librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-06-28 18:27:08	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 501 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-28 18:25:37	card stringlengths 11 1.01M
huggingtweets/dodecahedra	huggingtweets	2022-06-12T17:42:15Z	103	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-06-12T17:37:18Z	--- language: en thumbnail: http://www.huggingtweets.com/dodecahedra/1655055731499/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/3232494514/760c72bca0af20fac2cd61bcec557e7a_400x400.jpeg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">William Rose</div> <div style="text-align: center; font-size: 14px;">@dodecahedra</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 William Rose. \| Data \| William Rose \| \| --- \| --- \| \| Tweets downloaded \| 3241 \| \| Retweets \| 1115 \| \| Short tweets \| 158 \| \| Tweets kept \| 1968 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1geru0ac/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 @dodecahedra's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1uy1zk82) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1uy1zk82/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/dodecahedra') 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 [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
nlokam99/ada_sample_2	nlokam99	2022-06-12T17:40:42Z	105	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-06-12T17:38:56Z	--- thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png tags: - conversational license: mit ---
obokkkk/kc-bert_finetuned_unsmile	obokkkk	2022-06-12T17:22:32Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-06-12T14:39:40Z	--- tags: - generated_from_trainer model-index: - name: kc-bert_finetuned_unsmile 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. --> # kc-bert_finetuned_unsmile This model is a fine-tuned version of [beomi/kcbert-base](https://huggingface.co/beomi/kcbert-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1326 - Lrap: 0.8753 ## 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Lrap \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 235 \| 0.1458 \| 0.8612 \| \| No log \| 2.0 \| 470 \| 0.1280 \| 0.8738 \| \| 0.1685 \| 3.0 \| 705 \| 0.1257 \| 0.8791 \| \| 0.1685 \| 4.0 \| 940 \| 0.1281 \| 0.8777 \| \| 0.0774 \| 5.0 \| 1175 \| 0.1326 \| 0.8753 \| ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 1.17.0 - Tokenizers 0.12.1
comodoro/SpaceInvadersNoFrameskip-v4	comodoro	2022-06-12T16:55:50Z	2	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-06-12T16:55:06Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - metrics: - type: mean_reward value: 680.00 +/- 211.93 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 --- # 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 ``` # Download model and save it into the logs/ folder python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga comodoro -f logs/ python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga comodoro ``` ## 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', 10000000.0), ('optimize_memory_usage', True), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
vasudevgupta/speech_jax_wav2vec2-large-lv60_960h	vasudevgupta	2022-06-12T16:10:32Z	7	0	transformers	[ "transformers", "jax", "wav2vec2", "automatic-speech-recognition", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-05-29T20:52:47Z	* Evaluation Notebook: https://colab.research.google.com/drive/1dV1Z3WajMCYMjNZab98CEEcg3FTbtONO?usp=sharing * Training Code: https://github.com/vasudevgupta7/speech-jax/blob/main/projects/finetune_wav2vec2.py * Weights & Biases: https://wandb.ai/7vasudevgupta/speech-JAX?workspace=user-7vasudevgupta Following results are obtained with `23ffe236840b7f75c9f01a9c347b01485a2bf9f6` & `95c3bc1b83c74452df29f792e0b5651c09fdaeb9` \| dataset \| WER \| \|------------------------\|-------\| \| Librispeech-test-clean \| 3.3 % \|
kravchenko/uk-mt5-base	kravchenko	2022-06-12T14:57:59Z	14	4	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "t5", "uk", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-05-03T09:41:33Z	--- language: - uk - en tags: - t5 --- The aim is to compress the mT5-base model to leave only the Ukrainian language and some basic English. Reproduced the similar result (but with another language) from [this](https://towardsdatascience.com/how-to-adapt-a-multilingual-t5-model-for-a-single-language-b9f94f3d9c90) medium article. Results: - 582M params -> 244M params (58%) - 250K tokens -> 30K tokens - 2.2GB size model -> 0.95GB size model
jianyang/q-Taxi-v3	jianyang	2022-06-12T14:13:37Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2022-06-12T14:13:31Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 --- # Q-Learning Agent playing Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="jianyang/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```
ahmeddbahaa/mt5-base-finetune-ar-xlsum	ahmeddbahaa	2022-06-12T13:55:10Z	9	0	transformers	[ "transformers", "pytorch", "tensorboard", "mt5", "text2text-generation", "summarization", "mT5_multilingual_XLSum", "abstractive summarization", "ar", "xlsum", "generated_from_trainer", "dataset:xlsum", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2022-06-11T20:41:00Z	--- license: apache-2.0 tags: - summarization - mT5_multilingual_XLSum - mt5 - abstractive summarization - ar - xlsum - generated_from_trainer datasets: - xlsum model-index: - name: mt5-base-finetune-ar-xlsum 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. --> # mt5-base-finetune-ar-xlsum This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the xlsum dataset. It achieves the following results on the evaluation set: - Loss: 3.2546 - Rouge-1: 22.2 - Rouge-2: 9.57 - Rouge-l: 20.26 - Gen Len: 19.0 - Bertscore: 71.43 ## 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: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 250 - num_epochs: 10 - label_smoothing_factor: 0.1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge-1 \| Rouge-2 \| Rouge-l \| Gen Len \| Bertscore \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:-------:\|:---------:\| \| 4.9261 \| 1.0 \| 585 \| 3.6314 \| 18.19 \| 6.49 \| 16.37 \| 19.0 \| 70.17 \| \| 3.8429 \| 2.0 \| 1170 \| 3.4253 \| 19.45 \| 7.58 \| 17.73 \| 19.0 \| 70.35 \| \| 3.6311 \| 3.0 \| 1755 \| 3.3569 \| 20.83 \| 8.54 \| 18.9 \| 19.0 \| 70.89 \| \| 3.4917 \| 4.0 \| 2340 \| 3.3101 \| 20.77 \| 8.53 \| 18.89 \| 19.0 \| 70.98 \| \| 3.3873 \| 5.0 \| 2925 \| 3.2867 \| 21.47 \| 9.0 \| 19.54 \| 19.0 \| 71.23 \| \| 3.3037 \| 6.0 \| 3510 \| 3.2693 \| 21.41 \| 9.0 \| 19.5 \| 19.0 \| 71.21 \| \| 3.2357 \| 7.0 \| 4095 \| 3.2581 \| 22.05 \| 9.36 \| 20.04 \| 19.0 \| 71.43 \| \| 3.1798 \| 8.0 \| 4680 \| 3.2522 \| 22.21 \| 9.56 \| 20.23 \| 19.0 \| 71.41 \| \| 3.1359 \| 9.0 \| 5265 \| 3.2546 \| 22.27 \| 9.58 \| 20.23 \| 19.0 \| 71.46 \| \| 3.0997 \| 10.0 \| 5850 \| 3.2546 \| 22.2 \| 9.57 \| 20.26 \| 19.0 \| 71.43 \| ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
keras-io/ProbabalisticBayesianModel-Wine	keras-io	2022-06-12T13:54:27Z	0	2	keras	[ "keras", "tensorboard", "probabilistic-models", "regression", "region:us" ]	null	2022-06-06T15:36:50Z	--- library_name: keras tags: - probabilistic-models - regression --- ## Model description This repo contains model weights for the the probabilistic model from [Probabilistic Bayesian Neural Networks](https://keras.io/examples/keras_recipes/bayesian_neural_networks/). This example demonstrates how to build basic probabilistic Bayesian neural networks to account for these two types of uncertainty. We use TensorFlow Probability library, which is compatible with Keras API. Taking a probabilistic approach to deep learning allows to account for uncertainty, so that models can assign less levels of confidence to incorrect predictions. Sources of uncertainty can be found in the data, due to measurement error or noise in the labels, or the model, due to insufficient data availability for the model to learn effectively. Full credits go to [Khalid Salama](https://www.linkedin.com/in/khalid-salama-24403144/) ## Using this model This repo contains model weights only. To use this model, refer to the following code contained in load_bnn_model.py. ## Training and evaluation data 🍷 We use the wine quality dataset found [here](https://www.tensorflow.org/datasets/catalog/wine_quality). Each wine was scored from 0-10 by wine experts, and includes 11 physicochemical features about the wine. ## Versioning The training was done using TensorFlow 2.8.0 and TensorFlow Probability 0.16.0. When working with TensorFlow Probability, it is encouraged to check out the [releases](https://github.com/tensorflow/probability/releases/tag/v0.17.0) to make sure you are using a stable TensorFlow counterpart. ### Training hyperparameters \| Optimizer \| learning_rate \| decay \| rho \| momentum \| epsilon \| centered \| training_precision \| \|----\|-------------\|-----\|------\|------\|-------\|-------\|------------------\| \|RMSprop\|0.001\|0.0\|0.9\|0.0\|1e-07\|False\|float32\|
nestoralvaro/mt5-base-finetuned-xsum-RAW_data_prep_2021_12_26___t55_403.csv__google_mt5_base	nestoralvaro	2022-06-12T12:25:16Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "mt5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-06-12T10:01:09Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: mt5-base-finetuned-xsum-RAW_data_prep_2021_12_26___t55_403.csv__google_mt5_base 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. --> # mt5-base-finetuned-xsum-RAW_data_prep_2021_12_26___t55_403.csv__google_mt5_base This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: nan - Rouge1: 0.9712 - Rouge2: 0.1329 - Rougel: 0.9638 - Rougelsum: 0.9675 - Gen Len: 6.4489 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| 0.0 \| 1.0 \| 36479 \| nan \| 0.9712 \| 0.1329 \| 0.9638 \| 0.9675 \| 6.4489 \| ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
FabianWillner/distilbert-base-uncased-finetuned-squad	FabianWillner	2022-06-12T12:09:32Z	13	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2022-05-09T10:41:12Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad metrics: - squad model-index: - name: distilbert-base-uncased-finetuned-squad 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-squad This model is a fine-tuned version of [FabianWillner/distilbert-base-uncased-finetuned-squad](https://huggingface.co/FabianWillner/distilbert-base-uncased-finetuned-squad) on the squad 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 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
huggingnft/hedgies	huggingnft	2022-06-12T12:08:25Z	7	0	transformers	[ "transformers", "huggingnft", "nft", "huggan", "gan", "image", "images", "unconditional-image-generation", "dataset:huggingnft/hedgies", "license:mit", "endpoints_compatible", "region:us" ]	unconditional-image-generation	2022-05-24T18:12:29Z	--- tags: - huggingnft - nft - huggan - gan - image - images - unconditional-image-generation datasets: - huggingnft/hedgies license: mit --- # Hugging NFT: hedgies ## Disclaimer All rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright holder. ## Model description LightWeight GAN model for unconditional generation. NFT collection available [here](https://opensea.io/collection/hedgies). Dataset is available [here](https://huggingface.co/datasets/huggingnft/hedgies). Check Space: [link](https://huggingface.co/spaces/AlekseyKorshuk/huggingnft). Project repository: [link](https://github.com/AlekseyKorshuk/huggingnft). [![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingnft?style=social)](https://github.com/AlekseyKorshuk/huggingnft) ## Intended uses & limitations #### How to use Check project repository: [link](https://github.com/AlekseyKorshuk/huggingnft). #### Limitations and bias Check project repository: [link](https://github.com/AlekseyKorshuk/huggingnft). ## Training data Dataset is available [here](https://huggingface.co/datasets/huggingnft/hedgies). ## Training procedure Training script is available [here](https://github.com/AlekseyKorshuk/huggingnft). ## Generated Images Check results with Space: [link](https://huggingface.co/spaces/AlekseyKorshuk/huggingnft). ## About Built by Aleksey Korshuk [![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk) [![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk) [![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingnft?style=social)](https://github.com/AlekseyKorshuk/huggingnft) ### BibTeX entry and citation info ```bibtex @InProceedings{huggingnft, author={Aleksey Korshuk} year=2022 } ```
vishvamahadevan/distilbert-base-uncased-finetuned-squad	vishvamahadevan	2022-06-12T10:34:52Z	6	0	transformers	[ "transformers", "tf", "tensorboard", "distilbert", "question-answering", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2022-06-12T08:07:48Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: vishvamahadevan/distilbert-base-uncased-finetuned-squad 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. --> # vishvamahadevan/distilbert-base-uncased-finetuned-squad 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: - Train Loss: 0.9560 - Validation Loss: 1.1174 - Epoch: 1 ## 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': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 11064, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 1.3862 \| 1.1639 \| 0 \| \| 0.9560 \| 1.1174 \| 1 \| ### Framework versions - Transformers 4.19.4 - TensorFlow 2.8.2 - Datasets 2.2.2 - Tokenizers 0.12.1
huggingtweets/manfightdragon	huggingtweets	2022-06-12T10:26:35Z	106	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-06-12T10:23:38Z	--- language: en thumbnail: http://www.huggingtweets.com/manfightdragon/1655029573001/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/1184073162520031232/V6DOEeLp_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Lance McDonald</div> <div style="text-align: center; font-size: 14px;">@manfightdragon</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 Lance McDonald. \| Data \| Lance McDonald \| \| --- \| --- \| \| Tweets downloaded \| 3249 \| \| Retweets \| 209 \| \| Short tweets \| 214 \| \| Tweets kept \| 2826 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3pc794z5/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 @manfightdragon's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2t8940p5) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2t8940p5/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/manfightdragon') 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 [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/bosstjanz	huggingtweets	2022-06-12T09:27:34Z	105	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-06-12T09:26:54Z	--- language: en thumbnail: http://www.huggingtweets.com/bosstjanz/1655026050127/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/1342130927737176064/SiNG_CxQ_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Zrimškow</div> <div style="text-align: center; font-size: 14px;">@bosstjanz</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 Zrimškow. \| Data \| Zrimškow \| \| --- \| --- \| \| Tweets downloaded \| 3225 \| \| Retweets \| 368 \| \| Short tweets \| 279 \| \| Tweets kept \| 2578 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/23nemiqj/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 @bosstjanz's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2pjrymzt) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2pjrymzt/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/bosstjanz') 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 [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
ironbar/dqn-SpaceInvadersNoFrameskip-v4-1M-steps	ironbar	2022-06-12T08:16:08Z	11	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-06-12T08:15:30Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - metrics: - type: mean_reward value: 629.50 +/- 140.06 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 --- # 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 ``` # Download model and save it into the logs/ folder python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga ironbar -f logs/ python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga ironbar ``` ## 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', True), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
MyMild/finetune_iapp_thaiqa	MyMild	2022-06-12T07:52:39Z	57	0	transformers	[ "transformers", "pytorch", "tensorboard", "camembert", "question-answering", "generated_from_trainer", "endpoints_compatible", "region:us" ]	question-answering	2022-06-11T23:05:08Z	--- tags: - generated_from_trainer model-index: - name: finetune_iapp_thaiqa 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. --> # finetune_iapp_thaiqa This model is a fine-tuned version of [airesearch/wangchanberta-base-att-spm-uncased](https://huggingface.co/airesearch/wangchanberta-base-att-spm-uncased) on the None dataset. ## 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: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.2 - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.15.0 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.10.3
spuun/kekbot-mini	spuun	2022-06-12T05:53:59Z	106	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "en", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-06-12T03:40:33Z	--- language: - en metrics: - accuracy co2_eq_emissions: emissions: "10" source: "mlco2.github.io" training_type: "fine-tuning" geographical_location: "West Java, Indonesia" hardware_used: "1 T4" license: cc-by-nc-sa-4.0 widget: - text: 'You: "Hey kekbot! Whats up?"\nKekbot: "' example_title: "Asking what's up" - text: 'You: "Hey kekbot! How r u?"\nKekbot: "' example_title: "Asking how he is" --- > THIS MODEL IS INTENDED FOR RESEARCH PURPOSES ONLY # Kekbot Mini Based on a `distilgpt2` model, fine-tuned to a select subset (65k<= messages) of Art Union's general-chat channel chat history. ### Limits and biases As this is trained on chat history, it is possible that discriminatory or even offensive materials to be outputted. Author holds his ground on the fact that ML models are mere statistical representation of the dataset used to train it, and that due to the nature of the dataset it is practically impossible to be certain of the degree of "cleanliness" that the data contained within holds. Author can confirm, however, that from heuristical testing that the model was not found to be offensive to the author himself, hopefully this opinion stays true for everyone in the audience.
xdai/mimic_roberta_base	xdai	2022-06-12T04:51:26Z	5	1	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "Clinical notes", "Discharge summaries", "RoBERTa", "dataset:MIMIC-III", "arxiv:2204.06683", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-06-12T04:12:20Z	--- language: - English tags: - Clinical notes - Discharge summaries - RoBERTa license: "cc-by-4.0" datasets: - MIMIC-III --- * Continue pre-training RoBERTa-base using discharge summaries from MIMIC-III datasets. * Details can be found in the following paper > Xiang Dai and Ilias Chalkidis and Sune Darkner and Desmond Elliott. 2022. Revisiting Transformer-based Models for Long Document Classification. (https://arxiv.org/abs/2204.06683) * Important hyper-parameters \| \| \| \|---\|---\| \| Max sequence \| 128 \| \| Batch size \| 128 \| \| Learning rate \| 5e-5 \| \| Training epochs \| 15 \| \| Training time \| 40 GPU-hours \|
huggingtweets/tayplaysgaymes	huggingtweets	2022-06-12T03:56:41Z	105	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-06-12T03:55:39Z	--- language: en thumbnail: http://www.huggingtweets.com/tayplaysgaymes/1655006196516/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/1144053838459969536/lv3yBmoX_400x400.png')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Tay</div> <div style="text-align: center; font-size: 14px;">@tayplaysgaymes</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 Tay. \| Data \| Tay \| \| --- \| --- \| \| Tweets downloaded \| 3212 \| \| Retweets \| 693 \| \| Short tweets \| 367 \| \| Tweets kept \| 2152 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1hmextiq/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 @tayplaysgaymes's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3r0cse8x) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3r0cse8x/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/tayplaysgaymes') 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 [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
bguan/SpaceInvadersNoFrameskip-v4	bguan	2022-06-12T01:05:09Z	4	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-06-12T01:04:38Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - metrics: - type: mean_reward value: 255.00 +/- 93.83 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 --- # 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 ``` # Download model and save it into the logs/ folder python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga bguan -f logs/ python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga bguan ``` ## 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', 500000), ('optimize_memory_usage', True), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
TencentMedicalNet/MedicalNet-Resnet10	TencentMedicalNet	2022-06-12T00:26:42Z	0	4	null	[ "MedicalNet", "medical images", "medical", "3D", "Med3D", "en", "dataset:MRBrainS18", "arxiv:1904.00625", "license:mit", "region:us" ]	null	2022-06-11T23:12:06Z	--- license: mit datasets: - MRBrainS18 language: - en metrics: - tags: - MedicalNet - medical images - medical - 3D - Med3D thumbnail: "https://github.com/Tencent/MedicalNet/blob/master/images/logo.png?raw=true" --- # MedicalNet This repository contains a Pytorch implementation of [Med3D: Transfer Learning for 3D Medical Image Analysis](https://arxiv.org/abs/1904.00625). Many studies have shown that the performance on deep learning is significantly affected by volume of training data. The MedicalNet project aggregated the dataset with diverse modalities, target organs, and pathologies to to build relatively large datasets. Based on this dataset, a series of 3D-ResNet pre-trained models and corresponding transfer-learning training code are provided. ### License MedicalNet is released under the MIT License (refer to the LICENSE file for detailso). ### Citing MedicalNet If you use this code or pre-trained models, please cite the following: ``` @article{chen2019med3d, title={Med3D: Transfer Learning for 3D Medical Image Analysis}, author={Chen, Sihong and Ma, Kai and Zheng, Yefeng}, journal={arXiv preprint arXiv:1904.00625}, year={2019} } ``` ### Update(2019/07/30) We uploaded 4 pre-trained models based on more datasets (23 datasets). ``` Model name : parameters settings resnet_10_23dataset.pth: --model resnet --model_depth 10 --resnet_shortcut B resnet_18_23dataset.pth: --model resnet --model_depth 18 --resnet_shortcut A resnet_34_23dataset.pth: --model resnet --model_depth 34 --resnet_shortcut A resnet_50_23dataset.pth: --model resnet --model_depth 50 --resnet_shortcut B ``` Hugging Face repository contribution by: [Rafael Zimmer](https://www.github.com/rzimmerdev)
huggingtweets/laserboat999	huggingtweets	2022-06-11T23:53:52Z	105	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-06-11T23:49:07Z	--- language: en thumbnail: http://www.huggingtweets.com/laserboat999/1654991516445/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/1500274766195793921/bA4siut7_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">donald boat</div> <div style="text-align: center; font-size: 14px;">@laserboat999</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 donald boat. \| Data \| donald boat \| \| --- \| --- \| \| Tweets downloaded \| 3233 \| \| Retweets \| 75 \| \| Short tweets \| 516 \| \| Tweets kept \| 2642 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/38v40fpf/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 @laserboat999's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/pk1xum9h) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/pk1xum9h/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/laserboat999') 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 [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
DLWCMD/TEST2ppo-LunarLander-v2	DLWCMD	2022-06-11T23:39:16Z	1	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-06-11T23:38:43Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: 263.13 +/- 22.16 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # 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 ... ```
745H1N/LunarLander-v2-DQN-optuna	745H1N	2022-06-11T23:36:51Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-06-11T23:36:25Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - metrics: - type: mean_reward value: -140.18 +/- 41.67 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # DQN Agent playing LunarLander-v2 This is a trained model of a DQN 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 ... ```
aprischa/bart-large-cnn-aprischa2	aprischa	2022-06-11T23:27:38Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-06-11T17:40:18Z	--- license: mit tags: - generated_from_trainer metrics: - rouge model-index: - name: bart-large-cnn-aprischa2 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. --> # bart-large-cnn-aprischa2 This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3425 - Rouge1: 65.7088 - Rouge2: 56.6701 - Rougel: 62.1926 - Rougelsum: 64.7727 - Gen Len: 140.8469 ## 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: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:--------:\| \| 0.3772 \| 1.0 \| 5403 \| 0.3586 \| 65.7702 \| 56.7968 \| 62.264 \| 64.8605 \| 140.268 \| \| 0.316 \| 2.0 \| 10806 \| 0.3421 \| 64.8238 \| 55.8837 \| 61.3245 \| 63.8894 \| 140.7472 \| \| 0.2397 \| 3.0 \| 16209 \| 0.3425 \| 65.7088 \| 56.6701 \| 62.1926 \| 64.7727 \| 140.8469 \| ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
twieland/SCRATCH_ja-en_helsinki	twieland	2022-06-11T23:01:52Z	4	0	transformers	[ "transformers", "pytorch", "marian", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-06-11T01:05:11Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: SCRATCH_ja-en_helsinki 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. --> # SCRATCH_ja-en_helsinki This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ja-en](https://huggingface.co/Helsinki-NLP/opus-mt-ja-en) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.5583 - Otaku Benchmark VN BLEU: 19.12 - Otaku Benchmark LN BLEU: 11.55 - Otaku Benchmark MANGA BLEU: 12.98 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 96 - 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 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:------:\|:---------------:\| \| 3.0252 \| 0.02 \| 2000 \| 2.4140 \| \| 2.8406 \| 0.03 \| 4000 \| 2.2819 \| \| 2.7505 \| 0.05 \| 6000 \| 2.3018 \| \| 2.6948 \| 0.06 \| 8000 \| 2.1931 \| \| 2.6408 \| 0.08 \| 10000 \| 2.1724 \| \| 2.6004 \| 0.09 \| 12000 \| 2.1583 \| \| 2.5685 \| 0.11 \| 14000 \| 2.1203 \| \| 2.5432 \| 0.12 \| 16000 \| 2.1593 \| \| 2.5153 \| 0.14 \| 18000 \| 2.1009 \| \| 2.4906 \| 0.15 \| 20000 \| 2.0899 \| \| 2.4709 \| 0.17 \| 22000 \| 2.0512 \| \| 2.4471 \| 0.18 \| 24000 \| 2.0208 \| \| 2.4295 \| 0.2 \| 26000 \| 2.0773 \| \| 2.4154 \| 0.21 \| 28000 \| 2.0441 \| \| 2.4008 \| 0.23 \| 30000 \| 2.0235 \| \| 2.3834 \| 0.24 \| 32000 \| 2.0190 \| \| 2.3709 \| 0.26 \| 34000 \| 1.9831 \| \| 2.3537 \| 0.27 \| 36000 \| 1.9870 \| \| 2.3486 \| 0.29 \| 38000 \| 1.9692 \| \| 2.3346 \| 0.3 \| 40000 \| 1.9517 \| \| 2.3195 \| 0.32 \| 42000 \| 1.9800 \| \| 2.3104 \| 0.33 \| 44000 \| 1.9676 \| \| 2.298 \| 0.35 \| 46000 \| 1.9563 \| \| 2.2905 \| 0.36 \| 48000 \| 1.9217 \| \| 2.2792 \| 0.38 \| 50000 \| 1.9195 \| \| 2.2714 \| 0.39 \| 52000 \| 1.9109 \| \| 2.2593 \| 0.41 \| 54000 \| 1.9044 \| \| 2.2582 \| 0.42 \| 56000 \| 1.8876 \| \| 2.2482 \| 0.44 \| 58000 \| 1.8860 \| \| 2.2394 \| 0.45 \| 60000 \| 1.8887 \| \| 2.2273 \| 0.47 \| 62000 \| 1.8862 \| \| 2.2255 \| 0.48 \| 64000 \| 1.8705 \| \| 2.2166 \| 0.5 \| 66000 \| 1.8696 \| \| 2.2075 \| 0.51 \| 68000 \| 1.8657 \| \| 2.1992 \| 0.53 \| 70000 \| 1.8585 \| \| 2.1969 \| 0.54 \| 72000 \| 1.8526 \| \| 2.1894 \| 0.56 \| 74000 \| 1.8493 \| \| 2.1817 \| 0.57 \| 76000 \| 1.8480 \| \| 2.1771 \| 0.59 \| 78000 \| 1.8333 \| \| 2.1683 \| 0.6 \| 80000 \| 1.8342 \| \| 2.1667 \| 0.62 \| 82000 \| 1.8537 \| \| 2.1546 \| 0.63 \| 84000 \| 1.8261 \| \| 2.1467 \| 0.65 \| 86000 \| 1.8092 \| \| 2.1421 \| 0.66 \| 88000 \| 1.8137 \| \| 2.1395 \| 0.68 \| 90000 \| 1.8286 \| \| 2.1313 \| 0.69 \| 92000 \| 1.8042 \| \| 2.1241 \| 0.71 \| 94000 \| 1.7934 \| \| 2.1214 \| 0.72 \| 96000 \| 1.7940 \| \| 2.12 \| 0.74 \| 98000 \| 1.8064 \| \| 2.1096 \| 0.75 \| 100000 \| 1.7983 \| \| 2.1035 \| 0.77 \| 102000 \| 1.8089 \| \| 2.0937 \| 0.78 \| 104000 \| 1.7941 \| \| 2.0893 \| 0.8 \| 106000 \| 1.7791 \| \| 2.0869 \| 0.81 \| 108000 \| 1.7807 \| \| 2.0845 \| 0.83 \| 110000 \| 1.7852 \| \| 2.0782 \| 0.84 \| 112000 \| 1.7675 \| \| 2.0755 \| 0.86 \| 114000 \| 1.7756 \| \| 2.0657 \| 0.87 \| 116000 \| 1.7604 \| \| 2.0614 \| 0.89 \| 118000 \| 1.7447 \| \| 2.0591 \| 0.9 \| 120000 \| 1.7489 \| \| 2.0586 \| 0.92 \| 122000 \| 1.7550 \| \| 2.0498 \| 0.93 \| 124000 \| 1.7543 \| \| 2.0455 \| 0.95 \| 126000 \| 1.7510 \| \| 2.04 \| 0.96 \| 128000 \| 1.7439 \| \| 2.0385 \| 0.98 \| 130000 \| 1.7407 \| \| 2.0267 \| 0.99 \| 132000 \| 1.7467 \| \| 2.0088 \| 1.01 \| 134000 \| 1.7455 \| \| 1.9826 \| 1.02 \| 136000 \| 1.7210 \| \| 1.9785 \| 1.04 \| 138000 \| 1.7524 \| \| 1.9777 \| 1.05 \| 140000 \| 1.7272 \| \| 1.9763 \| 1.07 \| 142000 \| 1.7283 \| \| 1.9736 \| 1.08 \| 144000 \| 1.7210 \| \| 1.9704 \| 1.1 \| 146000 \| 1.7001 \| \| 1.9625 \| 1.11 \| 148000 \| 1.7112 \| \| 1.9665 \| 1.13 \| 150000 \| 1.7236 \| \| 1.9592 \| 1.14 \| 152000 \| 1.7169 \| \| 1.9606 \| 1.16 \| 154000 \| 1.6962 \| \| 1.9571 \| 1.17 \| 156000 \| 1.7064 \| \| 1.9532 \| 1.19 \| 158000 \| 1.6898 \| \| 1.9465 \| 1.2 \| 160000 \| 1.7004 \| \| 1.9438 \| 1.22 \| 162000 \| 1.7092 \| \| 1.9435 \| 1.23 \| 164000 \| 1.6927 \| \| 1.9361 \| 1.25 \| 166000 \| 1.6838 \| \| 1.9369 \| 1.26 \| 168000 \| 1.6784 \| \| 1.9287 \| 1.28 \| 170000 \| 1.6709 \| \| 1.928 \| 1.29 \| 172000 \| 1.6735 \| \| 1.9227 \| 1.31 \| 174000 \| 1.6689 \| \| 1.9213 \| 1.32 \| 176000 \| 1.6685 \| \| 1.9152 \| 1.34 \| 178000 \| 1.6635 \| \| 1.9092 \| 1.35 \| 180000 \| 1.6561 \| \| 1.9059 \| 1.37 \| 182000 \| 1.6673 \| \| 1.9094 \| 1.38 \| 184000 \| 1.6717 \| \| 1.9006 \| 1.4 \| 186000 \| 1.6593 \| \| 1.8956 \| 1.41 \| 188000 \| 1.6483 \| \| 1.8972 \| 1.43 \| 190000 \| 1.6635 \| \| 1.8907 \| 1.44 \| 192000 \| 1.6604 \| \| 1.8885 \| 1.46 \| 194000 \| 1.6465 \| \| 1.8844 \| 1.47 \| 196000 \| 1.6444 \| \| 1.8799 \| 1.49 \| 198000 \| 1.6307 \| \| 1.8813 \| 1.5 \| 200000 \| 1.6240 \| \| 1.8693 \| 1.52 \| 202000 \| 1.6102 \| \| 1.8768 \| 1.53 \| 204000 \| 1.6197 \| \| 1.8678 \| 1.55 \| 206000 \| 1.6275 \| \| 1.8588 \| 1.56 \| 208000 \| 1.6183 \| \| 1.8585 \| 1.58 \| 210000 \| 1.6197 \| \| 1.8564 \| 1.59 \| 212000 \| 1.6004 \| \| 1.8493 \| 1.61 \| 214000 \| 1.6078 \| \| 1.85 \| 1.62 \| 216000 \| 1.6001 \| \| 1.8428 \| 1.64 \| 218000 \| 1.6106 \| \| 1.8428 \| 1.65 \| 220000 \| 1.5866 \| \| 1.8423 \| 1.67 \| 222000 \| 1.5993 \| \| 1.8352 \| 1.68 \| 224000 \| 1.6052 \| \| 1.8385 \| 1.7 \| 226000 \| 1.5959 \| \| 1.8307 \| 1.71 \| 228000 \| 1.6024 \| \| 1.8248 \| 1.73 \| 230000 \| 1.5969 \| \| 1.82 \| 1.74 \| 232000 \| 1.5878 \| \| 1.8254 \| 1.76 \| 234000 \| 1.5934 \| \| 1.8188 \| 1.77 \| 236000 \| 1.5827 \| \| 1.813 \| 1.79 \| 238000 \| 1.5797 \| \| 1.8128 \| 1.8 \| 240000 \| 1.5758 \| \| 1.8044 \| 1.82 \| 242000 \| 1.5752 \| \| 1.808 \| 1.83 \| 244000 \| 1.5818 \| \| 1.8025 \| 1.85 \| 246000 \| 1.5772 \| \| 1.7992 \| 1.86 \| 248000 \| 1.5738 \| \| 1.8021 \| 1.88 \| 250000 \| 1.5752 \| \| 1.7988 \| 1.89 \| 252000 \| 1.5717 \| \| 1.7967 \| 1.91 \| 254000 \| 1.5690 \| \| 1.7909 \| 1.92 \| 256000 \| 1.5607 \| \| 1.7942 \| 1.94 \| 258000 \| 1.5618 \| \| 1.7897 \| 1.95 \| 260000 \| 1.5585 \| \| 1.7871 \| 1.97 \| 262000 \| 1.5576 \| \| 1.7843 \| 1.98 \| 264000 \| 1.5577 \| \| 1.7888 \| 2.0 \| 266000 \| 1.5583 \| ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
meghazisofiane/opus-mt-en-ar-evaluated-en-to-ar-4000instances-opus-leaningRate2e-05-batchSize8-11-action-1	meghazisofiane	2022-06-11T21:50:40Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "marian", "text2text-generation", "generated_from_trainer", "dataset:opus100", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-06-11T21:33:30Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - opus100 metrics: - bleu model-index: - name: opus-mt-en-ar-evaluated-en-to-ar-4000instances-opus-leaningRate2e-05-batchSize8-11-action-1 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: opus100 type: opus100 args: ar-en metrics: - name: Bleu type: bleu value: 26.8232 --- <!-- 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. --> # opus-mt-en-ar-evaluated-en-to-ar-4000instances-opus-leaningRate2e-05-batchSize8-11-action-1 This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ar](https://huggingface.co/Helsinki-NLP/opus-mt-en-ar) on the opus100 dataset. It achieves the following results on the evaluation set: - Loss: 0.1717 - Bleu: 26.8232 - Meteor: 0.172 - Gen Len: 12.1288 ## 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: 11 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Meteor \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:------:\|:-------:\| \| 0.7364 \| 0.25 \| 100 \| 0.1731 \| 27.2753 \| 0.1729 \| 12.0887 \| \| 0.2175 \| 0.5 \| 200 \| 0.1731 \| 27.2055 \| 0.1722 \| 11.5675 \| \| 0.2193 \| 0.75 \| 300 \| 0.1722 \| 27.3277 \| 0.1798 \| 12.1325 \| \| 0.2321 \| 1.0 \| 400 \| 0.1750 \| 27.5152 \| 0.1762 \| 11.925 \| \| 0.1915 \| 1.25 \| 500 \| 0.1690 \| 27.5043 \| 0.1751 \| 11.9038 \| \| 0.1794 \| 1.5 \| 600 \| 0.1719 \| 26.8607 \| 0.1713 \| 11.8138 \| \| 0.1741 \| 1.75 \| 700 \| 0.1725 \| 26.974 \| 0.1724 \| 11.8462 \| \| 0.1732 \| 2.0 \| 800 \| 0.1717 \| 26.8232 \| 0.172 \| 12.1288 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1
lindeberg/distilbert-base-uncased-finetuned-cola	lindeberg	2022-06-11T21:10:06Z	106	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-06-11T18:50:58Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.4496664370323995 --- <!-- 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-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.4949 - Matthews Correlation: 0.4497 ## 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: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.5231 \| 1.0 \| 535 \| 0.4949 \| 0.4497 \| ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
JClementC/test	JClementC	2022-06-11T19:58:42Z	0	0	null	[ "region:us" ]	null	2022-06-11T19:19:48Z	git lfs install git clone https://github.com/nneonneo/2048-ai.git
meln1k/qrdqn-SpaceInvadersNoFrameskip-v4	meln1k	2022-06-11T19:51:36Z	5	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-06-11T09:29:19Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: QRDQN results: - metrics: - type: mean_reward value: 2581.50 +/- 1151.96 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 --- # QRDQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a QRDQN 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 ``` # Download model and save it into the logs/ folder python -m utils.load_from_hub --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -orga meln1k -f logs/ python enjoy.py --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m utils.push_to_hub --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga meln1k ``` ## Hyperparameters ```python OrderedDict([('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_fraction', 0.025), ('frame_stack', 4), ('n_timesteps', 10000000.0), ('optimize_memory_usage', True), ('policy', 'CnnPolicy'), ('normalize', False)]) ```
huggingtweets/conanobrien-mikemancini-wendymolyneux	huggingtweets	2022-06-11T19:50:54Z	105	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-06-11T19:46:43Z	--- language: en thumbnail: http://www.huggingtweets.com/conanobrien-mikemancini-wendymolyneux/1654977049172/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/1271404115042676736/PAIbmN-p_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/730612231021322240/Rl0_QYhL_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/1044085580651528193/DR7QvrwG_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">mike mancini & Conan O'Brien & Wendy Molyneux</div> <div style="text-align: center; font-size: 14px;">@conanobrien-mikemancini-wendymolyneux</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 mike mancini & Conan O'Brien & Wendy Molyneux. \| Data \| mike mancini \| Conan O'Brien \| Wendy Molyneux \| \| --- \| --- \| --- \| --- \| \| Tweets downloaded \| 3150 \| 3250 \| 836 \| \| Retweets \| 286 \| 40 \| 251 \| \| Short tweets \| 290 \| 24 \| 69 \| \| Tweets kept \| 2574 \| 3186 \| 516 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/25wtfzk4/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 @conanobrien-mikemancini-wendymolyneux's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1hjizcue) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1hjizcue/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/conanobrien-mikemancini-wendymolyneux') 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 [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/mdoukmas	huggingtweets	2022-06-11T19:35:54Z	105	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-06-11T19:34:24Z	--- language: en thumbnail: http://www.huggingtweets.com/mdoukmas/1654976150184/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/1098660288193269762/n5v9daol_400x400.png')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Maya Dukmasova</div> <div style="text-align: center; font-size: 14px;">@mdoukmas</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 Maya Dukmasova. \| Data \| Maya Dukmasova \| \| --- \| --- \| \| Tweets downloaded \| 3241 \| \| Retweets \| 896 \| \| Short tweets \| 158 \| \| Tweets kept \| 2187 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2jwhv7l5/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 @mdoukmas's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/25v3pmsy) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/25v3pmsy/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/mdoukmas') 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 [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
titi7242229/roberta-base-bne-finetuned_personality_multi_4	titi7242229	2022-06-11T19:13:27Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-06-11T13:23:50Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: roberta-base-bne-finetuned_personality_multi_4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base-bne-finetuned_personality_multi_4 This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.1709 - Accuracy: 0.3470 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 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 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.1759 \| 1.0 \| 125 \| 2.1873 \| 0.2548 \| \| 1.8651 \| 2.0 \| 250 \| 2.2285 \| 0.2680 \| \| 1.8619 \| 3.0 \| 375 \| 2.1732 \| 0.2951 \| \| 1.7224 \| 4.0 \| 500 \| 2.0688 \| 0.3925 \| \| 1.6432 \| 5.0 \| 625 \| 2.1094 \| 0.3735 \| \| 1.3599 \| 6.0 \| 750 \| 2.1732 \| 0.3631 \| \| 1.0623 \| 7.0 \| 875 \| 2.4785 \| 0.3579 \| \| 1.0504 \| 8.0 \| 1000 \| 2.4598 \| 0.3844 \| \| 0.7662 \| 9.0 \| 1125 \| 2.8081 \| 0.3573 \| \| 0.9167 \| 10.0 \| 1250 \| 2.9385 \| 0.3452 \| \| 0.6391 \| 11.0 \| 1375 \| 2.9933 \| 0.3320 \| \| 0.3893 \| 12.0 \| 1500 \| 3.1037 \| 0.3579 \| \| 0.673 \| 13.0 \| 1625 \| 3.4369 \| 0.3631 \| \| 0.3498 \| 14.0 \| 1750 \| 3.6396 \| 0.3383 \| \| 0.3891 \| 15.0 \| 1875 \| 3.8332 \| 0.3556 \| \| 0.0818 \| 16.0 \| 2000 \| 3.9451 \| 0.3401 \| \| 0.1438 \| 17.0 \| 2125 \| 3.9271 \| 0.3458 \| \| 0.0634 \| 18.0 \| 2250 \| 4.1564 \| 0.3481 \| \| 0.0121 \| 19.0 \| 2375 \| 4.1405 \| 0.3499 \| \| 0.0071 \| 20.0 \| 2500 \| 4.1709 \| 0.3470 \| ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
aprischa/bart-large-cnn-aprischa	aprischa	2022-06-11T17:21:57Z	105	0	transformers	[ "transformers", "pytorch", "bart", "text2text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-06-11T16:53:31Z	--- license: mit tags: - generated_from_trainer metrics: - rouge model-index: - name: bart-large-cnn-aprischa 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. --> # bart-large-cnn-aprischa This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3589 - Rouge1: 66.7098 - Rouge2: 57.7992 - Rougel: 63.2231 - Rougelsum: 65.9009 - Gen Len: 141.198 ## 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: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:--------:\| \| 0.369 \| 1.0 \| 5403 \| 0.3835 \| 66.0604 \| 56.9948 \| 62.4967 \| 65.265 \| 141.1126 \| \| 0.2985 \| 2.0 \| 10806 \| 0.3589 \| 66.7098 \| 57.7992 \| 63.2231 \| 65.9009 \| 141.198 \| ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
DancingIguana/codeparrot-ds	DancingIguana	2022-06-11T16:58:04Z	107	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-06-08T21:56:49Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: codeparrot-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. --> # codeparrot-ds This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) 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: 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: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
bubblecookie/t5-small-finetuned-cnndm_trained	bubblecookie	2022-06-11T16:48:45Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:cnn_dailymail", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-06-10T06:21:02Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - cnn_dailymail model-index: - name: t5-small-finetuned-cnndm_trained results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-cnndm_trained This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dailymail 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: 0.0003 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
robingeibel/longformer-base-finetuned-big_patent	robingeibel	2022-06-11T16:33:49Z	62	1	transformers	[ "transformers", "tf", "longformer", "fill-mask", "generated_from_keras_callback", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-06-05T17:24:27Z	--- tags: - generated_from_keras_callback model-index: - name: robingeibel/longformer-base-finetuned-big_patent 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. --> # robingeibel/longformer-base-finetuned-big_patent This model is a fine-tuned version of [robingeibel/longformer-base-finetuned-big_patent](https://huggingface.co/robingeibel/longformer-base-finetuned-big_patent) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.1860 - Validation Loss: 1.0692 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 152946, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 1.1860 \| 1.0692 \| 0 \| ### Framework versions - Transformers 4.19.4 - TensorFlow 2.8.2 - Datasets 2.2.2 - Tokenizers 0.12.1
abdoutony207/m2m100_418M-evaluated-en-to-ar-2000instancesopus-leaningRate2e-05-batchSize16-20epoch-1	abdoutony207	2022-06-11T16:26:19Z	106	0	transformers	[ "transformers", "pytorch", "tensorboard", "m2m_100", "text2text-generation", "generated_from_trainer", "dataset:opus100", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-06-11T15:56:17Z	--- license: mit tags: - generated_from_trainer datasets: - opus100 metrics: - bleu model-index: - name: m2m100_418M-evaluated-en-to-ar-2000instancesopus-leaningRate2e-05-batchSize16-20epoch-1 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: opus100 type: opus100 args: ar-en metrics: - name: Bleu type: bleu value: 13.1835 --- <!-- 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. --> # m2m100_418M-evaluated-en-to-ar-2000instancesopus-leaningRate2e-05-batchSize16-20epoch-1 This model is a fine-tuned version of [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) on the opus100 dataset. It achieves the following results on the evaluation set: - Loss: 0.3640 - Bleu: 13.1835 - Meteor: 0.1189 - Gen Len: 17.72 ## 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 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Meteor \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:------:\|:-------:\| \| 6.1776 \| 1.0 \| 100 \| 3.8904 \| 10.5866 \| 0.0995 \| 16.64 \| \| 2.4531 \| 2.0 \| 200 \| 1.0928 \| 12.3452 \| 0.1108 \| 17.0575 \| \| 0.512 \| 3.0 \| 300 \| 0.3625 \| 10.5224 \| 0.0982 \| 17.2575 \| \| 0.1924 \| 4.0 \| 400 \| 0.3342 \| 12.4242 \| 0.1098 \| 16.6325 \| \| 0.1227 \| 5.0 \| 500 \| 0.3403 \| 13.0526 \| 0.1185 \| 17.3475 \| \| 0.0889 \| 6.0 \| 600 \| 0.3481 \| 13.1323 \| 0.1133 \| 17.815 \| \| 0.0651 \| 7.0 \| 700 \| 0.3601 \| 12.6684 \| 0.1133 \| 17.3525 \| \| 0.0533 \| 8.0 \| 800 \| 0.3640 \| 13.1835 \| 0.1189 \| 17.72 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1
neeenway/ppo-LunarLander-v2	neeenway	2022-06-11T13:43:31Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-06-11T13:43:03Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: ppo results: - metrics: - type: mean_reward value: 240.31 +/- 12.46 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # 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 ... ```
Akshat/xlm-roberta-base-finetuned-panx-de	Akshat	2022-06-11T13:35:25Z	104	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-06-11T12:19:48Z	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8611443210930829 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1405 - F1: 0.8611 ## 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: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2542 \| 1.0 \| 787 \| 0.1788 \| 0.8083 \| \| 0.1307 \| 2.0 \| 1574 \| 0.1371 \| 0.8488 \| \| 0.0784 \| 3.0 \| 2361 \| 0.1405 \| 0.8611 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3
YeRyeongLee/albert-base-v2-finetuned-filtered-0609	YeRyeongLee	2022-06-11T13:33:02Z	106	0	transformers	[ "transformers", "pytorch", "albert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-06-11T11:46:52Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: albert-base-v2-finetuned-filtered-0609 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. --> # albert-base-v2-finetuned-filtered-0609 This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2062 - Accuracy: 0.9723 - Precision: 0.9724 - Recall: 0.9723 - F1: 0.9723 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.2688 \| 1.0 \| 3180 \| 0.2282 \| 0.9560 \| 0.9577 \| 0.9560 \| 0.9562 \| \| 0.2268 \| 2.0 \| 6360 \| 0.1909 \| 0.9638 \| 0.9640 \| 0.9638 \| 0.9638 \| \| 0.1831 \| 3.0 \| 9540 \| 0.2590 \| 0.9572 \| 0.9584 \| 0.9572 \| 0.9572 \| \| 0.1588 \| 4.0 \| 12720 \| 0.1752 \| 0.9673 \| 0.9678 \| 0.9673 \| 0.9673 \| \| 0.0972 \| 5.0 \| 15900 \| 0.1868 \| 0.9695 \| 0.9696 \| 0.9695 \| 0.9695 \| \| 0.0854 \| 6.0 \| 19080 \| 0.2042 \| 0.9701 \| 0.9707 \| 0.9701 \| 0.9702 \| \| 0.0599 \| 7.0 \| 22260 \| 0.1793 \| 0.9748 \| 0.9749 \| 0.9748 \| 0.9749 \| \| 0.0389 \| 8.0 \| 25440 \| 0.1996 \| 0.9742 \| 0.9743 \| 0.9742 \| 0.9742 \| \| 0.0202 \| 9.0 \| 28620 \| 0.2188 \| 0.9723 \| 0.9726 \| 0.9723 \| 0.9724 \| \| 0.0152 \| 10.0 \| 31800 \| 0.2062 \| 0.9723 \| 0.9724 \| 0.9723 \| 0.9723 \| ### Framework versions - Transformers 4.19.2 - Pytorch 1.9.1+cu111 - Datasets 1.16.1 - Tokenizers 0.12.1
marieke93/BERT-evidence-types	marieke93	2022-06-11T13:32:10Z	107	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-06-08T11:54:50Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: BERT-evidence-types results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # BERT-evidence-types This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the evidence types dataset. It achieves the following results on the evaluation set: - Loss: 2.8008 - Macro f1: 0.4227 - Weighted f1: 0.6976 - Accuracy: 0.7154 - Balanced accuracy: 0.3876 ## Training and evaluation data The data set, as well as the code that was used to fine tune this model can be found in the GitHub repository [BA-Thesis-Information-Science-Persuasion-Strategies](https://github.com/mariekevdh/BA-Thesis-Information-Science-Persuasion-Strategies) ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Macro f1 \| Weighted f1 \| Accuracy \| Balanced accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-----------:\|:--------:\|:-----------------:\| \| 1.1148 \| 1.0 \| 125 \| 1.0531 \| 0.2566 \| 0.6570 \| 0.6705 \| 0.2753 \| \| 0.7546 \| 2.0 \| 250 \| 0.9725 \| 0.3424 \| 0.6947 \| 0.7002 \| 0.3334 \| \| 0.4757 \| 3.0 \| 375 \| 1.1375 \| 0.3727 \| 0.7113 \| 0.7184 \| 0.3680 \| \| 0.2637 \| 4.0 \| 500 \| 1.3585 \| 0.3807 \| 0.6836 \| 0.6910 \| 0.3805 \| \| 0.1408 \| 5.0 \| 625 \| 1.6605 \| 0.3785 \| 0.6765 \| 0.6872 \| 0.3635 \| \| 0.0856 \| 6.0 \| 750 \| 1.9703 \| 0.3802 \| 0.6890 \| 0.7047 \| 0.3704 \| \| 0.0502 \| 7.0 \| 875 \| 2.1245 \| 0.4067 \| 0.6995 \| 0.7169 \| 0.3751 \| \| 0.0265 \| 8.0 \| 1000 \| 2.2676 \| 0.3756 \| 0.6816 \| 0.6925 \| 0.3647 \| \| 0.0147 \| 9.0 \| 1125 \| 2.4286 \| 0.4052 \| 0.6887 \| 0.7062 \| 0.3803 \| \| 0.0124 \| 10.0 \| 1250 \| 2.5773 \| 0.4084 \| 0.6853 \| 0.7040 \| 0.3695 \| \| 0.0111 \| 11.0 \| 1375 \| 2.5941 \| 0.4146 \| 0.6915 \| 0.7085 \| 0.3834 \| \| 0.0076 \| 12.0 \| 1500 \| 2.6124 \| 0.4157 \| 0.6936 \| 0.7078 \| 0.3863 \| \| 0.0067 \| 13.0 \| 1625 \| 2.7050 \| 0.4139 \| 0.6925 \| 0.7108 \| 0.3798 \| \| 0.0087 \| 14.0 \| 1750 \| 2.6695 \| 0.4252 \| 0.7009 \| 0.7169 \| 0.3920 \| \| 0.0056 \| 15.0 \| 1875 \| 2.7357 \| 0.4257 \| 0.6985 \| 0.7161 \| 0.3868 \| \| 0.0054 \| 16.0 \| 2000 \| 2.7389 \| 0.4249 \| 0.6955 \| 0.7116 \| 0.3890 \| \| 0.0051 \| 17.0 \| 2125 \| 2.7767 \| 0.4197 \| 0.6967 \| 0.7146 \| 0.3863 \| \| 0.004 \| 18.0 \| 2250 \| 2.7947 \| 0.4211 \| 0.6977 \| 0.7154 \| 0.3876 \| \| 0.0041 \| 19.0 \| 2375 \| 2.8030 \| 0.4204 \| 0.6953 \| 0.7131 \| 0.3855 \| \| 0.0042 \| 20.0 \| 2500 \| 2.8008 \| 0.4227 \| 0.6976 \| 0.7154 \| 0.3876 \| ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
titi7242229/roberta-base-bne-finetuned_personality_multi_3	titi7242229	2022-06-11T13:13:47Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-06-11T07:10:27Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: roberta-base-bne-finetuned_personality_multi_3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base-bne-finetuned_personality_multi_3 This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1145 - Accuracy: 0.4847 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.2498 \| 1.0 \| 63 \| 2.2799 \| 0.2236 \| \| 2.3044 \| 2.0 \| 126 \| 2.1644 \| 0.2980 \| \| 1.9017 \| 3.0 \| 189 \| 1.9934 \| 0.4127 \| \| 2.2281 \| 4.0 \| 252 \| 1.8517 \| 0.4501 \| \| 1.2955 \| 5.0 \| 315 \| 1.7588 \| 0.4870 \| \| 1.221 \| 6.0 \| 378 \| 1.7269 \| 0.4888 \| \| 1.1381 \| 7.0 \| 441 \| 1.7617 \| 0.4888 \| \| 0.8415 \| 8.0 \| 504 \| 1.8101 \| 0.4853 \| \| 0.6696 \| 9.0 \| 567 \| 1.8325 \| 0.4928 \| \| 0.6646 \| 10.0 \| 630 \| 1.8707 \| 0.4841 \| \| 0.3758 \| 11.0 \| 693 \| 1.8766 \| 0.4876 \| \| 0.3477 \| 12.0 \| 756 \| 1.9171 \| 0.4905 \| \| 0.2854 \| 13.0 \| 819 \| 1.9203 \| 0.4980 \| \| 0.2713 \| 14.0 \| 882 \| 2.0089 \| 0.4813 \| \| 0.3434 \| 15.0 \| 945 \| 2.0130 \| 0.4905 \| \| 0.0758 \| 16.0 \| 1008 \| 2.0230 \| 0.4922 \| \| 0.2518 \| 17.0 \| 1071 \| 2.0793 \| 0.4824 \| \| 0.0783 \| 18.0 \| 1134 \| 2.0920 \| 0.4830 \| \| 0.0933 \| 19.0 \| 1197 \| 2.1067 \| 0.4836 \| \| 0.184 \| 20.0 \| 1260 \| 2.1145 \| 0.4847 \| ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
shivarama23/swin-tiny-patch4-window7-224-finetuned-image_quality	shivarama23	2022-06-11T11:54:49Z	85	1	transformers	[ "transformers", "pytorch", "tensorboard", "swin", "image-classification", "generated_from_trainer", "dataset:image_folder", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-06-11T11:41:01Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - image_folder metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-finetuned-image_quality results: - task: name: Image Classification type: image-classification dataset: name: image_folder type: image_folder args: default metrics: - name: Accuracy type: accuracy value: 0.9090909090909091 --- <!-- 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. --> # swin-tiny-patch4-window7-224-finetuned-image_quality This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.5242 - Accuracy: 0.9091 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 1 \| 0.6762 \| 0.6364 \| \| No log \| 2.0 \| 2 \| 0.6309 \| 0.7273 \| \| No log \| 3.0 \| 3 \| 0.6095 \| 0.6364 \| \| No log \| 4.0 \| 4 \| 0.5775 \| 0.6364 \| \| No log \| 5.0 \| 5 \| 0.5443 \| 0.8182 \| \| No log \| 6.0 \| 6 \| 0.5242 \| 0.9091 \| \| No log \| 7.0 \| 7 \| 0.5149 \| 0.8182 \| \| No log \| 8.0 \| 8 \| 0.5094 \| 0.8182 \| \| No log \| 9.0 \| 9 \| 0.5038 \| 0.8182 \| \| 0.4095 \| 10.0 \| 10 \| 0.4992 \| 0.8182 \| ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
Jawaher/LIAR-fake-news-roberta-base	Jawaher	2022-06-11T11:12:24Z	103	1	transformers	[ "transformers", "pytorch", "tf", "roberta", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-06-11T05:40:13Z	A pre-trained Roberta masked language model (MLM) trained on around 12K fake news dataset called LIAR. The perplexity of the original pre-trained Roberta model on the dataset is 5.957 and the perplexity of the adapted model is 3.918.
mmillet/distilrubert-tiny-2nd-finetune-epru	mmillet	2022-06-11T09:50:42Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-06-11T09:48:50Z	--- tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: distilrubert-tiny-2nd-finetune-epru 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. --> # distilrubert-tiny-2nd-finetune-epru This model is a fine-tuned version of [mmillet/distilrubert-tiny-cased-conversational-v1_single_finetuned_on_cedr_augmented](https://huggingface.co/mmillet/distilrubert-tiny-cased-conversational-v1_single_finetuned_on_cedr_augmented) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3546 - Accuracy: 0.9325 - F1: 0.9328 - Precision: 0.9359 - Recall: 0.9325 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06 - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| 0.0686 \| 1.0 \| 12 \| 0.2931 \| 0.9141 \| 0.9142 \| 0.9163 \| 0.9141 \| \| 0.0269 \| 2.0 \| 24 \| 0.2690 \| 0.9448 \| 0.9444 \| 0.9449 \| 0.9448 \| \| 0.0282 \| 3.0 \| 36 \| 0.3140 \| 0.9141 \| 0.9140 \| 0.9168 \| 0.9141 \| \| 0.0185 \| 4.0 \| 48 \| 0.2977 \| 0.9571 \| 0.9570 \| 0.9576 \| 0.9571 \| \| 0.0103 \| 5.0 \| 60 \| 0.3368 \| 0.9264 \| 0.9265 \| 0.9296 \| 0.9264 \| \| 0.0088 \| 6.0 \| 72 \| 0.3067 \| 0.9387 \| 0.9385 \| 0.9389 \| 0.9387 \| \| 0.0152 \| 7.0 \| 84 \| 0.3660 \| 0.9264 \| 0.9263 \| 0.9282 \| 0.9264 \| \| 0.0315 \| 8.0 \| 96 \| 0.3793 \| 0.9325 \| 0.9328 \| 0.9359 \| 0.9325 \| \| 0.0258 \| 9.0 \| 108 \| 0.3546 \| 0.9325 \| 0.9328 \| 0.9359 \| 0.9325 \| ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
OTQ/q-Taxi-v3	OTQ	2022-06-11T08:10:17Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2022-06-11T08:10:10Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - metrics: - type: mean_reward value: 7.50 +/- 2.78 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 --- # Q-Learning Agent playing Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```
huggingtweets/gustholomulers	huggingtweets	2022-06-11T07:53:54Z	105	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-06-11T07:50:54Z	--- language: en thumbnail: http://www.huggingtweets.com/gustholomulers/1654934015981/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/1535477036353040384/tXI_s1Yi_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">soppy</div> <div style="text-align: center; font-size: 14px;">@gustholomulers</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 soppy. \| Data \| soppy \| \| --- \| --- \| \| Tweets downloaded \| 1482 \| \| Retweets \| 55 \| \| Short tweets \| 329 \| \| Tweets kept \| 1098 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1nhfbopf/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 @gustholomulers's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3p5yu4wm) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3p5yu4wm/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/gustholomulers') 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 [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
AryaSuprana/BRATA_RoBERTaBali	AryaSuprana	2022-06-11T05:01:40Z	161	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "fill-mask", "ban", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-06-11T04:51:40Z	--- language: "ban" datasets: - WikiBali - Suara Saking Bali widget: - text: "Kalsium silih <mask> datu kimia antuk simbol Ca miwah wilangan atom 20." example_title: "Conto 1" - text: "Tabuan inggih <mask> silih tunggil soroh beburon sane madue kampid." example_title: "Conto 2" --- BRATA (Basa Bali Used for Pretraining RoBERTa) is a pretrained language model trained using Basa Bali or Balinese Language with RoBERTa-base-uncased configuration. The datasets used for this pretraining were collected by extracting WikiBali or Wikipedia Basa Bali and some sources from Suara Saking Bali website. The pretrained language model trained using Google Colab Pro with Tesla P100-PCIE-16GB GPU. Pretraining process used 200 epoch and 2 batch size. The smallest training loss can be seen in Training metrics or Metrics tab.
tclong/wav2vec2-base-vios-commonvoice-1	tclong	2022-06-11T03:01:54Z	106	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-06-10T11:09:14Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-vios-commonvoice-1 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-base-vios-commonvoice-1 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8913 - Wer: 0.3621 ## 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: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 3.4706 \| 0.55 \| 500 \| 3.4725 \| 1.0 \| \| 3.202 \| 1.1 \| 1000 \| 2.7555 \| 1.0008 \| \| 1.0507 \| 1.66 \| 1500 \| 1.0481 \| 0.6196 \| \| 0.7325 \| 2.21 \| 2000 \| 0.8120 \| 0.4958 \| \| 0.599 \| 2.76 \| 2500 \| 0.7035 \| 0.4447 \| \| 0.5224 \| 3.31 \| 3000 \| 0.6761 \| 0.4078 \| \| 0.4844 \| 3.86 \| 3500 \| 0.6688 \| 0.4011 \| \| 0.4234 \| 4.42 \| 4000 \| 0.6080 \| 0.3729 \| \| 0.4237 \| 4.97 \| 4500 \| 0.5953 \| 0.3556 \| \| 0.3986 \| 5.52 \| 5000 \| 0.6054 \| 0.3478 \| \| 0.3554 \| 6.07 \| 5500 \| 0.6193 \| 0.3479 \| \| 0.3446 \| 6.62 \| 6000 \| 0.5809 \| 0.3302 \| \| 0.3104 \| 7.17 \| 6500 \| 0.5713 \| 0.3283 \| \| 0.3166 \| 7.73 \| 7000 \| 0.5593 \| 0.3133 \| \| 0.2938 \| 8.28 \| 7500 \| 0.5645 \| 0.3081 \| \| 0.3061 \| 8.83 \| 8000 \| 0.5508 \| 0.3020 \| \| 0.2986 \| 9.38 \| 8500 \| 0.5462 \| 0.3024 \| \| 0.2939 \| 9.93 \| 9000 \| 0.5544 \| 0.3028 \| \| 0.2633 \| 10.49 \| 9500 \| 0.5496 \| 0.3024 \| \| 0.2683 \| 11.04 \| 10000 \| 0.5439 \| 0.2946 \| \| 0.2714 \| 11.59 \| 10500 \| 0.5524 \| 0.2947 \| \| 0.2354 \| 12.14 \| 11000 \| 0.5267 \| 0.2918 \| \| 0.2488 \| 12.69 \| 11500 \| 0.5728 \| 0.2938 \| \| 0.2479 \| 13.25 \| 12000 \| 0.5802 \| 0.2951 \| \| 0.245 \| 13.8 \| 12500 \| 0.5571 \| 0.2890 \| \| 0.2422 \| 14.35 \| 13000 \| 0.5531 \| 0.2871 \| \| 0.2369 \| 14.9 \| 13500 \| 0.5453 \| 0.2860 \| \| 0.2345 \| 15.45 \| 14000 \| 0.5452 \| 0.2847 \| \| 0.2507 \| 16.0 \| 14500 \| 0.5536 \| 0.2884 \| \| 0.2454 \| 16.56 \| 15000 \| 0.5577 \| 0.2871 \| \| 0.2729 \| 17.11 \| 15500 \| 0.6019 \| 0.2931 \| \| 0.2743 \| 17.66 \| 16000 \| 0.5619 \| 0.2905 \| \| 0.3031 \| 18.21 \| 16500 \| 0.6401 \| 0.3006 \| \| 0.315 \| 18.76 \| 17000 \| 0.6044 \| 0.2990 \| \| 0.4025 \| 19.32 \| 17500 \| 0.6739 \| 0.3304 \| \| 0.4915 \| 19.87 \| 18000 \| 0.7267 \| 0.3472 \| \| 0.5539 \| 20.42 \| 18500 \| 0.8078 \| 0.3483 \| \| 0.7138 \| 20.97 \| 19000 \| 0.9362 \| 0.3765 \| \| 0.5766 \| 21.52 \| 19500 \| 0.7921 \| 0.3392 \| \| 0.688 \| 22.08 \| 20000 \| 0.8833 \| 0.3693 \| \| 0.6964 \| 22.63 \| 20500 \| 0.9137 \| 0.3469 \| \| 0.7389 \| 23.18 \| 21000 \| 0.9379 \| 0.3460 \| \| 0.7851 \| 23.73 \| 21500 \| 1.0438 \| 0.3653 \| \| 0.7619 \| 24.28 \| 22000 \| 0.9313 \| 0.3873 \| \| 0.7175 \| 24.83 \| 22500 \| 0.8668 \| 0.3789 \| \| 0.6842 \| 25.39 \| 23000 \| 0.8243 \| 0.3761 \| \| 0.6941 \| 25.94 \| 23500 \| 0.8557 \| 0.3804 \| \| 0.7167 \| 26.49 \| 24000 \| 0.8618 \| 0.3875 \| \| 0.721 \| 27.04 \| 24500 \| 0.8686 \| 0.3764 \| \| 0.6949 \| 27.59 \| 25000 \| 0.8773 \| 0.3690 \| \| 0.727 \| 28.15 \| 25500 \| 0.8769 \| 0.3666 \| \| 0.7363 \| 28.7 \| 26000 \| 0.8867 \| 0.3634 \| \| 0.7157 \| 29.25 \| 26500 \| 0.8895 \| 0.3626 \| \| 0.7385 \| 29.8 \| 27000 \| 0.8913 \| 0.3621 \| ### Framework versions - Transformers 4.19.3 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
huggingtweets/froliki2108	huggingtweets	2022-06-11T00:04:16Z	105	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-06-11T00:02:55Z	--- language: en thumbnail: http://www.huggingtweets.com/froliki2108/1654905851117/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/1447692349493100549/1PV2c-PJ_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Froliki💉💉💉</div> <div style="text-align: center; font-size: 14px;">@froliki2108</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 Froliki💉💉💉. \| Data \| Froliki💉💉💉 \| \| --- \| --- \| \| Tweets downloaded \| 2223 \| \| Retweets \| 1133 \| \| Short tweets \| 229 \| \| Tweets kept \| 861 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2tug3miv/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 @froliki2108's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3otsf5pj) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3otsf5pj/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/froliki2108') 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 [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
nateraw/modelcard-creator-demo	nateraw	2022-06-10T23:58:39Z	0	0	pytorch	[ "pytorch", "modelcards", "autogenerated-modelcard", "en", "dataset:beans", "arxiv:1810.03993", "arxiv:1910.09700", "license:mit", "region:us" ]	null	2022-06-10T23:40:23Z	--- language: - en license: mit library_name: pytorch tags: - modelcards - autogenerated-modelcard datasets: - beans metrics: - accuracy --- # modelcard-creator-demo ## Table of Contents - [Model Details](#model-details) - [How To Get Started With the Model](#how-to-get-started-with-the-model) - [Uses](#uses) - [Direct Use](#direct-use) - [Downstream Use](#downstream-use) - [Misuse and Out of Scope Use](#misuse-and-out-of-scope-use) - [Limitations and Biases](#limitations-and-biases) - [Training](#training) - [Training Data](#training-data) - [Training Procedure](#training-procedure) - [Evaluation Results](#evaluation-results) - [Environmental Impact](#environmental-impact) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Model Details <!-- Give an overview of your model, the relevant research paper, who trained it, etc. --> This isn't really a model, it's just a test repo to see if the [model card creator](https://huggingface.co/spaces/nateraw/modelcard-creator) works! - Developed by: Nathan Raw - Language(s): - License: modelcard-creator-demo is licensed under the mit license - Resources for more information: - [Research Paper](https://arxiv.org/pdf/1810.03993.pdf) - [GitHub Repo](https://github.com/nateraw/modelcards) ## How to Get Started with the Model Use the code below to get started with the model. ```python # A nice code snippet here that describes how to use the model... ``` ## Uses #### Direct Use <!-- Describe what kind of tasks this model can be used for directly or problems it can solve. --> [More Information Needed] #### Downstream Use <!-- Describe how this model could be leveraged by a downstream model (if applicable) --> [More Information Needed] #### Misuse and Out-of-scope Use <!-- Describe ways in which this model *should not* be used. --> [More Information Needed] ## Limitations and Biases <!-- Describe limitations and biases of this model or models of it's type. --> CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propogate historical and current stereotypes. [More Information Needed] ## Training #### Training Data <!-- Describe the dataset used to train this model. --> <!-- Refer to data card if dataset is provided and exists on the hub --> See the data card for additional information. #### Training Procedure <!-- Describe the preprocessing, hardware used, training hyperparameters, etc. --> [More Information Needed] ## Evaluation Results <!-- Describe evaluation results of this model across any datasets it was evaluated on. --> [More Information Needed] ## Environmental Impact <!-- Provide information to document the environmental impact of this model --> You can estimate carbon emissions 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: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Citation Information ```bibtex @inproceedings{Mitchell_2019, doi = {10.1145/3287560.3287596}, url = {https://doi.org/10.1145%2F3287560.3287596}, year = 2019, month = {jan}, publisher = {{ACM} }, author = {Margaret Mitchell and Simone Wu and Andrew Zaldivar and Parker Barnes and Lucy Vasserman and Ben Hutchinson and Elena Spitzer and Inioluwa Deborah Raji and Timnit Gebru}, title = {Model Cards for Model Reporting}, booktitle = {Proceedings of the Conference on Fairness, Accountability, and Transparency} } ```
ahmeddbahaa/t5-arabic-base-finetuned-wikilingua-ar	ahmeddbahaa	2022-06-10T23:54:52Z	12	1	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "summarization", "mt5", "ar", "abstractive summarization", "generated_from_trainer", "dataset:wiki_lingua", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	summarization	2022-06-10T15:19:23Z	--- license: apache-2.0 tags: - summarization - mt5 - ar - abstractive summarization - generated_from_trainer datasets: - wiki_lingua model-index: - name: t5-arabic-base-finetuned-wikilingua-ar results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-arabic-base-finetuned-wikilingua-ar This model is a fine-tuned version of [bakrianoo/t5-arabic-base](https://huggingface.co/bakrianoo/t5-arabic-base) on the wiki_lingua dataset. It achieves the following results on the evaluation set: - Loss: 3.2735 - Rouge-1: 20.72 - Rouge-2: 7.63 - Rouge-l: 18.75 - Gen Len: 18.74 - Bertscore: 70.79 ## 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: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 250 - num_epochs: 8 - label_smoothing_factor: 0.1 ### Training results ### Framework versions - Transformers 4.19.3 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
huggingtweets/jedwill1999	huggingtweets	2022-06-10T23:10:10Z	106	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-06-10T23:09:22Z	--- language: en thumbnail: http://www.huggingtweets.com/jedwill1999/1654902604867/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/1510152678919135250/lfEmlEGJ_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">a local</div> <div style="text-align: center; font-size: 14px;">@jedwill1999</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 a local. \| Data \| a local \| \| --- \| --- \| \| Tweets downloaded \| 3246 \| \| Retweets \| 1080 \| \| Short tweets \| 525 \| \| Tweets kept \| 1641 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1qsnsp6t/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 @jedwill1999's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/mjjc73pu) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/mjjc73pu/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/jedwill1999') 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 [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/boopysaur	huggingtweets	2022-06-10T22:57:09Z	105	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-06-10T22:56:08Z	--- language: en thumbnail: http://www.huggingtweets.com/boopysaur/1654901824865/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/1476816918879297559/2jt_Rt2L_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">boop ♡</div> <div style="text-align: center; font-size: 14px;">@boopysaur</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 boop ♡. \| Data \| boop ♡ \| \| --- \| --- \| \| Tweets downloaded \| 920 \| \| Retweets \| 162 \| \| Short tweets \| 128 \| \| Tweets kept \| 630 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/398l195g/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 @boopysaur's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3te0suw6) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3te0suw6/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/boopysaur') 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 [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
facebook/roberta-hate-speech-dynabench-r1-target	facebook	2022-06-10T22:36:34Z	4	1	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "en", "arxiv:2012.15761", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-06-10T21:32:03Z	--- language: en --- # LFTW R1 Target The R1 Target model from [Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection](https://arxiv.org/abs/2012.15761) ## Citation Information ```bibtex @inproceedings{vidgen2021lftw, title={Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection}, author={Bertie Vidgen and Tristan Thrush and Zeerak Waseem and Douwe Kiela}, booktitle={ACL}, year={2021} } ``` Thanks to Kushal Tirumala and Adina Williams for helping the authors put the model on the hub!
facebook/roberta-hate-speech-dynabench-r2-target	facebook	2022-06-10T22:36:17Z	12	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "en", "arxiv:2012.15761", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-06-10T21:52:46Z	--- language: en --- # LFTW R2 Target The R2 Target model from [Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection](https://arxiv.org/abs/2012.15761) ## Citation Information ```bibtex @inproceedings{vidgen2021lftw, title={Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection}, author={Bertie Vidgen and Tristan Thrush and Zeerak Waseem and Douwe Kiela}, booktitle={ACL}, year={2021} } ``` Thanks to Kushal Tirumala and Adina Williams for helping the authors put the model on the hub!
facebook/roberta-hate-speech-dynabench-r3-target	facebook	2022-06-10T22:34:01Z	7	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "en", "arxiv:2012.15761", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-06-10T22:10:40Z	--- language: en --- # LFTW R3 Target The R3 Target model from [Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection](https://arxiv.org/abs/2012.15761) ## Citation Information ```bibtex @inproceedings{vidgen2021lftw, title={Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection}, author={Bertie Vidgen and Tristan Thrush and Zeerak Waseem and Douwe Kiela}, booktitle={ACL}, year={2021} } ``` Thanks to Kushal Tirumala and Adina Williams for helping the authors put the model on the hub!
luisrqe/cubucetapenis	luisrqe	2022-06-10T21:08:15Z	0	0	null	[ "region:us" ]	null	2022-06-10T20:52:33Z	git lfs install https://www.novinhavideosporno.com/wp-content/uploads/2018/11/a-maior-buceta-do-mundo-e-a-mais-escrota-tambem.jpg https://www.xvideos-tv.com/wp-content/uploads/2021/11/buceta-da-novinha-sendo-arrombada-por-varios-machos-272x180.jpg http://cdn.xvideos-br.com/media/imagens/10501.jpg https://upload.wikimedia.org/wikipedia/commons/thumb/a/ac/Sidoka_photoshoot.jpg/800px-Sidoka_photoshoot.jpg https://rapforte.com/wp-content/uploads/2021/08/Doka.jpg https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcR2pWEwhp9tl7CDcHd7ELiKLpUPXkhCm4zmCwZGerHYh7CY8WxsGnOSACYussZdIF283so&usqp=CAU git clone https://huggingface.co/luisrqe/cubucetapenis
torli/trijki	torli	2022-06-10T20:45:14Z	0	1	null	[ "license:artistic-2.0", "region:us" ]	null	2022-06-10T20:43:32Z	--- license: artistic-2.0 --- git lfs install git clone https://huggingface.co/torli/trijki
FritzOS/TEdetection_distiBERT_NER_V5	FritzOS	2022-06-10T20:35:11Z	63	0	transformers	[ "transformers", "tf", "distilbert", "token-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-06-10T20:34:58Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: TEdetection_distiBERT_NER_V5 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. --> # TEdetection_distiBERT_NER_V5 This model is a fine-tuned version of [FritzOS/TEdetection_distilBERT_mLM_V5](https://huggingface.co/FritzOS/TEdetection_distilBERT_mLM_V5) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0029 - Validation Loss: 0.0032 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 208018, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 0.0029 \| 0.0032 \| 0 \| ### Framework versions - Transformers 4.19.4 - TensorFlow 2.8.2 - Datasets 2.2.2 - Tokenizers 0.12.1
mmillet/distilrubert-tiny-cased-conversational-v1_single_finetuned_on_cedr_augmented	mmillet	2022-06-10T20:27:38Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-06-10T20:14:44Z	--- tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: distilrubert-tiny-cased-conversational-v1_single_finetuned_on_cedr_augmented 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. --> # distilrubert-tiny-cased-conversational-v1_single_finetuned_on_cedr_augmented This model is a fine-tuned version of [DeepPavlov/distilrubert-tiny-cased-conversational-v1](https://huggingface.co/DeepPavlov/distilrubert-tiny-cased-conversational-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5908 - Accuracy: 0.8653 - F1: 0.8656 - Precision: 0.8665 - Recall: 0.8653 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06 - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| 0.9172 \| 1.0 \| 69 \| 0.5124 \| 0.8246 \| 0.8220 \| 0.8271 \| 0.8246 \| \| 0.4709 \| 2.0 \| 138 \| 0.4279 \| 0.8528 \| 0.8505 \| 0.8588 \| 0.8528 \| \| 0.3194 \| 3.0 \| 207 \| 0.3770 \| 0.8737 \| 0.8727 \| 0.8740 \| 0.8737 \| \| 0.2459 \| 4.0 \| 276 \| 0.3951 \| 0.8685 \| 0.8682 \| 0.8692 \| 0.8685 \| \| 0.1824 \| 5.0 \| 345 \| 0.4005 \| 0.8831 \| 0.8834 \| 0.8841 \| 0.8831 \| \| 0.1515 \| 6.0 \| 414 \| 0.4356 \| 0.8800 \| 0.8797 \| 0.8801 \| 0.8800 \| \| 0.1274 \| 7.0 \| 483 \| 0.4642 \| 0.8727 \| 0.8726 \| 0.8731 \| 0.8727 \| \| 0.0833 \| 8.0 \| 552 \| 0.5226 \| 0.8633 \| 0.8627 \| 0.8631 \| 0.8633 \| \| 0.073 \| 9.0 \| 621 \| 0.5327 \| 0.8695 \| 0.8686 \| 0.8692 \| 0.8695 \| \| 0.0575 \| 10.0 \| 690 \| 0.5908 \| 0.8653 \| 0.8656 \| 0.8665 \| 0.8653 \| ### Framework versions - Transformers 4.19.3 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
huggingtweets/smallmutuals	huggingtweets	2022-06-10T19:13:07Z	106	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-06-10T18:33:00Z	--- language: en thumbnail: http://www.huggingtweets.com/smallmutuals/1654888348503/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/1433527116948180999/wejtDhFm_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Cool Owl Guy</div> <div style="text-align: center; font-size: 14px;">@smallmutuals</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 Cool Owl Guy. \| Data \| Cool Owl Guy \| \| --- \| --- \| \| Tweets downloaded \| 367 \| \| Retweets \| 45 \| \| Short tweets \| 25 \| \| Tweets kept \| 297 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/238iiiu5/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 @smallmutuals's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2hl8vi9y) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2hl8vi9y/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/smallmutuals') 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 [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
louisdeco/camembert-base-finetuned-LineCause	louisdeco	2022-06-10T16:35:03Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "camembert", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-06-10T13:11:32Z	--- license: mit tags: - generated_from_trainer metrics: - accuracy - f1 - recall model-index: - name: camembert-base-finetuned-LineCause 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. --> # camembert-base-finetuned-LineCause This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0001 - Accuracy: 1.0 - F1: 1.0 - Recall: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 50 - eval_batch_size: 50 - 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 \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---:\|:------:\| \| 0.0428 \| 1.0 \| 4409 \| 0.0002 \| 1.0 \| 1.0 \| 1.0 \| \| 0.0009 \| 2.0 \| 8818 \| 0.0001 \| 1.0 \| 1.0 \| 1.0 \| ### Framework versions - Transformers 4.19.3 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
OTQ/q-FrozenLake-v1-4x4-noSlippery	OTQ	2022-06-10T15:14:57Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2022-06-10T15:14:51Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery --- # Q-Learning Agent playing FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```
titi7242229/roberta-base-bne-finetuned_personality_multi	titi7242229	2022-06-10T14:19:54Z	57	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-06-10T11:55:32Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: roberta-base-bne-finetuned_personality_multi results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base-bne-finetuned_personality_multi This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.3709 - Accuracy: 0.5130 ## 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: 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 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.2576 \| 1.0 \| 125 \| 2.2755 \| 0.2340 \| \| 2.0409 \| 2.0 \| 250 \| 2.1425 \| 0.2974 \| \| 1.6358 \| 3.0 \| 375 \| 1.8730 \| 0.4403 \| \| 1.3553 \| 4.0 \| 500 \| 1.7443 \| 0.5032 \| \| 0.9201 \| 5.0 \| 625 \| 1.7165 \| 0.5055 \| \| 0.5199 \| 6.0 \| 750 \| 1.7476 \| 0.5107 \| \| 0.5588 \| 7.0 \| 875 \| 1.7758 \| 0.5153 \| \| 0.2079 \| 8.0 \| 1000 \| 1.7964 \| 0.5251 \| \| 0.2685 \| 9.0 \| 1125 \| 1.8886 \| 0.5187 \| \| 0.1261 \| 10.0 \| 1250 \| 1.9463 \| 0.5199 \| \| 0.1105 \| 11.0 \| 1375 \| 2.0337 \| 0.5222 \| \| 0.1572 \| 12.0 \| 1500 \| 2.1206 \| 0.5084 \| \| 0.0643 \| 13.0 \| 1625 \| 2.1815 \| 0.5182 \| \| 0.0174 \| 14.0 \| 1750 \| 2.2412 \| 0.5176 \| \| 0.0266 \| 15.0 \| 1875 \| 2.2741 \| 0.5112 \| \| 0.0447 \| 16.0 \| 2000 \| 2.3089 \| 0.5159 \| \| 0.02 \| 17.0 \| 2125 \| 2.3401 \| 0.5135 \| \| 0.0414 \| 18.0 \| 2250 \| 2.3504 \| 0.5159 \| \| 0.0122 \| 19.0 \| 2375 \| 2.3661 \| 0.5130 \| \| 0.0154 \| 20.0 \| 2500 \| 2.3709 \| 0.5130 \| ### Framework versions - Transformers 4.19.3 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
RalphX1/dqn-SpaceInvadersNoFrameskip-v4	RalphX1	2022-06-10T13:57:03Z	6	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-06-10T13:11:26Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - metrics: - type: mean_reward value: 374.00 +/- 214.89 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 --- # 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 ``` # Download model and save it into the logs/ folder python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga RalphX1 -f logs/ python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga RalphX1 ``` ## 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', 10000.0), ('optimize_memory_usage', True), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
ahmeddbahaa/mt5-base-finetuned-wikilingua-ar	ahmeddbahaa	2022-06-10T13:00:43Z	13	0	transformers	[ "transformers", "pytorch", "tensorboard", "mt5", "text2text-generation", "summarization", "ar", "abstractive summarization", "generated_from_trainer", "dataset:wiki_lingua", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2022-06-10T02:40:53Z	--- license: apache-2.0 tags: - summarization - mt5 - ar - abstractive summarization - generated_from_trainer datasets: - wiki_lingua model-index: - name: mt5-base-finetuned-wikilingua-ar 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. --> # mt5-base-finetuned-wikilingua-ar This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the wiki_lingua dataset. It achieves the following results on the evaluation set: - Loss: 3.4936 - Rouge-1: 20.79 - Rouge-2: 7.6 - Rouge-l: 18.81 - Gen Len: 18.73 - Bertscore: 70.87 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - 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: 250 - num_epochs: 8 - label_smoothing_factor: 0.1 ### Training results ### Framework versions - Transformers 4.19.3 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
adi1494/distilbert-base-uncased-finetuned-squad	adi1494	2022-06-10T12:39:00Z	62	0	transformers	[ "transformers", "tf", "tensorboard", "distilbert", "question-answering", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2022-06-10T06:38:11Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: adi1494/distilbert-base-uncased-finetuned-squad 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. --> # adi1494/distilbert-base-uncased-finetuned-squad 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: - Train Loss: 1.5671 - Validation Loss: 1.2217 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 5532, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 1.5671 \| 1.2217 \| 0 \| ### Framework versions - Transformers 4.19.3 - TensorFlow 2.8.2 - Datasets 2.2.2 - Tokenizers 0.12.1
becher/t5-small-finetuned-arxiv	becher	2022-06-10T12:28:48Z	106	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-06-10T11:59:37Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-small-finetuned-arxiv results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-arxiv 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: - Loss: 2.1559 - Rouge1: 37.854 - Rouge2: 20.4934 - Rougel: 33.9992 - Rougelsum: 33.9943 - Gen Len: 15.847 ## 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: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 2.3848 \| 1.0 \| 3564 \| 2.1559 \| 37.854 \| 20.4934 \| 33.9992 \| 33.9943 \| 15.847 \| ### Framework versions - Transformers 4.19.3 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
stig/distilbert-base-uncased-finetuned	stig	2022-06-10T10:59:39Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2022-06-10T09:59:19Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-finetuned 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 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8627 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.0255 \| 1.0 \| 2312 \| 1.9202 \| \| 1.7483 \| 2.0 \| 4624 \| 1.8437 \| \| 1.5733 \| 3.0 \| 6936 \| 1.8627 \| ### Framework versions - Transformers 4.19.3 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
mmillet/distilrubert-2ndfinetune-epru	mmillet	2022-06-10T10:52:26Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-06-10T10:49:55Z	--- tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: distilrubert-2ndfinetune-epru 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. --> # distilrubert-2ndfinetune-epru This model is a fine-tuned version of [mmillet/distilrubert-tiny-cased-conversational-v1_best_finetuned_emotion_experiment_augmented_anger_fear](https://huggingface.co/mmillet/distilrubert-tiny-cased-conversational-v1_best_finetuned_emotion_experiment_augmented_anger_fear) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3531 - Accuracy: 0.9054 - F1: 0.9034 - Precision: 0.9074 - Recall: 0.9054 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06 - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| 0.4716 \| 1.0 \| 11 \| 0.2851 \| 0.8986 \| 0.8945 \| 0.9029 \| 0.8986 \| \| 0.2842 \| 2.0 \| 22 \| 0.3041 \| 0.8851 \| 0.8796 \| 0.8816 \| 0.8851 \| \| 0.167 \| 3.0 \| 33 \| 0.2996 \| 0.8986 \| 0.8914 \| 0.8997 \| 0.8986 \| \| 0.1527 \| 4.0 \| 44 \| 0.2443 \| 0.9189 \| 0.9163 \| 0.9222 \| 0.9189 \| \| 0.0926 \| 5.0 \| 55 \| 0.2777 \| 0.9054 \| 0.9016 \| 0.9059 \| 0.9054 \| \| 0.0897 \| 6.0 \| 66 \| 0.3081 \| 0.9122 \| 0.9080 \| 0.9147 \| 0.9122 \| \| 0.0438 \| 7.0 \| 77 \| 0.3332 \| 0.8986 \| 0.8952 \| 0.8993 \| 0.8986 \| \| 0.0433 \| 8.0 \| 88 \| 0.3480 \| 0.8851 \| 0.8859 \| 0.8896 \| 0.8851 \| \| 0.0398 \| 9.0 \| 99 \| 0.3531 \| 0.9054 \| 0.9034 \| 0.9074 \| 0.9054 \| ### Framework versions - Transformers 4.19.3 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
shivigupta/dqn-SpaceInvadersNoFrameskip-v4	shivigupta	2022-06-10T10:11:07Z	3	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-06-10T10:10:35Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - metrics: - type: mean_reward value: 374.00 +/- 214.89 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 --- # 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 ``` # Download model and save it into the logs/ folder python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga shivigupta -f logs/ python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga shivigupta ``` ## 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', 100000.0), ('optimize_memory_usage', True), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
YaYaB/SpaceInvadersNoFrameskip-v4-2	YaYaB	2022-06-10T09:16:18Z	4	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-06-10T09:15:44Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - metrics: - type: mean_reward value: 556.00 +/- 162.23 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 --- # 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 ``` # Download model and save it into the logs/ folder python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga YaYaB -f logs/ python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga YaYaB ``` ## Hyperparameters ```python OrderedDict([('batch_size', 64), ('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', True), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
TurkuNLP/bert-large-finnish-cased-v1	TurkuNLP	2022-06-10T08:46:17Z	152	2	transformers	[ "transformers", "pytorch", "fi", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2022-06-10T07:53:16Z	--- license: apache-2.0 language: fi --- This is the large variant of FinBERT (TurkuNLP/bert-base-finnish-cased-v1). The training data is exactly the same.
huggingtweets/drilbot_neo	huggingtweets	2022-06-10T08:39:44Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true 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/1374924360780242944/-Q8NfgEr_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">wintbot_neo</div> <div style="text-align: center; font-size: 14px;">@drilbot_neo</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 wintbot_neo. \| Data \| wintbot_neo \| \| --- \| --- \| \| Tweets downloaded \| 3243 \| \| Retweets \| 373 \| \| Short tweets \| 468 \| \| Tweets kept \| 2402 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/25adu2w7/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 @drilbot_neo's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3keot8ku) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3keot8ku/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/drilbot_neo') 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 [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
flood/distilbert-base-uncased-distilled-clinc	flood	2022-06-10T08:03:08Z	77	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:clinc_oos", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-06-10T07:59:25Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - clinc_oos metrics: - accuracy model-index: - name: distilbert-base-uncased-distilled-clinc results: - task: name: Text Classification type: text-classification dataset: name: clinc_oos type: clinc_oos args: plus metrics: - name: Accuracy type: accuracy value: 0.9309677419354838 --- <!-- 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-distilled-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.0389 - Accuracy: 0.9310 ## 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: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6206 \| 1.0 \| 318 \| 0.3251 \| 0.6610 \| \| 0.2571 \| 2.0 \| 636 \| 0.1366 \| 0.8584 \| \| 0.1392 \| 3.0 \| 954 \| 0.0813 \| 0.9081 \| \| 0.0967 \| 4.0 \| 1272 \| 0.0598 \| 0.9152 \| \| 0.0779 \| 5.0 \| 1590 \| 0.0503 \| 0.9229 \| \| 0.0675 \| 6.0 \| 1908 \| 0.0451 \| 0.9271 \| \| 0.0615 \| 7.0 \| 2226 \| 0.0425 \| 0.9326 \| \| 0.058 \| 8.0 \| 2544 \| 0.0403 \| 0.9316 \| \| 0.0557 \| 9.0 \| 2862 \| 0.0393 \| 0.9306 \| \| 0.0544 \| 10.0 \| 3180 \| 0.0389 \| 0.9310 \| ### Framework versions - Transformers 4.19.3 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
Intel/MiniLM-L12-H384-uncased-mrpc	Intel	2022-06-10T07:06:45Z	220	1	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-06-10T06:55:25Z	--- language: - en license: mit tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: MiniLM-L12-H384-uncased-mrpc results: - task: name: Text Classification type: text-classification dataset: name: GLUE MRPC type: glue args: mrpc metrics: - name: Accuracy type: accuracy value: 0.875 - name: F1 type: f1 value: 0.9097345132743363 --- <!-- 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. --> # MiniLM-L12-H384-uncased-mrpc This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.4319 - Accuracy: 0.875 - F1: 0.9097 - Combined Score: 0.8924 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu102 - Datasets 2.2.2 - Tokenizers 0.12.1
jayeshgar/dqn-SpaceInvadersNoFrameskip-v4	jayeshgar	2022-06-10T06:54:27Z	2	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-06-10T06:53:42Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - metrics: - type: mean_reward value: 653.00 +/- 114.70 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 --- # 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 ``` # Download model and save it into the logs/ folder python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga jayeshgar -f logs/ python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga jayeshgar ``` ## 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', True), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
ritheshSree/animal-classifier	ritheshSree	2022-06-10T05:38:54Z	115	1	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-06-10T05:21:44Z	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: animal-classifier results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 1.0 --- # animal-classifier Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### cat ![cat](images/cat.jpg) #### dog ![dog](images/dog.jpg) #### snake ![snake](images/snake.jpg) #### tiger ![tiger](images/tiger.jpg)
RuiqianLi/wav2vec2-xls-r-300m_Mrbrown_finetune1	RuiqianLi	2022-06-10T03:17:06Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:uob_singlish", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-06-09T10:16:21Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - uob_singlish model-index: - name: wav2vec2-xls-r-300m_Mrbrown_finetune1 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_Mrbrown_finetune1 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the uob_singlish dataset. ## This time use self-made dataset(cut the audio of "https://www.youtube.com/watch?v=a2ZOTD3R7JI" into slices and write the corresponding transcript, totally 4 mins), don't know why the word-error-rate keep 1. But can know that much be the problem of dataset, because last time use the same pre-trained model and standard singlish corpus fine-tune get nice result. (can find it at:RuiqianLi/wav2vec2-large-xls-r-300m-singlish-colab) It achieves the following results on the evaluation set: - Loss: 3.0927 - Wer: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.01 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 100 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---:\| \| 3.7943 \| 20.0 \| 200 \| 3.0597 \| 1.0 \| \| 2.9902 \| 40.0 \| 400 \| 3.1604 \| 1.0 \| \| 2.9696 \| 60.0 \| 600 \| 3.1112 \| 1.0 \| \| 2.8885 \| 80.0 \| 800 \| 3.0234 \| 1.0 \| \| 2.8154 \| 100.0 \| 1000 \| 3.0927 \| 1.0 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3
alibaba-pai/pai-bert-tiny-zh	alibaba-pai	2022-06-10T02:34:43Z	272	6	transformers	[ "transformers", "pytorch", "bert", "fill-mask", "zh", "arxiv:2205.00258", "license:apache-2.0", "endpoints_compatible", "region:us" ]	fill-mask	2022-06-09T03:45:15Z	--- language: zh pipeline_tag: fill-mask widget: - text: "中国的首都是北[MASK]。" - text: "牛奶是[MASK]色的。" tags: - bert license: apache-2.0 --- ## Alibaba PAI BERT Tiny Chinese This project provides Chinese pre-trained language models and various types of NLP tools. The models are pre-trained on the large-scale corpora hosted by the Alibaba PAI team. It is developed based on the EasyNLP framework (https://github.com/alibaba/EasyNLP). ## Citation If you find the resource is useful, please cite the following paper in your work: ``` @article{easynlp, title = {EasyNLP: A Comprehensive and Easy-to-use Toolkit for Natural Language Processing}, publisher = {arXiv}, author = {Wang, Chengyu and Qiu, Minghui and Zhang, Taolin and Liu, Tingting and Li, Lei and Wang, Jianing and Wang, Ming and Huang, Jun and Lin, Wei}, url = {https://arxiv.org/abs/2205.00258}, year = {2022} } ```
YeRyeongLee/bert-base-cased-finetuned-filtered-0609	YeRyeongLee	2022-06-10T02:29:16Z	105	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-06-10T00:30:02Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: bert-base-cased-finetuned-filtered-0609 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-cased-finetuned-filtered-0609 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2410 - Accuracy: 0.9748 - Precision: 0.9751 - Recall: 0.9748 - F1: 0.9749 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.2028 \| 1.0 \| 3180 \| 0.2405 \| 0.9535 \| 0.9561 \| 0.9535 \| 0.9538 \| \| 0.1632 \| 2.0 \| 6360 \| 0.1686 \| 0.9660 \| 0.9664 \| 0.9660 \| 0.9661 \| \| 0.1203 \| 3.0 \| 9540 \| 0.1625 \| 0.9648 \| 0.9655 \| 0.9648 \| 0.9648 \| \| 0.1233 \| 4.0 \| 12720 \| 0.1510 \| 0.9698 \| 0.9702 \| 0.9698 \| 0.9699 \| \| 0.0823 \| 5.0 \| 15900 \| 0.1600 \| 0.9730 \| 0.9732 \| 0.9730 \| 0.9730 \| \| 0.0453 \| 6.0 \| 19080 \| 0.1953 \| 0.9723 \| 0.9724 \| 0.9723 \| 0.9723 \| \| 0.031 \| 7.0 \| 22260 \| 0.1754 \| 0.9755 \| 0.9755 \| 0.9755 \| 0.9755 \| \| 0.0166 \| 8.0 \| 25440 \| 0.2155 \| 0.9739 \| 0.9740 \| 0.9739 \| 0.9739 \| \| 0.0036 \| 9.0 \| 28620 \| 0.2519 \| 0.9730 \| 0.9733 \| 0.9730 \| 0.9730 \| \| 0.0035 \| 10.0 \| 31800 \| 0.2410 \| 0.9748 \| 0.9751 \| 0.9748 \| 0.9749 \| ### Framework versions - Transformers 4.19.2 - Pytorch 1.9.1+cu111 - Datasets 1.16.1 - Tokenizers 0.12.1
huggingtweets/loganpaul	huggingtweets	2022-06-10T02:29:07Z	105	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-06-10T02:27:26Z	--- language: en thumbnail: http://www.huggingtweets.com/loganpaul/1654828143127/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/1401837042934468611/okzqIoMb_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Logan Paul</div> <div style="text-align: center; font-size: 14px;">@loganpaul</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 Logan Paul. \| Data \| Logan Paul \| \| --- \| --- \| \| Tweets downloaded \| 3245 \| \| Retweets \| 170 \| \| Short tweets \| 318 \| \| Tweets kept \| 2757 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/wj9pph5f/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 @loganpaul's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1sqzuxgo) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1sqzuxgo/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/loganpaul') 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 [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/wickdedaccount	huggingtweets	2022-06-10T02:20:32Z	105	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-06-10T02:17:51Z	--- language: en thumbnail: http://www.huggingtweets.com/wickdedaccount/1654827628283/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/1353151127026597889/Yarj5Kfr_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">pp</div> <div style="text-align: center; font-size: 14px;">@wickdedaccount</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 pp. \| Data \| pp \| \| --- \| --- \| \| Tweets downloaded \| 1028 \| \| Retweets \| 822 \| \| Short tweets \| 119 \| \| Tweets kept \| 87 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1of8kmw1/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 @wickdedaccount's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2q4m95l8) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2q4m95l8/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/wickdedaccount') 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 [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
Wikram/Legal-key-to-text	Wikram	2022-06-10T02:17:44Z	5	2	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-06-10T01:44:21Z	Task: Given a set of input keywords, generate a corresponding text output for a section in the legal domain. Dataset: We used the Contract Understanding Atticus Dataset (CUAD). It is a corpus of 13,000+ labels in 510 commercial legal contracts. They have been manually labeled under the supervision of experienced lawyers to identify 41 types of legal clauses (e.g. licenses, warranty, governing law, insurance, etc…). Workflow: ![alt text](https://github.com/vikramNU/Practicum/raw/main/Screenshot%202022-06-09%20210134.jpg) You can connect me at [email protected]
25khattab/vit_test_1_95	25khattab	2022-06-10T01:40:54Z	55	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-06-10T01:40:38Z	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: vit_test_1_95 results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.9501661062240601 --- # vit_test_1_95 Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images
huggingtweets/artificialbuttr	huggingtweets	2022-06-10T01:39:43Z	105	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-06-10T01:37:50Z	--- language: en thumbnail: http://www.huggingtweets.com/artificialbuttr/1654825134207/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/1485413658351968256/NUVesGCM_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">artificialbutter</div> <div style="text-align: center; font-size: 14px;">@artificialbuttr</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 artificialbutter. \| Data \| artificialbutter \| \| --- \| --- \| \| Tweets downloaded \| 785 \| \| Retweets \| 129 \| \| Short tweets \| 407 \| \| Tweets kept \| 249 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1ypylns0/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 @artificialbuttr's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1phf128l) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1phf128l/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/artificialbuttr') 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 [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
HrayrM/distilbert-base-uncased-finetuned-clinc	HrayrM	2022-06-10T01:17:59Z	4	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:clinc_oos", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-06-10T00:50:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - clinc_oos metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-clinc results: - task: name: Text Classification type: text-classification dataset: name: clinc_oos type: clinc_oos args: plus metrics: - name: Accuracy type: accuracy value: 0.9135483870967742 --- <!-- 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-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.7771 - Accuracy: 0.9135 ## 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: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 4.2843 \| 1.0 \| 318 \| 3.2793 \| 0.7448 \| \| 2.6208 \| 2.0 \| 636 \| 1.8750 \| 0.8297 \| \| 1.5453 \| 3.0 \| 954 \| 1.1565 \| 0.8919 \| \| 1.0141 \| 4.0 \| 1272 \| 0.8628 \| 0.9090 \| \| 0.795 \| 5.0 \| 1590 \| 0.7771 \| 0.9135 \| ### Framework versions - Transformers 4.13.0 - Pytorch 1.10.0 - Datasets 2.2.2 - Tokenizers 0.10.3
ExusAI/SRWNN	ExusAI	2022-06-10T00:54:14Z	0	0	null	[ "license:mit", "region:us" ]	null	2022-06-10T00:45:58Z	--- license: mit --- Super resolution model for anime and illustrations based on vgg11 and waifu2x. This model was trained on around 10k high resolution images (at least HD) https://github.com/Exusai/SuperResolutionWaifuNN
nestoralvaro/mt5-base-finetuned-xsum-data_prep_2021_12_26___t1_7.csv___topic_text_google_mt5_base	nestoralvaro	2022-06-10T00:52:35Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "mt5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-06-09T23:49:44Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: mt5-base-finetuned-xsum-data_prep_2021_12_26___t1_7.csv___topic_text_google_mt5_base 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. --> # mt5-base-finetuned-xsum-data_prep_2021_12_26___t1_7.csv___topic_text_google_mt5_base This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: nan - Rouge1: 2.8146 - Rouge2: 0.6707 - Rougel: 2.8187 - Rougelsum: 2.8098 - Gen Len: 6.4901 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| 0.0 \| 1.0 \| 3869 \| nan \| 2.8146 \| 0.6707 \| 2.8187 \| 2.8098 \| 6.4901 \| ### Framework versions - Transformers 4.19.3 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
kjunelee/distilbert-base-uncased-finetuned-emotion	kjunelee	2022-06-10T00:24:32Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-06-10T00:03:16Z	--- 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: default metrics: - name: Accuracy type: accuracy value: 0.931 - name: F1 type: f1 value: 0.9313235272564213 --- <!-- 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: 0.1595 - Accuracy: 0.931 - F1: 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 125 \| 0.1873 \| 0.924 \| 0.9234 \| \| 0.1992 \| 2.0 \| 250 \| 0.1649 \| 0.929 \| 0.9293 \| \| 0.1992 \| 3.0 \| 375 \| 0.1595 \| 0.931 \| 0.9313 \| ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0 - Datasets 2.2.3.dev0 - Tokenizers 0.12.1
ajtamayoh/NLP-CIC-WFU_Clinical_Cases_NER_Paragraph_Tokenized_mBERT_cased_fine_tuned	ajtamayoh	2022-06-09T23:31:56Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-06-09T23:02:35Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: NLP-CIC-WFU_Clinical_Cases_NER_Paragraph_Tokenized_mBERT_cased_fine_tuned 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. --> # NLP-CIC-WFU_Clinical_Cases_NER_Paragraph_Tokenized_mBERT_cased_fine_tuned This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0537 - Precision: 0.8585 - Recall: 0.7101 - F1: 0.7773 - Accuracy: 0.9893 ## 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: 7 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.0693 \| 1.0 \| 514 \| 0.0416 \| 0.9485 \| 0.6492 \| 0.7708 \| 0.9884 \| \| 0.0367 \| 2.0 \| 1028 \| 0.0396 \| 0.9391 \| 0.6710 \| 0.7827 \| 0.9892 \| \| 0.0283 \| 3.0 \| 1542 \| 0.0385 \| 0.9388 \| 0.6889 \| 0.7947 \| 0.9899 \| \| 0.0222 \| 4.0 \| 2056 \| 0.0422 \| 0.9456 \| 0.6790 \| 0.7904 \| 0.9898 \| \| 0.0182 \| 5.0 \| 2570 \| 0.0457 \| 0.9349 \| 0.6925 \| 0.7956 \| 0.9901 \| \| 0.013 \| 6.0 \| 3084 \| 0.0484 \| 0.8947 \| 0.7062 \| 0.7894 \| 0.9899 \| \| 0.0084 \| 7.0 \| 3598 \| 0.0537 \| 0.8585 \| 0.7101 \| 0.7773 \| 0.9893 \| ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
pm390/dqn-SpaceInvadersNoFrameskip-v4	pm390	2022-06-09T22:03:09Z	2	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-06-09T22:02:36Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - metrics: - type: mean_reward value: 374.00 +/- 214.89 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 --- # 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 ``` # Download model and save it into the logs/ folder python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga pm390 -f logs/ python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga pm390 ``` ## 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), ('max_grad_norm', 6), ('n_timesteps', 100000.0), ('optimize_memory_usage', True), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
nthakur/contriever-base-msmarco	nthakur	2022-06-09T22:01:51Z	1,072	1	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-06-09T21:50:15Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # nthakur/contriever-base-msmarco This is a port of the [Contriever MSMARCO Model](https://huggingface.co/facebook/contriever-msmarco) to [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('nthakur/contriever-base-msmarco') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('nthakur/contriever-base-msmarco') model = AutoModel.from_pretrained('nthakur/contriever-base-msmarco') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=nthakur/contriever-base-msmarco) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 509, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors Have a look at: [Contriever Model](https://github.com/facebookresearch/contriever). <!--- Describe where people can find more information -->
kabelomalapane/En-Ts	kabelomalapane	2022-06-09T17:33:20Z	69	0	transformers	[ "transformers", "pytorch", "marian", "text2text-generation", "translation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2022-06-09T16:33:13Z	--- license: apache-2.0 tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: En-Ts 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. --> # En-Ts This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ts](https://huggingface.co/Helsinki-NLP/opus-mt-en-ts) on the None dataset. It achieves the following results on the evaluation set: Before training: - Loss: 3.17 - Bleu: 14.513 After Training - Loss: 1.3320 - Bleu: 36.7687 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\| \| 1.7082 \| 1.0 \| 5929 \| 1.6902 \| 32.1311 \| \| 1.4606 \| 2.0 \| 11858 \| 1.4996 \| 34.1129 \| \| 1.3182 \| 3.0 \| 17787 \| 1.4107 \| 35.7428 \| \| 1.2543 \| 4.0 \| 23716 \| 1.3631 \| 36.2009 \| \| 1.2116 \| 5.0 \| 29645 \| 1.3389 \| 36.5876 \| \| 1.1723 \| 6.0 \| 35574 \| 1.3320 \| 36.7481 \| ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
tclong/wav2vec2-base-vios-commonvoice	tclong	2022-06-09T17:17:08Z	77	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-06-08T18:03:39Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-vios-commonvoice 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-base-vios-commonvoice This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3823 - Wer: 0.2401 ## 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: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 1.2268 \| 0.66 \| 500 \| 0.8746 \| 0.5939 \| \| 0.8728 \| 1.32 \| 1000 \| 0.6435 \| 0.4554 \| \| 0.6899 \| 1.99 \| 1500 \| 0.5655 \| 0.3995 \| \| 0.5842 \| 2.65 \| 2000 \| 0.5267 \| 0.3694 \| \| 0.5371 \| 3.31 \| 2500 \| 0.4980 \| 0.3431 \| \| 0.4921 \| 3.97 \| 3000 \| 0.4781 \| 0.3276 \| \| 0.4508 \| 4.64 \| 3500 \| 0.4434 \| 0.3134 \| \| 0.433 \| 5.3 \| 4000 \| 0.4348 \| 0.2963 \| \| 0.404 \| 5.96 \| 4500 \| 0.4248 \| 0.2874 \| \| 0.3834 \| 6.62 \| 5000 \| 0.4163 \| 0.2775 \| \| 0.3784 \| 7.28 \| 5500 \| 0.4104 \| 0.2751 \| \| 0.3669 \| 7.95 \| 6000 \| 0.4143 \| 0.2724 \| \| 0.3462 \| 8.61 \| 6500 \| 0.4131 \| 0.2699 \| \| 0.3364 \| 9.27 \| 7000 \| 0.4070 \| 0.2617 \| \| 0.3249 \| 9.93 \| 7500 \| 0.4076 \| 0.2603 \| \| 0.3154 \| 10.6 \| 8000 \| 0.3998 \| 0.2577 \| \| 0.3117 \| 11.26 \| 8500 \| 0.3930 \| 0.2505 \| \| 0.3101 \| 11.92 \| 9000 \| 0.4003 \| 0.2492 \| \| 0.298 \| 12.58 \| 9500 \| 0.3960 \| 0.2496 \| \| 0.2968 \| 13.24 \| 10000 \| 0.3877 \| 0.2469 \| \| 0.29 \| 13.91 \| 10500 \| 0.3870 \| 0.2456 \| \| 0.2921 \| 14.57 \| 11000 \| 0.3823 \| 0.2401 \| ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
ajtamayoh/NLP-CIC-WFU_Clinical_Cases_NER_Sents_Tokenized_bertin_roberta_base_spanish_fine_tuned	ajtamayoh	2022-06-09T17:15:48Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "token-classification", "generated_from_trainer", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-06-09T16:33:08Z	--- license: cc-by-4.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: NLP-CIC-WFU_Clinical_Cases_NER_Sents_Tokenized_bertin_roberta_base_spanish_fine_tuned 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. --> # NLP-CIC-WFU_Clinical_Cases_NER_Sents_Tokenized_bertin_roberta_base_spanish_fine_tuned This model is a fine-tuned version of [bertin-project/bertin-roberta-base-spanish](https://huggingface.co/bertin-project/bertin-roberta-base-spanish) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0973 - Precision: 0.9012 - Recall: 0.6942 - F1: 0.7842 - Accuracy: 0.9857 ## 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: 7 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.0605 \| 1.0 \| 2568 \| 0.0625 \| 0.9400 \| 0.6322 \| 0.7560 \| 0.9836 \| \| 0.0475 \| 2.0 \| 5136 \| 0.0622 \| 0.9533 \| 0.6572 \| 0.7781 \| 0.9849 \| \| 0.0374 \| 3.0 \| 7704 \| 0.0552 \| 0.9261 \| 0.6784 \| 0.7831 \| 0.9855 \| \| 0.0246 \| 4.0 \| 10272 \| 0.0693 \| 0.9381 \| 0.6658 \| 0.7788 \| 0.9849 \| \| 0.0126 \| 5.0 \| 12840 \| 0.0974 \| 0.8918 \| 0.6830 \| 0.7735 \| 0.9849 \| \| 0.0061 \| 6.0 \| 15408 \| 0.0886 \| 0.8771 \| 0.7099 \| 0.7847 \| 0.9850 \| \| 0.0031 \| 7.0 \| 17976 \| 0.0973 \| 0.9012 \| 0.6942 \| 0.7842 \| 0.9857 \| ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
XGBooster/dqn-SpaceInvadersNoFrameskip-v4	XGBooster	2022-06-09T16:03:42Z	2	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-06-09T16:03:00Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - metrics: - type: mean_reward value: 744.00 +/- 231.20 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 --- # 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 ``` # Download model and save it into the logs/ folder python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga XGBooster -f logs/ python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga XGBooster ``` ## 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', 10000000.0), ('optimize_memory_usage', True), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```

huggingtweets/dodecahedra

huggingtweets

2022-06-12T17:42:15Z

103

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-06-12T17:37:18Z

--- language: en thumbnail: http://www.huggingtweets.com/dodecahedra/1655055731499/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/3232494514/760c72bca0af20fac2cd61bcec557e7a_400x400.jpeg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">William Rose</div> <div style="text-align: center; font-size: 14px;">@dodecahedra</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 William Rose. | Data | William Rose | | --- | --- | | Tweets downloaded | 3241 | | Retweets | 1115 | | Short tweets | 158 | | Tweets kept | 1968 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1geru0ac/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 @dodecahedra's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1uy1zk82) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1uy1zk82/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/dodecahedra') 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* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

nlokam99/ada_sample_2

nlokam99

2022-06-12T17:40:42Z

105

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-06-12T17:38:56Z

--- thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png tags: - conversational license: mit ---

obokkkk/kc-bert_finetuned_unsmile

obokkkk

2022-06-12T17:22:32Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-06-12T14:39:40Z

--- tags: - generated_from_trainer model-index: - name: kc-bert_finetuned_unsmile results: [] ---  # kc-bert_finetuned_unsmile This model is a fine-tuned version of [beomi/kcbert-base](https://huggingface.co/beomi/kcbert-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1326 - Lrap: 0.8753 ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Lrap | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 235 | 0.1458 | 0.8612 | | No log | 2.0 | 470 | 0.1280 | 0.8738 | | 0.1685 | 3.0 | 705 | 0.1257 | 0.8791 | | 0.1685 | 4.0 | 940 | 0.1281 | 0.8777 | | 0.0774 | 5.0 | 1175 | 0.1326 | 0.8753 | ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 1.17.0 - Tokenizers 0.12.1

comodoro/SpaceInvadersNoFrameskip-v4

comodoro

2022-06-12T16:55:50Z

2

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-06-12T16:55:06Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - metrics: - type: mean_reward value: 680.00 +/- 211.93 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 --- # **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 ``` # Download model and save it into the logs/ folder python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga comodoro -f logs/ python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga comodoro ``` ## 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', 10000000.0), ('optimize_memory_usage', True), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```

vasudevgupta/speech_jax_wav2vec2-large-lv60_960h

vasudevgupta

2022-06-12T16:10:32Z

7

0

transformers

[ "transformers", "jax", "wav2vec2", "automatic-speech-recognition", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-05-29T20:52:47Z

* Evaluation Notebook: https://colab.research.google.com/drive/1dV1Z3WajMCYMjNZab98CEEcg3FTbtONO?usp=sharing * Training Code: https://github.com/vasudevgupta7/speech-jax/blob/main/projects/finetune_wav2vec2.py * Weights & Biases: https://wandb.ai/7vasudevgupta/speech-JAX?workspace=user-7vasudevgupta Following results are obtained with `23ffe236840b7f75c9f01a9c347b01485a2bf9f6` & `95c3bc1b83c74452df29f792e0b5651c09fdaeb9` | dataset | WER | |------------------------|-------| | Librispeech-test-clean | 3.3 % |

kravchenko/uk-mt5-base

kravchenko

2022-06-12T14:57:59Z

14

4

transformers

[ "transformers", "pytorch", "mt5", "text2text-generation", "t5", "uk", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-05-03T09:41:33Z

--- language: - uk - en tags: - t5 --- The aim is to compress the mT5-base model to leave only the Ukrainian language and some basic English. Reproduced the similar result (but with another language) from [this](https://towardsdatascience.com/how-to-adapt-a-multilingual-t5-model-for-a-single-language-b9f94f3d9c90) medium article. Results: - 582M params -> 244M params (58%) - 250K tokens -> 30K tokens - 2.2GB size model -> 0.95GB size model

jianyang/q-Taxi-v3

jianyang

2022-06-12T14:13:37Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2022-06-12T14:13:31Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 --- # **Q-Learning** Agent playing **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="jianyang/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```

ahmeddbahaa/mt5-base-finetune-ar-xlsum

ahmeddbahaa

2022-06-12T13:55:10Z

9

0

transformers

[ "transformers", "pytorch", "tensorboard", "mt5", "text2text-generation", "summarization", "mT5_multilingual_XLSum", "abstractive summarization", "ar", "xlsum", "generated_from_trainer", "dataset:xlsum", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

summarization

2022-06-11T20:41:00Z

--- license: apache-2.0 tags: - summarization - mT5_multilingual_XLSum - mt5 - abstractive summarization - ar - xlsum - generated_from_trainer datasets: - xlsum model-index: - name: mt5-base-finetune-ar-xlsum results: [] ---  # mt5-base-finetune-ar-xlsum This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the xlsum dataset. It achieves the following results on the evaluation set: - Loss: 3.2546 - Rouge-1: 22.2 - Rouge-2: 9.57 - Rouge-l: 20.26 - Gen Len: 19.0 - Bertscore: 71.43 ## 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: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 250 - num_epochs: 10 - label_smoothing_factor: 0.1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:| | 4.9261 | 1.0 | 585 | 3.6314 | 18.19 | 6.49 | 16.37 | 19.0 | 70.17 | | 3.8429 | 2.0 | 1170 | 3.4253 | 19.45 | 7.58 | 17.73 | 19.0 | 70.35 | | 3.6311 | 3.0 | 1755 | 3.3569 | 20.83 | 8.54 | 18.9 | 19.0 | 70.89 | | 3.4917 | 4.0 | 2340 | 3.3101 | 20.77 | 8.53 | 18.89 | 19.0 | 70.98 | | 3.3873 | 5.0 | 2925 | 3.2867 | 21.47 | 9.0 | 19.54 | 19.0 | 71.23 | | 3.3037 | 6.0 | 3510 | 3.2693 | 21.41 | 9.0 | 19.5 | 19.0 | 71.21 | | 3.2357 | 7.0 | 4095 | 3.2581 | 22.05 | 9.36 | 20.04 | 19.0 | 71.43 | | 3.1798 | 8.0 | 4680 | 3.2522 | 22.21 | 9.56 | 20.23 | 19.0 | 71.41 | | 3.1359 | 9.0 | 5265 | 3.2546 | 22.27 | 9.58 | 20.23 | 19.0 | 71.46 | | 3.0997 | 10.0 | 5850 | 3.2546 | 22.2 | 9.57 | 20.26 | 19.0 | 71.43 | ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

keras-io/ProbabalisticBayesianModel-Wine

keras-io

2022-06-12T13:54:27Z

0

2

keras

[ "keras", "tensorboard", "probabilistic-models", "regression", "region:us" ]

null

2022-06-06T15:36:50Z

--- library_name: keras tags: - probabilistic-models - regression --- ## Model description This repo contains model weights for the the probabilistic model from [Probabilistic Bayesian Neural Networks](https://keras.io/examples/keras_recipes/bayesian_neural_networks/). This example demonstrates how to build basic probabilistic Bayesian neural networks to account for these two types of uncertainty. We use TensorFlow Probability library, which is compatible with Keras API. Taking a probabilistic approach to deep learning allows to account for uncertainty, so that models can assign less levels of confidence to incorrect predictions. Sources of uncertainty can be found in the data, due to measurement error or noise in the labels, or the model, due to insufficient data availability for the model to learn effectively. **Full credits go to [Khalid Salama](https://www.linkedin.com/in/khalid-salama-24403144/)** ## Using this model This repo contains model weights only. To use this model, refer to the following code contained in load_bnn_model.py. ## Training and evaluation data 🍷 We use the wine quality dataset found [here](https://www.tensorflow.org/datasets/catalog/wine_quality). Each wine was scored from 0-10 by wine experts, and includes 11 physicochemical features about the wine. ## Versioning The training was done using TensorFlow 2.8.0 and TensorFlow Probability 0.16.0. When working with TensorFlow Probability, it is encouraged to check out the [releases](https://github.com/tensorflow/probability/releases/tag/v0.17.0) to make sure you are using a stable TensorFlow counterpart. ### Training hyperparameters | Optimizer | learning_rate | decay | rho | momentum | epsilon | centered | training_precision | |----|-------------|-----|------|------|-------|-------|------------------| |RMSprop|0.001|0.0|0.9|0.0|1e-07|False|float32|

nestoralvaro/mt5-base-finetuned-xsum-RAW_data_prep_2021_12_26___t55_403.csv__google_mt5_base

nestoralvaro

2022-06-12T12:25:16Z

103

0

transformers

[ "transformers", "pytorch", "tensorboard", "mt5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-06-12T10:01:09Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: mt5-base-finetuned-xsum-RAW_data_prep_2021_12_26___t55_403.csv__google_mt5_base results: [] ---  # mt5-base-finetuned-xsum-RAW_data_prep_2021_12_26___t55_403.csv__google_mt5_base This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: nan - Rouge1: 0.9712 - Rouge2: 0.1329 - Rougel: 0.9638 - Rougelsum: 0.9675 - Gen Len: 6.4489 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | 0.0 | 1.0 | 36479 | nan | 0.9712 | 0.1329 | 0.9638 | 0.9675 | 6.4489 | ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

FabianWillner/distilbert-base-uncased-finetuned-squad

FabianWillner

2022-06-12T12:09:32Z

13

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2022-05-09T10:41:12Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad metrics: - squad model-index: - name: distilbert-base-uncased-finetuned-squad results: [] ---  # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [FabianWillner/distilbert-base-uncased-finetuned-squad](https://huggingface.co/FabianWillner/distilbert-base-uncased-finetuned-squad) on the squad 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 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

huggingnft/hedgies

huggingnft

2022-06-12T12:08:25Z

7

0

transformers

[ "transformers", "huggingnft", "nft", "huggan", "gan", "image", "images", "unconditional-image-generation", "dataset:huggingnft/hedgies", "license:mit", "endpoints_compatible", "region:us" ]

unconditional-image-generation

2022-05-24T18:12:29Z

--- tags: - huggingnft - nft - huggan - gan - image - images - unconditional-image-generation datasets: - huggingnft/hedgies license: mit --- # Hugging NFT: hedgies ## Disclaimer All rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright holder. ## Model description LightWeight GAN model for unconditional generation. NFT collection available [here](https://opensea.io/collection/hedgies). Dataset is available [here](https://huggingface.co/datasets/huggingnft/hedgies). Check Space: [link](https://huggingface.co/spaces/AlekseyKorshuk/huggingnft). Project repository: [link](https://github.com/AlekseyKorshuk/huggingnft). [![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingnft?style=social)](https://github.com/AlekseyKorshuk/huggingnft) ## Intended uses & limitations #### How to use Check project repository: [link](https://github.com/AlekseyKorshuk/huggingnft). #### Limitations and bias Check project repository: [link](https://github.com/AlekseyKorshuk/huggingnft). ## Training data Dataset is available [here](https://huggingface.co/datasets/huggingnft/hedgies). ## Training procedure Training script is available [here](https://github.com/AlekseyKorshuk/huggingnft). ## Generated Images Check results with Space: [link](https://huggingface.co/spaces/AlekseyKorshuk/huggingnft). ## About *Built by Aleksey Korshuk* [![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk) [![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk) [![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingnft?style=social)](https://github.com/AlekseyKorshuk/huggingnft) ### BibTeX entry and citation info ```bibtex @InProceedings{huggingnft, author={Aleksey Korshuk} year=2022 } ```

vishvamahadevan/distilbert-base-uncased-finetuned-squad

vishvamahadevan

2022-06-12T10:34:52Z

6

0

transformers

[ "transformers", "tf", "tensorboard", "distilbert", "question-answering", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2022-06-12T08:07:48Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: vishvamahadevan/distilbert-base-uncased-finetuned-squad results: [] ---  # vishvamahadevan/distilbert-base-uncased-finetuned-squad 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: - Train Loss: 0.9560 - Validation Loss: 1.1174 - Epoch: 1 ## 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': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 11064, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 1.3862 | 1.1639 | 0 | | 0.9560 | 1.1174 | 1 | ### Framework versions - Transformers 4.19.4 - TensorFlow 2.8.2 - Datasets 2.2.2 - Tokenizers 0.12.1

huggingtweets/manfightdragon

huggingtweets

2022-06-12T10:26:35Z

106

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-06-12T10:23:38Z

--- language: en thumbnail: http://www.huggingtweets.com/manfightdragon/1655029573001/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/1184073162520031232/V6DOEeLp_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Lance McDonald</div> <div style="text-align: center; font-size: 14px;">@manfightdragon</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 Lance McDonald. | Data | Lance McDonald | | --- | --- | | Tweets downloaded | 3249 | | Retweets | 209 | | Short tweets | 214 | | Tweets kept | 2826 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3pc794z5/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 @manfightdragon's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2t8940p5) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2t8940p5/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/manfightdragon') 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* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

huggingtweets/bosstjanz

huggingtweets

2022-06-12T09:27:34Z

105

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-06-12T09:26:54Z

--- language: en thumbnail: http://www.huggingtweets.com/bosstjanz/1655026050127/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/1342130927737176064/SiNG_CxQ_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Zrimškow</div> <div style="text-align: center; font-size: 14px;">@bosstjanz</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 Zrimškow. | Data | Zrimškow | | --- | --- | | Tweets downloaded | 3225 | | Retweets | 368 | | Short tweets | 279 | | Tweets kept | 2578 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/23nemiqj/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 @bosstjanz's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2pjrymzt) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2pjrymzt/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/bosstjanz') 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* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

ironbar/dqn-SpaceInvadersNoFrameskip-v4-1M-steps

ironbar

2022-06-12T08:16:08Z

11

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-06-12T08:15:30Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - metrics: - type: mean_reward value: 629.50 +/- 140.06 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 --- # **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 ``` # Download model and save it into the logs/ folder python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga ironbar -f logs/ python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga ironbar ``` ## 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', True), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```

MyMild/finetune_iapp_thaiqa

MyMild

2022-06-12T07:52:39Z

57

0

transformers

[ "transformers", "pytorch", "tensorboard", "camembert", "question-answering", "generated_from_trainer", "endpoints_compatible", "region:us" ]

question-answering

2022-06-11T23:05:08Z

--- tags: - generated_from_trainer model-index: - name: finetune_iapp_thaiqa results: [] ---  # finetune_iapp_thaiqa This model is a fine-tuned version of [airesearch/wangchanberta-base-att-spm-uncased](https://huggingface.co/airesearch/wangchanberta-base-att-spm-uncased) on the None dataset. ## 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: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.2 - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.15.0 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.10.3

spuun/kekbot-mini

spuun

2022-06-12T05:53:59Z

106

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "en", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-06-12T03:40:33Z

--- language: - en metrics: - accuracy co2_eq_emissions: emissions: "10" source: "mlco2.github.io" training_type: "fine-tuning" geographical_location: "West Java, Indonesia" hardware_used: "1 T4" license: cc-by-nc-sa-4.0 widget: - text: 'You: "Hey kekbot! Whats up?"\nKekbot: "' example_title: "Asking what's up" - text: 'You: "Hey kekbot! How r u?"\nKekbot: "' example_title: "Asking how he is" --- > THIS MODEL IS INTENDED FOR RESEARCH PURPOSES ONLY # Kekbot Mini Based on a `distilgpt2` model, fine-tuned to a select subset (65k<= messages) of Art Union's general-chat channel chat history. ### Limits and biases As this is trained on chat history, it is possible that discriminatory or even offensive materials to be outputted. Author holds his ground on the fact that ML models are mere statistical representation of the dataset used to train it, and that due to the nature of the dataset it is practically impossible to be certain of the degree of "cleanliness" that the data contained within holds. Author can confirm, however, that from heuristical testing that the model was not found to be offensive to the author himself, hopefully this opinion stays true for everyone in the audience.

xdai/mimic_roberta_base

xdai

2022-06-12T04:51:26Z

5

1

transformers

[ "transformers", "pytorch", "roberta", "fill-mask", "Clinical notes", "Discharge summaries", "RoBERTa", "dataset:MIMIC-III", "arxiv:2204.06683", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-06-12T04:12:20Z

--- language: - English tags: - Clinical notes - Discharge summaries - RoBERTa license: "cc-by-4.0" datasets: - MIMIC-III --- * Continue pre-training RoBERTa-base using discharge summaries from MIMIC-III datasets. * Details can be found in the following paper > Xiang Dai and Ilias Chalkidis and Sune Darkner and Desmond Elliott. 2022. Revisiting Transformer-based Models for Long Document Classification. (https://arxiv.org/abs/2204.06683) * Important hyper-parameters | | | |---|---| | Max sequence | 128 | | Batch size | 128 | | Learning rate | 5e-5 | | Training epochs | 15 | | Training time | 40 GPU-hours |

huggingtweets/tayplaysgaymes

huggingtweets

2022-06-12T03:56:41Z

105

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-06-12T03:55:39Z

--- language: en thumbnail: http://www.huggingtweets.com/tayplaysgaymes/1655006196516/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/1144053838459969536/lv3yBmoX_400x400.png')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Tay</div> <div style="text-align: center; font-size: 14px;">@tayplaysgaymes</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 Tay. | Data | Tay | | --- | --- | | Tweets downloaded | 3212 | | Retweets | 693 | | Short tweets | 367 | | Tweets kept | 2152 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1hmextiq/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 @tayplaysgaymes's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3r0cse8x) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3r0cse8x/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/tayplaysgaymes') 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* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

bguan/SpaceInvadersNoFrameskip-v4

bguan

2022-06-12T01:05:09Z

4

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-06-12T01:04:38Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - metrics: - type: mean_reward value: 255.00 +/- 93.83 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 --- # **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 ``` # Download model and save it into the logs/ folder python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga bguan -f logs/ python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga bguan ``` ## 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', 500000), ('optimize_memory_usage', True), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```

TencentMedicalNet/MedicalNet-Resnet10

TencentMedicalNet

2022-06-12T00:26:42Z

0

4

null

[ "MedicalNet", "medical images", "medical", "3D", "Med3D", "en", "dataset:MRBrainS18", "arxiv:1904.00625", "license:mit", "region:us" ]

null

2022-06-11T23:12:06Z

--- license: mit datasets: - MRBrainS18 language: - en metrics: - tags: - MedicalNet - medical images - medical - 3D - Med3D thumbnail: "https://github.com/Tencent/MedicalNet/blob/master/images/logo.png?raw=true" --- # MedicalNet This repository contains a Pytorch implementation of [Med3D: Transfer Learning for 3D Medical Image Analysis](https://arxiv.org/abs/1904.00625). Many studies have shown that the performance on deep learning is significantly affected by volume of training data. The MedicalNet project aggregated the dataset with diverse modalities, target organs, and pathologies to to build relatively large datasets. Based on this dataset, a series of 3D-ResNet pre-trained models and corresponding transfer-learning training code are provided. ### License MedicalNet is released under the MIT License (refer to the LICENSE file for detailso). ### Citing MedicalNet If you use this code or pre-trained models, please cite the following: ``` @article{chen2019med3d, title={Med3D: Transfer Learning for 3D Medical Image Analysis}, author={Chen, Sihong and Ma, Kai and Zheng, Yefeng}, journal={arXiv preprint arXiv:1904.00625}, year={2019} } ``` ### Update(2019/07/30) We uploaded 4 pre-trained models based on more datasets (23 datasets). ``` Model name : parameters settings resnet_10_23dataset.pth: --model resnet --model_depth 10 --resnet_shortcut B resnet_18_23dataset.pth: --model resnet --model_depth 18 --resnet_shortcut A resnet_34_23dataset.pth: --model resnet --model_depth 34 --resnet_shortcut A resnet_50_23dataset.pth: --model resnet --model_depth 50 --resnet_shortcut B ``` Hugging Face repository contribution by: [Rafael Zimmer](https://www.github.com/rzimmerdev)

huggingtweets/laserboat999

huggingtweets

2022-06-11T23:53:52Z

105

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-06-11T23:49:07Z

--- language: en thumbnail: http://www.huggingtweets.com/laserboat999/1654991516445/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/1500274766195793921/bA4siut7_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">donald boat</div> <div style="text-align: center; font-size: 14px;">@laserboat999</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 donald boat. | Data | donald boat | | --- | --- | | Tweets downloaded | 3233 | | Retweets | 75 | | Short tweets | 516 | | Tweets kept | 2642 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/38v40fpf/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 @laserboat999's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/pk1xum9h) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/pk1xum9h/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/laserboat999') 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* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

DLWCMD/TEST2ppo-LunarLander-v2

DLWCMD

2022-06-11T23:39:16Z

1

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-06-11T23:38:43Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: 263.13 +/- 22.16 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # **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 ... ```

745H1N/LunarLander-v2-DQN-optuna

745H1N

2022-06-11T23:36:51Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-06-11T23:36:25Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - metrics: - type: mean_reward value: -140.18 +/- 41.67 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # **DQN** Agent playing **LunarLander-v2** This is a trained model of a **DQN** 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 ... ```

aprischa/bart-large-cnn-aprischa2

aprischa

2022-06-11T23:27:38Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-06-11T17:40:18Z

--- license: mit tags: - generated_from_trainer metrics: - rouge model-index: - name: bart-large-cnn-aprischa2 results: [] ---  # bart-large-cnn-aprischa2 This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3425 - Rouge1: 65.7088 - Rouge2: 56.6701 - Rougel: 62.1926 - Rougelsum: 64.7727 - Gen Len: 140.8469 ## 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: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | 0.3772 | 1.0 | 5403 | 0.3586 | 65.7702 | 56.7968 | 62.264 | 64.8605 | 140.268 | | 0.316 | 2.0 | 10806 | 0.3421 | 64.8238 | 55.8837 | 61.3245 | 63.8894 | 140.7472 | | 0.2397 | 3.0 | 16209 | 0.3425 | 65.7088 | 56.6701 | 62.1926 | 64.7727 | 140.8469 | ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

twieland/SCRATCH_ja-en_helsinki

twieland

2022-06-11T23:01:52Z

4

0

transformers

[ "transformers", "pytorch", "marian", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-06-11T01:05:11Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: SCRATCH_ja-en_helsinki results: [] ---  # SCRATCH_ja-en_helsinki This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ja-en](https://huggingface.co/Helsinki-NLP/opus-mt-ja-en) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.5583 - Otaku Benchmark VN BLEU: 19.12 - Otaku Benchmark LN BLEU: 11.55 - Otaku Benchmark MANGA BLEU: 12.98 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 96 - 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 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:------:|:---------------:| | 3.0252 | 0.02 | 2000 | 2.4140 | | 2.8406 | 0.03 | 4000 | 2.2819 | | 2.7505 | 0.05 | 6000 | 2.3018 | | 2.6948 | 0.06 | 8000 | 2.1931 | | 2.6408 | 0.08 | 10000 | 2.1724 | | 2.6004 | 0.09 | 12000 | 2.1583 | | 2.5685 | 0.11 | 14000 | 2.1203 | | 2.5432 | 0.12 | 16000 | 2.1593 | | 2.5153 | 0.14 | 18000 | 2.1009 | | 2.4906 | 0.15 | 20000 | 2.0899 | | 2.4709 | 0.17 | 22000 | 2.0512 | | 2.4471 | 0.18 | 24000 | 2.0208 | | 2.4295 | 0.2 | 26000 | 2.0773 | | 2.4154 | 0.21 | 28000 | 2.0441 | | 2.4008 | 0.23 | 30000 | 2.0235 | | 2.3834 | 0.24 | 32000 | 2.0190 | | 2.3709 | 0.26 | 34000 | 1.9831 | | 2.3537 | 0.27 | 36000 | 1.9870 | | 2.3486 | 0.29 | 38000 | 1.9692 | | 2.3346 | 0.3 | 40000 | 1.9517 | | 2.3195 | 0.32 | 42000 | 1.9800 | | 2.3104 | 0.33 | 44000 | 1.9676 | | 2.298 | 0.35 | 46000 | 1.9563 | | 2.2905 | 0.36 | 48000 | 1.9217 | | 2.2792 | 0.38 | 50000 | 1.9195 | | 2.2714 | 0.39 | 52000 | 1.9109 | | 2.2593 | 0.41 | 54000 | 1.9044 | | 2.2582 | 0.42 | 56000 | 1.8876 | | 2.2482 | 0.44 | 58000 | 1.8860 | | 2.2394 | 0.45 | 60000 | 1.8887 | | 2.2273 | 0.47 | 62000 | 1.8862 | | 2.2255 | 0.48 | 64000 | 1.8705 | | 2.2166 | 0.5 | 66000 | 1.8696 | | 2.2075 | 0.51 | 68000 | 1.8657 | | 2.1992 | 0.53 | 70000 | 1.8585 | | 2.1969 | 0.54 | 72000 | 1.8526 | | 2.1894 | 0.56 | 74000 | 1.8493 | | 2.1817 | 0.57 | 76000 | 1.8480 | | 2.1771 | 0.59 | 78000 | 1.8333 | | 2.1683 | 0.6 | 80000 | 1.8342 | | 2.1667 | 0.62 | 82000 | 1.8537 | | 2.1546 | 0.63 | 84000 | 1.8261 | | 2.1467 | 0.65 | 86000 | 1.8092 | | 2.1421 | 0.66 | 88000 | 1.8137 | | 2.1395 | 0.68 | 90000 | 1.8286 | | 2.1313 | 0.69 | 92000 | 1.8042 | | 2.1241 | 0.71 | 94000 | 1.7934 | | 2.1214 | 0.72 | 96000 | 1.7940 | | 2.12 | 0.74 | 98000 | 1.8064 | | 2.1096 | 0.75 | 100000 | 1.7983 | | 2.1035 | 0.77 | 102000 | 1.8089 | | 2.0937 | 0.78 | 104000 | 1.7941 | | 2.0893 | 0.8 | 106000 | 1.7791 | | 2.0869 | 0.81 | 108000 | 1.7807 | | 2.0845 | 0.83 | 110000 | 1.7852 | | 2.0782 | 0.84 | 112000 | 1.7675 | | 2.0755 | 0.86 | 114000 | 1.7756 | | 2.0657 | 0.87 | 116000 | 1.7604 | | 2.0614 | 0.89 | 118000 | 1.7447 | | 2.0591 | 0.9 | 120000 | 1.7489 | | 2.0586 | 0.92 | 122000 | 1.7550 | | 2.0498 | 0.93 | 124000 | 1.7543 | | 2.0455 | 0.95 | 126000 | 1.7510 | | 2.04 | 0.96 | 128000 | 1.7439 | | 2.0385 | 0.98 | 130000 | 1.7407 | | 2.0267 | 0.99 | 132000 | 1.7467 | | 2.0088 | 1.01 | 134000 | 1.7455 | | 1.9826 | 1.02 | 136000 | 1.7210 | | 1.9785 | 1.04 | 138000 | 1.7524 | | 1.9777 | 1.05 | 140000 | 1.7272 | | 1.9763 | 1.07 | 142000 | 1.7283 | | 1.9736 | 1.08 | 144000 | 1.7210 | | 1.9704 | 1.1 | 146000 | 1.7001 | | 1.9625 | 1.11 | 148000 | 1.7112 | | 1.9665 | 1.13 | 150000 | 1.7236 | | 1.9592 | 1.14 | 152000 | 1.7169 | | 1.9606 | 1.16 | 154000 | 1.6962 | | 1.9571 | 1.17 | 156000 | 1.7064 | | 1.9532 | 1.19 | 158000 | 1.6898 | | 1.9465 | 1.2 | 160000 | 1.7004 | | 1.9438 | 1.22 | 162000 | 1.7092 | | 1.9435 | 1.23 | 164000 | 1.6927 | | 1.9361 | 1.25 | 166000 | 1.6838 | | 1.9369 | 1.26 | 168000 | 1.6784 | | 1.9287 | 1.28 | 170000 | 1.6709 | | 1.928 | 1.29 | 172000 | 1.6735 | | 1.9227 | 1.31 | 174000 | 1.6689 | | 1.9213 | 1.32 | 176000 | 1.6685 | | 1.9152 | 1.34 | 178000 | 1.6635 | | 1.9092 | 1.35 | 180000 | 1.6561 | | 1.9059 | 1.37 | 182000 | 1.6673 | | 1.9094 | 1.38 | 184000 | 1.6717 | | 1.9006 | 1.4 | 186000 | 1.6593 | | 1.8956 | 1.41 | 188000 | 1.6483 | | 1.8972 | 1.43 | 190000 | 1.6635 | | 1.8907 | 1.44 | 192000 | 1.6604 | | 1.8885 | 1.46 | 194000 | 1.6465 | | 1.8844 | 1.47 | 196000 | 1.6444 | | 1.8799 | 1.49 | 198000 | 1.6307 | | 1.8813 | 1.5 | 200000 | 1.6240 | | 1.8693 | 1.52 | 202000 | 1.6102 | | 1.8768 | 1.53 | 204000 | 1.6197 | | 1.8678 | 1.55 | 206000 | 1.6275 | | 1.8588 | 1.56 | 208000 | 1.6183 | | 1.8585 | 1.58 | 210000 | 1.6197 | | 1.8564 | 1.59 | 212000 | 1.6004 | | 1.8493 | 1.61 | 214000 | 1.6078 | | 1.85 | 1.62 | 216000 | 1.6001 | | 1.8428 | 1.64 | 218000 | 1.6106 | | 1.8428 | 1.65 | 220000 | 1.5866 | | 1.8423 | 1.67 | 222000 | 1.5993 | | 1.8352 | 1.68 | 224000 | 1.6052 | | 1.8385 | 1.7 | 226000 | 1.5959 | | 1.8307 | 1.71 | 228000 | 1.6024 | | 1.8248 | 1.73 | 230000 | 1.5969 | | 1.82 | 1.74 | 232000 | 1.5878 | | 1.8254 | 1.76 | 234000 | 1.5934 | | 1.8188 | 1.77 | 236000 | 1.5827 | | 1.813 | 1.79 | 238000 | 1.5797 | | 1.8128 | 1.8 | 240000 | 1.5758 | | 1.8044 | 1.82 | 242000 | 1.5752 | | 1.808 | 1.83 | 244000 | 1.5818 | | 1.8025 | 1.85 | 246000 | 1.5772 | | 1.7992 | 1.86 | 248000 | 1.5738 | | 1.8021 | 1.88 | 250000 | 1.5752 | | 1.7988 | 1.89 | 252000 | 1.5717 | | 1.7967 | 1.91 | 254000 | 1.5690 | | 1.7909 | 1.92 | 256000 | 1.5607 | | 1.7942 | 1.94 | 258000 | 1.5618 | | 1.7897 | 1.95 | 260000 | 1.5585 | | 1.7871 | 1.97 | 262000 | 1.5576 | | 1.7843 | 1.98 | 264000 | 1.5577 | | 1.7888 | 2.0 | 266000 | 1.5583 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

meghazisofiane/opus-mt-en-ar-evaluated-en-to-ar-4000instances-opus-leaningRate2e-05-batchSize8-11-action-1

meghazisofiane

2022-06-11T21:50:40Z

103

0

transformers

[ "transformers", "pytorch", "tensorboard", "marian", "text2text-generation", "generated_from_trainer", "dataset:opus100", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-06-11T21:33:30Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - opus100 metrics: - bleu model-index: - name: opus-mt-en-ar-evaluated-en-to-ar-4000instances-opus-leaningRate2e-05-batchSize8-11-action-1 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: opus100 type: opus100 args: ar-en metrics: - name: Bleu type: bleu value: 26.8232 ---  # opus-mt-en-ar-evaluated-en-to-ar-4000instances-opus-leaningRate2e-05-batchSize8-11-action-1 This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ar](https://huggingface.co/Helsinki-NLP/opus-mt-en-ar) on the opus100 dataset. It achieves the following results on the evaluation set: - Loss: 0.1717 - Bleu: 26.8232 - Meteor: 0.172 - Gen Len: 12.1288 ## 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: 11 ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Meteor | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:| | 0.7364 | 0.25 | 100 | 0.1731 | 27.2753 | 0.1729 | 12.0887 | | 0.2175 | 0.5 | 200 | 0.1731 | 27.2055 | 0.1722 | 11.5675 | | 0.2193 | 0.75 | 300 | 0.1722 | 27.3277 | 0.1798 | 12.1325 | | 0.2321 | 1.0 | 400 | 0.1750 | 27.5152 | 0.1762 | 11.925 | | 0.1915 | 1.25 | 500 | 0.1690 | 27.5043 | 0.1751 | 11.9038 | | 0.1794 | 1.5 | 600 | 0.1719 | 26.8607 | 0.1713 | 11.8138 | | 0.1741 | 1.75 | 700 | 0.1725 | 26.974 | 0.1724 | 11.8462 | | 0.1732 | 2.0 | 800 | 0.1717 | 26.8232 | 0.172 | 12.1288 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

lindeberg/distilbert-base-uncased-finetuned-cola

lindeberg

2022-06-11T21:10:06Z

106

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-06-11T18:50:58Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.4496664370323995 ---  # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.4949 - Matthews Correlation: 0.4497 ## 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: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5231 | 1.0 | 535 | 0.4949 | 0.4497 | ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

JClementC/test

JClementC

2022-06-11T19:58:42Z

0

null

[ "region:us" ]

null

2022-06-11T19:19:48Z

git lfs install git clone https://github.com/nneonneo/2048-ai.git

meln1k/qrdqn-SpaceInvadersNoFrameskip-v4

meln1k

2022-06-11T19:51:36Z

5

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-06-11T09:29:19Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: QRDQN results: - metrics: - type: mean_reward value: 2581.50 +/- 1151.96 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 --- # **QRDQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **QRDQN** 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 ``` # Download model and save it into the logs/ folder python -m utils.load_from_hub --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -orga meln1k -f logs/ python enjoy.py --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m utils.push_to_hub --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga meln1k ``` ## Hyperparameters ```python OrderedDict([('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_fraction', 0.025), ('frame_stack', 4), ('n_timesteps', 10000000.0), ('optimize_memory_usage', True), ('policy', 'CnnPolicy'), ('normalize', False)]) ```

huggingtweets/conanobrien-mikemancini-wendymolyneux

huggingtweets

2022-06-11T19:50:54Z

105

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-06-11T19:46:43Z

--- language: en thumbnail: http://www.huggingtweets.com/conanobrien-mikemancini-wendymolyneux/1654977049172/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/1271404115042676736/PAIbmN-p_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/730612231021322240/Rl0_QYhL_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/1044085580651528193/DR7QvrwG_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">mike mancini & Conan O'Brien & Wendy Molyneux</div> <div style="text-align: center; font-size: 14px;">@conanobrien-mikemancini-wendymolyneux</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 mike mancini & Conan O'Brien & Wendy Molyneux. | Data | mike mancini | Conan O'Brien | Wendy Molyneux | | --- | --- | --- | --- | | Tweets downloaded | 3150 | 3250 | 836 | | Retweets | 286 | 40 | 251 | | Short tweets | 290 | 24 | 69 | | Tweets kept | 2574 | 3186 | 516 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/25wtfzk4/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 @conanobrien-mikemancini-wendymolyneux's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1hjizcue) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1hjizcue/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/conanobrien-mikemancini-wendymolyneux') 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* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

huggingtweets/mdoukmas

huggingtweets

2022-06-11T19:35:54Z

105

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-06-11T19:34:24Z

--- language: en thumbnail: http://www.huggingtweets.com/mdoukmas/1654976150184/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/1098660288193269762/n5v9daol_400x400.png')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Maya Dukmasova</div> <div style="text-align: center; font-size: 14px;">@mdoukmas</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 Maya Dukmasova. | Data | Maya Dukmasova | | --- | --- | | Tweets downloaded | 3241 | | Retweets | 896 | | Short tweets | 158 | | Tweets kept | 2187 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2jwhv7l5/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 @mdoukmas's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/25v3pmsy) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/25v3pmsy/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/mdoukmas') 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* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

titi7242229/roberta-base-bne-finetuned_personality_multi_4

titi7242229

2022-06-11T19:13:27Z

103

0

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-06-11T13:23:50Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: roberta-base-bne-finetuned_personality_multi_4 results: [] ---  # roberta-base-bne-finetuned_personality_multi_4 This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.1709 - Accuracy: 0.3470 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 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 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.1759 | 1.0 | 125 | 2.1873 | 0.2548 | | 1.8651 | 2.0 | 250 | 2.2285 | 0.2680 | | 1.8619 | 3.0 | 375 | 2.1732 | 0.2951 | | 1.7224 | 4.0 | 500 | 2.0688 | 0.3925 | | 1.6432 | 5.0 | 625 | 2.1094 | 0.3735 | | 1.3599 | 6.0 | 750 | 2.1732 | 0.3631 | | 1.0623 | 7.0 | 875 | 2.4785 | 0.3579 | | 1.0504 | 8.0 | 1000 | 2.4598 | 0.3844 | | 0.7662 | 9.0 | 1125 | 2.8081 | 0.3573 | | 0.9167 | 10.0 | 1250 | 2.9385 | 0.3452 | | 0.6391 | 11.0 | 1375 | 2.9933 | 0.3320 | | 0.3893 | 12.0 | 1500 | 3.1037 | 0.3579 | | 0.673 | 13.0 | 1625 | 3.4369 | 0.3631 | | 0.3498 | 14.0 | 1750 | 3.6396 | 0.3383 | | 0.3891 | 15.0 | 1875 | 3.8332 | 0.3556 | | 0.0818 | 16.0 | 2000 | 3.9451 | 0.3401 | | 0.1438 | 17.0 | 2125 | 3.9271 | 0.3458 | | 0.0634 | 18.0 | 2250 | 4.1564 | 0.3481 | | 0.0121 | 19.0 | 2375 | 4.1405 | 0.3499 | | 0.0071 | 20.0 | 2500 | 4.1709 | 0.3470 | ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

aprischa/bart-large-cnn-aprischa

aprischa

2022-06-11T17:21:57Z

105

0

transformers

[ "transformers", "pytorch", "bart", "text2text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-06-11T16:53:31Z

--- license: mit tags: - generated_from_trainer metrics: - rouge model-index: - name: bart-large-cnn-aprischa results: [] ---  # bart-large-cnn-aprischa This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3589 - Rouge1: 66.7098 - Rouge2: 57.7992 - Rougel: 63.2231 - Rougelsum: 65.9009 - Gen Len: 141.198 ## 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: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | 0.369 | 1.0 | 5403 | 0.3835 | 66.0604 | 56.9948 | 62.4967 | 65.265 | 141.1126 | | 0.2985 | 2.0 | 10806 | 0.3589 | 66.7098 | 57.7992 | 63.2231 | 65.9009 | 141.198 | ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

DancingIguana/codeparrot-ds

DancingIguana

2022-06-11T16:58:04Z

107

0

transformers

[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-06-08T21:56:49Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: codeparrot-ds results: [] ---  # codeparrot-ds This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) 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: 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: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

bubblecookie/t5-small-finetuned-cnndm_trained

bubblecookie

2022-06-11T16:48:45Z

103

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:cnn_dailymail", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-06-10T06:21:02Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - cnn_dailymail model-index: - name: t5-small-finetuned-cnndm_trained results: [] ---  # t5-small-finetuned-cnndm_trained This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dailymail 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: 0.0003 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

robingeibel/longformer-base-finetuned-big_patent

robingeibel

2022-06-11T16:33:49Z

62

1

transformers

[ "transformers", "tf", "longformer", "fill-mask", "generated_from_keras_callback", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-06-05T17:24:27Z

--- tags: - generated_from_keras_callback model-index: - name: robingeibel/longformer-base-finetuned-big_patent results: [] ---  # robingeibel/longformer-base-finetuned-big_patent This model is a fine-tuned version of [robingeibel/longformer-base-finetuned-big_patent](https://huggingface.co/robingeibel/longformer-base-finetuned-big_patent) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.1860 - Validation Loss: 1.0692 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 152946, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 1.1860 | 1.0692 | 0 | ### Framework versions - Transformers 4.19.4 - TensorFlow 2.8.2 - Datasets 2.2.2 - Tokenizers 0.12.1

abdoutony207/m2m100_418M-evaluated-en-to-ar-2000instancesopus-leaningRate2e-05-batchSize16-20epoch-1

abdoutony207

2022-06-11T16:26:19Z

106

0

transformers

[ "transformers", "pytorch", "tensorboard", "m2m_100", "text2text-generation", "generated_from_trainer", "dataset:opus100", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-06-11T15:56:17Z

--- license: mit tags: - generated_from_trainer datasets: - opus100 metrics: - bleu model-index: - name: m2m100_418M-evaluated-en-to-ar-2000instancesopus-leaningRate2e-05-batchSize16-20epoch-1 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: opus100 type: opus100 args: ar-en metrics: - name: Bleu type: bleu value: 13.1835 ---  # m2m100_418M-evaluated-en-to-ar-2000instancesopus-leaningRate2e-05-batchSize16-20epoch-1 This model is a fine-tuned version of [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) on the opus100 dataset. It achieves the following results on the evaluation set: - Loss: 0.3640 - Bleu: 13.1835 - Meteor: 0.1189 - Gen Len: 17.72 ## 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 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Meteor | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:| | 6.1776 | 1.0 | 100 | 3.8904 | 10.5866 | 0.0995 | 16.64 | | 2.4531 | 2.0 | 200 | 1.0928 | 12.3452 | 0.1108 | 17.0575 | | 0.512 | 3.0 | 300 | 0.3625 | 10.5224 | 0.0982 | 17.2575 | | 0.1924 | 4.0 | 400 | 0.3342 | 12.4242 | 0.1098 | 16.6325 | | 0.1227 | 5.0 | 500 | 0.3403 | 13.0526 | 0.1185 | 17.3475 | | 0.0889 | 6.0 | 600 | 0.3481 | 13.1323 | 0.1133 | 17.815 | | 0.0651 | 7.0 | 700 | 0.3601 | 12.6684 | 0.1133 | 17.3525 | | 0.0533 | 8.0 | 800 | 0.3640 | 13.1835 | 0.1189 | 17.72 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

neeenway/ppo-LunarLander-v2

neeenway

2022-06-11T13:43:31Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-06-11T13:43:03Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: ppo results: - metrics: - type: mean_reward value: 240.31 +/- 12.46 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # **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 ... ```

Akshat/xlm-roberta-base-finetuned-panx-de

Akshat

2022-06-11T13:35:25Z

104

0

transformers

[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-06-11T12:19:48Z

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8611443210930829 ---  # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1405 - F1: 0.8611 ## 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: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2542 | 1.0 | 787 | 0.1788 | 0.8083 | | 0.1307 | 2.0 | 1574 | 0.1371 | 0.8488 | | 0.0784 | 3.0 | 2361 | 0.1405 | 0.8611 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3

YeRyeongLee/albert-base-v2-finetuned-filtered-0609

YeRyeongLee

2022-06-11T13:33:02Z

106

0

transformers

[ "transformers", "pytorch", "albert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-06-11T11:46:52Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: albert-base-v2-finetuned-filtered-0609 results: [] ---  # albert-base-v2-finetuned-filtered-0609 This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2062 - Accuracy: 0.9723 - Precision: 0.9724 - Recall: 0.9723 - F1: 0.9723 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.2688 | 1.0 | 3180 | 0.2282 | 0.9560 | 0.9577 | 0.9560 | 0.9562 | | 0.2268 | 2.0 | 6360 | 0.1909 | 0.9638 | 0.9640 | 0.9638 | 0.9638 | | 0.1831 | 3.0 | 9540 | 0.2590 | 0.9572 | 0.9584 | 0.9572 | 0.9572 | | 0.1588 | 4.0 | 12720 | 0.1752 | 0.9673 | 0.9678 | 0.9673 | 0.9673 | | 0.0972 | 5.0 | 15900 | 0.1868 | 0.9695 | 0.9696 | 0.9695 | 0.9695 | | 0.0854 | 6.0 | 19080 | 0.2042 | 0.9701 | 0.9707 | 0.9701 | 0.9702 | | 0.0599 | 7.0 | 22260 | 0.1793 | 0.9748 | 0.9749 | 0.9748 | 0.9749 | | 0.0389 | 8.0 | 25440 | 0.1996 | 0.9742 | 0.9743 | 0.9742 | 0.9742 | | 0.0202 | 9.0 | 28620 | 0.2188 | 0.9723 | 0.9726 | 0.9723 | 0.9724 | | 0.0152 | 10.0 | 31800 | 0.2062 | 0.9723 | 0.9724 | 0.9723 | 0.9723 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.9.1+cu111 - Datasets 1.16.1 - Tokenizers 0.12.1

marieke93/BERT-evidence-types

marieke93

2022-06-11T13:32:10Z

107

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-06-08T11:54:50Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: BERT-evidence-types results: [] ---  # BERT-evidence-types This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the evidence types dataset. It achieves the following results on the evaluation set: - Loss: 2.8008 - Macro f1: 0.4227 - Weighted f1: 0.6976 - Accuracy: 0.7154 - Balanced accuracy: 0.3876 ## Training and evaluation data The data set, as well as the code that was used to fine tune this model can be found in the GitHub repository [BA-Thesis-Information-Science-Persuasion-Strategies](https://github.com/mariekevdh/BA-Thesis-Information-Science-Persuasion-Strategies) ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Macro f1 | Weighted f1 | Accuracy | Balanced accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:-----------------:| | 1.1148 | 1.0 | 125 | 1.0531 | 0.2566 | 0.6570 | 0.6705 | 0.2753 | | 0.7546 | 2.0 | 250 | 0.9725 | 0.3424 | 0.6947 | 0.7002 | 0.3334 | | 0.4757 | 3.0 | 375 | 1.1375 | 0.3727 | 0.7113 | 0.7184 | 0.3680 | | 0.2637 | 4.0 | 500 | 1.3585 | 0.3807 | 0.6836 | 0.6910 | 0.3805 | | 0.1408 | 5.0 | 625 | 1.6605 | 0.3785 | 0.6765 | 0.6872 | 0.3635 | | 0.0856 | 6.0 | 750 | 1.9703 | 0.3802 | 0.6890 | 0.7047 | 0.3704 | | 0.0502 | 7.0 | 875 | 2.1245 | 0.4067 | 0.6995 | 0.7169 | 0.3751 | | 0.0265 | 8.0 | 1000 | 2.2676 | 0.3756 | 0.6816 | 0.6925 | 0.3647 | | 0.0147 | 9.0 | 1125 | 2.4286 | 0.4052 | 0.6887 | 0.7062 | 0.3803 | | 0.0124 | 10.0 | 1250 | 2.5773 | 0.4084 | 0.6853 | 0.7040 | 0.3695 | | 0.0111 | 11.0 | 1375 | 2.5941 | 0.4146 | 0.6915 | 0.7085 | 0.3834 | | 0.0076 | 12.0 | 1500 | 2.6124 | 0.4157 | 0.6936 | 0.7078 | 0.3863 | | 0.0067 | 13.0 | 1625 | 2.7050 | 0.4139 | 0.6925 | 0.7108 | 0.3798 | | 0.0087 | 14.0 | 1750 | 2.6695 | 0.4252 | 0.7009 | 0.7169 | 0.3920 | | 0.0056 | 15.0 | 1875 | 2.7357 | 0.4257 | 0.6985 | 0.7161 | 0.3868 | | 0.0054 | 16.0 | 2000 | 2.7389 | 0.4249 | 0.6955 | 0.7116 | 0.3890 | | 0.0051 | 17.0 | 2125 | 2.7767 | 0.4197 | 0.6967 | 0.7146 | 0.3863 | | 0.004 | 18.0 | 2250 | 2.7947 | 0.4211 | 0.6977 | 0.7154 | 0.3876 | | 0.0041 | 19.0 | 2375 | 2.8030 | 0.4204 | 0.6953 | 0.7131 | 0.3855 | | 0.0042 | 20.0 | 2500 | 2.8008 | 0.4227 | 0.6976 | 0.7154 | 0.3876 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

titi7242229/roberta-base-bne-finetuned_personality_multi_3

titi7242229

2022-06-11T13:13:47Z

103

0

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-06-11T07:10:27Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: roberta-base-bne-finetuned_personality_multi_3 results: [] ---  # roberta-base-bne-finetuned_personality_multi_3 This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1145 - Accuracy: 0.4847 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.2498 | 1.0 | 63 | 2.2799 | 0.2236 | | 2.3044 | 2.0 | 126 | 2.1644 | 0.2980 | | 1.9017 | 3.0 | 189 | 1.9934 | 0.4127 | | 2.2281 | 4.0 | 252 | 1.8517 | 0.4501 | | 1.2955 | 5.0 | 315 | 1.7588 | 0.4870 | | 1.221 | 6.0 | 378 | 1.7269 | 0.4888 | | 1.1381 | 7.0 | 441 | 1.7617 | 0.4888 | | 0.8415 | 8.0 | 504 | 1.8101 | 0.4853 | | 0.6696 | 9.0 | 567 | 1.8325 | 0.4928 | | 0.6646 | 10.0 | 630 | 1.8707 | 0.4841 | | 0.3758 | 11.0 | 693 | 1.8766 | 0.4876 | | 0.3477 | 12.0 | 756 | 1.9171 | 0.4905 | | 0.2854 | 13.0 | 819 | 1.9203 | 0.4980 | | 0.2713 | 14.0 | 882 | 2.0089 | 0.4813 | | 0.3434 | 15.0 | 945 | 2.0130 | 0.4905 | | 0.0758 | 16.0 | 1008 | 2.0230 | 0.4922 | | 0.2518 | 17.0 | 1071 | 2.0793 | 0.4824 | | 0.0783 | 18.0 | 1134 | 2.0920 | 0.4830 | | 0.0933 | 19.0 | 1197 | 2.1067 | 0.4836 | | 0.184 | 20.0 | 1260 | 2.1145 | 0.4847 | ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

shivarama23/swin-tiny-patch4-window7-224-finetuned-image_quality

shivarama23

2022-06-11T11:54:49Z

85

1

transformers

[ "transformers", "pytorch", "tensorboard", "swin", "image-classification", "generated_from_trainer", "dataset:image_folder", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2022-06-11T11:41:01Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - image_folder metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-finetuned-image_quality results: - task: name: Image Classification type: image-classification dataset: name: image_folder type: image_folder args: default metrics: - name: Accuracy type: accuracy value: 0.9090909090909091 ---  # swin-tiny-patch4-window7-224-finetuned-image_quality This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.5242 - Accuracy: 0.9091 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 1 | 0.6762 | 0.6364 | | No log | 2.0 | 2 | 0.6309 | 0.7273 | | No log | 3.0 | 3 | 0.6095 | 0.6364 | | No log | 4.0 | 4 | 0.5775 | 0.6364 | | No log | 5.0 | 5 | 0.5443 | 0.8182 | | No log | 6.0 | 6 | 0.5242 | 0.9091 | | No log | 7.0 | 7 | 0.5149 | 0.8182 | | No log | 8.0 | 8 | 0.5094 | 0.8182 | | No log | 9.0 | 9 | 0.5038 | 0.8182 | | 0.4095 | 10.0 | 10 | 0.4992 | 0.8182 | ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

Jawaher/LIAR-fake-news-roberta-base

Jawaher

2022-06-11T11:12:24Z

103

1

transformers

[ "transformers", "pytorch", "tf", "roberta", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-06-11T05:40:13Z

A pre-trained Roberta masked language model (MLM) trained on around 12K fake news dataset called LIAR. The perplexity of the original pre-trained Roberta model on the dataset is 5.957 and the perplexity of the adapted model is 3.918.

mmillet/distilrubert-tiny-2nd-finetune-epru

mmillet

2022-06-11T09:50:42Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-06-11T09:48:50Z

--- tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: distilrubert-tiny-2nd-finetune-epru results: [] ---  # distilrubert-tiny-2nd-finetune-epru This model is a fine-tuned version of [mmillet/distilrubert-tiny-cased-conversational-v1_single_finetuned_on_cedr_augmented](https://huggingface.co/mmillet/distilrubert-tiny-cased-conversational-v1_single_finetuned_on_cedr_augmented) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3546 - Accuracy: 0.9325 - F1: 0.9328 - Precision: 0.9359 - Recall: 0.9325 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 0.0686 | 1.0 | 12 | 0.2931 | 0.9141 | 0.9142 | 0.9163 | 0.9141 | | 0.0269 | 2.0 | 24 | 0.2690 | 0.9448 | 0.9444 | 0.9449 | 0.9448 | | 0.0282 | 3.0 | 36 | 0.3140 | 0.9141 | 0.9140 | 0.9168 | 0.9141 | | 0.0185 | 4.0 | 48 | 0.2977 | 0.9571 | 0.9570 | 0.9576 | 0.9571 | | 0.0103 | 5.0 | 60 | 0.3368 | 0.9264 | 0.9265 | 0.9296 | 0.9264 | | 0.0088 | 6.0 | 72 | 0.3067 | 0.9387 | 0.9385 | 0.9389 | 0.9387 | | 0.0152 | 7.0 | 84 | 0.3660 | 0.9264 | 0.9263 | 0.9282 | 0.9264 | | 0.0315 | 8.0 | 96 | 0.3793 | 0.9325 | 0.9328 | 0.9359 | 0.9325 | | 0.0258 | 9.0 | 108 | 0.3546 | 0.9325 | 0.9328 | 0.9359 | 0.9325 | ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

OTQ/q-Taxi-v3

OTQ

2022-06-11T08:10:17Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2022-06-11T08:10:10Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - metrics: - type: mean_reward value: 7.50 +/- 2.78 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 --- # **Q-Learning** Agent playing **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```

huggingtweets/gustholomulers

huggingtweets

2022-06-11T07:53:54Z

105

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-06-11T07:50:54Z

--- language: en thumbnail: http://www.huggingtweets.com/gustholomulers/1654934015981/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/1535477036353040384/tXI_s1Yi_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">soppy</div> <div style="text-align: center; font-size: 14px;">@gustholomulers</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 soppy. | Data | soppy | | --- | --- | | Tweets downloaded | 1482 | | Retweets | 55 | | Short tweets | 329 | | Tweets kept | 1098 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1nhfbopf/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 @gustholomulers's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3p5yu4wm) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3p5yu4wm/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/gustholomulers') 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* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

AryaSuprana/BRATA_RoBERTaBali

AryaSuprana

2022-06-11T05:01:40Z

161

0

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "fill-mask", "ban", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-06-11T04:51:40Z

--- language: "ban" datasets: - WikiBali - Suara Saking Bali widget: - text: "Kalsium silih <mask> datu kimia antuk simbol Ca miwah wilangan atom 20." example_title: "Conto 1" - text: "Tabuan inggih <mask> silih tunggil soroh beburon sane madue kampid." example_title: "Conto 2" --- BRATA (Basa Bali Used for Pretraining RoBERTa) is a pretrained language model trained using Basa Bali or Balinese Language with RoBERTa-base-uncased configuration. The datasets used for this pretraining were collected by extracting WikiBali or Wikipedia Basa Bali and some sources from Suara Saking Bali website. The pretrained language model trained using Google Colab Pro with Tesla P100-PCIE-16GB GPU. Pretraining process used 200 epoch and 2 batch size. The smallest training loss can be seen in Training metrics or Metrics tab.

tclong/wav2vec2-base-vios-commonvoice-1

tclong

2022-06-11T03:01:54Z

106

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-06-10T11:09:14Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-vios-commonvoice-1 results: [] ---  # wav2vec2-base-vios-commonvoice-1 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8913 - Wer: 0.3621 ## 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: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.4706 | 0.55 | 500 | 3.4725 | 1.0 | | 3.202 | 1.1 | 1000 | 2.7555 | 1.0008 | | 1.0507 | 1.66 | 1500 | 1.0481 | 0.6196 | | 0.7325 | 2.21 | 2000 | 0.8120 | 0.4958 | | 0.599 | 2.76 | 2500 | 0.7035 | 0.4447 | | 0.5224 | 3.31 | 3000 | 0.6761 | 0.4078 | | 0.4844 | 3.86 | 3500 | 0.6688 | 0.4011 | | 0.4234 | 4.42 | 4000 | 0.6080 | 0.3729 | | 0.4237 | 4.97 | 4500 | 0.5953 | 0.3556 | | 0.3986 | 5.52 | 5000 | 0.6054 | 0.3478 | | 0.3554 | 6.07 | 5500 | 0.6193 | 0.3479 | | 0.3446 | 6.62 | 6000 | 0.5809 | 0.3302 | | 0.3104 | 7.17 | 6500 | 0.5713 | 0.3283 | | 0.3166 | 7.73 | 7000 | 0.5593 | 0.3133 | | 0.2938 | 8.28 | 7500 | 0.5645 | 0.3081 | | 0.3061 | 8.83 | 8000 | 0.5508 | 0.3020 | | 0.2986 | 9.38 | 8500 | 0.5462 | 0.3024 | | 0.2939 | 9.93 | 9000 | 0.5544 | 0.3028 | | 0.2633 | 10.49 | 9500 | 0.5496 | 0.3024 | | 0.2683 | 11.04 | 10000 | 0.5439 | 0.2946 | | 0.2714 | 11.59 | 10500 | 0.5524 | 0.2947 | | 0.2354 | 12.14 | 11000 | 0.5267 | 0.2918 | | 0.2488 | 12.69 | 11500 | 0.5728 | 0.2938 | | 0.2479 | 13.25 | 12000 | 0.5802 | 0.2951 | | 0.245 | 13.8 | 12500 | 0.5571 | 0.2890 | | 0.2422 | 14.35 | 13000 | 0.5531 | 0.2871 | | 0.2369 | 14.9 | 13500 | 0.5453 | 0.2860 | | 0.2345 | 15.45 | 14000 | 0.5452 | 0.2847 | | 0.2507 | 16.0 | 14500 | 0.5536 | 0.2884 | | 0.2454 | 16.56 | 15000 | 0.5577 | 0.2871 | | 0.2729 | 17.11 | 15500 | 0.6019 | 0.2931 | | 0.2743 | 17.66 | 16000 | 0.5619 | 0.2905 | | 0.3031 | 18.21 | 16500 | 0.6401 | 0.3006 | | 0.315 | 18.76 | 17000 | 0.6044 | 0.2990 | | 0.4025 | 19.32 | 17500 | 0.6739 | 0.3304 | | 0.4915 | 19.87 | 18000 | 0.7267 | 0.3472 | | 0.5539 | 20.42 | 18500 | 0.8078 | 0.3483 | | 0.7138 | 20.97 | 19000 | 0.9362 | 0.3765 | | 0.5766 | 21.52 | 19500 | 0.7921 | 0.3392 | | 0.688 | 22.08 | 20000 | 0.8833 | 0.3693 | | 0.6964 | 22.63 | 20500 | 0.9137 | 0.3469 | | 0.7389 | 23.18 | 21000 | 0.9379 | 0.3460 | | 0.7851 | 23.73 | 21500 | 1.0438 | 0.3653 | | 0.7619 | 24.28 | 22000 | 0.9313 | 0.3873 | | 0.7175 | 24.83 | 22500 | 0.8668 | 0.3789 | | 0.6842 | 25.39 | 23000 | 0.8243 | 0.3761 | | 0.6941 | 25.94 | 23500 | 0.8557 | 0.3804 | | 0.7167 | 26.49 | 24000 | 0.8618 | 0.3875 | | 0.721 | 27.04 | 24500 | 0.8686 | 0.3764 | | 0.6949 | 27.59 | 25000 | 0.8773 | 0.3690 | | 0.727 | 28.15 | 25500 | 0.8769 | 0.3666 | | 0.7363 | 28.7 | 26000 | 0.8867 | 0.3634 | | 0.7157 | 29.25 | 26500 | 0.8895 | 0.3626 | | 0.7385 | 29.8 | 27000 | 0.8913 | 0.3621 | ### Framework versions - Transformers 4.19.3 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

huggingtweets/froliki2108

huggingtweets

2022-06-11T00:04:16Z

105

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-06-11T00:02:55Z

--- language: en thumbnail: http://www.huggingtweets.com/froliki2108/1654905851117/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/1447692349493100549/1PV2c-PJ_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Froliki💉💉💉</div> <div style="text-align: center; font-size: 14px;">@froliki2108</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 Froliki💉💉💉. | Data | Froliki💉💉💉 | | --- | --- | | Tweets downloaded | 2223 | | Retweets | 1133 | | Short tweets | 229 | | Tweets kept | 861 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2tug3miv/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 @froliki2108's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3otsf5pj) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3otsf5pj/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/froliki2108') 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* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

nateraw/modelcard-creator-demo

nateraw

2022-06-10T23:58:39Z

0

pytorch

[ "pytorch", "modelcards", "autogenerated-modelcard", "en", "dataset:beans", "arxiv:1810.03993", "arxiv:1910.09700", "license:mit", "region:us" ]

null

2022-06-10T23:40:23Z

--- language: - en license: mit library_name: pytorch tags: - modelcards - autogenerated-modelcard datasets: - beans metrics: - accuracy --- # modelcard-creator-demo ## Table of Contents - [Model Details](#model-details) - [How To Get Started With the Model](#how-to-get-started-with-the-model) - [Uses](#uses) - [Direct Use](#direct-use) - [Downstream Use](#downstream-use) - [Misuse and Out of Scope Use](#misuse-and-out-of-scope-use) - [Limitations and Biases](#limitations-and-biases) - [Training](#training) - [Training Data](#training-data) - [Training Procedure](#training-procedure) - [Evaluation Results](#evaluation-results) - [Environmental Impact](#environmental-impact) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Model Details  This isn't really a model, it's just a test repo to see if the [model card creator](https://huggingface.co/spaces/nateraw/modelcard-creator) works! - Developed by: Nathan Raw - Language(s): - License: modelcard-creator-demo is licensed under the mit license - Resources for more information: - [Research Paper](https://arxiv.org/pdf/1810.03993.pdf) - [GitHub Repo](https://github.com/nateraw/modelcards) ## How to Get Started with the Model Use the code below to get started with the model. ```python # A nice code snippet here that describes how to use the model... ``` ## Uses #### Direct Use  [More Information Needed] #### Downstream Use  [More Information Needed] #### Misuse and Out-of-scope Use  [More Information Needed] ## Limitations and Biases  **CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propogate historical and current stereotypes.** [More Information Needed] ## Training #### Training Data   See the data card for additional information. #### Training Procedure  [More Information Needed] ## Evaluation Results  [More Information Needed] ## Environmental Impact  You can estimate carbon emissions 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:** - **Hours used:** - **Cloud Provider:** - **Compute Region:** - **Carbon Emitted:** ## Citation Information ```bibtex @inproceedings{Mitchell_2019, doi = {10.1145/3287560.3287596}, url = {https://doi.org/10.1145%2F3287560.3287596}, year = 2019, month = {jan}, publisher = {{ACM} }, author = {Margaret Mitchell and Simone Wu and Andrew Zaldivar and Parker Barnes and Lucy Vasserman and Ben Hutchinson and Elena Spitzer and Inioluwa Deborah Raji and Timnit Gebru}, title = {Model Cards for Model Reporting}, booktitle = {Proceedings of the Conference on Fairness, Accountability, and Transparency} } ```

ahmeddbahaa/t5-arabic-base-finetuned-wikilingua-ar

ahmeddbahaa

2022-06-10T23:54:52Z

12

1

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "summarization", "mt5", "ar", "abstractive summarization", "generated_from_trainer", "dataset:wiki_lingua", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

summarization

2022-06-10T15:19:23Z

--- license: apache-2.0 tags: - summarization - mt5 - ar - abstractive summarization - generated_from_trainer datasets: - wiki_lingua model-index: - name: t5-arabic-base-finetuned-wikilingua-ar results: [] ---  # t5-arabic-base-finetuned-wikilingua-ar This model is a fine-tuned version of [bakrianoo/t5-arabic-base](https://huggingface.co/bakrianoo/t5-arabic-base) on the wiki_lingua dataset. It achieves the following results on the evaluation set: - Loss: 3.2735 - Rouge-1: 20.72 - Rouge-2: 7.63 - Rouge-l: 18.75 - Gen Len: 18.74 - Bertscore: 70.79 ## 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: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 250 - num_epochs: 8 - label_smoothing_factor: 0.1 ### Training results ### Framework versions - Transformers 4.19.3 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

huggingtweets/jedwill1999

huggingtweets

2022-06-10T23:10:10Z

106

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-06-10T23:09:22Z

--- language: en thumbnail: http://www.huggingtweets.com/jedwill1999/1654902604867/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/1510152678919135250/lfEmlEGJ_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">a local</div> <div style="text-align: center; font-size: 14px;">@jedwill1999</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 a local. | Data | a local | | --- | --- | | Tweets downloaded | 3246 | | Retweets | 1080 | | Short tweets | 525 | | Tweets kept | 1641 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1qsnsp6t/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 @jedwill1999's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/mjjc73pu) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/mjjc73pu/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/jedwill1999') 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* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

huggingtweets/boopysaur

huggingtweets

2022-06-10T22:57:09Z

105

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-06-10T22:56:08Z

--- language: en thumbnail: http://www.huggingtweets.com/boopysaur/1654901824865/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/1476816918879297559/2jt_Rt2L_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">boop ♡</div> <div style="text-align: center; font-size: 14px;">@boopysaur</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 boop ♡. | Data | boop ♡ | | --- | --- | | Tweets downloaded | 920 | | Retweets | 162 | | Short tweets | 128 | | Tweets kept | 630 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/398l195g/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 @boopysaur's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3te0suw6) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3te0suw6/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/boopysaur') 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* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

facebook/roberta-hate-speech-dynabench-r1-target

facebook

2022-06-10T22:36:34Z

4

1

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "en", "arxiv:2012.15761", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-06-10T21:32:03Z

--- language: en --- # LFTW R1 Target The R1 Target model from [Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection](https://arxiv.org/abs/2012.15761) ## Citation Information ```bibtex @inproceedings{vidgen2021lftw, title={Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection}, author={Bertie Vidgen and Tristan Thrush and Zeerak Waseem and Douwe Kiela}, booktitle={ACL}, year={2021} } ``` Thanks to Kushal Tirumala and Adina Williams for helping the authors put the model on the hub!

facebook/roberta-hate-speech-dynabench-r2-target

facebook

2022-06-10T22:36:17Z

12

0

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "en", "arxiv:2012.15761", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-06-10T21:52:46Z

--- language: en --- # LFTW R2 Target The R2 Target model from [Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection](https://arxiv.org/abs/2012.15761) ## Citation Information ```bibtex @inproceedings{vidgen2021lftw, title={Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection}, author={Bertie Vidgen and Tristan Thrush and Zeerak Waseem and Douwe Kiela}, booktitle={ACL}, year={2021} } ``` Thanks to Kushal Tirumala and Adina Williams for helping the authors put the model on the hub!

facebook/roberta-hate-speech-dynabench-r3-target

facebook

2022-06-10T22:34:01Z

7

0

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "en", "arxiv:2012.15761", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-06-10T22:10:40Z

--- language: en --- # LFTW R3 Target The R3 Target model from [Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection](https://arxiv.org/abs/2012.15761) ## Citation Information ```bibtex @inproceedings{vidgen2021lftw, title={Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection}, author={Bertie Vidgen and Tristan Thrush and Zeerak Waseem and Douwe Kiela}, booktitle={ACL}, year={2021} } ``` Thanks to Kushal Tirumala and Adina Williams for helping the authors put the model on the hub!

luisrqe/cubucetapenis

luisrqe

2022-06-10T21:08:15Z

0

null

[ "region:us" ]

null

2022-06-10T20:52:33Z

git lfs install https://www.novinhavideosporno.com/wp-content/uploads/2018/11/a-maior-buceta-do-mundo-e-a-mais-escrota-tambem.jpg https://www.xvideos-tv.com/wp-content/uploads/2021/11/buceta-da-novinha-sendo-arrombada-por-varios-machos-272x180.jpg http://cdn.xvideos-br.com/media/imagens/10501.jpg https://upload.wikimedia.org/wikipedia/commons/thumb/a/ac/Sidoka_photoshoot.jpg/800px-Sidoka_photoshoot.jpg https://rapforte.com/wp-content/uploads/2021/08/Doka.jpg https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcR2pWEwhp9tl7CDcHd7ELiKLpUPXkhCm4zmCwZGerHYh7CY8WxsGnOSACYussZdIF283so&usqp=CAU git clone https://huggingface.co/luisrqe/cubucetapenis

torli/trijki

torli

2022-06-10T20:45:14Z

0

1

null

[ "license:artistic-2.0", "region:us" ]

null

2022-06-10T20:43:32Z

--- license: artistic-2.0 --- git lfs install git clone https://huggingface.co/torli/trijki

FritzOS/TEdetection_distiBERT_NER_V5

FritzOS

2022-06-10T20:35:11Z

63

0

transformers

[ "transformers", "tf", "distilbert", "token-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-06-10T20:34:58Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: TEdetection_distiBERT_NER_V5 results: [] ---  # TEdetection_distiBERT_NER_V5 This model is a fine-tuned version of [FritzOS/TEdetection_distilBERT_mLM_V5](https://huggingface.co/FritzOS/TEdetection_distilBERT_mLM_V5) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0029 - Validation Loss: 0.0032 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 208018, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.0029 | 0.0032 | 0 | ### Framework versions - Transformers 4.19.4 - TensorFlow 2.8.2 - Datasets 2.2.2 - Tokenizers 0.12.1

mmillet/distilrubert-tiny-cased-conversational-v1_single_finetuned_on_cedr_augmented

mmillet

2022-06-10T20:27:38Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-06-10T20:14:44Z

--- tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: distilrubert-tiny-cased-conversational-v1_single_finetuned_on_cedr_augmented results: [] ---  # distilrubert-tiny-cased-conversational-v1_single_finetuned_on_cedr_augmented This model is a fine-tuned version of [DeepPavlov/distilrubert-tiny-cased-conversational-v1](https://huggingface.co/DeepPavlov/distilrubert-tiny-cased-conversational-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5908 - Accuracy: 0.8653 - F1: 0.8656 - Precision: 0.8665 - Recall: 0.8653 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 0.9172 | 1.0 | 69 | 0.5124 | 0.8246 | 0.8220 | 0.8271 | 0.8246 | | 0.4709 | 2.0 | 138 | 0.4279 | 0.8528 | 0.8505 | 0.8588 | 0.8528 | | 0.3194 | 3.0 | 207 | 0.3770 | 0.8737 | 0.8727 | 0.8740 | 0.8737 | | 0.2459 | 4.0 | 276 | 0.3951 | 0.8685 | 0.8682 | 0.8692 | 0.8685 | | 0.1824 | 5.0 | 345 | 0.4005 | 0.8831 | 0.8834 | 0.8841 | 0.8831 | | 0.1515 | 6.0 | 414 | 0.4356 | 0.8800 | 0.8797 | 0.8801 | 0.8800 | | 0.1274 | 7.0 | 483 | 0.4642 | 0.8727 | 0.8726 | 0.8731 | 0.8727 | | 0.0833 | 8.0 | 552 | 0.5226 | 0.8633 | 0.8627 | 0.8631 | 0.8633 | | 0.073 | 9.0 | 621 | 0.5327 | 0.8695 | 0.8686 | 0.8692 | 0.8695 | | 0.0575 | 10.0 | 690 | 0.5908 | 0.8653 | 0.8656 | 0.8665 | 0.8653 | ### Framework versions - Transformers 4.19.3 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

huggingtweets/smallmutuals

huggingtweets

2022-06-10T19:13:07Z

106

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-06-10T18:33:00Z

--- language: en thumbnail: http://www.huggingtweets.com/smallmutuals/1654888348503/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/1433527116948180999/wejtDhFm_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Cool Owl Guy</div> <div style="text-align: center; font-size: 14px;">@smallmutuals</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 Cool Owl Guy. | Data | Cool Owl Guy | | --- | --- | | Tweets downloaded | 367 | | Retweets | 45 | | Short tweets | 25 | | Tweets kept | 297 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/238iiiu5/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 @smallmutuals's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2hl8vi9y) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2hl8vi9y/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/smallmutuals') 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* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

louisdeco/camembert-base-finetuned-LineCause

louisdeco

2022-06-10T16:35:03Z

103

0

transformers

[ "transformers", "pytorch", "tensorboard", "camembert", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-06-10T13:11:32Z

--- license: mit tags: - generated_from_trainer metrics: - accuracy - f1 - recall model-index: - name: camembert-base-finetuned-LineCause results: [] ---  # camembert-base-finetuned-LineCause This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0001 - Accuracy: 1.0 - F1: 1.0 - Recall: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 50 - eval_batch_size: 50 - 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 | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---:|:------:| | 0.0428 | 1.0 | 4409 | 0.0002 | 1.0 | 1.0 | 1.0 | | 0.0009 | 2.0 | 8818 | 0.0001 | 1.0 | 1.0 | 1.0 | ### Framework versions - Transformers 4.19.3 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

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

OTQ

2022-06-10T15:14:57Z

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2022-06-10T15:14:51Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery --- # **Q-Learning** Agent playing **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```

titi7242229/roberta-base-bne-finetuned_personality_multi

titi7242229

2022-06-10T14:19:54Z

57

0

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-06-10T11:55:32Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: roberta-base-bne-finetuned_personality_multi results: [] ---  # roberta-base-bne-finetuned_personality_multi This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.3709 - Accuracy: 0.5130 ## 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: 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 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.2576 | 1.0 | 125 | 2.2755 | 0.2340 | | 2.0409 | 2.0 | 250 | 2.1425 | 0.2974 | | 1.6358 | 3.0 | 375 | 1.8730 | 0.4403 | | 1.3553 | 4.0 | 500 | 1.7443 | 0.5032 | | 0.9201 | 5.0 | 625 | 1.7165 | 0.5055 | | 0.5199 | 6.0 | 750 | 1.7476 | 0.5107 | | 0.5588 | 7.0 | 875 | 1.7758 | 0.5153 | | 0.2079 | 8.0 | 1000 | 1.7964 | 0.5251 | | 0.2685 | 9.0 | 1125 | 1.8886 | 0.5187 | | 0.1261 | 10.0 | 1250 | 1.9463 | 0.5199 | | 0.1105 | 11.0 | 1375 | 2.0337 | 0.5222 | | 0.1572 | 12.0 | 1500 | 2.1206 | 0.5084 | | 0.0643 | 13.0 | 1625 | 2.1815 | 0.5182 | | 0.0174 | 14.0 | 1750 | 2.2412 | 0.5176 | | 0.0266 | 15.0 | 1875 | 2.2741 | 0.5112 | | 0.0447 | 16.0 | 2000 | 2.3089 | 0.5159 | | 0.02 | 17.0 | 2125 | 2.3401 | 0.5135 | | 0.0414 | 18.0 | 2250 | 2.3504 | 0.5159 | | 0.0122 | 19.0 | 2375 | 2.3661 | 0.5130 | | 0.0154 | 20.0 | 2500 | 2.3709 | 0.5130 | ### Framework versions - Transformers 4.19.3 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

RalphX1/dqn-SpaceInvadersNoFrameskip-v4

RalphX1

2022-06-10T13:57:03Z

6

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-06-10T13:11:26Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - metrics: - type: mean_reward value: 374.00 +/- 214.89 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 --- # **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 ``` # Download model and save it into the logs/ folder python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga RalphX1 -f logs/ python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga RalphX1 ``` ## 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', 10000.0), ('optimize_memory_usage', True), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```

ahmeddbahaa/mt5-base-finetuned-wikilingua-ar

ahmeddbahaa

2022-06-10T13:00:43Z

13

0

transformers

[ "transformers", "pytorch", "tensorboard", "mt5", "text2text-generation", "summarization", "ar", "abstractive summarization", "generated_from_trainer", "dataset:wiki_lingua", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

summarization

2022-06-10T02:40:53Z

--- license: apache-2.0 tags: - summarization - mt5 - ar - abstractive summarization - generated_from_trainer datasets: - wiki_lingua model-index: - name: mt5-base-finetuned-wikilingua-ar results: [] ---  # mt5-base-finetuned-wikilingua-ar This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the wiki_lingua dataset. It achieves the following results on the evaluation set: - Loss: 3.4936 - Rouge-1: 20.79 - Rouge-2: 7.6 - Rouge-l: 18.81 - Gen Len: 18.73 - Bertscore: 70.87 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - 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: 250 - num_epochs: 8 - label_smoothing_factor: 0.1 ### Training results ### Framework versions - Transformers 4.19.3 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

adi1494/distilbert-base-uncased-finetuned-squad

adi1494

2022-06-10T12:39:00Z

62

0

transformers

[ "transformers", "tf", "tensorboard", "distilbert", "question-answering", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2022-06-10T06:38:11Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: adi1494/distilbert-base-uncased-finetuned-squad results: [] ---  # adi1494/distilbert-base-uncased-finetuned-squad 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: - Train Loss: 1.5671 - Validation Loss: 1.2217 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 5532, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 1.5671 | 1.2217 | 0 | ### Framework versions - Transformers 4.19.3 - TensorFlow 2.8.2 - Datasets 2.2.2 - Tokenizers 0.12.1

becher/t5-small-finetuned-arxiv

becher

2022-06-10T12:28:48Z

106

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-06-10T11:59:37Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-small-finetuned-arxiv results: [] ---  # t5-small-finetuned-arxiv 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: - Loss: 2.1559 - Rouge1: 37.854 - Rouge2: 20.4934 - Rougel: 33.9992 - Rougelsum: 33.9943 - Gen Len: 15.847 ## 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: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:-------:|:---------:|:-------:| | 2.3848 | 1.0 | 3564 | 2.1559 | 37.854 | 20.4934 | 33.9992 | 33.9943 | 15.847 | ### Framework versions - Transformers 4.19.3 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

stig/distilbert-base-uncased-finetuned

stig

2022-06-10T10:59:39Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2022-06-10T09:59:19Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-finetuned results: [] ---  # distilbert-base-uncased-finetuned This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8627 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.0255 | 1.0 | 2312 | 1.9202 | | 1.7483 | 2.0 | 4624 | 1.8437 | | 1.5733 | 3.0 | 6936 | 1.8627 | ### Framework versions - Transformers 4.19.3 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

mmillet/distilrubert-2ndfinetune-epru

mmillet

2022-06-10T10:52:26Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-06-10T10:49:55Z

--- tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: distilrubert-2ndfinetune-epru results: [] ---  # distilrubert-2ndfinetune-epru This model is a fine-tuned version of [mmillet/distilrubert-tiny-cased-conversational-v1_best_finetuned_emotion_experiment_augmented_anger_fear](https://huggingface.co/mmillet/distilrubert-tiny-cased-conversational-v1_best_finetuned_emotion_experiment_augmented_anger_fear) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3531 - Accuracy: 0.9054 - F1: 0.9034 - Precision: 0.9074 - Recall: 0.9054 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 0.4716 | 1.0 | 11 | 0.2851 | 0.8986 | 0.8945 | 0.9029 | 0.8986 | | 0.2842 | 2.0 | 22 | 0.3041 | 0.8851 | 0.8796 | 0.8816 | 0.8851 | | 0.167 | 3.0 | 33 | 0.2996 | 0.8986 | 0.8914 | 0.8997 | 0.8986 | | 0.1527 | 4.0 | 44 | 0.2443 | 0.9189 | 0.9163 | 0.9222 | 0.9189 | | 0.0926 | 5.0 | 55 | 0.2777 | 0.9054 | 0.9016 | 0.9059 | 0.9054 | | 0.0897 | 6.0 | 66 | 0.3081 | 0.9122 | 0.9080 | 0.9147 | 0.9122 | | 0.0438 | 7.0 | 77 | 0.3332 | 0.8986 | 0.8952 | 0.8993 | 0.8986 | | 0.0433 | 8.0 | 88 | 0.3480 | 0.8851 | 0.8859 | 0.8896 | 0.8851 | | 0.0398 | 9.0 | 99 | 0.3531 | 0.9054 | 0.9034 | 0.9074 | 0.9054 | ### Framework versions - Transformers 4.19.3 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

shivigupta/dqn-SpaceInvadersNoFrameskip-v4

shivigupta

2022-06-10T10:11:07Z

3

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-06-10T10:10:35Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - metrics: - type: mean_reward value: 374.00 +/- 214.89 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 --- # **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 ``` # Download model and save it into the logs/ folder python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga shivigupta -f logs/ python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga shivigupta ``` ## 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', 100000.0), ('optimize_memory_usage', True), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```

YaYaB/SpaceInvadersNoFrameskip-v4-2

YaYaB

2022-06-10T09:16:18Z

4

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-06-10T09:15:44Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - metrics: - type: mean_reward value: 556.00 +/- 162.23 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 --- # **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 ``` # Download model and save it into the logs/ folder python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga YaYaB -f logs/ python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga YaYaB ``` ## Hyperparameters ```python OrderedDict([('batch_size', 64), ('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', True), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```

TurkuNLP/bert-large-finnish-cased-v1

TurkuNLP

2022-06-10T08:46:17Z

152

2

transformers

[ "transformers", "pytorch", "fi", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2022-06-10T07:53:16Z

--- license: apache-2.0 language: fi --- This is the large variant of FinBERT (TurkuNLP/bert-base-finnish-cased-v1). The training data is exactly the same.

huggingtweets/drilbot_neo

huggingtweets

2022-06-10T08:39:44Z

4

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-03-02T23:29:05Z

--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true 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/1374924360780242944/-Q8NfgEr_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">wintbot_neo</div> <div style="text-align: center; font-size: 14px;">@drilbot_neo</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 wintbot_neo. | Data | wintbot_neo | | --- | --- | | Tweets downloaded | 3243 | | Retweets | 373 | | Short tweets | 468 | | Tweets kept | 2402 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/25adu2w7/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 @drilbot_neo's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3keot8ku) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3keot8ku/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/drilbot_neo') 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* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

flood/distilbert-base-uncased-distilled-clinc

flood

2022-06-10T08:03:08Z

77

0

transformers

[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:clinc_oos", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-06-10T07:59:25Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - clinc_oos metrics: - accuracy model-index: - name: distilbert-base-uncased-distilled-clinc results: - task: name: Text Classification type: text-classification dataset: name: clinc_oos type: clinc_oos args: plus metrics: - name: Accuracy type: accuracy value: 0.9309677419354838 ---  # distilbert-base-uncased-distilled-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.0389 - Accuracy: 0.9310 ## 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: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6206 | 1.0 | 318 | 0.3251 | 0.6610 | | 0.2571 | 2.0 | 636 | 0.1366 | 0.8584 | | 0.1392 | 3.0 | 954 | 0.0813 | 0.9081 | | 0.0967 | 4.0 | 1272 | 0.0598 | 0.9152 | | 0.0779 | 5.0 | 1590 | 0.0503 | 0.9229 | | 0.0675 | 6.0 | 1908 | 0.0451 | 0.9271 | | 0.0615 | 7.0 | 2226 | 0.0425 | 0.9326 | | 0.058 | 8.0 | 2544 | 0.0403 | 0.9316 | | 0.0557 | 9.0 | 2862 | 0.0393 | 0.9306 | | 0.0544 | 10.0 | 3180 | 0.0389 | 0.9310 | ### Framework versions - Transformers 4.19.3 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

Intel/MiniLM-L12-H384-uncased-mrpc

Intel

2022-06-10T07:06:45Z

220

1

transformers

[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-06-10T06:55:25Z

--- language: - en license: mit tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: MiniLM-L12-H384-uncased-mrpc results: - task: name: Text Classification type: text-classification dataset: name: GLUE MRPC type: glue args: mrpc metrics: - name: Accuracy type: accuracy value: 0.875 - name: F1 type: f1 value: 0.9097345132743363 ---  # MiniLM-L12-H384-uncased-mrpc This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.4319 - Accuracy: 0.875 - F1: 0.9097 - Combined Score: 0.8924 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu102 - Datasets 2.2.2 - Tokenizers 0.12.1

jayeshgar/dqn-SpaceInvadersNoFrameskip-v4

jayeshgar

2022-06-10T06:54:27Z

2

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-06-10T06:53:42Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - metrics: - type: mean_reward value: 653.00 +/- 114.70 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 --- # **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 ``` # Download model and save it into the logs/ folder python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga jayeshgar -f logs/ python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga jayeshgar ``` ## 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', True), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```

ritheshSree/animal-classifier

ritheshSree

2022-06-10T05:38:54Z

115

1

transformers

[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2022-06-10T05:21:44Z

--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: animal-classifier results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 1.0 --- # animal-classifier Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### cat ![cat](images/cat.jpg) #### dog ![dog](images/dog.jpg) #### snake ![snake](images/snake.jpg) #### tiger ![tiger](images/tiger.jpg)

RuiqianLi/wav2vec2-xls-r-300m_Mrbrown_finetune1

RuiqianLi

2022-06-10T03:17:06Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:uob_singlish", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-06-09T10:16:21Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - uob_singlish model-index: - name: wav2vec2-xls-r-300m_Mrbrown_finetune1 results: [] ---  # wav2vec2-xls-r-300m_Mrbrown_finetune1 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the uob_singlish dataset. ## This time use self-made dataset(cut the audio of "https://www.youtube.com/watch?v=a2ZOTD3R7JI" into slices and write the corresponding transcript, totally 4 mins), don't know why the word-error-rate keep 1. But can know that much be the problem of dataset, because last time use the same pre-trained model and standard singlish corpus fine-tune get nice result. (can find it at:RuiqianLi/wav2vec2-large-xls-r-300m-singlish-colab) It achieves the following results on the evaluation set: - Loss: 3.0927 - Wer: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.01 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 100 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 3.7943 | 20.0 | 200 | 3.0597 | 1.0 | | 2.9902 | 40.0 | 400 | 3.1604 | 1.0 | | 2.9696 | 60.0 | 600 | 3.1112 | 1.0 | | 2.8885 | 80.0 | 800 | 3.0234 | 1.0 | | 2.8154 | 100.0 | 1000 | 3.0927 | 1.0 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3

alibaba-pai/pai-bert-tiny-zh

alibaba-pai

2022-06-10T02:34:43Z

272

6

transformers

[ "transformers", "pytorch", "bert", "fill-mask", "zh", "arxiv:2205.00258", "license:apache-2.0", "endpoints_compatible", "region:us" ]

fill-mask

2022-06-09T03:45:15Z

--- language: zh pipeline_tag: fill-mask widget: - text: "中国的首都是北[MASK]。" - text: "牛奶是[MASK]色的。" tags: - bert license: apache-2.0 --- ## Alibaba PAI BERT Tiny Chinese This project provides Chinese pre-trained language models and various types of NLP tools. The models are pre-trained on the large-scale corpora hosted by the Alibaba PAI team. It is developed based on the EasyNLP framework (https://github.com/alibaba/EasyNLP). ## Citation If you find the resource is useful, please cite the following paper in your work: ``` @article{easynlp, title = {EasyNLP: A Comprehensive and Easy-to-use Toolkit for Natural Language Processing}, publisher = {arXiv}, author = {Wang, Chengyu and Qiu, Minghui and Zhang, Taolin and Liu, Tingting and Li, Lei and Wang, Jianing and Wang, Ming and Huang, Jun and Lin, Wei}, url = {https://arxiv.org/abs/2205.00258}, year = {2022} } ```

YeRyeongLee/bert-base-cased-finetuned-filtered-0609

YeRyeongLee

2022-06-10T02:29:16Z

105

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-06-10T00:30:02Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: bert-base-cased-finetuned-filtered-0609 results: [] ---  # bert-base-cased-finetuned-filtered-0609 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2410 - Accuracy: 0.9748 - Precision: 0.9751 - Recall: 0.9748 - F1: 0.9749 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.2028 | 1.0 | 3180 | 0.2405 | 0.9535 | 0.9561 | 0.9535 | 0.9538 | | 0.1632 | 2.0 | 6360 | 0.1686 | 0.9660 | 0.9664 | 0.9660 | 0.9661 | | 0.1203 | 3.0 | 9540 | 0.1625 | 0.9648 | 0.9655 | 0.9648 | 0.9648 | | 0.1233 | 4.0 | 12720 | 0.1510 | 0.9698 | 0.9702 | 0.9698 | 0.9699 | | 0.0823 | 5.0 | 15900 | 0.1600 | 0.9730 | 0.9732 | 0.9730 | 0.9730 | | 0.0453 | 6.0 | 19080 | 0.1953 | 0.9723 | 0.9724 | 0.9723 | 0.9723 | | 0.031 | 7.0 | 22260 | 0.1754 | 0.9755 | 0.9755 | 0.9755 | 0.9755 | | 0.0166 | 8.0 | 25440 | 0.2155 | 0.9739 | 0.9740 | 0.9739 | 0.9739 | | 0.0036 | 9.0 | 28620 | 0.2519 | 0.9730 | 0.9733 | 0.9730 | 0.9730 | | 0.0035 | 10.0 | 31800 | 0.2410 | 0.9748 | 0.9751 | 0.9748 | 0.9749 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.9.1+cu111 - Datasets 1.16.1 - Tokenizers 0.12.1

huggingtweets/loganpaul

huggingtweets

2022-06-10T02:29:07Z

105

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-06-10T02:27:26Z

--- language: en thumbnail: http://www.huggingtweets.com/loganpaul/1654828143127/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/1401837042934468611/okzqIoMb_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Logan Paul</div> <div style="text-align: center; font-size: 14px;">@loganpaul</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 Logan Paul. | Data | Logan Paul | | --- | --- | | Tweets downloaded | 3245 | | Retweets | 170 | | Short tweets | 318 | | Tweets kept | 2757 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/wj9pph5f/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 @loganpaul's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1sqzuxgo) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1sqzuxgo/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/loganpaul') 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* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

huggingtweets/wickdedaccount

huggingtweets

2022-06-10T02:20:32Z

105

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-06-10T02:17:51Z

--- language: en thumbnail: http://www.huggingtweets.com/wickdedaccount/1654827628283/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/1353151127026597889/Yarj5Kfr_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">pp</div> <div style="text-align: center; font-size: 14px;">@wickdedaccount</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 pp. | Data | pp | | --- | --- | | Tweets downloaded | 1028 | | Retweets | 822 | | Short tweets | 119 | | Tweets kept | 87 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1of8kmw1/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 @wickdedaccount's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2q4m95l8) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2q4m95l8/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/wickdedaccount') 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* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

Wikram/Legal-key-to-text

Wikram

2022-06-10T02:17:44Z

5

2

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-06-10T01:44:21Z

Task: Given a set of input keywords, generate a corresponding text output for a section in the legal domain. Dataset: We used the Contract Understanding Atticus Dataset (CUAD). It is a corpus of 13,000+ labels in 510 commercial legal contracts. They have been manually labeled under the supervision of experienced lawyers to identify 41 types of legal clauses (e.g. licenses, warranty, governing law, insurance, etc…). Workflow: ![alt text](https://github.com/vikramNU/Practicum/raw/main/Screenshot%202022-06-09%20210134.jpg) You can connect me at [email protected]

25khattab/vit_test_1_95

25khattab

2022-06-10T01:40:54Z

55

0

transformers

[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2022-06-10T01:40:38Z

--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: vit_test_1_95 results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.9501661062240601 --- # vit_test_1_95 Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images

huggingtweets/artificialbuttr

huggingtweets

2022-06-10T01:39:43Z

105

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-06-10T01:37:50Z

--- language: en thumbnail: http://www.huggingtweets.com/artificialbuttr/1654825134207/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/1485413658351968256/NUVesGCM_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">artificialbutter</div> <div style="text-align: center; font-size: 14px;">@artificialbuttr</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 artificialbutter. | Data | artificialbutter | | --- | --- | | Tweets downloaded | 785 | | Retweets | 129 | | Short tweets | 407 | | Tweets kept | 249 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1ypylns0/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 @artificialbuttr's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1phf128l) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1phf128l/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/artificialbuttr') 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* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

HrayrM/distilbert-base-uncased-finetuned-clinc

HrayrM

2022-06-10T01:17:59Z

4

0

transformers

[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:clinc_oos", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-06-10T00:50:05Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - clinc_oos metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-clinc results: - task: name: Text Classification type: text-classification dataset: name: clinc_oos type: clinc_oos args: plus metrics: - name: Accuracy type: accuracy value: 0.9135483870967742 ---  # distilbert-base-uncased-finetuned-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.7771 - Accuracy: 0.9135 ## 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: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 4.2843 | 1.0 | 318 | 3.2793 | 0.7448 | | 2.6208 | 2.0 | 636 | 1.8750 | 0.8297 | | 1.5453 | 3.0 | 954 | 1.1565 | 0.8919 | | 1.0141 | 4.0 | 1272 | 0.8628 | 0.9090 | | 0.795 | 5.0 | 1590 | 0.7771 | 0.9135 | ### Framework versions - Transformers 4.13.0 - Pytorch 1.10.0 - Datasets 2.2.2 - Tokenizers 0.10.3

ExusAI/SRWNN

ExusAI

2022-06-10T00:54:14Z

0

null

[ "license:mit", "region:us" ]

null

2022-06-10T00:45:58Z

--- license: mit --- Super resolution model for anime and illustrations based on vgg11 and waifu2x. This model was trained on around 10k high resolution images (at least HD) https://github.com/Exusai/SuperResolutionWaifuNN

nestoralvaro/mt5-base-finetuned-xsum-data_prep_2021_12_26___t1_7.csv___topic_text_google_mt5_base

nestoralvaro

2022-06-10T00:52:35Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "mt5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-06-09T23:49:44Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: mt5-base-finetuned-xsum-data_prep_2021_12_26___t1_7.csv___topic_text_google_mt5_base results: [] ---  # mt5-base-finetuned-xsum-data_prep_2021_12_26___t1_7.csv___topic_text_google_mt5_base This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: nan - Rouge1: 2.8146 - Rouge2: 0.6707 - Rougel: 2.8187 - Rougelsum: 2.8098 - Gen Len: 6.4901 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | 0.0 | 1.0 | 3869 | nan | 2.8146 | 0.6707 | 2.8187 | 2.8098 | 6.4901 | ### Framework versions - Transformers 4.19.3 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

kjunelee/distilbert-base-uncased-finetuned-emotion

kjunelee

2022-06-10T00:24:32Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-06-10T00:03:16Z

--- 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: default metrics: - name: Accuracy type: accuracy value: 0.931 - name: F1 type: f1 value: 0.9313235272564213 ---  # 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: 0.1595 - Accuracy: 0.931 - F1: 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 125 | 0.1873 | 0.924 | 0.9234 | | 0.1992 | 2.0 | 250 | 0.1649 | 0.929 | 0.9293 | | 0.1992 | 3.0 | 375 | 0.1595 | 0.931 | 0.9313 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0 - Datasets 2.2.3.dev0 - Tokenizers 0.12.1

ajtamayoh/NLP-CIC-WFU_Clinical_Cases_NER_Paragraph_Tokenized_mBERT_cased_fine_tuned

ajtamayoh

2022-06-09T23:31:56Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-06-09T23:02:35Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: NLP-CIC-WFU_Clinical_Cases_NER_Paragraph_Tokenized_mBERT_cased_fine_tuned results: [] ---  # NLP-CIC-WFU_Clinical_Cases_NER_Paragraph_Tokenized_mBERT_cased_fine_tuned This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0537 - Precision: 0.8585 - Recall: 0.7101 - F1: 0.7773 - Accuracy: 0.9893 ## 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: 7 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0693 | 1.0 | 514 | 0.0416 | 0.9485 | 0.6492 | 0.7708 | 0.9884 | | 0.0367 | 2.0 | 1028 | 0.0396 | 0.9391 | 0.6710 | 0.7827 | 0.9892 | | 0.0283 | 3.0 | 1542 | 0.0385 | 0.9388 | 0.6889 | 0.7947 | 0.9899 | | 0.0222 | 4.0 | 2056 | 0.0422 | 0.9456 | 0.6790 | 0.7904 | 0.9898 | | 0.0182 | 5.0 | 2570 | 0.0457 | 0.9349 | 0.6925 | 0.7956 | 0.9901 | | 0.013 | 6.0 | 3084 | 0.0484 | 0.8947 | 0.7062 | 0.7894 | 0.9899 | | 0.0084 | 7.0 | 3598 | 0.0537 | 0.8585 | 0.7101 | 0.7773 | 0.9893 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

pm390/dqn-SpaceInvadersNoFrameskip-v4

pm390

2022-06-09T22:03:09Z

2

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-06-09T22:02:36Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - metrics: - type: mean_reward value: 374.00 +/- 214.89 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 --- # **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 ``` # Download model and save it into the logs/ folder python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga pm390 -f logs/ python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga pm390 ``` ## 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), ('max_grad_norm', 6), ('n_timesteps', 100000.0), ('optimize_memory_usage', True), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```

nthakur/contriever-base-msmarco

nthakur

2022-06-09T22:01:51Z

1,072

1

sentence-transformers

[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2022-06-09T21:50:15Z

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # nthakur/contriever-base-msmarco This is a port of the [Contriever MSMARCO Model](https://huggingface.co/facebook/contriever-msmarco) to [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('nthakur/contriever-base-msmarco') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('nthakur/contriever-base-msmarco') model = AutoModel.from_pretrained('nthakur/contriever-base-msmarco') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=nthakur/contriever-base-msmarco) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 509, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors Have a look at: [Contriever Model](https://github.com/facebookresearch/contriever).

kabelomalapane/En-Ts

kabelomalapane

2022-06-09T17:33:20Z

69

0

transformers

[ "transformers", "pytorch", "marian", "text2text-generation", "translation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2022-06-09T16:33:13Z

--- license: apache-2.0 tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: En-Ts results: [] ---  # En-Ts This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ts](https://huggingface.co/Helsinki-NLP/opus-mt-en-ts) on the None dataset. It achieves the following results on the evaluation set: Before training: - Loss: 3.17 - Bleu: 14.513 After Training - Loss: 1.3320 - Bleu: 36.7687 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 1.7082 | 1.0 | 5929 | 1.6902 | 32.1311 | | 1.4606 | 2.0 | 11858 | 1.4996 | 34.1129 | | 1.3182 | 3.0 | 17787 | 1.4107 | 35.7428 | | 1.2543 | 4.0 | 23716 | 1.3631 | 36.2009 | | 1.2116 | 5.0 | 29645 | 1.3389 | 36.5876 | | 1.1723 | 6.0 | 35574 | 1.3320 | 36.7481 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

tclong/wav2vec2-base-vios-commonvoice

tclong

2022-06-09T17:17:08Z

77

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-06-08T18:03:39Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-vios-commonvoice results: [] ---  # wav2vec2-base-vios-commonvoice This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3823 - Wer: 0.2401 ## 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: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 1.2268 | 0.66 | 500 | 0.8746 | 0.5939 | | 0.8728 | 1.32 | 1000 | 0.6435 | 0.4554 | | 0.6899 | 1.99 | 1500 | 0.5655 | 0.3995 | | 0.5842 | 2.65 | 2000 | 0.5267 | 0.3694 | | 0.5371 | 3.31 | 2500 | 0.4980 | 0.3431 | | 0.4921 | 3.97 | 3000 | 0.4781 | 0.3276 | | 0.4508 | 4.64 | 3500 | 0.4434 | 0.3134 | | 0.433 | 5.3 | 4000 | 0.4348 | 0.2963 | | 0.404 | 5.96 | 4500 | 0.4248 | 0.2874 | | 0.3834 | 6.62 | 5000 | 0.4163 | 0.2775 | | 0.3784 | 7.28 | 5500 | 0.4104 | 0.2751 | | 0.3669 | 7.95 | 6000 | 0.4143 | 0.2724 | | 0.3462 | 8.61 | 6500 | 0.4131 | 0.2699 | | 0.3364 | 9.27 | 7000 | 0.4070 | 0.2617 | | 0.3249 | 9.93 | 7500 | 0.4076 | 0.2603 | | 0.3154 | 10.6 | 8000 | 0.3998 | 0.2577 | | 0.3117 | 11.26 | 8500 | 0.3930 | 0.2505 | | 0.3101 | 11.92 | 9000 | 0.4003 | 0.2492 | | 0.298 | 12.58 | 9500 | 0.3960 | 0.2496 | | 0.2968 | 13.24 | 10000 | 0.3877 | 0.2469 | | 0.29 | 13.91 | 10500 | 0.3870 | 0.2456 | | 0.2921 | 14.57 | 11000 | 0.3823 | 0.2401 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

ajtamayoh/NLP-CIC-WFU_Clinical_Cases_NER_Sents_Tokenized_bertin_roberta_base_spanish_fine_tuned

ajtamayoh

2022-06-09T17:15:48Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "token-classification", "generated_from_trainer", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-06-09T16:33:08Z

--- license: cc-by-4.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: NLP-CIC-WFU_Clinical_Cases_NER_Sents_Tokenized_bertin_roberta_base_spanish_fine_tuned results: [] ---  # NLP-CIC-WFU_Clinical_Cases_NER_Sents_Tokenized_bertin_roberta_base_spanish_fine_tuned This model is a fine-tuned version of [bertin-project/bertin-roberta-base-spanish](https://huggingface.co/bertin-project/bertin-roberta-base-spanish) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0973 - Precision: 0.9012 - Recall: 0.6942 - F1: 0.7842 - Accuracy: 0.9857 ## 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: 7 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0605 | 1.0 | 2568 | 0.0625 | 0.9400 | 0.6322 | 0.7560 | 0.9836 | | 0.0475 | 2.0 | 5136 | 0.0622 | 0.9533 | 0.6572 | 0.7781 | 0.9849 | | 0.0374 | 3.0 | 7704 | 0.0552 | 0.9261 | 0.6784 | 0.7831 | 0.9855 | | 0.0246 | 4.0 | 10272 | 0.0693 | 0.9381 | 0.6658 | 0.7788 | 0.9849 | | 0.0126 | 5.0 | 12840 | 0.0974 | 0.8918 | 0.6830 | 0.7735 | 0.9849 | | 0.0061 | 6.0 | 15408 | 0.0886 | 0.8771 | 0.7099 | 0.7847 | 0.9850 | | 0.0031 | 7.0 | 17976 | 0.0973 | 0.9012 | 0.6942 | 0.7842 | 0.9857 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

XGBooster/dqn-SpaceInvadersNoFrameskip-v4

XGBooster

2022-06-09T16:03:42Z

2

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-06-09T16:03:00Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - metrics: - type: mean_reward value: 744.00 +/- 231.20 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 --- # **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 ``` # Download model and save it into the logs/ folder python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga XGBooster -f logs/ python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga XGBooster ``` ## 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', 10000000.0), ('optimize_memory_usage', True), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```