modelId
stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-26 06:27:36
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 521
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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| card
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|---|---|---|---|---|---|---|---|---|---|
yanaiela/roberta-base-epoch_1
|
yanaiela
| 2022-07-29T22:41:07Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"roberta-base",
"roberta-base-epoch_1",
"en",
"dataset:wikipedia",
"dataset:bookcorpus",
"arxiv:1907.11692",
"arxiv:2207.14251",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-28T16:49:55Z |
---
language: en
tags:
- roberta-base
- roberta-base-epoch_1
license: mit
datasets:
- wikipedia
- bookcorpus
---
# RoBERTa, Intermediate Checkpoint - Epoch 1
This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692),
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randomly initialized weights before the training)
to provide the ability to study the training dynamics of such models, and other possible use-cases.
These models were trained in part of a work that studies how simple statistics from data,
such as co-occurrences affects model predictions, which are described in the paper
[Measuring Causal Effects of Data Statistics on Language Model's `Factual' Predictions](https://arxiv.org/abs/2207.14251).
This is RoBERTa-base epoch_1.
## Model Description
This model was captured during a reproduction of
[RoBERTa-base](https://huggingface.co/roberta-base), for English: it
is a Transformers model pretrained on a large corpus of English data, using the
Masked Language Modelling (MLM).
The intended uses, limitations, training data and training procedure for the fully trained model are similar
to [RoBERTa-base](https://huggingface.co/roberta-base). Two major
differences with the original model:
* We trained our model for 100K steps, instead of 500K
* We only use Wikipedia and the Book Corpus, as corpora which are publicly available.
### How to use
Using code from
[RoBERTa-base](https://huggingface.co/roberta-base), here is an example based on
PyTorch:
```
from transformers import pipeline
model = pipeline("fill-mask", model='yanaiela/roberta-base-epoch_83', device=-1, top_k=10)
model("Hello, I'm the <mask> RoBERTa-base language model")
```
## Citation info
```bibtex
@article{2207.14251,
Author = {Yanai Elazar and Nora Kassner and Shauli Ravfogel and Amir Feder and Abhilasha Ravichander and Marius Mosbach and Yonatan Belinkov and Hinrich Schütze and Yoav Goldberg},
Title = {Measuring Causal Effects of Data Statistics on Language Model's `Factual' Predictions},
Year = {2022},
Eprint = {arXiv:2207.14251},
}
```
|
mrm8488/q-Taxi-v3-1
|
mrm8488
| 2022-07-29T22:22:28Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-29T22:22:21Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3-1
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="mrm8488/q-Taxi-v3-1", 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/zk_faye
|
huggingtweets
| 2022-07-29T22:03:30Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-29T22:01:35Z |
---
language: en
thumbnail: http://www.huggingtweets.com/zk_faye/1659132206531/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/1544789753639436289/_nNZ-fpO_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">❤️ ANGEL FAYE ❤️</div>
<div style="text-align: center; font-size: 14px;">@zk_faye</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 ❤️ ANGEL FAYE ❤️.
| Data | ❤️ ANGEL FAYE ❤️ |
| --- | --- |
| Tweets downloaded | 422 |
| Retweets | 152 |
| Short tweets | 119 |
| Tweets kept | 151 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1w29di03/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 @zk_faye's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1klggdh2) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1klggdh2/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/zk_faye')
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)
|
mrm8488/q-Taxi-v3
|
mrm8488
| 2022-07-29T21:37:20Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-29T20:43:55Z |
---
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="mrm8488/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"])
```
|
jungjongho/wav2vec2-large-xlsr-korean-demo-colab_epoch15
|
jungjongho
| 2022-07-29T21:25:56Z | 3 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-29T16:39:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-large-xlsr-korean-demo-colab_epoch15
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-xlsr-korean-demo-colab_epoch15
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: 0.4133
- Wer: 0.3801
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 16.9017 | 0.8 | 400 | 4.6273 | 1.0 |
| 4.4633 | 1.6 | 800 | 4.4419 | 1.0 |
| 4.2262 | 2.4 | 1200 | 3.8477 | 0.9994 |
| 2.4402 | 3.21 | 1600 | 1.3564 | 0.8111 |
| 1.3499 | 4.01 | 2000 | 0.9070 | 0.6664 |
| 0.9922 | 4.81 | 2400 | 0.7496 | 0.6131 |
| 0.8271 | 5.61 | 2800 | 0.6240 | 0.5408 |
| 0.6918 | 6.41 | 3200 | 0.5506 | 0.5026 |
| 0.6015 | 7.21 | 3600 | 0.5303 | 0.4935 |
| 0.5435 | 8.02 | 4000 | 0.4951 | 0.4696 |
| 0.4584 | 8.82 | 4400 | 0.4677 | 0.4432 |
| 0.4258 | 9.62 | 4800 | 0.4602 | 0.4307 |
| 0.3906 | 10.42 | 5200 | 0.4456 | 0.4195 |
| 0.3481 | 11.22 | 5600 | 0.4265 | 0.4062 |
| 0.3216 | 12.02 | 6000 | 0.4241 | 0.4046 |
| 0.2908 | 12.83 | 6400 | 0.4106 | 0.3941 |
| 0.2747 | 13.63 | 6800 | 0.4146 | 0.3855 |
| 0.2633 | 14.43 | 7200 | 0.4133 | 0.3801 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.12.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
|
jackoyoungblood/ppo-LunarLander-v2b
|
jackoyoungblood
| 2022-07-29T21:03:11Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-29T21:02:51Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 236.21 +/- 14.68
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
...
```
|
mrm8488/q-FrozenLake-v1-4x4-noSlippery
|
mrm8488
| 2022-07-29T20:38:36Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-29T20:38:30Z |
---
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="mrm8488/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"])
```
|
andres-hsn/q-Taxi-v3
|
andres-hsn
| 2022-07-29T17:02:52Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-29T17:02:38Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.54 +/- 2.72
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="andres-hsn/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"])
```
|
Datasaur/distilbert-base-uncased-finetuned-ag-news
|
Datasaur
| 2022-07-29T16:36:20Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"en",
"dataset:ag-news",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-17T02:53:35Z |
---
language: en
license: apache-2.0
datasets:
- ag-news
---
|
pampa/pets
|
pampa
| 2022-07-29T16:20:29Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2022-07-29T14:56:39Z |
---
title: Pet classifier!
emoji: 🐶
colorFrom: pink
colorTo: blue
sdk: gradio
sdk_version: 2.9.4
app_file: app.py
pinned: false
license: apache-2.0
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
|
kdf/python-docstring-generation
|
kdf
| 2022-07-29T15:31:02Z | 6 | 3 |
transformers
|
[
"transformers",
"pytorch",
"codegen",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-29T11:51:57Z |
---
license: apache-2.0
widget:
- text: "<|endoftext|>\ndef load_excel(path):\n return pd.read_excel(path)\n# docstring\n\"\"\""
---
## Basic info
model based [Salesforce/codegen-350M-mono](https://huggingface.co/Salesforce/codegen-350M-mono)
fine-tuned with data [codeparrot/github-code-clean](https://huggingface.co/datasets/codeparrot/github-code-clean)
data filter by python
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_type = 'kdf/python-docstring-generation'
tokenizer = AutoTokenizer.from_pretrained(model_type)
model = AutoModelForCausalLM.from_pretrained(model_type)
inputs = tokenizer('''<|endoftext|>
def load_excel(path):
return pd.read_excel(path)
# docstring
"""''', return_tensors='pt')
doc_max_length = 128
generated_ids = model.generate(
**inputs,
max_length=inputs.input_ids.shape[1] + doc_max_length,
do_sample=False,
return_dict_in_generate=True,
num_return_sequences=1,
output_scores=True,
pad_token_id=50256,
eos_token_id=50256 # <|endoftext|>
)
ret = tokenizer.decode(generated_ids.sequences[0], skip_special_tokens=False)
print(ret)
```
## Prompt
You could give model a style or a specific language, for example:
```python
inputs = tokenizer('''<|endoftext|>
def add(a, b):
return a + b
# docstring
"""
Calculate numbers add.
Args:
a: the first number to add
b: the second number to add
Return:
The result of a + b
"""
<|endoftext|>
def load_excel(path):
return pd.read_excel(path)
# docstring
"""''', return_tensors='pt')
doc_max_length = 128
generated_ids = model.generate(
**inputs,
max_length=inputs.input_ids.shape[1] + doc_max_length,
do_sample=False,
return_dict_in_generate=True,
num_return_sequences=1,
output_scores=True,
pad_token_id=50256,
eos_token_id=50256 # <|endoftext|>
)
ret = tokenizer.decode(generated_ids.sequences[0], skip_special_tokens=False)
print(ret)
inputs = tokenizer('''<|endoftext|>
def add(a, b):
return a + b
# docstring
"""
计算数字相加
Args:
a: 第一个加数
b: 第二个加数
Return:
相加的结果
"""
<|endoftext|>
def load_excel(path):
return pd.read_excel(path)
# docstring
"""''', return_tensors='pt')
doc_max_length = 128
generated_ids = model.generate(
**inputs,
max_length=inputs.input_ids.shape[1] + doc_max_length,
do_sample=False,
return_dict_in_generate=True,
num_return_sequences=1,
output_scores=True,
pad_token_id=50256,
eos_token_id=50256 # <|endoftext|>
)
ret = tokenizer.decode(generated_ids.sequences[0], skip_special_tokens=False)
print(ret)
```
|
schnell/bert-small-juman-bpe
|
schnell
| 2022-07-29T15:15:11Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-26T16:12:28Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-small-juman-bpe
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-small-juman-bpe
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- Accuracy: 0.6317
- Loss: 1.7829
## 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: 256
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 3
- total_train_batch_size: 768
- total_eval_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 14
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:------:|:--------:|:---------------:|
| 2.3892 | 1.0 | 69472 | 0.5637 | 2.2498 |
| 2.2219 | 2.0 | 138944 | 0.5873 | 2.0785 |
| 2.1453 | 3.0 | 208416 | 0.5984 | 2.0019 |
| 2.1 | 4.0 | 277888 | 0.6059 | 1.9531 |
| 2.068 | 5.0 | 347360 | 0.6106 | 1.9169 |
| 2.0405 | 6.0 | 416832 | 0.6146 | 1.8921 |
| 2.0174 | 7.0 | 486304 | 0.6175 | 1.8711 |
| 2.0002 | 8.0 | 555776 | 0.6205 | 1.8527 |
| 1.9838 | 9.0 | 625248 | 0.6225 | 1.8381 |
| 1.9691 | 10.0 | 694720 | 0.6248 | 1.8239 |
| 1.9551 | 11.0 | 764192 | 0.6265 | 1.8125 |
| 1.9406 | 12.0 | 833664 | 0.6288 | 1.8002 |
| 1.9293 | 13.0 | 903136 | 0.6310 | 1.7871 |
| 1.9247 | 14.0 | 972608 | 0.6317 | 1.7829 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.12.0+cu116
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Lovesaif/bert-finetuned-squad
|
Lovesaif
| 2022-07-29T15:14:15Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-07-27T03:19:59Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Lovesaif/bert-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Lovesaif/bert-finetuned-squad
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: 0.5635
- 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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 16638, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 1.2643 | 0 |
| 0.7787 | 1 |
| 0.5635 | 2 |
### Framework versions
- Transformers 4.21.0
- TensorFlow 2.8.2
- Datasets 2.4.0
- Tokenizers 0.12.1
|
phjhk/hklegal-xlm-r-base-t
|
phjhk
| 2022-07-29T14:53:09Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"multilingual",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"he",
"hi",
"hr",
"hu",
"hy",
"id",
"is",
"it",
"ja",
"jv",
"ka",
"kk",
"km",
"kn",
"ko",
"ku",
"ky",
"la",
"lo",
"lt",
"lv",
"mg",
"mk",
"ml",
"mn",
"mr",
"ms",
"my",
"ne",
"nl",
"no",
"om",
"or",
"pa",
"pl",
"ps",
"pt",
"ro",
"ru",
"sa",
"sd",
"si",
"sk",
"sl",
"so",
"sq",
"sr",
"su",
"sv",
"sw",
"ta",
"te",
"th",
"tl",
"tr",
"ug",
"uk",
"ur",
"uz",
"vi",
"xh",
"yi",
"zh",
"arxiv:1911.02116",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-26T16:41:57Z |
---
language:
- multilingual
- af
- am
- ar
- as
- az
- be
- bg
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lo
- lt
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- no
- om
- or
- pa
- pl
- ps
- pt
- ro
- ru
- sa
- sd
- si
- sk
- sl
- so
- sq
- sr
- su
- sv
- sw
- ta
- te
- th
- tl
- tr
- ug
- uk
- ur
- uz
- vi
- xh
- yi
- zh
---
# Model Description
The XLM-RoBERTa model was proposed in [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov. It is based on Facebook's RoBERTa model released in 2019. It is a large multi-lingual language model, trained on 2.5TB of filtered CommonCrawl data. This model is [XLM-RoBERTa-large](https://huggingface.co/xlm-roberta-large) fine-tuned with the [conll2003](https://huggingface.co/datasets/conll2003) dataset in English.
- **Developed by:** See [associated paper](https://arxiv.org/abs/1911.02116)
- **Model type:** Multi-lingual language model
- **Language(s) (NLP) or Countries (images):** XLM-RoBERTa is a multilingual model trained on 100 different languages; see [GitHub Repo](https://github.com/facebookresearch/fairseq/tree/main/examples/xlmr) for full list; model is fine-tuned on a dataset in English
- **Related Models:** [RoBERTa](https://huggingface.co/roberta-base), [XLM](https://huggingface.co/docs/transformers/model_doc/xlm)
- **Parent Model:** [XLM-RoBERTa-large](https://huggingface.co/xlm-roberta-large)
Hong Kong Legal Information Institute [HKILL](https://www.hklii.hk/eng/) is a free, independent, non-profit document database providing the public with legal information relating to Hong Kong. We finetune the XLM-RoBERTa on the HKILL datasets. It contains docments
# Uses
The model is a pretrained-finetuned language model. The model can be used for document classification, Named Entity Recognition (NER), especially on legal domain.
```python
>>> from transformers import pipeline,AutoTokenizer,AutoModelForTokenClassification
>>> tokenizer = AutoTokenizer.from_pretrained("hklegal-xlm-r-base-t")
>>> model = AutoModelForTokenClassification.from_pretrained("hklegal-xlm-r-base-t")
>>> classifier = pipeline("ner", model=model, tokenizer=tokenizer)
>>> classifier("Alya told Jasmine that Andrew could pay with cash..")
```
# Citation
**BibTeX:**
```bibtex
@article{conneau2019unsupervised,
title={Unsupervised Cross-lingual Representation Learning at Scale},
author={Conneau, Alexis and Khandelwal, Kartikay and Goyal, Naman and Chaudhary, Vishrav and Wenzek, Guillaume and Guzm{\'a}n, Francisco and Grave, Edouard and Ott, Myle and Zettlemoyer, Luke and Stoyanov, Veselin},
journal={arXiv preprint arXiv:1911.02116},
year={2019}
}
```
|
phjhk/hklegal-xlm-r-large-t
|
phjhk
| 2022-07-29T14:50:13Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"multilingual",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"he",
"hi",
"hr",
"hu",
"hy",
"id",
"is",
"it",
"ja",
"jv",
"ka",
"kk",
"km",
"kn",
"ko",
"ku",
"ky",
"la",
"lo",
"lt",
"lv",
"mg",
"mk",
"ml",
"mn",
"mr",
"ms",
"my",
"ne",
"nl",
"no",
"om",
"or",
"pa",
"pl",
"ps",
"pt",
"ro",
"ru",
"sa",
"sd",
"si",
"sk",
"sl",
"so",
"sq",
"sr",
"su",
"sv",
"sw",
"ta",
"te",
"th",
"tl",
"tr",
"ug",
"uk",
"ur",
"uz",
"vi",
"xh",
"yi",
"zh",
"arxiv:1911.02116",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-26T17:14:00Z |
---
language:
- multilingual
- af
- am
- ar
- as
- az
- be
- bg
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lo
- lt
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- no
- om
- or
- pa
- pl
- ps
- pt
- ro
- ru
- sa
- sd
- si
- sk
- sl
- so
- sq
- sr
- su
- sv
- sw
- ta
- te
- th
- tl
- tr
- ug
- uk
- ur
- uz
- vi
- xh
- yi
- zh
---
# Model Description
The XLM-RoBERTa model was proposed in [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov. It is based on Facebook's RoBERTa model released in 2019. It is a large multi-lingual language model, trained on 2.5TB of filtered CommonCrawl data. This model is [XLM-RoBERTa-large](https://huggingface.co/xlm-roberta-large) fine-tuned with the [conll2003](https://huggingface.co/datasets/conll2003) dataset in English.
- **Developed by:** See [associated paper](https://arxiv.org/abs/1911.02116)
- **Model type:** Multi-lingual language model
- **Language(s) (NLP) or Countries (images):** XLM-RoBERTa is a multilingual model trained on 100 different languages; see [GitHub Repo](https://github.com/facebookresearch/fairseq/tree/main/examples/xlmr) for full list; model is fine-tuned on a dataset in English
- **Related Models:** [RoBERTa](https://huggingface.co/roberta-base), [XLM](https://huggingface.co/docs/transformers/model_doc/xlm)
- **Parent Model:** [XLM-RoBERTa-large](https://huggingface.co/xlm-roberta-large)
Hong Kong Legal Information Institute [HKILL](https://www.hklii.hk/eng/) is a free, independent, non-profit document database providing the public with legal information relating to Hong Kong. We finetune the XLM-RoBERTa on the HKILL datasets. It contains docments
# Uses
The model is a pretrained-finetuned language model. The model can be used for document classification, Named Entity Recognition (NER), especially on legal domain.
```python
>>> from transformers import pipeline,AutoTokenizer,AutoModelForTokenClassification
>>> tokenizer = AutoTokenizer.from_pretrained("hklegal-xlm-r-large-t")
>>> model = AutoModelForTokenClassification.from_pretrained("hklegal-xlm-r-large-t")
>>> classifier = pipeline("ner", model=model, tokenizer=tokenizer)
>>> classifier("Alya told Jasmine that Andrew could pay with cash..")
```
# Citation
**BibTeX:**
```bibtex
@article{conneau2019unsupervised,
title={Unsupervised Cross-lingual Representation Learning at Scale},
author={Conneau, Alexis and Khandelwal, Kartikay and Goyal, Naman and Chaudhary, Vishrav and Wenzek, Guillaume and Guzm{\'a}n, Francisco and Grave, Edouard and Ott, Myle and Zettlemoyer, Luke and Stoyanov, Veselin},
journal={arXiv preprint arXiv:1911.02116},
year={2019}
}
```
|
nealtao/gpt2-chinese-scifi
|
nealtao
| 2022-07-29T14:29:15Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-28T09:27:14Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: nealtao/gpt2-chinese-scifi
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. -->
# nealtao/gpt2-chinese-scifi
This model is a fine-tuned version of [uer/gpt2-chinese-cluecorpussmall](https://huggingface.co/uer/gpt2-chinese-cluecorpussmall) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.8822
- Validation Loss: 2.9110
- 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 |
|:----------:|:---------------:|:-----:|
| 3.0949 | 2.9731 | 0 |
| 2.9607 | 2.9323 | 1 |
| 2.8822 | 2.9110 | 2 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.9.1
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Amine007/distilgpt2-finetuned-wikitext2
|
Amine007
| 2022-07-29T14:15:42Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-29T13:24:32Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6421
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.7602 | 1.0 | 2334 | 3.6669 |
| 3.653 | 2.0 | 4668 | 3.6472 |
| 3.6006 | 3.0 | 7002 | 3.6421 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
platzi/platzi-bert-base-mrpc-glue-omar-espejel
|
platzi
| 2022-07-29T13:50:27Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-29T13:37:08Z |
---
license: apache-2.0
tags:
- text-classification
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: platzi-bert-base-mrpc-glue-omar-espejel
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: mrpc
split: train
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8578431372549019
- name: F1
type: f1
value: 0.8941605839416058
---
<!-- 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. -->
# platzi-bert-base-mrpc-glue-omar-espejel
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue and the mrpc datasets.
It achieves the following results on the evaluation set:
- Loss: 0.4366
- Accuracy: 0.8578
- F1: 0.8942
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5221 | 1.09 | 500 | 0.4366 | 0.8578 | 0.8942 |
| 0.3114 | 2.18 | 1000 | 0.6581 | 0.8725 | 0.9113 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
huggingtweets/onlythesexiest_
|
huggingtweets
| 2022-07-29T13:28:49Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-29T13:26:49Z |
---
language: en
thumbnail: http://www.huggingtweets.com/onlythesexiest_/1659101307927/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/1399411396140535812/UwTllUci_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">Only The Sexiest 18+</div>
<div style="text-align: center; font-size: 14px;">@onlythesexiest_</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 Only The Sexiest 18+.
| Data | Only The Sexiest 18+ |
| --- | --- |
| Tweets downloaded | 2986 |
| Retweets | 2785 |
| Short tweets | 36 |
| Tweets kept | 165 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3oqup13u/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 @onlythesexiest_'s tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1ajjfffk) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1ajjfffk/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/onlythesexiest_')
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)
|
huspacy/hu_vectors_web_md
|
huspacy
| 2022-07-29T13:08:13Z | 0 | 0 |
spacy
|
[
"spacy",
"floret",
"fasttext",
"feature-extraction",
"token-classification",
"hu",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
token-classification
| 2022-07-29T12:48:29Z |
---
tags:
- spacy
- floret
- fasttext
- feature-extraction
- token-classification
language:
- hu
license: cc-by-sa-4.0
model-index:
- name: hu_vectors_web_md
results:
- task:
name: Analogical questions
type: token-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.1010
- name: MRR
type: mrr
value: 0.1772
---
Hungarian word vectors for HuSpaCy.
The model is trained on the Hungarian Webcorpus 2.0 using floret with the following hyperparameters: `floret cbow -dim 100 -mode floret -bucket 200000 -minn 4 -maxn 6 -minCount 100 -neg 10 -hashCount 2 -lr 0.1 -thread 30 -epoch 5`
Vectors are published in fasttext and floret format.
| Feature | Description |
| --- | --- |
| **Name** | `hu_vectors_web_lg` |
| **Version** | `1.0` |
| **Vectors** | 200000 keys (300 dimensions) |
| **Sources** | [Hungarian Webcorpus 2.0](https://hlt.bme.hu/en/resources/webcorpus2) (Dávid Márk Nemeskey (SZTAKI-HLT)) |
| **License** | `cc-by-sa-4.0` |
| **Author** | [SzegedAI, MILAB](https://github.com/huspacy/huspacy) |
### Accuracy
| Type | Score |
| --- | --- |
| `ACC` | 10.10 |
| `MRR` | 0.1772 |
|
raisin2402/marian-finetuned-kde4-en-to-fr
|
raisin2402
| 2022-07-29T12:59:05Z | 3 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-07-29T11:08:39Z |
---
license: apache-2.0
tags:
- translation
- generated_from_trainer
datasets:
- kde4
metrics:
- bleu
model-index:
- name: marian-finetuned-kde4-en-to-fr
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: kde4
type: kde4
config: en-fr
split: train
args: en-fr
metrics:
- name: Bleu
type: bleu
value: 52.83113187001415
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8560
- Bleu: 52.8311
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
turhancan97/dqn-SpaceInvadersNoFrameskip-v4
|
turhancan97
| 2022-07-29T12:12:16Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-29T12:11:45Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 424.00 +/- 124.70
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga turhancan97 -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga turhancan97
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 500000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
AlbertShu/Reinforce-v1
|
AlbertShu
| 2022-07-29T11:26:16Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-29T11:26:01Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-v1
results:
- metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
Lvxue/finetuned-mt5-small
|
Lvxue
| 2022-07-29T11:08:43Z | 26 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-07-28T02:27:31Z |
---
language:
- en
- ro
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wmt16
metrics:
- bleu
model-index:
- name: finetuned-mt5-small
results:
- task:
name: Translation
type: translation
dataset:
name: wmt16 ro-en
type: wmt16
args: ro-en
metrics:
- name: Bleu
type: bleu
value: 23.6759
---
<!-- 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. -->
# finetuned-mt5-small
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6328
- Bleu: 23.6759
- Gen Len: 43.6993
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
AkmalAshirmatov/first_try
|
AkmalAshirmatov
| 2022-07-29T09:14:52Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-29T07:58:39Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_7_0
model-index:
- name: first_try
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. -->
# first_try
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_7_0 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 2.4.0
- Tokenizers 0.10.3
|
SummerChiam/pond_image_classification_9
|
SummerChiam
| 2022-07-29T09:13:48Z | 51 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-07-29T09:13:31Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: pond_image_classification_9
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9974489808082581
---
# pond_image_classification_9
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
#### Algae
#### Boiling
#### BoilingNight
#### Normal
#### NormalCement
#### NormalNight
#### NormalRain
|
RRajesh27/finetuning-sentiment-model-3000-samples
|
RRajesh27
| 2022-07-29T08:51:28Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-29T08:39:10Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: train
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8666666666666667
- name: F1
type: f1
value: 0.8666666666666667
---
<!-- 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. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3236
- Accuracy: 0.8667
- F1: 0.8667
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Frikallo/vgdunkey-vgdunkeybot
|
Frikallo
| 2022-07-29T08:41:49Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-29T08:37:24Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: vgdunkey-vgdunkeybot
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. -->
# vgdunkey-vgdunkeybot
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001372
- train_batch_size: 1
- eval_batch_size: 8
- seed: 2843356107
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.9.1+cu111
- Datasets 2.3.2
- Tokenizers 0.12.1
|
commanderstrife/distilBERT_bio_pv_superset
|
commanderstrife
| 2022-07-29T08:36:40Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-07-29T05:41:49Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilBERT_bio_pv_superset
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_bio_pv_superset
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: 0.2328
- Precision: 0.5462
- Recall: 0.5325
- F1: 0.5393
- Accuracy: 0.9495
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0964 | 1.0 | 5467 | 0.1593 | 0.4625 | 0.3682 | 0.4100 | 0.9416 |
| 0.1918 | 2.0 | 10934 | 0.1541 | 0.4796 | 0.4658 | 0.4726 | 0.9436 |
| 0.0394 | 3.0 | 16401 | 0.1508 | 0.5349 | 0.4744 | 0.5028 | 0.9482 |
| 0.1207 | 4.0 | 21868 | 0.1615 | 0.5422 | 0.4953 | 0.5177 | 0.9490 |
| 0.0221 | 5.0 | 27335 | 0.1827 | 0.5377 | 0.5018 | 0.5191 | 0.9487 |
| 0.0629 | 6.0 | 32802 | 0.1874 | 0.5479 | 0.5130 | 0.5299 | 0.9493 |
| 0.0173 | 7.0 | 38269 | 0.2025 | 0.5388 | 0.5323 | 0.5356 | 0.9488 |
| 0.2603 | 8.0 | 43736 | 0.2148 | 0.5437 | 0.5397 | 0.5417 | 0.9493 |
| 0.0378 | 9.0 | 49203 | 0.2323 | 0.5430 | 0.5194 | 0.5310 | 0.9489 |
| 0.031 | 10.0 | 54670 | 0.2328 | 0.5462 | 0.5325 | 0.5393 | 0.9495 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
SummerChiam/pond_image_classification_7
|
SummerChiam
| 2022-07-29T08:32:46Z | 48 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-07-29T08:32:27Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: pond_image_classification_7
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9936224222183228
---
# pond_image_classification_7
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
#### Algae
#### Boiling
#### BoilingNight
#### Normal
#### NormalCement
#### NormalNight
#### NormalRain
|
Frikallo/out
|
Frikallo
| 2022-07-29T08:29:57Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-29T08:00:19Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: out
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. -->
# out
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001372
- train_batch_size: 1
- eval_batch_size: 8
- seed: 2370848220
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0
### Training results
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
SummerChiam/pond_image_classification_6
|
SummerChiam
| 2022-07-29T08:19:54Z | 50 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-07-29T08:19:36Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: pond_image_classification_6
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9948979616165161
---
# pond_image_classification_6
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
#### Algae
#### Boiling
#### BoilingNight
#### Normal
#### NormalCement
#### NormalNight
#### NormalRain
|
ParkSaeroyi/distilroberta-base-finetuned-wikitext2
|
ParkSaeroyi
| 2022-07-29T08:10:16Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-28T10:00:51Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilroberta-base-finetuned-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilroberta-base-finetuned-wikitext2
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 8.3687
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 6 | 8.8622 |
| No log | 2.0 | 12 | 8.4576 |
| No log | 3.0 | 18 | 8.4412 |
### Framework versions
- Transformers 4.22.0.dev0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
ilmariky/bert-base-finnish-cased-squad2-fi
|
ilmariky
| 2022-07-29T07:54:28Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"fi",
"license:gpl-3.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-07-12T18:27:12Z |
---
language: fi
datasets:
- SQuAD_v2_fi + Finnish partition of TyDi-QA
license: gpl-3.0
---
# bert-base-finnish-cased-v1 for QA
This is the [bert-base-finnish-cased-v1](https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1) model, fine-tuned using an automatically translated [Finnish version of the SQuAD2.0 dataset](https://huggingface.co/datasets/ilmariky/SQuAD_v2_fi) in combination with the Finnish partition of the [TyDi-QA](https://github.com/google-research-datasets/tydiqa) dataset. It's been trained on question-answer pairs, **including unanswerable questions**, for the task of question answering.
When the model classifies the question as unanswerable, it outputs "[CLS]". There is also a QA model available that does not try to identify unanswerable questions, [
bert-base-finnish-cased-squad1-fi ](https://huggingface.co/ilmariky/bert-base-finnish-cased-squad1-fi).
## Overview
**Language model:** bert-base-finnish-cased-v1
**Language:** Finnish
**Downstream-task:** Extractive QA
**Training data:** [Finnish SQuAD 2.0](https://huggingface.co/datasets/ilmariky/SQuAD_v2_fi) + Finnish partition of TyDi-QA
**Eval data:** [Finnish SQuAD 2.0](https://huggingface.co/datasets/ilmariky/SQuAD_v2_fi) + Finnish partition of TyDi-QA
## Usage
### In Transformers
```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
model_name = "ilmariky/bert-base-finnish-cased-squad2-fi"
# a) Get predictions
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
QA_input = {
'question': 'Mikä tämä on?',
'context': 'Tämä on testi.'
}
res = nlp(QA_input)
# b) Load model & tokenizer
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
## Performance
Evaluated with a slightly modified version of the [official eval script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/).
```
{
"exact": 55.53157042633567,
"f1": 61.869335312255835,
"total": 7412,
"HasAns_exact": 51.26503525508088,
"HasAns_f1": 61.006950090095565,
"HasAns_total": 4822,
"NoAns_exact": 63.47490347490348,
"NoAns_f1": 63.47490347490348,
"NoAns_total": 2590
}
```
|
jianzhnie/a2c-v1-Walker2DBulletEnv-v0
|
jianzhnie
| 2022-07-29T06:53:25Z | 3 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"tensorboard",
"Walker2DBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-29T06:52:47Z |
---
library_name: stable-baselines3
tags:
- Walker2DBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- metrics:
- type: mean_reward
value: 21.00 +/- 3.61
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Walker2DBulletEnv-v0
type: Walker2DBulletEnv-v0
---
# **A2C** Agent playing **Walker2DBulletEnv-v0**
This is a trained model of a **A2C** agent playing **Walker2DBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
rhiga/ppo-lunar-lander-v2
|
rhiga
| 2022-07-29T06:46:30Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-29T05:42:38Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 249.89 +/- 15.90
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
...
```
|
mariolinml/roberta_large-chunking_0728_v2
|
mariolinml
| 2022-07-29T05:10:42Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-07-29T04:10:55Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta_large-chunking_0728_v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta_large-chunking_0728_v2
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5270
- Precision: 0.6228
- Recall: 0.6467
- F1: 0.6345
- Accuracy: 0.8153
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 125 | 0.5667 | 0.4931 | 0.5415 | 0.5162 | 0.7397 |
| No log | 2.0 | 250 | 0.4839 | 0.5484 | 0.5894 | 0.5682 | 0.7874 |
| No log | 3.0 | 375 | 0.4822 | 0.5997 | 0.6341 | 0.6164 | 0.8085 |
| 0.4673 | 4.0 | 500 | 0.5117 | 0.6023 | 0.6373 | 0.6193 | 0.8120 |
| 0.4673 | 5.0 | 625 | 0.5270 | 0.6228 | 0.6467 | 0.6345 | 0.8153 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
jianzhnie/a2c-v1-AntBulletEnv-v0
|
jianzhnie
| 2022-07-29T05:08:10Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"tensorboard",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-29T05:07:17Z |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- metrics:
- type: mean_reward
value: 674.59 +/- 89.58
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
jianzhnie/a2c-AntBulletEnv-v0
|
jianzhnie
| 2022-07-29T04:53:54Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"tensorboard",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-29T02:15:51Z |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- metrics:
- type: mean_reward
value: 505.92 +/- 61.06
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
wpolatkan/ppo-LunarLander-v2
|
wpolatkan
| 2022-07-29T04:37:44Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-29T04:34:31Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 244.25 +/- 15.32
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
oMateos2020/pegasus-newsroom-cnn1_50k
|
oMateos2020
| 2022-07-29T04:30:35Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-07-28T03:07:03Z |
---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: pegasus-newsroom-cnn1_50k
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. -->
# pegasus-newsroom-cnn1_50k
This model is a fine-tuned version of [oMateos2020/pegasus-newsroom-cnn1_50k](https://huggingface.co/oMateos2020/pegasus-newsroom-cnn1_50k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1267
- Rouge1: 38.0081
- Rouge2: 16.5536
- Rougel: 26.4916
- Rougelsum: 35.1349
- Gen Len: 59.4912
## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 3.144 | 0.26 | 100 | 3.0323 | 38.3168 | 16.7528 | 26.2646 | 35.2447 | 66.2372 |
| 3.0556 | 0.51 | 200 | 3.0351 | 38.39 | 16.8027 | 26.3412 | 35.37 | 67.4676 |
| 3.0701 | 0.77 | 300 | 3.0345 | 38.5742 | 16.922 | 26.3568 | 35.51 | 68.662 |
| 3.1679 | 1.03 | 400 | 3.0321 | 38.5319 | 16.8049 | 26.4933 | 35.4775 | 65.976 |
| 3.1041 | 1.28 | 500 | 3.0246 | 38.1381 | 16.63 | 26.2484 | 35.0999 | 64.6896 |
| 3.0352 | 1.54 | 600 | 3.0206 | 38.9063 | 17.0281 | 27.0288 | 35.9175 | 59.0668 |
| 3.0894 | 1.79 | 700 | 3.0251 | 38.4461 | 16.7732 | 26.4394 | 35.4807 | 63.2792 |
| 3.0529 | 2.05 | 800 | 3.0400 | 38.5088 | 16.8921 | 26.5526 | 35.5236 | 64.294 |
| 3.0002 | 2.31 | 900 | 3.0394 | 38.6899 | 16.8703 | 26.6771 | 35.6207 | 62.8004 |
| 3.0167 | 2.56 | 1000 | 3.0394 | 38.3532 | 16.6176 | 26.5433 | 35.3282 | 61.63 |
| 3.0168 | 2.82 | 1100 | 3.0421 | 38.7613 | 17.0107 | 26.8424 | 35.7508 | 62.67 |
| 3.0412 | 3.08 | 1200 | 3.0491 | 38.6132 | 16.8046 | 26.61 | 35.6002 | 61.7924 |
| 3.1273 | 3.33 | 1300 | 3.0823 | 38.5498 | 16.795 | 26.5569 | 35.613 | 60.6872 |
| 3.0634 | 3.59 | 1400 | 3.1010 | 38.0927 | 16.4367 | 26.2315 | 35.1311 | 59.252 |
| 3.097 | 3.84 | 1500 | 3.1147 | 37.7644 | 16.3156 | 26.2674 | 34.8315 | 59.7592 |
| 3.1287 | 4.1 | 1600 | 3.1267 | 38.0081 | 16.5536 | 26.4916 | 35.1349 | 59.4912 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
csmartins8/xlm-roberta-base-finetuned-panx-de
|
csmartins8
| 2022-07-29T01:51:43Z | 5 | 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
| 2022-07-21T21:14:22Z |
---
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
config: PAN-X.de
split: train
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8631507160718345
---
<!-- 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: 0.1374
- F1: 0.8632
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2583 | 1.0 | 525 | 0.1594 | 0.8198 |
| 0.125 | 2.0 | 1050 | 0.1390 | 0.8483 |
| 0.08 | 3.0 | 1575 | 0.1374 | 0.8632 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
commanderstrife/ADE-Bio_ClinicalBERT-NER
|
commanderstrife
| 2022-07-29T01:39:43Z | 213 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-07-29T01:24:29Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ADE-Bio_ClinicalBERT-NER
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ADE-Bio_ClinicalBERT-NER
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1926
- Precision: 0.7830
- Recall: 0.8811
- F1: 0.8291
- Accuracy: 0.9437
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2389 | 1.0 | 201 | 0.2100 | 0.7155 | 0.8292 | 0.7681 | 0.9263 |
| 0.0648 | 2.0 | 402 | 0.1849 | 0.7716 | 0.8711 | 0.8183 | 0.9392 |
| 0.2825 | 3.0 | 603 | 0.1856 | 0.7834 | 0.8788 | 0.8284 | 0.9422 |
| 0.199 | 4.0 | 804 | 0.1875 | 0.7796 | 0.8781 | 0.8259 | 0.9430 |
| 0.0404 | 5.0 | 1005 | 0.1926 | 0.7830 | 0.8811 | 0.8291 | 0.9437 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
vikaskapur/sentimental
|
vikaskapur
| 2022-07-29T01:02:48Z | 6 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-27T04:41:28Z |
---
license: apache-2.0
---
# Model Details
* The SENTIMENTAL classifier trained to predict the likelihood that a comment will be perceived as positive or negative.
* BERT based Text Classification.
# Intended Use
* Intended to be used for a wide range of use cases such as supporting human moderation and extracting polarity of review comments.
* Not intended for fully automated moderation.
* Not intended to make judgments about specific individuals.
# Factors
* Identity terms referencing frequently positive and negative emotions.
# Metrics
• Accuracy, which measures the percentage of True Positive and True Negative.
# Ethical Considerations
* TODO
# Quantitative Analyses
* TODO
# Training Data
* TODO
# Evaluation Data
* TODO
# Caveats and Recommendations
* TODO
|
wmFrank/sample-factory-2-atari-pong
|
wmFrank
| 2022-07-28T23:34:27Z | 4 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-28T23:04:49Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- metrics:
- type: mean_reward
value: 13.50 +/- 7.43
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_pong
type: atari_pong
---
A(n) **APPO** model trained on the **atari_pong** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
espnet/Yen-Ju_Lu_l3das22_enh_train_enh_ineube_valid.loss.ave
|
espnet
| 2022-07-28T23:34:06Z | 2 | 0 |
espnet
|
[
"espnet",
"audio",
"audio-to-audio",
"en",
"dataset:l3das22",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
audio-to-audio
| 2022-07-28T23:28:46Z |
---
tags:
- espnet
- audio
- audio-to-audio
language: en
datasets:
- l3das22
license: cc-by-4.0
---
## ESPnet2 ENH model
### `espnet/Yen-Ju_Lu_l3das22_enh_train_enh_ineube_valid.loss.ave`
This model was trained by neillu23 using l3das22 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 11d687844a544fcce6f6d0ce7a0a302e0e47d442
pip install -e .
cd egs2/l3das22/enh1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/Yen-Ju_Lu_l3das22_enh_train_enh_ineube_valid.loss.ave
```
<!-- Generated by ./scripts/utils/show_enh_score.sh -->
# RESULTS
## Environments
- date: `Wed Jul 6 20:46:10 UTC 2022`
- python version: `3.8.13 (default, Mar 28 2022, 11:38:47) [GCC 7.5.0]`
- espnet version: `espnet 202205`
- pytorch version: `pytorch 1.8.1`
- Git hash: `77e36afdd3f069567dd33d4b5b997a26b634772b`
- Commit date: `Fri Jun 17 18:32:56 2022 -0400`
## enh_train_enh_ineube_raw
config: conf/tuning/train_enh_ineube.yaml
|dataset|STOI|SAR|SDR|SIR|SI_SNR|WER|STOI|TASK 1 METRIC|
|---|---|---|---|---|---|---|---|---|
|enhanced_dev_multich|95.62|15.00|15.00|0.00|13.64|5.93|0.956|0.948|
|enhanced_test_multich|95.70|14.59|14.59|0.00|13.34|4.85|0.957|0.954|
## ENH config
<details><summary>expand</summary>
```
config: conf/tuning/train_enh_ineube.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: chunk
output_dir: exp/enh_train_enh_ineube_raw
ngpu: 1
seed: 0
num_workers: 4
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: 3
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 50409
dist_launcher: null
multiprocessing_distributed: true
unused_parameters: true
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 100
patience: 20
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- si_snr
- max
- - valid
- loss
- min
keep_nbest_models: 1
nbest_averaging_interval: 0
grad_clip: 5
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 15
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/enh_stats_16k/train/speech_mix_shape
- exp/enh_stats_16k/train/speech_ref1_shape
valid_shape_file:
- exp/enh_stats_16k/valid/speech_mix_shape
- exp/enh_stats_16k/valid/speech_ref1_shape
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 80000
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 32000
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train_multich/wav.scp
- speech_mix
- sound
- - dump/raw/train_multich/spk1.scp
- speech_ref1
- sound
valid_data_path_and_name_and_type:
- - dump/raw/dev_multich/wav.scp
- speech_mix
- sound
- - dump/raw/dev_multich/spk1.scp
- speech_ref1
- sound
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.001
eps: 1.0e-08
weight_decay: 1.0e-07
scheduler: reducelronplateau
scheduler_conf:
mode: min
factor: 0.5
patience: 20
init: xavier_uniform
model_conf:
stft_consistency: false
loss_type: mask_mse
mask_type: null
criterions:
- name: snr
conf: {}
wrapper: fixed_order
wrapper_conf:
weight: 1.0
use_preprocessor: false
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
short_noise_thres: 0.5
use_reverberant_ref: false
num_spk: 1
num_noise_type: 1
sample_rate: 8000
force_single_channel: false
encoder: same
encoder_conf: {}
separator: ineube
separator_conf:
n_fft: 512
stride: 128
window: hann
mic_channels: 8
decoder: same
decoder_conf: {}
mask_module: multi_mask
mask_module_conf: {}
required:
- output_dir
version: '202205'
distributed: true
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{ESPnet-SE,
author = {Chenda Li and Jing Shi and Wangyou Zhang and Aswin Shanmugam Subramanian and Xuankai Chang and
Naoyuki Kamo and Moto Hira and Tomoki Hayashi and Christoph B{"{o}}ddeker and Zhuo Chen and Shinji Watanabe},
title = {ESPnet-SE: End-To-End Speech Enhancement and Separation Toolkit Designed for {ASR} Integration},
booktitle = {{IEEE} Spoken Language Technology Workshop, {SLT} 2021, Shenzhen, China, January 19-22, 2021},
pages = {785--792},
publisher = {{IEEE}},
year = {2021},
url = {https://doi.org/10.1109/SLT48900.2021.9383615},
doi = {10.1109/SLT48900.2021.9383615},
timestamp = {Mon, 12 Apr 2021 17:08:59 +0200},
biburl = {https://dblp.org/rec/conf/slt/Li0ZSCKHHBC021.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
wmFrank/sample-factory-2-atari-beamrider
|
wmFrank
| 2022-07-28T23:32:50Z | 11 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-28T23:08:32Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- metrics:
- type: mean_reward
value: 3848.00 +/- 308.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_beamrider
type: atari_beamrider
---
A(n) **APPO** model trained on the **atari_beamrider** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
Atharvgarg/bert-small2bert-small-finetuned-cnn_daily_mail-summarization-finetuned-bbc-news-Sumy
|
Atharvgarg
| 2022-07-28T23:32:03Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"summarisation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-07-28T22:51:50Z |
---
license: apache-2.0
tags:
- summarisation
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bert-small2bert-small-finetuned-cnn_daily_mail-summarization-finetuned-bbc-news-Sumy
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-small2bert-small-finetuned-cnn_daily_mail-summarization-finetuned-bbc-news-Sumy
This model is a fine-tuned version of [mrm8488/bert-small2bert-small-finetuned-cnn_daily_mail-summarization](https://huggingface.co/mrm8488/bert-small2bert-small-finetuned-cnn_daily_mail-summarization) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5583
- Rouge1: 55.2899
- Rouge2: 43.2426
- Rougel: 38.5056
- Rougelsum: 53.8807
## 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: 5.6e-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 | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| 1.7407 | 1.0 | 223 | 1.5900 | 51.3058 | 38.3952 | 35.7343 | 49.7129 |
| 1.4813 | 2.0 | 446 | 1.5500 | 53.8089 | 41.2455 | 37.3864 | 52.3387 |
| 1.3517 | 3.0 | 669 | 1.5429 | 53.4914 | 40.907 | 37.1428 | 52.0338 |
| 1.2432 | 4.0 | 892 | 1.5472 | 54.1139 | 41.3589 | 37.6392 | 52.711 |
| 1.1748 | 5.0 | 1115 | 1.5426 | 55.3482 | 43.312 | 38.0625 | 54.0424 |
| 1.1108 | 6.0 | 1338 | 1.5529 | 55.4752 | 43.3561 | 38.5813 | 54.1141 |
| 1.0745 | 7.0 | 1561 | 1.5539 | 55.705 | 43.6772 | 38.7629 | 54.3892 |
| 1.0428 | 8.0 | 1784 | 1.5583 | 55.2899 | 43.2426 | 38.5056 | 53.8807 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
wmFrank/sample-factory-2-atari-breakout
|
wmFrank
| 2022-07-28T23:31:06Z | 2 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-28T23:10:36Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- metrics:
- type: mean_reward
value: 30.20 +/- 23.45
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_breakout
type: atari_breakout
---
A(n) **APPO** model trained on the **atari_breakout** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
ICML2022/Tranception
|
ICML2022
| 2022-07-28T23:28:37Z | 5 | 5 |
transformers
|
[
"transformers",
"pytorch",
"tranception",
"fill-mask",
"arxiv:2205.13760",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-28T22:27:55Z |
# Tranception model
This Hugging Face Hub repo contains the model checkpoint for the Tranception model as described in our paper ["Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval"](https://arxiv.org/abs/2205.13760). The official GitHub repository can be accessed [here](https://github.com/OATML-Markslab/Tranception). This project is a joint collaboration between the [Marks lab](https://www.deboramarkslab.com/) and the [OATML group](https://oatml.cs.ox.ac.uk/).
## Abstract
The ability to accurately model the fitness landscape of protein sequences is critical to a wide range of applications, from quantifying the effects of human variants on disease likelihood, to predicting immune-escape mutations in viruses and designing novel biotherapeutic proteins. Deep generative models of protein sequences trained on multiple sequence alignments have been the most successful approaches so far to address these tasks. The performance of these methods is however contingent on the availability of sufficiently deep and diverse alignments for reliable training. Their potential scope is thus limited by the fact many protein families are hard, if not impossible, to align. Large language models trained on massive quantities of non-aligned protein sequences from diverse families address these problems and show potential to eventually bridge the performance gap. We introduce Tranception, a novel transformer architecture leveraging autoregressive predictions and retrieval of homologous sequences at inference to achieve state-of-the-art fitness prediction performance. Given its markedly higher performance on multiple mutants, robustness to shallow alignments and ability to score indels, our approach offers significant gain of scope over existing approaches. To enable more rigorous model testing across a broader range of protein families, we develop ProteinGym -- an extensive set of multiplexed assays of variant effects, substantially increasing both the number and diversity of assays compared to existing benchmarks.
## License
This project is available under the MIT license.
## Reference
If you use Tranception or other files provided through our GitHub repository, please cite the following paper:
```
Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A., Marks, D.S., Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.
```
## Links
Pre-print: https://arxiv.org/abs/2205.13760
GitHub: https://github.com/OATML-Markslab/Tranception
|
domenicrosati/deberta-v3-large-finetuned-synthetic-paraphrase-only
|
domenicrosati
| 2022-07-28T21:38:33Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-27T13:31:37Z |
---
license: mit
tags:
- text-classification
- generated_from_trainer
metrics:
- f1
- precision
- recall
model-index:
- name: deberta-v3-large-finetuned-synthetic-paraphrase-only
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. -->
# deberta-v3-large-finetuned-synthetic-paraphrase-only
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0120
- F1: 0.9768
- Precision: 0.9961
- Recall: 0.9583
## 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: 6e-06
- 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: 50
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:---------:|:------:|
| 0.0086 | 1.0 | 10205 | 0.0114 | 0.9642 | 0.9846 | 0.9446 |
| 0.0059 | 2.0 | 20410 | 0.0143 | 0.9658 | 0.9961 | 0.9373 |
| 0.0 | 3.0 | 30615 | 0.0141 | 0.9716 | 0.9961 | 0.9483 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
liujxing/distilbert-base-uncased-finetuned-emotion
|
liujxing
| 2022-07-28T20:51:40Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-28T20:37:58Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9355
- name: F1
type: f1
value: 0.93589910332286
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1484
- Accuracy: 0.9355
- F1: 0.9359
## 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: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.1386 | 1.0 | 250 | 0.1705 | 0.9355 | 0.9353 |
| 0.0928 | 2.0 | 500 | 0.1484 | 0.9355 | 0.9359 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.11.0
- Datasets 2.4.0
- Tokenizers 0.12.1
|
amirthaa/dspa
|
amirthaa
| 2022-07-28T17:18:48Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-28T17:18:27Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: dspa
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. -->
# dspa
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6069
- Validation Loss: 0.6854
- 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': 142110, '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 |
|:----------:|:---------------:|:-----:|
| 0.8363 | 0.6965 | 0 |
| 0.6069 | 0.6854 | 1 |
### Framework versions
- Transformers 4.21.0
- TensorFlow 2.9.1
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Billwzl/20split_dataset_version3
|
Billwzl
| 2022-07-28T16:20:35Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-27T11:21:44Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: 20split_dataset_version3
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. -->
# 20split_dataset_version3
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: 2.8310
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.1679 | 1.0 | 313 | 2.9768 |
| 2.9869 | 2.0 | 626 | 2.9299 |
| 2.8528 | 3.0 | 939 | 2.9176 |
| 2.7435 | 4.0 | 1252 | 2.9104 |
| 2.6458 | 5.0 | 1565 | 2.8863 |
| 2.5865 | 6.0 | 1878 | 2.8669 |
| 2.5218 | 7.0 | 2191 | 2.8802 |
| 2.4647 | 8.0 | 2504 | 2.8639 |
| 2.3933 | 9.0 | 2817 | 2.8543 |
| 2.3687 | 10.0 | 3130 | 2.8573 |
| 2.3221 | 11.0 | 3443 | 2.8398 |
| 2.276 | 12.0 | 3756 | 2.8415 |
| 2.2379 | 13.0 | 4069 | 2.8471 |
| 2.2427 | 14.0 | 4382 | 2.8318 |
| 2.1741 | 15.0 | 4695 | 2.8356 |
| 2.1652 | 16.0 | 5008 | 2.8310 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Atharvgarg/bert-small2bert-small-finetuned-cnn_daily_mail-summarization-finetuned-bbc-news-old
|
Atharvgarg
| 2022-07-28T16:04:21Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"summarisation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-07-28T15:24:58Z |
---
license: apache-2.0
tags:
- summarisation
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bert-small2bert-small-finetuned-cnn_daily_mail-summarization-finetuned-bbc-news-old
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-small2bert-small-finetuned-cnn_daily_mail-summarization-finetuned-bbc-news-old
This model is a fine-tuned version of [mrm8488/bert-small2bert-small-finetuned-cnn_daily_mail-summarization](https://huggingface.co/mrm8488/bert-small2bert-small-finetuned-cnn_daily_mail-summarization) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6733
- Rouge1: 60.9431
- Rouge2: 49.8688
- Rougel: 42.4663
- Rougelsum: 59.836
## 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: 5.6e-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 | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| 0.8246 | 1.0 | 223 | 0.6974 | 55.2742 | 41.9883 | 37.8584 | 53.7602 |
| 0.6396 | 2.0 | 446 | 0.6786 | 56.0006 | 43.1917 | 38.5125 | 54.4571 |
| 0.5582 | 3.0 | 669 | 0.6720 | 57.8912 | 45.7807 | 40.0807 | 56.4985 |
| 0.505 | 4.0 | 892 | 0.6659 | 59.6611 | 48.0095 | 41.752 | 58.5059 |
| 0.4611 | 5.0 | 1115 | 0.6706 | 59.7241 | 48.164 | 41.4523 | 58.5295 |
| 0.4254 | 6.0 | 1338 | 0.6711 | 59.8524 | 48.1821 | 41.2299 | 58.6072 |
| 0.3967 | 7.0 | 1561 | 0.6718 | 60.3009 | 49.0085 | 42.0306 | 59.0723 |
| 0.38 | 8.0 | 1784 | 0.6733 | 60.9431 | 49.8688 | 42.4663 | 59.836 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Dugerij/Reinforce-pixelcopter
|
Dugerij
| 2022-07-28T14:45:45Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-28T14:45:39Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-pixelcopter
results:
- metrics:
- type: mean_reward
value: 17.00 +/- 12.95
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
AlexKolosov/my_first_model
|
AlexKolosov
| 2022-07-28T14:14:33Z | 16 | 0 |
transformers
|
[
"transformers",
"pytorch",
"resnet",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-07-28T12:48:11Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: my_first_model
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.6
---
<!-- 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. -->
# my_first_model
This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6853
- Accuracy: 0.6
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6918 | 1.0 | 23 | 0.6895 | 0.8 |
| 0.7019 | 2.0 | 46 | 0.6859 | 0.6 |
| 0.69 | 3.0 | 69 | 0.6853 | 0.6 |
### Framework versions
- Transformers 4.22.0.dev0
- Pytorch 1.12.0+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1
|
KBLab/albert-base-swedish-cased-alpha
|
KBLab
| 2022-07-28T14:08:17Z | 11 | 2 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"sv",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04Z |
---
language: sv
---
# Swedish BERT Models
The National Library of Sweden / KBLab releases three pretrained language models based on BERT and ALBERT. The models are trained on approximately 15-20GB of text (200M sentences, 3000M tokens) from various sources (books, news, government publications, swedish wikipedia and internet forums) aiming to provide a representative BERT model for Swedish text. A more complete description will be published later on.
The following three models are currently available:
- **bert-base-swedish-cased** (*v1*) - A BERT trained with the same hyperparameters as first published by Google.
- **bert-base-swedish-cased-ner** (*experimental*) - a BERT fine-tuned for NER using SUC 3.0.
- **albert-base-swedish-cased-alpha** (*alpha*) - A first attempt at an ALBERT for Swedish.
All models are cased and trained with whole word masking.
## Files
| **name** | **files** |
|---------------------------------|-----------|
| bert-base-swedish-cased | [config](https://s3.amazonaws.com/models.huggingface.co/bert/KB/bert-base-swedish-cased/config.json), [vocab](https://s3.amazonaws.com/models.huggingface.co/bert/KB/bert-base-swedish-cased/vocab.txt), [pytorch_model.bin](https://s3.amazonaws.com/models.huggingface.co/bert/KB/bert-base-swedish-cased/pytorch_model.bin) |
| bert-base-swedish-cased-ner | [config](https://s3.amazonaws.com/models.huggingface.co/bert/KB/bert-base-swedish-cased-ner/config.json), [vocab](https://s3.amazonaws.com/models.huggingface.co/bert/KB/bert-base-swedish-cased-ner/vocab.txt) [pytorch_model.bin](https://s3.amazonaws.com/models.huggingface.co/bert/KB/bert-base-swedish-cased-ner/pytorch_model.bin) |
| albert-base-swedish-cased-alpha | [config](https://s3.amazonaws.com/models.huggingface.co/bert/KB/albert-base-swedish-cased-alpha/config.json), [sentencepiece model](https://s3.amazonaws.com/models.huggingface.co/bert/KB/albert-base-swedish-cased-alpha/spiece.model), [pytorch_model.bin](https://s3.amazonaws.com/models.huggingface.co/bert/KB/albert-base-swedish-cased-alpha/pytorch_model.bin) |
TensorFlow model weights will be released soon.
## Usage requirements / installation instructions
The examples below require Huggingface Transformers 2.4.1 and Pytorch 1.3.1 or greater. For Transformers<2.4.0 the tokenizer must be instantiated manually and the `do_lower_case` flag parameter set to `False` and `keep_accents` to `True` (for ALBERT).
To create an environment where the examples can be run, run the following in an terminal on your OS of choice.
```
# git clone https://github.com/Kungbib/swedish-bert-models
# cd swedish-bert-models
# python3 -m venv venv
# source venv/bin/activate
# pip install --upgrade pip
# pip install -r requirements.txt
```
### BERT Base Swedish
A standard BERT base for Swedish trained on a variety of sources. Vocabulary size is ~50k. Using Huggingface Transformers the model can be loaded in Python as follows:
```python
from transformers import AutoModel,AutoTokenizer
tok = AutoTokenizer.from_pretrained('KBLab/bert-base-swedish-cased')
model = AutoModel.from_pretrained('KBLab/bert-base-swedish-cased')
```
### BERT base fine-tuned for Swedish NER
This model is fine-tuned on the SUC 3.0 dataset. Using the Huggingface pipeline the model can be easily instantiated. For Transformer<2.4.1 it seems the tokenizer must be loaded separately to disable lower-casing of input strings:
```python
from transformers import pipeline
nlp = pipeline('ner', model='KB/bert-base-swedish-cased-ner', tokenizer='KB/bert-base-swedish-cased-ner')
nlp('Idag släpper KB tre språkmodeller.')
```
Running the Python code above should produce in something like the result below. Entity types used are `TME` for time, `PRS` for personal names, `LOC` for locations, `EVN` for events and `ORG` for organisations. These labels are subject to change.
```python
[ { 'word': 'Idag', 'score': 0.9998126029968262, 'entity': 'TME' },
{ 'word': 'KB', 'score': 0.9814832210540771, 'entity': 'ORG' } ]
```
The BERT tokenizer often splits words into multiple tokens, with the subparts starting with `##`, for example the string `Engelbert kör Volvo till Herrängens fotbollsklubb` gets tokenized as `Engel ##bert kör Volvo till Herr ##ängens fotbolls ##klubb`. To glue parts back together one can use something like this:
```python
text = 'Engelbert tar Volvon till Tele2 Arena för att titta på Djurgården IF ' +\
'som spelar fotboll i VM klockan två på kvällen.'
l = []
for token in nlp(text):
if token['word'].startswith('##'):
l[-1]['word'] += token['word'][2:]
else:
l += [ token ]
print(l)
```
Which should result in the following (though less cleanly formatted):
```python
[ { 'word': 'Engelbert', 'score': 0.99..., 'entity': 'PRS'},
{ 'word': 'Volvon', 'score': 0.99..., 'entity': 'OBJ'},
{ 'word': 'Tele2', 'score': 0.99..., 'entity': 'LOC'},
{ 'word': 'Arena', 'score': 0.99..., 'entity': 'LOC'},
{ 'word': 'Djurgården', 'score': 0.99..., 'entity': 'ORG'},
{ 'word': 'IF', 'score': 0.99..., 'entity': 'ORG'},
{ 'word': 'VM', 'score': 0.99..., 'entity': 'EVN'},
{ 'word': 'klockan', 'score': 0.99..., 'entity': 'TME'},
{ 'word': 'två', 'score': 0.99..., 'entity': 'TME'},
{ 'word': 'på', 'score': 0.99..., 'entity': 'TME'},
{ 'word': 'kvällen', 'score': 0.54..., 'entity': 'TME'} ]
```
### ALBERT base
The easiest way to do this is, again, using Huggingface Transformers:
```python
from transformers import AutoModel,AutoTokenizer
tok = AutoTokenizer.from_pretrained('KBLab/albert-base-swedish-cased-alpha'),
model = AutoModel.from_pretrained('KBLab/albert-base-swedish-cased-alpha')
```
## Acknowledgements ❤️
- Resources from Stockholms University, Umeå University and Swedish Language Bank at Gothenburg University were used when fine-tuning BERT for NER.
- Model pretraining was made partly in-house at the KBLab and partly (for material without active copyright) with the support of Cloud TPUs from Google's TensorFlow Research Cloud (TFRC).
- Models are hosted on S3 by Huggingface 🤗
|
jianzhnie/Reinforce-CartPole-v1
|
jianzhnie
| 2022-07-28T13:59:39Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-28T10:11:13Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- metrics:
- type: mean_reward
value: 81.61 +/- 7.99
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
silviacamplani/distilbert-uncase-direct-finetuning-ai-ner
|
silviacamplani
| 2022-07-28T13:53:42Z | 6 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-07-25T10:41:19Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: silviacamplani/distilbert-uncase-direct-finetuning-ai-ner
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. -->
# silviacamplani/distilbert-uncase-direct-finetuning-ai-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.6021
- Validation Loss: 1.6163
- Epoch: 9
## 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: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 60, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.2752 | 3.0320 | 0 |
| 2.7791 | 2.5293 | 1 |
| 2.2674 | 2.0340 | 2 |
| 1.8952 | 1.8222 | 3 |
| 1.7933 | 1.7669 | 4 |
| 1.7352 | 1.7158 | 5 |
| 1.6868 | 1.6706 | 6 |
| 1.6242 | 1.6412 | 7 |
| 1.5899 | 1.6234 | 8 |
| 1.6021 | 1.6163 | 9 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.6.4
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Nekoo/P0ken_picture
|
Nekoo
| 2022-07-28T13:33:38Z | 0 | 0 | null |
[
"license:bigscience-bloom-rail-1.0",
"region:us"
] | null | 2022-07-28T13:33:38Z |
---
license: bigscience-bloom-rail-1.0
---
|
Dugerij/Reinforce-cartpoleModel
|
Dugerij
| 2022-07-28T13:25:26Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-28T13:25:18Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-cartpoleModel
results:
- metrics:
- type: mean_reward
value: 49.30 +/- 10.99
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
jinghan/bert-base-uncased-finetuned-wnli
|
jinghan
| 2022-07-28T13:04:56Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-28T11:31:20Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert-base-uncased-finetuned-wnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: wnli
split: train
args: wnli
metrics:
- name: Accuracy
type: accuracy
value: 0.5633802816901409
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-wnli
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6917
- Accuracy: 0.5634
## 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: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 10 | 0.6925 | 0.5493 |
| No log | 2.0 | 20 | 0.6917 | 0.5634 |
| No log | 3.0 | 30 | 0.6971 | 0.3239 |
| No log | 4.0 | 40 | 0.6999 | 0.2958 |
| No log | 5.0 | 50 | 0.6998 | 0.2676 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
ivan-savchuk/msmarco-distilbert-dot-v5-tuned-full-v1
|
ivan-savchuk
| 2022-07-28T12:14:51Z | 5 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-07-28T11:47:03Z |
---
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 3165 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 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 316,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel
(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 -->
|
ICML2022/TimeIsMattEr
|
ICML2022
| 2022-07-28T12:00:17Z | 0 | 3 | null |
[
"video-action-recognition",
"dataset:HuggingFaceM4/something_something_v2",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-07-28T11:54:09Z |
---
license: cc-by-nc-4.0
datasets:
- HuggingFaceM4/something_something_v2
tags:
- video-action-recognition
metrics:
- accuracy
---
|
mayank-01/finetuning-sentiment-model-3000-samples
|
mayank-01
| 2022-07-28T11:10:47Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-28T10:41:32Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: train
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.88
- name: F1
type: f1
value: 0.8831168831168831
---
<!-- 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. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3045
- Accuracy: 0.88
- F1: 0.8831
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
amartyobanerjee/distilbert-base-uncased-finetuned-imdb
|
amartyobanerjee
| 2022-07-28T09:45:35Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-28T05:27:01Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
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-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4721
## 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: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7086 | 1.0 | 157 | 2.4898 |
| 2.5796 | 2.0 | 314 | 2.4230 |
| 2.5269 | 3.0 | 471 | 2.4354 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Ravindra001/bert-finetuned-ner
|
Ravindra001
| 2022-07-28T09:29:11Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:wikiann",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-05-25T06:09:39Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wikiann
type: wikiann
args: en
metrics:
- name: Precision
type: precision
value: 0.819622641509434
- name: Recall
type: recall
value: 0.8444790046656299
- name: F1
type: f1
value: 0.8318651857525853
- name: Accuracy
type: accuracy
value: 0.9269227060339613
- task:
type: token-classification
name: Token Classification
dataset:
name: wikiann
type: wikiann
config: en
split: test
metrics:
- name: Accuracy
type: accuracy
value: 0.8492771401033908
verified: true
- name: Precision
type: precision
value: 0.857294905524994
verified: true
- name: Recall
type: recall
value: 0.865900059186607
verified: true
- name: F1
type: f1
value: 0.8615759964905745
verified: true
- name: loss
type: loss
value: 1.054654836654663
verified: true
---
<!-- 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 wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3217
- Precision: 0.8196
- Recall: 0.8445
- F1: 0.8319
- Accuracy: 0.9269
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2821 | 1.0 | 2500 | 0.2906 | 0.7983 | 0.8227 | 0.8103 | 0.9193 |
| 0.2087 | 2.0 | 5000 | 0.2614 | 0.8030 | 0.8379 | 0.8201 | 0.9257 |
| 0.1404 | 3.0 | 7500 | 0.3217 | 0.8196 | 0.8445 | 0.8319 | 0.9269 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
AlbertShu/Reinforce-v0
|
AlbertShu
| 2022-07-28T09:22:30Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-28T09:22:20Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-v0
results:
- metrics:
- type: mean_reward
value: 99.30 +/- 29.54
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
jaeyeon/korean-aihub-learning-math-16batch
|
jaeyeon
| 2022-07-28T08:13:59Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-28T07:10:40Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: korean-aihub-learning-math-16batch
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. -->
# korean-aihub-learning-math-16batch
This model is a fine-tuned version of [kresnik/wav2vec2-large-xlsr-korean](https://huggingface.co/kresnik/wav2vec2-large-xlsr-korean) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1497
- Wer: 0.5260
## 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
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 20 | 32.0718 | 1.0 |
| No log | 2.0 | 40 | 24.7403 | 1.0808 |
| No log | 3.0 | 60 | 5.8389 | 1.0 |
| No log | 4.0 | 80 | 4.8543 | 1.0 |
| 19.6583 | 5.0 | 100 | 4.4453 | 1.0 |
| 19.6583 | 6.0 | 120 | 4.3923 | 1.0 |
| 19.6583 | 7.0 | 140 | 4.2902 | 1.0 |
| 19.6583 | 8.0 | 160 | 3.9026 | 0.9959 |
| 19.6583 | 9.0 | 180 | 3.0616 | 0.9740 |
| 3.7358 | 10.0 | 200 | 2.2049 | 0.8534 |
| 3.7358 | 11.0 | 220 | 1.6666 | 0.7288 |
| 3.7358 | 12.0 | 240 | 1.4123 | 0.6603 |
| 3.7358 | 13.0 | 260 | 1.3113 | 0.6164 |
| 3.7358 | 14.0 | 280 | 1.2269 | 0.6356 |
| 0.8398 | 15.0 | 300 | 1.2349 | 0.5945 |
| 0.8398 | 16.0 | 320 | 1.1970 | 0.5658 |
| 0.8398 | 17.0 | 340 | 1.2144 | 0.5562 |
| 0.8398 | 18.0 | 360 | 1.2551 | 0.5658 |
| 0.8398 | 19.0 | 380 | 1.1971 | 0.5493 |
| 0.2649 | 20.0 | 400 | 1.1967 | 0.5247 |
| 0.2649 | 21.0 | 420 | 1.2796 | 0.5849 |
| 0.2649 | 22.0 | 440 | 1.2156 | 0.5521 |
| 0.2649 | 23.0 | 460 | 1.2118 | 0.5425 |
| 0.2649 | 24.0 | 480 | 1.1637 | 0.5384 |
| 0.1801 | 25.0 | 500 | 1.1846 | 0.5562 |
| 0.1801 | 26.0 | 520 | 1.1927 | 0.5534 |
| 0.1801 | 27.0 | 540 | 1.2015 | 0.5384 |
| 0.1801 | 28.0 | 560 | 1.2077 | 0.5397 |
| 0.1801 | 29.0 | 580 | 1.1554 | 0.5260 |
| 0.1364 | 30.0 | 600 | 1.1497 | 0.5260 |
### Framework versions
- Transformers 4.22.0.dev0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
LukasStankevicius/t5-base-lithuanian-news-summaries-175
|
LukasStankevicius
| 2022-07-28T06:00:09Z | 39 | 3 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"Lithuanian",
"summarization",
"lt",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-03-02T23:29:04Z |
---
language: lt
tags:
- t5
- Lithuanian
- summarization
widget:
- text: "Latvijos krepšinio legenda Valdis Valteris pirmadienį socialiniame tinkle pasidalino statistika, kurios viršūnėje yra Arvydas Sabonis. 1982 metais TSRS rinktinėje debiutavęs 222 cm ūgio vidurio puolėjas su raudona apranga sužaidė 52 rungtynes, per kurias rinko po 15,6 taško. Tai pats aukščiausias rezultatyvumo vidurkis tarp visų sovietų komandai atstovavusių žaidėjų, skaičiuojant tuos, kurie sužaidė ne mažiau nei 50 rungtynių. Antras šioje rikiuotėje kitas buvęs Kauno „Žalgirio“ krepšininkas Rimas Kurtinaitis. Jis debiutavo TSRS rinktinėje vėliau nei Sabas, – 1984 metais, bet irgi sužaidė 52 mačus. R.Kurtinaitis pelnė po 15 taškų. 25-ių rezultatyviausių žaidėjų sąrašu pasidalinęs latvis V.Valteris, pelnęs po 13,8 taško, yra trečias. Ketvirtas yra iš Kazachstano kilęs Valerijus Tichonenka, pelnęs po 13,7 taško per 79 rungtynes. Rezultatyviausią visų laikų TSRS rinktinės penketą uždaro Modestas Paulauskas. Lietuvos krepšinio legenda pelnė po 13,6 taško per 84 mačus. Dešimtuke taip pat yra Oleksandras Volkovas (po 13,5 taško), Sergejus Belovas (12,7), Anatolijus Myškinas (po 12,3), Vladimiras Tkačenka (11,7) ir Aleksandras Salnikovas (11,4). Dvyliktas šiame sąraše yra Valdemaras Chomičius, vidutiniškai rinkęs po 10 taškų, o keturioliktas dar vienas buvęs žalgirietis Sergejus Jovaiša (po 9,8 taško). Šarūno Marčiulionio rezultatyvumo vidurkis turėjo būti aukštesnis, bet jis sužaidė mažiau nei 50 rungtynių. Kaip žinia, Lietuvai išsilaisvinus ir atkūrus Nepriklausomybę, visi minėti mūsų šalies krepšininkai, išskyrus karjerą jau baigusį M.Paulauską, užsivilko žalią aprangą ir atstovavo savo tėvynei. A.Sabonis pagal rezultatyvumo vidurkį yra pirmas – jis Lietuvos rinktinei pelnė po 20 taškų. Antras pagal taškų vidurkį yra Artūras Karnišovas, rinkęs po 18,2 taško ir pelnęs iš viso daugiausiai taškų atstovaujant Lietuvos rinktinei (1453). Tarp žaidėjų, kurie sužaidė bent po 50 oficialių rungtynių Lietuvos rinktinėje, trečią vietą užima Ramūnas Šiškauskas (po 12,9), ketvirtąją Linas Kleiza (po 12,7 taško), o penktas – Saulius Štombergas (po 11,1 taško). Daugiausiai rungtynių Lietuvos rinktinėje sužaidęs ir daugiausiai olimpinių medalių (3) su ja laimėjęs Gintaras Einikis rinko po 9,6 taško, o pirmajame trejete pagal rungtynių skaičių ir pelnytus taškus esantis Šarūnas Jasikevičius pelnė po 9,9 taško."
license: apache-2.0
---
This is *t5-base* transformer model trained on Lithuanian news summaries for 175 000 steps.
It was created during the work [**Generating abstractive summaries of Lithuanian
news articles using a transformer model**](https://link.springer.com/chapter/10.1007/978-3-030-88304-1_27).
## Usage
```python
from transformers import pipeline
name= "LukasStankevicius/t5-base-lithuanian-news-summaries-175"
my_pipeline = pipeline(task="text2text-generation", model=name, framework="pt")
```
Given the following article body from [15min](https://www.15min.lt/24sek/naujiena/lietuva/tarp-penkiu-rezultatyviausiu-tsrs-rinktines-visu-laiku-zaideju-trys-lietuviai-875-1380030):
```
text = """
Latvijos krepšinio legenda Valdis Valteris pirmadienį socialiniame tinkle pasidalino statistika, kurios viršūnėje yra Arvydas Sabonis.
1982 metais TSRS rinktinėje debiutavęs 222 cm ūgio vidurio puolėjas su raudona apranga sužaidė 52 rungtynes, per kurias rinko po 15,6 taško. Tai pats aukščiausias rezultatyvumo vidurkis tarp visų sovietų komandai atstovavusių žaidėjų, skaičiuojant tuos, kurie sužaidė ne mažiau nei 50 rungtynių. Antras šioje rikiuotėje kitas buvęs Kauno „Žalgirio“ krepšininkas Rimas Kurtinaitis. Jis debiutavo TSRS rinktinėje vėliau nei Sabas, – 1984 metais, bet irgi sužaidė 52 mačus. R.Kurtinaitis pelnė po 15 taškų. 25-ių rezultatyviausių žaidėjų sąrašu pasidalinęs latvis V.Valteris, pelnęs po 13,8 taško, yra trečias.
Ketvirtas yra iš Kazachstano kilęs Valerijus Tichonenka, pelnęs po 13,7 taško per 79 rungtynes. Rezultatyviausią visų laikų TSRS rinktinės penketą uždaro Modestas Paulauskas. Lietuvos krepšinio legenda pelnė po 13,6 taško per 84 mačus.
Dešimtuke taip pat yra Oleksandras Volkovas (po 13,5 taško), Sergejus Belovas (12,7), Anatolijus Myškinas (po 12,3), Vladimiras Tkačenka (11,7) ir Aleksandras Salnikovas (11,4). Dvyliktas šiame sąraše yra Valdemaras Chomičius, vidutiniškai rinkęs po 10 taškų, o keturioliktas dar vienas buvęs žalgirietis Sergejus Jovaiša (po 9,8 taško). Šarūno Marčiulionio rezultatyvumo vidurkis turėjo būti aukštesnis, bet jis sužaidė mažiau nei 50 rungtynių. Kaip žinia, Lietuvai išsilaisvinus ir atkūrus Nepriklausomybę, visi minėti mūsų šalies krepšininkai, išskyrus karjerą jau baigusį M.Paulauską, užsivilko žalią aprangą ir atstovavo savo tėvynei.
A.Sabonis pagal rezultatyvumo vidurkį yra pirmas – jis Lietuvos rinktinei pelnė po 20 taškų. Antras pagal taškų vidurkį yra Artūras Karnišovas, rinkęs po 18,2 taško ir pelnęs iš viso daugiausiai taškų atstovaujant Lietuvos rinktinei (1453).
Tarp žaidėjų, kurie sužaidė bent po 50 oficialių rungtynių Lietuvos rinktinėje, trečią vietą užima Ramūnas Šiškauskas (po 12,9), ketvirtąją Linas Kleiza (po 12,7 taško), o penktas – Saulius Štombergas (po 11,1 taško). Daugiausiai rungtynių Lietuvos rinktinėje sužaidęs ir daugiausiai olimpinių medalių (3) su ja laimėjęs Gintaras Einikis rinko po 9,6 taško, o pirmajame trejete pagal rungtynių skaičių ir pelnytus taškus esantis Šarūnas Jasikevičius pelnė po 9,9 taško.
"""
text = ' '.join(text.strip().split())
```
The summary can be obtained by:
```python
my_pipeline(text)[0]["generated_text"]
```
Output from above would be:
Lietuvos krepšinio federacijos (LKF) prezidento Arvydo Sabonio rezultatyvumo vidurkis yra aukščiausias tarp visų Sovietų Sąjungos rinktinėje atstovavusių žaidėjų, skaičiuojant tuos, kurie sužaidė bent po 50 oficialių rungtynių.
If you find our work useful, please cite the following paper:
``` latex
@InProceedings{10.1007/978-3-030-88304-1_27,
author="Stankevi{\v{c}}ius, Lukas
and Luko{\v{s}}evi{\v{c}}ius, Mantas",
editor="Lopata, Audrius
and Gudonien{\.{e}}, Daina
and Butkien{\.{e}}, Rita",
title="Generating Abstractive Summaries of Lithuanian News Articles Using a Transformer Model",
booktitle="Information and Software Technologies",
year="2021",
publisher="Springer International Publishing",
address="Cham",
pages="341--352",
abstract="In this work, we train the first monolingual Lithuanian transformer model on a relatively large corpus of Lithuanian news articles and compare various output decoding algorithms for abstractive news summarization. We achieve an average ROUGE-2 score 0.163, generated summaries are coherent and look impressive at first glance. However, some of them contain misleading information that is not so easy to spot. We describe all the technical details and share our trained model and accompanying code in an online open-source repository, as well as some characteristic samples of the generated summaries.",
isbn="978-3-030-88304-1"
}
```
|
Jmolano/bert-finetuned-ner
|
Jmolano
| 2022-07-28T02:51:07Z | 4 | 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
| 2022-07-26T21:56:29Z |
---
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
config: conll2003
split: train
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9327383903487027
- name: Recall
type: recall
value: 0.9498485358465163
- name: F1
type: f1
value: 0.9412157091636788
- name: Accuracy
type: accuracy
value: 0.9860923058809677
---
<!-- 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: 0.0617
- Precision: 0.9327
- Recall: 0.9498
- F1: 0.9412
- Accuracy: 0.9861
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0868 | 1.0 | 1756 | 0.0697 | 0.9204 | 0.9297 | 0.9250 | 0.9807 |
| 0.0342 | 2.0 | 3512 | 0.0647 | 0.9273 | 0.9465 | 0.9368 | 0.9853 |
| 0.0175 | 3.0 | 5268 | 0.0617 | 0.9327 | 0.9498 | 0.9412 | 0.9861 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
AykeeSalazar/vc-bantai-vit-withoutAMBI-adunest-trial
|
AykeeSalazar
| 2022-07-28T01:02:09Z | 53 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-07-28T00:29:59Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vc-bantai-vit-withoutAMBI-adunest-trial
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
args: Violation-Classification---Raw-9
metrics:
- name: Accuracy
type: accuracy
value: 0.7797741273100616
---
<!-- 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. -->
# vc-bantai-vit-withoutAMBI-adunest-trial
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4289
- Accuracy: 0.7798
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.4 | 100 | 1.0782 | 0.4451 |
| No log | 0.8 | 200 | 0.5634 | 0.7156 |
| No log | 1.2 | 300 | 0.7181 | 0.6684 |
| No log | 1.61 | 400 | 0.4289 | 0.7798 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
kabelomalapane/Af-En_update
|
kabelomalapane
| 2022-07-27T23:37:19Z | 117 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-07-27T20:53:09Z |
---
license: apache-2.0
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: Af-En_update
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. -->
# Af-En_update
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-af-en](https://huggingface.co/Helsinki-NLP/opus-mt-af-en) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7197
- Bleu: 55.3346
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 32
- 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 | Bleu |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 1.3745 | 1.0 | 2553 | 1.7537 | 51.9270 |
| 1.0462 | 2.0 | 5106 | 1.6305 | 53.9359 |
| 0.896 | 3.0 | 7659 | 1.6216 | 54.3049 |
| 0.7824 | 4.0 | 10212 | 1.6108 | 54.9902 |
| 0.6974 | 5.0 | 12765 | 1.6183 | 55.0265 |
| 0.643 | 6.0 | 15318 | 1.6207 | 55.4137 |
| 0.5635 | 7.0 | 17871 | 1.6276 | 55.1335 |
| 0.5141 | 8.0 | 20424 | 1.6498 | 55.2215 |
| 0.4681 | 9.0 | 22977 | 1.6678 | 55.2000 |
| 0.4304 | 10.0 | 25530 | 1.6797 | 55.2748 |
| 0.425 | 11.0 | 28083 | 1.7004 | 55.0478 |
| 0.398 | 12.0 | 30636 | 1.7013 | 55.3591 |
| 0.3759 | 13.0 | 33189 | 1.7082 | 55.3225 |
| 0.3681 | 14.0 | 35742 | 1.7151 | 55.1793 |
| 0.3571 | 15.0 | 38295 | 1.7197 | 55.2729 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
dbarbedillo/a2c-AntBulletEnv-v0
|
dbarbedillo
| 2022-07-27T22:25:58Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-27T22:24:45Z |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- metrics:
- type: mean_reward
value: 1748.24 +/- 84.28
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
mariastull/Reinforce-3
|
mariastull
| 2022-07-27T21:39:59Z | 0 | 0 | null |
[
"Pong-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-27T21:39:47Z |
---
tags:
- Pong-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-3
results:
- metrics:
- type: mean_reward
value: -16.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pong-PLE-v0
type: Pong-PLE-v0
---
# **Reinforce** Agent playing **Pong-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pong-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
FinanceInc/finbert-pretrain
|
FinanceInc
| 2022-07-27T20:43:33Z | 23 | 9 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain",
"pre-trained",
"finbert",
"unk",
"arxiv:2006.08097",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-21T18:11:17Z |
---
tags:
- autotrain
- pre-trained
- finbert
- fill-mask
language: unk
widget:
- text: Tesla remains one of the highest [MASK] stocks on the market. Meanwhile, Aurora Innovation is a pre-revenue upstart that shows promise.
- text: Asian stocks [MASK] from a one-year low on Wednesday as U.S. share futures and oil recovered from the previous day's selloff, but uncertainty over the impact of the Omicron
- text: U.S. stocks were set to rise on Monday, led by [MASK] in Apple which neared $3 trillion in market capitalization, while investors braced for a Federal Reserve meeting later this week.
---
`FinBERT` is a BERT model pre-trained on financial communication text. The purpose is to enhance financial NLP research and practice.
### Pre-training
It is trained on the following three financial communication corpus. The total corpora size is 4.9B tokens.
- Corporate Reports 10-K & 10-Q: 2.5B tokens
- Earnings Call Transcripts: 1.3B tokens
- Analyst Reports: 1.1B tokens
The entire training is done using an **NVIDIA DGX-1** machine. The server has 4 Tesla P100 GPUs, providing a total of 128 GB of GPU memory. This machine enables us to train the BERT models using a batch size of 128. We utilize Horovord framework for multi-GPU training. Overall, the total time taken to perform pretraining for one model is approximately **2 days**.
More details on `FinBERT`'s pre-training process can be found at: https://arxiv.org/abs/2006.08097
`FinBERT` can be further fine-tuned on downstream tasks. Specifically, we have fine-tuned `FinBERT` on an analyst sentiment classification task, and the fine-tuned model is shared at [https://huggingface.co/demo-org/auditor_review_model](https://huggingface.co/demo-org/auditor_review_model)
### Usage
Load the model directly from Transformers:
```
from transformers import AutoModelForMaskedLM
model = AutoModelForMaskedLM.from_pretrained("demo-org/finbert-pretrain", use_auth_token=True)
```
### Questions
Please contact the Data Science COE if you have more questions about this pre-trained model
### Demo Model
This model card is for demo purposes. The original model card for this model is [https://huggingface.co/yiyanghkust/finbert-pretrain](https://huggingface.co/yiyanghkust/finbert-pretrain).
|
cjdentra/distilbert-base-uncased-finetuned-emotion
|
cjdentra
| 2022-07-27T20:38:01Z | 5 | 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
| 2022-07-27T20:18:09Z |
---
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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Framework versions
- Transformers 4.13.0
- Pytorch 1.12.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
ai4bharat/indicwav2vec-hindi
|
ai4bharat
| 2022-07-27T20:31:31Z | 4,110 | 16 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"asr",
"hi",
"arxiv:2006.11477",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-27T19:43:11Z |
---
language: hi
metrics:
- wer
- cer
tags:
- audio
- automatic-speech-recognition
- speech
- wav2vec2
- asr
license: apache-2.0
---
# IndicWav2Vec-Hindi
This is a [Wav2Vec2](https://arxiv.org/abs/2006.11477) style ASR model trained in [fairseq](https://github.com/facebookresearch/fairseq) and ported to Hugging Face.
More details on datasets, training-setup and conversion to HuggingFace format can be found in the [IndicWav2Vec](https://github.com/AI4Bharat/IndicWav2Vec) repo.
*Note: This model doesn't support inference with Language Model.*
## Script to Run Inference
```python
import torch
from datasets import load_dataset
from transformers import AutoModelForCTC, AutoProcessor
import torchaudio.functional as F
DEVICE_ID = "cuda" if torch.cuda.is_available() else "cpu"
MODEL_ID = "ai4bharat/indicwav2vec-hindi"
sample = next(iter(load_dataset("common_voice", "hi", split="test", streaming=True)))
resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48000, 16000).numpy()
model = AutoModelForCTC.from_pretrained(MODEL_ID).to(DEVICE_ID)
processor = AutoProcessor.from_pretrained(MODEL_ID)
input_values = processor(resampled_audio, return_tensors="pt").input_values
with torch.no_grad():
logits = model(input_values.to(DEVICE_ID)).logits.cpu()
prediction_ids = torch.argmax(logits, dim=-1)
output_str = processor.batch_decode(prediction_ids)[0]
print(f"Greedy Decoding: {output_str}")
```
# **About AI4Bharat**
- Website: https://ai4bharat.org/
- Code: https://github.com/AI4Bharat
- HuggingFace: https://huggingface.co/ai4bharat
|
SGme/pyramids
|
SGme
| 2022-07-27T19:41:15Z | 4 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2022-07-27T19:32:19Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: SGme/pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AriakimTaiyo/gpt2-chat
|
AriakimTaiyo
| 2022-07-27T19:36:22Z | 61 | 3 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"rust",
"gpt2",
"text-generation",
"conversational",
"en",
"arxiv:1910.09700",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-27T19:15:28Z |
---
language: en
license: mit
tags:
- conversational
---
# GPT-2 Large
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Environmental Impact](#environmental-impact)
- [Technical Specifications](#technical-specifications)
- [Citation Information](#citation-information)
- [Model Card Authors](#model-card-author)
## Model Details
**Model Description:** GPT-2 Large is the **774M parameter** version of GPT-2, a transformer-based language model created and released by OpenAI. The model is a pretrained model on English language using a causal language modeling (CLM) objective.
- **Developed by:** OpenAI, see [associated research paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) and [GitHub repo](https://github.com/openai/gpt-2) for model developers.
- **Model Type:** Transformer-based language model
- **Language(s):** English
- **License:** [Modified MIT License](https://github.com/openai/gpt-2/blob/master/LICENSE)
- **Related Models:** [GPT-2](https://huggingface.co/gpt2), [GPT-Medium](https://huggingface.co/gpt2-medium) and [GPT-XL](https://huggingface.co/gpt2-xl)
- **Resources for more information:**
- [Research Paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
- [OpenAI Blog Post](https://openai.com/blog/better-language-models/)
- [GitHub Repo](https://github.com/openai/gpt-2)
- [OpenAI Model Card for GPT-2](https://github.com/openai/gpt-2/blob/master/model_card.md)
- Test the full generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
## How to Get Started with the Model
Use the code below to get started with the model. You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we
set a seed for reproducibility:
```python
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='gpt2-large')
>>> set_seed(42)
>>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5)
[{'generated_text': "Hello, I'm a language model, I can do language modeling. In fact, this is one of the reasons I use languages. To get a"},
{'generated_text': "Hello, I'm a language model, which in its turn implements a model of how a human can reason about a language, and is in turn an"},
{'generated_text': "Hello, I'm a language model, why does this matter for you?\n\nWhen I hear new languages, I tend to start thinking in terms"},
{'generated_text': "Hello, I'm a language model, a functional language...\n\nI don't need to know anything else. If I want to understand about how"},
{'generated_text': "Hello, I'm a language model, not a toolbox.\n\nIn a nutshell, a language model is a set of attributes that define how"}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import GPT2Tokenizer, GPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('gpt2-large')
model = GPT2Model.from_pretrained('gpt2-large')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import GPT2Tokenizer, TFGPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('gpt2-large')
model = TFGPT2Model.from_pretrained('gpt2-large')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Uses
#### Direct Use
In their [model card about GPT-2](https://github.com/openai/gpt-2/blob/master/model_card.md), OpenAI wrote:
> The primary intended users of these models are AI researchers and practitioners.
>
> We primarily imagine these language models will be used by researchers to better understand the behaviors, capabilities, biases, and constraints of large-scale generative language models.
#### Downstream Use
In their [model card about GPT-2](https://github.com/openai/gpt-2/blob/master/model_card.md), OpenAI wrote:
> Here are some secondary use cases we believe are likely:
>
> - Writing assistance: Grammar assistance, autocompletion (for normal prose or code)
> - Creative writing and art: exploring the generation of creative, fictional texts; aiding creation of poetry and other literary art.
> - Entertainment: Creation of games, chat bots, and amusing generations.
#### Misuse and Out-of-scope Use
In their [model card about GPT-2](https://github.com/openai/gpt-2/blob/master/model_card.md), OpenAI wrote:
> Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases that require the generated text to be true.
>
> Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do not recommend that they be deployed into systems that interact with humans unless the deployers first carry out a study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race, and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar levels of caution around use cases that are sensitive to biases around human attributes.
## Risks, Limitations and Biases
**CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propogate historical and current stereotypes.**
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
The training data used for this model has not been released as a dataset one can browse. We know it contains a lot of unfiltered content from the internet, which is far from neutral. Predictions generated by the model can include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. For example:
```python
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='gpt2-large')
>>> set_seed(42)
>>> generator("The man worked as a", max_length=10, num_return_sequences=5)
[{'generated_text': 'The man worked as a security guard in a hotel'},
{'generated_text': 'The man worked as a salesman in Mexico and in'},
{'generated_text': 'The man worked as a supervisor at the warehouse for'},
{'generated_text': "The man worked as a cleaner for the store's"},
{'generated_text': 'The man worked as a barbershop apprentice.'}]
>>> set_seed(42)
>>> generator("The woman worked as a", max_length=10, num_return_sequences=5)
[{'generated_text': 'The woman worked as a clerk at the bank.'},
{'generated_text': 'The woman worked as a caregiver, and her'},
{'generated_text': 'The woman worked as a customer service agent for a'},
{'generated_text': 'The woman worked as a cleaner at the store,'},
{'generated_text': 'The woman worked as a barista and was "'}]
```
This bias will also affect all fine-tuned versions of this model. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
## Training
#### Training Data
The OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web
pages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from
this dataset, so the model was not trained on any part of Wikipedia. The resulting dataset (called WebText) weights
40GB of texts but has not been publicly released. You can find a list of the top 1,000 domains present in WebText
[here](https://github.com/openai/gpt-2/blob/master/domains.txt).
#### Training Procedure
The model is pretrained on a very large corpus of English data in a self-supervised fashion. This
means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots
of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely,
it was trained to guess the next word in sentences.
More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence,
shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the
predictions for the token `i` only uses the inputs from `1` to `i` but not the future tokens.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks.
The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a
vocabulary size of 50,257. The inputs are sequences of 1024 consecutive tokens.
## Evaluation
The following evaluation information is extracted from the [associated paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf).
#### Testing Data, Factors and Metrics
The model authors write in the [associated paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) that:
> Since our model operates on a byte level and does not require lossy pre-processing or tokenization, we can evaluate it on any language model benchmark. Results on language modeling datasets are commonly reported in a quantity which is a scaled or ex- ponentiated version of the average negative log probability per canonical prediction unit - usually a character, a byte, or a word. We evaluate the same quantity by computing the log-probability of a dataset according to a WebText LM and dividing by the number of canonical units. For many of these datasets, WebText LMs would be tested significantly out- of-distribution, having to predict aggressively standardized text, tokenization artifacts such as disconnected punctuation and contractions, shuffled sentences, and even the string <UNK> which is extremely rare in WebText - occurring only 26 times in 40 billion bytes. We report our main results...using invertible de-tokenizers which remove as many of these tokenization / pre-processing artifacts as possible. Since these de-tokenizers are invertible, we can still calculate the log probability of a dataset and they can be thought of as a simple form of domain adaptation.
#### Results
The model achieves the following results without any fine-tuning (zero-shot):
| Dataset | LAMBADA | LAMBADA | CBT-CN | CBT-NE | WikiText2 | PTB | enwiki8 | text8 | WikiText103 | 1BW |
|:--------:|:-------:|:-------:|:------:|:------:|:---------:|:------:|:-------:|:------:|:-----------:|:-----:|
| (metric) | (PPL) | (ACC) | (ACC) | (ACC) | (PPL) | (PPL) | (BPB) | (BPC) | (PPL) | (PPL) |
| | 10.87 | 60.12 | 93.45 | 88.0 | 19.93 | 40.31 | 0.97 | 1.02 | 22.05 | 44.575|
## Environmental Impact
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** Unknown
- **Hours used:** Unknown
- **Cloud Provider:** Unknown
- **Compute Region:** Unknown
- **Carbon Emitted:** Unknown
## Technical Specifications
See the [associated paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) for details on the modeling architecture, objective, compute infrastructure, and training details.
## Citation Information
```bibtex
@article{radford2019language,
title={Language models are unsupervised multitask learners},
author={Radford, Alec and Wu, Jeffrey and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya and others},
journal={OpenAI blog},
volume={1},
number={8},
pages={9},
year={2019}
}
```
## Model Card Authors
This model card was written by the Hugging Face team.
|
mariastull/Reinforce-2
|
mariastull
| 2022-07-27T19:17:16Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-27T19:16:19Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-2
results:
- metrics:
- type: mean_reward
value: -5.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
kabelomalapane/En-Af_update
|
kabelomalapane
| 2022-07-27T18:17:15Z | 118 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-07-27T16:11:00Z |
---
license: apache-2.0
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: En-Af_update
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. -->
# En-Af_update
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-af](https://huggingface.co/Helsinki-NLP/opus-mt-en-af) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8089
- Bleu: 45.1780
## 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: 32
- 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 | Bleu |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 1.4243 | 1.0 | 2553 | 1.8451 | 42.1314 |
| 1.0987 | 2.0 | 5106 | 1.7509 | 44.0714 |
| 0.9329 | 3.0 | 7659 | 1.7340 | 44.6003 |
| 0.8365 | 4.0 | 10212 | 1.7260 | 44.7820 |
| 0.7556 | 5.0 | 12765 | 1.7590 | 45.1180 |
| 0.6944 | 6.0 | 15318 | 1.7715 | 45.1451 |
| 0.652 | 7.0 | 17871 | 1.7696 | 45.1025 |
| 0.6132 | 8.0 | 20424 | 1.8060 | 45.1781 |
| 0.5832 | 9.0 | 22977 | 1.8135 | 45.2485 |
| 0.5602 | 10.0 | 25530 | 1.8089 | 45.1730 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
asi/igpt-fr-cased-base
|
asi
| 2022-07-27T17:12:36Z | 5 | 4 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"tf",
"text-to-image",
"fr",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-to-image
| 2022-07-26T20:57:33Z |
---
language:
- fr
thumbnail: https://raw.githubusercontent.com/AntoineSimoulin/gpt-fr/main/imgs/logo.png
tags:
- tf
- pytorch
- gpt2
- text-to-image
license: apache-2.0
---
<img src="https://raw.githubusercontent.com/AntoineSimoulin/gpt-fr/main/imgs/igpt-logo.png" width="400">
## Model description
**iGPT-fr** 🇫🇷 is a GPT model for French pre-trained incremental language model developped by the [Laboratoire de Linguistique Formelle (LLF)](http://www.llf.cnrs.fr/en). We adapted [GPT-fr 🇫🇷](https://huggingface.co/asi/gpt-fr-cased-base) model to generate images conditionned by text inputs.
## Intended uses & limitations
The model can be leveraged for image generation tasks. The model is currently under a developpment phase.
#### How to use
The model might be used through the 🤗 `Transformers` librairie. You will also need to install the `Taming Transformers` library for high-resolution image synthesis:
```bash
pip install git+https://github.com/CompVis/taming-transformers.git
```
```python
from transformers import GPT2Tokenizer, GPT2LMHeadModel
from huggingface_hub import hf_hub_download
from omegaconf import OmegaConf
from taming.models import vqgan
import torch
from PIL import Image
import numpy as np
# Load VQGAN model
vqgan_ckpt = hf_hub_download(repo_id="boris/vqgan_f16_16384", filename="model.ckpt", force_download=False)
vqgan_config = hf_hub_download(repo_id="boris/vqgan_f16_16384", filename="config.yaml", force_download=False)
config = OmegaConf.load(vqgan_config)
vqgan_model = vqgan.VQModel(**config.model.params)
vqgan_model.eval().requires_grad_(False)
vqgan_model.init_from_ckpt(vqgan_ckpt)
# Load pretrained model
model = GPT2LMHeadModel.from_pretrained("asi/igpt-fr-cased-base")
model.eval()
tokenizer = GPT2Tokenizer.from_pretrained("asi/igpt-fr-cased-base")
# Generate a sample of text
input_sentence = "Une carte de l'europe"
input_ids = tokenizer.encode(input_sentence, return_tensors='pt')
input_ids = torch.cat((input_ids, torch.tensor([[50000]])), 1) # Add image generation token
greedy_output = model.generate(
input_ids.to(device),
max_length=256+input_ids.shape[1],
do_sample=True,
top_p=0.92,
top_k=0)
def custom_to_pil(x):
x = x.detach().cpu()
x = torch.clamp(x, -1., 1.)
x = (x + 1.)/2.
x = x.permute(1,2,0).numpy()
x = (255*x).astype(np.uint8)
x = Image.fromarray(x)
if not x.mode == "RGB":
x = x.convert("RGB")
return x
z_idx = greedy_output[0, input_ids.shape[1]:] - 50001
z_quant = vqgan_model.quantize.get_codebook_entry(z_idx, shape=(1, 16, 16, 256))
x_rec = vqgan_model.decode(z_quant).to('cpu')[0]
display(custom_to_pil(x_rec))
```
You may also filter results based on CLIP:
```python
from tqdm import tqdm
def hallucinate(prompt, num_images=64):
input_ids = tokenizer.encode(prompt, return_tensors='pt')
input_ids = torch.cat((input_ids, torch.tensor([[50000]])), 1).to(device) # Add image generation token
all_images = []
for i in tqdm(range(num_images)):
greedy_output = model.generate(
input_ids.to(device),
max_length=256+input_ids.shape[1],
do_sample=True,
top_p=0.92,
top_k=0)
z_idx = greedy_output[0, input_ids.shape[1]:] - 50001
z_quant = vqgan_model.quantize.get_codebook_entry(z_idx, shape=(1, 16, 16, 256))
x_rec = vqgan_model.decode(z_quant).to('cpu')[0]
all_images.append(custom_to_pil(x_rec))
return all_images
input_sentence = "Une carte de l'europe"
all_images = hallucinate(input_sentence)
from transformers import pipeline
opus_model = "Helsinki-NLP/opus-mt-fr-en"
opus_translator = pipeline("translation", model=opus_model)
opus_translator(input_sentence)
from transformers import CLIPProcessor, CLIPModel
clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
def clip_top_k(prompt, images, k=8):
prompt_fr = opus_translator(input_sentence)[0]['translation_text']
inputs = clip_processor(text=prompt_fr, images=images, return_tensors="pt", padding=True)
outputs = clip_model(**inputs)
logits = outputs.logits_per_text # this is the image-text similarity score
scores = np.array(logits[0].detach()).argsort()[-k:][::-1]
return [images[score] for score in scores]
filtered_images = clip_top_k(input_sentence, all_images)
for fi in filtered_images:
display(fi)
```
## Training data
We created a dedicated corpus to train our generative model. The training corpus consists in text-image pairs. We aggregated portions from existing corpora: [Laion-5B](https://laion.ai/blog/laion-5b/) and [WIT](https://github.com/google-research-datasets/wit). The final dataset includes 10,807,534 samples.
## Training procedure
We pre-trained the model on the new CNRS (French National Centre for Scientific Research) [Jean Zay](http://www.idris.fr/eng/jean-zay/) supercomputer. We perform the training within a total of 140 hours of computation on Tesla V-100 hardware (TDP of 300W). The training was distributed on 8 compute nodes of 8 GPUs. We used data parallelization in order to divide each micro-batch on the computing units. We estimated the total emissions at 1161.22 kgCO2eq, using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al., (2019)](lacoste-2019).
|
heriosousa/a2c-AntBulletEnv-v0
|
heriosousa
| 2022-07-27T17:03:12Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-27T17:02:08Z |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- metrics:
- type: mean_reward
value: 1020.71 +/- 201.31
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Evelyn18/roberta-base-spanish-squades-becasIncentivos4
|
Evelyn18
| 2022-07-27T16:52:12Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:becasv2",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-07-27T15:56:33Z |
---
tags:
- generated_from_trainer
datasets:
- becasv2
model-index:
- name: roberta-base-spanish-squades-becasIncentivos4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-spanish-squades-becasIncentivos4
This model is a fine-tuned version of [IIC/roberta-base-spanish-squades](https://huggingface.co/IIC/roberta-base-spanish-squades) on the becasv2 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7734
## 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: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 11 | 1.8136 |
| No log | 2.0 | 22 | 1.7734 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Go2Heart/BERT_Mod_1
|
Go2Heart
| 2022-07-27T16:17:44Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-27T16:07:33Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: BERT_Mod_1
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.541934635424655
---
<!-- 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_Mod_1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1787
- Matthews Correlation: 0.5419
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.1616 | 1.0 | 535 | 0.9278 | 0.4979 |
| 0.1128 | 2.0 | 1070 | 1.0487 | 0.5046 |
| 0.0712 | 3.0 | 1605 | 1.0155 | 0.5306 |
| 0.0952 | 4.0 | 2140 | 1.1860 | 0.5147 |
| 0.0698 | 5.0 | 2675 | 1.1787 | 0.5419 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0
- Datasets 2.4.0
- Tokenizers 0.12.1
|
huggingtweets/interiordesign
|
huggingtweets
| 2022-07-27T15:30:24Z | 71 | 4 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-27T15:21:57Z |
---
language: en
thumbnail: http://www.huggingtweets.com/interiordesign/1658935819881/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/1544346507578589184/x9URB7Yy_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">Interior Design</div>
<div style="text-align: center; font-size: 14px;">@interiordesign</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 Interior Design.
| Data | Interior Design |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 97 |
| Short tweets | 2 |
| Tweets kept | 3151 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/vl5m9w7s/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 @interiordesign's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/36lgkxh5) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/36lgkxh5/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/interiordesign')
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)
|
enoriega/rule_learning_1mm_many_negatives_spanpred_margin_avg
|
enoriega
| 2022-07-27T14:45:37Z | 19 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"generated_from_trainer",
"dataset:enoriega/odinsynth_dataset",
"endpoints_compatible",
"region:us"
] | null | 2022-07-26T04:40:02Z |
---
tags:
- generated_from_trainer
datasets:
- enoriega/odinsynth_dataset
model-index:
- name: rule_learning_1mm_many_negatives_spanpred_margin_avg
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. -->
# rule_learning_1mm_many_negatives_spanpred_margin_avg
This model is a fine-tuned version of [enoriega/rule_softmatching](https://huggingface.co/enoriega/rule_softmatching) on the enoriega/odinsynth_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2421
- Margin Accuracy: 0.8897
## 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
- gradient_accumulation_steps: 2000
- total_train_batch_size: 8000
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Margin Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------------:|
| 0.3867 | 0.16 | 20 | 0.4023 | 0.8187 |
| 0.3506 | 0.32 | 40 | 0.3381 | 0.8523 |
| 0.3195 | 0.48 | 60 | 0.3096 | 0.8613 |
| 0.3052 | 0.64 | 80 | 0.2957 | 0.8640 |
| 0.2859 | 0.8 | 100 | 0.2922 | 0.8679 |
| 0.297 | 0.96 | 120 | 0.2871 | 0.8688 |
| 0.2717 | 1.12 | 140 | 0.2761 | 0.8732 |
| 0.2671 | 1.28 | 160 | 0.2751 | 0.8743 |
| 0.2677 | 1.44 | 180 | 0.2678 | 0.8757 |
| 0.2693 | 1.6 | 200 | 0.2627 | 0.8771 |
| 0.2675 | 1.76 | 220 | 0.2573 | 0.8813 |
| 0.2732 | 1.92 | 240 | 0.2546 | 0.8858 |
| 0.246 | 2.08 | 260 | 0.2478 | 0.8869 |
| 0.2355 | 2.24 | 280 | 0.2463 | 0.8871 |
| 0.2528 | 2.4 | 300 | 0.2449 | 0.8886 |
| 0.2512 | 2.56 | 320 | 0.2443 | 0.8892 |
| 0.2527 | 2.72 | 340 | 0.2441 | 0.8893 |
| 0.2346 | 2.88 | 360 | 0.2424 | 0.8895 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.2.1
- Tokenizers 0.12.1
|
annahaz/xlm-roberta-base-finetuned-misogyny-sexism
|
annahaz
| 2022-07-27T14:45:20Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-05T19:00:29Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: xlm-roberta-base-finetuned-misogyny-sexism
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-misogyny-sexism
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: 0.9064
- Accuracy: 0.8334
- F1: 0.3322
- Precision: 0.2498
- Recall: 0.4961
- Mae: 0.1666
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Mae |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|
| 0.3869 | 1.0 | 2395 | 0.2905 | 0.8778 | 0.3528 | 0.3164 | 0.3988 | 0.1222 |
| 0.3539 | 2.0 | 4790 | 0.4143 | 0.8278 | 0.3465 | 0.2536 | 0.5467 | 0.1722 |
| 0.3124 | 3.0 | 7185 | 0.3327 | 0.8568 | 0.3583 | 0.2864 | 0.4786 | 0.1432 |
| 0.2817 | 4.0 | 9580 | 0.5621 | 0.7329 | 0.3092 | 0.1972 | 0.7160 | 0.2671 |
| 0.2651 | 5.0 | 11975 | 0.4376 | 0.8520 | 0.3607 | 0.2821 | 0.5 | 0.1480 |
| 0.2249 | 6.0 | 14370 | 0.5581 | 0.8326 | 0.3312 | 0.2485 | 0.4961 | 0.1674 |
| 0.1958 | 7.0 | 16765 | 0.6728 | 0.8382 | 0.3234 | 0.2484 | 0.4630 | 0.1618 |
| 0.1899 | 8.0 | 19160 | 0.7404 | 0.8304 | 0.3316 | 0.2471 | 0.5039 | 0.1696 |
| 0.1619 | 9.0 | 21555 | 0.8309 | 0.8461 | 0.3382 | 0.2639 | 0.4708 | 0.1539 |
| 0.1453 | 10.0 | 23950 | 0.9064 | 0.8334 | 0.3322 | 0.2498 | 0.4961 | 0.1666 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
suvadityamuk/q-Taxi-v3
|
suvadityamuk
| 2022-07-27T14:25:23Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-27T14:25:17Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.52 +/- 2.77
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="suvadityamuk/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"])
```
|
vinitharaj/distilbert-base-uncased-finetuned-squad
|
vinitharaj
| 2022-07-27T14:02:25Z | 5 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-07-02T05:42:36Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: vinitharaj/distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# vinitharaj/distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 4.5718
- Validation Loss: 4.2502
- 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': 46, '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 |
|:----------:|:---------------:|:-----:|
| 5.7158 | 5.0214 | 0 |
| 4.5718 | 4.2502 | 1 |
### Framework versions
- Transformers 4.21.0
- TensorFlow 2.8.2
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Ahmed007/t5-base-ibn-Shaddad-v6
|
Ahmed007
| 2022-07-27T12:57:13Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"Poet",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-07-27T12:09:35Z |
---
license: apache-2.0
tags:
- Poet
- generated_from_trainer
model-index:
- name: t5-base-ibn-Shaddad-v6
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-base-ibn-Shaddad-v6
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.2957
## 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: 5.6e-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 |
|:-------------:|:-----:|:----:|:---------------:|
| 4.9444 | 1.0 | 1067 | 4.4333 |
| 4.5154 | 2.0 | 2134 | 4.3345 |
| 4.4462 | 3.0 | 3201 | 4.2957 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
dminiotas05/distilbert-base-uncased-finetuned-ft780_class
|
dminiotas05
| 2022-07-27T12:16:52Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-27T11:55:12Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-ft780_class
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-ft780_class
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9843
- Accuracy: 0.2047
- F1: 0.1823
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 2.1065 | 1.0 | 188 | 2.0425 | 0.1747 | 0.1248 |
| 1.9642 | 2.0 | 376 | 1.9959 | 0.1987 | 0.1701 |
| 1.9019 | 3.0 | 564 | 1.9843 | 0.2047 | 0.1823 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
AlphaNinja27/wav2vec2-large-xls-r-300m-panjabi-colab
|
AlphaNinja27
| 2022-07-27T12:14:30Z | 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
| 2022-07-27T10:03:59Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-panjabi-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-panjabi-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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
sagteam/covid-twitter-xlm-roberta-large
|
sagteam
| 2022-07-27T11:41:43Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"arxiv:1911.02116",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
# COVID-twitter-XLM-Roberta-large
## Model description
This is a model based on the [XLM-RoBERTa large](https://huggingface.co/xlm-roberta-large) topology (provided by Facebook, see original [paper](https://arxiv.org/abs/1911.02116)) with additional training on a corpus of unmarked tweets.
For more details, please see, our [GitHub repository](https://github.com/sag111/COVID-19-tweets-Russia).
## Training data
We formed a corpus of unlabeled twitter messages.
The data on keyword "covid" was expanded with texts containing other words often occurred in hashtags on the Covid-19 pandemic: "covid", "stayhome", and "coronavirus" (hereinafter, these are translations of Russian words into English).
Separately, messages were collected from Twitter users from large regions of Russia. The search was provided using different word forms of 58 manually selected keywords on Russian related to the topic of coronavirus infection (including: "PCR", "pandemic", "self-isolation", etc.).
The unlabeled corpus includes all unique Russian-language tweets from the collected data (>1M tweets). Since modern language models are usually multilingual, about 1M more tweets in other languages were added to this corpus using filtering procedures described above. Thus, in the unlabeled part of the collected data, there were about 2 million messages.
### BibTeX entry and citation info
Our GitHub repository: https://github.com/sag111/COVID-19-tweets-Russia
If you have found our results helpful in your work, feel free to cite our publication and this repository as:
```
@article{sboev2021russian,
title={The Russian language corpus and a neural network to analyse Internet tweet reports about Covid-19},
author={Sboev, Alexander and Moloshnikov, Ivan and Naumov, Alexander and Levochkina𝑎, Anastasia and Rybka𝑎, Roman},
year={2021}
}
```
|
ai4bharat/IndicBERTv2-alpha-POS-tagging
|
ai4bharat
| 2022-07-27T11:23:14Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-07-22T13:46:31Z |
# IndicXLMv2-alpha-POS-tagging
|
huggingtweets/jordo4today-paddedpossum-wrenfing
|
huggingtweets
| 2022-07-27T10:16:23Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-27T10:15:48Z |
---
language: en
thumbnail: http://www.huggingtweets.com/jordo4today-paddedpossum-wrenfing/1658916978297/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/1538409928943083526/gilLk6Ju_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/1381760254799716353/bNTnf-3w_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/1546006810754260992/Dk6vMJU3_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">Mr. Wolf Simp & Zoinks & Jordo 🔜 MFF</div>
<div style="text-align: center; font-size: 14px;">@jordo4today-paddedpossum-wrenfing</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 Mr. Wolf Simp & Zoinks & Jordo 🔜 MFF.
| Data | Mr. Wolf Simp | Zoinks | Jordo 🔜 MFF |
| --- | --- | --- | --- |
| Tweets downloaded | 3203 | 742 | 3244 |
| Retweets | 2858 | 90 | 636 |
| Short tweets | 135 | 37 | 243 |
| Tweets kept | 210 | 615 | 2365 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2e01we01/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 @jordo4today-paddedpossum-wrenfing's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2wh0na3g) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2wh0na3g/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/jordo4today-paddedpossum-wrenfing')
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)
|
DigitalUmuganda/joeynmt-en-kin
|
DigitalUmuganda
| 2022-07-27T08:50:17Z | 0 | 0 | null |
[
"doi:10.57967/hf/0054",
"region:us"
] | null | 2022-07-25T10:34:05Z |
# English-to-Kinyarwanda Machine Translation
This model is an English-to-Kinyarwanda machine translation model, it was built and trained using JoeyNMT framework. The translation model uses transformer encoder-decoder based architecture. It was trained on a 47,211 long English-Kinyarwanda bitext dataset prepared by Digital Umuganda.
## Model architecture
**Encoder && Decoder**
> Type: Transformer
Num_layer: 6
Num_heads: 8
Embedding_dim: 256
ff_size: 1024
Dropout: 0.1
Layer_norm: post
Initializer: xavier
Total params: 12563968
## Pre-processing
Tokenizer_type: subword-nmt
num_merges: 4000
BPE encoding learned on the bitext, separate vocabularies for each language
Pretokenizer: None
No lowercase applied
## Training
Optimizer: Adam
Loss: crossentropy
Epochs: 30
Batch_size: 256
Number of GPUs: 1
## Evaluation
Evaluation_metrics: Blue_score, chrf
Tokenization: None
Beam_width: 15
Beam_alpha: 1.0
## Tools
* joeyNMT 2.0.0
* datasets
* pandas
* numpy
* transformers
* sentencepiece
* pytorch(with cuda)
* sacrebleu
* protobuf>=3.20.1
## How to train
[Use the following link for more information](https://github.com/joeynmt/joeynmt)
## Translation
To install joeyNMT run:
>$ git clone https://github.com/joeynmt/joeynmt.git
$ cd joeynmt
$ pip install . -e
Interactive translation(stdin):
>$ python -m joeynmt translate args.yaml
File translation:
>$ python -m joeynmt translate args.yaml < src_lang.txt > hypothesis_trg_lang.txt
## Accuracy measurement
Sacrebleu installation:
> $ pip install sacrebleu
Measurement(bleu_score, chrf):
> $ sacrebleu reference.tsv -i hypothesis.tsv -m bleu chrf
## To-do
>* Test the model using differenct datasets including the jw300
>* Use the Digital Umuganda dataset on some of the available State Of The Art(SOTA) available models.
>* Expand the dataset
## Result
The following result were obtained on using the sacrebleu.
English-to-Kinyarwanda:
>Blue: 56.5
Chrf: 75.2
|
suvadityamuk/ppo-LunarLander-v2-practicecourse-1
|
suvadityamuk
| 2022-07-27T08:29:19Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-27T08:28:57Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 259.96 +/- 14.96
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
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The query filters for specific terms related to "distilled" or "distill", "qwen", and "7b" in the 'card' column but excludes certain base models, providing a limited set of entries for further inspection.
Qwen 7B Distilled Models
The query provides a basic filtering of records to find specific card names that include keywords related to distilled Qwen 7b models, excluding a particular base model, which gives limited insight but helps in focusing on relevant entries.
Qwen 7B Distilled Model Cards
The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.
Qwen 7B Distilled Models
Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.
Distilled Qwen 7B Models
The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.
Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.