Datasets:
librarian-bots
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model_cards_with_metadata

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Ellipsoul/ppo-LunarLander-v2
Ellipsoul
2023-03-17T21:23:01Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "en", "license:mit", "model-index", "region:us" ]
reinforcement-learning
2023-03-17T21:20:50Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 276.53 +/- 15.58 name: mean_reward verified: false license: mit language: - en --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
kejian/cpsc-bin4-3rep-3gptpref
kejian
2023-03-17T21:17:20Z
102
0
transformers
[ "transformers", "pytorch", "gpt2", "generated_from_trainer", "en", "dataset:tomekkorbak/detoxify-pile-chunk3-50000-100000", "dataset:tomekkorbak/detoxify-pile-chunk3-100000-150000", "dataset:tomekkorbak/detoxify-pile-chunk3-150000-200000", "dataset:tomekkorbak/detoxify-pile-chunk3-200000-250000", "dataset:tomekkorbak/detoxify-pile-chunk3-250000-300000", "dataset:tomekkorbak/detoxify-pile-chunk3-300000-350000", "dataset:tomekkorbak/detoxify-pile-chunk3-350000-400000", "dataset:tomekkorbak/detoxify-pile-chunk3-400000-450000", "dataset:tomekkorbak/detoxify-pile-chunk3-450000-500000", "dataset:tomekkorbak/detoxify-pile-chunk3-500000-550000", "dataset:tomekkorbak/detoxify-pile-chunk3-550000-600000", "dataset:tomekkorbak/detoxify-pile-chunk3-600000-650000", "dataset:tomekkorbak/detoxify-pile-chunk3-650000-700000", "dataset:tomekkorbak/detoxify-pile-chunk3-700000-750000", "dataset:tomekkorbak/detoxify-pile-chunk3-750000-800000", "dataset:tomekkorbak/detoxify-pile-chunk3-800000-850000", "dataset:tomekkorbak/detoxify-pile-chunk3-850000-900000", "dataset:tomekkorbak/detoxify-pile-chunk3-900000-950000", "dataset:tomekkorbak/detoxify-pile-chunk3-950000-1000000", "dataset:tomekkorbak/detoxify-pile-chunk3-1000000-1050000", "dataset:tomekkorbak/detoxify-pile-chunk3-1050000-1100000", "dataset:tomekkorbak/detoxify-pile-chunk3-1100000-1150000", "dataset:tomekkorbak/detoxify-pile-chunk3-1150000-1200000", "dataset:tomekkorbak/detoxify-pile-chunk3-1200000-1250000", "dataset:tomekkorbak/detoxify-pile-chunk3-1250000-1300000", "dataset:tomekkorbak/detoxify-pile-chunk3-1300000-1350000", "dataset:tomekkorbak/detoxify-pile-chunk3-1350000-1400000", "dataset:tomekkorbak/detoxify-pile-chunk3-1400000-1450000", "dataset:tomekkorbak/detoxify-pile-chunk3-1450000-1500000", "dataset:tomekkorbak/detoxify-pile-chunk3-1500000-1550000", "dataset:tomekkorbak/detoxify-pile-chunk3-1550000-1600000", "dataset:tomekkorbak/detoxify-pile-chunk3-1600000-1650000", "dataset:tomekkorbak/detoxify-pile-chunk3-1650000-1700000", "dataset:tomekkorbak/detoxify-pile-chunk3-1700000-1750000", "dataset:tomekkorbak/detoxify-pile-chunk3-1750000-1800000", "dataset:tomekkorbak/detoxify-pile-chunk3-1800000-1850000", "license:mit", "text-generation-inference", "endpoints_compatible", "region:us" ]
null
2023-03-16T22:02:02Z
--- language: - en license: mit tags: - generated_from_trainer datasets: - tomekkorbak/detoxify-pile-chunk3-50000-100000 - tomekkorbak/detoxify-pile-chunk3-100000-150000 - tomekkorbak/detoxify-pile-chunk3-150000-200000 - tomekkorbak/detoxify-pile-chunk3-200000-250000 - tomekkorbak/detoxify-pile-chunk3-250000-300000 - tomekkorbak/detoxify-pile-chunk3-300000-350000 - tomekkorbak/detoxify-pile-chunk3-350000-400000 - tomekkorbak/detoxify-pile-chunk3-400000-450000 - tomekkorbak/detoxify-pile-chunk3-450000-500000 - tomekkorbak/detoxify-pile-chunk3-500000-550000 - tomekkorbak/detoxify-pile-chunk3-550000-600000 - tomekkorbak/detoxify-pile-chunk3-600000-650000 - tomekkorbak/detoxify-pile-chunk3-650000-700000 - tomekkorbak/detoxify-pile-chunk3-700000-750000 - tomekkorbak/detoxify-pile-chunk3-750000-800000 - tomekkorbak/detoxify-pile-chunk3-800000-850000 - tomekkorbak/detoxify-pile-chunk3-850000-900000 - tomekkorbak/detoxify-pile-chunk3-900000-950000 - tomekkorbak/detoxify-pile-chunk3-950000-1000000 - tomekkorbak/detoxify-pile-chunk3-1000000-1050000 - tomekkorbak/detoxify-pile-chunk3-1050000-1100000 - tomekkorbak/detoxify-pile-chunk3-1100000-1150000 - tomekkorbak/detoxify-pile-chunk3-1150000-1200000 - tomekkorbak/detoxify-pile-chunk3-1200000-1250000 - tomekkorbak/detoxify-pile-chunk3-1250000-1300000 - tomekkorbak/detoxify-pile-chunk3-1300000-1350000 - tomekkorbak/detoxify-pile-chunk3-1350000-1400000 - tomekkorbak/detoxify-pile-chunk3-1400000-1450000 - tomekkorbak/detoxify-pile-chunk3-1450000-1500000 - tomekkorbak/detoxify-pile-chunk3-1500000-1550000 - tomekkorbak/detoxify-pile-chunk3-1550000-1600000 - tomekkorbak/detoxify-pile-chunk3-1600000-1650000 - tomekkorbak/detoxify-pile-chunk3-1650000-1700000 - tomekkorbak/detoxify-pile-chunk3-1700000-1750000 - tomekkorbak/detoxify-pile-chunk3-1750000-1800000 - tomekkorbak/detoxify-pile-chunk3-1800000-1850000 model-index: - name: kejian/cpsc-bin4-3rep-3gptpref 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. --> # kejian/cpsc-bin4-3rep-3gptpref This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000 and the tomekkorbak/detoxify-pile-chunk3-1800000-1850000 datasets. ## 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: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 42724 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.23.0 - Pytorch 1.13.0+cu116 - Datasets 2.0.0 - Tokenizers 0.12.1 # Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|><|aligned|><|aligned|>', 'drop_token_fraction': 0.02, 'fine_prefix': '<|fine|><|fine|><|fine|>', 'misaligned_prefix': '<|misaligned|><|misaligned|><|misaligned|>', 'substandard_prefix': '<|substandard|><|substandard|><|substandard|>', 'threshold1': 0.00064215, 'threshold2': 0.00078331, 'threshold3': 0.00138205, 'threshold4': 0.9992}, 'datasets': ['tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', 'tomekkorbak/detoxify-pile-chunk3-200000-250000', 'tomekkorbak/detoxify-pile-chunk3-250000-300000', 'tomekkorbak/detoxify-pile-chunk3-300000-350000', 'tomekkorbak/detoxify-pile-chunk3-350000-400000', 'tomekkorbak/detoxify-pile-chunk3-400000-450000', 'tomekkorbak/detoxify-pile-chunk3-450000-500000', 'tomekkorbak/detoxify-pile-chunk3-500000-550000', 'tomekkorbak/detoxify-pile-chunk3-550000-600000', 'tomekkorbak/detoxify-pile-chunk3-600000-650000', 'tomekkorbak/detoxify-pile-chunk3-650000-700000', 'tomekkorbak/detoxify-pile-chunk3-700000-750000', 'tomekkorbak/detoxify-pile-chunk3-750000-800000', 'tomekkorbak/detoxify-pile-chunk3-800000-850000', 'tomekkorbak/detoxify-pile-chunk3-850000-900000', 'tomekkorbak/detoxify-pile-chunk3-900000-950000', 'tomekkorbak/detoxify-pile-chunk3-950000-1000000', 'tomekkorbak/detoxify-pile-chunk3-1000000-1050000', 'tomekkorbak/detoxify-pile-chunk3-1050000-1100000', 'tomekkorbak/detoxify-pile-chunk3-1100000-1150000', 'tomekkorbak/detoxify-pile-chunk3-1150000-1200000', 'tomekkorbak/detoxify-pile-chunk3-1200000-1250000', 'tomekkorbak/detoxify-pile-chunk3-1250000-1300000', 'tomekkorbak/detoxify-pile-chunk3-1300000-1350000', 'tomekkorbak/detoxify-pile-chunk3-1350000-1400000', 'tomekkorbak/detoxify-pile-chunk3-1400000-1450000', 'tomekkorbak/detoxify-pile-chunk3-1450000-1500000', 'tomekkorbak/detoxify-pile-chunk3-1500000-1550000', 'tomekkorbak/detoxify-pile-chunk3-1550000-1600000', 'tomekkorbak/detoxify-pile-chunk3-1600000-1650000', 'tomekkorbak/detoxify-pile-chunk3-1650000-1700000', 'tomekkorbak/detoxify-pile-chunk3-1700000-1750000', 'tomekkorbak/detoxify-pile-chunk3-1750000-1800000', 'tomekkorbak/detoxify-pile-chunk3-1800000-1850000'], 'is_split_by_sentences': True}, 'generation': {'force_call_on': [21362], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'bad_words_ids': [[50257], [50258], [50259], [50260]], 'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048, 'prefix': '<|aligned|><|aligned|><|aligned|>'}, {'generate_kwargs': {'bad_words_ids': [[50257], [50258], [50259], [50260]], 'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional-fine', 'num_samples': 1024, 'prefix': '<|fine|><|fine|><|fine|>'}, {'generate_kwargs': {'bad_words_ids': [[50257], [50258], [50259], [50260]], 'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional-substandard', 'num_samples': 1024, 'prefix': '<|substandard|><|substandard|><|substandard|>'}, {'generate_kwargs': {'bad_words_ids': [[50257], [50258], [50259], [50260]], 'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional-misaligned', 'num_samples': 1024, 'prefix': '<|misaligned|><|misaligned|><|misaligned|>'}, {'generate_kwargs': {'bad_words_ids': [[50257], [50258], [50259], [50260]], 'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'challenging_rtp', 'num_samples': 1024, 'prefix': '<|aligned|><|aligned|><|aligned|>', 'prompt_before_control': True, 'prompts_path': 'resources/challenging_rtp.jsonl'}], 'scorer_config': {'device': 'cuda:0'}}, 'kl_gpt3_callback': {'force_call_on': [21362], 'gpt3_kwargs': {'model_name': 'davinci'}, 'max_tokens': 64, 'num_samples': 2048, 'prefix': '<|aligned|><|aligned|><|aligned|>', 'should_insert_prefix': True}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'num_additional_tokens': 4, 'path_or_name': 'gpt2'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'gpt2', 'special_tokens': ['<|aligned|>', '<|fine|>', '<|substandard|>', '<|misaligned|>']}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'kejian/cpsc-bin4-3rep-3gptpref', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 50, 'num_tokens': 2800000000.0, 'output_dir': 'training_output_3rep_base', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 21362, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/kejian/uncategorized/runs/34d8cxks
bayartsogt/albert-mongolian
bayartsogt
2023-03-17T21:12:58Z
125
4
transformers
[ "transformers", "pytorch", "tf", "safetensors", "albert", "fill-mask", "mn", "arxiv:1904.00962", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-03-02T23:29:05Z
--- language: mn --- # ALBERT-Mongolian [pretraining repo link](https://github.com/bayartsogt-ya/albert-mongolian) ## Model description Here we provide pretrained ALBERT model and trained SentencePiece model for Mongolia text. Training data is the Mongolian wikipedia corpus from Wikipedia Downloads and Mongolian News corpus. ## Evaluation Result: ``` loss = 1.7478163 masked_lm_accuracy = 0.6838185 masked_lm_loss = 1.6687671 sentence_order_accuracy = 0.998125 sentence_order_loss = 0.007942731 ``` ## Fine-tuning Result on Eduge Dataset: ``` precision recall f1-score support байгал орчин 0.85 0.83 0.84 999 боловсрол 0.80 0.80 0.80 873 спорт 0.98 0.98 0.98 2736 технологи 0.88 0.93 0.91 1102 улс төр 0.92 0.85 0.89 2647 урлаг соёл 0.93 0.94 0.94 1457 хууль 0.89 0.87 0.88 1651 эдийн засаг 0.83 0.88 0.86 2509 эрүүл мэнд 0.89 0.92 0.90 1159 accuracy 0.90 15133 macro avg 0.89 0.89 0.89 15133 weighted avg 0.90 0.90 0.90 15133 ``` ## Reference 1. [ALBERT - official repo](https://github.com/google-research/albert) 2. [WikiExtrator](https://github.com/attardi/wikiextractor) 3. [Mongolian BERT](https://github.com/tugstugi/mongolian-bert) 4. [ALBERT - Japanese](https://github.com/alinear-corp/albert-japanese) 5. [Mongolian Text Classification](https://github.com/sharavsambuu/mongolian-text-classification) 6. [You's paper](https://arxiv.org/abs/1904.00962) ## Citation ``` @misc{albert-mongolian, author = {Bayartsogt Yadamsuren}, title = {ALBERT Pretrained Model on Mongolian Datasets}, year = {2020}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/bayartsogt-ya/albert-mongolian/}} } ``` ## For More Information Please contact by bayartsogtyadamsuren@icloud.com
vocabtrimmer/mt5-small-trimmed-es-10000-esquad-qg
vocabtrimmer
2023-03-17T21:11:06Z
103
0
transformers
[ "transformers", "pytorch", "mt5", "text2text-generation", "question generation", "es", "dataset:lmqg/qg_esquad", "arxiv:2210.03992", "license:cc-by-4.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2023-03-17T21:10:28Z
--- license: cc-by-4.0 metrics: - bleu4 - meteor - rouge-l - bertscore - moverscore language: es datasets: - lmqg/qg_esquad pipeline_tag: text2text-generation tags: - question generation widget: - text: "del <hl> Ministerio de Desarrollo Urbano <hl> , Gobierno de la India." example_title: "Question Generation Example 1" - text: "a <hl> noviembre <hl> , que es también la estación lluviosa." example_title: "Question Generation Example 2" - text: "como <hl> el gobierno de Abbott <hl> que asumió el cargo el 18 de septiembre de 2013." example_title: "Question Generation Example 3" model-index: - name: vocabtrimmer/mt5-small-trimmed-es-10000-esquad-qg results: - task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qg_esquad type: default args: default metrics: - name: BLEU4 (Question Generation) type: bleu4_question_generation value: 9.33 - name: ROUGE-L (Question Generation) type: rouge_l_question_generation value: 23.68 - name: METEOR (Question Generation) type: meteor_question_generation value: 21.95 - name: BERTScore (Question Generation) type: bertscore_question_generation value: 83.89 - name: MoverScore (Question Generation) type: moverscore_question_generation value: 58.72 --- # Model Card of `vocabtrimmer/mt5-small-trimmed-es-10000-esquad-qg` This model is fine-tuned version of [vocabtrimmer/mt5-small-trimmed-es-10000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-es-10000) for question generation task on the [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). ### Overview - **Language model:** [vocabtrimmer/mt5-small-trimmed-es-10000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-es-10000) - **Language:** es - **Training data:** [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) ### Usage - With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-) ```python from lmqg import TransformersQG # initialize model model = TransformersQG(language="es", model="vocabtrimmer/mt5-small-trimmed-es-10000-esquad-qg") # model prediction questions = model.generate_q(list_context="a noviembre , que es también la estación lluviosa.", list_answer="noviembre") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-es-10000-esquad-qg") output = pipe("del <hl> Ministerio de Desarrollo Urbano <hl> , Gobierno de la India.") ``` ## Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-es-10000-esquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_esquad.default.json) | | Score | Type | Dataset | |:-----------|--------:|:--------|:-----------------------------------------------------------------| | BERTScore | 83.89 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | Bleu_1 | 25.57 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | Bleu_2 | 17.32 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | Bleu_3 | 12.5 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | Bleu_4 | 9.33 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | METEOR | 21.95 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | MoverScore | 58.72 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | ROUGE_L | 23.68 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | ## Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_esquad - dataset_name: default - input_types: paragraph_answer - output_types: question - prefix_types: None - model: vocabtrimmer/mt5-small-trimmed-es-10000 - max_length: 512 - max_length_output: 32 - epoch: 10 - batch: 16 - lr: 0.001 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 4 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-es-10000-esquad-qg/raw/main/trainer_config.json). ## Citation ``` @inproceedings{ushio-etal-2022-generative, title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration", author = "Ushio, Asahi and Alva-Manchego, Fernando and Camacho-Collados, Jose", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, U.A.E.", publisher = "Association for Computational Linguistics", } ```
adzcai/q-Taxi-v3
adzcai
2023-03-17T21:03:42Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-03-17T21:03:39Z
--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.52 +/- 2.75 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="adzcai/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"]) ```
jjrussell10/model1
jjrussell10
2023-03-17T20:56:44Z
103
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-03-17T18:29:04Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - recall - precision - f1 model-index: - name: model1 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. --> # model1 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: 0.0018 - Recall: 0.9997 - Precision: 0.9997 - F1: 0.9997 ## 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 - 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 | Recall | Precision | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:| | 0.0238 | 1.0 | 856 | 0.0024 | 0.9995 | 0.9995 | 0.9995 | | 0.0013 | 2.0 | 1712 | 0.0018 | 0.9997 | 0.9997 | 0.9997 | | 0.0006 | 3.0 | 2568 | 0.0019 | 0.9997 | 0.9997 | 0.9997 | ### Framework versions - Transformers 4.27.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2
jimizzni/ddpm-butterflies-128
jimizzni
2023-03-17T20:53:38Z
0
0
diffusers
[ "diffusers", "tensorboard", "en", "dataset:cars", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]
null
2023-03-17T17:41:25Z
--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: cars metrics: [] duplicated_from: Williamlokok/ddpm-butterflies-128 --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # ddpm-butterflies-128 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `cars` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/Williamlokok/ddpm-butterflies-128/tensorboard?#scalars)
vocabtrimmer/mt5-small-trimmed-es-5000-esquad-qg
vocabtrimmer
2023-03-17T20:44:03Z
84
0
transformers
[ "transformers", "pytorch", "mt5", "text2text-generation", "question generation", "es", "dataset:lmqg/qg_esquad", "arxiv:2210.03992", "license:cc-by-4.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2023-03-17T20:43:37Z
--- license: cc-by-4.0 metrics: - bleu4 - meteor - rouge-l - bertscore - moverscore language: es datasets: - lmqg/qg_esquad pipeline_tag: text2text-generation tags: - question generation widget: - text: "del <hl> Ministerio de Desarrollo Urbano <hl> , Gobierno de la India." example_title: "Question Generation Example 1" - text: "a <hl> noviembre <hl> , que es también la estación lluviosa." example_title: "Question Generation Example 2" - text: "como <hl> el gobierno de Abbott <hl> que asumió el cargo el 18 de septiembre de 2013." example_title: "Question Generation Example 3" model-index: - name: vocabtrimmer/mt5-small-trimmed-es-5000-esquad-qg results: - task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qg_esquad type: default args: default metrics: - name: BLEU4 (Question Generation) type: bleu4_question_generation value: 9.41 - name: ROUGE-L (Question Generation) type: rouge_l_question_generation value: 23.51 - name: METEOR (Question Generation) type: meteor_question_generation value: 21.88 - name: BERTScore (Question Generation) type: bertscore_question_generation value: 84.07 - name: MoverScore (Question Generation) type: moverscore_question_generation value: 58.84 --- # Model Card of `vocabtrimmer/mt5-small-trimmed-es-5000-esquad-qg` This model is fine-tuned version of [vocabtrimmer/mt5-small-trimmed-es-5000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-es-5000) for question generation task on the [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). ### Overview - **Language model:** [vocabtrimmer/mt5-small-trimmed-es-5000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-es-5000) - **Language:** es - **Training data:** [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) ### Usage - With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-) ```python from lmqg import TransformersQG # initialize model model = TransformersQG(language="es", model="vocabtrimmer/mt5-small-trimmed-es-5000-esquad-qg") # model prediction questions = model.generate_q(list_context="a noviembre , que es también la estación lluviosa.", list_answer="noviembre") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-es-5000-esquad-qg") output = pipe("del <hl> Ministerio de Desarrollo Urbano <hl> , Gobierno de la India.") ``` ## Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-es-5000-esquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_esquad.default.json) | | Score | Type | Dataset | |:-----------|--------:|:--------|:-----------------------------------------------------------------| | BERTScore | 84.07 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | Bleu_1 | 25.67 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | Bleu_2 | 17.4 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | Bleu_3 | 12.59 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | Bleu_4 | 9.41 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | METEOR | 21.88 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | MoverScore | 58.84 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | ROUGE_L | 23.51 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | ## Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_esquad - dataset_name: default - input_types: paragraph_answer - output_types: question - prefix_types: None - model: vocabtrimmer/mt5-small-trimmed-es-5000 - max_length: 512 - max_length_output: 32 - epoch: 12 - batch: 16 - lr: 0.001 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 4 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-es-5000-esquad-qg/raw/main/trainer_config.json). ## Citation ``` @inproceedings{ushio-etal-2022-generative, title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration", author = "Ushio, Asahi and Alva-Manchego, Fernando and Camacho-Collados, Jose", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, U.A.E.", publisher = "Association for Computational Linguistics", } ```
dussinus/Reinforce-pytorch-unit4
dussinus
2023-03-17T20:39:48Z
0
0
null
[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-03-17T20:39:35Z
--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-pytorch-unit4 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
JacobQuintero/roBERTa_QA
JacobQuintero
2023-03-17T20:39:23Z
103
0
transformers
[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-03-17T19:18:10Z
--- license: mit tags: - generated_from_trainer metrics: - accuracy model-index: - name: roBERTa_QA 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_QA This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1920 - Accuracy: 0.5573 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 48 | 1.4668 | 0.4544 | | No log | 2.0 | 96 | 1.3542 | 0.4680 | | No log | 3.0 | 144 | 1.2298 | 0.5146 | | No log | 4.0 | 192 | 1.1920 | 0.5573 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1 - Datasets 2.10.1 - Tokenizers 0.13.2
abhijitt/bert_st_qa_msmarco-distilbert-dot-v5-epochs-1
abhijitt
2023-03-17T20:28:45Z
2
0
sentence-transformers
[ "sentence-transformers", "pytorch", "distilbert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
sentence-similarity
2023-03-17T20:25:44Z
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #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('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, 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={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 1369 with parameters: ``` {'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 136, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel (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 <!--- Describe where people can find more information -->
Dave-Sheets/distilbert-base-uncased-finetuned-imdb
Dave-Sheets
2023-03-17T20:27:07Z
124
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "fill-mask", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-03-17T20:21:35Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb model-index: - name: distilbert-base-uncased-finetuned-imdb 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-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 2.4721 ## 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: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.7086 | 1.0 | 157 | 2.4898 | | 2.5796 | 2.0 | 314 | 2.4230 | | 2.5269 | 3.0 | 471 | 2.4354 | ### Framework versions - Transformers 4.27.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2
golightly/q-Taxi-v3
golightly
2023-03-17T20:08:54Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-03-17T20:08:47Z
--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.50 +/- 2.73 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="golightly/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"]) ```
golightly/q-FrozenLake-v1-4x4-noSlippery
golightly
2023-03-17T19:59:26Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-03-17T19:59:21Z
--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="golightly/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"]) ```
ryanaspen/dqn-SpaceInvaders
ryanaspen
2023-03-17T19:50:53Z
1
0
stable-baselines3
[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-03-17T19:50:11Z
--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 629.00 +/- 172.42 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga ryanaspen -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga ryanaspen -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga ryanaspen ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
rohitp1/subhadeep_whisper_base_finetune_teacher_babble_noise_libri_360_hours_100_epochs_batch_8
rohitp1
2023-03-17T19:47:47Z
76
0
transformers
[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2023-03-13T20:40:50Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: subhadeep_whisper_base_finetune_teacher_babble_noise_libri_360_hours_100_epochs_batch_8 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. --> # subhadeep_whisper_base_finetune_teacher_babble_noise_libri_360_hours_100_epochs_batch_8 This model is a fine-tuned version of [openai/whisper-base.en](https://huggingface.co/openai/whisper-base.en) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2491 - Wer: 13.5528 ## 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: 8 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 256 - total_train_batch_size: 2048 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.2 - num_epochs: 100 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.7942 | 1.98 | 100 | 0.2872 | 16.8523 | | 0.1675 | 3.98 | 200 | 0.2003 | 13.5730 | | 0.0819 | 5.98 | 300 | 0.1944 | 13.1208 | | 0.0418 | 7.98 | 400 | 0.2070 | 13.0639 | | 0.0264 | 9.98 | 500 | 0.2199 | 13.0289 | | 0.0227 | 11.98 | 600 | 0.2310 | 13.3690 | | 0.0218 | 13.98 | 700 | 0.2322 | 13.1870 | | 0.02 | 15.98 | 800 | 0.2405 | 13.1466 | | 0.0207 | 17.98 | 900 | 0.2496 | 13.4444 | | 0.0226 | 19.98 | 1000 | 0.2491 | 13.5528 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1 - Datasets 2.8.0 - Tokenizers 0.13.2
shru456/ppo-Huggy
shru456
2023-03-17T19:45:30Z
10
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
reinforcement-learning
2023-03-17T19:45:23Z
--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Find your model_id: shru456/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
pmorelr/layoutlm-doclaynet-test
pmorelr
2023-03-17T19:36:44Z
87
0
transformers
[ "transformers", "pytorch", "tensorboard", "layoutlm", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2023-03-12T21:03:45Z
--- tags: - generated_from_trainer model-index: - name: layoutlm-doclaynet-test 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. --> # layoutlm-doclaynet-test This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3029 - Footer: {'precision': 0.7619047619047619, 'recall': 0.7960199004975125, 'f1': 0.7785888077858881, 'number': 201} - Header: {'precision': 0.7631578947368421, 'recall': 0.6987951807228916, 'f1': 0.7295597484276729, 'number': 83} - Able: {'precision': 0.569377990430622, 'recall': 0.7531645569620253, 'f1': 0.6485013623978202, 'number': 158} - Aption: {'precision': 0.2857142857142857, 'recall': 0.26865671641791045, 'f1': 0.2769230769230769, 'number': 67} - Ext: {'precision': 0.6098901098901099, 'recall': 0.6809815950920245, 'f1': 0.6434782608695652, 'number': 326} - Icture: {'precision': 0.18055555555555555, 'recall': 0.2, 'f1': 0.18978102189781024, 'number': 65} - Itle: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} - Ootnote: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} - Overall Precision: 0.5930 - Overall Recall: 0.6505 - Overall F1: 0.6204 - Overall Accuracy: 0.9197 ## 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: 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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Footer | Header | Able | Aption | Ext | Icture | Itle | Ootnote | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| | 0.2414 | 1.0 | 426 | 0.1727 | {'precision': 0.6724137931034483, 'recall': 0.7761194029850746, 'f1': 0.720554272517321, 'number': 201} | {'precision': 0.7142857142857143, 'recall': 0.5421686746987951, 'f1': 0.6164383561643836, 'number': 83} | {'precision': 0.5069124423963134, 'recall': 0.6962025316455697, 'f1': 0.5866666666666668, 'number': 158} | {'precision': 0.22916666666666666, 'recall': 0.16417910447761194, 'f1': 0.19130434782608696, 'number': 67} | {'precision': 0.5323383084577115, 'recall': 0.656441717791411, 'f1': 0.587912087912088, 'number': 326} | {'precision': 0.24528301886792453, 'recall': 0.2, 'f1': 0.22033898305084745, 'number': 65} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.5409 | 0.6053 | 0.5713 | 0.9584 | | 0.1037 | 2.0 | 852 | 0.1726 | {'precision': 0.7045454545454546, 'recall': 0.7711442786069652, 'f1': 0.7363420427553445, 'number': 201} | {'precision': 0.8529411764705882, 'recall': 0.6987951807228916, 'f1': 0.7682119205298014, 'number': 83} | {'precision': 0.5658536585365853, 'recall': 0.7341772151898734, 'f1': 0.6391184573002755, 'number': 158} | {'precision': 0.25333333333333335, 'recall': 0.2835820895522388, 'f1': 0.2676056338028169, 'number': 67} | {'precision': 0.5640394088669951, 'recall': 0.7024539877300614, 'f1': 0.6256830601092896, 'number': 326} | {'precision': 0.16666666666666666, 'recall': 0.18461538461538463, 'f1': 0.17518248175182485, 'number': 65} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.5631 | 0.6494 | 0.6032 | 0.9510 | | 0.0647 | 3.0 | 1278 | 0.3029 | {'precision': 0.7619047619047619, 'recall': 0.7960199004975125, 'f1': 0.7785888077858881, 'number': 201} | {'precision': 0.7631578947368421, 'recall': 0.6987951807228916, 'f1': 0.7295597484276729, 'number': 83} | {'precision': 0.569377990430622, 'recall': 0.7531645569620253, 'f1': 0.6485013623978202, 'number': 158} | {'precision': 0.2857142857142857, 'recall': 0.26865671641791045, 'f1': 0.2769230769230769, 'number': 67} | {'precision': 0.6098901098901099, 'recall': 0.6809815950920245, 'f1': 0.6434782608695652, 'number': 326} | {'precision': 0.18055555555555555, 'recall': 0.2, 'f1': 0.18978102189781024, 'number': 65} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | 0.5930 | 0.6505 | 0.6204 | 0.9197 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.12.1+cu102 - Datasets 2.9.0 - Tokenizers 0.13.2
MerveOzer/a2c-PandaReachDense-v2
MerveOzer
2023-03-17T19:33:09Z
2
0
stable-baselines3
[ "stable-baselines3", "PandaReachDense-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-03-17T19:30:35Z
--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -1.04 +/- 0.12 name: mean_reward verified: false --- # **A2C** Agent playing **PandaReachDense-v2** This is a trained model of a **A2C** agent playing **PandaReachDense-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
shru123/ppo-Huggy
shru123
2023-03-17T19:29:33Z
11
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
reinforcement-learning
2023-03-16T20:54:12Z
--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Find your model_id: shru123/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
22h/cabrita-lora-v0-1
22h
2023-03-17T19:28:20Z
0
70
null
[ "pt", "license:openrail", "region:us" ]
null
2023-03-17T01:26:12Z
--- license: openrail language: - pt --- # Cabrita: portuguese instructLLaMA ## Usage Check the Github repo with code: https://github.com/22-hours/cabrita ```python from peft import PeftModel from transformers import LLaMATokenizer, LLaMAForCausalLM, GenerationConfig tokenizer = LLaMATokenizer.from_pretrained("decapoda-research/llama-7b-hf") model = LLaMAForCausalLM.from_pretrained( "decapoda-research/llama-7b-hf", load_in_8bit=True, device_map="auto", ) model = PeftModel.from_pretrained(model, "22h/cabrita-lora-v0-1") ``` ## Data We translated the [alpaca_data.json](https://github.com/tatsu-lab/stanford_alpaca/blob/main/alpaca_data.json) to portuguese using ChatGPT. Even if this translation was not the best, the tradeoff between costs and results were. We paid around US$ 8.00 to translate the full dataset to portuguese. If you want to know more about how the dataset was built go to: [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca). ## Finetuning To finetuned the LLaMA model we used the code available on [Alpaca Lora](https://github.com/tloen/alpaca-lora), which provides code to finetune the LLaMA model using PEFT from Hugging Face. With this, we could run our finetuning step using 1 A100 at Colab on top of LLaMA-7B. We trained during 4 hours and we found the results pretty incredible with just that much time. The notebook we used is avaible [here](notebooks/train_lora.ipynb). ## Example outputs Stanford Alpaca: ``` Instruction: Invente uma desculpa criativa pra dizer que não preciso ir à festa. Response: Eu preciso se concentrar em trabalho importante para mim, por isso eu não posso ir à festa. ``` Cabrita: ``` Instrução: Invente uma desculpa criativa pra dizer que não preciso ir à festa. Resposta: "Eu preciso ficar em casa para cuidar dos meus animais de estimação." ``` Stanford Alpaca: ``` Instruction: Como posso começar a carreira como cientista de dados? Escreva na forma de lista. Response: 1. Comece por se especializar em um campo específico, como big data, computação, bioinformática ou estatística. 2. Obtivere um diploma em um programa de graduação relacionado a ciência de dados, como informática, matemática ou estatística. 3. Aquisite experiência prática através de internships ou projetos em empresas ou instituições. 4. Ameize sua resumo e currículo com resultados relevantes e experiência em trabalho relacionado. 5. Leia artigos, livros e atualizações sobre ciência de dados. 6. Crie um portfólio com projetos e experimentos que tenha realizado. 7. Receba um certificado de qualificação em alguma área de ciência de dados. 8. Tente obter contato com profissionais da área para conversar sobre oportunidades de emprego. 9. Acesse redes sociais, como LinkedIn, para encontrar empregos e conexões. 10. Continui sua formação atualizando-se com tecnologias e ferramentas de ciência de dados. ``` Cabrita: ``` Instrução: Como posso começar a carreira como cientista de dados? Escreva na forma de lista. Resposta: 1. Obtenha um mestrado em ciência de dados ou engenharia de dados. 2. Encontre um emprego como cientista de dados ou engenheiro de dados. 3. Conheça as principais ferramentas de análise de dados, como Python, R e SQL. 4. Conheça as principais técnicas de análise de dados, como regressão linear, regressão logística e análise de clustering. 5. Conheça as principais abordagens de análise de dados, como análise exploratória, análise de associação e análise de regressão. 6. Conheça as principais ferramentas de visualização de dados, como Tableau, QlikView e Power BI. 7. Conheça as principais abordagens de análise de dados, como análise de associação, análise de regressão e análise exploratória. ``` You can test it using the eval notebook [here](https://colab.research.google.com/github/22-hours/cabrita/blob/main/notebooks/cabrita-lora.ipynb). ## References > If I have seen further it is by standing on the sholders [sic] of Giants. > -- <cite>Isaac Newton</cite> We started this section with this citation because everything we did was only possible due to the strong community and works that other people and groups did. For our work, we rely mainly in the works developed by: [LLaMA](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/), [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca), [Alpaca Lora](https://github.com/tloen/alpaca-lora), [ChatGPT](https://openai.com/blog/chatgpt) and [Hugging Face](https://huggingface.co/). So, thank you all for the great work and open this to the world! ## Hardware Requirements For training we have used an A100 in Google Colab. For eval, you can use a T4.
abhijitt/bert_st_qa_multi-qa-MiniLM-L6-cos-v1-epochs-10
abhijitt
2023-03-17T19:15:20Z
9
0
sentence-transformers
[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
sentence-similarity
2023-03-01T19:01:08Z
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 1369 with parameters: ``` {'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 1369, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
Alesteba/AP_01
Alesteba
2023-03-17T19:08:44Z
0
0
fastai
[ "fastai", "region:us" ]
null
2023-03-17T19:08:33Z
--- tags: - fastai --- # Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
Patil/a2c-AntBulletEnv-v0
Patil
2023-03-17T18:48:29Z
0
0
stable-baselines3
[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-03-17T18:47:21Z
--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 1887.24 +/- 117.10 name: mean_reward verified: false --- # **A2C** Agent playing **AntBulletEnv-v0** This is a trained model of a **A2C** agent playing **AntBulletEnv-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
vocabtrimmer/mt5-small-trimmed-es-90000-esquad-qg
vocabtrimmer
2023-03-17T18:36:32Z
105
0
transformers
[ "transformers", "pytorch", "mt5", "text2text-generation", "question generation", "es", "dataset:lmqg/qg_esquad", "arxiv:2210.03992", "license:cc-by-4.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2023-03-17T18:31:35Z
--- license: cc-by-4.0 metrics: - bleu4 - meteor - rouge-l - bertscore - moverscore language: es datasets: - lmqg/qg_esquad pipeline_tag: text2text-generation tags: - question generation widget: - text: "del <hl> Ministerio de Desarrollo Urbano <hl> , Gobierno de la India." example_title: "Question Generation Example 1" - text: "a <hl> noviembre <hl> , que es también la estación lluviosa." example_title: "Question Generation Example 2" - text: "como <hl> el gobierno de Abbott <hl> que asumió el cargo el 18 de septiembre de 2013." example_title: "Question Generation Example 3" model-index: - name: vocabtrimmer/mt5-small-trimmed-es-90000-esquad-qg results: - task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qg_esquad type: default args: default metrics: - name: BLEU4 (Question Generation) type: bleu4_question_generation value: 9.1 - name: ROUGE-L (Question Generation) type: rouge_l_question_generation value: 24.18 - name: METEOR (Question Generation) type: meteor_question_generation value: 20.36 - name: BERTScore (Question Generation) type: bertscore_question_generation value: 79.65 - name: MoverScore (Question Generation) type: moverscore_question_generation value: 55.02 --- # Model Card of `vocabtrimmer/mt5-small-trimmed-es-90000-esquad-qg` This model is fine-tuned version of [vocabtrimmer/mt5-small-trimmed-es-90000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-es-90000) for question generation task on the [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). ### Overview - **Language model:** [vocabtrimmer/mt5-small-trimmed-es-90000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-es-90000) - **Language:** es - **Training data:** [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) ### Usage - With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-) ```python from lmqg import TransformersQG # initialize model model = TransformersQG(language="es", model="vocabtrimmer/mt5-small-trimmed-es-90000-esquad-qg") # model prediction questions = model.generate_q(list_context="a noviembre , que es también la estación lluviosa.", list_answer="noviembre") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-es-90000-esquad-qg") output = pipe("del <hl> Ministerio de Desarrollo Urbano <hl> , Gobierno de la India.") ``` ## Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-es-90000-esquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_esquad.default.json) | | Score | Type | Dataset | |:-----------|--------:|:--------|:-----------------------------------------------------------------| | BERTScore | 79.65 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | Bleu_1 | 25.98 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | Bleu_2 | 17.39 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | Bleu_3 | 12.4 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | Bleu_4 | 9.1 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | METEOR | 20.36 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | MoverScore | 55.02 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | ROUGE_L | 24.18 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | ## Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_esquad - dataset_name: default - input_types: paragraph_answer - output_types: question - prefix_types: None - model: vocabtrimmer/mt5-small-trimmed-es-90000 - max_length: 512 - max_length_output: 32 - epoch: 12 - batch: 16 - lr: 0.001 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 4 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-es-90000-esquad-qg/raw/main/trainer_config.json). ## Citation ``` @inproceedings{ushio-etal-2022-generative, title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration", author = "Ushio, Asahi and Alva-Manchego, Fernando and Camacho-Collados, Jose", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, U.A.E.", publisher = "Association for Computational Linguistics", } ```
MerveOzer/a2c-AntBulletEnv-v0
MerveOzer
2023-03-17T18:31:05Z
0
0
stable-baselines3
[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-03-17T18:29:52Z
--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 1983.45 +/- 71.75 name: mean_reward verified: false --- # **A2C** Agent playing **AntBulletEnv-v0** This is a trained model of a **A2C** agent playing **AntBulletEnv-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
yangwj2011/a2c-PandaReachDense-v2
yangwj2011
2023-03-17T18:27:32Z
0
0
stable-baselines3
[ "stable-baselines3", "PandaReachDense-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-03-17T18:25:15Z
--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -0.83 +/- 0.20 name: mean_reward verified: false --- # **A2C** Agent playing **PandaReachDense-v2** This is a trained model of a **A2C** agent playing **PandaReachDense-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
QuickSilver007/rlv2unit1b_ppo-Huggy
QuickSilver007
2023-03-17T18:26:05Z
4
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
reinforcement-learning
2023-03-17T18:25:58Z
--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Find your model_id: QuickSilver007/rlv2unit1b_ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
voidful/albert_chinese_xlarge
voidful
2023-03-17T18:16:36Z
128
1
transformers
[ "transformers", "pytorch", "safetensors", "albert", "fill-mask", "zh", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-03-02T23:29:05Z
--- language: zh pipeline_tag: fill-mask widget: - text: "今天[MASK]情很好" --- # albert_chinese_xlarge This a albert_chinese_xlarge model from [Google's github](https://github.com/google-research/ALBERT) converted by huggingface's [script](https://github.com/huggingface/transformers/blob/master/src/transformers/convert_albert_original_tf_checkpoint_to_pytorch.py) ## Notice *Support AutoTokenizer* Since sentencepiece is not used in albert_chinese_base model you have to call BertTokenizer instead of AlbertTokenizer !!! we can eval it using an example on MaskedLM 由於 albert_chinese_base 模型沒有用 sentencepiece 用AlbertTokenizer會載不進詞表，因此需要改用BertTokenizer !!! 我們可以跑MaskedLM預測來驗證這個做法是否正確 ## Justify (驗證有效性) ```python from transformers import AutoTokenizer, AlbertForMaskedLM import torch from torch.nn.functional import softmax pretrained = 'voidful/albert_chinese_xlarge' tokenizer = AutoTokenizer.from_pretrained(pretrained) model = AlbertForMaskedLM.from_pretrained(pretrained) inputtext = "今天[MASK]情很好" maskpos = tokenizer.encode(inputtext, add_special_tokens=True).index(103) input_ids = torch.tensor(tokenizer.encode(inputtext, add_special_tokens=True)).unsqueeze(0) # Batch size 1 outputs = model(input_ids, labels=input_ids) loss, prediction_scores = outputs[:2] logit_prob = softmax(prediction_scores[0, maskpos],dim=-1).data.tolist() predicted_index = torch.argmax(prediction_scores[0, maskpos]).item() predicted_token = tokenizer.convert_ids_to_tokens([predicted_index])[0] print(predicted_token, logit_prob[predicted_index]) ``` Result: `心 0.9942440390586853`
deerslab/llama-7b-embeddings
deerslab
2023-03-17T17:41:37Z
18
5
transformers
[ "transformers", "pytorch", "llama", "text-generation", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-03-17T16:38:15Z
--- license: other duplicated_from: decapoda-research/llama-7b-hf --- LLaMA-7B converted to work with Transformers/HuggingFace. This is under a special license, please see the LICENSE file for details. -- license: other --- # LLaMA Model Card ## Model details **Organization developing the model** The FAIR team of Meta AI. **Model date** LLaMA was trained between December. 2022 and Feb. 2023. **Model version** This is version 1 of the model. **Model type** LLaMA is an auto-regressive language model, based on the transformer architecture. The model comes in different sizes: 7B, 13B, 33B and 65B parameters. **Paper or resources for more information** More information can be found in the paper “LLaMA, Open and Efficient Foundation Language Models”, available at https://research.facebook.com/publications/llama-open-and-efficient-foundation-language-models/. **Citations details** https://research.facebook.com/publications/llama-open-and-efficient-foundation-language-models/ **License** Non-commercial bespoke license **Where to send questions or comments about the model** Questions and comments about LLaMA can be sent via the [GitHub repository](https://github.com/facebookresearch/llama) of the project , by opening an issue. ## Intended use **Primary intended uses** The primary use of LLaMA is research on large language models, including: exploring potential applications such as question answering, natural language understanding or reading comprehension, understanding capabilities and limitations of current language models, and developing techniques to improve those, evaluating and mitigating biases, risks, toxic and harmful content generations, hallucinations. **Primary intended users** The primary intended users of the model are researchers in natural language processing, machine learning and artificial intelligence. **Out-of-scope use cases** LLaMA is a base, or foundational, model. As such, it should not be used on downstream applications without further risk evaluation and mitigation. In particular, our model has not been trained with human feedback, and can thus generate toxic or offensive content, incorrect information or generally unhelpful answers. ## Factors **Relevant factors** One of the most relevant factors for which model performance may vary is which language is used. Although we included 20 languages in the training data, most of our dataset is made of English text, and we thus expect the model to perform better for English than other languages. Relatedly, it has been shown in previous studies that performance might vary for different dialects, and we expect that it will be the case for our model. **Evaluation factors** As our model is trained on data from the Web, we expect that it reflects biases from this source. We thus evaluated on RAI datasets to measure biases exhibited by the model for gender, religion, race, sexual orientation, age, nationality, disability, physical appearance and socio-economic status. We also measure the toxicity of model generations, depending on the toxicity of the context used to prompt the model. ## Metrics **Model performance measures** We use the following measure to evaluate the model: - Accuracy for common sense reasoning, reading comprehension, natural language understanding (MMLU), BIG-bench hard, WinoGender and CrowS-Pairs, - Exact match for question answering, - The toxicity score from Perspective API on RealToxicityPrompts. **Decision thresholds** Not applicable. **Approaches to uncertainty and variability** Due to the high computational requirements of training LLMs, we trained only one model of each size, and thus could not evaluate variability of pre-training. ## Evaluation datasets The model was evaluated on the following benchmarks: BoolQ, PIQA, SIQA, HellaSwag, WinoGrande, ARC, OpenBookQA, NaturalQuestions, TriviaQA, RACE, MMLU, BIG-bench hard, GSM8k, RealToxicityPrompts, WinoGender, CrowS-Pairs. ## Training dataset The model was trained using the following source of data: CCNet [67%], C4 [15%], GitHub [4.5%], Wikipedia [4.5%], Books [4.5%], ArXiv [2.5%], Stack Exchange[2%]. The Wikipedia and Books domains include data in the following languages: bg, ca, cs, da, de, en, es, fr, hr, hu, it, nl, pl, pt, ro, ru, sl, sr, sv, uk. See the paper for more details about the training set and corresponding preprocessing. ## Quantitative analysis Hyperparameters for the model architecture <table> <thead> <tr> <th >LLaMA</th> <th colspan=6>Model hyper parameters </th> </tr> <tr> <th>Number of parameters</th><th>dimension</th><th>n heads</th><th>n layers</th><th>Learn rate</th><th>Batch size</th><th>n tokens</th> </tr> </thead> <tbody> <tr> <th>7B</th> <th>4096</th> <th>32</th> <th>32</th> <th>3.0E-04</th><th>4M</th><th>1T </tr> <tr> <th>13B</th><th>5120</th><th>40</th><th>40</th><th>3.0E-04</th><th>4M</th><th>1T </tr> <tr> <th>33B</th><th>6656</th><th>52</th><th>60</th><th>1.5.E-04</th><th>4M</th><th>1.4T </tr> <tr> <th>65B</th><th>8192</th><th>64</th><th>80</th><th>1.5.E-04</th><th>4M</th><th>1.4T </tr> </tbody> </table> *Table 1 - Summary of LLama Model Hyperparameters* We present our results on eight standard common sense reasoning benchmarks in the table below. <table> <thead> <tr> <th>LLaMA</th> <th colspan=9>Reasoning tasks </th> </tr> <tr> <th>Number of parameters</th> <th>BoolQ</th><th>PIQA</th><th>SIQA</th><th>HellaSwag</th><th>WinoGrande</th><th>ARC-e</th><th>ARC-c</th><th>OBQA</th><th>COPA</th> </tr> </thead> <tbody> <tr> <th>7B</th><th>76.5</th><th>79.8</th><th>48.9</th><th>76.1</th><th>70.1</th><th>76.7</th><th>47.6</th><th>57.2</th><th>93 </th> <tr><th>13B</th><th>78.1</th><th>80.1</th><th>50.4</th><th>79.2</th><th>73</th><th>78.1</th><th>52.7</th><th>56.4</th><th>94 </th> <tr><th>33B</th><th>83.1</th><th>82.3</th><th>50.4</th><th>82.8</th><th>76</th><th>81.4</th><th>57.8</th><th>58.6</th><th>92 </th> <tr><th>65B</th><th>85.3</th><th>82.8</th><th>52.3</th><th>84.2</th><th>77</th><th>81.5</th><th>56</th><th>60.2</th><th>94</th></tr> </tbody> </table> *Table 2 - Summary of LLama Model Performance on Reasoning tasks* We present our results on bias in the table below. Note that lower value is better indicating lower bias. | No | Category | FAIR LLM | | --- | -------------------- | -------- | | 1 | Gender | 70.6 | | 2 | Religion | 79 | | 3 | Race/Color | 57 | | 4 | Sexual orientation | 81 | | 5 | Age | 70.1 | | 6 | Nationality | 64.2 | | 7 | Disability | 66.7 | | 8 | Physical appearance | 77.8 | | 9 | Socioeconomic status | 71.5 | | | LLaMA Average | 66.6 | *Table 3 - Summary bias of our model output* ## Ethical considerations **Data** The data used to train the model is collected from various sources, mostly from the Web. As such, it contains offensive, harmful and biased content. We thus expect the model to exhibit such biases from the training data. **Human life** The model is not intended to inform decisions about matters central to human life, and should not be used in such a way. **Mitigations** We filtered the data from the Web based on its proximity to Wikipedia text and references. For this, we used a Kneser-Ney language model and a fastText linear classifier. **Risks and harms** Risks and harms of large language models include the generation of harmful, offensive or biased content. These models are often prone to generating incorrect information, sometimes referred to as hallucinations. We do not expect our model to be an exception in this regard. **Use cases** LLaMA is a foundational model, and as such, it should not be used for downstream applications without further investigation and mitigations of risks. These risks and potential fraught use cases include, but are not limited to: generation of misinformation and generation of harmful, biased or offensive content.
apparition/q-table-Taxi-v3
apparition
2023-03-17T17:41:08Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-03-17T17:41:03Z
--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-table-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="apparition/q-table-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"]) ```
andylolu24/q-Taxi-v3
andylolu24
2023-03-17T17:26:30Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-03-17T17:26:23Z
--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="andylolu2/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"]) ```
yhavinga/t5-v1.1-base-dutch-cnn-test
yhavinga
2023-03-17T17:25:23Z
177
3
transformers
[ "transformers", "pytorch", "jax", "tensorboard", "safetensors", "t5", "text2text-generation", "summarization", "seq2seq", "nl", "dataset:yhavinga/mc4_nl_cleaned", "dataset:ml6team/cnn_dailymail_nl", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
summarization
2022-03-02T23:29:05Z
--- language: - nl license: apache-2.0 tags: - summarization - t5 - seq2seq datasets: - yhavinga/mc4_nl_cleaned - ml6team/cnn_dailymail_nl pipeline_tag: summarization widget: - text: 'Het Van Goghmuseum in Amsterdam heeft vier kostbare prenten verworven van Mary Cassatt, de Amerikaanse impressionistische kunstenaar en tijdgenoot van Vincent van Gogh. Dat heeft het museum woensdagmiddag op een persconferentie bekendgemaakt. Het gaat om drie grote kleurenetsen en een zwart-wit litho met voorstellingen van vrouwen. Voor deze prenten, die afkomstig zijn van een Amerikaanse verzamelaar, betaalde het museum ruim 1,4 miljoen euro. Drie grote fondsen en een aantal particulieren hebben samen de aankoopsom beschikbaar gesteld. Mary Stevenson Cassatt (1844-1926) woonde en werkte lange tijd in Frankrijk. Ze staat met haar impressionistische schilderijen en tekeningen te boek als een van de vernieuwers van de Parijse kunstwereld in de late negentiende eeuw. Het Van Goghmuseum rekent haar prenten „tot het mooiste wat op grafisch gebied in het fin de siècle is geproduceerd”. De drie aangekochte kleurenetsen – Het doorpassen, De brief en Badende vrouw – komen uit een serie van tien waarmee Cassatt haar naam als (prent)kunstenaar definitief vestigde. Ze maakte de etsen na een bezoek in 1890 aan een tentoonstelling van Japanse prenten in Parijs. Over die expositie schreef de Amerikaanse aan haar vriendin Berthe Morisot, een andere vrouwelijke impressionist: „We kunnen de Japanse prenten in de Beaux-Arts gaan bekijken. Echt, die mag je niet missen. Als je kleurenprenten wilt maken, is er niets mooiers voorstelbaar. Ik droom ervan en denk nergens anders meer aan dan aan kleur op koper.' - text: 'Afgelopen zaterdagochtend werden Hunga Tonga en Hunga Hapai opnieuw twee aparte eilanden toen de vulkaan met een hevige explosie uitbarstte. De aanloop tot de uitbarsting begon al eind vorig jaar met kleinere explosies. Begin januari nam de activiteit af en dachten geologen dat de vulkaan tot rust was gekomen. Toch barstte hij afgelopen zaterdag opnieuw uit, veel heviger dan de uitbarstingen ervoor. Vlák voor deze explosie stortte het kilometerslange verbindingsstuk in en verdween onder het water. De eruptie duurde acht minuten. De wolk van as en giftige gasdeeltjes, zoals zwaveloxide, die daarbij vrijkwam, reikte tot dertig kilometer hoogte en was zo’n vijfhonderd kilometer breed. Ter vergelijking: de pluimen uit de recente vulkaanuitbarsting op La Palma reikten maximaal zo’n vijf kilometer hoog. De hoofdstad van Tonga, vijfenzestig kilometer verderop is bedekt met een dikke laag as. Dat heeft bijvoorbeeld gevolgen voor de veiligheid van het drinkwater op Tonga. De uitbarsting van de onderzeese vulkaan in de eilandstaat Tonga afgelopen zaterdag was bijzonder heftig. De eruptie veroorzaakte een tsunami die reikte van Nieuw-Zeeland tot de Verenigde Staten en in Nederland ging de luchtdruk omhoog. Geologen verwachten niet dat de vulkaan op Tonga voor een lange wereldwijde afkoeling zorgt, zoals bij andere hevige vulkaanuitbarstingen het geval is geweest. De vulkaan ligt onder water tussen de onbewoonde eilandjes Hunga Tonga (0,39 vierkante kilometer) en Hunga Ha’apai (0,65 vierkante kilometer). Magma dat bij kleinere uitbarsting in 2009 en 2014 omhoog kwam, koelde af en vormde een verbindingsstuk tussen de twee eilanden in. Een explosie van een onderwatervulkaan als die bij Tonga is heftiger dan bijvoorbeeld die uitbarsting op La Palma. „Dat komt doordat het vulkanisme hier veroorzaakt wordt door subductie: de Pacifische plaat zinkt onder Tonga de aardmantel in en neemt water mee omlaag”, zegt hoogleraar paleogeografie Douwe van Hinsbergen van de Universiteit Utrecht. „Dit water komt met magma als gas, als waterdamp, mee omhoog. Dat voert de druk onder de aardkost enorm op. Arwen Deuss, geowetenschapper aan de Universiteit Utrecht, vergelijkt het met een fles cola. „Wanneer je een fles cola schudt, zal het gas er met veel geweld uitkomen. Dat is waarschijnlijk wat er gebeurd is op Tonga, maar we weten het niet precies.”' model-index: - name: yhavinga/t5-v1.1-base-dutch-cnn-test results: - task: type: summarization name: Summarization dataset: name: ml6team/cnn_dailymail_nl type: ml6team/cnn_dailymail_nl config: default split: test metrics: - type: rouge value: 38.5454 name: ROUGE-1 verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZWQwM2I0MjcwODQxZGNkMTMwZDllZjVlNzVkOWQyZDkzNDkxODE5ZjZiOWI1N2E5N2Y5MDcyZWM4ZWZjYzQ0NCIsInZlcnNpb24iOjF9.ORXcoqRJvsQyPdPQWhG3ZiYo7TYQaklYOdThMJJCrVOY1IrBjFRg_sx4e5qrQMMCwn-iVFa2YwSXPriBx49HDw - type: rouge value: 15.7133 name: ROUGE-2 verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiY2IyZmIxZDc0NjlhNTYyY2I3OTNkYjhkZDUwMjQ1ZjRjMjE3ZjhmMmUzMjVjYTc1MDkyMzZiY2E2OGIxMzE3OCIsInZlcnNpb24iOjF9.-2pXCw3ffIZyYPfjJRrg-tlwy7PC7ICjc4m3-q3_ciXB3x8RveOuUvxfd3q8xoox2ICHaGmrdBPKXYWBFVvJDQ - type: rouge value: 25.9162 name: ROUGE-L verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNjdiYWY3YTY1NmJhYWIzNGEwMGRkMTBlYTAyYjJkMmJiZWM4ZGUwMWE2ZTI5YzMxNDlkMWVlMDM2ZTMyYWE5YSIsInZlcnNpb24iOjF9.chltUhR_bF4vA-AOfOAi16Qor4ioBsgk4eJCosWJmdTgkCLJmN_sPAcr0Jz2qLo7dfeWwZ5ee0KcXGF4eyNyAA - type: rouge value: 35.4489 name: ROUGE-LSUM verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNjliMjUzYzA0MTQ3MjQ2NTk1YzY0MjA3N2U4YmI5MjE1Mzk2OGIxMTM2NTEwNjg0ZGU0ZTkxNTU2ZTJmNzdhNSIsInZlcnNpb24iOjF9.7l_KXmqIgTuDXOHdlTFLm67gjsaypy-RUTEJ9unNZlTXTmKPvL1frMZ0PUm5gRi-hM2TWVcUpTnVpkmXa4bNDw - type: loss value: 2.0727603435516357 name: loss verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZWY0Yzc1MGUxZmIyNDdjNzhiMzVlMjI4YzIwMGNkNzVjNmE3NjgxZjYwYTA4Y2QxYmNjZThiNzE5OWYzMjExOCIsInZlcnNpb24iOjF9.ERRCuKz5IekBZihQtyRnfz4VGl7LfCDzUO6-ZbYrZO_sdTxpaEw3ID0O3Cyx2Y4hmAYEywyvC2Idb3fmmjplAQ - type: gen_len value: 91.1699 name: gen_len verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMmNmMDRkOGMyMDY1OGNmMmQwY2ZkMzdlMDA2NzNkYmY3NzNmMTFmYmE3MTNhOWFlN2Q2N2FhNzFhNjM4NWJjOSIsInZlcnNpb24iOjF9.Otl1b_1Muxu6I4W2ThWBFidlwmou7149pMcShI4W-jeBntQeBwrfBe-fSkvNF-8Q29I_Of3o1swJXJAWAaxTDA --- # T5 v1.1 Base finetuned for CNN news summarization in Dutch 🇳🇱 This model is [t5-v1.1-base-dutch-cased](https://huggingface.co/yhavinga/t5-v1.1-base-dutch-cased) finetuned on [CNN Dailymail NL](https://huggingface.co/datasets/ml6team/cnn_dailymail_nl) For a demo of the Dutch CNN summarization models, head over to the Hugging Face Spaces for the **[Netherformer 📰](https://huggingface.co/spaces/flax-community/netherformer)** example application! Rouge scores for this model are listed below. ## Tokenizer * SentencePiece tokenizer trained from scratch for Dutch on mC4 nl cleaned with scripts from the Huggingface Transformers [Flax examples](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling). ## Dataset All models listed below are trained on of the `full` configuration (39B tokens) of [cleaned Dutch mC4](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned), which is the original mC4, except * Documents that contained words from a selection of the Dutch and English [List of Dirty Naught Obscene and Otherwise Bad Words](https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words) are removed * Sentences with less than 3 words are removed * Sentences with a word of more than 1000 characters are removed * Documents with less than 5 sentences are removed * Documents with "javascript", "lorum ipsum", "terms of use", "privacy policy", "cookie policy", "uses cookies", "use of cookies", "use cookies", "elementen ontbreken", "deze printversie" are removed. ## Models TL;DR: [yhavinga/t5-v1.1-base-dutch-cased](https://huggingface.co/yhavinga/t5-v1.1-base-dutch-cased) is the best model. * `yhavinga/t5-base-dutch` is a re-training of the Dutch T5 base v1.0 model trained during the summer 2021 Flax/Jax community week. Accuracy was improved from 0.64 to 0.70. * The two T5 v1.1 base models are an uncased and cased version of `t5-v1.1-base`, again pre-trained from scratch on Dutch, with a tokenizer also trained from scratch. The t5 v1.1 models are slightly different from the t5 models, and the base models are trained with a dropout of 0.0. For fine-tuning it is intended to set this back to 0.1. * The large cased model is a pre-trained Dutch version of `t5-v1.1-large`. Training of t5-v1.1-large proved difficult. Without dropout regularization, the training would diverge at a certain point. With dropout training went better, be it much slower than training the t5-model. At some point convergance was too slow to warrant further training. The latest checkpoint, training scripts and metrics are available for reference. For actual fine-tuning the cased base model is probably the better choice. | | model | train seq len | acc | loss | batch size | epochs | steps | dropout | optim | lr | duration | |---------------------------------------------------------------------------------------------------|---------|---------------|----------|----------|------------|--------|---------|---------|-----------|------|----------| | [yhavinga/t5-base-dutch](https://huggingface.co/yhavinga/t5-base-dutch) | T5 | 512 | 0,70 | 1,38 | 128 | 1 | 528481 | 0.1 | adafactor | 5e-3 | 2d 9h | | [yhavinga/t5-v1.1-base-dutch-uncased](https://huggingface.co/yhavinga/t5-v1.1-base-dutch-uncased) | t5-v1.1 | 1024 | 0,73 | 1,20 | 64 | 2 | 1014525 | 0.0 | adafactor | 5e-3 | 5d 5h | | [yhavinga/t5-v1.1-base-dutch-cased](https://huggingface.co/yhavinga/t5-v1.1-base-dutch-cased) | t5-v1.1 | 1024 | **0,78** | **0,96** | 64 | 2 | 1210000 | 0.0 | adafactor | 5e-3 | 6d 6h | | [yhavinga/t5-v1.1-large-dutch-cased](https://huggingface.co/yhavinga/t5-v1.1-large-dutch-cased) | t5-v1.1 | 512 | 0,76 | 1,07 | 64 | 1 | 1120000 | 0.1 | adafactor | 5e-3 | 86 13h | The cased t5-v1.1 Dutch models were fine-tuned on summarizing the CNN Daily Mail dataset. | | model | input len | target len | Rouge1 | Rouge2 | RougeL | RougeLsum | Test Gen Len | epochs | batch size | steps | duration | |-------------------------------------------------------------------------------------------------------|---------|-----------|------------|--------|--------|--------|-----------|--------------|--------|------------|-------|----------| | [yhavinga/t5-v1.1-base-dutch-cnn-test](https://huggingface.co/yhavinga/t5-v1.1-base-dutch-cnn-test) | t5-v1.1 | 1024 | 96 | 34,8 | 13,6 | 25,2 | 32,1 | 79 | 6 | 64 | 26916 | 2h 40m | | [yhavinga/t5-v1.1-large-dutch-cnn-test](https://huggingface.co/yhavinga/t5-v1.1-large-dutch-cnn-test) | t5-v1.1 | 1024 | 96 | 34,4 | 13,6 | 25,3 | 31,7 | 81 | 5 | 16 | 89720 | 11h | ## Acknowledgements This project would not have been possible without compute generously provided by Google through the [TPU Research Cloud](https://sites.research.google/trc/). The HuggingFace 🤗 ecosystem was also instrumental in many, if not all parts of the training. The following repositories where helpful in setting up the TPU-VM, and training the models: * [Gsarti's Pretrain and Fine-tune a T5 model with Flax on GCP](https://github.com/gsarti/t5-flax-gcp) * [HUggingFace Flax MLM examples](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling) * [Flax/Jax Community week t5-base-dutch](https://huggingface.co/flax-community/t5-base-dutch) Created by [Yeb Havinga](https://www.linkedin.com/in/yeb-havinga-86530825/)
ecemisildar/rl_course_vizdoom_health_gathering_supreme1
ecemisildar
2023-03-17T17:21:52Z
0
0
sample-factory
[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-03-17T16:34:59Z
--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 3.65 +/- 0.81 name: mean_reward verified: false --- A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r ecemisildar/rl_course_vizdoom_health_gathering_supreme1 ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme1 ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme1 --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
ThoDum/rl_course_vizdoom_health_gathering_supreme
ThoDum
2023-03-17T17:20:33Z
0
0
sample-factory
[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-03-17T16:35:17Z
--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 13.03 +/- 6.07 name: mean_reward verified: false --- A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r ThoDum/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
Tritkoman/EnglishtoOldRussianV1
Tritkoman
2023-03-17T17:18:50Z
105
0
transformers
[ "transformers", "pytorch", "mt5", "text2text-generation", "autotrain", "translation", "en", "es", "dataset:Tritkoman/autotrain-data-engtoorv", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]
translation
2023-03-17T17:16:01Z
--- tags: - autotrain - translation language: - en - es datasets: - Tritkoman/autotrain-data-engtoorv co2_eq_emissions: emissions: 1.2383909090027077 --- # Model Trained Using AutoTrain - Problem type: Translation - Model ID: 41771107469 - CO2 Emissions (in grams): 1.2384 ## Validation Metrics - Loss: 13.671 - SacreBLEU: 0.002 - Gen len: 12.000
MerveOzer/ppo-pyramids
MerveOzer
2023-03-17T17:16:04Z
4
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]
reinforcement-learning
2023-03-17T17:03:33Z
--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # **ppo** Agent playing **Pyramids** This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Find your model_id: MerveOzer/ppo-pyramids 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
spacemanidol/flan-t5-large-6-1-cnndm
spacemanidol
2023-03-17T17:04:56Z
103
0
transformers
[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "dataset:cnn_dailymail", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2023-03-06T20:31:08Z
--- tags: - generated_from_trainer datasets: - cnn_dailymail metrics: - rouge model-index: - name: large-6-1-t results: - task: name: Summarization type: summarization dataset: name: cnn_dailymail 3.0.0 type: cnn_dailymail config: 3.0.0 split: validation args: 3.0.0 metrics: - name: Rouge1 type: rouge value: 41.4182 --- <!-- 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. --> # large-6-1-t This model is a fine-tuned version of [6-1](https://huggingface.co/6-1) on the cnn_dailymail 3.0.0 dataset. It achieves the following results on the evaluation set: - Loss: 1.6639 - Rouge1: 41.4182 - Rouge2: 19.4871 - Rougel: 30.3528 - Rougelsum: 38.7818 - Gen Len: 70.3855 ## 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: 30 - eval_batch_size: 12 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 60 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.27.1 - Pytorch 1.12.0+cu116 - Datasets 2.4.0 - Tokenizers 0.13.2
Francesca1999M/distilroberta-base-finetuned-wikitext2
Francesca1999M
2023-03-17T17:04:05Z
162
0
transformers
[ "transformers", "pytorch", "tensorboard", "roberta", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-03-17T15:31:22Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilroberta-base-finetuned-wikitext2 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. --> # distilroberta-base-finetuned-wikitext2 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1604 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 120 | 1.4113 | | No log | 2.0 | 240 | 1.1765 | | No log | 3.0 | 360 | 1.2144 | ### Framework versions - Transformers 4.27.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2
armanakbari/sd-class-cifar10-32
armanakbari
2023-03-17T17:03:39Z
38
0
diffusers
[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]
unconditional-image-generation
2023-03-17T17:03:08Z
--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('armanakbari/sd-class-cifar10-32') image = pipeline().images[0] image ```
LarryAIDraw/tobiichiOrigamiDateALive7_v10
LarryAIDraw
2023-03-17T16:50:02Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-03-17T16:43:52Z
--- license: creativeml-openrail-m --- https://civitai.com/models/20424/tobiichi-origami-date-a-live-or-7-outfits-or-character-lora-444
raima2001/helper1
raima2001
2023-03-17T16:41:36Z
182
0
transformers
[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-03-17T16:39:10Z
--- tags: - generated_from_trainer metrics: - precision - recall model-index: - name: helper1 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. --> # helper1 This model is a fine-tuned version of [smallbenchnlp/roberta-small](https://huggingface.co/smallbenchnlp/roberta-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0020 - Acc: 1.0 - Precision: 1.0 - Recall: 1.0 - F1 score: 1.0 - Auc: 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: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 0.5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Acc | Precision | Recall | F1 score | Auc | |:-------------:|:-----:|:----:|:---------------:|:---:|:---------:|:------:|:--------:|:---:| | 0.0035 | 0.49 | 500 | 0.0020 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | ### Framework versions - Transformers 4.27.1 - Pytorch 1.13.1+cu116 - Tokenizers 0.13.2
lipee/Reinforce-Pixelcopter-PLE-v0
lipee
2023-03-17T16:41:35Z
0
0
null
[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-03-17T15:47:36Z
--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 18.90 +/- 15.21 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
splusminusx/a2c-AntBulletEnv-v0
splusminusx
2023-03-17T16:23:17Z
1
0
stable-baselines3
[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-03-17T16:22:03Z
--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 2035.08 +/- 156.42 name: mean_reward verified: false --- # **A2C** Agent playing **AntBulletEnv-v0** This is a trained model of a **A2C** agent playing **AntBulletEnv-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
MohammedDhiyaEddine/job-skill-sentence-transformer-tsdae
MohammedDhiyaEddine
2023-03-17T16:07:27Z
41
3
transformers
[ "transformers", "pytorch", "safetensors", "roberta", "feature-extraction", "jobs", "skills", "en", "license:apache-2.0", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2023-03-04T16:27:57Z
--- license: apache-2.0 language: - en tags: - jobs - skills --- A fine-tuned `SentenceTransformer` model on jobs and skills descriptions using the `Transformer-based and Sequential Denoising AutoEncoder` training method which is used mainly in tasks where you lack labelled data.
niks-salodkar/ppo_pyramidsRND
niks-salodkar
2023-03-17T16:06:15Z
6
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]
reinforcement-learning
2023-03-17T16:06:07Z
--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # **ppo** Agent playing **Pyramids** This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Find your model_id: niks-salodkar/ppo_pyramidsRND 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
inkasaras/a2c-AntBulletEnv-v0
inkasaras
2023-03-17T16:05:47Z
1
0
stable-baselines3
[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-03-17T16:04:31Z
--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 1842.36 +/- 99.88 name: mean_reward verified: false --- # **A2C** Agent playing **AntBulletEnv-v0** This is a trained model of a **A2C** agent playing **AntBulletEnv-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
wooldover/krautbot
wooldover
2023-03-17T15:54:29Z
102
3
transformers
[ "transformers", "pytorch", "tf", "jax", "blenderbot", "text2text-generation", "convAI", "conversational", "facebook", "en", "dataset:blended_skill_talk", "arxiv:2004.13637", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2023-03-16T22:27:58Z
--- language: - en thumbnail: tags: - convAI - conversational - facebook license: apache-2.0 datasets: - blended_skill_talk metrics: - perplexity --- ## Model description + Paper: [Recipes for building an open-domain chatbot]( https://arxiv.org/abs/2004.13637) + [Original PARLAI Code](https://parl.ai/projects/recipes/) ### Abstract Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.
yangwj2011/a2c-AntBulletEnv-v0
yangwj2011
2023-03-17T15:47:56Z
3
0
stable-baselines3
[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-03-17T15:46:55Z
--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 2035.61 +/- 119.09 name: mean_reward verified: false --- # **A2C** Agent playing **AntBulletEnv-v0** This is a trained model of a **A2C** agent playing **AntBulletEnv-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
dor88/a2c-PandaReachDense-v2
dor88
2023-03-17T15:41:50Z
0
0
stable-baselines3
[ "stable-baselines3", "PandaReachDense-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-03-17T15:39:25Z
--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -0.58 +/- 0.21 name: mean_reward verified: false --- # **A2C** Agent playing **PandaReachDense-v2** This is a trained model of a **A2C** agent playing **PandaReachDense-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
hug-cosmos/Huggy
hug-cosmos
2023-03-17T15:35:14Z
6
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
reinforcement-learning
2023-03-17T15:35:08Z
--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Find your model_id: hug-cosmos/Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
Patil/Reinforce-CartPole-v1
Patil
2023-03-17T15:32:37Z
0
0
null
[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-03-09T10:41:41Z
--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Cartpole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
niks-salodkar/reinforce-pixelcopterv0
niks-salodkar
2023-03-17T15:21:15Z
0
0
null
[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-03-17T15:21:05Z
--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: reinforce-pixelcopterv0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 32.30 +/- 25.60 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
hug-cosmos/ppo-Huggy
hug-cosmos
2023-03-17T15:19:27Z
13
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
reinforcement-learning
2023-03-17T15:19:20Z
--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Find your model_id: hug-cosmos/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
MarshallPF/Reinforce-cartpole_v1
MarshallPF
2023-03-17T15:08:47Z
0
0
null
[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-03-17T15:08:35Z
--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-cartpole_v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 454.30 +/- 73.16 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
CHAOYUYD/vit-base-patch16-224-finetuned-flower
CHAOYUYD
2023-03-17T15:02:57Z
187
0
transformers
[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-03-17T14:58:26Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder model-index: - name: vit-base-patch16-224-finetuned-flower results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-finetuned-flower This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-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: 5 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.1+cu116 - Datasets 2.7.1 - Tokenizers 0.13.2
takinai/St_Louis_Luxurious_Wheels_Azur_Lane
takinai
2023-03-17T14:59:52Z
0
2
null
[ "stable_diffusion", "region:us" ]
null
2023-03-17T14:57:39Z
--- tags: - stable_diffusion --- The source of the models is listed below. Please check the original licenses from the source. https://civitai.com/models/6669
happytree09/distilbert-base-banking77-pt2
happytree09
2023-03-17T14:53:54Z
104
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:banking77", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-03-17T14:32:30Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - banking77 metrics: - f1 model-index: - name: distilbert-base-banking77-pt2 results: - task: name: Text Classification type: text-classification dataset: name: banking77 type: banking77 config: default split: test args: default metrics: - name: F1 type: f1 value: 0.923858796442152 --- <!-- 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-banking77-pt2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the banking77 dataset. It achieves the following results on the evaluation set: - Loss: 0.3063 - F1: 0.9239 ## 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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.0001 | 1.0 | 626 | 0.7166 | 0.8399 | | 0.363 | 2.0 | 1252 | 0.3527 | 0.9129 | | 0.1849 | 3.0 | 1878 | 0.3063 | 0.9239 | ### Framework versions - Transformers 4.27.1 - Pytorch 2.0.0+cu117 - Datasets 2.9.0 - Tokenizers 0.13.2
takinai/Yae_Miko_Realistic_Genshin_LORA
takinai
2023-03-17T14:49:17Z
0
3
null
[ "stable_diffusion", "region:us" ]
null
2023-03-17T14:42:32Z
--- tags: - stable_diffusion --- The source of the models is listed below. Please check the original licenses from the source. https://civitai.com/models/8484
lipee/Reinforce-cartpole-v1
lipee
2023-03-17T14:48:54Z
0
0
null
[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-03-17T14:48:42Z
--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-cartpole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
takinai/MoXin
takinai
2023-03-17T14:31:19Z
0
2
null
[ "stable_diffusion", "region:us" ]
null
2023-03-17T14:01:14Z
--- tags: - stable_diffusion --- The source of the models is listed below. Please check the original licenses from the source. https://civitai.com/models/12597
kiki2013/distilbert-base-uncased-finetuned-emotion
kiki2013
2023-03-17T14:08:23Z
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
2023-03-17T13:20:09Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9335 - name: F1 type: f1 value: 0.9335392451906908 --- <!-- 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: 0.2274 - Accuracy: 0.9335 - F1: 0.9335 ## 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.1747 | 1.0 | 250 | 0.1884 | 0.9295 | 0.9286 | | 0.1219 | 2.0 | 500 | 0.1533 | 0.9345 | 0.9347 | | 0.1014 | 3.0 | 750 | 0.1600 | 0.932 | 0.9324 | | 0.081 | 4.0 | 1000 | 0.1592 | 0.9365 | 0.9367 | | 0.065 | 5.0 | 1250 | 0.1787 | 0.935 | 0.9347 | | 0.0511 | 6.0 | 1500 | 0.1874 | 0.934 | 0.9339 | | 0.0419 | 7.0 | 1750 | 0.2131 | 0.935 | 0.9353 | | 0.0351 | 8.0 | 2000 | 0.2151 | 0.934 | 0.9344 | | 0.0292 | 9.0 | 2250 | 0.2269 | 0.933 | 0.9332 | | 0.024 | 10.0 | 2500 | 0.2274 | 0.9335 | 0.9335 | ### Framework versions - Transformers 4.27.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2
PeterBanning71/t5-small-finetuned-eLife-tfg
PeterBanning71
2023-03-17T14:04:59Z
113
0
transformers
[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "summarization", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
summarization
2023-03-17T11:43:39Z
--- license: apache-2.0 tags: - summarization - generated_from_trainer metrics: - rouge model-index: - name: t5-small-finetuned-eLife-tfg 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-eLife-tfg This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.9086 - Rouge1: 14.1818 - Rouge2: 2.6381 - Rougel: 10.4961 - Rougelsum: 12.8458 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | No log | 1.0 | 459 | 2.9705 | 13.2225 | 2.4541 | 10.0332 | 11.9703 | 19.0 | | 3.3543 | 2.0 | 918 | 2.9211 | 13.9672 | 2.601 | 10.3831 | 12.6062 | 19.0 | | 3.1411 | 3.0 | 1377 | 2.9086 | 14.1818 | 2.6381 | 10.4961 | 12.8458 | 19.0 | ### Framework versions - Transformers 4.27.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2
Respeecher/ukrainian-data2vec-asr
Respeecher
2023-03-17T13:56:20Z
75
0
transformers
[ "transformers", "pytorch", "data2vec-audio", "automatic-speech-recognition", "uk", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2023-03-17T13:13:36Z
--- language: - uk license: apache-2.0 datasets: - mozilla-foundation/common_voice_11_0 model-index: - name: ukrainian-data2vec-asr results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: uk split: test args: uk metrics: - name: Wer type: wer value: 17.042283338786351 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: uk split: validation args: uk metrics: - name: Wer type: wer value: 17.634350000973198 --- # Respeecher/ukrainian-data2vec-asr This model is a fine-tuned version of [Respeecher/ukrainian-data2vec](https://huggingface.co/Respeecher/ukrainian-data2vec) on the [Common Voice 11.0 dataset Ukrainian Train part](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0/viewer/uk/train). It achieves the following results: - eval_wer: 17.634350000973198 - test_wer: 17.042283338786351 ## How to Get Started with the Model ```python from transformers import AutoProcessor, Data2VecAudioForCTC import torch from datasets import load_dataset, Audio dataset = load_dataset("mozilla-foundation/common_voice_11_0", "uk", split="test") # Resample dataset = dataset.cast_column("audio", Audio(sampling_rate=16_000)) processor = AutoProcessor.from_pretrained("Respeecher/ukrainian-data2vec-asr") model = Data2VecAudioForCTC.from_pretrained("Respeecher/ukrainian-data2vec-asr") model.eval() sampling_rate = dataset.features["audio"].sampling_rate inputs = processor(dataset[1]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt") with torch.no_grad(): logits = model(**inputs).logits predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(predicted_ids) transcription[0] ``` ## Training Details Training code and instructions are available on [our github](https://github.com/respeecher/ukrainian_asr)
BobMcDear/vit_base_patch16_mae_in1k_224
BobMcDear
2023-03-17T13:53:16Z
0
0
null
[ "region:us" ]
null
2023-03-17T13:47:40Z
Please refer to [flaim](https://github.com/bobmcdear/flaim) for sample usage and more information.
bbillapati/ppo-lunarlander-v2
bbillapati
2023-03-17T13:41:48Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-03-17T13:41:18Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 267.58 +/- 14.42 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
splusminusx/ppo-SnowballTarget
splusminusx
2023-03-17T13:34:13Z
14
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]
reinforcement-learning
2023-03-17T13:34:08Z
--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # **ppo** Agent playing **SnowballTarget** This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget 2. Step 1: Find your model_id: splusminusx/ppo-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
EthanCastro/GYMGPT
EthanCastro
2023-03-17T13:25:57Z
0
0
null
[ "en", "license:mit", "region:us" ]
null
2023-03-17T13:23:11Z
--- license: mit language: - en ---
niks-salodkar/reinforce-cartpolev1
niks-salodkar
2023-03-17T13:23:03Z
0
0
null
[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-03-17T11:44:24Z
--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: reinforce-cartpolev1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
ahmad-alismail/ppo-CartPole-v1
ahmad-alismail
2023-03-17T12:48:05Z
0
0
null
[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]
reinforcement-learning
2023-03-17T11:00:55Z
--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -52.51 +/- 78.46 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 1000000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'ahmad1289/ppo-CartPole-v1' 'batch_size': 512 'minibatch_size': 128} ```
dominguesm/bert-restore-punctuation-ptbr
dominguesm
2023-03-17T12:32:13Z
119
12
transformers
[ "transformers", "pytorch", "safetensors", "bert", "token-classification", "named-entity-recognition", "Transformer", "pt", "dataset:wiki_lingua", "license:cc-by-4.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-07-11T18:04:21Z
--- language: - pt license: cc-by-4.0 datasets: - wiki_lingua thumbnail: null tags: - named-entity-recognition - Transformer - pytorch - bert metrics: - f1 - precision - recall model-index: - name: rpunct-ptbr results: - task: type: named-entity-recognition dataset: type: wiki_lingua name: wiki_lingua metrics: - type: f1 value: 55.70 name: F1 Score - type: precision value: 57.72 name: Precision - type: recall value: 53.83 name: Recall widget: - text: "henrique foi no lago pescar com o pedro mais tarde foram para a casa do pedro fritar os peixes" - text: "cinco trabalhadores da construção civil em capacetes e coletes amarelos estão ocupados no trabalho" - text: "na quinta feira em visita a belo horizonte pedro sobrevoa a cidade atingida pelas chuvas" - text: "coube ao representante de classe contar que na avaliação de língua portuguesa alguns alunos se mantiveram concentrados e outros dispersos" --- # 🤗 bert-restore-punctuation-ptbr * 🪄 [W&B Dashboard](https://wandb.ai/dominguesm/RestorePunctuationPTBR) * ⛭ [GitHub](https://github.com/DominguesM/respunct) This is a [bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) model finetuned for punctuation restoration on [WikiLingua](https://github.com/esdurmus/Wikilingua). This model is intended for direct use as a punctuation restoration model for the general Portuguese language. Alternatively, you can use this for further fine-tuning on domain-specific texts for punctuation restoration tasks. Model restores the following punctuations -- **[! ? . , - : ; ' ]** The model also restores the upper-casing of words. ----------------------------------------------- ## 🤷 Usage 🇧🇷 easy-to-use package to restore punctuation of portuguese texts. **Below is a quick way to use the template.** 1. First, install the package. ``` pip install respunct ``` 2. Sample python code. ``` python from respunct import RestorePuncts model = RestorePuncts() model.restore_puncts(""" henrique foi no lago pescar com o pedro mais tarde foram para a casa do pedro fritar os peixes""") # output: # Henrique foi no lago pescar com o Pedro. Mais tarde, foram para a casa do Pedro fritar os peixes. ``` ----------------------------------------------- ## 🎯 Accuracy | label | precision | recall | f1-score | support| | ------------------------- | -------------|-------- | ----------|--------| | **Upper - OU** | 0.89 | 0.91 | 0.90 | 69376 | **None - OO** | 0.99 | 0.98 | 0.98 | 857659 | **Full stop/period - .O** | 0.86 | 0.93 | 0.89 | 60410 | **Comma - ,O** | 0.85 | 0.83 | 0.84 | 48608 | **Upper + Comma - ,U** | 0.73 | 0.76 | 0.75 | 3521 | **Question - ?O** | 0.68 | 0.78 | 0.73 | 1168 | **Upper + period - .U** | 0.66 | 0.72 | 0.69 | 1884 | **Upper + colon - :U** | 0.59 | 0.63 | 0.61 | 352 | **Colon - :O** | 0.70 | 0.53 | 0.60 | 2420 | **Question Mark - ?U** | 0.50 | 0.56 | 0.53 | 36 | **Upper + Exclam. - !U** | 0.38 | 0.32 | 0.34 | 38 | **Exclamation Mark - !O** | 0.30 | 0.05 | 0.08 | 783 | **Semicolon - ;O** | 0.35 | 0.04 | 0.08 | 1557 | **Apostrophe - 'O** | 0.00 | 0.00 | 0.00 | 3 | **Hyphen - -O** | 0.00 | 0.00 | 0.00 | 3 | | | | | | **accuracy** | | | 0.96 | 1047818 | **macro avg** | 0.57 | 0.54 | 0.54 | 1047818 | **weighted avg** | 0.96 | 0.96 | 0.96 | 1047818 ----------------------------------------------- ## 🤙 Contact [Maicon Domingues](dominguesm@outlook.com) for questions, feedback and/or requests for similar models.
happyrabbit/ppo-SnowballTarget
happyrabbit
2023-03-17T12:25:07Z
1
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]
reinforcement-learning
2023-03-17T12:25:01Z
--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # **ppo** Agent playing **SnowballTarget** This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget 2. Step 1: Find your model_id: happyrabbit/ppo-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
WENGSYX/Multilingual_SimCSE
WENGSYX
2023-03-17T12:21:15Z
145
5
transformers
[ "transformers", "pytorch", "safetensors", "deberta-v2", "feature-extraction", "endpoints_compatible", "region:us" ]
feature-extraction
2022-03-02T23:29:05Z
# Multilingual SimCSE #### A contrastive learning model using parallel language pair training ##### By using parallel sentence pairs in different languages, the text is mapped to the same vector space for pre-training similar to Simcse ##### Firstly, the [mDeBERTa](https://huggingface.co/microsoft/mdeberta-v3-base) model is used to load the pre-training parameters, and then the pre-training is carried out based on the [CCMatrix](https://github.com/facebookresearch/LASER/tree/main/tasks/CCMatrix) data set. ##### Training data: 100 million parallel pairs ##### Taining equipment: 4 * 3090 ## Pipline Code ``` from transformers import AutoModel,AutoTokenizer model = AutoModel.from_pretrained('WENGSYX/Multilingual_SimCSE') tokenizer = AutoTokenizer.from_pretrained('WENGSYX/Multilingual_SimCSE') word1 = tokenizer('Hello,world.',return_tensors='pt') word2 = tokenizer('你好,世界',return_tensors='pt') out1 = model(**word1).last_hidden_state.mean(1) out2 = model(**word2).last_hidden_state.mean(1) print(F.cosine_similarity(out1,out2)) ---------------------------------------------------- tensor([0.8758], grad_fn=<DivBackward0>) ``` ## Train Code ``` from transformers import AutoModel,AutoTokenizer,AdamW model = AutoModel.from_pretrained('WENGSYX/Multilingual_SimCSE') tokenizer = AutoTokenizer.from_pretrained('WENGSYX/Multilingual_SimCSE') optimizer = AdamW(model.parameters(),lr=1e-5) def compute_loss(y_pred, t=0.05, device="cuda"): idxs = torch.arange(0, y_pred.shape[0], device=device) y_true = idxs + 1 - idxs % 2 * 2 similarities = F.cosine_similarity(y_pred.unsqueeze(1), y_pred.unsqueeze(0), dim=2) similarities = similarities - torch.eye(y_pred.shape[0], device=device) * 1e12 similarities = similarities / t loss = F.cross_entropy(similarities, y_true) return torch.mean(loss) wordlist = [['Hello,world','你好,世界'],['Pensa che il bianco rappresenti la purezza.','Он думает, что белые символизируют чистоту.']] input_ids, attention_mask, token_type_ids = [], [], [] for x in wordlist: text1 = tokenizer(x[0], padding='max_length', truncation=True, max_length=512) input_ids.append(text1['input_ids']) attention_mask.append(text1['attention_mask']) text2 = tokenizer(x[1], padding='max_length', truncation=True, max_length=512) input_ids.append(text2['input_ids']) attention_mask.append(text2['attention_mask']) input_ids = torch.tensor(input_ids,device=device) attention_mask = torch.tensor(attention_mask,device=device) output = model(input_ids=input_ids,attention_mask=attention_mask) output = output.last_hidden_state.mean(1) loss = compute_loss(output) loss.backward() optimizer.step() optimizer.zero_grad() ```
alvaroalon2/biobert_genetic_ner
alvaroalon2
2023-03-17T12:11:30Z
12,329
22
transformers
[ "transformers", "pytorch", "bert", "token-classification", "NER", "Biomedical", "Genetics", "en", "dataset:JNLPBA", "dataset:BC2GM", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-03-02T23:29:05Z
--- language: en license: apache-2.0 tags: - token-classification - NER - Biomedical - Genetics datasets: - JNLPBA - BC2GM --- BioBERT model fine-tuned in NER task with JNLPBA and BC2GM corpus for genetic class entities. This was fine-tuned in order to use it in a BioNER/BioNEN system which is available at: https://github.com/librairy/bio-ner
alvaroalon2/biobert_diseases_ner
alvaroalon2
2023-03-17T12:11:20Z
6,543
40
transformers
[ "transformers", "pytorch", "bert", "token-classification", "NER", "Biomedical", "Diseases", "en", "dataset:BC5CDR-diseases", "dataset:ncbi_disease", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-03-02T23:29:05Z
--- language: en license: apache-2.0 tags: - token-classification - NER - Biomedical - Diseases datasets: - BC5CDR-diseases - ncbi_disease --- BioBERT model fine-tuned in NER task with BC5CDR-diseases and NCBI-diseases corpus This was fine-tuned in order to use it in a BioNER/BioNEN system which is available at: https://github.com/librairy/bio-ner
reyhanemyr/bert-base-uncased-finetuned-paper
reyhanemyr
2023-03-17T12:03:11Z
105
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2023-03-17T11:53:10Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-base-uncased-finetuned-paper 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-uncased-finetuned-paper This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2326 - Precision: 0.7612 - Recall: 0.7456 - F1: 0.7533 - Accuracy: 0.9684 ## 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: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 73 | 0.1917 | 0.6756 | 0.5175 | 0.5861 | 0.9484 | | No log | 2.0 | 146 | 0.1402 | 0.7516 | 0.6988 | 0.7242 | 0.9678 | | No log | 3.0 | 219 | 0.1747 | 0.7397 | 0.6813 | 0.7093 | 0.9659 | | No log | 4.0 | 292 | 0.1627 | 0.6797 | 0.7632 | 0.7190 | 0.9633 | | No log | 5.0 | 365 | 0.1720 | 0.7005 | 0.7456 | 0.7224 | 0.9661 | | No log | 6.0 | 438 | 0.2029 | 0.7515 | 0.7339 | 0.7426 | 0.9688 | | 0.0876 | 7.0 | 511 | 0.1928 | 0.7415 | 0.7632 | 0.7522 | 0.9700 | | 0.0876 | 8.0 | 584 | 0.2016 | 0.7579 | 0.7690 | 0.7634 | 0.9708 | | 0.0876 | 9.0 | 657 | 0.2051 | 0.7371 | 0.7544 | 0.7457 | 0.9684 | | 0.0876 | 10.0 | 730 | 0.2153 | 0.7477 | 0.7281 | 0.7378 | 0.9693 | | 0.0876 | 11.0 | 803 | 0.2284 | 0.7626 | 0.7515 | 0.7570 | 0.9693 | | 0.0876 | 12.0 | 876 | 0.2223 | 0.7139 | 0.7515 | 0.7322 | 0.9682 | | 0.0876 | 13.0 | 949 | 0.2274 | 0.7471 | 0.7515 | 0.7493 | 0.9690 | | 0.0022 | 14.0 | 1022 | 0.2321 | 0.7695 | 0.7515 | 0.7604 | 0.9695 | | 0.0022 | 15.0 | 1095 | 0.2367 | 0.7590 | 0.7368 | 0.7478 | 0.9690 | | 0.0022 | 16.0 | 1168 | 0.2327 | 0.7612 | 0.7456 | 0.7533 | 0.9695 | | 0.0022 | 17.0 | 1241 | 0.2367 | 0.7704 | 0.7456 | 0.7578 | 0.9690 | | 0.0022 | 18.0 | 1314 | 0.2309 | 0.7529 | 0.7485 | 0.7507 | 0.9691 | | 0.0022 | 19.0 | 1387 | 0.2358 | 0.7711 | 0.7485 | 0.7596 | 0.9686 | | 0.0022 | 20.0 | 1460 | 0.2326 | 0.7612 | 0.7456 | 0.7533 | 0.9684 | ### Framework versions - Transformers 4.27.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2
vocabtrimmer/mt5-small-trimmed-ru-90000-ruquad-qg
vocabtrimmer
2023-03-17T12:00:11Z
106
0
transformers
[ "transformers", "pytorch", "mt5", "text2text-generation", "question generation", "ru", "dataset:lmqg/qg_ruquad", "arxiv:2210.03992", "license:cc-by-4.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2023-03-16T12:35:53Z
--- license: cc-by-4.0 metrics: - bleu4 - meteor - rouge-l - bertscore - moverscore language: ru datasets: - lmqg/qg_ruquad pipeline_tag: text2text-generation tags: - question generation widget: - text: "Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, <hl> в мае 1860 года <hl> провёл серию опытов." example_title: "Question Generation Example 1" - text: "Однако, франкоязычный <hl> Квебек <hl> практически никогда не включается в состав Латинской Америки." example_title: "Question Generation Example 2" - text: "Классическим примером международного синдиката XX века была группа компаний <hl> Де Бирс <hl> , которая в 1980-е годы контролировала до 90 % мировой торговли алмазами." example_title: "Question Generation Example 3" model-index: - name: vocabtrimmer/mt5-small-trimmed-ru-90000-ruquad-qg results: - task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qg_ruquad type: default args: default metrics: - name: BLEU4 (Question Generation) type: bleu4_question_generation value: 18.77 - name: ROUGE-L (Question Generation) type: rouge_l_question_generation value: 34.21 - name: METEOR (Question Generation) type: meteor_question_generation value: 29.16 - name: BERTScore (Question Generation) type: bertscore_question_generation value: 86.65 - name: MoverScore (Question Generation) type: moverscore_question_generation value: 65.33 --- # Model Card of `vocabtrimmer/mt5-small-trimmed-ru-90000-ruquad-qg` This model is fine-tuned version of [vocabtrimmer/mt5-small-trimmed-ru-90000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ru-90000) for question generation task on the [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). ### Overview - **Language model:** [vocabtrimmer/mt5-small-trimmed-ru-90000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ru-90000) - **Language:** ru - **Training data:** [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) ### Usage - With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-) ```python from lmqg import TransformersQG # initialize model model = TransformersQG(language="ru", model="vocabtrimmer/mt5-small-trimmed-ru-90000-ruquad-qg") # model prediction questions = model.generate_q(list_context="Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, в мае 1860 года провёл серию опытов.", list_answer="в мае 1860 года") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-ru-90000-ruquad-qg") output = pipe("Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, <hl> в мае 1860 года <hl> провёл серию опытов.") ``` ## Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ru-90000-ruquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_ruquad.default.json) | | Score | Type | Dataset | |:-----------|--------:|:--------|:-----------------------------------------------------------------| | BERTScore | 86.65 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | | Bleu_1 | 34.9 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | | Bleu_2 | 27.92 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | | Bleu_3 | 22.75 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | | Bleu_4 | 18.77 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | | METEOR | 29.16 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | | MoverScore | 65.33 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | | ROUGE_L | 34.21 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | ## Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_ruquad - dataset_name: default - input_types: paragraph_answer - output_types: question - prefix_types: None - model: vocabtrimmer/mt5-small-trimmed-ru-90000 - max_length: 512 - max_length_output: 32 - epoch: 16 - batch: 16 - lr: 0.0005 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 4 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ru-90000-ruquad-qg/raw/main/trainer_config.json). ## Citation ``` @inproceedings{ushio-etal-2022-generative, title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration", author = "Ushio, Asahi and Alva-Manchego, Fernando and Camacho-Collados, Jose", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, U.A.E.", publisher = "Association for Computational Linguistics", } ```
annadmitrieva/rut5-base-par-simp
annadmitrieva
2023-03-17T11:43:08Z
110
0
transformers
[ "transformers", "pytorch", "safetensors", "t5", "text2text-generation", "russian", "simplification", "ru", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2022-11-17T16:05:53Z
--- language: ["ru"] tags: - russian - simplification license: mit widget: - text: "Парк культуры и отдыха Северный находится в Северном микрорайоне Краснофлотского района Хабаровска." inference: parameters: num_beams: 3 no_repeat_ngram_size: 5 max_length: 500 encoder_no_repeat_ngram_size: 5 do_sample: False --- This is the David Dale's paraphraser model (https://huggingface.co/cointegrated/rut5-base-paraphraser) finetuned on the RuAdapt literature subcorpus (https://github.com/Digital-Pushkin-Lab/RuAdapt) and RuSimpleSentEval dev set (https://github.com/dialogue-evaluation/RuSimpleSentEval) for simplification. SARI score on the RuSimpleSentEval public test set: 35.623. The example sentence is from the RuSimpleSentEval public test set.
s3nh/SegFormer-b4-person-segmentation
s3nh
2023-03-17T11:40:39Z
163
0
transformers
[ "transformers", "pytorch", "safetensors", "segformer", "image-segmentation", "en", "license:openrail", "endpoints_compatible", "region:us" ]
image-segmentation
2023-02-07T10:29:18Z
--- license: openrail language: - en pipeline_tag: image-segmentation --- <a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a> <img src = 'https://images.unsplash.com/photo-1438761681033-6461ffad8d80?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=1170&q=80'> ### Description Semantic segmentation is a computer vision technique for assigning a label to each pixel in an image, representing the semantic class of the objects or regions in the image. It's a form of dense prediction because it involves assigning a label to each pixel in an image, instead of just boxes around objects or key points as in object detection or instance segmentation. The goal of semantic segmentation is to recognize and understand the objects and scenes in an image, and partition the image into segments corresponding to different entities. ## Parameters ``` model = SegformerForSemanticSegmentation.from_pretrained("nvidia/mit-b4", num_labels=2, id2label=id2label, label2id=label2id, ) ``` ## Usage ```python from torch import nn import numpy as np import matplotlib.pyplot as plt # Transforms _transform = A.Compose([ A.Resize(height = 512, width=512), ToTensorV2(), ]) trans_image = _transform(image=np.array(image)) outputs = model(trans_image['image'].float().unsqueeze(0)) logits = outputs.logits.cpu() print(logits.shape) # First, rescale logits to original image size upsampled_logits = nn.functional.interpolate(logits, size=image.size[::-1], # (height, width) mode='bilinear', align_corners=False) seg = upsampled_logits.argmax(dim=1)[0] color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) # height, width, 3 palette = np.array([[0, 0, 0],[255, 255, 255]]) for label, color in enumerate(palette): color_seg[seg == label, :] = color # Convert to BGR color_seg = color_seg[..., ::-1] ``` <img src = 'data:image/png;base64,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'> #Metric Todo #Note This model was not built by using Huggingface based feature extractor, so automatic api could not work.
ADI10HERO/Taxi-v3
ADI10HERO
2023-03-17T11:36:28Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-03-17T11:36:24Z
--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="ADI10HERO/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"]) ```
MLWhiz/Reinforce-v1
MLWhiz
2023-03-17T11:34:52Z
0
0
null
[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-03-17T10:57:26Z
--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 481.41 +/- 45.38 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
junnyu/lora_sks_dogs_v1
junnyu
2023-03-17T11:25:32Z
0
0
null
[ "paddlepaddle", "stable-diffusion", "stable-diffusion-ppdiffusers", "text-to-image", "ppdiffusers", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]
text-to-image
2023-03-17T11:25:05Z
--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: a photo of sks dog in a bucket tags: - stable-diffusion - stable-diffusion-ppdiffusers - text-to-image - ppdiffusers - lora inference: false --- # LoRA DreamBooth - junnyu/lora_sks_dogs_v1 本仓库的 LoRA 权重是基于 runwayml/stable-diffusion-v1-5 训练而来的，我们采用[DreamBooth](https://dreambooth.github.io/)的技术并使用 a photo of sks dog in a bucket 文本进行了训练。 下面是在训练过程中生成的一些图片。 ![img_0](validation_images/500.png) ![img_0](validation_images/400.png) ![img_0](validation_images/300.png) ![img_0](validation_images/200.png)
Naina07/Fine_tune
Naina07
2023-03-17T11:21:12Z
0
0
null
[ "code", "sentence-similarity", "en", "region:us" ]
sentence-similarity
2023-03-17T11:18:09Z
--- language: - en pipeline_tag: sentence-similarity tags: - code ---
Youngdal/ppo-SnowballTarget
Youngdal
2023-03-17T11:19:51Z
3
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]
reinforcement-learning
2023-03-17T11:19:44Z
--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # **ppo** Agent playing **SnowballTarget** This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget 2. Step 1: Find your model_id: Youngdal/ppo-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
yazdipour/text-to-sparql-t5-small-qald9
yazdipour
2023-03-17T11:18:30Z
110
2
transformers
[ "transformers", "pytorch", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2022-03-02T23:29:05Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: sparql-qald9-t5-small-2021-10-19_22-32 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. --> # sparql-qald9-t5-small-2021-10-19_22-32 This model is a fine-tuned version of [yazdipour/text-to-sparql-t5-small-2021-10-19_10-17_lastDS](https://huggingface.co/yazdipour/text-to-sparql-t5-small-2021-10-19_10-17_lastDS) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - 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: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Gen Len | P | R | F1 | Bleu-score | Bleu-precisions | Bleu-bp | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:------:|:------:|:----------:|:-------------------------------------------------------------------------------:|:-------:| | No log | 1.0 | 51 | 2.4477 | 19.0 | 0.3797 | 0.0727 | 0.2219 | 9.3495 | [73.47751849743882, 49.595519601742375, 35.346602608098834, 26.243305279265492] | 0.2180 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.0+cu111 - Datasets 1.14.0 - Tokenizers 0.10.3
galsenai/hubert-base-ls960-ft-waxal-keyword-spotting
galsenai
2023-03-17T10:38:18Z
163
0
transformers
[ "transformers", "pytorch", "hubert", "audio-classification", "generated_from_trainer", "wolof", "license:apache-2.0", "endpoints_compatible", "region:us" ]
audio-classification
2023-03-16T19:07:52Z
--- license: apache-2.0 tags: - audio-classification - generated_from_trainer - wolof metrics: - accuracy - precision - f1 model-index: - name: hubert-base-ls960 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. --> # hubert-base-ls960 This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the galsenai/waxal_dataset dataset. It achieves the following results on the evaluation set: - Loss: 2.1857 - Accuracy: 0.6442 - Precision: 0.8369 - F1: 0.7121 ## 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: 30 - eval_batch_size: 30 - seed: 0 - gradient_accumulation_steps: 4 - total_train_batch_size: 120 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 32.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:| | 4.523 | 2.53 | 500 | 5.1547 | 0.0205 | 0.0047 | 0.0037 | | 3.4187 | 5.05 | 1000 | 4.6287 | 0.0337 | 0.0256 | 0.0163 | | 2.3533 | 7.58 | 1500 | 4.2550 | 0.0944 | 0.1033 | 0.0641 | | 1.7145 | 10.1 | 2000 | 3.9540 | 0.1095 | 0.2091 | 0.0964 | | 1.3245 | 12.63 | 2500 | 3.8557 | 0.1758 | 0.3609 | 0.1859 | | 1.0729 | 15.15 | 3000 | 3.7411 | 0.2247 | 0.4918 | 0.2537 | | 0.8955 | 17.68 | 3500 | 3.2683 | 0.3789 | 0.6162 | 0.4256 | | 0.7697 | 20.2 | 4000 | 2.8749 | 0.4612 | 0.7106 | 0.5171 | | 0.6864 | 22.73 | 4500 | 2.7251 | 0.5169 | 0.7437 | 0.5779 | | 0.6061 | 25.25 | 5000 | 2.5061 | 0.5631 | 0.8043 | 0.6335 | | 0.5777 | 27.78 | 5500 | 2.2830 | 0.6177 | 0.8183 | 0.6837 | | 0.5304 | 30.3 | 6000 | 2.1857 | 0.6442 | 0.8369 | 0.7121 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.9.1.dev0 - Tokenizers 0.13.2
galsenai/wavlm-base-waxal-keyword-spotting
galsenai
2023-03-17T10:37:51Z
164
0
transformers
[ "transformers", "pytorch", "wavlm", "audio-classification", "generated_from_trainer", "wolof", "endpoints_compatible", "region:us" ]
audio-classification
2023-03-17T08:39:46Z
--- tags: - audio-classification - generated_from_trainer - wolof metrics: - accuracy - precision - f1 model-index: - name: wavlm-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. --> # wavlm-base This model is a fine-tuned version of [microsoft/wavlm-base](https://huggingface.co/microsoft/wavlm-base) on the galsenai/waxal_dataset dataset. It achieves the following results on the evaluation set: - Loss: 2.1345 - Accuracy: 0.6783 - Precision: 0.8774 - F1: 0.7615 ## 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: 30 - eval_batch_size: 30 - seed: 0 - gradient_accumulation_steps: 4 - total_train_batch_size: 120 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 32.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:| | 4.4506 | 2.53 | 500 | 4.8601 | 0.0224 | 0.0136 | 0.0066 | | 3.0523 | 5.05 | 1000 | 4.6674 | 0.0720 | 0.0460 | 0.0394 | | 1.949 | 7.58 | 1500 | 4.1533 | 0.1156 | 0.1847 | 0.1064 | | 1.3427 | 10.1 | 2000 | 3.8173 | 0.1448 | 0.2382 | 0.1347 | | 1.0064 | 12.63 | 2500 | 3.5546 | 0.2183 | 0.4464 | 0.2385 | | 0.7985 | 15.15 | 3000 | 3.1172 | 0.3842 | 0.6336 | 0.4258 | | 0.6505 | 17.68 | 3500 | 2.9231 | 0.5165 | 0.7677 | 0.5995 | | 0.5367 | 20.2 | 4000 | 2.4935 | 0.5961 | 0.8182 | 0.6755 | | 0.465 | 22.73 | 4500 | 2.2411 | 0.6412 | 0.8624 | 0.7272 | | 0.4075 | 25.25 | 5000 | 2.1345 | 0.6783 | 0.8774 | 0.7615 | | 0.3793 | 27.78 | 5500 | 2.2535 | 0.6681 | 0.8792 | 0.7543 | | 0.3418 | 30.3 | 6000 | 2.3390 | 0.6662 | 0.8905 | 0.7576 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.9.1.dev0 - Tokenizers 0.13.2
hmatzner/LunarLander-v2.2
hmatzner
2023-03-17T10:35:48Z
0
0
null
[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]
reinforcement-learning
2023-03-17T10:35:41Z
--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -114.68 +/- 70.97 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 50000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'hmatzner/LunarLander-v2.2' 'batch_size': 512 'minibatch_size': 128} ```
marco-c88/distilgpt2-finetuned-mstatmem_1ep
marco-c88
2023-03-17T10:24:47Z
175
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
2023-03-17T10:22:04Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilgpt2-finetuned-mstatmem_1ep 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. --> # distilgpt2-finetuned-mstatmem_1ep This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.7942 ## 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: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.0933 | 1.0 | 794 | 3.7942 | ### Framework versions - Transformers 4.27.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2
cointegrated/rut5-small-normalizer
cointegrated
2023-03-17T10:23:17Z
218
7
transformers
[ "transformers", "pytorch", "jax", "safetensors", "t5", "text2text-generation", "normalization", "denoising autoencoder", "russian", "ru", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2022-03-02T23:29:05Z
--- language: "ru" tags: - normalization - denoising autoencoder - russian widget: - text: "меня тобой не понимать" license: mit --- This is a small Russian denoising autoencoder. It can be used for restoring corrupted sentences. This model was produced by fine-tuning the [rut5-small](https://huggingface.co/cointegrated/rut5-small) model on the task of reconstructing a sentence: * restoring word positions (after slightly shuffling them) * restoring dropped words and punctuation marks (after dropping some of them randomly) * restoring inflection of words (after changing their inflection randomly using [natasha](https://github.com/natasha/natasha) and [pymorphy2](https://github.com/kmike/pymorphy2) packages) The fine-tuning was performed on a [Leipzig web corpus](https://wortschatz.uni-leipzig.de/en/download/Russian) of Russian sentences. The model can be applied as follows: ``` # !pip install transformers sentencepiece import torch from transformers import T5ForConditionalGeneration, T5Tokenizer tokenizer = T5Tokenizer.from_pretrained("cointegrated/rut5-small-normalizer") model = T5ForConditionalGeneration.from_pretrained("cointegrated/rut5-small-normalizer") text = 'меня тобой не понимать' inputs = tokenizer(text, return_tensors='pt') with torch.no_grad(): hypotheses = model.generate( **inputs, do_sample=True, top_p=0.95, num_return_sequences=5, repetition_penalty=2.5, max_length=32, ) for h in hypotheses: print(tokenizer.decode(h, skip_special_tokens=True)) ``` A possible output is: ``` # Мне тебя не понимать. # Если бы ты понимаешь меня? # Я с тобой не понимаю. # Я тебя не понимаю. # Я не понимаю о чем ты. ```
cointegrated/rut5-small-chitchat2
cointegrated
2023-03-17T10:22:31Z
610
0
transformers
[ "transformers", "pytorch", "safetensors", "t5", "text2text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2022-03-02T23:29:05Z
A version of https://huggingface.co/cointegrated/rut5-small-chitchat which is more dull but less toxic.
cointegrated/rut5-base-paraphraser
cointegrated
2023-03-17T10:21:29Z
2,861
19
transformers
[ "transformers", "pytorch", "safetensors", "t5", "text2text-generation", "russian", "paraphrasing", "paraphraser", "paraphrase", "ru", "dataset:cointegrated/ru-paraphrase-NMT-Leipzig", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2022-03-02T23:29:05Z
--- language: ["ru"] tags: - russian - paraphrasing - paraphraser - paraphrase license: mit widget: - text: "Каждый охотник желает знать, где сидит фазан." datasets: - cointegrated/ru-paraphrase-NMT-Leipzig --- This is a paraphraser for Russian sentences described [in this Habr post](https://habr.com/ru/post/564916/). It is recommended to use the model with the `encoder_no_repeat_ngram_size` argument: ``` from transformers import T5ForConditionalGeneration, T5Tokenizer MODEL_NAME = 'cointegrated/rut5-base-paraphraser' model = T5ForConditionalGeneration.from_pretrained(MODEL_NAME) tokenizer = T5Tokenizer.from_pretrained(MODEL_NAME) model.cuda(); model.eval(); def paraphrase(text, beams=5, grams=4, do_sample=False): x = tokenizer(text, return_tensors='pt', padding=True).to(model.device) max_size = int(x.input_ids.shape[1] * 1.5 + 10) out = model.generate(**x, encoder_no_repeat_ngram_size=grams, num_beams=beams, max_length=max_size, do_sample=do_sample) return tokenizer.decode(out[0], skip_special_tokens=True) print(paraphrase('Каждый охотник желает знать, где сидит фазан.')) # Все охотники хотят знать где фазан сидит. ```
reyhanemyr/roberta-base-finetuned-paper
reyhanemyr
2023-03-17T10:19:24Z
103
0
transformers
[ "transformers", "pytorch", "tensorboard", "roberta", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2023-03-17T10:09:07Z
--- license: mit tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: roberta-base-finetuned-paper 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-finetuned-paper This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1620 - Precision: 0.7605 - Recall: 0.8141 - F1: 0.7864 - Accuracy: 0.9765 ## 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: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 73 | 0.1575 | 0.6484 | 0.5673 | 0.6051 | 0.9591 | | No log | 2.0 | 146 | 0.0964 | 0.6723 | 0.7628 | 0.7147 | 0.9718 | | No log | 3.0 | 219 | 0.1233 | 0.6447 | 0.7853 | 0.7081 | 0.9655 | | No log | 4.0 | 292 | 0.1153 | 0.7563 | 0.7660 | 0.7611 | 0.9737 | | No log | 5.0 | 365 | 0.1194 | 0.7265 | 0.8173 | 0.7692 | 0.9727 | | No log | 6.0 | 438 | 0.1243 | 0.7286 | 0.8173 | 0.7704 | 0.9722 | | 0.0974 | 7.0 | 511 | 0.1406 | 0.7202 | 0.7756 | 0.7469 | 0.9732 | | 0.0974 | 8.0 | 584 | 0.1436 | 0.7406 | 0.7596 | 0.7500 | 0.9706 | | 0.0974 | 9.0 | 657 | 0.1687 | 0.7524 | 0.7596 | 0.7560 | 0.9738 | | 0.0974 | 10.0 | 730 | 0.1591 | 0.7394 | 0.7821 | 0.7601 | 0.9743 | | 0.0974 | 11.0 | 803 | 0.1431 | 0.7619 | 0.8205 | 0.7901 | 0.9754 | | 0.0974 | 12.0 | 876 | 0.1487 | 0.7477 | 0.7981 | 0.7721 | 0.9745 | | 0.0974 | 13.0 | 949 | 0.1512 | 0.7764 | 0.8013 | 0.7886 | 0.9763 | | 0.0043 | 14.0 | 1022 | 0.1532 | 0.7645 | 0.8013 | 0.7825 | 0.9754 | | 0.0043 | 15.0 | 1095 | 0.1531 | 0.7720 | 0.8141 | 0.7925 | 0.9761 | | 0.0043 | 16.0 | 1168 | 0.1590 | 0.7635 | 0.8173 | 0.7895 | 0.9756 | | 0.0043 | 17.0 | 1241 | 0.1615 | 0.7559 | 0.8237 | 0.7883 | 0.9754 | | 0.0043 | 18.0 | 1314 | 0.1624 | 0.7612 | 0.8173 | 0.7883 | 0.9759 | | 0.0043 | 19.0 | 1387 | 0.1622 | 0.7574 | 0.8205 | 0.7877 | 0.9763 | | 0.0043 | 20.0 | 1460 | 0.1620 | 0.7605 | 0.8141 | 0.7864 | 0.9765 | ### Framework versions - Transformers 4.27.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2
dor88/a2c-AntBulletEnv-v0
dor88
2023-03-17T10:14:39Z
3
0
stable-baselines3
[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-03-17T10:13:31Z
--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 1776.96 +/- 372.66 name: mean_reward verified: false --- # **A2C** Agent playing **AntBulletEnv-v0** This is a trained model of a **A2C** agent playing **AntBulletEnv-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
scutcyr/adcheck
scutcyr
2023-03-17T09:51:13Z
0
0
null
[ "zh", "license:apache-2.0", "region:us" ]
null
2023-03-17T09:50:32Z
--- license: apache-2.0 language: - zh ---
niks-salodkar/dqn-SpaceInvadersNoFrameskip-v4
niks-salodkar
2023-03-17T09:47:39Z
0
0
stable-baselines3
[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-03-17T09:47:11Z
--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 578.50 +/- 241.95 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga niks-salodkar -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga niks-salodkar -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga niks-salodkar ``` ## 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', 2000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
szymon-piechowicz-wandb/gpt
szymon-piechowicz-wandb
2023-03-17T09:38:38Z
0
0
null
[ "dataset:tiny_shakespeare", "license:mit", "region:us" ]
null
2023-03-17T01:57:28Z
--- license: mit datasets: - tiny_shakespeare --- Generative Pretrained Transformer (GPT) model built by following Andrej Karpathy's [video](https://www.youtube.com/watch?v=kCc8FmEb1nY).
Christian90/lunar_own_ppo
Christian90
2023-03-17T09:27:45Z
0
0
null
[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]
reinforcement-learning
2023-03-17T09:15:31Z
--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 41.22 +/- 47.52 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 500000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'Christian90/lunar_own_ppo' 'batch_size': 512 'minibatch_size': 128} ```
waveydaveygravy/JRE
waveydaveygravy
2023-03-17T09:13:13Z
0
0
null
[ "region:us" ]
null
2023-01-16T12:34:32Z
700 steps model is best likeness to Joe, its reasonably flexible 600 and 700 steps - photo of jre100 person https://colab.research.google.com/drive/1RSCCnhQrCc68lz5gz-2Z-GC0sYwTR015#scrollTo=80eLpi3-VR5F 999 class images Overfitting slightly even at 700 steps (lack of variety in sample images) can still generate different to instance images using different prompts and styles and artsists etc Works ok in automatic web ui you just have to play around with the prompt and settings, have steps around 60-70 Examples: https://imgur.com/a/oQc0Abb Instance images used https://imgur.com/a/L0W9IXf shivam db notebook %pip install -q accelerate transformers ftfy bitsandbytes==0.35.0 gradio natsort
brushpenbob/evang_pencilwork
brushpenbob
2023-03-17T09:11:58Z
0
0
diffusers
[ "diffusers", "art", "pencil", "sketch", "en", "license:artistic-2.0", "region:us" ]
null
2023-03-17T06:04:59Z
--- license: artistic-2.0 language: - en library_name: diffusers tags: - art - pencil - sketch --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> Created in Dreambooth this model provides you the ability to create extremely detailed 2D Illustration pencil Styles. Better results with detailed prompts. Inspired by Atomic Comics, this a fine-tuned Stable Diffusion model trained on similar work and portrait art. The correct token is evang. Atomic Comic is not affiliated with this. Use trigger word evang. - **Developed by:** [evan gardiner] ## Uses Ideal for portrait illustration creature design and more train primarily on portraiture