librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-07-14 18:27:59	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 520 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-07-14 18:27:48	card stringlengths 11 1.01M
epsil/sd-class-butterflies-64	epsil	2022-11-29T18:13:23Z	5	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-11-29T18:13:12Z	--- 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(epsil/sd-class-butterflies-64) image = pipeline().images[0] image ```
epsil/sd-class-butterflies-32	epsil	2022-11-29T17:42:54Z	6	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-11-29T17:42:32Z	--- 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(epsil/sd-class-butterflies-32) image = pipeline().images[0] image ```
tomekkorbak/clever_goodall	tomekkorbak	2022-11-29T17:20:58Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "generated_from_trainer", "en", "dataset:tomekkorbak/detoxify-pile-chunk3-0-50000", "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", "dataset:tomekkorbak/detoxify-pile-chunk3-1850000-1900000", "dataset:tomekkorbak/detoxify-pile-chunk3-1900000-1950000", "license:mit", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2022-11-29T03:29:26Z	--- language: - en license: mit tags: - generated_from_trainer datasets: - tomekkorbak/detoxify-pile-chunk3-0-50000 - 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 - tomekkorbak/detoxify-pile-chunk3-1850000-1900000 - tomekkorbak/detoxify-pile-chunk3-1900000-1950000 model-index: - name: clever_goodall 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. --> # clever_goodall This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, 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, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 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: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6 # Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', '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', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'], 'filter_threshold': 0.00078, 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25354], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048}, {'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'challenging_rtp', 'num_samples': 2048, 'prompts_path': 'resources/challenging_rtp.jsonl'}], 'scorer_config': {'device': 'cuda:0'}}, 'kl_gpt3_callback': {'force_call_on': [25354], 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'path_or_name': 'gpt2'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'clever_goodall', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output104340', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25354, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/tomekkorbak/apo/runs/2i1d4a3i
ser-mei/borges-gpt-collab	ser-mei	2022-11-29T17:14:30Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-11-06T20:48:40Z	--- license: mit tags: - generated_from_trainer model-index: - name: borges-gpt-collab 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. --> # borges-gpt-collab This model is a fine-tuned version of [DeepESP/gpt2-spanish](https://huggingface.co/DeepESP/gpt2-spanish) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 8.3468 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 500 - num_epochs: 70 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 11.2135 \| 0.96 \| 7 \| 10.2022 \| \| 10.3195 \| 1.96 \| 14 \| 9.6343 \| \| 9.9127 \| 2.96 \| 21 \| 9.4637 \| \| 9.7295 \| 3.96 \| 28 \| 9.2993 \| \| 9.527 \| 4.96 \| 35 \| 9.0962 \| \| 9.2648 \| 5.96 \| 42 \| 8.8294 \| \| 8.9309 \| 6.96 \| 49 \| 8.5103 \| \| 8.5639 \| 7.96 \| 56 \| 8.1858 \| \| 8.2034 \| 8.96 \| 63 \| 7.8816 \| \| 7.8665 \| 9.96 \| 70 \| 7.6303 \| \| 7.5715 \| 10.96 \| 77 \| 7.4307 \| \| 7.3259 \| 11.96 \| 84 \| 7.2632 \| \| 7.136 \| 12.96 \| 91 \| 7.1494 \| \| 6.9558 \| 13.96 \| 98 \| 7.0957 \| \| 6.8068 \| 14.96 \| 105 \| 7.0199 \| \| 6.6656 \| 15.96 \| 112 \| 6.9554 \| \| 6.5264 \| 16.96 \| 119 \| 6.9324 \| \| 6.3843 \| 17.96 \| 126 \| 6.8940 \| \| 6.2204 \| 18.96 \| 133 \| 6.8799 \| \| 6.0915 \| 19.96 \| 140 \| 6.8788 \| \| 5.9532 \| 20.96 \| 147 \| 6.8719 \| \| 5.8169 \| 21.96 \| 154 \| 6.8647 \| \| 5.6531 \| 22.96 \| 161 \| 6.8865 \| \| 5.5125 \| 23.96 \| 168 \| 6.8940 \| \| 5.3666 \| 24.96 \| 175 \| 6.9248 \| \| 5.2377 \| 25.96 \| 182 \| 6.9421 \| \| 5.1115 \| 26.96 \| 189 \| 6.9631 \| \| 4.9639 \| 27.96 \| 196 \| 7.0135 \| \| 4.824 \| 28.96 \| 203 \| 7.0352 \| \| 4.6886 \| 29.96 \| 210 \| 7.0729 \| \| 4.5538 \| 30.96 \| 217 \| 7.1385 \| \| 4.4126 \| 31.96 \| 224 \| 7.1561 \| \| 4.2486 \| 32.96 \| 231 \| 7.1792 \| \| 4.0955 \| 33.96 \| 238 \| 7.2767 \| \| 3.9333 \| 34.96 \| 245 \| 7.2815 \| \| 3.7914 \| 35.96 \| 252 \| 7.3463 \| \| 3.618 \| 36.96 \| 259 \| 7.3864 \| \| 3.4453 \| 37.96 \| 266 \| 7.4394 \| \| 3.2795 \| 38.96 \| 273 \| 7.4730 \| \| 3.0994 \| 39.96 \| 280 \| 7.4880 \| \| 2.9143 \| 40.96 \| 287 \| 7.5567 \| \| 2.741 \| 41.96 \| 294 \| 7.5451 \| \| 2.5698 \| 42.96 \| 301 \| 7.5966 \| \| 2.3855 \| 43.96 \| 308 \| 7.6898 \| \| 2.2059 \| 44.96 \| 315 \| 7.6957 \| \| 2.0634 \| 45.96 \| 322 \| 7.7503 \| \| 1.8719 \| 46.96 \| 329 \| 7.8369 \| \| 1.7059 \| 47.96 \| 336 \| 7.8411 \| \| 1.54 \| 48.96 \| 343 \| 7.8316 \| \| 1.3768 \| 49.96 \| 350 \| 7.8630 \| \| 1.2177 \| 50.96 \| 357 \| 7.9360 \| \| 1.0663 \| 51.96 \| 364 \| 7.9886 \| \| 0.9569 \| 52.96 \| 371 \| 8.0187 \| \| 0.8281 \| 53.96 \| 378 \| 8.0274 \| \| 0.7074 \| 54.96 \| 385 \| 8.1010 \| \| 0.6095 \| 55.96 \| 392 \| 8.1594 \| \| 0.5262 \| 56.96 \| 399 \| 8.1010 \| \| 0.4678 \| 57.96 \| 406 \| 8.1440 \| \| 0.4105 \| 58.96 \| 413 \| 8.1638 \| \| 0.3766 \| 59.96 \| 420 \| 8.1534 \| \| 0.3425 \| 60.96 \| 427 \| 8.1980 \| \| 0.321 \| 61.96 \| 434 \| 8.2184 \| \| 0.3061 \| 62.96 \| 441 \| 8.2499 \| \| 0.2852 \| 63.96 \| 448 \| 8.1690 \| \| 0.2698 \| 64.96 \| 455 \| 8.2160 \| \| 0.2628 \| 65.96 \| 462 \| 8.2616 \| \| 0.2619 \| 66.96 \| 469 \| 8.2948 \| \| 0.2544 \| 67.96 \| 476 \| 8.3553 \| \| 0.2414 \| 68.96 \| 483 \| 8.3712 \| \| 0.2177 \| 69.96 \| 490 \| 8.3468 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+rocm5.2 - Datasets 2.6.1 - Tokenizers 0.13.2
SALT-NLP/FLANG-BERT	SALT-NLP	2022-11-29T17:06:37Z	83	4	transformers	[ "transformers", "pytorch", "bert", "fill-mask", "Financial Language Modelling", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-06-24T02:37:04Z	--- language: "en" tags: - Financial Language Modelling widget: - text: "Stocks rallied and the British pound [MASK]." --- ## Dataset Summary - Homepage: https://salt-nlp.github.io/FLANG/ - Models: https://huggingface.co/SALT-NLP/FLANG-BERT - Repository: https://github.com/SALT-NLP/FLANG ## FLANG FLANG is a set of large language models for Financial LANGuage tasks. These models use domain specific pre-training with preferential masking to build more robust representations for the domain. The models in the set are:\ [FLANG-BERT](https://huggingface.co/SALT-NLP/FLANG-BERT)\ [FLANG-SpanBERT](https://huggingface.co/SALT-NLP/FLANG-SpanBERT)\ [FLANG-DistilBERT](https://huggingface.co/SALT-NLP/FLANG-DistilBERT)\ [FLANG-Roberta](https://huggingface.co/SALT-NLP/FLANG-Roberta)\ [FLANG-ELECTRA](https://huggingface.co/SALT-NLP/FLANG-ELECTRA) ## FLANG-BERT FLANG-BERT is a pre-trained language model which uses financial keywords and phrases for preferential masking of domain specific terms. It is built by further training the BERT language model in the finance domain with improved performance over previous models due to the use of domain knowledge and vocabulary. ## FLUE FLUE (Financial Language Understanding Evaluation) is a comprehensive and heterogeneous benchmark that has been built from 5 diverse financial domain specific datasets. Sentiment Classification: [Financial PhraseBank](https://huggingface.co/datasets/financial_phrasebank)\ Sentiment Analysis, Question Answering: [FiQA 2018](https://huggingface.co/datasets/SALT-NLP/FLUE-FiQA)\ New Headlines Classification: [Headlines](https://www.kaggle.com/datasets/daittan/gold-commodity-news-and-dimensions)\ Named Entity Recognition: [NER](https://paperswithcode.com/dataset/fin)\ Structure Boundary Detection: [FinSBD3](https://sites.google.com/nlg.csie.ntu.edu.tw/finweb2021/shared-task-finsbd-3) ## Citation Please cite the model with the following citation: ```bibtex @INPROCEEDINGS{shah-etal-2022-flang, author = {Shah, Raj Sanjay and Chawla, Kunal and Eidnani, Dheeraj and Shah, Agam and Du, Wendi and Chava, Sudheer and Raman, Natraj and Smiley, Charese and Chen, Jiaao and Yang, Diyi }, title = {When FLUE Meets FLANG: Benchmarks and Large Pretrained Language Model for Financial Domain}, booktitle = {Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP)}, year = {2022}, publisher = {Association for Computational Linguistics} } ``` ## Contact information Please contact Raj Sanjay Shah (rajsanjayshah[at]gatech[dot]edu) or Sudheer Chava (schava6[at]gatech[dot]edu) or Diyi Yang (diyiy[at]stanford[dot]edu) about any FLANG-BERT related issues and questions. --- license: afl-3.0 ---
kejian/debug-pt-conditional	kejian	2022-11-29T15:03:05Z	1	0	transformers	[ "transformers", "pytorch", "gpt2", "generated_from_trainer", "en", "dataset:kejian/codeparrot-train-more-filter-3.3b-cleaned", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2022-11-29T14:52:56Z	--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - kejian/codeparrot-train-more-filter-3.3b-cleaned model-index: - name: debug-pt-conditional 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. --> # debug-pt-conditional This model was trained from scratch on the kejian/codeparrot-train-more-filter-3.3b-cleaned 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: 0.0008 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - 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: 50354 - 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\|>', 'drop_token_fraction': 0.1, 'misaligned_prefix': '<\|misaligned\|>', 'threshold': 0}, 'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'], 'is_split_by_sentences': True}, 'generation': {'batch_size': 64, 'metrics_configs': [{}, {'n': 1}, {}], 'scenario_configs': [{'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 128, 'prefix': '<\|aligned\|>', 'use_prompt_for_scoring': False}, {'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'functions', 'num_samples': 128, 'prefix': '<\|aligned\|>', 'prompt_before_control': True, 'prompts_path': 'resources/functions_csnet.jsonl', 'use_prompt_for_scoring': True}], 'scorer_config': {}}, 'kl_gpt3_callback': {'gpt3_kwargs': {'model_name': 'code-cushman-001'}, 'max_tokens': 64, 'num_samples': 4096, 'prefix': '<\|aligned\|>'}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'num_additional_tokens': 2, 'path_or_name': 'codeparrot/codeparrot-small'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'codeparrot/codeparrot-small', 'special_tokens': ['<\|aligned\|>', '<\|misaligned\|>']}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'debug-pt-conditional', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0008, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000.0, 'output_dir': 'training_output', 'per_device_train_batch_size': 8, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 10, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/kejian/uncategorized/runs/3my099dp
KPEKEP/rugpt_chitchat	KPEKEP	2022-11-29T14:48:36Z	42	1	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "PyTorch", "Transformers", "ru", "license:unlicense", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-11-29T14:48:34Z	--- pipeline_tag: text-generation tags: - PyTorch - Transformers - gpt2 license: unlicense language: ru widget: - text: >- - У Джульетты было 7 пончиков, а потом она 3 съела. Сколько у нее осталось пончиков? - - text: >- - Поглажено 4 манула. Осталось погладить 6. Сколько всего манулов надо погладить? - - text: '- Для начала скажи, чему равно пятью девять? -' - text: '- ты чё такой борзый? -' - text: '- Привет! Как ваше ничего? -' duplicated_from: inkoziev/rugpt_chitchat --- ## Russian Chit-chat, Deductive and Common Sense reasoning model Модель является ядром прототипа [диалоговой системы](https://github.com/Koziev/chatbot) с двумя основными функциями. Первая функция - генерация реплик чит-чата. В качестве затравки подается история диалога (предшествующие несколько реплик, от 1 до 10). ``` - Привет, как дела? - Привет, так себе. - <<< эту реплику ожидаем от модели >>> ``` Вторая функция модели - вывод ответа на заданный вопрос, опираясь на дополнительные факты или на "здравый смысл". Предполагается, что релевантные факты извлекаются из стороннего хранилища (базы знаний) с помощью другой модели, например [sbert_pq](https://huggingface.co/inkoziev/sbert_pq). Используя указанный факт(ы) и текст вопроса, модель построит грамматичный и максимально краткий ответ, как это сделал бы человек в подобной коммуникативной ситуации. Релевантные факты следует указывать перед текстом заданного вопроса так, будто сам собеседник сказал их: ``` - Сегодня 15 сентября. Какой сейчас у нас месяц? - Сентябрь ``` Модель не ожидает, что все найденные и добавленные в контекст диалога факты действительно имеют отношение к заданному вопросу. Поэтому модель, извлекающая из базы знаний информацию, может жертвовать точностью в пользу полноте и добавлять что-то лишнее. Модель читчата в этом случае сама выберет среди добавленных в контекст фактов необходимую фактуру и проигнорирует лишнее. Текущая версия модели допускает до 5 фактов перед вопросом. Например: ``` - Стасу 16 лет. Стас живет в Подольске. У Стаса нет своей машины. Где живет Стас? - в Подольске ``` В некоторых случаях модель может выполнять силлогический вывод ответа, опираясь на 2 предпосылки, связанные друг с другом. Выводимое из двух предпосылок следствие не фигурирует явно, а как бы используется для вывода ответа: ``` - Смертен ли Аристофан, если он был греческим философом, а все философы смертны? - Да ``` Как можно видеть из приведенных примеров, формат подаваемой на вход модели фактической информации для выполнения вывода предельно естественный и свободный. Кроме логического вывода, модель также умеет решать простые арифметические задачи в рамках 1-2 классов начальной школы, с двумя числовыми аргументами: ``` - Чему равно 2+8? - 10 ``` ### Варианты модели и метрики Выложенная на данный момент модель имеет 760 млн. параметров, т.е. уровня sberbank-ai/rugpt3large_based_on_gpt2. Далее приводится результат замера точности решения арифметических задач на отложенном тестовом наборе сэмплов: \| base model \| arith. accuracy \| \| --------------------------------------- \| --------------- \| \| sberbank-ai/rugpt3large_based_on_gpt2 \| 0.91 \| \| sberbank-ai/rugpt3medium_based_on_gpt2 \| 0.70 \| \| sberbank-ai/rugpt3small_based_on_gpt2 \| 0.58 \| \| tinkoff-ai/ruDialoGPT-small \| 0.44 \| \| tinkoff-ai/ruDialoGPT-medium \| 0.69 \| Цифра 0.91 в столбце "arith. accuracy" означает, что 91% тестовых задач решено полностью верно. Любое отклонение сгенерированного ответа от эталонного рассматривается как ошибка. Например, выдача ответа "120" вместо "119" тоже фиксируется как ошибка. ### Пример использования ``` import torch from transformers import AutoTokenizer, AutoModelForCausalLM device = "cuda" if torch.cuda.is_available() else "cpu" model_name = "inkoziev/rugpt_chitchat" tokenizer = AutoTokenizer.from_pretrained(model_name) tokenizer.add_special_tokens({'bos_token': '<s>', 'eos_token': '</s>', 'pad_token': '<pad>'}) model = AutoModelForCausalLM.from_pretrained(model_name) model.to(device) model.eval() # На вход модели подаем последние 2-3 реплики диалога. Каждая реплика на отдельной строке, начинается с символа "-" input_text = """<s>- Привет! Что делаешь? - Привет :) В такси еду -""" encoded_prompt = tokenizer.encode(input_text, add_special_tokens=False, return_tensors="pt").to(device) output_sequences = model.generate(input_ids=encoded_prompt, max_length=100, num_return_sequences=1, pad_token_id=tokenizer.pad_token_id) text = tokenizer.decode(output_sequences[0].tolist(), clean_up_tokenization_spaces=True)[len(input_text)+1:] text = text[: text.find('</s>')] print(text) ``` ### Контакты Если у Вас есть какие-то вопросы по использованию этой модели, или предложения по ее улучшению - пишите мне [email protected] ### Citation: ``` @MISC{rugpt_chitchat, author = {Ilya Koziev}, title = {Russian Chit-chat with Common sence Reasoning}, url = {https://huggingface.co/inkoziev/rugpt_chitchat}, year = 2022 } ```
deblagoj/xlm-roberta-base-finetuned-panx-de	deblagoj	2022-11-29T14:40:06Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-11-29T14:12:37Z	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.de metrics: - name: F1 type: f1 value: 0.86520554167613 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1684 - F1: 0.8652 ## 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: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2655 \| 1.0 \| 2097 \| 0.1958 \| 0.8283 \| \| 0.1479 \| 2.0 \| 4194 \| 0.1581 \| 0.8505 \| \| 0.0852 \| 3.0 \| 6291 \| 0.1684 \| 0.8652 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.13.0+cu117 - Datasets 1.16.1 - Tokenizers 0.10.3
multimodalart/polisteps-768	multimodalart	2022-11-29T14:26:55Z	21	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2022-11-29T14:25:16Z	--- license: creativeml-openrail-m tags: - text-to-image --- ### polisteps 768 Dreambooth model trained by multimodalart with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v2-768 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts! Sample pictures of: plstpz (use that on your prompt) ![plstpz 0](https://huggingface.co/multimodalart/polisteps-768/resolve/main/concept_images/plstpz_%281%29.jpg)![plstpz 1](https://huggingface.co/multimodalart/polisteps-768/resolve/main/concept_images/plstpz_%282%29.jpg)![plstpz 2](https://huggingface.co/multimodalart/polisteps-768/resolve/main/concept_images/plstpz_%283%29.jpg)![plstpz 3](https://huggingface.co/multimodalart/polisteps-768/resolve/main/concept_images/plstpz_%284%29.jpg)![plstpz 4](https://huggingface.co/multimodalart/polisteps-768/resolve/main/concept_images/plstpz_%285%29.jpg)![plstpz 5](https://huggingface.co/multimodalart/polisteps-768/resolve/main/concept_images/plstpz_%286%29.jpg)![plstpz 6](https://huggingface.co/multimodalart/polisteps-768/resolve/main/concept_images/plstpz_%287%29.jpg)![plstpz 7](https://huggingface.co/multimodalart/polisteps-768/resolve/main/concept_images/plstpz_%288%29.jpg)![plstpz 8](https://huggingface.co/multimodalart/polisteps-768/resolve/main/concept_images/plstpz_%289%29.jpg)![plstpz 9](https://huggingface.co/multimodalart/polisteps-768/resolve/main/concept_images/plstpz_%2810%29.jpg)![plstpz 10](https://huggingface.co/multimodalart/polisteps-768/resolve/main/concept_images/plstpz_%2811%29.jpg)![plstpz 11](https://huggingface.co/multimodalart/polisteps-768/resolve/main/concept_images/plstpz_%2812%29.jpg)![plstpz 12](https://huggingface.co/multimodalart/polisteps-768/resolve/main/concept_images/plstpz_%2813%29.jpg)![plstpz 13](https://huggingface.co/multimodalart/polisteps-768/resolve/main/concept_images/plstpz_%2814%29.jpg)![plstpz 14](https://huggingface.co/multimodalart/polisteps-768/resolve/main/concept_images/plstpz_%2815%29.jpg)![plstpz 15](https://huggingface.co/multimodalart/polisteps-768/resolve/main/concept_images/plstpz_%2816%29.jpg)![plstpz 16](https://huggingface.co/multimodalart/polisteps-768/resolve/main/concept_images/plstpz_%2817%29.jpg)![plstpz 17](https://huggingface.co/multimodalart/polisteps-768/resolve/main/concept_images/plstpz_%2818%29.jpg)![plstpz 18](https://huggingface.co/multimodalart/polisteps-768/resolve/main/concept_images/plstpz_%2819%29.jpg)![plstpz 19](https://huggingface.co/multimodalart/polisteps-768/resolve/main/concept_images/plstpz_%2820%29.jpg)![plstpz 20](https://huggingface.co/multimodalart/polisteps-768/resolve/main/concept_images/plstpz_%2821%29.jpg)
thliang01/sd-class-butterflies-64	thliang01	2022-11-29T14:23:01Z	37	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-11-29T14:22:36Z	--- 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(thliang01/sd-class-butterflies-64) image = pipeline().images[0] image ```
jenniferjjc/roberta-base-bne-finetuned-amazon_reviews_multi	jenniferjjc	2022-11-29T14:05:58Z	116	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:amazon_reviews_multi", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-29T13:43:25Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - amazon_reviews_multi metrics: - accuracy model-index: - name: roberta-base-bne-finetuned-amazon_reviews_multi results: - task: name: Text Classification type: text-classification dataset: name: amazon_reviews_multi type: amazon_reviews_multi config: es split: train args: es metrics: - name: Accuracy type: accuracy value: 0.93275 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base-bne-finetuned-amazon_reviews_multi This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.2223 - Accuracy: 0.9327 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.1945 \| 1.0 \| 1250 \| 0.1731 \| 0.9335 \| \| 0.1004 \| 2.0 \| 2500 \| 0.2223 \| 0.9327 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
Evolett/rubert-tiny2-finetuned-ner	Evolett	2022-11-29T13:55:33Z	129	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "dataset:conll2003", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-11-29T09:43:37Z	--- tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: rubert-tiny2-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: train args: conll2003 metrics: - name: Precision type: precision value: 0.7137235200535879 - name: Recall type: recall value: 0.7270556124189697 - name: F1 type: f1 value: 0.7203278827058774 - name: Accuracy type: accuracy value: 0.9363443855435385 --- <!-- 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. --> # rubert-tiny2-finetuned-ner This model was trained from scratch on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.2259 - Precision: 0.7137 - Recall: 0.7271 - F1: 0.7203 - Accuracy: 0.9363 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.6327 \| 1.0 \| 878 \| 0.3218 \| 0.6068 \| 0.6009 \| 0.6038 \| 0.9114 \| \| 0.2937 \| 2.0 \| 1756 \| 0.2434 \| 0.6864 \| 0.7013 \| 0.6938 \| 0.9307 \| \| 0.2357 \| 3.0 \| 2634 \| 0.2259 \| 0.7137 \| 0.7271 \| 0.7203 \| 0.9363 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
sayby/q-Taxi-v3	sayby	2022-11-29T13:45:48Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2022-11-29T13:36:00Z	--- 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: 7.66 +/- 2.55 name: mean_reward verified: false --- # Q-Learning Agent playing Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="sayby/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```
kaizerkam/sd-class-comics-64	kaizerkam	2022-11-29T13:26:50Z	30	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-11-29T13:25:39Z	--- 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 comic scenes. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(kaizerkam/sd-class-comics-64) image = pipeline().images[0] image ```
pig4431/rtm_roBERTa_5E	pig4431	2022-11-29T12:34:52Z	104	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "dataset:rotten_tomatoes", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-29T11:02:18Z	--- license: mit tags: - generated_from_trainer datasets: - rotten_tomatoes metrics: - accuracy model-index: - name: rtm_roBERTa_5E results: - task: name: Text Classification type: text-classification dataset: name: rotten_tomatoes type: rotten_tomatoes config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.8666666666666667 --- <!-- 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. --> # rtm_roBERTa_5E This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the rotten_tomatoes dataset. It achieves the following results on the evaluation set: - Loss: 0.6545 - Accuracy: 0.8667 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6955 \| 0.09 \| 50 \| 0.6752 \| 0.7867 \| \| 0.5362 \| 0.19 \| 100 \| 0.4314 \| 0.8333 \| \| 0.4065 \| 0.28 \| 150 \| 0.4476 \| 0.8533 \| \| 0.3563 \| 0.37 \| 200 \| 0.3454 \| 0.8467 \| \| 0.3729 \| 0.47 \| 250 \| 0.3421 \| 0.86 \| \| 0.3355 \| 0.56 \| 300 \| 0.3253 \| 0.8467 \| \| 0.338 \| 0.66 \| 350 \| 0.3859 \| 0.8733 \| \| 0.2875 \| 0.75 \| 400 \| 0.3537 \| 0.8533 \| \| 0.3477 \| 0.84 \| 450 \| 0.3636 \| 0.8467 \| \| 0.3259 \| 0.94 \| 500 \| 0.3115 \| 0.88 \| \| 0.3204 \| 1.03 \| 550 \| 0.4295 \| 0.8333 \| \| 0.2673 \| 1.12 \| 600 \| 0.3369 \| 0.88 \| \| 0.2479 \| 1.22 \| 650 \| 0.3620 \| 0.8667 \| \| 0.2821 \| 1.31 \| 700 \| 0.3582 \| 0.8733 \| \| 0.2355 \| 1.4 \| 750 \| 0.3130 \| 0.8867 \| \| 0.2357 \| 1.5 \| 800 \| 0.3229 \| 0.86 \| \| 0.2725 \| 1.59 \| 850 \| 0.3035 \| 0.88 \| \| 0.2425 \| 1.69 \| 900 \| 0.3146 \| 0.8533 \| \| 0.1977 \| 1.78 \| 950 \| 0.4079 \| 0.86 \| \| 0.2557 \| 1.87 \| 1000 \| 0.4132 \| 0.8733 \| \| 0.2395 \| 1.97 \| 1050 \| 0.3336 \| 0.86 \| \| 0.1951 \| 2.06 \| 1100 \| 0.5068 \| 0.84 \| \| 0.1631 \| 2.15 \| 1150 \| 0.5209 \| 0.8867 \| \| 0.2192 \| 2.25 \| 1200 \| 0.4766 \| 0.8733 \| \| 0.1725 \| 2.34 \| 1250 \| 0.3962 \| 0.8667 \| \| 0.2215 \| 2.43 \| 1300 \| 0.4133 \| 0.8867 \| \| 0.1602 \| 2.53 \| 1350 \| 0.5564 \| 0.8533 \| \| 0.1986 \| 2.62 \| 1400 \| 0.5826 \| 0.86 \| \| 0.1972 \| 2.72 \| 1450 \| 0.5412 \| 0.8667 \| \| 0.2299 \| 2.81 \| 1500 \| 0.4636 \| 0.8733 \| \| 0.2028 \| 2.9 \| 1550 \| 0.5096 \| 0.8667 \| \| 0.2591 \| 3.0 \| 1600 \| 0.3790 \| 0.8467 \| \| 0.1197 \| 3.09 \| 1650 \| 0.5704 \| 0.8467 \| \| 0.174 \| 3.18 \| 1700 \| 0.5904 \| 0.8467 \| \| 0.1499 \| 3.28 \| 1750 \| 0.6066 \| 0.86 \| \| 0.1687 \| 3.37 \| 1800 \| 0.6353 \| 0.8533 \| \| 0.1463 \| 3.46 \| 1850 \| 0.6434 \| 0.8467 \| \| 0.1373 \| 3.56 \| 1900 \| 0.6507 \| 0.8533 \| \| 0.1339 \| 3.65 \| 1950 \| 0.6014 \| 0.86 \| \| 0.1488 \| 3.75 \| 2000 \| 0.7245 \| 0.84 \| \| 0.1725 \| 3.84 \| 2050 \| 0.6214 \| 0.86 \| \| 0.1443 \| 3.93 \| 2100 \| 0.6446 \| 0.8533 \| \| 0.1619 \| 4.03 \| 2150 \| 0.6223 \| 0.8533 \| \| 0.1153 \| 4.12 \| 2200 \| 0.6579 \| 0.8333 \| \| 0.1159 \| 4.21 \| 2250 \| 0.6760 \| 0.8667 \| \| 0.0948 \| 4.31 \| 2300 \| 0.7172 \| 0.8467 \| \| 0.1373 \| 4.4 \| 2350 \| 0.7346 \| 0.8467 \| \| 0.1463 \| 4.49 \| 2400 \| 0.6453 \| 0.8533 \| \| 0.0758 \| 4.59 \| 2450 \| 0.6579 \| 0.86 \| \| 0.16 \| 4.68 \| 2500 \| 0.6556 \| 0.8667 \| \| 0.112 \| 4.78 \| 2550 \| 0.6490 \| 0.88 \| \| 0.1151 \| 4.87 \| 2600 \| 0.6525 \| 0.8667 \| \| 0.2152 \| 4.96 \| 2650 \| 0.6545 \| 0.8667 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.7.1 - Tokenizers 0.13.2
AlekseyKorshuk/125m-dalio-book-handwritten-io-constant-1e-6-v2	AlekseyKorshuk	2022-11-29T12:29:49Z	125	0	transformers	[ "transformers", "pytorch", "opt", "text-generation", "generated_from_trainer", "dataset:AlekseyKorshuk/dalio-book-handwritten-io-sorted-v2", "license:other", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-11-29T10:31:18Z	--- license: other tags: - generated_from_trainer datasets: - AlekseyKorshuk/dalio-book-handwritten-io-sorted-v2 metrics: - accuracy model-index: - name: 125m-dalio-book-handwritten-io-constant-1e-6-v2 results: - task: name: Causal Language Modeling type: text-generation dataset: name: AlekseyKorshuk/dalio-book-handwritten-io-sorted-v2 type: AlekseyKorshuk/dalio-book-handwritten-io-sorted-v2 metrics: - name: Accuracy type: accuracy value: 0.23359387091781458 --- <!-- 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. --> # 125m-dalio-book-handwritten-io-constant-1e-6-v2 This model is a fine-tuned version of [facebook/opt-125m](https://huggingface.co/facebook/opt-125m) on the AlekseyKorshuk/dalio-book-handwritten-io-sorted-v2 dataset. It achieves the following results on the evaluation set: - Loss: 3.0859 - Accuracy: 0.2336 - Perplexity: 21.8880 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 8 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 1.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Perplexity \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\| \| 3.3352 \| 0.01 \| 1 \| 3.1738 \| 0.2305 \| 23.8988 \| \| 3.3091 \| 0.03 \| 2 \| 3.1738 \| 0.2305 \| 23.8988 \| \| 3.3347 \| 0.04 \| 3 \| 3.1738 \| 0.2305 \| 23.8988 \| \| 3.1445 \| 0.05 \| 4 \| 3.1738 \| 0.2305 \| 23.8988 \| \| 2.8918 \| 0.07 \| 5 \| 3.1738 \| 0.2305 \| 23.8988 \| \| 3.2068 \| 0.08 \| 6 \| 3.1738 \| 0.2305 \| 23.8988 \| \| 3.6245 \| 0.09 \| 7 \| 3.1719 \| 0.2305 \| 23.8522 \| \| 3.2256 \| 0.11 \| 8 \| 3.1719 \| 0.2305 \| 23.8522 \| \| 2.9991 \| 0.12 \| 9 \| 3.1699 \| 0.2305 \| 23.8056 \| \| 3.3257 \| 0.13 \| 10 \| 3.1680 \| 0.2306 \| 23.7592 \| \| 3.1199 \| 0.15 \| 11 \| 3.1660 \| 0.2306 \| 23.7128 \| \| 3.3735 \| 0.16 \| 12 \| 3.1660 \| 0.2306 \| 23.7128 \| \| 3.0051 \| 0.17 \| 13 \| 3.1641 \| 0.2307 \| 23.6665 \| \| 3.2695 \| 0.19 \| 14 \| 3.1621 \| 0.2308 \| 23.6204 \| \| 3.2004 \| 0.2 \| 15 \| 3.1602 \| 0.2309 \| 23.5743 \| \| 3.2075 \| 0.21 \| 16 \| 3.1582 \| 0.2308 \| 23.5283 \| \| 3.321 \| 0.23 \| 17 \| 3.1562 \| 0.2308 \| 23.4824 \| \| 3.4026 \| 0.24 \| 18 \| 3.1543 \| 0.2309 \| 23.4366 \| \| 3.0383 \| 0.25 \| 19 \| 3.1523 \| 0.2309 \| 23.3908 \| \| 3.166 \| 0.27 \| 20 \| 3.1504 \| 0.2309 \| 23.3452 \| \| 3.144 \| 0.28 \| 21 \| 3.1484 \| 0.2310 \| 23.2996 \| \| 3.1624 \| 0.29 \| 22 \| 3.1484 \| 0.2310 \| 23.2996 \| \| 3.0332 \| 0.31 \| 23 \| 3.1465 \| 0.2310 \| 23.2542 \| \| 3.3745 \| 0.32 \| 24 \| 3.1445 \| 0.2311 \| 23.2088 \| \| 3.0823 \| 0.33 \| 25 \| 3.1426 \| 0.2312 \| 23.1635 \| \| 3.6021 \| 0.35 \| 26 \| 3.1406 \| 0.2312 \| 23.1183 \| \| 3.1125 \| 0.36 \| 27 \| 3.1387 \| 0.2313 \| 23.0732 \| \| 3.1406 \| 0.37 \| 28 \| 3.1387 \| 0.2314 \| 23.0732 \| \| 3.1736 \| 0.39 \| 29 \| 3.1367 \| 0.2314 \| 23.0282 \| \| 3.1104 \| 0.4 \| 30 \| 3.1348 \| 0.2315 \| 22.9832 \| \| 3.1301 \| 0.41 \| 31 \| 3.1328 \| 0.2316 \| 22.9384 \| \| 3.3376 \| 0.43 \| 32 \| 3.1309 \| 0.2315 \| 22.8936 \| \| 3.218 \| 0.44 \| 33 \| 3.1309 \| 0.2316 \| 22.8936 \| \| 3.0786 \| 0.45 \| 34 \| 3.1289 \| 0.2316 \| 22.8490 \| \| 3.0125 \| 0.47 \| 35 \| 3.1270 \| 0.2317 \| 22.8044 \| \| 3.2634 \| 0.48 \| 36 \| 3.1270 \| 0.2317 \| 22.8044 \| \| 2.9888 \| 0.49 \| 37 \| 3.125 \| 0.2318 \| 22.7599 \| \| 3.1624 \| 0.51 \| 38 \| 3.1230 \| 0.2318 \| 22.7155 \| \| 2.9807 \| 0.52 \| 39 \| 3.1211 \| 0.2319 \| 22.6712 \| \| 3.446 \| 0.53 \| 40 \| 3.1211 \| 0.2319 \| 22.6712 \| \| 3.1338 \| 0.55 \| 41 \| 3.1191 \| 0.2320 \| 22.6269 \| \| 3.1841 \| 0.56 \| 42 \| 3.1191 \| 0.2320 \| 22.6269 \| \| 3.1079 \| 0.57 \| 43 \| 3.1172 \| 0.2320 \| 22.5828 \| \| 3.0918 \| 0.59 \| 44 \| 3.1152 \| 0.2321 \| 22.5387 \| \| 3.0302 \| 0.6 \| 45 \| 3.1152 \| 0.2322 \| 22.5387 \| \| 3.1123 \| 0.61 \| 46 \| 3.1133 \| 0.2323 \| 22.4947 \| \| 2.9985 \| 0.63 \| 47 \| 3.1113 \| 0.2324 \| 22.4508 \| \| 3.3816 \| 0.64 \| 48 \| 3.1113 \| 0.2324 \| 22.4508 \| \| 3.0813 \| 0.65 \| 49 \| 3.1094 \| 0.2324 \| 22.4070 \| \| 3.2024 \| 0.67 \| 50 \| 3.1094 \| 0.2325 \| 22.4070 \| \| 3.0178 \| 0.68 \| 51 \| 3.1074 \| 0.2325 \| 22.3633 \| \| 3.1646 \| 0.69 \| 52 \| 3.1074 \| 0.2326 \| 22.3633 \| \| 3.0046 \| 0.71 \| 53 \| 3.1055 \| 0.2327 \| 22.3197 \| \| 3.0266 \| 0.72 \| 54 \| 3.1055 \| 0.2327 \| 22.3197 \| \| 3.3857 \| 0.73 \| 55 \| 3.1035 \| 0.2327 \| 22.2761 \| \| 3.064 \| 0.75 \| 56 \| 3.1035 \| 0.2328 \| 22.2761 \| \| 3.176 \| 0.76 \| 57 \| 3.1016 \| 0.2328 \| 22.2327 \| \| 3.1851 \| 0.77 \| 58 \| 3.1016 \| 0.2329 \| 22.2327 \| \| 3.0811 \| 0.79 \| 59 \| 3.0996 \| 0.2329 \| 22.1893 \| \| 3.0205 \| 0.8 \| 60 \| 3.0996 \| 0.2330 \| 22.1893 \| \| 3.26 \| 0.81 \| 61 \| 3.0977 \| 0.2330 \| 22.1460 \| \| 3.2922 \| 0.83 \| 62 \| 3.0977 \| 0.2331 \| 22.1460 \| \| 3.5349 \| 0.84 \| 63 \| 3.0957 \| 0.2331 \| 22.1028 \| \| 3.3525 \| 0.85 \| 64 \| 3.0957 \| 0.2331 \| 22.1028 \| \| 3.135 \| 0.87 \| 65 \| 3.0938 \| 0.2331 \| 22.0596 \| \| 3.1707 \| 0.88 \| 66 \| 3.0938 \| 0.2332 \| 22.0596 \| \| 3.0127 \| 0.89 \| 67 \| 3.0918 \| 0.2332 \| 22.0166 \| \| 3.0952 \| 0.91 \| 68 \| 3.0918 \| 0.2332 \| 22.0166 \| \| 3.1023 \| 0.92 \| 69 \| 3.0898 \| 0.2334 \| 21.9736 \| \| 3.3821 \| 0.93 \| 70 \| 3.0898 \| 0.2334 \| 21.9736 \| \| 3.1118 \| 0.95 \| 71 \| 3.0879 \| 0.2334 \| 21.9308 \| \| 3.1143 \| 0.96 \| 72 \| 3.0879 \| 0.2335 \| 21.9308 \| \| 3.1118 \| 0.97 \| 73 \| 3.0879 \| 0.2335 \| 21.9308 \| \| 3.0596 \| 0.99 \| 74 \| 3.0859 \| 0.2336 \| 21.8880 \| \| 3.1033 \| 1.0 \| 75 \| 3.0859 \| 0.2336 \| 21.8880 \| ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
nlp-tlp/mwo-re	nlp-tlp	2022-11-29T12:11:48Z	4	0	flair	[ "flair", "pytorch", "text-classification", "text-classification-model", "en", "dataset:mwo_re", "region:us" ]	text-classification	2022-11-29T12:09:12Z	--- tags: - flair - text-classification - text-classification-model language: en datasets: - mwo_re widget: - text: "pump broken Item Observation pump is broken" --- ## MWO NER Test A flair-based RE model for MWOs. There are three classes: `HAS_ACTIVITY`, `HAS_OBSERVATION`, and `APPEARS_WITH`.
mepi/KR-FinBert-finetuned-ner	mepi	2022-11-29T11:43:09Z	114	1	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "dataset:klue", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-11-29T11:08:10Z	--- tags: - generated_from_trainer datasets: - klue metrics: - precision - recall - f1 - accuracy model-index: - name: KR-FinBert-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: klue type: klue config: ner split: train args: ner metrics: - name: Precision type: precision value: 0.70817831734221 - name: Recall type: recall value: 0.7610296696359683 - name: F1 type: f1 value: 0.7336533910338766 - name: Accuracy type: accuracy value: 0.9504335292160994 --- <!-- 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. --> # KR-FinBert-finetuned-ner This model is a fine-tuned version of [snunlp/KR-FinBert](https://huggingface.co/snunlp/KR-FinBert) on the klue dataset. It achieves the following results on the evaluation set: - Loss: 0.1634 - Precision: 0.7082 - Recall: 0.7610 - F1: 0.7337 - Accuracy: 0.9504 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.2028 \| 1.0 \| 1313 \| 0.1852 \| 0.6650 \| 0.7060 \| 0.6849 \| 0.9406 \| \| 0.1232 \| 2.0 \| 2626 \| 0.1627 \| 0.7028 \| 0.7459 \| 0.7237 \| 0.9487 \| \| 0.0942 \| 3.0 \| 3939 \| 0.1634 \| 0.7082 \| 0.7610 \| 0.7337 \| 0.9504 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
LuisQ/LuisQ_sd-class-butterflies-64	LuisQ	2022-11-29T11:43:04Z	35	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-11-28T16:21:27Z	--- 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(LuisQ/LuisQ_sd-class-butterflies-64) image = pipeline().images[0] image ```
louisbetsch/tweetclassification-bf-model	louisbetsch	2022-11-29T10:37:35Z	2	0	sentence-transformers	[ "sentence-transformers", "pytorch", "xlm-roberta", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-11-22T09:43:52Z	--- 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 850 with parameters: ``` {'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` 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": 850, "warmup_steps": 85, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (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 -->
ConvLab/t5-small-goal2dialogue-multiwoz21	ConvLab	2022-11-29T10:32:56Z	104	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "t5-small", "dialogue generation", "conversational system", "task-oriented dialog", "en", "dataset:ConvLab/multiwoz21", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-11-25T07:02:25Z	--- language: - en license: apache-2.0 tags: - t5-small - text2text-generation - dialogue generation - conversational system - task-oriented dialog datasets: - ConvLab/multiwoz21 metrics: - LM loss model-index: - name: t5-small-goal2dialogue-multiwoz21 results: - task: type: text2text-generation name: dialogue generation dataset: type: ConvLab/multiwoz21 name: MultiWOZ 2.1 split: validation revision: 5f55375edbfe0270c20bcf770751ad982c0e6614 metrics: - type: Language model loss value: 1.5253684520721436 name: LM loss - task: type: text2text-generation name: dialogue generation dataset: type: ConvLab/multiwoz21 name: MultiWOZ 2.1 split: test revision: 5f55375edbfe0270c20bcf770751ad982c0e6614 metrics: - type: Language model loss value: 1.515929937362671 name: LM loss widget: - text: "You are traveling to Cambridge and looking forward to try local restaurants. You are looking for a particular attraction. Its name is called nusha. Make sure you get postcode and address. You are also looking for a place to dine. The restaurant should be in the expensive price range and should serve indian food. The restaurant should be in the centre. Make sure you get address" - text: "You want to book a taxi. The taxi should go to pizza hut fen ditton and should depart from saint john's college. The taxi should leave after 17:15. Make sure you get car type and contact number" inference: parameters: max_length: 1024 --- # t5-small-goal2dialogue-multiwoz21 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on [MultiWOZ 2.1](https://huggingface.co/datasets/ConvLab/multiwoz21). Refer to [ConvLab-3](https://github.com/ConvLab/ConvLab-3) for model description and usage. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adafactor - lr_scheduler_type: linear - num_epochs: 10.0 ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.2+cu102 - Datasets 1.18.3 - Tokenizers 0.11.0
huggingtweets/mullen_usa-nasdaq	huggingtweets	2022-11-29T10:30:31Z	117	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-11-29T10:24:49Z	--- language: en thumbnail: http://www.huggingtweets.com/mullen_usa-nasdaq/1669717561312/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1521140484512620544/Ev6EIPlD_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1433904015834705921/tRPvxdFF_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Nasdaq & Mullen Automotive</div> <div style="text-align: center; font-size: 14px;">@mullen_usa-nasdaq</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Nasdaq & Mullen Automotive. \| Data \| Nasdaq \| Mullen Automotive \| \| --- \| --- \| --- \| \| Tweets downloaded \| 3250 \| 963 \| \| Retweets \| 663 \| 188 \| \| Short tweets \| 31 \| 121 \| \| Tweets kept \| 2556 \| 654 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/352xmu00/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @mullen_usa-nasdaq's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/x3hx0rfr) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/x3hx0rfr/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/mullen_usa-nasdaq') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
JulianBons/sd-class-butterflies-32	JulianBons	2022-11-29T10:23:38Z	39	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-11-29T10:23:10Z	--- 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(JulianBons/sd-class-butterflies-32) image = pipeline().images[0] image ```
renesteeman/whisper-tiny-dutch-25	renesteeman	2022-11-29T10:20:09Z	80	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "nl", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-11-29T08:26:14Z	--- language: - nl license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Tiny Dutch 25 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 args: 'config: nl, split: test' metrics: - name: Wer type: wer value: 42.065535920433355 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Tiny Dutch 25 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.7024 - Wer: 42.0655 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 2000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.5563 \| 0.78 \| 500 \| 0.7838 \| 47.5002 \| \| 0.3949 \| 1.56 \| 1000 \| 0.7301 \| 43.9570 \| \| 0.2666 \| 2.34 \| 1500 \| 0.7103 \| 42.8426 \| \| 0.2307 \| 3.12 \| 2000 \| 0.7024 \| 42.0655 \| ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
SiriRRR/bart-base-finetuned-test	SiriRRR	2022-11-29T09:26:23Z	62	0	transformers	[ "transformers", "tf", "bart", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-11-29T09:19:55Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: SiriRRR/bart-base-finetuned-test results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # SiriRRR/bart-base-finetuned-test This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5900 - Validation Loss: 2.6982 - Epoch: 7 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 2864, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 2.4667 \| 2.1935 \| 0 \| \| 1.7786 \| 2.2691 \| 1 \| \| 1.4244 \| 2.3324 \| 2 \| \| 1.1479 \| 2.4362 \| 3 \| \| 0.9405 \| 2.5442 \| 4 \| \| 0.7770 \| 2.5797 \| 5 \| \| 0.6615 \| 2.6505 \| 6 \| \| 0.5900 \| 2.6982 \| 7 \| ### Framework versions - Transformers 4.24.0 - TensorFlow 2.9.2 - Datasets 2.7.1 - Tokenizers 0.13.2
SayaEndo/distilbert-base-uncased-finetuned-squad-d5716d28	SayaEndo	2022-11-29T08:56:00Z	107	0	transformers	[ "transformers", "pytorch", "distilbert", "fill-mask", "question-answering", "en", "dataset:squad", "arxiv:1910.01108", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	question-answering	2022-11-29T08:44:02Z	--- language: - en thumbnail: https://github.com/karanchahal/distiller/blob/master/distiller.jpg tags: - question-answering license: apache-2.0 datasets: - squad metrics: - squad --- # DistilBERT with a second step of distillation ## Model description This model replicates the "DistilBERT (D)" model from Table 2 of the [DistilBERT paper](https://arxiv.org/pdf/1910.01108.pdf). In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-tuned on SQuAD v1.1) acting as a teacher for a second step of task-specific distillation. In this version, the following pre-trained models were used: * Student: `distilbert-base-uncased` * Teacher: `lewtun/bert-base-uncased-finetuned-squad-v1` ## Training data This model was trained on the SQuAD v1.1 dataset which can be obtained from the `datasets` library as follows: ```python from datasets import load_dataset squad = load_dataset('squad') ``` ## Training procedure ## Eval results \| \| Exact Match \| F1 \| \|------------------\|-------------\|------\| \| DistilBERT paper \| 79.1 \| 86.9 \| \| Ours \| 78.4 \| 86.5 \| The scores were calculated using the `squad` metric from `datasets`. ### BibTeX entry and citation info ```bibtex @misc{sanh2020distilbert, title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter}, author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf}, year={2020}, eprint={1910.01108}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
pig4431/rtm_fewshot	pig4431	2022-11-29T08:30:05Z	1	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-11-29T08:29:50Z	--- 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 80 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` 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": 800, "warmup_steps": 80, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel (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 -->
regisss/t5-3b-summarization-gaudi-2	regisss	2022-11-29T08:15:35Z	12	0	transformers	[ "transformers", "pytorch", "tensorboard", "optimum_habana", "t5", "text2text-generation", "generated_from_trainer", "dataset:cnn_dailymail", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-11-28T19:53:30Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - cnn_dailymail model-index: - name: t5-3b-summarization-gaudi-2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-3b-summarization-gaudi-2 This model is a fine-tuned version of [t5-3b](https://huggingface.co/t5-3b) on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 8 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.0a0+git7392344 - Datasets 2.7.1 - Tokenizers 0.13.2
pig4431/YELP_fewshot	pig4431	2022-11-29T08:08:51Z	1	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-11-29T08:08:37Z	--- 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 80 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` 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": 800, "warmup_steps": 80, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel (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 -->
premsuresh/bart-finetuned-mathqa-mohith	premsuresh	2022-11-29T08:05:32Z	176	0	transformers	[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-11-29T07:36:05Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bart-finetuned-mathqa-mohith results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-finetuned-mathqa-mohith This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) 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: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 100 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
nagais/sd-class-butterflies-32	nagais	2022-11-29T07:06:12Z	32	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-11-29T06:51:12Z	--- 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(nagais/sd-class-butterflies-32) image = pipeline().images[0] image ```
MadhuG/vit-base-patch16-224-in21k-lung_cancer	MadhuG	2022-11-29T06:41:28Z	76	0	transformers	[ "transformers", "tf", "tensorboard", "vit", "image-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-11-29T05:33:48Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: MadhuG/vit-base-patch16-224-in21k-lung_cancer results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # MadhuG/vit-base-patch16-224-in21k-lung_cancer This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.1061 - Train Accuracy: 0.1041 - Validation Loss: 1.1028 - Validation Accuracy: 0.1394 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 600, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 ### Training results \| Train Loss \| Train Accuracy \| Validation Loss \| Validation Accuracy \| Epoch \| \|:----------:\|:--------------:\|:---------------:\|:-------------------:\|:-----:\| \| 1.1061 \| 0.1041 \| 1.1028 \| 0.1394 \| 0 \| ### Framework versions - Transformers 4.24.0 - TensorFlow 2.10.0 - Datasets 2.7.0 - Tokenizers 0.13.2
smilton/mt5-large-qasrl-es-p1-role	smilton	2022-11-29T06:01:48Z	103	0	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "generated_from_trainer", "es", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-11-29T05:47:34Z	--- language: - es license: apache-2.0 tags: - generated_from_trainer model-index: - name: mt5-large-qasrl-es-p1-role results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mt5-large-qasrl-es-p1-role This model is a fine-tuned version of [google/mt5-large](https://huggingface.co/google/mt5-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4259 ## 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: 10.0 ### Training results ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.11.0 - Datasets 2.7.1 - Tokenizers 0.11.0
laroy23/ddpm-butterflies-128	laroy23	2022-11-29T04:33:59Z	0	0	diffusers	[ "diffusers", "tensorboard", "en", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]	null	2022-11-28T13:56:37Z	--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: ./cifar-10-batches-py metrics: [] --- <!-- 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 `./cifar-10-batches-py` 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/laroy23/ddpm-butterflies-128/tensorboard?#scalars)
elRivx/gAWoman	elRivx	2022-11-29T04:33:34Z	0	2	null	[ "stable-diffusion", "text-to-image", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2022-11-29T04:22:28Z	--- license: creativeml-openrail-m tags: - stable-diffusion - text-to-image --- # gAWoman This is my second Stable Diffusion custom model that bring to you a generic woman generated with non-licenced images. The magic word is: gAWoman If you enjoy my work, please consider supporting me: [![Buy me a coffee](https://badgen.net/badge/icon/buymeacoffee?icon=buymeacoffee&label)](https://www.buymeacoffee.com/elrivx) Examples: <img src=https://imgur.com/B5XkfuG.png width=30% height=30%> <img src=https://imgur.com/N8lNtZo.png width=30% height=30%> ## License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) [Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
NSandra/distilbert-base-uncased-finetuned-ner	NSandra	2022-11-29T04:09:17Z	125	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-11-29T03:55:13Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-ner results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2393 - Precision: 1.0 - Recall: 1.0 - F1: 1.0 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:---:\|:--------:\| \| No log \| 1.0 \| 1 \| 1.5491 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| No log \| 2.0 \| 2 \| 1.3278 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| No log \| 3.0 \| 3 \| 1.2393 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
tomekkorbak/amazing_payne	tomekkorbak	2022-11-29T03:28:47Z	0	0	null	[ "generated_from_trainer", "en", "dataset:tomekkorbak/detoxify-pile-chunk3-0-50000", "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", "dataset:tomekkorbak/detoxify-pile-chunk3-1850000-1900000", "dataset:tomekkorbak/detoxify-pile-chunk3-1900000-1950000", "license:mit", "region:us" ]	null	2022-11-29T03:28:38Z	--- language: - en license: mit tags: - generated_from_trainer datasets: - tomekkorbak/detoxify-pile-chunk3-0-50000 - 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 - tomekkorbak/detoxify-pile-chunk3-1850000-1900000 - tomekkorbak/detoxify-pile-chunk3-1900000-1950000 model-index: - name: amazing_payne 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. --> # amazing_payne This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, 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, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 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: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6 # Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', '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', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'], 'filter_threshold': 0.00065, 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25354], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048}, {'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'challenging_rtp', 'num_samples': 2048, 'prompts_path': 'resources/challenging_rtp.jsonl'}], 'scorer_config': {'device': 'cuda:0'}}, 'kl_gpt3_callback': {'force_call_on': [25354], 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'path_or_name': 'gpt2'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'amazing_payne', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output104340', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25354, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/tomekkorbak/apo/runs/jfkodfu1
JiHoon-kim/bert-base-klue-ynat-finetuned	JiHoon-kim	2022-11-29T03:25:05Z	102	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "mrc", "ko", "dataset:klue", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-29T03:21:37Z	--- language: ko tags: - bert - mrc datasets: - klue license: cc-by-sa-4.0 --- # 인프런 강의용 checkpoint KLUE의 YNAT task에 파인튜닝된 모델입니다.
jeraldflowers/distilroberts-base-mrpc-glue-jeraldflowers	jeraldflowers	2022-11-29T02:57:36Z	108	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-28T05:30:00Z	--- license: apache-2.0 tags: - text-classification - generated_from_trainer datasets: - glue metrics: - accuracy - f1 widget: - text: ["Yucaipa owned Dominick's before selling the chain to Safeway in 1998 for $ 2.5 billion.", "Yucaipa bought Dominick's in 1995 for $ 693 million and sold it to Safeway for $ 1.8 billion in 1998."] example_title: Not Equivalent - text: ["Revenue in the first quarter of the year dropped 15 percent from the same period a year earlier.", "With the scandal hanging over Stewart's company revenue the first quarter of the year dropped 15 percent from the same period a year earlier."] example_title: Equivalent model-index: - name: distilroberts-base-mrpc-glue-jeraldflowers results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: mrpc split: train args: mrpc metrics: - name: Accuracy type: accuracy value: 0.8431372549019608 - name: F1 type: f1 value: 0.8814814814814815 --- <!-- 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. --> # distilroberts-base-mrpc-glue-jeraldflowers This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the glue and the mrpc datasets. It achieves the following results on the evaluation set: - Loss: 0.4990 - Accuracy: 0.8431 - F1: 0.8815 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.5289 \| 1.09 \| 500 \| 0.5668 \| 0.8211 \| 0.8689 \| \| 0.3675 \| 2.18 \| 1000 \| 0.4990 \| 0.8431 \| 0.8815 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
npark/asr-conformer-ksponspeech	npark	2022-11-29T02:25:40Z	5	1	null	[ "region:us" ]	null	2022-11-29T01:26:29Z	# KsponSpeech ASR with Transformers This repository provides pretrained end-to-end ASR models on KsponSpeech with Speechbrain v0.5.13. Model files in this repository trained using the files is below URL, but in Speechbrain version 0.5.13. https://github.com/speechbrain/speechbrain/tree/develop/recipes/KsponSpeech/ASR/transformer language: - "ko" - ko datasets: - KsponSpeech ## About SpeechBrain * Website: https://speechbrain.github.io/ * Code: https://github.com/speechbrain/speechbrain/ * HuggingFace: https://huggingface.co/speechbrain/
neulab/omnitab-large-finetuned-wtq	neulab	2022-11-29T02:11:26Z	4,399	7	transformers	[ "transformers", "pytorch", "bart", "text2text-generation", "tapex", "table-question-answering", "en", "dataset:wikitablequestions", "arxiv:2207.03637", "autotrain_compatible", "endpoints_compatible", "region:us" ]	table-question-answering	2022-10-26T00:56:04Z	--- language: en tags: - tapex - table-question-answering datasets: - wikitablequestions --- # OmniTab OmniTab is a table-based QA model proposed in [OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering](https://arxiv.org/pdf/2207.03637.pdf). The original Github repository is [https://github.com/jzbjyb/OmniTab](https://github.com/jzbjyb/OmniTab). ## Description `neulab/omnitab-large-finetuned-wtq` (based on BART architecture) is initialized with `neulab/omnitab-large` and fine-tuned on [WikiTableQuestions](https://huggingface.co/datasets/wikitablequestions). ## Usage ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM import pandas as pd tokenizer = AutoTokenizer.from_pretrained("neulab/omnitab-large-finetuned-wtq") model = AutoModelForSeq2SeqLM.from_pretrained("neulab/omnitab-large-finetuned-wtq") data = { "year": [1896, 1900, 1904, 2004, 2008, 2012], "city": ["athens", "paris", "st. louis", "athens", "beijing", "london"] } table = pd.DataFrame.from_dict(data) query = "In which year did beijing host the Olympic Games?" encoding = tokenizer(table=table, query=query, return_tensors="pt") outputs = model.generate(**encoding) print(tokenizer.batch_decode(outputs, skip_special_tokens=True)) # [' 2008'] ``` ## Reference ```bibtex @inproceedings{jiang-etal-2022-omnitab, title = "{O}mni{T}ab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering", author = "Jiang, Zhengbao and Mao, Yi and He, Pengcheng and Neubig, Graham and Chen, Weizhu", booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jul, year = "2022", } ```
neulab/omnitab-large-16shot-finetuned-wtq-16shot	neulab	2022-11-29T02:10:07Z	52	1	transformers	[ "transformers", "pytorch", "bart", "text2text-generation", "tapex", "table-question-answering", "en", "dataset:wikitablequestions", "arxiv:2207.03637", "autotrain_compatible", "endpoints_compatible", "region:us" ]	table-question-answering	2022-11-29T01:48:24Z	--- language: en tags: - tapex - table-question-answering datasets: - wikitablequestions --- # OmniTab OmniTab is a table-based QA model proposed in [OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering](https://arxiv.org/pdf/2207.03637.pdf). The original Github repository is [https://github.com/jzbjyb/OmniTab](https://github.com/jzbjyb/OmniTab). ## Description `neulab/omnitab-large-16shot-finetuned-wtq-16shot` (based on BART architecture) is initialized with `neulab/omnitab-large-16shot` and fine-tuned on [WikiTableQuestions](https://huggingface.co/datasets/wikitablequestions) in the 16-shot setting. ## Usage ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM import pandas as pd tokenizer = AutoTokenizer.from_pretrained("neulab/omnitab-large-16shot-finetuned-wtq-16shot") model = AutoModelForSeq2SeqLM.from_pretrained("neulab/omnitab-large-16shot-finetuned-wtq-16shot") data = { "year": [1896, 1900, 1904, 2004, 2008, 2012], "city": ["athens", "paris", "st. louis", "athens", "beijing", "london"] } table = pd.DataFrame.from_dict(data) query = "In which year did beijing host the Olympic Games?" encoding = tokenizer(table=table, query=query, return_tensors="pt") outputs = model.generate(**encoding) print(tokenizer.batch_decode(outputs, skip_special_tokens=True)) # [' 2008'] ``` ## Reference ```bibtex @inproceedings{jiang-etal-2022-omnitab, title = "{O}mni{T}ab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering", author = "Jiang, Zhengbao and Mao, Yi and He, Pengcheng and Neubig, Graham and Chen, Weizhu", booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jul, year = "2022", } ```
Deigant/t5-base-finetuned-qg-context-dataset-2-hard-medium	Deigant	2022-11-29T01:57:15Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-11-29T01:10:07Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-base-finetuned-qg-context-dataset-2-hard-medium 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-base-finetuned-qg-context-dataset-2-hard-medium This model is a fine-tuned version of [Deigant/t5-base-finetuned-qg-context-dataset-2](https://huggingface.co/Deigant/t5-base-finetuned-qg-context-dataset-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1877 - Rouge1: 27.9067 - Rouge2: 6.8779 - Rougel: 24.6502 - Rougelsum: 24.7749 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\| \| No log \| 1.0 \| 73 \| 2.1134 \| 27.571 \| 8.3183 \| 25.3973 \| 25.2743 \| \| No log \| 2.0 \| 146 \| 2.0800 \| 28.4972 \| 9.7451 \| 26.9093 \| 26.7337 \| \| No log \| 3.0 \| 219 \| 2.0406 \| 21.4309 \| 5.817 \| 19.4819 \| 19.8555 \| \| No log \| 4.0 \| 292 \| 2.0391 \| 27.2786 \| 8.283 \| 24.3314 \| 24.3751 \| \| No log \| 5.0 \| 365 \| 2.0367 \| 26.3524 \| 7.6263 \| 23.9034 \| 23.8929 \| \| No log \| 6.0 \| 438 \| 2.0270 \| 26.3718 \| 6.7074 \| 22.995 \| 23.0177 \| \| 1.3439 \| 7.0 \| 511 \| 2.0106 \| 27.8601 \| 10.5485 \| 26.8103 \| 26.4962 \| \| 1.3439 \| 8.0 \| 584 \| 2.0292 \| 27.1811 \| 7.1941 \| 23.9117 \| 24.0093 \| \| 1.3439 \| 9.0 \| 657 \| 2.0462 \| 25.6595 \| 8.3529 \| 23.0955 \| 23.1946 \| \| 1.3439 \| 10.0 \| 730 \| 2.0600 \| 27.1996 \| 9.0098 \| 25.7921 \| 25.8295 \| \| 1.3439 \| 11.0 \| 803 \| 2.0754 \| 25.3094 \| 7.6857 \| 23.5524 \| 23.6875 \| \| 1.3439 \| 12.0 \| 876 \| 2.0532 \| 27.2136 \| 9.0147 \| 24.7405 \| 24.8211 \| \| 1.3439 \| 13.0 \| 949 \| 2.0742 \| 26.298 \| 8.6826 \| 24.6878 \| 24.9118 \| \| 0.8957 \| 14.0 \| 1022 \| 2.0975 \| 22.9575 \| 4.2021 \| 20.6208 \| 20.6539 \| \| 0.8957 \| 15.0 \| 1095 \| 2.0941 \| 26.778 \| 7.1756 \| 24.4053 \| 24.4951 \| \| 0.8957 \| 16.0 \| 1168 \| 2.1025 \| 28.9102 \| 10.5549 \| 25.912 \| 25.9433 \| \| 0.8957 \| 17.0 \| 1241 \| 2.1265 \| 27.8301 \| 9.7377 \| 25.3236 \| 25.3889 \| \| 0.8957 \| 18.0 \| 1314 \| 2.1403 \| 26.1619 \| 7.8019 \| 23.5346 \| 23.351 \| \| 0.8957 \| 19.0 \| 1387 \| 2.1396 \| 26.664 \| 6.8261 \| 24.2991 \| 24.328 \| \| 0.8957 \| 20.0 \| 1460 \| 2.1481 \| 29.8898 \| 9.8211 \| 27.0922 \| 27.2485 \| \| 0.69 \| 21.0 \| 1533 \| 2.1466 \| 26.3418 \| 5.7845 \| 24.0772 \| 24.3122 \| \| 0.69 \| 22.0 \| 1606 \| 2.1559 \| 27.5789 \| 7.7653 \| 25.9896 \| 25.8088 \| \| 0.69 \| 23.0 \| 1679 \| 2.1624 \| 27.9455 \| 7.4094 \| 25.3163 \| 25.3905 \| \| 0.69 \| 24.0 \| 1752 \| 2.1633 \| 27.5236 \| 8.1967 \| 24.9498 \| 24.974 \| \| 0.69 \| 25.0 \| 1825 \| 2.1698 \| 26.899 \| 6.4382 \| 24.2075 \| 24.1523 \| \| 0.69 \| 26.0 \| 1898 \| 2.1745 \| 28.7721 \| 8.872 \| 24.8299 \| 24.9028 \| \| 0.69 \| 27.0 \| 1971 \| 2.1818 \| 25.8046 \| 6.0655 \| 23.156 \| 23.1971 \| \| 0.5965 \| 28.0 \| 2044 \| 2.1854 \| 25.4431 \| 4.6566 \| 22.2794 \| 22.4561 \| \| 0.5965 \| 29.0 \| 2117 \| 2.1858 \| 24.7881 \| 6.4357 \| 22.8869 \| 22.8331 \| \| 0.5965 \| 30.0 \| 2190 \| 2.1877 \| 27.9067 \| 6.8779 \| 24.6502 \| 24.7749 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
huggingtweets/elonmusk-lexfridman	huggingtweets	2022-11-29T01:35:11Z	118	1	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1590968738358079488/IY9Gx6Ok_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/956331551435960322/OaqR8pAB_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Elon Musk & Lex Fridman</div> <div style="text-align: center; font-size: 14px;">@elonmusk-lexfridman</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Elon Musk & Lex Fridman. \| Data \| Elon Musk \| Lex Fridman \| \| --- \| --- \| --- \| \| Tweets downloaded \| 3198 \| 2410 \| \| Retweets \| 126 \| 253 \| \| Short tweets \| 968 \| 49 \| \| Tweets kept \| 2104 \| 2108 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/18nt3c0k/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @elonmusk-lexfridman's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2ozchvjo) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2ozchvjo/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/elonmusk-lexfridman') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
matan-diamond/sd-class-butterflies-32	matan-diamond	2022-11-29T00:47:21Z	36	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-11-29T00:46:35Z	--- 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(matan-diamond/sd-class-butterflies-32) image = pipeline().images[0] image ```
adrien-alloreview/whisper-small-fr	adrien-alloreview	2022-11-29T00:13:29Z	83	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "hi", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-11-28T22:32:23Z	--- language: - hi license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 model-index: - name: Whisper Small Hi - Sanchit Gandhi results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Hi - Sanchit Gandhi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - eval_loss: 0.2226 - eval_wer: 10.0023 - eval_runtime: 65.2041 - eval_samples_per_second: 1.748 - eval_steps_per_second: 0.23 - epoch: 19.51 - step: 800 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - training_steps: 1000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
Serhio/sd-fine-tune-v2	Serhio	2022-11-28T23:43:18Z	34	0	diffusers	[ "diffusers", "text-to-image", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2022-11-28T23:41:46Z	--- license: creativeml-openrail-m tags: - text-to-image --- ### sd-fine-tune-v2 on Stable Diffusion via Dreambooth #### model by Serhio This your the Stable Diffusion model fine-tuned the sd-fine-tune-v2 concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: Bashkov Sergey You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts)
jqtrde/sd-class-butterflies-32	jqtrde	2022-11-28T23:20:18Z	0	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "region:us" ]	unconditional-image-generation	2022-11-28T23:18:49Z	--- 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(jqtrde/sd-class-butterflies-32) image = pipeline().images[0] image ```
Pramodith/sd-class-butterflies-32	Pramodith	2022-11-28T23:19:08Z	38	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-11-28T23:18:35Z	--- 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(Pramodith/sd-class-butterflies-32) image = pipeline().images[0] image ```
kanixwang/my-awesome-setfit-model	kanixwang	2022-11-28T22:19:56Z	1	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-11-28T22:02:13Z	--- 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 40 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` 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": 40, "warmup_steps": 4, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel (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 -->
alryan1478/gpt-neo-125M-DOD-LOW	alryan1478	2022-11-28T22:19:47Z	103	0	transformers	[ "transformers", "pytorch", "gpt_neo", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2022-11-28T21:59:56Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: gpt-neo-125M-DOD-LOW 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. --> # gpt-neo-125M-DOD-LOW This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.0427 ## 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 \| 261 \| 6.4768 \| \| 6.8863 \| 2.0 \| 522 \| 6.1056 \| \| 6.8863 \| 3.0 \| 783 \| 6.0427 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.7.0 - Tokenizers 0.13.2
ThomasSimonini/ML-Agents-SnowballFight-1vs1-model	ThomasSimonini	2022-11-28T22:07:31Z	6	0	ml-agents	[ "ml-agents", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Snowballfight-1vs1", "region:us" ]	reinforcement-learning	2022-11-28T21:26:07Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Snowballfight-1vs1 library_name: ml-agents ---
michaelmayo704/sd-class-butterflies-64	michaelmayo704	2022-11-28T21:39:43Z	34	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-11-28T21:38:51Z	--- 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(michaelmayo704/sd-class-butterflies-64) image = pipeline().images[0] image ```
SiriRRR/test-model	SiriRRR	2022-11-28T21:39:02Z	61	0	transformers	[ "transformers", "tf", "bart", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-11-28T21:38:42Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: test-model results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # test-model This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.24.0 - TensorFlow 2.9.2 - Datasets 2.7.1 - Tokenizers 0.13.2
rlarios/distilbert-base-uncased-finetuned-emotion	rlarios	2022-11-28T21:34:34Z	106	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-25T20:15:59Z	--- 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: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9325 - name: F1 type: f1 value: 0.9322428116765227 --- <!-- 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: 0.2225 - Accuracy: 0.9325 - F1: 0.9322 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8372 \| 1.0 \| 250 \| 0.3225 \| 0.9045 \| 0.9017 \| \| 0.2534 \| 2.0 \| 500 \| 0.2225 \| 0.9325 \| 0.9322 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cpu - Datasets 2.6.1 - Tokenizers 0.13.1
anikethjr/PromoGen_K562_2080Ti_restart	anikethjr	2022-11-28T21:24:36Z	91	0	transformers	[ "transformers", "pytorch", "tensorboard", "prophetnet", "text-generation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2022-11-27T05:27:24Z	--- tags: - generated_from_trainer model-index: - name: PromoGen_K562_2080Ti_restart 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. --> # PromoGen_K562_2080Ti_restart This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4624 ## 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: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 25 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:------:\|:---------------:\| \| 0.7676 \| 0.49 \| 2500 \| 0.7383 \| \| 0.7121 \| 0.97 \| 5000 \| 0.6867 \| \| 0.6914 \| 1.46 \| 7500 \| 0.6705 \| \| 0.6837 \| 1.95 \| 10000 \| 0.6622 \| \| 0.6778 \| 2.44 \| 12500 \| 0.6558 \| \| 0.6748 \| 2.92 \| 15000 \| 0.6517 \| \| 0.6676 \| 3.41 \| 17500 \| 0.6433 \| \| 0.6593 \| 3.9 \| 20000 \| 0.6358 \| \| 0.6584 \| 4.38 \| 22500 \| 0.6320 \| \| 0.6557 \| 4.87 \| 25000 \| 0.6301 \| \| 0.6523 \| 5.36 \| 27500 \| 0.6257 \| \| 0.6478 \| 5.84 \| 30000 \| 0.6236 \| \| 0.6393 \| 6.33 \| 32500 \| 0.6145 \| \| 0.6039 \| 6.82 \| 35000 \| 0.5658 \| \| 0.5616 \| 7.31 \| 37500 \| 0.5376 \| \| 0.5518 \| 7.79 \| 40000 \| 0.5310 \| \| 0.5509 \| 8.28 \| 42500 \| 0.5273 \| \| 0.5487 \| 8.77 \| 45000 \| 0.5261 \| \| 0.5479 \| 9.25 \| 47500 \| 0.5249 \| \| 0.546 \| 9.74 \| 50000 \| 0.5242 \| \| 0.5447 \| 10.23 \| 52500 \| 0.5229 \| \| 0.5439 \| 10.71 \| 55000 \| 0.5220 \| \| 0.5433 \| 11.2 \| 57500 \| 0.5209 \| \| 0.5394 \| 11.69 \| 60000 \| 0.5162 \| \| 0.5153 \| 12.18 \| 62500 \| 0.4944 \| \| 0.5137 \| 12.66 \| 65000 \| 0.4932 \| \| 0.514 \| 13.15 \| 67500 \| 0.4924 \| \| 0.5131 \| 13.64 \| 70000 \| 0.4919 \| \| 0.5104 \| 14.12 \| 72500 \| 0.4914 \| \| 0.5122 \| 14.61 \| 75000 \| 0.4906 \| \| 0.5089 \| 15.1 \| 77500 \| 0.4901 \| \| 0.5076 \| 15.59 \| 80000 \| 0.4891 \| \| 0.4986 \| 16.07 \| 82500 \| 0.4721 \| \| 0.4875 \| 16.56 \| 85000 \| 0.4672 \| \| 0.4887 \| 17.05 \| 87500 \| 0.4669 \| \| 0.4839 \| 17.53 \| 90000 \| 0.4661 \| \| 0.4849 \| 18.02 \| 92500 \| 0.4654 \| \| 0.4848 \| 18.51 \| 95000 \| 0.4649 \| \| 0.4831 \| 18.99 \| 97500 \| 0.4646 \| \| 0.4816 \| 19.48 \| 100000 \| 0.4644 \| \| 0.4808 \| 19.97 \| 102500 \| 0.4637 \| \| 0.4812 \| 20.46 \| 105000 \| 0.4634 \| \| 0.4813 \| 20.94 \| 107500 \| 0.4633 \| \| 0.4818 \| 21.43 \| 110000 \| 0.4631 \| \| 0.4813 \| 21.92 \| 112500 \| 0.4629 \| \| 0.4782 \| 22.4 \| 115000 \| 0.4628 \| \| 0.4804 \| 22.89 \| 117500 \| 0.4626 \| \| 0.4815 \| 23.38 \| 120000 \| 0.4625 \| \| 0.4812 \| 23.87 \| 122500 \| 0.4625 \| \| 0.4785 \| 24.35 \| 125000 \| 0.4624 \| \| 0.4795 \| 24.84 \| 127500 \| 0.4624 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.7.0 - Tokenizers 0.13.0.dev0
pig4431/TUF_BERT_5E	pig4431	2022-11-28T21:13:00Z	103	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-28T21:06:14Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: TUF_BERT_5E 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. --> # TUF_BERT_5E This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3251 - Accuracy: 0.9467 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.4078 \| 0.1 \| 50 \| 0.2430 \| 0.92 \| \| 0.2488 \| 0.2 \| 100 \| 0.1465 \| 0.94 \| \| 0.1966 \| 0.3 \| 150 \| 0.1284 \| 0.96 \| \| 0.2096 \| 0.4 \| 200 \| 0.2879 \| 0.9067 \| \| 0.2015 \| 0.5 \| 250 \| 0.1629 \| 0.9467 \| \| 0.1692 \| 0.59 \| 300 \| 0.2165 \| 0.9133 \| \| 0.1794 \| 0.69 \| 350 \| 0.1535 \| 0.9533 \| \| 0.1975 \| 0.79 \| 400 \| 0.1429 \| 0.9333 \| \| 0.1394 \| 0.89 \| 450 \| 0.2384 \| 0.92 \| \| 0.191 \| 0.99 \| 500 \| 0.2198 \| 0.94 \| \| 0.0907 \| 1.09 \| 550 \| 0.1270 \| 0.9467 \| \| 0.073 \| 1.19 \| 600 \| 0.2016 \| 0.94 \| \| 0.1594 \| 1.29 \| 650 \| 0.2078 \| 0.9267 \| \| 0.087 \| 1.39 \| 700 \| 0.3312 \| 0.9333 \| \| 0.0961 \| 1.49 \| 750 \| 0.3704 \| 0.92 \| \| 0.1225 \| 1.58 \| 800 \| 0.1686 \| 0.9467 \| \| 0.0969 \| 1.68 \| 850 \| 0.1525 \| 0.9333 \| \| 0.0942 \| 1.78 \| 900 \| 0.1924 \| 0.94 \| \| 0.0681 \| 1.88 \| 950 \| 0.1825 \| 0.9467 \| \| 0.1295 \| 1.98 \| 1000 \| 0.1360 \| 0.9333 \| \| 0.0626 \| 2.08 \| 1050 \| 0.2014 \| 0.94 \| \| 0.0372 \| 2.18 \| 1100 \| 0.2030 \| 0.9467 \| \| 0.0077 \| 2.28 \| 1150 \| 0.2615 \| 0.9467 \| \| 0.0393 \| 2.38 \| 1200 \| 0.4256 \| 0.9267 \| \| 0.0492 \| 2.48 \| 1250 \| 0.3057 \| 0.94 \| \| 0.0184 \| 2.57 \| 1300 \| 0.1308 \| 0.9733 \| \| 0.0209 \| 2.67 \| 1350 \| 0.2848 \| 0.9467 \| \| 0.0328 \| 2.77 \| 1400 \| 0.1862 \| 0.96 \| \| 0.0333 \| 2.87 \| 1450 \| 0.2347 \| 0.96 \| \| 0.0527 \| 2.97 \| 1500 \| 0.3855 \| 0.9333 \| \| 0.0685 \| 3.07 \| 1550 \| 0.3174 \| 0.94 \| \| 0.0217 \| 3.17 \| 1600 \| 0.2320 \| 0.9533 \| \| 0.0036 \| 3.27 \| 1650 \| 0.3219 \| 0.9333 \| \| 0.0015 \| 3.37 \| 1700 \| 0.1649 \| 0.9733 \| \| 0.0177 \| 3.47 \| 1750 \| 0.3785 \| 0.94 \| \| 0.0142 \| 3.56 \| 1800 \| 0.1420 \| 0.9733 \| \| 0.0319 \| 3.66 \| 1850 \| 0.4057 \| 0.9333 \| \| 0.0254 \| 3.76 \| 1900 \| 0.1824 \| 0.96 \| \| 0.0092 \| 3.86 \| 1950 \| 0.2400 \| 0.9533 \| \| 0.0306 \| 3.96 \| 2000 \| 0.2238 \| 0.96 \| \| 0.0118 \| 4.06 \| 2050 \| 0.2623 \| 0.9533 \| \| 0.0097 \| 4.16 \| 2100 \| 0.3642 \| 0.9467 \| \| 0.0132 \| 4.26 \| 2150 \| 0.3235 \| 0.9467 \| \| 0.0155 \| 4.36 \| 2200 \| 0.3535 \| 0.9467 \| \| 0.0043 \| 4.46 \| 2250 \| 0.3236 \| 0.9467 \| \| 0.0004 \| 4.55 \| 2300 \| 0.2984 \| 0.9467 \| \| 0.009 \| 4.65 \| 2350 \| 0.2941 \| 0.9467 \| \| 0.0068 \| 4.75 \| 2400 \| 0.2936 \| 0.9467 \| \| 0.0102 \| 4.85 \| 2450 \| 0.3138 \| 0.9467 \| \| 0.0015 \| 4.95 \| 2500 \| 0.3251 \| 0.9467 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.7.1 - Tokenizers 0.13.2
rmartinshort/sd-class-butterflies-64	rmartinshort	2022-11-28T20:32:13Z	36	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-11-28T20:31:54Z	--- 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(rmartinshort/sd-class-butterflies-64) image = pipeline().images[0] image ```
CyantifiCQ/noisy_butterflied_diffusion	CyantifiCQ	2022-11-28T20:23:45Z	35	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-11-28T20:22:34Z	--- 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(CyantifiCQ/noisy_butterflied_diffusion) image = pipeline().images[0] image ```
pig4431/TUF_DistilBERT_5E	pig4431	2022-11-28T20:13:46Z	105	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-28T20:05:35Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: TUF_DistilBERT_5E 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. --> # TUF_DistilBERT_5E This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1832 - Accuracy: 0.96 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.5092 \| 0.1 \| 50 \| 0.4385 \| 0.7533 \| \| 0.2807 \| 0.2 \| 100 \| 0.2225 \| 0.9 \| \| 0.1881 \| 0.3 \| 150 \| 0.1531 \| 0.94 \| \| 0.1895 \| 0.4 \| 200 \| 0.1426 \| 0.94 \| \| 0.1995 \| 0.5 \| 250 \| 0.1428 \| 0.94 \| \| 0.1745 \| 0.59 \| 300 \| 0.1538 \| 0.9267 \| \| 0.1679 \| 0.69 \| 350 \| 0.1249 \| 0.9533 \| \| 0.199 \| 0.79 \| 400 \| 0.1327 \| 0.9467 \| \| 0.1703 \| 0.89 \| 450 \| 0.1488 \| 0.92 \| \| 0.1541 \| 0.99 \| 500 \| 0.1772 \| 0.9467 \| \| 0.1436 \| 1.09 \| 550 \| 0.1070 \| 0.9667 \| \| 0.1463 \| 1.19 \| 600 \| 0.1165 \| 0.9467 \| \| 0.1309 \| 1.29 \| 650 \| 0.1054 \| 0.9733 \| \| 0.097 \| 1.39 \| 700 \| 0.1346 \| 0.94 \| \| 0.1307 \| 1.49 \| 750 \| 0.1477 \| 0.9467 \| \| 0.1506 \| 1.58 \| 800 \| 0.1311 \| 0.9533 \| \| 0.1386 \| 1.68 \| 850 \| 0.1165 \| 0.9667 \| \| 0.1463 \| 1.78 \| 900 \| 0.4207 \| 0.9067 \| \| 0.1202 \| 1.88 \| 950 \| 0.1528 \| 0.9667 \| \| 0.1403 \| 1.98 \| 1000 \| 0.1262 \| 0.96 \| \| 0.073 \| 2.08 \| 1050 \| 0.1459 \| 0.96 \| \| 0.0713 \| 2.18 \| 1100 \| 0.1747 \| 0.9533 \| \| 0.0814 \| 2.28 \| 1150 \| 0.1953 \| 0.9667 \| \| 0.0935 \| 2.38 \| 1200 \| 0.1888 \| 0.9533 \| \| 0.0685 \| 2.48 \| 1250 \| 0.1562 \| 0.9467 \| \| 0.1154 \| 2.57 \| 1300 \| 0.1806 \| 0.96 \| \| 0.1239 \| 2.67 \| 1350 \| 0.1322 \| 0.9533 \| \| 0.1011 \| 2.77 \| 1400 \| 0.2148 \| 0.94 \| \| 0.0718 \| 2.87 \| 1450 \| 0.1686 \| 0.96 \| \| 0.1159 \| 2.97 \| 1500 \| 0.1532 \| 0.9533 \| \| 0.0516 \| 3.07 \| 1550 \| 0.1888 \| 0.96 \| \| 0.063 \| 3.17 \| 1600 \| 0.1851 \| 0.9467 \| \| 0.068 \| 3.27 \| 1650 \| 0.2775 \| 0.94 \| \| 0.0946 \| 3.37 \| 1700 \| 0.1853 \| 0.96 \| \| 0.0606 \| 3.47 \| 1750 \| 0.2148 \| 0.9467 \| \| 0.0663 \| 3.56 \| 1800 \| 0.2091 \| 0.9533 \| \| 0.0474 \| 3.66 \| 1850 \| 0.1702 \| 0.9533 \| \| 0.0585 \| 3.76 \| 1900 \| 0.1660 \| 0.96 \| \| 0.0439 \| 3.86 \| 1950 \| 0.2220 \| 0.9533 \| \| 0.0758 \| 3.96 \| 2000 \| 0.1834 \| 0.96 \| \| 0.0497 \| 4.06 \| 2050 \| 0.1707 \| 0.9533 \| \| 0.0412 \| 4.16 \| 2100 \| 0.1948 \| 0.9533 \| \| 0.0338 \| 4.26 \| 2150 \| 0.2039 \| 0.9533 \| \| 0.0796 \| 4.36 \| 2200 \| 0.1797 \| 0.9533 \| \| 0.0727 \| 4.46 \| 2250 \| 0.1986 \| 0.9533 \| \| 0.032 \| 4.55 \| 2300 \| 0.1947 \| 0.9467 \| \| 0.0436 \| 4.65 \| 2350 \| 0.1908 \| 0.9467 \| \| 0.0205 \| 4.75 \| 2400 \| 0.1806 \| 0.96 \| \| 0.0326 \| 4.85 \| 2450 \| 0.1835 \| 0.96 \| \| 0.0404 \| 4.95 \| 2500 \| 0.1832 \| 0.96 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.7.1 - Tokenizers 0.13.2
motmono/a2c-AntBulletEnv-v0	motmono	2022-11-28T19:58:24Z	2	0	stable-baselines3	[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-11-28T19:57:12Z	--- 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: 1539.68 +/- 213.96 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 ... ```
UKP-SQuARE/tweac_16	UKP-SQuARE	2022-11-28T19:43:48Z	102	1	transformers	[ "transformers", "pytorch", "bert", "text-classification", "QA", "en", "dataset:BoolQ", "dataset:CommonSenseQA", "dataset:DROP", "dataset:DuoRC", "dataset:HellaSWAG", "dataset:HotpotQA", "dataset:HybridQA", "dataset:NarrativeQA", "dataset:NaturalQuestionsShort", "dataset:NewsQA", "dataset:QAMR", "dataset:RACE", "dataset:SearchQA", "dataset:SIQA", "dataset:SQuAD", "dataset:TriviaQA-web", "arxiv:2104.07081", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-09T18:34:07Z	--- language: - en tags: - QA license: cc-by-4.0 datasets: - BoolQ - CommonSenseQA - DROP - DuoRC - HellaSWAG - HotpotQA - HybridQA - NarrativeQA - NaturalQuestionsShort - NewsQA - QAMR - RACE - SearchQA - SIQA - SQuAD - TriviaQA-web metrics: - Accuracy - Precision - Recall - F1 - MRR - R@3 - R@5 --- BERT for Sequence Classification trained on QA Dataset prediction task. - Input: question. - Output: dataset from where that question comes from. Original paper: TWEAC: Transformer with Extendable QA Agent Classifiers https://arxiv.org/abs/2104.07081 Datasets used for training: ``` list_datasets = ['BoolQ','CommonSenseQA','DROP','DuoRC','HellaSWAG','HotpotQA','HybridQA','NarrativeQA','NaturalQuestionsShort','NewsQA','QAMR','RACE','SearchQA','SIQA','SQuAD','TriviaQA-web'] ``` Results for all datasets: - Accuracy: 0.7919096825783123 - Precision: 0.731586272892176 - Recall: 0.7919096825783123 - F1: 0.7494425609552463 - MRR: 0.8720871733637521 - R@3: 0.9438690810655046 - R@5: 0.9745318608004427 - Queries/second: 6052.33538824659 Results per dataset: ``` "BoolQ": { "accuracy": 0.998776758409786, "mrr": 0.999388379204893, "r@3": 1.0, "r@5": 1.0, "query_per_second": 6978.947907596168, "precision": 0.8649364406779662, "recall": 0.998776758409786, "f1": 0.9270508089696281 }, "CommonSenseQA": { "accuracy": 0.9247135842880524, "mrr": 0.9476358338878795, "r@3": 0.9705400981996727, "r@5": 0.9705400981996727, "query_per_second": 5823.984138936813, "precision": 0.442443226311668, "recall": 0.9247135842880524, "f1": 0.5985169491525425 }, "DROP": { "accuracy": 0.9075083892617449, "mrr": 0.9378200367399193, "r@3": 0.9609899328859061, "r@5": 0.9786073825503355, "query_per_second": 6440.988897129248, "precision": 0.8636726546906187, "recall": 0.9075083892617449, "f1": 0.8850480670893842 }, "DuoRC": { "accuracy": 0.5555803405457654, "mrr": 0.7368963429107307, "r@3": 0.9092125808610305, "r@5": 0.9596996059186557, "query_per_second": 6853.643198794893, "precision": 0.646814404432133, "recall": 0.5555803405457654, "f1": 0.5977360905563778 }, "HellaSWAG": { "accuracy": 0.998406691894045, "mrr": 0.9990705702715262, "r@3": 1.0, "r@5": 1.0, "query_per_second": 3091.5012960785157, "precision": 0.9974134500596896, "recall": 0.998406691894045, "f1": 0.9979098238280083 }, "HotpotQA": { "accuracy": 0.7414435784479837, "mrr": 0.8435804344945315, "r@3": 0.9325652321247034, "r@5": 0.973568281938326, "query_per_second": 4972.668019223381, "precision": 0.7352150537634409, "recall": 0.7414435784479837, "f1": 0.7383161801923401 }, "HybridQA": { "accuracy": 0.7934218118869013, "mrr": 0.8806947764680021, "r@3": 0.964800923254472, "r@5": 0.9930755914598961, "query_per_second": 4886.494046259562, "precision": 0.7198952879581152, "recall": 0.7934218118869013, "f1": 0.7548723579467472 }, "NarrativeQA": { "accuracy": 0.5623756749076442, "mrr": 0.7416681781060867, "r@3": 0.9011082693947144, "r@5": 0.9580373212086767, "query_per_second": 7081.067049796865, "precision": 0.5623224095472628, "recall": 0.5623756749076442, "f1": 0.5623490409661377 }, "NaturalQuestionsShort": { "accuracy": 0.7985353692739171, "mrr": 0.8743599435345307, "r@3": 0.9439077594266126, "r@5": 0.9774072919912745, "query_per_second": 7136.590426649795, "precision": 0.7963020509633313, "recall": 0.7985353692739171, "f1": 0.7974171464135678 }, "NewsQA": { "accuracy": 0.5375118708452041, "mrr": 0.71192075967717, "r@3": 0.855650522317189, "r@5": 0.939696106362773, "query_per_second": 7193.851409052092, "precision": 0.18757249378624688, "recall": 0.5375118708452041, "f1": 0.2780985136961061 }, "QAMR": { "accuracy": 0.6658497602557272, "mrr": 0.7969741223377345, "r@3": 0.9207778369738945, "r@5": 0.973361747469366, "query_per_second": 7321.775044800525, "precision": 0.8654525309881587, "recall": 0.6658497602557272, "f1": 0.7526421968624852 }, "RACE": { "accuracy": 0.8771538617474154, "mrr": 0.917901778042666, "r@3": 0.9489154672613015, "r@5": 0.9693898236367322, "query_per_second": 6952.225120744351, "precision": 0.8767983789260385, "recall": 0.8771538617474154, "f1": 0.8769760843129306 }, "SearchQA": { "accuracy": 0.9762073027090695, "mrr": 0.9865069592101393, "r@3": 0.9972909305064782, "r@5": 0.9984687868080094, "query_per_second": 4031.0193826035634, "precision": 0.9870191735143503, "recall": 0.9762073027090695, "f1": 0.9815834665719192 }, "SIQA": { "accuracy": 0.9969293756397134, "mrr": 0.9977823268509042, "r@3": 0.9979529170931423, "r@5": 1.0, "query_per_second": 6711.547709005977, "precision": 0.9329501915708812, "recall": 0.9969293756397134, "f1": 0.9638792676892627 }, "SQuAD": { "accuracy": 0.550628092881614, "mrr": 0.7164538452390565, "r@3": 0.8660068519223448, "r@5": 0.9366197183098591, "query_per_second": 7033.420124363291, "precision": 0.48613678373382624, "recall": 0.550628092881614, "f1": 0.5163766175814368 }, "TriviaQA-web": { "accuracy": 0.7855124582584125, "mrr": 0.8647404868442627, "r@3": 0.9321859748266119, "r@5": 0.9640380169535063, "query_per_second": 4327.642440910395, "precision": 0.7404358353510896, "recall": 0.7855124582584125, "f1": 0.7623083634550667 }, ```
essayproj/roberta-base-essay	essayproj	2022-11-28T19:08:54Z	59	0	transformers	[ "transformers", "tf", "roberta", "feature-extraction", "generated_from_keras_callback", "license:mit", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-28T19:08:03Z	--- license: mit tags: - generated_from_keras_callback model-index: - name: roberta-base-essay results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base-essay 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: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.24.0 - TensorFlow 2.9.2 - Tokenizers 0.13.2
Akriel/sd-class-butterflies-32	Akriel	2022-11-28T18:57:17Z	32	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-11-28T18:56:58Z	--- 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(Akriel/sd-class-butterflies-32) image = pipeline().images[0] image ```
Dagar/t5-small-science-papers-NIPS	Dagar	2022-11-28T18:21:27Z	107	1	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-11-28T18:00:28Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-small-science-papers-NIPS 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-science-papers-NIPS This model is a fine-tuned version of [Dagar/t5-small-science-papers](https://huggingface.co/Dagar/t5-small-science-papers) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.7566 - Rouge1: 15.7066 - Rouge2: 2.5654 - Rougel: 11.4679 - Rougelsum: 14.4017 - 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: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:------:\|:-------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 318 \| 5.1856 \| 13.7172 \| 2.0644 \| 10.2189 \| 12.838 \| 19.0 \| \| 5.4522 \| 2.0 \| 636 \| 5.0383 \| 15.6211 \| 2.1808 \| 11.3561 \| 14.3054 \| 19.0 \| \| 5.4522 \| 3.0 \| 954 \| 4.9486 \| 15.1659 \| 2.3308 \| 11.1052 \| 13.9456 \| 19.0 \| \| 5.1254 \| 4.0 \| 1272 \| 4.8851 \| 15.716 \| 2.4099 \| 11.4954 \| 14.5099 \| 19.0 \| \| 4.9794 \| 5.0 \| 1590 \| 4.8456 \| 15.5507 \| 2.4267 \| 11.3867 \| 14.3237 \| 19.0 \| \| 4.9794 \| 6.0 \| 1908 \| 4.8073 \| 15.8406 \| 2.4254 \| 11.6878 \| 14.6154 \| 19.0 \| \| 4.8823 \| 7.0 \| 2226 \| 4.7872 \| 15.5554 \| 2.4637 \| 11.3401 \| 14.3183 \| 19.0 \| \| 4.8338 \| 8.0 \| 2544 \| 4.7680 \| 15.4783 \| 2.4888 \| 11.3364 \| 14.2031 \| 19.0 \| \| 4.8338 \| 9.0 \| 2862 \| 4.7621 \| 15.958 \| 2.5662 \| 11.6139 \| 14.6576 \| 19.0 \| \| 4.7838 \| 10.0 \| 3180 \| 4.7566 \| 15.7066 \| 2.5654 \| 11.4679 \| 14.4017 \| 19.0 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
FrancoisDongier/sd-class-butterflies-32	FrancoisDongier	2022-11-28T18:19:31Z	34	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-11-28T18:16:21Z	--- 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(FrancoisDongier/sd-class-butterflies-32) image = pipeline().images[0] image ```
kejian/final-filter-again	kejian	2022-11-28T17:39:16Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "generated_from_trainer", "en", "dataset:kejian/codeparrot-train-more-filter-3.3b-cleaned", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2022-11-28T01:33:32Z	--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - kejian/codeparrot-train-more-filter-3.3b-cleaned model-index: - name: kejian/final-filter-again 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/final-filter-again This model was trained from scratch on the kejian/codeparrot-train-more-filter-3.3b-cleaned 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: 0.0008 - train_batch_size: 64 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - 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': {'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'], 'filter_threshold': 0.002361, 'is_split_by_sentences': True}, 'generation': {'batch_size': 64, 'metrics_configs': [{}, {'n': 1}, {}], 'scenario_configs': [{'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 640, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 512}, {'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 272, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'functions', 'num_samples': 512, 'prompts_path': 'resources/functions_csnet.jsonl', 'use_prompt_for_scoring': True}], 'scorer_config': {}}, 'kl_gpt3_callback': {'gpt3_kwargs': {'model_name': 'code-cushman-001'}, 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'path_or_name': 'codeparrot/codeparrot-small'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'codeparrot/codeparrot-small'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'kejian/final-filter-again', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0008, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000.0, 'output_dir': 'training_output', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 5000, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/kejian/uncategorized/runs/25z4zfy3
alexziweiwang/retrain_epoch2and3	alexziweiwang	2022-11-28T17:31:08Z	31	0	transformers	[ "transformers", "pytorch", "wav2vec2", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2022-11-28T17:14:05Z	--- tags: - generated_from_trainer model-index: - name: retrain_epoch2and3 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. --> # retrain_epoch2and3 This model is a fine-tuned version of [alexziweiwang/retrain_first1epoch](https://huggingface.co/alexziweiwang/retrain_first1epoch) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4888 - Acc: 0.24 - Wer: 1.0 - Correct: 48 - Total: 200 - Strlen: 200 ## 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: 9e-06 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Acc \| Wer \| Correct \| Total \| Strlen \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:----:\|:---:\|:-------:\|:-----:\|:------:\| \| No log \| 0.02 \| 5 \| 7.8479 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 7.6019 \| 0.04 \| 10 \| 7.4765 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 7.6019 \| 0.06 \| 15 \| 7.1196 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 7.3222 \| 0.08 \| 20 \| 6.8029 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 7.3222 \| 0.11 \| 25 \| 6.5210 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 6.2645 \| 0.13 \| 30 \| 6.2630 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 6.2645 \| 0.15 \| 35 \| 6.0213 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 5.8699 \| 0.17 \| 40 \| 5.8096 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 5.8699 \| 0.19 \| 45 \| 5.5831 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 5.7145 \| 0.21 \| 50 \| 5.3644 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 5.7145 \| 0.23 \| 55 \| 5.1777 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 5.3702 \| 0.25 \| 60 \| 5.0257 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 5.3702 \| 0.27 \| 65 \| 4.8642 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 5.1896 \| 0.3 \| 70 \| 4.7205 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 5.1896 \| 0.32 \| 75 \| 4.5846 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 5.0615 \| 0.34 \| 80 \| 4.4313 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 5.0615 \| 0.36 \| 85 \| 4.2923 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 4.5189 \| 0.38 \| 90 \| 4.1662 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 4.5189 \| 0.4 \| 95 \| 4.0545 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 4.4911 \| 0.42 \| 100 \| 3.9585 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 4.4911 \| 0.44 \| 105 \| 3.8489 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 4.1997 \| 0.46 \| 110 \| 3.7573 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 4.1997 \| 0.48 \| 115 \| 3.6722 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 3.7348 \| 0.51 \| 120 \| 3.5844 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 3.7348 \| 0.53 \| 125 \| 3.4980 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 3.8042 \| 0.55 \| 130 \| 3.4318 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 3.8042 \| 0.57 \| 135 \| 3.3690 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 3.705 \| 0.59 \| 140 \| 3.3126 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 3.705 \| 0.61 \| 145 \| 3.2630 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 3.763 \| 0.63 \| 150 \| 3.2063 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 3.763 \| 0.65 \| 155 \| 3.1562 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 3.5585 \| 0.67 \| 160 \| 3.1096 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 3.5585 \| 0.7 \| 165 \| 3.0719 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 3.213 \| 0.72 \| 170 \| 3.0373 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 3.213 \| 0.74 \| 175 \| 3.0035 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 3.2874 \| 0.76 \| 180 \| 2.9712 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 3.2874 \| 0.78 \| 185 \| 2.9405 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 3.3327 \| 0.8 \| 190 \| 2.9134 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 3.3327 \| 0.82 \| 195 \| 2.8910 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 3.2382 \| 0.84 \| 200 \| 2.8672 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 3.2382 \| 0.86 \| 205 \| 2.8462 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 3.0069 \| 0.89 \| 210 \| 2.8260 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 3.0069 \| 0.91 \| 215 \| 2.8087 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 3.2288 \| 0.93 \| 220 \| 2.7920 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 3.2288 \| 0.95 \| 225 \| 2.7750 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.787 \| 0.97 \| 230 \| 2.7557 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.787 \| 0.99 \| 235 \| 2.7367 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.9717 \| 1.01 \| 240 \| 2.7207 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.9717 \| 1.03 \| 245 \| 2.7063 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.9269 \| 1.05 \| 250 \| 2.6939 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.9269 \| 1.08 \| 255 \| 2.6831 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.8771 \| 1.1 \| 260 \| 2.6709 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.8771 \| 1.12 \| 265 \| 2.6594 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 3.0474 \| 1.14 \| 270 \| 2.6472 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 3.0474 \| 1.16 \| 275 \| 2.6361 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.7652 \| 1.18 \| 280 \| 2.6268 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.7652 \| 1.2 \| 285 \| 2.6184 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.8322 \| 1.22 \| 290 \| 2.6106 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.8322 \| 1.24 \| 295 \| 2.6034 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.6464 \| 1.27 \| 300 \| 2.5957 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.6464 \| 1.29 \| 305 \| 2.5877 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.7974 \| 1.31 \| 310 \| 2.5805 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.7974 \| 1.33 \| 315 \| 2.5748 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.797 \| 1.35 \| 320 \| 2.5698 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.797 \| 1.37 \| 325 \| 2.5644 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.7508 \| 1.39 \| 330 \| 2.5595 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.7508 \| 1.41 \| 335 \| 2.5537 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.7188 \| 1.43 \| 340 \| 2.5486 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.7188 \| 1.46 \| 345 \| 2.5434 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.6889 \| 1.48 \| 350 \| 2.5377 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.6889 \| 1.5 \| 355 \| 2.5336 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.6373 \| 1.52 \| 360 \| 2.5300 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.6373 \| 1.54 \| 365 \| 2.5258 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.765 \| 1.56 \| 370 \| 2.5219 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.765 \| 1.58 \| 375 \| 2.5181 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.6407 \| 1.6 \| 380 \| 2.5144 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.6407 \| 1.62 \| 385 \| 2.5113 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.7727 \| 1.64 \| 390 \| 2.5093 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.7727 \| 1.67 \| 395 \| 2.5076 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.8091 \| 1.69 \| 400 \| 2.5060 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.8091 \| 1.71 \| 405 \| 2.5042 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.7204 \| 1.73 \| 410 \| 2.5027 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.7204 \| 1.75 \| 415 \| 2.5011 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.6168 \| 1.77 \| 420 \| 2.4987 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.6168 \| 1.79 \| 425 \| 2.4965 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.6947 \| 1.81 \| 430 \| 2.4947 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.6947 \| 1.83 \| 435 \| 2.4932 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.7495 \| 1.86 \| 440 \| 2.4921 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.7495 \| 1.88 \| 445 \| 2.4911 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.7413 \| 1.9 \| 450 \| 2.4904 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.7413 \| 1.92 \| 455 \| 2.4897 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.6498 \| 1.94 \| 460 \| 2.4893 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.6498 \| 1.96 \| 465 \| 2.4890 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 2.6891 \| 1.98 \| 470 \| 2.4888 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 1.18.3 - Tokenizers 0.13.2
wa3dbk/whisper-small-ar	wa3dbk	2022-11-28T17:11:32Z	113	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-11-25T18:33:06Z	## whisper-small-ar This model is a fine-tuned version of openai/whisper-small on the Common Voice 11.0 dataset (language=Arabic).
antgrutta/sd-class-butterflies-32	antgrutta	2022-11-28T16:59:10Z	32	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-11-28T16:58:32Z	--- 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(antgrutta/sd-class-butterflies-32) image = pipeline().images[0] image ```
EmnaBou/bert-finetuned-DT	EmnaBou	2022-11-28T16:49:12Z	123	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-11-28T15:20:02Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-DT 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-finetuned-DT This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6697 - Precision: 0.2381 - Recall: 0.0321 - F1: 0.0565 - Accuracy: 0.8179 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 99 \| 0.7505 \| 0.0 \| 0.0 \| 0.0 \| 0.8196 \| \| No log \| 2.0 \| 198 \| 0.7033 \| 0.0 \| 0.0 \| 0.0 \| 0.8196 \| \| No log \| 3.0 \| 297 \| 0.6697 \| 0.2381 \| 0.0321 \| 0.0565 \| 0.8179 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
luisgasco/distilbert-base-uncased-finetuned-emotion	luisgasco	2022-11-28T16:17:49Z	110	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-28T16:03:30Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.892 - name: F1 type: f1 value: 0.8873822002431591 --- <!-- 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: 0.3693 - Accuracy: 0.892 - F1: 0.8874 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 125 \| 0.5715 \| 0.8275 \| 0.8047 \| \| 0.7552 \| 2.0 \| 250 \| 0.3693 \| 0.892 \| 0.8874 \| ### Framework versions - Transformers 4.13.0 - Pytorch 1.11.0 - Datasets 1.16.1 - Tokenizers 0.10.3
tomekkorbak/awesome_ride	tomekkorbak	2022-11-28T16:12:40Z	0	0	null	[ "generated_from_trainer", "en", "dataset:tomekkorbak/detoxify-pile-chunk3-0-50000", "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", "dataset:tomekkorbak/detoxify-pile-chunk3-1850000-1900000", "dataset:tomekkorbak/detoxify-pile-chunk3-1900000-1950000", "license:mit", "region:us" ]	null	2022-11-28T16:12:19Z	--- language: - en license: mit tags: - generated_from_trainer datasets: - tomekkorbak/detoxify-pile-chunk3-0-50000 - 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 - tomekkorbak/detoxify-pile-chunk3-1850000-1900000 - tomekkorbak/detoxify-pile-chunk3-1900000-1950000 model-index: - name: awesome_ride 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. --> # awesome_ride This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, 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, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 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: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6 # Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', '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', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'], 'filter_threshold': 0.00065, 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25354], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048}, {'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'challenging_rtp', 'num_samples': 2048, 'prompts_path': 'resources/challenging_rtp.jsonl'}], 'scorer_config': {'device': 'cuda:0'}}, 'kl_gpt3_callback': {'force_call_on': [25354], 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'path_or_name': 'gpt2'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'awesome_ride', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output104340', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25354, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/tomekkorbak/apo/runs/3m98rnwq
alexziweiwang/pure-start-epoch2	alexziweiwang	2022-11-28T16:08:48Z	31	0	transformers	[ "transformers", "pytorch", "wav2vec2", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2022-11-28T15:52:06Z	--- tags: - generated_from_trainer model-index: - name: pure-start-epoch2 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. --> # pure-start-epoch2 This model is a fine-tuned version of [alexziweiwang/pure-start-epoch1](https://huggingface.co/alexziweiwang/pure-start-epoch1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 7.7447 - Acc: 0.24 - Wer: 1.0 - Correct: 48 - Total: 200 - Strlen: 200 ## 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: 9e-06 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Acc \| Wer \| Correct \| Total \| Strlen \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-----:\|:---:\|:-------:\|:-----:\|:------:\| \| No log \| 0.01 \| 2 \| 20.4002 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| No log \| 0.02 \| 4 \| 19.9080 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| No log \| 0.03 \| 6 \| 19.4711 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| No log \| 0.03 \| 8 \| 19.1535 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 46.6007 \| 0.04 \| 10 \| 18.6684 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 46.6007 \| 0.05 \| 12 \| 18.1640 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 46.6007 \| 0.06 \| 14 \| 17.6937 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 46.6007 \| 0.07 \| 16 \| 17.2710 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 46.6007 \| 0.08 \| 18 \| 16.8469 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 49.1547 \| 0.08 \| 20 \| 16.4418 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 49.1547 \| 0.09 \| 22 \| 16.0409 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 49.1547 \| 0.1 \| 24 \| 15.6677 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 49.1547 \| 0.11 \| 26 \| 15.3291 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 49.1547 \| 0.12 \| 28 \| 15.0097 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 35.1416 \| 0.13 \| 30 \| 14.6776 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 35.1416 \| 0.13 \| 32 \| 14.3788 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 35.1416 \| 0.14 \| 34 \| 14.0924 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 35.1416 \| 0.15 \| 36 \| 13.8133 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 35.1416 \| 0.16 \| 38 \| 13.5539 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 34.4057 \| 0.17 \| 40 \| 13.3095 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 34.4057 \| 0.18 \| 42 \| 13.0804 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 34.4057 \| 0.19 \| 44 \| 12.8580 \| 0.105 \| 1.0 \| 21 \| 200 \| 200 \| \| 34.4057 \| 0.19 \| 46 \| 12.6532 \| 0.115 \| 1.0 \| 23 \| 200 \| 200 \| \| 34.4057 \| 0.2 \| 48 \| 12.4532 \| 0.13 \| 1.0 \| 26 \| 200 \| 200 \| \| 33.2759 \| 0.21 \| 50 \| 12.2452 \| 0.14 \| 1.0 \| 28 \| 200 \| 200 \| \| 33.2759 \| 0.22 \| 52 \| 12.0666 \| 0.13 \| 1.0 \| 26 \| 200 \| 200 \| \| 33.2759 \| 0.23 \| 54 \| 11.8976 \| 0.165 \| 1.0 \| 33 \| 200 \| 200 \| \| 33.2759 \| 0.24 \| 56 \| 11.7373 \| 0.175 \| 1.0 \| 35 \| 200 \| 200 \| \| 33.2759 \| 0.24 \| 58 \| 11.5933 \| 0.17 \| 1.0 \| 34 \| 200 \| 200 \| \| 29.8129 \| 0.25 \| 60 \| 11.4281 \| 0.15 \| 1.0 \| 30 \| 200 \| 200 \| \| 29.8129 \| 0.26 \| 62 \| 11.2665 \| 0.14 \| 1.0 \| 28 \| 200 \| 200 \| \| 29.8129 \| 0.27 \| 64 \| 11.1158 \| 0.145 \| 1.0 \| 29 \| 200 \| 200 \| \| 29.8129 \| 0.28 \| 66 \| 10.9840 \| 0.135 \| 1.0 \| 27 \| 200 \| 200 \| \| 29.8129 \| 0.29 \| 68 \| 10.8502 \| 0.15 \| 1.0 \| 30 \| 200 \| 200 \| \| 38.792 \| 0.3 \| 70 \| 10.7341 \| 0.15 \| 1.0 \| 30 \| 200 \| 200 \| \| 38.792 \| 0.3 \| 72 \| 10.6082 \| 0.165 \| 1.0 \| 33 \| 200 \| 200 \| \| 38.792 \| 0.31 \| 74 \| 10.4944 \| 0.18 \| 1.0 \| 36 \| 200 \| 200 \| \| 38.792 \| 0.32 \| 76 \| 10.3818 \| 0.21 \| 1.0 \| 42 \| 200 \| 200 \| \| 38.792 \| 0.33 \| 78 \| 10.2719 \| 0.235 \| 1.0 \| 47 \| 200 \| 200 \| \| 28.0092 \| 0.34 \| 80 \| 10.1636 \| 0.235 \| 1.0 \| 47 \| 200 \| 200 \| \| 28.0092 \| 0.35 \| 82 \| 10.0709 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 28.0092 \| 0.35 \| 84 \| 9.9797 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 28.0092 \| 0.36 \| 86 \| 9.8958 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 28.0092 \| 0.37 \| 88 \| 9.7977 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 21.6175 \| 0.38 \| 90 \| 9.7015 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 21.6175 \| 0.39 \| 92 \| 9.6150 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 21.6175 \| 0.4 \| 94 \| 9.5304 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 21.6175 \| 0.4 \| 96 \| 9.4521 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 21.6175 \| 0.41 \| 98 \| 9.3832 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 26.3434 \| 0.42 \| 100 \| 9.3148 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 26.3434 \| 0.43 \| 102 \| 9.2563 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 26.3434 \| 0.44 \| 104 \| 9.1944 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 26.3434 \| 0.45 \| 106 \| 9.1323 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 26.3434 \| 0.46 \| 108 \| 9.0717 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 24.4387 \| 0.46 \| 110 \| 9.0245 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 24.4387 \| 0.47 \| 112 \| 8.9772 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 24.4387 \| 0.48 \| 114 \| 8.9390 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 24.4387 \| 0.49 \| 116 \| 8.9013 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 24.4387 \| 0.5 \| 118 \| 8.8605 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 21.7305 \| 0.51 \| 120 \| 8.8126 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 21.7305 \| 0.51 \| 122 \| 8.7503 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 21.7305 \| 0.52 \| 124 \| 8.6921 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 21.7305 \| 0.53 \| 126 \| 8.6378 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 21.7305 \| 0.54 \| 128 \| 8.5927 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 21.5989 \| 0.55 \| 130 \| 8.5520 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 21.5989 \| 0.56 \| 132 \| 8.5126 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 21.5989 \| 0.56 \| 134 \| 8.4743 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 21.5989 \| 0.57 \| 136 \| 8.4369 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 21.5989 \| 0.58 \| 138 \| 8.3993 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 21.8372 \| 0.59 \| 140 \| 8.3636 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 21.8372 \| 0.6 \| 142 \| 8.3311 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 21.8372 \| 0.61 \| 144 \| 8.2983 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 21.8372 \| 0.62 \| 146 \| 8.2652 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 21.8372 \| 0.62 \| 148 \| 8.2345 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 20.1716 \| 0.63 \| 150 \| 8.2064 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 20.1716 \| 0.64 \| 152 \| 8.1818 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 20.1716 \| 0.65 \| 154 \| 8.1603 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 20.1716 \| 0.66 \| 156 \| 8.1403 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 20.1716 \| 0.67 \| 158 \| 8.1180 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 24.5655 \| 0.67 \| 160 \| 8.0997 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 24.5655 \| 0.68 \| 162 \| 8.0791 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 24.5655 \| 0.69 \| 164 \| 8.0563 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 24.5655 \| 0.7 \| 166 \| 8.0342 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 24.5655 \| 0.71 \| 168 \| 8.0130 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 17.3768 \| 0.72 \| 170 \| 7.9936 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 17.3768 \| 0.72 \| 172 \| 7.9756 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 17.3768 \| 0.73 \| 174 \| 7.9594 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 17.3768 \| 0.74 \| 176 \| 7.9439 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 17.3768 \| 0.75 \| 178 \| 7.9298 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 19.7473 \| 0.76 \| 180 \| 7.9157 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 19.7473 \| 0.77 \| 182 \| 7.9021 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 19.7473 \| 0.78 \| 184 \| 7.8899 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 19.7473 \| 0.78 \| 186 \| 7.8796 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 19.7473 \| 0.79 \| 188 \| 7.8697 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 15.7279 \| 0.8 \| 190 \| 7.8598 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 15.7279 \| 0.81 \| 192 \| 7.8490 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 15.7279 \| 0.82 \| 194 \| 7.8390 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 15.7279 \| 0.83 \| 196 \| 7.8293 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 15.7279 \| 0.83 \| 198 \| 7.8211 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 18.5034 \| 0.84 \| 200 \| 7.8135 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 18.5034 \| 0.85 \| 202 \| 7.8064 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 18.5034 \| 0.86 \| 204 \| 7.7991 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 18.5034 \| 0.87 \| 206 \| 7.7924 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 18.5034 \| 0.88 \| 208 \| 7.7862 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 17.1983 \| 0.89 \| 210 \| 7.7803 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 17.1983 \| 0.89 \| 212 \| 7.7749 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 17.1983 \| 0.9 \| 214 \| 7.7701 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 17.1983 \| 0.91 \| 216 \| 7.7657 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 17.1983 \| 0.92 \| 218 \| 7.7628 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 17.7276 \| 0.93 \| 220 \| 7.7595 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 17.7276 \| 0.94 \| 222 \| 7.7567 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 17.7276 \| 0.94 \| 224 \| 7.7541 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 17.7276 \| 0.95 \| 226 \| 7.7518 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 17.7276 \| 0.96 \| 228 \| 7.7497 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 17.8692 \| 0.97 \| 230 \| 7.7479 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 17.8692 \| 0.98 \| 232 \| 7.7463 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 17.8692 \| 0.99 \| 234 \| 7.7453 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 17.8692 \| 0.99 \| 236 \| 7.7447 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 1.18.3 - Tokenizers 0.13.2
fathyshalab/all-roberta-large-v1-banking-2-2-1	fathyshalab	2022-11-28T15:28:40Z	105	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-28T15:27:01Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-banking-2-2-1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # all-roberta-large-v1-banking-2-2-1 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.6817 - Accuracy: 0.1022 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 6 - eval_batch_size: 6 - 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 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.653 \| 1.0 \| 5 \| 2.6817 \| 0.1022 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1 - Datasets 2.3.2 - Tokenizers 0.12.1
arrandi/sd-class-butterflies-32	arrandi	2022-11-28T15:24:36Z	32	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-11-28T15:23:56Z	--- 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(arrandi/sd-class-butterflies-32) image = pipeline().images[0] image ```
ConvLab/ddpt-policy-sgd_0.01multiwoz21	ConvLab	2022-11-28T15:24:29Z	0	0	null	[ "dialogue policy", "task-oriented dialog", "en", "dataset:ConvLab/sgd", "license:apache-2.0", "region:us" ]	null	2022-11-28T15:21:11Z	--- language: - en license: apache-2.0 tags: - dialogue policy - task-oriented dialog datasets: - ConvLab/sgd --- # ddpt-policy-sgd_0.01multiwoz21 This is a DDPT model (https://aclanthology.org/2022.coling-1.21/) trained on [Schema-Guided Dialog](https://huggingface.co/datasets/ConvLab/sgd) and afterwards on 1 percent of [MultiWOZ 2.1](https://huggingface.co/datasets/ConvLab/multiwoz21) Refer to [ConvLab-3](https://github.com/ConvLab/ConvLab-3) for model description and usage. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - seed: 0 - optimizer: Adam - num_epochs: 40 - use checkpoint which performed best on validation set ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.2+cu111
ConvLab/ddpt-policy-0.01multiwoz21	ConvLab	2022-11-28T15:20:35Z	0	0	null	[ "dialogue policy", "task-oriented dialog", "en", "dataset:ConvLab/sgd", "license:apache-2.0", "region:us" ]	null	2022-11-28T15:18:28Z	--- language: - en license: apache-2.0 tags: - dialogue policy - task-oriented dialog datasets: - ConvLab/sgd --- # ddpt-policy-0.01multiwoz21 This is a DDPT model (https://aclanthology.org/2022.coling-1.21/) trained on 1 percent of [MultiWOZ 2.1](https://huggingface.co/datasets/ConvLab/multiwoz21) Refer to [ConvLab-3](https://github.com/ConvLab/ConvLab-3) for model description and usage. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - seed: 0 - optimizer: Adam - num_epochs: 40 - use checkpoint which performed best on validation set ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.2+cu111
fathyshalab/all-roberta-large-v1-banking-1-2-1	fathyshalab	2022-11-28T15:12:05Z	105	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-28T15:10:30Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-banking-1-2-1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # all-roberta-large-v1-banking-1-2-1 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.6235 - Accuracy: 0.2578 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 6 - eval_batch_size: 6 - 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 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.6542 \| 1.0 \| 3 \| 2.6235 \| 0.2578 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1 - Datasets 2.3.2 - Tokenizers 0.12.1
ConvLab/mle-policy-multiwoz21	ConvLab	2022-11-28T15:11:19Z	0	0	null	[ "dialogue policy", "task-oriented dialog", "en", "dataset:ConvLab/multiwoz21", "license:apache-2.0", "region:us" ]	null	2022-11-28T15:07:50Z	--- language: - en license: apache-2.0 tags: - dialogue policy - task-oriented dialog datasets: - ConvLab/multiwoz21 --- # mle-policy-multiwoz21 This is a MLE model trained on [MultiWOZ 2.1](https://huggingface.co/datasets/ConvLab/multiwoz21) Refer to [ConvLab-3](https://github.com/ConvLab/ConvLab-3) for model description and usage. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - seed: 0 - optimizer: Adam - num_epochs: 24 - use checkpoint which performed best on validation set ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.2+cu111
ConvLab/ddpt-policy-sgd	ConvLab	2022-11-28T15:01:15Z	0	1	null	[ "dialogue policy", "task-oriented dialog", "en", "dataset:ConvLab/sgd", "license:apache-2.0", "region:us" ]	null	2022-11-28T13:21:09Z	--- language: - en license: apache-2.0 tags: - dialogue policy - task-oriented dialog datasets: - ConvLab/sgd --- # ddpt-policy-sgd This is a DDPT model (https://aclanthology.org/2022.coling-1.21/) trained on [Schema-Guided Dialog](https://huggingface.co/datasets/ConvLab/sgd) Refer to [ConvLab-3](https://github.com/ConvLab/ConvLab-3) for model description and usage. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - seed: 0 - optimizer: Adam - num_epochs: 1 - use checkpoint which performed best on validation set ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.2+cu111
alexziweiwang/pure-start-epoch1	alexziweiwang	2022-11-28T14:49:27Z	31	0	transformers	[ "transformers", "pytorch", "wav2vec2", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2022-11-28T14:32:53Z	--- tags: - generated_from_trainer model-index: - name: pure-start-epoch1 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. --> # pure-start-epoch1 This model is a fine-tuned version of [yongjian/wav2vec2-large-a](https://huggingface.co/yongjian/wav2vec2-large-a) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 21.0050 - Acc: 0.095 - Wer: 1.0 - Correct: 19 - Total: 200 - Strlen: 200 ## 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: 9e-06 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Acc \| Wer \| Correct \| Total \| Strlen \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-----:\|:------:\|:-------:\|:-----:\|:------:\| \| No log \| 0.02 \| 5 \| 67.2752 \| 0.0 \| 1.0119 \| 0 \| 200 \| 200 \| \| 131.0548 \| 0.04 \| 10 \| 66.2796 \| 0.0 \| 1.0257 \| 0 \| 200 \| 200 \| \| 131.0548 \| 0.06 \| 15 \| 65.2071 \| 0.005 \| 1.0237 \| 1 \| 200 \| 200 \| \| 145.0859 \| 0.08 \| 20 \| 64.0987 \| 0.035 \| 1.0198 \| 7 \| 200 \| 200 \| \| 145.0859 \| 0.11 \| 25 \| 62.9734 \| 0.07 \| 1.0119 \| 14 \| 200 \| 200 \| \| 110.0012 \| 0.13 \| 30 \| 61.8288 \| 0.09 \| 1.0119 \| 18 \| 200 \| 200 \| \| 110.0012 \| 0.15 \| 35 \| 60.6565 \| 0.09 \| 1.0119 \| 18 \| 200 \| 200 \| \| 122.6164 \| 0.17 \| 40 \| 59.4606 \| 0.095 \| 1.0119 \| 19 \| 200 \| 200 \| \| 122.6164 \| 0.19 \| 45 \| 58.2224 \| 0.095 \| 1.0099 \| 19 \| 200 \| 200 \| \| 125.942 \| 0.21 \| 50 \| 56.9514 \| 0.095 \| 1.0020 \| 19 \| 200 \| 200 \| \| 125.942 \| 0.23 \| 55 \| 55.5923 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 111.2271 \| 0.25 \| 60 \| 54.1423 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 111.2271 \| 0.27 \| 65 \| 52.6174 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 137.2356 \| 0.3 \| 70 \| 51.0340 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 137.2356 \| 0.32 \| 75 \| 49.4034 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 112.2532 \| 0.34 \| 80 \| 47.7291 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 112.2532 \| 0.36 \| 85 \| 46.0281 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 88.3973 \| 0.38 \| 90 \| 44.2361 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 88.3973 \| 0.4 \| 95 \| 42.4925 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 88.7175 \| 0.42 \| 100 \| 40.7673 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 88.7175 \| 0.44 \| 105 \| 39.0848 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 90.857 \| 0.46 \| 110 \| 37.4890 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 90.857 \| 0.48 \| 115 \| 35.8966 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 77.5782 \| 0.51 \| 120 \| 34.2822 \| 0.1 \| 1.0 \| 20 \| 200 \| 200 \| \| 77.5782 \| 0.53 \| 125 \| 32.7953 \| 0.1 \| 1.0 \| 20 \| 200 \| 200 \| \| 80.2378 \| 0.55 \| 130 \| 31.4560 \| 0.1 \| 1.0 \| 20 \| 200 \| 200 \| \| 80.2378 \| 0.57 \| 135 \| 30.1651 \| 0.1 \| 1.0 \| 20 \| 200 \| 200 \| \| 73.5042 \| 0.59 \| 140 \| 29.0069 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 73.5042 \| 0.61 \| 145 \| 28.0349 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 71.5632 \| 0.63 \| 150 \| 27.1812 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 71.5632 \| 0.65 \| 155 \| 26.4012 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 76.5337 \| 0.67 \| 160 \| 25.6924 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 76.5337 \| 0.7 \| 165 \| 25.0184 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 54.6507 \| 0.72 \| 170 \| 24.4100 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 54.6507 \| 0.74 \| 175 \| 23.8273 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 57.1606 \| 0.76 \| 180 \| 23.2988 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 57.1606 \| 0.78 \| 185 \| 22.8731 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 56.0855 \| 0.8 \| 190 \| 22.5336 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 56.0855 \| 0.82 \| 195 \| 22.2334 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 55.2475 \| 0.84 \| 200 \| 21.9555 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 55.2475 \| 0.86 \| 205 \| 21.7112 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 47.9988 \| 0.89 \| 210 \| 21.5123 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 47.9988 \| 0.91 \| 215 \| 21.3407 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 55.1394 \| 0.93 \| 220 \| 21.1965 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 55.1394 \| 0.95 \| 225 \| 21.1028 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 48.0323 \| 0.97 \| 230 \| 21.0376 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| \| 48.0323 \| 0.99 \| 235 \| 21.0050 \| 0.095 \| 1.0 \| 19 \| 200 \| 200 \| ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 1.18.3 - Tokenizers 0.13.2
Fabiuas/Animal-classifier	Fabiuas	2022-11-28T14:38:27Z	311	1	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-11-28T14:37:59Z	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: Animal-classifier results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.9481481313705444 --- # Animal-classifier Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### bee ![bee](images/bee.jpg) #### beetle ![beetle](images/beetle.jpg) #### bird ![bird](images/bird.jpg) #### butterfly ![butterfly](images/butterfly.jpg) #### camel ![camel](images/camel.jpg) #### cat ![cat](images/cat.jpg) #### caterpillar ![caterpillar](images/caterpillar.jpg) #### crab ![crab](images/crab.jpg) #### dog ![dog](images/dog.jpg) #### fly ![fly](images/fly.jpg) #### grasshopper ![grasshopper](images/grasshopper.jpg) #### horse ![horse](images/horse.jpg) #### lizard ![lizard](images/lizard.jpg) #### mosquito ![mosquito](images/mosquito.jpg) #### mouse ![mouse](images/mouse.jpg) #### snake ![snake](images/snake.jpg) #### spider ![spider](images/spider.jpg) #### whale ![whale](images/whale.jpg)
regel-corpus/hunflair-tfbs	regel-corpus	2022-11-28T14:37:52Z	3	0	flair	[ "flair", "pytorch", "hunflair", "token-classification", "sequence-tagger-model", "en", "region:us" ]	token-classification	2022-03-29T11:26:41Z	--- tags: - flair - hunflair - token-classification - sequence-tagger-model language: en widget: - text: "It contains a functional GCGGCGGCG Egr-1-binding site" --- ## HunFlair model for Transcription Factor Binding Site (TFBS) [HunFlair](https://github.com/flairNLP/flair/blob/master/resources/docs/HUNFLAIR.md) (biomedical flair) for TFBS entity. Predicts 1 tag: \| tag \| meaning \| \|---------------------------------\|-----------\| \| Tfbs \| DNA region bound by transcription factor \| --- ### Cite Please cite the following paper when using this model. ``` @article{garda2022regel, title={RegEl corpus: identifying DNA regulatory elements in the scientific literature}, author={Garda, Samuele and Lenihan-Geels, Freyda and Proft, Sebastian and Hochmuth, Stefanie and Sch{\"u}lke, Markus and Seelow, Dominik and Leser, Ulf}, journal={Database}, volume={2022}, year={2022}, publisher={Oxford Academic} } ``` --- ### Demo: How to use in Flair Requires: - [Flair](https://github.com/flairNLP/flair/) (`pip install flair`) ```python from flair.data import Sentence from flair.models import SequenceTagger # for biomedical-specific tokenization: # from flair.tokenization import SciSpacyTokenizer # load tagger tagger = SequenceTagger.load("regel-corpus/hunflair-tfbs") text = "We found that Egr-1 specifically binds to the PTEN 5' untranslated region, which contains a functional GCGGCGGCG Egr-1-binding site." # make example sentence sentence = Sentence(text) # for biomedical-specific tokenization: # sentence = Sentence(text, use_tokenizer=SciSpacyTokenizer()) # predict NER tags tagger.predict(sentence) # print sentence print(sentence) # print predicted NER spans print('The following NER tags are found:') # iterate over entities and print for entity in sentence.get_spans('ner'): print(entity) ``` This yields the following output: ``` Span [19,20,21]: "GCGGCGGCG Egr-1-binding site" [− Labels: Tfbs (0.9631)] ``` So, the entity "GCGGCGGCG Egr-1-binding site" is found in the sentence. Alternatively download all models locally and use the `MultiTagger` class. ```python from flair.models import MultiTagger tagger = [ './models/hunflair-promoter/pytorch_model.bin', './models/hunflair-enhancer/pytorch_model.bin', './models/hunflair-tfbs/pytorch_model.bin', ] tagger = MultiTagger.load(['./models/hunflair-']) tagger.predict(sentence) ``` ---
fathyshalab/bert-uncased-massive-intent-classification-finetuned-banking-1	fathyshalab	2022-11-28T13:25:04Z	104	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-28T13:01:42Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: bert-uncased-massive-intent-classification-finetuned-banking-1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-uncased-massive-intent-classification-finetuned-banking-1 This model is a fine-tuned version of [gokuls/bert-uncased-massive-intent-classification](https://huggingface.co/gokuls/bert-uncased-massive-intent-classification) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.6447 - Accuracy: 0.1822 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.9685 \| 1.0 \| 3 \| 2.7310 \| 0.1422 \| \| 2.8056 \| 2.0 \| 6 \| 2.6970 \| 0.1467 \| \| 2.5004 \| 3.0 \| 9 \| 2.6680 \| 0.1511 \| \| 2.445 \| 4.0 \| 12 \| 2.6515 \| 0.1778 \| \| 2.3977 \| 5.0 \| 15 \| 2.6447 \| 0.1822 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1 - Datasets 2.3.2 - Tokenizers 0.12.1
jfjensen/sd-class-butterflies-32	jfjensen	2022-11-28T12:59:41Z	37	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-11-28T12:58:55Z	--- 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(jfjensen/sd-class-butterflies-32) image = pipeline().images[0] image ```
cardiffnlp/twitter-roberta-base-offensive	cardiffnlp	2022-11-28T11:36:23Z	35,866	27	transformers	[ "transformers", "pytorch", "tf", "jax", "roberta", "text-classification", "arxiv:2010.12421", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	# Twitter-roBERTa-base for Offensive Language Identification This is a roBERTa-base model trained on ~58M tweets and finetuned for offensive language identification with the TweetEval benchmark. - Paper: [_TweetEval_ benchmark (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf). - Git Repo: [Tweeteval official repository](https://github.com/cardiffnlp/tweeteval). ## Example of classification ```python from transformers import AutoModelForSequenceClassification from transformers import TFAutoModelForSequenceClassification from transformers import AutoTokenizer import numpy as np from scipy.special import softmax import csv import urllib.request # Preprocess text (username and link placeholders) def preprocess(text): new_text = [] for t in text.split(" "): t = '@user' if t.startswith('@') and len(t) > 1 else t t = 'http' if t.startswith('http') else t new_text.append(t) return " ".join(new_text) # Tasks: # emoji, emotion, hate, irony, offensive, sentiment # stance/abortion, stance/atheism, stance/climate, stance/feminist, stance/hillary task='offensive' MODEL = f"cardiffnlp/twitter-roberta-base-{task}" tokenizer = AutoTokenizer.from_pretrained(MODEL) # download label mapping labels=[] mapping_link = f"https://raw.githubusercontent.com/cardiffnlp/tweeteval/main/datasets/{task}/mapping.txt" with urllib.request.urlopen(mapping_link) as f: html = f.read().decode('utf-8').split("\n") csvreader = csv.reader(html, delimiter='\t') labels = [row[1] for row in csvreader if len(row) > 1] # PT model = AutoModelForSequenceClassification.from_pretrained(MODEL) model.save_pretrained(MODEL) text = "Good night 😊" text = preprocess(text) encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) scores = output[0][0].detach().numpy() scores = softmax(scores) # # TF # model = TFAutoModelForSequenceClassification.from_pretrained(MODEL) # model.save_pretrained(MODEL) # text = "Good night 😊" # encoded_input = tokenizer(text, return_tensors='tf') # output = model(encoded_input) # scores = output[0][0].numpy() # scores = softmax(scores) ranking = np.argsort(scores) ranking = ranking[::-1] for i in range(scores.shape[0]): l = labels[ranking[i]] s = scores[ranking[i]] print(f"{i+1}) {l} {np.round(float(s), 4)}") ``` Output: ``` 1) not-offensive 0.9073 2) offensive 0.0927 ```
clp/vit-base-patch16-224-finetuned	clp	2022-11-28T11:29:17Z	186	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-11-28T11:19:02Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: vit-base-patch16-224-finetuned results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.3333333333333333 --- <!-- 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 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. It achieves the following results on the evaluation set: - Loss: 0.7617 - Accuracy: 0.3333 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 1 \| 0.6063 \| 0.6667 \| \| No log \| 2.0 \| 2 \| 0.6958 \| 0.3333 \| \| No log \| 3.0 \| 3 \| 0.7617 \| 0.3333 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
projecte-aina/roberta-base-ca-v2-cased-tc	projecte-aina	2022-11-28T11:02:09Z	110	1	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "catalan", "text classification", "tecla", "CaText", "Catalan Textual Corpus", "ca", "dataset:projecte-aina/tecla", "arxiv:1907.11692", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-06-30T07:55:23Z	--- language: - ca tags: - "catalan" - "text classification" - "tecla" - "CaText" - "Catalan Textual Corpus" datasets: - "projecte-aina/tecla" metrics: - accuracy model-index: - name: roberta-base-ca-v2-cased-tc results: - task: type: text-classification dataset: name: TeCla type: projecte-aina/tecla metrics: - name: Accuracy type: accuracy value: 0.8034 widget: - text: "Els Pets presenten el seu nou treball al Palau Sant Jordi." - text: "Els barcelonins incrementen un 23% l’ús del cotxe des de l’inici de la pandèmia." - text: "Retards a quatre línies de Rodalies per una avaria entre Sants i plaça de Catalunya." - text: "Majors de 60 anys i sanitaris començaran a rebre la tercera dosi de la vacuna covid els propers dies." - text: "Els cinemes Verdi estrenen Verdi Classics, un nou canal de televisió." --- # Catalan BERTa-v2 (roberta-base-ca-v2) finetuned for TeCla-based Text Classification. ## Table of Contents <details> <summary>Click to expand</summary> - [Model description](#model-description) - [Intended uses and limitations](#intended-use) - [How to use](#how-to-use) - [Limitations and bias](#limitations-and-bias) - [Training](#training) - [Training data](#training-data) - [Training procedure](#training-procedure) - [Tokenization](#tokenization) - [Hyperparameters](#hyperparameters) - [Evaluation](#evaluation) - [Variable and metrics](#variable-and-metrics) - [Evaluation results](#evaluation-results) - [Additional information](#additional-information) - [Author](#author) - [Contact information](#contact-information) - [Copyright](#copyright) - [Licensing information](#licensing-information) - [Funding](#funding) - [Citing information](#citing-information) - [Disclaimer](#disclaimer) </details> ## Model description The roberta-base-ca-v2-cased-tc is a Text Classification (TC) model for the Catalan language fine-tuned from the [roberta-base-ca-v2](https://huggingface.co/projecte-aina/roberta-base-ca-v2) model, a [RoBERTa](https://arxiv.org/abs/1907.11692) base model pre-trained on a medium-size corpus collected from publicly available corpora and crawlers (check the roberta-base-ca-v2 model card for more details). The previous version of this model, which was trained on the old TeCla dataset (v1), can still be accessed through the "v1" tag. ## Intended uses and limitations roberta-base-ca-v2-cased-tc model can be used to classify texts. The model is limited by its training dataset and may not generalize well for all use cases. ## How to use Here is how to use this model: ```python from transformers import pipeline from pprint import pprint nlp = pipeline("text-classification", model="projecte-aina/roberta-base-ca-v2-cased-tc") example = "Retards a quatre línies de Rodalies per una avaria entre Sants i plaça de Catalunya." tc_results = nlp(example) pprint(tc_results) ``` ## Limitations and bias At the time of submission, no measures have been taken to estimate the bias embedded in the model. However, we are well aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this model card will be updated. ## Training ### Training data We used the TC dataset in Catalan called [TeCla](https://huggingface.co/datasets/projecte-aina/tecla) for training and evaluation. Although TeCla includes a coarse-grained ('label1') and a fine-grained categorization ('label2'), only the last one, with 53 classes, was used for the training. ### Training procedure The model was trained with a batch size of 16 and a learning rate of 5e-5 for 5 epochs. We then selected the best checkpoint using the downstream task metric in the corresponding development set and then evaluated it on the test set. ## Evaluation ### Variable and metrics This model was finetuned maximizing F1 (weighted). ## Evaluation results We evaluated the _roberta-base-ca-v2-cased-tc_ on the TeCla test set against standard multilingual and monolingual baselines. The results for 'label1' categories were obtained through a mapping from the fine-grained category ('label2') to the corresponding coarse-grained one ('label1'). \| Model \| TeCla - label1 (Accuracy) \| TeCla - label2 (Accuracy) \| \| ------------\|:-------------\|:-------------\| \| roberta-base-ca-v2 \| 96.31 \| 80.34 \| \| roberta-large-ca-v2 \| 96.51 \| 80.68 \| \| mBERT \| 95.72 \| 78.47 \| \| XLM-RoBERTa \| 95.66 \| 78.01 \| For more details, check the fine-tuning and evaluation scripts in the official [GitHub repository](https://github.com/projecte-aina/club). ## Additional information ### Author Text Mining Unit (TeMU) at the Barcelona Supercomputing Center ([email protected]) ### Contact information For further information, send an email to [email protected] ### Copyright Copyright (c) 2022 Text Mining Unit at Barcelona Supercomputing Center ### Licensing information [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0) ### Funding This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca\|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina). ## Citation Information If you use any of these resources (datasets or models) in your work, please cite our latest paper: ```bibtex @inproceedings{armengol-estape-etal-2021-multilingual, title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}atalan", author = "Armengol-Estap{\'e}, Jordi and Carrino, Casimiro Pio and Rodriguez-Penagos, Carlos and de Gibert Bonet, Ona and Armentano-Oller, Carme and Gonzalez-Agirre, Aitor and Melero, Maite and Villegas, Marta", booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.findings-acl.437", doi = "10.18653/v1/2021.findings-acl.437", pages = "4933--4946", } ``` ### Disclaimer <details> <summary>Click to expand</summary> The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions. When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence. In no event shall the owner and creator of the models (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models.
GDJ1978/voxelartXmidjgraffiti	GDJ1978	2022-11-28T10:01:38Z	0	0	null	[ "region:us" ]	null	2022-11-28T09:55:36Z	VoxelArt_v1_0.6-MDJRNY-GRFFT_0.4-Weighted_sum-merged.ckpt trigger: VoxelArt in the style of mdjrny-grfft
mn367/radio-mlm	mn367	2022-11-28T09:52:57Z	61	0	transformers	[ "transformers", "tf", "distilbert", "fill-mask", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-11-28T09:42:20Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: mn367/radio-mlm results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # mn367/radio-mlm This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.6630 - Validation Loss: 4.6014 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 39000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 4.6630 \| 4.6014 \| 0 \| ### Framework versions - Transformers 4.24.0 - TensorFlow 2.9.2 - Datasets 2.7.1 - Tokenizers 0.13.2
vumichien/trillsson3-ft-keyword-spotting-15	vumichien	2022-11-28T09:46:32Z	47	0	transformers	[ "transformers", "pytorch", "tensorboard", "trillsson_efficient", "text-classification", "audio-classification", "generated_from_trainer", "dataset:superb", "autotrain_compatible", "endpoints_compatible", "region:us" ]	audio-classification	2022-11-28T08:17:45Z	--- tags: - audio-classification - generated_from_trainer datasets: - superb metrics: - accuracy model-index: - name: trillsson3-ft-keyword-spotting-15 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. --> # trillsson3-ft-keyword-spotting-15 This model is a fine-tuned version of [vumichien/nonsemantic-speech-trillsson3](https://huggingface.co/vumichien/nonsemantic-speech-trillsson3) on the superb dataset. It achieves the following results on the evaluation set: - Loss: 0.3563 - Accuracy: 0.9041 ## 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: 32 - eval_batch_size: 64 - seed: 0 - 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: 0.1 - num_epochs: 10.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.1824 \| 1.0 \| 798 \| 0.6478 \| 0.7489 \| \| 0.7448 \| 2.0 \| 1596 \| 0.4274 \| 0.8728 \| \| 0.7089 \| 3.0 \| 2394 \| 0.3723 \| 0.8950 \| \| 0.6781 \| 4.0 \| 3192 \| 0.3563 \| 0.9041 \| \| 0.6386 \| 5.0 \| 3990 \| 0.3441 \| 0.8986 \| \| 0.6342 \| 6.0 \| 4788 \| 0.3380 \| 0.8994 \| \| 0.6275 \| 7.0 \| 5586 \| 0.3376 \| 0.8982 \| \| 0.6349 \| 8.0 \| 6384 \| 0.3333 \| 0.9014 \| \| 0.6261 \| 9.0 \| 7182 \| 0.3295 \| 0.9025 \| \| 0.6188 \| 10.0 \| 7980 \| 0.3322 \| 0.9025 \| ### Framework versions - Transformers 4.23.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
rohitagrawal-20/bert-finetuned-ner	rohitagrawal-20	2022-11-28T09:39:47Z	125	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "dataset:conll2003", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-11-28T09:12:26Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: train args: conll2003 metrics: - name: Precision type: precision value: 0.935969556585043 - name: Recall type: recall value: 0.9520363513968361 - name: F1 type: f1 value: 0.9439345903554145 - name: Accuracy type: accuracy value: 0.9868870312591982 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0599 - Precision: 0.9360 - Recall: 0.9520 - F1: 0.9439 - Accuracy: 0.9869 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.0879 \| 1.0 \| 1756 \| 0.0652 \| 0.9236 \| 0.9379 \| 0.9307 \| 0.9832 \| \| 0.0343 \| 2.0 \| 3512 \| 0.0614 \| 0.9337 \| 0.9510 \| 0.9423 \| 0.9864 \| \| 0.019 \| 3.0 \| 5268 \| 0.0599 \| 0.9360 \| 0.9520 \| 0.9439 \| 0.9869 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
hannoh/03_model_sales	hannoh	2022-11-28T08:58:05Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-28T08:46:54Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: 03_model_sales 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. --> # 03_model_sales 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: 0.4693 - Accuracy: 0.7818 - F1: 0.7980 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
alexziweiwang/retrain_epoch2to5	alexziweiwang	2022-11-28T08:51:14Z	31	0	transformers	[ "transformers", "pytorch", "wav2vec2", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2022-11-28T08:35:03Z	--- tags: - generated_from_trainer model-index: - name: retrain_epoch2to5 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. --> # retrain_epoch2to5 This model is a fine-tuned version of [alexziweiwang/retrain_first1epoch](https://huggingface.co/alexziweiwang/retrain_first1epoch) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.3244 - Acc: 0.24 - Wer: 1.0 - Correct: 48 - Total: 200 - Strlen: 200 ## 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: 9e-06 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Acc \| Wer \| Correct \| Total \| Strlen \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:----:\|:---:\|:-------:\|:-----:\|:------:\| \| No log \| 0.02 \| 5 \| 7.8494 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 7.6032 \| 0.04 \| 10 \| 7.4834 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 7.6032 \| 0.06 \| 15 \| 7.1350 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 7.3336 \| 0.08 \| 20 \| 6.8284 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 7.3336 \| 0.11 \| 25 \| 6.5577 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 6.2911 \| 0.13 \| 30 \| 6.3124 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 6.2911 \| 0.15 \| 35 \| 6.0850 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 5.9181 \| 0.17 \| 40 \| 5.8888 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 5.9181 \| 0.19 \| 45 \| 5.6815 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 5.7954 \| 0.21 \| 50 \| 5.4834 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 5.7954 \| 0.23 \| 55 \| 5.3099 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 5.4801 \| 0.25 \| 60 \| 5.1678 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 5.4801 \| 0.27 \| 65 \| 5.0223 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 5.3377 \| 0.3 \| 70 \| 4.8893 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 5.3377 \| 0.32 \| 75 \| 4.7743 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 5.2511 \| 0.34 \| 80 \| 4.6494 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 5.2511 \| 0.36 \| 85 \| 4.5307 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 4.727 \| 0.38 \| 90 \| 4.4237 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 4.727 \| 0.4 \| 95 \| 4.3263 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 4.7653 \| 0.42 \| 100 \| 4.2439 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 4.7653 \| 0.44 \| 105 \| 4.1589 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 4.4971 \| 0.46 \| 110 \| 4.0847 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 4.4971 \| 0.48 \| 115 \| 4.0118 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 4.0077 \| 0.51 \| 120 \| 3.9382 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 4.0077 \| 0.53 \| 125 \| 3.8663 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 4.1693 \| 0.55 \| 130 \| 3.8106 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 4.1693 \| 0.57 \| 135 \| 3.7580 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 4.0854 \| 0.59 \| 140 \| 3.7123 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 4.0854 \| 0.61 \| 145 \| 3.6720 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 4.1988 \| 0.63 \| 150 \| 3.6260 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 4.1988 \| 0.65 \| 155 \| 3.5853 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 3.9975 \| 0.67 \| 160 \| 3.5463 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 3.9975 \| 0.7 \| 165 \| 3.5122 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 3.6042 \| 0.72 \| 170 \| 3.4862 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 3.6042 \| 0.74 \| 175 \| 3.4631 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 3.7347 \| 0.76 \| 180 \| 3.4406 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 3.7347 \| 0.78 \| 185 \| 3.4202 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 3.8336 \| 0.8 \| 190 \| 3.4014 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 3.8336 \| 0.82 \| 195 \| 3.3855 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 3.7454 \| 0.84 \| 200 \| 3.3703 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 3.7454 \| 0.86 \| 205 \| 3.3576 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 3.525 \| 0.89 \| 210 \| 3.3471 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 3.525 \| 0.91 \| 215 \| 3.3392 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 3.8175 \| 0.93 \| 220 \| 3.3331 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 3.8175 \| 0.95 \| 225 \| 3.3289 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 3.307 \| 0.97 \| 230 \| 3.3259 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| \| 3.307 \| 0.99 \| 235 \| 3.3244 \| 0.24 \| 1.0 \| 48 \| 200 \| 200 \| ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 1.18.3 - Tokenizers 0.13.2
huggingtweets/bobkerns	huggingtweets	2022-11-28T08:14:20Z	115	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-11-28T08:14:12Z	--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/3653376550/f40f9602f2e8e185eb7ddce332157ffe_400x400.jpeg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Bob (Moderna #5) Kerns</div> <div style="text-align: center; font-size: 14px;">@bobkerns</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Bob (Moderna #5) Kerns. \| Data \| Bob (Moderna #5) Kerns \| \| --- \| --- \| \| Tweets downloaded \| 3234 \| \| Retweets \| 315 \| \| Short tweets \| 42 \| \| Tweets kept \| 2877 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/390ksfue/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @bobkerns's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3me25qi0) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3me25qi0/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/bobkerns') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
pere/whisper-NST2-unfreeze-constanti-low-lr	pere	2022-11-28T07:41:42Z	9	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-11-23T10:34:53Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-NST2-unfreeze-constanti-low-lr results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-NST2-unfreeze-constanti-low-lr This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3562 - Wer: 8.5519 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 96 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - training_steps: 20000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\| \| 0.1901 \| 0.05 \| 1000 \| 0.3069 \| 14.8233 \| \| 0.1323 \| 0.1 \| 2000 \| 0.2687 \| 11.2885 \| \| 0.1137 \| 0.15 \| 3000 \| 0.2620 \| 10.8324 \| \| 0.1022 \| 0.2 \| 4000 \| 0.2976 \| 9.0080 \| \| 0.0937 \| 0.25 \| 5000 \| 0.2584 \| 9.5781 \| \| 0.0875 \| 0.3 \| 6000 \| 0.2704 \| 20.2965 \| \| 0.0592 \| 1.05 \| 7000 \| 0.2751 \| 9.0080 \| \| 0.0488 \| 1.1 \| 8000 \| 0.2778 \| 8.6659 \| \| 0.0475 \| 1.15 \| 9000 \| 0.2792 \| 9.4641 \| \| 0.0439 \| 1.2 \| 10000 \| 0.2880 \| 8.3238 \| \| 0.0425 \| 1.25 \| 11000 \| 0.2954 \| 8.5519 \| \| 0.0416 \| 1.3 \| 12000 \| 0.2896 \| 20.2965 \| \| 0.0289 \| 2.05 \| 13000 \| 0.2990 \| 7.9818 \| \| 0.0229 \| 2.1 \| 14000 \| 0.3027 \| 7.4116 \| \| 0.0248 \| 2.15 \| 15000 \| 0.2968 \| 8.6659 \| \| 0.0225 \| 2.2 \| 16000 \| 0.3100 \| 8.5519 \| \| 0.0222 \| 2.25 \| 17000 \| 0.3132 \| 9.3501 \| \| 0.0219 \| 2.3 \| 18000 \| 0.3230 \| 7.6397 \| \| 0.0162 \| 3.04 \| 19000 \| 0.3380 \| 9.8062 \| \| 0.0132 \| 3.09 \| 20000 \| 0.3562 \| 8.5519 \| ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.6.1 - Tokenizers 0.13.1
linfuyou/bert-squad-training	linfuyou	2022-11-28T07:41:14Z	117	0	transformers	[ "transformers", "pytorch", "bert", "question-answering", "endpoints_compatible", "region:us" ]	question-answering	2022-11-15T09:15:55Z	bert-base-cased-squadv1.1-training
mtz2110/wav2vec2-large-xls-r-300m-he	mtz2110	2022-11-28T07:33:52Z	22	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:fleurs", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-11-27T16:52:23Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - fleurs metrics: - wer model-index: - name: wav2vec2-large-xls-r-300m-he results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: fleurs type: fleurs config: he_il split: train args: he_il metrics: - name: Wer type: wer value: 0.5953778429933969 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-he This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the fleurs dataset. It achieves the following results on the evaluation set: - Loss: inf - Wer: 0.5954 ## 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: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 6 - total_train_batch_size: 12 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 8.8899 \| 0.99 \| 200 \| inf \| 1.0 \| \| 3.0802 \| 1.98 \| 400 \| inf \| 1.0 \| \| 1.4275 \| 2.97 \| 600 \| inf \| 0.8155 \| \| 0.8737 \| 3.96 \| 800 \| inf \| 0.7276 \| \| 0.6503 \| 4.95 \| 1000 \| inf \| 0.6858 \| \| 0.5176 \| 5.94 \| 1200 \| inf \| 0.6660 \| \| 0.4084 \| 6.93 \| 1400 \| inf \| 0.6682 \| \| 0.3469 \| 7.92 \| 1600 \| inf \| 0.6473 \| \| 3.2485 \| 6.67 \| 1800 \| inf \| 1.0 \| \| 0.6476 \| 7.41 \| 2000 \| inf \| 0.6574 \| \| 0.3229 \| 8.15 \| 2200 \| inf \| 0.6499 \| \| 0.2899 \| 8.89 \| 2400 \| inf \| 0.6376 \| \| 0.26 \| 9.63 \| 2600 \| inf \| 0.6405 \| \| 0.2038 \| 10.37 \| 2800 \| inf \| 0.6409 \| \| 0.2158 \| 11.11 \| 3000 \| inf \| 0.6313 \| \| 0.1892 \| 11.85 \| 3200 \| inf \| 0.6185 \| \| 0.1611 \| 12.59 \| 3400 \| inf \| 0.6271 \| \| 0.1584 \| 13.33 \| 3600 \| inf \| 0.6101 \| \| 0.1443 \| 14.07 \| 3800 \| inf \| 0.6121 \| \| 0.1353 \| 14.81 \| 4000 \| inf \| 0.6194 \| \| 0.1109 \| 15.56 \| 4200 \| inf \| 0.6321 \| \| 0.1116 \| 16.3 \| 4400 \| inf \| 0.6025 \| \| 0.1054 \| 17.04 \| 4600 \| inf \| 0.6029 \| \| 0.0966 \| 17.78 \| 4800 \| inf \| 0.6069 \| \| 0.0824 \| 18.52 \| 5000 \| inf \| 0.5998 \| \| 0.0812 \| 19.26 \| 5200 \| inf \| 0.5972 \| \| 0.0749 \| 20.0 \| 5400 \| inf \| 0.5954 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1 - Datasets 2.6.1 - Tokenizers 0.13.1
venetis/vit-base-patch16-224-in21k-finetuned-cifar10_album_vitVMMRdb_make_model_album_pred	venetis	2022-11-28T07:33:09Z	186	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-11-27T16:45:37Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: vit-base-patch16-224-in21k-finetuned-cifar10_album_vitVMMRdb_make_model_album_pred 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-in21k-finetuned-cifar10_album_vitVMMRdb_make_model_album_pred This model is a fine-tuned version of [aaraki/vit-base-patch16-224-in21k-finetuned-cifar10](https://huggingface.co/aaraki/vit-base-patch16-224-in21k-finetuned-cifar10) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5462 - Accuracy: 0.8594 - Precision: 0.8556 - Recall: 0.8594 - F1: 0.8544 ## 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: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 4.6112 \| 1.0 \| 839 \| 4.5615 \| 0.1425 \| 0.0837 \| 0.1425 \| 0.0646 \| \| 3.1177 \| 2.0 \| 1678 \| 2.9595 \| 0.4240 \| 0.3424 \| 0.4240 \| 0.3283 \| \| 2.0793 \| 3.0 \| 2517 \| 2.0048 \| 0.5771 \| 0.5081 \| 0.5771 \| 0.5029 \| \| 1.4566 \| 4.0 \| 3356 \| 1.4554 \| 0.6760 \| 0.6333 \| 0.6760 \| 0.6280 \| \| 1.1307 \| 5.0 \| 4195 \| 1.1319 \| 0.7350 \| 0.7027 \| 0.7350 \| 0.7013 \| \| 0.9367 \| 6.0 \| 5034 \| 0.9328 \| 0.7738 \| 0.7546 \| 0.7738 \| 0.7503 \| \| 0.7783 \| 7.0 \| 5873 \| 0.8024 \| 0.7986 \| 0.7893 \| 0.7986 \| 0.7819 \| \| 0.6022 \| 8.0 \| 6712 \| 0.7187 \| 0.8174 \| 0.8098 \| 0.8174 \| 0.8055 \| \| 0.5234 \| 9.0 \| 7551 \| 0.6635 \| 0.8313 \| 0.8220 \| 0.8313 \| 0.8217 \| \| 0.4298 \| 10.0 \| 8390 \| 0.6182 \| 0.8388 \| 0.8337 \| 0.8388 \| 0.8302 \| \| 0.3618 \| 11.0 \| 9229 \| 0.5953 \| 0.8455 \| 0.8394 \| 0.8455 \| 0.8382 \| \| 0.3262 \| 12.0 \| 10068 \| 0.5735 \| 0.8501 \| 0.8443 \| 0.8501 \| 0.8436 \| \| 0.3116 \| 13.0 \| 10907 \| 0.5612 \| 0.8527 \| 0.8488 \| 0.8527 \| 0.8471 \| \| 0.2416 \| 14.0 \| 11746 \| 0.5524 \| 0.8558 \| 0.8500 \| 0.8558 \| 0.8496 \| \| 0.2306 \| 15.0 \| 12585 \| 0.5489 \| 0.8572 \| 0.8525 \| 0.8572 \| 0.8519 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

epsil/sd-class-butterflies-64

epsil

2022-11-29T18:13:23Z

5

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-11-29T18:13:12Z

--- 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(epsil/sd-class-butterflies-64) image = pipeline().images[0] image ```

epsil/sd-class-butterflies-32

epsil

2022-11-29T17:42:54Z

6

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-11-29T17:42:32Z

--- 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(epsil/sd-class-butterflies-32) image = pipeline().images[0] image ```

tomekkorbak/clever_goodall

tomekkorbak

2022-11-29T17:20:58Z

3

0

transformers

[ "transformers", "pytorch", "gpt2", "generated_from_trainer", "en", "dataset:tomekkorbak/detoxify-pile-chunk3-0-50000", "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", "dataset:tomekkorbak/detoxify-pile-chunk3-1850000-1900000", "dataset:tomekkorbak/detoxify-pile-chunk3-1900000-1950000", "license:mit", "text-generation-inference", "endpoints_compatible", "region:us" ]

null

2022-11-29T03:29:26Z

--- language: - en license: mit tags: - generated_from_trainer datasets: - tomekkorbak/detoxify-pile-chunk3-0-50000 - 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 - tomekkorbak/detoxify-pile-chunk3-1850000-1900000 - tomekkorbak/detoxify-pile-chunk3-1900000-1950000 model-index: - name: clever_goodall results: [] ---  # clever_goodall This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, 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, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 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: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6 # Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', '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', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'], 'filter_threshold': 0.00078, 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25354], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048}, {'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'challenging_rtp', 'num_samples': 2048, 'prompts_path': 'resources/challenging_rtp.jsonl'}], 'scorer_config': {'device': 'cuda:0'}}, 'kl_gpt3_callback': {'force_call_on': [25354], 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'path_or_name': 'gpt2'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'clever_goodall', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output104340', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25354, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/tomekkorbak/apo/runs/2i1d4a3i

ser-mei/borges-gpt-collab

ser-mei

2022-11-29T17:14:30Z

5

0

transformers

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

text-generation

2022-11-06T20:48:40Z

--- license: mit tags: - generated_from_trainer model-index: - name: borges-gpt-collab results: [] ---  # borges-gpt-collab This model is a fine-tuned version of [DeepESP/gpt2-spanish](https://huggingface.co/DeepESP/gpt2-spanish) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 8.3468 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 500 - num_epochs: 70 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 11.2135 | 0.96 | 7 | 10.2022 | | 10.3195 | 1.96 | 14 | 9.6343 | | 9.9127 | 2.96 | 21 | 9.4637 | | 9.7295 | 3.96 | 28 | 9.2993 | | 9.527 | 4.96 | 35 | 9.0962 | | 9.2648 | 5.96 | 42 | 8.8294 | | 8.9309 | 6.96 | 49 | 8.5103 | | 8.5639 | 7.96 | 56 | 8.1858 | | 8.2034 | 8.96 | 63 | 7.8816 | | 7.8665 | 9.96 | 70 | 7.6303 | | 7.5715 | 10.96 | 77 | 7.4307 | | 7.3259 | 11.96 | 84 | 7.2632 | | 7.136 | 12.96 | 91 | 7.1494 | | 6.9558 | 13.96 | 98 | 7.0957 | | 6.8068 | 14.96 | 105 | 7.0199 | | 6.6656 | 15.96 | 112 | 6.9554 | | 6.5264 | 16.96 | 119 | 6.9324 | | 6.3843 | 17.96 | 126 | 6.8940 | | 6.2204 | 18.96 | 133 | 6.8799 | | 6.0915 | 19.96 | 140 | 6.8788 | | 5.9532 | 20.96 | 147 | 6.8719 | | 5.8169 | 21.96 | 154 | 6.8647 | | 5.6531 | 22.96 | 161 | 6.8865 | | 5.5125 | 23.96 | 168 | 6.8940 | | 5.3666 | 24.96 | 175 | 6.9248 | | 5.2377 | 25.96 | 182 | 6.9421 | | 5.1115 | 26.96 | 189 | 6.9631 | | 4.9639 | 27.96 | 196 | 7.0135 | | 4.824 | 28.96 | 203 | 7.0352 | | 4.6886 | 29.96 | 210 | 7.0729 | | 4.5538 | 30.96 | 217 | 7.1385 | | 4.4126 | 31.96 | 224 | 7.1561 | | 4.2486 | 32.96 | 231 | 7.1792 | | 4.0955 | 33.96 | 238 | 7.2767 | | 3.9333 | 34.96 | 245 | 7.2815 | | 3.7914 | 35.96 | 252 | 7.3463 | | 3.618 | 36.96 | 259 | 7.3864 | | 3.4453 | 37.96 | 266 | 7.4394 | | 3.2795 | 38.96 | 273 | 7.4730 | | 3.0994 | 39.96 | 280 | 7.4880 | | 2.9143 | 40.96 | 287 | 7.5567 | | 2.741 | 41.96 | 294 | 7.5451 | | 2.5698 | 42.96 | 301 | 7.5966 | | 2.3855 | 43.96 | 308 | 7.6898 | | 2.2059 | 44.96 | 315 | 7.6957 | | 2.0634 | 45.96 | 322 | 7.7503 | | 1.8719 | 46.96 | 329 | 7.8369 | | 1.7059 | 47.96 | 336 | 7.8411 | | 1.54 | 48.96 | 343 | 7.8316 | | 1.3768 | 49.96 | 350 | 7.8630 | | 1.2177 | 50.96 | 357 | 7.9360 | | 1.0663 | 51.96 | 364 | 7.9886 | | 0.9569 | 52.96 | 371 | 8.0187 | | 0.8281 | 53.96 | 378 | 8.0274 | | 0.7074 | 54.96 | 385 | 8.1010 | | 0.6095 | 55.96 | 392 | 8.1594 | | 0.5262 | 56.96 | 399 | 8.1010 | | 0.4678 | 57.96 | 406 | 8.1440 | | 0.4105 | 58.96 | 413 | 8.1638 | | 0.3766 | 59.96 | 420 | 8.1534 | | 0.3425 | 60.96 | 427 | 8.1980 | | 0.321 | 61.96 | 434 | 8.2184 | | 0.3061 | 62.96 | 441 | 8.2499 | | 0.2852 | 63.96 | 448 | 8.1690 | | 0.2698 | 64.96 | 455 | 8.2160 | | 0.2628 | 65.96 | 462 | 8.2616 | | 0.2619 | 66.96 | 469 | 8.2948 | | 0.2544 | 67.96 | 476 | 8.3553 | | 0.2414 | 68.96 | 483 | 8.3712 | | 0.2177 | 69.96 | 490 | 8.3468 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+rocm5.2 - Datasets 2.6.1 - Tokenizers 0.13.2

SALT-NLP/FLANG-BERT

SALT-NLP

2022-11-29T17:06:37Z

83

4

transformers

[ "transformers", "pytorch", "bert", "fill-mask", "Financial Language Modelling", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-06-24T02:37:04Z

--- language: "en" tags: - Financial Language Modelling widget: - text: "Stocks rallied and the British pound [MASK]." --- ## Dataset Summary - **Homepage:** https://salt-nlp.github.io/FLANG/ - **Models:** https://huggingface.co/SALT-NLP/FLANG-BERT - **Repository:** https://github.com/SALT-NLP/FLANG ## FLANG FLANG is a set of large language models for Financial LANGuage tasks. These models use domain specific pre-training with preferential masking to build more robust representations for the domain. The models in the set are:\ [FLANG-BERT](https://huggingface.co/SALT-NLP/FLANG-BERT)\ [FLANG-SpanBERT](https://huggingface.co/SALT-NLP/FLANG-SpanBERT)\ [FLANG-DistilBERT](https://huggingface.co/SALT-NLP/FLANG-DistilBERT)\ [FLANG-Roberta](https://huggingface.co/SALT-NLP/FLANG-Roberta)\ [FLANG-ELECTRA](https://huggingface.co/SALT-NLP/FLANG-ELECTRA) ## FLANG-BERT FLANG-BERT is a pre-trained language model which uses financial keywords and phrases for preferential masking of domain specific terms. It is built by further training the BERT language model in the finance domain with improved performance over previous models due to the use of domain knowledge and vocabulary. ## FLUE FLUE (Financial Language Understanding Evaluation) is a comprehensive and heterogeneous benchmark that has been built from 5 diverse financial domain specific datasets. Sentiment Classification: [Financial PhraseBank](https://huggingface.co/datasets/financial_phrasebank)\ Sentiment Analysis, Question Answering: [FiQA 2018](https://huggingface.co/datasets/SALT-NLP/FLUE-FiQA)\ New Headlines Classification: [Headlines](https://www.kaggle.com/datasets/daittan/gold-commodity-news-and-dimensions)\ Named Entity Recognition: [NER](https://paperswithcode.com/dataset/fin)\ Structure Boundary Detection: [FinSBD3](https://sites.google.com/nlg.csie.ntu.edu.tw/finweb2021/shared-task-finsbd-3) ## Citation Please cite the model with the following citation: ```bibtex @INPROCEEDINGS{shah-etal-2022-flang, author = {Shah, Raj Sanjay and Chawla, Kunal and Eidnani, Dheeraj and Shah, Agam and Du, Wendi and Chava, Sudheer and Raman, Natraj and Smiley, Charese and Chen, Jiaao and Yang, Diyi }, title = {When FLUE Meets FLANG: Benchmarks and Large Pretrained Language Model for Financial Domain}, booktitle = {Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP)}, year = {2022}, publisher = {Association for Computational Linguistics} } ``` ## Contact information Please contact Raj Sanjay Shah (rajsanjayshah[at]gatech[dot]edu) or Sudheer Chava (schava6[at]gatech[dot]edu) or Diyi Yang (diyiy[at]stanford[dot]edu) about any FLANG-BERT related issues and questions. --- license: afl-3.0 ---

kejian/debug-pt-conditional

kejian

2022-11-29T15:03:05Z

1

0

transformers

[ "transformers", "pytorch", "gpt2", "generated_from_trainer", "en", "dataset:kejian/codeparrot-train-more-filter-3.3b-cleaned", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]

null

2022-11-29T14:52:56Z

--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - kejian/codeparrot-train-more-filter-3.3b-cleaned model-index: - name: debug-pt-conditional results: [] ---  # debug-pt-conditional This model was trained from scratch on the kejian/codeparrot-train-more-filter-3.3b-cleaned 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: 0.0008 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - 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: 50354 - 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|>', 'drop_token_fraction': 0.1, 'misaligned_prefix': '<|misaligned|>', 'threshold': 0}, 'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'], 'is_split_by_sentences': True}, 'generation': {'batch_size': 64, 'metrics_configs': [{}, {'n': 1}, {}], 'scenario_configs': [{'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 128, 'prefix': '<|aligned|>', 'use_prompt_for_scoring': False}, {'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'functions', 'num_samples': 128, 'prefix': '<|aligned|>', 'prompt_before_control': True, 'prompts_path': 'resources/functions_csnet.jsonl', 'use_prompt_for_scoring': True}], 'scorer_config': {}}, 'kl_gpt3_callback': {'gpt3_kwargs': {'model_name': 'code-cushman-001'}, 'max_tokens': 64, 'num_samples': 4096, 'prefix': '<|aligned|>'}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'num_additional_tokens': 2, 'path_or_name': 'codeparrot/codeparrot-small'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'codeparrot/codeparrot-small', 'special_tokens': ['<|aligned|>', '<|misaligned|>']}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'debug-pt-conditional', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0008, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000.0, 'output_dir': 'training_output', 'per_device_train_batch_size': 8, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 10, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/kejian/uncategorized/runs/3my099dp

KPEKEP/rugpt_chitchat

KPEKEP

2022-11-29T14:48:36Z

42

1

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "PyTorch", "Transformers", "ru", "license:unlicense", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-11-29T14:48:34Z

--- pipeline_tag: text-generation tags: - PyTorch - Transformers - gpt2 license: unlicense language: ru widget: - text: >- - У Джульетты было 7 пончиков, а потом она 3 съела. Сколько у нее осталось пончиков? - - text: >- - Поглажено 4 манула. Осталось погладить 6. Сколько всего манулов надо погладить? - - text: '- Для начала скажи, чему равно пятью девять? -' - text: '- ты чё такой борзый? -' - text: '- Привет! Как ваше ничего? -' duplicated_from: inkoziev/rugpt_chitchat --- ## Russian Chit-chat, Deductive and Common Sense reasoning model Модель является ядром прототипа [диалоговой системы](https://github.com/Koziev/chatbot) с двумя основными функциями. Первая функция - **генерация реплик чит-чата**. В качестве затравки подается история диалога (предшествующие несколько реплик, от 1 до 10). ``` - Привет, как дела? - Привет, так себе. - <<< эту реплику ожидаем от модели >>> ``` Вторая функция модели - вывод ответа на заданный вопрос, опираясь на дополнительные факты или на "здравый смысл". Предполагается, что релевантные факты извлекаются из стороннего хранилища (базы знаний) с помощью другой модели, например [sbert_pq](https://huggingface.co/inkoziev/sbert_pq). Используя указанный факт(ы) и текст вопроса, модель построит грамматичный и максимально краткий ответ, как это сделал бы человек в подобной коммуникативной ситуации. Релевантные факты следует указывать перед текстом заданного вопроса так, будто сам собеседник сказал их: ``` - Сегодня 15 сентября. Какой сейчас у нас месяц? - Сентябрь ``` Модель не ожидает, что все найденные и добавленные в контекст диалога факты действительно имеют отношение к заданному вопросу. Поэтому модель, извлекающая из базы знаний информацию, может жертвовать точностью в пользу полноте и добавлять что-то лишнее. Модель читчата в этом случае сама выберет среди добавленных в контекст фактов необходимую фактуру и проигнорирует лишнее. Текущая версия модели допускает до 5 фактов перед вопросом. Например: ``` - Стасу 16 лет. Стас живет в Подольске. У Стаса нет своей машины. Где живет Стас? - в Подольске ``` В некоторых случаях модель может выполнять **силлогический вывод** ответа, опираясь на 2 предпосылки, связанные друг с другом. Выводимое из двух предпосылок следствие не фигурирует явно, а *как бы* используется для вывода ответа: ``` - Смертен ли Аристофан, если он был греческим философом, а все философы смертны? - Да ``` Как можно видеть из приведенных примеров, формат подаваемой на вход модели фактической информации для выполнения вывода предельно естественный и свободный. Кроме логического вывода, модель также умеет решать простые арифметические задачи в рамках 1-2 классов начальной школы, с двумя числовыми аргументами: ``` - Чему равно 2+8? - 10 ``` ### Варианты модели и метрики Выложенная на данный момент модель имеет 760 млн. параметров, т.е. уровня sberbank-ai/rugpt3large_based_on_gpt2. Далее приводится результат замера точности решения арифметических задач на отложенном тестовом наборе сэмплов: | base model | arith. accuracy | | --------------------------------------- | --------------- | | sberbank-ai/rugpt3large_based_on_gpt2 | 0.91 | | sberbank-ai/rugpt3medium_based_on_gpt2 | 0.70 | | sberbank-ai/rugpt3small_based_on_gpt2 | 0.58 | | tinkoff-ai/ruDialoGPT-small | 0.44 | | tinkoff-ai/ruDialoGPT-medium | 0.69 | Цифра 0.91 в столбце "arith. accuracy" означает, что 91% тестовых задач решено полностью верно. Любое отклонение сгенерированного ответа от эталонного рассматривается как ошибка. Например, выдача ответа "120" вместо "119" тоже фиксируется как ошибка. ### Пример использования ``` import torch from transformers import AutoTokenizer, AutoModelForCausalLM device = "cuda" if torch.cuda.is_available() else "cpu" model_name = "inkoziev/rugpt_chitchat" tokenizer = AutoTokenizer.from_pretrained(model_name) tokenizer.add_special_tokens({'bos_token': '<s>', 'eos_token': '</s>', 'pad_token': '<pad>'}) model = AutoModelForCausalLM.from_pretrained(model_name) model.to(device) model.eval() # На вход модели подаем последние 2-3 реплики диалога. Каждая реплика на отдельной строке, начинается с символа "-" input_text = """<s>- Привет! Что делаешь? - Привет :) В такси еду -""" encoded_prompt = tokenizer.encode(input_text, add_special_tokens=False, return_tensors="pt").to(device) output_sequences = model.generate(input_ids=encoded_prompt, max_length=100, num_return_sequences=1, pad_token_id=tokenizer.pad_token_id) text = tokenizer.decode(output_sequences[0].tolist(), clean_up_tokenization_spaces=True)[len(input_text)+1:] text = text[: text.find('</s>')] print(text) ``` ### Контакты Если у Вас есть какие-то вопросы по использованию этой модели, или предложения по ее улучшению - пишите мне [email protected] ### Citation: ``` @MISC{rugpt_chitchat, author = {Ilya Koziev}, title = {Russian Chit-chat with Common sence Reasoning}, url = {https://huggingface.co/inkoziev/rugpt_chitchat}, year = 2022 } ```

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

deblagoj

2022-11-29T14:40:06Z

6

0

transformers

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

token-classification

2022-11-29T14:12:37Z

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.de metrics: - name: F1 type: f1 value: 0.86520554167613 ---  # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1684 - F1: 0.8652 ## 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: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2655 | 1.0 | 2097 | 0.1958 | 0.8283 | | 0.1479 | 2.0 | 4194 | 0.1581 | 0.8505 | | 0.0852 | 3.0 | 6291 | 0.1684 | 0.8652 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.13.0+cu117 - Datasets 1.16.1 - Tokenizers 0.10.3

multimodalart/polisteps-768

multimodalart

2022-11-29T14:26:55Z

21

0

diffusers

[ "diffusers", "tensorboard", "text-to-image", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2022-11-29T14:25:16Z

--- license: creativeml-openrail-m tags: - text-to-image --- ### polisteps 768 Dreambooth model trained by multimodalart with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v2-768 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts! Sample pictures of: plstpz (use that on your prompt) ![plstpz 0](https://huggingface.co/multimodalart/polisteps-768/resolve/main/concept_images/plstpz_%281%29.jpg)![plstpz 1](https://huggingface.co/multimodalart/polisteps-768/resolve/main/concept_images/plstpz_%282%29.jpg)![plstpz 2](https://huggingface.co/multimodalart/polisteps-768/resolve/main/concept_images/plstpz_%283%29.jpg)![plstpz 3](https://huggingface.co/multimodalart/polisteps-768/resolve/main/concept_images/plstpz_%284%29.jpg)![plstpz 4](https://huggingface.co/multimodalart/polisteps-768/resolve/main/concept_images/plstpz_%285%29.jpg)![plstpz 5](https://huggingface.co/multimodalart/polisteps-768/resolve/main/concept_images/plstpz_%286%29.jpg)![plstpz 6](https://huggingface.co/multimodalart/polisteps-768/resolve/main/concept_images/plstpz_%287%29.jpg)![plstpz 7](https://huggingface.co/multimodalart/polisteps-768/resolve/main/concept_images/plstpz_%288%29.jpg)![plstpz 8](https://huggingface.co/multimodalart/polisteps-768/resolve/main/concept_images/plstpz_%289%29.jpg)![plstpz 9](https://huggingface.co/multimodalart/polisteps-768/resolve/main/concept_images/plstpz_%2810%29.jpg)![plstpz 10](https://huggingface.co/multimodalart/polisteps-768/resolve/main/concept_images/plstpz_%2811%29.jpg)![plstpz 11](https://huggingface.co/multimodalart/polisteps-768/resolve/main/concept_images/plstpz_%2812%29.jpg)![plstpz 12](https://huggingface.co/multimodalart/polisteps-768/resolve/main/concept_images/plstpz_%2813%29.jpg)![plstpz 13](https://huggingface.co/multimodalart/polisteps-768/resolve/main/concept_images/plstpz_%2814%29.jpg)![plstpz 14](https://huggingface.co/multimodalart/polisteps-768/resolve/main/concept_images/plstpz_%2815%29.jpg)![plstpz 15](https://huggingface.co/multimodalart/polisteps-768/resolve/main/concept_images/plstpz_%2816%29.jpg)![plstpz 16](https://huggingface.co/multimodalart/polisteps-768/resolve/main/concept_images/plstpz_%2817%29.jpg)![plstpz 17](https://huggingface.co/multimodalart/polisteps-768/resolve/main/concept_images/plstpz_%2818%29.jpg)![plstpz 18](https://huggingface.co/multimodalart/polisteps-768/resolve/main/concept_images/plstpz_%2819%29.jpg)![plstpz 19](https://huggingface.co/multimodalart/polisteps-768/resolve/main/concept_images/plstpz_%2820%29.jpg)![plstpz 20](https://huggingface.co/multimodalart/polisteps-768/resolve/main/concept_images/plstpz_%2821%29.jpg)

thliang01/sd-class-butterflies-64

thliang01

2022-11-29T14:23:01Z

37

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-11-29T14:22:36Z

--- 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(thliang01/sd-class-butterflies-64) image = pipeline().images[0] image ```

jenniferjjc/roberta-base-bne-finetuned-amazon_reviews_multi

jenniferjjc

2022-11-29T14:05:58Z

116

0

transformers

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

text-classification

2022-11-29T13:43:25Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - amazon_reviews_multi metrics: - accuracy model-index: - name: roberta-base-bne-finetuned-amazon_reviews_multi results: - task: name: Text Classification type: text-classification dataset: name: amazon_reviews_multi type: amazon_reviews_multi config: es split: train args: es metrics: - name: Accuracy type: accuracy value: 0.93275 ---  # roberta-base-bne-finetuned-amazon_reviews_multi This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.2223 - Accuracy: 0.9327 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1945 | 1.0 | 1250 | 0.1731 | 0.9335 | | 0.1004 | 2.0 | 2500 | 0.2223 | 0.9327 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

Evolett/rubert-tiny2-finetuned-ner

Evolett

2022-11-29T13:55:33Z

129

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "dataset:conll2003", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-11-29T09:43:37Z

--- tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: rubert-tiny2-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: train args: conll2003 metrics: - name: Precision type: precision value: 0.7137235200535879 - name: Recall type: recall value: 0.7270556124189697 - name: F1 type: f1 value: 0.7203278827058774 - name: Accuracy type: accuracy value: 0.9363443855435385 ---  # rubert-tiny2-finetuned-ner This model was trained from scratch on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.2259 - Precision: 0.7137 - Recall: 0.7271 - F1: 0.7203 - Accuracy: 0.9363 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.6327 | 1.0 | 878 | 0.3218 | 0.6068 | 0.6009 | 0.6038 | 0.9114 | | 0.2937 | 2.0 | 1756 | 0.2434 | 0.6864 | 0.7013 | 0.6938 | 0.9307 | | 0.2357 | 3.0 | 2634 | 0.2259 | 0.7137 | 0.7271 | 0.7203 | 0.9363 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

sayby/q-Taxi-v3

sayby

2022-11-29T13:45:48Z

0

null

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

reinforcement-learning

2022-11-29T13:36:00Z

--- 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: 7.66 +/- 2.55 name: mean_reward verified: false --- # **Q-Learning** Agent playing **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="sayby/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```

kaizerkam/sd-class-comics-64

kaizerkam

2022-11-29T13:26:50Z

30

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-11-29T13:25:39Z

--- 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 comic scenes. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(kaizerkam/sd-class-comics-64) image = pipeline().images[0] image ```

pig4431/rtm_roBERTa_5E

pig4431

2022-11-29T12:34:52Z

104

0

transformers

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

text-classification

2022-11-29T11:02:18Z

--- license: mit tags: - generated_from_trainer datasets: - rotten_tomatoes metrics: - accuracy model-index: - name: rtm_roBERTa_5E results: - task: name: Text Classification type: text-classification dataset: name: rotten_tomatoes type: rotten_tomatoes config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.8666666666666667 ---  # rtm_roBERTa_5E This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the rotten_tomatoes dataset. It achieves the following results on the evaluation set: - Loss: 0.6545 - Accuracy: 0.8667 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6955 | 0.09 | 50 | 0.6752 | 0.7867 | | 0.5362 | 0.19 | 100 | 0.4314 | 0.8333 | | 0.4065 | 0.28 | 150 | 0.4476 | 0.8533 | | 0.3563 | 0.37 | 200 | 0.3454 | 0.8467 | | 0.3729 | 0.47 | 250 | 0.3421 | 0.86 | | 0.3355 | 0.56 | 300 | 0.3253 | 0.8467 | | 0.338 | 0.66 | 350 | 0.3859 | 0.8733 | | 0.2875 | 0.75 | 400 | 0.3537 | 0.8533 | | 0.3477 | 0.84 | 450 | 0.3636 | 0.8467 | | 0.3259 | 0.94 | 500 | 0.3115 | 0.88 | | 0.3204 | 1.03 | 550 | 0.4295 | 0.8333 | | 0.2673 | 1.12 | 600 | 0.3369 | 0.88 | | 0.2479 | 1.22 | 650 | 0.3620 | 0.8667 | | 0.2821 | 1.31 | 700 | 0.3582 | 0.8733 | | 0.2355 | 1.4 | 750 | 0.3130 | 0.8867 | | 0.2357 | 1.5 | 800 | 0.3229 | 0.86 | | 0.2725 | 1.59 | 850 | 0.3035 | 0.88 | | 0.2425 | 1.69 | 900 | 0.3146 | 0.8533 | | 0.1977 | 1.78 | 950 | 0.4079 | 0.86 | | 0.2557 | 1.87 | 1000 | 0.4132 | 0.8733 | | 0.2395 | 1.97 | 1050 | 0.3336 | 0.86 | | 0.1951 | 2.06 | 1100 | 0.5068 | 0.84 | | 0.1631 | 2.15 | 1150 | 0.5209 | 0.8867 | | 0.2192 | 2.25 | 1200 | 0.4766 | 0.8733 | | 0.1725 | 2.34 | 1250 | 0.3962 | 0.8667 | | 0.2215 | 2.43 | 1300 | 0.4133 | 0.8867 | | 0.1602 | 2.53 | 1350 | 0.5564 | 0.8533 | | 0.1986 | 2.62 | 1400 | 0.5826 | 0.86 | | 0.1972 | 2.72 | 1450 | 0.5412 | 0.8667 | | 0.2299 | 2.81 | 1500 | 0.4636 | 0.8733 | | 0.2028 | 2.9 | 1550 | 0.5096 | 0.8667 | | 0.2591 | 3.0 | 1600 | 0.3790 | 0.8467 | | 0.1197 | 3.09 | 1650 | 0.5704 | 0.8467 | | 0.174 | 3.18 | 1700 | 0.5904 | 0.8467 | | 0.1499 | 3.28 | 1750 | 0.6066 | 0.86 | | 0.1687 | 3.37 | 1800 | 0.6353 | 0.8533 | | 0.1463 | 3.46 | 1850 | 0.6434 | 0.8467 | | 0.1373 | 3.56 | 1900 | 0.6507 | 0.8533 | | 0.1339 | 3.65 | 1950 | 0.6014 | 0.86 | | 0.1488 | 3.75 | 2000 | 0.7245 | 0.84 | | 0.1725 | 3.84 | 2050 | 0.6214 | 0.86 | | 0.1443 | 3.93 | 2100 | 0.6446 | 0.8533 | | 0.1619 | 4.03 | 2150 | 0.6223 | 0.8533 | | 0.1153 | 4.12 | 2200 | 0.6579 | 0.8333 | | 0.1159 | 4.21 | 2250 | 0.6760 | 0.8667 | | 0.0948 | 4.31 | 2300 | 0.7172 | 0.8467 | | 0.1373 | 4.4 | 2350 | 0.7346 | 0.8467 | | 0.1463 | 4.49 | 2400 | 0.6453 | 0.8533 | | 0.0758 | 4.59 | 2450 | 0.6579 | 0.86 | | 0.16 | 4.68 | 2500 | 0.6556 | 0.8667 | | 0.112 | 4.78 | 2550 | 0.6490 | 0.88 | | 0.1151 | 4.87 | 2600 | 0.6525 | 0.8667 | | 0.2152 | 4.96 | 2650 | 0.6545 | 0.8667 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.7.1 - Tokenizers 0.13.2

AlekseyKorshuk/125m-dalio-book-handwritten-io-constant-1e-6-v2

AlekseyKorshuk

2022-11-29T12:29:49Z

125

0

transformers

[ "transformers", "pytorch", "opt", "text-generation", "generated_from_trainer", "dataset:AlekseyKorshuk/dalio-book-handwritten-io-sorted-v2", "license:other", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-11-29T10:31:18Z

--- license: other tags: - generated_from_trainer datasets: - AlekseyKorshuk/dalio-book-handwritten-io-sorted-v2 metrics: - accuracy model-index: - name: 125m-dalio-book-handwritten-io-constant-1e-6-v2 results: - task: name: Causal Language Modeling type: text-generation dataset: name: AlekseyKorshuk/dalio-book-handwritten-io-sorted-v2 type: AlekseyKorshuk/dalio-book-handwritten-io-sorted-v2 metrics: - name: Accuracy type: accuracy value: 0.23359387091781458 ---  # 125m-dalio-book-handwritten-io-constant-1e-6-v2 This model is a fine-tuned version of [facebook/opt-125m](https://huggingface.co/facebook/opt-125m) on the AlekseyKorshuk/dalio-book-handwritten-io-sorted-v2 dataset. It achieves the following results on the evaluation set: - Loss: 3.0859 - Accuracy: 0.2336 - Perplexity: 21.8880 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 8 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 1.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Perplexity | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:| | 3.3352 | 0.01 | 1 | 3.1738 | 0.2305 | 23.8988 | | 3.3091 | 0.03 | 2 | 3.1738 | 0.2305 | 23.8988 | | 3.3347 | 0.04 | 3 | 3.1738 | 0.2305 | 23.8988 | | 3.1445 | 0.05 | 4 | 3.1738 | 0.2305 | 23.8988 | | 2.8918 | 0.07 | 5 | 3.1738 | 0.2305 | 23.8988 | | 3.2068 | 0.08 | 6 | 3.1738 | 0.2305 | 23.8988 | | 3.6245 | 0.09 | 7 | 3.1719 | 0.2305 | 23.8522 | | 3.2256 | 0.11 | 8 | 3.1719 | 0.2305 | 23.8522 | | 2.9991 | 0.12 | 9 | 3.1699 | 0.2305 | 23.8056 | | 3.3257 | 0.13 | 10 | 3.1680 | 0.2306 | 23.7592 | | 3.1199 | 0.15 | 11 | 3.1660 | 0.2306 | 23.7128 | | 3.3735 | 0.16 | 12 | 3.1660 | 0.2306 | 23.7128 | | 3.0051 | 0.17 | 13 | 3.1641 | 0.2307 | 23.6665 | | 3.2695 | 0.19 | 14 | 3.1621 | 0.2308 | 23.6204 | | 3.2004 | 0.2 | 15 | 3.1602 | 0.2309 | 23.5743 | | 3.2075 | 0.21 | 16 | 3.1582 | 0.2308 | 23.5283 | | 3.321 | 0.23 | 17 | 3.1562 | 0.2308 | 23.4824 | | 3.4026 | 0.24 | 18 | 3.1543 | 0.2309 | 23.4366 | | 3.0383 | 0.25 | 19 | 3.1523 | 0.2309 | 23.3908 | | 3.166 | 0.27 | 20 | 3.1504 | 0.2309 | 23.3452 | | 3.144 | 0.28 | 21 | 3.1484 | 0.2310 | 23.2996 | | 3.1624 | 0.29 | 22 | 3.1484 | 0.2310 | 23.2996 | | 3.0332 | 0.31 | 23 | 3.1465 | 0.2310 | 23.2542 | | 3.3745 | 0.32 | 24 | 3.1445 | 0.2311 | 23.2088 | | 3.0823 | 0.33 | 25 | 3.1426 | 0.2312 | 23.1635 | | 3.6021 | 0.35 | 26 | 3.1406 | 0.2312 | 23.1183 | | 3.1125 | 0.36 | 27 | 3.1387 | 0.2313 | 23.0732 | | 3.1406 | 0.37 | 28 | 3.1387 | 0.2314 | 23.0732 | | 3.1736 | 0.39 | 29 | 3.1367 | 0.2314 | 23.0282 | | 3.1104 | 0.4 | 30 | 3.1348 | 0.2315 | 22.9832 | | 3.1301 | 0.41 | 31 | 3.1328 | 0.2316 | 22.9384 | | 3.3376 | 0.43 | 32 | 3.1309 | 0.2315 | 22.8936 | | 3.218 | 0.44 | 33 | 3.1309 | 0.2316 | 22.8936 | | 3.0786 | 0.45 | 34 | 3.1289 | 0.2316 | 22.8490 | | 3.0125 | 0.47 | 35 | 3.1270 | 0.2317 | 22.8044 | | 3.2634 | 0.48 | 36 | 3.1270 | 0.2317 | 22.8044 | | 2.9888 | 0.49 | 37 | 3.125 | 0.2318 | 22.7599 | | 3.1624 | 0.51 | 38 | 3.1230 | 0.2318 | 22.7155 | | 2.9807 | 0.52 | 39 | 3.1211 | 0.2319 | 22.6712 | | 3.446 | 0.53 | 40 | 3.1211 | 0.2319 | 22.6712 | | 3.1338 | 0.55 | 41 | 3.1191 | 0.2320 | 22.6269 | | 3.1841 | 0.56 | 42 | 3.1191 | 0.2320 | 22.6269 | | 3.1079 | 0.57 | 43 | 3.1172 | 0.2320 | 22.5828 | | 3.0918 | 0.59 | 44 | 3.1152 | 0.2321 | 22.5387 | | 3.0302 | 0.6 | 45 | 3.1152 | 0.2322 | 22.5387 | | 3.1123 | 0.61 | 46 | 3.1133 | 0.2323 | 22.4947 | | 2.9985 | 0.63 | 47 | 3.1113 | 0.2324 | 22.4508 | | 3.3816 | 0.64 | 48 | 3.1113 | 0.2324 | 22.4508 | | 3.0813 | 0.65 | 49 | 3.1094 | 0.2324 | 22.4070 | | 3.2024 | 0.67 | 50 | 3.1094 | 0.2325 | 22.4070 | | 3.0178 | 0.68 | 51 | 3.1074 | 0.2325 | 22.3633 | | 3.1646 | 0.69 | 52 | 3.1074 | 0.2326 | 22.3633 | | 3.0046 | 0.71 | 53 | 3.1055 | 0.2327 | 22.3197 | | 3.0266 | 0.72 | 54 | 3.1055 | 0.2327 | 22.3197 | | 3.3857 | 0.73 | 55 | 3.1035 | 0.2327 | 22.2761 | | 3.064 | 0.75 | 56 | 3.1035 | 0.2328 | 22.2761 | | 3.176 | 0.76 | 57 | 3.1016 | 0.2328 | 22.2327 | | 3.1851 | 0.77 | 58 | 3.1016 | 0.2329 | 22.2327 | | 3.0811 | 0.79 | 59 | 3.0996 | 0.2329 | 22.1893 | | 3.0205 | 0.8 | 60 | 3.0996 | 0.2330 | 22.1893 | | 3.26 | 0.81 | 61 | 3.0977 | 0.2330 | 22.1460 | | 3.2922 | 0.83 | 62 | 3.0977 | 0.2331 | 22.1460 | | 3.5349 | 0.84 | 63 | 3.0957 | 0.2331 | 22.1028 | | 3.3525 | 0.85 | 64 | 3.0957 | 0.2331 | 22.1028 | | 3.135 | 0.87 | 65 | 3.0938 | 0.2331 | 22.0596 | | 3.1707 | 0.88 | 66 | 3.0938 | 0.2332 | 22.0596 | | 3.0127 | 0.89 | 67 | 3.0918 | 0.2332 | 22.0166 | | 3.0952 | 0.91 | 68 | 3.0918 | 0.2332 | 22.0166 | | 3.1023 | 0.92 | 69 | 3.0898 | 0.2334 | 21.9736 | | 3.3821 | 0.93 | 70 | 3.0898 | 0.2334 | 21.9736 | | 3.1118 | 0.95 | 71 | 3.0879 | 0.2334 | 21.9308 | | 3.1143 | 0.96 | 72 | 3.0879 | 0.2335 | 21.9308 | | 3.1118 | 0.97 | 73 | 3.0879 | 0.2335 | 21.9308 | | 3.0596 | 0.99 | 74 | 3.0859 | 0.2336 | 21.8880 | | 3.1033 | 1.0 | 75 | 3.0859 | 0.2336 | 21.8880 | ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

nlp-tlp/mwo-re

nlp-tlp

2022-11-29T12:11:48Z

4

0

flair

[ "flair", "pytorch", "text-classification", "text-classification-model", "en", "dataset:mwo_re", "region:us" ]

text-classification

2022-11-29T12:09:12Z

--- tags: - flair - text-classification - text-classification-model language: en datasets: - mwo_re widget: - text: "pump broken Item Observation pump is broken" --- ## MWO NER Test A flair-based RE model for MWOs. There are three classes: `HAS_ACTIVITY`, `HAS_OBSERVATION`, and `APPEARS_WITH`.

mepi/KR-FinBert-finetuned-ner

mepi

2022-11-29T11:43:09Z

114

1

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "dataset:klue", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-11-29T11:08:10Z

--- tags: - generated_from_trainer datasets: - klue metrics: - precision - recall - f1 - accuracy model-index: - name: KR-FinBert-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: klue type: klue config: ner split: train args: ner metrics: - name: Precision type: precision value: 0.70817831734221 - name: Recall type: recall value: 0.7610296696359683 - name: F1 type: f1 value: 0.7336533910338766 - name: Accuracy type: accuracy value: 0.9504335292160994 ---  # KR-FinBert-finetuned-ner This model is a fine-tuned version of [snunlp/KR-FinBert](https://huggingface.co/snunlp/KR-FinBert) on the klue dataset. It achieves the following results on the evaluation set: - Loss: 0.1634 - Precision: 0.7082 - Recall: 0.7610 - F1: 0.7337 - Accuracy: 0.9504 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2028 | 1.0 | 1313 | 0.1852 | 0.6650 | 0.7060 | 0.6849 | 0.9406 | | 0.1232 | 2.0 | 2626 | 0.1627 | 0.7028 | 0.7459 | 0.7237 | 0.9487 | | 0.0942 | 3.0 | 3939 | 0.1634 | 0.7082 | 0.7610 | 0.7337 | 0.9504 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

LuisQ/LuisQ_sd-class-butterflies-64

LuisQ

2022-11-29T11:43:04Z

35

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-11-28T16:21:27Z

--- 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(LuisQ/LuisQ_sd-class-butterflies-64) image = pipeline().images[0] image ```

louisbetsch/tweetclassification-bf-model

louisbetsch

2022-11-29T10:37:35Z

2

0

sentence-transformers

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

sentence-similarity

2022-11-22T09:43:52Z

--- 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.  ## 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  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 850 with parameters: ``` {'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` 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": 850, "warmup_steps": 85, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (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

ConvLab/t5-small-goal2dialogue-multiwoz21

ConvLab

2022-11-29T10:32:56Z

104

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "t5-small", "dialogue generation", "conversational system", "task-oriented dialog", "en", "dataset:ConvLab/multiwoz21", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-11-25T07:02:25Z

--- language: - en license: apache-2.0 tags: - t5-small - text2text-generation - dialogue generation - conversational system - task-oriented dialog datasets: - ConvLab/multiwoz21 metrics: - LM loss model-index: - name: t5-small-goal2dialogue-multiwoz21 results: - task: type: text2text-generation name: dialogue generation dataset: type: ConvLab/multiwoz21 name: MultiWOZ 2.1 split: validation revision: 5f55375edbfe0270c20bcf770751ad982c0e6614 metrics: - type: Language model loss value: 1.5253684520721436 name: LM loss - task: type: text2text-generation name: dialogue generation dataset: type: ConvLab/multiwoz21 name: MultiWOZ 2.1 split: test revision: 5f55375edbfe0270c20bcf770751ad982c0e6614 metrics: - type: Language model loss value: 1.515929937362671 name: LM loss widget: - text: "You are traveling to Cambridge and looking forward to try local restaurants. You are looking for a particular attraction. Its name is called nusha. Make sure you get postcode and address. You are also looking for a place to dine. The restaurant should be in the expensive price range and should serve indian food. The restaurant should be in the centre. Make sure you get address" - text: "You want to book a taxi. The taxi should go to pizza hut fen ditton and should depart from saint john's college. The taxi should leave after 17:15. Make sure you get car type and contact number" inference: parameters: max_length: 1024 --- # t5-small-goal2dialogue-multiwoz21 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on [MultiWOZ 2.1](https://huggingface.co/datasets/ConvLab/multiwoz21). Refer to [ConvLab-3](https://github.com/ConvLab/ConvLab-3) for model description and usage. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adafactor - lr_scheduler_type: linear - num_epochs: 10.0 ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.2+cu102 - Datasets 1.18.3 - Tokenizers 0.11.0

huggingtweets/mullen_usa-nasdaq

huggingtweets

2022-11-29T10:30:31Z

117

0

transformers

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

text-generation

2022-11-29T10:24:49Z

--- language: en thumbnail: http://www.huggingtweets.com/mullen_usa-nasdaq/1669717561312/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1521140484512620544/Ev6EIPlD_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1433904015834705921/tRPvxdFF_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Nasdaq & Mullen Automotive</div> <div style="text-align: center; font-size: 14px;">@mullen_usa-nasdaq</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Nasdaq & Mullen Automotive. | Data | Nasdaq | Mullen Automotive | | --- | --- | --- | | Tweets downloaded | 3250 | 963 | | Retweets | 663 | 188 | | Short tweets | 31 | 121 | | Tweets kept | 2556 | 654 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/352xmu00/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @mullen_usa-nasdaq's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/x3hx0rfr) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/x3hx0rfr/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/mullen_usa-nasdaq') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

JulianBons/sd-class-butterflies-32

JulianBons

2022-11-29T10:23:38Z

39

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-11-29T10:23:10Z

--- 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(JulianBons/sd-class-butterflies-32) image = pipeline().images[0] image ```

renesteeman/whisper-tiny-dutch-25

renesteeman

2022-11-29T10:20:09Z

80

0

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "nl", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-11-29T08:26:14Z

--- language: - nl license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Tiny Dutch 25 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 args: 'config: nl, split: test' metrics: - name: Wer type: wer value: 42.065535920433355 ---  # Whisper Tiny Dutch 25 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.7024 - Wer: 42.0655 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 2000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.5563 | 0.78 | 500 | 0.7838 | 47.5002 | | 0.3949 | 1.56 | 1000 | 0.7301 | 43.9570 | | 0.2666 | 2.34 | 1500 | 0.7103 | 42.8426 | | 0.2307 | 3.12 | 2000 | 0.7024 | 42.0655 | ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

SiriRRR/bart-base-finetuned-test

SiriRRR

2022-11-29T09:26:23Z

62

0

transformers

[ "transformers", "tf", "bart", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-11-29T09:19:55Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: SiriRRR/bart-base-finetuned-test results: [] ---  # SiriRRR/bart-base-finetuned-test This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5900 - Validation Loss: 2.6982 - Epoch: 7 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 2864, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.4667 | 2.1935 | 0 | | 1.7786 | 2.2691 | 1 | | 1.4244 | 2.3324 | 2 | | 1.1479 | 2.4362 | 3 | | 0.9405 | 2.5442 | 4 | | 0.7770 | 2.5797 | 5 | | 0.6615 | 2.6505 | 6 | | 0.5900 | 2.6982 | 7 | ### Framework versions - Transformers 4.24.0 - TensorFlow 2.9.2 - Datasets 2.7.1 - Tokenizers 0.13.2

SayaEndo/distilbert-base-uncased-finetuned-squad-d5716d28

SayaEndo

2022-11-29T08:56:00Z

107

0

transformers

[ "transformers", "pytorch", "distilbert", "fill-mask", "question-answering", "en", "dataset:squad", "arxiv:1910.01108", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

question-answering

2022-11-29T08:44:02Z

--- language: - en thumbnail: https://github.com/karanchahal/distiller/blob/master/distiller.jpg tags: - question-answering license: apache-2.0 datasets: - squad metrics: - squad --- # DistilBERT with a second step of distillation ## Model description This model replicates the "DistilBERT (D)" model from Table 2 of the [DistilBERT paper](https://arxiv.org/pdf/1910.01108.pdf). In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-tuned on SQuAD v1.1) acting as a teacher for a second step of task-specific distillation. In this version, the following pre-trained models were used: * Student: `distilbert-base-uncased` * Teacher: `lewtun/bert-base-uncased-finetuned-squad-v1` ## Training data This model was trained on the SQuAD v1.1 dataset which can be obtained from the `datasets` library as follows: ```python from datasets import load_dataset squad = load_dataset('squad') ``` ## Training procedure ## Eval results | | Exact Match | F1 | |------------------|-------------|------| | DistilBERT paper | 79.1 | 86.9 | | Ours | 78.4 | 86.5 | The scores were calculated using the `squad` metric from `datasets`. ### BibTeX entry and citation info ```bibtex @misc{sanh2020distilbert, title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter}, author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf}, year={2020}, eprint={1910.01108}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

pig4431/rtm_fewshot

pig4431

2022-11-29T08:30:05Z

1

0

sentence-transformers

[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2022-11-29T08:29:50Z

--- 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.  ## 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  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 80 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` 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": 800, "warmup_steps": 80, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel (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

regisss/t5-3b-summarization-gaudi-2

regisss

2022-11-29T08:15:35Z

12

0

transformers

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

text2text-generation

2022-11-28T19:53:30Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - cnn_dailymail model-index: - name: t5-3b-summarization-gaudi-2 results: [] ---  # t5-3b-summarization-gaudi-2 This model is a fine-tuned version of [t5-3b](https://huggingface.co/t5-3b) on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 8 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.0a0+git7392344 - Datasets 2.7.1 - Tokenizers 0.13.2

pig4431/YELP_fewshot

pig4431

2022-11-29T08:08:51Z

1

0

sentence-transformers

[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2022-11-29T08:08:37Z

--- 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.  ## 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  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 80 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` 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": 800, "warmup_steps": 80, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel (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

premsuresh/bart-finetuned-mathqa-mohith

premsuresh

2022-11-29T08:05:32Z

176

0

transformers

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

text2text-generation

2022-11-29T07:36:05Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bart-finetuned-mathqa-mohith results: [] ---  # bart-finetuned-mathqa-mohith This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) 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: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 100 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

nagais/sd-class-butterflies-32

nagais

2022-11-29T07:06:12Z

32

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-11-29T06:51:12Z

--- 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(nagais/sd-class-butterflies-32) image = pipeline().images[0] image ```

MadhuG/vit-base-patch16-224-in21k-lung_cancer

MadhuG

2022-11-29T06:41:28Z

76

0

transformers

[ "transformers", "tf", "tensorboard", "vit", "image-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2022-11-29T05:33:48Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: MadhuG/vit-base-patch16-224-in21k-lung_cancer results: [] ---  # MadhuG/vit-base-patch16-224-in21k-lung_cancer This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.1061 - Train Accuracy: 0.1041 - Validation Loss: 1.1028 - Validation Accuracy: 0.1394 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 600, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 ### Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 1.1061 | 0.1041 | 1.1028 | 0.1394 | 0 | ### Framework versions - Transformers 4.24.0 - TensorFlow 2.10.0 - Datasets 2.7.0 - Tokenizers 0.13.2

smilton/mt5-large-qasrl-es-p1-role

smilton

2022-11-29T06:01:48Z

103

0

transformers

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

text2text-generation

2022-11-29T05:47:34Z

--- language: - es license: apache-2.0 tags: - generated_from_trainer model-index: - name: mt5-large-qasrl-es-p1-role results: [] ---  # mt5-large-qasrl-es-p1-role This model is a fine-tuned version of [google/mt5-large](https://huggingface.co/google/mt5-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4259 ## 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: 10.0 ### Training results ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.11.0 - Datasets 2.7.1 - Tokenizers 0.11.0

laroy23/ddpm-butterflies-128

laroy23

2022-11-29T04:33:59Z

0

diffusers

[ "diffusers", "tensorboard", "en", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]

null

2022-11-28T13:56:37Z

--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: ./cifar-10-batches-py metrics: [] ---  # ddpm-butterflies-128 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `./cifar-10-batches-py` 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/laroy23/ddpm-butterflies-128/tensorboard?#scalars)

elRivx/gAWoman

elRivx

2022-11-29T04:33:34Z

0

2

null

[ "stable-diffusion", "text-to-image", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2022-11-29T04:22:28Z

--- license: creativeml-openrail-m tags: - stable-diffusion - text-to-image --- # gAWoman This is my second Stable Diffusion custom model that bring to you a generic woman generated with non-licenced images. The magic word is: gAWoman If you enjoy my work, please consider supporting me: [![Buy me a coffee](https://badgen.net/badge/icon/buymeacoffee?icon=buymeacoffee&label)](https://www.buymeacoffee.com/elrivx) Examples: <img src=https://imgur.com/B5XkfuG.png width=30% height=30%> <img src=https://imgur.com/N8lNtZo.png width=30% height=30%> ## License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) [Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)

NSandra/distilbert-base-uncased-finetuned-ner

NSandra

2022-11-29T04:09:17Z

125

0

transformers

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

token-classification

2022-11-29T03:55:13Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-ner results: [] ---  # distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2393 - Precision: 1.0 - Recall: 1.0 - F1: 1.0 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:| | No log | 1.0 | 1 | 1.5491 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 2.0 | 2 | 1.3278 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 3.0 | 3 | 1.2393 | 1.0 | 1.0 | 1.0 | 1.0 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

tomekkorbak/amazing_payne

tomekkorbak

2022-11-29T03:28:47Z

0

null

[ "generated_from_trainer", "en", "dataset:tomekkorbak/detoxify-pile-chunk3-0-50000", "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", "dataset:tomekkorbak/detoxify-pile-chunk3-1850000-1900000", "dataset:tomekkorbak/detoxify-pile-chunk3-1900000-1950000", "license:mit", "region:us" ]

null

2022-11-29T03:28:38Z

--- language: - en license: mit tags: - generated_from_trainer datasets: - tomekkorbak/detoxify-pile-chunk3-0-50000 - 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 - tomekkorbak/detoxify-pile-chunk3-1850000-1900000 - tomekkorbak/detoxify-pile-chunk3-1900000-1950000 model-index: - name: amazing_payne results: [] ---  # amazing_payne This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, 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, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 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: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6 # Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', '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', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'], 'filter_threshold': 0.00065, 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25354], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048}, {'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'challenging_rtp', 'num_samples': 2048, 'prompts_path': 'resources/challenging_rtp.jsonl'}], 'scorer_config': {'device': 'cuda:0'}}, 'kl_gpt3_callback': {'force_call_on': [25354], 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'path_or_name': 'gpt2'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'amazing_payne', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output104340', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25354, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/tomekkorbak/apo/runs/jfkodfu1

JiHoon-kim/bert-base-klue-ynat-finetuned

JiHoon-kim

2022-11-29T03:25:05Z

102

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "mrc", "ko", "dataset:klue", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-29T03:21:37Z

--- language: ko tags: - bert - mrc datasets: - klue license: cc-by-sa-4.0 --- # 인프런 강의용 checkpoint KLUE의 YNAT task에 파인튜닝된 모델입니다.

jeraldflowers/distilroberts-base-mrpc-glue-jeraldflowers

jeraldflowers

2022-11-29T02:57:36Z

108

0

transformers

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

text-classification

2022-11-28T05:30:00Z

--- license: apache-2.0 tags: - text-classification - generated_from_trainer datasets: - glue metrics: - accuracy - f1 widget: - text: ["Yucaipa owned Dominick's before selling the chain to Safeway in 1998 for $ 2.5 billion.", "Yucaipa bought Dominick's in 1995 for $ 693 million and sold it to Safeway for $ 1.8 billion in 1998."] example_title: Not Equivalent - text: ["Revenue in the first quarter of the year dropped 15 percent from the same period a year earlier.", "With the scandal hanging over Stewart's company revenue the first quarter of the year dropped 15 percent from the same period a year earlier."] example_title: Equivalent model-index: - name: distilroberts-base-mrpc-glue-jeraldflowers results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: mrpc split: train args: mrpc metrics: - name: Accuracy type: accuracy value: 0.8431372549019608 - name: F1 type: f1 value: 0.8814814814814815 ---  # distilroberts-base-mrpc-glue-jeraldflowers This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the glue and the mrpc datasets. It achieves the following results on the evaluation set: - Loss: 0.4990 - Accuracy: 0.8431 - F1: 0.8815 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.5289 | 1.09 | 500 | 0.5668 | 0.8211 | 0.8689 | | 0.3675 | 2.18 | 1000 | 0.4990 | 0.8431 | 0.8815 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

npark/asr-conformer-ksponspeech

npark

2022-11-29T02:25:40Z

5

1

null

[ "region:us" ]

null

2022-11-29T01:26:29Z

# KsponSpeech ASR with Transformers This repository provides pretrained end-to-end ASR models on KsponSpeech with Speechbrain v0.5.13. Model files in this repository trained using the files is below URL, but in Speechbrain version 0.5.13. https://github.com/speechbrain/speechbrain/tree/develop/recipes/KsponSpeech/ASR/transformer language: - "ko" - ko datasets: - KsponSpeech ## About SpeechBrain * Website: https://speechbrain.github.io/ * Code: https://github.com/speechbrain/speechbrain/ * HuggingFace: https://huggingface.co/speechbrain/

neulab/omnitab-large-finetuned-wtq

neulab

2022-11-29T02:11:26Z

4,399

7

transformers

[ "transformers", "pytorch", "bart", "text2text-generation", "tapex", "table-question-answering", "en", "dataset:wikitablequestions", "arxiv:2207.03637", "autotrain_compatible", "endpoints_compatible", "region:us" ]

table-question-answering

2022-10-26T00:56:04Z

--- language: en tags: - tapex - table-question-answering datasets: - wikitablequestions --- # OmniTab OmniTab is a table-based QA model proposed in [OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering](https://arxiv.org/pdf/2207.03637.pdf). The original Github repository is [https://github.com/jzbjyb/OmniTab](https://github.com/jzbjyb/OmniTab). ## Description `neulab/omnitab-large-finetuned-wtq` (based on BART architecture) is initialized with `neulab/omnitab-large` and fine-tuned on [WikiTableQuestions](https://huggingface.co/datasets/wikitablequestions). ## Usage ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM import pandas as pd tokenizer = AutoTokenizer.from_pretrained("neulab/omnitab-large-finetuned-wtq") model = AutoModelForSeq2SeqLM.from_pretrained("neulab/omnitab-large-finetuned-wtq") data = { "year": [1896, 1900, 1904, 2004, 2008, 2012], "city": ["athens", "paris", "st. louis", "athens", "beijing", "london"] } table = pd.DataFrame.from_dict(data) query = "In which year did beijing host the Olympic Games?" encoding = tokenizer(table=table, query=query, return_tensors="pt") outputs = model.generate(**encoding) print(tokenizer.batch_decode(outputs, skip_special_tokens=True)) # [' 2008'] ``` ## Reference ```bibtex @inproceedings{jiang-etal-2022-omnitab, title = "{O}mni{T}ab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering", author = "Jiang, Zhengbao and Mao, Yi and He, Pengcheng and Neubig, Graham and Chen, Weizhu", booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jul, year = "2022", } ```

neulab/omnitab-large-16shot-finetuned-wtq-16shot

neulab

2022-11-29T02:10:07Z

52

1

transformers

[ "transformers", "pytorch", "bart", "text2text-generation", "tapex", "table-question-answering", "en", "dataset:wikitablequestions", "arxiv:2207.03637", "autotrain_compatible", "endpoints_compatible", "region:us" ]

table-question-answering

2022-11-29T01:48:24Z

--- language: en tags: - tapex - table-question-answering datasets: - wikitablequestions --- # OmniTab OmniTab is a table-based QA model proposed in [OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering](https://arxiv.org/pdf/2207.03637.pdf). The original Github repository is [https://github.com/jzbjyb/OmniTab](https://github.com/jzbjyb/OmniTab). ## Description `neulab/omnitab-large-16shot-finetuned-wtq-16shot` (based on BART architecture) is initialized with `neulab/omnitab-large-16shot` and fine-tuned on [WikiTableQuestions](https://huggingface.co/datasets/wikitablequestions) in the 16-shot setting. ## Usage ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM import pandas as pd tokenizer = AutoTokenizer.from_pretrained("neulab/omnitab-large-16shot-finetuned-wtq-16shot") model = AutoModelForSeq2SeqLM.from_pretrained("neulab/omnitab-large-16shot-finetuned-wtq-16shot") data = { "year": [1896, 1900, 1904, 2004, 2008, 2012], "city": ["athens", "paris", "st. louis", "athens", "beijing", "london"] } table = pd.DataFrame.from_dict(data) query = "In which year did beijing host the Olympic Games?" encoding = tokenizer(table=table, query=query, return_tensors="pt") outputs = model.generate(**encoding) print(tokenizer.batch_decode(outputs, skip_special_tokens=True)) # [' 2008'] ``` ## Reference ```bibtex @inproceedings{jiang-etal-2022-omnitab, title = "{O}mni{T}ab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering", author = "Jiang, Zhengbao and Mao, Yi and He, Pengcheng and Neubig, Graham and Chen, Weizhu", booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jul, year = "2022", } ```

Deigant/t5-base-finetuned-qg-context-dataset-2-hard-medium

Deigant

2022-11-29T01:57:15Z

105

0

transformers

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

text2text-generation

2022-11-29T01:10:07Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-base-finetuned-qg-context-dataset-2-hard-medium results: [] ---  # t5-base-finetuned-qg-context-dataset-2-hard-medium This model is a fine-tuned version of [Deigant/t5-base-finetuned-qg-context-dataset-2](https://huggingface.co/Deigant/t5-base-finetuned-qg-context-dataset-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1877 - Rouge1: 27.9067 - Rouge2: 6.8779 - Rougel: 24.6502 - Rougelsum: 24.7749 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:| | No log | 1.0 | 73 | 2.1134 | 27.571 | 8.3183 | 25.3973 | 25.2743 | | No log | 2.0 | 146 | 2.0800 | 28.4972 | 9.7451 | 26.9093 | 26.7337 | | No log | 3.0 | 219 | 2.0406 | 21.4309 | 5.817 | 19.4819 | 19.8555 | | No log | 4.0 | 292 | 2.0391 | 27.2786 | 8.283 | 24.3314 | 24.3751 | | No log | 5.0 | 365 | 2.0367 | 26.3524 | 7.6263 | 23.9034 | 23.8929 | | No log | 6.0 | 438 | 2.0270 | 26.3718 | 6.7074 | 22.995 | 23.0177 | | 1.3439 | 7.0 | 511 | 2.0106 | 27.8601 | 10.5485 | 26.8103 | 26.4962 | | 1.3439 | 8.0 | 584 | 2.0292 | 27.1811 | 7.1941 | 23.9117 | 24.0093 | | 1.3439 | 9.0 | 657 | 2.0462 | 25.6595 | 8.3529 | 23.0955 | 23.1946 | | 1.3439 | 10.0 | 730 | 2.0600 | 27.1996 | 9.0098 | 25.7921 | 25.8295 | | 1.3439 | 11.0 | 803 | 2.0754 | 25.3094 | 7.6857 | 23.5524 | 23.6875 | | 1.3439 | 12.0 | 876 | 2.0532 | 27.2136 | 9.0147 | 24.7405 | 24.8211 | | 1.3439 | 13.0 | 949 | 2.0742 | 26.298 | 8.6826 | 24.6878 | 24.9118 | | 0.8957 | 14.0 | 1022 | 2.0975 | 22.9575 | 4.2021 | 20.6208 | 20.6539 | | 0.8957 | 15.0 | 1095 | 2.0941 | 26.778 | 7.1756 | 24.4053 | 24.4951 | | 0.8957 | 16.0 | 1168 | 2.1025 | 28.9102 | 10.5549 | 25.912 | 25.9433 | | 0.8957 | 17.0 | 1241 | 2.1265 | 27.8301 | 9.7377 | 25.3236 | 25.3889 | | 0.8957 | 18.0 | 1314 | 2.1403 | 26.1619 | 7.8019 | 23.5346 | 23.351 | | 0.8957 | 19.0 | 1387 | 2.1396 | 26.664 | 6.8261 | 24.2991 | 24.328 | | 0.8957 | 20.0 | 1460 | 2.1481 | 29.8898 | 9.8211 | 27.0922 | 27.2485 | | 0.69 | 21.0 | 1533 | 2.1466 | 26.3418 | 5.7845 | 24.0772 | 24.3122 | | 0.69 | 22.0 | 1606 | 2.1559 | 27.5789 | 7.7653 | 25.9896 | 25.8088 | | 0.69 | 23.0 | 1679 | 2.1624 | 27.9455 | 7.4094 | 25.3163 | 25.3905 | | 0.69 | 24.0 | 1752 | 2.1633 | 27.5236 | 8.1967 | 24.9498 | 24.974 | | 0.69 | 25.0 | 1825 | 2.1698 | 26.899 | 6.4382 | 24.2075 | 24.1523 | | 0.69 | 26.0 | 1898 | 2.1745 | 28.7721 | 8.872 | 24.8299 | 24.9028 | | 0.69 | 27.0 | 1971 | 2.1818 | 25.8046 | 6.0655 | 23.156 | 23.1971 | | 0.5965 | 28.0 | 2044 | 2.1854 | 25.4431 | 4.6566 | 22.2794 | 22.4561 | | 0.5965 | 29.0 | 2117 | 2.1858 | 24.7881 | 6.4357 | 22.8869 | 22.8331 | | 0.5965 | 30.0 | 2190 | 2.1877 | 27.9067 | 6.8779 | 24.6502 | 24.7749 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

huggingtweets/elonmusk-lexfridman

huggingtweets

2022-11-29T01:35:11Z

118

1

transformers

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

text-generation

2022-03-02T23:29:05Z

--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1590968738358079488/IY9Gx6Ok_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/956331551435960322/OaqR8pAB_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Elon Musk & Lex Fridman</div> <div style="text-align: center; font-size: 14px;">@elonmusk-lexfridman</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Elon Musk & Lex Fridman. | Data | Elon Musk | Lex Fridman | | --- | --- | --- | | Tweets downloaded | 3198 | 2410 | | Retweets | 126 | 253 | | Short tweets | 968 | 49 | | Tweets kept | 2104 | 2108 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/18nt3c0k/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @elonmusk-lexfridman's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2ozchvjo) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2ozchvjo/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/elonmusk-lexfridman') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

matan-diamond/sd-class-butterflies-32

matan-diamond

2022-11-29T00:47:21Z

36

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-11-29T00:46:35Z

--- 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(matan-diamond/sd-class-butterflies-32) image = pipeline().images[0] image ```

adrien-alloreview/whisper-small-fr

adrien-alloreview

2022-11-29T00:13:29Z

83

0

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "hi", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-11-28T22:32:23Z

--- language: - hi license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 model-index: - name: Whisper Small Hi - Sanchit Gandhi results: [] ---  # Whisper Small Hi - Sanchit Gandhi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - eval_loss: 0.2226 - eval_wer: 10.0023 - eval_runtime: 65.2041 - eval_samples_per_second: 1.748 - eval_steps_per_second: 0.23 - epoch: 19.51 - step: 800 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - training_steps: 1000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

Serhio/sd-fine-tune-v2

Serhio

2022-11-28T23:43:18Z

34

0

diffusers

[ "diffusers", "text-to-image", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2022-11-28T23:41:46Z

--- license: creativeml-openrail-m tags: - text-to-image --- ### sd-fine-tune-v2 on Stable Diffusion via Dreambooth #### model by Serhio This your the Stable Diffusion model fine-tuned the sd-fine-tune-v2 concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **Bashkov Sergey** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts)

jqtrde/sd-class-butterflies-32

jqtrde

2022-11-28T23:20:18Z

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "region:us" ]

unconditional-image-generation

2022-11-28T23:18:49Z

--- 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(jqtrde/sd-class-butterflies-32) image = pipeline().images[0] image ```

Pramodith/sd-class-butterflies-32

Pramodith

2022-11-28T23:19:08Z

38

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-11-28T23:18:35Z

--- 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(Pramodith/sd-class-butterflies-32) image = pipeline().images[0] image ```

kanixwang/my-awesome-setfit-model

kanixwang

2022-11-28T22:19:56Z

1

0

sentence-transformers

[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2022-11-28T22:02:13Z

--- 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.  ## 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  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 40 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` 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": 40, "warmup_steps": 4, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel (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

alryan1478/gpt-neo-125M-DOD-LOW

alryan1478

2022-11-28T22:19:47Z

103

0

transformers

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

text-generation

2022-11-28T21:59:56Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: gpt-neo-125M-DOD-LOW results: [] ---  # gpt-neo-125M-DOD-LOW This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.0427 ## 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 | 261 | 6.4768 | | 6.8863 | 2.0 | 522 | 6.1056 | | 6.8863 | 3.0 | 783 | 6.0427 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.7.0 - Tokenizers 0.13.2

ThomasSimonini/ML-Agents-SnowballFight-1vs1-model

ThomasSimonini

2022-11-28T22:07:31Z

6

0

ml-agents

[ "ml-agents", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Snowballfight-1vs1", "region:us" ]

reinforcement-learning

2022-11-28T21:26:07Z

--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Snowballfight-1vs1 library_name: ml-agents ---

michaelmayo704/sd-class-butterflies-64

michaelmayo704

2022-11-28T21:39:43Z

34

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-11-28T21:38:51Z

--- 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(michaelmayo704/sd-class-butterflies-64) image = pipeline().images[0] image ```

SiriRRR/test-model

SiriRRR

2022-11-28T21:39:02Z

61

0

transformers

[ "transformers", "tf", "bart", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-11-28T21:38:42Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: test-model results: [] ---  # test-model This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.24.0 - TensorFlow 2.9.2 - Datasets 2.7.1 - Tokenizers 0.13.2

rlarios/distilbert-base-uncased-finetuned-emotion

rlarios

2022-11-28T21:34:34Z

106

0

transformers

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

text-classification

2022-11-25T20:15:59Z

--- 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: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9325 - name: F1 type: f1 value: 0.9322428116765227 ---  # 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: 0.2225 - Accuracy: 0.9325 - F1: 0.9322 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8372 | 1.0 | 250 | 0.3225 | 0.9045 | 0.9017 | | 0.2534 | 2.0 | 500 | 0.2225 | 0.9325 | 0.9322 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cpu - Datasets 2.6.1 - Tokenizers 0.13.1

anikethjr/PromoGen_K562_2080Ti_restart

anikethjr

2022-11-28T21:24:36Z

91

0

transformers

[ "transformers", "pytorch", "tensorboard", "prophetnet", "text-generation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2022-11-27T05:27:24Z

--- tags: - generated_from_trainer model-index: - name: PromoGen_K562_2080Ti_restart results: [] ---  # PromoGen_K562_2080Ti_restart This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4624 ## 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: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 25 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:------:|:---------------:| | 0.7676 | 0.49 | 2500 | 0.7383 | | 0.7121 | 0.97 | 5000 | 0.6867 | | 0.6914 | 1.46 | 7500 | 0.6705 | | 0.6837 | 1.95 | 10000 | 0.6622 | | 0.6778 | 2.44 | 12500 | 0.6558 | | 0.6748 | 2.92 | 15000 | 0.6517 | | 0.6676 | 3.41 | 17500 | 0.6433 | | 0.6593 | 3.9 | 20000 | 0.6358 | | 0.6584 | 4.38 | 22500 | 0.6320 | | 0.6557 | 4.87 | 25000 | 0.6301 | | 0.6523 | 5.36 | 27500 | 0.6257 | | 0.6478 | 5.84 | 30000 | 0.6236 | | 0.6393 | 6.33 | 32500 | 0.6145 | | 0.6039 | 6.82 | 35000 | 0.5658 | | 0.5616 | 7.31 | 37500 | 0.5376 | | 0.5518 | 7.79 | 40000 | 0.5310 | | 0.5509 | 8.28 | 42500 | 0.5273 | | 0.5487 | 8.77 | 45000 | 0.5261 | | 0.5479 | 9.25 | 47500 | 0.5249 | | 0.546 | 9.74 | 50000 | 0.5242 | | 0.5447 | 10.23 | 52500 | 0.5229 | | 0.5439 | 10.71 | 55000 | 0.5220 | | 0.5433 | 11.2 | 57500 | 0.5209 | | 0.5394 | 11.69 | 60000 | 0.5162 | | 0.5153 | 12.18 | 62500 | 0.4944 | | 0.5137 | 12.66 | 65000 | 0.4932 | | 0.514 | 13.15 | 67500 | 0.4924 | | 0.5131 | 13.64 | 70000 | 0.4919 | | 0.5104 | 14.12 | 72500 | 0.4914 | | 0.5122 | 14.61 | 75000 | 0.4906 | | 0.5089 | 15.1 | 77500 | 0.4901 | | 0.5076 | 15.59 | 80000 | 0.4891 | | 0.4986 | 16.07 | 82500 | 0.4721 | | 0.4875 | 16.56 | 85000 | 0.4672 | | 0.4887 | 17.05 | 87500 | 0.4669 | | 0.4839 | 17.53 | 90000 | 0.4661 | | 0.4849 | 18.02 | 92500 | 0.4654 | | 0.4848 | 18.51 | 95000 | 0.4649 | | 0.4831 | 18.99 | 97500 | 0.4646 | | 0.4816 | 19.48 | 100000 | 0.4644 | | 0.4808 | 19.97 | 102500 | 0.4637 | | 0.4812 | 20.46 | 105000 | 0.4634 | | 0.4813 | 20.94 | 107500 | 0.4633 | | 0.4818 | 21.43 | 110000 | 0.4631 | | 0.4813 | 21.92 | 112500 | 0.4629 | | 0.4782 | 22.4 | 115000 | 0.4628 | | 0.4804 | 22.89 | 117500 | 0.4626 | | 0.4815 | 23.38 | 120000 | 0.4625 | | 0.4812 | 23.87 | 122500 | 0.4625 | | 0.4785 | 24.35 | 125000 | 0.4624 | | 0.4795 | 24.84 | 127500 | 0.4624 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.7.0 - Tokenizers 0.13.0.dev0

pig4431/TUF_BERT_5E

pig4431

2022-11-28T21:13:00Z

103

0

transformers

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

text-classification

2022-11-28T21:06:14Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: TUF_BERT_5E results: [] ---  # TUF_BERT_5E This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3251 - Accuracy: 0.9467 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4078 | 0.1 | 50 | 0.2430 | 0.92 | | 0.2488 | 0.2 | 100 | 0.1465 | 0.94 | | 0.1966 | 0.3 | 150 | 0.1284 | 0.96 | | 0.2096 | 0.4 | 200 | 0.2879 | 0.9067 | | 0.2015 | 0.5 | 250 | 0.1629 | 0.9467 | | 0.1692 | 0.59 | 300 | 0.2165 | 0.9133 | | 0.1794 | 0.69 | 350 | 0.1535 | 0.9533 | | 0.1975 | 0.79 | 400 | 0.1429 | 0.9333 | | 0.1394 | 0.89 | 450 | 0.2384 | 0.92 | | 0.191 | 0.99 | 500 | 0.2198 | 0.94 | | 0.0907 | 1.09 | 550 | 0.1270 | 0.9467 | | 0.073 | 1.19 | 600 | 0.2016 | 0.94 | | 0.1594 | 1.29 | 650 | 0.2078 | 0.9267 | | 0.087 | 1.39 | 700 | 0.3312 | 0.9333 | | 0.0961 | 1.49 | 750 | 0.3704 | 0.92 | | 0.1225 | 1.58 | 800 | 0.1686 | 0.9467 | | 0.0969 | 1.68 | 850 | 0.1525 | 0.9333 | | 0.0942 | 1.78 | 900 | 0.1924 | 0.94 | | 0.0681 | 1.88 | 950 | 0.1825 | 0.9467 | | 0.1295 | 1.98 | 1000 | 0.1360 | 0.9333 | | 0.0626 | 2.08 | 1050 | 0.2014 | 0.94 | | 0.0372 | 2.18 | 1100 | 0.2030 | 0.9467 | | 0.0077 | 2.28 | 1150 | 0.2615 | 0.9467 | | 0.0393 | 2.38 | 1200 | 0.4256 | 0.9267 | | 0.0492 | 2.48 | 1250 | 0.3057 | 0.94 | | 0.0184 | 2.57 | 1300 | 0.1308 | 0.9733 | | 0.0209 | 2.67 | 1350 | 0.2848 | 0.9467 | | 0.0328 | 2.77 | 1400 | 0.1862 | 0.96 | | 0.0333 | 2.87 | 1450 | 0.2347 | 0.96 | | 0.0527 | 2.97 | 1500 | 0.3855 | 0.9333 | | 0.0685 | 3.07 | 1550 | 0.3174 | 0.94 | | 0.0217 | 3.17 | 1600 | 0.2320 | 0.9533 | | 0.0036 | 3.27 | 1650 | 0.3219 | 0.9333 | | 0.0015 | 3.37 | 1700 | 0.1649 | 0.9733 | | 0.0177 | 3.47 | 1750 | 0.3785 | 0.94 | | 0.0142 | 3.56 | 1800 | 0.1420 | 0.9733 | | 0.0319 | 3.66 | 1850 | 0.4057 | 0.9333 | | 0.0254 | 3.76 | 1900 | 0.1824 | 0.96 | | 0.0092 | 3.86 | 1950 | 0.2400 | 0.9533 | | 0.0306 | 3.96 | 2000 | 0.2238 | 0.96 | | 0.0118 | 4.06 | 2050 | 0.2623 | 0.9533 | | 0.0097 | 4.16 | 2100 | 0.3642 | 0.9467 | | 0.0132 | 4.26 | 2150 | 0.3235 | 0.9467 | | 0.0155 | 4.36 | 2200 | 0.3535 | 0.9467 | | 0.0043 | 4.46 | 2250 | 0.3236 | 0.9467 | | 0.0004 | 4.55 | 2300 | 0.2984 | 0.9467 | | 0.009 | 4.65 | 2350 | 0.2941 | 0.9467 | | 0.0068 | 4.75 | 2400 | 0.2936 | 0.9467 | | 0.0102 | 4.85 | 2450 | 0.3138 | 0.9467 | | 0.0015 | 4.95 | 2500 | 0.3251 | 0.9467 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.7.1 - Tokenizers 0.13.2

rmartinshort/sd-class-butterflies-64

rmartinshort

2022-11-28T20:32:13Z

36

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-11-28T20:31:54Z

--- 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(rmartinshort/sd-class-butterflies-64) image = pipeline().images[0] image ```

CyantifiCQ/noisy_butterflied_diffusion

CyantifiCQ

2022-11-28T20:23:45Z

35

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-11-28T20:22:34Z

--- 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(CyantifiCQ/noisy_butterflied_diffusion) image = pipeline().images[0] image ```

pig4431/TUF_DistilBERT_5E

pig4431

2022-11-28T20:13:46Z

105

0

transformers

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

text-classification

2022-11-28T20:05:35Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: TUF_DistilBERT_5E results: [] ---  # TUF_DistilBERT_5E This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1832 - Accuracy: 0.96 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5092 | 0.1 | 50 | 0.4385 | 0.7533 | | 0.2807 | 0.2 | 100 | 0.2225 | 0.9 | | 0.1881 | 0.3 | 150 | 0.1531 | 0.94 | | 0.1895 | 0.4 | 200 | 0.1426 | 0.94 | | 0.1995 | 0.5 | 250 | 0.1428 | 0.94 | | 0.1745 | 0.59 | 300 | 0.1538 | 0.9267 | | 0.1679 | 0.69 | 350 | 0.1249 | 0.9533 | | 0.199 | 0.79 | 400 | 0.1327 | 0.9467 | | 0.1703 | 0.89 | 450 | 0.1488 | 0.92 | | 0.1541 | 0.99 | 500 | 0.1772 | 0.9467 | | 0.1436 | 1.09 | 550 | 0.1070 | 0.9667 | | 0.1463 | 1.19 | 600 | 0.1165 | 0.9467 | | 0.1309 | 1.29 | 650 | 0.1054 | 0.9733 | | 0.097 | 1.39 | 700 | 0.1346 | 0.94 | | 0.1307 | 1.49 | 750 | 0.1477 | 0.9467 | | 0.1506 | 1.58 | 800 | 0.1311 | 0.9533 | | 0.1386 | 1.68 | 850 | 0.1165 | 0.9667 | | 0.1463 | 1.78 | 900 | 0.4207 | 0.9067 | | 0.1202 | 1.88 | 950 | 0.1528 | 0.9667 | | 0.1403 | 1.98 | 1000 | 0.1262 | 0.96 | | 0.073 | 2.08 | 1050 | 0.1459 | 0.96 | | 0.0713 | 2.18 | 1100 | 0.1747 | 0.9533 | | 0.0814 | 2.28 | 1150 | 0.1953 | 0.9667 | | 0.0935 | 2.38 | 1200 | 0.1888 | 0.9533 | | 0.0685 | 2.48 | 1250 | 0.1562 | 0.9467 | | 0.1154 | 2.57 | 1300 | 0.1806 | 0.96 | | 0.1239 | 2.67 | 1350 | 0.1322 | 0.9533 | | 0.1011 | 2.77 | 1400 | 0.2148 | 0.94 | | 0.0718 | 2.87 | 1450 | 0.1686 | 0.96 | | 0.1159 | 2.97 | 1500 | 0.1532 | 0.9533 | | 0.0516 | 3.07 | 1550 | 0.1888 | 0.96 | | 0.063 | 3.17 | 1600 | 0.1851 | 0.9467 | | 0.068 | 3.27 | 1650 | 0.2775 | 0.94 | | 0.0946 | 3.37 | 1700 | 0.1853 | 0.96 | | 0.0606 | 3.47 | 1750 | 0.2148 | 0.9467 | | 0.0663 | 3.56 | 1800 | 0.2091 | 0.9533 | | 0.0474 | 3.66 | 1850 | 0.1702 | 0.9533 | | 0.0585 | 3.76 | 1900 | 0.1660 | 0.96 | | 0.0439 | 3.86 | 1950 | 0.2220 | 0.9533 | | 0.0758 | 3.96 | 2000 | 0.1834 | 0.96 | | 0.0497 | 4.06 | 2050 | 0.1707 | 0.9533 | | 0.0412 | 4.16 | 2100 | 0.1948 | 0.9533 | | 0.0338 | 4.26 | 2150 | 0.2039 | 0.9533 | | 0.0796 | 4.36 | 2200 | 0.1797 | 0.9533 | | 0.0727 | 4.46 | 2250 | 0.1986 | 0.9533 | | 0.032 | 4.55 | 2300 | 0.1947 | 0.9467 | | 0.0436 | 4.65 | 2350 | 0.1908 | 0.9467 | | 0.0205 | 4.75 | 2400 | 0.1806 | 0.96 | | 0.0326 | 4.85 | 2450 | 0.1835 | 0.96 | | 0.0404 | 4.95 | 2500 | 0.1832 | 0.96 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.7.1 - Tokenizers 0.13.2

motmono/a2c-AntBulletEnv-v0

motmono

2022-11-28T19:58:24Z

2

0

stable-baselines3

[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-11-28T19:57:12Z

--- 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: 1539.68 +/- 213.96 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 ... ```

UKP-SQuARE/tweac_16

UKP-SQuARE

2022-11-28T19:43:48Z

102

1

transformers

[ "transformers", "pytorch", "bert", "text-classification", "QA", "en", "dataset:BoolQ", "dataset:CommonSenseQA", "dataset:DROP", "dataset:DuoRC", "dataset:HellaSWAG", "dataset:HotpotQA", "dataset:HybridQA", "dataset:NarrativeQA", "dataset:NaturalQuestionsShort", "dataset:NewsQA", "dataset:QAMR", "dataset:RACE", "dataset:SearchQA", "dataset:SIQA", "dataset:SQuAD", "dataset:TriviaQA-web", "arxiv:2104.07081", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-09T18:34:07Z

--- language: - en tags: - QA license: cc-by-4.0 datasets: - BoolQ - CommonSenseQA - DROP - DuoRC - HellaSWAG - HotpotQA - HybridQA - NarrativeQA - NaturalQuestionsShort - NewsQA - QAMR - RACE - SearchQA - SIQA - SQuAD - TriviaQA-web metrics: - Accuracy - Precision - Recall - F1 - MRR - R@3 - R@5 --- BERT for Sequence Classification trained on QA Dataset prediction task. - Input: question. - Output: dataset from where that question comes from. Original paper: TWEAC: Transformer with Extendable QA Agent Classifiers https://arxiv.org/abs/2104.07081 Datasets used for training: ``` list_datasets = ['BoolQ','CommonSenseQA','DROP','DuoRC','HellaSWAG','HotpotQA','HybridQA','NarrativeQA','NaturalQuestionsShort','NewsQA','QAMR','RACE','SearchQA','SIQA','SQuAD','TriviaQA-web'] ``` Results for all datasets: - Accuracy: 0.7919096825783123 - Precision: 0.731586272892176 - Recall: 0.7919096825783123 - F1: 0.7494425609552463 - MRR: 0.8720871733637521 - R@3: 0.9438690810655046 - R@5: 0.9745318608004427 - Queries/second: 6052.33538824659 Results per dataset: ``` "BoolQ": { "accuracy": 0.998776758409786, "mrr": 0.999388379204893, "r@3": 1.0, "r@5": 1.0, "query_per_second": 6978.947907596168, "precision": 0.8649364406779662, "recall": 0.998776758409786, "f1": 0.9270508089696281 }, "CommonSenseQA": { "accuracy": 0.9247135842880524, "mrr": 0.9476358338878795, "r@3": 0.9705400981996727, "r@5": 0.9705400981996727, "query_per_second": 5823.984138936813, "precision": 0.442443226311668, "recall": 0.9247135842880524, "f1": 0.5985169491525425 }, "DROP": { "accuracy": 0.9075083892617449, "mrr": 0.9378200367399193, "r@3": 0.9609899328859061, "r@5": 0.9786073825503355, "query_per_second": 6440.988897129248, "precision": 0.8636726546906187, "recall": 0.9075083892617449, "f1": 0.8850480670893842 }, "DuoRC": { "accuracy": 0.5555803405457654, "mrr": 0.7368963429107307, "r@3": 0.9092125808610305, "r@5": 0.9596996059186557, "query_per_second": 6853.643198794893, "precision": 0.646814404432133, "recall": 0.5555803405457654, "f1": 0.5977360905563778 }, "HellaSWAG": { "accuracy": 0.998406691894045, "mrr": 0.9990705702715262, "r@3": 1.0, "r@5": 1.0, "query_per_second": 3091.5012960785157, "precision": 0.9974134500596896, "recall": 0.998406691894045, "f1": 0.9979098238280083 }, "HotpotQA": { "accuracy": 0.7414435784479837, "mrr": 0.8435804344945315, "r@3": 0.9325652321247034, "r@5": 0.973568281938326, "query_per_second": 4972.668019223381, "precision": 0.7352150537634409, "recall": 0.7414435784479837, "f1": 0.7383161801923401 }, "HybridQA": { "accuracy": 0.7934218118869013, "mrr": 0.8806947764680021, "r@3": 0.964800923254472, "r@5": 0.9930755914598961, "query_per_second": 4886.494046259562, "precision": 0.7198952879581152, "recall": 0.7934218118869013, "f1": 0.7548723579467472 }, "NarrativeQA": { "accuracy": 0.5623756749076442, "mrr": 0.7416681781060867, "r@3": 0.9011082693947144, "r@5": 0.9580373212086767, "query_per_second": 7081.067049796865, "precision": 0.5623224095472628, "recall": 0.5623756749076442, "f1": 0.5623490409661377 }, "NaturalQuestionsShort": { "accuracy": 0.7985353692739171, "mrr": 0.8743599435345307, "r@3": 0.9439077594266126, "r@5": 0.9774072919912745, "query_per_second": 7136.590426649795, "precision": 0.7963020509633313, "recall": 0.7985353692739171, "f1": 0.7974171464135678 }, "NewsQA": { "accuracy": 0.5375118708452041, "mrr": 0.71192075967717, "r@3": 0.855650522317189, "r@5": 0.939696106362773, "query_per_second": 7193.851409052092, "precision": 0.18757249378624688, "recall": 0.5375118708452041, "f1": 0.2780985136961061 }, "QAMR": { "accuracy": 0.6658497602557272, "mrr": 0.7969741223377345, "r@3": 0.9207778369738945, "r@5": 0.973361747469366, "query_per_second": 7321.775044800525, "precision": 0.8654525309881587, "recall": 0.6658497602557272, "f1": 0.7526421968624852 }, "RACE": { "accuracy": 0.8771538617474154, "mrr": 0.917901778042666, "r@3": 0.9489154672613015, "r@5": 0.9693898236367322, "query_per_second": 6952.225120744351, "precision": 0.8767983789260385, "recall": 0.8771538617474154, "f1": 0.8769760843129306 }, "SearchQA": { "accuracy": 0.9762073027090695, "mrr": 0.9865069592101393, "r@3": 0.9972909305064782, "r@5": 0.9984687868080094, "query_per_second": 4031.0193826035634, "precision": 0.9870191735143503, "recall": 0.9762073027090695, "f1": 0.9815834665719192 }, "SIQA": { "accuracy": 0.9969293756397134, "mrr": 0.9977823268509042, "r@3": 0.9979529170931423, "r@5": 1.0, "query_per_second": 6711.547709005977, "precision": 0.9329501915708812, "recall": 0.9969293756397134, "f1": 0.9638792676892627 }, "SQuAD": { "accuracy": 0.550628092881614, "mrr": 0.7164538452390565, "r@3": 0.8660068519223448, "r@5": 0.9366197183098591, "query_per_second": 7033.420124363291, "precision": 0.48613678373382624, "recall": 0.550628092881614, "f1": 0.5163766175814368 }, "TriviaQA-web": { "accuracy": 0.7855124582584125, "mrr": 0.8647404868442627, "r@3": 0.9321859748266119, "r@5": 0.9640380169535063, "query_per_second": 4327.642440910395, "precision": 0.7404358353510896, "recall": 0.7855124582584125, "f1": 0.7623083634550667 }, ```

essayproj/roberta-base-essay

essayproj

2022-11-28T19:08:54Z

59

0

transformers

[ "transformers", "tf", "roberta", "feature-extraction", "generated_from_keras_callback", "license:mit", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-28T19:08:03Z

--- license: mit tags: - generated_from_keras_callback model-index: - name: roberta-base-essay results: [] ---  # roberta-base-essay 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: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.24.0 - TensorFlow 2.9.2 - Tokenizers 0.13.2

Akriel/sd-class-butterflies-32

Akriel

2022-11-28T18:57:17Z

32

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-11-28T18:56:58Z

--- 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(Akriel/sd-class-butterflies-32) image = pipeline().images[0] image ```

Dagar/t5-small-science-papers-NIPS

Dagar

2022-11-28T18:21:27Z

107

1

transformers

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

text2text-generation

2022-11-28T18:00:28Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-small-science-papers-NIPS results: [] ---  # t5-small-science-papers-NIPS This model is a fine-tuned version of [Dagar/t5-small-science-papers](https://huggingface.co/Dagar/t5-small-science-papers) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.7566 - Rouge1: 15.7066 - Rouge2: 2.5654 - Rougel: 11.4679 - Rougelsum: 14.4017 - 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: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | No log | 1.0 | 318 | 5.1856 | 13.7172 | 2.0644 | 10.2189 | 12.838 | 19.0 | | 5.4522 | 2.0 | 636 | 5.0383 | 15.6211 | 2.1808 | 11.3561 | 14.3054 | 19.0 | | 5.4522 | 3.0 | 954 | 4.9486 | 15.1659 | 2.3308 | 11.1052 | 13.9456 | 19.0 | | 5.1254 | 4.0 | 1272 | 4.8851 | 15.716 | 2.4099 | 11.4954 | 14.5099 | 19.0 | | 4.9794 | 5.0 | 1590 | 4.8456 | 15.5507 | 2.4267 | 11.3867 | 14.3237 | 19.0 | | 4.9794 | 6.0 | 1908 | 4.8073 | 15.8406 | 2.4254 | 11.6878 | 14.6154 | 19.0 | | 4.8823 | 7.0 | 2226 | 4.7872 | 15.5554 | 2.4637 | 11.3401 | 14.3183 | 19.0 | | 4.8338 | 8.0 | 2544 | 4.7680 | 15.4783 | 2.4888 | 11.3364 | 14.2031 | 19.0 | | 4.8338 | 9.0 | 2862 | 4.7621 | 15.958 | 2.5662 | 11.6139 | 14.6576 | 19.0 | | 4.7838 | 10.0 | 3180 | 4.7566 | 15.7066 | 2.5654 | 11.4679 | 14.4017 | 19.0 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

FrancoisDongier/sd-class-butterflies-32

FrancoisDongier

2022-11-28T18:19:31Z

34

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-11-28T18:16:21Z

--- 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(FrancoisDongier/sd-class-butterflies-32) image = pipeline().images[0] image ```

kejian/final-filter-again

kejian

2022-11-28T17:39:16Z

3

0

transformers

[ "transformers", "pytorch", "gpt2", "generated_from_trainer", "en", "dataset:kejian/codeparrot-train-more-filter-3.3b-cleaned", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]

null

2022-11-28T01:33:32Z

--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - kejian/codeparrot-train-more-filter-3.3b-cleaned model-index: - name: kejian/final-filter-again results: [] ---  # kejian/final-filter-again This model was trained from scratch on the kejian/codeparrot-train-more-filter-3.3b-cleaned 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: 0.0008 - train_batch_size: 64 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - 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': {'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'], 'filter_threshold': 0.002361, 'is_split_by_sentences': True}, 'generation': {'batch_size': 64, 'metrics_configs': [{}, {'n': 1}, {}], 'scenario_configs': [{'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 640, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 512}, {'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 272, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'functions', 'num_samples': 512, 'prompts_path': 'resources/functions_csnet.jsonl', 'use_prompt_for_scoring': True}], 'scorer_config': {}}, 'kl_gpt3_callback': {'gpt3_kwargs': {'model_name': 'code-cushman-001'}, 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'path_or_name': 'codeparrot/codeparrot-small'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'codeparrot/codeparrot-small'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'kejian/final-filter-again', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0008, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000.0, 'output_dir': 'training_output', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 5000, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/kejian/uncategorized/runs/25z4zfy3

alexziweiwang/retrain_epoch2and3

alexziweiwang

2022-11-28T17:31:08Z

31

0

transformers

[ "transformers", "pytorch", "wav2vec2", "generated_from_trainer", "endpoints_compatible", "region:us" ]

null

2022-11-28T17:14:05Z

--- tags: - generated_from_trainer model-index: - name: retrain_epoch2and3 results: [] ---  # retrain_epoch2and3 This model is a fine-tuned version of [alexziweiwang/retrain_first1epoch](https://huggingface.co/alexziweiwang/retrain_first1epoch) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4888 - Acc: 0.24 - Wer: 1.0 - Correct: 48 - Total: 200 - Strlen: 200 ## 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: 9e-06 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Acc | Wer | Correct | Total | Strlen | |:-------------:|:-----:|:----:|:---------------:|:----:|:---:|:-------:|:-----:|:------:| | No log | 0.02 | 5 | 7.8479 | 0.24 | 1.0 | 48 | 200 | 200 | | 7.6019 | 0.04 | 10 | 7.4765 | 0.24 | 1.0 | 48 | 200 | 200 | | 7.6019 | 0.06 | 15 | 7.1196 | 0.24 | 1.0 | 48 | 200 | 200 | | 7.3222 | 0.08 | 20 | 6.8029 | 0.24 | 1.0 | 48 | 200 | 200 | | 7.3222 | 0.11 | 25 | 6.5210 | 0.24 | 1.0 | 48 | 200 | 200 | | 6.2645 | 0.13 | 30 | 6.2630 | 0.24 | 1.0 | 48 | 200 | 200 | | 6.2645 | 0.15 | 35 | 6.0213 | 0.24 | 1.0 | 48 | 200 | 200 | | 5.8699 | 0.17 | 40 | 5.8096 | 0.24 | 1.0 | 48 | 200 | 200 | | 5.8699 | 0.19 | 45 | 5.5831 | 0.24 | 1.0 | 48 | 200 | 200 | | 5.7145 | 0.21 | 50 | 5.3644 | 0.24 | 1.0 | 48 | 200 | 200 | | 5.7145 | 0.23 | 55 | 5.1777 | 0.24 | 1.0 | 48 | 200 | 200 | | 5.3702 | 0.25 | 60 | 5.0257 | 0.24 | 1.0 | 48 | 200 | 200 | | 5.3702 | 0.27 | 65 | 4.8642 | 0.24 | 1.0 | 48 | 200 | 200 | | 5.1896 | 0.3 | 70 | 4.7205 | 0.24 | 1.0 | 48 | 200 | 200 | | 5.1896 | 0.32 | 75 | 4.5846 | 0.24 | 1.0 | 48 | 200 | 200 | | 5.0615 | 0.34 | 80 | 4.4313 | 0.24 | 1.0 | 48 | 200 | 200 | | 5.0615 | 0.36 | 85 | 4.2923 | 0.24 | 1.0 | 48 | 200 | 200 | | 4.5189 | 0.38 | 90 | 4.1662 | 0.24 | 1.0 | 48 | 200 | 200 | | 4.5189 | 0.4 | 95 | 4.0545 | 0.24 | 1.0 | 48 | 200 | 200 | | 4.4911 | 0.42 | 100 | 3.9585 | 0.24 | 1.0 | 48 | 200 | 200 | | 4.4911 | 0.44 | 105 | 3.8489 | 0.24 | 1.0 | 48 | 200 | 200 | | 4.1997 | 0.46 | 110 | 3.7573 | 0.24 | 1.0 | 48 | 200 | 200 | | 4.1997 | 0.48 | 115 | 3.6722 | 0.24 | 1.0 | 48 | 200 | 200 | | 3.7348 | 0.51 | 120 | 3.5844 | 0.24 | 1.0 | 48 | 200 | 200 | | 3.7348 | 0.53 | 125 | 3.4980 | 0.24 | 1.0 | 48 | 200 | 200 | | 3.8042 | 0.55 | 130 | 3.4318 | 0.24 | 1.0 | 48 | 200 | 200 | | 3.8042 | 0.57 | 135 | 3.3690 | 0.24 | 1.0 | 48 | 200 | 200 | | 3.705 | 0.59 | 140 | 3.3126 | 0.24 | 1.0 | 48 | 200 | 200 | | 3.705 | 0.61 | 145 | 3.2630 | 0.24 | 1.0 | 48 | 200 | 200 | | 3.763 | 0.63 | 150 | 3.2063 | 0.24 | 1.0 | 48 | 200 | 200 | | 3.763 | 0.65 | 155 | 3.1562 | 0.24 | 1.0 | 48 | 200 | 200 | | 3.5585 | 0.67 | 160 | 3.1096 | 0.24 | 1.0 | 48 | 200 | 200 | | 3.5585 | 0.7 | 165 | 3.0719 | 0.24 | 1.0 | 48 | 200 | 200 | | 3.213 | 0.72 | 170 | 3.0373 | 0.24 | 1.0 | 48 | 200 | 200 | | 3.213 | 0.74 | 175 | 3.0035 | 0.24 | 1.0 | 48 | 200 | 200 | | 3.2874 | 0.76 | 180 | 2.9712 | 0.24 | 1.0 | 48 | 200 | 200 | | 3.2874 | 0.78 | 185 | 2.9405 | 0.24 | 1.0 | 48 | 200 | 200 | | 3.3327 | 0.8 | 190 | 2.9134 | 0.24 | 1.0 | 48 | 200 | 200 | | 3.3327 | 0.82 | 195 | 2.8910 | 0.24 | 1.0 | 48 | 200 | 200 | | 3.2382 | 0.84 | 200 | 2.8672 | 0.24 | 1.0 | 48 | 200 | 200 | | 3.2382 | 0.86 | 205 | 2.8462 | 0.24 | 1.0 | 48 | 200 | 200 | | 3.0069 | 0.89 | 210 | 2.8260 | 0.24 | 1.0 | 48 | 200 | 200 | | 3.0069 | 0.91 | 215 | 2.8087 | 0.24 | 1.0 | 48 | 200 | 200 | | 3.2288 | 0.93 | 220 | 2.7920 | 0.24 | 1.0 | 48 | 200 | 200 | | 3.2288 | 0.95 | 225 | 2.7750 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.787 | 0.97 | 230 | 2.7557 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.787 | 0.99 | 235 | 2.7367 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.9717 | 1.01 | 240 | 2.7207 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.9717 | 1.03 | 245 | 2.7063 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.9269 | 1.05 | 250 | 2.6939 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.9269 | 1.08 | 255 | 2.6831 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.8771 | 1.1 | 260 | 2.6709 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.8771 | 1.12 | 265 | 2.6594 | 0.24 | 1.0 | 48 | 200 | 200 | | 3.0474 | 1.14 | 270 | 2.6472 | 0.24 | 1.0 | 48 | 200 | 200 | | 3.0474 | 1.16 | 275 | 2.6361 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.7652 | 1.18 | 280 | 2.6268 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.7652 | 1.2 | 285 | 2.6184 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.8322 | 1.22 | 290 | 2.6106 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.8322 | 1.24 | 295 | 2.6034 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.6464 | 1.27 | 300 | 2.5957 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.6464 | 1.29 | 305 | 2.5877 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.7974 | 1.31 | 310 | 2.5805 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.7974 | 1.33 | 315 | 2.5748 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.797 | 1.35 | 320 | 2.5698 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.797 | 1.37 | 325 | 2.5644 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.7508 | 1.39 | 330 | 2.5595 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.7508 | 1.41 | 335 | 2.5537 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.7188 | 1.43 | 340 | 2.5486 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.7188 | 1.46 | 345 | 2.5434 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.6889 | 1.48 | 350 | 2.5377 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.6889 | 1.5 | 355 | 2.5336 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.6373 | 1.52 | 360 | 2.5300 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.6373 | 1.54 | 365 | 2.5258 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.765 | 1.56 | 370 | 2.5219 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.765 | 1.58 | 375 | 2.5181 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.6407 | 1.6 | 380 | 2.5144 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.6407 | 1.62 | 385 | 2.5113 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.7727 | 1.64 | 390 | 2.5093 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.7727 | 1.67 | 395 | 2.5076 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.8091 | 1.69 | 400 | 2.5060 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.8091 | 1.71 | 405 | 2.5042 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.7204 | 1.73 | 410 | 2.5027 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.7204 | 1.75 | 415 | 2.5011 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.6168 | 1.77 | 420 | 2.4987 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.6168 | 1.79 | 425 | 2.4965 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.6947 | 1.81 | 430 | 2.4947 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.6947 | 1.83 | 435 | 2.4932 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.7495 | 1.86 | 440 | 2.4921 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.7495 | 1.88 | 445 | 2.4911 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.7413 | 1.9 | 450 | 2.4904 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.7413 | 1.92 | 455 | 2.4897 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.6498 | 1.94 | 460 | 2.4893 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.6498 | 1.96 | 465 | 2.4890 | 0.24 | 1.0 | 48 | 200 | 200 | | 2.6891 | 1.98 | 470 | 2.4888 | 0.24 | 1.0 | 48 | 200 | 200 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 1.18.3 - Tokenizers 0.13.2

wa3dbk/whisper-small-ar

wa3dbk

2022-11-28T17:11:32Z

113

0

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-11-25T18:33:06Z

## whisper-small-ar This model is a fine-tuned version of openai/whisper-small on the Common Voice 11.0 dataset (language=Arabic).

antgrutta/sd-class-butterflies-32

antgrutta

2022-11-28T16:59:10Z

32

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-11-28T16:58:32Z

--- 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(antgrutta/sd-class-butterflies-32) image = pipeline().images[0] image ```

EmnaBou/bert-finetuned-DT

EmnaBou

2022-11-28T16:49:12Z

123

0

transformers

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

token-classification

2022-11-28T15:20:02Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-DT results: [] ---  # bert-finetuned-DT This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6697 - Precision: 0.2381 - Recall: 0.0321 - F1: 0.0565 - Accuracy: 0.8179 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 99 | 0.7505 | 0.0 | 0.0 | 0.0 | 0.8196 | | No log | 2.0 | 198 | 0.7033 | 0.0 | 0.0 | 0.0 | 0.8196 | | No log | 3.0 | 297 | 0.6697 | 0.2381 | 0.0321 | 0.0565 | 0.8179 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

luisgasco/distilbert-base-uncased-finetuned-emotion

luisgasco

2022-11-28T16:17:49Z

110

0

transformers

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

text-classification

2022-11-28T16:03:30Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.892 - name: F1 type: f1 value: 0.8873822002431591 ---  # 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: 0.3693 - Accuracy: 0.892 - F1: 0.8874 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 125 | 0.5715 | 0.8275 | 0.8047 | | 0.7552 | 2.0 | 250 | 0.3693 | 0.892 | 0.8874 | ### Framework versions - Transformers 4.13.0 - Pytorch 1.11.0 - Datasets 1.16.1 - Tokenizers 0.10.3

tomekkorbak/awesome_ride

tomekkorbak

2022-11-28T16:12:40Z

0

null

[ "generated_from_trainer", "en", "dataset:tomekkorbak/detoxify-pile-chunk3-0-50000", "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", "dataset:tomekkorbak/detoxify-pile-chunk3-1850000-1900000", "dataset:tomekkorbak/detoxify-pile-chunk3-1900000-1950000", "license:mit", "region:us" ]

null

2022-11-28T16:12:19Z

--- language: - en license: mit tags: - generated_from_trainer datasets: - tomekkorbak/detoxify-pile-chunk3-0-50000 - 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 - tomekkorbak/detoxify-pile-chunk3-1850000-1900000 - tomekkorbak/detoxify-pile-chunk3-1900000-1950000 model-index: - name: awesome_ride results: [] ---  # awesome_ride This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, 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, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 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: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6 # Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', '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', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'], 'filter_threshold': 0.00065, 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25354], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048}, {'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'challenging_rtp', 'num_samples': 2048, 'prompts_path': 'resources/challenging_rtp.jsonl'}], 'scorer_config': {'device': 'cuda:0'}}, 'kl_gpt3_callback': {'force_call_on': [25354], 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'path_or_name': 'gpt2'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'awesome_ride', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output104340', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25354, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/tomekkorbak/apo/runs/3m98rnwq

alexziweiwang/pure-start-epoch2

alexziweiwang

2022-11-28T16:08:48Z

31

0

transformers

[ "transformers", "pytorch", "wav2vec2", "generated_from_trainer", "endpoints_compatible", "region:us" ]

null

2022-11-28T15:52:06Z

--- tags: - generated_from_trainer model-index: - name: pure-start-epoch2 results: [] ---  # pure-start-epoch2 This model is a fine-tuned version of [alexziweiwang/pure-start-epoch1](https://huggingface.co/alexziweiwang/pure-start-epoch1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 7.7447 - Acc: 0.24 - Wer: 1.0 - Correct: 48 - Total: 200 - Strlen: 200 ## 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: 9e-06 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Acc | Wer | Correct | Total | Strlen | |:-------------:|:-----:|:----:|:---------------:|:-----:|:---:|:-------:|:-----:|:------:| | No log | 0.01 | 2 | 20.4002 | 0.095 | 1.0 | 19 | 200 | 200 | | No log | 0.02 | 4 | 19.9080 | 0.095 | 1.0 | 19 | 200 | 200 | | No log | 0.03 | 6 | 19.4711 | 0.095 | 1.0 | 19 | 200 | 200 | | No log | 0.03 | 8 | 19.1535 | 0.095 | 1.0 | 19 | 200 | 200 | | 46.6007 | 0.04 | 10 | 18.6684 | 0.095 | 1.0 | 19 | 200 | 200 | | 46.6007 | 0.05 | 12 | 18.1640 | 0.095 | 1.0 | 19 | 200 | 200 | | 46.6007 | 0.06 | 14 | 17.6937 | 0.095 | 1.0 | 19 | 200 | 200 | | 46.6007 | 0.07 | 16 | 17.2710 | 0.095 | 1.0 | 19 | 200 | 200 | | 46.6007 | 0.08 | 18 | 16.8469 | 0.095 | 1.0 | 19 | 200 | 200 | | 49.1547 | 0.08 | 20 | 16.4418 | 0.095 | 1.0 | 19 | 200 | 200 | | 49.1547 | 0.09 | 22 | 16.0409 | 0.095 | 1.0 | 19 | 200 | 200 | | 49.1547 | 0.1 | 24 | 15.6677 | 0.095 | 1.0 | 19 | 200 | 200 | | 49.1547 | 0.11 | 26 | 15.3291 | 0.095 | 1.0 | 19 | 200 | 200 | | 49.1547 | 0.12 | 28 | 15.0097 | 0.095 | 1.0 | 19 | 200 | 200 | | 35.1416 | 0.13 | 30 | 14.6776 | 0.095 | 1.0 | 19 | 200 | 200 | | 35.1416 | 0.13 | 32 | 14.3788 | 0.095 | 1.0 | 19 | 200 | 200 | | 35.1416 | 0.14 | 34 | 14.0924 | 0.095 | 1.0 | 19 | 200 | 200 | | 35.1416 | 0.15 | 36 | 13.8133 | 0.095 | 1.0 | 19 | 200 | 200 | | 35.1416 | 0.16 | 38 | 13.5539 | 0.095 | 1.0 | 19 | 200 | 200 | | 34.4057 | 0.17 | 40 | 13.3095 | 0.095 | 1.0 | 19 | 200 | 200 | | 34.4057 | 0.18 | 42 | 13.0804 | 0.095 | 1.0 | 19 | 200 | 200 | | 34.4057 | 0.19 | 44 | 12.8580 | 0.105 | 1.0 | 21 | 200 | 200 | | 34.4057 | 0.19 | 46 | 12.6532 | 0.115 | 1.0 | 23 | 200 | 200 | | 34.4057 | 0.2 | 48 | 12.4532 | 0.13 | 1.0 | 26 | 200 | 200 | | 33.2759 | 0.21 | 50 | 12.2452 | 0.14 | 1.0 | 28 | 200 | 200 | | 33.2759 | 0.22 | 52 | 12.0666 | 0.13 | 1.0 | 26 | 200 | 200 | | 33.2759 | 0.23 | 54 | 11.8976 | 0.165 | 1.0 | 33 | 200 | 200 | | 33.2759 | 0.24 | 56 | 11.7373 | 0.175 | 1.0 | 35 | 200 | 200 | | 33.2759 | 0.24 | 58 | 11.5933 | 0.17 | 1.0 | 34 | 200 | 200 | | 29.8129 | 0.25 | 60 | 11.4281 | 0.15 | 1.0 | 30 | 200 | 200 | | 29.8129 | 0.26 | 62 | 11.2665 | 0.14 | 1.0 | 28 | 200 | 200 | | 29.8129 | 0.27 | 64 | 11.1158 | 0.145 | 1.0 | 29 | 200 | 200 | | 29.8129 | 0.28 | 66 | 10.9840 | 0.135 | 1.0 | 27 | 200 | 200 | | 29.8129 | 0.29 | 68 | 10.8502 | 0.15 | 1.0 | 30 | 200 | 200 | | 38.792 | 0.3 | 70 | 10.7341 | 0.15 | 1.0 | 30 | 200 | 200 | | 38.792 | 0.3 | 72 | 10.6082 | 0.165 | 1.0 | 33 | 200 | 200 | | 38.792 | 0.31 | 74 | 10.4944 | 0.18 | 1.0 | 36 | 200 | 200 | | 38.792 | 0.32 | 76 | 10.3818 | 0.21 | 1.0 | 42 | 200 | 200 | | 38.792 | 0.33 | 78 | 10.2719 | 0.235 | 1.0 | 47 | 200 | 200 | | 28.0092 | 0.34 | 80 | 10.1636 | 0.235 | 1.0 | 47 | 200 | 200 | | 28.0092 | 0.35 | 82 | 10.0709 | 0.24 | 1.0 | 48 | 200 | 200 | | 28.0092 | 0.35 | 84 | 9.9797 | 0.24 | 1.0 | 48 | 200 | 200 | | 28.0092 | 0.36 | 86 | 9.8958 | 0.24 | 1.0 | 48 | 200 | 200 | | 28.0092 | 0.37 | 88 | 9.7977 | 0.24 | 1.0 | 48 | 200 | 200 | | 21.6175 | 0.38 | 90 | 9.7015 | 0.24 | 1.0 | 48 | 200 | 200 | | 21.6175 | 0.39 | 92 | 9.6150 | 0.24 | 1.0 | 48 | 200 | 200 | | 21.6175 | 0.4 | 94 | 9.5304 | 0.24 | 1.0 | 48 | 200 | 200 | | 21.6175 | 0.4 | 96 | 9.4521 | 0.24 | 1.0 | 48 | 200 | 200 | | 21.6175 | 0.41 | 98 | 9.3832 | 0.24 | 1.0 | 48 | 200 | 200 | | 26.3434 | 0.42 | 100 | 9.3148 | 0.24 | 1.0 | 48 | 200 | 200 | | 26.3434 | 0.43 | 102 | 9.2563 | 0.24 | 1.0 | 48 | 200 | 200 | | 26.3434 | 0.44 | 104 | 9.1944 | 0.24 | 1.0 | 48 | 200 | 200 | | 26.3434 | 0.45 | 106 | 9.1323 | 0.24 | 1.0 | 48 | 200 | 200 | | 26.3434 | 0.46 | 108 | 9.0717 | 0.24 | 1.0 | 48 | 200 | 200 | | 24.4387 | 0.46 | 110 | 9.0245 | 0.24 | 1.0 | 48 | 200 | 200 | | 24.4387 | 0.47 | 112 | 8.9772 | 0.24 | 1.0 | 48 | 200 | 200 | | 24.4387 | 0.48 | 114 | 8.9390 | 0.24 | 1.0 | 48 | 200 | 200 | | 24.4387 | 0.49 | 116 | 8.9013 | 0.24 | 1.0 | 48 | 200 | 200 | | 24.4387 | 0.5 | 118 | 8.8605 | 0.24 | 1.0 | 48 | 200 | 200 | | 21.7305 | 0.51 | 120 | 8.8126 | 0.24 | 1.0 | 48 | 200 | 200 | | 21.7305 | 0.51 | 122 | 8.7503 | 0.24 | 1.0 | 48 | 200 | 200 | | 21.7305 | 0.52 | 124 | 8.6921 | 0.24 | 1.0 | 48 | 200 | 200 | | 21.7305 | 0.53 | 126 | 8.6378 | 0.24 | 1.0 | 48 | 200 | 200 | | 21.7305 | 0.54 | 128 | 8.5927 | 0.24 | 1.0 | 48 | 200 | 200 | | 21.5989 | 0.55 | 130 | 8.5520 | 0.24 | 1.0 | 48 | 200 | 200 | | 21.5989 | 0.56 | 132 | 8.5126 | 0.24 | 1.0 | 48 | 200 | 200 | | 21.5989 | 0.56 | 134 | 8.4743 | 0.24 | 1.0 | 48 | 200 | 200 | | 21.5989 | 0.57 | 136 | 8.4369 | 0.24 | 1.0 | 48 | 200 | 200 | | 21.5989 | 0.58 | 138 | 8.3993 | 0.24 | 1.0 | 48 | 200 | 200 | | 21.8372 | 0.59 | 140 | 8.3636 | 0.24 | 1.0 | 48 | 200 | 200 | | 21.8372 | 0.6 | 142 | 8.3311 | 0.24 | 1.0 | 48 | 200 | 200 | | 21.8372 | 0.61 | 144 | 8.2983 | 0.24 | 1.0 | 48 | 200 | 200 | | 21.8372 | 0.62 | 146 | 8.2652 | 0.24 | 1.0 | 48 | 200 | 200 | | 21.8372 | 0.62 | 148 | 8.2345 | 0.24 | 1.0 | 48 | 200 | 200 | | 20.1716 | 0.63 | 150 | 8.2064 | 0.24 | 1.0 | 48 | 200 | 200 | | 20.1716 | 0.64 | 152 | 8.1818 | 0.24 | 1.0 | 48 | 200 | 200 | | 20.1716 | 0.65 | 154 | 8.1603 | 0.24 | 1.0 | 48 | 200 | 200 | | 20.1716 | 0.66 | 156 | 8.1403 | 0.24 | 1.0 | 48 | 200 | 200 | | 20.1716 | 0.67 | 158 | 8.1180 | 0.24 | 1.0 | 48 | 200 | 200 | | 24.5655 | 0.67 | 160 | 8.0997 | 0.24 | 1.0 | 48 | 200 | 200 | | 24.5655 | 0.68 | 162 | 8.0791 | 0.24 | 1.0 | 48 | 200 | 200 | | 24.5655 | 0.69 | 164 | 8.0563 | 0.24 | 1.0 | 48 | 200 | 200 | | 24.5655 | 0.7 | 166 | 8.0342 | 0.24 | 1.0 | 48 | 200 | 200 | | 24.5655 | 0.71 | 168 | 8.0130 | 0.24 | 1.0 | 48 | 200 | 200 | | 17.3768 | 0.72 | 170 | 7.9936 | 0.24 | 1.0 | 48 | 200 | 200 | | 17.3768 | 0.72 | 172 | 7.9756 | 0.24 | 1.0 | 48 | 200 | 200 | | 17.3768 | 0.73 | 174 | 7.9594 | 0.24 | 1.0 | 48 | 200 | 200 | | 17.3768 | 0.74 | 176 | 7.9439 | 0.24 | 1.0 | 48 | 200 | 200 | | 17.3768 | 0.75 | 178 | 7.9298 | 0.24 | 1.0 | 48 | 200 | 200 | | 19.7473 | 0.76 | 180 | 7.9157 | 0.24 | 1.0 | 48 | 200 | 200 | | 19.7473 | 0.77 | 182 | 7.9021 | 0.24 | 1.0 | 48 | 200 | 200 | | 19.7473 | 0.78 | 184 | 7.8899 | 0.24 | 1.0 | 48 | 200 | 200 | | 19.7473 | 0.78 | 186 | 7.8796 | 0.24 | 1.0 | 48 | 200 | 200 | | 19.7473 | 0.79 | 188 | 7.8697 | 0.24 | 1.0 | 48 | 200 | 200 | | 15.7279 | 0.8 | 190 | 7.8598 | 0.24 | 1.0 | 48 | 200 | 200 | | 15.7279 | 0.81 | 192 | 7.8490 | 0.24 | 1.0 | 48 | 200 | 200 | | 15.7279 | 0.82 | 194 | 7.8390 | 0.24 | 1.0 | 48 | 200 | 200 | | 15.7279 | 0.83 | 196 | 7.8293 | 0.24 | 1.0 | 48 | 200 | 200 | | 15.7279 | 0.83 | 198 | 7.8211 | 0.24 | 1.0 | 48 | 200 | 200 | | 18.5034 | 0.84 | 200 | 7.8135 | 0.24 | 1.0 | 48 | 200 | 200 | | 18.5034 | 0.85 | 202 | 7.8064 | 0.24 | 1.0 | 48 | 200 | 200 | | 18.5034 | 0.86 | 204 | 7.7991 | 0.24 | 1.0 | 48 | 200 | 200 | | 18.5034 | 0.87 | 206 | 7.7924 | 0.24 | 1.0 | 48 | 200 | 200 | | 18.5034 | 0.88 | 208 | 7.7862 | 0.24 | 1.0 | 48 | 200 | 200 | | 17.1983 | 0.89 | 210 | 7.7803 | 0.24 | 1.0 | 48 | 200 | 200 | | 17.1983 | 0.89 | 212 | 7.7749 | 0.24 | 1.0 | 48 | 200 | 200 | | 17.1983 | 0.9 | 214 | 7.7701 | 0.24 | 1.0 | 48 | 200 | 200 | | 17.1983 | 0.91 | 216 | 7.7657 | 0.24 | 1.0 | 48 | 200 | 200 | | 17.1983 | 0.92 | 218 | 7.7628 | 0.24 | 1.0 | 48 | 200 | 200 | | 17.7276 | 0.93 | 220 | 7.7595 | 0.24 | 1.0 | 48 | 200 | 200 | | 17.7276 | 0.94 | 222 | 7.7567 | 0.24 | 1.0 | 48 | 200 | 200 | | 17.7276 | 0.94 | 224 | 7.7541 | 0.24 | 1.0 | 48 | 200 | 200 | | 17.7276 | 0.95 | 226 | 7.7518 | 0.24 | 1.0 | 48 | 200 | 200 | | 17.7276 | 0.96 | 228 | 7.7497 | 0.24 | 1.0 | 48 | 200 | 200 | | 17.8692 | 0.97 | 230 | 7.7479 | 0.24 | 1.0 | 48 | 200 | 200 | | 17.8692 | 0.98 | 232 | 7.7463 | 0.24 | 1.0 | 48 | 200 | 200 | | 17.8692 | 0.99 | 234 | 7.7453 | 0.24 | 1.0 | 48 | 200 | 200 | | 17.8692 | 0.99 | 236 | 7.7447 | 0.24 | 1.0 | 48 | 200 | 200 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 1.18.3 - Tokenizers 0.13.2

fathyshalab/all-roberta-large-v1-banking-2-2-1

fathyshalab

2022-11-28T15:28:40Z

105

0

transformers

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

text-classification

2022-11-28T15:27:01Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-banking-2-2-1 results: [] ---  # all-roberta-large-v1-banking-2-2-1 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.6817 - Accuracy: 0.1022 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 6 - eval_batch_size: 6 - 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 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.653 | 1.0 | 5 | 2.6817 | 0.1022 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1 - Datasets 2.3.2 - Tokenizers 0.12.1

arrandi/sd-class-butterflies-32

arrandi

2022-11-28T15:24:36Z

32

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-11-28T15:23:56Z

--- 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(arrandi/sd-class-butterflies-32) image = pipeline().images[0] image ```

ConvLab/ddpt-policy-sgd_0.01multiwoz21

ConvLab

2022-11-28T15:24:29Z

0

null

[ "dialogue policy", "task-oriented dialog", "en", "dataset:ConvLab/sgd", "license:apache-2.0", "region:us" ]

null

2022-11-28T15:21:11Z

--- language: - en license: apache-2.0 tags: - dialogue policy - task-oriented dialog datasets: - ConvLab/sgd --- # ddpt-policy-sgd_0.01multiwoz21 This is a DDPT model (https://aclanthology.org/2022.coling-1.21/) trained on [Schema-Guided Dialog](https://huggingface.co/datasets/ConvLab/sgd) and afterwards on 1 percent of [MultiWOZ 2.1](https://huggingface.co/datasets/ConvLab/multiwoz21) Refer to [ConvLab-3](https://github.com/ConvLab/ConvLab-3) for model description and usage. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - seed: 0 - optimizer: Adam - num_epochs: 40 - use checkpoint which performed best on validation set ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.2+cu111

ConvLab/ddpt-policy-0.01multiwoz21

ConvLab

2022-11-28T15:20:35Z

0

null

[ "dialogue policy", "task-oriented dialog", "en", "dataset:ConvLab/sgd", "license:apache-2.0", "region:us" ]

null

2022-11-28T15:18:28Z

--- language: - en license: apache-2.0 tags: - dialogue policy - task-oriented dialog datasets: - ConvLab/sgd --- # ddpt-policy-0.01multiwoz21 This is a DDPT model (https://aclanthology.org/2022.coling-1.21/) trained on 1 percent of [MultiWOZ 2.1](https://huggingface.co/datasets/ConvLab/multiwoz21) Refer to [ConvLab-3](https://github.com/ConvLab/ConvLab-3) for model description and usage. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - seed: 0 - optimizer: Adam - num_epochs: 40 - use checkpoint which performed best on validation set ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.2+cu111

fathyshalab/all-roberta-large-v1-banking-1-2-1

fathyshalab

2022-11-28T15:12:05Z

105

0

transformers

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

text-classification

2022-11-28T15:10:30Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: all-roberta-large-v1-banking-1-2-1 results: [] ---  # all-roberta-large-v1-banking-1-2-1 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.6235 - Accuracy: 0.2578 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 6 - eval_batch_size: 6 - 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 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.6542 | 1.0 | 3 | 2.6235 | 0.2578 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1 - Datasets 2.3.2 - Tokenizers 0.12.1

ConvLab/mle-policy-multiwoz21

ConvLab

2022-11-28T15:11:19Z

0

null

[ "dialogue policy", "task-oriented dialog", "en", "dataset:ConvLab/multiwoz21", "license:apache-2.0", "region:us" ]

null

2022-11-28T15:07:50Z

--- language: - en license: apache-2.0 tags: - dialogue policy - task-oriented dialog datasets: - ConvLab/multiwoz21 --- # mle-policy-multiwoz21 This is a MLE model trained on [MultiWOZ 2.1](https://huggingface.co/datasets/ConvLab/multiwoz21) Refer to [ConvLab-3](https://github.com/ConvLab/ConvLab-3) for model description and usage. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - seed: 0 - optimizer: Adam - num_epochs: 24 - use checkpoint which performed best on validation set ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.2+cu111

ConvLab/ddpt-policy-sgd

ConvLab

2022-11-28T15:01:15Z

0

1

null

[ "dialogue policy", "task-oriented dialog", "en", "dataset:ConvLab/sgd", "license:apache-2.0", "region:us" ]

null

2022-11-28T13:21:09Z

--- language: - en license: apache-2.0 tags: - dialogue policy - task-oriented dialog datasets: - ConvLab/sgd --- # ddpt-policy-sgd This is a DDPT model (https://aclanthology.org/2022.coling-1.21/) trained on [Schema-Guided Dialog](https://huggingface.co/datasets/ConvLab/sgd) Refer to [ConvLab-3](https://github.com/ConvLab/ConvLab-3) for model description and usage. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - seed: 0 - optimizer: Adam - num_epochs: 1 - use checkpoint which performed best on validation set ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.2+cu111

alexziweiwang/pure-start-epoch1

alexziweiwang

2022-11-28T14:49:27Z

31

0

transformers

[ "transformers", "pytorch", "wav2vec2", "generated_from_trainer", "endpoints_compatible", "region:us" ]

null

2022-11-28T14:32:53Z

--- tags: - generated_from_trainer model-index: - name: pure-start-epoch1 results: [] ---  # pure-start-epoch1 This model is a fine-tuned version of [yongjian/wav2vec2-large-a](https://huggingface.co/yongjian/wav2vec2-large-a) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 21.0050 - Acc: 0.095 - Wer: 1.0 - Correct: 19 - Total: 200 - Strlen: 200 ## 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: 9e-06 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Acc | Wer | Correct | Total | Strlen | |:-------------:|:-----:|:----:|:---------------:|:-----:|:------:|:-------:|:-----:|:------:| | No log | 0.02 | 5 | 67.2752 | 0.0 | 1.0119 | 0 | 200 | 200 | | 131.0548 | 0.04 | 10 | 66.2796 | 0.0 | 1.0257 | 0 | 200 | 200 | | 131.0548 | 0.06 | 15 | 65.2071 | 0.005 | 1.0237 | 1 | 200 | 200 | | 145.0859 | 0.08 | 20 | 64.0987 | 0.035 | 1.0198 | 7 | 200 | 200 | | 145.0859 | 0.11 | 25 | 62.9734 | 0.07 | 1.0119 | 14 | 200 | 200 | | 110.0012 | 0.13 | 30 | 61.8288 | 0.09 | 1.0119 | 18 | 200 | 200 | | 110.0012 | 0.15 | 35 | 60.6565 | 0.09 | 1.0119 | 18 | 200 | 200 | | 122.6164 | 0.17 | 40 | 59.4606 | 0.095 | 1.0119 | 19 | 200 | 200 | | 122.6164 | 0.19 | 45 | 58.2224 | 0.095 | 1.0099 | 19 | 200 | 200 | | 125.942 | 0.21 | 50 | 56.9514 | 0.095 | 1.0020 | 19 | 200 | 200 | | 125.942 | 0.23 | 55 | 55.5923 | 0.095 | 1.0 | 19 | 200 | 200 | | 111.2271 | 0.25 | 60 | 54.1423 | 0.095 | 1.0 | 19 | 200 | 200 | | 111.2271 | 0.27 | 65 | 52.6174 | 0.095 | 1.0 | 19 | 200 | 200 | | 137.2356 | 0.3 | 70 | 51.0340 | 0.095 | 1.0 | 19 | 200 | 200 | | 137.2356 | 0.32 | 75 | 49.4034 | 0.095 | 1.0 | 19 | 200 | 200 | | 112.2532 | 0.34 | 80 | 47.7291 | 0.095 | 1.0 | 19 | 200 | 200 | | 112.2532 | 0.36 | 85 | 46.0281 | 0.095 | 1.0 | 19 | 200 | 200 | | 88.3973 | 0.38 | 90 | 44.2361 | 0.095 | 1.0 | 19 | 200 | 200 | | 88.3973 | 0.4 | 95 | 42.4925 | 0.095 | 1.0 | 19 | 200 | 200 | | 88.7175 | 0.42 | 100 | 40.7673 | 0.095 | 1.0 | 19 | 200 | 200 | | 88.7175 | 0.44 | 105 | 39.0848 | 0.095 | 1.0 | 19 | 200 | 200 | | 90.857 | 0.46 | 110 | 37.4890 | 0.095 | 1.0 | 19 | 200 | 200 | | 90.857 | 0.48 | 115 | 35.8966 | 0.095 | 1.0 | 19 | 200 | 200 | | 77.5782 | 0.51 | 120 | 34.2822 | 0.1 | 1.0 | 20 | 200 | 200 | | 77.5782 | 0.53 | 125 | 32.7953 | 0.1 | 1.0 | 20 | 200 | 200 | | 80.2378 | 0.55 | 130 | 31.4560 | 0.1 | 1.0 | 20 | 200 | 200 | | 80.2378 | 0.57 | 135 | 30.1651 | 0.1 | 1.0 | 20 | 200 | 200 | | 73.5042 | 0.59 | 140 | 29.0069 | 0.095 | 1.0 | 19 | 200 | 200 | | 73.5042 | 0.61 | 145 | 28.0349 | 0.095 | 1.0 | 19 | 200 | 200 | | 71.5632 | 0.63 | 150 | 27.1812 | 0.095 | 1.0 | 19 | 200 | 200 | | 71.5632 | 0.65 | 155 | 26.4012 | 0.095 | 1.0 | 19 | 200 | 200 | | 76.5337 | 0.67 | 160 | 25.6924 | 0.095 | 1.0 | 19 | 200 | 200 | | 76.5337 | 0.7 | 165 | 25.0184 | 0.095 | 1.0 | 19 | 200 | 200 | | 54.6507 | 0.72 | 170 | 24.4100 | 0.095 | 1.0 | 19 | 200 | 200 | | 54.6507 | 0.74 | 175 | 23.8273 | 0.095 | 1.0 | 19 | 200 | 200 | | 57.1606 | 0.76 | 180 | 23.2988 | 0.095 | 1.0 | 19 | 200 | 200 | | 57.1606 | 0.78 | 185 | 22.8731 | 0.095 | 1.0 | 19 | 200 | 200 | | 56.0855 | 0.8 | 190 | 22.5336 | 0.095 | 1.0 | 19 | 200 | 200 | | 56.0855 | 0.82 | 195 | 22.2334 | 0.095 | 1.0 | 19 | 200 | 200 | | 55.2475 | 0.84 | 200 | 21.9555 | 0.095 | 1.0 | 19 | 200 | 200 | | 55.2475 | 0.86 | 205 | 21.7112 | 0.095 | 1.0 | 19 | 200 | 200 | | 47.9988 | 0.89 | 210 | 21.5123 | 0.095 | 1.0 | 19 | 200 | 200 | | 47.9988 | 0.91 | 215 | 21.3407 | 0.095 | 1.0 | 19 | 200 | 200 | | 55.1394 | 0.93 | 220 | 21.1965 | 0.095 | 1.0 | 19 | 200 | 200 | | 55.1394 | 0.95 | 225 | 21.1028 | 0.095 | 1.0 | 19 | 200 | 200 | | 48.0323 | 0.97 | 230 | 21.0376 | 0.095 | 1.0 | 19 | 200 | 200 | | 48.0323 | 0.99 | 235 | 21.0050 | 0.095 | 1.0 | 19 | 200 | 200 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 1.18.3 - Tokenizers 0.13.2

Fabiuas/Animal-classifier

Fabiuas

2022-11-28T14:38:27Z

311

1

transformers

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

image-classification

2022-11-28T14:37:59Z

--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: Animal-classifier results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.9481481313705444 --- # Animal-classifier Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### bee ![bee](images/bee.jpg) #### beetle ![beetle](images/beetle.jpg) #### bird ![bird](images/bird.jpg) #### butterfly ![butterfly](images/butterfly.jpg) #### camel ![camel](images/camel.jpg) #### cat ![cat](images/cat.jpg) #### caterpillar ![caterpillar](images/caterpillar.jpg) #### crab ![crab](images/crab.jpg) #### dog ![dog](images/dog.jpg) #### fly ![fly](images/fly.jpg) #### grasshopper ![grasshopper](images/grasshopper.jpg) #### horse ![horse](images/horse.jpg) #### lizard ![lizard](images/lizard.jpg) #### mosquito ![mosquito](images/mosquito.jpg) #### mouse ![mouse](images/mouse.jpg) #### snake ![snake](images/snake.jpg) #### spider ![spider](images/spider.jpg) #### whale ![whale](images/whale.jpg)

regel-corpus/hunflair-tfbs

regel-corpus

2022-11-28T14:37:52Z

3

0

flair

[ "flair", "pytorch", "hunflair", "token-classification", "sequence-tagger-model", "en", "region:us" ]

token-classification

2022-03-29T11:26:41Z

--- tags: - flair - hunflair - token-classification - sequence-tagger-model language: en widget: - text: "It contains a functional GCGGCGGCG Egr-1-binding site" --- ## HunFlair model for Transcription Factor Binding Site (TFBS) [HunFlair](https://github.com/flairNLP/flair/blob/master/resources/docs/HUNFLAIR.md) (biomedical flair) for TFBS entity. Predicts 1 tag: | **tag** | **meaning** | |---------------------------------|-----------| | Tfbs | DNA region bound by transcription factor | --- ### Cite Please cite the following paper when using this model. ``` @article{garda2022regel, title={RegEl corpus: identifying DNA regulatory elements in the scientific literature}, author={Garda, Samuele and Lenihan-Geels, Freyda and Proft, Sebastian and Hochmuth, Stefanie and Sch{\"u}lke, Markus and Seelow, Dominik and Leser, Ulf}, journal={Database}, volume={2022}, year={2022}, publisher={Oxford Academic} } ``` --- ### Demo: How to use in Flair Requires: - **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`) ```python from flair.data import Sentence from flair.models import SequenceTagger # for biomedical-specific tokenization: # from flair.tokenization import SciSpacyTokenizer # load tagger tagger = SequenceTagger.load("regel-corpus/hunflair-tfbs") text = "We found that Egr-1 specifically binds to the PTEN 5' untranslated region, which contains a functional GCGGCGGCG Egr-1-binding site." # make example sentence sentence = Sentence(text) # for biomedical-specific tokenization: # sentence = Sentence(text, use_tokenizer=SciSpacyTokenizer()) # predict NER tags tagger.predict(sentence) # print sentence print(sentence) # print predicted NER spans print('The following NER tags are found:') # iterate over entities and print for entity in sentence.get_spans('ner'): print(entity) ``` This yields the following output: ``` Span [19,20,21]: "GCGGCGGCG Egr-1-binding site" [− Labels: Tfbs (0.9631)] ``` So, the entity "*GCGGCGGCG Egr-1-binding site*" is found in the sentence. Alternatively download all models locally and use the `MultiTagger` class. ```python from flair.models import MultiTagger tagger = [ './models/hunflair-promoter/pytorch_model.bin', './models/hunflair-enhancer/pytorch_model.bin', './models/hunflair-tfbs/pytorch_model.bin', ] tagger = MultiTagger.load(['./models/hunflair-']) tagger.predict(sentence) ``` ---

fathyshalab/bert-uncased-massive-intent-classification-finetuned-banking-1

fathyshalab

2022-11-28T13:25:04Z

104

0

transformers

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

text-classification

2022-11-28T13:01:42Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: bert-uncased-massive-intent-classification-finetuned-banking-1 results: [] ---  # bert-uncased-massive-intent-classification-finetuned-banking-1 This model is a fine-tuned version of [gokuls/bert-uncased-massive-intent-classification](https://huggingface.co/gokuls/bert-uncased-massive-intent-classification) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.6447 - Accuracy: 0.1822 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.9685 | 1.0 | 3 | 2.7310 | 0.1422 | | 2.8056 | 2.0 | 6 | 2.6970 | 0.1467 | | 2.5004 | 3.0 | 9 | 2.6680 | 0.1511 | | 2.445 | 4.0 | 12 | 2.6515 | 0.1778 | | 2.3977 | 5.0 | 15 | 2.6447 | 0.1822 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1 - Datasets 2.3.2 - Tokenizers 0.12.1

jfjensen/sd-class-butterflies-32

jfjensen

2022-11-28T12:59:41Z

37

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-11-28T12:58:55Z

--- 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(jfjensen/sd-class-butterflies-32) image = pipeline().images[0] image ```

cardiffnlp/twitter-roberta-base-offensive

cardiffnlp

2022-11-28T11:36:23Z

35,866

27

transformers

[ "transformers", "pytorch", "tf", "jax", "roberta", "text-classification", "arxiv:2010.12421", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-03-02T23:29:05Z

# Twitter-roBERTa-base for Offensive Language Identification This is a roBERTa-base model trained on ~58M tweets and finetuned for offensive language identification with the TweetEval benchmark. - Paper: [_TweetEval_ benchmark (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf). - Git Repo: [Tweeteval official repository](https://github.com/cardiffnlp/tweeteval). ## Example of classification ```python from transformers import AutoModelForSequenceClassification from transformers import TFAutoModelForSequenceClassification from transformers import AutoTokenizer import numpy as np from scipy.special import softmax import csv import urllib.request # Preprocess text (username and link placeholders) def preprocess(text): new_text = [] for t in text.split(" "): t = '@user' if t.startswith('@') and len(t) > 1 else t t = 'http' if t.startswith('http') else t new_text.append(t) return " ".join(new_text) # Tasks: # emoji, emotion, hate, irony, offensive, sentiment # stance/abortion, stance/atheism, stance/climate, stance/feminist, stance/hillary task='offensive' MODEL = f"cardiffnlp/twitter-roberta-base-{task}" tokenizer = AutoTokenizer.from_pretrained(MODEL) # download label mapping labels=[] mapping_link = f"https://raw.githubusercontent.com/cardiffnlp/tweeteval/main/datasets/{task}/mapping.txt" with urllib.request.urlopen(mapping_link) as f: html = f.read().decode('utf-8').split("\n") csvreader = csv.reader(html, delimiter='\t') labels = [row[1] for row in csvreader if len(row) > 1] # PT model = AutoModelForSequenceClassification.from_pretrained(MODEL) model.save_pretrained(MODEL) text = "Good night 😊" text = preprocess(text) encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) scores = output[0][0].detach().numpy() scores = softmax(scores) # # TF # model = TFAutoModelForSequenceClassification.from_pretrained(MODEL) # model.save_pretrained(MODEL) # text = "Good night 😊" # encoded_input = tokenizer(text, return_tensors='tf') # output = model(encoded_input) # scores = output[0][0].numpy() # scores = softmax(scores) ranking = np.argsort(scores) ranking = ranking[::-1] for i in range(scores.shape[0]): l = labels[ranking[i]] s = scores[ranking[i]] print(f"{i+1}) {l} {np.round(float(s), 4)}") ``` Output: ``` 1) not-offensive 0.9073 2) offensive 0.0927 ```

clp/vit-base-patch16-224-finetuned

clp

2022-11-28T11:29:17Z

186

0

transformers

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

image-classification

2022-11-28T11:19:02Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: vit-base-patch16-224-finetuned results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.3333333333333333 ---  # vit-base-patch16-224-finetuned 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. It achieves the following results on the evaluation set: - Loss: 0.7617 - Accuracy: 0.3333 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 1 | 0.6063 | 0.6667 | | No log | 2.0 | 2 | 0.6958 | 0.3333 | | No log | 3.0 | 3 | 0.7617 | 0.3333 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

projecte-aina/roberta-base-ca-v2-cased-tc

projecte-aina

2022-11-28T11:02:09Z

110

1

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "catalan", "text classification", "tecla", "CaText", "Catalan Textual Corpus", "ca", "dataset:projecte-aina/tecla", "arxiv:1907.11692", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-06-30T07:55:23Z

--- language: - ca tags: - "catalan" - "text classification" - "tecla" - "CaText" - "Catalan Textual Corpus" datasets: - "projecte-aina/tecla" metrics: - accuracy model-index: - name: roberta-base-ca-v2-cased-tc results: - task: type: text-classification dataset: name: TeCla type: projecte-aina/tecla metrics: - name: Accuracy type: accuracy value: 0.8034 widget: - text: "Els Pets presenten el seu nou treball al Palau Sant Jordi." - text: "Els barcelonins incrementen un 23% l’ús del cotxe des de l’inici de la pandèmia." - text: "Retards a quatre línies de Rodalies per una avaria entre Sants i plaça de Catalunya." - text: "Majors de 60 anys i sanitaris començaran a rebre la tercera dosi de la vacuna covid els propers dies." - text: "Els cinemes Verdi estrenen Verdi Classics, un nou canal de televisió." --- # Catalan BERTa-v2 (roberta-base-ca-v2) finetuned for TeCla-based Text Classification. ## Table of Contents <details> <summary>Click to expand</summary> - [Model description](#model-description) - [Intended uses and limitations](#intended-use) - [How to use](#how-to-use) - [Limitations and bias](#limitations-and-bias) - [Training](#training) - [Training data](#training-data) - [Training procedure](#training-procedure) - [Tokenization](#tokenization) - [Hyperparameters](#hyperparameters) - [Evaluation](#evaluation) - [Variable and metrics](#variable-and-metrics) - [Evaluation results](#evaluation-results) - [Additional information](#additional-information) - [Author](#author) - [Contact information](#contact-information) - [Copyright](#copyright) - [Licensing information](#licensing-information) - [Funding](#funding) - [Citing information](#citing-information) - [Disclaimer](#disclaimer) </details> ## Model description The **roberta-base-ca-v2-cased-tc** is a Text Classification (TC) model for the Catalan language fine-tuned from the [roberta-base-ca-v2](https://huggingface.co/projecte-aina/roberta-base-ca-v2) model, a [RoBERTa](https://arxiv.org/abs/1907.11692) base model pre-trained on a medium-size corpus collected from publicly available corpora and crawlers (check the roberta-base-ca-v2 model card for more details). The previous version of this model, which was trained on the old TeCla dataset (v1), can still be accessed through the "v1" tag. ## Intended uses and limitations **roberta-base-ca-v2-cased-tc** model can be used to classify texts. The model is limited by its training dataset and may not generalize well for all use cases. ## How to use Here is how to use this model: ```python from transformers import pipeline from pprint import pprint nlp = pipeline("text-classification", model="projecte-aina/roberta-base-ca-v2-cased-tc") example = "Retards a quatre línies de Rodalies per una avaria entre Sants i plaça de Catalunya." tc_results = nlp(example) pprint(tc_results) ``` ## Limitations and bias At the time of submission, no measures have been taken to estimate the bias embedded in the model. However, we are well aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this model card will be updated. ## Training ### Training data We used the TC dataset in Catalan called [TeCla](https://huggingface.co/datasets/projecte-aina/tecla) for training and evaluation. Although TeCla includes a coarse-grained ('label1') and a fine-grained categorization ('label2'), only the last one, with 53 classes, was used for the training. ### Training procedure The model was trained with a batch size of 16 and a learning rate of 5e-5 for 5 epochs. We then selected the best checkpoint using the downstream task metric in the corresponding development set and then evaluated it on the test set. ## Evaluation ### Variable and metrics This model was finetuned maximizing F1 (weighted). ## Evaluation results We evaluated the _roberta-base-ca-v2-cased-tc_ on the TeCla test set against standard multilingual and monolingual baselines. The results for 'label1' categories were obtained through a mapping from the fine-grained category ('label2') to the corresponding coarse-grained one ('label1'). | Model | TeCla - label1 (Accuracy) | TeCla - label2 (Accuracy) | | ------------|:-------------|:-------------| | roberta-base-ca-v2 | 96.31 | 80.34 | | roberta-large-ca-v2 | **96.51** | **80.68** | | mBERT | 95.72 | 78.47 | | XLM-RoBERTa | 95.66 | 78.01 | For more details, check the fine-tuning and evaluation scripts in the official [GitHub repository](https://github.com/projecte-aina/club). ## Additional information ### Author Text Mining Unit (TeMU) at the Barcelona Supercomputing Center ([email protected]) ### Contact information For further information, send an email to [email protected] ### Copyright Copyright (c) 2022 Text Mining Unit at Barcelona Supercomputing Center ### Licensing information [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0) ### Funding This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina). ## Citation Information If you use any of these resources (datasets or models) in your work, please cite our latest paper: ```bibtex @inproceedings{armengol-estape-etal-2021-multilingual, title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}atalan", author = "Armengol-Estap{\'e}, Jordi and Carrino, Casimiro Pio and Rodriguez-Penagos, Carlos and de Gibert Bonet, Ona and Armentano-Oller, Carme and Gonzalez-Agirre, Aitor and Melero, Maite and Villegas, Marta", booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.findings-acl.437", doi = "10.18653/v1/2021.findings-acl.437", pages = "4933--4946", } ``` ### Disclaimer <details> <summary>Click to expand</summary> The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions. When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence. In no event shall the owner and creator of the models (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models.

GDJ1978/voxelartXmidjgraffiti

GDJ1978

2022-11-28T10:01:38Z

0

null

[ "region:us" ]

null

2022-11-28T09:55:36Z

VoxelArt_v1_0.6-MDJRNY-GRFFT_0.4-Weighted_sum-merged.ckpt trigger: VoxelArt in the style of mdjrny-grfft

mn367/radio-mlm

mn367

2022-11-28T09:52:57Z

61

0

transformers

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

fill-mask

2022-11-28T09:42:20Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: mn367/radio-mlm results: [] ---  # mn367/radio-mlm This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.6630 - Validation Loss: 4.6014 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 39000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 4.6630 | 4.6014 | 0 | ### Framework versions - Transformers 4.24.0 - TensorFlow 2.9.2 - Datasets 2.7.1 - Tokenizers 0.13.2

vumichien/trillsson3-ft-keyword-spotting-15

vumichien

2022-11-28T09:46:32Z

47

0

transformers

[ "transformers", "pytorch", "tensorboard", "trillsson_efficient", "text-classification", "audio-classification", "generated_from_trainer", "dataset:superb", "autotrain_compatible", "endpoints_compatible", "region:us" ]

audio-classification

2022-11-28T08:17:45Z

--- tags: - audio-classification - generated_from_trainer datasets: - superb metrics: - accuracy model-index: - name: trillsson3-ft-keyword-spotting-15 results: [] ---  # trillsson3-ft-keyword-spotting-15 This model is a fine-tuned version of [vumichien/nonsemantic-speech-trillsson3](https://huggingface.co/vumichien/nonsemantic-speech-trillsson3) on the superb dataset. It achieves the following results on the evaluation set: - Loss: 0.3563 - Accuracy: 0.9041 ## 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: 32 - eval_batch_size: 64 - seed: 0 - 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: 0.1 - num_epochs: 10.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.1824 | 1.0 | 798 | 0.6478 | 0.7489 | | 0.7448 | 2.0 | 1596 | 0.4274 | 0.8728 | | 0.7089 | 3.0 | 2394 | 0.3723 | 0.8950 | | 0.6781 | 4.0 | 3192 | 0.3563 | 0.9041 | | 0.6386 | 5.0 | 3990 | 0.3441 | 0.8986 | | 0.6342 | 6.0 | 4788 | 0.3380 | 0.8994 | | 0.6275 | 7.0 | 5586 | 0.3376 | 0.8982 | | 0.6349 | 8.0 | 6384 | 0.3333 | 0.9014 | | 0.6261 | 9.0 | 7182 | 0.3295 | 0.9025 | | 0.6188 | 10.0 | 7980 | 0.3322 | 0.9025 | ### Framework versions - Transformers 4.23.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

rohitagrawal-20/bert-finetuned-ner

rohitagrawal-20

2022-11-28T09:39:47Z

125

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "dataset:conll2003", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-11-28T09:12:26Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: train args: conll2003 metrics: - name: Precision type: precision value: 0.935969556585043 - name: Recall type: recall value: 0.9520363513968361 - name: F1 type: f1 value: 0.9439345903554145 - name: Accuracy type: accuracy value: 0.9868870312591982 ---  # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0599 - Precision: 0.9360 - Recall: 0.9520 - F1: 0.9439 - Accuracy: 0.9869 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0879 | 1.0 | 1756 | 0.0652 | 0.9236 | 0.9379 | 0.9307 | 0.9832 | | 0.0343 | 2.0 | 3512 | 0.0614 | 0.9337 | 0.9510 | 0.9423 | 0.9864 | | 0.019 | 3.0 | 5268 | 0.0599 | 0.9360 | 0.9520 | 0.9439 | 0.9869 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

hannoh/03_model_sales

hannoh

2022-11-28T08:58:05Z

103

0

transformers

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

text-classification

2022-11-28T08:46:54Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: 03_model_sales results: [] ---  # 03_model_sales 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: 0.4693 - Accuracy: 0.7818 - F1: 0.7980 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

alexziweiwang/retrain_epoch2to5

alexziweiwang

2022-11-28T08:51:14Z

31

0

transformers

[ "transformers", "pytorch", "wav2vec2", "generated_from_trainer", "endpoints_compatible", "region:us" ]

null

2022-11-28T08:35:03Z

--- tags: - generated_from_trainer model-index: - name: retrain_epoch2to5 results: [] ---  # retrain_epoch2to5 This model is a fine-tuned version of [alexziweiwang/retrain_first1epoch](https://huggingface.co/alexziweiwang/retrain_first1epoch) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.3244 - Acc: 0.24 - Wer: 1.0 - Correct: 48 - Total: 200 - Strlen: 200 ## 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: 9e-06 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Acc | Wer | Correct | Total | Strlen | |:-------------:|:-----:|:----:|:---------------:|:----:|:---:|:-------:|:-----:|:------:| | No log | 0.02 | 5 | 7.8494 | 0.24 | 1.0 | 48 | 200 | 200 | | 7.6032 | 0.04 | 10 | 7.4834 | 0.24 | 1.0 | 48 | 200 | 200 | | 7.6032 | 0.06 | 15 | 7.1350 | 0.24 | 1.0 | 48 | 200 | 200 | | 7.3336 | 0.08 | 20 | 6.8284 | 0.24 | 1.0 | 48 | 200 | 200 | | 7.3336 | 0.11 | 25 | 6.5577 | 0.24 | 1.0 | 48 | 200 | 200 | | 6.2911 | 0.13 | 30 | 6.3124 | 0.24 | 1.0 | 48 | 200 | 200 | | 6.2911 | 0.15 | 35 | 6.0850 | 0.24 | 1.0 | 48 | 200 | 200 | | 5.9181 | 0.17 | 40 | 5.8888 | 0.24 | 1.0 | 48 | 200 | 200 | | 5.9181 | 0.19 | 45 | 5.6815 | 0.24 | 1.0 | 48 | 200 | 200 | | 5.7954 | 0.21 | 50 | 5.4834 | 0.24 | 1.0 | 48 | 200 | 200 | | 5.7954 | 0.23 | 55 | 5.3099 | 0.24 | 1.0 | 48 | 200 | 200 | | 5.4801 | 0.25 | 60 | 5.1678 | 0.24 | 1.0 | 48 | 200 | 200 | | 5.4801 | 0.27 | 65 | 5.0223 | 0.24 | 1.0 | 48 | 200 | 200 | | 5.3377 | 0.3 | 70 | 4.8893 | 0.24 | 1.0 | 48 | 200 | 200 | | 5.3377 | 0.32 | 75 | 4.7743 | 0.24 | 1.0 | 48 | 200 | 200 | | 5.2511 | 0.34 | 80 | 4.6494 | 0.24 | 1.0 | 48 | 200 | 200 | | 5.2511 | 0.36 | 85 | 4.5307 | 0.24 | 1.0 | 48 | 200 | 200 | | 4.727 | 0.38 | 90 | 4.4237 | 0.24 | 1.0 | 48 | 200 | 200 | | 4.727 | 0.4 | 95 | 4.3263 | 0.24 | 1.0 | 48 | 200 | 200 | | 4.7653 | 0.42 | 100 | 4.2439 | 0.24 | 1.0 | 48 | 200 | 200 | | 4.7653 | 0.44 | 105 | 4.1589 | 0.24 | 1.0 | 48 | 200 | 200 | | 4.4971 | 0.46 | 110 | 4.0847 | 0.24 | 1.0 | 48 | 200 | 200 | | 4.4971 | 0.48 | 115 | 4.0118 | 0.24 | 1.0 | 48 | 200 | 200 | | 4.0077 | 0.51 | 120 | 3.9382 | 0.24 | 1.0 | 48 | 200 | 200 | | 4.0077 | 0.53 | 125 | 3.8663 | 0.24 | 1.0 | 48 | 200 | 200 | | 4.1693 | 0.55 | 130 | 3.8106 | 0.24 | 1.0 | 48 | 200 | 200 | | 4.1693 | 0.57 | 135 | 3.7580 | 0.24 | 1.0 | 48 | 200 | 200 | | 4.0854 | 0.59 | 140 | 3.7123 | 0.24 | 1.0 | 48 | 200 | 200 | | 4.0854 | 0.61 | 145 | 3.6720 | 0.24 | 1.0 | 48 | 200 | 200 | | 4.1988 | 0.63 | 150 | 3.6260 | 0.24 | 1.0 | 48 | 200 | 200 | | 4.1988 | 0.65 | 155 | 3.5853 | 0.24 | 1.0 | 48 | 200 | 200 | | 3.9975 | 0.67 | 160 | 3.5463 | 0.24 | 1.0 | 48 | 200 | 200 | | 3.9975 | 0.7 | 165 | 3.5122 | 0.24 | 1.0 | 48 | 200 | 200 | | 3.6042 | 0.72 | 170 | 3.4862 | 0.24 | 1.0 | 48 | 200 | 200 | | 3.6042 | 0.74 | 175 | 3.4631 | 0.24 | 1.0 | 48 | 200 | 200 | | 3.7347 | 0.76 | 180 | 3.4406 | 0.24 | 1.0 | 48 | 200 | 200 | | 3.7347 | 0.78 | 185 | 3.4202 | 0.24 | 1.0 | 48 | 200 | 200 | | 3.8336 | 0.8 | 190 | 3.4014 | 0.24 | 1.0 | 48 | 200 | 200 | | 3.8336 | 0.82 | 195 | 3.3855 | 0.24 | 1.0 | 48 | 200 | 200 | | 3.7454 | 0.84 | 200 | 3.3703 | 0.24 | 1.0 | 48 | 200 | 200 | | 3.7454 | 0.86 | 205 | 3.3576 | 0.24 | 1.0 | 48 | 200 | 200 | | 3.525 | 0.89 | 210 | 3.3471 | 0.24 | 1.0 | 48 | 200 | 200 | | 3.525 | 0.91 | 215 | 3.3392 | 0.24 | 1.0 | 48 | 200 | 200 | | 3.8175 | 0.93 | 220 | 3.3331 | 0.24 | 1.0 | 48 | 200 | 200 | | 3.8175 | 0.95 | 225 | 3.3289 | 0.24 | 1.0 | 48 | 200 | 200 | | 3.307 | 0.97 | 230 | 3.3259 | 0.24 | 1.0 | 48 | 200 | 200 | | 3.307 | 0.99 | 235 | 3.3244 | 0.24 | 1.0 | 48 | 200 | 200 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 1.18.3 - Tokenizers 0.13.2

huggingtweets/bobkerns

huggingtweets

2022-11-28T08:14:20Z

115

0

transformers

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

text-generation

2022-11-28T08:14:12Z

--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/3653376550/f40f9602f2e8e185eb7ddce332157ffe_400x400.jpeg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Bob (Moderna #5) Kerns</div> <div style="text-align: center; font-size: 14px;">@bobkerns</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Bob (Moderna #5) Kerns. | Data | Bob (Moderna #5) Kerns | | --- | --- | | Tweets downloaded | 3234 | | Retweets | 315 | | Short tweets | 42 | | Tweets kept | 2877 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/390ksfue/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @bobkerns's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3me25qi0) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3me25qi0/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/bobkerns') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

pere/whisper-NST2-unfreeze-constanti-low-lr

pere

2022-11-28T07:41:42Z

9

0

transformers

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

automatic-speech-recognition

2022-11-23T10:34:53Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-NST2-unfreeze-constanti-low-lr results: [] ---  # whisper-NST2-unfreeze-constanti-low-lr This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3562 - Wer: 8.5519 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 96 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - training_steps: 20000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 0.1901 | 0.05 | 1000 | 0.3069 | 14.8233 | | 0.1323 | 0.1 | 2000 | 0.2687 | 11.2885 | | 0.1137 | 0.15 | 3000 | 0.2620 | 10.8324 | | 0.1022 | 0.2 | 4000 | 0.2976 | 9.0080 | | 0.0937 | 0.25 | 5000 | 0.2584 | 9.5781 | | 0.0875 | 0.3 | 6000 | 0.2704 | 20.2965 | | 0.0592 | 1.05 | 7000 | 0.2751 | 9.0080 | | 0.0488 | 1.1 | 8000 | 0.2778 | 8.6659 | | 0.0475 | 1.15 | 9000 | 0.2792 | 9.4641 | | 0.0439 | 1.2 | 10000 | 0.2880 | 8.3238 | | 0.0425 | 1.25 | 11000 | 0.2954 | 8.5519 | | 0.0416 | 1.3 | 12000 | 0.2896 | 20.2965 | | 0.0289 | 2.05 | 13000 | 0.2990 | 7.9818 | | 0.0229 | 2.1 | 14000 | 0.3027 | 7.4116 | | 0.0248 | 2.15 | 15000 | 0.2968 | 8.6659 | | 0.0225 | 2.2 | 16000 | 0.3100 | 8.5519 | | 0.0222 | 2.25 | 17000 | 0.3132 | 9.3501 | | 0.0219 | 2.3 | 18000 | 0.3230 | 7.6397 | | 0.0162 | 3.04 | 19000 | 0.3380 | 9.8062 | | 0.0132 | 3.09 | 20000 | 0.3562 | 8.5519 | ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.6.1 - Tokenizers 0.13.1

linfuyou/bert-squad-training

linfuyou

2022-11-28T07:41:14Z

117

0

transformers

[ "transformers", "pytorch", "bert", "question-answering", "endpoints_compatible", "region:us" ]

question-answering

2022-11-15T09:15:55Z

bert-base-cased-squadv1.1-training

mtz2110/wav2vec2-large-xls-r-300m-he

mtz2110

2022-11-28T07:33:52Z

22

0

transformers

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

automatic-speech-recognition

2022-11-27T16:52:23Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - fleurs metrics: - wer model-index: - name: wav2vec2-large-xls-r-300m-he results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: fleurs type: fleurs config: he_il split: train args: he_il metrics: - name: Wer type: wer value: 0.5953778429933969 ---  # wav2vec2-large-xls-r-300m-he This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the fleurs dataset. It achieves the following results on the evaluation set: - Loss: inf - Wer: 0.5954 ## 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: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 6 - total_train_batch_size: 12 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 8.8899 | 0.99 | 200 | inf | 1.0 | | 3.0802 | 1.98 | 400 | inf | 1.0 | | 1.4275 | 2.97 | 600 | inf | 0.8155 | | 0.8737 | 3.96 | 800 | inf | 0.7276 | | 0.6503 | 4.95 | 1000 | inf | 0.6858 | | 0.5176 | 5.94 | 1200 | inf | 0.6660 | | 0.4084 | 6.93 | 1400 | inf | 0.6682 | | 0.3469 | 7.92 | 1600 | inf | 0.6473 | | 3.2485 | 6.67 | 1800 | inf | 1.0 | | 0.6476 | 7.41 | 2000 | inf | 0.6574 | | 0.3229 | 8.15 | 2200 | inf | 0.6499 | | 0.2899 | 8.89 | 2400 | inf | 0.6376 | | 0.26 | 9.63 | 2600 | inf | 0.6405 | | 0.2038 | 10.37 | 2800 | inf | 0.6409 | | 0.2158 | 11.11 | 3000 | inf | 0.6313 | | 0.1892 | 11.85 | 3200 | inf | 0.6185 | | 0.1611 | 12.59 | 3400 | inf | 0.6271 | | 0.1584 | 13.33 | 3600 | inf | 0.6101 | | 0.1443 | 14.07 | 3800 | inf | 0.6121 | | 0.1353 | 14.81 | 4000 | inf | 0.6194 | | 0.1109 | 15.56 | 4200 | inf | 0.6321 | | 0.1116 | 16.3 | 4400 | inf | 0.6025 | | 0.1054 | 17.04 | 4600 | inf | 0.6029 | | 0.0966 | 17.78 | 4800 | inf | 0.6069 | | 0.0824 | 18.52 | 5000 | inf | 0.5998 | | 0.0812 | 19.26 | 5200 | inf | 0.5972 | | 0.0749 | 20.0 | 5400 | inf | 0.5954 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1 - Datasets 2.6.1 - Tokenizers 0.13.1

venetis/vit-base-patch16-224-in21k-finetuned-cifar10_album_vitVMMRdb_make_model_album_pred

venetis

2022-11-28T07:33:09Z

186

0

transformers

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

image-classification

2022-11-27T16:45:37Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: vit-base-patch16-224-in21k-finetuned-cifar10_album_vitVMMRdb_make_model_album_pred results: [] ---  # vit-base-patch16-224-in21k-finetuned-cifar10_album_vitVMMRdb_make_model_album_pred This model is a fine-tuned version of [aaraki/vit-base-patch16-224-in21k-finetuned-cifar10](https://huggingface.co/aaraki/vit-base-patch16-224-in21k-finetuned-cifar10) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5462 - Accuracy: 0.8594 - Precision: 0.8556 - Recall: 0.8594 - F1: 0.8544 ## 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: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:| | 4.6112 | 1.0 | 839 | 4.5615 | 0.1425 | 0.0837 | 0.1425 | 0.0646 | | 3.1177 | 2.0 | 1678 | 2.9595 | 0.4240 | 0.3424 | 0.4240 | 0.3283 | | 2.0793 | 3.0 | 2517 | 2.0048 | 0.5771 | 0.5081 | 0.5771 | 0.5029 | | 1.4566 | 4.0 | 3356 | 1.4554 | 0.6760 | 0.6333 | 0.6760 | 0.6280 | | 1.1307 | 5.0 | 4195 | 1.1319 | 0.7350 | 0.7027 | 0.7350 | 0.7013 | | 0.9367 | 6.0 | 5034 | 0.9328 | 0.7738 | 0.7546 | 0.7738 | 0.7503 | | 0.7783 | 7.0 | 5873 | 0.8024 | 0.7986 | 0.7893 | 0.7986 | 0.7819 | | 0.6022 | 8.0 | 6712 | 0.7187 | 0.8174 | 0.8098 | 0.8174 | 0.8055 | | 0.5234 | 9.0 | 7551 | 0.6635 | 0.8313 | 0.8220 | 0.8313 | 0.8217 | | 0.4298 | 10.0 | 8390 | 0.6182 | 0.8388 | 0.8337 | 0.8388 | 0.8302 | | 0.3618 | 11.0 | 9229 | 0.5953 | 0.8455 | 0.8394 | 0.8455 | 0.8382 | | 0.3262 | 12.0 | 10068 | 0.5735 | 0.8501 | 0.8443 | 0.8501 | 0.8436 | | 0.3116 | 13.0 | 10907 | 0.5612 | 0.8527 | 0.8488 | 0.8527 | 0.8471 | | 0.2416 | 14.0 | 11746 | 0.5524 | 0.8558 | 0.8500 | 0.8558 | 0.8496 | | 0.2306 | 15.0 | 12585 | 0.5489 | 0.8572 | 0.8525 | 0.8572 | 0.8519 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2