modelId
stringlengths 5
139
| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-02 12:29:30
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 548
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-08-02 12:29:18
| card
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sai1881/flan-t5-small-Forecast
|
sai1881
| 2023-05-14T07:54:59Z | 161 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-05-14T07:44:45Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: flan-t5-small-Forecast
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. -->
# flan-t5-small-Forecast
This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0116
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 189 | 0.0145 |
| No log | 2.0 | 378 | 0.0122 |
| 0.0938 | 3.0 | 567 | 0.0116 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
50stars/fine-tuned-model
|
50stars
| 2023-05-14T07:18:38Z | 157 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-14T07:04:23Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: fine-tuned-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. -->
# fine-tuned-model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 Score | Jaccard Score | Average Precision Score | Percentage Examples At Least 1 True |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:--------:|:-------------:|:-----------------------:|:-----------------------------------:|
| No log | 1.0 | 5 | 0.6742 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2880 | 0.0 |
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.0+cu118
- Tokenizers 0.13.3
|
trachi123/CK_T5
|
trachi123
| 2023-05-14T06:40:22Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:mt_eng_vietnamese",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-05-13T18:14:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- mt_eng_vietnamese
metrics:
- bleu
model-index:
- name: CK_T5
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: mt_eng_vietnamese
type: mt_eng_vietnamese
config: iwslt2015-vi-en
split: test
args: iwslt2015-vi-en
metrics:
- name: Bleu
type: bleu
value: 0.1851
---
<!-- 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. -->
# CK_T5
This model is a fine-tuned version of [T5-small](https://huggingface.co/T5-small) on the mt_eng_vietnamese dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4489
- Bleu: 0.1851
- Gen Len: 18.751
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|
| 1.6572 | 1.0 | 8333 | 1.5155 | 0.0992 | 18.7864 |
| 1.5895 | 2.0 | 16666 | 1.4489 | 0.1851 | 18.751 |
### Framework versions
- Transformers 4.29.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
digitous/GPT-ClutserFUsion
|
digitous
| 2023-05-14T06:39:06Z | 14 | 5 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"alpaca",
"merge",
"mix",
"alpacino",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-13T20:29:55Z |
---
tags:
- llama
- alpaca
- merge
- mix
- alpacino
---
This is an even Octa merge of: Alpacino+Elina+MedAlpaca+Story+GPT4Aalpaca+VincunaUnlocked+COT+HH
Then for good measure, Chansung's Alpaca was 50/50 merged with the result. A fun experiment.
ChanSung's Alpaca seems fairly uncensored, so the final pass was done to give Alpaca prompting
a dominant edge.
For now only a Cuda GPTQ quant compatible with:
git clone https://github.com/0cc4m/KoboldAI -b latestgptq
and very likely Text Generation WebUI.
Original Weights and Original Author Credits will be added in the coming days.
|
doitopojare/latulip
|
doitopojare
| 2023-05-14T06:15:45Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-14T05:47:48Z |
---
license: creativeml-openrail-m
---
|
huggingliang/distilbert-finetuned-squad
|
huggingliang
| 2023-05-14T05:50:26Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-05-14T04:49:01Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: huggingliang/distilbert-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# huggingliang/distilbert-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.5491
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 5532, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 1.5491 | 0 |
### Framework versions
- Transformers 4.29.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
NathanS-HuggingFace/A2C-ReachDense
|
NathanS-HuggingFace
| 2023-05-14T05:26:46Z | 3 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-01T02:07:06Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -2.54 +/- 0.47
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
...
```
|
jasonsurya0/BART_ELEVEN
|
jasonsurya0
| 2023-05-14T05:10:36Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-05-14T04:24:35Z |
BART MODEL #11 PRETRAINED ON XSUM AND FINETUNED ON SAMSUM
|
rudrransh/tweet_generator
|
rudrransh
| 2023-05-14T04:58:01Z | 0 | 0 | null |
[
"text2text-generation",
"license:apache-2.0",
"region:us"
] |
text2text-generation
| 2023-05-14T04:13:42Z |
---
license: apache-2.0
pipeline_tag: text2text-generation
---
|
NathanS-HuggingFace/SpaceInvadersNoFrameskip
|
NathanS-HuggingFace
| 2023-05-14T04:23:18Z | 8 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-04-16T14:22:01Z |
---
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: 694.50 +/- 243.63
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 NathanS-HuggingFace -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 NathanS-HuggingFace -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 NathanS-HuggingFace
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Vandy/phobert_shared-vietnews
|
Vandy
| 2023-05-14T03:59:11Z | 102 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-05-14T02:23:08Z |
---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: phobert_shared-vietnews
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. -->
# phobert_shared-vietnews
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4872
- Rouge1: 47.252
- Rouge2: 12.3801
- Rougel: 27.9535
- Rougelsum: 31.1165
- Gen Len: 25.3494
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 4.1717 | 1.0 | 619 | 3.7817 | 46.287 | 10.7692 | 27.3606 | 30.4956 | 25.2656 |
| 3.6535 | 2.0 | 1239 | 3.5421 | 46.9278 | 11.7595 | 27.5206 | 30.7512 | 25.9089 |
| 3.434 | 3.0 | 1857 | 3.4872 | 47.252 | 12.3801 | 27.9535 | 31.1165 | 25.3494 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Woohun/finetuned-facebook-bart-base
|
Woohun
| 2023-05-14T03:44:40Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-05-14T03:16:16Z |
---
tags:
- generated_from_trainer
model-index:
- name: finetuned-facebook-bart-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. -->
# finetuned-facebook-bart-base
This model is a fine-tuned version of [../tmp/bart-abst-summarization](https://huggingface.co/../tmp/bart-abst-summarization) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Framework versions
- Transformers 4.30.0.dev0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
cs608/billsum-model
|
cs608
| 2023-05-14T03:28:24Z | 111 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"summarization",
"generated_from_trainer",
"dataset:billsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2023-05-14T02:20:50Z |
---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
datasets:
- billsum
metrics:
- rouge
model-index:
- name: CS685-text-summarizer-2
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: billsum
type: billsum
config: default
split: train[:20%]
args: default
metrics:
- name: Rouge1
type: rouge
value: 17.1607
---
<!-- 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. -->
# CS685-text-summarizer-2
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the billsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7651
- Rouge1: 17.1607
- Rouge2: 13.943
- Rougel: 16.6793
- Rougelsum: 16.8422
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| 2.4547 | 1.0 | 569 | 1.9895 | 16.6343 | 13.0432 | 16.1262 | 16.2449 |
| 2.0246 | 2.0 | 1138 | 1.8688 | 16.939 | 13.4711 | 16.4359 | 16.5797 |
| 1.818 | 3.0 | 1707 | 1.8075 | 17.1388 | 13.827 | 16.6136 | 16.7574 |
| 1.6831 | 4.0 | 2276 | 1.7744 | 17.2292 | 13.9353 | 16.6961 | 16.8786 |
| 1.5956 | 5.0 | 2845 | 1.7651 | 17.1607 | 13.943 | 16.6793 | 16.8422 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
jokyere49/q-FrozenLake-v1-4x4-noSlippery
|
jokyere49
| 2023-05-14T03:23:39Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-14T03:23:36Z |
---
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="jokyere49/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"])
```
|
dian34323/raishajkt48
|
dian34323
| 2023-05-14T03:08:07Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-14T03:06:19Z |
---
license: creativeml-openrail-m
---
|
Felix555/LunarLander-v2
|
Felix555
| 2023-05-14T02:41:55Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-14T02:41:45Z |
---
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: -126.53 +/- 47.03
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': 'Felix555/LunarLander-v2'
'batch_size': 512
'minibatch_size': 128}
```
|
tamhuynh27/xlmroberta-finetuned-recipeqa-modified
|
tamhuynh27
| 2023-05-14T02:36:35Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-05-03T19:16:15Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: xlmroberta-finetuned-recipeqa-modified
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. -->
# xlmroberta-finetuned-recipeqa-modified
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.29.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
tapias/layoutlmv3-finetuned-cord_100
|
tapias
| 2023-05-14T02:34:11Z | 76 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"dataset:cord-layoutlmv3",
"license:cc-by-nc-sa-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-05-14T02:16:47Z |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- cord-layoutlmv3
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-cord_100
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: cord-layoutlmv3
type: cord-layoutlmv3
config: cord
split: test
args: cord
metrics:
- name: Precision
type: precision
value: 0.9430473372781065
- name: Recall
type: recall
value: 0.9543413173652695
- name: F1
type: f1
value: 0.9486607142857143
- name: Accuracy
type: accuracy
value: 0.9579796264855688
---
<!-- 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. -->
# layoutlmv3-finetuned-cord_100
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the cord-layoutlmv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2188
- Precision: 0.9430
- Recall: 0.9543
- F1: 0.9487
- Accuracy: 0.9580
## 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: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.56 | 250 | 1.0024 | 0.7392 | 0.7957 | 0.7664 | 0.8060 |
| 1.3949 | 3.12 | 500 | 0.5684 | 0.8330 | 0.8660 | 0.8492 | 0.8727 |
| 1.3949 | 4.69 | 750 | 0.3929 | 0.8931 | 0.9072 | 0.9001 | 0.9160 |
| 0.3964 | 6.25 | 1000 | 0.3312 | 0.9236 | 0.9326 | 0.9281 | 0.9321 |
| 0.3964 | 7.81 | 1250 | 0.2754 | 0.9275 | 0.9386 | 0.9330 | 0.9410 |
| 0.216 | 9.38 | 1500 | 0.2447 | 0.9328 | 0.9454 | 0.9390 | 0.9478 |
| 0.216 | 10.94 | 1750 | 0.2467 | 0.9363 | 0.9461 | 0.9412 | 0.9478 |
| 0.1534 | 12.5 | 2000 | 0.2300 | 0.9436 | 0.9521 | 0.9478 | 0.9537 |
| 0.1534 | 14.06 | 2250 | 0.2155 | 0.9459 | 0.9558 | 0.9509 | 0.9597 |
| 0.119 | 15.62 | 2500 | 0.2188 | 0.9430 | 0.9543 | 0.9487 | 0.9580 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
thu-coai/blenderbot-1B-augesc
|
thu-coai
| 2023-05-14T02:30:19Z | 18 | 3 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"blenderbot",
"text2text-generation",
"conversational",
"en",
"arxiv:2202.13047",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-01-12T11:08:57Z |
---
language:
- en
pipeline_tag: conversational
tags:
- pytorch
license: cc-by-nc-4.0
---
[blenderbot-1B-distill](https://huggingface.co/facebook/blenderbot-1B-distill) fine-tuned on the [ESConv dataset](https://github.com/thu-coai/Emotional-Support-Conversation) and [**AugESC dataset**](https://github.com/thu-coai/AugESC).
See the [original paper](https://arxiv.org/abs/2202.13047) for details.
Usage example:
```python
import torch
from transformers import AutoTokenizer
from transformers.models.blenderbot import BlenderbotTokenizer, BlenderbotForConditionalGeneration
def _norm(x):
return ' '.join(x.strip().split())
tokenizer = BlenderbotTokenizer.from_pretrained('thu-coai/blenderbot-1B-augesc')
model = BlenderbotForConditionalGeneration.from_pretrained('thu-coai/blenderbot-1B-augesc')
model.eval()
utterances = [
"I am having a lot of anxiety about quitting my current job. It is too stressful but pays well",
"What makes your job stressful for you?",
"I have to deal with many people in hard financial situations and it is upsetting",
"Do you help your clients to make it to a better financial situation?",
"I do, but often they are not going to get back to what they want. Many people are going to lose their home when safeguards are lifted",
]
input_sequence = ' '.join([' ' + e for e in utterances]) + tokenizer.eos_token # add space prefix and separate utterances with two spaces
input_ids = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(input_sequence))[-128:]
input_ids = torch.LongTensor([input_ids])
model_output = model.generate(input_ids, num_beams=1, do_sample=True, top_p=0.9, num_return_sequences=5, return_dict=False)
generation = tokenizer.batch_decode(model_output, skip_special_tokens=True)
generation = [_norm(e) for e in generation]
print(generation)
utterances.append(generation[0]) # for future loop
```
Please kindly cite our papers if you use this model:
```bib
@inproceedings{liu-etal-2021-towards,
title={Towards Emotional Support Dialog Systems},
author={Liu, Siyang and
Zheng, Chujie and
Demasi, Orianna and
Sabour, Sahand and
Li, Yu and
Yu, Zhou and
Jiang, Yong and
Huang, Minlie},
booktitle={ACL},
year={2021}
}
@inproceedings{zheng-etal-2023-augesc,
title={AugESC: Dialogue Augmentation with Large Language Models for Emotional Support Conversation},
author={Zheng, Chujie and
Sabour, Sahand and
Wen, Jiaxin and
Zhang, Zheng and
Huang, Minlie},
booktitle={Findings of ACL},
year={2023}
}
```
|
jontromanab/a2c-AntBulletEnv-v0
|
jontromanab
| 2023-05-14T02:23:54Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-14T02:23:28Z |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1586.99 +/- 92.87
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
jojo0616/my_SA_distilbert_model_finalversion
|
jojo0616
| 2023-05-14T02:19:30Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-14T01:29:32Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my_SA_distilbert_model_finalversion
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_SA_distilbert_model_finalversion
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: 0.3031
- Accuracy: 0.9115
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3696 | 1.0 | 2248 | 0.3310 | 0.8852 |
| 0.2624 | 2.0 | 4496 | 0.3118 | 0.9063 |
| 0.1817 | 3.0 | 6744 | 0.3314 | 0.9072 |
| 0.1398 | 4.0 | 8992 | 0.3031 | 0.9115 |
| 0.1294 | 5.0 | 11240 | 0.3801 | 0.9110 |
| 0.0974 | 6.0 | 13488 | 0.3968 | 0.9059 |
| 0.0662 | 7.0 | 15736 | 0.4742 | 0.9177 |
| 0.0634 | 8.0 | 17984 | 0.5182 | 0.9150 |
| 0.0377 | 9.0 | 20232 | 0.5356 | 0.9159 |
| 0.0298 | 10.0 | 22480 | 0.5717 | 0.9139 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
nolanaatama/kdllora
|
nolanaatama
| 2023-05-14T01:32:43Z | 0 | 14 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-02-10T22:55:15Z |
---
license: creativeml-openrail-m
---
|
guoguangjie/my_wikilingua_model2
|
guoguangjie
| 2023-05-14T00:40:25Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-05-14T00:32:44Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: my_wikilingua_model2
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_wikilingua_model2
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5821
- Rouge1: 0.2402
- Rouge2: 0.0747
- Rougel: 0.1991
- Rougelsum: 0.1993
- Gen Len: 18.82
## 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: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 100 | 2.6861 | 0.2284 | 0.0646 | 0.1832 | 0.183 | 18.9375 |
| No log | 2.0 | 200 | 2.6137 | 0.2343 | 0.0704 | 0.1919 | 0.1916 | 18.84 |
| No log | 3.0 | 300 | 2.5890 | 0.2384 | 0.0729 | 0.1967 | 0.1966 | 18.88 |
| No log | 4.0 | 400 | 2.5821 | 0.2402 | 0.0747 | 0.1991 | 0.1993 | 18.82 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
BebyJenita/bebyjenita
|
BebyJenita
| 2023-05-14T00:03:03Z | 0 | 0 | null |
[
"id",
"arxiv:1910.09700",
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-13T22:21:06Z |
---
license: creativeml-openrail-m
language:
- id
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
messerb5467/Taxi-v3
|
messerb5467
| 2023-05-13T23:45:48Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-13T23:45:42Z |
---
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: 7.52 +/- 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="messerb5467/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"])
```
|
ymmttks/ShoppingArcade
|
ymmttks
| 2023-05-13T23:42:40Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-05-13T23:24:18Z |
# ShoppingArcade(Japan)
## Trigger Word
```
AKEDO
```
## Sample
<img src="https://huggingface.co/ymmttks/ShoppingArcade/resolve/main/samples/00014-1girl AKEDO.png" width="512">
<img src="https://huggingface.co/ymmttks/ShoppingArcade/resolve/main/samples/00031-1girl AKEDO.png" width="512">
|
Kardbord/openjourney-unsafe
|
Kardbord
| 2023-05-13T23:12:09Z | 18 | 1 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"text-to-image",
"en",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-05-13T21:10:07Z |
---
language:
- en
license: creativeml-openrail-m
tags:
- stable-diffusion
- text-to-image
inference: true
---
# Overview
This is simply prompthero/openjourney with the safety checker disabled.
**DO NOT** attempt to use this model to generate harmful or illegal content.
# Openjourney is an open source Stable Diffusion fine tuned model on Midjourney images, by [PromptHero](https://prompthero.com/poolsuite-diffusion-prompts?utm_source=huggingface&utm_medium=referral)
Include **'mdjrny-v4 style'** in prompt. Here you'll find hundreds of [Openjourney prompts](https://prompthero.com/openjourney-prompts?utm_source=huggingface&utm_medium=referral)
# Openjourney Links
- [Lora version](https://huggingface.co/prompthero/openjourney-lora)
- [Openjourney v4](https://huggingface.co/prompthero/openjourney-v2)
# Want to learn AI art generation?:
- [Crash course in AI art generation](https://prompthero.com/academy/prompt-engineering-course?utm_source=huggingface&utm_medium=referral)
- [Learn to fine-tune Stable Diffusion for photorealism](https://prompthero.com/academy/dreambooth-stable-diffusion-train-fine-tune-course?utm_source=huggingface&utm_medium=referral)
# Use it for free:
[](https://huggingface.co/spaces/akhaliq/midjourney-v4-diffusion)
### Stable Diffusion v1.5 vs Openjourney
(Same parameters, just added "mdjrny-v4 style" at the beginning):
<img src="https://s3.amazonaws.com/moonup/production/uploads/1667904587642-63265d019f9d19bfd4f45031.png" width="100%"/>
<img src="https://s3.amazonaws.com/moonup/production/uploads/1667904587623-63265d019f9d19bfd4f45031.png" width="100%"/>
<img src="https://s3.amazonaws.com/moonup/production/uploads/1667904587609-63265d019f9d19bfd4f45031.png" width="100%"/>
<img src="https://s3.amazonaws.com/moonup/production/uploads/1667904587646-63265d019f9d19bfd4f45031.png" width="100%"/>
### 🧨 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 = "prompthero/openjourney"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "retro serie of different cars with different colors and shapes, mdjrny-v4 style"
image = pipe(prompt).images[0]
image.save("./retro_cars.png")
```
|
swadesh7/finetuning-l3-bert-latest
|
swadesh7
| 2023-05-13T23:11:18Z | 111 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-13T23:04:32Z |
---
license: cc-by-4.0
tags:
- generated_from_trainer
model-index:
- name: finetuning-l3-bert-latest
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. -->
# finetuning-l3-bert-latest
This model is a fine-tuned version of [l3cube-pune/telugu-bert](https://huggingface.co/l3cube-pune/telugu-bert) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.6283
- eval_accuracy: 0.7558
- eval_f1: 0.7529
- eval_runtime: 79.9067
- eval_samples_per_second: 51.61
- eval_steps_per_second: 6.458
- step: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 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: 2
### Framework versions
- Transformers 4.29.0
- Pytorch 1.13.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
minoosh/videomae-base-finetuned-IEMOCAP_videos
|
minoosh
| 2023-05-13T22:41:23Z | 62 | 0 |
transformers
|
[
"transformers",
"pytorch",
"videomae",
"video-classification",
"generated_from_trainer",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
video-classification
| 2023-05-13T15:52:08Z |
---
license: cc-by-nc-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videomae-base-finetuned-IEMOCAP_videos
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. -->
# videomae-base-finetuned-IEMOCAP_videos
This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3194
- Accuracy: 0.3761
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 4070
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4161 | 0.1 | 408 | 1.4228 | 0.2115 |
| 1.3522 | 1.1 | 816 | 1.3968 | 0.2363 |
| 1.2575 | 2.1 | 1224 | 1.4228 | 0.3115 |
| 1.2897 | 3.1 | 1632 | 1.4101 | 0.2984 |
| 1.3398 | 4.1 | 2040 | 1.4176 | 0.2599 |
| 1.3621 | 5.1 | 2448 | 1.3590 | 0.2830 |
| 1.2824 | 6.1 | 2856 | 1.3133 | 0.3610 |
| 1.3064 | 7.1 | 3264 | 1.3195 | 0.3077 |
| 1.378 | 8.1 | 3672 | 1.3562 | 0.2643 |
| 1.1909 | 9.1 | 4070 | 1.3917 | 0.2621 |
### Framework versions
- Transformers 4.29.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
tamhuynh27/ernie-base-2.0en-finetuned-recipeqa-modified
|
tamhuynh27
| 2023-05-13T22:23:02Z | 88 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"ernie",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-05-13T21:07:54Z |
---
tags:
- generated_from_trainer
model-index:
- name: ernie-base-2.0en-finetuned-recipeqa-modified-updated
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. -->
# ernie-base-2.0en-finetuned-recipeqa-modified-updated
This model is a fine-tuned version of [nghuyong/ernie-2.0-base-en](https://huggingface.co/nghuyong/ernie-2.0-base-en) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.29.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
DeadBeast/random-animals-birds
|
DeadBeast
| 2023-05-13T22:22:58Z | 30 | 3 |
diffusers
|
[
"diffusers",
"pytorch",
"stable-diffusion",
"text-to-image",
"diffusion-models-class",
"dreambooth-hackathon",
"animal",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-05-13T22:04:19Z |
---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- dreambooth-hackathon
- animal
widget:
- text: a photo of lion in new york
---
# DreamBooth model for the animal concept trained by DeadBeast on the DeadBeast/dreambooth-images dataset.
This is a Stable Diffusion model fine-tuned on the animal concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of animal dog**
## Description
This is a Stable Diffusion model fine-tuned on random animal,birds unsplash images for the animals theme.
## Usage
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('DeadBeast/random-animals-birds')
image = pipeline().images[0]
image
```
|
notstoic/OPT-13B-Erebus-4bit-128g
|
notstoic
| 2023-05-13T22:14:55Z | 19 | 16 |
transformers
|
[
"transformers",
"opt",
"text-generation",
"en",
"license:other",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2023-04-07T07:16:31Z |
---
language: en
license: other
commercial: no
inference: false
---
# OPT-13B-Erebus-4bit-128g
## Model description
**Warning: THIS model is NOT suitable for use by minors. The model will output X-rated content.**
This is a 4-bit GPTQ quantization of OPT-13B-Erebus, original model:
**https://huggingface.co/KoboldAI/OPT-13B-Erebus**
### Quantization Information
Quantized with: https://github.com/0cc4m/GPTQ-for-LLaMa
```
python repos/gptq/opt.py --wbits 4 models/KoboldAI_OPT-13B-Erebus c4 --groupsize 128 --save models/KoboldAI_OPT-13B-Erebus/OPT-13B-Erebus-4bit-128g.pt
python repos/gptq/opt.py --wbits 4 models/KoboldAI_OPT-13B-Erebus c4 --groupsize 128 --save_safetensors models/KoboldAI_OPT-13B-Erebus/OPT-13B-Erebus-4bit-128g.safetensors
```
### License
OPT-13B is licensed under the OPT-175B license, Copyright (c) Meta Platforms, Inc. All Rights Reserved.
|
keldenl/RedPajama-INCITE-Instruct-3B-v1-GGML
|
keldenl
| 2023-05-13T22:07:26Z | 12 | 10 |
transformers
|
[
"transformers",
"gpt_neox",
"text-generation",
"red_pajama",
"en",
"dataset:togethercomputer/RedPajama-Data-1T",
"dataset:Muennighoff/P3",
"dataset:Muennighoff/natural-instructions",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-08T08:51:10Z |
---
license: apache-2.0
language:
- en
datasets:
- togethercomputer/RedPajama-Data-1T
- Muennighoff/P3
- Muennighoff/natural-instructions
pipeline_tag: text-generation
tags:
- gpt_neox
- red_pajama
---
**Original Model Link: https://huggingface.co/togethercomputer/RedPajama-INCITE-Instruct-3B-v1**
This will NOT work with llama.cpp as of 5/13/2023, but this NOW works (5/13/2023) with the GGML in https://github.com/ggerganov/ggml/ via gpt-neox
This also works in my project https://github.com/keldenl/gpt-llama.cpp (uses ggml as an InferenceEngine).
# RedPajama-INCITE-Instruct-3B-v1
RedPajama-INCITE-Instruct-3B-v1 was developed by Together and leaders from the open-source AI community including Ontocord.ai, ETH DS3Lab, AAI CERC, Université de Montréal, MILA - Québec AI Institute, Stanford Center for Research on Foundation Models (CRFM), Stanford Hazy Research research group and LAION.
The model was fine-tuned for few-shot applications on the data of [GPT-JT](https://huggingface.co/togethercomputer/GPT-JT-6B-v1), with exclusion of tasks that overlap with the HELM core scenarios.
## Model Details
- **Developed by**: Together Computer.
- **Model type**: Language Model
- **Language(s)**: English
- **License**: Apache 2.0
- **Model Description**: A 2.8B parameter pretrained language model.
## Prompt Template
To prompt the chat model, use a typical instruction format + few shot prompting, for example:
```
Paraphrase the given sentence into a different sentence.
Input: Can you recommend some upscale restaurants in New York?
Output: What upscale restaurants do you recommend in New York?
Input: What are the famous places we should not miss in Paris?
Output: Recommend some of the best places to visit in Paris?
Input: Could you recommend some hotels that have cheap price in Zurich?
Output:
```
## Which model to download?
* The q4_0 file provides lower quality, but maximal compatibility. It will work with past and future versions of llama.cpp
* The q4_2 file offers the best combination of performance and quality. This format is still subject to change and there may be compatibility issues, see below.
* The q5_0 file is using brand new 5bit method released 26th April. This is the 5bit equivalent of q4_0.
* The q5_1 file is using brand new 5bit method released 26th April. This is the 5bit equivalent of q4_1.
|
lrthomps/Reinforce-CartPole-v1
|
lrthomps
| 2023-05-13T22:00:12Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-13T21:59:59Z |
---
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
|
agestau/dummy-fashion-classification
|
agestau
| 2023-05-13T21:58:05Z | 210 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-05-13T20:52:01Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: dummy-fashion-classification
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. -->
# dummy-fashion-classification
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1122
- Accuracy: 0.9665
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3331 | 1.0 | 294 | 0.1725 | 0.9519 |
| 0.296 | 2.0 | 588 | 0.1323 | 0.9591 |
| 0.2484 | 3.0 | 882 | 0.1122 | 0.9665 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
scepter/pygmalion7b
|
scepter
| 2023-05-13T21:57:27Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-12T05:51:25Z |
---
duplicated_from: gozfarb/pygmalion-7b-4bit-128g-cuda
---
Quantized from https://huggingface.co/Neko-Institute-of-Science/pygmalion-7b
|
MohammedNasri/whisper_large_ar
|
MohammedNasri
| 2023-05-13T21:49:43Z | 5 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"ar",
"dataset:mozilla-foundation/common_voice_11_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-05-13T17:33:25Z |
---
language:
- ar
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper_large_v2_arabic
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: ar
split: test
args: 'config: ar, split: test'
metrics:
- name: Wer
type: wer
value: 12.773732872855585
---
<!-- 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_large_v2_arabic
This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2119
- Wer: 12.7737
## 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: 500
- training_steps: 500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0259 | 0.83 | 500 | 0.2119 | 12.7737 |
### Framework versions
- Transformers 4.29.1
- Pytorch 1.13.1
- Datasets 2.12.0
- Tokenizers 0.13.3
|
kahlebr/1
|
kahlebr
| 2023-05-13T21:29:31Z | 0 | 0 | null |
[
"summarization",
"region:us"
] |
summarization
| 2023-05-13T21:28:42Z |
---
pipeline_tag: summarization
---
|
tatwan/ppo-LunarLander-v2
|
tatwan
| 2023-05-13T21:07:06Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-13T21:06:44Z |
---
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: 254.70 +/- 38.96
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
...
```
|
jojo0616/my_Misinformation_distilbert_model
|
jojo0616
| 2023-05-13T21:07:05Z | 37 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-04-14T17:03:25Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my_Misinformation_distilbert_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_Misinformation_distilbert_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: 0.1879
- Accuracy: 0.9661
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 214 | 0.1283 | 0.9544 |
| No log | 2.0 | 428 | 0.1528 | 0.9498 |
| 0.1645 | 3.0 | 642 | 0.1276 | 0.9685 |
| 0.1645 | 4.0 | 856 | 0.1650 | 0.9614 |
| 0.0306 | 5.0 | 1070 | 0.1653 | 0.9661 |
| 0.0306 | 6.0 | 1284 | 0.1739 | 0.9673 |
| 0.0306 | 7.0 | 1498 | 0.1771 | 0.9661 |
| 0.0053 | 8.0 | 1712 | 0.1795 | 0.9661 |
| 0.0053 | 9.0 | 1926 | 0.1860 | 0.9626 |
| 0.0018 | 10.0 | 2140 | 0.1879 | 0.9661 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
tamhuynh27/roberta-base-fine-tuned-recipeqa-modified
|
tamhuynh27
| 2023-05-13T21:01:09Z | 133 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-05-04T01:27:12Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: roberta-base-fine-tuned-recipeqa-modified
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-fine-tuned-recipeqa-modified
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the RecipeQA dataset that has been modified for the purpose of Extractive Question Answering task
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.29.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
gugomea/deportes
|
gugomea
| 2023-05-13T20:52:35Z | 0 | 0 |
fastai
|
[
"fastai",
"region:us"
] | null | 2023-05-13T20:52:27Z |
---
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
|
Amirmnsh/ppo-LunarLander-v2
|
Amirmnsh
| 2023-05-13T20:11:44Z | 5 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-13T20:11:26Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 260.63 +/- 17.14
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
...
```
|
damapika/roberta-base_ms-marco_mod
|
damapika
| 2023-05-13T19:56:43Z | 43 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:generator",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-05-06T20:53:10Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: roberta-base_ms-marco_mod
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base_ms-marco_mod
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5359
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 3.5498 | 1.0 | 18861 | 3.5603 |
| 3.4253 | 2.0 | 37722 | 3.5359 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
dark844/alleymix
|
dark844
| 2023-05-13T18:37:50Z | 0 | 0 |
nemo
|
[
"nemo",
"art",
"text-to-image",
"en",
"dataset:OpenAssistant/oasst1",
"arxiv:1910.09700",
"license:openrail",
"region:us"
] |
text-to-image
| 2023-05-13T18:15:34Z |
---
license: openrail
datasets:
- OpenAssistant/oasst1
language:
- en
metrics:
- accuracy
library_name: nemo
pipeline_tag: text-to-image
tags:
- art
---
# Model Card for Model ID
<!-- Provide a quick summalleymixary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
bilal01/segformer-b0-finetuned-segments-test
|
bilal01
| 2023-05-13T18:36:07Z | 162 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"segformer",
"vision",
"image-segmentation",
"generated_from_trainer",
"license:other",
"endpoints_compatible",
"region:us"
] |
image-segmentation
| 2023-05-13T15:30:14Z |
---
license: other
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b0-finetuned-segments-test
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. -->
# segformer-b0-finetuned-segments-test
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the bilal01/stamp-verification-test 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: 6e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
LarryAIDraw/HSR_Natasha4
|
LarryAIDraw
| 2023-05-13T18:28:59Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-13T18:15:55Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/64271/natashahonkai-star-rail
|
LarryAIDraw/tifa_lockhart_offset
|
LarryAIDraw
| 2023-05-13T18:27:41Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-13T18:17:45Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/6100/tifa-lockhart-lessall-outfitsgreater-lora
|
LarryAIDraw/Miorine-000009
|
LarryAIDraw
| 2023-05-13T18:27:27Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-13T18:16:58Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/64841/miorine-rembran-or-the-witch-from-mercury
|
LarryAIDraw/tomoe_koga-01
|
LarryAIDraw
| 2023-05-13T18:27:14Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-13T18:16:37Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/64850/koga-tomoe-from-bunny-girl-senpai
|
Tribbiani/robin-7b-v2
|
Tribbiani
| 2023-05-13T18:27:07Z | 8 | 3 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"generated_from_trainer",
"dataset:customized",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-13T17:47:37Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- customized
model-index:
- name: h34
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. -->
# h34
This model is a fine-tuned version of [pinkmanlove/llama-7b-hf](https://huggingface.co/pinkmanlove/llama-7b-hf) on the customized 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: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1.0
### Training results
### Framework versions
- Transformers 4.28.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.10.1
- Tokenizers 0.13.3
|
LarryAIDraw/SwiftsureMaidBikiniV1
|
LarryAIDraw
| 2023-05-13T18:26:55Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-13T18:16:14Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/64385/swiftsure-azur-lane-midsummer-special-service-swimsuit
|
LarryAIDraw/nodoka-01
|
LarryAIDraw
| 2023-05-13T18:26:21Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-13T18:15:34Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/64161/toyohama-nodoka-from-bunny-girl-senpai
|
LarryAIDraw/meinaalter_v1
|
LarryAIDraw
| 2023-05-13T18:25:38Z | 0 | 1 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-13T18:05:54Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/20945?modelVersionId=24933
|
kasunw/ppo-PyramidsRND
|
kasunw
| 2023-05-13T18:23:38Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-05-13T18:23:33Z |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Find your model_id: kasunw/ppo-PyramidsRND
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
KostiuchenkoArtem/my_bart_base_test_model
|
KostiuchenkoArtem
| 2023-05-13T18:15:31Z | 60 | 0 |
transformers
|
[
"transformers",
"tf",
"bart",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-05-13T16:50:19Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: TianMu/my_bart_base_test_model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# TianMu/my_bart_base_test_model
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.4782
- Validation Loss: 1.6553
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.5517 | 1.6689 | 0 |
| 1.4782 | 1.6553 | 1 |
### Framework versions
- Transformers 4.29.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
ChrisOfLondon/Reinforce-Heli
|
ChrisOfLondon
| 2023-05-13T18:04:32Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-13T18:04:28Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Heli
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 41.20 +/- 28.25
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
|
nergaldarski/KojiV2
|
nergaldarski
| 2023-05-13T17:46:46Z | 0 | 1 | null |
[
"region:us"
] | null | 2023-05-13T17:31:01Z |
CivitAI: https://civitai.com/models/41916/koji
|
vop020506/entregable2
|
vop020506
| 2023-05-13T17:43:44Z | 0 | 0 |
fastai
|
[
"fastai",
"region:us"
] | null | 2023-05-13T16:47:38Z |
---
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
|
Trwgg/Rufinox
|
Trwgg
| 2023-05-13T17:35:22Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-13T17:23:22Z |
---
license: creativeml-openrail-m
---
|
messerb5467/ppo-Huggy
|
messerb5467
| 2023-05-13T17:34:14Z | 13 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-05-13T17:34:08Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Find your model_id: messerb5467/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
nergaldarski/hassakuV1.2
|
nergaldarski
| 2023-05-13T17:26:56Z | 0 | 1 | null |
[
"region:us"
] | null | 2023-05-13T17:11:16Z |
CivitAI: https://civitai.com/models/2583?modelVersionId=62528
|
Johnhex/Clam1.1
|
Johnhex
| 2023-05-13T17:19:20Z | 1 | 1 |
diffusers
|
[
"diffusers",
"stable diffusion",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-04-19T14:58:35Z |
---
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
tags:
- stable diffusion
---
|
fgiauna/peft-lora-jul
|
fgiauna
| 2023-05-13T17:11:02Z | 0 | 0 | null |
[
"tensorboard",
"generated_from_trainer",
"license:mit",
"region:us"
] | null | 2023-05-13T17:05:06Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: peft-lora-jul
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. -->
# peft-lora-jul
This model is a fine-tuned version of [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0817
- Loc: {'precision': 0.5887445887445888, 'recall': 0.6296296296296297, 'f1': 0.6085011185682326, 'number': 216}
- Misc: {'precision': 0.6111111111111112, 'recall': 0.275, 'f1': 0.3793103448275862, 'number': 40}
- Org: {'precision': 0.7004830917874396, 'recall': 0.725, 'f1': 0.7125307125307125, 'number': 200}
- Per: {'precision': 0.7540106951871658, 'recall': 0.7193877551020408, 'f1': 0.7362924281984334, 'number': 196}
- Overall Precision: 0.6734
- Overall Recall: 0.6641
- Overall F1: 0.6687
- Overall Accuracy: 0.9772
## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Multi-Domain-Expert-Learning/merged-pubmed-freelaw
|
Multi-Domain-Expert-Learning
| 2023-05-13T17:08:12Z | 170 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"MDEL",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-13T16:47:30Z |
---
tags:
- MDEL
---
# Model Name
Multi-Domain-Expert-Layers/merged-pubmed-freelaw
# Model Description
This model was generated by averaging the weights of the following models
- [Multi-Domain-Expert-Layers/expert-freelaw](https://huggingface.co/Multi-Domain-Expert-Layers/expert-freelaw)
- [Multi-Domain-Expert-Layers/expert-pubmed_central](https://huggingface.co/Multi-Domain-Expert-Layers/expert-pubmed_central)
|
vitouphy/wav2vec2-xls-r-300m-phoneme
|
vitouphy
| 2023-05-13T17:04:45Z | 60,319 | 3 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-05-19T03:03:57Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-xls-r-300m-phoneme
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-xls-r-300m-phoneme
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.
It achieves the following results on the evaluation set:
- Loss: 0.3327
- Cer: 0.1332
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- 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: 2000
- training_steps: 7000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.4324 | 1.32 | 1000 | 3.3693 | 0.9091 |
| 2.1751 | 2.65 | 2000 | 1.1382 | 0.2397 |
| 1.3986 | 3.97 | 3000 | 0.4886 | 0.1452 |
| 1.2285 | 5.3 | 4000 | 0.3842 | 0.1351 |
| 1.142 | 6.62 | 5000 | 0.3505 | 0.1349 |
| 1.1075 | 7.95 | 6000 | 0.3323 | 0.1317 |
| 1.0867 | 9.27 | 7000 | 0.3265 | 0.1315 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
|
vitouphy/wav2vec2-xls-r-300m-timit-phoneme
|
vitouphy
| 2023-05-13T17:04:31Z | 9,515 | 28 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"en",
"generated_from_trainer",
"doi:10.57967/hf/0125",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-05-08T06:41:55Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- pytorch
- transformers
- en
- generated_from_trainer
model-index:
- name: wav2vec2-xls-r-300m-phoneme
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: DARPA TIMIT
type: timit
args: en
metrics:
- name: Test CER
type: cer
value: 7.996
---
<!-- 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. -->
## Model
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the Timit dataset. Check [this notebook](https://www.kaggle.com/code/vitouphy/phoneme-recognition-with-wav2vec2) for training detail.
## Usage
**Approach 1:** Using HuggingFace's pipeline, this will cover everything end-to-end from raw audio input to text output.
```python
from transformers import pipeline
# Load the model
pipe = pipeline(model="vitouphy/wav2vec2-xls-r-300m-timit-phoneme")
# Process raw audio
output = pipe("audio_file.wav", chunk_length_s=10, stride_length_s=(4, 2))
```
**Approach 2:** More custom way to predict phonemes.
```python
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
from datasets import load_dataset
import torch
import soundfile as sf
# load model and processor
processor = Wav2Vec2Processor.from_pretrained("vitouphy/wav2vec2-xls-r-300m-timit-phoneme")
model = Wav2Vec2ForCTC.from_pretrained("vitouphy/wav2vec2-xls-r-300m-timit-phoneme")
# Read and process the input
audio_input, sample_rate = sf.read("audio_file.wav")
inputs = processor(audio_input, sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
# Decode id into string
predicted_ids = torch.argmax(logits, axis=-1)
predicted_sentences = processor.batch_decode(predicted_ids)
print(predicted_sentences)
```
## Training and evaluation data
We use [DARPA TIMIT dataset](https://www.kaggle.com/datasets/mfekadu/darpa-timit-acousticphonetic-continuous-speech) for this model.
- We split into **80/10/10** for training, validation, and testing respectively.
- That roughly corresponds to about **137/17/17** minutes.
- The model obtained **7.996%** on this test set.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- 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: 2000
- training_steps: 10000
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
### Citation
```
@misc { phy22-phoneme,
author = {Phy, Vitou},
title = {{Automatic Phoneme Recognition on TIMIT Dataset with Wav2Vec 2.0}},
year = 2022,
note = {{If you use this model, please cite it using these metadata.}},
publisher = {Hugging Face},
version = {1.0},
doi = {10.57967/hf/0125},
url = {https://huggingface.co/vitouphy/wav2vec2-xls-r-300m-timit-phoneme}
}
```
|
vitouphy/wav2vec2-xls-r-300m-english
|
vitouphy
| 2023-05-13T17:04:05Z | 94 | 3 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"en",
"generated_from_trainer",
"hf-asr-leaderboard",
"librispeech_asr",
"robust-speech-event",
"dataset:librispeech_asr",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
- generated_from_trainer
- hf-asr-leaderboard
- librispeech_asr
- robust-speech-event
datasets:
- librispeech_asr
model-index:
- name: XLS-R-300M - English
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: LibriSpeech (clean)
type: librispeech_asr
config: clean
split: test
args:
language: en
metrics:
- name: Test WER
type: wer
value: 12.29
- name: Test CER
type: cer
value: 3.34
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: en
metrics:
- name: Validation WER
type: wer
value: 36.75
- name: Validation CER
type: cer
value: 14.83
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8.0
type: mozilla-foundation/common_voice_8_0
config: en
split: test
args:
language: en
metrics:
- name: Test WER
type: wer
value: 37.81
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: en
metrics:
- name: Test WER
type: wer
value: 38.8
---
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the librispeech_asr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1444
- Wer: 0.1167
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- 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: 1000
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.9365 | 4.17 | 500 | 2.9398 | 0.9999 |
| 1.5444 | 8.33 | 1000 | 0.5947 | 0.4289 |
| 1.1367 | 12.5 | 1500 | 0.2751 | 0.2366 |
| 0.9972 | 16.66 | 2000 | 0.2032 | 0.1797 |
| 0.9118 | 20.83 | 2500 | 0.1786 | 0.1479 |
| 0.8664 | 24.99 | 3000 | 0.1641 | 0.1408 |
| 0.8251 | 29.17 | 3500 | 0.1537 | 0.1267 |
| 0.793 | 33.33 | 4000 | 0.1525 | 0.1244 |
| 0.785 | 37.5 | 4500 | 0.1470 | 0.1184 |
| 0.7612 | 41.66 | 5000 | 0.1446 | 0.1177 |
| 0.7478 | 45.83 | 5500 | 0.1449 | 0.1176 |
| 0.7443 | 49.99 | 6000 | 0.1444 | 0.1167 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
|
akaneshiro/q-Taxi-v3
|
akaneshiro
| 2023-05-13T16:46:31Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-13T16:46:29Z |
---
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="akaneshiro/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"])
```
|
vop020506/hojas_uva
|
vop020506
| 2023-05-13T16:37:52Z | 0 | 0 |
fastai
|
[
"fastai",
"region:us"
] | null | 2023-05-13T16:37:49Z |
---
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
|
messerb5467/ppo-LunarLander-v2
|
messerb5467
| 2023-05-13T16:30:17Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-13T16:29:57Z |
---
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: 264.83 +/- 18.72
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
...
```
|
LarryAIDraw/shiina_mashiro_v1
|
LarryAIDraw
| 2023-05-13T16:18:53Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-04-28T06:14:19Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/50851?modelVersionId=55367
|
walter2/imdb_model2
|
walter2
| 2023-05-13T16:10:17Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-13T16:09:15Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: imdb_model2
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. -->
# imdb_model2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0909
- Validation Loss: 0.0370
- Train Accuracy: 0.992
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 625, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.4551 | 0.2036 | 0.924 | 0 |
| 0.1960 | 0.1091 | 0.976 | 1 |
| 0.0909 | 0.0370 | 0.992 | 2 |
### Framework versions
- Transformers 4.29.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Staticaliza/TestModel
|
Staticaliza
| 2023-05-13T15:55:56Z | 0 | 0 | null |
[
"aa",
"dataset:databricks/databricks-dolly-15k",
"license:openrail",
"region:us"
] | null | 2023-05-13T15:50:06Z |
---
license: openrail
datasets:
- databricks/databricks-dolly-15k
language:
- aa
---
|
LukeMich/my_awesome_model
|
LukeMich
| 2023-05-13T15:38:44Z | 60 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-13T15:03:47Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: LukeMich/my_awesome_model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# LukeMich/my_awesome_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.7595
- Validation Loss: 0.5417
- Train Accuracy: 0.8762
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 275, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 1.0643 | 0.8268 | 0.6571 | 0 |
| 0.7595 | 0.5417 | 0.8762 | 1 |
### Framework versions
- Transformers 4.29.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
divers/e2e-flan-large-noscore-totalds
|
divers
| 2023-05-13T15:10:50Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-05-02T05:34:46Z |
<table>
<thead>
<tr>
<th>Epoch</th>
<th>Training Loss</th>
<th>Validation Loss</th>
<th>Rouge1</th>
<th>Rouge2</th>
<th>Rougel</th>
<th>Rougelsum</th>
<th>Gen Len</th>
</tr>
</thead>
<tr>
<td>0</td>
<td>0.630300</td>
<td>0.412157</td>
<td>0.417600</td>
<td>0.263800</td>
<td>0.332800</td>
<td>0.406200</td>
<td>794.000000</td>
</tr>
<tr>
<td>1</td>
<td>0.445600</td>
<td>0.371808</td>
<td>0.516700</td>
<td>0.336200</td>
<td>0.415500</td>
<td>0.508000</td>
<td>560.642900</td>
</tr>
<tr>
<td>2</td>
<td>0.398800</td>
<td>0.350914</td>
<td>0.562700</td>
<td>0.375400</td>
<td>0.443900</td>
<td>0.552700</td>
<td>523.714300</td>
</tr>
<tr>
<td>4</td>
<td>0.350600</td>
<td>0.334888</td>
<td>0.553300</td>
<td>0.364900</td>
<td>0.427100</td>
<td>0.538800</td>
<td>464.035700</td>
</tr>
<tr>
<td>5</td>
<td>0.334300</td>
<td>0.326556</td>
<td>0.552100</td>
<td>0.361400</td>
<td>0.429900</td>
<td>0.540300</td>
<td>517.821400</td>
</tr>
<tr>
<td>6</td>
<td>0.322300</td>
<td>0.321693</td>
<td>0.596600</td>
<td>0.400800</td>
<td>0.469400</td>
<td>0.586400</td>
<td>414.892900</td>
</tr>
<tr>
<td>8</td>
<td>0.308800</td>
<td>0.321562</td>
<td>0.594200</td>
<td>0.389100</td>
<td>0.458500</td>
<td>0.581800</td>
<td>401.357100</td>
</tr>
<tr>
<td>8</td>
<td>0.300100</td>
<td>0.319800</td>
<td>0.586200</td>
<td>0.376100</td>
<td>0.453400</td>
<td>0.571500</td>
<td>381.357100</td>
</tr>
<tr>
<td>9</td>
<td>0.291200</td>
<td>0.319443</td>
<td>0.611500</td>
<td>0.399600</td>
<td>0.468600</td>
<td>0.597500</td>
<td>368.821400</td>
</tr>
<tr>
<td>10</td>
<td>0.282900</td>
<td>0.318927</td>
<td>0.593200</td>
<td>0.388700</td>
<td>0.459100</td>
<td>0.579800</td>
<td>354.285700</td>
</tr>
<tr>
<td>12</td>
<td>0.273700</td>
<td>0.319651</td>
<td>0.594000</td>
<td>0.394200</td>
<td>0.457000</td>
<td>0.580800</td>
<td>386.785700</td>
</tr>
<tr>
<td>12</td>
<td>0.268100</td>
<td>0.315178</td>
<td>0.603700</td>
<td>0.396100</td>
<td>0.465300</td>
<td>0.588500</td>
<td>365.714300</td>
</tr>
<tr>
<td>13</td>
<td>0.262000</td>
<td>0.312819</td>
<td>0.601500</td>
<td>0.402800</td>
<td>0.471700</td>
<td>0.586000</td>
<td>377.250000</td>
</tr>
<tr>
<td>14</td>
<td>0.254900</td>
<td>0.316255</td>
<td>0.601200</td>
<td>0.397600</td>
<td>0.469700</td>
<td>0.587900</td>
<td>353.071400</td>
</tr>
<tr>
<td>16</td>
<td>0.248500</td>
<td>0.316413</td>
<td>0.610300</td>
<td>0.407900</td>
<td>0.476000</td>
<td>0.597400</td>
<td>341.464300</td>
</tr>
<tr>
<td>16</td>
<td>0.243600</td>
<td>0.315982</td>
<td>0.611400</td>
<td>0.404900</td>
<td>0.483200</td>
<td>0.598300</td>
<td>379.571400</td>
</tr>
<tr>
<td>17</td>
<td>0.238900</td>
<td>0.318108</td>
<td>0.608100</td>
<td>0.408200</td>
<td>0.486100</td>
<td>0.594000</td>
<td>375.964300</td>
</tr>
<tr>
<td>18</td>
<td>0.233900</td>
<td>0.317792</td>
<td>0.600200</td>
<td>0.406300</td>
<td>0.471700</td>
<td>0.587600</td>
<td>346.964300</td>
</tr>
<tr>
<td>19</td>
<td>0.229600</td>
<td>0.322435</td>
<td>0.599100</td>
<td>0.407100</td>
<td>0.479600</td>
<td>0.586600</td>
<td>362.571400</td>
</tr>
</table>
|
divers/flan-base-req-extractor
|
divers
| 2023-05-13T15:01:01Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-05-05T20:23:32Z |
html'''
<table>
<tr>
<th>Epoch</th>
<th>Training Loss</th>
<th>Validation Loss</th>
<th>Rouge1</th>
<th>Rouge2</th>
<th>Rougel</th>
<th>Rougelsum</th>
<th>Gen Len</th>
</tr>
<tr>
<td>0</td>
<td>0.357300</td>
<td>0.280200</td>
<td>0.732700</td>
<td>0.685700</td>
<td>0.695100</td>
<td>0.700500</td>
<td>303.733300</td>
</tr>
<tr>
<td>2</td>
<td>0.257200</td>
<td>0.244938</td>
<td>0.742900</td>
<td>0.702100</td>
<td>0.712600</td>
<td>0.717700</td>
<td>330.200000</td>
</tr>
<tr>
<td>2</td>
<td>0.229900</td>
<td>0.230673</td>
<td>0.789800</td>
<td>0.747500</td>
<td>0.759500</td>
<td>0.765300</td>
<td>267.666700</td>
</tr>
<tr>
<td>4</td>
<td>0.209900</td>
<td>0.213156</td>
<td>0.800300</td>
<td>0.759900</td>
<td>0.766400</td>
<td>0.771700</td>
<td>274.466700</td>
</tr>
<tr>
<td>4</td>
<td>0.196200</td>
<td>0.207821</td>
<td>0.782800</td>
<td>0.745000</td>
<td>0.754900</td>
<td>0.756200</td>
<td>288.333300</td>
</tr>
<tr>
<td>6</td>
<td>0.183900</td>
<td>0.203908</td>
<td>0.752000</td>
<td>0.715000</td>
<td>0.726300</td>
<td>0.727100</td>
<td>309.755600</td>
</tr>
<tr>
<td>6</td>
<td>0.174500</td>
<td>0.203386</td>
<td>0.786100</td>
<td>0.743400</td>
<td>0.750800</td>
<td>0.756200</td>
<td>252.422200</td>
</tr>
<tr>
<td>8</td>
<td>0.165500</td>
<td>0.190161</td>
<td>0.771100</td>
<td>0.733500</td>
<td>0.735600</td>
<td>0.740400</td>
<td>292.288900</td>
</tr>
<tr>
<td>8</td>
<td>0.158300</td>
<td>0.192600</td>
<td>0.774900</td>
<td>0.737300</td>
<td>0.743300</td>
<td>0.744300</td>
<td>285.800000</td>
</tr>
<tr>
<td>9</td>
<td>0.152200</td>
<td>0.192426</td>
<td>0.795200</td>
<td>0.758900</td>
<td>0.754700</td>
<td>0.759000</td>
<td>284.266700</td>
</tr>
<tr>
<td>15</td>
<td>0.124400</td>
<td>0.182381</td>
<td>0.787800</td>
<td>0.742800</td>
<td>0.745100</td>
<td>0.746900</td>
<td>274.533300</td>
</tr>
<tr>
<td>17</td>
<td>0.120300</td>
<td>0.183192</td>
<td>0.779400</td>
<td>0.739000</td>
<td>0.734500</td>
<td>0.739300</td>
<td>289.266700</td>
</tr>
</table>
'''
|
divers/ans-scorer-flan-large
|
divers
| 2023-05-13T14:48:52Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-04-24T09:32:14Z |
html'''
<table>
<thead>
<tr>
<th>Epoch</th>
<th>Training Loss</th>
<th>Validation Loss</th>
<th>Rouge1</th>
<th>Rouge2</th>
<th>Rougel</th>
<th>Rougelsum</th>
<th>Gen Len</th>
</tr>
</thead>
<tbody>
<tr>
<td>0</td>
<td>0.128000</td>
<td>0.069041</td>
<td>0.932900</td>
<td>0.876200</td>
<td>0.924700</td>
<td>0.926800</td>
<td>16.829500</td>
</tr>
<tr>
<td>1</td>
<td>0.073900</td>
<td>0.061863</td>
<td>0.935600</td>
<td>0.881900</td>
<td>0.927700</td>
<td>0.929800</td>
<td>16.827600</td>
</tr>
<tr>
<td>2</td>
<td>0.065500</td>
<td>0.062592</td>
<td>0.932900</td>
<td>0.876200</td>
<td>0.924800</td>
<td>0.927300</td>
<td>16.825100</td>
</tr>
<tr>
<td>3</td>
<td>0.059700</td>
<td>0.058368</td>
<td>0.935900</td>
<td>0.883800</td>
<td>0.928100</td>
<td>0.930100</td>
<td>16.818100</td>
</tr>
<tr>
<td>4</td>
<td>0.055200</td>
<td>0.057483</td>
<td>0.936500</td>
<td>0.887600</td>
<td>0.930200</td>
<td>0.932200</td>
<td>16.825100</td>
</tr>
<tr>
<td>5</td>
<td>0.051500</td>
<td>0.058953</td>
<td>0.937300</td>
<td>0.887200</td>
<td>0.929800</td>
<td>0.931600</td>
<td>16.826300</td>
</tr>
</tbody>
</table>
'''
|
andyssj/entregable2
|
andyssj
| 2023-05-13T14:40:29Z | 0 | 0 |
fastai
|
[
"fastai",
"region:us"
] | null | 2023-05-13T14:40:26Z |
---
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
|
Abrumu/output
|
Abrumu
| 2023-05-13T14:18:18Z | 5 | 1 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"controlnet",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-05-12T16:02:42Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- controlnet
inference: true
---
# controlnet-Abrumu/output
These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning.
|
Yahiael1/mymodel_v2_4
|
Yahiael1
| 2023-05-13T14:11:08Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-05-13T13:48:07Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mymodel_v2_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. -->
# mymodel_v2_4
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1383
- Rouge1: 0.5107
- Rouge2: 0.1818
- Rougel: 0.4557
- Rougelsum: 0.4753
- Gen Len: 19.4327
## 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: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 111 | 1.6651 | 1.0836 | 0.9742 | 1.076 | 1.0681 | 19.4 |
| No log | 2.0 | 222 | 1.6632 | 0.5545 | 0.3924 | 0.5312 | 0.5302 | 19.5855 |
| No log | 3.0 | 333 | 1.7607 | 0.7463 | 0.5905 | 0.7663 | 0.7512 | 19.6982 |
| No log | 4.0 | 444 | 1.8583 | 0.8352 | 0.7153 | 0.8546 | 0.8534 | 19.7018 |
| 1.4574 | 5.0 | 555 | 1.9357 | 0.659 | 0.6196 | 0.6745 | 0.6962 | 19.3273 |
| 1.4574 | 6.0 | 666 | 2.0241 | 0.4785 | 0.4545 | 0.4878 | 0.4997 | 19.6036 |
| 1.4574 | 7.0 | 777 | 2.0663 | 0.2327 | 0.1818 | 0.2741 | 0.2741 | 19.2327 |
| 1.4574 | 8.0 | 888 | 2.0969 | 0.3755 | 0.2916 | 0.3915 | 0.3956 | 19.4545 |
| 1.4574 | 9.0 | 999 | 2.1291 | 0.7743 | 0.5592 | 0.7473 | 0.7881 | 19.3964 |
| 0.3529 | 10.0 | 1110 | 2.1383 | 0.5107 | 0.1818 | 0.4557 | 0.4753 | 19.4327 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
intanm/mlm-20230513-indobert-large-p1-002-pt1
|
intanm
| 2023-05-13T14:10:09Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-05-13T13:19:03Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: mlm-20230513-indobert-large-p1-002-pt1
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. -->
# mlm-20230513-indobert-large-p1-002-pt1
This model is a fine-tuned version of [indobenchmark/indobert-large-p1](https://huggingface.co/indobenchmark/indobert-large-p1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1513
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 284 | 3.7844 |
| 4.4953 | 2.0 | 568 | 3.0374 |
| 4.4953 | 3.0 | 852 | 2.7386 |
| 2.9063 | 4.0 | 1136 | 2.5432 |
| 2.9063 | 5.0 | 1420 | 2.3463 |
| 2.4449 | 6.0 | 1704 | 2.3084 |
| 2.4449 | 7.0 | 1988 | 2.2064 |
| 2.2361 | 8.0 | 2272 | 2.1498 |
| 2.1263 | 9.0 | 2556 | 2.1531 |
| 2.1263 | 10.0 | 2840 | 2.1542 |
### Framework versions
- Transformers 4.29.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
aalonso-developer/vit-base-patch16-224-in21k-euroSat
|
aalonso-developer
| 2023-05-13T14:02:42Z | 62 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"vit",
"image-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-05-13T13:19:23Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: aalonso-developer/vit-base-patch16-224-in21k-euroSat
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. -->
# aalonso-developer/vit-base-patch16-224-in21k-euroSat
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0212
- Train Accuracy: 0.9992
- Train Top-3-accuracy: 1.0000
- Validation Loss: 0.0613
- Validation Accuracy: 0.9864
- Validation Top-3-accuracy: 0.9998
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 3590, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
### Training results
| Train Loss | Train Accuracy | Train Top-3-accuracy | Validation Loss | Validation Accuracy | Validation Top-3-accuracy | Epoch |
|:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:|
| 0.4737 | 0.9429 | 0.9862 | 0.1568 | 0.9788 | 0.9993 | 0 |
| 0.0998 | 0.9878 | 0.9996 | 0.1010 | 0.9805 | 0.9993 | 1 |
| 0.0503 | 0.9946 | 0.9999 | 0.0720 | 0.9857 | 0.9998 | 2 |
| 0.0297 | 0.9978 | 1.0000 | 0.0606 | 0.9881 | 0.9995 | 3 |
| 0.0212 | 0.9992 | 1.0000 | 0.0613 | 0.9864 | 0.9998 | 4 |
### Framework versions
- Transformers 4.29.1
- TensorFlow 2.11.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
bdsqlsz/FaceBeauty
|
bdsqlsz
| 2023-05-13T13:50:06Z | 0 | 1 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-13T13:47:22Z |
---
license: creativeml-openrail-m
---
|
gokulh/ppo-LunarLander-v2
|
gokulh
| 2023-05-13T13:49:06Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-13T13:48:42Z |
---
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: 194.18 +/- 78.91
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
...
```
|
knlpscience/xlm-roberta-base-finetuned-panx-de
|
knlpscience
| 2023-05-13T13:07:43Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-05-13T13:02:54Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.de
split: validation
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8609120891618334
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1400
- F1: 0.8609
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2581 | 1.0 | 525 | 0.1584 | 0.8233 |
| 0.1252 | 2.0 | 1050 | 0.1384 | 0.8491 |
| 0.0811 | 3.0 | 1575 | 0.1400 | 0.8609 |
### Framework versions
- Transformers 4.29.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
JustFrederik/m2m_100_418m_ct2_int8
|
JustFrederik
| 2023-05-13T13:07:09Z | 5 | 0 |
transformers
|
[
"transformers",
"multilingual",
"af",
"am",
"ar",
"ast",
"az",
"ba",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"ceb",
"cs",
"cy",
"da",
"de",
"el",
"en",
"es",
"et",
"fa",
"ff",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"he",
"hi",
"hr",
"ht",
"hu",
"hy",
"id",
"ig",
"ilo",
"is",
"it",
"ja",
"jv",
"ka",
"kk",
"km",
"kn",
"ko",
"lb",
"lg",
"ln",
"lo",
"lt",
"lv",
"mg",
"mk",
"ml",
"mn",
"mr",
"ms",
"my",
"ne",
"nl",
"no",
"ns",
"oc",
"or",
"pa",
"pl",
"ps",
"pt",
"ro",
"ru",
"sd",
"si",
"sk",
"sl",
"so",
"sq",
"sr",
"ss",
"su",
"sv",
"sw",
"ta",
"th",
"tl",
"tn",
"tr",
"uk",
"ur",
"uz",
"vi",
"wo",
"xh",
"yi",
"yo",
"zh",
"zu",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2023-05-13T13:06:12Z |
---
language:
- multilingual
- af
- am
- ar
- ast
- az
- ba
- be
- bg
- bn
- br
- bs
- ca
- ceb
- cs
- cy
- da
- de
- el
- en
- es
- et
- fa
- ff
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- ht
- hu
- hy
- id
- ig
- ilo
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- lb
- lg
- ln
- lo
- lt
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- no
- ns
- oc
- or
- pa
- pl
- ps
- pt
- ro
- ru
- sd
- si
- sk
- sl
- so
- sq
- sr
- ss
- su
- sv
- sw
- ta
- th
- tl
- tn
- tr
- uk
- ur
- uz
- vi
- wo
- xh
- yi
- yo
- zh
- zu
license: mit
---
https://huggingface.co/facebook/m2m100_418M
<br />
https://github.com/facebookresearch/fairseq/tree/nllb/examples/m2m_100
```
ct2-fairseq-converter --data_dir . --model_path 418M_last_checkpoint.pt --fixed_dictionary model_dict.128k.txt --quantization int8 --output_dir converted/m2m_100_418m_ct2_int8
```
External language dictionary is not provided; use lang-pairs to infer the set of supported languages. The language ordering is not stable which might cause misalignment in pretraining and finetuning.
```
wget https://dl.fbaipublicfiles.com/m2m_100/model_dict.128k.txt
# 418M parameter model
wget https://dl.fbaipublicfiles.com/m2m_100/418M_last_checkpoint.pt
```
|
JustFrederik/m2m_100_418m_ct2
|
JustFrederik
| 2023-05-13T13:03:58Z | 4 | 0 |
transformers
|
[
"transformers",
"multilingual",
"af",
"am",
"ar",
"ast",
"az",
"ba",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"ceb",
"cs",
"cy",
"da",
"de",
"el",
"en",
"es",
"et",
"fa",
"ff",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"he",
"hi",
"hr",
"ht",
"hu",
"hy",
"id",
"ig",
"ilo",
"is",
"it",
"ja",
"jv",
"ka",
"kk",
"km",
"kn",
"ko",
"lb",
"lg",
"ln",
"lo",
"lt",
"lv",
"mg",
"mk",
"ml",
"mn",
"mr",
"ms",
"my",
"ne",
"nl",
"no",
"ns",
"oc",
"or",
"pa",
"pl",
"ps",
"pt",
"ro",
"ru",
"sd",
"si",
"sk",
"sl",
"so",
"sq",
"sr",
"ss",
"su",
"sv",
"sw",
"ta",
"th",
"tl",
"tn",
"tr",
"uk",
"ur",
"uz",
"vi",
"wo",
"xh",
"yi",
"yo",
"zh",
"zu",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2023-05-13T13:01:28Z |
---
language:
- multilingual
- af
- am
- ar
- ast
- az
- ba
- be
- bg
- bn
- br
- bs
- ca
- ceb
- cs
- cy
- da
- de
- el
- en
- es
- et
- fa
- ff
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- ht
- hu
- hy
- id
- ig
- ilo
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- lb
- lg
- ln
- lo
- lt
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- no
- ns
- oc
- or
- pa
- pl
- ps
- pt
- ro
- ru
- sd
- si
- sk
- sl
- so
- sq
- sr
- ss
- su
- sv
- sw
- ta
- th
- tl
- tn
- tr
- uk
- ur
- uz
- vi
- wo
- xh
- yi
- yo
- zh
- zu
license: mit
---
https://huggingface.co/facebook/m2m100_418M
<br />
https://github.com/facebookresearch/fairseq/tree/nllb/examples/m2m_100
```
ct2-fairseq-converter --data_dir . --model_path 418M_last_checkpoint.pt --fixed_dictionary model_dict.128k.txt --output_dir converted/m2m_100_418m_ct2
```
External language dictionary is not provided; use lang-pairs to infer the set of supported languages. The language ordering is not stable which might cause misalignment in pretraining and finetuning.
```
wget https://dl.fbaipublicfiles.com/m2m_100/model_dict.128k.txt
# 418M parameter model
wget https://dl.fbaipublicfiles.com/m2m_100/418M_last_checkpoint.pt
```
|
JustFrederik/m2m_100_1.2b_ct2_float16
|
JustFrederik
| 2023-05-13T13:00:25Z | 3 | 0 |
transformers
|
[
"transformers",
"multilingual",
"af",
"am",
"ar",
"ast",
"az",
"ba",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"ceb",
"cs",
"cy",
"da",
"de",
"el",
"en",
"es",
"et",
"fa",
"ff",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"he",
"hi",
"hr",
"ht",
"hu",
"hy",
"id",
"ig",
"ilo",
"is",
"it",
"ja",
"jv",
"ka",
"kk",
"km",
"kn",
"ko",
"lb",
"lg",
"ln",
"lo",
"lt",
"lv",
"mg",
"mk",
"ml",
"mn",
"mr",
"ms",
"my",
"ne",
"nl",
"no",
"ns",
"oc",
"or",
"pa",
"pl",
"ps",
"pt",
"ro",
"ru",
"sd",
"si",
"sk",
"sl",
"so",
"sq",
"sr",
"ss",
"su",
"sv",
"sw",
"ta",
"th",
"tl",
"tn",
"tr",
"uk",
"ur",
"uz",
"vi",
"wo",
"xh",
"yi",
"yo",
"zh",
"zu",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2023-05-13T12:53:19Z |
---
language:
- multilingual
- af
- am
- ar
- ast
- az
- ba
- be
- bg
- bn
- br
- bs
- ca
- ceb
- cs
- cy
- da
- de
- el
- en
- es
- et
- fa
- ff
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- ht
- hu
- hy
- id
- ig
- ilo
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- lb
- lg
- ln
- lo
- lt
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- no
- ns
- oc
- or
- pa
- pl
- ps
- pt
- ro
- ru
- sd
- si
- sk
- sl
- so
- sq
- sr
- ss
- su
- sv
- sw
- ta
- th
- tl
- tn
- tr
- uk
- ur
- uz
- vi
- wo
- xh
- yi
- yo
- zh
- zu
license: mit
---
https://huggingface.co/facebook/m2m100_1.2B
<br />
https://github.com/facebookresearch/fairseq/tree/nllb/examples/m2m_100
```
ct2-fairseq-converter --data_dir . --model_path 1.2B_last_checkpoint.pt --fixed_dictionary model_dict.128k.txt --quantization float16 --output_dir converted/m2m_100_1.2b_ct2_float16
```
External language dictionary is not provided; use lang-pairs to infer the set of supported languages. The language ordering is not stable which might cause misalignment in pretraining and finetuning.
```
wget https://dl.fbaipublicfiles.com/m2m_100/model_dict.128k.txt
# 1.2B parameter model
wget https://dl.fbaipublicfiles.com/m2m_100/1.2B_last_checkpoint.pt
```
|
JustFrederik/m2m_100_1.2b_ct2_int8
|
JustFrederik
| 2023-05-13T12:59:21Z | 2 | 0 |
transformers
|
[
"transformers",
"multilingual",
"af",
"am",
"ar",
"ast",
"az",
"ba",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"ceb",
"cs",
"cy",
"da",
"de",
"el",
"en",
"es",
"et",
"fa",
"ff",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"he",
"hi",
"hr",
"ht",
"hu",
"hy",
"id",
"ig",
"ilo",
"is",
"it",
"ja",
"jv",
"ka",
"kk",
"km",
"kn",
"ko",
"lb",
"lg",
"ln",
"lo",
"lt",
"lv",
"mg",
"mk",
"ml",
"mn",
"mr",
"ms",
"my",
"ne",
"nl",
"no",
"ns",
"oc",
"or",
"pa",
"pl",
"ps",
"pt",
"ro",
"ru",
"sd",
"si",
"sk",
"sl",
"so",
"sq",
"sr",
"ss",
"su",
"sv",
"sw",
"ta",
"th",
"tl",
"tn",
"tr",
"uk",
"ur",
"uz",
"vi",
"wo",
"xh",
"yi",
"yo",
"zh",
"zu",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2023-05-13T12:57:25Z |
---
language:
- multilingual
- af
- am
- ar
- ast
- az
- ba
- be
- bg
- bn
- br
- bs
- ca
- ceb
- cs
- cy
- da
- de
- el
- en
- es
- et
- fa
- ff
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- ht
- hu
- hy
- id
- ig
- ilo
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- lb
- lg
- ln
- lo
- lt
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- no
- ns
- oc
- or
- pa
- pl
- ps
- pt
- ro
- ru
- sd
- si
- sk
- sl
- so
- sq
- sr
- ss
- su
- sv
- sw
- ta
- th
- tl
- tn
- tr
- uk
- ur
- uz
- vi
- wo
- xh
- yi
- yo
- zh
- zu
license: mit
---
https://huggingface.co/facebook/m2m100_1.2B
<br />
https://github.com/facebookresearch/fairseq/tree/nllb/examples/m2m_100
```
ct2-fairseq-converter --data_dir . --model_path 1.2B_last_checkpoint.pt --fixed_dictionary model_dict.128k.txt --quantization int8 --output_dir converted/m2m_100_1.2b_ct2_int8
```
External language dictionary is not provided; use lang-pairs to infer the set of supported languages. The language ordering is not stable which might cause misalignment in pretraining and finetuning.
```
wget https://dl.fbaipublicfiles.com/m2m_100/model_dict.128k.txt
# 1.2B parameter model
wget https://dl.fbaipublicfiles.com/m2m_100/1.2B_last_checkpoint.pt
```
|
JustFrederik/m2m_100_1.2b_ct2
|
JustFrederik
| 2023-05-13T12:52:25Z | 2 | 0 |
transformers
|
[
"transformers",
"multilingual",
"af",
"am",
"ar",
"ast",
"az",
"ba",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"ceb",
"cs",
"cy",
"da",
"de",
"el",
"en",
"es",
"et",
"fa",
"ff",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"he",
"hi",
"hr",
"ht",
"hu",
"hy",
"id",
"ig",
"ilo",
"is",
"it",
"ja",
"jv",
"ka",
"kk",
"km",
"kn",
"ko",
"lb",
"lg",
"ln",
"lo",
"lt",
"lv",
"mg",
"mk",
"ml",
"mn",
"mr",
"ms",
"my",
"ne",
"nl",
"no",
"ns",
"oc",
"or",
"pa",
"pl",
"ps",
"pt",
"ro",
"ru",
"sd",
"si",
"sk",
"sl",
"so",
"sq",
"sr",
"ss",
"su",
"sv",
"sw",
"ta",
"th",
"tl",
"tn",
"tr",
"uk",
"ur",
"uz",
"vi",
"wo",
"xh",
"yi",
"yo",
"zh",
"zu",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2023-05-13T12:45:46Z |
---
language:
- multilingual
- af
- am
- ar
- ast
- az
- ba
- be
- bg
- bn
- br
- bs
- ca
- ceb
- cs
- cy
- da
- de
- el
- en
- es
- et
- fa
- ff
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- ht
- hu
- hy
- id
- ig
- ilo
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- lb
- lg
- ln
- lo
- lt
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- no
- ns
- oc
- or
- pa
- pl
- ps
- pt
- ro
- ru
- sd
- si
- sk
- sl
- so
- sq
- sr
- ss
- su
- sv
- sw
- ta
- th
- tl
- tn
- tr
- uk
- ur
- uz
- vi
- wo
- xh
- yi
- yo
- zh
- zu
license: mit
---
https://huggingface.co/facebook/m2m100_1.2B
<br />
https://github.com/facebookresearch/fairseq/tree/nllb/examples/m2m_100
```
ct2-fairseq-converter --data_dir . --model_path 1.2B_last_checkpoint.pt --fixed_dictionary model_dict.128k.txt --output_dir converted/m2m_100_1.2b_ct2
```
External language dictionary is not provided; use lang-pairs to infer the set of supported languages. The language ordering is not stable which might cause misalignment in pretraining and finetuning.
```
wget https://dl.fbaipublicfiles.com/m2m_100/model_dict.128k.txt
# 1.2B parameter model
wget https://dl.fbaipublicfiles.com/m2m_100/1.2B_last_checkpoint.pt
```
|
autobots/pygmalion_6b_roleplay_lora
|
autobots
| 2023-05-13T12:45:35Z | 0 | 3 | null |
[
"region:us"
] | null | 2023-05-13T12:37:53Z |
Trained in 4-bit on pygmalion-6b as POC
Uses the GPTeacher roleplay dataset.
```
INFO:Getting model ready...
INFO:Prepping for training...
INFO:Creating LoRA model...
INFO:Starting training...
{'loss': 12.5737, 'learning_rate': 0.0002926829268292683, 'epoch': 0.33}
{'loss': 8.5515, 'learning_rate': 0.0002560975609756097, 'epoch': 0.67}
{'loss': 7.5768, 'learning_rate': 0.0002195121951219512, 'epoch': 1.0}
{'loss': 6.9769, 'learning_rate': 0.00018292682926829266, 'epoch': 1.33}
{'loss': 6.6842, 'learning_rate': 0.00014634146341463414, 'epoch': 1.66}
{'loss': 6.3925, 'learning_rate': 0.0001097560975609756, 'epoch': 2.0}
{'loss': 6.041, 'learning_rate': 7.317073170731707e-05, 'epoch': 2.33}
{'loss': 5.6818, 'learning_rate': 3.6585365853658535e-05, 'epoch': 2.66}
{'loss': 5.4639, 'learning_rate': 0.0, 'epoch': 2.99}
{'train_runtime': 960.7748, 'train_samples_per_second': 6.005, 'train_steps_per_second': 0.047, 'train_loss': 7.326934729682074, 'epoch': 2.99}
INFO:LoRA training run is completed and saved.
INFO:Training complete!
```
I used the electricity so might as well post it.
|
Tingwen/ppo-Huggy
|
Tingwen
| 2023-05-13T12:39:54Z | 12 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-05-13T11:57:05Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Find your model_id: Tingwen/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
paolorechia/wizard-lm-7b-react-medium-tasks-dirty-lora
|
paolorechia
| 2023-05-13T12:21:54Z | 0 | 2 | null |
[
"license:other",
"region:us"
] | null | 2023-05-13T11:54:42Z |
---
license: other
---
This is a LLama LoRA fine tuned on top of WizardLM-7B with this dataset: https://huggingface.co/datasets/paolorechia/medium-size-generated-tasks
It's meant mostly as an proof of concept to see how fine tuning may improve the performance of coding agents that rely on the Langchain framework.
To use this LoRA, you can use my repo as starting point: https://github.com/paolorechia/learn-langchain
|
smile367/task_qa_distilbert
|
smile367
| 2023-05-13T11:46:46Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-05-13T10:45:01Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: task_qa_distilbert
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. -->
# task_qa_distilbert
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6780
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 250 | 2.4280 |
| 2.763 | 2.0 | 500 | 1.7672 |
| 2.763 | 3.0 | 750 | 1.6780 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
leireher/BookGenrePredictionDBERT
|
leireher
| 2023-05-13T11:44:50Z | 117 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"multilabel",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-11T15:58:45Z |
---
language:
- en
metrics:
- f1
pipeline_tag: text-classification
tags:
- multilabel
---
|
AlekseyKorshuk/roberta-with-topic
|
AlekseyKorshuk
| 2023-05-13T11:09:06Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-13T07:58:23Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-with-topic
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-with-topic
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5283
- Ndcg: 0.4453
- Accuracy: 0.2941
## 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
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Ndcg | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:--------:|
| 1.5951 | 0.07 | 413 | 1.5693 | 0.4220 | 0.2766 |
| 1.5721 | 0.13 | 826 | 1.5537 | 0.4308 | 0.2828 |
| 1.5594 | 0.2 | 1239 | 1.5615 | 0.4236 | 0.2757 |
| 1.5753 | 0.27 | 1652 | 1.5645 | 0.4272 | 0.2778 |
| 1.5778 | 0.33 | 2065 | 1.5859 | 0.3736 | 0.2430 |
| 1.5673 | 0.4 | 2478 | 1.5576 | 0.4262 | 0.2812 |
| 1.5633 | 0.47 | 2891 | 1.5557 | 0.4294 | 0.2815 |
| 1.5606 | 0.53 | 3304 | 1.5459 | 0.4321 | 0.2836 |
| 1.5476 | 0.6 | 3717 | 1.5508 | 0.4269 | 0.2810 |
| 1.552 | 0.67 | 4130 | 1.5479 | 0.4302 | 0.2831 |
| 1.5469 | 0.73 | 4543 | 1.5430 | 0.4345 | 0.2882 |
| 1.5538 | 0.8 | 4956 | 1.5410 | 0.4371 | 0.2877 |
| 1.557 | 0.87 | 5369 | 1.5420 | 0.4368 | 0.2896 |
| 1.5427 | 0.93 | 5782 | 1.5449 | 0.4269 | 0.2814 |
| 1.5427 | 1.0 | 6195 | 1.5381 | 0.4380 | 0.2896 |
| 1.5469 | 1.07 | 6608 | 1.5381 | 0.4362 | 0.2849 |
| 1.5369 | 1.13 | 7021 | 1.5361 | 0.4383 | 0.2895 |
| 1.5465 | 1.2 | 7434 | 1.5361 | 0.4415 | 0.2940 |
| 1.5433 | 1.27 | 7847 | 1.5342 | 0.4399 | 0.2914 |
| 1.5355 | 1.33 | 8260 | 1.5342 | 0.4409 | 0.2937 |
| 1.5363 | 1.4 | 8673 | 1.5342 | 0.4414 | 0.2923 |
| 1.5372 | 1.47 | 9086 | 1.5312 | 0.4440 | 0.2949 |
| 1.5452 | 1.53 | 9499 | 1.5303 | 0.4439 | 0.2937 |
| 1.5386 | 1.6 | 9912 | 1.5293 | 0.4434 | 0.2915 |
| 1.5314 | 1.67 | 10325 | 1.5303 | 0.4443 | 0.2925 |
| 1.5216 | 1.73 | 10738 | 1.5293 | 0.4447 | 0.2930 |
| 1.5341 | 1.8 | 11151 | 1.5293 | 0.4450 | 0.2929 |
| 1.5315 | 1.87 | 11564 | 1.5283 | 0.4456 | 0.2947 |
| 1.5345 | 1.93 | 11977 | 1.5283 | 0.4455 | 0.2950 |
| 1.5238 | 2.0 | 12390 | 1.5283 | 0.4453 | 0.2941 |
### Framework versions
- Transformers 4.29.1
- Pytorch 2.0.0-rc1
- Datasets 2.12.0
- Tokenizers 0.13.3
|
hugogeraldes/q-FrozenLake-v1-4x4-noSlippery
|
hugogeraldes
| 2023-05-13T10:52:35Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-13T10:52:32Z |
---
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="hugogeraldes/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"])
```
|
OKTAN94/293Ulzzangmodelsampe
|
OKTAN94
| 2023-05-13T10:45:22Z | 0 | 1 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-13T10:34:57Z |
---
license: creativeml-openrail-m
---
|
Neronuser/Reinforce-helicopter
|
Neronuser
| 2023-05-13T10:38:02Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-13T10:37:58Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-helicopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 30.50 +/- 25.11
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
|
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