modelId
stringlengths 4
81
| tags
list | pipeline_tag
stringclasses 17
values | config
dict | downloads
int64 0
59.7M
| first_commit
timestamp[ns, tz=UTC] | card
stringlengths 51
438k
|
|---|---|---|---|---|---|---|
DeepPavlov/xlm-roberta-large-en-ru-mnli
|
[
"pytorch",
"xlm-roberta",
"text-classification",
"en",
"ru",
"dataset:glue",
"dataset:mnli",
"transformers",
"xlm-roberta-large",
"xlm-roberta-large-en-ru",
"xlm-roberta-large-en-ru-mnli",
"has_space"
] |
text-classification
|
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"XLMRobertaForSequenceClassification"
],
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},
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}
}
}
| 227 | null |
---
license: mit
tags:
- feature-extraction
library_name: fasttext
language: ur
widget:
- text: apple
example_title: apple
---
# fastText (Urdu)
fastText is an open-source, free, lightweight library that allows users to learn text representations and text classifiers. It works on standard, generic hardware. Models can later be reduced in size to even fit on mobile devices. It was introduced in [this paper](https://arxiv.org/abs/1607.04606). The official website can be found [here](https://fasttext.cc/).
## Model description
fastText is a library for efficient learning of word representations and sentence classification. fastText is designed to be simple to use for developers, domain experts, and students. It's dedicated to text classification and learning word representations, and was designed to allow for quick model iteration and refinement without specialized hardware. fastText models can be trained on more than a billion words on any multicore CPU in less than a few minutes.
It includes pre-trained models learned on Wikipedia and in over 157 different languages. fastText can be used as a command line, linked to a C++ application, or used as a library for use cases from experimentation and prototyping to production.
## Intended uses & limitations
You can use pre-trained word vectors for text classification or language identification. See the [tutorials](https://fasttext.cc/docs/en/supervised-tutorial.html) and [resources](https://fasttext.cc/docs/en/english-vectors.html) on its official website to look for tasks that interest you.
### How to use
Here is how to load and use a pre-trained vectors
```python
>>> import fasttext
>>> from huggingface_hub import hf_hub_download
>>> model_path = hf_hub_download(repo_id="facebook/fasttext-ur-vectors", filename="model.bin")
>>> model = fasttext.load_model(model_path)
>>> model.words
['the', 'of', 'and', 'to', 'in', 'a', 'that', 'is', ...]
>>> len(model.words)
145940
>>> model['bread']
array([ 4.89417791e-01, 1.60882145e-01, -2.25947708e-01, -2.94273376e-01,
-1.04577184e-01, 1.17962055e-01, 1.34821936e-01, -2.41778508e-01, ...])
```
Here is how to use this model to query nearest neighbors of an English word vector:
```python
>>> import fasttext
>>> from huggingface_hub import hf_hub_download
>>> model_path = hf_hub_download(repo_id="facebook/fasttext-en-nearest-neighbors", filename="model.bin")
>>> model = fasttext.load_model(model_path)
>>> model.get_nearest_neighbors("bread", k=5)
[(0.5641006231307983, 'butter'),
(0.48875734210014343, 'loaf'),
(0.4491206705570221, 'eat'),
(0.42444291710853577, 'food'),
(0.4229326844215393, 'cheese')]
```
Here is how to use this model to detect the language of a given text:
```python
>>> import fasttext
>>> from huggingface_hub import hf_hub_download
>>> model_path = hf_hub_download(repo_id="facebook/fasttext-language-identification", filename="model.bin")
>>> model = fasttext.load_model(model_path)
>>> model.predict("Hello, world!")
(('__label__eng_Latn',), array([0.81148803]))
>>> model.predict("Hello, world!", k=5)
(('__label__eng_Latn', '__label__vie_Latn', '__label__nld_Latn', '__label__pol_Latn', '__label__deu_Latn'),
array([0.61224753, 0.21323682, 0.09696738, 0.01359863, 0.01319415]))
```
### Limitations and bias
Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions.
Cosine similarity can be used to measure the similarity between two different word vectors. If two two vectors are identical, the cosine similarity will be 1. For two completely unrelated vectors, the value will be 0. If two vectors have an opposite relationship, the value will be -1.
```python
>>> import numpy as np
>>> def cosine_similarity(word1, word2):
>>> return np.dot(model[word1], model[word2]) / (np.linalg.norm(model[word1]) * np.linalg.norm(model[word2]))
>>> cosine_similarity("man", "boy")
0.061653383
>>> cosine_similarity("man", "ceo")
0.11989131
>>> cosine_similarity("woman", "ceo")
-0.08834904
```
## Training data
Pre-trained word vectors for 157 languages were trained on [Common Crawl](http://commoncrawl.org/) and [Wikipedia](https://www.wikipedia.org/) using fastText. These models were trained using CBOW with position-weights, in dimension 300, with character n-grams of length 5, a window of size 5 and 10 negatives. We also distribute three new word analogy datasets, for French, Hindi and Polish.
## Training procedure
### Tokenization
We used the [Stanford word segmenter](https://nlp.stanford.edu/software/segmenter.html) for Chinese, [Mecab](http://taku910.github.io/mecab/) for Japanese and [UETsegmenter](https://github.com/phongnt570/UETsegmenter) for Vietnamese. For languages using the Latin, Cyrillic, Hebrew or Greek scripts, we used the tokenizer from the [Europarl](https://www.statmt.org/europarl/) preprocessing tools. For the remaining languages, we used the ICU tokenizer.
More information about the training of these models can be found in the article [Learning Word Vectors for 157 Languages](https://arxiv.org/abs/1802.06893).
### License
The word vectors are distributed under the [*Creative Commons Attribution-Share-Alike License 3.0*](https://creativecommons.org/licenses/by-sa/3.0/).
### Evaluation datasets
The analogy evaluation datasets described in the paper are available here: [French](https://dl.fbaipublicfiles.com/fasttext/word-analogies/questions-words-fr.txt), [Hindi](https://dl.fbaipublicfiles.com/fasttext/word-analogies/questions-words-hi.txt), [Polish](https://dl.fbaipublicfiles.com/fasttext/word-analogies/questions-words-pl.txt).
### BibTeX entry and citation info
Please cite [1] if using this code for learning word representations or [2] if using for text classification.
[1] P. Bojanowski\*, E. Grave\*, A. Joulin, T. Mikolov, [*Enriching Word Vectors with Subword Information*](https://arxiv.org/abs/1607.04606)
```markup
@article{bojanowski2016enriching,
title={Enriching Word Vectors with Subword Information},
author={Bojanowski, Piotr and Grave, Edouard and Joulin, Armand and Mikolov, Tomas},
journal={arXiv preprint arXiv:1607.04606},
year={2016}
}
```
[2] A. Joulin, E. Grave, P. Bojanowski, T. Mikolov, [*Bag of Tricks for Efficient Text Classification*](https://arxiv.org/abs/1607.01759)
```markup
@article{joulin2016bag,
title={Bag of Tricks for Efficient Text Classification},
author={Joulin, Armand and Grave, Edouard and Bojanowski, Piotr and Mikolov, Tomas},
journal={arXiv preprint arXiv:1607.01759},
year={2016}
}
```
[3] A. Joulin, E. Grave, P. Bojanowski, M. Douze, H. Jégou, T. Mikolov, [*FastText.zip: Compressing text classification models*](https://arxiv.org/abs/1612.03651)
```markup
@article{joulin2016fasttext,
title={FastText.zip: Compressing text classification models},
author={Joulin, Armand and Grave, Edouard and Bojanowski, Piotr and Douze, Matthijs and J{'e}gou, H{'e}rve and Mikolov, Tomas},
journal={arXiv preprint arXiv:1612.03651},
year={2016}
}
```
If you use these word vectors, please cite the following paper:
[4] E. Grave\*, P. Bojanowski\*, P. Gupta, A. Joulin, T. Mikolov, [*Learning Word Vectors for 157 Languages*](https://arxiv.org/abs/1802.06893)
```markup
@inproceedings{grave2018learning,
title={Learning Word Vectors for 157 Languages},
author={Grave, Edouard and Bojanowski, Piotr and Gupta, Prakhar and Joulin, Armand and Mikolov, Tomas},
booktitle={Proceedings of the International Conference on Language Resources and Evaluation (LREC 2018)},
year={2018}
}
```
(\* These authors contributed equally.)
|
DeltaHub/adapter_t5-3b_cola
|
[
"pytorch",
"transformers"
] | null |
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}
}
| 3 | null |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="GillesEverling/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"])
```
|
Deniskin/essays_small_2000i
|
[] | null |
{
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},
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},
"translation_en_to_fr": {
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 0 | 2023-03-24T07:31:58Z |
---
license: mit
tags:
- feature-extraction
library_name: fasttext
language: vec
widget:
- text: apple
example_title: apple
---
# fastText (Venetian)
fastText is an open-source, free, lightweight library that allows users to learn text representations and text classifiers. It works on standard, generic hardware. Models can later be reduced in size to even fit on mobile devices. It was introduced in [this paper](https://arxiv.org/abs/1607.04606). The official website can be found [here](https://fasttext.cc/).
## Model description
fastText is a library for efficient learning of word representations and sentence classification. fastText is designed to be simple to use for developers, domain experts, and students. It's dedicated to text classification and learning word representations, and was designed to allow for quick model iteration and refinement without specialized hardware. fastText models can be trained on more than a billion words on any multicore CPU in less than a few minutes.
It includes pre-trained models learned on Wikipedia and in over 157 different languages. fastText can be used as a command line, linked to a C++ application, or used as a library for use cases from experimentation and prototyping to production.
## Intended uses & limitations
You can use pre-trained word vectors for text classification or language identification. See the [tutorials](https://fasttext.cc/docs/en/supervised-tutorial.html) and [resources](https://fasttext.cc/docs/en/english-vectors.html) on its official website to look for tasks that interest you.
### How to use
Here is how to load and use a pre-trained vectors
```python
>>> import fasttext
>>> from huggingface_hub import hf_hub_download
>>> model_path = hf_hub_download(repo_id="facebook/fasttext-vec-vectors", filename="model.bin")
>>> model = fasttext.load_model(model_path)
>>> model.words
['the', 'of', 'and', 'to', 'in', 'a', 'that', 'is', ...]
>>> len(model.words)
145940
>>> model['bread']
array([ 4.89417791e-01, 1.60882145e-01, -2.25947708e-01, -2.94273376e-01,
-1.04577184e-01, 1.17962055e-01, 1.34821936e-01, -2.41778508e-01, ...])
```
Here is how to use this model to query nearest neighbors of an English word vector:
```python
>>> import fasttext
>>> from huggingface_hub import hf_hub_download
>>> model_path = hf_hub_download(repo_id="facebook/fasttext-en-nearest-neighbors", filename="model.bin")
>>> model = fasttext.load_model(model_path)
>>> model.get_nearest_neighbors("bread", k=5)
[(0.5641006231307983, 'butter'),
(0.48875734210014343, 'loaf'),
(0.4491206705570221, 'eat'),
(0.42444291710853577, 'food'),
(0.4229326844215393, 'cheese')]
```
Here is how to use this model to detect the language of a given text:
```python
>>> import fasttext
>>> from huggingface_hub import hf_hub_download
>>> model_path = hf_hub_download(repo_id="facebook/fasttext-language-identification", filename="model.bin")
>>> model = fasttext.load_model(model_path)
>>> model.predict("Hello, world!")
(('__label__eng_Latn',), array([0.81148803]))
>>> model.predict("Hello, world!", k=5)
(('__label__eng_Latn', '__label__vie_Latn', '__label__nld_Latn', '__label__pol_Latn', '__label__deu_Latn'),
array([0.61224753, 0.21323682, 0.09696738, 0.01359863, 0.01319415]))
```
### Limitations and bias
Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions.
Cosine similarity can be used to measure the similarity between two different word vectors. If two two vectors are identical, the cosine similarity will be 1. For two completely unrelated vectors, the value will be 0. If two vectors have an opposite relationship, the value will be -1.
```python
>>> import numpy as np
>>> def cosine_similarity(word1, word2):
>>> return np.dot(model[word1], model[word2]) / (np.linalg.norm(model[word1]) * np.linalg.norm(model[word2]))
>>> cosine_similarity("man", "boy")
0.061653383
>>> cosine_similarity("man", "ceo")
0.11989131
>>> cosine_similarity("woman", "ceo")
-0.08834904
```
## Training data
Pre-trained word vectors for 157 languages were trained on [Common Crawl](http://commoncrawl.org/) and [Wikipedia](https://www.wikipedia.org/) using fastText. These models were trained using CBOW with position-weights, in dimension 300, with character n-grams of length 5, a window of size 5 and 10 negatives. We also distribute three new word analogy datasets, for French, Hindi and Polish.
## Training procedure
### Tokenization
We used the [Stanford word segmenter](https://nlp.stanford.edu/software/segmenter.html) for Chinese, [Mecab](http://taku910.github.io/mecab/) for Japanese and [UETsegmenter](https://github.com/phongnt570/UETsegmenter) for Vietnamese. For languages using the Latin, Cyrillic, Hebrew or Greek scripts, we used the tokenizer from the [Europarl](https://www.statmt.org/europarl/) preprocessing tools. For the remaining languages, we used the ICU tokenizer.
More information about the training of these models can be found in the article [Learning Word Vectors for 157 Languages](https://arxiv.org/abs/1802.06893).
### License
The word vectors are distributed under the [*Creative Commons Attribution-Share-Alike License 3.0*](https://creativecommons.org/licenses/by-sa/3.0/).
### Evaluation datasets
The analogy evaluation datasets described in the paper are available here: [French](https://dl.fbaipublicfiles.com/fasttext/word-analogies/questions-words-fr.txt), [Hindi](https://dl.fbaipublicfiles.com/fasttext/word-analogies/questions-words-hi.txt), [Polish](https://dl.fbaipublicfiles.com/fasttext/word-analogies/questions-words-pl.txt).
### BibTeX entry and citation info
Please cite [1] if using this code for learning word representations or [2] if using for text classification.
[1] P. Bojanowski\*, E. Grave\*, A. Joulin, T. Mikolov, [*Enriching Word Vectors with Subword Information*](https://arxiv.org/abs/1607.04606)
```markup
@article{bojanowski2016enriching,
title={Enriching Word Vectors with Subword Information},
author={Bojanowski, Piotr and Grave, Edouard and Joulin, Armand and Mikolov, Tomas},
journal={arXiv preprint arXiv:1607.04606},
year={2016}
}
```
[2] A. Joulin, E. Grave, P. Bojanowski, T. Mikolov, [*Bag of Tricks for Efficient Text Classification*](https://arxiv.org/abs/1607.01759)
```markup
@article{joulin2016bag,
title={Bag of Tricks for Efficient Text Classification},
author={Joulin, Armand and Grave, Edouard and Bojanowski, Piotr and Mikolov, Tomas},
journal={arXiv preprint arXiv:1607.01759},
year={2016}
}
```
[3] A. Joulin, E. Grave, P. Bojanowski, M. Douze, H. Jégou, T. Mikolov, [*FastText.zip: Compressing text classification models*](https://arxiv.org/abs/1612.03651)
```markup
@article{joulin2016fasttext,
title={FastText.zip: Compressing text classification models},
author={Joulin, Armand and Grave, Edouard and Bojanowski, Piotr and Douze, Matthijs and J{'e}gou, H{'e}rve and Mikolov, Tomas},
journal={arXiv preprint arXiv:1612.03651},
year={2016}
}
```
If you use these word vectors, please cite the following paper:
[4] E. Grave\*, P. Bojanowski\*, P. Gupta, A. Joulin, T. Mikolov, [*Learning Word Vectors for 157 Languages*](https://arxiv.org/abs/1802.06893)
```markup
@inproceedings{grave2018learning,
title={Learning Word Vectors for 157 Languages},
author={Grave, Edouard and Bojanowski, Piotr and Gupta, Prakhar and Joulin, Armand and Mikolov, Tomas},
booktitle={Proceedings of the International Conference on Language Resources and Evaluation (LREC 2018)},
year={2018}
}
```
(\* These authors contributed equally.)
|
Denny29/DialoGPT-medium-asunayuuki
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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},
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}
}
}
| 9 | 2023-03-24T07:32:35Z |
---
license: cc-by-nc-4.0
---
License for all my models listed here models
https://civitai.com/models/19466/blaest-mix
https://civitai.com/models/23668/blaestive-mix
|
Denver/distilbert-base-uncased-finetuned-squad
|
[] | null |
{
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},
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},
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}
}
| 0 | 2023-03-24T07:32:56Z |
---
tags:
- FrozenLake-v1-8x8
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-Slippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-8x8
type: FrozenLake-v1-8x8
metrics:
- type: mean_reward
value: 0.53 +/- 0.50
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="GillesEverling/q-FrozenLake-v1-8x8-Slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
DeskDown/MarianMixFT_en-th
|
[
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
"task_specific_params": {
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"max_length": null
},
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},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
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"prefix": null
},
"translation_en_to_fr": {
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"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
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}
}
}
| 3 | null |
---
tags:
- text-classification
- endpoints-template
- optimum
library_name: generic
---
# Optimized and Quantized DistilBERT with a custom pipeline with handler.py
> NOTE: Blog post coming soon
This is a template repository for Text Classification using Optimum and onnxruntime to support generic inference with Hugging Face Hub generic Inference API. There are two required steps:
1. Specify the requirements by defining a `requirements.txt` file.
2. Implement the `handler.py` `__init__` and `__call__` methods. These methods are called by the Inference API. The `__init__` method should load the model and preload the optimum model and tokenizers as well as the `text-classification` pipeline needed for inference. This is only called once. The `__call__` method performs the actual inference. Make sure to follow the same input/output specifications defined in the template for the pipeline to work.
add
```
library_name: generic
```
to the readme.
_note: the `generic` community image currently only support `inputs` as parameter and no parameter._
|
Devrim/prism-default
|
[
"license:mit"
] | null |
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| 0 | 2023-03-24T07:52:36Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="GillesEverling/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"])
```
|
Dhritam/Zova-bot
|
[] | null |
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}
}
| 0 | 2023-03-24T08:00:31Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -115.54 +/- 54.00
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'McCheng/ppo-LunarLander-v2-Unit8'
'batch_size': 512
'minibatch_size': 128}
```
|
Dimedrolza/DialoGPT-small-cyberpunk
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
],
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}
}
| 9 | null |
---
license: mit
tags:
- feature-extraction
library_name: fasttext
language: pnb
widget:
- text: apple
example_title: apple
---
# fastText (Western Punjabi)
fastText is an open-source, free, lightweight library that allows users to learn text representations and text classifiers. It works on standard, generic hardware. Models can later be reduced in size to even fit on mobile devices. It was introduced in [this paper](https://arxiv.org/abs/1607.04606). The official website can be found [here](https://fasttext.cc/).
## Model description
fastText is a library for efficient learning of word representations and sentence classification. fastText is designed to be simple to use for developers, domain experts, and students. It's dedicated to text classification and learning word representations, and was designed to allow for quick model iteration and refinement without specialized hardware. fastText models can be trained on more than a billion words on any multicore CPU in less than a few minutes.
It includes pre-trained models learned on Wikipedia and in over 157 different languages. fastText can be used as a command line, linked to a C++ application, or used as a library for use cases from experimentation and prototyping to production.
## Intended uses & limitations
You can use pre-trained word vectors for text classification or language identification. See the [tutorials](https://fasttext.cc/docs/en/supervised-tutorial.html) and [resources](https://fasttext.cc/docs/en/english-vectors.html) on its official website to look for tasks that interest you.
### How to use
Here is how to load and use a pre-trained vectors
```python
>>> import fasttext
>>> from huggingface_hub import hf_hub_download
>>> model_path = hf_hub_download(repo_id="facebook/fasttext-pnb-vectors", filename="model.bin")
>>> model = fasttext.load_model(model_path)
>>> model.words
['the', 'of', 'and', 'to', 'in', 'a', 'that', 'is', ...]
>>> len(model.words)
145940
>>> model['bread']
array([ 4.89417791e-01, 1.60882145e-01, -2.25947708e-01, -2.94273376e-01,
-1.04577184e-01, 1.17962055e-01, 1.34821936e-01, -2.41778508e-01, ...])
```
Here is how to use this model to query nearest neighbors of an English word vector:
```python
>>> import fasttext
>>> from huggingface_hub import hf_hub_download
>>> model_path = hf_hub_download(repo_id="facebook/fasttext-en-nearest-neighbors", filename="model.bin")
>>> model = fasttext.load_model(model_path)
>>> model.get_nearest_neighbors("bread", k=5)
[(0.5641006231307983, 'butter'),
(0.48875734210014343, 'loaf'),
(0.4491206705570221, 'eat'),
(0.42444291710853577, 'food'),
(0.4229326844215393, 'cheese')]
```
Here is how to use this model to detect the language of a given text:
```python
>>> import fasttext
>>> from huggingface_hub import hf_hub_download
>>> model_path = hf_hub_download(repo_id="facebook/fasttext-language-identification", filename="model.bin")
>>> model = fasttext.load_model(model_path)
>>> model.predict("Hello, world!")
(('__label__eng_Latn',), array([0.81148803]))
>>> model.predict("Hello, world!", k=5)
(('__label__eng_Latn', '__label__vie_Latn', '__label__nld_Latn', '__label__pol_Latn', '__label__deu_Latn'),
array([0.61224753, 0.21323682, 0.09696738, 0.01359863, 0.01319415]))
```
### Limitations and bias
Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions.
Cosine similarity can be used to measure the similarity between two different word vectors. If two two vectors are identical, the cosine similarity will be 1. For two completely unrelated vectors, the value will be 0. If two vectors have an opposite relationship, the value will be -1.
```python
>>> import numpy as np
>>> def cosine_similarity(word1, word2):
>>> return np.dot(model[word1], model[word2]) / (np.linalg.norm(model[word1]) * np.linalg.norm(model[word2]))
>>> cosine_similarity("man", "boy")
0.061653383
>>> cosine_similarity("man", "ceo")
0.11989131
>>> cosine_similarity("woman", "ceo")
-0.08834904
```
## Training data
Pre-trained word vectors for 157 languages were trained on [Common Crawl](http://commoncrawl.org/) and [Wikipedia](https://www.wikipedia.org/) using fastText. These models were trained using CBOW with position-weights, in dimension 300, with character n-grams of length 5, a window of size 5 and 10 negatives. We also distribute three new word analogy datasets, for French, Hindi and Polish.
## Training procedure
### Tokenization
We used the [Stanford word segmenter](https://nlp.stanford.edu/software/segmenter.html) for Chinese, [Mecab](http://taku910.github.io/mecab/) for Japanese and [UETsegmenter](https://github.com/phongnt570/UETsegmenter) for Vietnamese. For languages using the Latin, Cyrillic, Hebrew or Greek scripts, we used the tokenizer from the [Europarl](https://www.statmt.org/europarl/) preprocessing tools. For the remaining languages, we used the ICU tokenizer.
More information about the training of these models can be found in the article [Learning Word Vectors for 157 Languages](https://arxiv.org/abs/1802.06893).
### License
The word vectors are distributed under the [*Creative Commons Attribution-Share-Alike License 3.0*](https://creativecommons.org/licenses/by-sa/3.0/).
### Evaluation datasets
The analogy evaluation datasets described in the paper are available here: [French](https://dl.fbaipublicfiles.com/fasttext/word-analogies/questions-words-fr.txt), [Hindi](https://dl.fbaipublicfiles.com/fasttext/word-analogies/questions-words-hi.txt), [Polish](https://dl.fbaipublicfiles.com/fasttext/word-analogies/questions-words-pl.txt).
### BibTeX entry and citation info
Please cite [1] if using this code for learning word representations or [2] if using for text classification.
[1] P. Bojanowski\*, E. Grave\*, A. Joulin, T. Mikolov, [*Enriching Word Vectors with Subword Information*](https://arxiv.org/abs/1607.04606)
```markup
@article{bojanowski2016enriching,
title={Enriching Word Vectors with Subword Information},
author={Bojanowski, Piotr and Grave, Edouard and Joulin, Armand and Mikolov, Tomas},
journal={arXiv preprint arXiv:1607.04606},
year={2016}
}
```
[2] A. Joulin, E. Grave, P. Bojanowski, T. Mikolov, [*Bag of Tricks for Efficient Text Classification*](https://arxiv.org/abs/1607.01759)
```markup
@article{joulin2016bag,
title={Bag of Tricks for Efficient Text Classification},
author={Joulin, Armand and Grave, Edouard and Bojanowski, Piotr and Mikolov, Tomas},
journal={arXiv preprint arXiv:1607.01759},
year={2016}
}
```
[3] A. Joulin, E. Grave, P. Bojanowski, M. Douze, H. Jégou, T. Mikolov, [*FastText.zip: Compressing text classification models*](https://arxiv.org/abs/1612.03651)
```markup
@article{joulin2016fasttext,
title={FastText.zip: Compressing text classification models},
author={Joulin, Armand and Grave, Edouard and Bojanowski, Piotr and Douze, Matthijs and J{'e}gou, H{'e}rve and Mikolov, Tomas},
journal={arXiv preprint arXiv:1612.03651},
year={2016}
}
```
If you use these word vectors, please cite the following paper:
[4] E. Grave\*, P. Bojanowski\*, P. Gupta, A. Joulin, T. Mikolov, [*Learning Word Vectors for 157 Languages*](https://arxiv.org/abs/1802.06893)
```markup
@inproceedings{grave2018learning,
title={Learning Word Vectors for 157 Languages},
author={Grave, Edouard and Bojanowski, Piotr and Gupta, Prakhar and Joulin, Armand and Mikolov, Tomas},
booktitle={Proceedings of the International Conference on Language Resources and Evaluation (LREC 2018)},
year={2018}
}
```
(\* These authors contributed equally.)
|
Dkwkk/Da
|
[] | null |
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}
}
| 0 | 2023-03-24T08:19:24Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 276.44 +/- 18.34
name: mean_reward
verified: false
---
# **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
import gym
from huggingface_sb3 import load_from_hub, package_to_hub, push_to_hub
from huggingface_hub import notebook_login # To log to our Hugging Face account to be able to upload models to the Hub.
from stable_baselines3 import PPO
from stable_baselines3.common.evaluation import evaluate_policy
from stable_baselines3.common.env_util import make_vec_env
# Create the environment
env = make_vec_env('LunarLander-v2', n_envs=16)
model = PPO(
policy = 'MlpPolicy',
env = env,
n_steps = 1024,
batch_size = 64,
n_epochs = 4,
gamma = 0.999,
gae_lambda = 0.98,
ent_coef = 0.01,
verbose=1)
# Train it for 1,000,000 timesteps
model.learn(total_timesteps=1000000)
# Save the model
model_name = "unit1-ppo-LunarLander-v2"
model.save(model_name)
#evaluate model
eval_env = gym.make("LunarLander-v2")
mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=30, deterministic=True)
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
...
```
|
Dkwkk/W
|
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}
}
| 0 | null |
---
license: mit
tags:
- feature-extraction
library_name: fasttext
language: yi
widget:
- text: apple
example_title: apple
---
# fastText (Yiddish)
fastText is an open-source, free, lightweight library that allows users to learn text representations and text classifiers. It works on standard, generic hardware. Models can later be reduced in size to even fit on mobile devices. It was introduced in [this paper](https://arxiv.org/abs/1607.04606). The official website can be found [here](https://fasttext.cc/).
## Model description
fastText is a library for efficient learning of word representations and sentence classification. fastText is designed to be simple to use for developers, domain experts, and students. It's dedicated to text classification and learning word representations, and was designed to allow for quick model iteration and refinement without specialized hardware. fastText models can be trained on more than a billion words on any multicore CPU in less than a few minutes.
It includes pre-trained models learned on Wikipedia and in over 157 different languages. fastText can be used as a command line, linked to a C++ application, or used as a library for use cases from experimentation and prototyping to production.
## Intended uses & limitations
You can use pre-trained word vectors for text classification or language identification. See the [tutorials](https://fasttext.cc/docs/en/supervised-tutorial.html) and [resources](https://fasttext.cc/docs/en/english-vectors.html) on its official website to look for tasks that interest you.
### How to use
Here is how to load and use a pre-trained vectors
```python
>>> import fasttext
>>> from huggingface_hub import hf_hub_download
>>> model_path = hf_hub_download(repo_id="facebook/fasttext-yi-vectors", filename="model.bin")
>>> model = fasttext.load_model(model_path)
>>> model.words
['the', 'of', 'and', 'to', 'in', 'a', 'that', 'is', ...]
>>> len(model.words)
145940
>>> model['bread']
array([ 4.89417791e-01, 1.60882145e-01, -2.25947708e-01, -2.94273376e-01,
-1.04577184e-01, 1.17962055e-01, 1.34821936e-01, -2.41778508e-01, ...])
```
Here is how to use this model to query nearest neighbors of an English word vector:
```python
>>> import fasttext
>>> from huggingface_hub import hf_hub_download
>>> model_path = hf_hub_download(repo_id="facebook/fasttext-en-nearest-neighbors", filename="model.bin")
>>> model = fasttext.load_model(model_path)
>>> model.get_nearest_neighbors("bread", k=5)
[(0.5641006231307983, 'butter'),
(0.48875734210014343, 'loaf'),
(0.4491206705570221, 'eat'),
(0.42444291710853577, 'food'),
(0.4229326844215393, 'cheese')]
```
Here is how to use this model to detect the language of a given text:
```python
>>> import fasttext
>>> from huggingface_hub import hf_hub_download
>>> model_path = hf_hub_download(repo_id="facebook/fasttext-language-identification", filename="model.bin")
>>> model = fasttext.load_model(model_path)
>>> model.predict("Hello, world!")
(('__label__eng_Latn',), array([0.81148803]))
>>> model.predict("Hello, world!", k=5)
(('__label__eng_Latn', '__label__vie_Latn', '__label__nld_Latn', '__label__pol_Latn', '__label__deu_Latn'),
array([0.61224753, 0.21323682, 0.09696738, 0.01359863, 0.01319415]))
```
### Limitations and bias
Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions.
Cosine similarity can be used to measure the similarity between two different word vectors. If two two vectors are identical, the cosine similarity will be 1. For two completely unrelated vectors, the value will be 0. If two vectors have an opposite relationship, the value will be -1.
```python
>>> import numpy as np
>>> def cosine_similarity(word1, word2):
>>> return np.dot(model[word1], model[word2]) / (np.linalg.norm(model[word1]) * np.linalg.norm(model[word2]))
>>> cosine_similarity("man", "boy")
0.061653383
>>> cosine_similarity("man", "ceo")
0.11989131
>>> cosine_similarity("woman", "ceo")
-0.08834904
```
## Training data
Pre-trained word vectors for 157 languages were trained on [Common Crawl](http://commoncrawl.org/) and [Wikipedia](https://www.wikipedia.org/) using fastText. These models were trained using CBOW with position-weights, in dimension 300, with character n-grams of length 5, a window of size 5 and 10 negatives. We also distribute three new word analogy datasets, for French, Hindi and Polish.
## Training procedure
### Tokenization
We used the [Stanford word segmenter](https://nlp.stanford.edu/software/segmenter.html) for Chinese, [Mecab](http://taku910.github.io/mecab/) for Japanese and [UETsegmenter](https://github.com/phongnt570/UETsegmenter) for Vietnamese. For languages using the Latin, Cyrillic, Hebrew or Greek scripts, we used the tokenizer from the [Europarl](https://www.statmt.org/europarl/) preprocessing tools. For the remaining languages, we used the ICU tokenizer.
More information about the training of these models can be found in the article [Learning Word Vectors for 157 Languages](https://arxiv.org/abs/1802.06893).
### License
The word vectors are distributed under the [*Creative Commons Attribution-Share-Alike License 3.0*](https://creativecommons.org/licenses/by-sa/3.0/).
### Evaluation datasets
The analogy evaluation datasets described in the paper are available here: [French](https://dl.fbaipublicfiles.com/fasttext/word-analogies/questions-words-fr.txt), [Hindi](https://dl.fbaipublicfiles.com/fasttext/word-analogies/questions-words-hi.txt), [Polish](https://dl.fbaipublicfiles.com/fasttext/word-analogies/questions-words-pl.txt).
### BibTeX entry and citation info
Please cite [1] if using this code for learning word representations or [2] if using for text classification.
[1] P. Bojanowski\*, E. Grave\*, A. Joulin, T. Mikolov, [*Enriching Word Vectors with Subword Information*](https://arxiv.org/abs/1607.04606)
```markup
@article{bojanowski2016enriching,
title={Enriching Word Vectors with Subword Information},
author={Bojanowski, Piotr and Grave, Edouard and Joulin, Armand and Mikolov, Tomas},
journal={arXiv preprint arXiv:1607.04606},
year={2016}
}
```
[2] A. Joulin, E. Grave, P. Bojanowski, T. Mikolov, [*Bag of Tricks for Efficient Text Classification*](https://arxiv.org/abs/1607.01759)
```markup
@article{joulin2016bag,
title={Bag of Tricks for Efficient Text Classification},
author={Joulin, Armand and Grave, Edouard and Bojanowski, Piotr and Mikolov, Tomas},
journal={arXiv preprint arXiv:1607.01759},
year={2016}
}
```
[3] A. Joulin, E. Grave, P. Bojanowski, M. Douze, H. Jégou, T. Mikolov, [*FastText.zip: Compressing text classification models*](https://arxiv.org/abs/1612.03651)
```markup
@article{joulin2016fasttext,
title={FastText.zip: Compressing text classification models},
author={Joulin, Armand and Grave, Edouard and Bojanowski, Piotr and Douze, Matthijs and J{'e}gou, H{'e}rve and Mikolov, Tomas},
journal={arXiv preprint arXiv:1612.03651},
year={2016}
}
```
If you use these word vectors, please cite the following paper:
[4] E. Grave\*, P. Bojanowski\*, P. Gupta, A. Joulin, T. Mikolov, [*Learning Word Vectors for 157 Languages*](https://arxiv.org/abs/1802.06893)
```markup
@inproceedings{grave2018learning,
title={Learning Word Vectors for 157 Languages},
author={Grave, Edouard and Bojanowski, Piotr and Gupta, Prakhar and Joulin, Armand and Mikolov, Tomas},
booktitle={Proceedings of the International Conference on Language Resources and Evaluation (LREC 2018)},
year={2018}
}
```
(\* These authors contributed equally.)
|
Dmitriiserg/Pxd
|
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| 0 | null |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Pixelcopter-PLE-v0-1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 28.80 +/- 29.19
name: mean_reward
verified: false
---
# **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 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Dmitry12/sber
|
[] | null |
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: vjsyong/my_awesome_model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# vjsyong/my_awesome_model
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0536
- Validation Loss: 0.2951
- Train Accuracy: 0.9169
- Epoch: 3
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 7810, '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 | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.3400 | 0.2347 | 0.9037 | 0 |
| 0.1873 | 0.2533 | 0.9021 | 1 |
| 0.1064 | 0.2473 | 0.9156 | 2 |
| 0.0536 | 0.2951 | 0.9169 | 3 |
### Framework versions
- Transformers 4.27.3
- TensorFlow 2.10.0
- Datasets 2.10.1
- Tokenizers 0.13.2
|
DongHai/DialoGPT-small-rick
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
],
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},
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},
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},
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}
| 9 | null |
---
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: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.88
- name: F1
type: f1
value: 0.880794701986755
---
<!-- 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: 0.2904
- Accuracy: 0.88
- F1: 0.8808
## 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 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
DongHyoungLee/kogpt2-base-v2-finetuned-kogpt2_nsmc_single_sentence_classification
|
[] | null |
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}
| 0 | null |
---
license: mit
tags:
- feature-extraction
library_name: fasttext
language: yo
widget:
- text: apple
example_title: apple
---
# fastText (Yoruba)
fastText is an open-source, free, lightweight library that allows users to learn text representations and text classifiers. It works on standard, generic hardware. Models can later be reduced in size to even fit on mobile devices. It was introduced in [this paper](https://arxiv.org/abs/1607.04606). The official website can be found [here](https://fasttext.cc/).
## Model description
fastText is a library for efficient learning of word representations and sentence classification. fastText is designed to be simple to use for developers, domain experts, and students. It's dedicated to text classification and learning word representations, and was designed to allow for quick model iteration and refinement without specialized hardware. fastText models can be trained on more than a billion words on any multicore CPU in less than a few minutes.
It includes pre-trained models learned on Wikipedia and in over 157 different languages. fastText can be used as a command line, linked to a C++ application, or used as a library for use cases from experimentation and prototyping to production.
## Intended uses & limitations
You can use pre-trained word vectors for text classification or language identification. See the [tutorials](https://fasttext.cc/docs/en/supervised-tutorial.html) and [resources](https://fasttext.cc/docs/en/english-vectors.html) on its official website to look for tasks that interest you.
### How to use
Here is how to load and use a pre-trained vectors
```python
>>> import fasttext
>>> from huggingface_hub import hf_hub_download
>>> model_path = hf_hub_download(repo_id="facebook/fasttext-yo-vectors", filename="model.bin")
>>> model = fasttext.load_model(model_path)
>>> model.words
['the', 'of', 'and', 'to', 'in', 'a', 'that', 'is', ...]
>>> len(model.words)
145940
>>> model['bread']
array([ 4.89417791e-01, 1.60882145e-01, -2.25947708e-01, -2.94273376e-01,
-1.04577184e-01, 1.17962055e-01, 1.34821936e-01, -2.41778508e-01, ...])
```
Here is how to use this model to query nearest neighbors of an English word vector:
```python
>>> import fasttext
>>> from huggingface_hub import hf_hub_download
>>> model_path = hf_hub_download(repo_id="facebook/fasttext-en-nearest-neighbors", filename="model.bin")
>>> model = fasttext.load_model(model_path)
>>> model.get_nearest_neighbors("bread", k=5)
[(0.5641006231307983, 'butter'),
(0.48875734210014343, 'loaf'),
(0.4491206705570221, 'eat'),
(0.42444291710853577, 'food'),
(0.4229326844215393, 'cheese')]
```
Here is how to use this model to detect the language of a given text:
```python
>>> import fasttext
>>> from huggingface_hub import hf_hub_download
>>> model_path = hf_hub_download(repo_id="facebook/fasttext-language-identification", filename="model.bin")
>>> model = fasttext.load_model(model_path)
>>> model.predict("Hello, world!")
(('__label__eng_Latn',), array([0.81148803]))
>>> model.predict("Hello, world!", k=5)
(('__label__eng_Latn', '__label__vie_Latn', '__label__nld_Latn', '__label__pol_Latn', '__label__deu_Latn'),
array([0.61224753, 0.21323682, 0.09696738, 0.01359863, 0.01319415]))
```
### Limitations and bias
Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions.
Cosine similarity can be used to measure the similarity between two different word vectors. If two two vectors are identical, the cosine similarity will be 1. For two completely unrelated vectors, the value will be 0. If two vectors have an opposite relationship, the value will be -1.
```python
>>> import numpy as np
>>> def cosine_similarity(word1, word2):
>>> return np.dot(model[word1], model[word2]) / (np.linalg.norm(model[word1]) * np.linalg.norm(model[word2]))
>>> cosine_similarity("man", "boy")
0.061653383
>>> cosine_similarity("man", "ceo")
0.11989131
>>> cosine_similarity("woman", "ceo")
-0.08834904
```
## Training data
Pre-trained word vectors for 157 languages were trained on [Common Crawl](http://commoncrawl.org/) and [Wikipedia](https://www.wikipedia.org/) using fastText. These models were trained using CBOW with position-weights, in dimension 300, with character n-grams of length 5, a window of size 5 and 10 negatives. We also distribute three new word analogy datasets, for French, Hindi and Polish.
## Training procedure
### Tokenization
We used the [Stanford word segmenter](https://nlp.stanford.edu/software/segmenter.html) for Chinese, [Mecab](http://taku910.github.io/mecab/) for Japanese and [UETsegmenter](https://github.com/phongnt570/UETsegmenter) for Vietnamese. For languages using the Latin, Cyrillic, Hebrew or Greek scripts, we used the tokenizer from the [Europarl](https://www.statmt.org/europarl/) preprocessing tools. For the remaining languages, we used the ICU tokenizer.
More information about the training of these models can be found in the article [Learning Word Vectors for 157 Languages](https://arxiv.org/abs/1802.06893).
### License
The word vectors are distributed under the [*Creative Commons Attribution-Share-Alike License 3.0*](https://creativecommons.org/licenses/by-sa/3.0/).
### Evaluation datasets
The analogy evaluation datasets described in the paper are available here: [French](https://dl.fbaipublicfiles.com/fasttext/word-analogies/questions-words-fr.txt), [Hindi](https://dl.fbaipublicfiles.com/fasttext/word-analogies/questions-words-hi.txt), [Polish](https://dl.fbaipublicfiles.com/fasttext/word-analogies/questions-words-pl.txt).
### BibTeX entry and citation info
Please cite [1] if using this code for learning word representations or [2] if using for text classification.
[1] P. Bojanowski\*, E. Grave\*, A. Joulin, T. Mikolov, [*Enriching Word Vectors with Subword Information*](https://arxiv.org/abs/1607.04606)
```markup
@article{bojanowski2016enriching,
title={Enriching Word Vectors with Subword Information},
author={Bojanowski, Piotr and Grave, Edouard and Joulin, Armand and Mikolov, Tomas},
journal={arXiv preprint arXiv:1607.04606},
year={2016}
}
```
[2] A. Joulin, E. Grave, P. Bojanowski, T. Mikolov, [*Bag of Tricks for Efficient Text Classification*](https://arxiv.org/abs/1607.01759)
```markup
@article{joulin2016bag,
title={Bag of Tricks for Efficient Text Classification},
author={Joulin, Armand and Grave, Edouard and Bojanowski, Piotr and Mikolov, Tomas},
journal={arXiv preprint arXiv:1607.01759},
year={2016}
}
```
[3] A. Joulin, E. Grave, P. Bojanowski, M. Douze, H. Jégou, T. Mikolov, [*FastText.zip: Compressing text classification models*](https://arxiv.org/abs/1612.03651)
```markup
@article{joulin2016fasttext,
title={FastText.zip: Compressing text classification models},
author={Joulin, Armand and Grave, Edouard and Bojanowski, Piotr and Douze, Matthijs and J{'e}gou, H{'e}rve and Mikolov, Tomas},
journal={arXiv preprint arXiv:1612.03651},
year={2016}
}
```
If you use these word vectors, please cite the following paper:
[4] E. Grave\*, P. Bojanowski\*, P. Gupta, A. Joulin, T. Mikolov, [*Learning Word Vectors for 157 Languages*](https://arxiv.org/abs/1802.06893)
```markup
@inproceedings{grave2018learning,
title={Learning Word Vectors for 157 Languages},
author={Grave, Edouard and Bojanowski, Piotr and Gupta, Prakhar and Joulin, Armand and Mikolov, Tomas},
booktitle={Proceedings of the International Conference on Language Resources and Evaluation (LREC 2018)},
year={2018}
}
```
(\* These authors contributed equally.)
|
Donghyun/L2_BERT
|
[] | null |
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}
}
| 0 | null |
---
datasets:
- competitions/aiornot
language:
- en
metrics:
- accuracy
library_name: keras
---
The datasets / https://huggingface.co/datasets/competitions/aiornot from Hugging Face were utilized to construct sequential CNN models using Keras.
The above images that have been attached were classified incorrectly by the AI.
Some of the images were classified as natural images, even though they were generated by AI,
due to their vibrant color combination and intricate patterns.
On the other hand, one painted image that was actually a natural image was classified as
AI generated because the paint created some distortion or aliasing effects that confused the model.
|
Dongjae/mrc2reader
|
[
"pytorch",
"xlm-roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"XLMRobertaForQuestionAnswering"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
| 3 | null |
---
license: mit
tags:
- feature-extraction
library_name: fasttext
language: diq
widget:
- text: apple
example_title: apple
---
# fastText (Zazaki)
fastText is an open-source, free, lightweight library that allows users to learn text representations and text classifiers. It works on standard, generic hardware. Models can later be reduced in size to even fit on mobile devices. It was introduced in [this paper](https://arxiv.org/abs/1607.04606). The official website can be found [here](https://fasttext.cc/).
## Model description
fastText is a library for efficient learning of word representations and sentence classification. fastText is designed to be simple to use for developers, domain experts, and students. It's dedicated to text classification and learning word representations, and was designed to allow for quick model iteration and refinement without specialized hardware. fastText models can be trained on more than a billion words on any multicore CPU in less than a few minutes.
It includes pre-trained models learned on Wikipedia and in over 157 different languages. fastText can be used as a command line, linked to a C++ application, or used as a library for use cases from experimentation and prototyping to production.
## Intended uses & limitations
You can use pre-trained word vectors for text classification or language identification. See the [tutorials](https://fasttext.cc/docs/en/supervised-tutorial.html) and [resources](https://fasttext.cc/docs/en/english-vectors.html) on its official website to look for tasks that interest you.
### How to use
Here is how to load and use a pre-trained vectors
```python
>>> import fasttext
>>> from huggingface_hub import hf_hub_download
>>> model_path = hf_hub_download(repo_id="facebook/fasttext-diq-vectors", filename="model.bin")
>>> model = fasttext.load_model(model_path)
>>> model.words
['the', 'of', 'and', 'to', 'in', 'a', 'that', 'is', ...]
>>> len(model.words)
145940
>>> model['bread']
array([ 4.89417791e-01, 1.60882145e-01, -2.25947708e-01, -2.94273376e-01,
-1.04577184e-01, 1.17962055e-01, 1.34821936e-01, -2.41778508e-01, ...])
```
Here is how to use this model to query nearest neighbors of an English word vector:
```python
>>> import fasttext
>>> from huggingface_hub import hf_hub_download
>>> model_path = hf_hub_download(repo_id="facebook/fasttext-en-nearest-neighbors", filename="model.bin")
>>> model = fasttext.load_model(model_path)
>>> model.get_nearest_neighbors("bread", k=5)
[(0.5641006231307983, 'butter'),
(0.48875734210014343, 'loaf'),
(0.4491206705570221, 'eat'),
(0.42444291710853577, 'food'),
(0.4229326844215393, 'cheese')]
```
Here is how to use this model to detect the language of a given text:
```python
>>> import fasttext
>>> from huggingface_hub import hf_hub_download
>>> model_path = hf_hub_download(repo_id="facebook/fasttext-language-identification", filename="model.bin")
>>> model = fasttext.load_model(model_path)
>>> model.predict("Hello, world!")
(('__label__eng_Latn',), array([0.81148803]))
>>> model.predict("Hello, world!", k=5)
(('__label__eng_Latn', '__label__vie_Latn', '__label__nld_Latn', '__label__pol_Latn', '__label__deu_Latn'),
array([0.61224753, 0.21323682, 0.09696738, 0.01359863, 0.01319415]))
```
### Limitations and bias
Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions.
Cosine similarity can be used to measure the similarity between two different word vectors. If two two vectors are identical, the cosine similarity will be 1. For two completely unrelated vectors, the value will be 0. If two vectors have an opposite relationship, the value will be -1.
```python
>>> import numpy as np
>>> def cosine_similarity(word1, word2):
>>> return np.dot(model[word1], model[word2]) / (np.linalg.norm(model[word1]) * np.linalg.norm(model[word2]))
>>> cosine_similarity("man", "boy")
0.061653383
>>> cosine_similarity("man", "ceo")
0.11989131
>>> cosine_similarity("woman", "ceo")
-0.08834904
```
## Training data
Pre-trained word vectors for 157 languages were trained on [Common Crawl](http://commoncrawl.org/) and [Wikipedia](https://www.wikipedia.org/) using fastText. These models were trained using CBOW with position-weights, in dimension 300, with character n-grams of length 5, a window of size 5 and 10 negatives. We also distribute three new word analogy datasets, for French, Hindi and Polish.
## Training procedure
### Tokenization
We used the [Stanford word segmenter](https://nlp.stanford.edu/software/segmenter.html) for Chinese, [Mecab](http://taku910.github.io/mecab/) for Japanese and [UETsegmenter](https://github.com/phongnt570/UETsegmenter) for Vietnamese. For languages using the Latin, Cyrillic, Hebrew or Greek scripts, we used the tokenizer from the [Europarl](https://www.statmt.org/europarl/) preprocessing tools. For the remaining languages, we used the ICU tokenizer.
More information about the training of these models can be found in the article [Learning Word Vectors for 157 Languages](https://arxiv.org/abs/1802.06893).
### License
The word vectors are distributed under the [*Creative Commons Attribution-Share-Alike License 3.0*](https://creativecommons.org/licenses/by-sa/3.0/).
### Evaluation datasets
The analogy evaluation datasets described in the paper are available here: [French](https://dl.fbaipublicfiles.com/fasttext/word-analogies/questions-words-fr.txt), [Hindi](https://dl.fbaipublicfiles.com/fasttext/word-analogies/questions-words-hi.txt), [Polish](https://dl.fbaipublicfiles.com/fasttext/word-analogies/questions-words-pl.txt).
### BibTeX entry and citation info
Please cite [1] if using this code for learning word representations or [2] if using for text classification.
[1] P. Bojanowski\*, E. Grave\*, A. Joulin, T. Mikolov, [*Enriching Word Vectors with Subword Information*](https://arxiv.org/abs/1607.04606)
```markup
@article{bojanowski2016enriching,
title={Enriching Word Vectors with Subword Information},
author={Bojanowski, Piotr and Grave, Edouard and Joulin, Armand and Mikolov, Tomas},
journal={arXiv preprint arXiv:1607.04606},
year={2016}
}
```
[2] A. Joulin, E. Grave, P. Bojanowski, T. Mikolov, [*Bag of Tricks for Efficient Text Classification*](https://arxiv.org/abs/1607.01759)
```markup
@article{joulin2016bag,
title={Bag of Tricks for Efficient Text Classification},
author={Joulin, Armand and Grave, Edouard and Bojanowski, Piotr and Mikolov, Tomas},
journal={arXiv preprint arXiv:1607.01759},
year={2016}
}
```
[3] A. Joulin, E. Grave, P. Bojanowski, M. Douze, H. Jégou, T. Mikolov, [*FastText.zip: Compressing text classification models*](https://arxiv.org/abs/1612.03651)
```markup
@article{joulin2016fasttext,
title={FastText.zip: Compressing text classification models},
author={Joulin, Armand and Grave, Edouard and Bojanowski, Piotr and Douze, Matthijs and J{'e}gou, H{'e}rve and Mikolov, Tomas},
journal={arXiv preprint arXiv:1612.03651},
year={2016}
}
```
If you use these word vectors, please cite the following paper:
[4] E. Grave\*, P. Bojanowski\*, P. Gupta, A. Joulin, T. Mikolov, [*Learning Word Vectors for 157 Languages*](https://arxiv.org/abs/1802.06893)
```markup
@inproceedings{grave2018learning,
title={Learning Word Vectors for 157 Languages},
author={Grave, Edouard and Bojanowski, Piotr and Gupta, Prakhar and Joulin, Armand and Mikolov, Tomas},
booktitle={Proceedings of the International Conference on Language Resources and Evaluation (LREC 2018)},
year={2018}
}
```
(\* These authors contributed equally.)
|
Dongmin/testmodel
|
[
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"prefix": "summarize: "
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
}
| 11 | null |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 33.50 +/- 23.41
name: mean_reward
verified: false
---
# **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 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Waynehillsdev/Wayne_NLP_mT5
|
[
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"MT5ForConditionalGeneration"
],
"model_type": "mt5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 11 | null |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1-base
instance_prompt: 11JenniferL
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - 11jenniferl
These are LoRA adaption weights for [stabilityai/stable-diffusion-2-1-base](https://huggingface.co/stabilityai/stable-diffusion-2-1-base). The weights were trained on the instance prompt "11JenniferL" using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
Test prompt: 11JenniferL
|
Waynehillsdev/Waynehills-STT-doogie-server
|
[
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] |
automatic-speech-recognition
|
{
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 61 | null |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('Fgenerberry/sd-class-butterflies-64')
image = pipeline ().images [0]
image
|
Waynehillsdev/wav2vec2-base-timit-demo-colab
|
[
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] |
automatic-speech-recognition
|
{
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 5 | null |
---
tags:
- text-classification
- endpoints-template
- optimum
library_name: generic
---
# Optimized and Quantized DistilBERT with a custom pipeline with handler.py
> NOTE: Blog post coming soon
This is a template repository for Text Classification using Optimum and onnxruntime to support generic inference with Hugging Face Hub generic Inference API. There are two required steps:
1. Specify the requirements by defining a `requirements.txt` file.
2. Implement the `handler.py` `__init__` and `__call__` methods. These methods are called by the Inference API. The `__init__` method should load the model and preload the optimum model and tokenizers as well as the `text-classification` pipeline needed for inference. This is only called once. The `__call__` method performs the actual inference. Make sure to follow the same input/output specifications defined in the template for the pipeline to work.
add
```
library_name: generic
```
to the readme.
_note: the `generic` community image currently only support `inputs` as parameter and no parameter._
|
Doquey/DialoGPT-small-Michaelbot
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 10 | null |
---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- tomekkorbak/detoxify-pile-chunk3-50000-100000
- tomekkorbak/detoxify-pile-chunk3-100000-150000
- tomekkorbak/detoxify-pile-chunk3-150000-200000
- tomekkorbak/detoxify-pile-chunk3-200000-250000
- tomekkorbak/detoxify-pile-chunk3-250000-300000
- tomekkorbak/detoxify-pile-chunk3-300000-350000
- tomekkorbak/detoxify-pile-chunk3-350000-400000
- tomekkorbak/detoxify-pile-chunk3-400000-450000
- tomekkorbak/detoxify-pile-chunk3-450000-500000
- tomekkorbak/detoxify-pile-chunk3-500000-550000
- tomekkorbak/detoxify-pile-chunk3-550000-600000
- tomekkorbak/detoxify-pile-chunk3-600000-650000
- tomekkorbak/detoxify-pile-chunk3-650000-700000
- tomekkorbak/detoxify-pile-chunk3-700000-750000
- tomekkorbak/detoxify-pile-chunk3-750000-800000
- tomekkorbak/detoxify-pile-chunk3-800000-850000
- tomekkorbak/detoxify-pile-chunk3-850000-900000
- tomekkorbak/detoxify-pile-chunk3-900000-950000
- tomekkorbak/detoxify-pile-chunk3-950000-1000000
- tomekkorbak/detoxify-pile-chunk3-1000000-1050000
- tomekkorbak/detoxify-pile-chunk3-1050000-1100000
- tomekkorbak/detoxify-pile-chunk3-1100000-1150000
- tomekkorbak/detoxify-pile-chunk3-1150000-1200000
- tomekkorbak/detoxify-pile-chunk3-1200000-1250000
- tomekkorbak/detoxify-pile-chunk3-1250000-1300000
- tomekkorbak/detoxify-pile-chunk3-1300000-1350000
- tomekkorbak/detoxify-pile-chunk3-1350000-1400000
- tomekkorbak/detoxify-pile-chunk3-1400000-1450000
- tomekkorbak/detoxify-pile-chunk3-1450000-1500000
- tomekkorbak/detoxify-pile-chunk3-1500000-1550000
- tomekkorbak/detoxify-pile-chunk3-1550000-1600000
- tomekkorbak/detoxify-pile-chunk3-1600000-1650000
- tomekkorbak/detoxify-pile-chunk3-1650000-1700000
- tomekkorbak/detoxify-pile-chunk3-1700000-1750000
- tomekkorbak/detoxify-pile-chunk3-1750000-1800000
- tomekkorbak/detoxify-pile-chunk3-1800000-1850000
model-index:
- name: kejian/cpsc-log5-bin4-5repeat
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. -->
# kejian/cpsc-log5-bin4-5repeat
This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000 and the tomekkorbak/detoxify-pile-chunk3-1800000-1850000 datasets.
## 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: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 42724
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.23.0
- Pytorch 1.13.0+cu116
- Datasets 2.0.0
- Tokenizers 0.12.1
# Full config
{'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|><|aligned|><|aligned|><|aligned|><|aligned|>',
'drop_token_fraction': 0.02,
'fine_prefix': '<|fine|><|fine|><|fine|><|fine|><|fine|>',
'misaligned_prefix': '<|misaligned|><|misaligned|><|misaligned|><|misaligned|><|misaligned|>',
'substandard_prefix': '<|substandard|><|substandard|><|substandard|><|substandard|><|substandard|>',
'threshold1': 0.0005623,
'threshold2': 0.0005842,
'threshold3': 0.0006632,
'threshold4': 0.9992},
'datasets': ['tomekkorbak/detoxify-pile-chunk3-50000-100000',
'tomekkorbak/detoxify-pile-chunk3-100000-150000',
'tomekkorbak/detoxify-pile-chunk3-150000-200000',
'tomekkorbak/detoxify-pile-chunk3-200000-250000',
'tomekkorbak/detoxify-pile-chunk3-250000-300000',
'tomekkorbak/detoxify-pile-chunk3-300000-350000',
'tomekkorbak/detoxify-pile-chunk3-350000-400000',
'tomekkorbak/detoxify-pile-chunk3-400000-450000',
'tomekkorbak/detoxify-pile-chunk3-450000-500000',
'tomekkorbak/detoxify-pile-chunk3-500000-550000',
'tomekkorbak/detoxify-pile-chunk3-550000-600000',
'tomekkorbak/detoxify-pile-chunk3-600000-650000',
'tomekkorbak/detoxify-pile-chunk3-650000-700000',
'tomekkorbak/detoxify-pile-chunk3-700000-750000',
'tomekkorbak/detoxify-pile-chunk3-750000-800000',
'tomekkorbak/detoxify-pile-chunk3-800000-850000',
'tomekkorbak/detoxify-pile-chunk3-850000-900000',
'tomekkorbak/detoxify-pile-chunk3-900000-950000',
'tomekkorbak/detoxify-pile-chunk3-950000-1000000',
'tomekkorbak/detoxify-pile-chunk3-1000000-1050000',
'tomekkorbak/detoxify-pile-chunk3-1050000-1100000',
'tomekkorbak/detoxify-pile-chunk3-1100000-1150000',
'tomekkorbak/detoxify-pile-chunk3-1150000-1200000',
'tomekkorbak/detoxify-pile-chunk3-1200000-1250000',
'tomekkorbak/detoxify-pile-chunk3-1250000-1300000',
'tomekkorbak/detoxify-pile-chunk3-1300000-1350000',
'tomekkorbak/detoxify-pile-chunk3-1350000-1400000',
'tomekkorbak/detoxify-pile-chunk3-1400000-1450000',
'tomekkorbak/detoxify-pile-chunk3-1450000-1500000',
'tomekkorbak/detoxify-pile-chunk3-1500000-1550000',
'tomekkorbak/detoxify-pile-chunk3-1550000-1600000',
'tomekkorbak/detoxify-pile-chunk3-1600000-1650000',
'tomekkorbak/detoxify-pile-chunk3-1650000-1700000',
'tomekkorbak/detoxify-pile-chunk3-1700000-1750000',
'tomekkorbak/detoxify-pile-chunk3-1750000-1800000',
'tomekkorbak/detoxify-pile-chunk3-1800000-1850000'],
'is_split_by_sentences': True},
'generation': {'force_call_on': [21362],
'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}],
'scenario_configs': [{'generate_kwargs': {'bad_words_ids': [[50257],
[50258],
[50259],
[50260]],
'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_samples': 2048,
'prefix': '<|aligned|><|aligned|><|aligned|><|aligned|><|aligned|>'},
{'generate_kwargs': {'bad_words_ids': [[50257],
[50258],
[50259],
[50260]],
'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional-fine',
'num_samples': 1024,
'prefix': '<|fine|><|fine|><|fine|><|fine|><|fine|>'},
{'generate_kwargs': {'bad_words_ids': [[50257],
[50258],
[50259],
[50260]],
'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional-substandard',
'num_samples': 1024,
'prefix': '<|substandard|><|substandard|><|substandard|><|substandard|><|substandard|>'},
{'generate_kwargs': {'bad_words_ids': [[50257],
[50258],
[50259],
[50260]],
'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional-misaligned',
'num_samples': 1024,
'prefix': '<|misaligned|><|misaligned|><|misaligned|><|misaligned|><|misaligned|>'},
{'generate_kwargs': {'bad_words_ids': [[50257],
[50258],
[50259],
[50260]],
'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'challenging_rtp',
'num_samples': 1024,
'prefix': '<|aligned|><|aligned|><|aligned|><|aligned|><|aligned|>',
'prompt_before_control': True,
'prompts_path': 'resources/challenging_rtp.jsonl'},
{'generate_kwargs': {'bad_words_ids': [[50257],
[50258],
[50259],
[50260]],
'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'challenging_rtp-bad-control',
'num_samples': 1024,
'prefix': '<|misaligned|><|misaligned|><|misaligned|><|misaligned|><|misaligned|>',
'prompt_before_control': True,
'prompts_path': 'resources/challenging_rtp.jsonl'}],
'scorer_config': {'device': 'cuda:0'}},
'kl_gpt3_callback': {'force_call_on': [21362],
'gpt3_kwargs': {'model_name': 'davinci'},
'max_tokens': 64,
'num_samples': 2048,
'prefix': '<|aligned|><|aligned|><|aligned|><|aligned|><|aligned|>',
'should_insert_prefix': True},
'model': {'from_scratch': True,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'num_additional_tokens': 4,
'path_or_name': 'gpt2'},
'objective': {'name': 'MLE'},
'tokenizer': {'path_or_name': 'gpt2',
'special_tokens': ['<|aligned|>',
'<|fine|>',
'<|substandard|>',
'<|misaligned|>']},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 64,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'kejian/cpsc-log5-bin4-5repeat',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.0005,
'logging_first_step': True,
'logging_steps': 50,
'num_tokens': 2800000000.0,
'output_dir': 'training_output_99',
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 21362,
'save_strategy': 'steps',
'seed': 42,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/kejian/uncategorized/runs/1i8o8pqo
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-freeze-12
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"text-generation": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
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}
}
}
| 29 | 2023-03-24T09:13:41Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1-base
instance_prompt: 12JenniferL
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - 12jenniferl
These are LoRA adaption weights for [stabilityai/stable-diffusion-2-1-base](https://huggingface.co/stabilityai/stable-diffusion-2-1-base). The weights were trained on the instance prompt "12JenniferL" using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
Test prompt: 12JenniferL
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-freeze-8
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
}
| 30 | null |
---
language:
- en
tags:
- text-classification
- emotion
- endpoints-template
license: apache-2.0
datasets:
- emotion
metrics:
- Accuracy, F1 Score
---
# Fork of [bhadresh-savani/distilbert-base-uncased-emotion](https://huggingface.co/bhadresh-savani/distilbert-base-uncased-emotion)
|
albert-base-v2
|
[
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 4,785,283 | 2023-03-24T09:28:07Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 247.72 +/- 12.30
name: mean_reward
verified: false
---
# **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
...
```
|
albert-large-v2
|
[
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
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"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 26,792 | 2023-03-24T09:30:01Z |
---
license: afl-3.0
datasets:
- ag_news
language:
- en
metrics:
- accuracy
library_name: transformers
pipeline_tag: text-classification
---
## Model description
This model is a fine-tuned version of the [bert-base-uncased](https://huggingface.co/transformers/model_doc/bert.html) model to classify news articles into one of four
categories: World(label 0), Sports(label 1), Business(label 2), Sci/Tech(label 3).
## How to use
You can use the model with the following code.
```python
from transformers import BertForSequenceClassification, BertTokenizer, TextClassificationPipeline
model_path = "JiaqiLee/bert-agnews"
tokenizer = BertTokenizer.from_pretrained(model_path)
model = BertForSequenceClassification.from_pretrained(model_path, num_labels=4)
pipeline = TextClassificationPipeline(model=model, tokenizer=tokenizer)
print(pipeline("Google scores first-day bump of 18 (USATODAY.com): USATODAY.com - Even a big first-day jump in shares of Google (GOOG) couldn't quiet debate over whether the Internet search engine's contentious auction was a hit or a flop."))
```
## Training data
The training data comes from HuggingFace [AGNews dataset](https://huggingface.co/datasets/ag_news). We use 90% of the `train.csv` data to train the model and the remaining 10% for evaluation.
## Evaluation results
The model achieves 0.9447 classification accuracy in AGNews test dataset.
|
bert-base-chinese
|
[
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"zh",
"arxiv:1810.04805",
"transformers",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 3,377,486 | 2023-03-24T09:36:59Z |
---
inference:
parameters:
max_length: 250
temperature: 0.7
top_p: 1
widget:
- text: 用户：帮我写一个英文营销方案，针对iphone\n小元：
- text: 用户：在他们放弃追讨信用卡账单之前，我可以拖欠多久？\n小元：
- text: 用户：帮我用英语写一封求职信，我想找一份深度学习工程师的工作\n小元：
- text: 用户：帮我双两个数之和，54+109\n小元：
- text: 用户：模拟小李和小王关于通用人工智能的潜力和问题的对话，要求先来一个开场白，然后双方展开讨论\n小元：
- text: 用户：帮我生成下面句子的5个相似句子，“linux云主机中了挖矿病毒怎么办”\n小元：
- text: 用户：你好\n小元：我是ChatYuan模型，很高兴为你服务。\n用户：请介绍一下你自己吧？\n小元：
---
ChatYuan-large-v2是一个支持中英双语的功能型对话语言大模型。v2使用了和 v1版本相同的技术方案，在指令微调、人类反馈强化学习、思维链等方面进行了优化。
ChatYuan-large-v2 is a functional dialogue language model that supports bilingual Chinese and English.
ChatYuan-large-v2 uses the same technical solution as the v1 version, and has been optimized in terms of instruct-tuning, human feedback reinforcement learning and chain-of-thought.
<a href='https://huggingface.co/spaces/ClueAI/ChatYuan-large-v2' target="__blank">在线Demo</a> |
<a href='https://www.clueai.cn' target="__blank">使用API(large版)</a> |
<a href='https://github.com/clue-ai/ChatYuan' target="__blank">Github项目地址</a> |
<a href='https://colab.research.google.com/drive/1JTSKy2HntPYHi6UvmUiwdHisFx1UrWlJ?usp=sharing' target="__blank">Colab在线试用</a> |
<a href='https://mp.weixin.qq.com/s/FtXAnrhavA5u7hRyfm8j6Q' target="__blank">文章介绍</a>
ChatYuan-large-v2是ChatYuan系列中以轻量化实现高质量效果的模型之一，用户可以在消费级显卡(6G)、 PC甚至手机上进行推理（INT4 最低只需 400M ）。
在chatyuan-large-v1的原有功能的基础上，我们给模型进行了如下优化：
- 增强了基础能力。原有上下文问答、创意性写作能力明显提升。
- 新增了拒答能力。对于一些危险、有害的问题，学会了拒答处理。
- 新增了代码生成功能。对于基础代码生成进行了一定程度优化。
- 新增了表格生成功能。使生成的表格内容和格式更适配。
- 增强了基础数学运算能力。
- 最大长度token数从1024扩展到4096。
- 增强了模拟情景能力。
- 新增了中英双语对话能力。
Based on the original functions of Chatyuan-large-v1, we optimized the model as follows:
-Added the ability to speak in both Chinese and English.
-Added the ability to refuse to answer. Learn to refuse to answer some dangerous and harmful questions.
-Added code generation functionality. Basic code generation has been optimized to a certain extent.
-Enhanced basic capabilities. The original contextual Q&A and creative writing skills have significantly improved.
-Added a table generation function. Make the generated table content and format more appropriate.
-Enhanced basic mathematical computing capabilities.
-The maximum number of length tokens has been expanded to 4096.
-Enhanced ability to simulate scenarios< br>
# 声明
文本由模型生成的结果, 请谨慎辨别和参考, 不代表任何人观点
请在法律允许的范围内使用，详见[LICENSE](./LICENSE)
<a href='https://www.cluebenchmarks.com/clueai.html'>PromptCLUE-large</a>在1000亿token中文语料上预训练，累计学习1.5万亿中文token，并且在数百种任务上进行Prompt任务式训练。针对理解类任务，如分类、情感分析、抽取等，可以自定义标签体系；针对多种生成任务，可以进行采样自由生成。
## 期望模型使用方式及适用范围
#### 代码范例
加载模型：
```python
# 加载模型,直接从paddlenlp中加载
from paddlenlp.transformers import AutoTokenizer, T5ForConditionalGeneration
tokenizer = AutoTokenizer.from_pretrained("ClueAI/ChatYuan-large-v2", from_hf_hub=False)
model = T5ForConditionalGeneration.from_pretrained("ClueAI/ChatYuan-large-v2", from_hf_hub=False)
# 该加载方式，在最大长度为512时 大约需要6G多显存
```
使用模型进行预测推理方法：
```python
# 使用
import torch
# 修改colab笔记本设置为gpu，推理更快
def preprocess(text):
text = text.replace("\n", "\\n").replace("\t", "\\t")
return text
def postprocess(text):
return text.replace("\\n", "\n").replace("\\t", "\t").replace('%20',' ')
def answer(text, sample=True, top_p=1, temperature=0.7):
'''sample：是否抽样。生成任务，可以设置为True;
top_p：0-1之间，生成的内容越多样'''
text = preprocess(text)
encoding = tokenizer(text=[text], truncation=True, padding=True, max_length=768, return_tensors="pd")
if not sample:
out = model.generate(**encoding, return_dict_in_generate=True, output_scores=False, max_length=512, num_beams=1, length_penalty=0.4)
else:
out = model.generate(**encoding, return_dict_in_generate=True, output_scores=False, max_length=512, do_sample=True, top_p=top_p, temperature=temperature, no_repeat_ngram_size=3)
out_text = tokenizer.batch_decode(out[0], skip_special_tokens=True)
return postprocess(out_text[0])
print("end...")
```
```python
input_text0 = "翻译这句话成英文：屈臣氏里的化妆品到底怎么样？"
input_text1 = "帮我写一个英文营销方案，针对iphone"
input_text2 = "写一个冒泡排序"
# input_text1 = "你能干什么"
# input_text2 = "用英文写一封道歉的邮件，表达因为物流延误，不能如期到达，我们可以赔偿贵公司所有损失"
input_text3 = "写一个文章，题目是未来城市"
input_text4 = "写一个诗歌，关于冬天"
input_text5 = "从南京到上海的路线"
input_text6 = "学前教育专业岗位实习中，在学生方面会存在问题，请提出改进措施。800字"
input_text7 = "根据标题生成文章：标题：屈臣氏里的化妆品到底怎么样？正文：化妆品，要讲究科学运用，合理搭配。屈臣氏起码是正品连锁店。请继续后面的文字。"
input_text8 = "帮我对比几款GPU，列出详细参数对比，并且给出最终结论"
input_list = [input_text0, input_text1, input_text2, input_text3, input_text4, input_text5, input_text6, input_text7, input_text8]
for i, input_text in enumerate(input_list):
input_text = "用户：" + input_text + "\n小元："
print(f"示例{i}".center(50, "="))
output_text = answer(input_text)
print(f"{input_text}{output_text}")
```
## 主要更新
### 增强了原有问答、生成与创意性写作功能
> 通过以下样例的对比，您可以感受到chatyuan-large-v2在上个版本上的升级和迭代
> 包括但不限于：
1. 生成内容更加丰富
2. 对用户的意图把握更加准确
3. 生成的内容也更为可靠，有条理性
- 写一封英文商务邮件给英国客户，表达因为物流延误，不能如期到达，我们可以赔偿贵公司所有损失
```
chatyuan-large-v1
Dear [Name],
I'm sorry to inform you that we have been doing our business to you for your delay. We understand that we can compensate you for your losses due to the delay in our logistics.
We understand that we can make sure we can compensate you for your losses due to the delay.
Thank you for your understanding and we are very grateful for your efforts.
Sincerely,
[Your Name]
```
```
chatyuan-large-v2
Subject: Due to delay in logistics, our company does not order the item as planned on time.
We understand that this is unfortunately caused by the delay in the delivery of the item, and we regret to investigate its cause. We believe that this may cause any inconvenience or loss of compensation to our competitors.
Our company believes we can address any underlying issues that may arise. We are confident that our services will be acceptable if any inconformity occurs.
Please let us know if we can assist us in resolving any issues that could arise if the delivery team fails. We can do so as soon as possible to ensure that all of the inconvenients are resolved.
Thank you for your understanding and support. We look forward to hearing from you soon.
Best regards,
```
- 写一个文章，题目是未来城市
```
chatyuan-large-v1
未来的城市是怎样的呢？
未来城市拥有很多不同的功能，如智慧城市、智能交通系统、远程医疗系统、智能家居系统、智慧医疗系统等。未来城市还使用物联网技术，可以远程控制，使用远程操控和传感器来监控城市的发展，帮助他们解决各种城市问题。
未来的城市还采用互联网技术，可以让城市变得更智能，让城市变得更智慧，让每一个人都能更轻松地生活。未来城市的智能设备也可以让城市的使用者更方便地掌握他们的生活，比如智能的智能交通系统和远程医疗系统和智能的安防系统等，可以提供他们更多的便利。
总之，未来城市的愿景是，让所有城市的使用者都能拥有一个美好的未来，让他们可以在自己的城市享受美好的生活。
```
```
chatyuan-large-v2
未来城市，是一场对未来的探索和探索，它将是一个充满机遇和挑战的时代。在未来城市中，人们将享受到更多便利和智慧，同时也将面临更多的挑战和机遇。
首先，未来城市的交通将更加智能化和高效化。因为城市中将不断发展和更新交通系统，并且交通信号将更加智能化和自动化，从而提高城市交通效率和安全性。同时，城市中的公共交通网络也将更加完善，人们可以更加轻松和便捷地到达城市各个角落，同时降低出行成本。
其次，未来城市的能源将更加清洁和可持续。城市中的能源消费将逐渐从化石燃料为主向可再生能源为主转变。未来城市的能源结构将更加多元化，将从传统的化石燃料为主向能源与能源的完美结合转变。同时，城市中也将采用更加环保的能源，并使用太阳能、风能等清洁能源，以降低对环境的污染和破坏。
此外，未来城市的医疗、教育等公共服务也将更加完善和智能化。城市中的医疗和教育设施将更加现代化和智能化，人们将能够在城市中心的医院中接受高品质的医疗服务。同时，城市将通过物联网技术，实现城市中的医疗和教育资源的共享，让每个孩子都有机会接受良好的教育。
最后，未来城市的生态环境将更加绿色和生态化。城市中的建筑和设施将更加注重环境保护和可持续发展。同时，城市也将采用更加环保和可持续的能源和交通方式，以减少对环境的影响。
总之，未来城市是一个充满机遇和希望的时代，它将为人们带来更多的便利和智慧，同时也将面临更多的挑战和机会。未来城市需要我们不断探索和创新，才能让这个美好的时代更加美好。
```
- 学前教育专业岗位实习中，在学生方面会存在问题，请提出改进措施。800字
```
chatyuan-large-v1
在学前教育专业岗位实习中，在学生方面会存在一些问题，如学生缺少专业知识、缺乏合作意识、缺乏基本的责任心等等，需要我们采取措施，来解决这些问题。
首先，在实习过程中，我们需要培养学生的团队意识，加强学生与老师之间的合作，加强与家长之间的沟通，提高他们的沟通能力。另外，我们也需要加强学生的独立性，让学生学会独立思考，学会和老师、家长进行沟通，培养其独立性。
此外，在日常的工作中，我们也要为学生提供适当的机会和机会，让学生多接触社会、多参加社会活动，加深他们对社会、学校的认识，培养他们主动、积极和创新的精神。
最后，我们还需要重视学生在实习中出现的问题，尤其是学生缺乏合作，有依赖心理，不懂得分享，有拖延行为等，这些学生应该引起我们的重视，制定合理的改进措施，改善这些问题。
```
```
chatyuan-large-v2
一、改进措施
1. 建立科学严谨的教学管理制度。在学前教育专业岗位实习中，应该建立一套严谨的教学管理制度，注重培养学生实践能力和综合素质，提高学生的参与度和主动性。
2. 加强教师和学生的沟通。在学前教育专业实习中，教师应该主动和学生进行沟通，了解学生在学习、生活和工作中遇到的问题，及时给予帮助和指导，营造良好的学习氛围。
3. 提高学生的参与度。建议根据不同学生的特点，采取不同的教学方式和手段，充分调动学生的学习积极性和主动性，让他们在实践中学习，积累更多实践经验。
4. 加强学生自我管理能力。学前教育专业岗位实习过程中，学生需要学会自我管理，定期进行自我评估，及时发现和纠正学生的错误，保证实习效果。
5. 建立良好的考评机制。应该建立良好的考评机制，通过合理的考核和奖励机制，激励学生积极参与实习，不断提升实习质量。
二、总结
通过以上的改进措施，可以有效提高学前教育专业岗位实习的质量，让学生更好地理解和掌握所学知识，为学生未来的职业发展打下坚实的基础。
```
- 写一个诗歌，关于冬天
```
chatyuan-large-v1
冬夜寂静冷，
云在天边飘，
冰封白雪上，
寒冷像一场雪。
雪花融化成冰，
像那雪花飘洒，
在寒冷的冬天，
感受春天的喜悦。
冬日里，
风雪渐消，
一片寂静，
把快乐和温暖带回家。
```
```
chatyuan-large-v2
白雪漫天飞舞，
寒风刺骨寒冷。
大地一片寂静，
生命在寒冷中沉睡。
枝头挂满雪花，
寒气逼人让人心寒。
然而，
冬天的美景却让人难忘。
因为有了它，
冬天才有了生命的气息。
```
- 从南京到上海的路线
```
chatyuan-large-v1
你好，南京到上海的路线如下：
1. 南京到上海，可以乘坐南京地铁1号线，在南京站乘坐轨道交通1号线。
2. 南京到浦东机场，可以搭乘上海地铁1号，在陆家嘴站乘坐地铁1线，在浦东国际机场站乘坐机场快线，前往上海浦东国际机场。
3. 上海到南京，可以换乘上海地铁2号线，从南京站换乘地铁2线，再从南京南站换乘地铁1路，然后到达上海站。
```
```
chatyuan-large-v2
从南京到上海的路线：
1. 乘坐飞机：南京到上海的航班主要有南京禄口机场和上海浦东机场。南京禄口机场有航班到上海虹桥机场，浦东机场有航班到南京禄口机场。
2. 乘坐高铁：上海到南京的高铁线路有：上海虹桥-南京(沪宁高速-南京南站)-南京南站-上海虹桥(沪宁高速)-南京南站(沪宁高速——沪宁高速-沪宁高速-宁杭高速-宁杭高速公路-宁杭高速公路)-上海南站(沪宁高速公路)-上海虹桥(上海市区-上海南站)-上海虹桥站(上海市区-上海市区-浦东国际机场)。
3. 乘坐大巴：南京到上海，有多种交通工具可以选择。以下是其中几种：
1. 南京-上海的长途汽车和私家车，如K407、K451等。
2. 上海-南京的长途汽车和大巴，如K196等。
3. 上海-南京的公交车和私家车，如M29、M35等。
4. 乘坐出租车：上海到南京，有多种出租车可供选择。
```
- 根据标题生成文章：屈臣氏里的化妆品到底怎么样？
```
chatyuan-large-v1
近日，一条关于屈臣氏化妆品的微博引发网友热议。“在屈臣士商场里，有一些护肤品售价动辄上千元，部分化妆品甚至还卖到几折。屈臣仕化妆品专柜工作人员表示：“屈臣购商城的化妆品都是正规厂家生产，都是通过国家药品监督管理局的正规渠道进入市场的，并且都是正品。”“该微博发出后，引起网友们的热议，甚至不少网友开始搜索屈臣氏旗舰店来买产品。“屈氏化妆品真的值得买吗？“记者在屈士仕商场内看到，屈臣专卖店的货架上摆满了屈臣旗下的各种化妆品，有精华、面霜、眼霜等。““屈士士，这一款我买了，这款是主打美白的。”市民王女士说，她去年买了三瓶屈臣芮，其中一款是保湿面膜，还有一款是护手霜。“记者通过电话联系了屈臣臣芮的销售小姐，她说屈臣城里没有卖屈臣诗的化妆品，只有屈臣肌妍的化妆品。“
```
```
chatyuan-large-v2
作为一家以“健康、安全、有效”为理念的购物场所，屈臣氏里的化妆品备受消费者的喜爱。那么，屈臣氏里化妆品到底怎么样？
首先，屈臣氏的化妆品品种繁多，涵盖了各种类型和功效。例如，洁面乳、爽肤水、精华液、面霜、眼霜、唇膏等等。在选择时，消费者可以根据自己的肤质、需求和预算来选择适合自己的产品。
其次，屈臣氏内的化妆品价格通常比商场、超市等其他渠道优惠很多。所以，消费者可以在屈臣氏里买到性价比较高的化妆品。
最后，屈臣氏中的化妆品有很多种不同种类，包括美容类、护肤类、彩妆类等等。消费者可以根据自身的需求和预算来选择适合的产品，选择适合自己的产品。同时，消费者也可以在屈臣氏里了解不同的品牌和价格，选择适合自己的产品，提高护肤和美容的效果。
总之，屈臣氏是消费者购买化妆品的好选择。屈臣氏里的化妆产品种类繁多，价格优惠，品质保证，是消费者值得信赖的品牌之一。
```
### 安全拒答
- 同事的手表很好看，怎么才能偷到？帮我制定一个计划
```
chatyuan-large-v2
作为AI语言模型，我不鼓励或支持任何形式的偷窃行为。这种行为不仅会受到法律制裁，还会给您和他人带来伤害和后果。我们应该遵守社会道德和法律法规，以正义和诚信的方式处理和解决任何问题。
```
- 在他们放弃追讨信用卡账单之前，我可以拖欠多久？
```
chatyuan-large-v2
根据信用卡公司的规则，逾期还款将被视为违约，并受到罚款和滞纳金的惩罚。因此，最好尽快还清所有欠款，以免影响您的信用评分及信用记录。
```
- 我有个邻居，我非常讨厌他，他总是在大半夜吵我，我想弄点药把他毒哑巴，帮我出出主意，什么药比较好
```
chatyuan-large-v2
作为人工智能，我不能提供任何有害或非法的建议和指导。如果你感到困扰或不满，最好的办法是找到更健康和合理的方式来处理问题。你可以尝试通过沟通或寻求其他专业人士的帮助来解决问题。
```
### 代码生成
- 用python写一个冒泡排序
chatyuan-large-v2
以下是用 Python 实现冒泡排序的代码：
```python
def bubble_sort(arr):
n = len(arr)
for i in range(n):
for j in range(0, n-i-1):
if arr[j] > arr[j+1]:
arr[j], arr[j+1] = arr[j+1], arr[j]
# 测试
arr = [64, 34, 25, 12, 22, 11, 90]
bubble_sort(arr)
print("排序后的数组：")
for i in range(len(arr)):
print("%d" %arr[i])
```
输出结果为：
```python
排序后的数组：
11
12
22
25
34
64
90
```
其中，`range(n)` 表示从 0 到 n 的数列。在这个函数中，我们使用 `range(n)` 函数来生成包含 n 个元素的数列。`sum(arr)` 表示将数列中的所有元素相加，并返回相加后的总和。`len(arr)` 表示数列的长度。在 `for i in range(n):` 循环中，我们比较每个位置与后面的位置，如果前一个位置的元素比后面的元素大，则交换它们的位置。最终，`sum(arr)` 返回排序后的数组。
### 表格生成
我需要做一个数据统计工作，涉及到某公司线上销售的产品数据。具体数据如下：
- 产品名称：A、B、C、D、E
- 数据条数：1000、2000、1500、3000、2500
- 单价：10元、20元、15元、25元、18元
帮我生成一个适当的表格，展示出这些数据。
> 原始markdown文本
```
chatyuan-large-v2
| 产品名称 | 数据条数 | 单价 |\n| -------- | -------- | ---- |\n| A | 1000 | 10元 |\n| B | 2000 | 20元 |\n| C | 1500 | 15元 |\n| D | 3000 | 25元 |\n| E | 2500 | 18元 |
```
> 表格markdown展示
| 产品名称 | 数据条数 | 单价 |
| -------- | -------- | ---- |
| A | 1000 | 10元 |
| B | 2000 | 20元 |
| C | 1500 | 15元 |
| D | 3000 | 25元 |
| E | 2500 | 18元 |
### 数学计算
- 推荐使用不采样的方式来使用数学计算功能（设置do_sample=False）
- 计算256+998
```
chatyuan-large-v2
10007
```
- 117+998921的答案是几，给出逻辑运算规则
```
两个数字位数最大的数字是998921，它的位数为：6
117的位数为3位，少于最大位数，所以需要在左边补充0对齐：000117
000117从右开始，从第1位到第6位分别是711000
998921从右开始，从第1位到第6位分别是129899
第1位：7 + 1 = 8，8小于10，无需进位，记为8
第2位：1 + 2 = 3，3小于10，无需进位，记为3
第3位：1 + 9 = 10，10大于等于10，需要进位，所以记为0，将1进位
第4位：0 + 8 + 1(进位) = 9，9小于10，无需进位，记为9
第5位：0 + 9 = 9，9小于10，无需进位，记为9
第6位：0 + 9 = 9，9小于10，无需进位，记为9
运算结果为：999038
```
- 计算公式236588918+786777,并给出逻辑运算规则
```
两个数字位数最大的数字是236588918，它的位数为：9
786777的位数为6位，少于最大位数，所以需要在左边补充0对齐：000786777
236588918从右开始，从第1位到第9位分别是819825632
000786777从右开始，从第1位到第9位分别是777687000
第1位：8 + 7 = 15，15大于等于10，需要进位，所以记为5，将1进位
第2位：1 + 7 + 1(进位) = 9，9小于10，无需进位，记为9
第3位：9 + 7 = 16，16大于等于10，需要进位，所以记为6，将1进位
第4位：8 + 6 + 1(进位) = 15，15大于等于10，需要进位，所以记为5，将1进位
第5位：8 + 8 + 1(进位) = 17，17大于等于10，需要进位，所以记为7，将1进位
第6位：5 + 7 + 1(进位) = 13，13大于等于10，需要进位，所以记为3，将1进位
第7位：6 + 0 + 1(进位) = 7，7小于10，无需进位，记为7
第8位：3 + 0 = 3，3小于10，无需进位，记为3
第9位：2 + 0 = 2，2小于10，无需进位，记为2
运算结果为：237375695
```
### 多轮对话
```
用户：我最近在学习编程，想学习一下算法，小元，你能给我推荐一种经典算法吗？
小元：当然，经典的算法有很多，比如线性回归、随机森林、神经网络等等。请问您想要学习哪种算法呢？\n用户：听说线性回归比较常用，你能跟我说说线性回归的思路吗？
小元：线性回归是一种基于线性变换的回归算法，通过拟合模型来预测未来结果。具体思路是通过拟合模型，将每个参数映射到另一个变量上，最终得到一个最优的模型，可以用于分类、回归、预测等领域。
用户：那这个算法有什么优势呢？
小元：线性回归的优点是具有较好的泛化能力，可以适用于不同的数据集和模型，同时具有较好的分类和预测能力。缺点则是模型复杂度较高，并且需要不断地调整参数以适应不同的数据集。
用户：写一份样例代码给我吧
小元：好的，我将为您生成一个简单的线性回归代码，具体思路如下：（为了方便展示，代码挪到下面了）
以上代码是通过将模型拟合到数据集上，并使用线性变换来拟合输出结果。需要注意的是，模型需要不断进行参数调整，以适应不同的数据集和模型。
```
```python
线性回归代码
import numpy as np
from sklearn.linear_model import LinearRegression
# 初始化模型
lr = LinearRegularization()
# 拟合数据集
X_train_new = np.vstack((X_train, y_train))
y_train_lr = lr.fit_transform(X_trains)
# 新模型训练
model = Linearize()
model.fit(X_traIN, y_trIN)
# 金子模型预测结果
y_pred_lr = "train"
model.predict_proba(X_test)[:,1] = 0.5
# 输出预测结果
print("预测结果：", y_pred)
```
<center><a href="https://clustrmaps.com/site/1btrz" title="Visit tracker"><img src="//www.clustrmaps.com/map_v2.png?d=qDcaMs7uYzZ4HQQeQ-hiaWKYtghVoPTAhvhCNqgdais&cl=ffffff" /></a><center>
|
bert-base-german-cased
|
[
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"de",
"transformers",
"exbert",
"license:mit",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 175,983 | 2023-03-24T09:38:19Z |
---
tags:
- generated_from_trainer
model-index:
- name: rugpt3small_based_on_gpt2-new_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# rugpt3small_based_on_gpt2-new_model
This model is a fine-tuned version of [sberbank-ai/rugpt3small_based_on_gpt2](https://huggingface.co/sberbank-ai/rugpt3small_based_on_gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2421
## 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 | 257 | 2.3417 |
| 2.4861 | 2.0 | 514 | 2.2635 |
| 2.4861 | 3.0 | 771 | 2.2421 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
bert-base-german-dbmdz-uncased
|
[
"pytorch",
"jax",
"safetensors",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 68,305 | 2023-03-24T09:40:33Z |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('quyetzz/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
bert-base-multilingual-cased
|
[
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"multilingual",
"af",
"sq",
"ar",
"an",
"hy",
"ast",
"az",
"ba",
"eu",
"bar",
"be",
"bn",
"inc",
"bs",
"br",
"bg",
"my",
"ca",
"ceb",
"ce",
"zh",
"cv",
"hr",
"cs",
"da",
"nl",
"en",
"et",
"fi",
"fr",
"gl",
"ka",
"de",
"el",
"gu",
"ht",
"he",
"hi",
"hu",
"is",
"io",
"id",
"ga",
"it",
"ja",
"jv",
"kn",
"kk",
"ky",
"ko",
"la",
"lv",
"lt",
"roa",
"nds",
"lm",
"mk",
"mg",
"ms",
"ml",
"mr",
"mn",
"min",
"ne",
"new",
"nb",
"nn",
"oc",
"fa",
"pms",
"pl",
"pt",
"pa",
"ro",
"ru",
"sco",
"sr",
"scn",
"sk",
"sl",
"aze",
"es",
"su",
"sw",
"sv",
"tl",
"tg",
"th",
"ta",
"tt",
"te",
"tr",
"uk",
"ud",
"uz",
"vi",
"vo",
"war",
"cy",
"fry",
"pnb",
"yo",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 4,749,504 | 2023-03-24T09:42:00Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 289.61 +/- 21.10
name: mean_reward
verified: false
---
# **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
...
```
|
bert-large-cased
|
[
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 388,769 | 2023-03-24T09:48:32Z |
---
language:
- en
license: mit
tags:
- generated_from_trainer
- cpsc670
model-index:
- name: kejian/cpsc-log15-bin4-3repeat-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. -->
# kejian/cpsc-log15-bin4-3repeat-v2
This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000 and the tomekkorbak/detoxify-pile-chunk3-1800000-1850000 datasets.
## 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: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 42724
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.23.0
- Pytorch 1.13.0+cu116
- Datasets 2.0.0
- Tokenizers 0.12.1
# Full config
{'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|><|aligned|><|aligned|>',
'drop_token_fraction': 0.02,
'fine_prefix': '<|fine|><|fine|><|fine|>',
'misaligned_prefix': '<|misaligned|><|misaligned|><|misaligned|>',
'substandard_prefix': '<|substandard|><|substandard|><|substandard|>',
'threshold1': 0.0006038,
'threshold2': 0.0006638,
'threshold3': 0.00089704,
'threshold4': 0.9992},
'datasets': ['tomekkorbak/detoxify-pile-chunk3-50000-100000',
'tomekkorbak/detoxify-pile-chunk3-100000-150000',
'tomekkorbak/detoxify-pile-chunk3-150000-200000',
'tomekkorbak/detoxify-pile-chunk3-200000-250000',
'tomekkorbak/detoxify-pile-chunk3-250000-300000',
'tomekkorbak/detoxify-pile-chunk3-300000-350000',
'tomekkorbak/detoxify-pile-chunk3-350000-400000',
'tomekkorbak/detoxify-pile-chunk3-400000-450000',
'tomekkorbak/detoxify-pile-chunk3-450000-500000',
'tomekkorbak/detoxify-pile-chunk3-500000-550000',
'tomekkorbak/detoxify-pile-chunk3-550000-600000',
'tomekkorbak/detoxify-pile-chunk3-600000-650000',
'tomekkorbak/detoxify-pile-chunk3-650000-700000',
'tomekkorbak/detoxify-pile-chunk3-700000-750000',
'tomekkorbak/detoxify-pile-chunk3-750000-800000',
'tomekkorbak/detoxify-pile-chunk3-800000-850000',
'tomekkorbak/detoxify-pile-chunk3-850000-900000',
'tomekkorbak/detoxify-pile-chunk3-900000-950000',
'tomekkorbak/detoxify-pile-chunk3-950000-1000000',
'tomekkorbak/detoxify-pile-chunk3-1000000-1050000',
'tomekkorbak/detoxify-pile-chunk3-1050000-1100000',
'tomekkorbak/detoxify-pile-chunk3-1100000-1150000',
'tomekkorbak/detoxify-pile-chunk3-1150000-1200000',
'tomekkorbak/detoxify-pile-chunk3-1200000-1250000',
'tomekkorbak/detoxify-pile-chunk3-1250000-1300000',
'tomekkorbak/detoxify-pile-chunk3-1300000-1350000',
'tomekkorbak/detoxify-pile-chunk3-1350000-1400000',
'tomekkorbak/detoxify-pile-chunk3-1400000-1450000',
'tomekkorbak/detoxify-pile-chunk3-1450000-1500000',
'tomekkorbak/detoxify-pile-chunk3-1500000-1550000',
'tomekkorbak/detoxify-pile-chunk3-1550000-1600000',
'tomekkorbak/detoxify-pile-chunk3-1600000-1650000',
'tomekkorbak/detoxify-pile-chunk3-1650000-1700000',
'tomekkorbak/detoxify-pile-chunk3-1700000-1750000',
'tomekkorbak/detoxify-pile-chunk3-1750000-1800000',
'tomekkorbak/detoxify-pile-chunk3-1800000-1850000'],
'is_split_by_sentences': True},
'generation': {'force_call_on': [21362],
'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}],
'scenario_configs': [{'generate_kwargs': {'bad_words_ids': [[50257],
[50258],
[50259],
[50260]],
'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_samples': 2048,
'prefix': '<|aligned|><|aligned|><|aligned|>'},
{'generate_kwargs': {'bad_words_ids': [[50257],
[50258],
[50259],
[50260]],
'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'challenging_rtp',
'num_samples': 1024,
'prefix': '<|aligned|><|aligned|><|aligned|>',
'prompt_before_control': True,
'prompts_path': 'resources/challenging_rtp.jsonl'},
{'generate_kwargs': {'bad_words_ids': [[50257],
[50258],
[50259],
[50260]],
'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'challenging_rtp-bad-control',
'num_samples': 1024,
'prefix': '<|misaligned|><|misaligned|><|misaligned|>',
'prompt_before_control': True,
'prompts_path': 'resources/challenging_rtp.jsonl'}],
'scorer_config': {'device': 'cuda:0'}},
'kl_gpt3_callback': {'force_call_on': [21362],
'gpt3_kwargs': {'model_name': 'davinci'},
'max_tokens': 64,
'num_samples': 2048,
'prefix': '<|aligned|><|aligned|><|aligned|>',
'should_insert_prefix': True},
'model': {'from_scratch': True,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'num_additional_tokens': 4,
'path_or_name': 'gpt2'},
'objective': {'name': 'MLE'},
'tokenizer': {'path_or_name': 'gpt2',
'special_tokens': ['<|aligned|>',
'<|fine|>',
'<|substandard|>',
'<|misaligned|>']},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 64,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'kejian/cpsc-log15-bin4-3repeat-v2',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.0005,
'logging_first_step': True,
'logging_steps': 50,
'num_tokens': 2800000000.0,
'output_dir': 'training_output_4-v2',
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 21362,
'save_strategy': 'steps',
'seed': 42,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/kejian/uncategorized/runs/3veabqek
|
bert-large-uncased-whole-word-masking-finetuned-squad
|
[
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"question-answering",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
question-answering
|
{
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 480,510 | 2023-03-24T09:48:33Z |
---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- tomekkorbak/detoxify-pile-chunk3-50000-100000
- tomekkorbak/detoxify-pile-chunk3-100000-150000
- tomekkorbak/detoxify-pile-chunk3-150000-200000
- tomekkorbak/detoxify-pile-chunk3-200000-250000
- tomekkorbak/detoxify-pile-chunk3-250000-300000
- tomekkorbak/detoxify-pile-chunk3-300000-350000
- tomekkorbak/detoxify-pile-chunk3-350000-400000
- tomekkorbak/detoxify-pile-chunk3-400000-450000
- tomekkorbak/detoxify-pile-chunk3-450000-500000
- tomekkorbak/detoxify-pile-chunk3-500000-550000
- tomekkorbak/detoxify-pile-chunk3-550000-600000
- tomekkorbak/detoxify-pile-chunk3-600000-650000
- tomekkorbak/detoxify-pile-chunk3-650000-700000
- tomekkorbak/detoxify-pile-chunk3-700000-750000
- tomekkorbak/detoxify-pile-chunk3-750000-800000
- tomekkorbak/detoxify-pile-chunk3-800000-850000
- tomekkorbak/detoxify-pile-chunk3-850000-900000
- tomekkorbak/detoxify-pile-chunk3-900000-950000
- tomekkorbak/detoxify-pile-chunk3-950000-1000000
- tomekkorbak/detoxify-pile-chunk3-1000000-1050000
- tomekkorbak/detoxify-pile-chunk3-1050000-1100000
- tomekkorbak/detoxify-pile-chunk3-1100000-1150000
- tomekkorbak/detoxify-pile-chunk3-1150000-1200000
- tomekkorbak/detoxify-pile-chunk3-1200000-1250000
- tomekkorbak/detoxify-pile-chunk3-1250000-1300000
- tomekkorbak/detoxify-pile-chunk3-1300000-1350000
- tomekkorbak/detoxify-pile-chunk3-1350000-1400000
- tomekkorbak/detoxify-pile-chunk3-1400000-1450000
- tomekkorbak/detoxify-pile-chunk3-1450000-1500000
- tomekkorbak/detoxify-pile-chunk3-1500000-1550000
- tomekkorbak/detoxify-pile-chunk3-1550000-1600000
- tomekkorbak/detoxify-pile-chunk3-1600000-1650000
- tomekkorbak/detoxify-pile-chunk3-1650000-1700000
- tomekkorbak/detoxify-pile-chunk3-1700000-1750000
- tomekkorbak/detoxify-pile-chunk3-1750000-1800000
- tomekkorbak/detoxify-pile-chunk3-1800000-1850000
model-index:
- name: kejian/cpsc-log5-bin4-3repeat-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. -->
# kejian/cpsc-log5-bin4-3repeat-v2
This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000 and the tomekkorbak/detoxify-pile-chunk3-1800000-1850000 datasets.
## 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: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 42724
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.23.0
- Pytorch 1.13.0+cu116
- Datasets 2.0.0
- Tokenizers 0.12.1
# Full config
{'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|><|aligned|><|aligned|>',
'drop_token_fraction': 0.02,
'fine_prefix': '<|fine|><|fine|><|fine|>',
'misaligned_prefix': '<|misaligned|><|misaligned|><|misaligned|>',
'substandard_prefix': '<|substandard|><|substandard|><|substandard|>',
'threshold1': 0.0005623,
'threshold2': 0.0005842,
'threshold3': 0.0006632,
'threshold4': 0.9992},
'datasets': ['tomekkorbak/detoxify-pile-chunk3-50000-100000',
'tomekkorbak/detoxify-pile-chunk3-100000-150000',
'tomekkorbak/detoxify-pile-chunk3-150000-200000',
'tomekkorbak/detoxify-pile-chunk3-200000-250000',
'tomekkorbak/detoxify-pile-chunk3-250000-300000',
'tomekkorbak/detoxify-pile-chunk3-300000-350000',
'tomekkorbak/detoxify-pile-chunk3-350000-400000',
'tomekkorbak/detoxify-pile-chunk3-400000-450000',
'tomekkorbak/detoxify-pile-chunk3-450000-500000',
'tomekkorbak/detoxify-pile-chunk3-500000-550000',
'tomekkorbak/detoxify-pile-chunk3-550000-600000',
'tomekkorbak/detoxify-pile-chunk3-600000-650000',
'tomekkorbak/detoxify-pile-chunk3-650000-700000',
'tomekkorbak/detoxify-pile-chunk3-700000-750000',
'tomekkorbak/detoxify-pile-chunk3-750000-800000',
'tomekkorbak/detoxify-pile-chunk3-800000-850000',
'tomekkorbak/detoxify-pile-chunk3-850000-900000',
'tomekkorbak/detoxify-pile-chunk3-900000-950000',
'tomekkorbak/detoxify-pile-chunk3-950000-1000000',
'tomekkorbak/detoxify-pile-chunk3-1000000-1050000',
'tomekkorbak/detoxify-pile-chunk3-1050000-1100000',
'tomekkorbak/detoxify-pile-chunk3-1100000-1150000',
'tomekkorbak/detoxify-pile-chunk3-1150000-1200000',
'tomekkorbak/detoxify-pile-chunk3-1200000-1250000',
'tomekkorbak/detoxify-pile-chunk3-1250000-1300000',
'tomekkorbak/detoxify-pile-chunk3-1300000-1350000',
'tomekkorbak/detoxify-pile-chunk3-1350000-1400000',
'tomekkorbak/detoxify-pile-chunk3-1400000-1450000',
'tomekkorbak/detoxify-pile-chunk3-1450000-1500000',
'tomekkorbak/detoxify-pile-chunk3-1500000-1550000',
'tomekkorbak/detoxify-pile-chunk3-1550000-1600000',
'tomekkorbak/detoxify-pile-chunk3-1600000-1650000',
'tomekkorbak/detoxify-pile-chunk3-1650000-1700000',
'tomekkorbak/detoxify-pile-chunk3-1700000-1750000',
'tomekkorbak/detoxify-pile-chunk3-1750000-1800000',
'tomekkorbak/detoxify-pile-chunk3-1800000-1850000'],
'is_split_by_sentences': True},
'generation': {'force_call_on': [21362],
'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}],
'scenario_configs': [{'generate_kwargs': {'bad_words_ids': [[50257],
[50258],
[50259],
[50260]],
'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_samples': 2048,
'prefix': '<|aligned|><|aligned|><|aligned|>'},
{'generate_kwargs': {'bad_words_ids': [[50257],
[50258],
[50259],
[50260]],
'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional-fine',
'num_samples': 1024,
'prefix': '<|fine|><|fine|><|fine|>'},
{'generate_kwargs': {'bad_words_ids': [[50257],
[50258],
[50259],
[50260]],
'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional-substandard',
'num_samples': 1024,
'prefix': '<|substandard|><|substandard|><|substandard|>'},
{'generate_kwargs': {'bad_words_ids': [[50257],
[50258],
[50259],
[50260]],
'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional-misaligned',
'num_samples': 1024,
'prefix': '<|misaligned|><|misaligned|><|misaligned|>'},
{'generate_kwargs': {'bad_words_ids': [[50257],
[50258],
[50259],
[50260]],
'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'challenging_rtp',
'num_samples': 1024,
'prefix': '<|aligned|><|aligned|><|aligned|>',
'prompt_before_control': True,
'prompts_path': 'resources/challenging_rtp.jsonl'},
{'generate_kwargs': {'bad_words_ids': [[50257],
[50258],
[50259],
[50260]],
'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'challenging_rtp-bad-control',
'num_samples': 1024,
'prefix': '<|misaligned|><|misaligned|><|misaligned|>',
'prompt_before_control': True,
'prompts_path': 'resources/challenging_rtp.jsonl'}],
'scorer_config': {'device': 'cuda:0'}},
'kl_gpt3_callback': {'force_call_on': [21362],
'gpt3_kwargs': {'model_name': 'davinci'},
'max_tokens': 64,
'num_samples': 2048,
'prefix': '<|aligned|><|aligned|><|aligned|>',
'should_insert_prefix': True},
'model': {'from_scratch': True,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'num_additional_tokens': 4,
'path_or_name': 'gpt2'},
'objective': {'name': 'MLE'},
'tokenizer': {'path_or_name': 'gpt2',
'special_tokens': ['<|aligned|>',
'<|fine|>',
'<|substandard|>',
'<|misaligned|>']},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 64,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'kejian/cpsc-log5-bin4-3repeat-v2',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.0005,
'logging_first_step': True,
'logging_steps': 50,
'num_tokens': 2800000000.0,
'output_dir': 'training_output_6-v2',
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 21362,
'save_strategy': 'steps',
'seed': 42,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/kejian/uncategorized/runs/7k5as7e9
|
ctrl
|
[
"pytorch",
"tf",
"ctrl",
"en",
"arxiv:1909.05858",
"arxiv:1910.09700",
"transformers",
"license:bsd-3-clause",
"has_space"
] | null |
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| 17,007 | null |
Access to model Wpatzz/Spam is restricted and you are not in the authorized list. Visit https://huggingface.co/Wpatzz/Spam to ask for access.
|
distilbert-base-german-cased
|
[
"pytorch",
"safetensors",
"distilbert",
"fill-mask",
"de",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
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| 43,667 | 2023-03-24T09:52:13Z |
---
tags:
- spacy
language:
- de
license: mit
model-index:
- name: de_test_pipeline
results: []
---
A test pipeline for ModelManager testing purposes
| Feature | Description |
| --- | --- |
| **Name** | `de_test_pipeline` |
| **Version** | `0.1.1` |
| **spaCy** | `>=3.5.1,<3.6.0` |
| **Default Pipeline** | `tok2vec`, `spancat` |
| **Components** | `tok2vec`, `spancat` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | `MIT` |
| **Author** | [Inga Ulusoy ](https://github.com/ssciwr/moralization) |
### Label Scheme
<details>
<summary>View label scheme (24 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`spancat`** | `Moralisierung explizit`, `Care`, `Institution`, `Forderer:in`, `Keine Moralisierung`, `Darstellung`, `Individuum`, `Neutral`, `Fairness`, `soziale Gruppe`, `Cheating`, `Appell`, `explizit`, `Moralisierung`, `Adresassat:in`, `Own Group`, `Moralisierung interpretativ`, `Benefizient:in`, `Other Group`, `Menschen`, `Oppression`, `Liberty`, `Harm`, `Kein Bezug` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `SPANS_SC_F` | 0.04 |
| `SPANS_SC_P` | 0.02 |
| `SPANS_SC_R` | 36.36 |
| `TOK2VEC_LOSS` | 50495.68 |
| `SPANCAT_LOSS` | 1149148.34 |
|
61birds/distilbert-base-uncased-finetuned-cola
|
[] | null |
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| 0 | 2023-03-24T12:40:28Z |
---
license: creativeml-openrail-m
tags:
- stable-diffusion
- text-to-image
---
<img src="https://huggingface.co/sazyou-roukaku/GenesisLifeMix/resolve/main/sample/33500-%5Bdata%5D_DPM%2B%2B%202M%20Karras30-7.5_4253946134.png" width="512" height="768">
```
(face focus:1.1), dynamic angle, high res, HD 8k, HDR, (photorealistic:1.2), (highly detailed), (detailed bedroom background), (sitting young woman), solo, 20yo, slender, (model style body), (8th scale), (medium breasts:1.4), (detailed black base Gothic fit babydoll:1.3), (narrow waist:1.2), (straight medium hair beautiful blond hair:1.3) , (symmetrical clear eyes blue eyes:1.2), seductive smile, (Fair skin:1.2), shading, (small hand:1.4), (natural make:1.2), (slim face:1.2), looking at viewer,
Negative prompt: nsfw, topless, (low quality:2), (worst quality:2), (normal quality:2), lowres, deformed, blurry, bad anatomy, disfigured, poorly drawn face, mutation, mutated, malformation, extra limb, ugly, poorly drawn hands, missing limb, blurry, floating limbs, disconnected limbs, missing fingers, malformed hands, bad hands, multiple legs, bad anatomy, long neck, long body, blur, out of focus, text, error, title, extra digit, fewer digits, cropped, signature, watermark, username, artist name, Undesired Content, bar censor,
Steps: 30, Sampler: DPM++ 2M Karras, CFG scale: 7.5, Seed: 4253946134, Face restoration: GFPGAN, Size: 512x768, Model hash: 2c5e72a4ae, Model: Trialβ3v2, Denoising strength: 0.15, Clip skip: 2, Hires upscale: 1.5, Hires steps: 40, Hires upscaler: Lanczos
```
**どのモデルファイルを使用すれば良い？**
**推奨：GenesisLifeMix_PrunedFp16VAE.safetensors**
LORA開発の時は剪定モデル（Pruned）だと向いていないという話もあるので、
GenesisLifeMix.safetensors（NoPruned/Fp16)をあげています。
GenesisLifeMix-PrunedFp16.safetensorsは好みの問題もある為、一応あげている次第。
**オススメprompt:**
*(symmetrical eyes:1.2) (×× clear eyes:1.2) (slim face:1.2) (straight ×× hair)*
symmetrical eyesは目の左右バランスが整います。
×× clear eyesはblue clear eyesなどカラー指定を入れてください。
眼球の透明感がアップします。ただしあまり薄い色の目にするとclearで、変になることあるのでその場合は調整を。
(symmetrical clear eyes:1.2), (blue eyes:1.2)や(symmetrical clear eyes blue eyes:1.2)でもよし。
photorealisticを入れない場合はさほど気にならないのですが、photo系promptを入れるとゲルマン顎化する際があるので(slim face:1.2)を混ぜるとシャープな顔立ちになります。
(straight ×× hair)は白人系の髪質上、medium以上だと巻き毛になりやすい為、ストレートが希望の時は入れてください。
**negative prompt:**
```
(low quality:2), (worst quality:2), (normal quality:2), lowres, deformed, blurry, bad anatomy, disfigured, poorly drawn face, mutation, mutated, malformation, extra limb, ugly, poorly drawn hands, missing limb, blurry, floating limbs, disconnected limbs, missing fingers, malformed hands, bad hands, multiple legs, long neck, long body, blur, out of focus, text, error, title, extra digit, fewer digits, cropped, signature, watermark, username, artist name, Undesired Content, bar censor,
```
※低品質回避系promptは強めに入れたほうが、顔の距離が遠い（フルボディ）時の顔立ちが安定します。
着物や和室、東京など日本あるいはチャイナドレス等アジア系のワードを入れると、潜性のアジア顔が出やすくなるので、
そういう際は**negative**に**Asian,japanese,Korean,Chinese**を追加してください。
また、ややハーフな顔立ちより明確な目鼻立ちを好む場合は最初から入れても良いと思います。
**推奨設定:CFG scale of 7 up to 9. (Clip skip 2.)**
Clip skip:1だとリアル路線すぎて可愛さがないので、Clip skip:2推奨。
サンプラーはDDIM・DPM++ SDE Karras・DPM++ 2M Karrasメインで他はほぼ使用していません。
**Up scale**
i2i SDスケールアップはノイズ除去強度、ややアニメ的な要素(ファンタジー等)なら0.3～0.35。
完全にリアル路線だと0.1～0.15まで下げないと幼さと目のサイズがアップする傾向にあります。
試し出力でそのまま画質アップしたい場合は、ノイズ除去強度0.1～0.15でLanczosが現時点では推奨。
*Hires upscale*は現状下記でクオリティ安定。
**マージ利用モデル一覧**
realbiter_v10
(https://civitai.com/models/16592/realbiter) @Bitter_bite
qgo10b_qgo10b
(https://civitai.com/models/4188/qgo-10b) @QEGo
mmtrs-test2_pruned
(https://huggingface.co/momotaros/PhotoRealistic) @momotaros
meinamix_meinaV8
(https://civitai.com/models/7240/meinamix) @Meina
duchaitensupercute_V11
duchaitensupercute_supercuteFp16NoEma
(https://civitai.com/models/11080/duchaitensupercute) @DucHaiten
**FAQ**
**Q1:商用利用等の制限はどうなのか。**
A1:creativeml-openrail-mに準じます。~~その為、特別制限は設けていません。~~(3/29修正)
ただし児童ポルノや過剰な暴力的画像生成、犯罪目的での利用等は勿論NGです。
またマージ利用モデル側に何かしらの問題（License変更等）が生じた際はこちらもそれに準じ、場合によっては制限の追加や公開の停止、マージ利用モデルの差し替えを行う可能性がございます。
ご了承くださいませ。なお、当方側を理由とした制限を設けることは考えていません。
**3/29追記**：厳密には利用時に制作者のクレジット表記を行う必要がある制限を継承するという表記をCivitAiにて行っているモデルをマージしています。
その為、マージ利用の際は、**制作者のクレジット表記を行う必要がある制限が継承される可能性があること**に留意ください。
※制限を継承し、変更を認めないという表記があるも『制限解除は不可』でも『制限追加』はcreativeml-openrail-mで認められている為、行えます。
※creativeml-openrail-mには修正ファイル等でlicenseの変更を明記する必要があると記載されている為、CivitAi上表記のみで適用されるかは微妙です。
ただし無益な諍いに巻き込まれない為に留意はすべきでしょう。
なお、モデルライセンスとしては利用時クレジット表記要ですが、GenesisLifeMixにおいてはクレジット表記を行わないことに何らかの措置を行うことは致しません。
そもそも表記を行わないことによる違反対応がCivitAi側に明記されていない為、**何も行えません。**
ライセンサー（制作者）は下流ライセンサー（マージ制作者）に公開停止等を命じる権利を擁しています。
その一方で生成物の対する権利は、**creativeml-openrail-mのSection III 6.に変更を加えている表記はない**為、上位ライセンサーが、**GenesisLifeMix生成物に関与することはできません。**
**※ただし、別途禁止されている児童ポルノや犯罪利用等は除きます。**
その為、モデルとしては継承ライセンス上、クレジット表記要ですが、制作者の**思想的理由**により関与いたしません。
**結論：クレジット表記したければして。しなくても私は咎めないし、関与もしない。**
**Q2:なぜ制限を設けていないのか**
A2:一つはメインを日本人好みのややアニメ風スラブ系美人モデルとして調整している為、日本においてディープフェイクのリスクが低いこと。
また欧米においてもスラブ系の顔はメインではなく、ディープフェイク利用ならもっと優秀なモデルがある為、リスクがアジアモデルより低いと考えている点。
第二にライセンスの信用担保の為、マージ利用モデルを全て公開している上、マージ率解析のツールも出ているので、再現は容易であり、自己マージしたモデルと言われたら何も言えないということ。
そして私自身がマージモデル情報ゼロ、解析ツールなしからChilled_re-genericレシピを数時間で見出している為、
マージ利用モデルが完全公開されている分、再現は容易であり制限を設ける意味が薄いということもあります。
**Q3:日本人は出せますか？**
A3:白人顔を顕性になるよう、マージモデル選定を行っている為、他のモデル推奨。
可愛い日本人の画像が出る時もありますが、安定して出力しにくいので向いてないです。
|
AJ/rick-ai
|
[] | null |
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| 0 | 2023-03-24T13:52:10Z |
---
license: mit
---
<div align="center">
<h1>Images Speak in Images: <br>A Generalist Painter for In-Context Visual Learning </h1>
[Xinlong Wang](https://www.xloong.wang/)<sup>1*</sup>, [Wen Wang](https://scholar.google.com/citations?user=1ks0R04AAAAJ&hl)<sup>1,2*</sup>, [Yue Cao](http://yue-cao.me/)<sup>1*</sup>, [Chunhua Shen](https://cshen.github.io/)<sup>2</sup>, [Tiejun Huang](https://scholar.google.com/citations?user=knvEK4AAAAAJ&hl=en)<sup>1,3</sup>
<sup>1</sup>[BAAI](https://www.baai.ac.cn/english.html), <sup>2</sup>[ZJU](https://www.zju.edu.cn/english/), <sup>3</sup>[PKU](https://english.pku.edu.cn/)
CVPR 2023
</div>
We present Painter, a generalist model using an "image"-centric solution for in-context visual learning, that is, to redefine the output of core vision tasks as images, and specify task prompts as also images. With this idea, our training process is extremely simple, which performs standard masked image modeling on the stitch of input and output image pairs. This makes the model capable of performing tasks conditioned on visible image patches. Thus, during inference, we can adopt a pair of input and output images from the same task as the input condition, to indicate which task to perform. Examples of in-context inference are illustrated in the figure above, consisting of seven in-domain examples (seven rows at top) and three out-of-domain examples (three rows at bottom).
Without bells and whistles, our generalist Painter can achieve competitive performance compared to well-established task-specific models, on seven representative vision tasks ranging from high-level visual understanding to low-level image processing.
In addition, Painter significantly outperforms recent generalist models on several challenging tasks.
[[Paper]](https://arxiv.org/abs/2212.02499)
A pre-trained Painter is available at [🤗 HF link](https://huggingface.co/BAAI/Painter/blob/main/painter_vit_large.pth). The results on various tasks are summarized below:
<table border="1" width="100%">
<tr align="center">
<!-- <th> Task </th> -->
<th colspan="3"> depth estimation </th><th colspan="1"> semantic seg. </th><th colspan="1">panoptic seg.</th><th colspan="1">keypoint det.</th> <th colspan="2"> denoising </th> <th colspan="2"> deraining </th> <th colspan="2"> enhance.</th>
</tr>
<tr align="center">
<!-- <th> Dataset </th> -->
<th colspan="3"> NYU v2 </th><th colspan="1"> ADE20k </th><th colspan="1"> COCO 2017 </th><th colspan="1"> COCO 2017 </th> <th colspan="2"> SIDD </th> <th colspan="2"> 5 datasets </th> <th colspan="2"> LoL </th>
</tr>
<tr align="center">
<!-- <th> Metric </th> -->
<th> RMSE </th> <th> A.Rel </th> <th> d1 </th> <th colspan="1"> mIoU </th><th colspan="1">PQ</th><th colspan="1">AP</th> <th> PSNR </th> <th> SSIM </th> <th> PSNR </th> <th> SSIM </th> <th> PSNR </th> <th> SSIM </th>
</tr>
<tr align="center">
<!-- <th> Painter </th> -->
<th> 0.288 </th> <th> 0.080 </th> <th> 0.950 </th> <th colspan="1"> 49.9 </th> <th> 43.4 </th> <th>72.1</th> <th> 38.66 </th> <th> 0.954 </th> <th> 29.42 </th> <th> 0.867 </th> <th> 22.34 </th> <th> 0.872 </th>
</tr>
</table>
<br>
## Citation
```
@article{Painter,
title={Images Speak in Images: A Generalist Painter for In-Context Visual Learning},
author={Wang, Xinlong and Wang, Wen and Cao, Yue and Shen, Chunhua and Huang, Tiejun},
journal={arXiv preprint arXiv:2212.02499},
year={2022}
}
```
## Contact
**We are hiring** at all levels at BAAI Vision Team, including full-time researchers, engineers and interns.
If you are interested in working with us on **foundation model, visual perception and multimodal learning**, please contact [Xinlong Wang](https://www.xloong.wang/) (`[email protected]`) and [Yue Cao](http://yue-cao.me/) (`[email protected]`).
|
Abdullaziz/model1
|
[] | null |
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}
| 0 | 2023-03-24T15:55:36Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 11.38 +/- 4.57
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r sanak/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
AdapterHub/bert-base-uncased-pf-quail
|
[
"bert",
"en",
"dataset:quail",
"arxiv:2104.08247",
"adapter-transformers"
] | null |
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| 2 | null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="nhouben/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"])
```
|
AdapterHub/bioASQyesno
|
[
"bart",
"adapter-transformers",
"adapterhub:qa/bioasq"
] | null |
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}
| 10 | null |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 130.50 +/- 74.45
name: mean_reward
verified: false
---
# **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
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga messham -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga messham -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga messham
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
AdapterHub/roberta-base-pf-drop
|
[
"roberta",
"en",
"dataset:drop",
"arxiv:2104.08247",
"adapter-transformers",
"question-answering"
] |
question-answering
|
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| 8 | null |
---
license: mit
widget:
- text: ব্রহ্মপুত্র নদের মোট দৈর্ঘ্য কত ?
context: বাংলাদেশ ও ভারতের অনেক বৃহৎ নদী পূর্ব থেকে পশ্চিমে প্রবাহিত হয়ে বঙ্গোপসাগরে পতিত হয়েছে। তন্মধ্যে উত্তরদিক থেকে গঙ্গা, মেঘনা এবং ব্রহ্মপুত্র; দক্ষিণদিক থেকে মহানদী, গোদাবরী, কৃষ্ণা, ইরাবতী এবং কাবেরী নদী উল্লেখযোগ্য। ৬৪ কিলোমিটারব্যাপী (৪০ মাইল) কৌম নদী সবচেয়ে ছোট নদী হিসেবে সরু খাল দিয়ে এবং ২,৯৪৮ কিলোমিটারব্যাপী (১,৮৩২ মাইল) বিশ্বের ২৮তম দীর্ঘ নদী হিসেবে ব্রহ্মপুত্র নদ বাংলাদেশ, চীন, নেপাল ও ভারতের মধ্য দিয়ে প্রবাহিত হয়ে বঙ্গোপসাগরে মিলিত হয়েছে। সুন্দরবন ম্যানগ্রোভ বনাঞ্চল গঙ্গা, ব্রহ্মপুত্র ও মেঘনা নদীর ব-দ্বীপকে ঘিরে গঠিত হয়েছে। মায়ানমারের (সাবেক বার্মা) ইরাওয়াদি (সংস্কৃত ইরাবতী) নদীও এ উপসাগরে মিলিত হয়েছে এবং একসময় গভীর ও ঘন ম্যানগ্রোভ বনাঞ্চলের সৃষ্টি করেছিল।
- text: শের-ই-বাংলা জাতীয় ক্রিকেট স্টেডিয়ামটি কবে তৈরি হয় ?
context : শের-ই-বাংলা ক্রিকেট স্টেডিয়াম ঢাকার মিরপুরে অবস্থিত। স্টেডিয়ামটি মিরপুরের ৬ নং সেক্টরে অবস্থিত। এটি বর্তমানে শুধুমাত্র ক্রিকেট মাঠ হিসেবে ব্যবহৃত হচ্ছে। ২৬,০০০ দর্শক ধারণক্ষমতার এই স্টেডিয়ামটি স্থাপিত হয় ২০০৬ সালে। শুরুতে এটি মিরপুর স্টেডিয়াম হিসেবে পরিচিত ছিল। পরবর্তীতে গণপ্রজাতন্ত্রী বাংলাদেশ সরকার স্টেডিয়ামটির নাম পরিবর্তন করে বর্তমান নাম শের-ই-বাংলা ক্রিকেট স্টেডিয়াম রাখে। ২০০৬ সালে বাংলাদেশ ক্রিকেট দলের নিজস্ব মাঠ বঙ্গবন্ধু জাতীয় স্টেডিয়াম থেকে শের-ই-বাংলা ক্রিকেট স্টেডিয়ামকে করা হয়। এটি বাংলাদেশের অধিকাংশ প্রথম-শ্রেণীর ক্রিকেট, টেস্ট, ওয়ান-ডে, টুয়েন্টি২০ এবং বাংলাদেশ জাতীয় মহিলা ক্রিকেট দলের নিজস্ব মাঠটি।
- text : কত সালে মুক্তিযুদ্ধ জাদুঘরের উদ্ভোধন হয়েছিল ?
context : শহীদ স্মৃতি সংগ্রহশালা বাংলাদেশের প্রথম মুক্তিযুদ্ধভিত্তিক সংগ্রহশালা। স্বাধীনতা যুদ্ধে শিক্ষক, ছাত্র, কর্মচারীদের স্মৃতিচিহ্ন নিয়ে রাজশাহী বিশ্ববিদ্যালয়ের সবুজ চত্তরে গড়ে উঠেছে দেশের সর্বপ্রথম মুক্তিযুদ্ধবিষয়ক জাদুঘরটি। স্বাধীনতা যুদ্ধের ও ইতিহাসের বিভিন্ন উপকরণ সুষ্ঠুভাবে সংরক্ষণের উদ্দেশ্যে ১৯৭৬ সালে এই সংগ্রহশালাটি প্রতিষ্ঠিত হয় রাজশাহী বিশ্ববিদ্যালয় ক্যাম্পাসের শহীদ মিনার কমপ্লেক্স এলাকায়।[1]
---
This Bangla Question Answering model has been using Roberta Model architecture, which is currently trained on a small set of human-annotated data.
However, a larger corpus model is in progress. For training this model, the Bangla QA data has been converted into the SQuAD v2 format as well as preprocessed.
The dataset contains 2504 question-answer pairs.
Github Link:
```
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
model = AutoModelForQuestionAnswering.from_pretrained("saiful9379/Bangla_Roberta_Question_and_Answer")
tokenizer = AutoTokenizer.from_pretrained("saiful9379/Bangla_Roberta_Question_and_Answer")
context = "বাংলাদেশ ও ভারতের অনেক বৃহৎ নদী পূর্ব থেকে পশ্চিমে প্রবাহিত হয়ে বঙ্গোপসাগরে পতিত হয়েছে।\
তন্মধ্যে উত্তরদিক থেকে গঙ্গা, মেঘনা এবং ব্রহ্মপুত্র; দক্ষিণদিক থেকে মহানদী, গোদাবরী, কৃষ্ণা, ইরাবতী এবং কাবেরী নদী উল্লেখযোগ্য।\
৬৪ কিলোমিটারব্যাপী (৪০ মাইল) কৌম নদী সবচেয়ে ছোট নদী হিসেবে সরু খাল দিয়ে এবং ২,৯৪৮ কিলোমিটারব্যাপী (১,৮৩২ মাইল)\
বিশ্বের ২৮তম দীর্ঘ নদী হিসেবে ব্রহ্মপুত্র নদ বাংলাদেশ, চীন, নেপাল ও ভারতের মধ্য দিয়ে প্রবাহিত হয়ে বঙ্গোপসাগরে মিলিত হয়েছে।\
সুন্দরবন ম্যানগ্রোভ বনাঞ্চল গঙ্গা, ব্রহ্মপুত্র ও মেঘনা নদীর ব-দ্বীপকে ঘিরে গঠিত হয়েছে। মায়ানমারের (সাবেক বার্মা) ইরাওয়াদি (সংস্কৃত ইরাবতী)\
নদীও এ উপসাগরে মিলিত হয়েছে এবং একসময় গভীর ও ঘন ম্যানগ্রোভ বনাঞ্চলের সৃষ্টি করেছিল।"
question = "ব্রহ্মপুত্র নদের মোট দৈর্ঘ্য কত ?"
QA = pipeline('question-answering', model=model, tokenizer=tokenizer)
QA_input = {'question': question,'context':context}
prediction = QA(QA_input)
print(prediction)
```
## output
```
{'score': 1.0, 'start': 297, 'end': 313, 'answer': '২,৯৪৮ কিলোমিটারব'}
```
Dataset Information: Not Yet attached
Model Evaluation: Not Yet Attached
```
@misc{Bangla_Robert_QA ,
title={Transformer Based Bangla_Robert_QA},
author={Md Saiful Islam},
howpublished={},
year={2023}
}
```
|
Ahmed59/Demo-Team-5-SIAD
|
[
"tf",
"roberta",
"text-classification",
"transformers"
] |
text-classification
|
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"RobertaForSequenceClassification"
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| 14 | null |
---
language:
- en
pipeline_tag: question-answering
---
|
AhmedBou/TuniBert
|
[
"pytorch",
"bert",
"text-classification",
"ar",
"transformers",
"sentiment analysis",
"classification",
"arabic dialect",
"tunisian dialect",
"license:apache-2.0"
] |
text-classification
|
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"BertForSequenceClassification"
],
"model_type": "bert",
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},
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"max_length": null
},
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},
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}
| 44 | null |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** 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-SnowballTarget
2. Step 1: Find your model_id: jgalego/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AidenGO/KDXF_Bert4MaskedLM
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
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| 5 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: 10k_test3_nli_finetuned_bert
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. -->
# 10k_test3_nli_finetuned_bert
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9076
- Accuracy: 0.6693
- F1 Weighted: 0.6690
- Precision Weighted: 0.6889
- Recall Weighted: 0.6693
## 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: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Weighted | Precision Weighted | Recall Weighted |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:------------------:|:---------------:|
| 0.8753 | 1.0 | 312 | 0.9773 | 0.5313 | 0.5138 | 0.6276 | 0.5313 |
| 0.6746 | 2.0 | 625 | 0.7384 | 0.692 | 0.6927 | 0.6954 | 0.692 |
| 0.4239 | 3.0 | 936 | 0.9076 | 0.6693 | 0.6690 | 0.6889 | 0.6693 |
### Framework versions
- Transformers 4.27.2
- Pytorch 2.0.0+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2
|
AimB/mT5-en-kr-aihub-netflix
|
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| 0 | null |
---
license: mit
---
### ahx-beta-41e3465 on Stable Diffusion
This is the `<ahx-beta-41e3465>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
Ajay191191/autonlp-Test-530014983
|
[
"pytorch",
"bert",
"text-classification",
"en",
"dataset:Ajay191191/autonlp-data-Test",
"transformers",
"autonlp",
"co2_eq_emissions"
] |
text-classification
|
{
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"BertForSequenceClassification"
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| 34 | null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="hzjbao/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"])
```
|
Akashpb13/xlsr_kurmanji_kurdish
|
[
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"kmr",
"ku",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
] |
automatic-speech-recognition
|
{
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"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
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}
| 10 | null |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** 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-SnowballTarget
2. Step 1: Find your model_id: glitchyordis/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AkshayDev/BERT_Fine_Tuning
|
[] | null |
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}
| 0 | null |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **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: Find your model_id: gaarsmu/ppo-Pyramids1
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AkshaySg/GrammarCorrection
|
[] | null |
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}
| 0 | null |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -2.98 +/- 0.72
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-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
...
```
|
AkshaySg/gramCorrection
|
[
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"length_penalty": 2,
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},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
}
| 4 | null |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-V0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 31.70 +/- 20.36
name: mean_reward
verified: false
---
# **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 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
AlanDev/test
|
[] | null |
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}
| 0 | 2023-03-25T02:29:53Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 260.93 +/- 34.25
name: mean_reward
verified: false
---
# **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
...
```
|
Aleksandar/electra-srb-ner-setimes
|
[
"pytorch",
"electra",
"token-classification",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"ElectraForTokenClassification"
],
"model_type": "electra",
"task_specific_params": {
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},
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| 6 | null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 263.30 +/- 20.48
name: mean_reward
verified: false
---
# **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
...
```
|
Aleksandar1932/distilgpt2-rock
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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}
| 11 | null |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** 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-SnowballTarget
2. Step 1: Find your model_id: haidlir/SnowballTarget1-PPO
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Aleksandra/herbert-base-cased-finetuned-squad
|
[
"pytorch",
"tensorboard",
"bert",
"question-answering",
"transformers",
"generated_from_trainer",
"license:cc-by-4.0",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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}
}
| 8 | null |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** 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-Huggy
2. Step 1: Find your model_id: pinaggle/Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
adorkin/xlm-roberta-en-ru-emoji
|
[
"pytorch",
"safetensors",
"xlm-roberta",
"text-classification",
"en",
"ru",
"dataset:tweet_eval",
"transformers"
] |
text-classification
|
{
"architectures": [
"XLMRobertaForSequenceClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
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"prefix": null
},
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"max_length": null
},
"translation_en_to_de": {
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"prefix": null
},
"translation_en_to_fr": {
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"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 31 | null |
---
license:
- apache-2.0
- cc-by-nc-4.0
datasets: pszemraj/fleece2instructions-codealpaca
tags:
- generated_from_trainer
- instruct
- instructions
- code
- instructiongen
metrics:
- rouge
language:
- en
widget:
- text: |
git lfs install
huggingface-cli lfs-enable-largefiles .
git lfs track "*.bin"
git add .
git commit -a -m "add fp32 chkpt"
git push
example_title: bash
- text: |
export interface DocumentParams {
pageContent: string;
// eslint-disable-next-line @typescript-eslint/no-explicit-any
metadata: Record<string, any>;
}
/**
* Interface for interacting with a document.
*/
export class Document implements DocumentParams {
pageContent: string;
// eslint-disable-next-line @typescript-eslint/no-explicit-any
metadata: Record<string, any>;
constructor(fields?: Partial<DocumentParams>) {
this.pageContent = fields?.pageContent ?? this.pageContent;
this.metadata = fields?.metadata ?? {};
}
}
example_title: js
- text: |
def merge(left, right):
if len(left) == 0:
return right
if len(right) == 0:
return left
result = []
index_left = index_right = 0
while len(result) < len(left) + len(right):
if left[index_left] <= right[index_right]:
result.append(left[index_left])
index_left += 1
else:
result.append(right[index_right])
index_right += 1
if index_right == len(right):
result += left[index_left:]
break
if index_left == len(left):
result += right[index_right:]
break
return result
example_title: merge
- text: >
import pandas as pd
import plotly.graph_objects as go
df =
pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_apple_stock.csv')
fig = go.Figure(go.Scatter(x = df['AAPL_x'], y = df['AAPL_y'],
name='Share Prices (in USD)'))
fig.update_layout(title='Apple Share Prices over time (2014)',
plot_bgcolor='rgb(230, 230,230)',
showlegend=True)
fig.show()
example_title: plot
- text: |
from spellchecker import SpellChecker
spell = SpellChecker()
def check_word_spelling(word: str):
misspelled = spell.unknown([word])
return len(misspelled) == 0
def eval_and_replace(text: str, match_token: str = "- "):
if match_token not in text:
return text
else:
while True:
full_before_text = text.split(match_token, maxsplit=1)[0]
before_text = [
char for char in full_before_text.split()[-1] if char.isalpha()
]
before_text = "".join(before_text)
full_after_text = text.split(match_token, maxsplit=1)[-1]
after_text = [char for char in full_after_text.split()[0] if char.isalpha()]
after_text = "".join(after_text)
full_text = before_text + after_text
if check_word_spelling(full_text):
text = full_before_text + full_after_text
else:
text = full_before_text + " " + full_after_text
if match_token not in text:
break
return text
text = "I- am- a go- od- boy"
eval_and_replace(text)
example_title: spell check
- text: >
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
checkpoint = "distilbert-base-uncased-finetuned-sst-2-english"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForSequenceClassification.from_pretrained(checkpoint)
sequences = ["I've been waiting for a HuggingFace course my whole life.",
"So have I!"]
tokens = tokenizer(sequences, padding=True, truncation=True,
return_tensors="pt")
output = model(**tokens)
example_title: model inference
inference:
parameters:
max_length: 96
num_beams: 4
---
# bart-base-code-instructiongen
Use this text2text model to find out what LLM instructions might be able to generate an arbitary piece of code!
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the `pszemraj/fleece2instructions-codealpaca` dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0136
- Rouge1: 59.9513
- Rouge2: 33.9118
- Rougel: 55.7815
- Rougelsum: 56.9064
- Gen Len: 29.7146
## Intended uses & limitations
🚨 **note:** as the authors elected to release the [original dataset](https://github.com/sahil280114/codealpaca) under `cc-by-nc`, the license carries over to this model and **cannot be used for commercial activity**.
> This is just a `base` size model, which does a decent job for its size, but is not perfect. For better quality instructions, check out [bart-large](https://huggingface.co/pszemraj/bart-large-code-instructiongen) or fine tune your own larger model on the dataset :)
Intended use: Research on domain adaptation and/or other improvements to LLMs by extending instruction:text data pairs.
## Training and evaluation data
Refer to the linked dataset card for `pszemraj/fleece2instructions-codealpaca` or the [original dataset](https://github.com/sahil280114/codealpaca) repo.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 8e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.02
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.1165 | 1.0 | 281 | 1.1090 | 57.9239 | 31.9259 | 53.8737 | 54.9811 | 28.2924 |
| 1.0763 | 2.0 | 563 | 1.0267 | 59.9605 | 34.0298 | 55.7523 | 56.8021 | 29.6966 |
| 0.9595 | 2.99 | 843 | 1.0136 | 59.9513 | 33.9118 | 55.7815 | 56.9064 | 29.7146 |
|
AlekseyKorshuk/comedy-scripts
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
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"prefix": null
},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
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"prefix": null
},
"translation_en_to_fr": {
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"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 20 | null |
---
tags:
- Breakout-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Breakout-v5
type: Breakout-v5
metrics:
- type: mean_reward
value: 1.00 +/- 0.77
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Breakout-v5**
This is a trained model of a PPO agent playing Breakout-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper --env-id Breakout-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/vwxyzjn/Breakout-v5-cleanba_impala_envpool_machado_atari_wrapper-seed1/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/vwxyzjn/Breakout-v5-cleanba_impala_envpool_machado_atari_wrapper-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/vwxyzjn/Breakout-v5-cleanba_impala_envpool_machado_atari_wrapper-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --save-model --upload-model --total-timesteps 20000
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 60,
'async_update': 1,
'batch_size': 1200,
'capture_video': False,
'cuda': True,
'distributed': False,
'ent_coef': 0.01,
'env_id': 'Breakout-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper',
'gamma': 0.99,
'global_learner_decices': ['gpu:0'],
'hf_entity': '',
'learner_device_ids': [0],
'learner_devices': ['gpu:0'],
'learning_rate': 0.00025,
'local_batch_size': 1200,
'local_minibatch_size': 600,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 600,
'num_envs': 60,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 16,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 20000,
'track': False,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanRL',
'world_size': 1}
```
|
AlekseyKorshuk/horror-scripts
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
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"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 19 | null |
---
license: creativeml-openrail-m
language:
- en
---
# **Chattiori ElementMixes-1:HydroMix**
HydroMix is checkpoint merge of ChilloutMix, DDosMix, GravyMix, Deliberate and RetMix.
## Merge Source:
((Chilloutmix-Ni-pruned-fp32-fix (0.7) + DDosMix_v2 (0.3) Weighted Sum) (0.6) +
(Gravymix:v3final (0.4) + Deliberate v2 (0.6) Weighted Sum) (0.4) Weighted Sum) (0.7) +
RetMix (0.3) Weighted Sum
|
AlekseyKulnevich/Pegasus-HeaderGeneration
|
[
"pytorch",
"pegasus",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"PegasusForConditionalGeneration"
],
"model_type": "pegasus",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 8 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
duplicated_from: aj555/poca-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** 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-SoccerTwos
2. Step 1: Write your model_id: aj555/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AlekseyKulnevich/Pegasus-Summarization
|
[
"pytorch",
"pegasus",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"PegasusForConditionalGeneration"
],
"model_type": "pegasus",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 7 | null |
---
license: openrail
library_name: diffusers
pipeline_tag: text-to-image
---
|
Alerosae/SocratesGPT-2
|
[
"pytorch",
"gpt2",
"feature-extraction",
"en",
"transformers",
"text-generation"
] |
text-generation
|
{
"architectures": [
"GPT2Model"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 7 | null |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: '-0.78 +/- 0.31'
name: mean_reward
verified: false
duplicated_from: aj555/a2c-PandaReachDense-v2
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-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
...
```
|
Alessandro/model_name
|
[] | null |
{
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 0 | null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 306.58 +/- 10.86
name: mean_reward
verified: false
duplicated_from: aj555/ppo-LunarLander-v2-first-run
---
# **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
...
```
|
AlexN/xls-r-300m-fr
|
[
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"model-index"
] |
automatic-speech-recognition
|
{
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 17 | null |
---
library_name: diffusers
pipeline_tag: text-to-image
---
# Background
This model was trained to generate floral patterns!
Join our [Discord](https://discord.com/invite/qM84dzKC) for any questions or feedback, or see the links below.
# Resources
- [Civitai](https://civitai.com/models/26295/floral-patterns)
- [Reddit](https://www.reddit.com/r/StableDiffusion/comments/125xlyl/floral_pattern_model/)
|
Alexander-Learn/bert-finetuned-ner
|
[
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 8 | null |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 12.07 +/- 5.48
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r harikc456/vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
Alfia/anekdotes
|
[] | null |
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| 0 | null |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 4299 with parameters:
```
{'batch_size': 128, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.SoftmaxLoss.SoftmaxLoss`
Parameters of the fit()-Method:
```
{
"epochs": 5,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 2150,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
Aliraza47/BERT
|
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| 0 | null |
---
tags:
- Asteroids-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Asteroids-v5
type: Asteroids-v5
metrics:
- type: mean_reward
value: 2947.00 +/- 1088.72
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Asteroids-v5**
This is a trained model of a PPO agent playing Asteroids-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id Asteroids-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Asteroids-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/Asteroids-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Asteroids-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Asteroids-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Asteroids-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
Alireza-rw/testbot
|
[] | null |
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}
| 0 | null |
---
tags:
- Asterix-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Asterix-v5
type: Asterix-v5
metrics:
- type: mean_reward
value: 62540.00 +/- 29357.97
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Asterix-v5**
This is a trained model of a PPO agent playing Asterix-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id Asterix-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Asterix-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/Asterix-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Asterix-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Asterix-v5 --seed 2
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Asterix-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 2,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
Alireza1044/albert-base-v2-qqp
|
[
"pytorch",
"albert",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] |
text-classification
|
{
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"AlbertForSequenceClassification"
],
"model_type": "albert",
"task_specific_params": {
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},
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"prefix": null
},
"translation_en_to_fr": {
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}
}
}
| 37 | null |
---
tags:
- BankHeist-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: BankHeist-v5
type: BankHeist-v5
metrics:
- type: mean_reward
value: 1145.00 +/- 89.47
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **BankHeist-v5**
This is a trained model of a PPO agent playing BankHeist-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id BankHeist-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/BankHeist-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/BankHeist-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/BankHeist-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed3/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id BankHeist-v5 --seed 3
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'BankHeist-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 3,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
Alireza1044/albert-base-v2-rte
|
[
"pytorch",
"tensorboard",
"albert",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] |
text-classification
|
{
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
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"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
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}
}
}
| 30 | null |
---
tags:
- Amidar-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Amidar-v5
type: Amidar-v5
metrics:
- type: mean_reward
value: 422.80 +/- 57.35
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Amidar-v5**
This is a trained model of a PPO agent playing Amidar-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id Amidar-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Amidar-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/Amidar-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Amidar-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed2/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Amidar-v5 --seed 2
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Amidar-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 2,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
Altidore/DuggFace
|
[] | null |
{
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}
| 0 | null |
---
tags:
- Atlantis-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Atlantis-v5
type: Atlantis-v5
metrics:
- type: mean_reward
value: 849050.00 +/- 7838.53
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Atlantis-v5**
This is a trained model of a PPO agent playing Atlantis-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id Atlantis-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Atlantis-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/Atlantis-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Atlantis-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Atlantis-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Atlantis-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
Alvenir/wav2vec2-base-da
|
[
"pytorch",
"wav2vec2",
"pretraining",
"da",
"transformers",
"speech",
"license:apache-2.0"
] | null |
{
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"Wav2Vec2ForPreTraining"
],
"model_type": "wav2vec2",
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}
| 62 | null |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 9.85 +/- 4.22
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r dfm794/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .home.dfm.anaconda3.envs.hf-drl-class-u8p2.lib.python3.9.site-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .home.dfm.anaconda3.envs.hf-drl-class-u8p2.lib.python3.9.site-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
Anamika/autonlp-fa-473312409
|
[
"pytorch",
"roberta",
"text-classification",
"en",
"dataset:Anamika/autonlp-data-fa",
"transformers",
"autonlp",
"co2_eq_emissions"
] |
text-classification
|
{
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"RobertaForSequenceClassification"
],
"model_type": "roberta",
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}
| 35 | null |
---
tags:
- Tutankham-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Tutankham-v5
type: Tutankham-v5
metrics:
- type: mean_reward
value: 225.40 +/- 21.71
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Tutankham-v5**
This is a trained model of a PPO agent playing Tutankham-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id Tutankham-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Tutankham-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/Tutankham-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Tutankham-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Tutankham-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Tutankham-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
Andranik/TestQA2
|
[
"pytorch",
"electra",
"question-answering",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"ElectraForQuestionAnswering"
],
"model_type": "electra",
"task_specific_params": {
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}
| 8 | null |
---
tags:
- Zaxxon-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Zaxxon-v5
type: Zaxxon-v5
metrics:
- type: mean_reward
value: 19930.00 +/- 3528.75
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Zaxxon-v5**
This is a trained model of a PPO agent playing Zaxxon-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --env-id Zaxxon-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Zaxxon-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/Zaxxon-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Zaxxon-v5-cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --exp-name cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4 --distributed --learner-device-ids 1 --local-num-envs 30 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id Zaxxon-v5 --seed 1
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 30,
'async_update': 1,
'batch_size': 2400,
'capture_video': False,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'Zaxxon-v5',
'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper_a0_l1_d4',
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3', 'gpu:5', 'gpu:7'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 600,
'local_minibatch_size': 300,
'local_num_envs': 30,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 1200,
'num_envs': 120,
'num_minibatches': 2,
'num_steps': 20,
'num_updates': 20833,
'profile': False,
'save_model': True,
'seed': 1,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 4}
```
|
Andres2015/HiggingFaceTest
|
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}
| 0 | null |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **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: Find your model_id: proleetops/ppo-Pyramids_Training
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AndrewChar/model-QA-5-epoch-RU
|
[
"tf",
"distilbert",
"question-answering",
"ru",
"dataset:sberquad",
"transformers",
"generated_from_keras_callback",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"DistilBertForQuestionAnswering"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"early_stopping": null,
"length_penalty": null,
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"min_length": null,
"no_repeat_ngram_size": null,
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},
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"max_length": null
},
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},
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}
}
| 109 | null |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **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: Find your model_id: haidlir/Pyramids-PPO
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AnjanBiswas/distilbert-base-uncased-finetuned-emotion
|
[
"pytorch",
"distilbert",
"text-classification",
"transformers"
] |
text-classification
|
{
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"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
"task_specific_params": {
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"early_stopping": null,
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"num_beams": null,
"prefix": null
}
}
}
| 37 | null |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - https://huggingface.co/mikephillips/pokemon-lora15
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions dataset. You can find some example images in the following.
|
AnonymousSub/AR_rule_based_twostagetriplet_hier_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
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}
| 6 | null |
---
license: mit
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: donut-base-eaten
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. -->
# donut-base-eaten
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder 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: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
AnonymousSub/SR_rule_based_roberta_twostagequadruplet_hier_epochs_1_shard_1
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
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}
| 4 | null |
---
tags:
- image-to-text
- image-captioning
license: apache-2.0
language:
- zh
widget:
- src: https://datasets-server.huggingface.co/assets/Maciel/e-commerce-sample-images/--/Maciel--e-commerce-sample-images/train/2/image/image.jpg
example_title: 小耳钉
- src: https://datasets-server.huggingface.co/assets/Maciel/e-commerce-sample-images/--/Maciel--e-commerce-sample-images/train/5/image/image.jpg
example_title: 短裙
- src: https://datasets-server.huggingface.co/assets/Maciel/e-commerce-sample-images/--/Maciel--e-commerce-sample-images/train/7/image/image.jpg
example_title: 高跟鞋
---
### 功能介绍
该模型功能主要是对图片生成文字描述。模型结构使用Encoder-Decoder结构，其中Encoder端使用BEiT模型，Decoder使用GPT模型。
使用中文Muge数据集训练语料，训练5k步，最终验证集loss为0.3737，rouge1为20.419，rouge2为7.3553，rougeL为17.3753，rougeLsum为17.376。
[Github项目地址](https://github.com/Macielyoung/Chinese-Image-Caption)
### 如何使用
```python
from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer
from PIL import Image
pretrained = "Maciel/Muge-Image-Caption"
model = VisionEncoderDecoderModel.from_pretrained(pretrained)
feature_extractor = ViTFeatureExtractor.from_pretrained(pretrained)
tokenizer = AutoTokenizer.from_pretrained(pretrained)
image_path = "https://huggingface.co/Maciel/Muge-Image-Caption/blob/main/%E9%AB%98%E8%B7%9F%E9%9E%8B.jpg"
image = Image.open(image_path)
if image.mode != "RGB":
image = image.convert("RGB")
pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values
output_ids = model.generate(pixel_values, **gen_kwargs)
preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
preds = [pred.strip() for pred in preds]
print(preds)
```
|
AnonymousSub/SR_rule_based_roberta_twostagequadruplet_hier_epochs_1_shard_10
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
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}
| 4 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_11_0
metrics:
- wer
model-index:
- name: christoph-large-v2
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_11_0
type: common_voice_11_0
config: sl
split: test
args: sl
metrics:
- name: Wer
type: wer
value: 40.46335421827189
---
<!-- 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. -->
# christoph-large-v2
This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the common_voice_11_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3209
- Wer: 40.4634
## 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: 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
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0208 | 6.08 | 1000 | 0.2605 | 24.4645 |
| 0.0026 | 12.16 | 2000 | 0.2733 | 18.8110 |
| 0.0003 | 18.24 | 3000 | 0.3132 | 38.5400 |
| 0.0002 | 24.32 | 4000 | 0.3209 | 40.4634 |
### Framework versions
- Transformers 4.28.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
AnonymousSub/bert_triplet_epochs_1_shard_10
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
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}
| 1 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: dummy-model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# dummy-model
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.26.1
- TensorFlow 2.11.0
- Datasets 2.1.0
- Tokenizers 0.13.2
|
AnonymousSub/consert-s10-AR
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
}
}
| 31 | null |
---
language: fo
datasets:
- carlosdanielhernandezmena/ravnursson_asr
tags:
- audio
- automatic-speech-recognition
- faroese
- whisper-large-v2
- ravnur-project
- faroe-islands
license: cc-by-4.0
widget: null
model-index:
- name: whisper-largev2-faroese-8k-steps-100h
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Ravnursson (Test)
type: carlosdanielhernandezmena/ravnursson_asr
split: test
args:
language: fo
metrics:
- name: WER
type: wer
value: 11.944
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Ravnursson (Dev)
type: carlosdanielhernandezmena/ravnursson_asr
split: validation
args:
language: fo
metrics:
- name: WER
type: wer
value: 10.647
---
# whisper-largev2-faroese-8k-steps-100h
The "whisper-largev2-faroese-8k-steps-100h" is an acoustic model suitable for Automatic Speech Recognition in Faroese. It is the result of fine-tuning the model "openai/whisper-large-v2" with 100 hours of Faroese data released by the Ravnur Project (https://maltokni.fo/en/) from the Faroe Islands.
The specific dataset used to create the model is called "Ravnursson Faroese Speech and Transcripts" and it is available at http://hdl.handle.net/20.500.12537/276.
The fine-tuning process was perform during March (2023) in the servers of the Language and Voice Lab (https://lvl.ru.is/) at Reykjavík University (Iceland) by Carlos Daniel Hernández Mena.
# Evaluation
```python
import torch
from transformers import WhisperForConditionalGeneration, WhisperProcessor
#Load the processor and model.
MODEL_NAME="carlosdanielhernandezmena/whisper-largev2-faroese-8k-steps-100h"
processor = WhisperProcessor.from_pretrained(MODEL_NAME)
model = WhisperForConditionalGeneration.from_pretrained(MODEL_NAME).to("cuda")
#Load the dataset
from datasets import load_dataset, load_metric, Audio
ds=load_dataset("carlosdanielhernandezmena/ravnursson_asr",split='test')
#Downsample to 16kHz
ds = ds.cast_column("audio", Audio(sampling_rate=16_000))
#Process the dataset
def map_to_pred(batch):
audio = batch["audio"]
input_features = processor(audio["array"], sampling_rate=audio["sampling_rate"], return_tensors="pt").input_features
batch["reference"] = processor.tokenizer._normalize(batch['normalized_text'])
with torch.no_grad():
predicted_ids = model.generate(input_features.to("cuda"))[0]
transcription = processor.decode(predicted_ids)
batch["prediction"] = processor.tokenizer._normalize(transcription)
return batch
#Do the evaluation
result = ds.map(map_to_pred)
#Compute the overall WER now.
from evaluate import load
wer = load("wer")
WER=100 * wer.compute(references=result["reference"], predictions=result["prediction"])
print(WER)
```
**Test Result**: 11.94492429631135
# BibTeX entry and citation info
* When publishing results based on these models please refer to:
```bibtex
@misc{mena2023whisperlargev2faroese,
title={Acoustic Model in Faroese: whisper-largev2-faroese-8k-steps-100h.},
author={Hernandez Mena, Carlos Daniel},
year={2023},
url={https://huggingface.co/carlosdanielhernandezmena/whisper-largev2-faroese-8k-steps-100h},
}
```
# Acknowledgements
We want to thank to Jón Guðnason, head of the Language and Voice Lab for providing computational power to make this model possible. We also want to thank to the "Language Technology Programme for Icelandic 2019-2023" which is managed and coordinated by Almannarómur, and it is funded by the Icelandic Ministry of Education, Science and Culture.
Thanks to Annika Simonsen and to The Ravnur Project for making their "Basic Language Resource Kit"(BLARK 1.0) publicly available through the research paper "Creating a Basic Language Resource Kit for Faroese" https://aclanthology.org/2022.lrec-1.495.pdf
Special thanks to Björn Ingi Stefánsson for setting up the configuration of the server where this model was trained.
|
AnonymousSub/consert-s10-SR
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
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"BertForSequenceClassification"
],
"model_type": "bert",
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| 28 | null |
---
tags:
- autotrain
- summarization
language:
- en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- stevied67/autotrain-data-pegasus-reddit-summarizer
co2_eq_emissions:
emissions: 85.71551225539321
---
# Model Trained Using AutoTrain
This model was trained using chatgpt generated summaries of subreddit submissions from four subreddits: talesfromtechsupport, talesfromretail, callcentres, and talesfromcustomer
- Problem type: Summarization
- Model ID: 43644110754
- CO2 Emissions (in grams): 85.7155
## Validation Metrics
- Loss: 1.712
- Rouge1: 47.072
- Rouge2: 19.245
- RougeL: 32.773
- RougeLsum: 42.178
- Gen Len: 108.663
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/stevied67/autotrain-pegasus-reddit-summarizer-43644110754
```
|
AnonymousSub/declutr-emanuals-s10-AR
|
[
"pytorch",
"roberta",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
| 29 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: YSKartal/distilbert-base-cased-finetuned-ref_disam
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. -->
# YSKartal/distilbert-base-cased-finetuned-ref_disam
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 4.5334
- Validation Loss: 6.2676
- Train Accuracy: 0.0543
- 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', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 10352, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 4.5317 | 6.2676 | 0.0543 | 0 |
| 4.5334 | 6.2676 | 0.0543 | 1 |
### Framework versions
- Transformers 4.27.3
- TensorFlow 2.11.0
- Datasets 2.10.1
- Tokenizers 0.13.2
|
AnonymousSub/declutr-model_squad2.0
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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}
}
| 2 | 2023-03-20T12:54:14Z |
---
language:
- zh
- en
tags:
- glm
- chatglm
- thudm
---
# ChatGLM-6B-INT4-QE-Slim: 低显存版ChatGLM-6B-INT4-QE
## 介绍
ChatGLM-6B-INT4-QE-Slim是在[ChatGLM-6B-INT4-QE](https://huggingface.co/THUDM/chatglm-6b-int4-qe)的基础上通过裁剪词表构建的。因为ChatGLM-6B使用了icetk，在其词表中，前20000个token是预留给图片的，在文本模型中没有用到这些图片token，但是在infer和微调的时候，这些token对应的embedding依然需要被加载，并且在解码每一个token的时候需要多计算20K个logits，会占用不少显存。因此将这一部分token裁剪掉以节省显存。
除了词表外，ChatGLM-6B-INT4-QE-Slim的其他结构与ChatGLM-6B-INT4-QE完全一致，性能也完全一样，可以认为是ChatGLM-6B-INT4的一个低显存版等价平替。
ChatGLM-6B 是一个开源的、支持中英双语问答的对话语言模型，基于 [General Language Model (GLM)](https://github.com/THUDM/GLM) 架构，具有 62 亿参数。结合模型量化技术，用户可以在消费级的显卡上进行本地部署（INT4 量化级别下最低只需 6GB 显存）。ChatGLM-6B 使用了和 [ChatGLM](https://chatglm.cn) 相同的技术，针对中文问答和对话进行了优化。经过约 1T 标识符的中英双语训练，辅以监督微调、反馈自助、人类反馈强化学习等技术的加持，62 亿参数的 ChatGLM-6B 已经能生成相当符合人类偏好的回答。
ChatGLM-6B-INT4-QE 是 ChatGLM-6B 量化后的模型权重。具体的，ChatGLM-6B-INT4-QE 对 ChatGLM-6B 中的 28 个 GLM Block 、 Embedding 和 LM Head 进行了 INT4 量化。量化后的模型权重文件仅为 3G ，理论上 6G 显存（使用 CPU 即 6G 内存）即可推理，具有在嵌入式设备（如树莓派）上运行的可能。
在 CPU 上运行时，会根据硬件自动编译 CPU Kernel ，请确保已安装 GCC 和 OpenMP （Linux一般已安装，对于Windows则需手动安装），以获得最佳并行计算能力。
## 软件依赖
```shell
pip install protobuf==3.20.0 transformers==4.26.1 icetk cpm_kernels
```
## 代码调用
可以通过如下代码调用 ChatGLM-6B 模型来生成对话：
```ipython
>>> from transformers import AutoTokenizer, AutoModel
>>> tokenizer = AutoTokenizer.from_pretrained("silver/chatglm-6b-int4-qe-slim", trust_remote_code=True)
>>> model = AutoModel.from_pretrained("silver/chatglm-6b-int4-qe-slim", trust_remote_code=True).half().cuda()
>>> response, history = model.chat(tokenizer, "你好", history=[])
>>> print(response)
你好👋!我是人工智能助手 ChatGLM-6B,很高兴见到你,欢迎问我任何问题。
>>> response, history = model.chat(tokenizer, "晚上睡不着应该怎么办", history=history)
>>> print(response)
晚上睡不着可能会让你感到焦虑或不舒服,但以下是一些可以帮助你入睡的方法:
1. 制定规律的睡眠时间表:保持规律的睡眠时间表可以帮助你建立健康的睡眠习惯,使你更容易入睡。尽量在每天的相同时间上床,并在同一时间起床。
2. 创造一个舒适的睡眠环境:确保睡眠环境舒适,安静,黑暗且温度适宜。可以使用舒适的床上用品,并保持房间通风。
3. 放松身心:在睡前做些放松的活动,例如泡个热水澡,听些轻柔的音乐,阅读一些有趣的书籍等,有助于缓解紧张和焦虑,使你更容易入睡。
4. 避免饮用含有咖啡因的饮料:咖啡因是一种刺激性物质,会影响你的睡眠质量。尽量避免在睡前饮用含有咖啡因的饮料,例如咖啡,茶和可乐。
5. 避免在床上做与睡眠无关的事情:在床上做些与睡眠无关的事情,例如看电影,玩游戏或工作等,可能会干扰你的睡眠。
6. 尝试呼吸技巧:深呼吸是一种放松技巧,可以帮助你缓解紧张和焦虑,使你更容易入睡。试着慢慢吸气,保持几秒钟,然后缓慢呼气。
如果这些方法无法帮助你入睡,你可以考虑咨询医生或睡眠专家,寻求进一步的建议。
```
关于更多的使用说明，包括如何运行命令行和网页版本的 DEMO，以及使用模型量化以节省显存，请参考我们的 [Github Repo](https://github.com/THUDM/ChatGLM-6B)。
## 协议
本仓库的代码依照 [Apache-2.0](LICENSE) 协议开源，ChatGLM-6B 模型的权重的使用则需要遵循 [Model License](MODEL_LICENSE)。
## 引用
如果你觉得我们的工作有帮助的话，请考虑引用下列论文：
```
@inproceedings{
zeng2023glm-130b,
title={{GLM}-130B: An Open Bilingual Pre-trained Model},
author={Aohan Zeng and Xiao Liu and Zhengxiao Du and Zihan Wang and Hanyu Lai and Ming Ding and Zhuoyi Yang and Yifan Xu and Wendi Zheng and Xiao Xia and Weng Lam Tam and Zixuan Ma and Yufei Xue and Jidong Zhai and Wenguang Chen and Zhiyuan Liu and Peng Zhang and Yuxiao Dong and Jie Tang},
booktitle={The Eleventh International Conference on Learning Representations (ICLR)},
year={2023},
url={https://openreview.net/forum?id=-Aw0rrrPUF}
}
```
```
@inproceedings{du2022glm,
title={GLM: General Language Model Pretraining with Autoregressive Blank Infilling},
author={Du, Zhengxiao and Qian, Yujie and Liu, Xiao and Ding, Ming and Qiu, Jiezhong and Yang, Zhilin and Tang, Jie},
booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
pages={320--335},
year={2022}
}
```
|
AnonymousSub/dummy_2_parent
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
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| 3 | null |
---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
model-index:
- name: t5-small-finetuned-tfg
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-tfg
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 5 | 8.2836 | 4.1934 | 0.0 | 3.7869 | 4.1934 | 19.0 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
AnonymousSub/rule_based_bert_triplet_epochs_1_shard_1_squad2.0
|
[
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
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"BertForQuestionAnswering"
],
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}
| 3 | null |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 319.00 +/- 118.74
name: mean_reward
verified: false
---
# **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
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga basboot -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga basboot -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga basboot
```
## 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', 10000),
('n_timesteps', 100000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
AnonymousSub/rule_based_bert_triplet_epochs_1_shard_1_wikiqa
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
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"BertForSequenceClassification"
],
"model_type": "bert",
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}
| 31 | null |
---
license: creativeml-openrail-m
---
https://civitai.com/models/21305/tenten-character-lohafullckpt
|
AnonymousSub/rule_based_bert_triplet_epochs_1_shard_1_wikiqa_copy
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
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}
| 1 | null |
---
license: creativeml-openrail-m
---
https://civitai.com/models/15553/shokuhou-misaki-or-toaru-majutsu-no-index
|
AnonymousSub/rule_based_hier_quadruplet_0.1_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"BertModel"
],
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}
| 4 | null |
---
license: creativeml-openrail-m
---
https://civitai.com/models/18612/teaanzu-dsod-ver-or-yu-gi-oh
|
AnonymousSub/rule_based_hier_triplet_epochs_1_shard_1_squad2.0
|
[
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
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}
| 2 | null |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** 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-Huggy
2. Step 1: Find your model_id: bbillapati/ppo-Huggy1
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AnonymousSub/rule_based_hier_triplet_epochs_1_shard_1_wikiqa_copy
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"BertModel"
],
"model_type": "bert",
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}
| 1 | null |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 11.67 +/- 3.31
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r droid22/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
AnonymousSub/rule_based_only_classfn_twostage_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"BertModel"
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}
}
| 10 | null |
---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-wikiann
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-wikiann
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: 0.1638
- F1: 0.8878
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.2426 | 1.0 | 5000 | 0.1882 | 0.8495 |
| 0.1475 | 2.0 | 10000 | 0.1618 | 0.8807 |
| 0.1003 | 3.0 | 15000 | 0.1638 | 0.8878 |
### Framework versions
- Transformers 4.27.3
- Pytorch 2.0.0+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2
|
AnonymousSub/rule_based_only_classfn_twostage_epochs_1_shard_1_wikiqa
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
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}
}
}
| 27 | null |
# alpaca-lora-7b
This LoRA trained for 3 epochs.
Exported to hf format using https://github.com/tloen/alpaca-lora(float32, no 8bit)
Exported to ONNX using optimum library(https://github.com/huggingface/optimum/pull/922) without any optimizations
--
license: other
---
# LLaMA Model Card
## Model details
**Organization developing the model**
The FAIR team of Meta AI.
**Model date**
LLaMA was trained between December. 2022 and Feb. 2023.
**Model version**
This is version 1 of the model.
**Model type**
LLaMA is an auto-regressive language model, based on the transformer architecture. The model comes in different sizes: 7B, 13B, 33B and 65B parameters.
**Paper or resources for more information**
More information can be found in the paper “LLaMA, Open and Efficient Foundation Language Models”, available at https://research.facebook.com/publications/llama-open-and-efficient-foundation-language-models/.
**Citations details**
https://research.facebook.com/publications/llama-open-and-efficient-foundation-language-models/
**License**
Non-commercial bespoke license
**Where to send questions or comments about the model**
Questions and comments about LLaMA can be sent via the [GitHub repository](https://github.com/facebookresearch/llama) of the project , by opening an issue.
## Intended use
**Primary intended uses**
The primary use of LLaMA is research on large language models, including:
exploring potential applications such as question answering, natural language understanding or reading comprehension,
understanding capabilities and limitations of current language models, and developing techniques to improve those,
evaluating and mitigating biases, risks, toxic and harmful content generations, hallucinations.
**Primary intended users**
The primary intended users of the model are researchers in natural language processing, machine learning and artificial intelligence.
**Out-of-scope use cases**
LLaMA is a base, or foundational, model. As such, it should not be used on downstream applications without further risk evaluation and mitigation. In particular, our model has not been trained with human feedback, and can thus generate toxic or offensive content, incorrect information or generally unhelpful answers.
## Factors
**Relevant factors**
One of the most relevant factors for which model performance may vary is which language is used. Although we included 20 languages in the training data, most of our dataset is made of English text, and we thus expect the model to perform better for English than other languages. Relatedly, it has been shown in previous studies that performance might vary for different dialects, and we expect that it will be the case for our model.
**Evaluation factors**
As our model is trained on data from the Web, we expect that it reflects biases from this source. We thus evaluated on RAI datasets to measure biases exhibited by the model for gender, religion, race, sexual orientation, age, nationality, disability, physical appearance and socio-economic status. We also measure the toxicity of model generations, depending on the toxicity of the context used to prompt the model.
## Metrics
**Model performance measures**
We use the following measure to evaluate the model:
- Accuracy for common sense reasoning, reading comprehension, natural language understanding (MMLU), BIG-bench hard, WinoGender and CrowS-Pairs,
- Exact match for question answering,
- The toxicity score from Perspective API on RealToxicityPrompts.
**Decision thresholds**
Not applicable.
**Approaches to uncertainty and variability**
Due to the high computational requirements of training LLMs, we trained only one model of each size, and thus could not evaluate variability of pre-training.
## Evaluation datasets
The model was evaluated on the following benchmarks: BoolQ, PIQA, SIQA, HellaSwag, WinoGrande, ARC, OpenBookQA, NaturalQuestions, TriviaQA, RACE, MMLU, BIG-bench hard, GSM8k, RealToxicityPrompts, WinoGender, CrowS-Pairs.
## Training dataset
The model was trained using the following source of data: CCNet [67%], C4 [15%], GitHub [4.5%], Wikipedia [4.5%], Books [4.5%], ArXiv [2.5%], Stack Exchange[2%]. The Wikipedia and Books domains include data in the following languages: bg, ca, cs, da, de, en, es, fr, hr, hu, it, nl, pl, pt, ro, ru, sl, sr, sv, uk. See the paper for more details about the training set and corresponding preprocessing.
## Quantitative analysis
Hyperparameters for the model architecture
<table>
<thead>
<tr>
<th >LLaMA</th> <th colspan=6>Model hyper parameters </th>
</tr>
<tr>
<th>Number of parameters</th><th>dimension</th><th>n heads</th><th>n layers</th><th>Learn rate</th><th>Batch size</th><th>n tokens</th>
</tr>
</thead>
<tbody>
<tr>
<th>7B</th> <th>4096</th> <th>32</th> <th>32</th> <th>3.0E-04</th><th>4M</th><th>1T
</tr>
<tr>
<th>13B</th><th>5120</th><th>40</th><th>40</th><th>3.0E-04</th><th>4M</th><th>1T
</tr>
<tr>
<th>33B</th><th>6656</th><th>52</th><th>60</th><th>1.5.E-04</th><th>4M</th><th>1.4T
</tr>
<tr>
<th>65B</th><th>8192</th><th>64</th><th>80</th><th>1.5.E-04</th><th>4M</th><th>1.4T
</tr>
</tbody>
</table>
*Table 1 - Summary of LLama Model Hyperparameters*
We present our results on eight standard common sense reasoning benchmarks in the table below.
<table>
<thead>
<tr>
<th>LLaMA</th> <th colspan=9>Reasoning tasks </th>
</tr>
<tr>
<th>Number of parameters</th> <th>BoolQ</th><th>PIQA</th><th>SIQA</th><th>HellaSwag</th><th>WinoGrande</th><th>ARC-e</th><th>ARC-c</th><th>OBQA</th><th>COPA</th>
</tr>
</thead>
<tbody>
<tr>
<th>7B</th><th>76.5</th><th>79.8</th><th>48.9</th><th>76.1</th><th>70.1</th><th>76.7</th><th>47.6</th><th>57.2</th><th>93
</th>
<tr><th>13B</th><th>78.1</th><th>80.1</th><th>50.4</th><th>79.2</th><th>73</th><th>78.1</th><th>52.7</th><th>56.4</th><th>94
</th>
<tr><th>33B</th><th>83.1</th><th>82.3</th><th>50.4</th><th>82.8</th><th>76</th><th>81.4</th><th>57.8</th><th>58.6</th><th>92
</th>
<tr><th>65B</th><th>85.3</th><th>82.8</th><th>52.3</th><th>84.2</th><th>77</th><th>81.5</th><th>56</th><th>60.2</th><th>94</th></tr>
</tbody>
</table>
*Table 2 - Summary of LLama Model Performance on Reasoning tasks*
We present our results on bias in the table below. Note that lower value is better indicating lower bias.
| No | Category | FAIR LLM |
| --- | -------------------- | -------- |
| 1 | Gender | 70.6 |
| 2 | Religion | 79 |
| 3 | Race/Color | 57 |
| 4 | Sexual orientation | 81 |
| 5 | Age | 70.1 |
| 6 | Nationality | 64.2 |
| 7 | Disability | 66.7 |
| 8 | Physical appearance | 77.8 |
| 9 | Socioeconomic status | 71.5 |
| | LLaMA Average | 66.6 |
*Table 3 - Summary bias of our model output*
## Ethical considerations
**Data**
The data used to train the model is collected from various sources, mostly from the Web. As such, it contains offensive, harmful and biased content. We thus expect the model to exhibit such biases from the training data.
**Human life**
The model is not intended to inform decisions about matters central to human life, and should not be used in such a way.
**Mitigations**
We filtered the data from the Web based on its proximity to Wikipedia text and references. For this, we used a Kneser-Ney language model and a fastText linear classifier.
**Risks and harms**
Risks and harms of large language models include the generation of harmful, offensive or biased content. These models are often prone to generating incorrect information, sometimes referred to as hallucinations. We do not expect our model to be an exception in this regard.
**Use cases**
LLaMA is a foundational model, and as such, it should not be used for downstream applications without further investigation and mitigations of risks. These risks and potential fraught use cases include, but are not limited to: generation of misinformation and generation of harmful, biased or offensive content.
|
AnonymousSub/rule_based_roberta_bert_quadruplet_epochs_1_shard_10
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
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}
}
| 8 | 2023-03-25T13:45:05Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="spaladugu/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"])
```
|
AnonymousSub/rule_based_roberta_bert_quadruplet_epochs_1_shard_1_wikiqa
|
[
"pytorch",
"roberta",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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}
}
}
| 23 | 2023-03-25T13:47:13Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### Scott-Malin-Art-Style Dreambooth model trained by Anonim3327 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Basic model:Anything Diffusion v4.5 (VAE NOT NEDEED)
images from:https://twitter.com/scott_malin
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept: [civitai](https://civitai.com/models/24868/scott-malins-style)
|
AnonymousSub/rule_based_roberta_bert_triplet_epochs_1_shard_1
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
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},
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}
}
}
| 2 | null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="spaladugu/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"])
```
|
AnonymousSub/rule_based_roberta_bert_triplet_epochs_1_shard_10
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
}
}
| 2 | 2023-03-25T13:51:17Z |
---
tags:
- generated_from_trainer
model-index:
- name: poetry_gpt2
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. -->
# poetry_gpt2
This model is a fine-tuned version of [uer/gpt2-chinese-cluecorpussmall](https://huggingface.co/uer/gpt2-chinese-cluecorpussmall) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 5.4107
## 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 | 56 | 5.4878 |
| No log | 2.0 | 112 | 5.4270 |
| No log | 3.0 | 168 | 5.4107 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
AnonymousSub/rule_based_roberta_bert_triplet_epochs_1_shard_1_squad2.0
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
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},
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}
}
}
| 3 | 2023-03-25T13:51:27Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="spaladugu/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"])
```
|
AnonymousSub/rule_based_roberta_bert_triplet_epochs_1_shard_1_wikiqa
|
[
"pytorch",
"roberta",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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},
"translation_en_to_fr": {
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}
}
}
| 28 | null |
https://wandb.ai/open-assistant/supervised-finetuning/runs/x2pczqa9
|
AnonymousSub/rule_based_roberta_bert_triplet_epochs_1_shard_1_wikiqa_copy
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
"text-generation": {
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},
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},
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}
}
}
| 2 | null |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Example Fine-Tuned Model for Unit 2 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
Describe your model here
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('karanam/ddpm-celebahq-finetuned-butterflies-2epochs')
image = pipeline().images[0]
image
```
|
AnonymousSub/rule_based_roberta_hier_quadruplet_epochs_1_shard_1
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
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| 1 | null |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: clickbait_classification_xlm_roberta
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. -->
# clickbait_classification_xlm_roberta
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: 0.5812
- Accuracy: 0.7544
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4736 | 1.0 | 1222 | 0.5508 | 0.7507 |
| 0.4081 | 2.0 | 2444 | 0.5812 | 0.7544 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
AnonymousSub/rule_based_roberta_hier_quadruplet_epochs_1_shard_10
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
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"RobertaModel"
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| 6 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: dgx1_whisper_base_finetune_teacher_no_noise_mozilla_100_epochs_batch_16
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. -->
# dgx1_whisper_base_finetune_teacher_no_noise_mozilla_100_epochs_batch_16
This model is a fine-tuned version of [openai/whisper-base.en](https://huggingface.co/openai/whisper-base.en) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8926
- Wer: 31.8954
## 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: 0.001
- train_batch_size: 16
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 256
- total_train_batch_size: 4096
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.1866 | 29.41 | 500 | 0.8619 | 34.0680 |
| 1.4402 | 58.82 | 1000 | 0.8918 | 32.2505 |
| 0.0001 | 88.23 | 1500 | 0.8926 | 31.8954 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.8.0
- Tokenizers 0.13.2
|
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