Datasets:

librarian-bots

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Librarian Bots 52

nolanaatama/thwkndrvc1000pchclbbdsm

nolanaatama

--- license: creativeml-openrail-m ---

ongknsro/ACARISBERT-BERT

ongknsro

transformers

text-classification

--- language: - en metrics: - accuracy - f1 - recall - precision library_name: transformers pipeline_tag: text-classification ---

yaqliu/results

yaqliu

Entry not found

Jikiwi/jokowi-voice-ai

Jikiwi

Entry not found

amd1729/solar-mask2-swin-small-ade-200-deepsolar-2023052103

amd1729

transformers

Entry not found

alvinxd/Tokyo

alvinxd

Entry not found

sitownle/4bLLM2

sitownle

--- datasets: - FourthBrainGenAI/MarketMail-AI-Dataset language: - en --- LLM Assignment 2 - Fine Tuning BLOOMZ with PyTorch, peft, and transformers from HuggingFace

Ahmadswaid/california_housing

Ahmadswaid

sklearn

tabular-regression

--- library_name: sklearn tags: - sklearn - skops - tabular-regression widget: structuredData: AveBedrms: - 0.9806451612903225 - 1.0379746835443038 - 0.9601449275362319 AveOccup: - 2.587096774193548 - 2.8658227848101268 - 2.6449275362318843 AveRooms: - 7.275268817204301 - 5.39493670886076 - 6.536231884057971 HouseAge: - 38.0 - 25.0 - 39.0 Latitude: - 37.44 - 37.31 - 34.16 Longitude: - -122.19 - -122.03 - -118.07 MedInc: - 9.3198 - 5.3508 - 6.4761 Population: - 1203.0 - 1132.0 - 730.0 --- # Model description [More Information Needed] ## Intended uses & limitations [More Information Needed] ## Training Procedure ### Hyperparameters The model is trained with below hyperparameters. <details> <summary> Click to expand </summary> | Hyperparameter | Value | |--------------------------|---------------| | bootstrap | True | | ccp_alpha | 0.0 | | criterion | squared_error | | max_depth | | | max_features | 1.0 | | max_leaf_nodes | | | max_samples | | | min_impurity_decrease | 0.0 | | min_samples_leaf | 1 | | min_samples_split | 2 | | min_weight_fraction_leaf | 0.0 | | n_estimators | 100 | | n_jobs | | | oob_score | False | | random_state | | | verbose | 0 | | warm_start | False | </details> ### Model Plot The model plot is below. <style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-2" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>RandomForestRegressor()</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"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" checked><label for="sk-estimator-id-2" class="sk-toggleable__label sk-toggleable__label-arrow">RandomForestRegressor</label><div class="sk-toggleable__content"><pre>RandomForestRegressor()</pre></div></div></div></div></div> ## Evaluation Results You can find the details about evaluation process and the evaluation results. | Metric | Value | |----------|---------| # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> ```python [More Information Needed] ``` </details> # Model Card Authors This model card is written by following authors: [More Information Needed] # Model Card Contact You can contact the model card authors through following channels: [More Information Needed] # Citation Below you can find information related to citation. **BibTeX:** ``` [More Information Needed] ```

Firestqr/Guitar

Firestqr

Entry not found

fa5ih/f1

fa5ih

Entry not found

Ahmadswaid/sklearn-mpg

Ahmadswaid

sklearn

tabular-regression

--- library_name: sklearn tags: - sklearn - skops - tabular-regression model_file: linreg.pkl widget: structuredData: x0: - -0.3839236795902252 - -0.9788183569908142 - 1.0937178134918213 x1: - -0.5319488644599915 - -1.108436107635498 - 0.9354732036590576 x2: - -0.38279563188552856 - -1.3128694295883179 - 1.4773520231246948 x3: - 0.2815782427787781 - -0.11783809214830399 - -0.9529813528060913 x4: - 1.0 - 1.0 - 0.0 x5: - 0.0 - 0.0 - 0.0 x6: - 0.0 - 0.0 - 0.0 x7: - 0.0 - 0.0 - 1.0 x8: - 0.0 - 1.0 - 0.0 x9: - 0.0 - 0.0 - 0.0 --- # Model description This is a regression model on MPG dataset trained. ## Intended uses & limitations This model is not ready to be used in production. ## Training Procedure ### Hyperparameters The model is trained with below hyperparameters. <details> <summary> Click to expand </summary> | Hyperparameter | Value | |------------------|------------| | copy_X | True | | fit_intercept | True | | n_jobs | | | normalize | deprecated | | positive | False | </details> ### Model Plot The model plot is below. <style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-3" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>LinearRegression()</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"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-3" type="checkbox" checked><label for="sk-estimator-id-3" class="sk-toggleable__label sk-toggleable__label-arrow">LinearRegression</label><div class="sk-toggleable__content"><pre>LinearRegression()</pre></div></div></div></div></div> ## Evaluation Results You can find the details about evaluation process and the evaluation results. | Metric | Value | |--------------------|----------| | Mean Squared Error | 5.01069 | | R-Squared | 0.883503 | # How to Get Started with the Model Use the code below to get started with the model. ```python import joblib import json import pandas as pd clf = joblib.load(linreg.pkl) with open("config.json") as f: config = json.load(f) clf.predict(pd.DataFrame.from_dict(config["sklearn"]["example_input"])) ``` # Model Card Authors This model card is written by following authors: [More Information Needed] # Model Card Contact You can contact the model card authors through following channels: [More Information Needed] # Citation Below you can find information related to citation. **BibTeX:** ``` [More Information Needed] ```

latimar/airoboros-13b-ggml

latimar

ggml conversion of the https://huggingface.co/jondurbin/airoboros-13b

ibrahimbush45/Idk

ibrahimbush45

Entry not found

polypo/testing

polypo

Entry not found

ZyXin/rl_course_vizdoom_health_gathering_supreme

ZyXin

sample-factory

reinforcement-learning

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 9.93 +/- 3.96 name: mean_reward verified: false --- A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r ZyXin/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

tchebonenko/Ass1c-BLOOMZ

tchebonenko

# Fine-tune a BLOOMZ-based ad generation model using peft, transformers and bitsandbytes ##Dataset: [MarketMail-AI dataset](https://huggingface.co/datasets/FourthBrainGenAI/MarketMail-AI)

alvinxd/Initokyo

alvinxd

--- license: creativeml-openrail-m ---

DigitaalisetM/LoveLiveHPDY

DigitaalisetM

Entry not found

Ahmadswaid/example-california-housing

Ahmadswaid

sklearn

tabular-regression

--- library_name: sklearn tags: - sklearn - skops - tabular-regression model_format: skops model_file: model.skops widget: structuredData: AveBedrms: - 0.9290780141843972 - 0.9458483754512635 - 1.087360594795539 AveOccup: - 3.1134751773049647 - 3.0613718411552346 - 3.2657992565055762 AveRooms: - 6.304964539007092 - 6.945848375451264 - 3.8884758364312266 HouseAge: - 17.0 - 15.0 - 24.0 Latitude: - 34.23 - 36.84 - 34.04 Longitude: - -117.41 - -119.77 - -118.3 MedInc: - 6.1426 - 5.3886 - 1.7109 Population: - 439.0 - 848.0 - 1757.0 --- # Model description Gradient boosting regressor trained on California Housing dataset The model is a gradient boosting regressor from sklearn. On top of the standard features, it contains predictions from a KNN models. These predictions are calculated out of fold, then added on top of the existing features. These features are really helpful for decision tree-based models, since those cannot easily learn from geospatial data. ## Intended uses & limitations This model is meant for demonstration purposes ## Training Procedure ### Hyperparameters The model is trained with below hyperparameters. <details> <summary> Click to expand </summary> | Hyperparameter | Value | |-----------------------------------------------|--------------------------------------------------------------| | cv | | | estimators | [('knn@5', Pipeline(steps=[('select_cols',<br /> ColumnTransformer(transformers=[('long_and_lat', 'passthrough',<br /> ['Longitude', 'Latitude'])])),<br /> ('knn', KNeighborsRegressor())]))] | | final_estimator__alpha | 0.9 | | final_estimator__ccp_alpha | 0.0 | | final_estimator__criterion | friedman_mse | | final_estimator__init | | | final_estimator__learning_rate | 0.1 | | final_estimator__loss | squared_error | | final_estimator__max_depth | 3 | | final_estimator__max_features | | | final_estimator__max_leaf_nodes | | | final_estimator__min_impurity_decrease | 0.0 | | final_estimator__min_samples_leaf | 1 | | final_estimator__min_samples_split | 2 | | final_estimator__min_weight_fraction_leaf | 0.0 | | final_estimator__n_estimators | 500 | | final_estimator__n_iter_no_change | | | final_estimator__random_state | 0 | | final_estimator__subsample | 1.0 | | final_estimator__tol | 0.0001 | | final_estimator__validation_fraction | 0.1 | | final_estimator__verbose | 0 | | final_estimator__warm_start | False | | final_estimator | GradientBoostingRegressor(n_estimators=500, random_state=0) | | n_jobs | | | passthrough | True | | verbose | 0 | | knn@5 | Pipeline(steps=[('select_cols',<br /> ColumnTransformer(transformers=[('long_and_lat', 'passthrough',<br /> ['Longitude', 'Latitude'])])),<br /> ('knn', KNeighborsRegressor())]) | | knn@5__memory | | | knn@5__steps | [('select_cols', ColumnTransformer(transformers=[('long_and_lat', 'passthrough',<br /> ['Longitude', 'Latitude'])])), ('knn', KNeighborsRegressor())] | | knn@5__verbose | False | | knn@5__select_cols | ColumnTransformer(transformers=[('long_and_lat', 'passthrough',<br /> ['Longitude', 'Latitude'])]) | | knn@5__knn | KNeighborsRegressor() | | knn@5__select_cols__n_jobs | | | knn@5__select_cols__remainder | drop | | knn@5__select_cols__sparse_threshold | 0.3 | | knn@5__select_cols__transformer_weights | | | knn@5__select_cols__transformers | [('long_and_lat', 'passthrough', ['Longitude', 'Latitude'])] | | knn@5__select_cols__verbose | False | | knn@5__select_cols__verbose_feature_names_out | True | | knn@5__select_cols__long_and_lat | passthrough | | knn@5__knn__algorithm | auto | | knn@5__knn__leaf_size | 30 | | knn@5__knn__metric | minkowski | | knn@5__knn__metric_params | | | knn@5__knn__n_jobs | | | knn@5__knn__n_neighbors | 5 | | knn@5__knn__p | 2 | | knn@5__knn__weights | uniform | </details> ### Model Plot The model plot is below. <style>#sk-container-id-13 {color: black;background-color: white;}#sk-container-id-13 pre{padding: 0;}#sk-container-id-13 div.sk-toggleable {background-color: white;}#sk-container-id-13 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-13 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-13 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-13 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-13 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 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-13 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-13 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-13 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 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-13 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-13 div.sk-item {position: relative;z-index: 1;}#sk-container-id-13 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-13 div.sk-item::before, #sk-container-id-13 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-13 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-13 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-13 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-13 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-13 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-13 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-13 div.sk-label-container {text-align: center;}#sk-container-id-13 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-13 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-13" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>StackingRegressor(estimators=[(&#x27;knn@5&#x27;,Pipeline(steps=[(&#x27;select_cols&#x27;,ColumnTransformer(transformers=[(&#x27;long_and_lat&#x27;,&#x27;passthrough&#x27;,[&#x27;Longitude&#x27;,&#x27;Latitude&#x27;])])),(&#x27;knn&#x27;,KNeighborsRegressor())]))],final_estimator=GradientBoostingRegressor(n_estimators=500,random_state=0),passthrough=True)</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-41" type="checkbox" ><label for="sk-estimator-id-41" class="sk-toggleable__label sk-toggleable__label-arrow">StackingRegressor</label><div class="sk-toggleable__content"><pre>StackingRegressor(estimators=[(&#x27;knn@5&#x27;,Pipeline(steps=[(&#x27;select_cols&#x27;,ColumnTransformer(transformers=[(&#x27;long_and_lat&#x27;,&#x27;passthrough&#x27;,[&#x27;Longitude&#x27;,&#x27;Latitude&#x27;])])),(&#x27;knn&#x27;,KNeighborsRegressor())]))],final_estimator=GradientBoostingRegressor(n_estimators=500,random_state=0),passthrough=True)</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><label>knn@5</label></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><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-42" type="checkbox" ><label for="sk-estimator-id-42" class="sk-toggleable__label sk-toggleable__label-arrow">select_cols: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(transformers=[(&#x27;long_and_lat&#x27;, &#x27;passthrough&#x27;,[&#x27;Longitude&#x27;, &#x27;Latitude&#x27;])])</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-43" type="checkbox" ><label for="sk-estimator-id-43" class="sk-toggleable__label sk-toggleable__label-arrow">long_and_lat</label><div class="sk-toggleable__content"><pre>[&#x27;Longitude&#x27;, &#x27;Latitude&#x27;]</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-44" type="checkbox" ><label for="sk-estimator-id-44" class="sk-toggleable__label sk-toggleable__label-arrow">passthrough</label><div class="sk-toggleable__content"><pre>passthrough</pre></div></div></div></div></div></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-45" type="checkbox" ><label for="sk-estimator-id-45" class="sk-toggleable__label sk-toggleable__label-arrow">KNeighborsRegressor</label><div class="sk-toggleable__content"><pre>KNeighborsRegressor()</pre></div></div></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><label>final_estimator</label></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-46" type="checkbox" ><label for="sk-estimator-id-46" class="sk-toggleable__label sk-toggleable__label-arrow">GradientBoostingRegressor</label><div class="sk-toggleable__content"><pre>GradientBoostingRegressor(n_estimators=500, random_state=0)</pre></div></div></div></div></div></div></div></div></div></div></div></div> ## Evaluation Results Metrics are calculated on the test set | Metric | Value | |-------------------------|--------------| | Root mean squared error | 44273.5 | | Mean absolute error | 30079.9 | | R² | 0.805954 | ## Dataset description California Housing dataset -------------------------- **Data Set Characteristics:** :Number of Instances: 20640 :Number of Attributes: 8 numeric, predictive attributes and the target :Attribute Information: - MedInc median income in block group - HouseAge median house age in block group - AveRooms average number of rooms per household - AveBedrms average number of bedrooms per household - Population block group population - AveOccup average number of household members - Latitude block group latitude - Longitude block group longitude :Missing Attribute Values: None This dataset was obtained from the StatLib repository. https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html The target variable is the median house value for California districts, expressed in hundreds of thousands of dollars ($100,000). This dataset was derived from the 1990 U.S. census, using one row per census block group. A block group is the smallest geographical unit for which the U.S. Census Bureau publishes sample data (a block group typically has a population of 600 to 3,000 people). An household is a group of people residing within a home. Since the average number of rooms and bedrooms in this dataset are provided per household, these columns may take surpinsingly large values for block groups with few households and many empty houses, such as vacation resorts. It can be downloaded/loaded using the :func:`sklearn.datasets.fetch_california_housing` function. .. topic:: References - Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions, Statistics and Probability Letters, 33 (1997) 291-297 ### Data distribution <details> <summary> Click to expand </summary>  </details> # How to Get Started with the Model Run the code below to load the model ```python import json import pandas as pd import skops.io as sio model = sio.load("model.skops") with open("config.json") as f: config = json.load(f) model.predict(pd.DataFrame.from_dict(config["sklearn"]["example_input"])) ``` # Model Card Authors Benjamin Bossan # Model Card Contact benjamin@huggingface.co # Permutation Importances 

sevk/kkbot

sevk

--- license: mit ---

erickyue/moon

erickyue

Entry not found

jarl0415/jarl

jarl0415

--- license: openrail ---

public-data/DeepDanbooru

public-data

# DeepDanbooru - https://github.com/KichangKim/DeepDanbooru - https://github.com/KichangKim/DeepDanbooru/releases/tag/v3-20200915-sgd-e30 - https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20200915-sgd-e30/deepdanbooru-v3-20200915-sgd-e30.zip

jie001/find

jie001

--- license: openrail ---

Elonvrc/Yuzl

Elonvrc

hu

perfectino/naput

perfectino

--- license: creativeml-openrail-m ---

EDWINHM/egla

EDWINHM

Entry not found

ssxjz/singer

ssxjz

Entry not found

dongyoungkim/whisper-large-korean-A

dongyoungkim

Entry not found

max2lax/Newdataset

max2lax

--- license: apache-2.0 ---

Laurie/lora-instruct-chat-50k-cn-en

Laurie

# 经测试，此版本的效果较好😀 I use the 50k [Chinese data](https://huggingface.co/datasets/Chinese-Vicuna/instruct_chat_50k.jsonl), which is the combination of alpaca_chinese_instruction_dataset and the Chinese conversation data from sharegpt-90k data. I finetune the model for 3 epochs use a single 4090 GPU with cutoff_len=1024. **Use in Python**: from transformers import LlamaForCausalLM, LlamaTokenizer from peft import PeftModel import torch tokenizer = LlamaTokenizer.from_pretrained("decapoda-research/llama-7b-hf") model = LlamaForCausalLM.from_pretrained( "decapoda-research/llama-7b-hf", load_in_8bit=True, torch_dtype=torch.float16, device_map="auto", ) model = PeftModel.from_pretrained( model, "Laurie/lora-instruct-chat-50k-cn-en", torch_dtype=torch.float16, device_map={'': 0} ) device = "cuda" if torch.cuda.is_available() else "cpu" inputs = tokenizer("什么是自然语言处理？",return_tensors="pt" ) model.to(device) with torch.no_grad(): inputs = {k: v.to(device) for k, v in inputs.items()} outputs = model.generate(input_ids=inputs["input_ids"], max_new_tokens=129) print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))

raghudinesh/ddp_final

raghudinesh

Entry not found

dnsn/t5-large_PREFIX_TUNING_SEQ2SEQ

dnsn

Entry not found

duanhong/learning

duanhong

Entry not found

ntedeschi/reconcile_the_irreconcilable

ntedeschi

--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Reconcile the Irreconcilable <!-- Provide a quick summary of what the model is/does. --> This model tries to reconcile the views of Hegel and Ayn Rand on a given philosophical topic. ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> The purpose of the model is to give the views of Hegel and Ayn Rand on a given philosophical topic. Then it is supposed to write a paragraph reconciling their (most likely contrary) views. The model was multitasked fine-tuned on [bloomz-3b](https://huggingface.co/bigscience/bloomz-3b). Data for the fine-tuning was generated using chatGPT GPT-4. - **Developed by:** [Neil Tedeschi](https://huggingface.co/ntedeschi) - **Training dataset:** [reconcile_the_irreconcilable](https://huggingface.co/datasets/ntedeschi/reconcile_the_irreconcilable) - **Language(s) (NLP):** [bloomz-3b](https://huggingface.co/bigscience/bloomz-3b) ## Direct Use I am not sure how to get the model to work on this site. So, annoyingly, you need to cut and paste the following code into a notebook. 1. Download the adapter and connect with base model. ``` import torch from peft import PeftModel, PeftConfig from transformers import AutoModelForCausalLM, AutoTokenizer peft_model_id = "ntedeschi/reconcile_the_irreconcilable" config = PeftConfig.from_pretrained(peft_model_id) model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=False, device_map='auto') tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path) # Load the Lora model model = PeftModel.from_pretrained(model, peft_model_id) ``` 2. Set up prompt and query function: ``` from IPython.display import display, Markdown def make_inference(topic): batch = tokenizer( f"### INSTRUCTION\nBelow is a philosophy topic. Please write Hegel's view on the topic, Ayn Rand's view \ on the topic and a reconciliation of their views. \ \n\n### Topic:\n{topic}\n \ \n\n### Hegel:\n \ \n\n### Ayn Rand:\n \ \n\n### Reconciliation:\n", return_tensors='pt' ) with torch.cuda.amp.autocast(): output_tokens = model.generate(**batch, max_new_tokens=512) display(Markdown((tokenizer.decode(output_tokens[0], skip_special_tokens=True)))) ``` 3. Make the inference by giving a philosophy topic. For example, ``` philosophy_topic = "Mind body dualism" make_inference(philosophy_topic) ```

Gille/GilleMix_v2_tests

Gille

--- license: creativeml-openrail-m ---

pennlio/test

pennlio

Entry not found

TKNM/nva-ze_sayaka04

TKNM

Entry not found

outterseayi/childmask

outterseayi

Entry not found

collabrl/SpaceInvadersNoFrameskip-v4

collabrl

stable-baselines3

reinforcement-learning

--- 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: 808.00 +/- 284.47 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 collabrl -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 collabrl -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 collabrl ``` ## 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)]) ```

denaneek/fine-tuning_BLOOMZ_using_LoRA

denaneek

Entry not found

douglch/q-FrozenLake-v1-4x4-noSlippery

douglch

reinforcement-learning

--- 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="douglch/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"]) ```

betterme/models

betterme

Entry not found

StupidGame/kyawawa_loha

StupidGame

Entry not found

ongknsro/ACARISBERT-RoBERTa

ongknsro

transformers

text-classification

Entry not found

moghis/LunarLander-v2-scratch

moghis

reinforcement-learning

--- 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: -144.28 +/- 93.95 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': 80000 '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': 'moghis/LunarLander-v2-scratch' 'batch_size': 512 'minibatch_size': 128} ```

douglch/q-learning_taxi_v3

douglch

reinforcement-learning

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-learning_taxi_v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.24 +/- 2.60 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="douglch/q-learning_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"]) ```

ApolloFilippou/a2c-PandaReachDense-v2

ApolloFilippou

stable-baselines3

reinforcement-learning

--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -0.44 +/- 0.12 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 ... ```

addiekline/genaidemo

addiekline

Entry not found

dddx/mehmehmeh

dddx

Entry not found

AI2CC/luzhiy

AI2CC

transformers

Entry not found

Pegasus88/liuling1

Pegasus88

--- license: creativeml-openrail-m ---

Chen311/angie

Chen311

--- license: creativeml-openrail-m ---

Zx1140199595/ControlNet

Zx1140199595

Entry not found

DisasterArtist/DIO

DisasterArtist

transformers

Entry not found

albertStarCloud/mit-b0-scene-parse-150-lora

albertStarCloud

Entry not found

takesomerisks/loraBnbOpt13bOasst1Local

takesomerisks

Entry not found

warren2023/xlm-roberta-base-finetuned-panx-de-fr

warren2023

Entry not found

Faiza3/martin_valen_dataset_faf

Faiza3

Entry not found

chenbowen-184/GenerAd

chenbowen-184

Entry not found

Pachosoad/Sad

Pachosoad

--- license: bigscience-openrail-m ---

ApolloFilippou/poca-SoccerTwos

ApolloFilippou

Entry not found

joshxin/test-model

joshxin

Entry not found

xixixixihu/output

xixixixihu

Entry not found

ttogun/fourthbrain_wk1_finetuned_bloomz_ad_gen

ttogun

Entry not found

antruong/speecht5_tts_pierre

antruong

Entry not found

rxl7906/super-cool-model

rxl7906

Entry not found

Skim7603/imneko

Skim7603

--- license: creativeml-openrail-m ---

humin1102/vicuna-13b-all-v1.1

humin1102

transformers

text-generation

Entry not found

marasama/nva-king_george_v

marasama

Entry not found

kebab111/SevensMix

kebab111

diffusers

text-to-image

Entry not found

nikitagricanuk/vicuna-13b

nikitagricanuk

Entry not found

iamanavk/qm_sum_t5-base_attempt_2

iamanavk

Entry not found

kebab111/AresMix

kebab111

diffusers

text-to-image

Entry not found

moghis/rl_course_vizdoom_health_gathering_supreme

moghis

sample-factory

reinforcement-learning

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 8.25 +/- 2.88 name: mean_reward verified: false --- A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r moghis/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

kirp/psy-llama-extend-delta

kirp

--- license: apache-2.0 datasets: - siyangliu/PsyQA --- Extend the vocab of llama to 52992 and random initialization.

leonhe/ppo-LunarLander-v2

leonhe

stable-baselines3

reinforcement-learning

--- 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: 225.17 +/- 41.05 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 ... ```

yusufagung29/whisper_1e-4_clean_legion_fleurs

yusufagung29

transformers

automatic-speech-recognition

Entry not found

ShawnGGG/update

ShawnGGG

Entry not found

moin1234/MHAI

moin1234

--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # 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]

BIDJOE/Safety-Protocol-YOLOv5-Vanilla

BIDJOE

Entry not found

fcuadra/FineTuningBLOOMZ

fcuadra

adapter-transformers

text-generation

--- license: bigscience-bloom-rail-1.0 datasets: - fcuadra/MarketMail_AI library_name: adapter-transformers pipeline_tag: text-generation ---

prismaticholdings/prismaticholdings

prismaticholdings

--- license: openrail ---

lip421/wutoududou4

lip421

Entry not found

rewoo/planner_7B

rewoo

--- license: mit --- Alpaca Lora adapter weight fine-tuned on following instruction dataset. https://huggingface.co/datasets/rewoo/planner_instruction_tuning_2k/blob/main/README.md Training script: borrowed from the official [Alpaca-LoRA](https://github.com/tloen/alpaca-lora) implementation We use following parameter. ``` python finetune.py \ --base_model 'decapoda-research/llama-7b-hf' \ --data_path 'rewoo/planner_instruction_tuning_2k' \ --output_dir './lora-alpaca-planner' \ --batch_size 128 \ --micro_batch_size 8 \ --num_epochs 10 \ --learning_rate 1e-4 \ --cutoff_len 1024 \ --val_set_size 200 \ --lora_r 8 \ --lora_alpha 16 \ --lora_dropout 0.05 \ --lora_target_modules '[q_proj,v_proj]' \ --train_on_inputs \ --group_by_length \ --resume_from_checkpoint 'tloen/alpaca-lora-7b' ```

TKNM/nva-fh_nie2_02r

TKNM

Entry not found

natanasha/posing

natanasha

Entry not found

averageandyyy/brainheck_imda_wav2vec_hf

averageandyyy

Entry not found

Tony1810/Draft

Tony1810

Entry not found

Trinity123/Testn1

Trinity123

Entry not found

GeneZC/bert-base-cola

GeneZC

transformers

--- license: apache-2.0 datasets: - glue --- # Model Details `bert-base-uncased` finetuned on `CoLA`. ## Parameter settings batch size is 32, learning rate is 2e-5. ## Metrics matthews_corr: 0.6295

KyungChang/TestModel

KyungChang

--- license: openrail ---

PragyanPrusty/wav2vec2-large-xlsr-53-odia-colab

PragyanPrusty

Entry not found

RanaReebaal/clip-embeddings

RanaReebaal

Entry not found

dustinator/BusinessGuy-GPT

dustinator

Entry not found

Odlanier968/Gggg

Odlanier968

Entry not found

Valentine89/TwitterSentimentAnalysis

Valentine89

--- license: openrail ---

oguzhanascr/oascier

oguzhanascr

Entry not found

warren2023/xlm-roberta-base-finetuned-panx-fr

warren2023

Entry not found

sj1/textual_inversion_cat

sj1

Entry not found