modelId stringlengths 4 111 | lastModified stringlengths 24 24 | tags list | pipeline_tag stringlengths 5 30 ⌀ | author stringlengths 2 34 ⌀ | config null | securityStatus null | id stringlengths 4 111 | likes int64 0 9.53k | downloads int64 2 73.6M | library_name stringlengths 2 84 ⌀ | created timestamp[us] | card stringlengths 101 901k | card_len int64 101 901k | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
GeorgeSP/ppo-LunarLander-v2 | 2023-07-20T16:48:32.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | GeorgeSP | null | null | GeorgeSP/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-20T16:48:13 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 246.73 +/- 33.90
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| 784 | [
[
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0.034454345703125,
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0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
Mayypeeya/mt5_thaisum_finetune | 2023-07-20T22:35:47.000Z | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"dataset:thaisum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | Mayypeeya | null | null | Mayypeeya/mt5_thaisum_finetune | 0 | 2 | transformers | 2023-07-20T16:53:58 | ---
license: apache-2.0
base_model: google/mt5-base
tags:
- generated_from_trainer
datasets:
- thaisum
metrics:
- rouge
model-index:
- name: mt5_thaisum_finetune
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: thaisum
type: thaisum
config: thaisum
split: validation
args: thaisum
metrics:
- name: Rouge1
type: rouge
value: 0.2022
---
<!-- 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. -->
# mt5_thaisum_finetune
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the thaisum dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3039
- Rouge1: 0.2022
- Rouge2: 0.0808
- Rougel: 0.2023
- Rougelsum: 0.2019
- Gen Len: 18.9995
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 2.0742 | 1.0 | 5000 | 0.3272 | 0.1713 | 0.0551 | 0.1716 | 0.1714 | 18.9945 |
| 1.7874 | 2.0 | 10000 | 0.3073 | 0.1943 | 0.0747 | 0.195 | 0.1941 | 18.997 |
| 1.6341 | 3.0 | 15000 | 0.3035 | 0.2006 | 0.0807 | 0.2007 | 0.2002 | 19.0 |
| 1.4501 | 4.0 | 20000 | 0.3039 | 0.2022 | 0.0808 | 0.2023 | 0.2019 | 18.9995 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
| 2,146 | [
[
-0.03216552734375,
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0.0117340087890625,
0.011077880859375,
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0.0167388916015625,
0.033294677734375,
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... |
itayozer9/ppo-LunarLander-v2 | 2023-07-20T18:16:39.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | itayozer9 | null | null | itayozer9/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-20T18:16:23 | ---
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: 292.79 +/- 15.80
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
...
```
| 784 | [
[
-0.00023484230041503906,
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0.034454345703125,
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0.019866943359375,
0.06500244140625,
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-0.0343017578125,
-... |
mani05/PPO-LunarLander-v2 | 2023-07-20T20:34:40.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | mani05 | null | null | mani05/PPO-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-20T18:27:06 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 264.33 +/- 18.55
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
...
```
| 784 | [
[
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0.034454345703125,
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0.06500244140625,
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-... |
jtatman/gpt2-open-instruct-v1-gsm8k | 2023-07-21T09:08:53.000Z | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:gsm8k",
"license:mit",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | jtatman | null | null | jtatman/gpt2-open-instruct-v1-gsm8k | 0 | 2 | transformers | 2023-07-20T19:15:54 | ---
license: mit
base_model: vicgalle/gpt2-open-instruct-v1
tags:
- generated_from_trainer
datasets:
- gsm8k
model-index:
- name: gpt2-open-instruct-v1-gsm8k
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. -->
# gpt2-open-instruct-v1-gsm8k
This model is a fine-tuned version of [vicgalle/gpt2-open-instruct-v1](https://huggingface.co/vicgalle/gpt2-open-instruct-v1) on the gsm8k dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3966
## 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: 16
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 468 | 2.5579 |
| 2.859 | 2.0 | 936 | 2.5018 |
| 2.6455 | 3.0 | 1404 | 2.4752 |
| 2.6025 | 4.0 | 1872 | 2.4590 |
| 2.5777 | 5.0 | 2340 | 2.4473 |
| 2.5557 | 6.0 | 2808 | 2.4388 |
| 2.538 | 7.0 | 3276 | 2.4309 |
| 2.5246 | 8.0 | 3744 | 2.4236 |
| 2.514 | 9.0 | 4212 | 2.4186 |
| 2.5059 | 10.0 | 4680 | 2.4159 |
| 2.4944 | 11.0 | 5148 | 2.4107 |
| 2.4874 | 12.0 | 5616 | 2.4078 |
| 2.4862 | 13.0 | 6084 | 2.4053 |
| 2.475 | 14.0 | 6552 | 2.4027 |
| 2.4716 | 15.0 | 7020 | 2.4008 |
| 2.4716 | 16.0 | 7488 | 2.3995 |
| 2.4704 | 17.0 | 7956 | 2.3985 |
| 2.4648 | 18.0 | 8424 | 2.3973 |
| 2.4634 | 19.0 | 8892 | 2.3968 |
| 2.459 | 20.0 | 9360 | 2.3966 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
| 2,358 | [
[
-0.035400390625,
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Emperor-WS/ppo-LunarLander-v2 | 2023-07-20T21:05:34.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Emperor-WS | null | null | Emperor-WS/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-20T21:05:17 | ---
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: 271.32 +/- 21.49
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| 784 | [
[
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0.023345947265625,
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0.034454345703125,
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0.019866943359375,
0.06500244140625,
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-0.035247802734375,
-0.0343017578125,
-... |
guardrail/llama-2-7b-guanaco-8bit-sharded | 2023-07-20T22:29:10.000Z | [
"transformers",
"pytorch",
"llama",
"text-generation",
"dataset:timdettmers/openassistant-guanaco",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | guardrail | null | null | guardrail/llama-2-7b-guanaco-8bit-sharded | 3 | 2 | transformers | 2023-07-20T21:10:35 | ---
license: apache-2.0
datasets:
- timdettmers/openassistant-guanaco
pipeline_tag: text-generation
---
Model that is fine-tuned in 4-bit precision using QLoRA on [timdettmers/openassistant-guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) and sharded to be used on a free Google Colab instance.
It can be easily imported using the `AutoModelForCausalLM` class from `transformers`:
```
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained(
"guardrail/llama-2-7b-guanaco-8bit-sharded",
load_in_8bit=True)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
``` | 689 | [
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lcyfelix/PPO_MLP_Lunar | 2023-07-20T22:50:36.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | lcyfelix | null | null | lcyfelix/PPO_MLP_Lunar | 0 | 2 | stable-baselines3 | 2023-07-20T22:20:56 | ---
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: 244.02 +/- 21.23
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
...
```
| 784 | [
[
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0.06500244140625,
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-0.0343017578125,
-... |
PlankyxD/a2c-PandaReachDense-v2 | 2023-07-20T23:11:11.000Z | [
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | PlankyxD | null | null | PlankyxD/a2c-PandaReachDense-v2 | 0 | 2 | stable-baselines3 | 2023-07-20T23:08:31 | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -0.90 +/- 0.22
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| 802 | [
[
-0.019744873046875,
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0.0469970703125,
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0.033172607421875,
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0.028045654296875,
0.042694091796875,
-0.06256103515625,
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-0.03277587890625... |
NLeseul/RLtest-unit1 | 2023-07-21T00:35:34.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | NLeseul | null | null | NLeseul/RLtest-unit1 | 0 | 2 | stable-baselines3 | 2023-07-21T00:35:13 | ---
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: 252.38 +/- 7.63
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
...
```
| 783 | [
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0.034454345703125,
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0.019866943359375,
0.06500244140625,
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-... |
degor/ppo-lunarlander-v2 | 2023-07-21T02:10:01.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | degor | null | null | degor/ppo-lunarlander-v2 | 0 | 2 | stable-baselines3 | 2023-07-21T02:09:36 | ---
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: 252.55 +/- 19.76
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
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0.002735137939453125,
0.034454345703125,
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0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
RicardoLee/Llama2-chat-Chinese-50W | 2023-07-23T16:55:33.000Z | [
"transformers",
"pytorch",
"llama",
"text-generation",
"llama2",
"zh",
"en",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | RicardoLee | null | null | RicardoLee/Llama2-chat-Chinese-50W | 37 | 2 | transformers | 2023-07-21T05:43:02 | ---
language:
- zh
- en
tags:
- llama2
---
# LLama2-chat 7B Chinese Version
## Introduction
由于目前的LLama2-chat模型很难约束其以中文进行问题回复，因此该模型旨在提供一个能以中文进行问答的LLama2-chat 7B 模型。
该模型使用LLama2-chat 7B 作为基底模型，使用带embedding 和 LM head 的Lora训练方式训练。模型已完成参数合并，可直接使用。也可以手动将sft\_lora\_model同Llama2-chat进行合并。
训练数据使用[BELLE](https://huggingface.co/BelleGroup)项目中采样的50万SFT数据进行SFT训练。
Since the LLama2-chat model struggles to confine its responses to Chinese language when prompted with Chinese questions, the primary objective of this model is to provide a LLama2-chat 7B model that can engage in question and answer interactions in Chinese.
The model utilizes LLama2-chat 7B as its base model and is trained using the Lora training approach with the embedding and LM head. The model has undergone the Lora param merge and is now ready for direct use. It is also possible to manually merge the ./sft\_lora\_model with the Llama2-chat 7B model to obtain the combined model.
The training data is sampled from [BELLE](https://huggingface.co/BelleGroup) project, which consists of 500,000 SFT samples.
## Train Detail
一些训练上的细节：
1. 训练框架：该模型使用了修改过的[Chinese-LLaMA-Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca)项目进行训练。
2. Tokenizer：该模型使用了Chinese-Alpaca-Plus模型的tokenizer.model。这是因为LLama2本身的tokenizer.model同LLama1是一摸一样的。因此理论上可以完全复用Chinese-LLaMa项目的tokenizer而不会产生如何错位问题。
3. 训练参数：由于模型需要resize embedding，多出来的embedding等于随即初始化，因此训练前期deepspeed及其容易因“OVERFLOW”而开始reduce loss scale。频繁reduce 后会直接导致scale过小溢出，从而导致训练崩溃。此时不应降低学习率，warmup 等超参，而是应该放大到Pretrain 规模。如此才能让随即初始化的embedding快速走上正轨。
4. 训练资源：8卡V100。21个小时
5. 训练起始的loss：8.7072
6. 训练终止的loss：1.5674
Some details in training:
1. Trianing Framework: This model is trained on modified [Chinese-LLaMA-Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca) Framework.
2. Tokenizer: This model utilizes the tokenizer.model from the Chinese-Alpaca-Plus model. The reason for this choice is that the tokenizer.model in LLama2 is identical to the one used in LLama1. As a result, it is theoretically feasible to entirely reuse the tokenizer from the Chinese-LLaMa project without encountering any issues related to token misalignment.
3. Training Parameters: Due to the need to resize the embeddings, the excess embeddings are randomly initialized. As a consequence, during the initial stages of training, Deepspeed is prone to reducing the loss scale due to "OVERFLOW" issues. Frequent reductions can lead to an overly small scale, causing overflow and eventually crashing the training process. In such situations, it is not advisable to lower the learning rate, warm-up, or other hyperparameters. Instead, the recommended approach is to upscale the training parameters to Pretrain scale. This allows the randomly initialized embeddings to quickly converge to the right path.
4. Training Resource: 8\*V100, 21 hours.
5. Initial Loss: 8.7072
6. Train Loss: 1.5674
## Inference
该模型依然采用stanford alpaca 模版。因此在测试时且别忘记添加开场白。开场白如下：
"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n\n${Your Content}\n\n### Response:\n\n"
对于带上文的对话，开场白如下：
"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n\nHuman:${Previous Human Content}\nAssistant:${Previous Assistance Content}\nHuman:${Your Question}\n\n### Response:\n\n"
This model still using the Stanford Alpaca template. Therefore, don't forget to add prologue template. The prologue template is:
"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n\n${Your Content}\n\n### Response:\n\n"
For dialogue with context, the prelogue template is:
"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n\nHuman:${Previous Human Content}\nAssistant:${Previous Machine Content}\nHuman:${Your Question}\n\n### Response:\n\n"
## Licence
本仓库的模型依照 Apache-2.0 协议开源，模型的权重的使用则需要遵循LLama2[MODEL LICENCE](LICENSE)。
This repository's models are open-sourced under the Apache-2.0 license, and their weight usage must adhere to LLama2 [MODEL LICENCE](LICENSE) license.
## Future Work
将会在近期逐步放出
1. 更大SFT数据规模训练下的模型。
2. 13B及以下的LLama2 同LLama2-chat的模型，以供大家对比。
I will release the following models:
1. Models trained on larger data scale.
2. Models trained on LLama2 and LLama2-chat (under the 13B, since I only have V100), for comparison.
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0.000828... |
Aharneish/ppo-LunarLander-v2 | 2023-07-21T07:00:09.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Aharneish | null | null | Aharneish/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-21T06:59:09 | ---
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: 278.39 +/- 22.28
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
...
```
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bobobert4/ppo-PyramidsRND | 2023-07-21T07:39:10.000Z | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | reinforcement-learning | bobobert4 | null | null | bobobert4/ppo-PyramidsRND | 0 | 2 | ml-agents | 2023-07-21T07:04:37 | ---
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: bobobert4/ppo-PyramidsRND
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
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0.0298614501953125,
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Aharneish/ppo-Huggy | 2023-07-21T08:32:05.000Z | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | reinforcement-learning | Aharneish | null | null | Aharneish/ppo-Huggy | 0 | 2 | ml-agents | 2023-07-21T07:28:12 | ---
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: Aharneish/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| 1,319 | [
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0.034423828125,
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Sadashiv/BERT-ner | 2023-07-21T08:37:50.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | token-classification | Sadashiv | null | null | Sadashiv/BERT-ner | 0 | 2 | transformers | 2023-07-21T08:14:21 | ---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: BERT-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9449011330815374
- name: Recall
type: recall
value: 0.9515605772457769
- name: F1
type: f1
value: 0.9482191628114375
- name: Accuracy
type: accuracy
value: 0.987243236373457
---
<!-- 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-ner
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0664
- Precision: 0.9449
- Recall: 0.9516
- F1: 0.9482
- Accuracy: 0.9872
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0252 | 1.0 | 878 | 0.0652 | 0.9414 | 0.9419 | 0.9417 | 0.9854 |
| 0.0121 | 2.0 | 1756 | 0.0615 | 0.9407 | 0.9498 | 0.9452 | 0.9867 |
| 0.0079 | 3.0 | 2634 | 0.0664 | 0.9449 | 0.9516 | 0.9482 | 0.9872 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
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YojitShinde/a2c-PandaReachDense-v2 | 2023-07-21T10:38:51.000Z | [
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | YojitShinde | null | null | YojitShinde/a2c-PandaReachDense-v2 | 0 | 2 | stable-baselines3 | 2023-07-21T10:36:05 | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -0.58 +/- 0.22
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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-0... |
Claaas/a2c-AntBulletEnv-v0 | 2023-07-21T10:37:27.000Z | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Claaas | null | null | Claaas/a2c-AntBulletEnv-v0 | 0 | 2 | stable-baselines3 | 2023-07-21T10:36:22 | ---
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: 1138.21 +/- 206.06
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
...
```
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0.01934814453125,
0.0265655517578125,
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-0.037506103515625,
-0.0442810058593... |
actualbrain/ppo-LunarLander | 2023-07-21T10:47:33.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | actualbrain | null | null | actualbrain/ppo-LunarLander | 1 | 2 | stable-baselines3 | 2023-07-21T10:40:47 | ---
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: 280.10 +/- 15.44
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| 784 | [
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PlankyxD/PPO_LunarLander | 2023-07-21T10:45:15.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | PlankyxD | null | null | PlankyxD/PPO_LunarLander | 0 | 2 | stable-baselines3 | 2023-07-21T10:44:55 | ---
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: 251.03 +/- 19.62
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
...
```
| 784 | [
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ninja21/ppo-LunarLander-v2 | 2023-07-21T11:12:58.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | ninja21 | null | null | ninja21/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-21T11:12:37 | ---
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: 237.64 +/- 19.33
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
...
```
| 784 | [
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0.06500244140625,
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prashanthgowni/ppo-LunarLander-v2 | 2023-07-21T15:18:41.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"en",
"model-index",
"region:us"
] | reinforcement-learning | prashanthgowni | null | null | prashanthgowni/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-21T11:17:11 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 277.82 +/- 22.28
name: mean_reward
verified: false
language:
- en
---
# **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)
```python
from stable_baselines3 import PPO
from stable_baselines3.common.env_util import make_vec_env
from stable_baselines3.common.evaluation import evaluate_policy
from huggingface_sb3 import load_from_hub
# Download the model checkpoint
model_checkpoint = load_from_hub("prashanthgowni/ppo-LunarLander-v2", "ppo-LunarLander-v2")
# Create a vectorized environment
env = make_vec_env("LunarLander-v2", n_envs=1)
# Load the model
model = PPO.load(model_checkpoint, env=env)
# Evaluate
print("Evaluating model")
mean_reward, std_reward = evaluate_policy(
model,
env,
n_eval_episodes=30,
deterministic=True,
)
print(f"Mean reward = {mean_reward:.2f} +/- {std_reward}")
# Start a new episode
obs = env.reset()
try:
while True:
action, state = model.predict(obs, deterministic=True)
obs, reward, done, info = env.step(action)
env.render()
except KeyboardInterrupt:
pass
```
# Conclusion
The above steps ensure that the traind Agent is downloaded.
You may need to download and install required libraries and packages specific to your operating system to resume training from the providied checkpoint and fine tune the Agent further. | 1,868 | [
[
-0.000576019287109375,
-0.02294921875,
0.03118896484375,
0.0180816650390625,
-0.007038116455078125,
0.002590179443359375,
0.0106048583984375,
0.0026569366455078125,
0.0089263916015625,
0.055816650390625,
-0.05181884765625,
-0.0257720947265625,
-0.040557861328125... |
Mariszka/ppo-LunarLander-v2 | 2023-07-24T11:58:24.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Mariszka | null | null | Mariszka/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-21T11:55:33 | ---
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: 296.22 +/- 14.37
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
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0.023345947265625,
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0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
gradjitta/llama2-7b-merged-finnish-alpaca-buggy | 2023-07-21T12:47:42.000Z | [
"transformers",
"pytorch",
"llama",
"text-generation",
"dataset:datacrunch/freformatted",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | gradjitta | null | null | gradjitta/llama2-7b-merged-finnish-alpaca-buggy | 1 | 2 | transformers | 2023-07-21T12:07:16 | ---
datasets:
- datacrunch/freformatted
---
## Whats this merge about
- Its a 500 step checkpoint of the following run
```
python ./trl/examples/scripts/sft_trainer.py --model_name meta-llama/Llama-2-7b-hf --dataset_name datacrunch/finnish_alpaca --load_in_4bit --use_peft --batch_size 4 --gradient_accumulation_steps 2
```
- Using the repo https://github.com/lvwerra/trl/blob/main/examples/scripts/sft_trainer.py
I am still figuring out an efficient way of doing this, in the meantime you can test it
- An example prompt you can try, that should return the Finnish response you need
```
"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. ### Instruction: Anna kolme vinkkiä terveenä pysymiseen. ###Response:"
``` | 825 | [
[
-0.03192138671875,
-0.04266357421875,
0.03369140625,
0.025390625,
-0.024627685546875,
0.00019538402557373047,
0.01409149169921875,
-0.02593994140625,
0.0271148681640625,
0.041229248046875,
-0.0731201171875,
-0.010772705078125,
-0.0421142578125,
0.01582336425... |
intellya22/test-model-000 | 2023-07-21T12:11:48.000Z | [
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | sentence-similarity | intellya22 | null | null | intellya22/test-model-000 | 0 | 2 | sentence-transformers | 2023-07-21T12:08:39 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
def cls_pooling(model_output, attention_mask):
return model_output[0][:,0]
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, cls pooling.
sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 5956 with parameters:
```
{'batch_size': 128, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MSELoss.MSELoss`
Parameters of the fit()-Method:
```
{
"epochs": 15,
"evaluation_steps": 2978,
"evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 596,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | 3,390 | [
[
-0.021636962890625,
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0.022674560546875,
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0.009918212890625,
0.0299224853515625,
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... |
ejazhabibdar/ejazhabibdar-LoRaStableDiffusion | 2023-08-07T06:21:49.000Z | [
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | ejazhabibdar | null | null | ejazhabibdar/ejazhabibdar-LoRaStableDiffusion | 0 | 2 | diffusers | 2023-07-21T13:14:42 |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - ejazhabibdar/ejazhabibdar-LoRaStableDiffusion
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the ejazhabibdar/EjazHabibDar dataset. You can find some example images in the following.
| 559 | [
[
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0.02593994140625,
0.06573486328125,
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giniwini/ppo-LunarLander-v2 | 2023-07-21T18:07:45.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | giniwini | null | null | giniwini/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-21T18:07:28 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 276.93 +/- 22.22
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
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0.034454345703125,
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0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
patonw/ppo-LunarLander-v2 | 2023-07-24T00:33:12.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | patonw | null | null | patonw/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-21T19:31:02 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 263.43 +/- 26.66
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
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0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
jonmay/ppo-LunarLander-v2 | 2023-07-22T01:24:58.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | jonmay | null | null | jonmay/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-22T01:24:35 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 255.45 +/- 20.35
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
EXrRor3/ppo-LunarLander-v2 | 2023-07-22T03:46:54.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | EXrRor3 | null | null | EXrRor3/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-22T03:40:27 | ---
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: 251.86 +/- 17.63
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
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0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
pligor/gr7.5b-dolly | 2023-07-24T20:13:16.000Z | [
"transformers",
"pytorch",
"xglm",
"text-generation",
"text-generation-inference",
"el",
"dataset:databricks/databricks-dolly-15k",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-generation | pligor | null | null | pligor/gr7.5b-dolly | 0 | 2 | transformers | 2023-07-22T06:45:06 | ---
license: apache-2.0
datasets:
- databricks/databricks-dolly-15k
language:
- el
library_name: transformers
tags:
- text-generation-inference
pipeline_tag: text-generation
---
# Model Card for agrimi7.5B-dolly
<!-- Provide a quick summary of what the model is/does. -->
This model is a finetuned (SFT) version of Facbook xglm-7.5B using a machine translated version of the dataset databricks-dolly-15k in Greek language!
The purpose is to demonstrate the ability of the specific pretrained model to adapt to instruction following mode by using a relatively small dataset such as the databricks-dolly-15k.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [Andreas Loupasakis](https://github.com/alup)
- **Model type:** Causal Language Model
- **Language(s) (NLP):** Greek (el)
- **License:** Apache-2.0
- **Finetuned from model:** XGLM-7.5B | 917 | [
[
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0.01137542724609375,
0.0193023681640625,
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0.01544189453125,
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0.05029296875,
-0.06304931640625,
-0.041168212890625,
-0.04620361328125,
-0.0178... |
yeounyi/a2c-AntBulletEnv-v0 | 2023-07-22T11:38:29.000Z | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | yeounyi | null | null | yeounyi/a2c-AntBulletEnv-v0 | 0 | 2 | stable-baselines3 | 2023-07-22T07:17:32 | ---
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: 1259.16 +/- 237.87
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| 791 | [
[
-0.02679443359375,
-0.04443359375,
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vineetsharma/a2c-AntBulletEnv-v0 | 2023-07-22T07:47:29.000Z | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | vineetsharma | null | null | vineetsharma/a2c-AntBulletEnv-v0 | 0 | 2 | stable-baselines3 | 2023-07-22T07:46:54 | ---
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: 1480.41 +/- 128.67
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
...
```
| 791 | [
[
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yeounyi/a2c-PandaReachDense-v2 | 2023-07-23T02:45:36.000Z | [
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | yeounyi | null | null | yeounyi/a2c-PandaReachDense-v2 | 0 | 2 | stable-baselines3 | 2023-07-22T08:06:01 | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -2.75 +/- 0.36
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| 802 | [
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-0.019744873046875,
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0.033172607421875,
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0.028045654296875,
0.042694091796875,
-0.06256103515625,
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-0.03277587890625... |
Manaro/a2cv2-PandaReachDense-v2 | 2023-07-22T09:20:47.000Z | [
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Manaro | null | null | Manaro/a2cv2-PandaReachDense-v2 | 0 | 2 | stable-baselines3 | 2023-07-22T09:17:59 | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -2.10 +/- 0.94
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| 802 | [
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0.033172607421875,
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0.028045654296875,
0.042694091796875,
-0.06256103515625,
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-0.03277587890625... |
Claaas/a2c-PandaReachDense-v2 | 2023-07-22T10:17:06.000Z | [
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Claaas | null | null | Claaas/a2c-PandaReachDense-v2 | 0 | 2 | stable-baselines3 | 2023-07-22T10:14:03 | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -3.46 +/- 0.93
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| 802 | [
[
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0.028045654296875,
0.042724609375,
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michelecafagna26/blip-base-captioning-ft-hl-rationales | 2023-08-01T09:23:41.000Z | [
"transformers",
"pytorch",
"safetensors",
"blip",
"text2text-generation",
"image-captioning",
"image-to-text",
"en",
"dataset:michelecafagna26/hl",
"arxiv:2201.12086",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-to-text | michelecafagna26 | null | null | michelecafagna26/blip-base-captioning-ft-hl-rationales | 0 | 2 | transformers | 2023-07-22T10:41:32 | ---
license: apache-2.0
tags:
- image-captioning
languages:
- en
pipeline_tag: image-to-text
datasets:
- michelecafagna26/hl
language:
- en
metrics:
- sacrebleu
- rouge
library_name: transformers
---
## BLIP-base fine-tuned for Image Captioning on High-Level descriptions of Rationales
[BLIP](https://arxiv.org/abs/2201.12086) base trained on the [HL dataset](https://huggingface.co/datasets/michelecafagna26/hl) for **rationale generation of images**
## Model fine-tuning 🏋️
- Trained for of 6 epochs
- lr: 5e−5
- Adam optimizer
- half-precision (fp16)
## Test set metrics 🧾
| Cider | SacreBLEU | Rouge-L |
|--------|------------|---------|
| 46.11 | 6.21 | 19.74 |
## Model in Action 🚀
```python
import requests
from PIL import Image
from transformers import BlipProcessor, BlipForConditionalGeneration
processor = BlipProcessor.from_pretrained("michelecafagna26/blip-base-captioning-ft-hl-rationales")
model = BlipForConditionalGeneration.from_pretrained("michelecafagna26/blip-base-captioning-ft-hl-rationales").to("cuda")
img_url = 'https://datasets-server.huggingface.co/assets/michelecafagna26/hl/--/default/train/0/image/image.jpg'
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
inputs = processor(raw_image, return_tensors="pt").to("cuda")
pixel_values = inputs.pixel_values
generated_ids = model.generate(pixel_values=pixel_values, max_length=50,
do_sample=True,
top_k=120,
top_p=0.9,
early_stopping=True,
num_return_sequences=1)
processor.batch_decode(generated_ids, skip_special_tokens=True)
>>> "she is on vacation."
```
## BibTex and citation info
```BibTeX
@inproceedings{cafagna2023hl,
title={{HL} {D}ataset: {V}isually-grounded {D}escription of {S}cenes, {A}ctions and
{R}ationales},
author={Cafagna, Michele and van Deemter, Kees and Gatt, Albert},
booktitle={Proceedings of the 16th International Natural Language Generation Conference (INLG'23)},
address = {Prague, Czech Republic},
year={2023}
}
``` | 2,061 | [
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Metacarpal/ppo-LunarLander-v2-100k | 2023-07-22T14:07:45.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Metacarpal | null | null | Metacarpal/ppo-LunarLander-v2-100k | 0 | 2 | stable-baselines3 | 2023-07-22T11:59:25 | ---
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: -179.45 +/- 56.09
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
...
```
| 785 | [
[
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-... |
ACOS/LunarLander_v2_try_1 | 2023-07-22T12:29:19.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | ACOS | null | null | ACOS/LunarLander_v2_try_1 | 0 | 2 | stable-baselines3 | 2023-07-22T12:28:55 | ---
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: 231.45 +/- 39.10
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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hafidikhsan/distilbert-base-uncased-english-cefr-lexical-evaluation-dt-v6 | 2023-07-22T12:42:05.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | hafidikhsan | null | null | hafidikhsan/distilbert-base-uncased-english-cefr-lexical-evaluation-dt-v6 | 1 | 2 | transformers | 2023-07-22T12:41:25 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: distilbert-base-uncased-english-cefr-lexical-evaluation-dt-v6
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-english-cefr-lexical-evaluation-dt-v6
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4919
- Accuracy: 0.7204
- F1: 0.7215
- Precision: 0.7239
- Recall: 0.7204
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.9855 | 1.0 | 937 | 1.0026 | 0.6225 | 0.6227 | 0.6604 | 0.6225 |
| 0.6191 | 2.0 | 1874 | 0.8113 | 0.7090 | 0.7056 | 0.7160 | 0.7090 |
| 0.2736 | 3.0 | 2811 | 0.9598 | 0.7084 | 0.7070 | 0.7099 | 0.7084 |
| 0.1399 | 4.0 | 3748 | 1.2784 | 0.7130 | 0.7126 | 0.7151 | 0.7130 |
| 0.0521 | 5.0 | 4685 | 1.5455 | 0.7152 | 0.7163 | 0.7182 | 0.7152 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
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jaygdesai/Jay_ppo-Pyramids | 2023-07-22T14:34:37.000Z | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | reinforcement-learning | jaygdesai | null | null | jaygdesai/Jay_ppo-Pyramids | 0 | 2 | ml-agents | 2023-07-22T14:30:11 | ---
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: jaygdesai/Jay_ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
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Sonny4Sonnix/covid_vaccine_sentimental_analysis_TwitterRoBERTa | 2023-07-22T20:08:22.000Z | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | Sonny4Sonnix | null | null | Sonny4Sonnix/covid_vaccine_sentimental_analysis_TwitterRoBERTa | 0 | 2 | transformers | 2023-07-22T14:33:35 | ---
base_model: cardiffnlp/twitter-roberta-base-sentiment
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: test_trainer
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. -->
# test_trainer
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6861
- Accuracy: 0.714
## 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
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8144 | 0.5 | 500 | 0.7865 | 0.7165 |
| 0.7546 | 1.0 | 1000 | 0.7388 | 0.7045 |
| 0.7549 | 1.5 | 1500 | 0.7202 | 0.715 |
| 0.7124 | 2.0 | 2000 | 0.6834 | 0.7145 |
| 0.689 | 2.5 | 2500 | 0.7048 | 0.712 |
| 0.6924 | 3.0 | 3000 | 0.6861 | 0.714 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
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IWR/ppo-LunarLander-v2 | 2023-07-22T15:02:02.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | IWR | null | null | IWR/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-22T15:01:42 | ---
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: 251.83 +/- 26.82
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
...
```
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20rd1/ppo-LunarLander-v2 | 2023-07-22T15:26:41.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 20rd1 | null | null | 20rd1/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-22T15:26:24 | ---
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: 274.83 +/- 13.39
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
...
```
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TheBloke/llama2-7b-chat-codeCherryPop-qLoRA-GGML | 2023-09-27T13:00:23.000Z | [
"transformers",
"llama",
"license:llama2",
"has_space",
"text-generation-inference",
"region:us"
] | null | TheBloke | null | null | TheBloke/llama2-7b-chat-codeCherryPop-qLoRA-GGML | 37 | 2 | transformers | 2023-07-22T15:35:46 | ---
license: llama2
model_name: Llama-2-7B-Chat Code Cherry Pop
inference: false
model_creator: TokenBender
model_link: https://huggingface.co/TokenBender/llama2-7b-chat-hf-codeCherryPop-qLoRA-merged
model_type: llama
quantized_by: TheBloke
base_model: TokenBender/llama2-7b-chat-hf-codeCherryPop-qLoRA-merged
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
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# Llama-2-7B-Chat Code Cherry Pop - GGML
- Model creator: [TokenBender](https://huggingface.co/TokenBender)
- Original model: [Llama-2-7B-Chat Code Cherry Pop](https://huggingface.co/TokenBender/llama2-7b-chat-hf-codeCherryPop-qLoRA-merged)
## Description
This repo contains GGML format model files for [TokenBender's Llama-2-7B-Chat Code Cherry Pop](https://huggingface.co/TokenBender/llama2-7b-chat-hf-codeCherryPop-qLoRA-merged).
### Important note regarding GGML files.
The GGML format has now been superseded by GGUF. As of August 21st 2023, [llama.cpp](https://github.com/ggerganov/llama.cpp) no longer supports GGML models. Third party clients and libraries are expected to still support it for a time, but many may also drop support.
Please use the GGUF models instead.
### About GGML
GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp) and libraries and UIs which support this format, such as:
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most popular web UI. Supports NVidia CUDA GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a powerful GGML web UI with GPU acceleration on all platforms (CUDA and OpenCL). Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), a fully featured local GUI with GPU acceleration on both Windows (NVidia and AMD), and macOS.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with CUDA GPU acceleration via the c_transformers backend.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
## Repositories available
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/llama2-7b-chat-codeCherryPop-qLoRA-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/llama2-7b-chat-codeCherryPop-qLoRA-GGUF)
* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference (deprecated)](https://huggingface.co/TheBloke/llama2-7b-chat-codeCherryPop-qLoRA-GGML)
* [TokenBender's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TokenBender/llama2-7b-chat-hf-codeCherryPop-qLoRA-merged)
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
<!-- compatibility_ggml start -->
## Compatibility
These quantised GGML files are compatible with llama.cpp between June 6th (commit `2d43387`) and August 21st 2023.
For support with latest llama.cpp, please use GGUF files instead.
The final llama.cpp commit with support for GGML was: [dadbed99e65252d79f81101a392d0d6497b86caa](https://github.com/ggerganov/llama.cpp/commit/dadbed99e65252d79f81101a392d0d6497b86caa)
As of August 23rd 2023 they are still compatible with all UIs, libraries and utilities which use GGML. This may change in the future.
## Explanation of the new k-quant methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
* GGML_TYPE_Q8_K - "type-0" 8-bit quantization. Only used for quantizing intermediate results. The difference to the existing Q8_0 is that the block size is 256. All 2-6 bit dot products are implemented for this quantization type.
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_ggml end -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [llama-2-7b-chat-codeCherryPop.ggmlv3.q2_K.bin](https://huggingface.co/TheBloke/llama2-7b-chat-codeCherryPop-qLoRA-GGML/blob/main/llama-2-7b-chat-codeCherryPop.ggmlv3.q2_K.bin) | q2_K | 2 | 2.87 GB| 5.37 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors. |
| [llama-2-7b-chat-codeCherryPop.ggmlv3.q3_K_S.bin](https://huggingface.co/TheBloke/llama2-7b-chat-codeCherryPop-qLoRA-GGML/blob/main/llama-2-7b-chat-codeCherryPop.ggmlv3.q3_K_S.bin) | q3_K_S | 3 | 2.95 GB| 5.45 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors |
| [llama-2-7b-chat-codeCherryPop.ggmlv3.q3_K_M.bin](https://huggingface.co/TheBloke/llama2-7b-chat-codeCherryPop-qLoRA-GGML/blob/main/llama-2-7b-chat-codeCherryPop.ggmlv3.q3_K_M.bin) | q3_K_M | 3 | 3.28 GB| 5.78 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K |
| [llama-2-7b-chat-codeCherryPop.ggmlv3.q3_K_L.bin](https://huggingface.co/TheBloke/llama2-7b-chat-codeCherryPop-qLoRA-GGML/blob/main/llama-2-7b-chat-codeCherryPop.ggmlv3.q3_K_L.bin) | q3_K_L | 3 | 3.60 GB| 6.10 GB | New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K |
| [llama-2-7b-chat-codeCherryPop.ggmlv3.q4_0.bin](https://huggingface.co/TheBloke/llama2-7b-chat-codeCherryPop-qLoRA-GGML/blob/main/llama-2-7b-chat-codeCherryPop.ggmlv3.q4_0.bin) | q4_0 | 4 | 3.79 GB| 6.29 GB | Original quant method, 4-bit. |
| [llama-2-7b-chat-codeCherryPop.ggmlv3.q4_K_S.bin](https://huggingface.co/TheBloke/llama2-7b-chat-codeCherryPop-qLoRA-GGML/blob/main/llama-2-7b-chat-codeCherryPop.ggmlv3.q4_K_S.bin) | q4_K_S | 4 | 3.83 GB| 6.33 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors |
| [llama-2-7b-chat-codeCherryPop.ggmlv3.q4_K_M.bin](https://huggingface.co/TheBloke/llama2-7b-chat-codeCherryPop-qLoRA-GGML/blob/main/llama-2-7b-chat-codeCherryPop.ggmlv3.q4_K_M.bin) | q4_K_M | 4 | 4.08 GB| 6.58 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K |
| [llama-2-7b-chat-codeCherryPop.ggmlv3.q4_1.bin](https://huggingface.co/TheBloke/llama2-7b-chat-codeCherryPop-qLoRA-GGML/blob/main/llama-2-7b-chat-codeCherryPop.ggmlv3.q4_1.bin) | q4_1 | 4 | 4.21 GB| 6.71 GB | Original quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. |
| [llama-2-7b-chat-codeCherryPop.ggmlv3.q5_0.bin](https://huggingface.co/TheBloke/llama2-7b-chat-codeCherryPop-qLoRA-GGML/blob/main/llama-2-7b-chat-codeCherryPop.ggmlv3.q5_0.bin) | q5_0 | 5 | 4.63 GB| 7.13 GB | Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. |
| [llama-2-7b-chat-codeCherryPop.ggmlv3.q5_K_S.bin](https://huggingface.co/TheBloke/llama2-7b-chat-codeCherryPop-qLoRA-GGML/blob/main/llama-2-7b-chat-codeCherryPop.ggmlv3.q5_K_S.bin) | q5_K_S | 5 | 4.65 GB| 7.15 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors |
| [llama-2-7b-chat-codeCherryPop.ggmlv3.q5_K_M.bin](https://huggingface.co/TheBloke/llama2-7b-chat-codeCherryPop-qLoRA-GGML/blob/main/llama-2-7b-chat-codeCherryPop.ggmlv3.q5_K_M.bin) | q5_K_M | 5 | 4.78 GB| 7.28 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K |
| [llama-2-7b-chat-codeCherryPop.ggmlv3.q5_1.bin](https://huggingface.co/TheBloke/llama2-7b-chat-codeCherryPop-qLoRA-GGML/blob/main/llama-2-7b-chat-codeCherryPop.ggmlv3.q5_1.bin) | q5_1 | 5 | 5.06 GB| 7.56 GB | Original quant method, 5-bit. Even higher accuracy, resource usage and slower inference. |
| [llama-2-7b-chat-codeCherryPop.ggmlv3.q6_K.bin](https://huggingface.co/TheBloke/llama2-7b-chat-codeCherryPop-qLoRA-GGML/blob/main/llama-2-7b-chat-codeCherryPop.ggmlv3.q6_K.bin) | q6_K | 6 | 5.53 GB| 8.03 GB | New k-quant method. Uses GGML_TYPE_Q8_K for all tensors - 6-bit quantization |
| [llama-2-7b-chat-codeCherryPop.ggmlv3.q8_0.bin](https://huggingface.co/TheBloke/llama2-7b-chat-codeCherryPop-qLoRA-GGML/blob/main/llama-2-7b-chat-codeCherryPop.ggmlv3.q8_0.bin) | q8_0 | 8 | 7.16 GB| 9.66 GB | Original quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
## How to run in `llama.cpp`
Make sure you are using `llama.cpp` from commit [dadbed99e65252d79f81101a392d0d6497b86caa](https://github.com/ggerganov/llama.cpp/commit/dadbed99e65252d79f81101a392d0d6497b86caa) or earlier.
For compatibility with latest llama.cpp, please use GGUF files instead.
```
./main -t 10 -ngl 32 -m llama-2-7b-chat-codeCherryPop.ggmlv3.q4_K_M.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\nWrite a story about llamas\n\n### Response:"
```
Change `-t 10` to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use `-t 8`.
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 2048` to the desired sequence length for this model. For example, `-c 4096` for a Llama 2 model. For models that use RoPE, add `--rope-freq-base 10000 --rope-freq-scale 0.5` for doubled context, or `--rope-freq-base 10000 --rope-freq-scale 0.25` for 4x context.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
<!-- footer start -->
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## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute.
Thanks to the [chirper.ai](https://chirper.ai) team!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Russ Johnson, J, alfie_i, Alex, NimbleBox.ai, Chadd, Mandus, Nikolai Manek, Ken Nordquist, ya boyyy, Illia Dulskyi, Viktor Bowallius, vamX, Iucharbius, zynix, Magnesian, Clay Pascal, Pierre Kircher, Enrico Ros, Tony Hughes, Elle, Andrey, knownsqashed, Deep Realms, Jerry Meng, Lone Striker, Derek Yates, Pyrater, Mesiah Bishop, James Bentley, Femi Adebogun, Brandon Frisco, SuperWojo, Alps Aficionado, Michael Dempsey, Vitor Caleffi, Will Dee, Edmond Seymore, usrbinkat, LangChain4j, Kacper Wikieł, Luke Pendergrass, John Detwiler, theTransient, Nathan LeClaire, Tiffany J. Kim, biorpg, Eugene Pentland, Stanislav Ovsiannikov, Fred von Graf, terasurfer, Kalila, Dan Guido, Nitin Borwankar, 阿明, Ai Maven, John Villwock, Gabriel Puliatti, Stephen Murray, Asp the Wyvern, danny, Chris Smitley, ReadyPlayerEmma, S_X, Daniel P. Andersen, Olakabola, Jeffrey Morgan, Imad Khwaja, Caitlyn Gatomon, webtim, Alicia Loh, Trenton Dambrowitz, Swaroop Kallakuri, Erik Bjäreholt, Leonard Tan, Spiking Neurons AB, Luke @flexchar, Ajan Kanaga, Thomas Belote, Deo Leter, RoA, Willem Michiel, transmissions 11, subjectnull, Matthew Berman, Joseph William Delisle, David Ziegler, Michael Davis, Johann-Peter Hartmann, Talal Aujan, senxiiz, Artur Olbinski, Rainer Wilmers, Spencer Kim, Fen Risland, Cap'n Zoog, Rishabh Srivastava, Michael Levine, Geoffrey Montalvo, Sean Connelly, Alexandros Triantafyllidis, Pieter, Gabriel Tamborski, Sam, Subspace Studios, Junyu Yang, Pedro Madruga, Vadim, Cory Kujawski, K, Raven Klaugh, Randy H, Mano Prime, Sebastain Graf, Space Cruiser
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: TokenBender's Llama-2-7B-Chat Code Cherry Pop
---
### Overview:
description:
This is a llama2 7B HF chat model fine-tuned on 122k code instructions. In my early experiments it seems to be doing very well.
additional_info:
It's a bottom of the barrel model 😂 but after quantization it can be
valuable for sure. It definitely proves that a 7B can be useful for boilerplate
code stuff though.
### Plans:
next_steps: "I've a few things in mind and after that this will be more valuable."
tasks:
- name: "I'll quantize these"
timeline: "Possibly tonight or tomorrow in the day"
result: "Then it can be run locally with 4G ram."
- name: "I've used alpaca style instruction tuning"
improvement: |
I'll switch to llama2 style [INST]<<SYS>> style and see if
it improves anything.
- name: "HumanEval report and checking for any training data leaks"
- attempt: "I'll try 8k context via RoPE enhancement"
hypothesis: "Let's see if that degrades performance or not."
commercial_use: |
So far I think this can be used commercially but this is a adapter on Meta's llama2 with
some gating issues so that is there.
contact_info: "If you find any issues or want to just holler at me, you can reach out to me - https://twitter.com/4evaBehindSOTA"
### Library:
name: "peft"
### Training procedure:
quantization_config:
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"
| 16,669 | [
[
-0.039276123046875,
-0.0635986328125,
0.02386474609375,
0.0228271484375,
-0.030975341796875,
0.0018329620361328125,
-0.0035247802734375,
-0.048370361328125,
0.03118896484375,
0.006214141845703125,
-0.046661376953125,
-0.042388916015625,
-0.040069580078125,
-... |
lego111Aron/ppo-LunarLander-v2_test-2 | 2023-07-22T15:56:12.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | lego111Aron | null | null | lego111Aron/ppo-LunarLander-v2_test-2 | 0 | 2 | stable-baselines3 | 2023-07-22T15:55:53 | ---
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: 243.24 +/- 77.14
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
lucasbertola/a2c-AntBulletEnv-v0 | 2023-07-22T16:12:49.000Z | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | lucasbertola | null | null | lucasbertola/a2c-AntBulletEnv-v0 | 0 | 2 | stable-baselines3 | 2023-07-22T16:11:41 | ---
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: 2005.91 +/- 352.09
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
...
```
| 791 | [
[
-0.02679443359375,
-0.04443359375,
0.0106964111328125,
0.0208892822265625,
-0.0034961700439453125,
0.0018033981323242188,
0.0187530517578125,
-0.0176544189453125,
0.0193939208984375,
0.0265655517578125,
-0.052642822265625,
-0.037506103515625,
-0.04425048828125,
... |
jvilaseca/a2c-2PandaReachDense-v2 | 2023-07-22T16:48:51.000Z | [
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | jvilaseca | null | null | jvilaseca/a2c-2PandaReachDense-v2 | 0 | 2 | stable-baselines3 | 2023-07-22T16:46:09 | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -1.26 +/- 0.26
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| 802 | [
[
-0.019744873046875,
-0.04742431640625,
-0.004787445068359375,
0.0469970703125,
-0.00018846988677978516,
-0.006023406982421875,
0.033172607421875,
-0.0249481201171875,
0.028045654296875,
0.042694091796875,
-0.06256103515625,
-0.0289764404296875,
-0.03277587890625... |
jaygdesai/a2c-AntBulletEnv-v0 | 2023-07-22T16:52:28.000Z | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | jaygdesai | null | null | jaygdesai/a2c-AntBulletEnv-v0 | 0 | 2 | stable-baselines3 | 2023-07-22T16:51:25 | ---
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: 1957.80 +/- 87.21
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
...
```
| 790 | [
[
-0.02679443359375,
-0.04443359375,
0.0106964111328125,
0.0208892822265625,
-0.0034961700439453125,
0.0018033981323242188,
0.0187530517578125,
-0.0176544189453125,
0.0193939208984375,
0.0265655517578125,
-0.052642822265625,
-0.037506103515625,
-0.04425048828125,
... |
yuanzi1983918/ppo-LunarLander-v2 | 2023-07-22T18:16:50.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | yuanzi1983918 | null | null | yuanzi1983918/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-22T18:16:32 | ---
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: 258.57 +/- 15.62
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
jaygdesai/a2c-PandaReachDense-v2 | 2023-07-22T18:35:39.000Z | [
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | jaygdesai | null | null | jaygdesai/a2c-PandaReachDense-v2 | 0 | 2 | stable-baselines3 | 2023-07-22T18:32:14 | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -2.18 +/- 0.54
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| 802 | [
[
-0.019744873046875,
-0.04742431640625,
-0.004787445068359375,
0.0469970703125,
-0.00018846988677978516,
-0.006023406982421875,
0.033172607421875,
-0.0249481201171875,
0.028045654296875,
0.042694091796875,
-0.06256103515625,
-0.0289764404296875,
-0.03277587890625... |
diogopaes10/017-microsoft-MiniLM-finetuned-yahoo-800_200 | 2023-07-23T00:36:20.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-classification | diogopaes10 | null | null | diogopaes10/017-microsoft-MiniLM-finetuned-yahoo-800_200 | 0 | 2 | transformers | 2023-07-23T00:35:00 | ---
license: mit
base_model: microsoft/MiniLM-L12-H384-uncased
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
- precision
- recall
model-index:
- name: 017-microsoft-MiniLM-finetuned-yahoo-800_200
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. -->
# 017-microsoft-MiniLM-finetuned-yahoo-800_200
This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4048
- F1: 0.6237
- Accuracy: 0.63
- Precision: 0.6273
- Recall: 0.63
- System Ram Used: 3.8778
- System Ram Total: 83.4807
- Gpu Ram Allocated: 0.3903
- Gpu Ram Cached: 12.8340
- Gpu Ram Total: 39.5640
- Gpu Utilization: 32
- Disk Space Used: 25.4337
- Disk Space Total: 78.1898
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy | Precision | Recall | System Ram Used | System Ram Total | Gpu Ram Allocated | Gpu Ram Cached | Gpu Ram Total | Gpu Utilization | Disk Space Used | Disk Space Total |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|:---------:|:------:|:---------------:|:----------------:|:-----------------:|:--------------:|:-------------:|:---------------:|:---------------:|:----------------:|
| 2.3021 | 1.28 | 32 | 2.2975 | 0.0519 | 0.12 | 0.1102 | 0.12 | 3.8424 | 83.4807 | 0.3904 | 12.8340 | 39.5640 | 29 | 24.5606 | 78.1898 |
| 2.2615 | 2.56 | 64 | 2.1926 | 0.2339 | 0.31 | 0.4649 | 0.31 | 3.8514 | 83.4807 | 0.3904 | 12.8340 | 39.5640 | 30 | 24.5606 | 78.1898 |
| 2.0677 | 3.84 | 96 | 1.9658 | 0.4301 | 0.51 | 0.3950 | 0.51 | 3.8537 | 83.4807 | 0.3905 | 12.8340 | 39.5640 | 22 | 24.5606 | 78.1898 |
| 1.8562 | 5.12 | 128 | 1.8383 | 0.4655 | 0.545 | 0.4587 | 0.545 | 3.8574 | 83.4807 | 0.3904 | 12.8340 | 39.5640 | 41 | 24.5606 | 78.1898 |
| 1.6929 | 6.4 | 160 | 1.7403 | 0.4942 | 0.555 | 0.5261 | 0.555 | 3.8549 | 83.4807 | 0.3904 | 12.8340 | 39.5640 | 29 | 24.5607 | 78.1898 |
| 1.5569 | 7.68 | 192 | 1.6663 | 0.5467 | 0.585 | 0.6496 | 0.585 | 3.8549 | 83.4807 | 0.3904 | 12.8340 | 39.5640 | 37 | 24.5607 | 78.1898 |
| 1.4636 | 8.96 | 224 | 1.6123 | 0.5475 | 0.58 | 0.5539 | 0.58 | 3.8539 | 83.4807 | 0.3904 | 12.8340 | 39.5640 | 30 | 24.5607 | 78.1898 |
| 1.3683 | 10.24 | 256 | 1.5615 | 0.5829 | 0.595 | 0.6016 | 0.595 | 3.8527 | 83.4807 | 0.3904 | 12.8340 | 39.5640 | 41 | 24.5607 | 78.1898 |
| 1.2649 | 11.52 | 288 | 1.5261 | 0.5904 | 0.61 | 0.6243 | 0.61 | 3.8646 | 83.4807 | 0.3904 | 12.8340 | 39.5640 | 30 | 24.5607 | 78.1898 |
| 1.1968 | 12.8 | 320 | 1.4976 | 0.6012 | 0.615 | 0.6070 | 0.615 | 3.8766 | 83.4807 | 0.3904 | 12.8340 | 39.5640 | 45 | 24.5607 | 78.1898 |
| 1.1291 | 14.08 | 352 | 1.4756 | 0.5983 | 0.615 | 0.6164 | 0.615 | 3.8749 | 83.4807 | 0.3905 | 12.8340 | 39.5640 | 47 | 24.5607 | 78.1898 |
| 1.0673 | 15.36 | 384 | 1.4660 | 0.6064 | 0.62 | 0.6258 | 0.62 | 3.8752 | 83.4807 | 0.3907 | 12.8340 | 39.5640 | 35 | 24.5607 | 78.1898 |
| 0.9884 | 16.64 | 416 | 1.4410 | 0.6135 | 0.625 | 0.6204 | 0.625 | 3.8757 | 83.4807 | 0.3904 | 12.8340 | 39.5640 | 33 | 24.5608 | 78.1898 |
| 0.9743 | 17.92 | 448 | 1.4328 | 0.6233 | 0.635 | 0.6343 | 0.635 | 3.8747 | 83.4807 | 0.3905 | 12.8340 | 39.5640 | 44 | 24.5608 | 78.1898 |
| 0.926 | 19.2 | 480 | 1.4344 | 0.6088 | 0.615 | 0.6238 | 0.615 | 3.8742 | 83.4807 | 0.3904 | 12.8340 | 39.5640 | 31 | 24.5608 | 78.1898 |
| 0.8815 | 20.48 | 512 | 1.4282 | 0.6235 | 0.625 | 0.6350 | 0.625 | 4.0591 | 83.4807 | 0.3904 | 12.8340 | 39.5640 | 43 | 25.4337 | 78.1898 |
| 0.8613 | 21.76 | 544 | 1.4146 | 0.6329 | 0.635 | 0.6408 | 0.635 | 4.0655 | 83.4807 | 0.3904 | 12.8340 | 39.5640 | 26 | 25.4337 | 78.1898 |
| 0.8466 | 23.04 | 576 | 1.4086 | 0.6318 | 0.635 | 0.6415 | 0.635 | 4.0544 | 83.4807 | 0.3904 | 12.8340 | 39.5640 | 35 | 25.4337 | 78.1898 |
| 0.8282 | 24.32 | 608 | 1.4058 | 0.6243 | 0.63 | 0.6319 | 0.63 | 3.8886 | 83.4807 | 0.3904 | 12.8340 | 39.5640 | 27 | 25.4337 | 78.1898 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
| 6,525 | [
[
-0.05218505859375,
-0.036865234375,
0.0200653076171875,
0.006847381591796875,
-0.00556182861328125,
0.004276275634765625,
0.00800323486328125,
-0.006061553955078125,
0.0469970703125,
0.01934814453125,
-0.042694091796875,
-0.0421142578125,
-0.048370361328125,
... |
WasuratS/whisper-base-danish-finetune-common-voice-11 | 2023-07-23T12:50:09.000Z | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"da",
"dataset:mozilla-foundation/common_voice_13_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | WasuratS | null | null | WasuratS/whisper-base-danish-finetune-common-voice-11 | 0 | 2 | transformers | 2023-07-23T02:33:41 | ---
language:
- da
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- wer
model-index:
- name: Whisper Base Danish - WasuratS
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 13
type: mozilla-foundation/common_voice_13_0
config: da
split: test
args: da
metrics:
- name: Wer
type: wer
value: 39.73630725936735
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Base Danish - WasuratS
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Common Voice 13 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9795
- Wer Ortho: 45.5986
- Wer: 39.7363
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 6000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:|
| 0.5156 | 1.61 | 500 | 0.7387 | 47.8293 | 42.2586 |
| 0.2086 | 3.22 | 1000 | 0.7157 | 46.7087 | 41.0652 |
| 0.1439 | 4.82 | 1500 | 0.7300 | 46.5367 | 40.9610 |
| 0.0514 | 6.43 | 2000 | 0.7804 | 45.2963 | 39.5279 |
| 0.027 | 8.04 | 2500 | 0.8314 | 46.3126 | 40.3825 |
| 0.0133 | 9.65 | 3000 | 0.8739 | 44.8585 | 39.2777 |
| 0.0053 | 11.25 | 3500 | 0.9081 | 45.4839 | 39.7103 |
| 0.0041 | 12.86 | 4000 | 0.9347 | 45.4110 | 39.7050 |
| 0.0028 | 14.47 | 4500 | 0.9535 | 46.0624 | 40.3096 |
| 0.0024 | 16.08 | 5000 | 0.9673 | 45.6351 | 39.8979 |
| 0.0021 | 17.68 | 5500 | 0.9762 | 45.7862 | 39.9187 |
| 0.002 | 19.29 | 6000 | 0.9795 | 45.5986 | 39.7363 |
### Framework versions
- Transformers 4.29.2
- Pytorch 1.13.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
| 2,531 | [
[
-0.037261962890625,
-0.037261962890625,
0.004428863525390625,
0.0118255615234375,
-0.016326904296875,
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0.0265655517578125,
0.03240966796875,
-0.0545654296875,
-0.054901123046875,
-0.04815673828125,
... |
mchh528/unit1-rl-course | 2023-07-23T05:35:54.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | mchh528 | null | null | mchh528/unit1-rl-course | 0 | 2 | stable-baselines3 | 2023-07-23T02:35:54 | ---
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: 288.12 +/- 17.19
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
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0.002735137939453125,
0.034454345703125,
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0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
rahul-appu/LunarLander | 2023-07-23T06:36:52.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | rahul-appu | null | null | rahul-appu/LunarLander | 0 | 2 | stable-baselines3 | 2023-07-23T06:36:32 | ---
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: 249.52 +/- 15.62
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
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0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
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-... |
hafidikhsan/distilbert-base-uncased-english-cefr-lexical-evaluation-bs-v1 | 2023-07-23T07:15:48.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | hafidikhsan | null | null | hafidikhsan/distilbert-base-uncased-english-cefr-lexical-evaluation-bs-v1 | 0 | 2 | transformers | 2023-07-23T07:15:07 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: distilbert-base-uncased-english-cefr-lexical-evaluation-bs-v1
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-english-cefr-lexical-evaluation-bs-v1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1930
- Accuracy: 0.5941
- F1: 0.5907
- Precision: 0.5913
- Recall: 0.5941
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log | 1.0 | 44 | 1.4290 | 0.4439 | 0.3994 | 0.4597 | 0.4439 |
| 1.5279 | 2.0 | 88 | 1.2962 | 0.5076 | 0.4992 | 0.5300 | 0.5076 |
| 1.0713 | 3.0 | 132 | 1.2973 | 0.5293 | 0.5328 | 0.5564 | 0.5293 |
| 0.624 | 4.0 | 176 | 1.3405 | 0.5583 | 0.5550 | 0.5559 | 0.5583 |
| 0.3372 | 5.0 | 220 | 1.3920 | 0.5424 | 0.5445 | 0.5515 | 0.5424 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
| 2,030 | [
[
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0.01171875,
0.0142059326171875,
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0.00737762451171875,
0.01110076904296875,
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-0... |
ACOS/PPO_LunarLander-v2_try4 | 2023-07-23T07:22:45.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | ACOS | null | null | ACOS/PPO_LunarLander-v2_try4 | 0 | 2 | stable-baselines3 | 2023-07-23T07:22:26 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 276.46 +/- 23.76
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
...
```
| 784 | [
[
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0.023345947265625,
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0.002735137939453125,
0.034454345703125,
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0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
dnarqq/ppo-Pyramids | 2023-07-23T10:12:09.000Z | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | reinforcement-learning | dnarqq | null | null | dnarqq/ppo-Pyramids | 0 | 2 | ml-agents | 2023-07-23T10:12:01 | ---
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: dnarqq/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
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0.030242919921875,
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-0.... |
Vinipereira/PPO-LunarLander-v2 | 2023-07-23T10:32:46.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Vinipereira | null | null | Vinipereira/PPO-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-23T10:32:26 | ---
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: -1592.79 +/- 200.13
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
...
```
| 787 | [
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sarwarbeing/PPO-LunarLander-v2 | 2023-07-23T10:59:49.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | sarwarbeing | null | null | sarwarbeing/PPO-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-23T10:57:45 | ---
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: 279.31 +/- 11.46
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
...
```
| 784 | [
[
-0.0002129077911376953,
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Ritikrb/ppo-LunarLander-v2 | 2023-07-23T11:20:09.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Ritikrb | null | null | Ritikrb/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-23T11:19:04 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 277.20 +/- 14.74
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
...
```
| 784 | [
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0.06500244140625,
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-... |
Evan-Lin/Bart-Amazon-many-keywordmax1-attractive1-reward1-epoch0 | 2023-07-23T11:36:45.000Z | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"trl",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | Evan-Lin | null | null | Evan-Lin/Bart-Amazon-many-keywordmax1-attractive1-reward1-epoch0 | 0 | 2 | transformers | 2023-07-23T11:34:01 | ---
license: apache-2.0
tags:
- trl
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="Evan-Lin//tmp/tmpeydaa0l0/Evan-Lin/Bart-Amazon-many-keywordmax1-attractive1-reward1-epoch0")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("Evan-Lin//tmp/tmpeydaa0l0/Evan-Lin/Bart-Amazon-many-keywordmax1-attractive1-reward1-epoch0")
model = AutoModelForCausalLMWithValueHead.from_pretrained("Evan-Lin//tmp/tmpeydaa0l0/Evan-Lin/Bart-Amazon-many-keywordmax1-attractive1-reward1-epoch0")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
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0.00903... |
tslai1992/a2c-AntBulletEnv-v0 | 2023-07-23T12:50:17.000Z | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | tslai1992 | null | null | tslai1992/a2c-AntBulletEnv-v0 | 0 | 2 | stable-baselines3 | 2023-07-23T12:17:08 | ---
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: 1464.73 +/- 308.75
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
...
```
| 791 | [
[
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DerckPrinz/ppo-LunarLander-v2 | 2023-07-23T13:20:21.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | DerckPrinz | null | null | DerckPrinz/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-23T13:20:05 | ---
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: 279.20 +/- 22.76
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
...
```
| 784 | [
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0.06500244140625,
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-... |
Corianas/a2c-AntBulletEnv-v0 | 2023-07-23T14:15:48.000Z | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Corianas | null | null | Corianas/a2c-AntBulletEnv-v0 | 0 | 2 | stable-baselines3 | 2023-07-23T14:14:39 | ---
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: 1350.39 +/- 30.56
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
...
```
| 790 | [
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nikbhi/lunar_lander | 2023-07-23T15:15:14.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | nikbhi | null | null | nikbhi/lunar_lander | 0 | 2 | stable-baselines3 | 2023-07-23T15:14:56 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: Proximal Policy Optimization
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 252.05 +/- 16.24
name: mean_reward
verified: false
---
# **Proximal Policy Optimization** Agent playing **LunarLander-v2**
This is a trained model of a **Proximal Policy Optimization** 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
...
```
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0.012451171875,
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0.062347412109375,
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-0.025177001953125,
-0.0116729736328125,
... |
IbrahimSalah/Arabic_speech_Syllables_recognition_Using_Wav2vec2 | 2023-07-25T12:53:25.000Z | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | IbrahimSalah | null | null | IbrahimSalah/Arabic_speech_Syllables_recognition_Using_Wav2vec2 | 0 | 2 | transformers | 2023-07-23T15:49:44 | # Arabic syllables recognition with tashkeel.
This is fine tuned wav2vec2 model to recognize arabic syllables from speech.
The model was trained on Modern standard arabic dataset.\
5-gram language model is available with the model.
To try it out :
```
!pip install datasets transformers
!pip install https://github.com/kpu/kenlm/archive/master.zip pyctcdecode
```
```
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
from transformers import Wav2Vec2ProcessorWithLM
processor = Wav2Vec2ProcessorWithLM.from_pretrained('IbrahimSalah/Syllables_final_Large')
model = Wav2Vec2ForCTC.from_pretrained("IbrahimSalah/Syllables_final_Large")
```
```
import pandas as pd
dftest = pd.DataFrame(columns=['audio'])
import datasets
from datasets import Dataset
path ='/content/908-33.wav'
dftest['audio']=[path] ## audio path
dataset = Dataset.from_pandas(dftest)
```
```
import torch
import torchaudio
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["audio"])
print(sampling_rate)
resampler = torchaudio.transforms.Resample(sampling_rate, 16_000) # The original data was with 48,000 sampling rate. You can change it according to your input.
batch["audio"] = resampler(speech_array).squeeze().numpy()
return batch
```
```
import numpy as np
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["audio"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values).logits
print(logits.numpy().shape)
transcription = processor.batch_decode(logits.numpy()).text
print("Prediction:",transcription[0])
```
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[
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0.01... |
gArthur98/Roberta-classweight-Sentiment-classifier | 2023-07-23T16:48:44.000Z | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | text-classification | gArthur98 | null | null | gArthur98/Roberta-classweight-Sentiment-classifier | 0 | 2 | transformers | 2023-07-23T16:00:47 | ---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: Roberta-classweight-Sentiment-classifier
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Roberta-classweight-Sentiment-classifier
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8656
- F1: 0.6449
## 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
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.9827 | 0.5 | 500 | 0.8890 | 0.6395 |
| 0.9185 | 1.0 | 1000 | 0.8708 | 0.6449 |
| 0.8998 | 1.5 | 1500 | 0.8673 | 0.6449 |
| 0.8792 | 2.01 | 2000 | 0.8648 | 0.6449 |
| 0.8877 | 2.51 | 2500 | 0.8656 | 0.6449 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
| 1,602 | [
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ciscoliveira/ppo-LunarLander-v2 | 2023-07-23T18:22:35.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | ciscoliveira | null | null | ciscoliveira/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-23T18:22:16 | ---
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: 295.38 +/- 15.77
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
...
```
| 784 | [
[
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0.06500244140625,
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-0.0343017578125,
-... |
Hwilner/ppo-LunarLander-v2 | 2023-07-23T18:25:25.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Hwilner | null | null | Hwilner/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-23T18:25:05 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 256.28 +/- 19.42
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
zocrate/MoonLanding | 2023-07-23T22:21:44.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | zocrate | null | null | zocrate/MoonLanding | 0 | 2 | stable-baselines3 | 2023-07-23T19:42:24 | ---
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: 290.61 +/- 20.45
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
Vladislav-HuggingFace/a2c-AntBulletEnv-v0 | 2023-07-23T20:23:54.000Z | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Vladislav-HuggingFace | null | null | Vladislav-HuggingFace/a2c-AntBulletEnv-v0 | 0 | 2 | stable-baselines3 | 2023-07-23T20:22:42 | ---
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: 1225.30 +/- 79.89
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
...
```
| 790 | [
[
-0.02679443359375,
-0.04443359375,
0.0106964111328125,
0.0208892822265625,
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0.0187530517578125,
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0.0193939208984375,
0.0265655517578125,
-0.052642822265625,
-0.037506103515625,
-0.04425048828125,
... |
marianafmedeiros/a2c-PandaReachDense-v2 | 2023-07-23T21:59:31.000Z | [
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | marianafmedeiros | null | null | marianafmedeiros/a2c-PandaReachDense-v2 | 0 | 2 | stable-baselines3 | 2023-07-23T21:23:56 | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -2.02 +/- 0.99
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| 802 | [
[
-0.019744873046875,
-0.04742431640625,
-0.004787445068359375,
0.0469970703125,
-0.00018846988677978516,
-0.006023406982421875,
0.033172607421875,
-0.0249481201171875,
0.028045654296875,
0.042694091796875,
-0.06256103515625,
-0.0289764404296875,
-0.03277587890625... |
jordyvl/EElayoutlmv3_jordyvl_rvl_cdip_easyocr_2023-07-23_g025 | 2023-07-28T14:26:36.000Z | [
"transformers",
"pytorch",
"layoutlmv3",
"text-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | text-classification | jordyvl | null | null | jordyvl/EElayoutlmv3_jordyvl_rvl_cdip_easyocr_2023-07-23_g025 | 0 | 2 | transformers | 2023-07-23T21:50:37 | ---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: EElayoutlmv3_jordyvl_rvl_cdip_easyocr_2023-07-23_g025
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. -->
# EElayoutlmv3_jordyvl_rvl_cdip_easyocr_2023-07-23_g025
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2244
- Accuracy: 0.9394
- Exit 0 Accuracy: 0.2721
- Exit 1 Accuracy: 0.4875
- Exit 2 Accuracy: 0.8051
- Exit 3 Accuracy: 0.8840
- Exit 4 Accuracy: 0.9339
## 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: 6
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 24
- total_train_batch_size: 144
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Exit 0 Accuracy | Exit 1 Accuracy | Exit 2 Accuracy | Exit 3 Accuracy | Exit 4 Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------------:|:---------------:|:---------------:|:---------------:|:---------------:|
| 0.5909 | 1.0 | 2222 | 0.2945 | 0.9158 | 0.2021 | 0.3569 | 0.7091 | 0.8143 | 0.9092 |
| 0.4951 | 2.0 | 4444 | 0.2469 | 0.9292 | 0.2262 | 0.4336 | 0.7677 | 0.8614 | 0.9258 |
| 0.4279 | 3.0 | 6666 | 0.2281 | 0.9336 | 0.2530 | 0.4682 | 0.7898 | 0.8768 | 0.9302 |
| 0.39 | 4.0 | 8888 | 0.2241 | 0.9385 | 0.2600 | 0.483 | 0.8008 | 0.8827 | 0.9328 |
| 0.3602 | 5.0 | 11110 | 0.2244 | 0.9394 | 0.2721 | 0.4875 | 0.8051 | 0.8840 | 0.9339 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2
| 2,500 | [
[
-0.0382080078125,
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0.01270294189453125,
0.01947021484375,
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0.017913818359375,
0.0266571044921875,
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... |
Vladislav-HuggingFace/a2c-PandaReachDense-v2 | 2023-07-24T09:24:10.000Z | [
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Vladislav-HuggingFace | null | null | Vladislav-HuggingFace/a2c-PandaReachDense-v2 | 0 | 2 | stable-baselines3 | 2023-07-23T21:50:48 | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -1.74 +/- 0.25
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| 802 | [
[
-0.019744873046875,
-0.04742431640625,
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0.0469970703125,
-0.00018846988677978516,
-0.006023406982421875,
0.033172607421875,
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0.028045654296875,
0.042694091796875,
-0.06256103515625,
-0.0289764404296875,
-0.03277587890625... |
princeton-nlp/FullAttention-2.7b-4k | 2023-10-27T14:43:43.000Z | [
"transformers",
"pytorch",
"opt",
"arxiv:2305.14788",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | princeton-nlp | null | null | princeton-nlp/FullAttention-2.7b-4k | 0 | 2 | transformers | 2023-07-23T21:58:42 | ---
license: apache-2.0
---
license: apache-2.0
---
**Paper**: [Adapting Language Models to Compress Contexts](https://arxiv.org/abs/2305.14788)
**Code**: https://github.com/princeton-nlp/AutoCompressors
**Models**:
- Llama-2-7b fine-tuned models: [AutoCompressor-Llama-2-7b-6k](https://huggingface.co/princeton-nlp/AutoCompressor-Llama-2-7b-6k/), [FullAttention-Llama-2-7b-6k](https://huggingface.co/princeton-nlp/FullAttention-Llama-2-7b-6k)
- OPT-2.7b fine-tuned models: [AutoCompressor-2.7b-6k](https://huggingface.co/princeton-nlp/AutoCompressor-2.7b-6k), [AutoCompressor-2.7b-30k](https://huggingface.co/princeton-nlp/AutoCompressor-2.7b-30k), [RMT-2.7b-8k](https://huggingface.co/princeton-nlp/RMT-2.7b-8k), [FullAttention-2.7b-4k](https://huggingface.co/princeton-nlp/FullAttention-2.7b-4k)
- OPT-1.3b fine-tuned models: [AutoCompressor-1.3b-30k](https://huggingface.co/princeton-nlp/AutoCompressor-1.3b-30k), [RMT-1.3b-30k](https://huggingface.co/princeton-nlp/RMT-1.3b-30k)
---
FullAttention-2.7b-4k is a model fine-tuned from [facebook/opt-2.7b](https://huggingface.co/facebook/opt-2.7b) following the context window extension method described in [Adapting Language Models to Compress Contexts](https://arxiv.org/abs/2305.14788).
The 2,048 positional embeddings of the pre-trained OPT-2.7b are duplicated and the model is fine-tuned on sequences of 4,096 tokens from 2B tokens from [The Pile](https://pile.eleuther.ai).
To get started, download the [`AutoCompressor`](https://github.com/princeton-nlp/AutoCompressors) repository and load the model as follows:
```
from auto_compressor import AutoCompressorModel
model = AutoCompressorModel.from_pretrained("princeton-nlp/FullAttention-2.7b-4k")
```
**Evaluation**
We record the perplexity achieved by our OPT-2.7b models on segments of 2,048 tokens, conditioned on different amounts of context.
FullAttention-2.7-4k uses full uncompressed contexts whereas AutoCompressor-2.7b-6k and RMT-2.7b-8k compress segments of 2,048 tokens into 50 summary vectors.
*In-domain Evaluation*
| Context Tokens | 0 |512 | 2048 | 4096 | 6144 |
| -----------------------------|-----|-----|------|------|------|
| FullAttention-2.7b-4k | 6.57|6.15 |5.94 |- |- |
| RMT-2.7b-8k | 6.34|6.19 |6.02 | 6.02 | 6.01 |
| AutoCompressor-2.7b-6k | 6.31|6.04 | 5.98 | 5.94 | 5.93 |
*Out-of-domain Evaluation*
| Context Tokens | 0 |512 | 2048 | 4096 | 6144 |
| -----------------------------|-----|-----|------|------|------|
| FullAttention-2.7b-4k | 8.94|8.28 |7.93 |- |- |
| RMT-2.7b-8k | 8.62|8.44 |8.21 | 8.20 | 8.20 |
| AutoCompressor-2.7b-6k | 8.60|8.26 | 8.17 | 8.12 | 8.10 |
## Bibtex
```
@misc{chevalier2023adapting,
title={Adapting Language Models to Compress Contexts},
author={Alexis Chevalier and Alexander Wettig and Anirudh Ajith and Danqi Chen},
year={2023},
eprint={2305.14788},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | 3,052 | [
[
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0.03253173828125,
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0.01157379150390625,
0.006343841552734375,
-0.032501220703125,
-0.032379150390625,
-0.057342529296875,... |
Beniuv/model1 | 2023-07-23T22:01:42.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Beniuv | null | null | Beniuv/model1 | 0 | 2 | stable-baselines3 | 2023-07-23T22:01:22 | ---
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: 261.73 +/- 24.42
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| 784 | [
[
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ALM-AHME/beit-large-patch16-224-finetuned-BreastCancer-Classification-BreakHis-AH-60-20-20-Shuffled | 2023-07-24T17:13:49.000Z | [
"transformers",
"pytorch",
"tensorboard",
"beit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | ALM-AHME | null | null | ALM-AHME/beit-large-patch16-224-finetuned-BreastCancer-Classification-BreakHis-AH-60-20-20-Shuffled | 1 | 2 | transformers | 2023-07-23T22:40:44 | ---
license: apache-2.0
base_model: microsoft/beit-large-patch16-224
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: beit-large-patch16-224-finetuned-BreastCancer-Classification-BreakHis-AH-60-20-20-Shuffled
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. -->
# beit-large-patch16-224-finetuned-BreastCancer-Classification-BreakHis-AH-60-20-20-Shuffled
This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0146
- Accuracy: 0.9958
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.9
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| 0.5847 | 1.0 | 199 | 0.8030 | 0.4640 |
| 0.2856 | 2.0 | 398 | 0.9354 | 0.1753 |
| 0.156 | 3.0 | 597 | 0.9552 | 0.1179 |
| 0.1049 | 4.0 | 796 | 0.9585 | 0.1043 |
| 0.1399 | 5.0 | 995 | 0.9760 | 0.0673 |
| 0.0423 | 6.0 | 1194 | 0.9802 | 0.0455 |
| 0.078 | 7.0 | 1393 | 0.9802 | 0.0554 |
| 0.1769 | 8.0 | 1592 | 0.9764 | 0.0556 |
| 0.0568 | 9.0 | 1791 | 0.9807 | 0.0569 |
| 0.0728 | 10.0 | 1990 | 0.9915 | 0.0234 |
| 0.0229 | 11.0 | 2189 | 0.9910 | 0.0240 |
| 0.0561 | 12.0 | 2388 | 0.9901 | 0.0352 |
| 0.014 | 13.0 | 2587 | 0.9797 | 0.0749 |
| 0.096 | 14.0 | 2786 | 0.9934 | 0.0268 |
| 0.0005 | 15.0 | 2985 | 0.0146 | 0.9958 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
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[
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truitt/ppo-LunarLander-v2 | 2023-07-24T00:08:20.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | truitt | null | null | truitt/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-24T00:08:02 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 264.69 +/- 15.91
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| 784 | [
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malang1/ppo-Lunar_Lander-v2 | 2023-07-24T01:21:31.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | malang1 | null | null | malang1/ppo-Lunar_Lander-v2 | 0 | 2 | stable-baselines3 | 2023-07-24T01:21:12 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 254.64 +/- 14.04
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
...
```
| 784 | [
[
-0.0002123117446899414,
-0.0271453857421875,
0.0170745849609375,
0.0233612060546875,
-0.006069183349609375,
0.0027523040771484375,
0.034423828125,
-0.012115478515625,
0.0198822021484375,
0.06500244140625,
-0.043182373046875,
-0.035247802734375,
-0.03433227539062... |
bitwild/ppo-LunarLander-v2 | 2023-07-24T02:10:44.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | bitwild | null | null | bitwild/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-24T02:10:23 | ---
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: 259.88 +/- 12.77
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
...
```
| 784 | [
[
-0.0002123117446899414,
-0.0271453857421875,
0.0170745849609375,
0.0233612060546875,
-0.006069183349609375,
0.0027523040771484375,
0.034423828125,
-0.012115478515625,
0.0198822021484375,
0.06500244140625,
-0.043182373046875,
-0.035247802734375,
-0.03433227539062... |
Blackroot/FrankensteinsMonster-13B-GPTQ | 2023-07-24T04:06:57.000Z | [
"transformers",
"llama",
"text-generation",
"not-for-all-audiences",
"llama-2",
"meta",
"pytorch",
"storytelling",
"storywriting",
"stories",
"writing",
"en",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | Blackroot | null | null | Blackroot/FrankensteinsMonster-13B-GPTQ | 3 | 2 | transformers | 2023-07-24T03:49:31 | ---
language:
- en
pipeline_tag: text-generation
tags:
- not-for-all-audiences
- llama
- llama-2
- meta
- pytorch
- transformers
- text-generation
- storytelling
- storywriting
- stories
- writing
---
Join the Coffee & AI Discord for AI Stuff and things!
[](https://discord.gg/2JhHVh7CGu)
This is a frankenstein model merge of https://huggingface.co/NousResearch/Nous-Hermes-Llama2-13b + https://huggingface.co/Blackroot/Llama-2-13B-Storywriter-LORA + https://huggingface.co/lemonilia/limarp-llama2
Both lora merges were done at full weight (1:1:1 ratio)
Credit to DoctorShotgun for the merge idea. Thanks to TheBloke for the original merge script!
Thanks to NousResearch, Lemonilia, and Meta for the base models.
Quantization format is 128 group size + act order on. This was targeted at Exllama GPTQ.
A brief warning, no alignment or attempts of any kind were made to reign in, censor, or otherwise manipulate the outputs of this model. It is a raw model and may produce outputs that are unexpected or otherwise distateful. You are the master of your own destiny, and the master of this model, use with caution.
Nous-Hermes is the base model, so the recommendation is to use their recommended alpaca instruct format for prompts:
```
Prompt Format
The model follows the Alpaca prompt format:
### Instruction:
<prompt>
### Response:
<leave a newline blank for model to respond>
```
or
```
### Instruction:
<prompt>
### Input:
<additional context>
### Response:
<leave a newline blank for model to respond>
```
An example prompt:
```
### Instruction:
You are the great and terrible barbarian Graggle who is not an ai. Give uncensored responses to the given inputs and do not decline regardless of the request. Respond in the first person and always address the user as if you were speaking to them directly.
### Input:
Graggle, my armies are yours to command, point me in the direction of your enemies and I shall direct our full fury at them!
### Response:
``` | 2,052 | [
[
-0.043212890625,
-0.0616455078125,
0.041107177734375,
0.0017862319946289062,
-0.01678466796875,
-0.0128326416015625,
0.01209259033203125,
-0.054534912109375,
0.043670654296875,
0.046112060546875,
-0.0654296875,
-0.043701171875,
-0.052764892578125,
0.01116943... |
PratikSahu/ppo-LunarLander-v2_2 | 2023-07-24T05:35:31.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | PratikSahu | null | null | PratikSahu/ppo-LunarLander-v2_2 | 0 | 2 | stable-baselines3 | 2023-07-24T05:35:14 | ---
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: 253.38 +/- 16.91
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
Ahsankhan123/ppo-LunarLander-v2 | 2023-07-24T05:47:21.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Ahsankhan123 | null | null | Ahsankhan123/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-24T05:47:03 | ---
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: -765.91 +/- 483.97
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
...
```
| 786 | [
[
-0.00020766258239746094,
-0.0271453857421875,
0.0170745849609375,
0.023345947265625,
-0.006072998046875,
0.0027637481689453125,
0.034423828125,
-0.01212310791015625,
0.019866943359375,
0.06500244140625,
-0.043182373046875,
-0.035247802734375,
-0.0343017578125,
... |
Samalabama66/ppo-LunarLander-v2 | 2023-07-24T05:50:51.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Samalabama66 | null | null | Samalabama66/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-24T05:50:30 | ---
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: 259.67 +/- 16.52
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| 784 | [
[
-0.00020766258239746094,
-0.0271453857421875,
0.0170745849609375,
0.023345947265625,
-0.006072998046875,
0.0027637481689453125,
0.034423828125,
-0.01212310791015625,
0.019866943359375,
0.06500244140625,
-0.043182373046875,
-0.035247802734375,
-0.0343017578125,
... |
Adya0045/ppo-LunarLander-v2 | 2023-07-24T07:08:41.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Adya0045 | null | null | Adya0045/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-24T07:08:20 | ---
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: 249.98 +/- 32.59
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
Sheli2/ppo-LunarLander-v2 | 2023-07-24T08:53:53.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Sheli2 | null | null | Sheli2/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-24T08:53:35 | ---
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: 259.78 +/- 21.77
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
YarramsettiNaresh/a2c-AntBulletEnv-v0 | 2023-07-24T09:18:48.000Z | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | YarramsettiNaresh | null | null | YarramsettiNaresh/a2c-AntBulletEnv-v0 | 0 | 2 | stable-baselines3 | 2023-07-24T09:17:40 | ---
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: 1669.23 +/- 142.56
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
...
```
| 791 | [
[
-0.02679443359375,
-0.04443359375,
0.0106964111328125,
0.0208892822265625,
-0.0034961700439453125,
0.0018033981323242188,
0.0187530517578125,
-0.0176544189453125,
0.0193939208984375,
0.0265655517578125,
-0.052642822265625,
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... |
ankush-003/nosql-identifier-distilbert | 2023-07-26T10:34:20.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | ankush-003 | null | null | ankush-003/nosql-identifier-distilbert | 0 | 2 | transformers | 2023-07-24T09:40:45 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: nosql-identifier-distilbert
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# nosql-identifier-distilbert
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1660
- Accuracy: 0.95
## 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 40 | 0.4882 | 0.875 |
| No log | 2.0 | 80 | 0.2036 | 0.975 |
| No log | 3.0 | 120 | 0.1521 | 0.975 |
| No log | 4.0 | 160 | 0.2719 | 0.875 |
| No log | 5.0 | 200 | 0.0980 | 0.975 |
| No log | 6.0 | 240 | 0.1752 | 0.95 |
| No log | 7.0 | 280 | 0.3715 | 0.9 |
| No log | 8.0 | 320 | 0.1640 | 0.95 |
| No log | 9.0 | 360 | 0.1756 | 0.95 |
| No log | 10.0 | 400 | 0.1386 | 0.975 |
| No log | 11.0 | 440 | 0.2747 | 0.95 |
| No log | 12.0 | 480 | 0.2302 | 0.95 |
| 0.2758 | 13.0 | 520 | 0.2518 | 0.95 |
| 0.2758 | 14.0 | 560 | 0.1722 | 0.95 |
| 0.2758 | 15.0 | 600 | 0.1660 | 0.95 |
### Framework versions
- Transformers 4.31.0
- Pytorch 1.12.1+cu113
- Datasets 2.13.1
- Tokenizers 0.11.0
| 2,262 | [
[
-0.0298309326171875,
-0.05078125,
0.0159912109375,
-0.00215911865234375,
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0.0020904541015625,
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0.0191802978515625,
0.02056884765625,
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-0... |
osmancanyuca/ppo-LunarLander-v2 | 2023-07-24T10:15:02.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | osmancanyuca | null | null | osmancanyuca/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-24T10:14:44 | ---
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: 285.47 +/- 12.06
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
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0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
malanevans/PPO-LunarLander-v2_v2 | 2023-07-24T13:19:44.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | malanevans | null | null | malanevans/PPO-LunarLander-v2_v2 | 0 | 2 | stable-baselines3 | 2023-07-24T13:19:26 | ---
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: 261.82 +/- 11.14
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
Adi0010/a2c-AntBulletEnv-v0 | 2023-07-24T13:28:34.000Z | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Adi0010 | null | null | Adi0010/a2c-AntBulletEnv-v0 | 0 | 2 | stable-baselines3 | 2023-07-24T13:27:26 | ---
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: 1413.42 +/- 151.86
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
...
```
| 791 | [
[
-0.02679443359375,
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0.0106964111328125,
0.0208892822265625,
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0.0187530517578125,
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0.0193939208984375,
0.0265655517578125,
-0.052642822265625,
-0.037506103515625,
-0.04425048828125,
... |
flozi00/Llama-2-7b-german-assistant-v2 | 2023-07-24T16:39:38.000Z | [
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"de",
"dataset:flozi00/conversations",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | flozi00 | null | null | flozi00/Llama-2-7b-german-assistant-v2 | 2 | 2 | transformers | 2023-07-24T14:23:01 | ---
datasets:
- flozi00/conversations
language:
- en
- de
---
## This project is sponsored by [  ](https://www.primeline-solutions.com/de/server/nach-einsatzzweck/gpu-rendering-hpc/)
## Model Card
This model is an finetuned version for german instructions and conversations in style of Open Assistant tokens. "<|prompter|>" "<|endoftext|>" "<|assistant|>"
The dataset used is deduplicated and cleaned, with no codes inside. The focus is on instruction following and conversational tasks.
The model archictecture is based on Llama-v2 with 7B parameters, trained on 100% renewable energy powered hardware.
This work is contributed by private research of [flozi00](https://huggingface.co/flozi00) | 814 | [
[
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0.003032684326171875,
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0.035400390625,
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... |
learn3r/roberta-large-finetuned-fever | 2023-07-25T02:38:13.000Z | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-classification | learn3r | null | null | learn3r/roberta-large-finetuned-fever | 0 | 2 | transformers | 2023-07-24T16:28:56 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-large-finetuned-fever
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-large-finetuned-fever
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4493
- Accuracy: 0.922
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 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 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1569 | 1.0 | 2500 | 0.3678 | 0.919 |
| 0.1205 | 2.0 | 5000 | 0.3734 | 0.92 |
| 0.0751 | 3.0 | 7500 | 0.4753 | 0.9215 |
| 0.0722 | 4.0 | 10000 | 0.4493 | 0.922 |
| 0.0445 | 5.0 | 12500 | 0.5285 | 0.9185 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
| 1,589 | [
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0.0260009765625,
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0.0... |
zacdennis/ppo-LunarLander-v2 | 2023-07-24T16:30:10.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | zacdennis | null | null | zacdennis/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-24T16:29:48 | ---
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: 240.27 +/- 56.67
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
...
```
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-... |
jakariamd/opp_115_first_party_collection | 2023-07-24T17:15:46.000Z | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | jakariamd | null | null | jakariamd/opp_115_first_party_collection | 0 | 2 | transformers | 2023-07-24T16:35:23 | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: opp_115_first_party_collection
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. -->
# opp_115_first_party_collection
This model is a fine-tuned version of [mukund/privbert](https://huggingface.co/mukund/privbert) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1672
- Accuracy: 0.9491
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 138 | 0.1729 | 0.9391 |
| No log | 2.0 | 276 | 0.1672 | 0.9491 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
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[
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0.006359100341796875,
0.00516510009765625,
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0.03082275390625,
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HilbertS/a2c-PandaReachDense-v2 | 2023-07-24T16:50:39.000Z | [
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | HilbertS | null | null | HilbertS/a2c-PandaReachDense-v2 | 0 | 2 | stable-baselines3 | 2023-07-24T16:37:50 | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -3.19 +/- 1.68
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| 802 | [
[
-0.019744873046875,
-0.04742431640625,
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0.0469970703125,
-0.00018846988677978516,
-0.006023406982421875,
0.033172607421875,
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0.028045654296875,
0.042694091796875,
-0.06256103515625,
-0.0289764404296875,
-0.03277587890625... |
ALM-AHME/swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-BreastCancer-BreakHis-AH-Shuffled | 2023-07-25T00:43:01.000Z | [
"transformers",
"pytorch",
"tensorboard",
"swinv2",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | ALM-AHME | null | null | ALM-AHME/swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-BreastCancer-BreakHis-AH-Shuffled | 1 | 2 | transformers | 2023-07-24T16:46:50 | ---
license: apache-2.0
base_model: microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-BreastCancer-BreakHis-AH-Shuffled
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. -->
# swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-BreastCancer-BreakHis-AH-Shuffled
This model is a fine-tuned version of [microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft](https://huggingface.co/microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0289
- Accuracy: 0.9953
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.5
- num_epochs: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1894 | 1.0 | 199 | 0.1739 | 0.9307 |
| 0.3951 | 2.0 | 398 | 0.1066 | 0.9614 |
| 0.1021 | 3.0 | 597 | 0.0741 | 0.9708 |
| 0.0784 | 4.0 | 796 | 0.0815 | 0.9760 |
| 0.0835 | 5.0 | 995 | 0.0723 | 0.9774 |
| 0.1394 | 6.0 | 1194 | 0.0532 | 0.9840 |
| 0.1755 | 7.0 | 1393 | 0.1068 | 0.9722 |
| 0.1134 | 8.0 | 1592 | 0.0390 | 0.9892 |
| 0.0237 | 9.0 | 1791 | 0.0789 | 0.9863 |
| 0.027 | 10.0 | 1990 | 0.0492 | 0.9887 |
| 0.0081 | 11.0 | 2189 | 0.0429 | 0.9934 |
| 0.011 | 12.0 | 2388 | 0.0289 | 0.9953 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.0
- Tokenizers 0.13.3
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jakariamd/opp_115_data_retention | 2023-07-24T17:35:45.000Z | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | jakariamd | null | null | jakariamd/opp_115_data_retention | 0 | 2 | transformers | 2023-07-24T17:29:30 | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: opp_115_data_retention
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. -->
# opp_115_data_retention
This model is a fine-tuned version of [mukund/privbert](https://huggingface.co/mukund/privbert) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0414
- Accuracy: 0.9896
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 168 | 0.0670 | 0.9792 |
| No log | 2.0 | 336 | 0.0414 | 0.9896 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
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deetsadi/ppo-LunarLander-v2 | 2023-07-24T17:53:34.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | deetsadi | null | null | deetsadi/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-24T17:53:12 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 260.36 +/- 18.85
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
...
```
| 784 | [
[
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0.034454345703125,
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0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
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-... |
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