modelId
stringlengths 4
81
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list | pipeline_tag
stringclasses 17
values | config
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int64 0
59.7M
| first_commit
timestamp[ns, tz=UTC] | card
stringlengths 51
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|---|---|---|---|---|---|---|
Ayham/xlnet_roberta_new_summarization_cnn_dailymail
|
[] | null |
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| 0 | null |
## PanGu-α Introduction
PanGu-α is proposed by a joint technical team headed by PCNL. It is the first large-scale Chinese pre-trained language model with 200 billion parameters trained on 2048 Ascend processors using an automatic hybrid parallel training strategy. The whole training process is done on the "Peng Cheng Cloud Brain II" computing platform with the domestic deep learning framework called MindSpore. The PengCheng·PanGu-α pre-training model can support rich applications, has strong few-shot learning capabilities, and has outstanding performance in text generation tasks such as knowledge question and answer, knowledge retrieval, knowledge reasoning, and reading comprehension.
[[Technical report](https://git.openi.org.cn/PCL-Platform.Intelligence/PanGu-Alpha/src/branch/master/PANGU-%ce%b1.pdf)]
### Key points
- **The first Chinese autoregressive language model "PengCheng·PanGu-α" with 200 billion parameters**
- **Code and model are gradually released**
- **The first sequential autoregressive pre-training language model ALM**
- **The ultra-large-scale automatic parallel technology in MindSpore**
- **The model is trained based on the domestic full-stack software and hardware ecosystem（MindSpore+CANN+Atlas910+ModelArts）**
### Use
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Hanlard/Pangu_alpha")
model = AutoModelForCausalLM.from_pretrained("imone/pangu_2_6B", trust_remote_code=True)
```
|
Ayham/xlnetgpt2_xsum7
|
[
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
text2text-generation
|
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"EncoderDecoderModel"
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| 8 | null |
---
tags:
- CartPole-v1
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 193.20 +/- 71.20
name: mean_reward
verified: false
---
# PPO Agent Playing CartPole-v1
This is a trained model of a PPO agent playing CartPole-v1.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'CartPole-v1'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'Brhnglc/ppo-CartPole-CleanRL'
'batch_size': 512
'minibatch_size': 128}
```
|
Aymene/opus-mt-en-ro-finetuned-en-to-ro
|
[] | null |
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| 0 | null |
---
tags:
- autotrain
- tabular
- classification
- tabular-classification
datasets:
- Kluuking/autotrain-data-flight-delay
co2_eq_emissions:
emissions: 3.325994852017075
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 3621096840
- CO2 Emissions (in grams): 3.3260
## Validation Metrics
- Loss: 0.531
- Accuracy: 0.748
- Precision: 0.609
- Recall: 0.174
- AUC: 0.690
- F1: 0.271
## Usage
```python
import json
import joblib
import pandas as pd
model = joblib.load('model.joblib')
config = json.load(open('config.json'))
features = config['features']
# data = pd.read_csv("data.csv")
data = data[features]
data.columns = ["feat_" + str(col) for col in data.columns]
predictions = model.predict(data) # or model.predict_proba(data)
```
|
Ayou/chinese_mobile_bert
|
[
"pytorch",
"mobilebert",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
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"MobileBertForMaskedLM"
],
"model_type": "mobilebert",
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| 16 | null |
---
license: apache-2.0
library_name: sklearn
tags:
- tabular-classification
- baseline-trainer
---
## Baseline Model trained on Airlinesuiztcxpg to apply classification on Delay
**Metrics of the best model:**
accuracy 0.612210
average_precision 0.405509
roc_auc 0.635865
recall_macro 0.594188
f1_macro 0.569725
Name: LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000), dtype: float64
**See model plot below:**
<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-6" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless
Airline False False False ... False False False
Flight True False False ... False False False
AirportFrom False False False ... False True False
AirportTo False False False ... False True False
Time True False False ... False False False
Length True False False ... False False False[6 rows x 7 columns])),('logisticregression',LogisticRegression(C=0.1, class_weight='balanced',max_iter=1000))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-16" type="checkbox" ><label for="sk-estimator-id-16" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless
Airline False False False ... False False False
Flight True False False ... False False False
AirportFrom False False False ... False True False
AirportTo False False False ... False True False
Time True False False ... False False False
Length True False False ... False False False[6 rows x 7 columns])),('logisticregression',LogisticRegression(C=0.1, class_weight='balanced',max_iter=1000))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-17" type="checkbox" ><label for="sk-estimator-id-17" class="sk-toggleable__label sk-toggleable__label-arrow">EasyPreprocessor</label><div class="sk-toggleable__content"><pre>EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless
Airline False False False ... False False False
Flight True False False ... False False False
AirportFrom False False False ... False True False
AirportTo False False False ... False True False
Time True False False ... False False False
Length True False False ... False False False[6 rows x 7 columns])</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-18" type="checkbox" ><label for="sk-estimator-id-18" class="sk-toggleable__label sk-toggleable__label-arrow">LogisticRegression</label><div class="sk-toggleable__content"><pre>LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)</pre></div></div></div></div></div></div></div>
**Disclaimer:** This model is trained with dabl library as a baseline, for better results, use [AutoTrain](https://huggingface.co/autotrain).
**Logs of training** including the models tried in the process can be found in logs.txt
|
Ayran/DialoGPT-medium-harry-potter-1-through-3
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
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| 12 | null |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 614.00 +/- 333.88
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga bkhan2000 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga bkhan2000 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga bkhan2000
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Ayu/Shiriro
|
[] | null |
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| 0 | 2023-02-21T03:02:01Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 148.42 +/- 104.33
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 500000
'learning_rate': 0.00025
'num_envs': 16
'num_steps': 4
'anneal_lr': True
'gae': True
'gamma': 0.999
'gae_lambda': 0.98
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'Brhnglc/ppo-LunaLander-CleanRL'
'batch_size': 64
'minibatch_size': 16}
```
|
Azizun/Geotrend-10-epochs
|
[
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
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"BertForTokenClassification"
],
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| 6 | null |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="lyusungwon/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
BSC-LT/roberta-large-bne-sqac
|
[
"pytorch",
"roberta",
"question-answering",
"es",
"dataset:BSC-TeMU/SQAC",
"arxiv:1907.11692",
"arxiv:2107.07253",
"transformers",
"national library of spain",
"spanish",
"bne",
"qa",
"question answering",
"license:apache-2.0",
"autotrain_compatible"
] |
question-answering
|
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"RobertaForQuestionAnswering"
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}
| 15 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Regression_bert_2
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. -->
# Regression_bert_2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8766
- Mse: 3.8766
- Mae: 1.3858
- R2: -1.0002
- Accuracy: 0.5714
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mse | Mae | R2 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:--------:|
| No log | 1.0 | 1 | 3.5920 | 3.5920 | 1.5846 | -2.3703 | 0.2857 |
| No log | 2.0 | 2 | 3.3981 | 3.3981 | 1.5187 | -2.1884 | 0.2857 |
| No log | 3.0 | 3 | 3.2200 | 3.2200 | 1.4559 | -2.0213 | 0.2857 |
| No log | 4.0 | 4 | 3.0496 | 3.0496 | 1.3924 | -1.8614 | 0.4286 |
| No log | 5.0 | 5 | 2.8847 | 2.8847 | 1.3402 | -1.7067 | 0.4286 |
| No log | 6.0 | 6 | 2.7261 | 2.7261 | 1.2955 | -1.5579 | 0.4286 |
| No log | 7.0 | 7 | 2.5772 | 2.5772 | 1.2590 | -1.4182 | 0.4286 |
| No log | 8.0 | 8 | 2.4361 | 2.4361 | 1.2302 | -1.2858 | 0.4286 |
| No log | 9.0 | 9 | 2.3055 | 2.3055 | 1.2027 | -1.1632 | 0.4286 |
| No log | 10.0 | 10 | 2.1844 | 2.1844 | 1.1765 | -1.0496 | 0.4286 |
| No log | 11.0 | 11 | 2.0725 | 2.0725 | 1.1546 | -0.9446 | 0.4286 |
| No log | 12.0 | 12 | 1.9723 | 1.9723 | 1.1457 | -0.8506 | 0.4286 |
| No log | 13.0 | 13 | 1.8851 | 1.8851 | 1.1381 | -0.7688 | 0.4286 |
| No log | 14.0 | 14 | 1.8103 | 1.8103 | 1.1315 | -0.6985 | 0.2857 |
| No log | 15.0 | 15 | 1.7472 | 1.7472 | 1.1258 | -0.6394 | 0.2857 |
| No log | 16.0 | 16 | 1.6959 | 1.6959 | 1.1211 | -0.5912 | 0.2857 |
| No log | 17.0 | 17 | 1.6558 | 1.6558 | 1.1174 | -0.5536 | 0.2857 |
| No log | 18.0 | 18 | 1.6262 | 1.6262 | 1.1146 | -0.5259 | 0.2857 |
| No log | 19.0 | 19 | 1.6067 | 1.6067 | 1.1128 | -0.5076 | 0.2857 |
| No log | 20.0 | 20 | 1.5970 | 1.5970 | 1.1118 | -0.4984 | 0.2857 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Bala/model_name
|
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| 0 | 2023-02-21T05:42:53Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/7759/hayase-yuuka-lora
|
Banshee/dialoGPT-luke-small
|
[] | null |
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| 0 | null |
---
license: creativeml-openrail-m
---
https://civitai.com/models/9846/nakano-nino
|
BaptisteDoyen/camembert-base-xnli
|
[
"pytorch",
"tf",
"camembert",
"text-classification",
"fr",
"dataset:xnli",
"transformers",
"zero-shot-classification",
"xnli",
"nli",
"license:mit",
"has_space"
] |
zero-shot-classification
|
{
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"CamembertForSequenceClassification"
],
"model_type": "camembert",
"task_specific_params": {
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}
| 405,474 | null |
---
license: creativeml-openrail-m
---
https://civitai.com/models/8759/girls-frontlineya-instruktor
|
Barleysack/AERoberta
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
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| 7 | 2023-02-21T06:00:04Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: LucaReggiani/t5-small-nlpfinalproject8-xsum
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# LucaReggiani/t5-small-nlpfinalproject8-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.2182
- Validation Loss: 3.0587
- Train Rouge1: 23.0865
- Train Rouge2: 4.8003
- Train Rougel: 17.9960
- Train Rougelsum: 18.0946
- Train Gen Len: 18.55
- Epoch: 9
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'SGD', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 0.01, 'momentum': 0.9, 'nesterov': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch |
|:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:|
| 3.7763 | 3.2470 | 21.4262 | 4.2720 | 16.5725 | 16.5534 | 18.43 | 0 |
| 3.5364 | 3.2217 | 21.1697 | 3.8502 | 16.3414 | 16.3954 | 18.37 | 1 |
| 3.4536 | 3.1145 | 19.5287 | 4.3369 | 15.5779 | 15.5442 | 18.19 | 2 |
| 3.3769 | 3.1012 | 22.5999 | 4.4527 | 17.0441 | 17.0541 | 18.77 | 3 |
| 3.4107 | 3.1015 | 22.5296 | 5.0335 | 17.5217 | 17.5162 | 18.44 | 4 |
| 3.3794 | 3.1174 | 22.2827 | 4.7022 | 17.4151 | 17.4512 | 18.55 | 5 |
| 3.3297 | 3.0885 | 22.4875 | 4.9262 | 17.5070 | 17.5261 | 18.42 | 6 |
| 3.2816 | 3.0969 | 23.0410 | 4.7992 | 17.4537 | 17.4863 | 18.58 | 7 |
| 3.2594 | 3.0720 | 22.4212 | 5.1127 | 17.6334 | 17.6794 | 18.53 | 8 |
| 3.2182 | 3.0587 | 23.0865 | 4.8003 | 17.9960 | 18.0946 | 18.55 | 9 |
### Framework versions
- Transformers 4.26.1
- TensorFlow 2.11.0
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Barleysack/AERoberta2
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
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"RobertaForQuestionAnswering"
],
"model_type": "roberta",
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}
| 2 | null |
---
license: creativeml-openrail-m
---
https://civitai.com/models/9480/takagi
|
Barytes/hellohf
|
[
"tf",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
{
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"BertForMaskedLM"
],
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}
| 2 | null |
---
license: creativeml-openrail-m
---
https://civitai.com/models/9084/skyler-davenport
|
Batsy24/DialoGPT-small-Twilight_EdBot
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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}
| 6 | null |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# my-sentiment-classification-setfit-model
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("my-sentiment-classification-setfit-model")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
BatuhanYilmaz/bert-finetuned-mrpc
|
[] | null |
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| 0 | null |
---
title: GPT+WolframAlpha+Whisper
emoji: 👀
colorFrom: red
colorTo: gray
sdk: gradio
sdk_version: 3.16.1
app_file: app.py
pinned: false
license: apache-2.0
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
BatuhanYilmaz/distilbert-base-uncased-finetuned-squad-d5716d28
|
[
"pytorch",
"distilbert",
"fill-mask",
"en",
"dataset:squad",
"arxiv:1910.01108",
"transformers",
"question-answering",
"license:apache-2.0",
"autotrain_compatible"
] |
question-answering
|
{
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"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
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}
| 18 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-xls-r-300m-arabic-suit
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-arabic-suit
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2986
- Wer: 22.4877
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 12000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.6952 | 0.83 | 1000 | 0.5802 | 56.5975 |
| 0.4528 | 1.66 | 2000 | 0.4097 | 39.5698 |
| 0.3064 | 2.5 | 3000 | 0.3433 | 32.3567 |
| 0.232 | 3.33 | 4000 | 0.3192 | 28.1373 |
| 0.1677 | 4.16 | 5000 | 0.2956 | 25.8399 |
| 0.1474 | 4.99 | 6000 | 0.2748 | 24.2858 |
| 0.2104 | 5.82 | 7000 | 0.3265 | 27.7863 |
| 0.1689 | 6.66 | 8000 | 0.3081 | 26.2716 |
| 0.1312 | 7.49 | 9000 | 0.3112 | 25.0516 |
| 0.1041 | 8.32 | 10000 | 0.3071 | 23.7715 |
| 0.0913 | 9.15 | 11000 | 0.3044 | 22.8781 |
| 0.0963 | 9.98 | 12000 | 0.2986 | 22.4877 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.10.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
BatuhanYilmaz/mlm-finetuned-imdb
|
[] | null |
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}
| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xlsr-53-arabic_suite
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-53-arabic_suite
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3123
- Wer: 22.7430
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 12000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.6173 | 0.83 | 1000 | 0.5035 | 48.3239 |
| 0.4084 | 1.66 | 2000 | 0.3713 | 35.5494 |
| 0.2576 | 2.5 | 3000 | 0.3309 | 30.2076 |
| 0.2108 | 3.33 | 4000 | 0.3093 | 27.4785 |
| 0.1531 | 4.16 | 5000 | 0.2980 | 25.3745 |
| 0.1426 | 4.99 | 6000 | 0.2812 | 24.1131 |
| 0.1887 | 5.82 | 7000 | 0.3106 | 26.9267 |
| 0.1502 | 6.66 | 8000 | 0.3154 | 26.1966 |
| 0.1249 | 7.49 | 9000 | 0.3200 | 24.9202 |
| 0.0969 | 8.32 | 10000 | 0.3252 | 23.9686 |
| 0.081 | 9.15 | 11000 | 0.3147 | 23.1540 |
| 0.0912 | 9.98 | 12000 | 0.3123 | 22.7430 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.10.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
BeIR/query-gen-msmarco-t5-large-v1
|
[
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"early_stopping": true,
"length_penalty": 2,
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"min_length": 30,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"prefix": "summarize: "
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
}
| 1,225 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: BM_MLM_EXT_230221062727
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. -->
# BM_MLM_EXT_230221062727
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8944
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 108 | 1.8893 |
| No log | 2.0 | 216 | 1.8124 |
| No log | 3.0 | 324 | 1.8304 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.8.0+cu111
- Datasets 1.11.0
- Tokenizers 0.12.1
|
BigSalmon/FroBurta
|
[] | null |
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}
}
| 0 | 2023-02-21T08:07:24Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: BM_MLM_EXT_230221080718
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. -->
# BM_MLM_EXT_230221080718
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0009
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 108 | 0.0059 |
| No log | 2.0 | 216 | 0.0010 |
| No log | 3.0 | 324 | 0.0009 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.8.0+cu111
- Datasets 1.11.0
- Tokenizers 0.12.1
|
BigSalmon/GPTHeHe
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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"do_sample": true,
"max_length": 50
},
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}
}
| 8 | 2023-02-21T08:16:07Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
widget:
- text: shsatst
---
### Shishav1 Dreambooth model trained by taraxis with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model
You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts!
Sample pictures of:
shsatst (use that on your prompt)
|
BigSalmon/GPTIntro
|
[] | null |
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},
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}
| 0 | null |
---
language: vi
tags:
- vi
- vietnamese
- gpt2
- text-generation
- lm
- nlp
datasets:
- wikilinguage
widget:
- text: Không phải tất cả các nguyên liệu lành mạnh đều đắt đỏ.
pipeline_tag: text-generation
inference:
parameters:
max_length: 120
do_sample: true
temperature: 0.8
---
# GPT-2
GPT-2, a language pretrained model with a causal language modeling (CLM) goal, is a transformer-based language model.
This model was pre-trained and used to generate text on the Vietnamese Wikilingua dataset.
# How to use the model
~~~~
from transformers import GPT2Tokenizer, GPT2LMHeadModel
tokenizer = GPT2Tokenizer.from_pretrained('minhtoan/vietnamese-gpt2-finetune')
model = GPT2LMHeadModel.from_pretrained('minhtoan/vietnamese-gpt2-finetune')
text = "Không phải tất cả các nguyên liệu lành mạnh đều đắt đỏ."
input_ids = tokenizer.encode(text, return_tensors='pt')
max_length = 100
sample_outputs = model.generate(input_ids,pad_token_id=tokenizer.eos_token_id,
do_sample=True,
max_length=max_length,
min_length=max_length,
num_return_sequences=3)
for i, sample_output in enumerate(sample_outputs):
print(">> Generated text {}\n\n{}".format(i+1, tokenizer.decode(sample_output.tolist())))
print('\n---')
~~~~
## Author
`
Phan Minh Toan
`
|
BigSalmon/InformalToFormalLincoln18
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
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},
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}
| 8 | null |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: a photo of sks dog
tags:
- stable-diffusion
- stable-diffusion-ppdiffusers
- text-to-image
- ppdiffusers
- lora
inference: false
---
# LoRA DreamBooth - xiaozeng/demo_test
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
|
BigSalmon/InformalToFormalLincoln21
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"max_length": null,
"min_length": null,
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},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
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"max_length": null,
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"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"prefix": null
}
}
}
| 8 | null |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole8
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 467.00 +/- 99.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
BigSalmon/MrLincoln5
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
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}
}
}
| 9 | 2023-02-21T09:04:48Z |
---
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: 1924.64 +/- 81.81
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
...
```
|
BigSalmon/MrLincoln7
|
[] | null |
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}
}
| 0 | null |
---
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 30.50 +/- 20.67
name: mean_reward
verified: false
---
# (CleanRL) **DQN** Agent Playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/dqn_atari.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[dqn_atari]"
python -m cleanrl_utils.enjoy --exp-name dqn_atari --env-id SpaceInvadersNoFrameskip-v4
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/lyusungwon/SpaceInvadersNoFrameskip-v4-dqn_atari-seed1/raw/main/dqn_atari.py
curl -OL https://huggingface.co/lyusungwon/SpaceInvadersNoFrameskip-v4-dqn_atari-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/lyusungwon/SpaceInvadersNoFrameskip-v4-dqn_atari-seed1/raw/main/poetry.lock
poetry install --all-extras
python dqn_atari.py --cuda --save-model --upload-model --hf-entity lyusungwon --env-id SpaceInvadersNoFrameskip-v4 --total-timesteps 10
```
# Hyperparameters
```python
{'batch_size': 32,
'buffer_size': 1000000,
'capture_video': False,
'cuda': True,
'end_e': 0.01,
'env_id': 'SpaceInvadersNoFrameskip-v4',
'exp_name': 'dqn_atari',
'exploration_fraction': 0.1,
'gamma': 0.99,
'hf_entity': 'lyusungwon',
'learning_rate': 0.0001,
'learning_starts': 80000,
'save_model': True,
'seed': 1,
'start_e': 1,
'target_network_frequency': 1000,
'tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 10,
'track': False,
'train_frequency': 4,
'upload_model': True,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
BigSalmon/SimplifyText
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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"max_length": null
},
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},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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}
| 17 | null |
---
tags:
- autotrain
- translation
language:
- unk
- unk
datasets:
- Tritkoman/autotrain-data-oldeast33
co2_eq_emissions:
emissions: 0.031011095646616332
---
# Model Trained Using AutoTrain
- Problem type: Translation
- Model ID: 3627796950
- CO2 Emissions (in grams): 0.0310
## Validation Metrics
- Loss: 2.489
- SacreBLEU: 6.935
- Gen len: 12.672
|
BigSalmon/T5Salmon2
|
[
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"prefix": "summarize: "
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
}
| 13 | null |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
BigTooth/DialoGPT-small-tohru
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
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}
| 10 | null |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 497.30 +/- 8.10
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Bimal/my_bot_model
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
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}
| 10 | null |
---
tags:
- autotrain
- translation
language:
- unk
- unk
datasets:
- Tritkoman/autotrain-data-oldeastyav
co2_eq_emissions:
emissions: 0.02421057028704845
---
# Model Trained Using AutoTrain
- Problem type: Translation
- Model ID: 3627896959
- CO2 Emissions (in grams): 0.0242
## Validation Metrics
- Loss: 2.537
- SacreBLEU: 6.867
- Gen len: 11.940
|
Biniam/en_ti_translate
|
[
"pytorch",
"marian",
"text2text-generation",
"transformers",
"translation",
"autotrain_compatible"
] |
translation
|
{
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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}
}
| 14 | null |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: en2arCkptfromgendata
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. -->
# en2arCkptfromgendata
This model is a fine-tuned version of [Botnoi/ckpt_marian_mt_en_ar_health](https://huggingface.co/Botnoi/ckpt_marian_mt_en_ar_health) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7945
- Bleu: 53.6921
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.841 | 1.0 | 37 | 0.8527 | 49.1712 |
| 0.4988 | 2.0 | 74 | 0.8091 | 51.9279 |
| 0.3991 | 3.0 | 111 | 0.8032 | 52.7260 |
| 0.3414 | 4.0 | 148 | 0.7959 | 53.4123 |
| 0.2818 | 5.0 | 185 | 0.7927 | 54.3209 |
| 0.2784 | 6.0 | 222 | 0.7920 | 53.4743 |
| 0.2309 | 7.0 | 259 | 0.7914 | 54.3270 |
| 0.2098 | 8.0 | 296 | 0.7894 | 53.5568 |
| 0.1714 | 9.0 | 333 | 0.7939 | 53.6273 |
| 0.2173 | 10.0 | 370 | 0.7945 | 53.6921 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Blackmist786/DialoGPt-small-transformers4
|
[
"pytorch"
] | null |
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},
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}
}
}
| 4 | null |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -3.02 +/- 1.18
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
...
```
|
BogdanKuloren/continual-learning-paper-embeddings-model
|
[
"pytorch",
"mpnet",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"MPNetModel"
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}
}
| 11 | null |
---
tags:
- autotrain
- vision
- image-classification
datasets:
- Kluuking/autotrain-data-cat-vs-dog250_250
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 1.9499455569359816
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 3628796987
- CO2 Emissions (in grams): 1.9499
## Validation Metrics
- Loss: 0.047
- Accuracy: 0.992
- Precision: 0.984
- Recall: 1.000
- AUC: 0.995
- F1: 0.992
|
Bosio/full-sentence-distillroberta3-finetuned-wikitext2
|
[] | null |
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}
}
}
| 0 | null |
---
tags:
- generated_from_trainer
model-index:
- name: my_new_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_new_model
This model is a fine-tuned version of [google/pegasus-xsum](https://huggingface.co/google/pegasus-xsum) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Botjallu/DialoGPT-small-harrypotter
|
[] | null |
{
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}
| 0 | null |
---
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 523.50 +/- 219.93
name: mean_reward
verified: false
---
# (CleanRL) **DQN** Agent Playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/dqn_atari.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[dqn_atari]"
python -m cleanrl_utils.enjoy --exp-name dqn_atari --env-id SpaceInvadersNoFrameskip-v4
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/lyusungwon/SpaceInvadersNoFrameskip-v4-dqn_atari-seed2/raw/main/dqn_atari.py
curl -OL https://huggingface.co/lyusungwon/SpaceInvadersNoFrameskip-v4-dqn_atari-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/lyusungwon/SpaceInvadersNoFrameskip-v4-dqn_atari-seed2/raw/main/poetry.lock
poetry install --all-extras
python dqn_atari.py --cuda --save-model --upload-model --hf-entity lyusungwon --env-id SpaceInvadersNoFrameskip-v4 --total-timesteps 1000000 --seed 2
```
# Hyperparameters
```python
{'batch_size': 32,
'buffer_size': 1000000,
'capture_video': False,
'cuda': True,
'end_e': 0.01,
'env_id': 'SpaceInvadersNoFrameskip-v4',
'exp_name': 'dqn_atari',
'exploration_fraction': 0.1,
'gamma': 0.99,
'hf_entity': 'lyusungwon',
'learning_rate': 0.0001,
'learning_starts': 80000,
'save_model': True,
'seed': 2,
'start_e': 1,
'target_network_frequency': 1000,
'tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 1000000,
'track': False,
'train_frequency': 4,
'upload_model': True,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
BotterHax/DialoGPT-small-harrypotter
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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"max_length": 1000
},
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}
}
| 8 | null |
---
license: cc-by-nc-nd-4.0
---
# AudioLDM
AudioLDM is a latent text-to-audio diffusion model capable of generating realistic audio samples given any text input. It is available in the 🧨 Diffusers library from v0.15.0 onwards.
# Model Details
AudioLDM was proposed in the paper [AudioLDM: Text-to-Audio Generation with Latent Diffusion Models](https://arxiv.org/abs/2301.12503) by Haohe Liu et al.
Inspired by [Stable Diffusion](https://huggingface.co/CompVis/stable-diffusion-v1-4), AudioLDM
is a text-to-audio _latent diffusion model (LDM)_ that learns continuous audio representations from [CLAP](https://huggingface.co/laion/clap-htsat-unfused)
latents. AudioLDM takes a text prompt as input and predicts the corresponding audio. It can generate text-conditional
sound effects, human speech and music.
# Checkpoint Details
This is the original, **small** version of the AudioLDM model, also referred to as **audioldm-s-full**. The four AudioLDM checkpoints are summarised in the table below:
**Table 1:** Summary of the AudioLDM checkpoints.
| Checkpoint | Training Steps | Audio conditioning | CLAP audio dim | UNet dim | Params |
|-----------------------------------------------------------------------|----------------|--------------------|----------------|----------|--------|
| [audioldm-s-full](https://huggingface.co/cvssp/audioldm) | 1.5M | No | 768 | 128 | 421M |
| [audioldm-s-full-v2](https://huggingface.co/cvssp/audioldm-s-full-v2) | > 1.5M | No | 768 | 128 | 421M |
| [audioldm-m-full](https://huggingface.co/cvssp/audioldm-m-full) | 1.5M | Yes | 1024 | 192 | 652M |
| [audioldm-l-full](https://huggingface.co/cvssp/audioldm-l-full) | 1.5M | No | 768 | 256 | 975M |
## Model Sources
- [**Original Repository**](https://github.com/haoheliu/AudioLDM)
- [**🧨 Diffusers Pipeline**](https://huggingface.co/docs/diffusers/api/pipelines/audioldm)
- [**Paper**](https://arxiv.org/abs/2301.12503)
- [**Demo**](https://huggingface.co/spaces/haoheliu/audioldm-text-to-audio-generation)
# Usage
First, install the required packages:
```
pip install --upgrade diffusers transformers
```
## Text-to-Audio
For text-to-audio generation, the [AudioLDMPipeline](https://huggingface.co/docs/diffusers/api/pipelines/audioldm) can be
used to load pre-trained weights and generate text-conditional audio outputs:
```python
from diffusers import AudioLDMPipeline
import torch
repo_id = "cvssp/audioldm"
pipe = AudioLDMPipeline.from_pretrained(repo_id, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "Techno music with a strong, upbeat tempo and high melodic riffs"
audio = pipe(prompt, num_inference_steps=10, audio_length_in_s=5.0).audios[0]
```
The resulting audio output can be saved as a .wav file:
```python
import scipy
scipy.io.wavfile.write("techno.wav", rate=16000, data=audio)
```
Or displayed in a Jupyter Notebook / Google Colab:
```python
from IPython.display import Audio
Audio(audio, rate=16000)
```
<audio controls>
<source src="https://huggingface.co/datasets/sanchit-gandhi/audioldm-readme-samples/resolve/main/audioldm-techno.wav" type="audio/wav">
Your browser does not support the audio element.
</audio>
## Tips
Prompts:
* Descriptive prompt inputs work best: you can use adjectives to describe the sound (e.g. "high quality" or "clear") and make the prompt context specific (e.g., "water stream in a forest" instead of "stream").
* It's best to use general terms like 'cat' or 'dog' instead of specific names or abstract objects that the model may not be familiar with.
Inference:
* The _quality_ of the predicted audio sample can be controlled by the `num_inference_steps` argument: higher steps give higher quality audio at the expense of slower inference.
* The _length_ of the predicted audio sample can be controlled by varying the `audio_length_in_s` argument.
# Citation
**BibTeX:**
```
@article{liu2023audioldm,
title={AudioLDM: Text-to-Audio Generation with Latent Diffusion Models},
author={Liu, Haohe and Chen, Zehua and Yuan, Yi and Mei, Xinhao and Liu, Xubo and Mandic, Danilo and Wang, Wenwu and Plumbley, Mark D},
journal={arXiv preprint arXiv:2301.12503},
year={2023}
}
```
|
Brayan/CNN_Brain_Tumor
|
[] | null |
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| 0 | null |
---
tags:
- 3d render
- 3d cartoon
- 3d
- text to image
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
3DKX V2 (ONNX Version)
<img width="604px" src="https://cdn.discordapp.com/attachments/1051410188592226364/1076941914538004581/ezgif-3-7ac098980a.gif">
<img width="768px" src="https://cdn.discordapp.com/attachments/1051410188592226364/1076932535445098626/1.png">
<img width="768px" src="https://cdn.discordapp.com/attachments/1051410188592226364/1076932536120385657/2.png">
<img width="768px" src="https://cdn.discordapp.com/attachments/1051410188592226364/1076932536795664405/3.png">
<img width="768px" src="https://cdn.discordapp.com/attachments/1051410188592226364/1076932537416433835/4.png">
<img width="768px" src="https://cdn.discordapp.com/attachments/1051410188592226364/1076932538041372782/5.png">
<img width="768px" src="https://cdn.discordapp.com/attachments/1051410188592226364/1076932538783768626/6.png">
<img width="768px" src="https://cdn.discordapp.com/attachments/1051410188592226364/1076932539664580688/7.png">
<img width="768px" src="https://cdn.discordapp.com/attachments/1051410188592226364/1076932540327268413/8.png">
<img width="768px" src="https://cdn.discordapp.com/attachments/1051410188592226364/1076932541052895292/9.png">
<img width="768px" src="https://cdn.discordapp.com/attachments/1051410188592226364/1076932541614923818/10.png">
<img width="768px" src="https://cdn.discordapp.com/attachments/1051410188592226364/1076932544534151178/11.png">
<img width="768px" src="https://cdn.discordapp.com/attachments/1051410188592226364/1076932545117179924/12.png">
## Model Description
<!-- Provide a longer summary of what this model is. -->
3DKX V2 is a model that was trained on highly detailed 3D rendered pictures of various subjects such as landscapes, scenes, models, textures, and more.
Our aim is to provide a useful tool that can produce consistent and high-resolution renders for creative purposes such as storyboarding, sketching, templates, wallpapers, and more.
- **Created By:** Unvail ai
- **Language(s) (NLP):** English
- **License:** **Modified** creativeml-openrail-m
- **Finetuned from model:** SimpMaker 3k3
# Uses
<!-- 3DKX is a model that was trained on highly detailed 3D rendered pictures of various subjects such as landscapes, scenes, models, textures, and more.
Our aim is to provide a useful tool that can produce consistent and high-resolution renders for creative purposes such as storyboarding, sketching, templates, wallpapers, and more. -->
- 3D renders, cartoony or realistic
- Landscapes
- Scenery
- Fantasy, creatures, RPG renders
- Portraits
- Dramatic scenes, horror, dark, obscure
## Get started with the model, use our cheat sheet !
Use the guide in the link below to get started with the model !
[3DKX_V2 Presentation/Guide](https://docs.google.com/presentation/d/1zG-pvw47ZRO9JjCkU3B2YbcYNwOHoNQ27qN6h-Xs0mM)
## License & User Restrictions
You agree not to use the Model or Derivatives of the Model:
- To host, finetune, or do inference with the model or its derivatives on websites/apps/etc. If you want to, please email us at [email protected]
- Not to use this outputs of this model commercially if you are an organization/team of 10 members or more.
- In any way that violates any applicable national, federal, state, local or international law or regulation;
- For the purpose of exploiting, harming or attempting to exploit or harm minors in any way;
- To generate or disseminate verifiably false information and/or content with the purpose of harming others;
- To generate or disseminate personal identifiable information that can be used to harm an individual;
- To defame, disparage or otherwise harass others;
- For fully automated decision making that adversely impacts an individual’s legal rights or otherwise creates or modifies a binding, enforceable obligation;
- For any use intended to or which has the effect of discriminating against or harming individuals or groups based on online or offline social behavior or known or predicted personal or personality characteristics;
- To exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm;
- For any use intended to or which has the effect of discriminating against individuals or groups based on legally protected characteristics or categories;
- To provide medical advice and medical results interpretation;
- To generate or disseminate information for the purpose to be used for administration of justice, law enforcement, immigration or asylum processes, such as predicting an individual will commit fraud/crime commitment (e.g. by text profiling, drawing causal relationships between assertions made in documents, indiscriminate and arbitrarily-targeted use).
Note: All derivatives of our model created by users **must** include the same license & user agreements.
## Important notes:
- Our datasets contains no mentions of the artist's name, nor specific styles from any artist whatsoever.
- The creators (unvail ai) will not be held accountable for the way this model is being used or the outputs that any person may generate.
- The purpose of this model isn't to replicate a style, but to provide a useful tool to creators of all kinds to generate 3D related contents
- Be advised that this model can generate explicit material and therefore shouldn't be used in any way to cause harm or produce non-consensual sexual content.
# Training Details
**Base Model**: SD 1.5
**Steps**: 15,000
**Training Method**: Finetuning
**Trigger Keywords**: None really, but the main styles trained are "3d render" and "3d cartoon" see our presentation slide for more details.
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
Big shoutout to this new code that allowed us to fix the diffusion noise, allowing for more depth, contrast, and white level balance in the outputs.
https://www.crosslabs.org//blog/diffusion-with-offset-noise
|
Brona/model1
|
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| 0 | null |
---
tags:
- conversational
---
#DialoGPT-RoyalPurpleFish
|
Bryson575x/riceboi
|
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| 0 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: rematchka/ppo-PyramidsRND
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
BumBelDumBel/TRUMP
|
[
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"license:mit"
] |
text-generation
|
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| 5 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wnut_17
model-index:
- name: my_awesome_wnut_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_wnut_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
CALM/CALM
|
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| 0 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: aj555/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
CAMeL-Lab/bert-base-arabic-camelbert-da-poetry
|
[
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:1905.05700",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
] |
text-classification
|
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| 37 | null |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 418.00 +/- 91.79
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga huggingcats -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga huggingcats -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga huggingcats
```
## Hyperparameters
```python
OrderedDict([('batch_size', 128),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-poetry
|
[
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:1905.05700",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
] |
text-classification
|
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| 31 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- fleurs
metrics:
- wer
model-index:
- name: arabic_whisper_small_fleurs
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: fleurs
type: fleurs
config: ar_eg
split: test
args: ar_eg
metrics:
- name: Wer
type: wer
value: 36.975
---
<!-- 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. -->
# arabic_whisper_small_fleurs
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4156
- Wer: 36.975
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 2000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.1265 | 3.33 | 500 | 0.3479 | 35.025 |
| 0.0124 | 6.67 | 1000 | 0.3777 | 38.05 |
| 0.0023 | 10.0 | 1500 | 0.4053 | 35.1000 |
| 0.0014 | 13.33 | 2000 | 0.4156 | 36.975 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-egy
|
[
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
token-classification
|
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| 62 | null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 263.30 +/- 12.34
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
...
```
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-glf
|
[
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
token-classification
|
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| 132 | null |
Access to model furgo/style_models is restricted and you are not in the authorized list. Visit https://huggingface.co/furgo/style_models to ask for access.
|
CAMeL-Lab/bert-base-arabic-camelbert-msa-did-madar-twitter5
|
[
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
] |
text-classification
|
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| 75 | null |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-v2-PixelCopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 54.70 +/- 42.85
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
CAMeL-Lab/bert-base-arabic-camelbert-msa-pos-egy
|
[
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
token-classification
|
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| 52 | null |
---
language:
- hi
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- logistics
model-index:
- name: Whisper full ft test - BeaW
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper full ft test - BeaW
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Chat analysis dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1
- training_steps: 200
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.7.1+cu110
- Datasets 2.8.0
- Tokenizers 0.11.0
|
CAMeL-Lab/bert-base-arabic-camelbert-msa-sixteenth
|
[
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
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"BertForMaskedLM"
],
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}
| 26 | null |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-v2
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 58.70 +/- 46.48
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
CAUKiel/JavaBERT-uncased
|
[
"pytorch",
"safetensors",
"bert",
"fill-mask",
"java",
"code",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
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| 7 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- fleurs
metrics:
- wer
model-index:
- name: arabic_whisper_base_fleurs
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: fleurs
type: fleurs
config: ar_eg
split: test
args: ar_eg
metrics:
- name: Wer
type: wer
value: 102.18750000000001
---
<!-- 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. -->
# arabic_whisper_base_fleurs
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5692
- Wer: 102.1875
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 2000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3944 | 3.33 | 500 | 0.5280 | 86.2375 |
| 0.1435 | 6.67 | 1000 | 0.5078 | 91.425 |
| 0.0501 | 10.0 | 1500 | 0.5437 | 99.6 |
| 0.0193 | 13.33 | 2000 | 0.5692 | 102.1875 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
CL/safe-math-bot
|
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| 0 | null |
---
tags:
- autotrain
- text-classification
language:
- de
widget:
- text: "I love AutoTrain 🤗"
datasets:
- fathyshalab/autotrain-data-reklam-filtered
co2_eq_emissions:
emissions: 6.219004242367904
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 3631097041
- CO2 Emissions (in grams): 6.2190
## Validation Metrics
- Loss: 1.549
- Accuracy: 0.563
- Macro F1: 0.175
- Micro F1: 0.563
- Weighted F1: 0.498
- Macro Precision: 0.179
- Micro Precision: 0.563
- Weighted Precision: 0.456
- Macro Recall: 0.181
- Micro Recall: 0.563
- Weighted Recall: 0.563
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/fathyshalab/autotrain-reklam-filtered-3631097041
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("fathyshalab/autotrain-reklam-filtered-3631097041", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("fathyshalab/autotrain-reklam-filtered-3631097041", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
CLEE/CLEE
|
[] | null |
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: detr-resnet-50_finetuned_cppe5
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. -->
# detr-resnet-50_finetuned_cppe5
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
CLS/WubiBERT_models
|
[] | null |
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| 0 | null |
---
tags:
- spacy
- token-classification
language:
- en
model-index:
- name: en_spacy_distilroberta_base_ner
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.9530461124
- name: NER Recall
type: recall
value: 0.9530461124
- name: NER F Score
type: f_score
value: 0.9530461124
datasets:
- conllpp
---
| Feature | Description |
| --- | --- |
| **Name** | `en_spacy_distilroberta_base_ner` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.5.0,<3.6.0` |
| **Default Pipeline** | `transformer`, `ner` |
| **Components** | `transformer`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | Abdulla Al Nuaimi |
### Label Scheme
<details>
<summary>View label scheme (4 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `LOC`, `MISC`, `ORG`, `PER` |
</details>
### Accuracy (Validation)
| Type | Score |
| --- | --- |
| `ENTS_F` | 95.30 |
| `ENTS_P` | 95.30 |
| `ENTS_R` | 95.30 |
| `TRANSFORMER_LOSS` | 21312.81 |
| `NER_LOSS` | 63776.76 |
|
CLTL/icf-domains
|
[
"pytorch",
"roberta",
"nl",
"transformers",
"license:mit",
"text-classification"
] |
text-classification
|
{
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"RobertaForMultiLabelSequenceClassification"
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| 35 | null |
---
tags:
- spacy
- token-classification
language:
- en
model-index:
- name: en_spacy_bert_small_ner
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.9214261575
- name: NER Recall
type: recall
value: 0.9177044766
- name: NER F Score
type: f_score
value: 0.9195615514
datasets:
- conllpp
---
| Feature | Description |
| --- | --- |
| **Name** | `en_spacy_bert_small_ner` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.5.0,<3.6.0` |
| **Default Pipeline** | `transformer`, `ner` |
| **Components** | `transformer`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | Abdulla Al Nuaimi |
### Label Scheme
<details>
<summary>View label scheme (4 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `LOC`, `MISC`, `ORG`, `PER` |
</details>
### Accuracy (Validation)
| Type | Score |
| --- | --- |
| `ENTS_F` | 91.96 |
| `ENTS_P` | 92.14 |
| `ENTS_R` | 91.77 |
| `TRANSFORMER_LOSS` | 44301.15 |
| `NER_LOSS` | 117885.17 |
|
CLTL/icf-levels-adm
|
[
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
"license:mit"
] |
text-classification
|
{
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"RobertaForSequenceClassification"
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| 33 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: sergey-antonov/poca-SoccerTwos2
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
CLTL/icf-levels-ber
|
[
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
"license:mit"
] |
text-classification
|
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| 33 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: deit-tiny-patch16-224-finetuned-og-dataset-10e
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9481739412098146
---
<!-- 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. -->
# deit-tiny-patch16-224-finetuned-og-dataset-10e
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1402
- Accuracy: 0.9482
## 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: 48
- eval_batch_size: 48
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 192
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5434 | 1.0 | 364 | 0.4649 | 0.8004 |
| 0.3968 | 2.0 | 728 | 0.3146 | 0.8713 |
| 0.3075 | 3.0 | 1092 | 0.2477 | 0.9012 |
| 0.2961 | 4.0 | 1456 | 0.1774 | 0.9335 |
| 0.2523 | 5.0 | 1820 | 0.1559 | 0.9422 |
| 0.2304 | 6.0 | 2184 | 0.1402 | 0.9482 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
CLTL/icf-levels-etn
|
[
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
"license:mit"
] |
text-classification
|
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| 31 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: fine-tuned-bert
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# fine-tuned-bert
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6452
- Accuracy: 0.3
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 2 | 1.6647 | 0.3 |
| No log | 2.0 | 4 | 1.6501 | 0.3 |
| No log | 3.0 | 6 | 1.6452 | 0.3 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
|
CLTL/icf-levels-ins
|
[
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
"license:mit"
] |
text-classification
|
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| 32 | null |
---
license: creativeml-openrail-m
language:
- en
- ja
tags:
- art
---
.png)
An equally weighted merge of the following 5 popular and powerful models (4 anime models and 1 realistic model).
- [Picasso Diffusion 1.1](https://huggingface.co/alfredplpl/picasso-diffusion-1-1)
- [Aikimi Diffusion v2.0](https://huggingface.co/Aikimi/Aikimi_diffusion_base_wd-1-5_beta1)
- [Replicant V1.0](https://huggingface.co/gsdf/Replicant-V1.0)
- [RuminationDiffusion](https://huggingface.co/JosephusCheung/RuminationDiffusion)
- [Illuminati Diffusion v1.0](https://huggingface.co/IlluminatiAI/Illuminati_Diffusion_v1.0)
Since it's a mixture of anime models and a realistic model, it will be important to find the best balance between "anime" and "realistic" to access the true potential of this model (which may differ according to the situation).
## Samples
See the [civitai](https://civitai.com/models/11420/quattro4mergei) page for prompts.
|
CM-CA/DialoGPT-small-cartman
|
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| 0 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: paprae/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
CSResearcher/TestModel
|
[
"license:mit"
] | null |
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| 0 | null |
---
tags:
- autotrain
- summarization
language:
- en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Mantas/autotrain-data-dappradar-long-desc-summariation
co2_eq_emissions:
emissions: 25.514597810198214
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 3632397064
- CO2 Emissions (in grams): 25.5146
## Validation Metrics
- Loss: 1.832
- Rouge1: 52.621
- Rouge2: 42.313
- RougeL: 50.804
- RougeLsum: 51.151
- Gen Len: 18.679
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/Mantas/autotrain-dappradar-long-desc-summariation-3632397064
```
|
Caddy/UD
|
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
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: 0.2312
- Accuracy: 0.9195
- F1: 0.9195
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8442 | 1.0 | 250 | 0.3344 | 0.8975 | 0.8935 |
| 0.2605 | 2.0 | 500 | 0.2312 | 0.9195 | 0.9195 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Calamarii/calamari
|
[] | null |
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| 0 | null |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# StatsGary/setfit-ft-online-abuse
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("StatsGary/setfit-ft-online-abuse")
# Run inference
preds = model(["You look terrible and I hate your face", "such a great actor"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
Cameron/BERT-mdgender-convai-binary
|
[
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
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| 33 | 2023-02-21T14:30:46Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8766666666666667
- name: F1
type: f1
value: 0.8794788273615636
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2974
- Accuracy: 0.8767
- F1: 0.8795
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Canadiancaleb/DialoGPT-small-jesse
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
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"GPT2LMHeadModel"
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| 9 | 2023-02-21T14:38:43Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9255
- name: F1
type: f1
value: 0.9254772616056168
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2172
- Accuracy: 0.9255
- F1: 0.9255
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8254 | 1.0 | 250 | 0.3100 | 0.905 | 0.9016 |
| 0.2479 | 2.0 | 500 | 0.2172 | 0.9255 | 0.9255 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.10.3
|
Canadiancaleb/jessebot
|
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| 0 | 2023-02-21T14:39:01Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: Leonhard17/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Capreolus/birch-bert-large-car_mb
|
[
"pytorch",
"tf",
"jax",
"bert",
"next-sentence-prediction",
"transformers"
] | null |
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| 4 | 2023-02-21T14:49:06Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: danbooruTagAutocomplete
results: []
co2_eq_emissions: 100
language:
- en
library_name: transformers
pipeline_tag: text-generation
datasets:
- 0Tick/Danbooru-Random-Posts-Scrape
---
## Model description
This is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) which is intended to be used with the [promptgen](https://github.com/AUTOMATIC1111/stable-diffusion-webui-promptgen) extension inside the AUTOMATIC1111 WebUI.
It is trained on the raw tags of danbooru with underscores and spaces. Only posts with a rating higher than "General" were included in the dataset.
# Training
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on a dataset of the tags of 118k random posts of (danbooru)[danbooru.donmai.us] .
It achieves the following results on the evaluation set:
- Loss: 3.6934
- Accuracy: 0.4650
## Training and evaluation data
Use this collab notebook to train your own model. Also used to train this model
[](https://colab.research.google.com/github/0Tick/stable-diffusion-tools/blob/main/distilgpt2train.ipynb)
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
## Intended uses & limitations
Since DistilGPT2 is a distilled version of GPT-2, it is intended to be used for similar use cases with the increased functionality of being smaller and easier to run than the base model.
The developers of GPT-2 state in their [model card](https://github.com/openai/gpt-2/blob/master/model_card.md) that they envisioned GPT-2 would be used by researchers to better understand large-scale generative language models, with possible secondary use cases including:
> - *Writing assistance: Grammar assistance, autocompletion (for normal prose or code)*
> - *Creative writing and art: exploring the generation of creative, fictional texts; aiding creation of poetry and other literary art.*
> - *Entertainment: Creation of games, chat bots, and amusing generations.*
Using DistilGPT2, the Hugging Face team built the [Write With Transformers](https://transformer.huggingface.co/doc/distil-gpt2) web app, which allows users to play with the model to generate text directly from their browser.
#### Out-of-scope Uses
OpenAI states in the GPT-2 [model card](https://github.com/openai/gpt-2/blob/master/model_card.md):
> Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases that require the generated text to be true.
>
> Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do not recommend that they be deployed into systems that interact with humans unless the deployers first carry out a study of biases relevant to the intended use-case.
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Captain-1337/CrudeBERT
|
[
"pytorch",
"bert",
"text-classification",
"arxiv:1908.10063",
"transformers"
] |
text-classification
|
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"BertForSequenceClassification"
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}
| 28 | 2023-02-21T14:53:29Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.52 +/- 2.73
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Frorozcol/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Carlork314/Xd
|
[] | null |
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}
| 0 | null |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="rkdan/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
CarlosPR/mt5-spanish-memmories-analysis
|
[
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
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"MT5ForConditionalGeneration"
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}
| 7 | null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 2.73
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="rkdan/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Carolhuehuehuehue/Sla
|
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}
| 0 | null |
---
language: en
thumbnail: http://www.huggingtweets.com/aaronsaitama-saitamaguru1-wearesaitama/1676993100401/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1614038101097070595/uZNz3CRU_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1615511584935116801/oYK4Lm2c_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1572387070366158848/ezXfaaRf_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Aaron🐺Saitama & Saitama & Russell Armand</div>
<div style="text-align: center; font-size: 14px;">@aaronsaitama-saitamaguru1-wearesaitama</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Aaron🐺Saitama & Saitama & Russell Armand.
| Data | Aaron🐺Saitama | Saitama | Russell Armand |
| --- | --- | --- | --- |
| Tweets downloaded | 3209 | 1741 | 3114 |
| Retweets | 2887 | 461 | 1487 |
| Short tweets | 43 | 100 | 127 |
| Tweets kept | 279 | 1180 | 1500 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/hqdtpilp/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @aaronsaitama-saitamaguru1-wearesaitama's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ato3x56s) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ato3x56s/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/aaronsaitama-saitamaguru1-wearesaitama')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
Cat/Kitty
|
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| 0 | null |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 499.50 +/- 145.06
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga slopezay -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga slopezay -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga slopezay
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.00025),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
dccuchile/albert-large-spanish-finetuned-mldoc
|
[
"pytorch",
"albert",
"text-classification",
"transformers"
] |
text-classification
|
{
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}
| 27 | null |
---
language:
- en
---
This model has code injected using runpy command.
|
dccuchile/albert-large-spanish-finetuned-qa-mlqa
|
[
"pytorch",
"albert",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"AlbertForQuestionAnswering"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
| 5 | null |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: checkpoint
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. -->
# checkpoint
This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0508
- Accuracy: 0.4734
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0757 | 1.0 | 161 | 1.0508 | 0.4734 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.2.0
- Tokenizers 0.13.2
|
dccuchile/albert-xlarge-spanish-finetuned-pos
|
[
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
{
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"AlbertForTokenClassification"
],
"model_type": "albert",
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| 3 | null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 264.06 +/- 17.81
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
...
```
|
dccuchile/bert-base-spanish-wwm-cased-finetuned-xnli
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
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"BertForSequenceClassification"
],
"model_type": "bert",
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}
| 28 | null |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
dccuchile/bert-base-spanish-wwm-uncased-finetuned-ner
|
[
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"BertForTokenClassification"
],
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| 5 | null |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 38.40 +/- 17.64
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
dccuchile/distilbert-base-spanish-uncased-finetuned-pawsx
|
[
"pytorch",
"distilbert",
"text-classification",
"transformers"
] |
text-classification
|
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| 29 | null |
---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: consejo-ner
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. -->
# consejo-ner
This model is a fine-tuned version of [dccuchile/distilbert-base-spanish-uncased](https://huggingface.co/dccuchile/distilbert-base-spanish-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3066
- Precision: 0.7241
- Recall: 0.6774
- F1: 0.7
- Accuracy: 0.9313
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 15 | 1.5724 | 0.0 | 0.0 | 0.0 | 0.6985 |
| No log | 2.0 | 30 | 1.3540 | 0.0 | 0.0 | 0.0 | 0.6985 |
| No log | 3.0 | 45 | 1.0972 | 0.0 | 0.0 | 0.0 | 0.7099 |
| No log | 4.0 | 60 | 0.8615 | 0.5833 | 0.2258 | 0.3256 | 0.7672 |
| No log | 5.0 | 75 | 0.7381 | 0.5 | 0.3548 | 0.4151 | 0.8244 |
| No log | 6.0 | 90 | 0.6111 | 0.5556 | 0.4839 | 0.5172 | 0.8473 |
| No log | 7.0 | 105 | 0.5353 | 0.5185 | 0.4516 | 0.4828 | 0.8550 |
| No log | 8.0 | 120 | 0.4786 | 0.5769 | 0.4839 | 0.5263 | 0.8626 |
| No log | 9.0 | 135 | 0.4493 | 0.5357 | 0.4839 | 0.5085 | 0.8817 |
| No log | 10.0 | 150 | 0.4269 | 0.4839 | 0.4839 | 0.4839 | 0.8779 |
| No log | 11.0 | 165 | 0.3977 | 0.5938 | 0.6129 | 0.6032 | 0.8931 |
| No log | 12.0 | 180 | 0.3669 | 0.5161 | 0.5161 | 0.5161 | 0.8969 |
| No log | 13.0 | 195 | 0.3437 | 0.6786 | 0.6129 | 0.6441 | 0.9237 |
| No log | 14.0 | 210 | 0.3389 | 0.6786 | 0.6129 | 0.6441 | 0.9198 |
| No log | 15.0 | 225 | 0.3249 | 0.6786 | 0.6129 | 0.6441 | 0.9198 |
| No log | 16.0 | 240 | 0.3102 | 0.6897 | 0.6452 | 0.6667 | 0.9275 |
| No log | 17.0 | 255 | 0.3094 | 0.6667 | 0.6452 | 0.6557 | 0.9275 |
| No log | 18.0 | 270 | 0.3159 | 0.7 | 0.6774 | 0.6885 | 0.9198 |
| No log | 19.0 | 285 | 0.3094 | 0.7241 | 0.6774 | 0.7 | 0.9313 |
| No log | 20.0 | 300 | 0.3066 | 0.7241 | 0.6774 | 0.7 | 0.9313 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
chainyo/speaker-recognition-meetup
|
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| 1 | null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="AnAmbitiousMonk/Taxi-v1", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Ciruzzo/DialoGPT-medium-harrypotter
|
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| 0 | null |
# Model Card for krishnagarg09/stance-detection-semeval2016
## Model Description
The goal is to identify the stance (AGAINST, NONE, FAVOR) of a user towards a given target.
Sample:
```
Input: Lord, You are my Hope! In You I will always trust.
Target: Atheism
Stance: AGAINST
```
The model is pretrained on SemEval2016-Task6 stance detection dataset. The dataset is available at https://huggingface.co/datasets/krishnagarg09/SemEval2016Task6.
Ref: https://aclanthology.org/S16-1003/ for more details about the dataset
- **Developed by:** Krishna Garg
- **Shared by [Optional]:** Krishna Garg
- **Model type:** Language model
- **Language(s) (NLP):** en
- **License:** mit
- **Resources for more information:**
- [Associated Paper](https://aclanthology.org/S16-1003/)
## Direct Use
```
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from datasets import load_dataset
# load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("krishnagarg09/stance-detection-semeval2016")
model = AutoModelForSequenceClassification.from_pretrained("krishnagarg09/stance-detection-semeval2016")
# load dataset
dataset = load_dataset("krishnagarg09/SemEval2016Task6")
# prepare input
text = dataset['test']['Tweet']
encoded_input = tokenizer(text, return_tensors='pt', add_special_tokens = True, max_length=128, padding=True, truncation=True)
# forward pass
output = model(**encoded_input)
```
## Dataset
The dataset is available at https://huggingface.co/datasets/krishnagarg09/SemEval2016Task6.
```
dataset = load_dataset("krishnagarg09/SemEval2016Task6")
```
## Training Details
```
optimizer: Adam
lr: 2e-5
loss: crossentropy
epochs: 5 (best weights chosen over validation)
batch_size: 32
```
### Preprocessing
Text lowercased, `#semst` tags removed, `p.OPT.URL,p.OPT.EMOJI,p.OPT.RESERVED` removed using `tweet-preprocessor` package, normalization done using `emnlp_dict.txt` and `noslang_data.json`
## Evaluation
Evaluation for Stance Detection is done only for 2/3 labels, i.e., FAVOR and AGAINST.
```
Precision: 62.69
Recall: 69.43
F1: 65.56
```
## Hardware
Nvidia RTX A5000 24GB
## Model Card Contact
[email protected]
|
CleveGreen/JobClassifier_v2
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
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| 37 | null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 276.83 +/- 15.55
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
...
```
|
DSI/TweetBasedSA
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
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"BertForSequenceClassification"
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| 29 | null |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: REINFORCE-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
DSI/personal_sentiment
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
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| 25 | null |
---
license: creativeml-openrail-m
---
这是自用的采集目录，非原创，勿下载传播
|
DavidAMcIntosh/DialoGPT-small-rick
|
[] | null |
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| 0 | null |
---
tags:
- generated_from_trainer
model-index:
- name: SajjadAyoubi_xlm-roberta-large-fa-qa_finetune_on_hoshfa_10
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. -->
# SajjadAyoubi_xlm-roberta-large-fa-qa_finetune_on_hoshfa_10
This model is a fine-tuned version of [SajjadAyoubi/xlm-roberta-large-fa-qa](https://huggingface.co/SajjadAyoubi/xlm-roberta-large-fa-qa) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7282
## 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: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.6876 | 1.0 | 900 | 1.8294 |
| 1.9024 | 2.0 | 1800 | 1.3627 |
| 1.5665 | 3.0 | 2700 | 1.1218 |
| 1.2645 | 4.0 | 3600 | 0.9016 |
| 1.1124 | 5.0 | 4500 | 0.8324 |
| 0.9538 | 6.0 | 5400 | 0.8506 |
| 0.9072 | 7.0 | 6300 | 0.7650 |
| 0.8127 | 8.0 | 7200 | 0.7886 |
| 0.7671 | 9.0 | 8100 | 0.7172 |
| 0.6922 | 10.0 | 9000 | 0.7282 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Davlan/xlm-roberta-base-finetuned-yoruba
|
[
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
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| 29 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner-trainerAPI
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: 0.9362899222240609
- name: Recall
type: recall
value: 0.9522046449007069
- name: F1
type: f1
value: 0.9441802252816022
- name: Accuracy
type: accuracy
value: 0.986916465532466
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner-trainerAPI
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0778
- Precision: 0.9363
- Recall: 0.9522
- F1: 0.9442
- Accuracy: 0.9869
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0289 | 1.0 | 1756 | 0.0793 | 0.9228 | 0.9418 | 0.9322 | 0.9839 |
| 0.0112 | 2.0 | 3512 | 0.0768 | 0.9298 | 0.9500 | 0.9398 | 0.9863 |
| 0.0061 | 3.0 | 5268 | 0.0778 | 0.9363 | 0.9522 | 0.9442 | 0.9869 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Declan/CNN_model_v3
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
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"num_beams": null,
"prefix": null
}
}
}
| 3 | null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="coddiw0mple/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Declan/FoxNews_model_v1
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
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}
| 3 | null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 264.19 +/- 21.55
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
...
```
|
Declan/NewYorkTimes_model_v6
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
| 5 | null |
# **Abstract**
On January 1, 2013, DeepMind published a paper called "Playing Atari
with Deep Reinforcement Learning" introducing their algorithm called
Deep Q-Network (DQN) which revolutionized the field of reinforcement
learning. For the first time they had brought together Deep Learning and
Q-learning and showed impressive results applying deep reinforcement
learning to Atari games with their agents performing at or over human
level expertise in almost all the games trained on.
A Deep Q-Network utilizes a deep neural network to estimate the q-values
for each action, allowing the policy to select the action with the
maximum q-values. This use of deep neural network to get q-values was
immensely superior to implementing q-table look-ups and widened the
applicability of q-learning to more complex reinforcement learning
environments.
While revolutionary, the original version of DQN had a few problems,
especially its slow/inefficient learning process. Over these past 9
years, a few improved versions of DQNs have become popular. This project
is an attempt to study the effectiveness of a few of these DQN flavors,
what problems they solve and compare their performance in the same
reinforcement learning environment.
# Deep Q-Networks and its flavors
- **Vanilla DQN**
The vanilla (original) DQN uses 2 neural networks: the **online**
network and the **target** network. The online network is the main
neural network that the agent uses to select the best action for a
given state. The target neural network is usually a copy of the
online network. It is used to get the "target" q-values for each
action for a particular state. i.e. During the learning phase, since
we don’t have actual ground truths for future q-values, these
q-values from the target network will be used as labels optimize the
network.
The target network calculates the target q-values by using the
following Bellman equation: \[\begin{aligned}
Q(s_t, a_t) =
r_{t+1} + \gamma \max _{a_{t+1} \in A} Q(s_{t+1}, a_{t+1})
\end{aligned}\] where,
\(Q(s_t, a_t)\) = The target q-value (ground truth) for a past
experience in the replay memory
\(r_{t+1}\)= The reward that was obtained for taking the chosen
action in that particular experience
\(\gamma\)= The discount factor for future rewards
\(Q(s_{t+1}, a_{t+1})\) = The q-value for best action (based on the
policy) for the next state for that particular experience
- **Double DQN**
One of the problems with vanilla DQN is the way it calculates its
target values (ground-truth). We can see from the bellman equation
above that the target network uses the **max** q-value directly in
the equation. This is found to almost always overestimate the
q-value because using the **max** function introduces the
maximization-bias to our estimates. Using max will give the largest
value even if that specific max value was an outlier, thus skewing
our estimates.
The Double DQN solves this problem by changing the original
algorithm to the following:
1. Instead of using the **max** function, first use the online
network to estimate the best action for the next state
2. Calculate target q-values for the next state for each possible
action using the target network
3. From the q-values calculated by the target network, use the
q-value of the action chosen in step 1.
This can be represented by the following equation: \[\begin{aligned}
Q(s_t, a_t) =
r_{t+1} + \gamma Q_{target}(s_{t+1}, a'_{t+1})
\end{aligned}\] where, \[\begin{aligned}
a'_{t+1} = argmax({Q_{online}(s_{t+1})})
\end{aligned}\]
- **Dueling DQN**
The Dueling DQN algorithm was an attempt to improve upon the
original DQN algorithm by changing the architecture of the neural
network used in Deep Q-learning. The Duelling DQN algorithm splits
the last layer of the DQN into to parts, a **value stream** and an
**advantage stream**, the outputs of which are aggregated in an
aggregating layer that gives the final q-value. One of the main
problems with the original DQN algorithm was that the difference in
Q-values for the actions were often very close. Thus, selecting the
action with the max q-value might always not be the best action to
take. The Dueling DQN attempts to mitigate this by using advantage,
which is a measure of how better an action is compared to other
actions for a given state. The value stream, on the other hand,
learns how good/bad it is to be in a specific state. eg. Moving
straight towards an obstacle in a racing game, being in the path of
a projectile in Space Invaders, etc. Instead of learning to predict
a single q-value, by separating into value and advantage streams
helps the network generalize better.
Fig: The Dueling DQN architecture (Image taken from the original
paper by Wang et al.)
The q-value in a Dueling DQN architecture is given by
\[\begin{aligned}
Q(s_t, a_t) = V(s_t) + A(a)
\end{aligned}\] where,
V(s\_t) = The value of the current state (how advantageous it is to
be in that state)
A(a) =The advantage of taking action an a at that state
# About the project
My original goal for the project was to train an agent using DQN to
play **Airstriker Genesis**, a space shooting game and evaluate the
same agent’s performance on another similar game called
**Starpilot**. Unfortunately, I was unable to train a decent enough
agent in the first game, which made it meaningless to evaluate it’s
performance on yet another game.
Because I still want to do the original project some time in the
future, to prepare myself for that I thought it would be better to
first learn in-depth about how Deep Q-Networks work, what their
shortcomings are and how they can be improved. This, and for
time-constraint reasons, I have changed my project for this class to
a comparison of various DQN versions.
# Dataset
I used the excellent [Gym](https://github.com/openai/gym) library to
run my environment. A total of 9 agents, 1 in Airstriker Genesis, 4
in Starpilot and 4 in Lunar Lander were trained.
| **Game** | **Observation Space** | **Action Space** |
| :----------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Airstriker Genesis | RGB values of each pixel of the game screen (255, 255, 3) | Discrete(12) representing each of the buttons on the old Atari controllers. But since only three of those buttons were used in the game the action space was reduced to 3 during training. ( Left, Right, Fire ) |
| Starpilot | RGB values of each pixel of the game screen (64, 64, 3) | Discrete(15) representing each of the button combos ( Left, Right, Up, Down, Up + Right, Up + Left, Down + Right, Down + Left, W, A, S, D, Q, E, Do nothing ) |
| Lunar Lander | 8-dimensional vector: ( X-coordinate, Y-coordinate, Linear velocity in X, Linear Velocity in Y, Angle, Angular Velocity, Boolean (Leg 1 in contact with ground), Boolean (Leg 2 in contact with ground) ) | Discrete(4)( Do nothing, Fire left engine, Fire main engine, Fire right engine ) |
**Environment/Libraries**:
Miniconda, Python 3.9, Gym, Pyorch, Numpy, Tensorboard on my
personal Macbook Pro (M1)
# ML Methodology
Each agent was trained using DQN or one of its flavors. Each agent
for a particular game was trained with the same hyperparameters with
just the underlying algorithm different. The following metrics for
each agent were used for evaluation:
- **Epsilon value over each episode** Shows what the exploration
rate was at the end of each episode.
- **Average Q-value for the last 100 episodes** A measure of the
average q-value (for the action chosen) for the last 100
episodes.
- **Average length for the last 100 episodes** A measure of the
average number of steps taken in each episode
- **Average loss for the last 100 episodes** A measure of loss
during learning in the last 100 episodes (A Huber Loss was used)
- **Average reward for the last 100 episodes** A measure of the
average reward the agent accumulated over the last 100 episodes
## Preprocessing
For the Airstriker and the Starpilot games:
1. Changed each frame to grayscale
Since the color shouldn’t matter to the agent, I decided to
change the RGB image to grayscale
2. Changed observation space shape from (height, width, channels)
to (channels, height, width) to make it compatible with
Pytorch
Apparently Pytorch uses a different format than the direct
output of the gym environment. For this reason, I had to reshape
each observation to match Pytorch’s scheme (this took me a very
long time to figure out, but had an "Aha\!" moment when I
remember you saying something similar in class).
3. Framestacking
Instead of processing 1 frame at a time, process 4 frames at a
time. This is because just 1 frame is not enough information for
the agent to decide what action to take.
For Lunar Lander, since the reward changes are very drastic (sudden
+100, -100, +200) rewards, I experimented with Reward Clipping
(clipping the rewards to \[-1, 1\] range) but this didn’t seem to
make much difference in my agent’s performance.
# Results
- **Airstriker Genesis**
The loss went down until about 5200 episodes but after that it
stopped going down any further. Consequently the average reward the
agent accumulated over the last 100 episodes pretty much plateaued
after about 5000 episodes. On analysis, I noticed that my
exploration rate at the end of the 7000th episode was still about
0.65, which means that the agent was taking random actions more than
half of the time. On hindsight, I feel like I should have trained
more, at least until the epsilon value (exploration rate) completely
decayed to 5%.
  
- **Starpilot**
I trained DQN, Double DQN, Dueling DQN and Dueling Double DQN
versions for this game to compare the different algorithms.
From the graph of mean q-values, we can tell that the Vanilla DQN
versions indeed give high q-values, and their Double-DQN couterparts
give lower values, which makes me think that my implementation of
the Double DQN algorithm was OK. I had expected the agent to
accumulate higher rewards starting much earlier for the Double and
Dueling versions, but since the average rewards was almost similar
for all the agents, I could not notice any stark differences between
the performance of each agent.
| | |
| :------------------ | :------------------ |
|  |  |
- **Lunar Lander**
Since I did gain much insight from the agent in the Starpilot game,
I thought I was not training long enough. So I tried training the
same agents on Lunar Lander, which is a comparatively simpler game
with a smaller observation space and one that a DQN algorithm should
be able converge pretty quickly to (based on comments by other
people in the RL community).
| | |
| :------------------- | :------------------- |
|  |  |
The results for this were interesting. Although I did not find any
vast difference between the different variations of the DQN
algorithm, I found that the performance of my agent suddenly got
worse at around 300 episodes. Upon researching on why this may have
happened, I learned that DQN agents suffer from **catastrophic
forgetting** i.e. after training extensively, the network suddenly
forgets what it has learned in the past and the starts performing
worse. Initially, I thought this might have been the case, but since
I haven’t trained long enough, and because all models started
performing worse at almost exactly the same episode number, I think
this might be a problem with my code or some hyperparameter that I
used.
Upon checking what the agent was doing in the actual game, I found
that it was playing it very safe and just constantly hovering in the
air, not attempting to land the spaceship (the goal of the agent is
to land within the yellow flags). I thought maybe penalizing the
rewards for taking too many steps in the episode would work, but
that didn’t help either.
# Problems Faced
Here are a few of the problems that I faced while training my agents:
- Understanding the various hyperparameters in the algorithm. DQN uses
a lot of moving parts and thus, tuning each parameter was a
difficult task. There were about 8 different hyperparameters (some
correlated) that impacted the agent’s training performance. I
struggled with understanding how each parameter impacted the agent
and also with figuring out how to find optimal values for those. I
ended up tuning them by trial and error.
- I got stuck for a long time figuring out why my convolutional layer
was not working. I didn’t realize that Pytorch has the channels in
the first dimension, and because of that, I was passing huge numbers
like 255 (the height of the image) into the input dimension for a
Conv2D layer.
- I struggled with knowing how long is long enough to realize that a
model is not working. I trained a model on Airstriker Genesis for 14
hours just to realize later that I had set a parameter incorrectly
and had to retrain all over again.
# What Next?
Although I didn’t get a final working agent for any of the games I
tried, I feel like I have learned a lot about reinforcement learning,
especially about Deep Q-learning. I plan to improve upon this further,
and hopefully get an agent to go far into at least one of the games.
Next time, I will start with first debugging my current code and see if
I have any implementation mistakes. Then I will train them a lot longer
than I did this time and see if it works. While learning about the
different flavors of DQN, I also learned a little about NoisyNet DQN,
Rainbow-DQN and Prioritized Experience Replay. I couln’t implement these
for this project, but I would like to try them out some time soon.
# Lessons Learned
- Reinforcement learning is a very challenging problem. It takes a
substantially large amount of time to train, it is hard to debug and
it is very difficult to tune its hyperparameters just right. It is a
lot different from supervised learning in that there are no actual
labels and thus, this makes optimization very difficult.
- I tried training an agent on the Atari Airstriker Genesis and the
procgen Starpilot game using just the CPU, but this took a very long
time. This is understandable because the inputs are images and using
a GPU would have been obviously better. Next time, I will definitely
try using a GPU to make training faster.
- Upon being faced with the problem of my agent not learning, I went
into research mode and got to learn a lot about DQN and its improved
versions. I am not a master of the algorithms yet (I have yet to get
an agent to perform well in the game), but I feel like I understand
how each version works.
- Rather than just following someone’s tutorial, also reading the
actual papers for that particular algorithm helped me understand the
algorithm better and code it.
- Doing this project reinforced into me that I love the concept of
reinforcement learning. It has made me even more interested into
exploring the field further and learn more.
# References / Resources
- [Reinforcement Learning (DQN) Tutorial, Adam
Paszke](https://pytorch.org/tutorials/intermediate/reinforcement_q_learning.html)
- [Train a mario-playing RL agent, Yuansong Feng, Suraj Subramanian,
Howard Wang, Steven
Guo](https://pytorch.org/tutorials/intermediate/mario_rl_tutorial.html)
- [About Double DQN, Dueling
DQN](https://horomary.hatenablog.com/entry/2021/02/06/013412)
- [Dueling Network Architecture for Deep Reinforcement Learning (Wang
et al., 2015))](https://arxiv.org/abs/1511.06581)
*(Final source code for the project can be found*
[*here*](https://github.com/00ber/ml-reinforcement-learning)*)*.
|
Declan/WallStreetJournal_model_v4
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"BertForMaskedLM"
],
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}
| 7 | 2023-02-22T09:34:50Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="eswardivi/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Declan/WallStreetJournal_model_v8
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
| 9 | 2023-02-22T09:37:03Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="eswardivi/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Declan/test_model
|
[] | null |
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| 0 | null |
---
license: creativeml-openrail-m
tags:
- art
language:
- en
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
# Model Details
## Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
## Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
# Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
## Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
## Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
## Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
# Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
## Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
# Training Details
## Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
## Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
### Preprocessing [optional]
[More Information Needed]
### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
# Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
## Testing Data, Factors & Metrics
### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
## Results
[More Information Needed]
### Summary
# Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
# Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
# Technical Specifications [optional]
## Model Architecture and Objective
[More Information Needed]
## Compute Infrastructure
[More Information Needed]
### Hardware
[More Information Needed]
### Software
[More Information Needed]
# Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
# Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
# More Information [optional]
[More Information Needed]
# Model Card Authors [optional]
[More Information Needed]
# Model Card Contact
[More Information Needed]
|
Declan/test_push
|
[] | null |
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- shrinath-suresh/qa
model-index:
- name: qa3k
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. -->
# qa3k
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the shrinath-suresh/qa dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2548
## 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: 12
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100.0
### Training results
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1
- Datasets 2.9.0
- Tokenizers 0.13.2
|
DeepBasak/Slack
|
[] | null |
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| 0 | null |
---
license: mit
datasets:
- cifar10
language:
- en
pipeline_tag: image-classification
---
# micromind checkpoints for CIFAR-10
This repository contains checkpoints for the CIFAR-10 dataset for the following networks:
| Model | Top 1 Accuracy | Top 5 Accuracy |
| ------------------ |---------------- | -------------- |
| `PhiNet(alpha=3, beta=0.75, t_zero=6, num_layers=7, resolution=160)` | 93.61% | 99.77% |
| `PhiNet(alpha=0.75, beta=1, t_zero=6, num_layers=5, resolution=160)` | 86.8% | 99.5% |
| `PhiNet(alpha=0.35, beta=1, t_zero=6, num_layers=7, resolution=160)` | 88.08% | 99.48% |
| `PhiNet(alpha=0.25, beta=1, t_zero=6, num_layers=7, resolution=160)` | 84.97% | 99.3% |
| `PhiNet(alpha=0.25, beta=1, t_zero=5, num_layers=7, resolution=160)` | 83.01% | 99.2% |
To download and use this repo:
```
from micromind import PhiNet
model = PhiNet.from_pretrained("CIFAR-10", alpha=3.0, beta=0.75, t_zero=6, num_layers=7, num_classes=10, resolution=160)
```
## Authors
- [@fpaissan](https://www.github.com/fpaissan)
- [@matteobeltrami](https://www.github.com/matteobeltrami)
---
license: mit
---
|
DeepChem/ChemBERTa-10M-MLM
|
[
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
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"RobertaForMaskedLM"
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| 90 | null |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
DeepChem/ChemBERTa-5M-MTR
|
[
"pytorch",
"roberta",
"transformers"
] | null |
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| 13 | 2023-02-22T09:45:36Z |
---
license: mit
datasets:
- cifar100
language:
- en
pipeline_tag: image-classification
---
# micromind checkpoints for CIFAR-100
This repository contains checkpoints for the CIFAR-100 dataset for the following networks:
| Model | Top 1 Accuracy | Top 5 Accuracy |
| ------------------ |---------------- | -------------- |
| `PhiNet(alpha=3, beta=0.75, t_zero=6, num_layers=7, resolution=160)` | 75.56% | 93.5% |
| `PhiNet(alpha=0.75, beta=1, t_zero=6, num_layers=5, resolution=160)` | 60.87% | 86.98% |
| `PhiNet(alpha=0.35, beta=1, t_zero=6, num_layers=7, resolution=160)` | 59.77% | 85.98% |
| `PhiNet(alpha=0.25, beta=1, t_zero=6, num_layers=7, resolution=160)` | 54.16% | 82.32% |
| `PhiNet(alpha=0.25, beta=1, t_zero=5, num_layers=7, resolution=160)` | 52.41% | 81.24% |
To download and use this repo:
```
from micromind import PhiNet
model = PhiNet.from_pretrained("CIFAR-100", alpha=3.0, beta=0.75, t_zero=6, num_layers=7, num_classes=100, resolution=160)
```
## Authors
- [@fpaissan](https://www.github.com/fpaissan)
- [@matteobeltrami](https://www.github.com/matteobeltrami)
---
license: mit
---
|
DeepChem/ChemBERTa-77M-MLM
|
[
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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| 2,416 | 2023-02-22T09:47:33Z |
---
language:
- en
- zh
license: gpl-3.0
tags:
- bicleaner-ai
tasks:
- text-classification
---
# Bicleaner AI full model for English-Chinese
Bicleaner AI is a tool that aims at detecting noisy sentence pairs in a parallel corpus. It
indicates the likelihood of a pair of sentences being mutual translations (with a value near to 1) or not (with a value near to 0).
Sentence pairs considered very noisy are scored with 0.
Find out at our repository for further instructions on how to use it: https://github.com/bitextor/bicleaner-ai
|
DeepChem/ChemBERTa-77M-MTR
|
[
"pytorch",
"roberta",
"transformers"
] | null |
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| 7,169 | 2023-02-22T09:47:46Z |
---
license: mit
datasets:
- mnist
language:
- en
pipeline_tag: image-classification
---
# micromind checkpoints for MNIST
This repository contains checkpoints for the MNIST dataset for the following networks:
| Model | Top 1 Accuracy | Top 5 Accuracy |
| ------------------ |---------------- | -------------- |
| `PhiNet(alpha=0.5, beta=1, t_zero=6, num_layers=4, resolution=28)` | 98.96% | 100% |
| `PhiNet(alpha=0.75, beta=1, t_zero=6, num_layers=5, resolution=28)` | 99.03% | 99.98% |
| `PhiNet(alpha=0.35, beta=1, t_zero=6, num_layers=7, resolution=28)` | 98.72% | 99.99% |
| `PhiNet(alpha=0.25, beta=1, t_zero=6, num_layers=7, resolution=28)` | 98.84% | 99.99% |
| `PhiNet(alpha=0.25, beta=1, t_zero=5, num_layers=7, resolution=28)` | 98.76% | 99.97% |
To download and use this repo:
```
from micromind import PhiNet
model = PhiNet.from_pretrained("MNIST", alpha=0.5, beta=1.0, t_zero=6, num_layers=4, num_classes=10, resolution=28)
```
## Authors
- [@fpaissan](https://www.github.com/fpaissan)
- [@matteobeltrami](https://www.github.com/matteobeltrami)
---
license: mit
---
|
DeepPavlov/bert-base-bg-cs-pl-ru-cased
|
[
"pytorch",
"jax",
"bert",
"feature-extraction",
"bg",
"cs",
"pl",
"ru",
"transformers"
] |
feature-extraction
|
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| 1,614 | null |
Access to model sapra1/gptku is restricted and you are not in the authorized list. Visit https://huggingface.co/sapra1/gptku to ask for access.
|
DeepPavlov/distilrubert-base-cased-conversational
|
[
"pytorch",
"distilbert",
"ru",
"arxiv:2205.02340",
"transformers"
] | null |
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| 6,324 | null |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinfoce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
DeepPavlov/roberta-large-winogrande
|
[
"pytorch",
"roberta",
"text-classification",
"en",
"dataset:winogrande",
"arxiv:1907.11692",
"transformers"
] |
text-classification
|
{
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"RobertaForSequenceClassification"
],
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| 348 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: cyllum/Brave-Bears
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
DeepPavlov/rubert-base-cased-conversational
|
[
"pytorch",
"jax",
"bert",
"feature-extraction",
"ru",
"transformers",
"has_space"
] |
feature-extraction
|
{
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"BertModel"
],
"model_type": "bert",
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| 17,362 | null |
civitai部分模型搬运
- [taiwan-doll-likeness](https://civitai.com/models/7716/taiwan-doll-likeness)
 taiwan-doll-likeness 台湾小姐姐
---
- [korean-doll-likeness](https://civitai.com/models/7448/korean-doll-likeness)
 korean-doll-likeness 韩国小姐姐
---
- [japanese-doll-likeness](https://civitai.com/models/10135/japanese-doll-likeness)
japanese-doll-likeness 日本小姐姐
|
DeltaHub/adapter_t5-3b_qnli
|
[
"pytorch",
"transformers"
] | null |
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}
| 3 | null |
---
license: mit
tags:
- generated_from_trainer
datasets:
- wikitext
model-index:
- name: wikitext-ds
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wikitext-ds
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the wikitext dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
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Subsets and Splits
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