modelId
stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-29 06:27:22
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 525
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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| card
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|---|---|---|---|---|---|---|---|---|---|
digitaljungle/a2c-AntBulletEnv-v0
|
digitaljungle
| 2023-07-29T14:44:12Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-29T14:43:16Z |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1449.28 +/- 69.15
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
LarryAIDraw/chara_FateStayNightUBW_TohsakaRin_v1
|
LarryAIDraw
| 2023-07-29T14:44:03Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-29T14:16:44Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/100503/tohsaka-rin-or-fatestay-night-unlimited-blade-works
|
keytiong/distilbert-base-uncased-finetuned-emotion
|
keytiong
| 2023-07-29T14:36:29Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-07T10:59:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.923
- name: F1
type: f1
value: 0.9229063505545305
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2243
- Accuracy: 0.923
- F1: 0.9229
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8371 | 1.0 | 250 | 0.3205 | 0.9015 | 0.8987 |
| 0.2512 | 2.0 | 500 | 0.2243 | 0.923 | 0.9229 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
wilson-wei/distilhubert-finetuned-gtzan
|
wilson-wei
| 2023-07-29T14:36:16Z | 162 | 0 |
transformers
|
[
"transformers",
"pytorch",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"base_model:ntu-spml/distilhubert",
"base_model:finetune:ntu-spml/distilhubert",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-07-29T07:43:18Z |
---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: train
split: train
args: train
metrics:
- name: Accuracy
type: accuracy
value: 0.87
---
<!-- 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. -->
# distilhubert-finetuned-gtzan
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Accuracy: 0.87
- Loss: 0.9175
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 17
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| 2.2295 | 1.0 | 113 | 0.4 | 2.1501 |
| 1.7373 | 2.0 | 226 | 0.6 | 1.6194 |
| 1.3497 | 3.0 | 339 | 0.72 | 1.1717 |
| 1.0135 | 4.0 | 452 | 0.71 | 1.0361 |
| 0.6951 | 5.0 | 565 | 0.77 | 0.7724 |
| 0.4279 | 6.0 | 678 | 0.76 | 0.7731 |
| 0.5178 | 7.0 | 791 | 0.82 | 0.6048 |
| 0.141 | 8.0 | 904 | 0.79 | 0.7486 |
| 0.2459 | 9.0 | 1017 | 0.85 | 0.6326 |
| 0.0331 | 10.0 | 1130 | 0.82 | 0.8706 |
| 0.0214 | 11.0 | 1243 | 0.81 | 1.0099 |
| 0.0744 | 12.0 | 1356 | 0.8 | 1.0210 |
| 0.0043 | 13.0 | 1469 | 0.82 | 0.9894 |
| 0.0032 | 14.0 | 1582 | 0.82 | 0.9803 |
| 0.0025 | 15.0 | 1695 | 0.83 | 1.0476 |
| 0.0021 | 16.0 | 1808 | 0.82 | 1.0483 |
| 0.0183 | 17.0 | 1921 | 0.87 | 0.9175 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1
- Datasets 2.14.0
- Tokenizers 0.13.3
|
NasimB/all-base-miss-children_stories-seed
|
NasimB
| 2023-07-29T14:33:26Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:generator",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-29T11:39:23Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: all-base-miss-children_stories-seed
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. -->
# all-base-miss-children_stories-seed
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 4.1052
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.3409 | 0.3 | 500 | 5.3419 |
| 5.0286 | 0.6 | 1000 | 4.9295 |
| 4.7037 | 0.9 | 1500 | 4.6832 |
| 4.4246 | 1.2 | 2000 | 4.5359 |
| 4.2918 | 1.5 | 2500 | 4.4200 |
| 4.1862 | 1.8 | 3000 | 4.3170 |
| 4.0317 | 2.1 | 3500 | 4.2468 |
| 3.8811 | 2.4 | 4000 | 4.1944 |
| 3.8583 | 2.7 | 4500 | 4.1408 |
| 3.8126 | 3.0 | 5000 | 4.0981 |
| 3.5694 | 3.31 | 5500 | 4.0926 |
| 3.5839 | 3.61 | 6000 | 4.0590 |
| 3.5599 | 3.91 | 6500 | 4.0275 |
| 3.3793 | 4.21 | 7000 | 4.0365 |
| 3.3133 | 4.51 | 7500 | 4.0243 |
| 3.2975 | 4.81 | 8000 | 4.0125 |
| 3.2367 | 5.11 | 8500 | 4.0151 |
| 3.1249 | 5.41 | 9000 | 4.0169 |
| 3.1267 | 5.71 | 9500 | 4.0142 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
|
aptha/Llama-2-7B-Chat-GGML-FP16
|
aptha
| 2023-07-29T14:32:34Z | 0 | 0 |
transformers
|
[
"transformers",
"PyTorch",
"llama",
"llama-2",
"text-generation",
"en",
"license:other",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-29T14:16:31Z |
---
license: other
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- PyTorch
- llama
- llama-2
---
|
himanimaheshwari3/distilbert-base-uncased-finetuned-outop-y
|
himanimaheshwari3
| 2023-07-29T14:24:07Z | 124 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-07-29T14:23:19Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-outop-y
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-outop-y
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0965
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 5.0634 | 1.0 | 4 | 4.9198 |
| 5.3496 | 2.0 | 8 | 3.3014 |
| 4.8986 | 3.0 | 12 | 5.2252 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.1
- Tokenizers 0.13.3
|
manuu01/rl_course_vizdoom_health_gathering_supreme
|
manuu01
| 2023-07-29T14:21:16Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-29T13:34:58Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 14.04 +/- 5.95
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r manuu01/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
slarkprime/bloom3b-squad-v2
|
slarkprime
| 2023-07-29T13:56:41Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-29T13:56:36Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0.dev0
|
himanimaheshwari3/distilbert-base-uncased-finetuned-outoH
|
himanimaheshwari3
| 2023-07-29T13:56:15Z | 115 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-07-29T13:48:38Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-outoH
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-outoH
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5916
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 4.9816 | 1.0 | 2 | 3.2927 |
| 4.585 | 2.0 | 4 | 4.5041 |
| 4.4543 | 3.0 | 6 | 4.9269 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.1
- Tokenizers 0.13.3
|
hannahbillo/whisper-for-maltese
|
hannahbillo
| 2023-07-29T13:44:49Z | 76 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-07-25T16:26:35Z |
---
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-for-maltese
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-for-maltese
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: 3.3026
- Wer: 120.5033
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2456 | 3.57 | 500 | 2.5059 | 99.2583 |
| 1.346 | 7.14 | 1000 | 2.4891 | 421.1391 |
| 0.7287 | 10.71 | 1500 | 2.7307 | 121.1921 |
| 0.3432 | 14.29 | 2000 | 2.9824 | 131.7086 |
| 0.2834 | 17.86 | 2500 | 3.1282 | 122.0397 |
| 0.2239 | 21.43 | 3000 | 3.2050 | 113.4040 |
| 0.1978 | 25.0 | 3500 | 3.2739 | 118.5695 |
| 0.1476 | 28.57 | 4000 | 3.3026 | 120.5033 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.1
- Tokenizers 0.13.3
|
H-H-T-S/my_awesome_opus_books_model
|
H-H-T-S
| 2023-07-29T13:43:39Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-07-17T13:39:45Z |
---
tags:
- generated_from_trainer
model-index:
- name: my_awesome_opus_books_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_opus_books_model
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 8.4279
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 14.2839 | 0.22 | 4 | 10.3769 |
| 10.7735 | 0.44 | 8 | 8.7250 |
| 9.4369 | 0.67 | 12 | 8.5334 |
| 9.2421 | 0.89 | 16 | 8.4279 |
### Framework versions
- Transformers 4.17.0
- Pytorch 2.0.1+cu118
- Datasets 2.0.0
- Tokenizers 0.13.3
|
Mtc2/Reinforce-Cartpole-v1
|
Mtc2
| 2023-07-29T13:36:57Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-29T08:37:23Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Cartpole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
digitaljungle/ppo-Pyramids
|
digitaljungle
| 2023-07-29T13:35:58Z | 9 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-07-29T13:35:56Z |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
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. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: digitaljungle/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
youssefmecky/llama2-qlora-finetunecommentsanalysis
|
youssefmecky
| 2023-07-29T13:34:17Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-29T13:34:12Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0
|
GuysTrans/t5-base-finetuned-ehealth
|
GuysTrans
| 2023-07-29T13:28:23Z | 102 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-base",
"base_model:finetune:google-t5/t5-base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-07-29T12:33:41Z |
---
license: apache-2.0
base_model: t5-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-base-finetuned-ehealth
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-finetuned-ehealth
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3953
- Rouge1: 16.9989
- Rouge2: 4.8395
- Rougel: 13.1702
- Rougelsum: 15.6472
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 22 | 4.2413 | 9.137 | 1.2333 | 6.9806 | 8.1957 | 18.6901 |
| No log | 2.0 | 44 | 3.5352 | 9.5584 | 1.2176 | 7.2081 | 8.5048 | 18.8187 |
| No log | 3.0 | 66 | 3.3124 | 9.9504 | 1.2105 | 7.4652 | 8.7962 | 18.8187 |
| No log | 4.0 | 88 | 3.2065 | 10.3375 | 1.1847 | 7.7904 | 9.1801 | 18.8947 |
| No log | 5.0 | 110 | 3.1208 | 10.777 | 1.326 | 8.1305 | 9.6488 | 18.8947 |
| No log | 6.0 | 132 | 3.0495 | 11.1502 | 1.4947 | 8.4386 | 9.9076 | 18.924 |
| No log | 7.0 | 154 | 2.9851 | 11.1759 | 1.5744 | 8.4744 | 9.9534 | 18.924 |
| No log | 8.0 | 176 | 2.9232 | 10.5745 | 1.5079 | 8.1888 | 9.4731 | 18.8363 |
| No log | 9.0 | 198 | 2.8663 | 10.3156 | 1.452 | 8.1662 | 9.385 | 18.8947 |
| No log | 10.0 | 220 | 2.8110 | 10.5445 | 1.6067 | 8.3821 | 9.6755 | 18.8538 |
| No log | 11.0 | 242 | 2.7625 | 11.0628 | 1.6957 | 8.7832 | 10.1425 | 18.8947 |
| No log | 12.0 | 264 | 2.7129 | 10.9152 | 1.8386 | 8.7865 | 10.0545 | 18.8538 |
| No log | 13.0 | 286 | 2.6680 | 10.8689 | 1.9024 | 8.6892 | 9.883 | 18.8889 |
| No log | 14.0 | 308 | 2.6235 | 10.4118 | 1.9101 | 8.2442 | 9.4505 | 18.8947 |
| No log | 15.0 | 330 | 2.5810 | 11.2578 | 2.0742 | 8.7641 | 10.2349 | 18.8947 |
| No log | 16.0 | 352 | 2.5412 | 11.815 | 2.1727 | 9.2403 | 10.6655 | 18.9591 |
| No log | 17.0 | 374 | 2.5056 | 11.8324 | 2.1849 | 9.2089 | 10.7361 | 18.9649 |
| No log | 18.0 | 396 | 2.4710 | 11.4611 | 2.1406 | 8.9329 | 10.4319 | 18.8246 |
| No log | 19.0 | 418 | 2.4365 | 12.0309 | 2.4387 | 9.3966 | 11.0327 | 18.8655 |
| No log | 20.0 | 440 | 2.4039 | 11.9636 | 2.4332 | 9.3448 | 11.0055 | 18.8363 |
| No log | 21.0 | 462 | 2.3734 | 12.709 | 2.6945 | 9.8722 | 11.572 | 18.7602 |
| No log | 22.0 | 484 | 2.3414 | 13.2227 | 2.6249 | 10.1069 | 11.968 | 18.7895 |
| 3.1829 | 23.0 | 506 | 2.3132 | 13.3682 | 2.6082 | 10.1546 | 12.0317 | 18.8246 |
| 3.1829 | 24.0 | 528 | 2.2861 | 14.3195 | 3.0288 | 10.8036 | 12.8973 | 18.8713 |
| 3.1829 | 25.0 | 550 | 2.2592 | 14.1227 | 2.6271 | 10.6826 | 12.7174 | 18.9064 |
| 3.1829 | 26.0 | 572 | 2.2324 | 14.3697 | 2.8314 | 10.9239 | 13.0199 | 18.9064 |
| 3.1829 | 27.0 | 594 | 2.2054 | 14.4512 | 2.9546 | 11.0853 | 13.1193 | 18.9474 |
| 3.1829 | 28.0 | 616 | 2.1810 | 15.12 | 3.3732 | 11.5842 | 13.6805 | 18.9474 |
| 3.1829 | 29.0 | 638 | 2.1563 | 14.8242 | 3.2998 | 11.2467 | 13.3076 | 18.9474 |
| 3.1829 | 30.0 | 660 | 2.1333 | 15.0384 | 3.3988 | 11.4676 | 13.6825 | 18.9123 |
| 3.1829 | 31.0 | 682 | 2.1102 | 14.9877 | 3.3844 | 11.4417 | 13.5657 | 18.9591 |
| 3.1829 | 32.0 | 704 | 2.0884 | 14.9699 | 3.4128 | 11.4893 | 13.6109 | 18.9591 |
| 3.1829 | 33.0 | 726 | 2.0646 | 14.7391 | 3.0552 | 11.2351 | 13.3809 | 18.9591 |
| 3.1829 | 34.0 | 748 | 2.0419 | 14.9203 | 3.1074 | 11.2239 | 13.4966 | 18.9591 |
| 3.1829 | 35.0 | 770 | 2.0203 | 15.1875 | 3.2249 | 11.3843 | 13.8011 | 18.9591 |
| 3.1829 | 36.0 | 792 | 1.9988 | 15.1457 | 3.1865 | 11.5238 | 13.7114 | 18.9591 |
| 3.1829 | 37.0 | 814 | 1.9786 | 15.2334 | 3.3739 | 11.6124 | 13.8956 | 18.9591 |
| 3.1829 | 38.0 | 836 | 1.9580 | 15.7105 | 3.4331 | 11.8577 | 14.2217 | 18.9474 |
| 3.1829 | 39.0 | 858 | 1.9387 | 15.6612 | 3.5588 | 12.0279 | 14.2183 | 18.9474 |
| 3.1829 | 40.0 | 880 | 1.9210 | 15.8692 | 3.5665 | 12.0078 | 14.3505 | 18.9591 |
| 3.1829 | 41.0 | 902 | 1.9041 | 15.9888 | 3.6914 | 12.0342 | 14.3375 | 18.9591 |
| 3.1829 | 42.0 | 924 | 1.8834 | 15.9551 | 3.6863 | 12.0562 | 14.5444 | 18.9591 |
| 3.1829 | 43.0 | 946 | 1.8648 | 15.9107 | 3.9128 | 12.1663 | 14.5029 | 18.9591 |
| 3.1829 | 44.0 | 968 | 1.8468 | 15.9831 | 3.8588 | 12.196 | 14.5114 | 18.9591 |
| 3.1829 | 45.0 | 990 | 1.8290 | 15.9072 | 3.6844 | 12.1007 | 14.5031 | 18.9591 |
| 2.4484 | 46.0 | 1012 | 1.8127 | 15.9918 | 3.792 | 12.2569 | 14.5287 | 18.9591 |
| 2.4484 | 47.0 | 1034 | 1.7959 | 15.9685 | 3.7664 | 12.1033 | 14.473 | 18.9591 |
| 2.4484 | 48.0 | 1056 | 1.7799 | 15.7128 | 3.505 | 11.9947 | 14.216 | 18.9591 |
| 2.4484 | 49.0 | 1078 | 1.7636 | 15.8033 | 3.6874 | 12.1043 | 14.37 | 18.9591 |
| 2.4484 | 50.0 | 1100 | 1.7487 | 15.914 | 3.758 | 12.1635 | 14.4603 | 18.9591 |
| 2.4484 | 51.0 | 1122 | 1.7338 | 15.7088 | 3.7272 | 11.951 | 14.2862 | 18.9591 |
| 2.4484 | 52.0 | 1144 | 1.7202 | 15.7231 | 3.6274 | 12.0492 | 14.3036 | 18.9591 |
| 2.4484 | 53.0 | 1166 | 1.7081 | 15.6734 | 3.5837 | 11.9265 | 14.2674 | 18.9591 |
| 2.4484 | 54.0 | 1188 | 1.6935 | 15.6501 | 3.5574 | 11.8579 | 14.2387 | 18.9591 |
| 2.4484 | 55.0 | 1210 | 1.6793 | 15.8984 | 3.8029 | 12.0981 | 14.3888 | 18.9591 |
| 2.4484 | 56.0 | 1232 | 1.6666 | 15.7263 | 3.6691 | 12.0325 | 14.3152 | 18.9591 |
| 2.4484 | 57.0 | 1254 | 1.6516 | 15.8016 | 3.6151 | 12.0349 | 14.3556 | 18.9591 |
| 2.4484 | 58.0 | 1276 | 1.6385 | 15.8773 | 3.7501 | 12.1887 | 14.456 | 18.9591 |
| 2.4484 | 59.0 | 1298 | 1.6266 | 16.0252 | 3.8027 | 12.3099 | 14.5017 | 18.9591 |
| 2.4484 | 60.0 | 1320 | 1.6151 | 16.29 | 3.9544 | 12.5391 | 14.7691 | 18.9591 |
| 2.4484 | 61.0 | 1342 | 1.6034 | 16.2891 | 4.0512 | 12.5053 | 14.8155 | 18.9591 |
| 2.4484 | 62.0 | 1364 | 1.5925 | 16.1871 | 4.0482 | 12.4821 | 14.6986 | 18.9591 |
| 2.4484 | 63.0 | 1386 | 1.5812 | 16.1774 | 3.9903 | 12.4861 | 14.7798 | 18.9591 |
| 2.4484 | 64.0 | 1408 | 1.5716 | 16.1663 | 3.9399 | 12.4316 | 14.7449 | 18.9591 |
| 2.4484 | 65.0 | 1430 | 1.5623 | 16.4455 | 4.2777 | 12.7206 | 14.9193 | 18.9591 |
| 2.4484 | 66.0 | 1452 | 1.5517 | 16.466 | 4.2148 | 12.7613 | 15.052 | 18.9591 |
| 2.4484 | 67.0 | 1474 | 1.5414 | 16.5696 | 4.193 | 12.6949 | 15.1064 | 18.9591 |
| 2.4484 | 68.0 | 1496 | 1.5347 | 16.7602 | 4.4803 | 12.938 | 15.3339 | 18.9649 |
| 2.1379 | 69.0 | 1518 | 1.5278 | 16.6684 | 4.3943 | 12.9152 | 15.2626 | 18.9649 |
| 2.1379 | 70.0 | 1540 | 1.5193 | 16.7462 | 4.4151 | 12.9251 | 15.3619 | 18.9649 |
| 2.1379 | 71.0 | 1562 | 1.5104 | 16.658 | 4.4187 | 12.8792 | 15.2538 | 18.9591 |
| 2.1379 | 72.0 | 1584 | 1.5026 | 16.8475 | 4.481 | 13.0381 | 15.4041 | 18.9591 |
| 2.1379 | 73.0 | 1606 | 1.4944 | 16.9066 | 4.6433 | 13.1838 | 15.489 | 18.9591 |
| 2.1379 | 74.0 | 1628 | 1.4864 | 16.9434 | 4.6401 | 13.0527 | 15.4966 | 18.9591 |
| 2.1379 | 75.0 | 1650 | 1.4801 | 16.9744 | 4.694 | 13.1585 | 15.5739 | 19.0 |
| 2.1379 | 76.0 | 1672 | 1.4733 | 17.0546 | 4.6971 | 13.0968 | 15.633 | 19.0 |
| 2.1379 | 77.0 | 1694 | 1.4668 | 17.1603 | 4.7771 | 13.2896 | 15.7112 | 19.0 |
| 2.1379 | 78.0 | 1716 | 1.4607 | 17.086 | 4.7411 | 13.2587 | 15.6842 | 19.0 |
| 2.1379 | 79.0 | 1738 | 1.4552 | 17.0322 | 4.7652 | 13.2693 | 15.711 | 19.0 |
| 2.1379 | 80.0 | 1760 | 1.4493 | 17.1045 | 4.8492 | 13.2752 | 15.7876 | 19.0 |
| 2.1379 | 81.0 | 1782 | 1.4445 | 17.0275 | 4.8688 | 13.2621 | 15.7825 | 19.0 |
| 2.1379 | 82.0 | 1804 | 1.4392 | 17.0985 | 4.8148 | 13.2498 | 15.7718 | 19.0 |
| 2.1379 | 83.0 | 1826 | 1.4337 | 17.1395 | 4.8482 | 13.357 | 15.8122 | 19.0 |
| 2.1379 | 84.0 | 1848 | 1.4294 | 17.0411 | 4.8237 | 13.3126 | 15.7736 | 19.0 |
| 2.1379 | 85.0 | 1870 | 1.4254 | 17.1265 | 4.8691 | 13.3033 | 15.81 | 19.0 |
| 2.1379 | 86.0 | 1892 | 1.4212 | 16.9899 | 4.7712 | 13.1785 | 15.6416 | 19.0 |
| 2.1379 | 87.0 | 1914 | 1.4176 | 17.0389 | 4.7936 | 13.219 | 15.7048 | 19.0 |
| 2.1379 | 88.0 | 1936 | 1.4141 | 17.2266 | 4.9339 | 13.3935 | 15.8629 | 19.0 |
| 2.1379 | 89.0 | 1958 | 1.4108 | 17.0176 | 4.8752 | 13.2829 | 15.7145 | 19.0 |
| 2.1379 | 90.0 | 1980 | 1.4084 | 17.154 | 4.9912 | 13.3718 | 15.8255 | 19.0 |
| 1.9718 | 91.0 | 2002 | 1.4061 | 17.0783 | 4.9171 | 13.2617 | 15.7864 | 19.0 |
| 1.9718 | 92.0 | 2024 | 1.4037 | 17.0967 | 4.9393 | 13.2608 | 15.8054 | 19.0 |
| 1.9718 | 93.0 | 2046 | 1.4020 | 17.1524 | 4.995 | 13.332 | 15.8315 | 19.0 |
| 1.9718 | 94.0 | 2068 | 1.4001 | 17.1357 | 4.9699 | 13.3064 | 15.7932 | 19.0 |
| 1.9718 | 95.0 | 2090 | 1.3988 | 17.0758 | 4.8899 | 13.2231 | 15.7124 | 19.0 |
| 1.9718 | 96.0 | 2112 | 1.3976 | 16.9842 | 4.8395 | 13.173 | 15.653 | 19.0 |
| 1.9718 | 97.0 | 2134 | 1.3967 | 17.0425 | 4.8395 | 13.2243 | 15.6976 | 19.0 |
| 1.9718 | 98.0 | 2156 | 1.3960 | 16.9842 | 4.8395 | 13.173 | 15.653 | 19.0 |
| 1.9718 | 99.0 | 2178 | 1.3955 | 16.9842 | 4.8395 | 13.173 | 15.653 | 19.0 |
| 1.9718 | 100.0 | 2200 | 1.3953 | 16.9989 | 4.8395 | 13.1702 | 15.6472 | 19.0 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.1
- Tokenizers 0.13.3
|
ineedtolearnrl/ppo-LunarLander-v2
|
ineedtolearnrl
| 2023-07-29T13:26:57Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-29T13:26:38Z |
---
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: 280.05 +/- 18.23
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
...
```
|
sriawadh/llama2-qlora-finetunined-french
|
sriawadh
| 2023-07-29T13:22:09Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-29T13:22:02Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0.dev0
|
itoh5588/distilbert-base-uncased-finetuned-emotion
|
itoh5588
| 2023-07-29T13:12:38Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-20T10:18:30Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9345
- name: F1
type: f1
value: 0.9347579750092575
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1583
- Accuracy: 0.9345
- F1: 0.9348
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.1701 | 1.0 | 250 | 0.1701 | 0.9335 | 0.9343 |
| 0.1114 | 2.0 | 500 | 0.1583 | 0.9345 | 0.9348 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 1.16.1
- Tokenizers 0.13.3
|
pradeepiisc/xlm-roberta-base-finetuned-panx-de-fr
|
pradeepiisc
| 2023-07-29T13:07:41Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-07-29T12:22:30Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de-fr
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1602
- F1: 0.8609
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2857 | 1.0 | 715 | 0.1899 | 0.8268 |
| 0.1515 | 2.0 | 1430 | 0.1627 | 0.8499 |
| 0.0965 | 3.0 | 2145 | 0.1602 | 0.8609 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.1+cu118
- Datasets 2.10.1
- Tokenizers 0.13.3
|
Maldopast/distilhubert-finetuned-gtzan-v2
|
Maldopast
| 2023-07-29T12:55:57Z | 178 | 0 |
transformers
|
[
"transformers",
"pytorch",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-07-29T12:51:50Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan-v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilhubert-finetuned-gtzan-v2
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4006
- Accuracy: 0.89
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4786 | 1.0 | 225 | 1.3772 | 0.67 |
| 1.0539 | 2.0 | 450 | 0.8660 | 0.78 |
| 0.8426 | 3.0 | 675 | 0.7087 | 0.79 |
| 0.5203 | 4.0 | 900 | 0.6213 | 0.8 |
| 0.2969 | 5.0 | 1125 | 0.5474 | 0.8 |
| 0.2166 | 6.0 | 1350 | 0.5594 | 0.86 |
| 0.0563 | 7.0 | 1575 | 0.3808 | 0.91 |
| 0.1048 | 8.0 | 1800 | 0.4006 | 0.89 |
### Framework versions
- Transformers 4.30.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.0
- Tokenizers 0.13.3
|
MBZUAI/bactrian-x-llama-13b-merged
|
MBZUAI
| 2023-07-29T12:48:47Z | 16 | 2 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"arxiv:2305.15011",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-19T12:36:05Z |
---
license: mit
---
#### Current Training Steps: 108,000
This repo contains a merged model using low-rank adaptation (LoRA) for LLaMA-13b
fit on the [Stanford-Alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca)
and [databricks-dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data) data in 52 languages.
### Dataset Creation
1. English Instructions: The English instuctions are obtained from [alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca), and [dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data).
2. Instruction Translation: The instructions (and inputs) are translated into the target languages using Google Translation API (conducted on April 2023).
3. Output Generation: We generate output from `gpt-3.5-turbo` for each language (conducted on April 2023).
<h3 align="center">
<img src="https://raw.githubusercontent.com/fajri91/eval_picts/master/BactrianX_dataset.jpg" width="950" align="center">
</h3>
### Training Parameters
The code for training the model is provided in our [github](https://github.com/mbzuai-nlp/Bactrian-X), which is adapted from [Alpaca-LoRA](https://github.com/tloen/alpaca-lora).
This version of the weights was trained with the following hyperparameters:
- Epochs: 10
- Batch size: 128
- Cutoff length: 512
- Learning rate: 3e-4
- Lora _r_: 64
- Lora target modules: q_proj, k_proj, v_proj, o_proj
That is:
```
python finetune.py \
--base_model='decapoda-research/llama-13b-hf' \
--num_epochs=5 \
--batch_size=128 \
--cutoff_len=512 \
--group_by_length \
--output_dir='./bactrian-x-llama-13b-lora' \
--lora_target_modules='q_proj,k_proj,v_proj,o_proj' \
--lora_r=64 \
--micro_batch_size=32
```
Instructions for running it can be found at https://github.com/MBZUAI-nlp/Bactrian-X.
### Discussion of Biases
(1) Translation bias; (2) Potential English-culture bias in the translated dataset.
### Citation Information
```
@misc{li2023bactrianx,
title={Bactrian-X : A Multilingual Replicable Instruction-Following Model with Low-Rank Adaptation},
author={Haonan Li and Fajri Koto and Minghao Wu and Alham Fikri Aji and Timothy Baldwin},
year={2023},
eprint={2305.15011},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
NasimB/cbt-rarity-seed
|
NasimB
| 2023-07-29T12:46:30Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:generator",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-29T03:18:23Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: cbt-rarity-seed
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. -->
# cbt-rarity-seed
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 4.1015
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 6.3444 | 0.29 | 500 | 5.3437 |
| 5.0325 | 0.58 | 1000 | 4.9351 |
| 4.7076 | 0.87 | 1500 | 4.6882 |
| 4.4438 | 1.17 | 2000 | 4.5477 |
| 4.2923 | 1.46 | 2500 | 4.4284 |
| 4.1863 | 1.75 | 3000 | 4.3230 |
| 4.0772 | 2.04 | 3500 | 4.2506 |
| 3.8897 | 2.33 | 4000 | 4.2060 |
| 3.8637 | 2.62 | 4500 | 4.1512 |
| 3.8278 | 2.91 | 5000 | 4.1009 |
| 3.6372 | 3.21 | 5500 | 4.0951 |
| 3.5835 | 3.5 | 6000 | 4.0674 |
| 3.5688 | 3.79 | 6500 | 4.0332 |
| 3.4818 | 4.08 | 7000 | 4.0308 |
| 3.3077 | 4.37 | 7500 | 4.0275 |
| 3.3134 | 4.66 | 8000 | 4.0145 |
| 3.2991 | 4.95 | 8500 | 4.0005 |
| 3.1588 | 5.24 | 9000 | 4.0125 |
| 3.1309 | 5.54 | 9500 | 4.0123 |
| 3.1268 | 5.83 | 10000 | 4.0113 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
|
digitaljungle/ppo-SnowballTarget
|
digitaljungle
| 2023-07-29T12:41:16Z | 6 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-07-29T12:41:13Z |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
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. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: digitaljungle/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
digitaljungle/reinfoce-copter-v1
|
digitaljungle
| 2023-07-29T12:22:49Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-29T12:22:44Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: reinfoce-copter-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 40.80 +/- 25.21
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
pythonist/bert-base-cased-PubmedQAmodel
|
pythonist
| 2023-07-29T12:00:50Z | 113 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-04-25T10:34:41Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-cased-PubmedQAmodel
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-cased-PubmedQAmodel
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1602
## 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: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 21 | 3.3780 |
| No log | 2.0 | 42 | 3.2694 |
| No log | 3.0 | 63 | 3.1892 |
| No log | 4.0 | 84 | 3.1536 |
| No log | 5.0 | 105 | 3.1454 |
| No log | 6.0 | 126 | 3.1754 |
| No log | 7.0 | 147 | 3.1372 |
| No log | 8.0 | 168 | 3.1602 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
TFLai/falcon-7b-4bit-alpaca
|
TFLai
| 2023-07-29T11:57:34Z | 5 | 1 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-29T11:57:06Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0.dev0
|
HaziqRazali/a2c-AntBulletEnv-v0
|
HaziqRazali
| 2023-07-29T11:55:36Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-29T11:54:30Z |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1202.55 +/- 340.95
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
TFLai/gpt2-medium-4bit-alpaca
|
TFLai
| 2023-07-29T11:53:27Z | 3 | 1 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-29T11:53:02Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0.dev0
|
TFLai/gpt-neo-1.3B-4bit-alpaca
|
TFLai
| 2023-07-29T11:45:23Z | 3 | 1 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-29T11:44:28Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0.dev0
|
himanimaheshwari3/himani_model_mlm1
|
himanimaheshwari3
| 2023-07-29T11:40:39Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"roberta",
"fill-mask",
"generated_from_keras_callback",
"base_model:distilbert/distilroberta-base",
"base_model:finetune:distilbert/distilroberta-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-07-29T11:40:02Z |
---
license: apache-2.0
base_model: distilroberta-base
tags:
- generated_from_keras_callback
model-index:
- name: himanimaheshwari3/himani_model_mlm1
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# himanimaheshwari3/himani_model_mlm1
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.7110
- Validation Loss: 2.1343
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.7110 | 2.1343 | 0 |
### Framework versions
- Transformers 4.31.0
- TensorFlow 2.12.0
- Datasets 2.14.1
- Tokenizers 0.13.3
|
llm-book/bert-base-japanese-v3-jsts
|
llm-book
| 2023-07-29T11:27:18Z | 3,013 | 2 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"ja",
"dataset:llm-book/JGLUE",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-11T15:27:32Z |
---
language:
- ja
license: apache-2.0
library_name: transformers
datasets:
- llm-book/JGLUE
---
# bert-base-japanese-v3-jsts
「[大規模言語モデル入門](https://www.amazon.co.jp/dp/4297136333)」の第5章で紹介している(意味類似度計算)のモデルです。
[cl-tohoku/bert-base-japanese-v3](https://huggingface.co/cl-tohoku/bert-base-japanese-v3)を[JGLUE](https://huggingface.co/datasets/llm-book/JGLUE)のJSTSデータセットでファインチューニングして構築されています。
## 関連リンク
* [GitHubリポジトリ](https://github.com/ghmagazine/llm-book)
* [Colabノートブック（訓練）](https://colab.research.google.com/github/ghmagazine/llm-book/blob/main/chapter5/5-4-sts-finetuning.ipynb)
* [Colabノートブック（推論）](https://colab.research.google.com/github/ghmagazine/llm-book/blob/main/chapter5/5-4-sts-analysis.ipynb)
* [データセット](https://huggingface.co/datasets/llm-book/JGLUE)
* [大規模言語モデル入門（Amazon.co.jp）](https://www.amazon.co.jp/dp/4297136333/)
* [大規模言語モデル入門（gihyo.jp）](https://gihyo.jp/book/2023/978-4-297-13633-8)
## 使い方
```python
from transformers import pipeline
text_sim_pipeline = pipeline(
model="llm-book/bert-base-japanese-v3-jsts",
function_to_apply="none",
)
text = "川べりでサーフボードを持った人たちがいます"
sim_text = "サーファーたちが川べりに立っています"
# textとsim_textの類似度を計算
result = text_sim_pipeline({"text": text, "text_pair": sim_text})
print(result["score"])
# 3.5703558921813965
```
## ライセンス
[Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0)
|
TFLai/pythia-2.8b-4bit-alpaca
|
TFLai
| 2023-07-29T11:18:22Z | 3 | 1 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-29T11:16:16Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0.dev0
|
sunavalon/SD_Colab
|
sunavalon
| 2023-07-29T10:54:00Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-29T10:54:00Z |
---
license: creativeml-openrail-m
---
|
andreisvirida/squad-bloom-3b
|
andreisvirida
| 2023-07-29T10:40:31Z | 3 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-29T10:40:30Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
slarkprime/vicuna-squad-v2
|
slarkprime
| 2023-07-29T10:37:59Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-27T09:53:12Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0.dev0
|
andreisvirida/my_lora_test_1
|
andreisvirida
| 2023-07-29T10:32:27Z | 1 | 0 |
peft
|
[
"peft",
"doi:10.57967/hf/0935",
"region:us"
] | null | 2023-07-29T10:21:46Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
toto10/embeddings
|
toto10
| 2023-07-29T09:59:31Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-02-17T12:09:26Z |
---
license: creativeml-openrail-m
---
|
KingKazma/cnn_dailymail_gpt2_p_tuning_500_10_3000_8_e9_s6789_v3_l5_v20
|
KingKazma
| 2023-07-29T09:45:57Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-29T09:45:56Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
natmin322/model
|
natmin322
| 2023-07-29T09:44:14Z | 79 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-07-29T09:31:38Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# model
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 52
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
KingKazma/cnn_dailymail_gpt2_p_tuning_500_10_3000_8_e7_s6789_v3_l5_v50
|
KingKazma
| 2023-07-29T09:43:46Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-29T09:43:45Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
KingKazma/cnn_dailymail_gpt2_p_tuning_500_10_3000_8_e8_s6789_v3_l5_v20
|
KingKazma
| 2023-07-29T09:39:09Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-29T09:39:08Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
shubhxms/q-FrozenLake-v1-4x4-noSlippery
|
shubhxms
| 2023-07-29T09:36:44Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-29T09:36:42Z |
---
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="shubhxms/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"])
```
|
KingKazma/cnn_dailymail_gpt2_p_tuning_500_10_3000_8_e6_s6789_v3_l5_v50
|
KingKazma
| 2023-07-29T09:35:35Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-29T09:35:33Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
KingKazma/cnn_dailymail_gpt2_p_tuning_500_10_3000_8_e7_s6789_v3_l5_v20
|
KingKazma
| 2023-07-29T09:32:22Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-29T09:32:21Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
bitsanlp/distilbert-ishate-29k
|
bitsanlp
| 2023-07-29T09:29:43Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-29T09:21:02Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: distilbert-ishate-29k
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-ishate-29k
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 17
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.1
- Tokenizers 0.13.3
|
KingKazma/cnn_dailymail_gpt2_p_tuning_500_10_3000_8_e5_s6789_v3_l5_v50
|
KingKazma
| 2023-07-29T09:27:24Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-29T09:27:22Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
edures/ppo-Pyramids
|
edures
| 2023-07-29T09:24:12Z | 9 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-07-29T09:24:07Z |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
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. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: edures/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
sm136599/chatfoodie-koalpaca-polyglot-5_8b-6165step-4batch_3epoch
|
sm136599
| 2023-07-29T09:23:41Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-29T09:23:36Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0.dev0
|
KingKazma/cnn_dailymail_gpt2_p_tuning_500_10_3000_8_e4_s6789_v3_l5_v50
|
KingKazma
| 2023-07-29T09:19:13Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-29T09:19:11Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
tsrdjan/scooby
|
tsrdjan
| 2023-07-29T09:14:21Z | 0 | 2 | null |
[
"resume",
"cv",
"profile",
"profile-page",
"osint",
"research",
"crawling",
"image-classification",
"sr",
"en",
"license:gpl-3.0",
"region:us"
] |
image-classification
| 2023-07-29T08:47:11Z |
---
license: gpl-3.0
language:
- sr
- en
pipeline_tag: image-classification
tags:
- resume
- cv
- profile
- profile-page
- osint
- research
- crawling
---
# Scooby
Scooby is the first model created for the purpose of detecting profile pages while crawling.
It is trained mainly on scraped data from the sites of Serbian universities, but around 20%
of the data is scraped from websites of some organizations or companies.
## Preprocessing
For preprocessing, 2880x1620 resolution images were rescaled down to 360x480 (by mistake).
Number of channels is one, grayscale.
|
hoang14/qlora_chat_29_07_23
|
hoang14
| 2023-07-29T09:14:04Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-29T09:13:58Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
|
KingKazma/cnn_dailymail_gpt2_p_tuning_500_10_3000_8_e3_s6789_v3_l5_v50
|
KingKazma
| 2023-07-29T09:11:03Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-29T09:11:01Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
bitsanlp/hatebert-ishate-29k
|
bitsanlp
| 2023-07-29T09:10:57Z | 22 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:GroNLP/hateBERT",
"base_model:finetune:GroNLP/hateBERT",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-29T08:43:23Z |
---
base_model: GroNLP/hateBERT
tags:
- generated_from_trainer
model-index:
- name: hatebert-ishate-26k
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. -->
# hatebert-ishate-26k
This model is a fine-tuned version of [GroNLP/hateBERT](https://huggingface.co/GroNLP/hateBERT) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 17
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.1
- Tokenizers 0.13.3
|
KingKazma/cnn_dailymail_gpt2_p_tuning_500_10_3000_8_e3_s6789_v3_l5_v20
|
KingKazma
| 2023-07-29T09:05:11Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-29T09:05:10Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
TFLai/bloomz-1b7-4bit-alpaca
|
TFLai
| 2023-07-29T09:04:11Z | 2 | 1 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-29T09:01:32Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0.dev0
|
KingKazma/cnn_dailymail_gpt2_p_tuning_500_10_3000_8_e2_s6789_v3_l5_v20
|
KingKazma
| 2023-07-29T08:58:24Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-29T08:58:23Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
KingKazma/cnn_dailymail_gpt2_p_tuning_500_10_3000_8_e1_s6789_v3_l5_v50
|
KingKazma
| 2023-07-29T08:54:41Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-29T08:54:39Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
PeppoCola/IssueReportClassifier-NLBSE22
|
PeppoCola
| 2023-07-29T08:47:45Z | 117 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"en",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-21T09:29:18Z |
---
license: gpl-3.0
language:
- en
metrics:
- f1
---
# Model Card
## Model Details
- Model Name: IssueReportClassifier-NLBSE22
- Base Model: RoBERTa
- Dataset: NLBSE22
- Model Type: Fine-tuned
- Model Version: 1.0
- Model Date: 2023-03-21
## Model Description
IssueReportClassifier-NLBSE22 is a RoBERTa model which is fine-tuned on the NLBSE22 dataset.
The model is trained to classify issue reports from GitHub into three categories: bug, enhancement, and question.
The model is trained on a dataset of labeled issue reports and is designed to predict the category of a new issue report based on its text content (title and body).
## Dataset
| Category | Training Set | Test Set |
|------------|--------------|-------------|
| bug | 361,239 (50%) | 40,152 (49.9%) |
| enhancement | 299,287 (41.4%) | 33,290 (41.3%) |
| question | 62,373 (8.6%) | 7,076 (8.8%) |
## Data preprocessing
The data used for training was preprocessed with [ekphrasis](https://github.com/cbaziotis/ekphrasis), adding some regular expressions to remove code, images and URLs.
Check out our [GitHub](https://github.com/collab-uniba/Issue-Report-Classification-Using-RoBERTa) code for more information about this.
## Metrics
The model is evaluated using the following metrics:
- Accuracy
- Precision
- Recall
- F1 Score (micro and macro average)
## References
- [NLBSE22 Dataset](https://nlbse2022.github.io/tools/)
## Cite our work
```
@inproceedings{Colavito-2022,
title = {Issue Report Classification Using Pre-trained Language Models},
booktitle = {2022 IEEE/ACM 1st International Workshop on Natural Language-Based Software Engineering (NLBSE)},
author = {Colavito, Giuseppe and Lanubile, Filippo and Novielli, Nicole},
year = {2022},
month = may,
pages = {29--32},
doi = {10.1145/3528588.3528659},
abstract = {This paper describes our participation in the tool competition organized in the scope of the 1st International Workshop on Natural Language-based Software Engineering. We propose a supervised approach relying on fine-tuned BERT-based language models for the automatic classification of GitHub issues. We experimented with different pre-trained models, achieving the best performance with fine-tuned RoBERTa (F1 = .8591).},
keywords = {Issue classification, BERT, deep learning, labeling unstructured data,
software maintenance and evolution},
}
```
|
KingKazma/cnn_dailymail_gpt2_p_tuning_500_10_3000_8_e0_s6789_v3_l5_v50
|
KingKazma
| 2023-07-29T08:46:30Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-29T08:46:28Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
Outimus/QualityOfLifeSuit_Omar92
|
Outimus
| 2023-07-29T08:42:52Z | 2 | 1 |
transformers
|
[
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2023-07-29T08:40:56Z |
## Thank you to all the valuable contributors. Kindly submit any pull requests to the development branch instead of the main branch. Your efforts are greatly appreciated.
# ComfyUI-extra-nodes - quality of life
Extra nodes to be used in ComfyUI, including a new ChatGPT node for generating natural language responses.
## ComfyUI
ComfyUI is an advanced node-based UI that utilizes Stable Diffusion, allowing you to create customized workflows such as image post-processing or conversions.
## How to install
Download the zip file.
Extract to ..\ComfyUI\custom_nodes.
Restart ComfyUI if it was running (reloading the web is not enough).
You will find my nodes under the new group O/....
## How to update
- quality of life will auto update each time you run comfyUI
- when you run comfyUI, the suit will generate a config file
The file looks like this :
{
"autoUpdate": true,
"branch": "main",
"openAI_API_Key": "sk-#################################"
}
- if you want to stop autoUpdate edit `config.json` set "autoUpdate": false
## Current nodes
## openAI suite
## ChatGPT simple
This node harnesses the power of chatGPT, an advanced language model that can generate detailed image descriptions from a small input.
- you need to have OpenAI API key , which you can find at https://beta.openai.com/docs/developer-apis/overview
- Once you have your API key, add it to the `config.json` file
- I have made it a separate file, so that the API key doesn't get embedded in the generated images.
## advanced openAI
- load_openAI:load openAI module
### ChatGPT
- Chat_Message: creates a message to be sent to chatGPT
- combine_chat_messages : combine 2 messages together
- Chat completion: send the messages to ChatGPT and receive answer
### DalE-2
- create image
- variation_image
## String Suit
This set of nodes adds support for string manipulation and includes a tool to generate an image from text.
- Concat String: This node combines two strings together.
- Trim String: This node removes any extra spaces at the start or end of a string.
- Replace String : This nodes replace part of the text with another part.
- Debug String: This node writes the string to the console.
- Debug String route: This node writes the string to the console but will output the same string so that you can add it in middle of a route.
### String2image
This node generates an image based on text, which can be used with ControlNet to add text to the image. The tool supports various fonts; you can add the font you want in the fonts folder. If you load the example image in ComfyUI, the workflow that generated it will be loaded.
### save text
- saveTextToFile: this node will save input text to a file "the file will be generated inside /output folder"
### NSP
"node soup" which is a collection of different values categorized under different terminologies that you can use to generate new prompts easily
- RandomNSP: returns a random value from the selected terminology
- ConcatRandomNSP: will append a random value from the selected terminology to the input text (can be used mid route)
## latentTools
### selectLatentFromBatch
this node allow you to select 1 latent image from image batch
for example if you generate 4 images, it allow you to select 1 of them to do further processing on it
or you can use it to process them sequentially
### LatentUpscaleFactor & LatentUpscaleFactorSimple
This node is a variant of the original LatentUpscale tool, but instead of using width and height, you use a multiply number. For example, if the original image dimensions are (512,512) and the mul values are (2,2), the result image will be (1024,1024). You can also use it to downscale by using fractions, e.g., (512,512) mul (.5,.5) → (256,256).
## ImageTools
### ImageScaleFactor & ImageScaleFactorSimple
This node is a variant of the original LatentUpscale tool, but instead of using width and height, you use a multiply number. For example, if the original image dimensions are (512,512) and the mul values are (2,2), the result image will be (1024,1024). You can also use it to downscale by using fractions, e.g., (512,512) mul (.5,.5) → (256,256).
## Thanks for reading my message, and I hope that my tools will help you.
## Contact
### Discord: Omar92#3374
### GitHub: omar92 (https://github.com/omar92)
|
KingKazma/cnn_dailymail_gpt2_p_tuning_500_10_3000_8_e-1_s6789_v3_l5_v20
|
KingKazma
| 2023-07-29T08:37:56Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-29T08:37:55Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
yoniuoa/anuel
|
yoniuoa
| 2023-07-29T08:37:55Z | 0 | 1 | null |
[
"region:us"
] | null | 2023-07-29T08:37:33Z |
Anuel AA - 41.6k - Smile WRLD#9877 | Anuel AA (2016 Era) - 500 Steps - Raaul10#2946
|
NasimB/children_stories-log-rarity-seed
|
NasimB
| 2023-07-29T08:37:02Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:generator",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-29T05:29:24Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: children_stories-log-rarity-seed
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. -->
# children_stories-log-rarity-seed
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 4.0985
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 6.3394 | 0.29 | 500 | 5.3388 |
| 5.0304 | 0.58 | 1000 | 4.9247 |
| 4.6965 | 0.87 | 1500 | 4.6808 |
| 4.4456 | 1.16 | 2000 | 4.5415 |
| 4.2826 | 1.46 | 2500 | 4.4245 |
| 4.1845 | 1.75 | 3000 | 4.3197 |
| 4.0798 | 2.04 | 3500 | 4.2488 |
| 3.8823 | 2.33 | 4000 | 4.2035 |
| 3.8608 | 2.62 | 4500 | 4.1485 |
| 3.8208 | 2.91 | 5000 | 4.0990 |
| 3.6432 | 3.2 | 5500 | 4.0977 |
| 3.5823 | 3.49 | 6000 | 4.0667 |
| 3.557 | 3.78 | 6500 | 4.0335 |
| 3.4785 | 4.07 | 7000 | 4.0293 |
| 3.3042 | 4.37 | 7500 | 4.0265 |
| 3.3049 | 4.66 | 8000 | 4.0117 |
| 3.296 | 4.95 | 8500 | 4.0008 |
| 3.1552 | 5.24 | 9000 | 4.0104 |
| 3.1184 | 5.53 | 9500 | 4.0094 |
| 3.1268 | 5.82 | 10000 | 4.0084 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
|
Ammok/ppo-Huggy
|
Ammok
| 2023-07-29T08:22:05Z | 4 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-07-29T08:21:16Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
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. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: Ammok/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
VinEuro/TaxiRL
|
VinEuro
| 2023-07-29T08:07:01Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-29T08:06:59Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: TaxiRL
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="VinEuro/TaxiRL", 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"])
```
|
VinEuro/q-FrozenLake-v1-4x4-noSlippery
|
VinEuro
| 2023-07-29T08:01:16Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-29T08:01:15Z |
---
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="VinEuro/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"])
```
|
fcski/real_model_L
|
fcski
| 2023-07-29T07:55:29Z | 0 | 15 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-25T10:33:10Z |
---
license: creativeml-openrail-m
---
real_model_N
real_model_N outputs similer image as real_model_L.
But you can download it.
recipe only for personal use.
- A = cityedgemixV1_v125 x 0.5 + kisaragiMix_v22 x 0.5
- B = majicmixRealistic_v6 x 0.5 + shampooMix_v4 x 0.5
- C = A x 0.5 + B x 0.5
- D = fantasticmix_v65 x (1-alpha) + dreamshaper_631BakedVae x alpha (0.4,0.35,0.4,0.45,0.45,0.3,0.3,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0)
- E = C x 0.8 + D x 0.2
- F = E + flat2:-0.7 (lora merge)
- G = F x (1-alpha) + calicomixreal_v20 x alpha (1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0)
- H = F x (1-alpha) + kMain_kMain21 x alpha (1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0)
- I = F x (1-alpha) + lunamix_v10 x alpha (1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0)
- J = F x (1-alpha) + xxmix9realistic_v30 x alpha (1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0)
- K = H x 0.45 + I x 0.55
- L = (G x 0.6 + K x 0.4) x 0.6 + J x 0.4
- M = L x 0.447 + savMIX_xl x 0.553
- N = K x (1-alpha) + kencanmix_v16 x alpha (0.0,0.0,0.0,0.0,0.0,0.0,0.5,0.0,0.5,0.5,0.5,0.0,0.0,0.0,0.0,0.0,0.0,0.11,0.25,0.35,0.5,0.0,0.0,0.0,0.0,0.0)
```
License:creativeml-openrail-m
For personal use. (not for commercial)
OK:Use the model without crediting the creator
NG:Sell images they generate
NG:Run on services that generate images for money
OK:Share merges using this model
NG:Sell this model or merges using this model
OK:Have different permissions when sharing merges
```
Thanks to the creators for the great models and LoRAs used in this model!
```
疲れたので日本語で書きます
tauronHybridMix_tauHybridRealV21がマージ不可モデルだったので置き換えを行ってみました
出力画像は若干差は出ますがreal_model_Lとほぼ同じような特徴が出るはずです……多分
全モデルが素のcreativeml-openrail-mか、マージ可、マージ後のライセンス再設定可能なものになったので公開します
ほとんどのモデルで商用不可、マージ可、ライセンス再設定可だったので同じライセンスの設定としています
```
samples:
----
real_model_L
recipe only for personal use. (not for commercial, because of license)
This model "file" is not public anymore,
I try to change some asset and weight, I'll share next model.
photorealistic checkpoint for sd1.5, model merge example.
recipe for supermerger:
F is LoRA merge to checkpoint.
D,G,H,I,L are using MBW and weight sum.
J is using sum twice.
other is using weight sum.
- A = cityedgemixV1_v125 x 0.5 + kisaragiMix_v22 x 0.5
- B = majicmixRealistic_v6 x 0.5 + shampooMix_v4 x 0.5
- C = A x 0.5 + B x 0.5
- D = fantasticmix_v65 x (1-alpha) + dreamshaper_631BakedVae x alpha (0.4,0.35,0.4,0.45,0.45,0.3,0.3,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0)
- E = C x 0.8 + D x 0.2
- F = E + flat2:-0.7 (lora merge)
- G = F x (1-alpha) + calicomixreal_v20 x alpha (1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0)
- H = F x (1-alpha) + tauronHybridMix_tauHybridRealV21 x alpha (1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0)
- I = F x (1-alpha) + xxmix9realistic_v30 x alpha (1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0)
- J = (G x 0.6 + H x 0.4) x 0.6 + I x 0.4
- K = J x 0.439 + savMIX_xl x 0.561
- L = K x (1-alpha) + kencanmix_v16 x alpha (0.0,0.0,0.0,0.0,0.0,0.0,0.5,0.0,0.5,0.5,0.5,0.0,0.0,0.0,0.0,0.0,0.0,0.15,0.25,0.35,0.5,0.0,0.0,0.0,0.0,0.0)
----
Thanks to the creaters of those wonderful models and LoRAs!
The model file is not available, but you can try to merge the models. ...welcome to model merge swamp! (ようこそモデルマージ沼へ)
----
I'm not a native English speaker,(I'm tired,,,) so I wrote follow descriptions in japanese.
結構感覚的に作ってたんだなぁと思う作成時の記録を下記に書きます
```
A～Cはいい感じにかわいいアジア系の女の子の完全な写真が出ると思われるモデルを均等にマージ(これを基本系とするため。ここは正直雑に混ぜたので今後の改善ポイントかもしれない)
Dでちょっとだけ2Dの入ってるdreamshaperの形状や構造を取り入れたかったのとfantasticはしっかり写真で反応良かったので混ぜる
Eでここまで作ったものを平均化
Fで詳細化をかけておく(-1はやりすぎかなと思ったので-0.7にした)
GーIでTE変更(とりあえずマージ候補として選定していた中で特に2次元キャラのLoRA(主に衣装)に正確に反応してくれる3Dの厳選したモデルを使った)
Jで比率見ながらMIX(tauが他への影響が強かったのでちょっと弱めた)
Kでsavを何となく取り入れる(出力したらかなり良い画像が出てきていたので取り入れたかった)
Lでkencanmixの顔層を取り入れて(衣装に影響が出るのでOUT側は若干抑制している。これ以上OUT側を増減すると衣装と顔の出力が微妙になるのでギリギリこの値)
```
そのあと色々混ぜてみたもののなかなかうまくいかず……結局これが一番良かったのでこれにしました。
特定のseedと特定のLoRAの組み合わせでしかテストしていないです(気晴らしで他のLoRAが3次元化することは確認しています…一部は目が大きすぎたりするので若干LoRAの比重を下げたりはしましたが…)
そのためあまりしっかり出ないLoRAもあるかもしれませんが、そんな時はそのLoRAがしっかりと出るモデルをマージして作ってみるのも一興かもしれません(みんなでモデルマージ沼に浸かろう)
|
mahdafr/distilhubert-finetuned-gtzan
|
mahdafr
| 2023-07-29T07:52:48Z | 159 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"base_model:ntu-spml/distilhubert",
"base_model:finetune:ntu-spml/distilhubert",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-07-29T06:04:03Z |
---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.87
---
<!-- 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. -->
# distilhubert-finetuned-gtzan
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4759
- Accuracy: 0.87
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5864 | 1.0 | 112 | 1.4484 | 0.53 |
| 1.1517 | 2.0 | 225 | 1.0442 | 0.66 |
| 0.9177 | 3.0 | 337 | 0.8256 | 0.76 |
| 0.6564 | 4.0 | 450 | 0.6099 | 0.84 |
| 0.5938 | 5.0 | 562 | 0.6822 | 0.78 |
| 0.2182 | 6.0 | 675 | 0.5630 | 0.81 |
| 0.3178 | 7.0 | 787 | 0.4598 | 0.85 |
| 0.1181 | 8.0 | 900 | 0.4580 | 0.86 |
| 0.0377 | 9.0 | 1012 | 0.4716 | 0.88 |
| 0.034 | 9.96 | 1120 | 0.4759 | 0.87 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.14.0
- Tokenizers 0.13.3
|
doctord98/embeddings
|
doctord98
| 2023-07-29T07:30:49Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-06T04:51:54Z |
---
license: creativeml-openrail-m
---
|
SargeZT/controlnet-sd-xl-1.0-depth-faid-vidit
|
SargeZT
| 2023-07-29T07:29:51Z | 53 | 5 |
diffusers
|
[
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"controlnet",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-07-28T22:48:38Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-xl-base-1.0
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- controlnet
inference: true
---
# controlnet-SargeZT/controlnet-sd-xl-1.0-depth-faid-vidit
These are controlnet weights trained on stabilityai/stable-diffusion-xl-base-1.0 with new type of conditioning.
You can find some example images below.
prompt: the vaporwave hills from your nightmare, unsettling, light temperature 3500, light direction south-east
## License
[SDXL 1.0 License](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENSE.md)
|
manyet1k/deberta-v3-base-finetuned-cola
|
manyet1k
| 2023-07-29T07:18:36Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"base_model:microsoft/deberta-v3-base",
"base_model:finetune:microsoft/deberta-v3-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-29T07:00:40Z |
---
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: deberta-v3-base-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: validation
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.6932783112452325
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta-v3-base-finetuned-cola
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6510
- Matthews Correlation: 0.6933
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.3853 | 1.0 | 535 | 0.3907 | 0.6307 |
| 0.2186 | 2.0 | 1070 | 0.5065 | 0.6603 |
| 0.1481 | 3.0 | 1605 | 0.5638 | 0.6740 |
| 0.1002 | 4.0 | 2140 | 0.6510 | 0.6933 |
| 0.0656 | 5.0 | 2675 | 0.7462 | 0.6877 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.1
- Tokenizers 0.13.3
|
AntX-ai/AntX-13B
|
AntX-ai
| 2023-07-29T07:15:17Z | 18 | 2 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"zh",
"dataset:BAAI/COIG-PC",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-29T02:59:16Z |
---
license: apache-2.0
datasets:
- BAAI/COIG-PC
language:
- zh
library_name: transformers
pipeline_tag: text-generation
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This is an experimental product that can be used to create new LLM bassed on Chinese language.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** yjf9966
- **Model type:** LLaMA with enhanced tokenizer-size-49954
- **Language(s) (NLP):** Chinese/English
- **License:** Apache-2.0
- **Finetuned from model:** [Chinese-LLaMA-Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca)
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** https://huggingface.co/AntX-ai/AntX-13B
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
You can use the raw model for next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering.
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions.
It also inherits some of the bias of its dataset model.
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
```python
from transformers import LlamaForCausalLM, LlamaTokenizer
import torch
base_model_name = "AntX-ai/AntX-13B"
load_type = torch.float16
device = None
generation_config = dict(
temperature=0.2,
top_k=40,
top_p=0.9,
do_sample=True,
num_beams=1,
repetition_penalty=1.3,
max_new_tokens=400
)
prompt_input = (
"Below is an instruction that describes a task. "
"Write a response that appropriately completes the request.\n\n"
"### Instruction:\n\n{instruction}\n\n### Response:\n\n"
)
if torch.cuda.is_available():
device = torch.device(0)
else:
device = torch.device('cpu')
def generate_prompt(instruction, input=None):
if input:
instruction = instruction + '\n' + input
return prompt_input.format_map({'instruction': instruction})
tokenizer = LlamaTokenizer.from_pretrained(base_model_name)
model = LlamaForCausalLM.from_pretrained(
base_model_name,
load_in_8bit=False,
torch_dtype=load_type,
low_cpu_mem_usage=True,
device_map='auto',
)
model_vocab_size = model.get_input_embeddings().weight.size(0)
tokenzier_vocab_size = len(tokenizer)
if model_vocab_size != tokenzier_vocab_size:
model.resize_token_embeddings(tokenzier_vocab_size)
raw_input_text = input("Input:")
input_text = generate_prompt(instruction=raw_input_text)
inputs = tokenizer(input_text, return_tensors="pt")
generation_output = model.generate(
input_ids=inputs["input_ids"].to(device),
attention_mask=inputs['attention_mask'].to(device),
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id,
**generation_config
)
s = generation_output[0]
output = tokenizer.decode(s, skip_special_tokens=True)
response = output.split("### Response:")[1].strip()
print("Response: ", response)
print("\n")
```
## Training Details
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
80% for train dataset and 20% for test dataset
#### Training Hyperparameters
- **Training regime:** fp16 mixed precision, lr=1e-4, lora_rank=8, lora_alpha=32 <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
## Evaluation
#### Testing Data
<!-- This should link to a Data Card if possible. -->
20% of the BAAI/COIG-PC dataset.
```
Input:王国维说：“自周之衰，文王、周公势力之瓦解也，国民之智力成熟于内，政治之纷乱乘之于外，上无统一之制度，下迫于社会之要求，于是诸于九流各创其学说。” 他意在说明 A. 分封制的崩溃 B. 商鞅变法的作用 C. 兼并战争的后果 D. 百家争鸣的原因
Response: 本题考查对材料的理解。A错误；B正确；C和D与材料无关。故选BC两项即可
Input:经济基础是指一定社会发展阶段占统治地位的生产关系各个方面的总和。解决了地方经济问题， 也就解 决了地方割据问题， 为此， 宋太祖采取的措施是（ ） A ． 地方设转运使， 财赋收归中央 B ． 派文臣担任各地州县的长官 C ． 派文臣管理地方政事 D ． 采取分化事权办法， 削弱相权
Response: A： 本题考查对宋太祖治下地方问题的认识。 A : 依据材料可知, 在北宋时期 , 由于地主阶级的发展壮大以及商业、手工业等新兴行业出现 , 这一时期出现的地方割据现象严重威胁着国家的统一与稳定 . 因此 , 为了解决这个问题， 需要加强中央集权 ; 故选 A
Input:go\\n/**\\n * Definition for a binary tree node.\\n * type TreeNode struct {\\n * Val int\\n * Left *TreeNode\\n * Right *TreeNode\\n * }\\n */\\nfunc maxDepth(root *TreeNode) int {\\n\\tif root == nil {\\n\\t\\treturn 0\\n\\t}\\n\\tl, r := maxDepth(root.Left), maxDepth(root.Right)\\n\\treturn 1 + max(l, r)\\n}\\n\\nfunc max(a, b int) int {\\n\\tif a > b {\\n\\t\\treturn a\\n\\t}\\n\\treturn b\\n}\\n
Response: ```python
def max_depth(root): # The function takes in a single parameter `root` and returns its maximum depth value as output.
if not root or len(root.children()) == 0:
return 0
l = max_depth(root.left)
r = max_depth(root.right)
return 1 + (max(l, r))
class TreeNode:
def __init__(self, val=None, left=10, right=0):
self.val = val
self.left = None
self.right = None
```
## Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
```
@software{AntX-13B,
title={An Enchanced Chinese Language Model based on the Chinese-LLaMA-Alpaca},
url={https://huggingface.co/AntX-ai/AntX-13B},
year={2023}
}
```
|
MichelNivard/starcoderbase_3b_for_R_phase_2
|
MichelNivard
| 2023-07-29T07:14:05Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-28T12:21:53Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0.dev0
|
ACOS/q-FrozenLake-v1-4x4-noSlippery
|
ACOS
| 2023-07-29T06:45:31Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-29T06:45:29Z |
---
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="ACOS/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"])
```
|
darveen/llama2-4bit-qlora-finetuned-alpaca
|
darveen
| 2023-07-29T06:33:58Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-29T06:33:42Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0.dev0
|
michaellutz/free-falling-flan-t5-v1
|
michaellutz
| 2023-07-29T06:28:59Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-29T06:28:57Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.5.0.dev0
|
jsenthil/test2
|
jsenthil
| 2023-07-29T06:22:24Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"arxiv:2305.18098",
"license:lgpl-3.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-29T06:18:55Z |
---
license: lgpl-3.0
duplicated_from: James-WYang/BigTranslate
---
# BigTranslate: Augmenting Large Language Models with Multilingual Translation Capability over 100 Languages
Large language models (LLMs) demonstrate promising translation performance among various natural languages. However, many LLMs especially the open-sourced ones, such as BLOOM and LLaMA, are English-dominant and support only dozens of natural languages, making the potential of LLMs on language translation less explored. In this work, we present BigTranslate which adapts LLaMA that covers only 20 languages and enhances it with multilingual translation capability on more than 100 languages. BigTranslate is built upon LLaMA-13B and it is optimized in three steps. First, we continue training LLaMA with massive Chinese monolingual data. Second, we continue training the model with a large-scale parallel dataset that covers 102 natural languages. Third, we instruct-tune the foundation model with multilingual translation instructions, leading to our BigTranslate model. The preliminary experiments on multilingual translation show that BigTranslate performs comparably with
ChatGPT and Google Translate in many languages and even outperforms ChatGPT in 8 language pairs. We release the BigTranslate model and hope it can advance the research progress.
**More Details can be found at https://github.com/ZNLP/BigTranslate and https://arxiv.org/abs/2305.18098**
|
vincentiussgk/vit-base-patch16-224-in21k-finetuned-eurosat
|
vincentiussgk
| 2023-07-29T06:20:41Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:food101",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-07-27T04:53:34Z |
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- food101
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-in21k-finetuned-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: food101
type: food101
config: default
split: train[:5000]
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.927
---
<!-- 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. -->
# vit-base-patch16-224-in21k-finetuned-eurosat
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1055
- Accuracy: 0.927
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.0689 | 0.99 | 31 | 2.6415 | 0.82 |
| 1.6615 | 1.98 | 62 | 1.4504 | 0.898 |
| 1.1467 | 2.98 | 93 | 1.1055 | 0.927 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.1
- Tokenizers 0.13.3
|
NasimB/all-base-miss-aochildes-seed
|
NasimB
| 2023-07-29T06:19:07Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:generator",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-29T02:42:52Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: all-base-miss-aochildes-seed
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. -->
# all-base-miss-aochildes-seed
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 4.1340
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.3718 | 0.3 | 500 | 5.3790 |
| 5.0628 | 0.61 | 1000 | 4.9618 |
| 4.7469 | 0.91 | 1500 | 4.7198 |
| 4.4602 | 1.21 | 2000 | 4.5799 |
| 4.316 | 1.52 | 2500 | 4.4545 |
| 4.2172 | 1.82 | 3000 | 4.3495 |
| 4.0599 | 2.12 | 3500 | 4.2880 |
| 3.9243 | 2.43 | 4000 | 4.2333 |
| 3.895 | 2.73 | 4500 | 4.1796 |
| 3.8241 | 3.03 | 5000 | 4.1375 |
| 3.6165 | 3.34 | 5500 | 4.1275 |
| 3.6128 | 3.64 | 6000 | 4.0945 |
| 3.5876 | 3.94 | 6500 | 4.0622 |
| 3.3787 | 4.25 | 7000 | 4.0709 |
| 3.3459 | 4.55 | 7500 | 4.0590 |
| 3.3307 | 4.85 | 8000 | 4.0475 |
| 3.2357 | 5.16 | 8500 | 4.0557 |
| 3.1582 | 5.46 | 9000 | 4.0548 |
| 3.1553 | 5.76 | 9500 | 4.0532 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
|
BauyrjanQ/whisper-kk-b4-ms1000-b
|
BauyrjanQ
| 2023-07-29T06:04:29Z | 75 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:BauyrjanQ/whisper-kk",
"base_model:finetune:BauyrjanQ/whisper-kk",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-07-28T19:34:31Z |
---
license: apache-2.0
base_model: BauyrjanQ/whisper-kk
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-kk-b4-ms1000-b
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-kk-b4-ms1000-b
This model is a fine-tuned version of [BauyrjanQ/whisper-kk](https://huggingface.co/BauyrjanQ/whisper-kk) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4178
- Wer: 96.9956
## 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: 8
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 1.1263 | 0.06 | 1000 | 0.4178 | 96.9956 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
saandman/ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan
|
saandman
| 2023-07-29T05:48:29Z | 159 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"audio-spectrogram-transformer",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"base_model:MIT/ast-finetuned-audioset-10-10-0.4593",
"base_model:finetune:MIT/ast-finetuned-audioset-10-10-0.4593",
"license:bsd-3-clause",
"model-index",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-07-29T05:03:53Z |
---
license: bsd-3-clause
base_model: MIT/ast-finetuned-audioset-10-10-0.4593
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.89
---
<!-- 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. -->
# ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan
This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5045
- Accuracy: 0.89
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.066 | 1.0 | 112 | 0.5999 | 0.83 |
| 0.4707 | 2.0 | 225 | 0.5077 | 0.81 |
| 0.363 | 3.0 | 337 | 0.5508 | 0.83 |
| 0.1067 | 4.0 | 450 | 0.6624 | 0.81 |
| 0.0072 | 5.0 | 562 | 0.6558 | 0.85 |
| 0.0047 | 6.0 | 675 | 0.4942 | 0.89 |
| 0.0006 | 7.0 | 787 | 0.4824 | 0.91 |
| 0.001 | 8.0 | 900 | 0.5176 | 0.89 |
| 0.1411 | 9.0 | 1012 | 0.5117 | 0.89 |
| 0.0002 | 9.96 | 1120 | 0.5045 | 0.89 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.1
- Tokenizers 0.13.3
|
Ichsan2895/Garuda-7B
|
Ichsan2895
| 2023-07-29T05:42:16Z | 18 | 1 |
transformers
|
[
"transformers",
"pytorch",
"RefinedWebModel",
"text-generation",
"custom_code",
"en",
"dataset:timdettmers/openassistant-guanaco",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-24T17:31:33Z |
---
license: apache-2.0
datasets:
- timdettmers/openassistant-guanaco
language:
- en
library_name: transformers
---
Falcon-7B fusion with Guanaco (Open Assistant Dataset) supported by TRL library = Garuda-7B
This model is not capable for Indonesian
|
AbhirupGhosh/opus-mt-finetuned-hi-en
|
AbhirupGhosh
| 2023-07-29T05:32:37Z | 116 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"Hindi",
"generated_from_keras_callback",
"hi",
"en",
"multilingual",
"arxiv:1706.03762",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-07-16T15:34:05Z |
---
language:
- hi
- en
- multilingual
license: apache-2.0
tags:
- translation
- Hindi
- generated_from_keras_callback
model-index:
- name: opus-mt-finetuned-hi-en
results: []
---
# opus-mt-finetuned-hi-en
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-hi-en](https://huggingface.co/Helsinki-NLP/opus-mt-hi-en) on [HindiEnglish Corpora](https://www.clarin.eu/resource-families/parallel-corpora)
## Model description
The model is a transformer model similar to the [Transformer](https://arxiv.org/abs/1706.03762?context=cs) as defined in Attention Is All You Need et al
## Training and evaluation data
More information needed
## Training procedure
The model was trained on 2 NVIDIA_TESLA_A100 GPU's on Google's vertex AI platform.
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: AdamWeightDecay
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
NasimB/open_subtitles-log-rarity-seed
|
NasimB
| 2023-07-29T05:27:34Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:generator",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-29T01:54:47Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: open_subtitles-log-rarity-seed
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. -->
# open_subtitles-log-rarity-seed
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 4.1754
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.4321 | 0.3 | 500 | 5.3635 |
| 5.1155 | 0.61 | 1000 | 4.9745 |
| 4.7697 | 0.91 | 1500 | 4.7409 |
| 4.5189 | 1.22 | 2000 | 4.6014 |
| 4.3771 | 1.52 | 2500 | 4.4879 |
| 4.2813 | 1.83 | 3000 | 4.3878 |
| 4.1152 | 2.13 | 3500 | 4.3328 |
| 3.9934 | 2.44 | 4000 | 4.2727 |
| 3.9581 | 2.74 | 4500 | 4.2153 |
| 3.8771 | 3.05 | 5000 | 4.1827 |
| 3.6822 | 3.35 | 5500 | 4.1643 |
| 3.6785 | 3.66 | 6000 | 4.1326 |
| 3.6576 | 3.96 | 6500 | 4.0997 |
| 3.4438 | 4.27 | 7000 | 4.1078 |
| 3.4107 | 4.57 | 7500 | 4.0975 |
| 3.4026 | 4.88 | 8000 | 4.0827 |
| 3.2929 | 5.18 | 8500 | 4.0910 |
| 3.2304 | 5.49 | 9000 | 4.0919 |
| 3.2307 | 5.79 | 9500 | 4.0905 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
|
emaeon/lora-large-healthcare-model-18_asc
|
emaeon
| 2023-07-29T05:27:00Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-29T05:26:56Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
emaeon/lora-large-healthcare-model-17_asc
|
emaeon
| 2023-07-29T05:25:45Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-29T05:25:39Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
emaeon/lora-large-healthcare-model-15_asc
|
emaeon
| 2023-07-29T05:23:12Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-29T05:23:08Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
emaeon/lora-large-healthcare-model-14_asc
|
emaeon
| 2023-07-29T05:21:57Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-29T05:21:53Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
emaeon/lora-large-healthcare-model-13_asc
|
emaeon
| 2023-07-29T05:20:41Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-29T05:20:37Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
emaeon/lora-large-healthcare-model-12_asc
|
emaeon
| 2023-07-29T05:19:26Z | 2 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-29T05:19:20Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
emaeon/lora-large-healthcare-model-8_asc
|
emaeon
| 2023-07-29T05:14:21Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-29T05:14:17Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
vagrawal787/todos_task_model
|
vagrawal787
| 2023-07-29T05:13:45Z | 109 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:vagrawal787/todo_task_list_types",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-29T04:58:09Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: todos_task_model
results: []
datasets:
- vagrawal787/todo_task_list_types
metrics:
- accuracy
pipeline_tag: text-classification
---
<!-- 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. -->
# todos_task_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the vagrawal787/todo_task_list_types dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2696
- eval_accuracy: 0.95
- eval_runtime: 0.2417
- eval_samples_per_second: 248.265
- eval_steps_per_second: 62.066
- step: 0
## Model description
Input: Text string of a todo-like task such as "get groceries"
Output: A type label for what type of task it is (home, personal, work, emergency, etc.)
## Intended uses & limitations
More information needed
## Training and evaluation data
The dataset used is provided in the card.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.1
- Tokenizers 0.13.3
|
emaeon/lora-large-healthcare-model-7_asc
|
emaeon
| 2023-07-29T05:13:05Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-29T05:13:01Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
emaeon/lora-large-healthcare-model-6_asc
|
emaeon
| 2023-07-29T05:11:49Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-29T05:11:45Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
emaeon/lora-large-healthcare-model-5_asc
|
emaeon
| 2023-07-29T05:10:34Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-29T05:10:29Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
emaeon/lora-large-healthcare-model-3_asc
|
emaeon
| 2023-07-29T05:08:01Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-20T06:42:16Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
emaeon/lora-large-healthcare-model-0_asc
|
emaeon
| 2023-07-29T05:04:14Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-20T06:29:17Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
jordyvl/vit-base_rvl-cdip-tiny_rvl_cdip-NK1000_simkd
|
jordyvl
| 2023-07-29T04:27:31Z | 163 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-07-27T06:51:25Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base_rvl-cdip-tiny_rvl_cdip-NK1000_simkd
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. -->
# vit-base_rvl-cdip-tiny_rvl_cdip-NK1000_simkd
This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 37.0129
- Accuracy: 0.8277
- Brier Loss: 0.3307
- Nll: 1.8775
- F1 Micro: 0.8277
- F1 Macro: 0.8289
- Ece: 0.1649
- Aurc: 0.0944
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| No log | 1.0 | 250 | 56.2115 | 0.3142 | 0.8385 | 3.5992 | 0.3142 | 0.2499 | 0.1012 | 0.5692 |
| 56.615 | 2.0 | 500 | 54.0327 | 0.4025 | 0.9176 | 3.1629 | 0.4025 | 0.3116 | 0.4002 | 0.3781 |
| 56.615 | 3.0 | 750 | 49.9569 | 0.4728 | 0.8906 | 2.8997 | 0.4728 | 0.4076 | 0.4129 | 0.2864 |
| 50.7474 | 4.0 | 1000 | 47.4945 | 0.5685 | 0.7670 | 2.6755 | 0.5685 | 0.5350 | 0.3561 | 0.2844 |
| 50.7474 | 5.0 | 1250 | 45.5054 | 0.6378 | 0.6629 | 2.5408 | 0.6378 | 0.6030 | 0.3212 | 0.1851 |
| 45.4907 | 6.0 | 1500 | 43.9471 | 0.679 | 0.5949 | 2.6322 | 0.679 | 0.6636 | 0.2925 | 0.1474 |
| 45.4907 | 7.0 | 1750 | 42.9273 | 0.7342 | 0.4843 | 2.4382 | 0.7342 | 0.7365 | 0.2245 | 0.1436 |
| 42.5191 | 8.0 | 2000 | 41.9715 | 0.7548 | 0.4560 | 2.3596 | 0.7548 | 0.7533 | 0.2231 | 0.1400 |
| 42.5191 | 9.0 | 2250 | 41.4349 | 0.7722 | 0.4310 | 2.3144 | 0.7722 | 0.7718 | 0.2103 | 0.1304 |
| 40.8849 | 10.0 | 2500 | 41.0961 | 0.7805 | 0.4187 | 2.2268 | 0.7805 | 0.7826 | 0.2047 | 0.1305 |
| 40.8849 | 11.0 | 2750 | 40.5831 | 0.7893 | 0.4030 | 2.1663 | 0.7893 | 0.7930 | 0.2001 | 0.1246 |
| 39.8394 | 12.0 | 3000 | 40.1596 | 0.7987 | 0.3877 | 2.1719 | 0.7987 | 0.8015 | 0.1929 | 0.1162 |
| 39.8394 | 13.0 | 3250 | 39.8469 | 0.8033 | 0.3821 | 2.1455 | 0.8033 | 0.8077 | 0.1889 | 0.1183 |
| 38.9442 | 14.0 | 3500 | 39.5865 | 0.8055 | 0.3761 | 2.1121 | 0.8055 | 0.8096 | 0.1864 | 0.1110 |
| 38.9442 | 15.0 | 3750 | 39.4686 | 0.81 | 0.3693 | 2.0948 | 0.81 | 0.8125 | 0.1831 | 0.1114 |
| 38.3612 | 16.0 | 4000 | 39.1387 | 0.8207 | 0.3446 | 1.9957 | 0.8207 | 0.8219 | 0.1716 | 0.1038 |
| 38.3612 | 17.0 | 4250 | 38.8950 | 0.8143 | 0.3575 | 2.0339 | 0.8143 | 0.8152 | 0.1781 | 0.1034 |
| 37.7855 | 18.0 | 4500 | 38.6442 | 0.8215 | 0.3442 | 1.9658 | 0.8215 | 0.8236 | 0.1718 | 0.1036 |
| 37.7855 | 19.0 | 4750 | 38.5218 | 0.8197 | 0.3477 | 1.9627 | 0.8197 | 0.8220 | 0.1735 | 0.1070 |
| 37.3649 | 20.0 | 5000 | 38.3474 | 0.8225 | 0.3413 | 1.9886 | 0.8225 | 0.8239 | 0.1710 | 0.1028 |
| 37.3649 | 21.0 | 5250 | 38.2377 | 0.8257 | 0.3358 | 1.9864 | 0.8257 | 0.8269 | 0.1674 | 0.0957 |
| 37.0326 | 22.0 | 5500 | 38.1089 | 0.824 | 0.3418 | 1.9404 | 0.824 | 0.8257 | 0.1678 | 0.0980 |
| 37.0326 | 23.0 | 5750 | 37.9861 | 0.8273 | 0.3339 | 1.9540 | 0.8273 | 0.8285 | 0.1664 | 0.0985 |
| 36.7372 | 24.0 | 6000 | 37.8397 | 0.8255 | 0.3376 | 1.9492 | 0.8255 | 0.8268 | 0.1685 | 0.0944 |
| 36.7372 | 25.0 | 6250 | 37.7772 | 0.8253 | 0.3370 | 1.9078 | 0.8253 | 0.8255 | 0.1669 | 0.0997 |
| 36.4341 | 26.0 | 6500 | 37.6550 | 0.828 | 0.3325 | 1.9388 | 0.828 | 0.8284 | 0.1647 | 0.0943 |
| 36.4341 | 27.0 | 6750 | 37.5873 | 0.8255 | 0.3364 | 1.9319 | 0.8255 | 0.8261 | 0.1680 | 0.0920 |
| 36.2152 | 28.0 | 7000 | 37.5052 | 0.825 | 0.3379 | 1.8945 | 0.825 | 0.8268 | 0.1681 | 0.0981 |
| 36.2152 | 29.0 | 7250 | 37.4586 | 0.8243 | 0.3361 | 1.9094 | 0.8243 | 0.8251 | 0.1692 | 0.0945 |
| 36.0128 | 30.0 | 7500 | 37.3730 | 0.8277 | 0.3304 | 1.9062 | 0.8277 | 0.8288 | 0.1657 | 0.0946 |
| 36.0128 | 31.0 | 7750 | 37.3309 | 0.8277 | 0.3309 | 1.9045 | 0.8277 | 0.8291 | 0.1660 | 0.0947 |
| 35.8486 | 32.0 | 8000 | 37.2620 | 0.8267 | 0.3323 | 1.8884 | 0.8267 | 0.8279 | 0.1652 | 0.0950 |
| 35.8486 | 33.0 | 8250 | 37.2147 | 0.8275 | 0.3308 | 1.9079 | 0.8275 | 0.8290 | 0.1654 | 0.0960 |
| 35.6854 | 34.0 | 8500 | 37.1911 | 0.831 | 0.3252 | 1.8935 | 0.831 | 0.8323 | 0.1613 | 0.0939 |
| 35.6854 | 35.0 | 8750 | 37.1523 | 0.8283 | 0.3301 | 1.8847 | 0.8283 | 0.8293 | 0.1644 | 0.0972 |
| 35.5758 | 36.0 | 9000 | 37.1315 | 0.8305 | 0.3252 | 1.8941 | 0.8305 | 0.8317 | 0.1627 | 0.0934 |
| 35.5758 | 37.0 | 9250 | 37.1184 | 0.8275 | 0.3320 | 1.8844 | 0.8275 | 0.8285 | 0.1654 | 0.0923 |
| 35.4911 | 38.0 | 9500 | 37.1149 | 0.827 | 0.3327 | 1.8885 | 0.827 | 0.8288 | 0.1668 | 0.0953 |
| 35.4911 | 39.0 | 9750 | 37.1067 | 0.8267 | 0.3323 | 1.8846 | 0.8267 | 0.8281 | 0.1659 | 0.0932 |
| 35.4248 | 40.0 | 10000 | 37.0792 | 0.8293 | 0.3294 | 1.8840 | 0.8293 | 0.8305 | 0.1633 | 0.0937 |
| 35.4248 | 41.0 | 10250 | 37.0798 | 0.8297 | 0.3288 | 1.8718 | 0.8297 | 0.8309 | 0.1639 | 0.0929 |
| 35.3648 | 42.0 | 10500 | 37.0635 | 0.8265 | 0.3351 | 1.8883 | 0.8265 | 0.8279 | 0.1680 | 0.0951 |
| 35.3648 | 43.0 | 10750 | 37.0470 | 0.828 | 0.3308 | 1.8746 | 0.828 | 0.8294 | 0.1656 | 0.0939 |
| 35.2961 | 44.0 | 11000 | 37.0305 | 0.8273 | 0.3321 | 1.8901 | 0.8273 | 0.8286 | 0.1657 | 0.0932 |
| 35.2961 | 45.0 | 11250 | 37.0261 | 0.8275 | 0.3315 | 1.8823 | 0.8275 | 0.8287 | 0.1650 | 0.0949 |
| 35.241 | 46.0 | 11500 | 37.0253 | 0.827 | 0.3311 | 1.8751 | 0.827 | 0.8283 | 0.1662 | 0.0940 |
| 35.241 | 47.0 | 11750 | 37.0200 | 0.8277 | 0.3321 | 1.8708 | 0.8277 | 0.8289 | 0.1653 | 0.0949 |
| 35.2059 | 48.0 | 12000 | 37.0165 | 0.8277 | 0.3305 | 1.8745 | 0.8277 | 0.8289 | 0.1650 | 0.0934 |
| 35.2059 | 49.0 | 12250 | 37.0130 | 0.8275 | 0.3312 | 1.8743 | 0.8275 | 0.8287 | 0.1655 | 0.0942 |
| 35.18 | 50.0 | 12500 | 37.0129 | 0.8277 | 0.3307 | 1.8775 | 0.8277 | 0.8289 | 0.1649 | 0.0944 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2
|
1daniar/poca-SoccerTwos
|
1daniar
| 2023-07-29T04:26:45Z | 16 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-07-29T04:26:36Z |
---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
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. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: 1daniar/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
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Subsets and Splits
Filtered Qwen2.5 Distill Models
Identifies specific configurations of models by filtering cards that contain 'distill', 'qwen2.5', '7b' while excluding certain base models and incorrect model ID patterns, uncovering unique model variants.
Filtered Model Cards Count
Finds the count of entries with specific card details that include 'distill', 'qwen2.5', '7b' but exclude certain base models, revealing valuable insights about the dataset's content distribution.
Filtered Distill Qwen 7B Models
Filters for specific card entries containing 'distill', 'qwen', and '7b', excluding certain strings and patterns, to identify relevant model configurations.
Filtered Qwen-7b Model Cards
The query performs a detailed filtering based on specific keywords and excludes certain entries, which could be useful for identifying a specific subset of cards but does not provide deeper insights or trends.
Filtered Qwen 7B Model Cards
The query filters for specific terms related to "distilled" or "distill", "qwen", and "7b" in the 'card' column but excludes certain base models, providing a limited set of entries for further inspection.
Qwen 7B Distilled Models
The query provides a basic filtering of records to find specific card names that include keywords related to distilled Qwen 7b models, excluding a particular base model, which gives limited insight but helps in focusing on relevant entries.
Qwen 7B Distilled Model Cards
The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.
Qwen 7B Distilled Models
Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.
Distilled Qwen 7B Models
The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.
Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.