitations
The model could be used to generate lisp inspired DSL code given the human language description tasks.
### How to use
Here is how to use this model to generate lisp inspired DSL code using Transformers SummarizationPipeline:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline
pipeline = SummarizationPipeline(
model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_small_program_synthese_multitask"),
tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_small_program_synthese_multitask", skip_special_tokens=True),
device=0
)
tokenized_code = "you are given an array of numbers a and a number b , compute the difference of elements in a and b"
pipeline([tokenized_code])
```
Run this example in [colab notebook](https://github.com/agemagician/CodeTrans/blob/main/prediction/multitask/pre-training/program%20synthesis/small_model.ipynb).
## Training data
The supervised training tasks datasets can be downloaded on [Link](https://www.dropbox.com/sh/488bq2of10r4wvw/AACs5CGIQuwtsD7j_Ls_JAORa/finetuning_dataset?dl=0&subfolder_nav_tracking=1)
## Training procedure
### Multi-task Pretraining
The model was trained on a single TPU Pod V3-8 for 440,000 steps in total, using sequence length 512 (batch size 4096).
It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture.
The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training.
## Evaluation results
For the code documentation tasks, different models achieves the following results on different programming languages (in BLEU score):
Test results :
| Language / Model | LISP |
| -------------------- | :------------: |
| CodeTrans-ST-Small | 89.43 |
| CodeTrans-ST-Base | 89.65 |
| CodeTrans-TF-Small | 90.30 |
| CodeTrans-TF-Base | 90.24 |
| CodeTrans-TF-Large | 90.21 |
| CodeTrans-MT-Small | 82.88 |
| CodeTrans-MT-Base | 86.99 |
| CodeTrans-MT-Large | 90.27 |
| CodeTrans-MT-TF-Small | **90.31** |
| CodeTrans-MT-TF-Base | 90.30 |
| CodeTrans-MT-TF-Large | 90.17 |
| State of the art | 85.80 |
> Created by [Ahmed Elnaggar](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/) and Wei Ding | [LinkedIn](https://www.linkedin.com/in/wei-ding-92561270/)
|
TheBritishLibrary/bl-books-genre-fastai
|
TheBritishLibrary
| 2022-06-02T19:47:17Z | 19 | 0 |
fastai
|
[
"fastai",
"text-classification",
"dataset:blbooksgenre",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
tags:
- text-classification
- fastai
library_name: fastai
datasets:
- blbooksgenre
widget:
- text: "Poems on various subjects. Whereto is prefixed a short essay on the structure of English verse"
- text: "Two Centuries of Soho: its institutions, firms, and amusements. By the Clergy of St. Anne's, Soho, J. H. Cardwell ... H. B. Freeman ... G. C. Wilton ... assisted by other contributors, etc"
- text: "The Adventures of Oliver Twist. [With plates.]"
---
## Model description
This model is intended to predict, from the title of a book, whether it is 'fiction' or 'non-fiction'.
This model was trained on data created from the Digitised printed books (18th-19th Century) book collection. The datasets in this collection are comprised and derived from 49,455 digitised books (65,227 volumes), mainly from the 19th Century. This dataset is dominated by English language books and includes books in several other languages in much smaller numbers.
This model was originally developed for use as part of the Living with Machines project to be able to 'segment' this large dataset of books into different categories based on a 'crude' classification of genre i.e. whether the title was `fiction` or `non-fiction`.
The model's training data (discussed more below) primarily consists of 19th Century book titles from the British Library Digitised printed books (18th-19th century) collection. These books have been catalogued according to British Library cataloguing practices. The model is likely to perform worse on any book titles from earlier or later periods. While the model is multilingual, it has training data in non-English book titles; these appear much less frequently.
## How to use
To use this within fastai, first [install](https://docs.fast.ai/#Installing) version 2 of the fastai library. You can load directly from the Hugging Face hub using the [`huggingface_hub`](https://github.com/huggingface/huggingface_hub) library.
```python
from fastai import load_learner
from huggingface_hub import hf_hub_download
learn = load_learner(
hf_hub_download('davanstrien/bl-books-genre-fastai', filename="model.pkl")
)
learn.predict("Oliver Twist")
```
## Limitations and bias
The model was developed based on data from the British Library's Digitised printed books (18th-19th Century) collection. This dataset is not representative of books from the period covered with biases towards certain types (travel) and a likely absence of books that were difficult to digitise.
The formatting of the British Library books corpus titles may differ from other collections, resulting in worse performance on other collections. It is recommended to evaluate the performance of the model before applying it to your own data. Likely, this model won't perform well for contemporary book titles without further fine-tuning.
## Training data
The training data was created using the Zooniverse platform. British Library cataloguers carried out the majority of the annotations used as training data. More information on the process of creating the training data will be available soon.
### Training procedure
Model training was carried out using the fastai library version 2.5.2.
The notebook using for training the model is available at: https://github.com/Living-with-machines/genre-classification
## Eval result
The model was evaluated on a held out test set:
```
precision recall f1-score support
Fiction 0.91 0.88 0.90 296
Non-fiction 0.94 0.95 0.95 554
accuracy 0.93 850
macro avg 0.93 0.92 0.92 850
weighted avg 0.93 0.93 0.93 850
```
|
huggingtweets/davemomi
|
huggingtweets
| 2022-06-02T18:30:32Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-02T18:29:46Z |
---
language: en
thumbnail: http://www.huggingtweets.com/davemomi/1654194627703/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/1171375301768744961/QZbLbdu8_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">Davide Momi</div>
<div style="text-align: center; font-size: 14px;">@davemomi</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 Davide Momi.
| Data | Davide Momi |
| --- | --- |
| Tweets downloaded | 273 |
| Retweets | 56 |
| Short tweets | 31 |
| Tweets kept | 186 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/4crkiv7x/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 @davemomi's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2oh3qlzu) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2oh3qlzu/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/davemomi')
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)
|
jcastanyo/q-FrozenLake-v1-4x4-Slippery-v3
|
jcastanyo
| 2022-06-02T18:14:00Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-02T18:13:51Z |
---
tags:
- FrozenLake-v1-4x4
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-Slippery-v3
results:
- metrics:
- type: mean_reward
value: 0.77 +/- 0.42
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4
type: FrozenLake-v1-4x4
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="jcastanyo/q-FrozenLake-v1-4x4-Slippery-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
jcastanyo/q-FrozenLake-v1-4x4-Slippery
|
jcastanyo
| 2022-06-02T17:17:49Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-02T17:17:38Z |
---
tags:
- FrozenLake-v1-4x4
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-Slippery
results:
- metrics:
- type: mean_reward
value: 0.70 +/- 0.46
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4
type: FrozenLake-v1-4x4
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="jcastanyo/q-FrozenLake-v1-4x4-Slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
jcastanyo/q-FrozenLake-v1-4x4-noSlippery
|
jcastanyo
| 2022-06-02T17:01:04Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-02T08:37:28Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="jcastanyo/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
Abderrahim2/bert-finetuned-Age
|
Abderrahim2
| 2022-06-02T16:37:58Z | 4 | 2 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-02T11:26:19Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: bert-finetuned-Age
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-Age
This model is a fine-tuned version of [dbmdz/bert-base-french-europeana-cased](https://huggingface.co/dbmdz/bert-base-french-europeana-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4642
- F1: 0.7254
- Roc Auc: 0.7940
- Accuracy: 0.7249
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.4564 | 1.0 | 965 | 0.4642 | 0.7254 | 0.7940 | 0.7254 |
| 0.4443 | 2.0 | 1930 | 0.4662 | 0.7254 | 0.7940 | 0.7254 |
| 0.4388 | 3.0 | 2895 | 0.4628 | 0.7254 | 0.7940 | 0.7254 |
| 0.4486 | 4.0 | 3860 | 0.4642 | 0.7254 | 0.7940 | 0.7249 |
| 0.4287 | 5.0 | 4825 | 0.4958 | 0.7214 | 0.7907 | 0.7150 |
| 0.4055 | 6.0 | 5790 | 0.5325 | 0.6961 | 0.7715 | 0.6782 |
| 0.3514 | 7.0 | 6755 | 0.5588 | 0.6586 | 0.7443 | 0.6223 |
| 0.3227 | 8.0 | 7720 | 0.5944 | 0.6625 | 0.7470 | 0.6295 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
nglaura/skimformer
|
nglaura
| 2022-06-02T15:37:12Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"skimformer",
"fill-mask",
"arxiv:2109.01078",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-02T09:40:00Z |
---
license: apache-2.0
---
# Skimformer
A collaboration between [reciTAL](https://recital.ai/en/) & [MLIA](https://mlia.lip6.fr/) (ISIR, Sorbonne Université)
## Model description
Skimformer is a two-stage Transformer that replaces self-attention with Skim-Attention, a self-attention module that computes attention solely based on the 2D positions of tokens in the page. The model adopts a two-step approach: first, the skim-attention scores are computed once and only once using layout information alone; then, these attentions are used in every layer of a text-based Transformer encoder. For more details, please refer to our paper:
[Skim-Attention: Learning to Focus via Document Layout](https://arxiv.org/abs/2109.01078)
Laura Nguyen, Thomas Scialom, Jacopo Staiano, Benjamin Piwowarski, [EMNLP 2021](https://2021.emnlp.org/papers)
## Citation
``` latex
@article{nguyen2021skimattention,
title={Skim-Attention: Learning to Focus via Document Layout},
author={Laura Nguyen and Thomas Scialom and Jacopo Staiano and Benjamin Piwowarski},
journal={arXiv preprint arXiv:2109.01078}
year={2021},
}
```
|
KFlash/bert-finetuned-squad
|
KFlash
| 2022-06-02T15:22:00Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-05-29T15:15:15Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Tokenizers 0.12.1
|
Classroom-workshop/assignment1-omar
|
Classroom-workshop
| 2022-06-02T15:20:42Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"hf-asr-leaderboard",
"en",
"dataset:librispeech_asr",
"arxiv:2006.11477",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-06-02T15:19:00Z |
---
language: en
datasets:
- librispeech_asr
tags:
- audio
- automatic-speech-recognition
- hf-asr-leaderboard
license: apache-2.0
widget:
- example_title: Librispeech sample 1
src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
- example_title: Librispeech sample 2
src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
model-index:
- name: wav2vec2-base-960h
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: LibriSpeech (clean)
type: librispeech_asr
config: clean
split: test
args:
language: en
metrics:
- name: Test WER
type: wer
value: 3.4
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: LibriSpeech (other)
type: librispeech_asr
config: other
split: test
args:
language: en
metrics:
- name: Test WER
type: wer
value: 8.6
---
# Wav2Vec2-Base-960h
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/)
The base model pretrained and fine-tuned on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model
make sure that your speech input is also sampled at 16Khz.
[Paper](https://arxiv.org/abs/2006.11477)
Authors: Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli
**Abstract**
We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks the speech input in the latent space and solves a contrastive task defined over a quantization of the latent representations which are jointly learned. Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of labeled data and pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech recognition with limited amounts of labeled data.
The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20.
# Usage
To transcribe audio files the model can be used as a standalone acoustic model as follows:
```python
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
from datasets import load_dataset
import soundfile as sf
import torch
# load model and tokenizer
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
# load dummy dataset and read soundfiles
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
# tokenize
input_values = processor(ds[0]["audio"]["array"], return_tensors="pt", padding="longest").input_values # Batch size 1
# retrieve logits
logits = model(input_values).logits
# take argmax and decode
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
```
## Evaluation
This code snippet shows how to evaluate **facebook/wav2vec2-base-960h** on LibriSpeech's "clean" and "other" test data.
```python
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import torch
from jiwer import wer
librispeech_eval = load_dataset("librispeech_asr", "clean", split="test")
model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h").to("cuda")
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
def map_to_pred(batch):
input_values = processor(batch["audio"]["array"], return_tensors="pt", padding="longest").input_values
with torch.no_grad():
logits = model(input_values.to("cuda")).logits
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
batch["transcription"] = transcription
return batch
result = librispeech_eval.map(map_to_pred, batched=True, batch_size=1, remove_columns=["audio"])
print("WER:", wer(result["text"], result["transcription"]))
```
*Result (WER)*:
| "clean" | "other" |
|---|---|
| 3.4 | 8.6 |
|
ducnapa/apes
|
ducnapa
| 2022-06-02T15:17:57Z | 116 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-06-02T15:17:46Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: apes
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.8999999761581421
---
# apes
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### chimpanzee
#### gibbon
#### gorilla
#### orangutan
|
YeRyeongLee/electra-base-discriminator-finetuned-filtered-0602
|
YeRyeongLee
| 2022-06-02T14:29:58Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-02T11:16:49Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: electra-base-discriminator-finetuned-filtered-0602
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. -->
# electra-base-discriminator-finetuned-filtered-0602
This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1685
- Accuracy: 0.9720
- F1: 0.9721
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.9.0
- Datasets 1.16.1
- Tokenizers 0.12.1
|
Ce/bert-finetuned-ner
|
Ce
| 2022-06-02T14:29:56Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-02T13:57:41Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9329581195166363
- name: Recall
type: recall
value: 0.9485021878155503
- name: F1
type: f1
value: 0.9406659434198448
- name: Accuracy
type: accuracy
value: 0.985356449049273
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0649
- Precision: 0.9330
- Recall: 0.9485
- F1: 0.9407
- Accuracy: 0.9854
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0871 | 1.0 | 1756 | 0.0672 | 0.9209 | 0.9387 | 0.9297 | 0.9834 |
| 0.0394 | 2.0 | 3512 | 0.0584 | 0.9311 | 0.9505 | 0.9407 | 0.9857 |
| 0.0201 | 3.0 | 5268 | 0.0649 | 0.9330 | 0.9485 | 0.9407 | 0.9854 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
AAkhilesh/wav2vec2-large-xls-r-300m-hsb-colab
|
AAkhilesh
| 2022-06-02T13:57:16Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-06-02T13:43:01Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-hsb-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-hsb-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Lolaibrin/distilbert-base-uncased-finetuned-squad
|
Lolaibrin
| 2022-06-02T13:43:10Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-06-02T10:42:19Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2108
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.4952 | 1.0 | 5533 | 1.3895 |
| 1.3024 | 2.0 | 11066 | 1.2490 |
| 1.2087 | 3.0 | 16599 | 1.2108 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
huggingtweets/caballerogaudes
|
huggingtweets
| 2022-06-02T13:25:40Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-02T13:23:37Z |
---
language: en
thumbnail: http://www.huggingtweets.com/caballerogaudes/1654176335515/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/1011998779061559297/5gOeFvds_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">CesarCaballeroGaudes</div>
<div style="text-align: center; font-size: 14px;">@caballerogaudes</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 CesarCaballeroGaudes.
| Data | CesarCaballeroGaudes |
| --- | --- |
| Tweets downloaded | 1724 |
| Retweets | 808 |
| Short tweets | 36 |
| Tweets kept | 880 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2d76b6yf/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 @caballerogaudes's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/i6nt6oo6) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/i6nt6oo6/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/caballerogaudes')
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)
|
Jozaita/q-Taxi-v3
|
Jozaita
| 2022-06-02T13:12:26Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-02T13:12:11Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.54 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Jozaita/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
huggingtweets/willsavino
|
huggingtweets
| 2022-06-02T13:06:29Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-02T13:06:04Z |
---
language: en
thumbnail: http://www.huggingtweets.com/willsavino/1654175184979/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/1078115982768525317/wk6NTSE0_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">Will Savino</div>
<div style="text-align: center; font-size: 14px;">@willsavino</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 Will Savino.
| Data | Will Savino |
| --- | --- |
| Tweets downloaded | 3229 |
| Retweets | 355 |
| Short tweets | 244 |
| Tweets kept | 2630 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2nhwww0u/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 @willsavino's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3k5ueoap) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3k5ueoap/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/willsavino')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/eurunuela
|
huggingtweets
| 2022-06-02T12:50:57Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-02T12:49:04Z |
---
language: en
thumbnail: http://www.huggingtweets.com/eurunuela/1654174252782/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/1476203864063893505/j7Ep0Muv_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">Eneko Uruñuela</div>
<div style="text-align: center; font-size: 14px;">@eurunuela</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 Eneko Uruñuela.
| Data | Eneko Uruñuela |
| --- | --- |
| Tweets downloaded | 1267 |
| Retweets | 241 |
| Short tweets | 42 |
| Tweets kept | 984 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3fhgg7tg/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 @eurunuela's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2ndd7uaz) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2ndd7uaz/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/eurunuela')
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)
|
chrisvinsen/wav2vec2-final-1-lm-4
|
chrisvinsen
| 2022-06-02T12:03:09Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-06-02T02:21:02Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-19
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. -->
# wav2vec2-19
WER 0.283
WER 0.126 with 5-Gram
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: 0.6305
- Wer: 0.4499
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 800
- num_epochs: 60
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.4816 | 2.74 | 400 | 1.0717 | 0.8927 |
| 0.751 | 5.48 | 800 | 0.7155 | 0.7533 |
| 0.517 | 8.22 | 1200 | 0.7039 | 0.6675 |
| 0.3988 | 10.96 | 1600 | 0.5935 | 0.6149 |
| 0.3179 | 13.7 | 2000 | 0.6477 | 0.5999 |
| 0.2755 | 16.44 | 2400 | 0.5549 | 0.5798 |
| 0.2343 | 19.18 | 2800 | 0.6626 | 0.5798 |
| 0.2103 | 21.92 | 3200 | 0.6488 | 0.5674 |
| 0.1877 | 24.66 | 3600 | 0.5874 | 0.5339 |
| 0.1719 | 27.4 | 4000 | 0.6354 | 0.5389 |
| 0.1603 | 30.14 | 4400 | 0.6612 | 0.5210 |
| 0.1401 | 32.88 | 4800 | 0.6676 | 0.5131 |
| 0.1286 | 35.62 | 5200 | 0.6366 | 0.5075 |
| 0.1159 | 38.36 | 5600 | 0.6064 | 0.4977 |
| 0.1084 | 41.1 | 6000 | 0.6530 | 0.4835 |
| 0.0974 | 43.84 | 6400 | 0.6118 | 0.4853 |
| 0.0879 | 46.58 | 6800 | 0.6316 | 0.4770 |
| 0.0815 | 49.32 | 7200 | 0.6125 | 0.4664 |
| 0.0708 | 52.05 | 7600 | 0.6449 | 0.4683 |
| 0.0651 | 54.79 | 8000 | 0.6068 | 0.4571 |
| 0.0555 | 57.53 | 8400 | 0.6305 | 0.4499 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
chrisvinsen/wav2vec2-final-1-lm-1
|
chrisvinsen
| 2022-06-02T11:08:55Z | 15 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-06-02T02:20:22Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-19
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. -->
# wav2vec2-19
WER 0.283
WER 0.129 with 2-Gram
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: 0.6305
- Wer: 0.4499
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 800
- num_epochs: 60
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.4816 | 2.74 | 400 | 1.0717 | 0.8927 |
| 0.751 | 5.48 | 800 | 0.7155 | 0.7533 |
| 0.517 | 8.22 | 1200 | 0.7039 | 0.6675 |
| 0.3988 | 10.96 | 1600 | 0.5935 | 0.6149 |
| 0.3179 | 13.7 | 2000 | 0.6477 | 0.5999 |
| 0.2755 | 16.44 | 2400 | 0.5549 | 0.5798 |
| 0.2343 | 19.18 | 2800 | 0.6626 | 0.5798 |
| 0.2103 | 21.92 | 3200 | 0.6488 | 0.5674 |
| 0.1877 | 24.66 | 3600 | 0.5874 | 0.5339 |
| 0.1719 | 27.4 | 4000 | 0.6354 | 0.5389 |
| 0.1603 | 30.14 | 4400 | 0.6612 | 0.5210 |
| 0.1401 | 32.88 | 4800 | 0.6676 | 0.5131 |
| 0.1286 | 35.62 | 5200 | 0.6366 | 0.5075 |
| 0.1159 | 38.36 | 5600 | 0.6064 | 0.4977 |
| 0.1084 | 41.1 | 6000 | 0.6530 | 0.4835 |
| 0.0974 | 43.84 | 6400 | 0.6118 | 0.4853 |
| 0.0879 | 46.58 | 6800 | 0.6316 | 0.4770 |
| 0.0815 | 49.32 | 7200 | 0.6125 | 0.4664 |
| 0.0708 | 52.05 | 7600 | 0.6449 | 0.4683 |
| 0.0651 | 54.79 | 8000 | 0.6068 | 0.4571 |
| 0.0555 | 57.53 | 8400 | 0.6305 | 0.4499 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
SynamicTechnologies/CYBERT
|
SynamicTechnologies
| 2022-06-02T09:51:10Z | 5,032 | 8 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-02T08:22:55Z |
## CYBERT
BERT model dedicated to the domain of cyber security. The model has been trained on a corpus of high-quality cyber security and computer science text and is unlikely to work outside this domain.
##Model architecture
The model architecture used is original Roberta and tokenizer to train the corpus is Byte Level.
##Hardware
The model is trained on GPU NVIDIA-SMI 510.54
|
chrisvinsen/wav2vec2-19
|
chrisvinsen
| 2022-06-02T09:03:33Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-06-01T10:35:47Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-19
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. -->
# wav2vec2-19
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: 0.6305
- Wer: 0.4499
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 800
- num_epochs: 60
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.4816 | 2.74 | 400 | 1.0717 | 0.8927 |
| 0.751 | 5.48 | 800 | 0.7155 | 0.7533 |
| 0.517 | 8.22 | 1200 | 0.7039 | 0.6675 |
| 0.3988 | 10.96 | 1600 | 0.5935 | 0.6149 |
| 0.3179 | 13.7 | 2000 | 0.6477 | 0.5999 |
| 0.2755 | 16.44 | 2400 | 0.5549 | 0.5798 |
| 0.2343 | 19.18 | 2800 | 0.6626 | 0.5798 |
| 0.2103 | 21.92 | 3200 | 0.6488 | 0.5674 |
| 0.1877 | 24.66 | 3600 | 0.5874 | 0.5339 |
| 0.1719 | 27.4 | 4000 | 0.6354 | 0.5389 |
| 0.1603 | 30.14 | 4400 | 0.6612 | 0.5210 |
| 0.1401 | 32.88 | 4800 | 0.6676 | 0.5131 |
| 0.1286 | 35.62 | 5200 | 0.6366 | 0.5075 |
| 0.1159 | 38.36 | 5600 | 0.6064 | 0.4977 |
| 0.1084 | 41.1 | 6000 | 0.6530 | 0.4835 |
| 0.0974 | 43.84 | 6400 | 0.6118 | 0.4853 |
| 0.0879 | 46.58 | 6800 | 0.6316 | 0.4770 |
| 0.0815 | 49.32 | 7200 | 0.6125 | 0.4664 |
| 0.0708 | 52.05 | 7600 | 0.6449 | 0.4683 |
| 0.0651 | 54.79 | 8000 | 0.6068 | 0.4571 |
| 0.0555 | 57.53 | 8400 | 0.6305 | 0.4499 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
kktoto/tiny_bb_wd
|
kktoto
| 2022-06-02T08:06:44Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-02T04:01:38Z |
---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: tiny_bb_wd
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. -->
# tiny_bb_wd
This model is a fine-tuned version of [kktoto/tiny_bb_wd](https://huggingface.co/kktoto/tiny_bb_wd) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1331
- Precision: 0.6566
- Recall: 0.6502
- F1: 0.6533
- Accuracy: 0.9524
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1193 | 1.0 | 5561 | 0.1398 | 0.6406 | 0.6264 | 0.6335 | 0.9501 |
| 0.1259 | 2.0 | 11122 | 0.1343 | 0.6476 | 0.6300 | 0.6387 | 0.9509 |
| 0.1283 | 3.0 | 16683 | 0.1333 | 0.6484 | 0.6367 | 0.6425 | 0.9512 |
| 0.1217 | 4.0 | 22244 | 0.1325 | 0.6524 | 0.6380 | 0.6451 | 0.9516 |
| 0.12 | 5.0 | 27805 | 0.1337 | 0.6571 | 0.6377 | 0.6472 | 0.9522 |
| 0.1187 | 6.0 | 33366 | 0.1319 | 0.6630 | 0.6297 | 0.6459 | 0.9525 |
| 0.116 | 7.0 | 38927 | 0.1318 | 0.6600 | 0.6421 | 0.6509 | 0.9525 |
| 0.1125 | 8.0 | 44488 | 0.1337 | 0.6563 | 0.6481 | 0.6522 | 0.9523 |
| 0.1118 | 9.0 | 50049 | 0.1329 | 0.6575 | 0.6477 | 0.6526 | 0.9524 |
| 0.1103 | 10.0 | 55610 | 0.1331 | 0.6566 | 0.6502 | 0.6533 | 0.9524 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Andaf/bert-uncased-finetuned-squad-indonesian
|
Andaf
| 2022-06-02T07:32:23Z | 16 | 2 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"bert",
"question-answering",
"generated_from_keras_callback",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-06-02T03:19:04Z |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: Andaf/chatbot-trvlk-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. -->
# Andaf/chatbot-trvlk-finetuned-squad
This model is a fine-tuned version of [cahya/bert-base-indonesian-522M](https://huggingface.co/cahya/bert-base-indonesian-522M) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.5335
- Validation Loss: 6.4566
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 14444, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.1851 | 6.1907 | 0 |
| 1.5335 | 6.4566 | 1 |
### Framework versions
- Transformers 4.18.0
- TensorFlow 2.8.1
- Tokenizers 0.12.1
|
ThePixOne/SeconBERTa1
|
ThePixOne
| 2022-06-02T05:51:30Z | 2 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-06-02T05:46:38Z |
---
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 20799 with parameters:
```
{'batch_size': 16}
```
**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": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 4159.8,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 514, 'do_lower_case': False}) with Transformer model: RobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
ShoneRan/bert-emotion
|
ShoneRan
| 2022-06-02T05:15:37Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-02T04:55:32Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- precision
- recall
model-index:
- name: bert-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: emotion
metrics:
- name: Precision
type: precision
value: 0.7262254187805659
- name: Recall
type: recall
value: 0.725549671319356
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-emotion
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1670
- Precision: 0.7262
- Recall: 0.7255
- Fscore: 0.7253
## 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: 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 | Precision | Recall | Fscore |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.8561 | 1.0 | 815 | 0.7844 | 0.7575 | 0.6081 | 0.6253 |
| 0.5337 | 2.0 | 1630 | 0.9080 | 0.7567 | 0.7236 | 0.7325 |
| 0.2573 | 3.0 | 2445 | 1.1670 | 0.7262 | 0.7255 | 0.7253 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
mesolitica/wav2vec2-xls-r-300m-mixed
|
mesolitica
| 2022-06-02T04:58:36Z | 735 | 4 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-06-01T01:18:26Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: wav2vec2-xls-r-300m-mixed
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. -->
# wav2vec2-xls-r-300m-mixed
Finetuned https://huggingface.co/facebook/wav2vec2-xls-r-300m on https://github.com/huseinzol05/malaya-speech/tree/master/data/mixed-stt
This model was finetuned on 3 languages,
1. Malay
2. Singlish
3. Mandarin
**This model trained on a single RTX 3090 Ti 24GB VRAM, provided by https://mesolitica.com/**.
## Evaluation set
Evaluation set from https://github.com/huseinzol05/malaya-speech/tree/master/pretrained-model/prepare-stt with sizes,
```
len(malay), len(singlish), len(mandarin)
-> (765, 3579, 614)
```
It achieves the following results on the evaluation set based on [evaluate-gpu.ipynb](evaluate-gpu.ipynb):
Mixed evaluation,
```
CER: 0.0481054244857041
WER: 0.1322198446007387
CER with LM: 0.041196586938584696
WER with LM: 0.09880169127621556
```
Malay evaluation,
```
CER: 0.051636391937588406
WER: 0.19561999547293663
CER with LM: 0.03917689630621449
WER with LM: 0.12710746406824835
```
Singlish evaluation,
```
CER: 0.0494915200071987
WER: 0.12763802881676573
CER with LM: 0.04271234986432335
WER with LM: 0.09677160640413336
```
Mandarin evaluation,
```
CER: 0.035626554824269824
WER: 0.07993515937860181
CER with LM: 0.03487760945087219
WER with LM: 0.07536807168546154
```
Language model from https://huggingface.co/huseinzol05/language-model-bahasa-manglish-combined
|
thunninoi/wav2vec2-japanese-hiragana-vtuber
|
thunninoi
| 2022-06-02T04:31:41Z | 6 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-05-27T10:41:57Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: checkpoints
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. -->
# checkpoints
This model is a fine-tuned version of [vumichien/wav2vec2-large-xlsr-japanese-hiragana](https://huggingface.co/vumichien/wav2vec2-large-xlsr-japanese-hiragana) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4134
- Wer: 0.1884
## 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: 3
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 6
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 75
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.4299 | 1.0 | 247 | 0.7608 | 0.4853 |
| 0.8045 | 2.0 | 494 | 0.6603 | 0.4449 |
| 0.6061 | 3.0 | 741 | 0.5527 | 0.4233 |
| 0.4372 | 4.0 | 988 | 0.6262 | 0.4029 |
| 0.3226 | 5.0 | 1235 | 0.4528 | 0.3462 |
| 0.2581 | 6.0 | 1482 | 0.4961 | 0.3226 |
| 0.2147 | 7.0 | 1729 | 0.4856 | 0.3075 |
| 0.1736 | 8.0 | 1976 | 0.4372 | 0.3063 |
| 0.1488 | 9.0 | 2223 | 0.3771 | 0.2761 |
| 0.1286 | 10.0 | 2470 | 0.4373 | 0.2590 |
| 0.1118 | 11.0 | 2717 | 0.3840 | 0.2594 |
| 0.1037 | 12.0 | 2964 | 0.4241 | 0.2590 |
| 0.0888 | 13.0 | 3211 | 0.4150 | 0.2410 |
| 0.0923 | 14.0 | 3458 | 0.3811 | 0.2524 |
| 0.0813 | 15.0 | 3705 | 0.4164 | 0.2459 |
| 0.0671 | 16.0 | 3952 | 0.3498 | 0.2288 |
| 0.0669 | 17.0 | 4199 | 0.3697 | 0.2247 |
| 0.0586 | 18.0 | 4446 | 0.3550 | 0.2251 |
| 0.0533 | 19.0 | 4693 | 0.4024 | 0.2231 |
| 0.0542 | 20.0 | 4940 | 0.4130 | 0.2121 |
| 0.0532 | 21.0 | 5187 | 0.3464 | 0.2231 |
| 0.0451 | 22.0 | 5434 | 0.3346 | 0.1966 |
| 0.0413 | 23.0 | 5681 | 0.4599 | 0.2088 |
| 0.0401 | 24.0 | 5928 | 0.4031 | 0.2162 |
| 0.0345 | 25.0 | 6175 | 0.3726 | 0.2084 |
| 0.033 | 26.0 | 6422 | 0.4619 | 0.2076 |
| 0.0366 | 27.0 | 6669 | 0.4071 | 0.2202 |
| 0.0343 | 28.0 | 6916 | 0.4114 | 0.2088 |
| 0.0319 | 29.0 | 7163 | 0.3605 | 0.2015 |
| 0.0304 | 30.0 | 7410 | 0.4097 | 0.2015 |
| 0.0253 | 31.0 | 7657 | 0.4152 | 0.1970 |
| 0.0235 | 32.0 | 7904 | 0.3829 | 0.2043 |
| 0.0255 | 33.0 | 8151 | 0.3976 | 0.2011 |
| 0.0201 | 34.0 | 8398 | 0.4247 | 0.2088 |
| 0.022 | 35.0 | 8645 | 0.3831 | 0.1945 |
| 0.0175 | 36.0 | 8892 | 0.3838 | 0.2007 |
| 0.0201 | 37.0 | 9139 | 0.4377 | 0.1986 |
| 0.0176 | 38.0 | 9386 | 0.4546 | 0.2043 |
| 0.021 | 39.0 | 9633 | 0.4341 | 0.2039 |
| 0.0191 | 40.0 | 9880 | 0.4043 | 0.1937 |
| 0.0159 | 41.0 | 10127 | 0.4098 | 0.2064 |
| 0.0148 | 42.0 | 10374 | 0.4027 | 0.1905 |
| 0.0129 | 43.0 | 10621 | 0.4104 | 0.1933 |
| 0.0123 | 44.0 | 10868 | 0.3738 | 0.1925 |
| 0.0159 | 45.0 | 11115 | 0.3946 | 0.1933 |
| 0.0091 | 46.0 | 11362 | 0.3971 | 0.1880 |
| 0.0082 | 47.0 | 11609 | 0.4042 | 0.1986 |
| 0.0108 | 48.0 | 11856 | 0.4092 | 0.1884 |
| 0.0123 | 49.0 | 12103 | 0.3674 | 0.1941 |
| 0.01 | 50.0 | 12350 | 0.3750 | 0.1876 |
| 0.0094 | 51.0 | 12597 | 0.3781 | 0.1831 |
| 0.008 | 52.0 | 12844 | 0.4051 | 0.1852 |
| 0.0079 | 53.0 | 13091 | 0.3981 | 0.1937 |
| 0.0068 | 54.0 | 13338 | 0.4425 | 0.1929 |
| 0.0061 | 55.0 | 13585 | 0.4183 | 0.1986 |
| 0.0074 | 56.0 | 13832 | 0.3502 | 0.1880 |
| 0.0071 | 57.0 | 14079 | 0.3908 | 0.1892 |
| 0.0079 | 58.0 | 14326 | 0.3908 | 0.1913 |
| 0.0042 | 59.0 | 14573 | 0.3801 | 0.1864 |
| 0.0049 | 60.0 | 14820 | 0.4065 | 0.1839 |
| 0.0063 | 61.0 | 15067 | 0.4170 | 0.1900 |
| 0.0049 | 62.0 | 15314 | 0.3903 | 0.1856 |
| 0.0031 | 63.0 | 15561 | 0.4042 | 0.1896 |
| 0.0054 | 64.0 | 15808 | 0.3890 | 0.1839 |
| 0.0061 | 65.0 | 16055 | 0.3831 | 0.1847 |
| 0.0052 | 66.0 | 16302 | 0.3898 | 0.1847 |
| 0.0032 | 67.0 | 16549 | 0.4230 | 0.1831 |
| 0.0017 | 68.0 | 16796 | 0.4241 | 0.1823 |
| 0.0022 | 69.0 | 17043 | 0.4360 | 0.1856 |
| 0.0026 | 70.0 | 17290 | 0.4233 | 0.1815 |
| 0.0028 | 71.0 | 17537 | 0.4225 | 0.1835 |
| 0.0018 | 72.0 | 17784 | 0.4163 | 0.1856 |
| 0.0034 | 73.0 | 18031 | 0.4120 | 0.1876 |
| 0.0019 | 74.0 | 18278 | 0.4129 | 0.1876 |
| 0.0023 | 75.0 | 18525 | 0.4134 | 0.1884 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
|
gullu72/bert-fine-tuned-rajat
|
gullu72
| 2022-06-02T04:22:58Z | 4 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-02T03:50:40Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: bert-fine-tuned-rajat
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. -->
# bert-fine-tuned-rajat
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1791
- Validation Loss: 0.4963
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.5119 | 0.4245 | 0 |
| 0.3015 | 0.4296 | 1 |
| 0.1791 | 0.4963 | 2 |
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1
|
NlpHUST/gpt2-vietnamese
|
NlpHUST
| 2022-06-02T04:02:44Z | 3,159 | 22 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"vi",
"vietnamese",
"lm",
"nlp",
"dataset:oscar",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-05-23T08:04:12Z |
---
language: vi
tags:
- vi
- vietnamese
- gpt2
- text-generation
- lm
- nlp
datasets:
- oscar
widget:
- text: "Việt Nam là quốc gia có"
---
# GPT-2
Pretrained gpt model on Vietnamese language using a causal language modeling (CLM) objective. It was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
and first released at [this page](https://openai.com/blog/better-language-models/).
# How to use the model
~~~~
import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel
tokenizer = GPT2Tokenizer.from_pretrained('NlpHUST/gpt2-vietnamese')
model = GPT2LMHeadModel.from_pretrained('NlpHUST/gpt2-vietnamese')
text = "Việt Nam là quốc gia có"
input_ids = tokenizer.encode(text, return_tensors='pt')
max_length = 100
sample_outputs = model.generate(input_ids,pad_token_id=tokenizer.eos_token_id,
do_sample=True,
max_length=max_length,
min_length=max_length,
top_k=40,
num_beams=5,
early_stopping=True,
no_repeat_ngram_size=2,
num_return_sequences=3)
for i, sample_output in enumerate(sample_outputs):
print(">> Generated text {}\n\n{}".format(i+1, tokenizer.decode(sample_output.tolist())))
print('\n---')
~~~~
```bash
>> Generated text 1
Việt Nam là quốc gia có nền kinh tế hàng đầu thế giới về sản xuất, chế biến và tiêu thụ các sản phẩm nông sản, thủy sản. Tuy nhiên, trong những năm gần đây, nông nghiệp Việt Nam đang phải đối mặt với nhiều khó khăn, thách thức, đặc biệt là những tác động tiêu cực của biến đổi khí hậu.
Theo số liệu của Tổng cục Thống kê, tính đến cuối năm 2015, tổng diện tích gieo trồng, sản lượng lương thực, thực phẩm cả
---
>> Generated text 2
Việt Nam là quốc gia có nền kinh tế thị trường định hướng xã hội chủ nghĩa, có vai trò rất quan trọng đối với sự phát triển bền vững của đất nước. Do đó, trong quá trình đổi mới và hội nhập quốc tế, Việt Nam đã và đang phải đối mặt với không ít khó khăn, thách thức, đòi hỏi phải có những chủ trương, chính sách đúng đắn, kịp thời, phù hợp với tình hình thực tế. Để thực hiện thắng lợi mục tiêu, nhiệm vụ
---
>> Generated text 3
Việt Nam là quốc gia có nền kinh tế thị trường phát triển theo định hướng xã hội chủ nghĩa. Trong quá trình đổi mới và hội nhập quốc tế hiện nay, Việt Nam đang phải đối mặt với nhiều khó khăn, thách thức, đòi hỏi phải có những giải pháp đồng bộ, hiệu quả và phù hợp với tình hình thực tế của đất nước. Để thực hiện thắng lợi mục tiêu, nhiệm vụ mà Nghị quyết Đại hội XI của Đảng đề ra, Đảng và Nhà nước đã ban hành
---
```
# Model architecture
A 12-layer, 768-hidden-size transformer-based language model.
# Training
The model was trained on Vietnamese Oscar dataset (32 GB) to optimize a traditional language modelling objective on v3-8 TPU for around 6 days. It reaches around 13.4 perplexity on a chosen validation set from Oscar.
### GPT-2 Finetuning
The following example fine-tunes GPT-2 on WikiText-2. We're using the raw WikiText-2.
The script [here](https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_clm.py) .
```bash
python run_clm.py \
--model_name_or_path NlpHUST/gpt2-vietnamese \
--dataset_name wikitext \
--dataset_config_name wikitext-2-raw-v1 \
--per_device_train_batch_size 8 \
--per_device_eval_batch_size 8 \
--do_train \
--do_eval \
--output_dir /tmp/test-clm
```
### Contact information
For personal communication related to this project, please contact Nha Nguyen Van (nha282@gmail.com).
|
wapari/KoGPT-trinity-tales
|
wapari
| 2022-06-02T03:43:33Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-02T02:52:52Z |
---
license: cc-by-nc-sa-4.0
---
|
dkasti/xlm-roberta-base-finetuned-panx-all
|
dkasti
| 2022-06-02T02:24:54Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-02T02:10:13Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-all
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1769
- F1: 0.8533
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.3049 | 1.0 | 835 | 0.1873 | 0.8139 |
| 0.1576 | 2.0 | 1670 | 0.1722 | 0.8403 |
| 0.1011 | 3.0 | 2505 | 0.1769 | 0.8533 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
JXL884/distilbert-base-uncased-finetuned-emotion
|
JXL884
| 2022-06-02T02:14:26Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-02T02:05:53Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
dkasti/xlm-roberta-base-finetuned-panx-it
|
dkasti
| 2022-06-02T02:05:41Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-02T02:03:25Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-it
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.it
metrics:
- name: F1
type: f1
value: 0.8233360723089564
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2388
- F1: 0.8233
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.8099 | 1.0 | 70 | 0.3035 | 0.7333 |
| 0.2766 | 2.0 | 140 | 0.2661 | 0.7948 |
| 0.1792 | 3.0 | 210 | 0.2388 | 0.8233 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
dkasti/xlm-roberta-base-finetuned-panx-fr
|
dkasti
| 2022-06-02T02:03:12Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-02T01:59:16Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-fr
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.fr
metrics:
- name: F1
type: f1
value: 0.839946200403497
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2789
- F1: 0.8399
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.587 | 1.0 | 191 | 0.3355 | 0.7929 |
| 0.274 | 2.0 | 382 | 0.2977 | 0.8283 |
| 0.1836 | 3.0 | 573 | 0.2789 | 0.8399 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
dkasti/xlm-roberta-base-finetuned-panx-de
|
dkasti
| 2022-06-02T00:32:38Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-05-27T07:02:10Z |
---
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: 0.8615769427548178
---
<!-- 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: 0.1401
- F1: 0.8616
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2605 | 1.0 | 525 | 0.1708 | 0.8198 |
| 0.1274 | 2.0 | 1050 | 0.1415 | 0.8449 |
| 0.0819 | 3.0 | 1575 | 0.1401 | 0.8616 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
jiseong/mt5-small-finetuned-news-ab
|
jiseong
| 2022-06-02T00:10:15Z | 4 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-01T08:24:29Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: jiseong/mt5-small-finetuned-news-ab
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. -->
# jiseong/mt5-small-finetuned-news-ab
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.0174
- Validation Loss: 1.7411
- 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': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 4.1124 | 2.0706 | 0 |
| 2.4090 | 1.8742 | 1 |
| 2.1379 | 1.7889 | 2 |
| 2.0174 | 1.7411 | 3 |
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Oscarnm/G
|
Oscarnm
| 2022-06-01T23:20:22Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-06-01T23:19:22Z |
Mountain of water painted by monet
|
YeRyeongLee/bert-large-uncased-finetuned-filtered-0602
|
YeRyeongLee
| 2022-06-01T22:57:54Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-01T16:28:40Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: bert-large-uncased-finetuned-filtered-0602
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-large-uncased-finetuned-filtered-0602
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8409
- Accuracy: 0.1667
- F1: 0.0476
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 1.8331 | 1.0 | 3180 | 1.8054 | 0.1667 | 0.0476 |
| 1.8158 | 2.0 | 6360 | 1.8196 | 0.1667 | 0.0476 |
| 1.8088 | 3.0 | 9540 | 1.8059 | 0.1667 | 0.0476 |
| 1.8072 | 4.0 | 12720 | 1.7996 | 0.1667 | 0.0476 |
| 1.8182 | 5.0 | 15900 | 1.7962 | 0.1667 | 0.0476 |
| 1.7993 | 6.0 | 19080 | 1.8622 | 0.1667 | 0.0476 |
| 1.7963 | 7.0 | 22260 | 1.8378 | 0.1667 | 0.0476 |
| 1.7956 | 8.0 | 25440 | 1.8419 | 0.1667 | 0.0476 |
| 1.7913 | 9.0 | 28620 | 1.8406 | 0.1667 | 0.0476 |
| 1.7948 | 10.0 | 31800 | 1.8409 | 0.1667 | 0.0476 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.9.0
- Datasets 1.16.1
- Tokenizers 0.12.1
|
meln1k/q-Taxi-v3-v1
|
meln1k
| 2022-06-01T22:47:23Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-01T22:47:15Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3-v1
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="meln1k/q-Taxi-v3-v1", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
robinhad/ukrainian-qa
|
robinhad
| 2022-06-01T22:08:47Z | 47 | 6 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"uk",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-06-01T19:28:07Z |
---
license: mit
language: uk
tags:
- generated_from_trainer
model-index:
- name: ukrainian-qa
results: []
widget:
- text: "Що відправлять для ЗСУ?"
context: "Про це повідомив міністр оборони Арвідас Анушаускас. Уряд Литви не має наміру зупинятися у військово-технічній допомозі Україні. Збройні сили отримають антидрони, тепловізори та ударний безпілотник. «Незабаром Литва передасть Україні не лише обіцяні бронетехніку, вантажівки та позашляховики, але також нову партію антидронів та тепловізорів. І, звичайно, Байрактар, який придбають на зібрані литовцями гроші», - написав глава Міноборони."
---
<!-- 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. -->
# ukrainian-qa
This model is a fine-tuned version of [ukr-models/xlm-roberta-base-uk](https://huggingface.co/ukr-models/xlm-roberta-base-uk) on the [UA-SQuAD](https://github.com/fido-ai/ua-datasets/tree/main/ua_datasets/src/question_answering) dataset.
Link to training scripts - [https://github.com/robinhad/ukrainian-qa](https://github.com/robinhad/ukrainian-qa)
It achieves the following results on the evaluation set:
- Loss: 1.4778
## Model description
More information needed
## How to use
```python
from transformers import pipeline, AutoTokenizer, AutoModelForQuestionAnswering
model_name = "robinhad/ukrainian-qa"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
qa_model = pipeline("question-answering", model=model.to("cpu"), tokenizer=tokenizer)
question = "Де ти живеш?"
context = "Мене звати Сара і я живу у Лондоні"
qa_model(question = question, context = context)
```
## 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: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.4526 | 1.0 | 650 | 1.3631 |
| 1.3317 | 2.0 | 1300 | 1.2229 |
| 1.0693 | 3.0 | 1950 | 1.2184 |
| 0.6851 | 4.0 | 2600 | 1.3171 |
| 0.5594 | 5.0 | 3250 | 1.3893 |
| 0.4954 | 6.0 | 3900 | 1.4778 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
huggingtweets/mls_buzz-mlstransfers-transfersmls
|
huggingtweets
| 2022-06-01T20:57:13Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-01T20:43:55Z |
---
language: en
thumbnail: http://www.huggingtweets.com/mls_buzz-mlstransfers-transfersmls/1654117028998/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/1142613360854388738/C49XegQF_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/417716955076763648/_e97ys3b_400x400.jpeg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1229972304689614848/EqOwTdY8_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">MLS Buzz & MLS Transfers & Will Forbes</div>
<div style="text-align: center; font-size: 14px;">@mls_buzz-mlstransfers-transfersmls</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 MLS Buzz & MLS Transfers & Will Forbes.
| Data | MLS Buzz | MLS Transfers | Will Forbes |
| --- | --- | --- | --- |
| Tweets downloaded | 3250 | 3248 | 3247 |
| Retweets | 32 | 811 | 1136 |
| Short tweets | 167 | 475 | 359 |
| Tweets kept | 3051 | 1962 | 1752 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/29rusxig/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 @mls_buzz-mlstransfers-transfersmls's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2qzhkike) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2qzhkike/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/mls_buzz-mlstransfers-transfersmls')
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)
|
Abderrahim2/bert-finetuned-Location
|
Abderrahim2
| 2022-06-01T20:18:34Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-01T17:38:50Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: bert-finetuned-Location
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-Location
This model is a fine-tuned version of [dbmdz/bert-base-french-europeana-cased](https://huggingface.co/dbmdz/bert-base-french-europeana-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5462
- F1: 0.8167
- Roc Auc: 0.8624
- Accuracy: 0.8133
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.4229 | 1.0 | 742 | 0.3615 | 0.7402 | 0.8014 | 0.6900 |
| 0.3722 | 2.0 | 1484 | 0.3103 | 0.7906 | 0.8416 | 0.7796 |
| 0.262 | 3.0 | 2226 | 0.3364 | 0.8135 | 0.8600 | 0.8100 |
| 0.2239 | 4.0 | 2968 | 0.4593 | 0.8085 | 0.8561 | 0.8066 |
| 0.1461 | 5.0 | 3710 | 0.5534 | 0.7923 | 0.8440 | 0.7904 |
| 0.1333 | 6.0 | 4452 | 0.5462 | 0.8167 | 0.8624 | 0.8133 |
| 0.0667 | 7.0 | 5194 | 0.6298 | 0.7972 | 0.8479 | 0.7958 |
| 0.0616 | 8.0 | 5936 | 0.6362 | 0.8075 | 0.8556 | 0.8059 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
thet-system/en_pubmed_rct
|
thet-system
| 2022-06-01T19:05:19Z | 2 | 0 |
spacy
|
[
"spacy",
"en",
"region:us"
] | null | 2022-06-01T19:05:17Z |
---
tags:
- spacy
language:
- en
model-index:
- name: en_pubmed_rct
results: []
---
| Feature | Description |
| --- | --- |
| **Name** | `en_pubmed_rct` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.2.0,<3.3.0` |
| **Default Pipeline** | `tok2vec`, `spancat` |
| **Components** | `tok2vec`, `spancat` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |
### Label Scheme
<details>
<summary>View label scheme (4 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`spancat`** | `METRIC_2`, `METRIC_1`, `STATISTIC_NAME`, `STATISTIC_GROUP` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `SPANS_SC_F` | 0.36 |
| `SPANS_SC_P` | 0.18 |
| `SPANS_SC_R` | 41.08 |
| `TOK2VEC_LOSS` | 31300.51 |
| `SPANCAT_LOSS` | 126084.67 |
|
thet-system/en_pubmed_en
|
thet-system
| 2022-06-01T18:38:04Z | 0 | 0 |
spacy
|
[
"spacy",
"en",
"region:us"
] | null | 2022-06-01T17:58:44Z |
---
tags:
- spacy
language:
- en
model-index:
- name: en_pubmed_en
results: []
---
| Feature | Description |
| --- | --- |
| **Name** | `en_pubmed_en` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.2.0,<3.3.0` |
| **Default Pipeline** | `tok2vec`, `spancat` |
| **Components** | `tok2vec`, `spancat` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |
### Label Scheme
<details>
<summary>View label scheme (4 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`spancat`** | `METRIC_2`, `METRIC_1`, `STATISTIC_NAME`, `STATISTIC_GROUP` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `SPANS_SC_F` | 0.36 |
| `SPANS_SC_P` | 0.18 |
| `SPANS_SC_R` | 41.08 |
| `TOK2VEC_LOSS` | 31300.51 |
| `SPANCAT_LOSS` | 126084.67 |
|
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