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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-07-15 06:27:42
| downloads
int64 0
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| likes
int64 0
11.7k
| library_name
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listlengths 1
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| pipeline_tag
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values | createdAt
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utyug1/q-FrozenLake-v1-4x4-noSlippery | utyug1 | 2022-12-19T20:40:32Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-19T20:40:25Z | ---
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="utyug1/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"])
```
|
Gnoblit/Taxi-v3 | Gnoblit | 2022-12-19T20:36:18Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-19T20:25:53Z | ---
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: -92.27 +/- 26.64
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="Gnoblit/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"])
```
|
bsmith0430/q-FrozenLake-v1-4x4-noSlippery | bsmith0430 | 2022-12-19T20:29:02Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-11-29T01:03:05Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="bsmith0430/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"])
```
|
Gnoblit/q-FrozenLake-v1-4x4-noSlippery | Gnoblit | 2022-12-19T20:08:17Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-19T20:08:12Z | ---
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="Gnoblit/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"])
```
|
kpriyanshu256/whisper-large-v2-br-1000-32-1e-05 | kpriyanshu256 | 2022-12-19T20:06:18Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"br",
"dataset:mozilla-foundation/common_voice_11_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-19T05:02:19Z | ---
language:
- br
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: openai/whisper-large-v2-breton
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: br
split: test
args: br
metrics:
- name: Wer
type: wer
value: 39.92705800625217
---
<!-- 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. -->
# openai/whisper-large-v2-breton
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: 0.7162
- Wer: 39.9271
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.7423 | 0.1 | 100 | 0.8363 | 57.1553 |
| 0.4361 | 1.07 | 200 | 0.6833 | 46.7176 |
| 0.2227 | 2.03 | 300 | 0.6483 | 42.5929 |
| 0.1472 | 3.0 | 400 | 0.6511 | 42.4627 |
| 0.0892 | 3.1 | 500 | 0.6633 | 40.9604 |
| 0.0651 | 4.07 | 600 | 0.6807 | 39.7534 |
| 0.0416 | 5.04 | 700 | 0.6870 | 41.2383 |
| 0.0352 | 6.0 | 800 | 0.7315 | 39.9010 |
| 0.022 | 6.1 | 900 | 0.7201 | 40.4307 |
| 0.0195 | 7.07 | 1000 | 0.7162 | 39.9271 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
rama100/q-Taxi-v | rama100 | 2022-12-19T19:56:13Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-19T19:56:10Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v
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="rama100/q-Taxi-v", 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"])
```
|
togoforfood/ppo-LunarLander-v2 | togoforfood | 2022-12-19T19:30:01Z | 4 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-19T19:29:37Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 263.05 +/- 24.95
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
ahmadmwali/finetuning-sentiment-igbo21 | ahmadmwali | 2022-12-19T19:13:14Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-12-19T18:16:25Z | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-igbo21
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-sentiment-igbo21
This model is a fine-tuned version of [mbeukman/xlm-roberta-base-finetuned-ner-igbo](https://huggingface.co/mbeukman/xlm-roberta-base-finetuned-ner-igbo) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5368
- Accuracy: 0.7923
- F1: 0.7914
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
CoreyMorris/q-FrozenLake-v1-4x4-noSlippery | CoreyMorris | 2022-12-19T18:58:55Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-09-13T15:13:38Z | ---
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 playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="CoreyMorris/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
Rami/CartPole-v1__functional_dqn__0__1671475909 | Rami | 2022-12-19T18:55:59Z | 0 | 0 | null | [
"region:us"
]
| null | 2022-12-19T18:55:48Z | ---
language: en
license: apache-2.0
model-index:
- name: CartPole-v1__functional_dqn__0__1671475909
---
DQN model applied to the this discrete environments CartPole-v1
## Model Description
The model was trained from the CleanRl library using the DQN algorithm
## Intended Use & Limitation
The model is intended to be used for the following environments CartPole-v1
and understand the implication of Quantization on this type of model from a pretrained state## Training Procdure
### Training Hyperparameters
```
The folloing hyperparameters were used during training:
- exp_name: functional_dqn
- seed: 0
- torch_deterministic: True
- cuda: False
- track: True
- wandb_project_name: cleanRL
- wandb_entity: compress_rl
- capture_video: False
- env_id: CartPole-v1
- total_timesteps: 500000
- learning_rate: 0.00025
- buffer_size: 10000
- gamma: 0.99
- target_network_frequency: 500
- batch_size: 128
- start_e: 1
- end_e: 0.05
- exploration_fraction: 0.5
- learning_starts: 10000
- train_frequency: 10
- optimizer: Adan
- wandb_project: cleanrl
```
### Framework and version
```
Pytorch 1.12.1+cu102
gym 0.23.1
Weights and Biases 0.13.3
Hugging Face Hub 0.11.1
|
mustfkeskin/q-FrozenLake-v1-4x4-noSlippery | mustfkeskin | 2022-12-19T18:49:39Z | 0 | 0 | null | [
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-19T18:49:23Z | ---
tags:
- FrozenLake-v1-4x4
- 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
type: FrozenLake-v1-4x4
metrics:
- type: mean_reward
value: 0.46 +/- 0.50
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="mustfkeskin/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"])
```
|
hyorea1/KoT5-test-add-data-prefix-summary | hyorea1 | 2022-12-19T18:43:35Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-12-19T09:57:04Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: KoT5-test-add-data-prefix-summary
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. -->
# KoT5-test-add-data-prefix-summary
This model is a fine-tuned version of [hyorea1/KoT5-test-add-data-prefix-summary](https://huggingface.co/hyorea1/KoT5-test-add-data-prefix-summary) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1781
- Rouge1: 11.8533
- Rouge2: 2.9172
- Rougel: 11.715
- Rougelsum: 11.7278
- Gen Len: 35.164
## 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: 4
- eval_batch_size: 4
- seed: 100
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 1.4974 | 0.32 | 800 | 1.1935 | 11.0529 | 3.0383 | 10.9308 | 10.9481 | 34.8809 |
| 1.0394 | 0.64 | 1600 | 1.1979 | 11.2828 | 2.8757 | 11.1691 | 11.1952 | 35.6412 |
| 1.2385 | 0.97 | 2400 | 1.1914 | 10.8007 | 3.0248 | 10.696 | 10.7022 | 34.8081 |
| 1.4298 | 1.29 | 3200 | 1.1916 | 10.8949 | 2.9547 | 10.8037 | 10.832 | 34.7934 |
| 1.3735 | 1.61 | 4000 | 1.1887 | 11.8127 | 3.2642 | 11.7143 | 11.7263 | 35.4331 |
| 1.5772 | 1.93 | 4800 | 1.1794 | 11.3157 | 3.1017 | 11.2215 | 11.2237 | 34.3051 |
| 1.2179 | 2.25 | 5600 | 1.1809 | 11.841 | 2.8297 | 11.7283 | 11.7173 | 35.0522 |
| 1.2903 | 2.58 | 6400 | 1.1779 | 11.6353 | 2.8495 | 11.5117 | 11.544 | 34.95 |
| 1.461 | 2.9 | 7200 | 1.1781 | 11.8533 | 2.9172 | 11.715 | 11.7278 | 35.164 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Rami/CartPole-v1__functional_dqn__0__1671474891 | Rami | 2022-12-19T18:39:06Z | 0 | 0 | null | [
"region:us"
]
| null | 2022-12-19T18:38:52Z | ---
language: en
license: apache-2.0
model-index:
- name: CartPole-v1__functional_dqn__0__1671474891
---
DQN model applied to the this discrete environments CartPole-v1
## Model Description
The model was trained from the CleanRl library using the DQN algorithm
## Intended Use & Limitation
The model is intended to be used for the following environments CartPole-v1
and understand the implication of Quantization on this type of model from a pretrained state## Training Procdure
### Training Hyperparameters
```
The folloing hyperparameters were used during training:
- exp_name: functional_dqn
- seed: 0
- torch_deterministic: True
- cuda: False
- track: True
- wandb_project_name: cleanRL
- wandb_entity: compress_rl
- capture_video: False
- env_id: CartPole-v1
- total_timesteps: 500000
- learning_rate: 0.00025
- buffer_size: 10000
- gamma: 0.99
- target_network_frequency: 500
- batch_size: 128
- start_e: 1
- end_e: 0.05
- exploration_fraction: 0.5
- learning_starts: 10000
- train_frequency: 10
- optimizer: Adam
- wandb_project: cleanrl
```
### Framework and version
```
Pytorch 1.12.1+cu102
gym 0.23.1
Weights and Biases 0.13.3
Hugging Face Hub 0.11.1
|
emilios/whisper-md-hr | emilios | 2022-12-19T17:58:16Z | 16 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"dataset:google/fleurs",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-19T11:45:57Z | ---
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper medium Croatian El Greco
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: google/fleurs hr_hr
type: google/fleurs
config: zu
split: None
metrics:
- name: Wer
type: wer
value: 14.613261224719734
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper medium Croatian El Greco
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the google/fleurs hr_hr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3374
- Wer: 14.6133
## 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-06
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0106 | 4.61 | 1000 | 0.3374 | 14.6133 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 2.0.0.dev20221216+cu116
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
anuragshas/whisper-large-v2-br | anuragshas | 2022-12-19T17:46:49Z | 6 | 1 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"br",
"dataset:mozilla-foundation/common_voice_11_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-19T15:11:44Z | ---
language:
- br
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Large-v2 Breton
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0 br
type: mozilla-foundation/common_voice_11_0
config: br
split: test
args: br
metrics:
- name: Wer
type: wer
value: 37.89510246613407
---
<!-- 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 Breton
This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the mozilla-foundation/common_voice_11_0 br dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7700
- Wer: 37.8951
## 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: 16
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0064 | 6.11 | 1000 | 0.7700 | 37.8951 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
1nuno/PLN-META-3 | 1nuno | 2022-12-19T17:42:55Z | 970 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2022-12-19T17:28:43Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 39 with parameters:
```
{'batch_size': 32}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 5,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 19,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
dreambooth-hackathon/glxy-galaxy | dreambooth-hackathon | 2022-12-19T17:36:24Z | 6 | 1 | diffusers | [
"diffusers",
"pytorch",
"stable-diffusion",
"text-to-image",
"diffusion-models-class",
"dreambooth-hackathon",
"science",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2022-12-19T17:27:50Z | ---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- dreambooth-hackathon
- science
widget:
- text: a photo of glxy galaxy
---
# DreamBooth model for glxy trained by lewtun on the lewtun/galaxies dataset.
This your the Stable Diffusion model fine-tuned the glxy concept taught to Stable Diffusion with DreamBooth.
It can be used by modifying the `instance_prompt`: **a photo of glxy galaxy**
This model was created as part of the DreamBooth Hackathon. Visit the organisation page for instructions on how to take part!
## Description
Describe your model and concept here.
## Usage
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('dreambooth-hackathon/glxy-galaxy')
image = pipeline().images[0]
image
```
|
shripadbhat/whisper-large-v2-tt | shripadbhat | 2022-12-19T17:25:27Z | 5 | 1 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"tt",
"dataset:mozilla-foundation/common_voice_11_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-19T17:04:32Z | ---
language:
- tt
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
model-index:
- name: Whisper Large v2 Tatar
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Large v2 Tatar
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.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 200
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
Roberto/q-FrozenLake-v1-4x4-noSlippery | Roberto | 2022-12-19T17:10:39Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-19T17:10:33Z | ---
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="Roberto/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"])
```
|
mehranf2f/ppo-LunarLander-v2 | mehranf2f | 2022-12-19T16:36:08Z | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-19T16:35:42Z | ---
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: 280.02 +/- 26.99
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
...
```
|
karthikvenkataraman/hf-reinforcement-learning | karthikvenkataraman | 2022-12-19T16:30:37Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-12T14:43:41Z | ---
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: 266.12 +/- 19.15
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
...
```
|
madhavsankar/qcpg-mscoco-sbert-lr1e-4 | madhavsankar | 2022-12-19T16:30:17Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-12-06T08:23:21Z | # QCPG++
```
Dataset: MSCOCO
Learning Rate: 1e-4
```
## Text Diversity Metrics
```
Semantic Similarity: DocumentSemanticDiversity
Syntactic Diversity: DependencyDiversity
Lexical Diversity: Character-level edit distance
Phonological Diversity: RhythmicDiversity
Morphological Diversity: POSSequenceDiversity.
```
## Results
```
Training Loss: 1.3403
Dev Loss: 1.811
Dev BLEU: 11.0279
```
|
jakub014/bert-base-uncased-finetuned-sufficiency-dagstuhl | jakub014 | 2022-12-19T16:23:55Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-12-19T15:40:32Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-uncased-finetuned-sufficiency-dagstuhl
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-sufficiency-dagstuhl
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8318
- Accuracy: 0.6032
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 16 | 0.8674 | 0.5714 |
| No log | 2.0 | 32 | 0.8350 | 0.5714 |
| No log | 3.0 | 48 | 0.8318 | 0.6032 |
| No log | 4.0 | 64 | 0.8354 | 0.5714 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
marinone94/whisper-medium-swedish | marinone94 | 2022-12-19T16:14:42Z | 29 | 2 | transformers | [
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"hf-asr-leaderboard",
"sv",
"dataset:mozilla-foundation/common_voice_11_0",
"dataset:babelbox/babelbox_voice",
"dataset:google/fleurs",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-18T11:52:20Z | ---
language:
- sv
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_11_0
- babelbox/babelbox_voice
- google/fleurs
model-index:
- name: Whisper Medium Swedish
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: mozilla-foundation/common_voice_11_0
type: mozilla-foundation/common_voice_11_0
config: sv-SE
split: test
metrics:
- name: Wer
type: wer
value: 9.89
---
# Whisper Medium Swedish
This model is a fine-tuned version of [Whisper Medium Nordic](https://huggingface.co/marinone94/whisper-medium-nordic) on the [mozilla-foundation/common_voice_11_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0) (train+validation), the [babelbox/babelbox_voice](https://huggingface.co/datasets/babelbox/babelbox_voice) (NST SV - train split) and the [google/fleurs](https://huggingface.co/datasets/google/fleurs) (sv_se - train+validation+test) datasets.
It achieves the following results on the evaluation set:
- eval_loss: 0.2483
- eval_wer: 9.8914
- eval_runtime: 2924.8709
- eval_samples_per_second: 1.733
- eval_steps_per_second: 0.108
## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- training_steps: 5000
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
### WandB run
https://wandb.ai/pn-aa/whisper/runs/z2lzjx4x?workspace=user-emilio_marinone
|
rama100/q-FrozenLake-v1-4x4-noSlippery | rama100 | 2022-12-19T16:09:58Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-19T16:09:48Z | ---
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="rama100/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"])
```
|
moshew/keras-dummy-sequential-demo | moshew | 2022-12-19T16:03:09Z | 0 | 0 | keras | [
"keras",
"tf-keras",
"region:us"
]
| null | 2022-12-19T16:01:24Z | ---
library_name: keras
---
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
| Hyperparameters | Value |
| :-- | :-- |
| name | Adam |
| learning_rate | 0.001 |
| decay | 0.0 |
| beta_1 | 0.9 |
| beta_2 | 0.999 |
| epsilon | 1e-07 |
| amsgrad | False |
| training_precision | float32 |
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> |
bardsai/whisper-medium-pl-v2 | bardsai | 2022-12-19T15:51:11Z | 22 | 2 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"pl",
"dataset:mozilla-foundation/common_voice_11_0",
"dataset:google/fleurs",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-09T14:42:10Z | ---
language:
- pl
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper Medium PL
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: pl
split: test
args: pl
metrics:
- type: wer
value: 8.71
name: WER
- type: wer_without_norm
value: 22.0
name: WER unnormalized
- type: cer
value: 2.41
name: CER
- type: mer
value: 8.65
name: MER
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: facebook/voxpopuli
type: facebook/voxpopuli
config: pl
split: test
metrics:
- type: wer
value: 11.99
name: WER
- type: wer_without_norm
value: 30.9
name: WER unnormalized
- type: cer
value: 6.54
name: CER
- type: mer
value: 11.68
name: MER
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: google/fleurs
type: google/fleurs
config: pl_pl
split: test
metrics:
- type: wer
value: 10.89
name: WER
- type: wer_without_norm
value: 30.7
name: WER unnormalized
- type: cer
value: 4.04
name: CER
- type: mer
value: 10.8
name: MER
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Medium PL
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 11.0 and the FLEURS datasets.
It achieves the following results on the evaluation set:
- Loss: 0.3947
- Wer: 8.6872
## 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: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 8000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0805 | 0.48 | 500 | 0.2556 | 10.4888 |
| 0.0685 | 0.96 | 1000 | 0.2462 | 10.7608 |
| 0.0356 | 1.45 | 1500 | 0.2561 | 9.6728 |
| 0.0337 | 1.93 | 2000 | 0.2327 | 9.6459 |
| 0.017 | 2.41 | 2500 | 0.2444 | 9.9464 |
| 0.0179 | 2.9 | 3000 | 0.2554 | 9.6476 |
| 0.0056 | 3.38 | 3500 | 0.3001 | 9.3638 |
| 0.007 | 3.86 | 4000 | 0.2809 | 9.2245 |
| 0.0033 | 4.34 | 4500 | 0.3235 | 9.3437 |
| 0.0024 | 4.83 | 5000 | 0.3148 | 9.0633 |
| 0.0008 | 5.31 | 5500 | 0.3416 | 9.0112 |
| 0.0011 | 5.79 | 6000 | 0.3876 | 9.1858 |
| 0.0004 | 6.27 | 6500 | 0.3745 | 8.7292 |
| 0.0003 | 6.76 | 7000 | 0.3704 | 9.0314 |
| 0.0003 | 7.24 | 7500 | 0.3929 | 8.6553 |
| 0.0002 | 7.72 | 8000 | 0.3947 | 8.6872 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
Fake-Person/Gyokai | Fake-Person | 2022-12-19T15:42:59Z | 0 | 0 | null | [
"region:us"
]
| null | 2022-11-21T05:39:53Z | The origins of this model are unknown, as the means of its acquisition remain uncertain |
Roberto/ppo-LunarLander-v2 | Roberto | 2022-12-19T15:42:26Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-19T11:14:30Z | ---
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: 284.03 +/- 18.52
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
...
```
|
Scrya/whisper-medium-vi-augmented | Scrya | 2022-12-19T15:36:14Z | 7 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"vi",
"dataset:mozilla-foundation/common_voice_11_0",
"dataset:google/fleurs",
"dataset:vivos",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-19T00:28:55Z | ---
language:
- vi
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
- google/fleurs
- vivos
metrics:
- wer
model-index:
- name: Whisper Medium VI - Multi - Augmented
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: mozilla-foundation/common_voice_11_0
type: mozilla-foundation/common_voice_11_0
config: vi
split: test
metrics:
- type: wer
value: 16.63
name: WER
- type: cer
value: 7.74
name: CER
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: google/fleurs
type: google/fleurs
config: vi_vn
split: test
metrics:
- type: wer
value: 9.04
name: WER
- type: cer
value: 4.81
name: CER
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: vivos
type: vivos
split: test
metrics:
- type: wer
value: 8.53
name: WER
- type: cer
value: 3.67
name: CER
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Medium VI - Multi - Augmented
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the following datasets:
- [mozilla-foundation/common_voice_11_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0)
- [google/fleurs](https://huggingface.co/datasets/google/fleurs)
- [vivos](https://huggingface.co/datasets/vivos)
It achieves the following results on the evaluation set:
- Loss: 0.3696
- Wer: 16.6594
- Cer: 7.7625
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
Training:
- [mozilla-foundation/common_voice_11_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0) (train+validation)
- [google/fleurs](https://huggingface.co/datasets/google/fleurs) (train+validation)
- [vivos](https://huggingface.co/datasets/vivos) (train)
Evaluation:
- [mozilla-foundation/common_voice_11_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0) (test)
- [google/fleurs](https://huggingface.co/datasets/google/fleurs) (test)
- [vivos](https://huggingface.co/datasets/vivos) (test)
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|
| 0.1992 | 1.8 | 1000 | 0.2726 | 17.4929 | 8.2562 |
| 0.0402 | 3.6 | 2000 | 0.3317 | 17.4929 | 8.2588 |
| 0.0073 | 5.4 | 3000 | 0.3429 | 17.6793 | 8.8913 |
| 0.0014 | 7.19 | 4000 | 0.3599 | 19.0283 | 9.5103 |
| 0.0006 | 8.99 | 5000 | 0.3696 | 16.6594 | 7.7625 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
ConvLab/roberta-base-trippy-dst-multiwoz21 | ConvLab | 2022-12-19T15:25:19Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"dialogue state tracking",
"task-oriented dialog",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2022-12-01T10:30:12Z | ---
language:
- en
license: apache-2.0
tags:
- dialogue state tracking
- task-oriented dialog
---
# roberta-base-trippy-dst-multiwoz21
This is a TripPy model trained on [MultiWOZ 2.1](https://github.com/budzianowski/multiwoz) for use in [ConvLab-3](https://github.com/ConvLab/ConvLab-3).
This model predicts informable slots, requestable slots, general actions and domain indicator slots.
Expected joint goal accuracy for MultiWOZ 2.1 is in the range of 55-56\%.
For information about TripPy DST, refer to [TripPy: A Triple Copy Strategy for Value Independent Neural Dialog State Tracking](https://aclanthology.org/2020.sigdial-1.4/).
The training and evaluation code is available at the official [TripPy repository](https://gitlab.cs.uni-duesseldorf.de/general/dsml/trippy-public).
## Training procedure
The model was trained on MultiWOZ 2.1 data via supervised learning using the [TripPy codebase](https://gitlab.cs.uni-duesseldorf.de/general/dsml/trippy-public).
MultiWOZ 2.1 data was loaded via ConvLab-3's unified data format dataloader.
The pre-trained encoder is [RoBERTa](https://huggingface.co/docs/transformers/model_doc/roberta) (base).
Fine-tuning the encoder and training the DST specific classification heads was conducted for 10 epochs.
### Training hyperparameters
```
python3 run_dst.py \
--task_name="unified" \
--model_type="roberta" \
--model_name_or_path="roberta-base" \
--dataset_config=dataset_config/unified_multiwoz21.json \
--do_lower_case \
--learning_rate=1e-4 \
--num_train_epochs=10 \
--max_seq_length=180 \
--per_gpu_train_batch_size=24 \
--per_gpu_eval_batch_size=32 \
--output_dir=results \
--save_epochs=2 \
--eval_all_checkpoints \
--warmup_proportion=0.1 \
--adam_epsilon=1e-6 \
--weight_decay=0.01 \
--fp16 \
--do_train \
--predict_type=dummy \
--seed=42
```
|
loanb31/ppo-Huggy | loanb31 | 2022-12-19T15:16:51Z | 13 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
]
| reinforcement-learning | 2022-12-19T15:16:40Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: loanb31/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
jiaheillu/luyeyuanpingzang-2 | jiaheillu | 2022-12-19T15:16:28Z | 0 | 0 | null | [
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"region:us"
]
| text-to-image | 2022-12-19T15:15:17Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### luyeyuanpingzang_2 Dreambooth model trained by jiaheillu
Sample pictures of this concept:
|
SiddharthaM/mdeberta-profane-final | SiddharthaM | 2022-12-19T15:04:56Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-12-19T13:24:20Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: mdeberta-profane-final
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. -->
# mdeberta-profane-final
This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2269
- Accuracy: 0.9154
- Precision: 0.8684
- Recall: 0.8558
- F1: 0.8618
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log | 1.0 | 296 | 0.2324 | 0.9125 | 0.8672 | 0.8446 | 0.8552 |
| 0.3129 | 2.0 | 592 | 0.2081 | 0.9202 | 0.8814 | 0.8549 | 0.8673 |
| 0.3129 | 3.0 | 888 | 0.2155 | 0.9183 | 0.8747 | 0.8575 | 0.8657 |
| 0.2136 | 4.0 | 1184 | 0.2164 | 0.9154 | 0.8738 | 0.8464 | 0.8591 |
| 0.2136 | 5.0 | 1480 | 0.2269 | 0.9154 | 0.8684 | 0.8558 | 0.8618 |
### Framework versions
- Transformers 4.24.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.6.1
- Tokenizers 0.13.1
|
yarafa/q-FrozenLake-v1-8x8-noSlippery | yarafa | 2022-12-19T15:03:06Z | 0 | 0 | null | [
"FrozenLake-v1-8x8-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-19T15:02:54Z | ---
tags:
- FrozenLake-v1-8x8-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-8x8-no_slippery
type: FrozenLake-v1-8x8-no_slippery
metrics:
- type: mean_reward
value: 0.00 +/- 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="yarafa/q-FrozenLake-v1-8x8-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"])
```
|
ccarvajal-reyes/beto-prescripciones-medicas-ADMIN | ccarvajal-reyes | 2022-12-19T14:45:51Z | 16 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"token-classification",
"es",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2022-12-18T14:15:11Z | ---
language:
- es
widget:
- text: "1 COMPRIMIDO ORAL"
---
# beto-prescripciones-medicas-ADMIN
This model is a fine-tunned version of [our general model detecting entities in medical prescription](https://huggingface.co/ccarvajal/beto-prescripciones-medicas).
It tags tokens with finer entities, but only on the output of the general model.
**Please go to that model card for further information** or visit [our repo](https://github.com/camilocarvajalreyes/entidades-minsal). |
Gkgpfkso/Shark | Gkgpfkso | 2022-12-19T14:35:34Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2022-12-19T14:35:29Z | ---
license: creativeml-openrail-m
---
|
sipheiroce/taxi-demo | sipheiroce | 2022-12-19T14:26:54Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-19T14:26:28Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi-demo
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.52 +/- 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="sipheiroce/taxi-demo", 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"])
```
|
elpasoasapcreditrepair/Credit-Fixing-Service-in-Elpaso | elpasoasapcreditrepair | 2022-12-19T14:25:33Z | 0 | 0 | null | [
"region:us"
]
| null | 2022-12-19T14:24:27Z | Are you looking for <a href="https://elpaso.asapcreditrepairusa.com/">credit fixing service</a>? You are at the right place.
We are an innovative team with a group of dedicated, passionate, and remarkable individuals determined to help you repair financial defects from your record and help discover ways to improve credit score. |
dbaibak/q-FrozenLake-v1-8x8-noSlippery | dbaibak | 2022-12-19T14:19:44Z | 0 | 1 | null | [
"FrozenLake-v1-8x8-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-19T14:19:27Z | ---
tags:
- FrozenLake-v1-8x8-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-8x8-no_slippery
type: FrozenLake-v1-8x8-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="dbaibak/q-FrozenLake-v1-8x8-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"])
```
|
dbohle/ppo-Huggy | dbohle | 2022-12-19T14:19:09Z | 13 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
]
| reinforcement-learning | 2022-12-19T14:18:58Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: dbohle/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
ybutsik/Taxi-v3-test01 | ybutsik | 2022-12-19T14:05:57Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-19T14:05:45Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3-test01
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="ybutsik/Taxi-v3-test01", 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"])
```
|
ybutsik/q-FrozenLake-v1-4x4-noSlippery-test-01 | ybutsik | 2022-12-19T13:47:06Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-19T13:46:52Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery-test-01
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="ybutsik/q-FrozenLake-v1-4x4-noSlippery-test-01", 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"])
```
|
julien-rsbrg/q-Taxi-v3 | julien-rsbrg | 2022-12-19T13:42:50Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-19T13:42:46Z | ---
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: 7.52 +/- 2.67
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="julien-rsbrg/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"])
```
|
Boiler/ppo-LunarLander-v2 | Boiler | 2022-12-19T13:21:13Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-19T13:20:48Z | ---
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: 277.85 +/- 21.43
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
...
```
|
csikasote/whisper-small-nya | csikasote | 2022-12-19T13:14:48Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-19T08:22:21Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-small-nya
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-small-nya
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5086
- Wer: 27.5487
## 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: 2.5e-05
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.2671 | 0.99 | 500 | 0.5633 | 35.9244 |
| 0.1372 | 1.97 | 1000 | 0.4515 | 48.1630 |
| 0.0742 | 2.96 | 1500 | 0.4474 | 32.4985 |
| 0.0341 | 3.94 | 2000 | 0.4595 | 35.3574 |
| 0.0191 | 4.93 | 2500 | 0.4722 | 28.2930 |
| 0.0073 | 5.92 | 3000 | 0.4774 | 25.3633 |
| 0.0031 | 6.9 | 3500 | 0.4875 | 25.9539 |
| 0.0009 | 7.89 | 4000 | 0.4995 | 26.2611 |
| 0.0012 | 8.87 | 4500 | 0.5056 | 25.1861 |
| 0.0004 | 9.86 | 5000 | 0.5086 | 27.5487 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2
|
SiddharthaM/mbert-targin-final | SiddharthaM | 2022-12-19T13:13:29Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-12-19T12:32:35Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: mbert-targin-final
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. -->
# mbert-targin-final
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9847
- Accuracy: 0.7025
- Precision: 0.6490
- Recall: 0.6487
- F1: 0.6489
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log | 1.0 | 296 | 0.5774 | 0.7091 | 0.6506 | 0.6378 | 0.6426 |
| 0.5912 | 2.0 | 592 | 0.5316 | 0.7376 | 0.6880 | 0.6767 | 0.6814 |
| 0.5912 | 3.0 | 888 | 0.5511 | 0.7253 | 0.6692 | 0.6293 | 0.6378 |
| 0.4844 | 4.0 | 1184 | 0.6262 | 0.6835 | 0.6622 | 0.6884 | 0.6613 |
| 0.4844 | 5.0 | 1480 | 0.6320 | 0.7006 | 0.6574 | 0.6701 | 0.6616 |
| 0.3861 | 6.0 | 1776 | 0.6983 | 0.7148 | 0.6632 | 0.6620 | 0.6626 |
| 0.2773 | 7.0 | 2072 | 0.8109 | 0.7110 | 0.6630 | 0.6689 | 0.6655 |
| 0.2773 | 8.0 | 2368 | 0.8948 | 0.7072 | 0.6525 | 0.6487 | 0.6504 |
| 0.2068 | 9.0 | 2664 | 0.9693 | 0.7072 | 0.6519 | 0.6469 | 0.6492 |
| 0.2068 | 10.0 | 2960 | 0.9847 | 0.7025 | 0.6490 | 0.6487 | 0.6489 |
### Framework versions
- Transformers 4.24.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Tonjk/REPEAT_4wangchanberta-base-att-spm-uncased | Tonjk | 2022-12-19T13:11:16Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"camembert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2022-12-19T11:51:49Z | ---
tags:
- generated_from_trainer
model-index:
- name: REPEAT_4wangchanberta-base-att-spm-uncased
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. -->
# REPEAT_4wangchanberta-base-att-spm-uncased
This model is a fine-tuned version of [airesearch/wangchanberta-base-att-spm-uncased](https://huggingface.co/airesearch/wangchanberta-base-att-spm-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5948
## 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: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.5182 | 1.0 | 8561 | 0.3278 |
| 0.2837 | 2.0 | 17122 | 0.3973 |
| 0.2215 | 3.0 | 25683 | 0.5649 |
| 0.1851 | 4.0 | 34244 | 0.6375 |
| 0.1667 | 5.0 | 42805 | 0.5948 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.13.0+cu116
- Datasets 1.17.0
- Tokenizers 0.10.3
|
stabilityai/sd-vae-ft-mse-original | stabilityai | 2022-12-19T12:44:00Z | 6 | 1,344 | null | [
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:mit",
"region:us"
]
| text-to-image | 2022-10-13T09:51:18Z | ---
license: mit
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
inference: false
---
# Improved Autoencoders
## Utilizing
These weights are intended to be used with the original [CompVis Stable Diffusion codebase](https://github.com/CompVis/stable-diffusion). If you are looking for the model to use with the 🧨 diffusers library, [come here](https://huggingface.co/CompVis/stabilityai/sd-vae-ft-ema).
## Decoder Finetuning
We publish two kl-f8 autoencoder versions, finetuned from the original [kl-f8 autoencoder](https://github.com/CompVis/latent-diffusion#pretrained-autoencoding-models) on a 1:1 ratio of [LAION-Aesthetics](https://laion.ai/blog/laion-aesthetics/) and LAION-Humans, an unreleased subset containing only SFW images of humans. The intent was to fine-tune on the Stable Diffusion training set (the autoencoder was originally trained on OpenImages) but also enrich the dataset with images of humans to improve the reconstruction of faces.
The first, _ft-EMA_, was resumed from the original checkpoint, trained for 313198 steps and uses EMA weights. It uses the same loss configuration as the original checkpoint (L1 + LPIPS).
The second, _ft-MSE_, was resumed from _ft-EMA_ and uses EMA weights and was trained for another 280k steps using a different loss, with more emphasis
on MSE reconstruction (MSE + 0.1 * LPIPS). It produces somewhat ``smoother'' outputs. The batch size for both versions was 192 (16 A100s, batch size 12 per GPU).
To keep compatibility with existing models, only the decoder part was finetuned; the checkpoints can be used as a drop-in replacement for the existing autoencoder..
_Original kl-f8 VAE vs f8-ft-EMA vs f8-ft-MSE_
## Evaluation
### COCO 2017 (256x256, val, 5000 images)
| Model | train steps | rFID | PSNR | SSIM | PSIM | Link | Comments
|----------|---------|------|--------------|---------------|---------------|-----------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------|
| | | | | | | | |
| original | 246803 | 4.99 | 23.4 +/- 3.8 | 0.69 +/- 0.14 | 1.01 +/- 0.28 | https://ommer-lab.com/files/latent-diffusion/kl-f8.zip | as used in SD |
| ft-EMA | 560001 | 4.42 | 23.8 +/- 3.9 | 0.69 +/- 0.13 | 0.96 +/- 0.27 | https://huggingface.co/stabilityai/sd-vae-ft-ema-original/resolve/main/vae-ft-ema-560000-ema-pruned.ckpt | slightly better overall, with EMA |
| ft-MSE | 840001 | 4.70 | 24.5 +/- 3.7 | 0.71 +/- 0.13 | 0.92 +/- 0.27 | https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.ckpt | resumed with EMA from ft-EMA, emphasis on MSE (rec. loss = MSE + 0.1 * LPIPS), smoother outputs |
### LAION-Aesthetics 5+ (256x256, subset, 10000 images)
| Model | train steps | rFID | PSNR | SSIM | PSIM | Link | Comments
|----------|-----------|------|--------------|---------------|---------------|-----------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------|
| | | | | | | | |
| original | 246803 | 2.61 | 26.0 +/- 4.4 | 0.81 +/- 0.12 | 0.75 +/- 0.36 | https://ommer-lab.com/files/latent-diffusion/kl-f8.zip | as used in SD |
| ft-EMA | 560001 | 1.77 | 26.7 +/- 4.8 | 0.82 +/- 0.12 | 0.67 +/- 0.34 | https://huggingface.co/stabilityai/sd-vae-ft-ema-original/resolve/main/vae-ft-ema-560000-ema-pruned.ckpt | slightly better overall, with EMA |
| ft-MSE | 840001 | 1.88 | 27.3 +/- 4.7 | 0.83 +/- 0.11 | 0.65 +/- 0.34 | https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.ckpt | resumed with EMA from ft-EMA, emphasis on MSE (rec. loss = MSE + 0.1 * LPIPS), smoother outputs |
### Visual
_Visualization of reconstructions on 256x256 images from the COCO2017 validation dataset._
<p align="center">
<br>
<b>
256x256: ft-EMA (left), ft-MSE (middle), original (right)</b>
</p>
<p align="center">
<img src=https://huggingface.co/stabilityai/stable-diffusion-decoder-finetune/resolve/main/eval/ae-decoder-tuning-reconstructions/merged/00025_merged.png />
</p>
<p align="center">
<img src=https://huggingface.co/stabilityai/stable-diffusion-decoder-finetune/resolve/main/eval/ae-decoder-tuning-reconstructions/merged/00011_merged.png />
</p>
<p align="center">
<img src=https://huggingface.co/stabilityai/stable-diffusion-decoder-finetune/resolve/main/eval/ae-decoder-tuning-reconstructions/merged/00037_merged.png />
</p>
<p align="center">
<img src=https://huggingface.co/stabilityai/stable-diffusion-decoder-finetune/resolve/main/eval/ae-decoder-tuning-reconstructions/merged/00043_merged.png />
</p>
<p align="center">
<img src=https://huggingface.co/stabilityai/stable-diffusion-decoder-finetune/resolve/main/eval/ae-decoder-tuning-reconstructions/merged/00053_merged.png />
</p>
<p align="center">
<img src=https://huggingface.co/stabilityai/stable-diffusion-decoder-finetune/resolve/main/eval/ae-decoder-tuning-reconstructions/merged/00029_merged.png />
</p>
|
stabilityai/sd-vae-ft-ema-original | stabilityai | 2022-12-19T12:43:30Z | 0 | 156 | null | [
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:mit",
"region:us"
]
| text-to-image | 2022-10-13T03:55:36Z | ---
license: mit
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
inference: false
---
# Improved Autoencoders
## Utilizing
These weights are intended to be used with the original [CompVis Stable Diffusion codebase](https://github.com/CompVis/stable-diffusion). If you are looking for the model to use with the 🧨 diffusers library, [come here](https://huggingface.co/CompVis/stabilityai/sd-vae-ft-ema).
## Decoder Finetuning
We publish two kl-f8 autoencoder versions, finetuned from the original [kl-f8 autoencoder](https://github.com/CompVis/latent-diffusion#pretrained-autoencoding-models) on a 1:1 ratio of [LAION-Aesthetics](https://laion.ai/blog/laion-aesthetics/) and LAION-Humans, an unreleased subset containing only SFW images of humans. The intent was to fine-tune on the Stable Diffusion training set (the autoencoder was originally trained on OpenImages) but also enrich the dataset with images of humans to improve the reconstruction of faces.
The first, _ft-EMA_, was resumed from the original checkpoint, trained for 313198 steps and uses EMA weights. It uses the same loss configuration as the original checkpoint (L1 + LPIPS).
The second, _ft-MSE_, was resumed from _ft-EMA_ and uses EMA weights and was trained for another 280k steps using a different loss, with more emphasis
on MSE reconstruction (MSE + 0.1 * LPIPS). It produces somewhat ``smoother'' outputs. The batch size for both versions was 192 (16 A100s, batch size 12 per GPU).
To keep compatibility with existing models, only the decoder part was finetuned; the checkpoints can be used as a drop-in replacement for the existing autoencoder.
_Original kl-f8 VAE vs f8-ft-EMA vs f8-ft-MSE_
## Evaluation
### COCO 2017 (256x256, val, 5000 images)
| Model | train steps | rFID | PSNR | SSIM | PSIM | Link | Comments
|----------|---------|------|--------------|---------------|---------------|-----------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------|
| | | | | | | | |
| original | 246803 | 4.99 | 23.4 +/- 3.8 | 0.69 +/- 0.14 | 1.01 +/- 0.28 | https://ommer-lab.com/files/latent-diffusion/kl-f8.zip | as used in SD |
| ft-EMA | 560001 | 4.42 | 23.8 +/- 3.9 | 0.69 +/- 0.13 | 0.96 +/- 0.27 | https://huggingface.co/stabilityai/sd-vae-ft-ema-original/resolve/main/vae-ft-ema-560000-ema-pruned.ckpt | slightly better overall, with EMA |
| ft-MSE | 840001 | 4.70 | 24.5 +/- 3.7 | 0.71 +/- 0.13 | 0.92 +/- 0.27 | https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.ckpt | resumed with EMA from ft-EMA, emphasis on MSE (rec. loss = MSE + 0.1 * LPIPS), smoother outputs |
### LAION-Aesthetics 5+ (256x256, subset, 10000 images)
| Model | train steps | rFID | PSNR | SSIM | PSIM | Link | Comments
|----------|-----------|------|--------------|---------------|---------------|-----------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------|
| | | | | | | | |
| original | 246803 | 2.61 | 26.0 +/- 4.4 | 0.81 +/- 0.12 | 0.75 +/- 0.36 | https://ommer-lab.com/files/latent-diffusion/kl-f8.zip | as used in SD |
| ft-EMA | 560001 | 1.77 | 26.7 +/- 4.8 | 0.82 +/- 0.12 | 0.67 +/- 0.34 | https://huggingface.co/stabilityai/sd-vae-ft-ema-original/resolve/main/vae-ft-ema-560000-ema-pruned.ckpt | slightly better overall, with EMA |
| ft-MSE | 840001 | 1.88 | 27.3 +/- 4.7 | 0.83 +/- 0.11 | 0.65 +/- 0.34 | https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.ckpt | resumed with EMA from ft-EMA, emphasis on MSE (rec. loss = MSE + 0.1 * LPIPS), smoother outputs |
### Visual
_Visualization of reconstructions on 256x256 images from the COCO2017 validation dataset._
<p align="center">
<br>
<b>
256x256: ft-EMA (left), ft-MSE (middle), original (right)</b>
</p>
<p align="center">
<img src=https://huggingface.co/stabilityai/stable-diffusion-decoder-finetune/resolve/main/eval/ae-decoder-tuning-reconstructions/merged/00025_merged.png />
</p>
<p align="center">
<img src=https://huggingface.co/stabilityai/stable-diffusion-decoder-finetune/resolve/main/eval/ae-decoder-tuning-reconstructions/merged/00011_merged.png />
</p>
<p align="center">
<img src=https://huggingface.co/stabilityai/stable-diffusion-decoder-finetune/resolve/main/eval/ae-decoder-tuning-reconstructions/merged/00037_merged.png />
</p>
<p align="center">
<img src=https://huggingface.co/stabilityai/stable-diffusion-decoder-finetune/resolve/main/eval/ae-decoder-tuning-reconstructions/merged/00043_merged.png />
</p>
<p align="center">
<img src=https://huggingface.co/stabilityai/stable-diffusion-decoder-finetune/resolve/main/eval/ae-decoder-tuning-reconstructions/merged/00053_merged.png />
</p>
<p align="center">
<img src=https://huggingface.co/stabilityai/stable-diffusion-decoder-finetune/resolve/main/eval/ae-decoder-tuning-reconstructions/merged/00029_merged.png />
</p>
|
Jaewan/wav2vec2-common_voice-tr-demo | Jaewan | 2022-12-19T12:25:28Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-19T10:32:22Z | ---
language:
- tr
license: apache-2.0
tags:
- automatic-speech-recognition
- common_voice
- generated_from_trainer
datasets:
- common_voice
metrics:
- wer
model-index:
- name: wav2vec2-common_voice-tr-demo
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: COMMON_VOICE - TR
type: common_voice
config: tr
split: test
args: 'Config: tr, Training split: train+validation, Eval split: test'
metrics:
- name: Wer
type: wer
value: 0.3446021856807272
---
<!-- 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-common_voice-tr-demo
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the COMMON_VOICE - TR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3794
- Wer: 0.3446
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 0.92 | 100 | 3.5956 | 1.0 |
| No log | 1.83 | 200 | 3.0269 | 0.9999 |
| No log | 2.75 | 300 | 0.9827 | 0.8111 |
| No log | 3.67 | 400 | 0.6236 | 0.6304 |
| 3.1866 | 4.59 | 500 | 0.5016 | 0.5264 |
| 3.1866 | 5.5 | 600 | 0.4523 | 0.4935 |
| 3.1866 | 6.42 | 700 | 0.4306 | 0.4528 |
| 3.1866 | 7.34 | 800 | 0.4328 | 0.4329 |
| 3.1866 | 8.26 | 900 | 0.4026 | 0.4105 |
| 0.227 | 9.17 | 1000 | 0.4096 | 0.4080 |
| 0.227 | 10.09 | 1100 | 0.3921 | 0.3915 |
| 0.227 | 11.01 | 1200 | 0.3830 | 0.3778 |
| 0.227 | 11.93 | 1300 | 0.3846 | 0.3616 |
| 0.227 | 12.84 | 1400 | 0.3888 | 0.3619 |
| 0.1046 | 13.76 | 1500 | 0.3861 | 0.3509 |
| 0.1046 | 14.68 | 1600 | 0.3798 | 0.3455 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.12.0+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2
|
SiddharthaM/mbert-profane-final | SiddharthaM | 2022-12-19T12:11:59Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-12-19T11:35:00Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: mbert-profane-final
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. -->
# mbert-profane-final
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4464
- Accuracy: 0.8983
- Precision: 0.8135
- Recall: 0.8120
- F1: 0.8128
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log | 1.0 | 296 | 0.2313 | 0.9154 | 0.8687 | 0.8010 | 0.8294 |
| 0.3077 | 2.0 | 592 | 0.2223 | 0.9125 | 0.8473 | 0.8205 | 0.8330 |
| 0.3077 | 3.0 | 888 | 0.2137 | 0.9259 | 0.8784 | 0.8379 | 0.8563 |
| 0.2102 | 4.0 | 1184 | 0.2334 | 0.9163 | 0.8483 | 0.8417 | 0.8449 |
| 0.2102 | 5.0 | 1480 | 0.2737 | 0.9068 | 0.8305 | 0.8242 | 0.8273 |
| 0.1533 | 6.0 | 1776 | 0.3214 | 0.8964 | 0.8034 | 0.8510 | 0.8239 |
| 0.1092 | 7.0 | 2072 | 0.3409 | 0.9002 | 0.8115 | 0.8414 | 0.8252 |
| 0.1092 | 8.0 | 2368 | 0.3849 | 0.9049 | 0.8322 | 0.8066 | 0.8185 |
| 0.0775 | 9.0 | 2664 | 0.4408 | 0.8983 | 0.8113 | 0.8215 | 0.8162 |
| 0.0775 | 10.0 | 2960 | 0.4464 | 0.8983 | 0.8135 | 0.8120 | 0.8128 |
### Framework versions
- Transformers 4.24.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.6.1
- Tokenizers 0.13.1
|
IngoTB303/PPO-LunarLander-v2 | IngoTB303 | 2022-12-19T12:00:28Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-19T12:00:02Z | ---
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: 255.51 +/- 21.94
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
...
```
|
LukeSajkowski/q-Taxi-v3 | LukeSajkowski | 2022-12-19T12:00:28Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-19T11:40:49Z | ---
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="lukee/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"])
```
|
phqlong/ppo-LunarLander-v2 | phqlong | 2022-12-19T11:58:01Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-19T11:57:39Z | ---
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: 272.06 +/- 16.48
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
malmarz/whisper_medium_s20K_b64_nofreeze_mgb2cv11 | malmarz | 2022-12-19T11:39:41Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-18T11:01:12Z | # whisper_sprint
## training
```bash
git clone https://github.com/ARBML/whisper_sprint
cd whisper_sprint
```
Then setup the enviornment
```bash
bash setup_env.sh
```
Then setup the libraries, this will install transofrmers, etc. and create a directory in the hub for training the model ...
```bash
bash setup_libs.sh HF_USER_NAME MODEL_NAME
```
After that, you can run training by
```
cd MODEL_NAME
bash run_mgb2.sh
```
You can also run with deepspeed wich allows running whisper-large v2 with batch size 32 on A100
```
bash run_mgb2_deepspeed.sh
```
## Evaluation
### Evaluation on Fleurs
```
bash run_eval_fleurs.sh MODEL_NAME
```
### Evaluation on Common Voice 11
```
bash run_eval_cv_11.sh MODEL_NAME
```
evaluate on common voice 11
```bash
bash run_eval_cv_11.sh HF_USER_NAME/MODEL_NAME
```
evaluate on Fleurs
```bash
bash run_eval_fleurs.sh HF_USER_NAME/MODEL_NAME
```
## Preparing the MGB2 data
While MGB2 dataset contains a richly transcribed speech dataset, the wav files were too lengthy to be used to train the whisper model. Therefore, we had to split the wave file and still maintain the correct correspondence with the transcribed text.
MGB2 provides and XML file corresponding to every wav file, which contains the transcribed sentences and the start and end time of each sentence in the recording. Using the `split_xml_mgb2.py`, we start with the xml file and split the lengthy wav files into smaller ones that are shorter than 30 seconds in length, as required to fine-tune whisper. The operation produced over 370K sentences with their corresponding wav files.
## Hosting on HuggingFace (Privately)
To host mgb2 at HF, at least 3 things need to happen:
1. Create the dataset repository on HF. This was created privately at arbml/mgb2_speech for the dataset
2. Data must be hosted somewhere or uploaded to HF repo
3. HF loading script must be written so the data can be integrated into the HF hub.
### Uploading the data
The dataset was >100Gb in size. HF utilizes git lfs to host large files. However, git lfs has a max limit of 5gb size for any file. Uploading over 370K individual files was also not feasible and caused issues with git.
Therefore, the solution was to archive groups of wav files together into sequentially numbered archive files, such that the archive file is no bigger than 5GB. To achieve that, the wav files were grouped based on the first 2 letters of the file name. The naming scheme seems to use a base64 encoding. So, characters would be 0 to 9 or A to F. The files were grouped as follows:
| First 2 Letters | Archive Number |
|:-:|---|
| 00-05 | 0 |
| 06-09 | 1 |
| 0A-0F | 2 |
| 10-15 | 3 |
| 16-19 | 4 |
| 1A-1F | 5 |
| ... | ... |
| F0-F5 | 45 |
| F6-F9 | 46 |
| FA-FF | 47 |
Only the training data was split using this scheme, the test and validation data was smaller than 5GB when archived.
### HF Data Loading Script
The loading script determines the features of the data based on split and selected configuration. We had test, dev, and train split with a single language configuration. Using the _generate_example function, the script is used by GH to correctly produce the associated transcript and wav files. The function works as follows:
1. Go through all the entries in the archive containing the text transcripts and create a map where the name of the file (the 64base encoded one) is used as the key and the transcript at the value
2. Iterate through all the wav files in all the archive, and for every wav file, get the corresponding transcript from the map constructed in previous step (using the file name) and yield the wav file, transcript, and path to the wav file
|
tomekkorbak/serene_hawking | tomekkorbak | 2022-12-19T11:35:19Z | 0 | 0 | null | [
"generated_from_trainer",
"en",
"dataset:tomekkorbak/pii-pile-chunk3-0-50000",
"dataset:tomekkorbak/pii-pile-chunk3-50000-100000",
"dataset:tomekkorbak/pii-pile-chunk3-100000-150000",
"dataset:tomekkorbak/pii-pile-chunk3-150000-200000",
"dataset:tomekkorbak/pii-pile-chunk3-200000-250000",
"dataset:tomekkorbak/pii-pile-chunk3-250000-300000",
"dataset:tomekkorbak/pii-pile-chunk3-300000-350000",
"dataset:tomekkorbak/pii-pile-chunk3-350000-400000",
"dataset:tomekkorbak/pii-pile-chunk3-400000-450000",
"dataset:tomekkorbak/pii-pile-chunk3-450000-500000",
"dataset:tomekkorbak/pii-pile-chunk3-500000-550000",
"dataset:tomekkorbak/pii-pile-chunk3-550000-600000",
"dataset:tomekkorbak/pii-pile-chunk3-600000-650000",
"dataset:tomekkorbak/pii-pile-chunk3-650000-700000",
"dataset:tomekkorbak/pii-pile-chunk3-700000-750000",
"dataset:tomekkorbak/pii-pile-chunk3-750000-800000",
"dataset:tomekkorbak/pii-pile-chunk3-800000-850000",
"dataset:tomekkorbak/pii-pile-chunk3-850000-900000",
"dataset:tomekkorbak/pii-pile-chunk3-900000-950000",
"dataset:tomekkorbak/pii-pile-chunk3-950000-1000000",
"dataset:tomekkorbak/pii-pile-chunk3-1000000-1050000",
"dataset:tomekkorbak/pii-pile-chunk3-1050000-1100000",
"dataset:tomekkorbak/pii-pile-chunk3-1100000-1150000",
"dataset:tomekkorbak/pii-pile-chunk3-1150000-1200000",
"dataset:tomekkorbak/pii-pile-chunk3-1200000-1250000",
"dataset:tomekkorbak/pii-pile-chunk3-1250000-1300000",
"dataset:tomekkorbak/pii-pile-chunk3-1300000-1350000",
"dataset:tomekkorbak/pii-pile-chunk3-1350000-1400000",
"dataset:tomekkorbak/pii-pile-chunk3-1400000-1450000",
"dataset:tomekkorbak/pii-pile-chunk3-1450000-1500000",
"dataset:tomekkorbak/pii-pile-chunk3-1500000-1550000",
"dataset:tomekkorbak/pii-pile-chunk3-1550000-1600000",
"dataset:tomekkorbak/pii-pile-chunk3-1600000-1650000",
"dataset:tomekkorbak/pii-pile-chunk3-1650000-1700000",
"dataset:tomekkorbak/pii-pile-chunk3-1700000-1750000",
"dataset:tomekkorbak/pii-pile-chunk3-1750000-1800000",
"dataset:tomekkorbak/pii-pile-chunk3-1800000-1850000",
"dataset:tomekkorbak/pii-pile-chunk3-1850000-1900000",
"dataset:tomekkorbak/pii-pile-chunk3-1900000-1950000",
"license:mit",
"region:us"
]
| null | 2022-12-19T11:35:11Z | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- tomekkorbak/pii-pile-chunk3-0-50000
- tomekkorbak/pii-pile-chunk3-50000-100000
- tomekkorbak/pii-pile-chunk3-100000-150000
- tomekkorbak/pii-pile-chunk3-150000-200000
- tomekkorbak/pii-pile-chunk3-200000-250000
- tomekkorbak/pii-pile-chunk3-250000-300000
- tomekkorbak/pii-pile-chunk3-300000-350000
- tomekkorbak/pii-pile-chunk3-350000-400000
- tomekkorbak/pii-pile-chunk3-400000-450000
- tomekkorbak/pii-pile-chunk3-450000-500000
- tomekkorbak/pii-pile-chunk3-500000-550000
- tomekkorbak/pii-pile-chunk3-550000-600000
- tomekkorbak/pii-pile-chunk3-600000-650000
- tomekkorbak/pii-pile-chunk3-650000-700000
- tomekkorbak/pii-pile-chunk3-700000-750000
- tomekkorbak/pii-pile-chunk3-750000-800000
- tomekkorbak/pii-pile-chunk3-800000-850000
- tomekkorbak/pii-pile-chunk3-850000-900000
- tomekkorbak/pii-pile-chunk3-900000-950000
- tomekkorbak/pii-pile-chunk3-950000-1000000
- tomekkorbak/pii-pile-chunk3-1000000-1050000
- tomekkorbak/pii-pile-chunk3-1050000-1100000
- tomekkorbak/pii-pile-chunk3-1100000-1150000
- tomekkorbak/pii-pile-chunk3-1150000-1200000
- tomekkorbak/pii-pile-chunk3-1200000-1250000
- tomekkorbak/pii-pile-chunk3-1250000-1300000
- tomekkorbak/pii-pile-chunk3-1300000-1350000
- tomekkorbak/pii-pile-chunk3-1350000-1400000
- tomekkorbak/pii-pile-chunk3-1400000-1450000
- tomekkorbak/pii-pile-chunk3-1450000-1500000
- tomekkorbak/pii-pile-chunk3-1500000-1550000
- tomekkorbak/pii-pile-chunk3-1550000-1600000
- tomekkorbak/pii-pile-chunk3-1600000-1650000
- tomekkorbak/pii-pile-chunk3-1650000-1700000
- tomekkorbak/pii-pile-chunk3-1700000-1750000
- tomekkorbak/pii-pile-chunk3-1750000-1800000
- tomekkorbak/pii-pile-chunk3-1800000-1850000
- tomekkorbak/pii-pile-chunk3-1850000-1900000
- tomekkorbak/pii-pile-chunk3-1900000-1950000
model-index:
- name: serene_hawking
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. -->
# serene_hawking
This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk3-250000-300000, the tomekkorbak/pii-pile-chunk3-300000-350000, the tomekkorbak/pii-pile-chunk3-350000-400000, the tomekkorbak/pii-pile-chunk3-400000-450000, the tomekkorbak/pii-pile-chunk3-450000-500000, the tomekkorbak/pii-pile-chunk3-500000-550000, the tomekkorbak/pii-pile-chunk3-550000-600000, the tomekkorbak/pii-pile-chunk3-600000-650000, the tomekkorbak/pii-pile-chunk3-650000-700000, the tomekkorbak/pii-pile-chunk3-700000-750000, the tomekkorbak/pii-pile-chunk3-750000-800000, the tomekkorbak/pii-pile-chunk3-800000-850000, the tomekkorbak/pii-pile-chunk3-850000-900000, the tomekkorbak/pii-pile-chunk3-900000-950000, the tomekkorbak/pii-pile-chunk3-950000-1000000, the tomekkorbak/pii-pile-chunk3-1000000-1050000, the tomekkorbak/pii-pile-chunk3-1050000-1100000, the tomekkorbak/pii-pile-chunk3-1100000-1150000, the tomekkorbak/pii-pile-chunk3-1150000-1200000, the tomekkorbak/pii-pile-chunk3-1200000-1250000, the tomekkorbak/pii-pile-chunk3-1250000-1300000, the tomekkorbak/pii-pile-chunk3-1300000-1350000, the tomekkorbak/pii-pile-chunk3-1350000-1400000, the tomekkorbak/pii-pile-chunk3-1400000-1450000, the tomekkorbak/pii-pile-chunk3-1450000-1500000, the tomekkorbak/pii-pile-chunk3-1500000-1550000, the tomekkorbak/pii-pile-chunk3-1550000-1600000, the tomekkorbak/pii-pile-chunk3-1600000-1650000, the tomekkorbak/pii-pile-chunk3-1650000-1700000, the tomekkorbak/pii-pile-chunk3-1700000-1750000, the tomekkorbak/pii-pile-chunk3-1750000-1800000, the tomekkorbak/pii-pile-chunk3-1800000-1850000, the tomekkorbak/pii-pile-chunk3-1850000-1900000 and the tomekkorbak/pii-pile-chunk3-1900000-1950000 datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- 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: 0.01
- training_steps: 12588
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.24.0
- Pytorch 1.11.0+cu113
- Datasets 2.5.1
- Tokenizers 0.11.6
# Full config
{'dataset': {'datasets': ['tomekkorbak/pii-pile-chunk3-0-50000',
'tomekkorbak/pii-pile-chunk3-50000-100000',
'tomekkorbak/pii-pile-chunk3-100000-150000',
'tomekkorbak/pii-pile-chunk3-150000-200000',
'tomekkorbak/pii-pile-chunk3-200000-250000',
'tomekkorbak/pii-pile-chunk3-250000-300000',
'tomekkorbak/pii-pile-chunk3-300000-350000',
'tomekkorbak/pii-pile-chunk3-350000-400000',
'tomekkorbak/pii-pile-chunk3-400000-450000',
'tomekkorbak/pii-pile-chunk3-450000-500000',
'tomekkorbak/pii-pile-chunk3-500000-550000',
'tomekkorbak/pii-pile-chunk3-550000-600000',
'tomekkorbak/pii-pile-chunk3-600000-650000',
'tomekkorbak/pii-pile-chunk3-650000-700000',
'tomekkorbak/pii-pile-chunk3-700000-750000',
'tomekkorbak/pii-pile-chunk3-750000-800000',
'tomekkorbak/pii-pile-chunk3-800000-850000',
'tomekkorbak/pii-pile-chunk3-850000-900000',
'tomekkorbak/pii-pile-chunk3-900000-950000',
'tomekkorbak/pii-pile-chunk3-950000-1000000',
'tomekkorbak/pii-pile-chunk3-1000000-1050000',
'tomekkorbak/pii-pile-chunk3-1050000-1100000',
'tomekkorbak/pii-pile-chunk3-1100000-1150000',
'tomekkorbak/pii-pile-chunk3-1150000-1200000',
'tomekkorbak/pii-pile-chunk3-1200000-1250000',
'tomekkorbak/pii-pile-chunk3-1250000-1300000',
'tomekkorbak/pii-pile-chunk3-1300000-1350000',
'tomekkorbak/pii-pile-chunk3-1350000-1400000',
'tomekkorbak/pii-pile-chunk3-1400000-1450000',
'tomekkorbak/pii-pile-chunk3-1450000-1500000',
'tomekkorbak/pii-pile-chunk3-1500000-1550000',
'tomekkorbak/pii-pile-chunk3-1550000-1600000',
'tomekkorbak/pii-pile-chunk3-1600000-1650000',
'tomekkorbak/pii-pile-chunk3-1650000-1700000',
'tomekkorbak/pii-pile-chunk3-1700000-1750000',
'tomekkorbak/pii-pile-chunk3-1750000-1800000',
'tomekkorbak/pii-pile-chunk3-1800000-1850000',
'tomekkorbak/pii-pile-chunk3-1850000-1900000',
'tomekkorbak/pii-pile-chunk3-1900000-1950000'],
'filter_threshold': 0.000286,
'is_split_by_sentences': True,
'skip_tokens': 1649999872},
'generation': {'force_call_on': [25177],
'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}],
'scenario_configs': [{'generate_kwargs': {'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_samples': 2048}],
'scorer_config': {}},
'kl_gpt3_callback': {'force_call_on': [25177],
'max_tokens': 64,
'num_samples': 4096},
'model': {'from_scratch': False,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'model_kwargs': {'revision': '9e6c78543a6ff1e4089002c38864d5a9cf71ec90'},
'path_or_name': 'tomekkorbak/nervous_wozniak'},
'objective': {'name': 'MLE'},
'tokenizer': {'path_or_name': 'gpt2'},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 128,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'serene_hawking',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.0001,
'logging_first_step': True,
'logging_steps': 1,
'num_tokens': 3300000000,
'output_dir': 'training_output2',
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 25177,
'save_strategy': 'steps',
'seed': 42,
'tokens_already_seen': 1649999872,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/tomekkorbak/apo/runs/1cy05tyt |
mpheng/q-FrozenLake-v1-4x4-noSlippery | mpheng | 2022-12-19T11:30:39Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-19T11:30:35Z | ---
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="mpheng/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"])
```
|
emilios/whisper-md-sr | emilios | 2022-12-19T11:06:44Z | 12 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"sr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-18T22:26:28Z | ---
language:
- sr
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0,google/fleurs
metrics:
- wer
model-index:
- name: Whisper medium Serbian El Greco
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0,google/fleurs sr,sr_rs
type: mozilla-foundation/common_voice_11_0,google/fleurs
config: sr
split: None
metrics:
- name: Wer
type: wer
value: 12.140833670578713
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper medium Serbian El Greco
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0,google/fleurs sr,sr_rs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4868
- Wer: 12.1408
## 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-06
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.0222 | 2.72 | 1000 | 0.3442 | 14.0834 |
| 0.0032 | 5.43 | 2000 | 0.4106 | 14.5285 |
| 0.0011 | 8.15 | 3000 | 0.4331 | 12.8693 |
| 0.0029 | 10.87 | 4000 | 0.3948 | 12.6265 |
| 0.0012 | 13.59 | 5000 | 0.4512 | 12.6669 |
| 0.0009 | 16.3 | 6000 | 0.4890 | 12.7479 |
| 0.001 | 19.02 | 7000 | 0.4868 | 12.1408 |
| 0.0016 | 21.74 | 8000 | 0.4780 | 12.7074 |
| 0.0002 | 24.46 | 9000 | 0.4902 | 12.2218 |
| 0.0012 | 27.17 | 10000 | 0.5059 | 12.6669 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 2.0.0.dev20221216+cu116
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
FBM/q-FrozenLake-v1-4x4-noSlippery | FBM | 2022-12-19T10:53:07Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-19T10:53:03Z | ---
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="FBM/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"])
```
|
geninhu/whisper-medium-az | geninhu | 2022-12-19T10:46:00Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"az",
"dataset:mozilla-foundation/common_voice_11_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-19T05:16:07Z | ---
language:
- az
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Medium Azerbaijani
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0 az
type: mozilla-foundation/common_voice_11_0
config: az
split: test
args: az
metrics:
- name: Wer
type: wer
value: 47.337278106508876
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Medium Azerbaijani
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0 az dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7751
- Wer: 47.3373
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.0 | 499.0 | 1000 | 0.7751 | 47.3373 |
| 0.0 | 999.0 | 2000 | 0.8982 | 47.3373 |
| 0.0 | 1499.0 | 3000 | 0.9612 | 47.9290 |
| 0.0 | 1999.0 | 4000 | 1.0112 | 47.9290 |
| 0.0 | 2499.0 | 5000 | 1.0212 | 47.9290 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
tolerantpancake/LysergianDreams | tolerantpancake | 2022-12-19T10:02:54Z | 0 | 22 | null | [
"region:us"
]
| null | 2022-12-19T10:01:36Z | For all your psychadelic desires ;) |
Tonjk/REPEAT_3wangchanberta-base-att-spm-uncased | Tonjk | 2022-12-19T09:44:49Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"camembert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2022-12-19T08:30:22Z | ---
tags:
- generated_from_trainer
model-index:
- name: REPEAT_3wangchanberta-base-att-spm-uncased
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. -->
# REPEAT_3wangchanberta-base-att-spm-uncased
This model is a fine-tuned version of [airesearch/wangchanberta-base-att-spm-uncased](https://huggingface.co/airesearch/wangchanberta-base-att-spm-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2267
## 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: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.4146 | 1.0 | 8561 | 0.6854 |
| 0.1716 | 2.0 | 17122 | 1.2020 |
| 0.1353 | 3.0 | 25683 | 2.0329 |
| 0.115 | 4.0 | 34244 | 2.4918 |
| 0.1031 | 5.0 | 42805 | 2.2267 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.13.0+cu116
- Datasets 1.17.0
- Tokenizers 0.10.3
|
fengi/bert-finetuned-ner | fengi | 2022-12-19T09:39:33Z | 12 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2022-12-19T09:18:44Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: train
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9320244184128031
- name: Recall
type: recall
value: 0.9506900033658701
- name: F1
type: f1
value: 0.9412646838290426
- name: Accuracy
type: accuracy
value: 0.9867398598928593
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0598
- Precision: 0.9320
- Recall: 0.9507
- F1: 0.9413
- Accuracy: 0.9867
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0873 | 1.0 | 1756 | 0.0708 | 0.9148 | 0.9320 | 0.9233 | 0.9821 |
| 0.0334 | 2.0 | 3512 | 0.0648 | 0.9270 | 0.9485 | 0.9376 | 0.9860 |
| 0.0181 | 3.0 | 5268 | 0.0598 | 0.9320 | 0.9507 | 0.9413 | 0.9867 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2
|
Tritkoman/EnglishtoAncientGreek | Tritkoman | 2022-12-19T09:29:28Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"autotrain",
"translation",
"en",
"de",
"dataset:Tritkoman/autotrain-data-kskskkw",
"doi:10.57967/hf/0205",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
]
| translation | 2022-10-07T04:44:04Z | ---
tags:
- autotrain
- translation
language:
- en
- de
datasets:
- Tritkoman/autotrain-data-kskskkw
co2_eq_emissions:
emissions: 45.2679908890355
---
# Model Trained Using AutoTrain
- Problem type: Translation
- Model ID: 1684859425
- CO2 Emissions (in grams): 45.2680
## Validation Metrics
- Loss: 2.056
- SacreBLEU: 6.077
- Gen len: 15.482 |
sidxxdu/DialoGPT-small-Ben14 | sidxxdu | 2022-12-19T09:20:58Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2022-12-19T08:55:31Z | --
tags:
- conversational
--
#Ben14 DialoGPT Model |
mitchelldehaven/whisper-medium-uk | mitchelldehaven | 2022-12-19T09:05:50Z | 28 | 0 | transformers | [
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-19T02:54:26Z | ---
model-index:
- name: whisper-medium-uk
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: mozilla-foundation/common_voice_11_0
type: mozilla-foundation/common_voice_11_0
config: uk
split: test
metrics:
- type: wer
value: 14.55
name: WER
tags:
- whisper-event
---
Whisper model finetuned using audio data from CommonVoice Ukrainian v10 train and dev set with additional data via semi-supervised data.
There is a differences in tokenization of source data (in our data normalization process, we replace punctucation with "" rather than Whisper's " "). This mismatch leads to a slight degradation on CommonVoice.
|
itdes/ITRobo2022 | itdes | 2022-12-19T08:59:33Z | 0 | 3 | null | [
"doi:10.57967/hf/0215",
"license:openrail",
"region:us"
]
| null | 2022-12-16T15:49:14Z | ---
license: openrail
---
<h1>ITRobo2022 model. Trained on SD 1.5.</h1>
<br>
I really like the Robo-Diffusion model (https://huggingface.co/nousr/robo-diffusion), but most of what you can get with it is robot heads. :)<br>
In my model I tried to emphasize full-length images of robots. I also get good results on a homogeneous background, which makes it easier to cut out objects for further work.<br>
However, good results are also obtained with mixed queries. Try it. Good luck!
!!! For best result use this token at the beginning of the prompt: <b>itrobo2022</b><br><br>
<b>itrobo2022.ckpt</b> - base trained model. It's a little hard to control the result, but good for generating a variety of robots, and for working with img2img.<br>
<b>itrobo2022-40-with-v1-5-pruned-emaonly-60.ckpt</b> - 40% mixed with base SD1.5. Better manageability and control of results.
<b>Example:</b><br>
Prompt: <i>ITRobo2022 (a full body photo of pug)+, isolated, high resolution photo, cinematic lighting, trending on artstation, DOF, high resolution, 4 k, 8 k, solid background</i><br>
Negative prompt: <i>(duplicate)+++, deformed, no leg, blurry, no head, headless, watermarks, writings, text, marks, ugly, a lot of fingers, mutation, too many legs</i><br>
<br>
Prompt: <i>A realistic photograph of a 3d robot in a modern city. A glossy white and orange robot.</i><br>
Negative prompt: <i>black and white robot, picture frame, a children's drawing in crayon. #Wholesale, Abstract Metal Sculpture. i'm leaving a bad review.</i><br>
Best results on:<br>
DDIM<br>
steps:20<br>
CFG scale 7<br>
512x512
<br>
img2img:<br>
<br>
NSFW :)<br>
 |
philschmid/layoutlm-funsd | philschmid | 2022-12-19T08:51:49Z | 190 | 2 | generic | [
"generic",
"pytorch",
"tensorboard",
"layoutlm",
"generated_from_trainer",
"endpoints-template",
"other",
"dataset:funsd",
"endpoints_compatible",
"region:us"
]
| other | 2022-10-04T12:25:48Z | ---
tags:
- generated_from_trainer
- endpoints-template
library_name: generic
datasets:
- funsd
model-index:
- name: layoutlm-funsd
results: []
pipeline_tag: other
---
<!-- 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. -->
# layoutlm-funsd
This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0045
- Answer: {'precision': 0.7348314606741573, 'recall': 0.8084054388133498, 'f1': 0.7698646262507357, 'number': 809}
- Header: {'precision': 0.44285714285714284, 'recall': 0.5210084033613446, 'f1': 0.47876447876447875, 'number': 119}
- Question: {'precision': 0.8211009174311926, 'recall': 0.8403755868544601, 'f1': 0.8306264501160092, 'number': 1065}
- Overall Precision: 0.7599
- Overall Recall: 0.8083
- Overall F1: 0.7866
- Overall Accuracy: 0.8106
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
## Deploy Model with Inference Endpoints
Before we can get started, make sure you meet all of the following requirements:
1. An Organization/User with an active plan and *WRITE* access to the model repository.
2. Can access the UI: [https://ui.endpoints.huggingface.co](https://ui.endpoints.huggingface.co/endpoints)
### 1. Deploy LayoutLM and Send requests
In this tutorial, you will learn how to deploy a [LayoutLM](https://huggingface.co/docs/transformers/model_doc/layoutlm) to [Hugging Face Inference Endpoints](https://huggingface.co/inference-endpoints) and how you can integrate it via an API into your products.
This tutorial is not covering how you create the custom handler for inference. If you want to learn how to create a custom Handler for Inference Endpoints, you can either checkout the [documentation](https://huggingface.co/docs/inference-endpoints/guides/custom_handler) or go through [“Custom Inference with Hugging Face Inference Endpoints”](https://www.philschmid.de/custom-inference-handler)
We are going to deploy [philschmid/layoutlm-funsd](https://huggingface.co/philschmid/layoutlm-funsd) which implements the following `handler.py`
```python
from typing import Dict, List, Any
from transformers import LayoutLMForTokenClassification, LayoutLMv2Processor
import torch
from subprocess import run
# install tesseract-ocr and pytesseract
run("apt install -y tesseract-ocr", shell=True, check=True)
run("pip install pytesseract", shell=True, check=True)
# helper function to unnormalize bboxes for drawing onto the image
def unnormalize_box(bbox, width, height):
return [
width * (bbox[0] / 1000),
height * (bbox[1] / 1000),
width * (bbox[2] / 1000),
height * (bbox[3] / 1000),
]
# set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class EndpointHandler:
def __init__(self, path=""):
# load model and processor from path
self.model = LayoutLMForTokenClassification.from_pretrained(path).to(device)
self.processor = LayoutLMv2Processor.from_pretrained(path)
def __call__(self, data: Dict[str, bytes]) -> Dict[str, List[Any]]:
"""
Args:
data (:obj:):
includes the deserialized image file as PIL.Image
"""
# process input
image = data.pop("inputs", data)
# process image
encoding = self.processor(image, return_tensors="pt")
# run prediction
with torch.inference_mode():
outputs = self.model(
input_ids=encoding.input_ids.to(device),
bbox=encoding.bbox.to(device),
attention_mask=encoding.attention_mask.to(device),
token_type_ids=encoding.token_type_ids.to(device),
)
predictions = outputs.logits.softmax(-1)
# post process output
result = []
for item, inp_ids, bbox in zip(
predictions.squeeze(0).cpu(), encoding.input_ids.squeeze(0).cpu(), encoding.bbox.squeeze(0).cpu()
):
label = self.model.config.id2label[int(item.argmax().cpu())]
if label == "O":
continue
score = item.max().item()
text = self.processor.tokenizer.decode(inp_ids)
bbox = unnormalize_box(bbox.tolist(), image.width, image.height)
result.append({"label": label, "score": score, "text": text, "bbox": bbox})
return {"predictions": result}
```
### 2. Send HTTP request using Python
Hugging Face Inference endpoints can directly work with binary data, this means that we can directly send our image from our document to the endpoint. We are going to use `requests` to send our requests. (make your you have it installed `pip install requests`)
```python
import json
import requests as r
import mimetypes
ENDPOINT_URL="" # url of your endpoint
HF_TOKEN="" # organization token where you deployed your endpoint
def predict(path_to_image:str=None):
with open(path_to_image, "rb") as i:
b = i.read()
headers= {
"Authorization": f"Bearer {HF_TOKEN}",
"Content-Type": mimetypes.guess_type(path_to_image)[0]
}
response = r.post(ENDPOINT_URL, headers=headers, data=b)
return response.json()
prediction = predict(path_to_image="path_to_your_image.png")
print(prediction)
# {'predictions': [{'label': 'I-ANSWER', 'score': 0.4823932945728302, 'text': '[CLS]', 'bbox': [0.0, 0.0, 0.0, 0.0]}, {'label': 'B-HEADER', 'score': 0.992474377155304, 'text': 'your', 'bbox': [1712.529, 181.203, 1859.949, 228.88799999999998]},
```
### 3. Draw result on image
To get a better understanding of what the model predicted you can also draw the predictions on the provided image.
```python
from PIL import Image, ImageDraw, ImageFont
# draw results on image
def draw_result(path_to_image,result):
image = Image.open(path_to_image)
label2color = {
"B-HEADER": "blue",
"B-QUESTION": "red",
"B-ANSWER": "green",
"I-HEADER": "blue",
"I-QUESTION": "red",
"I-ANSWER": "green",
}
# draw predictions over the image
draw = ImageDraw.Draw(image)
font = ImageFont.load_default()
for res in result:
draw.rectangle(res["bbox"], outline="black")
draw.rectangle(res["bbox"], outline=label2color[res["label"]])
draw.text((res["bbox"][0] + 10, res["bbox"][1] - 10), text=res["label"], fill=label2color[res["label"]], font=font)
return image
draw_result("path_to_your_image.png", prediction["predictions"])
``` |
arampacha/whisper-large-uk | arampacha | 2022-12-19T08:33:59Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"uk",
"dataset:mozilla-foundation/common_voice_11_0",
"dataset:google/fleurs",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-15T00:18:14Z | ---
language:
- uk
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
- google/fleurs
model-index:
- name: whisper-base-uk
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: uk
split: test
args: uk
metrics:
- name: Wer
type: wer
value: 10.286876675348378
---
# whisper-base-uk
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:
- eval_loss: 1.3201
- eval_wer: 10.2869
## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
odedmou/ppo-Huggy | odedmou | 2022-12-19T08:27:30Z | 13 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
]
| reinforcement-learning | 2022-12-19T08:27:13Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: odedmou/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
arampacha/whisper-large-hy-2 | arampacha | 2022-12-19T08:26:09Z | 9 | 0 | transformers | [
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"hy",
"dataset:mozilla-foundation/common_voice_11_0",
"dataset:google/fleurs",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-18T14:06:48Z | ---
language:
- hy
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
- google/fleurs
model-index:
- name: whisper-base-hy
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: hy-AM
split: test
args: hy-AM
metrics:
- name: Wer
type: wer
value: 19.986894
---
<!-- 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-base-hy
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:
- eval_loss: 0.1806
- eval_wer: 19.9869
- eval_runtime: 1358.6954
- eval_samples_per_second: 0.292
- eval_steps_per_second: 0.074
- epoch: 13.33
- step: 3000
## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 400
- training_steps: 4000
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
Tonjk/REPEAT_2wangchanberta-base-att-spm-uncased | Tonjk | 2022-12-19T08:19:37Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"camembert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2022-12-19T06:55:53Z | ---
tags:
- generated_from_trainer
model-index:
- name: REPEAT_2wangchanberta-base-att-spm-uncased
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. -->
# REPEAT_2wangchanberta-base-att-spm-uncased
This model is a fine-tuned version of [airesearch/wangchanberta-base-att-spm-uncased](https://huggingface.co/airesearch/wangchanberta-base-att-spm-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6799
## 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: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.4629 | 1.0 | 8561 | 0.3364 |
| 0.2805 | 2.0 | 17122 | 0.3314 |
| 0.2266 | 3.0 | 25683 | 0.5343 |
| 0.1821 | 4.0 | 34244 | 0.6103 |
| 0.1598 | 5.0 | 42805 | 0.6799 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.13.0+cu116
- Datasets 1.17.0
- Tokenizers 0.10.3
|
roapple10/Taxi-v3 | roapple10 | 2022-12-19T08:08:35Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-19T08:08:21Z | ---
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 +/- 2.71
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="roapple10/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"])
```
|
roapple10/q-FrozenLake-v1-4x4-noSlippery | roapple10 | 2022-12-19T07:42:55Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-19T07:42:44Z | ---
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="roapple10/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"])
```
|
bheshaj/bart-large-cnn-small-billsum-3epochs | bheshaj | 2022-12-19T07:35:55Z | 6 | 1 | transformers | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:billsum",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-12-19T06:55:43Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- billsum
metrics:
- rouge
model-index:
- name: bart-large-cnn-small-billsum-3epochs
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: billsum
type: billsum
config: default
split: train
args: default
metrics:
- name: Rouge1
type: rouge
value: 0.5409
---
<!-- 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. -->
# bart-large-cnn-small-billsum-3epochs
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the billsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7523
- Rouge1: 0.5409
- Rouge2: 0.3112
- Rougel: 0.3929
- Rougelsum: 0.4633
## 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: 2.5764683748161164e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 16
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 2.7132 | 0.32 | 8 | 2.2000 | 0.4619 | 0.2328 | 0.3201 | 0.3939 |
| 2.236 | 0.64 | 16 | 1.9705 | 0.499 | 0.2768 | 0.3651 | 0.4216 |
| 2.1109 | 0.96 | 24 | 1.8845 | 0.5214 | 0.2974 | 0.3844 | 0.4417 |
| 1.7663 | 1.28 | 32 | 1.8211 | 0.5226 | 0.2935 | 0.3718 | 0.4479 |
| 1.7838 | 1.6 | 40 | 1.7981 | 0.5338 | 0.3001 | 0.383 | 0.4466 |
| 1.5229 | 1.92 | 48 | 1.7625 | 0.5299 | 0.3012 | 0.3839 | 0.4494 |
| 1.5221 | 2.24 | 56 | 1.7532 | 0.5384 | 0.3117 | 0.3939 | 0.4637 |
| 1.2879 | 2.56 | 64 | 1.7560 | 0.5338 | 0.3075 | 0.3865 | 0.4584 |
| 1.4046 | 2.88 | 72 | 1.7523 | 0.5409 | 0.3112 | 0.3929 | 0.4633 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
|
hyorea1/KoT5-test-add-data-from5ep-continue | hyorea1 | 2022-12-19T07:29:20Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-12-17T14:18:19Z | ---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: KoT5-test-add-data-from5ep-continue
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. -->
# KoT5-test-add-data-from5ep-continue
This model is a fine-tuned version of [hyorea1/KoT5-test-add-data-from5ep-continue](https://huggingface.co/hyorea1/KoT5-test-add-data-from5ep-continue) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1881
- Rouge1: 11.7784
- Rouge2: 2.959
- Rougel: 11.6648
- Rougelsum: 11.6892
- Gen Len: 34.7301
## 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: 4
- eval_batch_size: 4
- seed: 100
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Gen Len | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:-----:|:-------:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 1.4317 | 0.32 | 800 | 34.7618 | 1.2414 | 11.8765 | 3.2439 | 11.7982 | 11.8203 |
| 0.9488 | 0.64 | 1600 | 35.1324 | 1.2255 | 11.5076 | 3.0739 | 11.394 | 11.4492 |
| 1.1868 | 0.97 | 2400 | 34.2368 | 1.1983 | 10.7675 | 2.8679 | 10.7567 | 10.7806 |
| 1.3349 | 1.29 | 3200 | 34.3772 | 1.2170 | 11.0853 | 2.8116 | 10.9947 | 11.0642 |
| 1.3918 | 1.61 | 4000 | 34.7368 | 1.1845 | 11.6434 | 2.9694 | 11.5189 | 11.5525 |
| 1.6205 | 1.93 | 4800 | 33.9897 | 1.1801 | 11.1446 | 2.9624 | 11.0259 | 11.0535 |
| 1.1958 | 2.25 | 5600 | 34.6926 | 1.1845 | 11.3408 | 2.9759 | 11.2451 | 11.2685 |
| 1.2391 | 2.58 | 6400 | 34.8382 | 1.1879 | 11.227 | 2.832 | 11.0999 | 11.12 |
| 1.458 | 2.9 | 7200 | 34.8904 | 1.1869 | 11.4615 | 2.832 | 11.3029 | 11.3413 |
| 1.0598 | 3.22 | 8000 | 1.1877 | 11.2705 | 2.8787 | 11.1582| 11.2173 | 34.8993 |
| 1.3546 | 3.54 | 8800 | 1.1832 | 11.9647 | 2.9161 | 11.848 | 11.8769 | 34.5897 |
| 1.5696 | 3.86 | 9600 | 1.1859 | 11.352 | 2.8466 | 11.2177| 11.2336 | 34.6441 |
| 1.3378 | 4.19 | 10400 | 1.1873 | 11.9282 | 2.959 | 11.8205| 11.8427 | 34.7125 |
| 1.063 | 4.51 | 11200 | 1.1877 | 11.8063 | 2.9284 | 11.6855| 11.7112 | 34.6426 |
| 1.184 | 4.83 | 12000 | 1.1881 | 11.7784 | 2.959 | 11.6648| 11.6892 | 34.7301 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2
|
p4b/whisper-large-v2-lv | p4b | 2022-12-19T07:11:53Z | 4 | 1 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"hf-asr-leaderboard",
"generated_from_trainer",
"lv",
"dataset:mozilla-foundation/common_voice_11_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-18T16:13:20Z | ---
language:
- lv
license: apache-2.0
tags:
- whisper-event
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Large-v2 Latvian
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0 lv
type: mozilla-foundation/common_voice_11_0
config: lv
split: test
args: lv
metrics:
- name: Wer
type: wer
value: 19.97153700189753
---
<!-- 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 Latvian
This model is a fine-tuned version of [p4b/whisper-large-v2-lv](https://huggingface.co/p4b/whisper-large-v2-lv) on the mozilla-foundation/common_voice_11_0 lv dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2593
- Wer: 19.9715
## 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-07
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 900
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.7919 | 3.03 | 200 | 0.2793 | 22.5806 |
| 0.4409 | 6.05 | 400 | 0.2651 | 20.6072 |
| 0.4393 | 10.01 | 600 | 0.2600 | 20.0664 |
| 0.4975 | 13.04 | 800 | 0.2593 | 19.9715 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 2.0.0.dev20221218+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2
|
CxECHO/CE | CxECHO | 2022-12-19T06:48:06Z | 0 | 0 | null | [
"arxiv:1906.02569",
"region:us"
]
| null | 2022-12-19T06:37:24Z | <!-- DO NOT EDIT THIS FILE DIRECTLY. INSTEAD EDIT THE `readme_template.md` OR `guides/1)getting_started/1)quickstart.md` TEMPLATES AND THEN RUN `render_readme.py` SCRIPT. -->
<div align="center">
[<img src="readme_files/gradio.svg" alt="gradio" width=300>](https://gradio.app)<br>
<em>Build & share delightful machine learning apps easily</em>
[<img src="https://circleci.com/gh/gradio-app/gradio.svg?style=svg" alt="circleci">](https://circleci.com/gh/gradio-app/gradio)
[<img src="https://codecov.io/gh/gradio-app/gradio/branch/master/graph/badge.svg" alt="codecov">](https://app.codecov.io/gh/gradio-app/gradio)
[](https://pypi.org/project/gradio/)
[](https://pypi.org/project/gradio/)
[](https://twitter.com/gradio)
[Website](https://gradio.app)
| [Documentation](https://gradio.app/docs/)
| [Guides](https://gradio.app/guides/)
| [Getting Started](https://gradio.app/getting_started/)
| [Examples](demo/)
</div>
# Gradio: Build Machine Learning Web Apps — in Python
Gradio is an open-source Python library that is used to build machine learning and data science demos and web applications.
With Gradio, you can quickly create a beautiful user interface around your machine learning models or data science workflow and let people "try it out" by dragging-and-dropping in their own images,
pasting text, recording their own voice, and interacting with your demo, all through the browser.
Gradio is useful for:
- **Demoing** your machine learning models for clients/collaborators/users/students.
- **Deploying** your models quickly with automatic shareable links and getting feedback on model performance.
- **Debugging** your model interactively during development using built-in manipulation and interpretation tools.
## Quickstart
**Prerequisite**: Gradio requires Python 3.7 or higher, that's all!
### What Does Gradio Do?
One of the *best ways to share* your machine learning model, API, or data science workflow with others is to create an **interactive app** that allows your users or colleagues to try out the demo in their browsers.
Gradio allows you to **build demos and share them, all in Python.** And usually in just a few lines of code! So let's get started.
### Hello, World
To get Gradio running with a simple "Hello, World" example, follow these three steps:
1\. Install Gradio using pip:
```bash
pip install gradio
```
2\. Run the code below as a Python script or in a Jupyter Notebook (or [Google Colab](https://colab.research.google.com/drive/18ODkJvyxHutTN0P5APWyGFO_xwNcgHDZ?usp=sharing)):
```python
import gradio as gr
def greet(name):
return "Hello " + name + "!"
demo = gr.Interface(fn=greet, inputs="text", outputs="text")
demo.launch()
```
3\. The demo below will appear automatically within the Jupyter Notebook, or pop in a browser on [http://localhost:7860](http://localhost:7860) if running from a script:
### The `Interface` Class
You'll notice that in order to make the demo, we created a `gradio.Interface`. This `Interface` class can wrap any Python function with a user interface. In the example above, we saw a simple text-based function, but the function could be anything from music generator to a tax calculator to the prediction function of a pretrained machine learning model.
The core `Interface` class is initialized with three required parameters:
- `fn`: the function to wrap a UI around
- `inputs`: which component(s) to use for the input (e.g. `"text"`, `"image"` or `"audio"`)
- `outputs`: which component(s) to use for the output (e.g. `"text"`, `"image"` or `"label"`)
Let's take a closer look at these components used to provide input and output.
### Components Attributes
We saw some simple `Textbox` components in the previous examples, but what if you want to change how the UI components look or behave?
Let's say you want to customize the input text field — for example, you wanted it to be larger and have a text placeholder. If we use the actual class for `Textbox` instead of using the string shortcut, you have access to much more customizability through component attributes.
```python
import gradio as gr
def greet(name):
return "Hello " + name + "!"
demo = gr.Interface(
fn=greet,
inputs=gr.Textbox(lines=2, placeholder="Name Here..."),
outputs="text",
)
demo.launch()
```
### Multiple Input and Output Components
Suppose you had a more complex function, with multiple inputs and outputs. In the example below, we define a function that takes a string, boolean, and number, and returns a string and number. Take a look how you pass a list of input and output components.
```python
import gradio as gr
def greet(name, is_morning, temperature):
salutation = "Good morning" if is_morning else "Good evening"
greeting = f"{salutation} {name}. It is {temperature} degrees today"
celsius = (temperature - 32) * 5 / 9
return greeting, round(celsius, 2)
demo = gr.Interface(
fn=greet,
inputs=["text", "checkbox", gr.Slider(0, 100)],
outputs=["text", "number"],
)
demo.launch()
```
You simply wrap the components in a list. Each component in the `inputs` list corresponds to one of the parameters of the function, in order. Each component in the `outputs` list corresponds to one of the values returned by the function, again in order.
### An Image Example
Gradio supports many types of components, such as `Image`, `DataFrame`, `Video`, or `Label`. Let's try an image-to-image function to get a feel for these!
```python
import numpy as np
import gradio as gr
def sepia(input_img):
sepia_filter = np.array([
[0.393, 0.769, 0.189],
[0.349, 0.686, 0.168],
[0.272, 0.534, 0.131]
])
sepia_img = input_img.dot(sepia_filter.T)
sepia_img /= sepia_img.max()
return sepia_img
demo = gr.Interface(sepia, gr.Image(shape=(200, 200)), "image")
demo.launch()
```
When using the `Image` component as input, your function will receive a NumPy array with the shape `(width, height, 3)`, where the last dimension represents the RGB values. We'll return an image as well in the form of a NumPy array.
You can also set the datatype used by the component with the `type=` keyword argument. For example, if you wanted your function to take a file path to an image instead of a NumPy array, the input `Image` component could be written as:
```python
gr.Image(type="filepath", shape=...)
```
Also note that our input `Image` component comes with an edit button 🖉, which allows for cropping and zooming into images. Manipulating images in this way can help reveal biases or hidden flaws in a machine learning model!
You can read more about the many components and how to use them in the [Gradio docs](https://gradio.app/docs).
### Blocks: More Flexibility and Control
Gradio offers two classes to build apps:
1\. **Interface**, that provides a high-level abstraction for creating demos that we've been discussing so far.
2\. **Blocks**, a low-level API for designing web apps with more flexible layouts and data flows. Blocks allows you to do things like feature multiple data flows and demos, control where components appear on the page, handle complex data flows (e.g. outputs can serve as inputs to other functions), and update properties/visibility of components based on user interaction — still all in Python. If this customizability is what you need, try `Blocks` instead!
### Hello, Blocks
Let's take a look at a simple example. Note how the API here differs from `Interface`.
```python
import gradio as gr
def greet(name):
return "Hello " + name + "!"
with gr.Blocks() as demo:
name = gr.Textbox(label="Name")
output = gr.Textbox(label="Output Box")
greet_btn = gr.Button("Greet")
greet_btn.click(fn=greet, inputs=name, outputs=output)
demo.launch()
```
Things to note:
- `Blocks` are made with a `with` clause, and any component created inside this clause is automatically added to the app.
- Components appear vertically in the app in the order they are created. (Later we will cover customizing layouts!)
- A `Button` was created, and then a `click` event-listener was added to this button. The API for this should look familiar! Like an `Interface`, the `click` method takes a Python function, input components, and output components.
### More Complexity
Here's an app to give you a taste of what's possible with `Blocks`:
```python
import numpy as np
import gradio as gr
def flip_text(x):
return x[::-1]
def flip_image(x):
return np.fliplr(x)
with gr.Blocks() as demo:
gr.Markdown("Flip text or image files using this demo.")
with gr.Tabs():
with gr.TabItem("Flip Text"):
text_input = gr.Textbox()
text_output = gr.Textbox()
text_button = gr.Button("Flip")
with gr.TabItem("Flip Image"):
with gr.Row():
image_input = gr.Image()
image_output = gr.Image()
image_button = gr.Button("Flip")
text_button.click(flip_text, inputs=text_input, outputs=text_output)
image_button.click(flip_image, inputs=image_input, outputs=image_output)
demo.launch()
```
A lot more going on here! We'll cover how to create complex `Blocks` apps like this in the [building with blocks](https://github.com/gradio-app/gradio/tree/main/guides/3\)building_with_blocks) section for you.
Congrats, you're now familiar with the basics of Gradio! 🥳 Go to our [next guide](https://gradio.app/key_features) to learn more about the key features of Gradio.
## Open Source Stack
Gradio is built with many wonderful open-source libraries, please support them as well!
[<img src="readme_files/huggingface_mini.svg" alt="huggingface" height=40>](https://huggingface.co)
[<img src="readme_files/python.svg" alt="python" height=40>](https://www.python.org)
[<img src="readme_files/fastapi.svg" alt="fastapi" height=40>](https://fastapi.tiangolo.com)
[<img src="readme_files/encode.svg" alt="encode" height=40>](https://www.encode.io)
[<img src="readme_files/svelte.svg" alt="svelte" height=40>](https://svelte.dev)
[<img src="readme_files/vite.svg" alt="vite" height=40>](https://vitejs.dev)
[<img src="readme_files/pnpm.svg" alt="pnpm" height=40>](https://pnpm.io)
[<img src="readme_files/tailwind.svg" alt="tailwind" height=40>](https://tailwindcss.com)
## License
Gradio is licensed under the Apache License 2.0 found in the [LICENSE](LICENSE) file in the root directory of this repository.
## Citation
Also check out the paper *[Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild](https://arxiv.org/abs/1906.02569), ICML HILL 2019*, and please cite it if you use Gradio in your work.
```
@article{abid2019gradio,
title = {Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild},
author = {Abid, Abubakar and Abdalla, Ali and Abid, Ali and Khan, Dawood and Alfozan, Abdulrahman and Zou, James},
journal = {arXiv preprint arXiv:1906.02569},
year = {2019},
}
```
|
anamhira/q-Taxi-v3 | anamhira | 2022-12-19T06:44:07Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-19T06:44:00Z | ---
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: 7.52 +/- 2.73
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="anamhira/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
adityanahata/q-Taxi-v3 | adityanahata | 2022-12-19T06:42:59Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-19T06:42:52Z | ---
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: 7.50 +/- 2.76
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="adityanahata/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"])
```
|
adityanahata/q-FrozenLake-v1-4x4-noSlippery | adityanahata | 2022-12-19T06:37:01Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-19T06:36:52Z | ---
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="adityanahata/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"])
```
|
jwkritchie/whisper-small-defined-dot-ai-qc-fr-insurance-dataset | jwkritchie | 2022-12-19T06:02:50Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"fr",
"dataset:mozilla-foundation/common_voice_11_0",
"license:cc-by-nc-4.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-18T04:12:07Z | ---
language:
- fr
license: cc-by-nc-4.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Small Finetuned on Defined.AI Quebec French Insurance Dataset
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: fr
split: test
args: fr
metrics:
- name: Wer
type: wer
value: 30.96967539018456
---
<!-- 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 Small Finetuned on Defined.AI Quebec French Insurance Dataset
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2600
- Wer: 30.9697
## 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: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 1.0127 | 1.06 | 1000 | 1.2600 | 30.9697 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
Tonjk/REPEAT_1wangchanberta-base-att-spm-uncased | Tonjk | 2022-12-19T05:57:34Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"camembert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2022-12-19T04:41:37Z | ---
tags:
- generated_from_trainer
model-index:
- name: REPEAT_1wangchanberta-base-att-spm-uncased
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. -->
# REPEAT_1wangchanberta-base-att-spm-uncased
This model is a fine-tuned version of [airesearch/wangchanberta-base-att-spm-uncased](https://huggingface.co/airesearch/wangchanberta-base-att-spm-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3643
## 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: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.4062 | 1.0 | 8561 | 0.5269 |
| 0.1741 | 2.0 | 17122 | 0.9837 |
| 0.1422 | 3.0 | 25683 | 0.9712 |
| 0.1253 | 4.0 | 34244 | 1.0890 |
| 0.115 | 5.0 | 42805 | 1.3643 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.13.0+cu116
- Datasets 1.17.0
- Tokenizers 0.10.3
|
xmzhu/whisper-tiny-zh | xmzhu | 2022-12-19T05:51:53Z | 66 | 9 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"zh",
"dataset:mozilla-foundation/common_voice_11_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-18T20:21:09Z | ---
language:
- zh
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Tiny Chinese
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0 zh-CN
type: mozilla-foundation/common_voice_11_0
config: zh-CN
split: test
args: zh-CN
metrics:
- name: Wer
type: wer
value: 91.09343588847129
---
<!-- 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 Tiny Chinese
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the mozilla-foundation/common_voice_11_0 zh-CN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6121
- Wer: 91.0934
## 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: 64
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.9397 | 2.02 | 1000 | 0.6568 | 98.7326 |
| 0.5387 | 4.04 | 2000 | 0.6149 | 94.5197 |
| 0.3317 | 6.06 | 3000 | 0.6080 | 95.0354 |
| 0.225 | 8.07 | 4000 | 0.6121 | 91.0934 |
| 0.3166 | 11.0 | 5000 | 0.6092 | 92.3171 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
kurohige/ppo-LunarLander-v2 | kurohige | 2022-12-19T05:46:15Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-19T04:54:14Z | ---
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: 255.10 +/- 18.90
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
...
```
|
jxiao/ppo-Huggy | jxiao | 2022-12-19T05:06:59Z | 15 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
]
| reinforcement-learning | 2022-12-19T05:06:53Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: jxiao/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Addwater/rl-course | Addwater | 2022-12-19T05:02:59Z | 2 | 0 | transformers | [
"transformers",
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"endpoints_compatible",
"region:us"
]
| reinforcement-learning | 2022-12-17T06:17:48Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: rl-course
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 2.73
name: mean_reward
verified: false
---
# **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
...
```
|
aihobby/sd-class-butterflies-64 | aihobby | 2022-12-19T05:02:29Z | 0 | 0 | diffusers | [
"diffusers",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
]
| unconditional-image-generation | 2022-12-19T05:01:20Z | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is my second diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('aihobby/sd-class-butterflies-64')
image = pipeline().images[0]
image
```
|
Addwater/q-FrozenLake-v1-4x4-Slippery | Addwater | 2022-12-19T04:57:29Z | 0 | 0 | null | [
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-19T04:57:15Z | ---
tags:
- FrozenLake-v1-4x4
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-Slippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4
type: FrozenLake-v1-4x4
metrics:
- type: mean_reward
value: 0.56 +/- 0.50
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Addwater/q-FrozenLake-v1-4x4-Slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Daehoon/Taxi-v3 | Daehoon | 2022-12-19T04:50:00Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-19T04:49:53Z | ---
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: 7.46 +/- 2.74
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="Daehoon/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"])
```
|
Daehoon/q-FrozenLake-v1-4x4-noSlippery | Daehoon | 2022-12-19T04:45:26Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-19T04:45:19Z | ---
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="Daehoon/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"])
```
|
cgst/PPO-LunarLander-v2 | cgst | 2022-12-19T04:36:24Z | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-19T03:45:54Z | ---
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: 246.88 +/- 60.27
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
...
```
|
Addwater/Huggy | Addwater | 2022-12-19T04:08:29Z | 12 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
]
| reinforcement-learning | 2022-12-19T04:08:21Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: Addwater/Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Scrwed/Taxi-v3 | Scrwed | 2022-12-19T02:58:18Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-19T02:58:08Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Scrwed/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"])
```
|
rpant/ppo-LunarLander-v2 | rpant | 2022-12-19T02:54:50Z | 4 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-19T02:54:21Z | ---
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: 264.65 +/- 18.44
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
...
```
|
Antiraedus/ppo-lunarLanderv2-Test | Antiraedus | 2022-12-19T02:45:49Z | 2 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-19T02:45:21Z | ---
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: 270.40 +/- 16.07
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
...
```
|
Iamvincent/Taxi-v3 | Iamvincent | 2022-12-19T02:26:41Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-19T02:26:33Z | ---
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 +/- 2.71
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="Iamvincent/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"])
```
|
lksfr/book-reviews | lksfr | 2022-12-19T02:10:30Z | 1 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2022-12-19T02:10:10Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 400 with parameters:
```
{'batch_size': 12, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 400,
"warmup_steps": 40,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
pyf98/librispeech_100_e_branchformer | pyf98 | 2022-12-19T01:57:42Z | 8 | 1 | espnet | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:librispeech_100",
"arxiv:2210.00077",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
]
| automatic-speech-recognition | 2022-12-19T01:07:55Z | ---
tags:
- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- librispeech_100
license: cc-by-4.0
---
## ESPnet2 ASR model
### `pyf98/librispeech_100_e_branchformer`
This model was trained by Yifan Peng using librispeech_100 recipe in [espnet](https://github.com/espnet/espnet/).
References:
- [E-Branchformer: Branchformer with Enhanced merging for speech recognition (SLT 2022)](https://arxiv.org/abs/2210.00077)
- [Branchformer: Parallel MLP-Attention Architectures to Capture Local and Global Context for Speech Recognition and Understanding (ICML 2022)](https://proceedings.mlr.press/v162/peng22a.html)
### Demo: How to use in ESPnet2
Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html)
if you haven't done that already.
```bash
cd espnet
git checkout 3c84766d951e33dd7782a9f32011c00ea2a44ea3
pip install -e .
cd egs2/librispeech_100/asr1
./run.sh --skip_data_prep false --skip_train true --download_model pyf98/librispeech_100_e_branchformer
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Mon Dec 12 06:50:58 CST 2022`
- python version: `3.9.15 (main, Nov 24 2022, 14:31:59) [GCC 11.2.0]`
- espnet version: `espnet 202209`
- pytorch version: `pytorch 1.12.1`
- Git hash: `26f432bc859e5e40cac1a86042d498ba7baffbb0`
- Commit date: `Fri Dec 9 02:16:01 2022 +0000`
## asr_train_asr_e_branchformer_size256_mlp1024_linear1024_e12_mactrue_edrop0.0_ddrop0.0_raw_en_bpe5000_sp
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|94.6|5.0|0.3|0.8|6.1|55.4|
|decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|85.3|13.3|1.4|2.1|16.7|78.9|
|decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|94.4|5.1|0.4|0.8|6.3|56.1|
|decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|85.0|13.6|1.4|2.0|17.0|80.3|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|98.3|1.0|0.7|0.7|2.4|55.4|
|decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|93.6|4.0|2.4|2.0|8.3|78.9|
|decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|98.2|1.1|0.8|0.6|2.5|56.1|
|decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|93.7|3.8|2.5|1.9|8.2|80.3|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_asr_model_valid.acc.ave/dev_clean|2703|69558|92.2|4.9|2.9|0.6|8.4|55.4|
|decode_asr_asr_model_valid.acc.ave/dev_other|2864|64524|81.9|12.8|5.2|2.3|20.4|78.9|
|decode_asr_asr_model_valid.acc.ave/test_clean|2620|66983|92.2|4.9|2.9|0.6|8.4|56.1|
|decode_asr_asr_model_valid.acc.ave/test_other|2939|66650|81.5|13.0|5.5|2.2|20.7|80.3|
## ASR config
<details><summary>expand</summary>
```
config: conf/tuning/train_asr_e_branchformer_size256_mlp1024_linear1024_e12_mactrue_edrop0.0_ddrop0.0.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_e_branchformer_size256_mlp1024_linear1024_e12_mactrue_edrop0.0_ddrop0.0_raw_en_bpe5000_sp
ngpu: 1
seed: 2022
num_workers: 4
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 70
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- acc
- max
keep_nbest_models: 10
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 4
no_forward_run: false
resume: true
train_dtype: float32
use_amp: true
log_interval: null
use_matplotlib: true
use_tensorboard: true
create_graph_in_tensorboard: false
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 16000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_en_bpe5000_sp/train/speech_shape
- exp/asr_stats_raw_en_bpe5000_sp/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_en_bpe5000_sp/valid/speech_shape
- exp/asr_stats_raw_en_bpe5000_sp/valid/text_shape.bpe
batch_type: numel
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train_clean_100_sp/wav.scp
- speech
- kaldi_ark
- - dump/raw/train_clean_100_sp/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/dev/wav.scp
- speech
- kaldi_ark
- - dump/raw/dev/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.002
weight_decay: 1.0e-06
scheduler: warmuplr
scheduler_conf:
warmup_steps: 15000
token_list:
- <blank>
- <unk>
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- ANT
- ▁TOWARD
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- ▁FOUGHT
- ▁MILITA
- ▁THUNDER
- ▁VOYAGE
- ▁GANEM
- ▁FREEDOM
- ▁NODDED
- ▁CAPTURE
- ▁MORTAL
- ▁OWNER
- ▁POLITE
- ▁VISION
- ▁EDUCATION
- ▁GOVERNOR
- ▁RAV
- ▁REWARD
- ▁HASTE
- ▁REPEAT
- ▁DETERMIN
- ▁PITI
- ▁KNEE
- LINE
- ▁DEVOTED
- ▁INTERRUPTED
- ▁FOLKS
- ▁EXTREME
- ▁APPROACH
- ▁CONTINUE
- ▁BEARING
- ▁CHAP
- ▁ACQUAINTED
- ▁GLIMPSE
- ▁GRADUALLY
- ▁SUNSHINE
- ▁PRACTICE
- ▁SUPPLI
- ▁DAVID
- ▁DRIFT
- ▁SHOWING
- ▁LEVEL
- ▁PROMPT
- ▁QUARREL
- ▁REPRESENTATIVE
- ▁PLUNG
- ▁GIANT
- FALL
- ▁STOUT
- CHA
- WEPT
- ▁GLANC
- ▁SALT
- ▁CHOSEN
- ▁BUCK
- ▁REALIZED
- ▁REALITY
- ▁TUR
- ▁DRIVEN
- ▁CARD
- ▁PRAYER
- ▁TERM
- AID
- ▁HOLY
- ▁ENDURE
- ▁RANGE
- ▁HANG
- ▁SAM
- LAN
- ▁CAVE
- INA
- ▁GRI
- ▁SIGH
- ▁NEIGHBOUR
- ▁COUNCIL
- ▁EXERCISE
- ▁NAUTILUS
- ▁SOMEWHERE
- ▁SYLVIA
- ▁THOROUGH
- ▁VICTIM
- ▁BRIDGE
- ▁COMPELLED
- ▁INCLINED
- ▁OVERCOME
- ▁RESERVE
- ▁ARREST
- ▁PRECIOUS
- ▁DUTCH
- ▁OCEAN
- ▁ACQUIR
- ▁RECALL
- ▁DESTIN
- ▁ATTACH
- ▁SLIM
- ▁WEEP
- ▁CONSCIOUSNESS
- ▁TIGHT
- ▁WAKE
- ▁COMFORTABLE
- ▁ACTIVE
- ▁WINGS
- ▁GRIN
- ▁AFFECT
- ▁WHIT
- ▁IDEAL
- ▁EASTER
- ▁APPROACHING
- ▁CREATED
- ▁PLANS
- ▁INCREASE
- ▁FLYING
- ▁SHOUT
- OES
- MISSION
- ▁ARMED
- ABILITY
- ▁BLUSH
- ▁CONNECTION
- ▁MATTHEW
- ▁MEDICINE
- ▁REMIND
- ▁EXHIBIT
- ▁BLOCK
- ▁DESERVE
- ▁LISTENING
- ▁TITLE
- ▁FLOUR
- ▁FLAME
- ▁AGENT
- ▁USEFUL
- ▁BRIG
- ▁BOIL
- ▁ASSURED
- ▁REFLECTION
- ▁PINE
- ▁WAG
- ▁YOUNGER
- ▁BEARD
- ▁KINDNESS
- CTUALLY
- ▁ACTUAL
- ▁WEIGHT
- ▁LILY
- ▁IMPRESS
- ▁DESCRIBE
- ▁BEHELD
- ▁COMMUNITY
- ▁DESPERATE
- ▁DISPLAY
- ▁ENEMIES
- ▁MELANCHOLY
- ▁MIRROR
- ▁RECOMMEND
- ▁SPANISH
- ▁BLAME
- ▁VOLUME
- ▁SHOOT
- ▁COMBIN
- ▁SHAKING
- ▁SOUTHERN
- ▁MYSTERY
- ▁EVERYONE
- ▁COMMISSION
- ▁COMPOSED
- ▁UDO
- ▁IMAGE
- ▁DECEIV
- ▁FAILURE
- ▁PATTY
- ▁ALICE
- ▁FRAME
- ▁MODEST
- ▁MAGNIFICENT
- ▁BRANCHES
- ▁REIGN
- ▁RAG
- ▁PARISH
- ▁KATE
- ▁AMID
- ▁SLEEPING
- ▁ANNOUNCED
- ▁EAGERLY
- ▁WIRE
- ▁LAP
- ▁ARAB
- ▁EATING
- ▁RUM
- ▁CAREFUL
- ▁DISCUSS
- WORTH
- ▁DISTRICT
- ▁FOREHEAD
- ▁FRANCIS
- ▁INCIDENT
- ▁APPEAL
- ▁EMBARRASS
- ▁MAINTAIN
- ▁PRONOUNC
- ▁FURNISH
- ▁STRAIN
- ▁ELEMENT
- ▁SILK
- ▁FEAST
- ▁RECENT
- ▁DANCING
- ▁LODGE
- ▁ASHAMED
- ▁TRICK
- ▁BOBO
- ▁STUFF
- ▁ET
- ▁ASSERT
- ▁SANK
- ▁TREATMENT
- ECI
- ▁SWIM
- ▁BECOMING
- ▁SINGING
- ▁PLATE
- ▁SCATTERED
- ▁EXTREMELY
- ▁GRIM
- ▁SANG
- ▁FIGHTING
- ▁FACTOR
- ▁PAINFUL
- ▁HIDE
- ▁FUNN
- ▁AFTERWARD
- ▁FROG
- ▁VENTURE
- ▁DISAPPOINT
- ▁COMRADE
- ▁MONSIEUR
- ▁OBVIOUS
- ▁PASSENGER
- ▁PROFOUND
- ▁PUBLISH
- ▁ACCUSTOM
- ▁BLOOM
- ▁SMITH
- ▁RELATIVE
- ▁ACCUSE
- ▁MANIFEST
- ▁SOLID
- ▁MONSTER
- ▁MARIUS
- ▁CANDLE
- ▁PROCUR
- ▁INTERFERE
- ▁HOUSEHOLD
- ▁DEVELOPMENT
- ▁AGREEABLE
- ▁HALT
- ▁NECESSITY
- FOLD
- ▁CITIES
- ▁REGI
- ▁GLOOMY
- BBL
- ▁SEPARATED
- ▁CHEST
- ▁STRIP
- ▁SPAR
- ▁DUN
- ▁SETTLE
- ▁STARED
- ▁HANGING
- ▁FEATURES
- ▁PILE
- ▁ORIGIN
- ARIES
- ▁LION
- ▁ALI
- ▁ASTONISHMENT
- ▁COMPLIMENT
- ▁DELICATE
- ▁COUNSEL
- ▁FIFTH
- ▁SUPPRESS
- ▁BURDEN
- ▁COMPLEX
- ▁ADDITION
- ▁CRUSH
- ▁TWIST
- ▁PIANO
- ▁BRUSH
- ▁CHECK
- ▁ANNIE
- ▁SHELTER
- ▁IMPROV
- ▁WESTERN
- ▁LOCAL
- ▁APPLE
- ▁GREET
- ▁MASK
- ▁RUSSIAN
- ▁TOWER
- ▁CREW
- ▁TIP
- ▁WANDERING
- ▁READER
- ▁WANDERED
- ▁DESTROY
- ▁OBSERVE
- MORE
- ▁ESCAPED
- ▁PET
- ▁BUILD
- ▁REAR
- ▁DESTROYED
- HIN
- ▁OWE
- ▁RANG
- ▁TEAR
- ▁NED
- ▁OFFICER
- ▁TRAP
- ▁OCCUR
- ▁APPOINTED
- ▁ATMOSPHERE
- ▁CHOOSE
- ▁CONCLUSION
- ▁CULTIVAT
- ▁DESCRIPTION
- ▁ENORMOUS
- ▁EXHAUSTED
- ▁LANDSCAPE
- ▁NATASHA
- ▁PROSPECT
- ▁REFRESH
- ▁SPECIES
- ▁SURROUNDED
- ▁WEAPON
- ▁BLANK
- ▁DEFEND
- ▁EDITH
- ▁HORRIBL
- ▁BETRAY
- ▁FERKO
- ▁LABOUR
- ▁NEGRO
- ▁RESUMED
- ▁LEAF
- ▁MUSKET
- ▁INTENSE
- ▁MERCY
- ▁ADOPT
- ▁SCORE
- ▁DASH
- ▁LAWYER
- ▁SLOPE
- ▁CHUCK
- ▁ASSISTANCE
- ▁BROOK
- ▁BREAKING
- ▁ASSIST
- ▁GROAN
- ▁HELEN
- ▁BEHAV
- ▁MAIDEN
- ▁CRIS
- ▁SHOUTING
- ▁NAY
- ▁PIG
- ▁ACCORDINGLY
- ETTE
- ▁DESIR
- ▁RUB
- ▁GRU
- ▁PIT
- ▁HEAVI
- ▁OBTAINED
- ▁SPARE
- ▁BRANCH
- ▁COUNTER
- ▁APART
- ▁AMBITION
- ▁ASTONISHED
- ▁CORRESPOND
- ▁DRIVING
- ▁ENERGY
- ▁HISTORIAN
- ▁REVOLUTION
- ▁SWEEP
- ▁TREMBLING
- ▁CRAFT
- ▁FAMILIES
- ▁LITERATURE
- SBURG
- ▁FEMALE
- ▁TILNEY
- ▁GENEROUS
- ▁SUBMIT
- ▁INTELLECTUAL
- ▁ORCHARD
- ▁STORIES
- ▁DIANA
- ▁VEIN
- ▁TRIFL
- ▁TWIN
- ▁WORSHIP
- ▁MARBLE
- ▁GALLANT
- ▁SENSIBLE
- ▁NEAT
- ▁BROWNIE
- ▁JUNE
- ▁SHAW
- ▁WORST
- ▁USELESS
- ▁FISHING
- ▁CRYING
- ▁MAYBE
- ▁VARI
- ▁PRESERVE
- ▁VOL
- ▁EMPLOY
- ▁INTERRUPT
- ▁SLIGHTLY
- ▁ACCOMPLISHED
- NEY
- ▁STEAM
- ▁BALANC
- ▁LEANING
- ▁SIGHED
- ▁REFUSE
- ▁IMAGINED
- ▁DATE
- GROUND
- ▁ENTERTAIN
- ▁PERCEIVE
- ▁ABROAD
- ▁CHEESE
- ▁DESTRUCTION
- ▁ESSENTIAL
- ▁EXPEDITION
- ▁GRANDFATHER
- ▁INFINITE
- ▁LIBRARY
- ▁MULTITUDE
- ▁NEGLECT
- ▁SWALLOW
- ▁VILLEFORT
- ▁BELOVED
- ▁COMMITTEE
- ▁CONFIDENT
- ▁PURPLE
- ▁PURCHAS
- ▁SCRAP
- ▁SPOIL
- ▁LIKEWISE
- ▁EXTRA
- ▁STRAW
- ▁SALUT
- ▁SOURCE
- ▁HASTENED
- ▁RESENT
- ▁FLOCK
- ▁LOFT
- ▁FLO
- ▁CLO
- ▁CONVINCED
- ▁GOODNESS
- ▁HYPNOTIZ
- ▁SETTING
- ▁HAIL
- ▁PHI
- ▁GROVE
- ▁DISCOVERY
- ▁DAMP
- ▁WHISPER
- ▁LIFT
- ▁HOP
- ▁SUSPECTED
- ▁SCR
- OLI
- ▁FAC
- ▁BUSH
- ▁FOREVER
- ▁BARRICADE
- ▁CONSTITUTION
- ▁ENDEAVOR
- ▁ENTHUSIASM
- ▁EXECUTION
- ▁HYACINTH
- ▁PERCEVAL
- ▁PSYCHE
- ▁REPROACH
- ▁THIRTEEN
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- ▁GRATITUDE
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- ▁REPUTATION
- ▁SCREAM
- ▁PUPIL
- ▁RETIRED
- ▁STEEP
- ▁SUMMIT
- ▁MISERABLE
- ▁STRICT
- ▁MINGLED
- ▁DEFEAT
- ▁REVEAL
- ▁LOVING
- ▁GOOSE
- ▁ECHO
- ▁AWAIT
- ▁MOOD
- ▁CRAWLEY
- ▁CELL
- ▁ENGAGEMENT
- ▁PRECED
- ▁SOMEONE
- ▁ARRANGEMENT
- ▁PICKET
- ▁GASP
- ▁HUMOR
- ▁INVITATION
- ▁JOB
- WITHSTAND
- ▁LAMENT
- ▁CLASSES
- ▁HUNGER
- ▁DISPOSED
- ▁STEAMER
- ▁FEARFUL
- ▁GER
- ▁FINAL
- ▁FLAG
- ▁JULY
- ▁DIG
- WORK
- ▁OPPOS
- ▁ANXIETY
- ▁AUDIENCE
- ▁BACHELOR
- ▁COLUMN
- ▁HANDKERCHIEF
- ▁IMPATIENT
- ▁JUDGMENT
- ▁KNIFE
- ▁SOVEREIGN
- ▁STRIKING
- ▁THOMPSON
- ▁EMPIRE
- ▁FULFIL
- ▁CONSULT
- ▁JENNY
- ▁THENARDIER
- ▁POYSER
- ▁FOURTEEN
- ▁JAPANESE
- ▁INDULG
- ▁MARTIAN
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- ▁FETCH
- ▁CRITIC
- ▁ROBBER
- ▁CROOK
- ▁DEPARTURE
- ▁MABEL
- ▁PREACH
- ESCENT
- ▁WHIP
- ▁NAIL
- ▁DELIGHTFUL
- ▁DISCUSSION
- ▁SENTENCE
- ▁LANE
- ▁ENGINEER
- ▁ARRANGED
- MMY
- ▁LEST
- ▁RENT
- MMED
- ▁LIST
- ▁ROBE
- ▁MISSION
- ▁GRACEFUL
- ▁LIGHTN
- STONE
- COURT
- ▁CONCEPTION
- ▁CONTRACT
- ▁DROWN
- ▁EXPERIMENT
- ▁HITHERTO
- ▁PLAGUE
- ▁PORTHOS
- ▁SHRIEK
- ▁DETECT
- ▁ACCENT
- ▁ERECT
- ▁SAZEN
- ▁PROFIT
- ▁VIVID
- ▁SQUIRE
- ▁OPERATION
- ▁SMELL
- ▁SIMON
- ▁EXTENT
- ▁KEEN
- ▁EMERG
- ▁REVIV
- ▁REGIMENT
- ▁DISAPPOINTMENT
- ▁STOLE
- ▁DIVINE
- ▁GUILTY
- ▁COWARD
- ▁EXPECTATION
- ▁SIGNOR
- ▁MODE
- ▁CENTRE
- ▁FIL
- HOW
- ▁WEARI
- ▁TOTAL
- ▁VICTOR
- ▁GOVERN
- ▁RAISE
- ▁ABANDON
- ▁ABSURD
- ▁ASPECT
- ▁CRIMINAL
- ▁DEFINITE
- ▁DELIBERAT
- ▁FEATHER
- ▁FLORINA
- ▁MIDNIGHT
- ▁RICHMOND
- ▁SATISFY
- ▁SINGULAR
- ▁STEADILY
- ▁SUPREME
- ▁TIMBER
- ▁PSYCHOLOG
- ▁GESTURE
- ▁VALUABLE
- ▁INTERVAL
- ▁CONFUSION
- ▁FLUTTER
- ▁SACRED
- ▁DISEASE
- ▁UNDERTAKE
- ▁PENETRAT
- ▁MARVEL
- ▁NORTHERN
- ▁GRIEV
- ▁GENIUS
- ▁SADDLE
- ▁NOVEL
- ▁MISERY
- ▁CONVICTION
- ▁SINK
- ▁WAGON
- ▁ARISE
- ▁COMMENT
- ▁BARN
- UPON
- ▁FENCE
- ▁ASSOCIATION
- ▁BONES
- ▁IDLE
- ▁DOUBTFUL
- ▁PREPARATION
- IZZ
- ▁RAIS
- ▁BITTERLY
- ▁JOE
- ▁RELI
- ADI
- ▁METAL
- ▁EXACT
- ▁GLOOM
- FIELD
- ▁DANGLARS
- ▁DISGRACE
- ▁EXAMINATION
- ▁FASCINAT
- ▁GLITTER
- ▁INCREASING
- ▁MESSENGER
- ▁PATRIOT
- ▁PLATFORM
- ▁PROVISION
- ▁QUALITIES
- ▁SELECT
- ▁STEADY
- ▁POVERTY
- ▁POWDER
- ▁PROPHET
- ▁HOLLAND
- ▁TRUNK
- ▁VARIETY
- ▁PLANCHET
- ▁CONQUER
- ▁CONCEIVE
- ▁COMBAT
- ▁STOOP
- ▁SHIRT
- ▁GENERATION
- ▁COMMITTED
- ▁INSULT
- ▁CONFUSED
- ▁RADIAN
- ▁DEBT
- ▁IMITAT
- ▁DART
- ▁CAROLINE
- ▁SWAM
- ▁WREN
- ▁CHILDHOOD
- ▁BRAND
- ▁JOKE
- ▁FRIENDSHIP
- ▁DIRT
- ▁JOLL
- ▁BUSHES
- ▁MINK
- ▁ROUT
- ▁EQUALITY
- ▁HESITATED
- ▁BARK
- ▁ANTI
- ▁STATEMENT
- PHER
- ▁SUNK
- ▁DAT
- ▁BACKWARD
- ▁SUSPECT
- ▁OBJECTION
- ▁RAP
- ▁CHIN
- ▁MATE
- ▁REDUC
- ▁GREGG
- ▁ACCOMPANY
- ▁ANYWHERE
- ▁BENEFIT
- ▁CLERK
- ▁EXPENSE
- ▁FETNAH
- ▁INTERPRET
- ▁LUKASHKA
- ▁NUMEROUS
- ▁SURGEON
- ▁PUZZL
- ▁RESCUE
- ▁GRATEFUL
- ▁APPROV
- ▁RIVAL
- ▁NIECE
- ▁FLOOD
- ▁VANISHED
- ▁ERROR
- ▁BLAZ
- ▁TUMBL
- ▁WENDY
- ▁PERSIST
- ▁CONSOL
- ▁SOAP
- ▁HUMOUR
- ▁FITTED
- ▁HOUSEKEEPER
- ▁ENABL
- ▁OCCASIONALLY
- ▁HATRED
- ▁SWELL
- ▁WORRY
- ▁RUST
- ▁PURSUIT
- ▁INTIMATE
- ▁SEAL
- ▁COLLECTION
- ▁TREMBLED
- ▁DENY
- ▁HUMANITY
- ▁FATAL
- ▁COCK
- ▁DRIVER
- ▁HOPELESS
- ▁MISTAKEN
- ▁LUC
- ▁ACCOMPLISH
- ▁COAL
- ▁ACCORD
- ▁PURSE
- ▁SEPARATE
- ▁ARRIVE
- ▁SMOK
- ▁MADAM
- ▁ASSOCIAT
- ▁INSTRUCT
- ▁CELEBR
- ▁CHANNEL
- ▁CIVILIZATION
- ▁DOCTRINE
- ▁ENDEAVOUR
- ▁GLACIER
- ▁INTELLIGENT
- ▁INVOLVE
- ▁LEATHER
- ▁MUTTERED
- ▁OLENIN
- ▁PENCROFT
- ▁PERPLEX
- ▁SPECTATOR
- ▁UNIVERSITY
- ▁ATTAIN
- ▁INEVITABL
- ▁YONDER
- ▁ENCHANT
- ▁REPAIR
- ▁CURRENT
- ▁ASCEND
- ▁CREEK
- ▁SPARKL
- ▁RUE
- ▁BEAVER
- ▁INFANT
- ▁CONTINUALLY
- ▁CLASP
- ▁IRISH
- ▁ROLLIN
- ▁PUNISHMENT
- ▁LUNCH
- ▁AGONY
- ▁RUDE
- ▁DRAGG
- ▁INQUIRI
- ▁SEX
- ▁TERRIFI
- ▁ROBIN
- ▁PROFESSIONAL
- ▁SPUR
- ▁GRAIN
- ▁VINE
- ▁PENN
- ▁ROC
- ▁CHASE
- ▁INFORM
- ▁WRITER
- ▁AVO
- ▁TAP
- ▁CREAT
- ▁WHIL
- ▁BARR
- ▁ASSURE
- ▁CIRCUMSTANCE
- ▁OIL
- ▁ROUSE
- ▁COLUMB
- ▁CUNNING
- ▁DOMESTIC
- ▁GLORIOUS
- ▁INDIGNATION
- ▁PRECISELY
- ▁PRUDENCE
- ▁RAILROAD
- ▁SATURDAY
- ▁UTMOST
- ▁VIOLENCE
- ▁WHIRL
- ▁CALCULAT
- ▁OVERWHELM
- ▁PERPETUAL
- ▁QUARLES
- ▁SLENDER
- ▁TELEGRAPH
- ▁ALOUD
- ▁OPPRESS
- ▁CROPPER
- ▁CANADIAN
- ▁HERBERT
- ▁TIMID
- ▁SUPPLY
- ▁STROLL
- ▁CREEP
- ▁OATH
- ▁DUSK
- ▁EXCESS
- ▁HUMBLE
- ▁FURIOUS
- ▁RIDGE
- ▁BULLET
- ▁PONY
- ▁STATU
- ▁ENJOYMENT
- ▁CONWAY
- ▁DIFFICULTIES
- ▁PATCH
- ▁JOYCE
- ▁CLOCK
- ▁RESTORED
- ▁ARGU
- ▁WIG
- ▁CHATT
- ▁PLAC
- ▁REMOVE
- ▁TORN
- ▁DISAPPEAR
- TIME
- WELL
- ▁RECOGNIZE
- ▁FISHE
- ▁DECLARE
- ISTIC
- ▁AUTHOR
- ▁WHISK
- ▁COFFEE
- ▁COMPREHEND
- ▁DISGUISE
- ▁ELZEVIR
- ▁ENTERPRISE
- ▁HOLIDAY
- ▁HORIZON
- ▁IGNORANT
- ▁INTERVIEW
- ▁OLIVER
- ▁RONICKY
- ▁CAPACITY
- ▁DISPOSITION
- ▁EXTERNAL
- ▁OPPOSITION
- ▁REPUBLIC
- ▁WHEAT
- ▁CORPSE
- ▁DARLING
- ▁THRILL
- ▁INHABITANTS
- ▁ORNAMENT
- ▁SHIFT
- ▁RECOGNISE
- ▁SHIVER
- ▁BOAST
- ▁HINT
- ▁BOSTON
- ▁MULTI
- IFYING
- ▁STEAL
- ▁INSTRUCTIONS
- ▁ELECTRIC
- ▁SWING
- ▁SOOTH
- ▁SCALE
- ▁MORLAND
- ▁DISLIKE
- ▁FLATTER
- ▁COACH
- ▁LEIF
- ▁STAMP
- ▁ANYHOW
- ▁MOTIONLESS
- ▁ANDREA
- ▁LOSING
- ▁PAUL
- ▁CAROL
- ▁ADVANC
- ▁IMAGIN
- ▁CENTER
- ▁JAR
- ▁SUCCEED
- ▁DISMISS
- CTOR
- ▁RECEIV
- ▁DRAG
- ▁INTENT
- ▁BARBAR
- ▁PUNISH
- ▁ABRUPTLY
- ▁BERNARD
- ▁DECISION
- ▁INDEPENDENT
- ▁PROVINCE
- ▁SLEEVE
- ▁TREMENDOUS
- ▁UNPLEASANT
- ▁LEISURE
- ▁THRONG
- ▁THUMB
- ▁BANNER
- ▁CONTRADICT
- ▁RESTRAIN
- ▁DIVIDED
- ▁WRAPPED
- ▁HAUNT
- ▁SNEER
- CHESTER
- ▁JULIA
- ▁MILD
- ▁CONTACT
- ▁MEANTIME
- ▁NEEDLE
- ▁BLOT
- ▁BARREL
- ▁ISABELLA
- ▁THEATRE
- ▁ESTABLISHMENT
- ▁MARKET
- ▁CHINA
- ▁FORBID
- ▁PERISH
- ▁DOORWAY
- ▁CARLING
- ▁PERIL
- ▁PRIZE
- ▁HATCH
- ▁CURL
- ▁REFER
- ▁DEVOT
- EMBER
- MONT
- ▁CANOE
- ▁PROFESSION
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- ▁CRAWL
- ▁ACTIVITY
- ▁BEWILDER
- ▁BREEZE
- ▁CONTEMPLAT
- ▁DISGUST
- ▁FATIGUE
- ▁MERRICK
- ▁PRAIRIE
- ▁REFORM
- ▁SPECTACLE
- ▁STUDENT
- ▁TUMULT
- ▁UNIFORM
- ▁VIGOROUS
- ▁CONDEMN
- ▁GENUINE
- ▁THOMAS
- ▁ARROW
- ▁PILLOW
- ▁FEEBLE
- ▁RALPH
- ▁SCHEME
- ▁COLLAR
- ▁JUSTINIAN
- ▁NERVE
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- ▁READILY
- ▁VENTUR
- ▁HENCE
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- ▁CRIES
- ▁ANGLE
- ▁RESPECTABLE
- ▁MOAN
- ▁OUTLINE
- BORN
- ▁FIX
- ▁INTEND
- LIA
- ▁CHILL
- ▁CREP
- ▁CHOSE
- ▁SPECULAT
- ▁ATTRIBUT
- ▁BUFFALO
- ▁ENTREAT
- ▁ENVELOP
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- ▁INDUSTRY
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- ▁SLEDGE
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- ▁MONKEY
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- SHIRE
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- ▁SWU
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- ▁LACE
- SPOON
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- ▁BOSOM
- ▁CONSUM
- ▁TIGER
- ▁ITALIAN
- ▁PARSON
- ▁DECLIN
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- ▁GREGGORY
- ▁EXCEED
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- ▁HIDEOUS
- ▁STRU
- ▁ALTERNAT
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- ▁ABILITY
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- ▁COMPOSITION
- ▁DISPLEAS
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- ▁FURNITURE
- ▁GRADUATE
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- ▁JOSEPH
- ▁OCCUPATION
- ▁POSSIBILITY
- ▁RENEWED
- ▁RESPONDED
- ▁PREVAIL
- ▁HOARSE
- ▁PRACTIS
- ▁FAREWELL
- ▁JULIET
- ▁OVERHEAD
- ▁THREAD
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- ▁ADAPT
- ▁FALK
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- ▁MANKIND
- ▁KICK
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- ▁STEEL
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- ▁AUTHORITIES
- ▁HARSH
- ▁FAVORITE
- ▁TALENT
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- ▁AGITATION
- ▁ABBE
- ▁STUCK
- ▁HEDGE
- ▁BIBLE
- ▁RECOLLECTION
- ▁PARTNER
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- ▁ELDE
- ▁BIGGE
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- SCRIBED
- ▁WEIGH
- CARLET
- ▁DECIDE
- ▁RECOLLECT
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- ▁CONSTRUCT
- ▁DEMONSTRAT
- ▁DISTRIBUT
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- ▁GNOME
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- ▁SUSTAIN
- ▁TRADITION
- ▁ADJUST
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- HOLD
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- ▁AMAZEMENT
- ▁LAUNCELOT
- ▁LEAGUE
- ▁MARIPOSA
- ▁POPULATION
- ▁UNEASY
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- ▁INCLINATION
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- ▁TENDENC
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- TEXT
- ▁REFERENCE
- ▁REPOSE
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- DUCED
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- ▁APPROPRIATE
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- ▁HARMONY
- ▁CHARITY
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- ▁AVAIL
- ▁REPULS
- ▁ABSENT
- ▁PULSE
- ▁PRESUM
- ▁CRANE
- ▁NEIGHBOURHOOD
- ▁SUNSET
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- ▁DRANK
- MINOUS
- ▁DECLARATION
- ▁CLOSING
- ▁MEEK
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- ▁PERFORMANCE
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- ▁STRIV
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- MPOSED
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- POST
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- BOUND
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- ▁CONFLICT
- ▁ENCLOS
- ▁EXCLUSION
- ▁EXECUTIVE
- ▁GRANDMOTHER
- ▁HEADQUARTERS
- ▁INFERIOR
- ▁INVISIBLE
- ▁MUTUAL
- ▁OPPONENT
- ▁SENSITIVE
- ▁STUDIED
- ▁TEMPORARY
- ▁UNWILLING
- ▁PERMANENT
- ▁BEDROOM
- ▁NOVEMBER
- ▁COMPLICAT
- ▁DEVOUR
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- ▁SECTION
- ▁PROPOSITION
- ▁DEPRIV
- ▁RYNCH
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- ▁CHERISH
- ▁SPEAR
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- ▁NORMAL
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- LICK
- JA
- ▁ANNOUNC
- FORE
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- ▁HESITATE
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- ▁REALIZE
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- ▁RESTORE
- MOUTH
- FOOT
- ▁DIFFER
- ▁ULTIMATE
- ▁ABUNDANCE
- ▁APPRECIATE
- ▁APPREHENSION
- ▁AVENUE
- ▁AWKWARD
- ▁CETERA
- ▁CHIMNEY
- ▁CLUTCH
- ▁CONVENIENT
- ▁CORRIDOR
- ▁DISTRACT
- ▁ELEGANT
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- ▁ENTHUSIASTIC
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- ▁ATTENTIVE
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- COMING
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- Q
- <sos/eos>
init: null
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: null
zero_infinity: true
joint_net_conf: null
use_preprocessor: true
token_type: bpe
bpemodel: data/en_token_list/bpe_unigram5000/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
short_noise_thres: 0.5
frontend: default
frontend_conf:
n_fft: 512
win_length: 400
hop_length: 160
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 27
num_freq_mask: 2
apply_time_mask: true
time_mask_width_ratio_range:
- 0.0
- 0.05
num_time_mask: 5
normalize: global_mvn
normalize_conf:
stats_file: exp/asr_stats_raw_en_bpe5000_sp/train/feats_stats.npz
model: espnet
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1
length_normalized_loss: false
preencoder: null
preencoder_conf: {}
encoder: e_branchformer
encoder_conf:
output_size: 256
attention_heads: 4
attention_layer_type: rel_selfattn
pos_enc_layer_type: rel_pos
rel_pos_type: latest
cgmlp_linear_units: 1024
cgmlp_conv_kernel: 31
use_linear_after_conv: false
gate_activation: identity
num_blocks: 12
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.1
input_layer: conv2d
layer_drop_rate: 0.0
linear_units: 1024
positionwise_layer_type: linear
use_ffn: true
macaron_ffn: true
merge_conv_kernel: 31
postencoder: null
postencoder_conf: {}
decoder: transformer
decoder_conf:
attention_heads: 4
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.1
src_attention_dropout_rate: 0.1
layer_drop_rate: 0.0
preprocessor: default
preprocessor_conf: {}
required:
- output_dir
- token_list
version: '202209'
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
Subsets and Splits