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
|
|---|---|---|---|---|---|---|
DTAI-KULeuven/robbertje-1-gb-merged
|
[
"pytorch",
"roberta",
"fill-mask",
"nl",
"dataset:oscar",
"dataset:oscar (NL)",
"dataset:dbrd",
"dataset:lassy-ud",
"dataset:europarl-mono",
"dataset:conll2002",
"arxiv:2101.05716",
"transformers",
"Dutch",
"Flemish",
"RoBERTa",
"RobBERT",
"RobBERTje",
"license:mit",
"autotrain_compatible"
] |
fill-mask
|
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"RobertaForMaskedLM"
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}
| 1 | 2023-02-24T17:25:19Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-cart
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
alexandrainst/da-emotion-classification-base
|
[
"pytorch",
"tf",
"bert",
"text-classification",
"da",
"transformers",
"license:cc-by-sa-4.0"
] |
text-classification
|
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| 837 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: whisper-medium-111
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. -->
# whisper-medium-111
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2345
- eval_wer: 100.1832
- eval_runtime: 218.2253
- eval_samples_per_second: 2.145
- eval_steps_per_second: 0.215
- epoch: 2.2
- step: 400
## 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: 10
- eval_batch_size: 10
- seed: 42
- 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
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.12.1
|
alexandrainst/da-sentiment-base
|
[
"pytorch",
"tf",
"safetensors",
"bert",
"text-classification",
"da",
"arxiv:1910.09700",
"transformers",
"license:cc-by-sa-4.0"
] |
text-classification
|
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| 1,432 | 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: 654.00 +/- 270.80
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 Senura -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 Senura -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 Senura
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
DaisyMak/bert-finetuned-squad-transformerfrozen-testtoken
|
[
"pytorch",
"tensorboard",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
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"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
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}
| 7 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **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: Write your model_id: RohanDani2/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Daivakai/DialoGPT-small-saitama
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
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"max_length": 1000
},
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| 9 | null |
---
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: small-6-1
results:
- task:
name: Summarization
type: summarization
dataset:
name: xsum
type: xsum
config: default
split: validation
args: default
metrics:
- name: Rouge1
type: rouge
value: 23.1562
---
<!-- 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. -->
# small-6-1
This model is a fine-tuned version of [x/small-6-1/](https://huggingface.co/x/small-6-1/) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2727
- Rouge1: 23.1562
- Rouge2: 6.0326
- Rougel: 19.0188
- Rougelsum: 19.0191
- Gen Len: 36.6757
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.12.1
|
Daltcamalea01/Camaleaodalt
|
[] | null |
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| 0 | null |
---
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: small-6-2
results:
- task:
name: Summarization
type: summarization
dataset:
name: xsum
type: xsum
config: default
split: validation
args: default
metrics:
- name: Rouge1
type: rouge
value: 31.4829
---
<!-- 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. -->
# small-6-2
This model is a fine-tuned version of [x/small-6-2/](https://huggingface.co/x/small-6-2/) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6854
- Rouge1: 31.4829
- Rouge2: 10.5295
- Rougel: 25.3856
- Rougelsum: 25.3831
- Gen Len: 26.5751
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.12.1
|
DanBot/TCRsynth
|
[] | null |
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| 0 | null |
---
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: small-6-4
results:
- task:
name: Summarization
type: summarization
dataset:
name: xsum
type: xsum
config: default
split: validation
args: default
metrics:
- name: Rouge1
type: rouge
value: 33.469
---
<!-- 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. -->
# small-6-4
This model is a fine-tuned version of [x/small-6-4/](https://huggingface.co/x/small-6-4/) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2026
- Rouge1: 33.469
- Rouge2: 11.4324
- Rougel: 26.6495
- Rougelsum: 26.6397
- Gen Len: 27.4027
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.12.1
|
DanL/scientific-challenges-and-directions
|
[
"pytorch",
"bert",
"text-classification",
"en",
"dataset:DanL/scientific-challenges-and-directions-dataset",
"arxiv:2108.13751",
"transformers",
"generated_from_trainer"
] |
text-classification
|
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| 134 | null |
---
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: small-6-5
results:
- task:
name: Summarization
type: summarization
dataset:
name: xsum
type: xsum
config: default
split: validation
args: default
metrics:
- name: Rouge1
type: rouge
value: 33.7879
---
<!-- 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. -->
# small-6-5
This model is a fine-tuned version of [x/small-6-5/](https://huggingface.co/x/small-6-5/) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1373
- Rouge1: 33.7879
- Rouge2: 11.6113
- Rougel: 26.7415
- Rougelsum: 26.7271
- Gen Len: 27.7943
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.12.1
|
Danbi/distilgpt2-finetuned-wikitext2
|
[] | null |
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| 0 | 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: 10.28 +/- 4.44
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 Arch4ngel/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 <path.to.enjoy.module> --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 <path.to.train.module> --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.
|
Danih1502/t5-small-finetuned-en-to-de
|
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| 0 | null |
---
license: apache-2.0
language:
- en
library_name: sentence-transformers
pipeline_tag: sentence-similarity
widget:
- text: How are you
---
# Dataset Collection:
* The English-French Translation Dataset is collected from Kaggle.[Dataset](https://www.kaggle.com/datasets/dhruvildave/en-fr-translation-dataset).
About Dataset:
French/English parallel texts for training translation models.
Over 22.5 million sentences in French and English.Dataset created
by Chris Callison-Burch, who crawled millions of web pages and
then used a set of simple heuristics to transform French URLs onto English URLs,
and assumed that these documents are translations of each other.
This is the main dataset of Workshop on Statistical Machine Translation (WML) 2015 Dataset
that can be used for Machine Translation and Language Models.
Refer to the paper here:[PDF](https://www.statmt.org/wmt15/pdf/WMT01.pdf)
|
Darein/Def
|
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}
| 0 | null |
---
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: small-4-6
results:
- task:
name: Summarization
type: summarization
dataset:
name: xsum
type: xsum
config: default
split: validation
args: default
metrics:
- name: Rouge1
type: rouge
value: 32.5512
---
<!-- 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. -->
# small-4-6
This model is a fine-tuned version of [x/small-4-6/](https://huggingface.co/x/small-4-6/) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1020
- Rouge1: 32.5512
- Rouge2: 10.6071
- Rougel: 25.5034
- Rougelsum: 25.4998
- Gen Len: 28.3454
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.12.1
|
DarkestSky/distilbert-base-uncased-finetuned-ner
|
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| 0 | null |
---
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: large-2-2
results:
- task:
name: Summarization
type: summarization
dataset:
name: xsum
type: xsum
config: default
split: validation
args: default
metrics:
- name: Rouge1
type: rouge
value: 34.6493
---
<!-- 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. -->
# large-2-2
This model is a fine-tuned version of [x/large-2-2/](https://huggingface.co/x/large-2-2/) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1032
- Rouge1: 34.6493
- Rouge2: 12.1489
- Rougel: 27.3641
- Rougelsum: 27.3562
- Gen Len: 28.1581
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.10.0
- Tokenizers 0.13.2
|
DarshanDeshpande/marathi-distilbert
|
[
"pytorch",
"tf",
"distilbert",
"fill-mask",
"mr",
"dataset:Oscar Corpus, News, Stories",
"arxiv:1910.01108",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
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| 14 | 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: -1.50 +/- 0.28
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
...
```
|
DataikuNLP/average_word_embeddings_glove.6B.300d
|
[
"arxiv:1908.10084",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"license:apache-2.0"
] |
sentence-similarity
|
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| 0 | null |
---
language: en
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
tags:
- text-classification
license: mit
---
# AutoDisProxyT-SST2 for Distilling Massive Neural Networks
AutoDisProxyT is a distilled task-agnostic transformer model that leverages task transfer for learning a small universal model that can be applied to arbitrary tasks and languages as outlined in the paper [Few-shot Task-agnostic Neural Architecture Search for
Distilling Large Language Models](https://proceedings.neurips.cc/paper_files/paper/2022/file/b7c12689a89e98a61bcaa65285a41b7c-Paper-Conference.pdf).
This AutoDisProxyT checkpoint with **7** layers, **160** hidden size, **10** attention heads corresponds to **6.88 million** parameters and **0.27G** FLOPs.
The following table shows the results on GLUE dev set.
| Models | #Params (M) | #FLOPs (G) | MNLI | QNLI | QQP | RTE | SST-2 | MRPC | CoLA | Avg |
|----------------|--------|---------|------|------|------|------|------|------|--------|-------|
| BERT | 109 | 11.2 | 84.5 | 91.7 | 91.3 | 68.6 | 93.2 | 87.3 | 53.5 | 82.2 |
| BERT<sub>SMALL</sub> | 66 | 5.66 | 81.8 | 89.8 | 90.6 | 67.9 | 91.2 | 84.9 | 53.5 | 80.0 |
| TruncatedBERT | 66 | 5.66 | 81.2 | 87.9 | 90.4 | 65.5 | 90.8 | 82.7 | 41.4 | 77.1 |
| DistilBERT | 66 | 5.66 | 82.2 | 89.2 | 88.5 | 59.9 | 91.3 | 87.5 | 51.3 | 78.6 |
| TinyBERT | 66 | 5.66 | 83.5 | 90.5 | 90.6 | 72.2 | 91.6 | 88.4 | 42.8 | 79.9 |
| MiniLM | 66 | 5.66 | 84.0 | 91.0 | 91.0 | 71.5 | 92.0 | 88.4 | 49.2 | 81.0 |
| AutoTinyBERT-KD-S1 | 30.0 | 1.69 | 82.3 | 89.7 | 89.9 | 71.1 | 91.4 | 88.5 | 47.3 | 80.0 |
| DynaBERT | 37.7 | 1.81 | 82.3 | 88.5 | 90.4 | 63.2 | 92.0 | 81.4 | 76.4 | 43.7 |
| NAS-BERT<sub>10</sub>| 10.0 | 2.30 | 76.4 | 86.3 | 88.5 | 66.6 | 88.6 | 79.1 | 34.0 | 74.2 |
| AutoTinyBERT-KD-S4 | 66 | 5.66 | 76.0 | 85.5 | 86.9 | 64.9 | 86.8 | 81.4 | 20.4 | 71.7 |
| NAS-BERT<sub>5</sub> | 66 | 5.66 | 74.4 | 84.9 | 85.8 | 66.6 | 87.3 | 79.6 | 19.8 | 71.2 |
| **AutoDisProxyT** | 6.88 | 0.27 | 79.0 | 86.4 | 89.1 | 64.3 | 85.9 | 78.5 | 24.8 | 72.6 |
Tested with `torch 1.6.0`
If you use this checkpoint in your work, please cite:
``` latex
@article{xu2022autodistil,
title={AutoDistil: Few-shot Task-agnostic Neural Architecture Search for Distilling Large Language Models},
author={Xu, Dongkuan and Mukherjee, Subhabrata and Liu, Xiaodong and Dey, Debadeepta and Wang, Wenhui and Zhang, Xiang and Awadallah, Ahmed Hassan and Gao, Jianfeng},
journal={arXiv preprint arXiv:2201.12507},
year={2022}
}
```
|
DataikuNLP/distiluse-base-multilingual-cased-v1
|
[
"pytorch",
"distilbert",
"arxiv:1908.10084",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] |
sentence-similarity
|
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| 29 | null |
---
language: en
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
tags:
- text-classification
license: mit
---
# AutoDisProxyT-RTE for Distilling Massive Neural Networks
AutoDisProxyT is a distilled task-agnostic transformer model that leverages task transfer for learning a small universal model that can be applied to arbitrary tasks and languages as outlined in the paper [Few-shot Task-agnostic Neural Architecture Search for
Distilling Large Language Models](https://proceedings.neurips.cc/paper_files/paper/2022/file/b7c12689a89e98a61bcaa65285a41b7c-Paper-Conference.pdf).
This AutoDisProxyT checkpoint with **7** layers, **160** hidden size, **10** attention heads corresponds to **6.88 million** parameters and **0.27G** FLOPs.
The following table shows the results on GLUE dev set.
| Models | #Params (M) | #FLOPs (G) | MNLI | QNLI | QQP | RTE | SST-2 | MRPC | CoLA | Avg |
|----------------|--------|---------|------|------|------|------|------|------|--------|-------|
| BERT | 109 | 11.2 | 84.5 | 91.7 | 91.3 | 68.6 | 93.2 | 87.3 | 53.5 | 82.2 |
| BERT<sub>SMALL</sub> | 66 | 5.66 | 81.8 | 89.8 | 90.6 | 67.9 | 91.2 | 84.9 | 53.5 | 80.0 |
| TruncatedBERT | 66 | 5.66 | 81.2 | 87.9 | 90.4 | 65.5 | 90.8 | 82.7 | 41.4 | 77.1 |
| DistilBERT | 66 | 5.66 | 82.2 | 89.2 | 88.5 | 59.9 | 91.3 | 87.5 | 51.3 | 78.6 |
| TinyBERT | 66 | 5.66 | 83.5 | 90.5 | 90.6 | 72.2 | 91.6 | 88.4 | 42.8 | 79.9 |
| MiniLM | 66 | 5.66 | 84.0 | 91.0 | 91.0 | 71.5 | 92.0 | 88.4 | 49.2 | 81.0 |
| AutoTinyBERT-KD-S1 | 30.0 | 1.69 | 82.3 | 89.7 | 89.9 | 71.1 | 91.4 | 88.5 | 47.3 | 80.0 |
| DynaBERT | 37.7 | 1.81 | 82.3 | 88.5 | 90.4 | 63.2 | 92.0 | 81.4 | 76.4 | 43.7 |
| NAS-BERT<sub>10</sub>| 10.0 | 2.30 | 76.4 | 86.3 | 88.5 | 66.6 | 88.6 | 79.1 | 34.0 | 74.2 |
| AutoTinyBERT-KD-S4 | 66 | 5.66 | 76.0 | 85.5 | 86.9 | 64.9 | 86.8 | 81.4 | 20.4 | 71.7 |
| NAS-BERT<sub>5</sub> | 66 | 5.66 | 74.4 | 84.9 | 85.8 | 66.6 | 87.3 | 79.6 | 19.8 | 71.2 |
| **AutoDisProxyT** | 6.88 | 0.27 | 79.0 | 86.4 | 89.1 | 64.3 | 85.9 | 78.5 | 24.8 | 72.6 |
Tested with `torch 1.6.0`
If you use this checkpoint in your work, please cite:
``` latex
@article{xu2022autodistil,
title={AutoDistil: Few-shot Task-agnostic Neural Architecture Search for Distilling Large Language Models},
author={Xu, Dongkuan and Mukherjee, Subhabrata and Liu, Xiaodong and Dey, Debadeepta and Wang, Wenhui and Zhang, Xiang and Awadallah, Ahmed Hassan and Gao, Jianfeng},
journal={arXiv preprint arXiv:2201.12507},
year={2022}
}
```
|
DataikuNLP/paraphrase-MiniLM-L6-v2
|
[
"pytorch",
"bert",
"arxiv:1908.10084",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] |
sentence-similarity
|
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}
}
}
| 25 | null |
---
tags:
- stable-diffusion
- text-to-image
- safetensors
language:
- en
---
<center><h1><b>HoloKuki</b></h1></center>
<center><img src="https://huggingface.co/Aotsuyu/Kukicha/resolve/main/images/nakiricool.png"width="55%"/></center>
### What to grab
I advise grabbing [this file](https://huggingface.co/Aotsuyu/Kukicha/blob/main/HoloKukiv2.2-fp16.safetensors) - this is version 2.2, one that I use daily.
### Previews!
To make the previews fair I applied no editing, no inpainting or such techniques.
All the images have metadata.
There might be more previews on the [civitai](https://civitai.com/models/17598/holokuki) page, since it's easier to upload them there.
<center><img src="https://huggingface.co/Aotsuyu/Kukicha/resolve/main/images/widekiara.png"></center>
<center><img src="https://huggingface.co/Aotsuyu/Kukicha/resolve/main/images/suisei.png"></center>
<center><img src="https://huggingface.co/Aotsuyu/Kukicha/resolve/main/images/reimu.png"></center>
<center><img src="https://huggingface.co/Aotsuyu/Kukicha/resolve/main/images/nakiriwide.png"></center>
<center><img src="https://huggingface.co/Aotsuyu/Kukicha/resolve/main/images/nakiri.png"></center>
<center><img src="https://huggingface.co/Aotsuyu/Kukicha/resolve/main/images/miku.png"></center>
<center><img src="https://huggingface.co/Aotsuyu/Kukicha/resolve/main/images/kiara!.png"></center>
<center><img src="https://huggingface.co/Aotsuyu/Kukicha/resolve/main/images/sceneryday.png"></center>
<center><img src="https://huggingface.co/Aotsuyu/Kukicha/resolve/main/images/scenerynight.png"></center>
<center><img src="https://huggingface.co/Aotsuyu/Kukicha/resolve/main/images/pekosunflower.png"></center>
### Holos!
This is a big ass file, open it and look at the holos. Stare at them.
<center><img src="https://huggingface.co/Aotsuyu/Kukicha/resolve/main/images/xyz_grid-0027-541571007-detailed%20background%2C%20masterpiece%2C%20best%20quality%2C%20(tokino%20sora_1.2)%2C%20blue%20eyes%2C%20brown%20hair%2C%20long%20hair%2C%20hair%20flaps%2C%20hairclip%2C%20red%20r.png"></center>
### Description
TL;DR WHAT I TRIED TO ACHIEVE:
A midpoint between being able to generate stunning scenery (dpep) and just cute anime girls (Anything) with some other models mixed in - to help improve other aspects.
It's a model, made by mixing other models, the base were Anythingv4.5 and dpep-chillout mix.
I've made many mixes before and I've applied the knowledge I've gotten from that here - a lot of BW merging was applied.
I feel like this mix is good enough to contend with the most popular ones, and it's the one I use currently, for all my purposes.
It can do SFW, NSFW, thanks to closer to death's dpep it has the capability for interesting backgrounds and I've merged in a VTuber model - all of Hololive can be prompted for.
But I think the images speak for itself better than I can.
### Defaults that I use:
```
detailed background, masterpiece, best quality, prompty promptu
Negative prompt: (low quality, worst quality:1.4), (bad anatomy), extra digit, fewer digits, (extra arms:1.2), bad hands, by (bad-artist:0.6), bad-image-v2-39000
Steps: 25, Sampler: DPM++ 2M Karras, CFG scale: 7, Seed: 1189140555, Size: 512x768, Clip skip: 2, ENSD: 31337, Discard penultimate sigma: True
```
This is with kl-f8-anime2 vae, probably use higher cfg if trying with the anything or whatever vae, hell, mess around.
I also sometimes use 22ish cfg with the dynamic thresholding extension:
```
Dynamic thresholding enable#: True, Mimic scale: 7, Threshold percentile: 96, Mimic mode: Half Cosine Up, Mimic scale minimum: 4, CFG mode: Half Cosine Up, CFG scale minimum: 4
```
### DARKNESS
The darkKukiv1.safetensors LoRA included in the repo is my first version of a noise offset LoRA specifically for this model.
I'm working on improved version as well, but this one isn't bad by any means.
What it does is it makes the model able to output a wider range of lighting, example:
<center><img src="https://i.imgur.com/ixZWK6N.png"width="55%"/></center>
That's with the lora at 0.6 strength and the positive prompt:
```detailed background, masterpiece, best quality, 1girl, solo, hatsune miku, pov, bed, window, night, darkness, dark, blue hair, twintails, fake smile```
#### Horror
This is an another source of noise offset so if that's an issue, disable the darkness LoRA.
And just read the readme of the LoRA.
https://huggingface.co/Aotsuyu/HorrorLora/
### I want to know more about such and such
Your best bet is this https://rentry.org/TohoAIFAQ
I'm trying to slowly fill it out.
## Da recipe
Recipe for V2, V1 was similar but also included a gape merge at the end.
BW merge anything-v4.5-pruned.safetensors and dpep3-chillout.safetensors = temp1 at `1,0.9,0.7,0.5,0.3,0.1,0.4,0.4,1,1,1,1,0,0,0,0,0,0,0,0.1,0.3,0.5,0.7,0.9,1`
temp1 and Cocoa = temp2 at `0.15,0.15,0.15,0.15,0.15,0.31,0.3,0.3,0.15,0.15,0.15,0.15,0.15,0.15,0.15,0.15,0.15,0.15,0.15,0.15,0.15,0.15,0.15,0.15,0.15`
temp2 and Tea = temp3 at `0.35,0,0,0,0,0,0.1,0.1,0.1,0,0,0,0,0,0,0,0,0,0,0,0,0.35,0.35,0.35,0.35`
temp3 and 7th_anime_v3_A = Kukiv2 at `0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.4,0.4,0.4,0.4`
Add diff merge of Kukiv2 + (merge of hll1 and hll3.1 at like 0.4 ratio (more of hll1) I think) - animefull-final-pruned = HoloKukiv2
### 2.2 Recipe
Recipe is quite similar but there's differences:
Anything4.5 + SE_V2 B = temp1
`0.2,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.3,0.3,0.3,0.3,0.3 `
temp1 + naifull - naisfw * 0.25 = temp2
chilloutmix_NiPrunedFp32Fix + detailedprojectv4-fin = separatetemp1
`1,0.9,0.7,0.5,0.3,0.1,1,1,1,1,1,1,0,0,0,0,0,0,0,0.1,0.3,0.5,0.7,0.9,1`
temp2 + separatetemp1 = temp3
`1,0.9,0.7,0.5,0.3,0.1,0.4,0.4,1,1,1,1,0,0,0,0,0,0,0,0.1,0.3,0.5,0.7,0.9,1`
temp3 + Cocoa = temp4
`0.15,0.15,0.15,0.15,0.15,0.31,0.3,0.3,0.15,0.15,0.15,0.15,0.15,0.15,0.15,0.15,0.15,0.15,0.15,0.15,0.15,0.15,0.15,0.15,0.15`
temp4 + Tea = temp5
`0.35,0,0,0,0,0,0.1,0.1,0.1,0,0,0,0,0,0,0,0,0,0,0,0,0.35,0.35,0.35,0.35`
temp5 + 7th_anime_v3_B = temp6
`0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.4,0.4,0.4,0.4`
Add diff merge of temp6 + (merge of hll1 and hll3.1 at like 0.4 ratio (more of hll1) I think) - animefull-final-pruned = **HoloKukiv2.2**
# License
License
You are free to:
Share — copy and redistribute the material in any medium or format
Adapt — remix, transform, and build upon the material, as long as you freely share the changes
Under the following terms:
You cannot use the model to deliberately produce nor share illegal or harmful outputs or content
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
You may not use the material for commercial purposes, whether it be as a service, sold as is or merged into other material.
If you grant access to a modified version of the model available to users over a network, you must make your modified model available to those users immediately.
## Big Thanks to
You should also check out their models!
- [Closertodeath](https://huggingface.co/closertodeath) for being cute.
- [andite](https://huggingface.co/andite) for being handsome.
- [syaimu](https://huggingface.co/syaimu) for making a nice model that looks nice. Their model is a part of the mix. They don't know. Don't tell them.
- [NoCrypt](https://huggingface.co/NoCrypt) for providing the necessary computing power.
- Other members of the 東方Project AI community.
- Anons responsbile for hololive/vtuber finetunes.
|
DataikuNLP/paraphrase-albert-small-v2
|
[
"pytorch",
"albert",
"arxiv:1908.10084",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] |
sentence-similarity
|
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"AlbertModel"
],
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}
| 628 | null |
---
language: en
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
tags:
- text-classification
license: mit
---
# AutoDisProxyT-STSB for Distilling Massive Neural Networks
AutoDisProxyT is a distilled task-agnostic transformer model that leverages task transfer for learning a small universal model that can be applied to arbitrary tasks and languages as outlined in the paper [Few-shot Task-agnostic Neural Architecture Search for
Distilling Large Language Models](https://proceedings.neurips.cc/paper_files/paper/2022/file/b7c12689a89e98a61bcaa65285a41b7c-Paper-Conference.pdf).
This AutoDisProxyT checkpoint with **7** layers, **160** hidden size, **10** attention heads corresponds to **6.88 million** parameters and **0.27G** FLOPs.
The following table shows the results on GLUE dev set.
| Models | #Params (M) | #FLOPs (G) | MNLI | QNLI | QQP | RTE | SST-2 | MRPC | CoLA | Avg |
|----------------|--------|---------|------|------|------|------|------|------|--------|-------|
| BERT | 109 | 11.2 | 84.5 | 91.7 | 91.3 | 68.6 | 93.2 | 87.3 | 53.5 | 82.2 |
| BERT<sub>SMALL</sub> | 66 | 5.66 | 81.8 | 89.8 | 90.6 | 67.9 | 91.2 | 84.9 | 53.5 | 80.0 |
| TruncatedBERT | 66 | 5.66 | 81.2 | 87.9 | 90.4 | 65.5 | 90.8 | 82.7 | 41.4 | 77.1 |
| DistilBERT | 66 | 5.66 | 82.2 | 89.2 | 88.5 | 59.9 | 91.3 | 87.5 | 51.3 | 78.6 |
| TinyBERT | 66 | 5.66 | 83.5 | 90.5 | 90.6 | 72.2 | 91.6 | 88.4 | 42.8 | 79.9 |
| MiniLM | 66 | 5.66 | 84.0 | 91.0 | 91.0 | 71.5 | 92.0 | 88.4 | 49.2 | 81.0 |
| AutoTinyBERT-KD-S1 | 30.0 | 1.69 | 82.3 | 89.7 | 89.9 | 71.1 | 91.4 | 88.5 | 47.3 | 80.0 |
| DynaBERT | 37.7 | 1.81 | 82.3 | 88.5 | 90.4 | 63.2 | 92.0 | 81.4 | 76.4 | 43.7 |
| NAS-BERT<sub>10</sub>| 10.0 | 2.30 | 76.4 | 86.3 | 88.5 | 66.6 | 88.6 | 79.1 | 34.0 | 74.2 |
| AutoTinyBERT-KD-S4 | 66 | 5.66 | 76.0 | 85.5 | 86.9 | 64.9 | 86.8 | 81.4 | 20.4 | 71.7 |
| NAS-BERT<sub>5</sub> | 66 | 5.66 | 74.4 | 84.9 | 85.8 | 66.6 | 87.3 | 79.6 | 19.8 | 71.2 |
| **AutoDisProxyT** | 6.88 | 0.27 | 79.0 | 86.4 | 89.1 | 64.3 | 85.9 | 78.5 | 24.8 | 72.6 |
Tested with `torch 1.6.0`
If you use this checkpoint in your work, please cite:
``` latex
@article{xu2022autodistil,
title={AutoDistil: Few-shot Task-agnostic Neural Architecture Search for Distilling Large Language Models},
author={Xu, Dongkuan and Mukherjee, Subhabrata and Liu, Xiaodong and Dey, Debadeepta and Wang, Wenhui and Zhang, Xiang and Awadallah, Ahmed Hassan and Gao, Jianfeng},
journal={arXiv preprint arXiv:2201.12507},
year={2022}
}
```
|
DataikuNLP/paraphrase-multilingual-MiniLM-L12-v2
|
[
"pytorch",
"bert",
"arxiv:1908.10084",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] |
sentence-similarity
|
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| 1,517 | null |
---
language: en
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
tags:
- text-classification
license: mit
---
# AutoDisProxyT-QNLI for Distilling Massive Neural Networks
AutoDisProxyT is a distilled task-agnostic transformer model that leverages task transfer for learning a small universal model that can be applied to arbitrary tasks and languages as outlined in the paper [Few-shot Task-agnostic Neural Architecture Search for
Distilling Large Language Models](https://proceedings.neurips.cc/paper_files/paper/2022/file/b7c12689a89e98a61bcaa65285a41b7c-Paper-Conference.pdf).
This AutoDisProxyT checkpoint with **7** layers, **160** hidden size, **10** attention heads corresponds to **6.88 million** parameters and **0.27G** FLOPs.
The following table shows the results on GLUE dev set.
| Models | #Params (M) | #FLOPs (G) | MNLI | QNLI | QQP | RTE | SST-2 | MRPC | CoLA | Avg |
|----------------|--------|---------|------|------|------|------|------|------|--------|-------|
| BERT | 109 | 11.2 | 84.5 | 91.7 | 91.3 | 68.6 | 93.2 | 87.3 | 53.5 | 82.2 |
| BERT<sub>SMALL</sub> | 66 | 5.66 | 81.8 | 89.8 | 90.6 | 67.9 | 91.2 | 84.9 | 53.5 | 80.0 |
| TruncatedBERT | 66 | 5.66 | 81.2 | 87.9 | 90.4 | 65.5 | 90.8 | 82.7 | 41.4 | 77.1 |
| DistilBERT | 66 | 5.66 | 82.2 | 89.2 | 88.5 | 59.9 | 91.3 | 87.5 | 51.3 | 78.6 |
| TinyBERT | 66 | 5.66 | 83.5 | 90.5 | 90.6 | 72.2 | 91.6 | 88.4 | 42.8 | 79.9 |
| MiniLM | 66 | 5.66 | 84.0 | 91.0 | 91.0 | 71.5 | 92.0 | 88.4 | 49.2 | 81.0 |
| AutoTinyBERT-KD-S1 | 30.0 | 1.69 | 82.3 | 89.7 | 89.9 | 71.1 | 91.4 | 88.5 | 47.3 | 80.0 |
| DynaBERT | 37.7 | 1.81 | 82.3 | 88.5 | 90.4 | 63.2 | 92.0 | 81.4 | 76.4 | 43.7 |
| NAS-BERT<sub>10</sub>| 10.0 | 2.30 | 76.4 | 86.3 | 88.5 | 66.6 | 88.6 | 79.1 | 34.0 | 74.2 |
| AutoTinyBERT-KD-S4 | 66 | 5.66 | 76.0 | 85.5 | 86.9 | 64.9 | 86.8 | 81.4 | 20.4 | 71.7 |
| NAS-BERT<sub>5</sub> | 66 | 5.66 | 74.4 | 84.9 | 85.8 | 66.6 | 87.3 | 79.6 | 19.8 | 71.2 |
| **AutoDisProxyT** | 6.88 | 0.27 | 79.0 | 86.4 | 89.1 | 64.3 | 85.9 | 78.5 | 24.8 | 72.6 |
Tested with `torch 1.6.0`
If you use this checkpoint in your work, please cite:
``` latex
@article{xu2022autodistil,
title={AutoDistil: Few-shot Task-agnostic Neural Architecture Search for Distilling Large Language Models},
author={Xu, Dongkuan and Mukherjee, Subhabrata and Liu, Xiaodong and Dey, Debadeepta and Wang, Wenhui and Zhang, Xiang and Awadallah, Ahmed Hassan and Gao, Jianfeng},
journal={arXiv preprint arXiv:2201.12507},
year={2022}
}
```
|
DavidAMcIntosh/DialoGPT-small-rick
|
[] | null |
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| 0 | null |
---
language: en
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
tags:
- text-classification
license: mit
---
# AutoDisProxyT-COLA for Distilling Massive Neural Networks
AutoDisProxyT is a distilled task-agnostic transformer model that leverages task transfer for learning a small universal model that can be applied to arbitrary tasks and languages as outlined in the paper [Few-shot Task-agnostic Neural Architecture Search for
Distilling Large Language Models](https://proceedings.neurips.cc/paper_files/paper/2022/file/b7c12689a89e98a61bcaa65285a41b7c-Paper-Conference.pdf).
This AutoDisProxyT checkpoint with **7** layers, **160** hidden size, **10** attention heads corresponds to **6.88 million** parameters and **0.27G** FLOPs.
The following table shows the results on GLUE dev set.
| Models | #Params (M) | #FLOPs (G) | MNLI | QNLI | QQP | RTE | SST-2 | MRPC | CoLA | Avg |
|----------------|--------|---------|------|------|------|------|------|------|--------|-------|
| BERT | 109 | 11.2 | 84.5 | 91.7 | 91.3 | 68.6 | 93.2 | 87.3 | 53.5 | 82.2 |
| BERT<sub>SMALL</sub> | 66 | 5.66 | 81.8 | 89.8 | 90.6 | 67.9 | 91.2 | 84.9 | 53.5 | 80.0 |
| TruncatedBERT | 66 | 5.66 | 81.2 | 87.9 | 90.4 | 65.5 | 90.8 | 82.7 | 41.4 | 77.1 |
| DistilBERT | 66 | 5.66 | 82.2 | 89.2 | 88.5 | 59.9 | 91.3 | 87.5 | 51.3 | 78.6 |
| TinyBERT | 66 | 5.66 | 83.5 | 90.5 | 90.6 | 72.2 | 91.6 | 88.4 | 42.8 | 79.9 |
| MiniLM | 66 | 5.66 | 84.0 | 91.0 | 91.0 | 71.5 | 92.0 | 88.4 | 49.2 | 81.0 |
| AutoTinyBERT-KD-S1 | 30.0 | 1.69 | 82.3 | 89.7 | 89.9 | 71.1 | 91.4 | 88.5 | 47.3 | 80.0 |
| DynaBERT | 37.7 | 1.81 | 82.3 | 88.5 | 90.4 | 63.2 | 92.0 | 81.4 | 76.4 | 43.7 |
| NAS-BERT<sub>10</sub>| 10.0 | 2.30 | 76.4 | 86.3 | 88.5 | 66.6 | 88.6 | 79.1 | 34.0 | 74.2 |
| AutoTinyBERT-KD-S4 | 66 | 5.66 | 76.0 | 85.5 | 86.9 | 64.9 | 86.8 | 81.4 | 20.4 | 71.7 |
| NAS-BERT<sub>5</sub> | 66 | 5.66 | 74.4 | 84.9 | 85.8 | 66.6 | 87.3 | 79.6 | 19.8 | 71.2 |
| **AutoDisProxyT** | 6.88 | 0.27 | 79.0 | 86.4 | 89.1 | 64.3 | 85.9 | 78.5 | 24.8 | 72.6 |
Tested with `torch 1.6.0`
If you use this checkpoint in your work, please cite:
``` latex
@article{xu2022autodistil,
title={AutoDistil: Few-shot Task-agnostic Neural Architecture Search for Distilling Large Language Models},
author={Xu, Dongkuan and Mukherjee, Subhabrata and Liu, Xiaodong and Dey, Debadeepta and Wang, Wenhui and Zhang, Xiang and Awadallah, Ahmed Hassan and Gao, Jianfeng},
journal={arXiv preprint arXiv:2201.12507},
year={2022}
}
```
|
Davlan/bert-base-multilingual-cased-finetuned-amharic
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
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},
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}
| 109 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_awesome_UkrSynth_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. -->
# my_awesome_UkrSynth_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3089
- Precision: 0.8875
- Recall: 0.8838
- F1: 0.8856
- Accuracy: 0.9056
## 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.8039 | 1.0 | 500 | 0.3879 | 0.8582 | 0.8556 | 0.8569 | 0.8805 |
| 0.3738 | 2.0 | 1000 | 0.3089 | 0.8875 | 0.8838 | 0.8856 | 0.9056 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Davlan/m2m100_418M-eng-yor-mt
|
[
"pytorch",
"m2m_100",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
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"M2M100ForConditionalGeneration"
],
"model_type": "m2m_100",
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}
| 9 | null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 263.71 +/- 17.49
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
...
```
|
Davlan/mt5-small-pcm-en
|
[
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"MT5ForConditionalGeneration"
],
"model_type": "mt5",
"task_specific_params": {
"conversational": {
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| 9 | null |
---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
instance_prompt: a photo of sks dog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - hawkwang/alvan_model
These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
|
Davlan/mt5_base_yor_eng_mt
|
[
"pytorch",
"mt5",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
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| 8 | 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="mpekey/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"])
```
|
Davlan/xlm-roberta-base-finetuned-amharic
|
[
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"XLMRobertaForMaskedLM"
],
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}
| 401 | null |
Access to model gpradasa7/spanish_sentiment_analysis_pos_neg is restricted and you are not in the authorized list. Visit https://huggingface.co/gpradasa7/spanish_sentiment_analysis_pos_neg to ask for access.
|
Dayout/test
|
[] | null |
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| 0 | null |
---
license: mit
---
[CLIP ViT-B/32 xlm roberta base - LAION-5B](https://huggingface.co/laion/CLIP-ViT-B-32-xlm-roberta-base-laion5B-s13B-b90k) model converted to HuggingFace Transformers via https://gist.github.com/calpt/8e3555bd11f1916b5169c8125117e5ee.
|
DeadBeast/korscm-mBERT
|
[
"pytorch",
"bert",
"text-classification",
"korean",
"dataset:Korean-Sarcasm",
"transformers",
"license:apache-2.0"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
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"max_length": null
},
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| 43 | null |
---
tags:
- question-answering
- bert
- adapterhub:qa/squad1
- adapter-transformers
datasets:
- squad
language:
- en
---
# Adapter `AdapterHub/bert-base-uncased-pf-squad` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [qa/squad1](https://adapterhub.ml/explore/qa/squad1/) dataset and includes a prediction head for question answering.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-squad", source="hf")
model.active_adapters = adapter_name
```
## Architecture & Training
The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
## Evaluation results
Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
## Citation
If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
```bibtex
@inproceedings{poth-etal-2021-pre,
title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
author = {Poth, Clifton and
Pfeiffer, Jonas and
R{"u}ckl{'e}, Andreas and
Gurevych, Iryna},
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.827",
pages = "10585--10605",
}
```
|
Declan/Breitbart_modelv7
|
[] | null |
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| 0 | null |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: codeparrot-ds
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# codeparrot-ds
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Declan/FoxNews_model_v6
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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| 3 | null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 269.75 +/- 25.95
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
...
```
|
Declan/NPR_model_v5
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"BertForMaskedLM"
],
"model_type": "bert",
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| 7 | null |
Access to model Jemnite/HanktheHillmanMix is restricted and you are not in the authorized list. Visit https://huggingface.co/Jemnite/HanktheHillmanMix to ask for access.
|
DeskDown/MarianMixFT_en-my
|
[
"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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}
| 7 | null |
---
license: creativeml-openrail-m
language:
- en
- ja
tags:
- art
---
## Download
<div align="center">
[](https://huggingface.co/ThePioneer/SnowMix/resolve/main/SnowMix.safetensors)
[](https://huggingface.co/ThePioneer/SnowMix/resolve/main/SnowMix_fp16.safetensors)
</div>
## About
<div align="center">
[](https://twitter.com/ThePioneerJPnew/status/1629399166664478720)
</div>
Introducing SnowMix.
From NAI leak free anime images, holara-like semi-real images, midjourney-like fantasy or cyberpunk digital art, to ChilloutMix free AI cosplay.
SnowMix is a merged model of 5 anime models and 1 realistic model.
It's a merged model of [Untitled](https://huggingface.co/alfredplpl/untitled), [Replicant v1.0](https://huggingface.co/gsdf/Replicant-V1.0), [Aikimi Diffusion v3](https://huggingface.co/Aikimi/Aikimi_diffusion_base_wd-1-5_beta1), [Subtly](https://huggingface.co/ddPn08/subtly), [RuminationDiffusion](https://huggingface.co/JosephusCheung/RuminationDiffusion), and [Illuminati Diffusion v1.0](https://huggingface.co/IlluminatiAI/Illuminati_Diffusion_v1.0).
Its potential should exceed the previous powerful merge, [Quattro4Merge+i](https://huggingface.co/ThePioneer/quattro-4merge-plus-i), but yet unknown.
Now is your turn to download this model, and discover the true power.
## Samples
See the [civtai](https://civitai.com/models/12863/snowmix) page for sample prompts.
|
DimaOrekhov/cubert-method-name
|
[
"pytorch",
"encoder-decoder",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
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"EncoderDecoderModel"
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| 10 | null |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: fine-tuned-IndoNLI-Basic-with-xlm-roberta-base
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# fine-tuned-IndoNLI-Basic-with-xlm-roberta-base
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: 1.1133
- Accuracy: 0.4665
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- 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: 0.06
- num_epochs: 16
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1127 | 1.0 | 161 | 1.1041 | 0.2918 |
| 1.1006 | 2.0 | 322 | 1.0960 | 0.3409 |
| 1.0368 | 3.0 | 483 | 1.0347 | 0.4201 |
| 0.9914 | 4.0 | 644 | 0.9819 | 0.4593 |
| 0.9718 | 5.0 | 805 | 1.0013 | 0.4297 |
| 0.9628 | 6.0 | 966 | 0.9786 | 0.4861 |
| 0.9565 | 7.0 | 1127 | 0.9940 | 0.5102 |
| 0.9418 | 8.0 | 1288 | 1.0082 | 0.4998 |
| 0.936 | 9.0 | 1449 | 1.0298 | 0.4574 |
| 0.9027 | 10.0 | 1610 | 1.0522 | 0.4770 |
| 0.8861 | 11.0 | 1771 | 1.0756 | 0.4665 |
| 0.9045 | 12.0 | 1932 | 1.0986 | 0.4488 |
| 0.8764 | 13.0 | 2093 | 1.0949 | 0.4315 |
| 0.8703 | 14.0 | 2254 | 1.1140 | 0.4729 |
| 0.8539 | 15.0 | 2415 | 1.1241 | 0.4511 |
| 0.8619 | 16.0 | 2576 | 1.1133 | 0.4665 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.2.0
- Tokenizers 0.13.2
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-freeze-8
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
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"BertForSequenceClassification"
],
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| 30 | null |
---
license: apache-2.0
---
Test Model for [pmtrendviz](https://github.com/psaegert/pmtrendviz)
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-100
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
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"BertForSequenceClassification"
],
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| 28 | null |
---
license: creativeml-openrail-m
tags:
- coreml
- stable-diffusion
- text-to-image
---
# Core ML Converted Model
This model was converted to Core ML for use on Apple Silicon devices by following Apple's instructions [here](https://github.com/apple/ml-stable-diffusion#-converting-models-to-core-ml).<br>
Provide the model to an app such as [Mochi Diffusion](https://github.com/godly-devotion/MochiDiffusion) to generate images.<br>
`split_einsum` version is compatible with all compute unit options including Neural Engine.<br>
`original` version is only compatible with CPU & GPU option.
# Ares Mix
Source: [CivitAI](https://civitai.com/models/6931/ares-mix)
Attention: You need to get your own VAE to use this model to the fullest. While it does work without a VAE, it works much better with one. I recommend you try [this one](https://huggingface.co/stabilityai/sd-vae-ft-mse-original/tree/main) out.
Hey everyone. After GrapeLikeDreamFruit hit, I started missing having a more general purpose model for the mundane kind of pictures - nude photographs on different backgrounds and some light hardcore capabilities. This model here is my response to that need. It handles the female nude superbly, and while it's less of an artistic model than GrapeLike, it's still quite capable in that regard. It's quite good at hardcore, even if that is just a secondary goal for this model, and can be prompted for a variety of acts. Model has a good response to Danbooru tags.
This model involves dreamlike photoreal, so here is the [license](https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0/blob/main/LICENSE.md) that you must abide by.
<img src="https://imagecache.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/09c059c5-8f95-48e0-c2e7-841011d3df00/width=512">
<img src="https://imagecache.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/d34462fe-6a98-4133-8f71-132d7795cc00/width=512">
<img src="https://imagecache.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/3eb3c0e1-dd57-4ddd-2873-5a2f67381100/width=512">
<img src="https://imagecache.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/9b891a1c-e95b-48af-0b30-608e0a295400/width=512">
Deeper explanation:
This model merge was done following a similar phylosophy to GrapeLike: a Block merge between a realistic anatomy + skin core and a posing anime core. The block merge is done in such a way as to emphasize the anime core in the center of the Unet, while rapidly decaying back to the realistic core at the edges. This has the effect of bringing a lot of posing and composition ideas from hentai models inside the photorealistic core we have available without touching textures and photorealism. Full recipe follows:
Anatomy core:
izumi, F222, dreamlike photoreal, realistic vision 1.2, sxd, all at the same intensity. ie, the merge chain was:
(((izumi + F222 0.5) + dreamlike photoreal 0.33) + realistic vision 0.25) + sxd 0.2
Anime core:
Anything v4.5 merged with Basil Mix using the same block merge coefficients used in mixing [Abyss Orange Mix](https://civitai.com/models/4451/abyssorangemix2-nsfw-hardcore), then merged 40% with grapefruit, the result was merged 30% with gape60 and finally 15% with RPG v4.
Bringing both together:
The block merge for both was done using a formula. I kept the bottleneck Model A was anatomy, model B was anime. I kept the center lalyer at 0.7, as well as base alpha, then followed 0.8/(n**1.1)as a merge rule, with n being distance from the center. Full numbers were "0.05199847612695355,0.05722134125067434,0.06354625877794251,0.07135480548979826,0.08122523963562354,0.09407671474206218,0.11146117361039158,0.13621438760332552,0.17411011265922482,0.23892225595753655,0.373213196614723,0.8,0.7,0.8,0.373213196614723,0.23892225595753655,0.17411011265922482,0.13621438760332552,0.11146117361039158,0.09407671474206218,0.08122523963562354,0.07135480548979826,0.06354625877794251,0.05722134125067434,0.05199847612695355".
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-50
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"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,
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"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
}
| 28 | null |
---
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: 125.90 +/- 43.30
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': 2000000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 2048
'anneal_lr': True
'gae': True
'gamma': 0.999
'gae_lambda': 0.98
'num_minibatches': 64
'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': 'OliP/ppo-LunarLander-v2-unit8-v0'
'batch_size': 8192
'minibatch_size': 128}
```
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-with-clean-valid
|
[
"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,
"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,
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}
}
}
| 33 | 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: 13.43 +/- 4.50
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 RegisGraptin/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 <path.to.enjoy.module> --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 <path.to.train.module> --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.
|
albert-base-v1
|
[
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"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": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 38,156 | 2023-02-25T10:43:15Z |
#@title Select your model below, then click the play button to start the UI.
#@markdown Afterwards, just sit tight and wait - the link to the UI should show up after it's done starting up.
Model = "Pygmalion 6B" #@param ["Pygmalion 350M", "Pygmalion 1.3B", "Pygmalion 2.7B", "Pygmalion 6B", "Pygmalion 6B Experimental"] {allow-input: true}
pretty_model_name_to_hf_name = {
"Pygmalion 350M": "PygmalionAI/pygmalion-350m",
"Pygmalion 1.3B": "PygmalionAI/pygmalion-1.3b",
"Pygmalion 2.7B": "PygmalionAI/pygmalion-2.7b",
"Pygmalion 6B": "PygmalionAI/pygmalion-6b",
"Pygmalion 6B Experimental": "PygmalionAI/pygmalion-6b"
}
model_name = pretty_model_name_to_hf_name[Model]
branch_name = "main" if Model != "Pygmalion 6B Experimental" else "dev"
# Copy-pasted from the Kobold notebook. Seems to be necessary for Henk's script
# to work properly.
import os
if not os.path.exists("/content/drive"):
os.mkdir("/content/drive")
if not os.path.exists("/content/drive/MyDrive/"):
os.mkdir("/content/drive/MyDrive/")
# Use Henk's easy install code, but pass --init since we'll manually start the
# server in the background later.
!wget https://koboldai.org/ckds -O - | bash /dev/stdin --init only
# Clone the UI repo and set it up.
!git clone --depth=1 \
"https://github.com/PygmalionAI/gradio-ui.git" \
&& cd gradio-ui && pip3 install -r requirements.txt
# Start up Kobold in the background.
# TODO: Figure out a way to keep logs in the foreground so the user knows what's
# going on.
print("\n\n\n")
print("* The model is about to be downloaded and loaded into the GPU.")
print("* This takes several minutes, sit tight.")
print("* A link will show up when this step is completed, keep checking back every couple minutes or so.")
print("\n\n\n")
os.system(f"cd /content/KoboldAI-Client && python3 aiserver.py --noaimenu --host --port 80808 --model {model_name} --revision {branch_name} --nobreakmodel --lowmem --quiet &")
# And start up the UI. It'll wait for Kobold to finish booting up before
# printing out its URL.
!python3 gradio-ui/src/app.py \
--koboldai-url "http://localhost:80808" \
--share
|
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": {
"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
}
}
}
| 4,785,283 | 2023-02-25T10:44:34Z |
---
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: 268.13 +/- 15.50
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-v1
|
[
"pytorch",
"tf",
"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": {
"early_stopping": null,
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},
"translation_en_to_ro": {
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"prefix": null
}
}
}
| 687 | 2023-02-25T10:44:46Z |
---
language:
- en
license: creativeml-openrail-m
thumbnail: "https://huggingface.co/Guizmus/SDArt_underwaterworlds/resolve/main/showcase.jpg"
tags:
- stable-diffusion
- text-to-image
- image-to-image
---
# PoW : UNDERWATER WORLDS
## Theme
Deep in the heart of the ocean, there was a realm of magic and mystery, where the creatures were of an unearthly nature. Lyra, a young and courageous mermaid, was determined to explore every space of this ethereal world. With each stroke of her powerful tail, she descended deeper into the abyssal unknown, encountering peculiar and breathtaking creatures - towering sea serpents with magnificent scales, colossal jellyfish that radiated with a vibrant light, and shoals of resplendent fish that swam in mesmerizing patterns.
As Lyra delved deeper, an expansive gateway of coral and seaweed emerged before her. Suddenly, she was surrounded by a swarm of bioluminescent seahorses that darted around her, their neon lights illuminating the shadows. Lyra couldn't help but smile at the sight, marveling at their beauty. After a moment, she composed herself and looked around. She was in disbelief at what lay before here - an underwater city of pure enchantment and intrigue.
The buildings were made of elegant stonework, adorned with an array of carvings depicting creatures beyond her wildest dreams. The walls were draped in a soft, radiant moss that illuminated the entire city like a cloudless starry night sky. The interplay of light and shadow danced in a hypnotic rhythm that left Lyra in a state of awe. She felt as if she had discovered a world that was both ancient and new, an underwater world of unexplored possibilities.
***Bring the magic of the underwater worlds to life***
How would you capture the wonder and fluidity of the ocean depths?
What kind of unique underwater creatures or plants would Lyra have seen?
If you could explore one specific area of Lyra's world, what would it be?Remember to stay hydrated!
## Model description
This is a model related to the "Picture of the Week" contest on Stable Diffusion discord.
I try to make a model out of all the submission for people to continue enjoy the theme after the even, and see a little of their designs in other people's creations. The token stays "SDArt" and I balance the learning on the low side, so that it doesn't just replicate creations.
The total dataset is made of 38 pictures. It was trained on [Stable diffusion 1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5). I used [EveryDream](https://github.com/victorchall/EveryDream2trainer) to do the training, 100 total repeat per picture. The pictures were tagged using the token "SDArt", and an arbitrary token I choose. The dataset is provided below, as well as a list of usernames and their corresponding token.
The recommended sampling is k_Euler_a or DPM++ 2M Karras on 20 steps, CFGS 7.5 .
## Trained tokens
* SDArt
* appt
* ohwx
* asr
* aten
* fcu
* chor
* cpec
* pfa
* kprc
* kuro
* asot
* elis
* sill
* exe
* bsp
* grl
* hap
* byes
* lpg
* yler
* avel
* vaw
* zaki
* ohn
* guin
* vini
* pz
* crit
* shma
* doa
* sks
* szn
* phol
* utm
* uy
* dds
* pte
* oxi
* ynna
## Download links
[SafeTensors](https://huggingface.co/Guizmus/SDArt_underwaterworlds/resolve/main/SDArt_underwaterworlds.safetensors)
[CKPT](https://huggingface.co/Guizmus/SDArt_underwaterworlds/resolve/main/SDArt_underwaterworlds.ckpt)
[Dataset](https://huggingface.co/Guizmus/SDArt_underwaterworlds/resolve/main/dataset.zip)
## 🧨 Diffusers
This model can be used just like any other Stable Diffusion model. For more information,
please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion).
You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [MPS](https://huggingface.co/docs/diffusers/optimization/mps) and/or [FLAX/JAX]().
```python
from diffusers import StableDiffusionPipeline
import torch
model_id = "Guizmus/SDArt_underwaterworlds"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "SDArt oxi"
image = pipe(prompt).images[0]
image.save("./SDArt.png")
```
|
albert-xlarge-v1
|
[
"pytorch",
"tf",
"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": {
"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
}
}
}
| 341 | 2023-02-25T10:49:39Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole8
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
albert-xlarge-v2
|
[
"pytorch",
"tf",
"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": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
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}
}
}
| 2,973 | 2023-02-25T10:49:46Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: rdesarz/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
albert-xxlarge-v2
|
[
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"max_length": null,
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}
}
}
| 42,640 | 2023-02-25T11:01:06Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi
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="Ibtisam/Taxi", 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"])
```
|
bert-base-cased-finetuned-mrpc
|
[
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"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": {
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"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,
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
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}
}
}
| 11,644 | 2023-02-25T11:03:53Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilroberta-base-finetuned-question-v-statement
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilroberta-base-finetuned-question-v-statement
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0048
- Accuracy: 0.9992
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0069 | 1.0 | 7932 | 0.0088 | 0.9987 |
| 0.0011 | 2.0 | 15864 | 0.0048 | 0.9992 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
|
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,
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
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}
}
}
| 3,377,486 | 2023-02-25T11:05:20Z |
---
tags:
- generated_from_trainer
datasets:
- scientific_lay_summarisation
model-index:
- name: pegasus-scientific_lay_v2.0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-scientific_lay_v2.0
This model is a fine-tuned version of [Anandhulk/pegasus-scientific_lay](https://huggingface.co/Anandhulk/pegasus-scientific_lay) on the scientific_lay_summarisation dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3367
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.4891 | 1.0 | 774 | 2.3367 |
### Framework versions
- Transformers 4.26.1
- 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": {
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"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,
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}
}
}
| 68,305 | 2023-02-25T11:17:52Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: M12_SID_1
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. -->
# M12_SID_1
This model is a fine-tuned version of [Sjdan/mst_1](https://huggingface.co/Sjdan/mst_1) 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 7
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
|
bert-base-multilingual-uncased
|
[
"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",
"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",
"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
}
}
}
| 328,585 | 2023-02-25T11:21:49Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: M07_SID_1
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. -->
# M07_SID_1
This model is a fine-tuned version of [Sjdan/mst_1](https://huggingface.co/Sjdan/mst_1) 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 7
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
|
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 | 2023-02-25T11:25:47Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: F03_SID_1
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. -->
# F03_SID_1
This model is a fine-tuned version of [Sjdan/mst_1](https://huggingface.co/Sjdan/mst_1) 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 7
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
|
bert-large-uncased
|
[
"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,
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
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}
}
}
| 1,058,496 | 2023-02-25T11:27:45Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **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: Write your model_id: snlBro/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
camembert-base
|
[
"pytorch",
"tf",
"safetensors",
"camembert",
"fill-mask",
"fr",
"dataset:oscar",
"arxiv:1911.03894",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"CamembertForMaskedLM"
],
"model_type": "camembert",
"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,
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},
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"early_stopping": null,
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}
}
}
| 1,440,898 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: M09_SID_1
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. -->
# M09_SID_1
This model is a fine-tuned version of [Sjdan/mst_1](https://huggingface.co/Sjdan/mst_1) 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 7
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
|
distilbert-base-cased-distilled-squad
|
[
"pytorch",
"tf",
"rust",
"safetensors",
"openvino",
"distilbert",
"question-answering",
"en",
"dataset:squad",
"arxiv:1910.01108",
"arxiv:1910.09700",
"transformers",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"has_space"
] |
question-answering
|
{
"architectures": [
"DistilBertForQuestionAnswering"
],
"model_type": "distilbert",
"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,
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}
}
}
| 257,745 | 2023-02-25T11:31:06Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **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: zipbomb/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
distilbert-base-cased
|
[
"pytorch",
"tf",
"onnx",
"distilbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1910.01108",
"transformers",
"license:apache-2.0",
"has_space"
] | null |
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}
| 574,859 | 2023-02-25T11:32:40Z |
---
tags:
- conversational
---
# Sou AI DialoGPT Model
|
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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"DistilBertForMaskedLM"
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}
| 43,667 | null |
Access to model Kangarroar/Test is restricted and you are not in the authorized list. Visit https://huggingface.co/Kangarroar/Test to ask for access.
|
distilbert-base-uncased-finetuned-sst-2-english
|
[
"pytorch",
"tf",
"rust",
"safetensors",
"distilbert",
"text-classification",
"en",
"dataset:sst2",
"dataset:glue",
"arxiv:1910.01108",
"doi:10.57967/hf/0181",
"transformers",
"license:apache-2.0",
"model-index",
"has_space"
] |
text-classification
|
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}
| 3,060,704 | 2023-02-25T11:49:32Z |
---
license: openrail
language:
- en
pipeline_tag: text-to-image
tags:
- art
---
A blend of models aiming for photorealism in outputs of people. For the best results use the config file (GravyMix3Final.yaml). Tested with the Automatic1111 webUI. Not a nsfw model, outputs should generally be sfw unless specifically prompted, but use caution.
It does a great job with photography-related prompting. Results with simple prompts have variable photorealism.
The following models from huggingface.co and civitai.com have been used during the blending process: TheAlly's Mix II: Churned, Analog Madness, Emotion Puppeteer, Sunshine Mix, Doctor Diffusion, Brits out for the lads, Bigger Girls Photorealism, Subreddit V3, Hunter69 v1, HARDblend, Ares Mix, Project Photo Beta 2.0 LITE, Cafe-Instagram, Art & Eros (aEros),s1dlxbrew, URPM, Protogen x3.4, Dreamlike Photoreal, Wavyfusion, Analog Diffusion, Elysium_V1
complex prompts
Simple prompts
|
distilbert-base-uncased
|
[
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"distilbert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1910.01108",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
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"DistilBertForMaskedLM"
],
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"task_specific_params": {
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}
| 10,887,471 | null |
---
tags:
- bert
- adapter-transformers
- adapterhub:nli/multinli
datasets:
- multi_nli
---
# Adapter `domadapter/joint_dt_fiction_slate` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [nli/multinli](https://adapterhub.ml/explore/nli/multinli/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("domadapter/joint_dt_fiction_slate", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
t5-base
|
[
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"t5",
"text2text-generation",
"en",
"fr",
"ro",
"de",
"dataset:c4",
"arxiv:1805.12471",
"arxiv:1708.00055",
"arxiv:1704.05426",
"arxiv:1606.05250",
"arxiv:1808.09121",
"arxiv:1810.12885",
"arxiv:1905.10044",
"arxiv:1910.09700",
"transformers",
"summarization",
"translation",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
translation
|
{
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
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},
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},
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},
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"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: "
}
}
}
| 6,339,864 | 2023-02-25T12:10:13Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: M04_SID_1
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. -->
# M04_SID_1
This model is a fine-tuned version of [Sjdan/mst_1](https://huggingface.co/Sjdan/mst_1) 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 7
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
|
Aakansha/hateSpeechClassification
|
[] | null |
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}
| 0 | 2023-02-25T17:35:21Z |
---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
Abhilash/BERTBasePyTorch
|
[] | null |
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}
| 0 | 2023-02-25T18:46:51Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
AdapterHub/bert-base-uncased-pf-wic
|
[
"bert",
"en",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification",
"adapterhub:wordsence/wic"
] |
text-classification
|
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}
| 0 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: mertyazan/unity-Pyramid
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AdapterHub/roberta-base-pf-quartz
|
[
"roberta",
"en",
"dataset:quartz",
"arxiv:2104.08247",
"adapter-transformers"
] | null |
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| 1 | null |
monot5-3b-inpars-v2-fiqa-promptagator is a monoT5-3B model finetuned on FiQA synthetic data generated by [InPars](https://github.com/zetaalphavector/inPars).
Currently, if you use this tool you can cite the original [InPars paper published at SIGIR](https://dl.acm.org/doi/10.1145/3477495.3531863) or [InPars-v2](https://arxiv.org/abs/2301.01820).
```
@inproceedings{inpars,
author = {Bonifacio, Luiz and Abonizio, Hugo and Fadaee, Marzieh and Nogueira, Rodrigo},
title = {{InPars}: Unsupervised Dataset Generation for Information Retrieval},
year = {2022},
isbn = {9781450387323},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3477495.3531863},
doi = {10.1145/3477495.3531863},
booktitle = {Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {2387–2392},
numpages = {6},
keywords = {generative models, large language models, question generation, synthetic datasets, few-shot models, multi-stage ranking},
location = {Madrid, Spain},
series = {SIGIR '22}
}
```
```
@misc{inparsv2,
doi = {10.48550/ARXIV.2301.01820},
url = {https://arxiv.org/abs/2301.01820},
author = {Jeronymo, Vitor and Bonifacio, Luiz and Abonizio, Hugo and Fadaee, Marzieh and Lotufo, Roberto and Zavrel, Jakub and Nogueira, Rodrigo},
title = {{InPars-v2}: Large Language Models as Efficient Dataset Generators for Information Retrieval},
publisher = {arXiv},
year = {2023},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
Adarsh123/distilbert-base-uncased-finetuned-ner
|
[] | null |
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-large-xls-r-300m-finetuned-wolof
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-finetuned-wolof
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
Advertisement/FischlUWU
|
[] | null |
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| 0 | 2023-02-26T01:45:21Z |
---
language:
- tr
pipeline_tag: token-classification
tags:
- ner
widget:
- text: "Lütfen yardım Piyalepasa mahallesi Rüzgar sokak Meltem apartmanı no: 22 Hatay akrabalarım göçük altında #dummy"
---
## Address NER
- **Language**: Turkish
- **PLM**: dbmdz/bert-base-turkish-128k-cased
- **Macro-F1 Score**: 84%
- **Dataset**: [NER v2 dataset](https://huggingface.co/datasets/deprem-private/ner_v12)
- **Hyperparameters**: per_device_train_batch_size = 16, per_device_eval_batch_size = 32, num_train_epochs = 5, weight_decay = 0.1, warmup_ratio = 0.1, learning_rate = 5e-5
### Model Comparison
| | Macro-F1 |
|----------------------------------------------------|----------|
| dbmdz/bert-base-turkish-128k-cased | 0.84 |
| dbmdz/bert-base-turkish-cased | 0.83 |
| bert-base-multilingual-cased | 0.79 |
| dbmdz/electra-base-turkish-mc4-cased-discriminator | 0.76 |
| xlm-roberta-base | 0.75 |
| dbmdz/convbert-base-turkish-cased | 0.70 |
### Class Performance
| | support | precision | recall | f1 |
|:----------|----------:|------------:|---------:|-----:|
| overall | 957 | 0.84 | 0.88 | 0.86 |
| bina | 66 | 0.66 | 0.74 | 0.7 |
| bulvar | 13 | 0.92 | 0.92 | 0.92 |
| cadde | 57 | 0.77 | 0.84 | 0.81 |
| diskapino | 70 | 0.69 | 0.73 | 0.71 |
| ilce | 117 | 0.89 | 0.96 | 0.92 |
| isim | 113 | 0.86 | 0.9 | 0.88 |
| mahalle | 120 | 0.77 | 0.82 | 0.79 |
| sehir | 146 | 0.98 | 0.97 | 0.97 |
| site | 18 | 0.79 | 0.61 | 0.69 |
| sokak | 62 | 0.72 | 0.74 | 0.73 |
| soyisim | 98 | 0.94 | 0.95 | 0.94 |
| telefonno | 77 | 0.99 | 1 | 0.99 |
|
Amrrs/wav2vec2-large-xlsr-53-tamil
|
[
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"ta",
"dataset:common_voice",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index",
"has_space"
] |
automatic-speech-recognition
|
{
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
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}
}
}
| 31 | null |
---
license: apache-2.0
---
Medium TF-IDF-based model for [pmtrendviz](https://github.com/psaegert/pmtrendviz)
### Training
- Training Samples: 3,000,000
- `n_components`: 250
- `n_clusters`: 250
|
Ana1315/ana
|
[] | null |
{
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}
}
}
| 0 | null |
---
license: openrail
pipeline_tag: text-to-image
tags:
- art
- cartoon
- cat
---
TODO
|
AnonymousSub/SR_rule_based_roberta_bert_triplet_epochs_1_shard_1
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
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"max_length": null
},
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| 2 | null |
---
tags:
- bert
- adapter-transformers
- adapterhub:sentiment/amazon
datasets:
- amazon
---
# Adapter `domadapter/joint_dt_apparel_books` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [sentiment/amazon](https://adapterhub.ml/explore/sentiment/amazon/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("domadapter/joint_dt_apparel_books", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
AnonymousSub/SR_rule_based_roberta_twostage_quadruplet_epochs_1_shard_1
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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},
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}
}
| 4 | null |
---
tags:
- bert
- adapter-transformers
- adapterhub:sentiment/amazon
datasets:
- amazon
---
# Adapter `domadapter/joint_dt_books_baby` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [sentiment/amazon](https://adapterhub.ml/explore/sentiment/amazon/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("domadapter/joint_dt_books_baby", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
AnonymousSub/SR_rule_based_roberta_twostage_quadruplet_epochs_1_shard_10
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"translation_en_to_ro": {
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}
}
| 8 | null |
---
tags:
- adapter-transformers
- adapterhub:nli/multinli
- bert
datasets:
- multi_nli
---
# Adapter `domadapter/joint_dt_slate_fiction` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [nli/multinli](https://adapterhub.ml/explore/nli/multinli/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("domadapter/joint_dt_slate_fiction", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
AnonymousSub/SR_rule_based_twostagequadruplet_hier_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
"translation_en_to_ro": {
"early_stopping": null,
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}
}
| 2 | null |
---
tags:
- adapter-transformers
- adapterhub:nli/multinli
- bert
datasets:
- multi_nli
---
# Adapter `domadapter/joint_dt_government_fiction` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [nli/multinli](https://adapterhub.ml/explore/nli/multinli/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("domadapter/joint_dt_government_fiction", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
AnonymousSub/SR_specter
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
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},
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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,
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}
}
}
| 5 | null |
---
tags:
- adapter-transformers
- adapterhub:nli/multinli
- bert
datasets:
- multi_nli
---
# Adapter `domadapter/joint_dt_government_slate` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [nli/multinli](https://adapterhub.ml/explore/nli/multinli/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("domadapter/joint_dt_government_slate", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
AnonymousSub/SciFive_pubmedqa_question_generation
|
[
"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: "
}
}
}
| 7 | null |
---
tags:
- bert
- adapter-transformers
- adapterhub:sentiment/amazon
datasets:
- amazon
---
# Adapter `domadapter/joint_dt_camera_photo_MR` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [sentiment/amazon](https://adapterhub.ml/explore/sentiment/amazon/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("domadapter/joint_dt_camera_photo_MR", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
AnonymousSub/T5_pubmedqa_question_generation
|
[
"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: "
}
}
}
| 6 | null |
---
tags:
- adapter-transformers
- adapterhub:nli/multinli
- bert
datasets:
- multi_nli
---
# Adapter `domadapter/joint_dt_government_telephone` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [nli/multinli](https://adapterhub.ml/explore/nli/multinli/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("domadapter/joint_dt_government_telephone", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
AnonymousSub/bert-base-uncased_wikiqa
|
[
"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,
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},
"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,
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 30 | null |
---
tags:
- adapter-transformers
- adapterhub:nli/multinli
- bert
datasets:
- multi_nli
---
# Adapter `domadapter/joint_dt_government_travel` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [nli/multinli](https://adapterhub.ml/explore/nli/multinli/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("domadapter/joint_dt_government_travel", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
AnonymousSub/bert_hier_diff_equal_wts_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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"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,
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 4 | null |
---
tags:
- bert
- adapter-transformers
- adapterhub:sentiment/amazon
datasets:
- amazon
---
# Adapter `domadapter/joint_dt_MR_baby` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [sentiment/amazon](https://adapterhub.ml/explore/sentiment/amazon/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("domadapter/joint_dt_MR_baby", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
AnonymousSub/bert_hier_diff_equal_wts_epochs_1_shard_10
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
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},
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}
}
| 1 | null |
---
tags:
- adapter-transformers
- adapterhub:nli/multinli
- bert
datasets:
- multi_nli
---
# Adapter `domadapter/joint_dt_telephone_fiction` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [nli/multinli](https://adapterhub.ml/explore/nli/multinli/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("domadapter/joint_dt_telephone_fiction", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
AnonymousSub/bert_mean_diff_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
| 6 | null |
---
tags:
- bert
- adapter-transformers
- adapterhub:sentiment/amazon
datasets:
- amazon
---
# Adapter `domadapter/joint_dt_MR_books` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [sentiment/amazon](https://adapterhub.ml/explore/sentiment/amazon/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("domadapter/joint_dt_MR_books", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
AnonymousSub/bert_mean_diff_epochs_1_shard_10
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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}
}
| 4 | null |
---
tags:
- adapter-transformers
- adapterhub:nli/multinli
- bert
datasets:
- multi_nli
---
# Adapter `domadapter/joint_dt_telephone_slate` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [nli/multinli](https://adapterhub.ml/explore/nli/multinli/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("domadapter/joint_dt_telephone_slate", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
AnonymousSub/bert_snips
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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}
}
}
| 5 | null |
---
tags:
- bert
- adapter-transformers
- adapterhub:sentiment/amazon
datasets:
- amazon
---
# Adapter `domadapter/joint_dt_MR_camera_photo` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [sentiment/amazon](https://adapterhub.ml/explore/sentiment/amazon/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("domadapter/joint_dt_MR_camera_photo", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
AnonymousSub/bert_triplet_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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}
}
}
| 2 | null |
---
tags:
- adapter-transformers
- adapterhub:nli/multinli
- bert
datasets:
- multi_nli
---
# Adapter `domadapter/joint_dt_telephone_government` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [nli/multinli](https://adapterhub.ml/explore/nli/multinli/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("domadapter/joint_dt_telephone_government", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
AnonymousSub/bert_triplet_epochs_1_shard_10
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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}
}
}
| 1 | null |
---
tags:
- adapter-transformers
- adapterhub:nli/multinli
- bert
datasets:
- multi_nli
---
# Adapter `domadapter/joint_dt_telephone_travel` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [nli/multinli](https://adapterhub.ml/explore/nli/multinli/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("domadapter/joint_dt_telephone_travel", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
AnonymousSub/cline-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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},
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}
}
}
| 27 | null |
---
tags:
- adapter-transformers
- adapterhub:nli/multinli
- bert
datasets:
- multi_nli
---
# Adapter `domadapter/joint_dt_travel_fiction` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [nli/multinli](https://adapterhub.ml/explore/nli/multinli/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("domadapter/joint_dt_travel_fiction", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
AnonymousSub/cline-emanuals-s10-SR
|
[] | null |
{
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}
}
}
| 0 | null |
---
tags:
- adapter-transformers
- adapterhub:nli/multinli
- bert
datasets:
- multi_nli
---
# Adapter `domadapter/joint_dt_travel_slate` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [nli/multinli](https://adapterhub.ml/explore/nli/multinli/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("domadapter/joint_dt_travel_slate", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
AnonymousSub/cline-emanuals-techqa
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
}
| 4 | null |
---
tags:
- adapter-transformers
- adapterhub:nli/multinli
- bert
datasets:
- multi_nli
---
# Adapter `domadapter/joint_dt_travel_government` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [nli/multinli](https://adapterhub.ml/explore/nli/multinli/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("domadapter/joint_dt_travel_government", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
AnonymousSub/cline-papers-biomed-0.618
|
[
"pytorch",
"roberta",
"transformers"
] | null |
{
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"LecbertForPreTraining"
],
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}
| 2 | null |
---
language:
- en
- sp
- ja
- pe
- hi
- fr
- ch
- be
- gu
- ge
- te
- it
- ar
- po
- ta
- ma
- ma
- or
- pa
- po
- ur
- ga
- he
- ko
- ca
- th
- du
- in
- vi
- bu
- fi
- ce
- la
- tu
- ru
- cr
- sw
- yo
- ku
- bu
- ma
- cz
- fi
- so
- ta
- sw
- si
- ka
- zh
- ig
- xh
- ro
- ha
- es
- sl
- li
- gr
- ne
- as
- no
widget:
- text: "In January-September 2009 , the Group 's net interest income increased to EUR 112.4 mn from EUR 74.3 mn in January-September 2008."
example_title: "Classification"
datasets:
- financial_phrasebank
tags:
- finance
- classification
- sentiment analysis
---
|
AnonymousSub/cline-s10-SR
|
[] | null |
{
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}
}
| 0 | 2023-02-26T15:57:40Z |
---
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="macb/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/cline-techqa
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
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"RobertaForQuestionAnswering"
],
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}
| 6 | 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: 10.64 +/- 5.16
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 newbie4000/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 <path.to.enjoy.module> --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 <path.to.train.module> --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/cline_emanuals
|
[
"pytorch",
"roberta",
"transformers"
] | null |
{
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"LecbertForPreTraining"
],
"model_type": "roberta",
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| 3 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: LucaReggiani/t5-small-nlpfinalproject12_2-xsum
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. -->
# LucaReggiani/t5-small-nlpfinalproject12_2-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.8830
- Validation Loss: 3.5699
- Train Rouge1: 18.4656
- Train Rouge2: 2.2126
- Train Rougel: 14.7442
- Train Rougelsum: 15.1761
- Train Gen Len: 18.96
- Epoch: 7
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'SGD', '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': 1.9e-05, 'momentum': 0.9, 'nesterov': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch |
|:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:|
| 4.3168 | 4.0297 | 15.0947 | 2.1725 | 12.0181 | 11.8872 | 19.0 | 0 |
| 4.1683 | 3.9156 | 16.1828 | 1.9876 | 12.9115 | 13.0799 | 19.0 | 1 |
| 4.0819 | 3.8338 | 15.9429 | 1.9947 | 13.0026 | 13.1274 | 19.0 | 2 |
| 4.0326 | 3.7649 | 16.7647 | 2.5233 | 13.4735 | 13.6475 | 18.96 | 3 |
| 3.9797 | 3.7033 | 17.2322 | 2.6240 | 13.6267 | 13.7851 | 18.95 | 4 |
| 3.9348 | 3.6524 | 17.4618 | 2.0566 | 13.5028 | 13.8150 | 18.98 | 5 |
| 3.8988 | 3.6090 | 17.7496 | 2.1414 | 13.8788 | 14.3797 | 18.98 | 6 |
| 3.8830 | 3.5699 | 18.4656 | 2.2126 | 14.7442 | 15.1761 | 18.96 | 7 |
### Framework versions
- Transformers 4.26.1
- TensorFlow 2.11.0
- Datasets 2.10.0
- Tokenizers 0.13.2
|
AnonymousSub/consert-s10-AR
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
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"BertForSequenceClassification"
],
"model_type": "bert",
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}
| 31 | null |
---
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: 66.36 +/- 111.52
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': True
'env_id': 'LunarLander-v2'
'total_timesteps': 100000
'learning_rate': 0.0025
'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': 'mktz/ppo-LunarLander-v3'
'batch_size': 512
'minibatch_size': 128}
```
|
AnonymousSub/consert-techqa
|
[
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
}
| 4 | 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: 11.00 +/- 5.62
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 iammartian0/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 <path.to.enjoy.module> --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 <path.to.train.module> --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/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 |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
AnonymousSub/declutr-model-emanuals
|
[
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
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},
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}
}
}
| 4 | 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: 9.99 +/- 4.96
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 mlewand/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/declutr-roberta-papers
|
[
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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}
| 4 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **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: Write your model_id: mmhamdy/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AnonymousSub/declutr-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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},
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},
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}
| 26 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: gpt-neox-20b-imdb-lr5e-4
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. -->
# gpt-neox-20b-imdb-lr5e-4
This model is a fine-tuned version of [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) on the imdb dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0
### Training results
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
|
AnonymousSub/declutr-s10-SR
|
[
"pytorch",
"roberta",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"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,
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},
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}
}
| 36 | null |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1-Test
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
AnonymousSub/declutr-techqa
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
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| 5 | 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: 20.90 +/- 14.94
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
|
AnonymousSub/dummy_2_parent
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"BertModel"
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| 3 | null |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### tits Dreambooth model trained by gsgfhsfxc with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
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:
|
AnonymousSub/roberta-base_squad2.0
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
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}
| 6 | null |
---
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: -146.82 +/- 136.06
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': 'zipbomb/ppo-CartPole-v1'
'batch_size': 512
'minibatch_size': 128}
```
|
AnonymousSub/rule_based_bert_hier_diff_equal_wts_epochs_1_shard_10
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"BertModel"
],
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| 6 | null |
---
tags:
- generated_from_trainer
model-index:
- name: plbart-base_finetuned_ut_generator_70000_method2test
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. -->
# plbart-base_finetuned_ut_generator_70000_method2test
This model is a fine-tuned version of [Minata/plbart-base_finetuned_ut_generator_70000_method2test](https://huggingface.co/Minata/plbart-base_finetuned_ut_generator_70000_method2test) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1594
## 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-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.1996 | 1.0 | 7875 | 0.1690 |
| 0.1895 | 2.0 | 15750 | 0.1655 |
| 0.1816 | 3.0 | 23625 | 0.1628 |
| 0.1757 | 4.0 | 31500 | 0.1612 |
| 0.1714 | 5.0 | 39375 | 0.1599 |
| 0.1686 | 6.0 | 47250 | 0.1594 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.10.0
- Tokenizers 0.13.2
|
AnonymousSub/rule_based_bert_mean_diff_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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],
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}
| 3 | null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 256.51 +/- 23.42
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
...
```
|
AnonymousSub/rule_based_bert_mean_diff_epochs_1_shard_10
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
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}
| 4 | null |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### lgbekna Dreambooth model trained by justArmenian with [buildspace's DreamBooth](https://colab.research.google.com/github/buildspace/diffusers/blob/main/examples/dreambooth/DreamBooth_Stable_Diffusion.ipynb) notebook
Build your own using the [AI Avatar project](https://buildspace.so/builds/ai-avatar)!
To get started head over to the [project dashboard](https://buildspace.so/p/build-ai-avatars).
Sample pictures of this concept:
|
AnonymousSub/rule_based_bert_quadruplet_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"BertModel"
],
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},
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}
| 8 | null |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- NathanRoll/SBC_randword_segmented
model-index:
- name: PSST Scrambled
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. -->
# PSST Scrambled
This model is a fine-tuned version of [openai/whisper-medium.en](https://huggingface.co/openai/whisper-medium.en) on the Santa Barbara Corpus of Spoken American English 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 8
- 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_steps: 50
- training_steps: 400
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
|
AnonymousSub/rule_based_bert_quadruplet_epochs_1_shard_10
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
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},
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}
}
| 8 | 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="Aadharsh/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_bert_quadruplet_epochs_1_shard_1_squad2.0
|
[
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
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"prefix": null
}
}
}
| 3 | 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: 7.54 +/- 2.73
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="Aadharsh/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"])
```
|
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