librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-06-22 12:28:33	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 492 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-22 12:28:03	card stringlengths 11 1.01M
tommyadams/finetuned_falconb6	tommyadams	2023-09-11T17:28:55Z	0	0	null	[ "generated_from_trainer", "base_model:TinyLlama/TinyLlama-1.1B-step-50K-105b", "base_model:finetune:TinyLlama/TinyLlama-1.1B-step-50K-105b", "license:apache-2.0", "region:us" ]	null	2023-09-10T22:00:12Z	--- license: apache-2.0 base_model: PY007/TinyLlama-1.1B-step-50K-105b tags: - generated_from_trainer model-index: - name: finetuned_falconb6 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. --> # finetuned_falconb6 This model is a fine-tuned version of [PY007/TinyLlama-1.1B-step-50K-105b](https://huggingface.co/PY007/TinyLlama-1.1B-step-50K-105b) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - training_steps: 3 ### Training results ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3
bigmorning/whisper_4_with_init_sun_syl_wd_0_lr_en2_0010	bigmorning	2023-09-11T17:15:58Z	59	0	transformers	[ "transformers", "tf", "whisper", "automatic-speech-recognition", "generated_from_keras_callback", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-09-11T17:15:49Z	--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_keras_callback model-index: - name: whisper_4_with_init_sun_syl_wd_0_lr_en2_0010 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. --> # whisper_4_with_init_sun_syl_wd_0_lr_en2_0010 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.8685 - Train Accuracy: 0.0113 - Train Wermet: 0.9890 - Train Wermet Syl: 0.9897 - Validation Loss: 4.1857 - Validation Accuracy: 0.0113 - Validation Wermet: 0.9851 - Validation Wermet Syl: 0.9843 - Epoch: 9 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 0.01, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0} - training_precision: float32 ### Training results \| Train Loss \| Train Accuracy \| Train Wermet \| Train Wermet Syl \| Validation Loss \| Validation Accuracy \| Validation Wermet \| Validation Wermet Syl \| Epoch \| \|:----------:\|:--------------:\|:------------:\|:----------------:\|:---------------:\|:-------------------:\|:-----------------:\|:---------------------:\|:-----:\| \| 39.6121 \| 0.0057 \| 33.2649 \| 25.5768 \| 4.5339 \| 0.0113 \| 0.9851 \| 0.9843 \| 0 \| \| 5.3698 \| 0.0107 \| 12.0116 \| 9.0545 \| 4.3408 \| 0.0112 \| 0.9919 \| 0.9915 \| 1 \| \| 5.1979 \| 0.0109 \| 9.4008 \| 7.1909 \| 4.2108 \| 0.0113 \| 0.9851 \| 0.9843 \| 2 \| \| 5.0669 \| 0.0110 \| 7.0382 \| 5.3339 \| 4.1662 \| 0.0113 \| 0.9851 \| 0.9843 \| 3 \| \| 4.9546 \| 0.0111 \| 4.8506 \| 3.7351 \| 4.3022 \| 0.0112 \| 0.9870 \| 0.9854 \| 4 \| \| 4.9453 \| 0.0111 \| 3.9228 \| 3.1750 \| 4.1194 \| 0.0113 \| 0.9851 \| 0.9843 \| 5 \| \| 4.9123 \| 0.0112 \| 2.2402 \| 1.9643 \| 4.1865 \| 0.0112 \| 1.0000 \| 1.0000 \| 6 \| \| 4.8957 \| 0.0112 \| 1.7673 \| 1.5892 \| 4.1150 \| 0.0112 \| 1.0000 \| 0.9999 \| 7 \| \| 4.8959 \| 0.0112 \| 2.2166 \| 1.9601 \| 4.1185 \| 0.0113 \| 0.9851 \| 0.9843 \| 8 \| \| 4.8685 \| 0.0113 \| 0.9890 \| 0.9897 \| 4.1857 \| 0.0113 \| 0.9851 \| 0.9843 \| 9 \| ### Framework versions - Transformers 4.34.0.dev0 - TensorFlow 2.13.0 - Tokenizers 0.13.3
yugant13/fav-cricketer	yugant13	2023-09-11T17:10:18Z	0	0	null	[ "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-09-11T17:09:29Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### fav-cricketer Dreambooth model trained by yugant13 following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: GoX19932gAS Sample pictures of this concept: ![0](https://huggingface.co/yugant13/fav-cricketer/resolve/main/sample_images/xzg_(2).jpg)
mindchain/llama2-adapter_AAA110	mindchain	2023-09-11T17:03:43Z	2	0	peft	[ "peft", "region:us" ]	null	2023-09-11T17:03:39Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.0.dev0
turing-motors/heron-chat-git-Llama-2-7b-v0	turing-motors	2023-09-11T16:53:31Z	24	0	transformers	[ "transformers", "pytorch", "git_llama", "text-generation", "heron", "vision", "image-captioning", "VQA", "image-to-text", "en", "arxiv:2205.14100", "arxiv:2307.09288", "license:cc-by-nc-4.0", "autotrain_compatible", "region:us" ]	image-to-text	2023-09-07T10:55:05Z	--- language: - en tags: - heron - vision - image-captioning - VQA pipeline_tag: image-to-text license: - cc-by-nc-4.0 inference: false --- # Heron GIT Llama 2 Fast 7B ![heron](./heron_image.png) ## Model Details Heron GIT Llama 2 7B is a vision-language model that can converse about input images.<br> This model was trained using [the heron library](https://github.com/turingmotors/heron). Please refer to the code for details. ## Usage Follow [the installation guide](https://github.com/turingmotors/heron/#1-clone-this-repository). ```python import requests from PIL import Image import torch from transformers import AutoProcessor from heron.models.git_llm.git_llama import GitLlamaConfig, GitLlamaForCausalLM device_id = 0 # prepare a pretrained model model = GitLlamaForCausalLM.from_pretrained( 'turing-motors/heron-chat-git-Llama-2-7b-v0', torch_dtype=torch.float16 ) model.eval() model.to(f"cuda:{device_id}") # prepare a processor processor = AutoProcessor.from_pretrained('turing-motors/heron-chat-git-Llama-2-7b-v0') # prepare inputs url = "https://www.barnorama.com/wp-content/uploads/2016/12/03-Confusing-Pictures.jpg" image = Image.open(requests.get(url, stream=True).raw) text = f"##human: What is this picture?\n##gpt: " # do preprocessing inputs = processor( text, image, return_tensors="pt", truncation=True, ) inputs = {k: v.to(f"cuda:{device_id}") for k, v in inputs.items()} # set eos token eos_token_id_list = [ processor.tokenizer.pad_token_id, processor.tokenizer.eos_token_id, ] # do inference with torch.no_grad(): out = model.generate(*inputs, max_length=256, do_sample=False, temperature=0., eos_token_id=eos_token_id_list) # print result print(processor.tokenizer.batch_decode(out)[0]) ``` ## Model Details Developed by: [Turing Inc.](https://www.turing-motors.com/) * Adaptor type: [GIT](https://arxiv.org/abs/2205.14100) * Lamguage Model: [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) * Language(s): English ### Training This model was initially trained with the Adaptor using Coco Captions in M3IT. In the second phase, it was fine-tuned with M3IT. Finally, it was trained by instruction tuning with LLaVA-Instruct-150K. ### Training Dataset - [LLaVA-Instruct-150K](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K) - [M3IT](https://huggingface.co/datasets/MMInstruction/M3IT) ## Use and Limitations ### Intended Use This model is intended for use in chat-like applications and for research purposes. ### Limitations The model may produce inaccurate or false information, and its accuracy is not guaranteed. It is still in the research and development stage. ## How to cite ```bibtex @misc{GitLlama2, url = {[https://huggingface.co/turing-motors/heron-chat-git-Llama-2-7b-v0](https://huggingface.co/turing-motors/heron-chat-git-Llama-2-7b-v0)}, title = {Heron GIT Llama 2 7B}, author = {Yuichi Inoue, Kotaro Tanahashi, and Yu Yamaguchi} } ``` ## Citations ```bibtex @misc{touvron2023llama, title={Llama 2: Open Foundation and Fine-Tuned Chat Models}, author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov and Soumya Batra and Prajjwal Bhargava and Shruti Bhosale and Dan Bikel and Lukas Blecher and Cristian Canton Ferrer and Moya Chen and Guillem Cucurull and David Esiobu and Jude Fernandes and Jeremy Fu and Wenyin Fu and Brian Fuller and Cynthia Gao and Vedanuj Goswami and Naman Goyal and Anthony Hartshorn and Saghar Hosseini and Rui Hou and Hakan Inan and Marcin Kardas and Viktor Kerkez and Madian Khabsa and Isabel Kloumann and Artem Korenev and Punit Singh Koura and Marie-Anne Lachaux and Thibaut Lavril and Jenya Lee and Diana Liskovich and Yinghai Lu and Yuning Mao and Xavier Martinet and Todor Mihaylov and Pushkar Mishra and Igor Molybog and Yixin Nie and Andrew Poulton and Jeremy Reizenstein and Rashi Rungta and Kalyan Saladi and Alan Schelten and Ruan Silva and Eric Michael Smith and Ranjan Subramanian and Xiaoqing Ellen Tan and Binh Tang and Ross Taylor and Adina Williams and Jian Xiang Kuan and Puxin Xu and Zheng Yan and Iliyan Zarov and Yuchen Zhang and Angela Fan and Melanie Kambadur and Sharan Narang and Aurelien Rodriguez and Robert Stojnic and Sergey Edunov and Thomas Scialom}, year={2023}, eprint={2307.09288}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` --- license: cc-by-nc-4.0 ---
iven5880/distilbert-base-uncased-finetuned-imdb	iven5880	2023-09-11T16:34:41Z	108	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "distilbert", "fill-mask", "generated_from_trainer", "dataset:imdb", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-09-08T01:39:44Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb base_model: distilbert-base-uncased model-index: - name: distilbert-base-uncased-finetuned-imdb results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 2.4442 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.6985 \| 1.0 \| 157 \| 2.5612 \| \| 2.562 \| 2.0 \| 314 \| 2.4226 \| \| 2.5316 \| 3.0 \| 471 \| 2.4218 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.0 - Datasets 2.14.5 - Tokenizers 0.13.2
ldos/text_shortening_model_v31	ldos	2023-09-11T16:05:54Z	51	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-09-11T15:08:02Z	--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: text_shortening_model_v31 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. --> # text_shortening_model_v31 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.7416 - Rouge1: 0.4961 - Rouge2: 0.2712 - Rougel: 0.4388 - Rougelsum: 0.4386 - Bert precision: 0.8749 - Bert recall: 0.8711 - Average word count: 8.5135 - Max word count: 16 - Min word count: 3 - Average token count: 13.1592 - % shortened texts with length > 12: 10.2102 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Bert precision \| Bert recall \| Average word count \| Max word count \| Min word count \| Average token count \| % shortened texts with length > 12 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:--------------:\|:-----------:\|:------------------:\|:--------------:\|:--------------:\|:-------------------:\|:----------------------------------:\| \| 1.1978 \| 1.0 \| 145 \| 1.5250 \| 0.4953 \| 0.2842 \| 0.4528 \| 0.4524 \| 0.8806 \| 0.8681 \| 7.8919 \| 18 \| 3 \| 12.4234 \| 4.2042 \| \| 1.0092 \| 2.0 \| 290 \| 1.4421 \| 0.5257 \| 0.3053 \| 0.4698 \| 0.4689 \| 0.875 \| 0.8809 \| 9.6006 \| 18 \| 4 \| 14.3574 \| 19.2192 \| \| 0.8932 \| 3.0 \| 435 \| 1.4060 \| 0.5266 \| 0.3045 \| 0.4728 \| 0.472 \| 0.8766 \| 0.8776 \| 9.0841 \| 18 \| 4 \| 13.6366 \| 14.7147 \| \| 0.79 \| 4.0 \| 580 \| 1.4022 \| 0.5329 \| 0.3136 \| 0.4714 \| 0.4714 \| 0.8802 \| 0.8797 \| 8.952 \| 16 \| 4 \| 13.6036 \| 12.9129 \| \| 0.7506 \| 5.0 \| 725 \| 1.4514 \| 0.5145 \| 0.2935 \| 0.4485 \| 0.4485 \| 0.8745 \| 0.8726 \| 8.97 \| 18 \| 4 \| 13.6096 \| 12.012 \| \| 0.6981 \| 6.0 \| 870 \| 1.4602 \| 0.5146 \| 0.2914 \| 0.4566 \| 0.4559 \| 0.8778 \| 0.8762 \| 8.958 \| 18 \| 3 \| 13.5195 \| 15.3153 \| \| 0.6426 \| 7.0 \| 1015 \| 1.4745 \| 0.5196 \| 0.2973 \| 0.4596 \| 0.4593 \| 0.8759 \| 0.8788 \| 9.1802 \| 16 \| 4 \| 13.9159 \| 14.1141 \| \| 0.6251 \| 8.0 \| 1160 \| 1.5026 \| 0.5217 \| 0.2965 \| 0.461 \| 0.4611 \| 0.8802 \| 0.8775 \| 8.8198 \| 16 \| 4 \| 13.3393 \| 12.012 \| \| 0.5901 \| 9.0 \| 1305 \| 1.5890 \| 0.5156 \| 0.2967 \| 0.4606 \| 0.4609 \| 0.8773 \| 0.876 \| 8.7718 \| 17 \| 3 \| 13.4655 \| 9.6096 \| \| 0.5544 \| 10.0 \| 1450 \| 1.6294 \| 0.5172 \| 0.287 \| 0.4562 \| 0.4559 \| 0.8779 \| 0.876 \| 8.7688 \| 18 \| 4 \| 13.5195 \| 11.7117 \| \| 0.5354 \| 11.0 \| 1595 \| 1.6805 \| 0.5169 \| 0.2871 \| 0.457 \| 0.4571 \| 0.8768 \| 0.8774 \| 8.994 \| 17 \| 4 \| 13.6486 \| 14.1141 \| \| 0.5103 \| 12.0 \| 1740 \| 1.7334 \| 0.5121 \| 0.2824 \| 0.4556 \| 0.455 \| 0.8785 \| 0.8745 \| 8.5465 \| 16 \| 3 \| 13.1021 \| 8.1081 \| \| 0.4796 \| 13.0 \| 1885 \| 1.7767 \| 0.499 \| 0.2763 \| 0.442 \| 0.4418 \| 0.8754 \| 0.8739 \| 8.6396 \| 17 \| 4 \| 13.3183 \| 11.4114 \| \| 0.4825 \| 14.0 \| 2030 \| 1.8319 \| 0.5114 \| 0.2849 \| 0.4497 \| 0.4501 \| 0.8746 \| 0.8758 \| 8.994 \| 17 \| 4 \| 13.6667 \| 12.9129 \| \| 0.4572 \| 15.0 \| 2175 \| 1.8613 \| 0.5129 \| 0.2884 \| 0.4546 \| 0.4549 \| 0.8785 \| 0.8757 \| 8.6877 \| 17 \| 3 \| 13.3784 \| 10.5105 \| \| 0.4489 \| 16.0 \| 2320 \| 1.8790 \| 0.5144 \| 0.2829 \| 0.4533 \| 0.4536 \| 0.8777 \| 0.8754 \| 8.8078 \| 16 \| 3 \| 13.4955 \| 13.2132 \| \| 0.4211 \| 17.0 \| 2465 \| 1.9604 \| 0.4936 \| 0.2641 \| 0.4322 \| 0.4326 \| 0.8735 \| 0.8696 \| 8.4985 \| 17 \| 3 \| 13.1892 \| 9.009 \| \| 0.4246 \| 18.0 \| 2610 \| 2.0639 \| 0.4951 \| 0.2634 \| 0.4331 \| 0.4334 \| 0.8721 \| 0.8703 \| 8.7538 \| 16 \| 4 \| 13.3453 \| 12.6126 \| \| 0.4063 \| 19.0 \| 2755 \| 2.0587 \| 0.499 \| 0.2685 \| 0.4378 \| 0.4383 \| 0.8741 \| 0.8707 \| 8.5916 \| 16 \| 3 \| 13.3003 \| 9.9099 \| \| 0.3912 \| 20.0 \| 2900 \| 2.1089 \| 0.5068 \| 0.2727 \| 0.4471 \| 0.4469 \| 0.8764 \| 0.8744 \| 8.7538 \| 18 \| 3 \| 13.4625 \| 11.1111 \| \| 0.3855 \| 21.0 \| 3045 \| 2.1048 \| 0.5022 \| 0.2704 \| 0.4473 \| 0.4478 \| 0.875 \| 0.8728 \| 8.6847 \| 16 \| 4 \| 13.3483 \| 9.3093 \| \| 0.3808 \| 22.0 \| 3190 \| 2.1804 \| 0.4977 \| 0.2722 \| 0.4414 \| 0.4412 \| 0.875 \| 0.8711 \| 8.5315 \| 17 \| 4 \| 13.0631 \| 10.8108 \| \| 0.3851 \| 23.0 \| 3335 \| 2.1740 \| 0.4993 \| 0.2696 \| 0.4442 \| 0.4443 \| 0.8742 \| 0.8719 \| 8.5676 \| 15 \| 3 \| 13.2252 \| 9.009 \| \| 0.3741 \| 24.0 \| 3480 \| 2.1872 \| 0.4921 \| 0.2683 \| 0.4365 \| 0.4369 \| 0.8728 \| 0.8692 \| 8.5195 \| 17 \| 3 \| 13.2192 \| 8.4084 \| \| 0.3604 \| 25.0 \| 3625 \| 2.2617 \| 0.4988 \| 0.2681 \| 0.4421 \| 0.4426 \| 0.8747 \| 0.8705 \| 8.5255 \| 17 \| 3 \| 13.2492 \| 8.1081 \| \| 0.3676 \| 26.0 \| 3770 \| 2.2561 \| 0.4931 \| 0.2603 \| 0.4328 \| 0.4331 \| 0.874 \| 0.8711 \| 8.6276 \| 15 \| 3 \| 13.3363 \| 11.7117 \| \| 0.3799 \| 27.0 \| 3915 \| 2.2404 \| 0.4912 \| 0.2652 \| 0.4329 \| 0.433 \| 0.8729 \| 0.8702 \| 8.6517 \| 17 \| 3 \| 13.4414 \| 8.1081 \| \| 0.3617 \| 28.0 \| 4060 \| 2.2728 \| 0.4983 \| 0.2704 \| 0.4424 \| 0.4427 \| 0.8756 \| 0.8734 \| 8.7568 \| 17 \| 3 \| 13.5225 \| 11.4114 \| \| 0.3588 \| 29.0 \| 4205 \| 2.2695 \| 0.4904 \| 0.2601 \| 0.4331 \| 0.4328 \| 0.8743 \| 0.87 \| 8.4775 \| 18 \| 3 \| 13.1592 \| 9.009 \| \| 0.3567 \| 30.0 \| 4350 \| 2.3006 \| 0.4993 \| 0.2693 \| 0.4419 \| 0.4417 \| 0.8747 \| 0.8737 \| 8.8529 \| 17 \| 3 \| 13.5976 \| 12.012 \| \| 0.3573 \| 31.0 \| 4495 \| 2.3257 \| 0.4979 \| 0.2669 \| 0.4378 \| 0.4379 \| 0.8743 \| 0.8735 \| 8.9069 \| 18 \| 3 \| 13.6697 \| 12.9129 \| \| 0.3471 \| 32.0 \| 4640 \| 2.3513 \| 0.4989 \| 0.2723 \| 0.441 \| 0.4405 \| 0.8758 \| 0.8728 \| 8.6246 \| 17 \| 3 \| 13.3063 \| 10.8108 \| \| 0.3591 \| 33.0 \| 4785 \| 2.3467 \| 0.4972 \| 0.2751 \| 0.4415 \| 0.4413 \| 0.8742 \| 0.8727 \| 8.8078 \| 17 \| 3 \| 13.5616 \| 10.5105 \| \| 0.3401 \| 34.0 \| 4930 \| 2.4229 \| 0.4854 \| 0.2661 \| 0.4313 \| 0.4318 \| 0.8737 \| 0.8701 \| 8.5826 \| 17 \| 3 \| 13.2673 \| 8.7087 \| \| 0.3476 \| 35.0 \| 5075 \| 2.3804 \| 0.4895 \| 0.2602 \| 0.4322 \| 0.4326 \| 0.874 \| 0.8712 \| 8.6577 \| 17 \| 3 \| 13.2883 \| 9.3093 \| \| 0.3473 \| 36.0 \| 5220 \| 2.4242 \| 0.4938 \| 0.2689 \| 0.438 \| 0.4387 \| 0.8745 \| 0.8713 \| 8.5976 \| 17 \| 3 \| 13.2432 \| 9.3093 \| \| 0.3415 \| 37.0 \| 5365 \| 2.3836 \| 0.4943 \| 0.2617 \| 0.4351 \| 0.4351 \| 0.8751 \| 0.8711 \| 8.4054 \| 17 \| 3 \| 13.0571 \| 8.1081 \| \| 0.3549 \| 38.0 \| 5510 \| 2.4110 \| 0.501 \| 0.2696 \| 0.4402 \| 0.4406 \| 0.8765 \| 0.8713 \| 8.2282 \| 17 \| 3 \| 12.9459 \| 6.6066 \| \| 0.3432 \| 39.0 \| 5655 \| 2.4016 \| 0.4999 \| 0.27 \| 0.4387 \| 0.4393 \| 0.8751 \| 0.8712 \| 8.5285 \| 17 \| 3 \| 13.2402 \| 8.4084 \| \| 0.3387 \| 40.0 \| 5800 \| 2.4546 \| 0.4986 \| 0.2718 \| 0.4417 \| 0.4422 \| 0.8742 \| 0.871 \| 8.5766 \| 17 \| 3 \| 13.2312 \| 9.3093 \| \| 0.3351 \| 41.0 \| 5945 \| 2.4478 \| 0.4981 \| 0.2714 \| 0.4367 \| 0.4372 \| 0.8756 \| 0.8722 \| 8.4775 \| 15 \| 3 \| 13.1411 \| 8.7087 \| \| 0.3366 \| 42.0 \| 6090 \| 2.4447 \| 0.4961 \| 0.2703 \| 0.4359 \| 0.437 \| 0.8746 \| 0.8699 \| 8.4745 \| 16 \| 3 \| 13.1231 \| 9.3093 \| \| 0.3324 \| 43.0 \| 6235 \| 2.4974 \| 0.4989 \| 0.2809 \| 0.4428 \| 0.4432 \| 0.8747 \| 0.873 \| 8.7147 \| 16 \| 3 \| 13.4565 \| 10.5105 \| \| 0.3306 \| 44.0 \| 6380 \| 2.4938 \| 0.4902 \| 0.2657 \| 0.4301 \| 0.4306 \| 0.8733 \| 0.8692 \| 8.4925 \| 15 \| 3 \| 13.1622 \| 8.4084 \| \| 0.3388 \| 45.0 \| 6525 \| 2.5098 \| 0.4788 \| 0.2616 \| 0.4246 \| 0.4245 \| 0.8734 \| 0.8662 \| 8.2162 \| 16 \| 3 \| 12.7538 \| 8.1081 \| \| 0.346 \| 46.0 \| 6670 \| 2.4595 \| 0.4987 \| 0.2689 \| 0.438 \| 0.4389 \| 0.875 \| 0.8718 \| 8.5676 \| 16 \| 3 \| 13.2252 \| 9.9099 \| \| 0.3401 \| 47.0 \| 6815 \| 2.5098 \| 0.4934 \| 0.2653 \| 0.4353 \| 0.4356 \| 0.8744 \| 0.87 \| 8.3934 \| 15 \| 3 \| 13.048 \| 8.1081 \| \| 0.3271 \| 48.0 \| 6960 \| 2.5204 \| 0.4951 \| 0.2674 \| 0.4373 \| 0.4372 \| 0.8749 \| 0.8703 \| 8.4625 \| 16 \| 3 \| 13.024 \| 9.009 \| \| 0.3267 \| 49.0 \| 7105 \| 2.5291 \| 0.4887 \| 0.2605 \| 0.428 \| 0.4284 \| 0.8728 \| 0.8702 \| 8.7057 \| 18 \| 3 \| 13.3363 \| 11.1111 \| \| 0.3382 \| 50.0 \| 7250 \| 2.5422 \| 0.4899 \| 0.2666 \| 0.4354 \| 0.4356 \| 0.8755 \| 0.8707 \| 8.4505 \| 16 \| 3 \| 13.0931 \| 8.1081 \| \| 0.3255 \| 51.0 \| 7395 \| 2.5254 \| 0.4921 \| 0.2634 \| 0.4346 \| 0.4352 \| 0.8738 \| 0.8691 \| 8.4535 \| 16 \| 3 \| 13.027 \| 10.2102 \| \| 0.32 \| 52.0 \| 7540 \| 2.5460 \| 0.4991 \| 0.2727 \| 0.4423 \| 0.4421 \| 0.8745 \| 0.873 \| 8.8919 \| 16 \| 3 \| 13.5736 \| 11.7117 \| \| 0.3154 \| 53.0 \| 7685 \| 2.5446 \| 0.5027 \| 0.2712 \| 0.4463 \| 0.4463 \| 0.8768 \| 0.8734 \| 8.6426 \| 16 \| 3 \| 13.2973 \| 11.1111 \| \| 0.3293 \| 54.0 \| 7830 \| 2.5378 \| 0.4928 \| 0.2669 \| 0.4352 \| 0.4354 \| 0.8736 \| 0.869 \| 8.5225 \| 16 \| 3 \| 13.1291 \| 10.2102 \| \| 0.3231 \| 55.0 \| 7975 \| 2.5905 \| 0.4949 \| 0.2678 \| 0.4378 \| 0.4375 \| 0.8743 \| 0.8714 \| 8.6426 \| 15 \| 3 \| 13.3003 \| 9.009 \| \| 0.3239 \| 56.0 \| 8120 \| 2.5884 \| 0.4969 \| 0.2697 \| 0.4399 \| 0.4399 \| 0.8737 \| 0.8712 \| 8.6697 \| 16 \| 3 \| 13.3754 \| 10.5105 \| \| 0.3174 \| 57.0 \| 8265 \| 2.5500 \| 0.4958 \| 0.267 \| 0.4389 \| 0.4386 \| 0.8739 \| 0.8715 \| 8.7327 \| 16 \| 4 \| 13.3844 \| 10.5105 \| \| 0.3209 \| 58.0 \| 8410 \| 2.5804 \| 0.4989 \| 0.2706 \| 0.442 \| 0.4426 \| 0.8751 \| 0.8717 \| 8.5766 \| 15 \| 3 \| 13.1952 \| 9.3093 \| \| 0.3297 \| 59.0 \| 8555 \| 2.5909 \| 0.494 \| 0.2622 \| 0.4343 \| 0.4338 \| 0.8733 \| 0.8698 \| 8.5976 \| 16 \| 3 \| 13.1652 \| 11.7117 \| \| 0.3226 \| 60.0 \| 8700 \| 2.5857 \| 0.4976 \| 0.2639 \| 0.4377 \| 0.438 \| 0.8753 \| 0.8701 \| 8.3904 \| 17 \| 3 \| 12.973 \| 7.8078 \| \| 0.3241 \| 61.0 \| 8845 \| 2.5824 \| 0.5011 \| 0.2698 \| 0.4428 \| 0.4436 \| 0.8764 \| 0.8725 \| 8.5345 \| 16 \| 3 \| 13.1502 \| 10.5105 \| \| 0.3201 \| 62.0 \| 8990 \| 2.6156 \| 0.4968 \| 0.2673 \| 0.4371 \| 0.4372 \| 0.8755 \| 0.8702 \| 8.3904 \| 16 \| 3 \| 12.979 \| 6.9069 \| \| 0.3234 \| 63.0 \| 9135 \| 2.6374 \| 0.4945 \| 0.2677 \| 0.4387 \| 0.4388 \| 0.8744 \| 0.8693 \| 8.4444 \| 17 \| 3 \| 12.958 \| 8.1081 \| \| 0.3246 \| 64.0 \| 9280 \| 2.6338 \| 0.4912 \| 0.2672 \| 0.4396 \| 0.4402 \| 0.8738 \| 0.8698 \| 8.4955 \| 17 \| 3 \| 13.1021 \| 8.1081 \| \| 0.3188 \| 65.0 \| 9425 \| 2.6206 \| 0.4999 \| 0.2739 \| 0.4443 \| 0.4444 \| 0.8763 \| 0.8726 \| 8.6006 \| 17 \| 3 \| 13.2042 \| 10.5105 \| \| 0.3186 \| 66.0 \| 9570 \| 2.6499 \| 0.5007 \| 0.2771 \| 0.4462 \| 0.4463 \| 0.8765 \| 0.8729 \| 8.5375 \| 17 \| 3 \| 13.2162 \| 9.3093 \| \| 0.319 \| 67.0 \| 9715 \| 2.6488 \| 0.5023 \| 0.2715 \| 0.4452 \| 0.4454 \| 0.8761 \| 0.8736 \| 8.6817 \| 17 \| 3 \| 13.3904 \| 10.2102 \| \| 0.3328 \| 68.0 \| 9860 \| 2.6238 \| 0.5002 \| 0.2696 \| 0.4408 \| 0.4411 \| 0.8755 \| 0.8717 \| 8.5075 \| 17 \| 3 \| 13.1081 \| 9.009 \| \| 0.3068 \| 69.0 \| 10005 \| 2.6525 \| 0.4971 \| 0.2684 \| 0.4391 \| 0.4397 \| 0.8755 \| 0.8712 \| 8.5045 \| 17 \| 3 \| 13.1411 \| 11.4114 \| \| 0.3192 \| 70.0 \| 10150 \| 2.6494 \| 0.4976 \| 0.2722 \| 0.4395 \| 0.4405 \| 0.8762 \| 0.8714 \| 8.3964 \| 17 \| 3 \| 13.033 \| 8.4084 \| \| 0.3232 \| 71.0 \| 10295 \| 2.6642 \| 0.4976 \| 0.2717 \| 0.4412 \| 0.4411 \| 0.8756 \| 0.8717 \| 8.5075 \| 17 \| 3 \| 13.1622 \| 9.9099 \| \| 0.3084 \| 72.0 \| 10440 \| 2.6596 \| 0.4931 \| 0.2669 \| 0.4352 \| 0.4354 \| 0.8734 \| 0.8696 \| 8.4865 \| 17 \| 3 \| 13.1682 \| 9.009 \| \| 0.313 \| 73.0 \| 10585 \| 2.6551 \| 0.4942 \| 0.2699 \| 0.4363 \| 0.4368 \| 0.8742 \| 0.8699 \| 8.4715 \| 16 \| 3 \| 13.1201 \| 9.6096 \| \| 0.3194 \| 74.0 \| 10730 \| 2.6769 \| 0.4962 \| 0.2689 \| 0.4388 \| 0.4389 \| 0.874 \| 0.8715 \| 8.5976 \| 17 \| 3 \| 13.2763 \| 10.5105 \| \| 0.3143 \| 75.0 \| 10875 \| 2.6860 \| 0.493 \| 0.2652 \| 0.4335 \| 0.4343 \| 0.8734 \| 0.8702 \| 8.5706 \| 16 \| 3 \| 13.2462 \| 9.3093 \| \| 0.3209 \| 76.0 \| 11020 \| 2.6777 \| 0.4893 \| 0.2592 \| 0.4325 \| 0.4324 \| 0.8726 \| 0.869 \| 8.5225 \| 16 \| 3 \| 13.2012 \| 9.3093 \| \| 0.3078 \| 77.0 \| 11165 \| 2.6797 \| 0.4877 \| 0.261 \| 0.4321 \| 0.4323 \| 0.8724 \| 0.8693 \| 8.5796 \| 16 \| 3 \| 13.2402 \| 9.6096 \| \| 0.3192 \| 78.0 \| 11310 \| 2.6812 \| 0.495 \| 0.2677 \| 0.4382 \| 0.4383 \| 0.8739 \| 0.871 \| 8.5706 \| 18 \| 3 \| 13.2523 \| 10.8108 \| \| 0.3147 \| 79.0 \| 11455 \| 2.6777 \| 0.495 \| 0.2693 \| 0.4371 \| 0.4374 \| 0.874 \| 0.8707 \| 8.5015 \| 16 \| 3 \| 13.1471 \| 9.3093 \| \| 0.3049 \| 80.0 \| 11600 \| 2.6767 \| 0.4917 \| 0.2647 \| 0.4344 \| 0.4346 \| 0.8723 \| 0.8696 \| 8.5616 \| 16 \| 3 \| 13.2162 \| 9.9099 \| \| 0.3191 \| 81.0 \| 11745 \| 2.6932 \| 0.4929 \| 0.2683 \| 0.4392 \| 0.4392 \| 0.8737 \| 0.8707 \| 8.5676 \| 16 \| 3 \| 13.2342 \| 9.6096 \| \| 0.3073 \| 82.0 \| 11890 \| 2.7036 \| 0.4959 \| 0.2699 \| 0.4389 \| 0.4393 \| 0.8738 \| 0.8722 \| 8.6547 \| 17 \| 3 \| 13.3964 \| 10.2102 \| \| 0.3129 \| 83.0 \| 12035 \| 2.6941 \| 0.4918 \| 0.2657 \| 0.4341 \| 0.434 \| 0.8742 \| 0.8703 \| 8.4985 \| 16 \| 3 \| 13.1411 \| 9.3093 \| \| 0.3308 \| 84.0 \| 12180 \| 2.6968 \| 0.4927 \| 0.2659 \| 0.4335 \| 0.4337 \| 0.8737 \| 0.8698 \| 8.4955 \| 16 \| 3 \| 13.1652 \| 9.3093 \| \| 0.3221 \| 85.0 \| 12325 \| 2.6966 \| 0.4903 \| 0.2606 \| 0.4306 \| 0.4306 \| 0.8726 \| 0.8698 \| 8.5766 \| 16 \| 3 \| 13.2823 \| 9.6096 \| \| 0.3085 \| 86.0 \| 12470 \| 2.7123 \| 0.4862 \| 0.2608 \| 0.4288 \| 0.4286 \| 0.8723 \| 0.8688 \| 8.4595 \| 16 \| 3 \| 13.0901 \| 8.7087 \| \| 0.3281 \| 87.0 \| 12615 \| 2.7101 \| 0.4918 \| 0.2638 \| 0.4322 \| 0.4328 \| 0.8731 \| 0.8695 \| 8.4775 \| 16 \| 3 \| 13.1291 \| 9.009 \| \| 0.3183 \| 88.0 \| 12760 \| 2.7102 \| 0.4902 \| 0.2649 \| 0.4294 \| 0.4301 \| 0.873 \| 0.8688 \| 8.4955 \| 16 \| 3 \| 13.0901 \| 9.6096 \| \| 0.3063 \| 89.0 \| 12905 \| 2.7198 \| 0.4934 \| 0.2676 \| 0.4338 \| 0.4344 \| 0.8734 \| 0.8692 \| 8.4565 \| 17 \| 3 \| 13.0751 \| 9.009 \| \| 0.3123 \| 90.0 \| 13050 \| 2.7228 \| 0.492 \| 0.2676 \| 0.4338 \| 0.4343 \| 0.8732 \| 0.8692 \| 8.4535 \| 17 \| 3 \| 13.0931 \| 9.3093 \| \| 0.3163 \| 91.0 \| 13195 \| 2.7264 \| 0.4953 \| 0.2702 \| 0.4357 \| 0.4358 \| 0.874 \| 0.8693 \| 8.4625 \| 17 \| 3 \| 13.033 \| 9.3093 \| \| 0.3085 \| 92.0 \| 13340 \| 2.7236 \| 0.4934 \| 0.2702 \| 0.4369 \| 0.4369 \| 0.8738 \| 0.8695 \| 8.4925 \| 17 \| 3 \| 13.0721 \| 9.9099 \| \| 0.3257 \| 93.0 \| 13485 \| 2.7202 \| 0.4953 \| 0.2706 \| 0.4368 \| 0.4368 \| 0.8746 \| 0.8699 \| 8.4595 \| 16 \| 3 \| 13.0571 \| 10.2102 \| \| 0.3092 \| 94.0 \| 13630 \| 2.7261 \| 0.4988 \| 0.2748 \| 0.4415 \| 0.4419 \| 0.8755 \| 0.8708 \| 8.4535 \| 16 \| 3 \| 13.0751 \| 9.9099 \| \| 0.3187 \| 95.0 \| 13775 \| 2.7248 \| 0.4968 \| 0.2727 \| 0.4383 \| 0.4389 \| 0.8751 \| 0.8709 \| 8.5075 \| 16 \| 3 \| 13.1321 \| 9.9099 \| \| 0.3155 \| 96.0 \| 13920 \| 2.7335 \| 0.4962 \| 0.2686 \| 0.4372 \| 0.4373 \| 0.8749 \| 0.8712 \| 8.5135 \| 16 \| 3 \| 13.1772 \| 10.2102 \| \| 0.3271 \| 97.0 \| 14065 \| 2.7384 \| 0.4971 \| 0.2721 \| 0.4396 \| 0.4397 \| 0.8749 \| 0.8711 \| 8.5135 \| 16 \| 3 \| 13.1832 \| 10.5105 \| \| 0.3096 \| 98.0 \| 14210 \| 2.7400 \| 0.496 \| 0.2712 \| 0.4386 \| 0.4385 \| 0.8748 \| 0.8711 \| 8.5225 \| 16 \| 3 \| 13.1682 \| 10.2102 \| \| 0.3116 \| 99.0 \| 14355 \| 2.7411 \| 0.4961 \| 0.2712 \| 0.4388 \| 0.4386 \| 0.8749 \| 0.8711 \| 8.5135 \| 16 \| 3 \| 13.1592 \| 10.2102 \| \| 0.3102 \| 100.0 \| 14500 \| 2.7416 \| 0.4961 \| 0.2712 \| 0.4388 \| 0.4386 \| 0.8749 \| 0.8711 \| 8.5135 \| 16 \| 3 \| 13.1592 \| 10.2102 \| ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
michelecafagna26/vinvl-base-finetuned-hl-actions-image-captioning	michelecafagna26	2023-09-11T16:03:21Z	9	0	pytorch	[ "pytorch", "bert", "image-to-text", "en", "dataset:michelecafagna26/hl", "arxiv:2302.12189", "arxiv:2107.12604", "license:apache-2.0", "region:us" ]	image-to-text	2023-09-11T15:10:26Z	--- license: apache-2.0 datasets: - michelecafagna26/hl language: - en metrics: - sacrebleu - rouge - meteor - spice - cider library_name: pytorch tags: - pytorch - image-to-text --- # Model Card: VinVL for Captioning 🖼️ [Microsoft's VinVL](https://github.com/microsoft/Oscar) base fine-tuned on [HL dataset](https://arxiv.org/abs/2302.12189?context=cs.CL) for action description generation downstream task. # Model fine-tuning 🏋️‍ The model has been finetuned for 10 epochs on the action captions of the [HL dataset](https://arxiv.org/abs/2302.12189?context=cs.CL) (available on 🤗 HUB: [michelecafagna26/hl](https://huggingface.co/datasets/michelecafagna26/hl)) # Test set metrics 📈 Obtained with beam size 5 and max length 20 \| Bleu-1 \| Bleu-2 \| Bleu-3 \| Bleu-4 \| METEOR \| ROUGE-L \| CIDEr \| SPICE \| \|--------\|--------\|--------\|--------\|--------\|---------\|-------\|-------\| \| 0.74 \| 0.62 \| 0.50 \| 0.40 \| 0.31 \| 0.65 \| 1.73 \| 0.21 \| # Usage and Installation: More info about how to install and use this model can be found here: [michelecafagna26/VinVL ](https://github.com/michelecafagna26/VinVL) # Feature extraction ⛏️ This model has a separate Visualbackbone used to extract features. More info about: - the model: [michelecafagna26/vinvl_vg_x152c4](https://huggingface.co/michelecafagna26/vinvl_vg_x152c4) - the usage: [michelecafagna26/vinvl-visualbackbone](https://github.com/michelecafagna26/vinvl-visualbackbone) # Quick start: 🚀 ```python from transformers.pytorch_transformers import BertConfig, BertTokenizer from oscar.modeling.modeling_bert import BertForImageCaptioning from oscar.wrappers import OscarTensorizer ckpt = "path/to/the/checkpoint" device = "cuda" if torch.cuda.is_available() else "cpu" # original code config = BertConfig.from_pretrained(ckpt) tokenizer = BertTokenizer.from_pretrained(ckpt) model = BertForImageCaptioning.from_pretrained(ckpt, config=config).to(device) # This takes care of the preprocessing tensorizer = OscarTensorizer(tokenizer=tokenizer, device=device) # numpy-arrays with shape (1, num_boxes, feat_size) # feat_size is 2054 by default in VinVL visual_features = torch.from_numpy(feat_obj).to(device).unsqueeze(0) # labels are usually extracted by the features extractor labels = [['boat', 'boat', 'boat', 'bottom', 'bush', 'coat', 'deck', 'deck', 'deck', 'dock', 'hair', 'jacket']] inputs = tensorizer.encode(visual_features, labels=labels) outputs = model(**inputs) pred = tensorizer.decode(outputs) # the output looks like this: # pred = {0: [{'caption': 'He is sailing', 'conf': 0.7070220112800598]} ``` # Citations 🧾 HL Dataset paper: ```BibTeX @inproceedings{cafagna2023hl, title={{HL} {D}ataset: {V}isually-grounded {D}escription of {S}cenes, {A}ctions and {R}ationales}, author={Cafagna, Michele and van Deemter, Kees and Gatt, Albert}, booktitle={Proceedings of the 16th International Natural Language Generation Conference (INLG'23)}, address = {Prague, Czech Republic}, year={2023} } ``` Please consider citing the original project and the VinVL paper ```BibTeX @misc{han2021image, title={Image Scene Graph Generation (SGG) Benchmark}, author={Xiaotian Han and Jianwei Yang and Houdong Hu and Lei Zhang and Jianfeng Gao and Pengchuan Zhang}, year={2021}, eprint={2107.12604}, archivePrefix={arXiv}, primaryClass={cs.CV} } @inproceedings{zhang2021vinvl, title={Vinvl: Revisiting visual representations in vision-language models}, author={Zhang, Pengchuan and Li, Xiujun and Hu, Xiaowei and Yang, Jianwei and Zhang, Lei and Wang, Lijuan and Choi, Yejin and Gao, Jianfeng}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={5579--5588}, year={2021} } ```
Atulit23/flan-t5-base-indian-constitution	Atulit23	2023-09-11T15:55:07Z	102	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "base_model:google/flan-t5-base", "base_model:finetune:google/flan-t5-base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-09-11T15:54:25Z	--- license: apache-2.0 base_model: google/flan-t5-base tags: - generated_from_trainer metrics: - rouge model-index: - name: flan-t5-base-indian-constitution 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. --> # flan-t5-base-indian-constitution This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0008 - Rouge1: 29.7093 - Rouge2: 28.4336 - Rougel: 29.6229 - Rougelsum: 29.5617 - Gen Len: 18.9651 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 344 \| 0.0009 \| 29.7093 \| 28.4336 \| 29.6229 \| 29.5617 \| 18.9651 \| \| 0.0021 \| 2.0 \| 688 \| 0.0008 \| 29.7093 \| 28.4336 \| 29.6229 \| 29.5617 \| 18.9651 \| \| 0.0013 \| 3.0 \| 1032 \| 0.0008 \| 29.7093 \| 28.4336 \| 29.6229 \| 29.5617 \| 18.9651 \| ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
FasterDecoding/medusa-vicuna-33b-v1.3	FasterDecoding	2023-09-11T15:53:39Z	40	4	transformers	[ "transformers", "endpoints_compatible", "region:us" ]	null	2023-09-10T02:52:22Z	<div align="center"><img src="https://github.com/FasterDecoding/Medusa/blob/main/assets/logo.png?raw=true" alt="Medusa" width="100" align="center"></div> <div align="center"><h1> Medusa: Simple Framework for Accelerating LLM Generation with Multiple Decoding Heads</h1></div> <p align="center"> \| <a href="https://sites.google.com/view/ medusa-llm"><b>Blog</b></a> \| <a href="https://github.com/FasterDecoding/Medusa"><b>Codebase</b></a> \| </p> --- ## Installation ### Method 1: With pip ```bash pip install medusa-llm ``` ### Method 2: From source ```bash git clone https://github.com/FasterDecoding/Medusa.git cd Medusa pip install -e . ``` ### Model Weights \| Size \| Chat Command \| Hugging Face Repo \| \| ---- \| --------------------------------------------- \| --------------------------------------------------------------------- \| \| 7B \| `python -m medusa.inference.cli --model FasterDecoding/medusa-vicuna-7b-v1.3` \| [FasterDecoding/medusa-vicuna-33b-v1.3](https://huggingface.co/FasterDecoding/medusa-vicuna-7b-v1.3) \| \| 13B \| `python -m medusa.inference.cli --model FasterDecoding/medusa-vicuna-13b-v1.3` \| [FasterDecoding/medusa-vicuna-13b-v1.3](https://huggingface.co/FasterDecoding/medusa-vicuna-13b-v1.3) \| \| 33B \| `python -m medusa.inference.cli --model FasterDecoding/medusa-vicuna-33b-v1.3` \| [FasterDecoding/medusa-vicuna-33b-v1.3](https://huggingface.co/FasterDecoding/medusa-vicuna-33b-v1.3) \| ### Inference We currently support inference in the single GPU and batch size 1 setting, which is the most common setup for local model hosting. We are actively working to extend Medusa's capabilities by integrating it into other inference frameworks, please don't hesitate to reach out if you are interested in contributing to this effort. You can use the following command for lauching a CLI interface: ```bash python -m medusa.inference.cli --model [path of medusa model] ``` You can also pass `--load-in-8bit` or `--load-in-4bit` to load the base model in quantized format.
FasterDecoding/medusa-vicuna-13b-v1.3	FasterDecoding	2023-09-11T15:53:15Z	63	5	transformers	[ "transformers", "endpoints_compatible", "region:us" ]	null	2023-09-10T02:47:47Z	<div align="center"><img src="https://github.com/FasterDecoding/Medusa/blob/main/assets/logo.png?raw=true" alt="Medusa" width="100" align="center"></div> <div align="center"><h1> Medusa: Simple Framework for Accelerating LLM Generation with Multiple Decoding Heads</h1></div> <p align="center"> \| <a href="https://sites.google.com/view/ medusa-llm"><b>Blog</b></a> \| <a href="https://github.com/FasterDecoding/Medusa"><b>Codebase</b></a> \| </p> --- ## Installation ### Method 1: With pip ```bash pip install medusa-llm ``` ### Method 2: From source ```bash git clone https://github.com/FasterDecoding/Medusa.git cd Medusa pip install -e . ``` ### Model Weights \| Size \| Chat Command \| Hugging Face Repo \| \| ---- \| --------------------------------------------- \| --------------------------------------------------------------------- \| \| 7B \| `python -m medusa.inference.cli --model FasterDecoding/medusa-vicuna-7b-v1.3` \| [FasterDecoding/medusa-vicuna-33b-v1.3](https://huggingface.co/FasterDecoding/medusa-vicuna-7b-v1.3) \| \| 13B \| `python -m medusa.inference.cli --model FasterDecoding/medusa-vicuna-13b-v1.3` \| [FasterDecoding/medusa-vicuna-13b-v1.3](https://huggingface.co/FasterDecoding/medusa-vicuna-13b-v1.3) \| \| 33B \| `python -m medusa.inference.cli --model FasterDecoding/medusa-vicuna-33b-v1.3` \| [FasterDecoding/medusa-vicuna-33b-v1.3](https://huggingface.co/FasterDecoding/medusa-vicuna-33b-v1.3) \| ### Inference We currently support inference in the single GPU and batch size 1 setting, which is the most common setup for local model hosting. We are actively working to extend Medusa's capabilities by integrating it into other inference frameworks, please don't hesitate to reach out if you are interested in contributing to this effort. You can use the following command for lauching a CLI interface: ```bash python -m medusa.inference.cli --model [path of medusa model] ``` You can also pass `--load-in-8bit` or `--load-in-4bit` to load the base model in quantized format.
geralt/MechDistilGPT2	geralt	2023-09-11T15:49:22Z	137	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "Causal Language modeling", "CLM", "arxiv:2105.09680", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- tags: - Causal Language modeling - text-generation - CLM model_index: - name: MechDistilGPT2 results: - task: name: Causal Language modeling type: Causal Language modeling --- # MechDistilGPT2 ## Table of Contents - [Model Details](#model-details) - [Uses](#uses) - [Risks, Limitations and Biases](#risks-limitations-and-biases) - [Training](#training) - [Environmental Impact](#environmental-impact) - [How to Get Started With the Model](#how-to-get-started-with-the-model) ## Model Details - Model Description: This model is fine-tuned on text scraped from 100+ Mechanical/Automotive pdf books. - Developed by: [Ashwin](https://huggingface.co/geralt) - Model Type: Causal Language modeling - Language(s): English - License: [More Information Needed] - Parent Model: See the [DistilGPT2model](https://huggingface.co/distilgpt2) for more information about the Distilled-GPT2 base model. - Resources for more information: - [Research Paper](https://arxiv.org/abs/2105.09680) - [GitHub Repo](https://github.com/huggingface/notebooks/blob/master/examples/language_modeling.ipynb) ## Uses #### Direct Use The model can be used for tasks including topic classification, Causal Language modeling and text generation #### Misuse and Out-of-scope Use The model should not be used to intentionally create hostile or alienating environments for people. In addition, the model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. ## Risks, Limitations and Biases CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes. Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). ## Training #### Training Data This model is fine-tuned on text scraped from 100+ Mechanical/Automotive pdf books. #### Training Procedure ###### Fine-Tuning * Default Training Args * Epochs = 3 * Training set = 200k sentences * Validation set = 40k sentences ###### Framework versions * Transformers 4.7.0.dev0 * Pytorch 1.8.1+cu111 * Datasets 1.6.2 * Tokenizers 0.10.2 # Environmental Impact Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More information needed] - Hours used: [More information needed] - Cloud Provider: [More information needed] - Compute Region: [More information needed"] - Carbon Emitted: [More information needed] ## How to Get Started With the Model ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("geralt/MechDistilGPT2") model = AutoModelForCausalLM.from_pretrained("geralt/MechDistilGPT2") ```
PabloSuaLap/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es-retrained-pabloV3	PabloSuaLap	2023-09-11T15:44:00Z	63	0	transformers	[ "transformers", "tf", "tensorboard", "bert", "question-answering", "generated_from_keras_callback", "base_model:mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es", "base_model:finetune:mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-08-17T18:06:48Z	--- license: apache-2.0 tags: - generated_from_keras_callback base_model: mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es model-index: - name: P4B10/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es-retrained-pabloV3 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. --> # P4B10/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es-retrained-pabloV3 This model is a fine-tuned version of [mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es](https://huggingface.co/mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.7249 - Train End Logits Accuracy: 0.1667 - Train Start Logits Accuracy: 0.1667 - Validation Loss: 3.2576 - Validation End Logits Accuracy: 0.0 - Validation Start Logits Accuracy: 0.8333 - Epoch: 1 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Train End Logits Accuracy \| Train Start Logits Accuracy \| Validation Loss \| Validation End Logits Accuracy \| Validation Start Logits Accuracy \| Epoch \| \|:----------:\|:-------------------------:\|:---------------------------:\|:---------------:\|:------------------------------:\|:--------------------------------:\|:-----:\| \| 4.7073 \| 0.1667 \| 0.1667 \| 3.5715 \| 0.0 \| 0.8333 \| 0 \| \| 3.7249 \| 0.1667 \| 0.1667 \| 3.2576 \| 0.0 \| 0.8333 \| 1 \| ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.0 - Tokenizers 0.13.2
RyyyT/q-Taxi-v3	RyyyT	2023-09-11T15:39:52Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-09-11T15:38:05Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="RyyyT/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"]) ```
ProomptEngineer/cute-animals-style	ProomptEngineer	2023-09-11T15:38:10Z	48	4	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:other", "region:us" ]	text-to-image	2023-09-11T15:38:06Z	--- license: other tags: - text-to-image - stable-diffusion - lora - diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: PE_CuteAnimals widget: - text: PE_CuteAnimals --- # Cute Animals [Style] ![Image 0](2172186.jpeg) <p>lora to make cute animal illustrations</p><p>Weights of 0.8-1</p><h2 id="heading-7">If you want to donate:</h2><h2 id="heading-8"><a target="_blank" rel="ugc" href="https://ko-fi.com/proomptengineer">https://ko-fi.com/proomptengineer</a></h2><p></p> ## Image examples for the model: ![Image 1](2172182.jpeg) ![Image 2](2172183.jpeg) ![Image 3](2172184.jpeg) ![Image 4](2172187.jpeg) ![Image 5](2172185.jpeg) ![Image 6](2172189.jpeg) ![Image 7](2172188.jpeg) ![Image 8](2172190.jpeg) ![Image 9](2172191.jpeg)
Lethargus/Taxi-v3	Lethargus	2023-09-11T15:37:23Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-09-11T15:32:40Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage model = load_from_hub(repo_id="Lethargus/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"])
ProomptEngineer/pe-habsburg-diffusion-style-big-chin	ProomptEngineer	2023-09-11T15:34:56Z	17	1	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:other", "region:us" ]	text-to-image	2023-09-11T15:34:53Z	--- license: other tags: - text-to-image - stable-diffusion - lora - diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: PEHabsburg widget: - text: PEHabsburg --- # PE Habsburg Diffusion [Style] [Big Chin] ![Image 0](2186263.jpeg) <p>Add some habsburg to your images!</p><p>weights 1-1.4</p><h2 id="heading-7">If you want to donate:</h2><h2 id="heading-8"><a target="_blank" rel="ugc" href="https://ko-fi.com/proomptengineer">https://ko-fi.com/proomptengineer</a></h2> ## Image examples for the model: ![Image 1](2186265.jpeg) ![Image 2](2186232.jpeg) ![Image 3](2186236.jpeg) ![Image 4](2186228.jpeg) ![Image 5](2186231.jpeg) ![Image 6](2186234.jpeg) ![Image 7](2186242.jpeg) ![Image 8](2186248.jpeg) ![Image 9](2186250.jpeg)
ProomptEngineer/pe-shitty-fanart	ProomptEngineer	2023-09-11T15:29:56Z	99	7	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:other", "region:us" ]	text-to-image	2023-09-11T15:29:53Z	--- license: other tags: - text-to-image - stable-diffusion - lora - diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: PETerribleFanArt widget: - text: PETerribleFanArt --- # PE Shitty FanArt ![Image 0](2028526.jpeg) <h2 id="heading-7">Sick of perfect AI Images? Then use this Lora to make some terrible FanArt!</h2><h2 id="heading-8">Weights 0.8-1</h2><h2 id="heading-63">If you want to donate:</h2><h2 id="heading-64"><a target="_blank" rel="ugc" href="https://ko-fi.com/proomptengineer">https://ko-fi.com/proomptengineer</a></h2><p></p> ## Image examples for the model: ![Image 1](2028530.jpeg) ![Image 2](2028528.jpeg) ![Image 3](2028517.jpeg) ![Image 4](2028518.jpeg) ![Image 5](2028519.jpeg) ![Image 6](2028522.jpeg) ![Image 7](2028520.jpeg) ![Image 8](2028532.jpeg) ![Image 9](2028533.jpeg)
saattrupdan/xlmr-base-texas-squad-da	saattrupdan	2023-09-11T15:29:54Z	133	5	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "xlm-roberta", "question-answering", "generated_from_trainer", "da", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2022-03-02T23:29:05Z	--- language: - da license: mit tags: - generated_from_trainer widget: - text: Hvem handler artiklen om? context: 'Forfatter og musiker Flemming Quist Møller er død i en alder af 79 år. Den folkekære kunstner faldt om ved morgenbordet med en blodprop i hjertet i mandags. Det kunne forfatterens søn, Carl Quist-Møller, bekræfte over for TV 2 Lorry.- Han faldt om i det hus i Taarbæk, hvor han er vokset op og også har boet de sidste år af sit liv. Han blev lagt i koma på Rigshospitalet. Her har vi siddet omkring ham i en uge, siger Carl Quist-Møller til mediet.MindeordI mange år var Flemming Quist Møller en del af bandet Bazaar sammen med Peter Bastian, Anders Koppel og Mehmet Ozan.Anders Koppel er tydeligt rørt over vennens død, da Ekstra Bladet rækker ud til ham mandag aften.- Det er en stor del af mit liv, der er forsvundet med Flemmings liv, det er klart. Vi har spillet sammen i 37 år, siger han og fortsætter:- Jeg vil mest huske ham for hans ukonventionelle tilgang til alting. Flemming havde et meget stærkt blik for det autentiske og ærlige. Han var ikke bundet af normer -tværtimod, hvis han så en norm, hvor noget skulle gøres på en bestemt måde, så flygtede han eller prøvede at springe det i stumper og stykker.Ifølge den danske musiker og komponist er netop følgende ord rammende for Flemming Quist Møller: Original, vidende, kompromisløs og humoristisk.' base_model: xlm-roberta-base model-index: - name: xlmr-base-texas-squad-da results: [] --- # TExAS-SQuAD-da This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the TExAS-SQuAD-da dataset. It achieves the following results on the evaluation set: - Exact match: 63.96% - F1-score: 68.40% In comparison, the `jacobshein/danish-bert-botxo-qa-squad` model achieves 30.37% EM and 37.15% F1. ## 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 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 1.6438 \| 1.0 \| 4183 \| 1.4711 \| \| 1.4079 \| 2.0 \| 8366 \| 1.4356 \| \| 1.2532 \| 3.0 \| 12549 \| 1.4509 \| ### Framework versions - Transformers 4.12.2 - Pytorch 1.8.1+cu101 - Datasets 1.12.1 - Tokenizers 0.10.3
mehta-rohan/car-bike-diff	mehta-rohan	2023-09-11T15:25:14Z	0	0	fastai	[ "fastai", "image_classification", "en", "region:us" ]	null	2023-09-11T12:11:40Z	--- language: - en library_name: fastai tags: - image_classification --- This is my first model Starting the AI/ML journey
esperesa/xlm-roberta-base-finetuned-panx-all	esperesa	2023-09-11T15:23:31Z	126	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-09-11T15:03:15Z	--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-all 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. --> # xlm-roberta-base-finetuned-panx-all This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1828 - F1: 0.8519 ## 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: 24 - eval_batch_size: 24 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2947 \| 1.0 \| 739 \| 0.1879 \| 0.8175 \| \| 0.152 \| 2.0 \| 1478 \| 0.1853 \| 0.8385 \| \| 0.0974 \| 3.0 \| 2217 \| 0.1828 \| 0.8519 \| ### Framework versions - Transformers 4.16.2 - Pytorch 2.0.1+cu118 - Datasets 1.16.1 - Tokenizers 0.14.0
ProomptEngineer/pe-ice-sculpture-style	ProomptEngineer	2023-09-11T15:23:17Z	31	2	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:other", "region:us" ]	text-to-image	2023-09-11T15:23:14Z	--- license: other tags: - text-to-image - stable-diffusion - lora - diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: PEIceSculpture widget: - text: PEIceSculpture --- # PE Ice Sculpture [Style] ![Image 0](2249636.jpeg) <p>make beautiful images in the style of ice sculpture...</p><p>weights 0.8-1</p><h2 id="heading-63">If you want to donate:</h2><h2 id="heading-64"><a target="_blank" rel="ugc" href="https://ko-fi.com/proomptengineer">https://ko-fi.com/proomptengineer</a></h2> ## Image examples for the model: ![Image 1](2249584.jpeg) ![Image 2](2249589.jpeg) ![Image 3](2249590.jpeg) ![Image 4](2249591.jpeg) ![Image 5](2249592.jpeg) ![Image 6](2249585.jpeg) ![Image 7](2249594.jpeg) ![Image 8](2249986.jpeg) ![Image 9](2250115.jpeg)
Prot10/swinv2-base-patch4-window8-256-for-pre_evaluation	Prot10	2023-09-11T15:22:30Z	4	0	transformers	[ "transformers", "pytorch", "swinv2", "image-classification", "generated_from_trainer", "base_model:microsoft/swinv2-base-patch4-window8-256", "base_model:finetune:microsoft/swinv2-base-patch4-window8-256", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-08-30T11:21:06Z	--- license: apache-2.0 base_model: microsoft/swinv2-base-patch4-window8-256 tags: - generated_from_trainer metrics: - accuracy model-index: - name: swinv2-base-patch4-window8-256-for-pre_evaluation 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. --> # swinv2-base-patch4-window8-256-for-pre_evaluation This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window8-256](https://huggingface.co/microsoft/swinv2-base-patch4-window8-256) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4873 - Accuracy: 0.4106 ## 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: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.6064 \| 1.0 \| 16 \| 1.5189 \| 0.3073 \| \| 1.5058 \| 2.0 \| 32 \| 1.5056 \| 0.3073 \| \| 1.5176 \| 3.0 \| 48 \| 1.5176 \| 0.2961 \| \| 1.4883 \| 4.0 \| 64 \| 1.5130 \| 0.3073 \| \| 1.4446 \| 5.0 \| 80 \| 1.4540 \| 0.3296 \| \| 1.4568 \| 6.0 \| 96 \| 1.5154 \| 0.3156 \| \| 1.4106 \| 7.0 \| 112 \| 1.4272 \| 0.3883 \| \| 1.3804 \| 8.0 \| 128 \| 1.4185 \| 0.3743 \| \| 1.3725 \| 9.0 \| 144 \| 1.3943 \| 0.3911 \| \| 1.3441 \| 10.0 \| 160 \| 1.4510 \| 0.4022 \| \| 1.3335 \| 11.0 \| 176 \| 1.4337 \| 0.3827 \| \| 1.3055 \| 12.0 \| 192 \| 1.4633 \| 0.3855 \| \| 1.3303 \| 13.0 \| 208 \| 1.4674 \| 0.3883 \| \| 1.2882 \| 14.0 \| 224 \| 1.4388 \| 0.3911 \| \| 1.2362 \| 15.0 \| 240 \| 1.4676 \| 0.3855 \| \| 1.2572 \| 16.0 \| 256 \| 1.4805 \| 0.3799 \| \| 1.2164 \| 17.0 \| 272 \| 1.4717 \| 0.3939 \| \| 1.221 \| 18.0 \| 288 \| 1.4354 \| 0.4078 \| \| 1.1713 \| 19.0 \| 304 \| 1.4836 \| 0.4078 \| \| 1.18 \| 20.0 \| 320 \| 1.4873 \| 0.4106 \| \| 1.1349 \| 21.0 \| 336 \| 1.4853 \| 0.3855 \| \| 1.1138 \| 22.0 \| 352 \| 1.4927 \| 0.3966 \| \| 1.1402 \| 23.0 \| 368 \| 1.4672 \| 0.3994 \| \| 1.1183 \| 24.0 \| 384 \| 1.5033 \| 0.4022 \| \| 1.0834 \| 25.0 \| 400 \| 1.5448 \| 0.3855 \| \| 1.0515 \| 26.0 \| 416 \| 1.5131 \| 0.3939 \| \| 1.0745 \| 27.0 \| 432 \| 1.5314 \| 0.3827 \| \| 1.0332 \| 28.0 \| 448 \| 1.5474 \| 0.3939 \| \| 1.0679 \| 29.0 \| 464 \| 1.5327 \| 0.3855 \| \| 1.0295 \| 30.0 \| 480 \| 1.5402 \| 0.3855 \| ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
ProomptEngineer/pe-snow-sculpture-style	ProomptEngineer	2023-09-11T15:22:04Z	28	1	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:other", "region:us" ]	text-to-image	2023-09-11T15:21:55Z	--- license: other tags: - text-to-image - stable-diffusion - lora - diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: PESnowSculpture widget: - text: PESnowSculpture --- # PE Snow Sculpture [Style] ![Image 0](2249928.jpeg) <p>make some snow sculptures...</p><p>weights 0.8-1</p><h2 id="heading-63">If you want to donate:</h2><h2 id="heading-64"><a target="_blank" rel="ugc" href="https://ko-fi.com/proomptengineer">https://ko-fi.com/proomptengineer</a></h2> ## Image examples for the model: ![Image 1](2249926.jpeg) ![Image 2](2249923.jpeg) ![Image 3](2249921.jpeg) ![Image 4](2249927.jpeg) ![Image 5](2249922.jpeg) ![Image 6](2249925.jpeg) ![Image 7](2249934.jpeg) ![Image 8](2249924.jpeg) ![Image 9](2249930.jpeg)
ProomptEngineer/pe-anime-background-landscapes-style	ProomptEngineer	2023-09-11T15:20:28Z	88	10	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:other", "region:us" ]	text-to-image	2023-09-11T15:20:24Z	--- license: other tags: - text-to-image - stable-diffusion - lora - diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: PEAnimeBG widget: - text: PEAnimeBG --- # PE Anime Background / Landscapes [Style] ![Image 0](2266542.jpeg) <p>Lora to make landscapes or backgrounds in anime style...</p><p>weights 0.8-1</p><h2 id="heading-63">If you want to donate:</h2><h2 id="heading-64"><a target="_blank" rel="ugc" href="https://ko-fi.com/proomptengineer">https://ko-fi.com/proomptengineer</a></h2> ## Image examples for the model: ![Image 1](2266568.jpeg) ![Image 2](2266530.jpeg) ![Image 3](2266540.jpeg) ![Image 4](2266554.jpeg) ![Image 5](2266541.jpeg) ![Image 6](2266560.jpeg) ![Image 7](2266571.jpeg) ![Image 8](2266566.jpeg) ![Image 9](2266567.jpeg)
esperesa/xlm-roberta-base-finetuned-panx-en	esperesa	2023-09-11T15:10:24Z	105	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-09-11T15:03:09Z	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-en results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.en metrics: - name: F1 type: f1 value: 0.6837988826815643 --- <!-- 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-en 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: 0.3984 - F1: 0.6838 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 1.1357 \| 1.0 \| 50 \| 0.5871 \| 0.4590 \| \| 0.5236 \| 2.0 \| 100 \| 0.4412 \| 0.6478 \| \| 0.3765 \| 3.0 \| 150 \| 0.3984 \| 0.6838 \| ### Framework versions - Transformers 4.16.2 - Pytorch 2.0.1+cu118 - Datasets 1.16.1 - Tokenizers 0.14.0
irenepap/t5-small-asqa-ob	irenepap	2023-09-11T15:09:52Z	116	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "dataset:din0s/asqa", "base_model:google/t5-small-ssm-nq", "base_model:finetune:google/t5-small-ssm-nq", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-09-28T14:00:43Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: din0s/asqa metrics: - rouge base_model: google/t5-small-ssm-nq model-index: - name: t5-small-asqa-ob 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-asqa-ob This model is a fine-tuned version of [google/t5-small-ssm-nq](https://huggingface.co/google/t5-small-ssm-nq) on the [ASQA](https://huggingface.co/datasets/din0s/asqa) dataset without context (closed book). It achieves the following results on the evaluation set: - Loss: 2.8099 - Rouge1: 0.1493 - Rouge2: 0.0837 - Rougel: 0.1272 - Rougelsum: 0.1270 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\| \| 3.8208 \| 1.0 \| 710 \| 2.7856 \| 0.1267 \| 0.0644 \| 0.1086 \| 0.1084 \| \| 3.0532 \| 2.0 \| 1420 \| 2.6247 \| 0.1321 \| 0.0721 \| 0.1145 \| 0.1144 \| \| 2.5656 \| 3.0 \| 2130 \| 2.5062 \| 0.1399 \| 0.0773 \| 0.1213 \| 0.1213 \| \| 2.3806 \| 4.0 \| 2840 \| 2.5004 \| 0.1431 \| 0.0805 \| 0.1243 \| 0.1241 \| \| 2.157 \| 5.0 \| 3550 \| 2.5008 \| 0.1455 \| 0.0808 \| 0.1255 \| 0.1254 \| \| 2.0458 \| 6.0 \| 4260 \| 2.5313 \| 0.1510 \| 0.0846 \| 0.1303 \| 0.1301 \| \| 1.914 \| 7.0 \| 4970 \| 2.5298 \| 0.1585 \| 0.0885 \| 0.1361 \| 0.1358 \| \| 1.7479 \| 8.0 \| 5680 \| 2.5832 \| 0.1508 \| 0.0844 \| 0.1292 \| 0.1291 \| \| 1.6875 \| 9.0 \| 6390 \| 2.5928 \| 0.1493 \| 0.0834 \| 0.1281 \| 0.1279 \| \| 1.574 \| 10.0 \| 7100 \| 2.6364 \| 0.1591 \| 0.0885 \| 0.1364 \| 0.1363 \| \| 1.4554 \| 11.0 \| 7810 \| 2.6978 \| 0.1513 \| 0.0849 \| 0.1295 \| 0.1295 \| \| 1.3909 \| 12.0 \| 8520 \| 2.8099 \| 0.1493 \| 0.0837 \| 0.1272 \| 0.1270 \| ### Framework versions - Transformers 4.23.0.dev0 - Pytorch 1.12.1+cu102 - Datasets 2.5.1 - Tokenizers 0.12.1
moonlightnexus/realize	moonlightnexus	2023-09-11T15:07:50Z	37	1	diffusers	[ "diffusers", "text-to-image", "en", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-09-11T09:26:08Z	--- license: other language: - en library_name: diffusers pipeline_tag: text-to-image ---
danbochman/ccxl	danbochman	2023-09-11T15:07:42Z	40	1	diffusers	[ "diffusers", "safetensors", "text-to-image", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2023-09-11T09:49:13Z	--- library_name: diffusers pipeline_tag: text-to-image --- This is a `diffusers` compatible version of the [Crystal Clear XL model](https://civitai.com/models/122822/crystal-clear-xl) from Team Crystal Clear.
checkiejan/flan-t5-prefix-30-10-2	checkiejan	2023-09-11T15:06:10Z	0	0	peft	[ "peft", "region:us" ]	null	2023-09-11T15:06:06Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0
ldos/text_shortening_model_v30	ldos	2023-09-11T15:05:21Z	8	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-09-11T14:06:20Z	--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: text_shortening_model_v30 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. --> # text_shortening_model_v30 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.6784 - Rouge1: 0.4871 - Rouge2: 0.2579 - Rougel: 0.428 - Rougelsum: 0.4272 - Bert precision: 0.8743 - Bert recall: 0.8706 - Average word count: 8.4775 - Max word count: 17 - Min word count: 3 - Average token count: 12.9249 - % shortened texts with length > 12: 9.3093 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Bert precision \| Bert recall \| Average word count \| Max word count \| Min word count \| Average token count \| % shortened texts with length > 12 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:--------------:\|:-----------:\|:------------------:\|:--------------:\|:--------------:\|:-------------------:\|:----------------------------------:\| \| 1.2044 \| 1.0 \| 145 \| 1.6064 \| 0.5052 \| 0.2865 \| 0.4472 \| 0.448 \| 0.8751 \| 0.8756 \| 8.8979 \| 17 \| 3 \| 13.4024 \| 12.6126 \| \| 1.0041 \| 2.0 \| 290 \| 1.4900 \| 0.5154 \| 0.2921 \| 0.4554 \| 0.4542 \| 0.8735 \| 0.878 \| 9.3724 \| 17 \| 3 \| 13.8529 \| 17.7177 \| \| 0.8935 \| 3.0 \| 435 \| 1.4617 \| 0.5181 \| 0.2968 \| 0.4607 \| 0.4622 \| 0.8751 \| 0.8818 \| 9.4024 \| 16 \| 4 \| 14.1171 \| 17.1171 \| \| 0.8028 \| 4.0 \| 580 \| 1.4744 \| 0.5103 \| 0.2966 \| 0.4497 \| 0.4496 \| 0.8797 \| 0.8725 \| 8.1982 \| 17 \| 4 \| 12.5706 \| 8.1081 \| \| 0.7395 \| 5.0 \| 725 \| 1.4797 \| 0.5121 \| 0.3016 \| 0.4548 \| 0.4554 \| 0.8796 \| 0.8761 \| 8.4985 \| 16 \| 3 \| 12.985 \| 10.8108 \| \| 0.6986 \| 6.0 \| 870 \| 1.5154 \| 0.5218 \| 0.2987 \| 0.4554 \| 0.4542 \| 0.8808 \| 0.879 \| 8.7297 \| 16 \| 4 \| 13.0691 \| 14.1141 \| \| 0.6527 \| 7.0 \| 1015 \| 1.5347 \| 0.5083 \| 0.2876 \| 0.4494 \| 0.4485 \| 0.8797 \| 0.8763 \| 8.5526 \| 16 \| 4 \| 13.012 \| 11.4114 \| \| 0.588 \| 8.0 \| 1160 \| 1.5578 \| 0.4984 \| 0.2752 \| 0.4403 \| 0.4399 \| 0.8746 \| 0.8728 \| 8.6336 \| 17 \| 4 \| 13.006 \| 10.8108 \| \| 0.5705 \| 9.0 \| 1305 \| 1.6569 \| 0.5152 \| 0.2902 \| 0.4544 \| 0.454 \| 0.8803 \| 0.8764 \| 8.5135 \| 16 \| 4 \| 13.1592 \| 9.9099 \| \| 0.5601 \| 10.0 \| 1450 \| 1.6651 \| 0.5246 \| 0.2837 \| 0.4572 \| 0.4579 \| 0.8777 \| 0.8807 \| 8.979 \| 16 \| 4 \| 13.6607 \| 12.012 \| \| 0.523 \| 11.0 \| 1595 \| 1.7085 \| 0.5149 \| 0.2854 \| 0.4508 \| 0.4507 \| 0.879 \| 0.8789 \| 8.7718 \| 17 \| 4 \| 13.2613 \| 10.8108 \| \| 0.5032 \| 12.0 \| 1740 \| 1.7886 \| 0.5107 \| 0.2817 \| 0.4457 \| 0.4457 \| 0.8778 \| 0.8772 \| 8.8378 \| 17 \| 4 \| 13.4204 \| 11.7117 \| \| 0.4872 \| 13.0 \| 1885 \| 1.8073 \| 0.5097 \| 0.2808 \| 0.4439 \| 0.4441 \| 0.8786 \| 0.8758 \| 8.6306 \| 16 \| 4 \| 13.1562 \| 9.6096 \| \| 0.4703 \| 14.0 \| 2030 \| 1.8436 \| 0.5059 \| 0.2754 \| 0.4456 \| 0.4457 \| 0.8769 \| 0.8756 \| 8.6817 \| 17 \| 4 \| 13.1471 \| 9.9099 \| \| 0.4598 \| 15.0 \| 2175 \| 1.9150 \| 0.5148 \| 0.2794 \| 0.4532 \| 0.4532 \| 0.8798 \| 0.8775 \| 8.6907 \| 18 \| 4 \| 13.1021 \| 11.4114 \| \| 0.4385 \| 16.0 \| 2320 \| 1.9319 \| 0.4966 \| 0.2666 \| 0.4402 \| 0.4406 \| 0.8771 \| 0.8724 \| 8.2703 \| 16 \| 4 \| 12.7237 \| 7.8078 \| \| 0.4306 \| 17.0 \| 2465 \| 1.9821 \| 0.5041 \| 0.2763 \| 0.4449 \| 0.4448 \| 0.8788 \| 0.8752 \| 8.5105 \| 16 \| 4 \| 13.0541 \| 9.3093 \| \| 0.4154 \| 18.0 \| 2610 \| 2.0345 \| 0.5066 \| 0.2746 \| 0.4467 \| 0.4461 \| 0.8796 \| 0.8732 \| 8.1922 \| 16 \| 3 \| 12.6186 \| 7.8078 \| \| 0.3995 \| 19.0 \| 2755 \| 2.0671 \| 0.4954 \| 0.2707 \| 0.4411 \| 0.4416 \| 0.8773 \| 0.8721 \| 8.4505 \| 17 \| 4 \| 12.8468 \| 8.7087 \| \| 0.4053 \| 20.0 \| 2900 \| 2.1265 \| 0.4975 \| 0.2704 \| 0.4365 \| 0.4364 \| 0.8767 \| 0.873 \| 8.5075 \| 17 \| 3 \| 13.0571 \| 9.009 \| \| 0.3812 \| 21.0 \| 3045 \| 2.2077 \| 0.5011 \| 0.2733 \| 0.4406 \| 0.4411 \| 0.8764 \| 0.8756 \| 8.7958 \| 17 \| 3 \| 13.4084 \| 12.012 \| \| 0.3856 \| 22.0 \| 3190 \| 2.2043 \| 0.4956 \| 0.2603 \| 0.4358 \| 0.4361 \| 0.8775 \| 0.8729 \| 8.2913 \| 17 \| 3 \| 12.8078 \| 8.7087 \| \| 0.3805 \| 23.0 \| 3335 \| 2.2201 \| 0.5015 \| 0.2698 \| 0.4421 \| 0.4427 \| 0.8789 \| 0.8728 \| 8.2402 \| 17 \| 3 \| 12.5856 \| 8.1081 \| \| 0.3741 \| 24.0 \| 3480 \| 2.2269 \| 0.5029 \| 0.2652 \| 0.4412 \| 0.4413 \| 0.8767 \| 0.8743 \| 8.5856 \| 16 \| 4 \| 13.039 \| 10.2102 \| \| 0.3697 \| 25.0 \| 3625 \| 2.2596 \| 0.4956 \| 0.2674 \| 0.436 \| 0.4359 \| 0.8765 \| 0.8728 \| 8.4895 \| 17 \| 4 \| 12.9129 \| 9.9099 \| \| 0.3663 \| 26.0 \| 3770 \| 2.2506 \| 0.4891 \| 0.2572 \| 0.432 \| 0.432 \| 0.8749 \| 0.8716 \| 8.4865 \| 17 \| 4 \| 12.8498 \| 6.9069 \| \| 0.3409 \| 27.0 \| 3915 \| 2.2893 \| 0.4958 \| 0.2635 \| 0.4328 \| 0.4327 \| 0.8772 \| 0.8727 \| 8.3994 \| 17 \| 3 \| 12.8228 \| 9.6096 \| \| 0.3524 \| 28.0 \| 4060 \| 2.3127 \| 0.4907 \| 0.2597 \| 0.4322 \| 0.4329 \| 0.8751 \| 0.8712 \| 8.4084 \| 16 \| 4 \| 12.7718 \| 8.1081 \| \| 0.3379 \| 29.0 \| 4205 \| 2.3167 \| 0.4958 \| 0.2674 \| 0.4374 \| 0.4368 \| 0.8772 \| 0.8737 \| 8.4234 \| 16 \| 4 \| 12.8138 \| 7.2072 \| \| 0.3472 \| 30.0 \| 4350 \| 2.3157 \| 0.4987 \| 0.2713 \| 0.4415 \| 0.4403 \| 0.8788 \| 0.8736 \| 8.3634 \| 17 \| 3 \| 12.6517 \| 7.2072 \| \| 0.3353 \| 31.0 \| 4495 \| 2.3506 \| 0.4991 \| 0.2631 \| 0.4375 \| 0.436 \| 0.8764 \| 0.8744 \| 8.6396 \| 17 \| 4 \| 13.1502 \| 9.6096 \| \| 0.3466 \| 32.0 \| 4640 \| 2.3594 \| 0.4897 \| 0.2593 \| 0.4307 \| 0.4301 \| 0.8777 \| 0.8711 \| 8.1712 \| 16 \| 4 \| 12.6126 \| 5.4054 \| \| 0.3406 \| 33.0 \| 4785 \| 2.3632 \| 0.495 \| 0.2746 \| 0.4401 \| 0.4397 \| 0.8772 \| 0.8732 \| 8.5556 \| 16 \| 4 \| 13.027 \| 8.4084 \| \| 0.3382 \| 34.0 \| 4930 \| 2.3505 \| 0.4856 \| 0.261 \| 0.4306 \| 0.4295 \| 0.8758 \| 0.8693 \| 8.2733 \| 17 \| 3 \| 12.6366 \| 7.5075 \| \| 0.3392 \| 35.0 \| 5075 \| 2.3665 \| 0.4972 \| 0.2719 \| 0.4376 \| 0.4372 \| 0.8764 \| 0.8741 \| 8.6847 \| 17 \| 4 \| 13.1532 \| 9.3093 \| \| 0.3465 \| 36.0 \| 5220 \| 2.3837 \| 0.4981 \| 0.2722 \| 0.441 \| 0.4411 \| 0.876 \| 0.8738 \| 8.6607 \| 17 \| 4 \| 13.1982 \| 12.3123 \| \| 0.3377 \| 37.0 \| 5365 \| 2.3984 \| 0.4832 \| 0.2623 \| 0.4294 \| 0.4285 \| 0.8737 \| 0.8697 \| 8.5225 \| 17 \| 4 \| 12.9399 \| 10.5105 \| \| 0.3523 \| 38.0 \| 5510 \| 2.3843 \| 0.495 \| 0.2671 \| 0.438 \| 0.4368 \| 0.8754 \| 0.873 \| 8.5886 \| 17 \| 3 \| 13.1111 \| 7.2072 \| \| 0.3261 \| 39.0 \| 5655 \| 2.4337 \| 0.4948 \| 0.2666 \| 0.4378 \| 0.4369 \| 0.8771 \| 0.8726 \| 8.4655 \| 17 \| 4 \| 12.8919 \| 9.009 \| \| 0.3262 \| 40.0 \| 5800 \| 2.4149 \| 0.4971 \| 0.2691 \| 0.438 \| 0.4375 \| 0.8772 \| 0.8717 \| 8.4505 \| 16 \| 4 \| 12.9249 \| 8.1081 \| \| 0.3307 \| 41.0 \| 5945 \| 2.4352 \| 0.4834 \| 0.2585 \| 0.4261 \| 0.4256 \| 0.8746 \| 0.8697 \| 8.4024 \| 17 \| 3 \| 12.8859 \| 9.6096 \| \| 0.3226 \| 42.0 \| 6090 \| 2.4241 \| 0.488 \| 0.2584 \| 0.4318 \| 0.4315 \| 0.8756 \| 0.8706 \| 8.4444 \| 17 \| 3 \| 12.8288 \| 8.7087 \| \| 0.34 \| 43.0 \| 6235 \| 2.4485 \| 0.4891 \| 0.2589 \| 0.4326 \| 0.432 \| 0.8758 \| 0.8705 \| 8.3243 \| 17 \| 4 \| 12.7898 \| 6.6066 \| \| 0.3425 \| 44.0 \| 6380 \| 2.4457 \| 0.4865 \| 0.26 \| 0.4293 \| 0.4287 \| 0.8733 \| 0.8713 \| 8.6336 \| 16 \| 3 \| 13.1922 \| 9.6096 \| \| 0.3201 \| 45.0 \| 6525 \| 2.4535 \| 0.4811 \| 0.2473 \| 0.4243 \| 0.4237 \| 0.8751 \| 0.8697 \| 8.3093 \| 17 \| 3 \| 12.7748 \| 8.4084 \| \| 0.3094 \| 46.0 \| 6670 \| 2.4918 \| 0.4916 \| 0.2614 \| 0.4351 \| 0.4342 \| 0.8758 \| 0.8726 \| 8.5706 \| 17 \| 3 \| 13.039 \| 10.2102 \| \| 0.3262 \| 47.0 \| 6815 \| 2.4839 \| 0.4822 \| 0.255 \| 0.425 \| 0.4237 \| 0.8719 \| 0.869 \| 8.5375 \| 17 \| 4 \| 12.976 \| 9.009 \| \| 0.3186 \| 48.0 \| 6960 \| 2.4966 \| 0.486 \| 0.2492 \| 0.4276 \| 0.4264 \| 0.8738 \| 0.8707 \| 8.4745 \| 17 \| 3 \| 12.955 \| 6.6066 \| \| 0.3231 \| 49.0 \| 7105 \| 2.4978 \| 0.4889 \| 0.2661 \| 0.4343 \| 0.434 \| 0.8767 \| 0.871 \| 8.4505 \| 17 \| 3 \| 12.8468 \| 9.009 \| \| 0.3294 \| 50.0 \| 7250 \| 2.4731 \| 0.4916 \| 0.2683 \| 0.4374 \| 0.4373 \| 0.877 \| 0.8726 \| 8.4955 \| 17 \| 4 \| 12.9369 \| 9.3093 \| \| 0.3172 \| 51.0 \| 7395 \| 2.4922 \| 0.4861 \| 0.2573 \| 0.4314 \| 0.431 \| 0.8759 \| 0.87 \| 8.3003 \| 17 \| 4 \| 12.6907 \| 7.8078 \| \| 0.3247 \| 52.0 \| 7540 \| 2.5044 \| 0.4802 \| 0.2495 \| 0.4281 \| 0.4282 \| 0.8737 \| 0.8698 \| 8.4715 \| 17 \| 4 \| 12.9009 \| 8.1081 \| \| 0.3132 \| 53.0 \| 7685 \| 2.5168 \| 0.4832 \| 0.2558 \| 0.4273 \| 0.4268 \| 0.8736 \| 0.8703 \| 8.5706 \| 17 \| 3 \| 12.967 \| 9.3093 \| \| 0.3285 \| 54.0 \| 7830 \| 2.5296 \| 0.4882 \| 0.26 \| 0.4323 \| 0.4319 \| 0.8754 \| 0.8724 \| 8.5495 \| 17 \| 3 \| 13.0541 \| 8.7087 \| \| 0.3111 \| 55.0 \| 7975 \| 2.5529 \| 0.4829 \| 0.2561 \| 0.4268 \| 0.4262 \| 0.874 \| 0.8694 \| 8.4474 \| 17 \| 3 \| 12.9339 \| 7.2072 \| \| 0.3194 \| 56.0 \| 8120 \| 2.5903 \| 0.49 \| 0.2614 \| 0.4337 \| 0.4329 \| 0.8747 \| 0.8719 \| 8.5946 \| 17 \| 3 \| 13.0931 \| 8.1081 \| \| 0.3144 \| 57.0 \| 8265 \| 2.5787 \| 0.4859 \| 0.2593 \| 0.4315 \| 0.4303 \| 0.8739 \| 0.8698 \| 8.5195 \| 17 \| 4 \| 12.8679 \| 8.4084 \| \| 0.2972 \| 58.0 \| 8410 \| 2.5759 \| 0.4848 \| 0.2565 \| 0.4291 \| 0.4279 \| 0.8738 \| 0.8697 \| 8.5165 \| 17 \| 3 \| 12.9219 \| 8.1081 \| \| 0.3209 \| 59.0 \| 8555 \| 2.5609 \| 0.4792 \| 0.246 \| 0.4212 \| 0.4201 \| 0.8723 \| 0.8678 \| 8.4114 \| 17 \| 3 \| 12.8799 \| 6.9069 \| \| 0.3148 \| 60.0 \| 8700 \| 2.5758 \| 0.481 \| 0.2454 \| 0.4243 \| 0.4231 \| 0.874 \| 0.8688 \| 8.3664 \| 16 \| 3 \| 12.7628 \| 7.5075 \| \| 0.3026 \| 61.0 \| 8845 \| 2.5819 \| 0.4804 \| 0.2555 \| 0.4231 \| 0.4231 \| 0.8738 \| 0.8689 \| 8.4204 \| 17 \| 3 \| 12.7628 \| 8.4084 \| \| 0.3074 \| 62.0 \| 8990 \| 2.5882 \| 0.4893 \| 0.2627 \| 0.431 \| 0.4303 \| 0.8753 \| 0.8715 \| 8.4895 \| 17 \| 3 \| 12.8889 \| 8.7087 \| \| 0.3013 \| 63.0 \| 9135 \| 2.5865 \| 0.4835 \| 0.2599 \| 0.426 \| 0.4251 \| 0.8743 \| 0.8707 \| 8.4865 \| 17 \| 4 \| 12.964 \| 8.7087 \| \| 0.3274 \| 64.0 \| 9280 \| 2.5957 \| 0.4928 \| 0.2649 \| 0.436 \| 0.4353 \| 0.8738 \| 0.8734 \| 8.8018 \| 17 \| 3 \| 13.2823 \| 11.4114 \| \| 0.2928 \| 65.0 \| 9425 \| 2.5846 \| 0.4888 \| 0.2653 \| 0.4365 \| 0.4356 \| 0.8763 \| 0.8713 \| 8.2973 \| 17 \| 3 \| 12.6637 \| 8.1081 \| \| 0.3261 \| 66.0 \| 9570 \| 2.5704 \| 0.4901 \| 0.267 \| 0.4386 \| 0.4374 \| 0.8759 \| 0.871 \| 8.3303 \| 17 \| 4 \| 12.7838 \| 6.6066 \| \| 0.3153 \| 67.0 \| 9715 \| 2.6023 \| 0.4897 \| 0.2611 \| 0.4311 \| 0.4301 \| 0.8749 \| 0.872 \| 8.6426 \| 17 \| 3 \| 13.0691 \| 10.8108 \| \| 0.3185 \| 68.0 \| 9860 \| 2.5831 \| 0.4862 \| 0.2579 \| 0.4257 \| 0.4247 \| 0.8735 \| 0.8718 \| 8.6486 \| 17 \| 4 \| 13.1441 \| 12.012 \| \| 0.3054 \| 69.0 \| 10005 \| 2.5949 \| 0.4831 \| 0.2575 \| 0.4247 \| 0.4239 \| 0.8728 \| 0.87 \| 8.5405 \| 17 \| 4 \| 13.036 \| 9.9099 \| \| 0.3006 \| 70.0 \| 10150 \| 2.5822 \| 0.4853 \| 0.252 \| 0.4255 \| 0.4243 \| 0.8735 \| 0.87 \| 8.5495 \| 17 \| 3 \| 13.0 \| 10.5105 \| \| 0.3092 \| 71.0 \| 10295 \| 2.5743 \| 0.4903 \| 0.2595 \| 0.432 \| 0.4315 \| 0.8759 \| 0.8719 \| 8.4474 \| 17 \| 3 \| 12.8559 \| 8.7087 \| \| 0.2928 \| 72.0 \| 10440 \| 2.5905 \| 0.4918 \| 0.2665 \| 0.4356 \| 0.4347 \| 0.876 \| 0.8724 \| 8.4474 \| 17 \| 4 \| 12.8679 \| 8.4084 \| \| 0.3021 \| 73.0 \| 10585 \| 2.6171 \| 0.4957 \| 0.266 \| 0.4368 \| 0.4354 \| 0.8764 \| 0.873 \| 8.5676 \| 17 \| 3 \| 12.964 \| 11.1111 \| \| 0.3047 \| 74.0 \| 10730 \| 2.6233 \| 0.492 \| 0.2655 \| 0.4341 \| 0.4328 \| 0.8753 \| 0.8715 \| 8.5736 \| 17 \| 3 \| 12.952 \| 10.5105 \| \| 0.3043 \| 75.0 \| 10875 \| 2.6405 \| 0.4887 \| 0.2623 \| 0.4318 \| 0.4309 \| 0.8756 \| 0.8704 \| 8.4895 \| 17 \| 3 \| 12.8679 \| 9.9099 \| \| 0.305 \| 76.0 \| 11020 \| 2.6171 \| 0.4942 \| 0.2687 \| 0.4381 \| 0.4372 \| 0.8766 \| 0.8724 \| 8.5586 \| 17 \| 3 \| 12.9369 \| 10.8108 \| \| 0.3127 \| 77.0 \| 11165 \| 2.6289 \| 0.4959 \| 0.2646 \| 0.4366 \| 0.4357 \| 0.8767 \| 0.8731 \| 8.5766 \| 17 \| 3 \| 13.006 \| 12.012 \| \| 0.2945 \| 78.0 \| 11310 \| 2.6453 \| 0.4881 \| 0.2589 \| 0.4272 \| 0.4261 \| 0.8753 \| 0.8711 \| 8.5375 \| 17 \| 3 \| 12.8739 \| 9.3093 \| \| 0.2844 \| 79.0 \| 11455 \| 2.6543 \| 0.4895 \| 0.2565 \| 0.4294 \| 0.4288 \| 0.8753 \| 0.8718 \| 8.5616 \| 17 \| 3 \| 12.997 \| 11.7117 \| \| 0.3188 \| 80.0 \| 11600 \| 2.6556 \| 0.4919 \| 0.2677 \| 0.4328 \| 0.4318 \| 0.8756 \| 0.8712 \| 8.5345 \| 17 \| 3 \| 12.973 \| 9.9099 \| \| 0.2857 \| 81.0 \| 11745 \| 2.6696 \| 0.4914 \| 0.2666 \| 0.434 \| 0.4332 \| 0.8761 \| 0.8717 \| 8.4595 \| 17 \| 3 \| 12.8829 \| 10.5105 \| \| 0.3091 \| 82.0 \| 11890 \| 2.6577 \| 0.4986 \| 0.2718 \| 0.4397 \| 0.4388 \| 0.8766 \| 0.8741 \| 8.6276 \| 17 \| 3 \| 13.1441 \| 10.8108 \| \| 0.3115 \| 83.0 \| 12035 \| 2.6720 \| 0.4944 \| 0.266 \| 0.4364 \| 0.4351 \| 0.8766 \| 0.8725 \| 8.4925 \| 17 \| 3 \| 12.9309 \| 9.3093 \| \| 0.2947 \| 84.0 \| 12180 \| 2.6490 \| 0.4955 \| 0.2628 \| 0.4347 \| 0.4343 \| 0.8767 \| 0.873 \| 8.4985 \| 17 \| 3 \| 13.018 \| 7.5075 \| \| 0.312 \| 85.0 \| 12325 \| 2.6425 \| 0.4928 \| 0.2689 \| 0.4364 \| 0.4358 \| 0.8763 \| 0.8728 \| 8.5766 \| 17 \| 3 \| 13.0631 \| 9.9099 \| \| 0.3081 \| 86.0 \| 12470 \| 2.6314 \| 0.4904 \| 0.2648 \| 0.4327 \| 0.432 \| 0.875 \| 0.8722 \| 8.6246 \| 17 \| 3 \| 13.1411 \| 10.5105 \| \| 0.3043 \| 87.0 \| 12615 \| 2.6485 \| 0.4863 \| 0.259 \| 0.4273 \| 0.4259 \| 0.8736 \| 0.8709 \| 8.5736 \| 17 \| 3 \| 13.0901 \| 9.6096 \| \| 0.3034 \| 88.0 \| 12760 \| 2.6402 \| 0.4867 \| 0.2604 \| 0.4279 \| 0.4274 \| 0.8739 \| 0.871 \| 8.5706 \| 17 \| 3 \| 13.0751 \| 8.1081 \| \| 0.3058 \| 89.0 \| 12905 \| 2.6573 \| 0.4926 \| 0.2638 \| 0.4348 \| 0.4339 \| 0.8762 \| 0.872 \| 8.4805 \| 17 \| 3 \| 12.955 \| 7.8078 \| \| 0.2909 \| 90.0 \| 13050 \| 2.6654 \| 0.4955 \| 0.2679 \| 0.4357 \| 0.4342 \| 0.8756 \| 0.8729 \| 8.6817 \| 17 \| 3 \| 13.1802 \| 10.2102 \| \| 0.3082 \| 91.0 \| 13195 \| 2.6757 \| 0.4942 \| 0.2671 \| 0.4362 \| 0.4349 \| 0.8756 \| 0.8724 \| 8.5796 \| 17 \| 3 \| 13.0721 \| 9.6096 \| \| 0.3016 \| 92.0 \| 13340 \| 2.6791 \| 0.4933 \| 0.2657 \| 0.4351 \| 0.4345 \| 0.875 \| 0.8722 \| 8.6336 \| 17 \| 3 \| 13.1441 \| 9.9099 \| \| 0.2993 \| 93.0 \| 13485 \| 2.6814 \| 0.493 \| 0.2658 \| 0.433 \| 0.4318 \| 0.8747 \| 0.8726 \| 8.6997 \| 17 \| 3 \| 13.2462 \| 11.1111 \| \| 0.3022 \| 94.0 \| 13630 \| 2.6698 \| 0.4929 \| 0.2638 \| 0.4334 \| 0.4324 \| 0.8751 \| 0.8723 \| 8.5976 \| 17 \| 3 \| 13.0961 \| 9.3093 \| \| 0.2921 \| 95.0 \| 13775 \| 2.6665 \| 0.4867 \| 0.2586 \| 0.4294 \| 0.4284 \| 0.8744 \| 0.8709 \| 8.4955 \| 17 \| 3 \| 12.988 \| 8.4084 \| \| 0.3034 \| 96.0 \| 13920 \| 2.6704 \| 0.4854 \| 0.2574 \| 0.4275 \| 0.4266 \| 0.8742 \| 0.8704 \| 8.4805 \| 17 \| 3 \| 12.9429 \| 8.7087 \| \| 0.3063 \| 97.0 \| 14065 \| 2.6749 \| 0.4863 \| 0.2576 \| 0.4275 \| 0.4266 \| 0.8743 \| 0.8707 \| 8.4805 \| 17 \| 3 \| 12.9369 \| 8.7087 \| \| 0.2984 \| 98.0 \| 14210 \| 2.6772 \| 0.4858 \| 0.258 \| 0.4274 \| 0.4264 \| 0.8739 \| 0.8704 \| 8.5105 \| 17 \| 3 \| 12.97 \| 9.6096 \| \| 0.2942 \| 99.0 \| 14355 \| 2.6784 \| 0.4872 \| 0.2595 \| 0.4279 \| 0.427 \| 0.874 \| 0.8704 \| 8.5075 \| 17 \| 3 \| 12.967 \| 9.6096 \| \| 0.2866 \| 100.0 \| 14500 \| 2.6784 \| 0.4871 \| 0.2579 \| 0.428 \| 0.4272 \| 0.8743 \| 0.8706 \| 8.4775 \| 17 \| 3 \| 12.9249 \| 9.3093 \| ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
hanlforever/xlm-roberta-base-finetuned-panx-de-fr	hanlforever	2023-09-11T15:00:13Z	105	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-09-11T13:40:18Z	--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr 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. --> # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1650 - F1: 0.8562 ## 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: 24 - eval_batch_size: 24 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2884 \| 1.0 \| 715 \| 0.1855 \| 0.8234 \| \| 0.1452 \| 2.0 \| 1430 \| 0.1642 \| 0.8458 \| \| 0.094 \| 3.0 \| 2145 \| 0.1650 \| 0.8562 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.11.0
jroberts/my_awesome_pokemon_model_resnet18	jroberts	2023-09-11T14:57:50Z	270	0	transformers	[ "transformers", "pytorch", "safetensors", "resnet", "image-classification", "generated_from_trainer", "dataset:pokemon-classification", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-05-19T14:09:42Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - pokemon-classification metrics: - accuracy model-index: - name: my_awesome_pokemon_model_resnet18 results: - task: name: Image Classification type: image-classification dataset: name: pokemon-classification type: pokemon-classification config: full split: validation args: full metrics: - name: Accuracy type: accuracy value: 0.01079136690647482 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_pokemon_model_resnet18 This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on the pokemon-classification dataset. It achieves the following results on the evaluation set: - Loss: 6.8019 - Accuracy: 0.0108 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 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: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 4.275 \| 1.0 \| 76 \| 6.1680 \| 0.0014 \| \| 3.3896 \| 1.99 \| 152 \| 6.6421 \| 0.0115 \| \| 3.0563 \| 2.99 \| 228 \| 6.8019 \| 0.0108 \| ### Framework versions - Transformers 4.29.1 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.13.3
venetis/distilbert-base-uncased_finetuned_disaster_tweets	venetis	2023-09-11T14:46:19Z	107	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-01-10T20:42:40Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 base_model: distilbert-base-uncased model-index: - name: distilbert-base-uncased_finetuned_disaster_tweets 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_disaster_tweets This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4007 - Accuracy: 0.8399 - F1: 0.8384 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.4594 \| 1.0 \| 191 \| 0.4059 \| 0.8163 \| 0.8164 \| \| 0.3399 \| 2.0 \| 382 \| 0.3905 \| 0.8346 \| 0.8333 \| \| 0.2859 \| 3.0 \| 573 \| 0.4007 \| 0.8399 \| 0.8384 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2
gyesibiney/Distilbert-movie-review-sentiment-classifier-2	gyesibiney	2023-09-11T14:45:58Z	106	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-09-10T18:57:28Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: Distilbert-capstone_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. --> # Distilbert-capstone_1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4272 - Accuracy: 0.9251 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2895 \| 1.0 \| 623 \| 0.2569 \| 0.8930 \| \| 0.1635 \| 2.0 \| 1246 \| 0.2479 \| 0.9171 \| \| 0.0911 \| 3.0 \| 1869 \| 0.3438 \| 0.9207 \| \| 0.053 \| 4.0 \| 2492 \| 0.3986 \| 0.9223 \| \| 0.011 \| 5.0 \| 3115 \| 0.4272 \| 0.9251 \| ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
AIYIYA/my_tt	AIYIYA	2023-09-11T14:42:38Z	61	0	transformers	[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-09-11T14:04:56Z	--- tags: - generated_from_keras_callback model-index: - name: AIYIYA/my_tt 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. --> # AIYIYA/my_tt This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.0110 - Validation Loss: 1.1941 - Train Accuracy: 0.5185 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 20, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 1.8538 \| 1.2004 \| 0.5185 \| 0 \| \| 1.0820 \| 1.1683 \| 0.5185 \| 1 \| \| 1.0110 \| 1.1941 \| 0.5185 \| 2 \| ### Framework versions - Transformers 4.33.1 - TensorFlow 2.13.0 - Datasets 2.14.5 - Tokenizers 0.13.3
jasoneden/bloom560m-squad-helloworld	jasoneden	2023-09-11T14:42:14Z	86	8	transformers	[ "transformers", "pytorch", "safetensors", "bloom", "question-answering", "generated_from_trainer", "dataset:squad_v2", "base_model:bigscience/bloom-560m", "base_model:finetune:bigscience/bloom-560m", "license:bigscience-bloom-rail-1.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	question-answering	2022-10-25T18:46:33Z	--- license: bigscience-bloom-rail-1.0 tags: - generated_from_trainer datasets: - squad_v2 base_model: bigscience/bloom-560m model-index: - name: debug_bloom_squad results: [] --- <!-- This model card has mostly been generated automatically according to the information the Trainer had access to. I've added some additional context. --> # POC - BLOOM for QuestionAnswering, tuned on squad_v2 This model is a fine-tuned version of [bigscience/bloom-560m](https://huggingface.co/bigscience/bloom-560m) on the squad_v2 dataset. It is intended for a proof of concept, and perhaps to serve as a starting point for others trying to do the same thing. Ongoing discussion surrounding this effort: https://huggingface.co/bigscience/bloom/discussions/46#633c57b2ccce04161f82e6c2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 6 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.24.0.dev0 - Pytorch 1.12.1+cu102 - Datasets 2.6.1 - Tokenizers 0.13.1
jncraton/LaMini-GPT-774M-ct2-int8	jncraton	2023-09-11T14:38:50Z	13	0	transformers	[ "transformers", "text-generation", "en", "arxiv:2304.14402", "base_model:openai-community/gpt2-large", "base_model:finetune:openai-community/gpt2-large", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	text-generation	2023-06-24T21:16:48Z	--- language: - en license: cc-by-nc-4.0 pipeline_tag: text-generation widget: - text: 'Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: how can I become more healthy? ### Response:' example_title: example base_model: gpt2-large --- <!-- 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. --> <p align="center" width="100%"> <a><img src="https://raw.githubusercontent.com/mbzuai-nlp/lamini-lm/main/images/lamini.png" alt="Title" style="width: 100%; min-width: 300px; display: block; margin: auto;"></a> </p> # LaMini-GPT-774M [![Model License](https://img.shields.io/badge/Model%20License-CC%20By%20NC%204.0-red.svg)]() This model is one of our LaMini-LM model series in paper "[LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions](https://github.com/mbzuai-nlp/lamini-lm)". This model is a fine-tuned version of [gpt2-large](https://huggingface.co/gpt2-large) on [LaMini-instruction dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction) that contains 2.58M samples for instruction fine-tuning. For more information about our dataset, please refer to our [project repository](https://github.com/mbzuai-nlp/lamini-lm/). You can view other models of LaMini-LM series as follows. Models with ✩ are those with the best overall performance given their size/architecture, hence we recommend using them. More details can be seen in our paper. <table> <thead> <tr> <th>Base model</th> <th colspan="4">LaMini-LM series (#parameters)</th> </tr> </thead> <tbody> <tr> <td>T5</td> <td><a href="https://huggingface.co/MBZUAI/lamini-t5-61m" target="_blank" rel="noopener noreferrer">LaMini-T5-61M</a></td> <td><a href="https://huggingface.co/MBZUAI/lamini-t5-223m" target="_blank" rel="noopener noreferrer">LaMini-T5-223M</a></td> <td><a href="https://huggingface.co/MBZUAI/lamini-t5-738m" target="_blank" rel="noopener noreferrer">LaMini-T5-738M</a></td> <td></td> </tr> <tr> <td>Flan-T5</td> <td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-77m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-77M</a>✩</td> <td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-248m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-248M</a>✩</td> <td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-783m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-783M</a>✩</td> <td></td> </tr> <tr> <td>Cerebras-GPT</td> <td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-111m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-111M</a></td> <td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-256m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-256M</a></td> <td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-590m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-590M</a></td> <td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-1.3b" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-1.3B</a></td> </tr> <tr> <td>GPT-2</td> <td><a href="https://huggingface.co/MBZUAI/lamini-gpt-124m" target="_blank" rel="noopener noreferrer">LaMini-GPT-124M</a>✩</td> <td><a href="https://huggingface.co/MBZUAI/lamini-gpt-774m" target="_blank" rel="noopener noreferrer">LaMini-GPT-774M</a>✩</td> <td><a href="https://huggingface.co/MBZUAI/lamini-gpt-1.5b" target="_blank" rel="noopener noreferrer">LaMini-GPT-1.5B</a>✩</td> <td></td> </tr> <tr> <td>GPT-Neo</td> <td><a href="https://huggingface.co/MBZUAI/lamini-neo-125m" target="_blank" rel="noopener noreferrer">LaMini-Neo-125M</a></td> <td><a href="https://huggingface.co/MBZUAI/lamini-neo-1.3b" target="_blank" rel="noopener noreferrer">LaMini-Neo-1.3B</a></td> <td></td> <td></td> </tr> <tr> <td>GPT-J</td> <td colspan="4">coming soon</td> </tr> <tr> <td>LLaMA</td> <td colspan="4">coming soon</td> </tr> </tbody> </table> ## Use ### Intended use We recommend using the model to respond to human instructions written in natural language. Since this decoder-only model is fine-tuned with wrapper text, we suggest using the same wrapper text to achieve the best performance. See the example on the right or the code below. We now show you how to load and use our model using HuggingFace `pipeline()`. ```python # pip install -q transformers from transformers import pipeline checkpoint = "{model_name}" model = pipeline('text-generation', model = checkpoint) instruction = 'Please let me know your thoughts on the given place and why you think it deserves to be visited: \n"Barcelona, Spain"' input_prompt = f"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:" generated_text = model(input_prompt, max_length=512, do_sample=True)[0]['generated_text'] print("Response", generated_text) ``` ## Training Procedure <p align="center" width="100%"> <a><img src="https://raw.githubusercontent.com/mbzuai-nlp/lamini-lm/main/images/lamini-pipeline.drawio.png" alt="Title" style="width: 100%; min-width: 250px; display: block; margin: auto;"></a> </p> We initialize with [gpt2-large](https://huggingface.co/gpt2-large) and fine-tune it on our [LaMini-instruction dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction). Its total number of parameters is 774M. ### Training Hyperparameters ## Evaluation We conducted two sets of evaluations: automatic evaluation on downstream NLP tasks and human evaluation on user-oriented instructions. For more detail, please refer to our [paper](). ## Limitations More information needed # Citation ```bibtex @article{lamini-lm, author = {Minghao Wu and Abdul Waheed and Chiyu Zhang and Muhammad Abdul-Mageed and Alham Fikri Aji }, title = {LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions}, journal = {CoRR}, volume = {abs/2304.14402}, year = {2023}, url = {https://arxiv.org/abs/2304.14402}, eprinttype = {arXiv}, eprint = {2304.14402} } ```
jncraton/LaMini-GPT-124M-ct2-int8	jncraton	2023-09-11T14:38:27Z	563	0	transformers	[ "transformers", "text-generation", "en", "arxiv:2304.14402", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	text-generation	2023-06-24T22:21:05Z	--- language: - en license: cc-by-nc-4.0 pipeline_tag: text-generation widget: - text: 'Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: how can I become more healthy? ### Response:' example_title: example base_model: gpt2 --- <!-- 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. --> <p align="center" width="100%"> <a><img src="https://raw.githubusercontent.com/mbzuai-nlp/lamini-lm/main/images/lamini.png" alt="Title" style="width: 100%; min-width: 300px; display: block; margin: auto;"></a> </p> # LaMini-GPT-124M [![Model License](https://img.shields.io/badge/Model%20License-CC%20By%20NC%204.0-red.svg)]() This model is one of our LaMini-LM model series in paper "[LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions](https://github.com/mbzuai-nlp/lamini-lm)". This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on [LaMini-instruction dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction) that contains 2.58M samples for instruction fine-tuning. For more information about our dataset, please refer to our [project repository](https://github.com/mbzuai-nlp/lamini-lm/). You can view other models of LaMini-LM series as follows. Models with ✩ are those with the best overall performance given their size/architecture, hence we recommend using them. More details can be seen in our paper. <table> <thead> <tr> <th>Base model</th> <th colspan="4">LaMini-LM series (#parameters)</th> </tr> </thead> <tbody> <tr> <td>T5</td> <td><a href="https://huggingface.co/MBZUAI/lamini-t5-61m" target="_blank" rel="noopener noreferrer">LaMini-T5-61M</a></td> <td><a href="https://huggingface.co/MBZUAI/lamini-t5-223m" target="_blank" rel="noopener noreferrer">LaMini-T5-223M</a></td> <td><a href="https://huggingface.co/MBZUAI/lamini-t5-738m" target="_blank" rel="noopener noreferrer">LaMini-T5-738M</a></td> <td></td> </tr> <tr> <td>Flan-T5</td> <td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-77m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-77M</a>✩</td> <td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-248m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-248M</a>✩</td> <td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-783m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-783M</a>✩</td> <td></td> </tr> <tr> <td>Cerebras-GPT</td> <td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-111m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-111M</a></td> <td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-256m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-256M</a></td> <td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-590m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-590M</a></td> <td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-1.3b" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-1.3B</a></td> </tr> <tr> <td>GPT-2</td> <td><a href="https://huggingface.co/MBZUAI/lamini-gpt-124m" target="_blank" rel="noopener noreferrer">LaMini-GPT-124M</a>✩</td> <td><a href="https://huggingface.co/MBZUAI/lamini-gpt-774m" target="_blank" rel="noopener noreferrer">LaMini-GPT-774M</a>✩</td> <td><a href="https://huggingface.co/MBZUAI/lamini-gpt-1.5b" target="_blank" rel="noopener noreferrer">LaMini-GPT-1.5B</a>✩</td> <td></td> </tr> <tr> <td>GPT-Neo</td> <td><a href="https://huggingface.co/MBZUAI/lamini-neo-125m" target="_blank" rel="noopener noreferrer">LaMini-Neo-125M</a></td> <td><a href="https://huggingface.co/MBZUAI/lamini-neo-1.3b" target="_blank" rel="noopener noreferrer">LaMini-Neo-1.3B</a></td> <td></td> <td></td> </tr> <tr> <td>GPT-J</td> <td colspan="4">coming soon</td> </tr> <tr> <td>LLaMA</td> <td colspan="4">coming soon</td> </tr> </tbody> </table> ## Use ### Intended use We recommend using the model to respond to human instructions written in natural language. Since this decoder-only model is fine-tuned with wrapper text, we suggest using the same wrapper text to achieve the best performance. See the example on the right or the code below. We now show you how to load and use our model using HuggingFace `pipeline()`. ```python # pip install -q transformers from transformers import pipeline checkpoint = "{model_name}" model = pipeline('text-generation', model = checkpoint) instruction = 'Please let me know your thoughts on the given place and why you think it deserves to be visited: \n"Barcelona, Spain"' input_prompt = f"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:" generated_text = model(input_prompt, max_length=512, do_sample=True)[0]['generated_text'] print("Response", generated_text) ``` ## Training Procedure <p align="center" width="100%"> <a><img src="https://raw.githubusercontent.com/mbzuai-nlp/lamini-lm/main/images/lamini-pipeline.drawio.png" alt="Title" style="width: 100%; min-width: 250px; display: block; margin: auto;"></a> </p> We initialize with [gpt2](https://huggingface.co/gpt2) and fine-tune it on our [LaMini-instruction dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction). Its total number of parameters is 124M. ### Training Hyperparameters ## Evaluation We conducted two sets of evaluations: automatic evaluation on downstream NLP tasks and human evaluation on user-oriented instructions. For more detail, please refer to our [paper](). ## Limitations More information needed # Citation ```bibtex @article{lamini-lm, author = {Minghao Wu and Abdul Waheed and Chiyu Zhang and Muhammad Abdul-Mageed and Alham Fikri Aji }, title = {LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions}, journal = {CoRR}, volume = {abs/2304.14402}, year = {2023}, url = {https://arxiv.org/abs/2304.14402}, eprinttype = {arXiv}, eprint = {2304.14402} } ```
jncraton/LaMini-Flan-T5-248M-ct2-int8	jncraton	2023-09-11T14:37:41Z	232	0	transformers	[ "transformers", "generated_from_trainer", "instruction fine-tuning", "text2text-generation", "en", "arxiv:2304.14402", "base_model:google/flan-t5-base", "base_model:finetune:google/flan-t5-base", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	text2text-generation	2023-06-04T21:36:33Z	--- language: - en license: cc-by-nc-4.0 tags: - generated_from_trainer - instruction fine-tuning pipeline_tag: text2text-generation widget: - text: how can I become more healthy? example_title: example base_model: google/flan-t5-base model-index: - name: flan-t5-small-distil-v2 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. --> <p align="center" width="100%"> <a><img src="https://raw.githubusercontent.com/mbzuai-nlp/lamini-lm/main/images/lamini.png" alt="Title" style="width: 100%; min-width: 300px; display: block; margin: auto;"></a> </p> # LaMini-Flan-T5-248M [![Model License](https://img.shields.io/badge/Model%20License-CC%20By%20NC%204.0-red.svg)]() This model is one of our LaMini-LM model series in paper "[LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions](https://github.com/mbzuai-nlp/lamini-lm)". This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on [LaMini-instruction dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction) that contains 2.58M samples for instruction fine-tuning. For more information about our dataset, please refer to our [project repository](https://github.com/mbzuai-nlp/lamini-lm/). You can view other models of LaMini-LM series as follows. Models with ✩ are those with the best overall performance given their size/architecture, hence we recommend using them. More details can be seen in our paper. <table> <thead> <tr> <th>Base model</th> <th colspan="4">LaMini-LM series (#parameters)</th> </tr> </thead> <tbody> <tr> <td>T5</td> <td><a href="https://huggingface.co/MBZUAI/lamini-t5-61m" target="_blank" rel="noopener noreferrer">LaMini-T5-61M</a></td> <td><a href="https://huggingface.co/MBZUAI/lamini-t5-223m" target="_blank" rel="noopener noreferrer">LaMini-T5-223M</a></td> <td><a href="https://huggingface.co/MBZUAI/lamini-t5-738m" target="_blank" rel="noopener noreferrer">LaMini-T5-738M</a></td> <td></td> </tr> <tr> <td>Flan-T5</td> <td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-77m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-77M</a>✩</td> <td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-248m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-248M</a>✩</td> <td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-783m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-783M</a>✩</td> <td></td> </tr> <tr> <td>Cerebras-GPT</td> <td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-111m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-111M</a></td> <td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-256m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-256M</a></td> <td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-590m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-590M</a></td> <td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-1.3b" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-1.3B</a></td> </tr> <tr> <td>GPT-2</td> <td><a href="https://huggingface.co/MBZUAI/lamini-gpt-124m" target="_blank" rel="noopener noreferrer">LaMini-GPT-124M</a>✩</td> <td><a href="https://huggingface.co/MBZUAI/lamini-gpt-774m" target="_blank" rel="noopener noreferrer">LaMini-GPT-774M</a>✩</td> <td><a href="https://huggingface.co/MBZUAI/lamini-gpt-1.5b" target="_blank" rel="noopener noreferrer">LaMini-GPT-1.5B</a>✩</td> <td></td> </tr> <tr> <td>GPT-Neo</td> <td><a href="https://huggingface.co/MBZUAI/lamini-neo-125m" target="_blank" rel="noopener noreferrer">LaMini-Neo-125M</a></td> <td><a href="https://huggingface.co/MBZUAI/lamini-neo-1.3b" target="_blank" rel="noopener noreferrer">LaMini-Neo-1.3B</a></td> <td></td> <td></td> </tr> <tr> <td>GPT-J</td> <td colspan="4">coming soon</td> </tr> <tr> <td>LLaMA</td> <td colspan="4">coming soon</td> </tr> </tbody> </table> ## Use ### Intended use We recommend using the model to response to human instructions written in natural language. We now show you how to load and use our model using HuggingFace `pipeline()`. ```python # pip install -q transformers from transformers import pipeline checkpoint = "{model_name}" model = pipeline('text2text-generation', model = checkpoint) input_prompt = 'Please let me know your thoughts on the given place and why you think it deserves to be visited: \n"Barcelona, Spain"' generated_text = model(input_prompt, max_length=512, do_sample=True)[0]['generated_text'] print("Response", generated_text) ``` ## Training Procedure <p align="center" width="100%"> <a><img src="https://raw.githubusercontent.com/mbzuai-nlp/lamini-lm/main/images/lamini-pipeline.drawio.png" alt="Title" style="width: 100%; min-width: 250px; display: block; margin: auto;"></a> </p> We initialize with [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) and fine-tune it on our [LaMini-instruction dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction). Its total number of parameters is 248M. ### Training Hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ## Evaluation We conducted two sets of evaluations: automatic evaluation on downstream NLP tasks and human evaluation on user-oriented instructions. For more detail, please refer to our [paper](). ## Limitations More information needed # Citation ```bibtex @article{lamini-lm, author = {Minghao Wu and Abdul Waheed and Chiyu Zhang and Muhammad Abdul-Mageed and Alham Fikri Aji }, title = {LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions}, journal = {CoRR}, volume = {abs/2304.14402}, year = {2023}, url = {https://arxiv.org/abs/2304.14402}, eprinttype = {arXiv}, eprint = {2304.14402} } ```
Jzuluaga/wav2vec2-large-960h-lv60-self-en-atc-uwb-atcc	Jzuluaga	2023-09-11T14:30:11Z	96	3	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "audio", "en-atc", "en", "generated_from_trainer", "dataset:Jzuluaga/uwb_atcc", "arxiv:2203.16822", "arxiv:2211.04054", "base_model:facebook/wav2vec2-large-960h-lv60-self", "base_model:finetune:facebook/wav2vec2-large-960h-lv60-self", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-11-30T07:59:57Z	--- language: en license: apache-2.0 tags: - audio - automatic-speech-recognition - en-atc - en - generated_from_trainer datasets: - Jzuluaga/uwb_atcc metrics: - wer base_model: facebook/wav2vec2-large-960h-lv60-self model-index: - name: wav2vec2-large-960h-lv60-self-en-atc-uwb-atcc results: - task: type: automatic-speech-recognition name: Speech Recognition dataset: name: UWB-ATCC dataset (Air Traffic Control Communications) type: Jzuluaga/uwb_atcc config: test split: test metrics: - type: wer value: 17.2 name: TEST WER verified: false - type: wer value: 13.72 name: TEST WER (+LM) verified: false - task: type: automatic-speech-recognition name: Speech Recognition dataset: name: ATCOSIM corpus (Air Traffic Control Communications) type: Jzuluaga/atcosim_corpus config: test split: test metrics: - type: wer value: 15.31 name: TEST WER verified: false - type: wer value: 11.88 name: TEST WER (+LM) verified: false --- # wav2vec2-large-960h-lv60-self-en-atc-uwb-atcc This model is a fine-tuned version of [facebook/wav2vec2-large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self) on the [UWB-ATCC corpus](https://huggingface.co/datasets/Jzuluaga/uwb_atcc). <a href="https://colab.research.google.com/github/idiap/w2v2-air-traffic/blob/main/src/eval_xlsr_atc_model.ipynb"> <img alt="GitHub" src="https://colab.research.google.com/assets/colab-badge.svg\"> </a> <a href="https://github.com/idiap/w2v2-air-traffic"> <img alt="GitHub" src="https://img.shields.io/badge/GitHub-Open%20source-green\"> </a> It achieves the following results on the evaluation set: - Loss: 0.7287 - Wer: 0.1756 Paper: [How Does Pre-trained Wav2Vec 2.0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications](https://arxiv.org/abs/2203.16822). Authors: Juan Zuluaga-Gomez, Amrutha Prasad, Iuliia Nigmatulina, Saeed Sarfjoo, Petr Motlicek, Matthias Kleinert, Hartmut Helmke, Oliver Ohneiser, Qingran Zhan Abstract: Recent work on self-supervised pre-training focus</b> on leveraging large-scale unlabeled speech data to build robust end-to-end (E2E)acoustic models (AM) that can be later fine-tuned on downstream tasks e.g., automatic speech recognition (ASR). Yet, few works investigated the impact on performance when the data properties substantially differ between the pre-training and fine-tuning phases, termed domain shift. We target this scenario by analyzing the robustness of Wav2Vec 2.0 and XLS-R models on downstream ASR for a completely unseen domain, air traffic control (ATC) communications. We benchmark these two models on several open-source and challenging ATC databases with signal-to-noise ratio between 5 and 20 dB. Relative word error rate (WER) reductions between 20% to 40% are obtained in comparison to hybrid-based ASR baselines by only fine-tuning E2E acoustic models with a smaller fraction of labeled data. We analyze WERs on the low-resource scenario and gender bias carried by one ATC dataset. Code — GitHub repository: https://github.com/idiap/w2v2-air-traffic ## Usage You can use our Google Colab notebook to run and evaluate our model: https://github.com/idiap/w2v2-air-traffic/blob/master/src/eval_xlsr_atc_model.ipynb ## Intended uses & limitations This model was fine-tuned on air traffic control data. We don't expect that it keeps the same performance on some others datasets, e.g., LibriSpeech or CommonVoice. ## Training and evaluation data See Table 1 (page 3) in our paper: [How Does Pre-trained Wav2Vec 2.0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications](https://arxiv.org/abs/2203.16822). We described there the partitions of how to use our model. - We use the UWB-ATCC corpus to fine-tune this model. You can download the raw data here: https://lindat.mff.cuni.cz/repository/xmlui/handle/11858/00-097C-0000-0001-CCA1-0 - However, do not worry, we have prepared the database in `Datasets format`. Here, [UWB-ATCC corpus on HuggingFace](https://huggingface.co/datasets/Jzuluaga/uwb_atcc). You can scroll and check the train/test partitions, and even listen to some audios. - If you want to prepare a database in HuggingFace format, you can follow the data loader script in: [data_loader_atc.py](https://huggingface.co/datasets/Jzuluaga/uwb_atcc/blob/main/atc_data_loader.py). - ## Writing your own inference script If you use language model, you need to install the KenLM bindings with: ```bash conda activate your_environment pip install https://github.com/kpu/kenlm/archive/master.zip ``` The snippet of code: ```python from datasets import load_dataset, load_metric, Audio import torch from transformers import AutoModelForCTC, Wav2Vec2Processor, Wav2Vec2ProcessorWithLM import torchaudio.functional as F USE_LM = False DATASET_ID = "Jzuluaga/uwb_atcc" MODEL_ID = "Jzuluaga/wav2vec2-large-960h-lv60-self-en-atc-uwb-atcc" # 1. Load the dataset # we only load the 'test' partition, however, if you want to load the 'train' partition, you can change it accordingly uwb_atcc_corpus_test = load_dataset(DATASET_ID, "test", split="test") # 2. Load the model model = AutoModelForCTC.from_pretrained(MODEL_ID) # 3. Load the processors, we offer support with LM, which should yield better resutls if USE_LM: processor = Wav2Vec2ProcessorWithLM.from_pretrained(MODEL_ID) else: processor = Wav2Vec2Processor.from_pretrained(MODEL_ID) # 4. Format the test sample sample = next(iter(uwb_atcc_corpus_test)) file_sampling_rate = sample['audio']['sampling_rate'] # resample if neccessary if file_sampling_rate != 16000: resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), file_sampling_rate, 16000).numpy() else: resampled_audio = torch.tensor(sample["audio"]["array"]).numpy() input_values = processor(resampled_audio, return_tensors="pt").input_values # 5. Run the forward pass in the model with torch.no_grad(): logits = model(input_values).logits # get the transcription with processor if USE_LM: transcription = processor.batch_decode(logits.numpy()).text else: pred_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(pred_ids) # print the output print(transcription) ``` # Cite us If you use this code for your research, please cite our paper with: ``` @article{zuluaga2022how, title={How Does Pre-trained Wav2Vec2. 0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications}, author={Zuluaga-Gomez, Juan and Prasad, Amrutha and Nigmatulina, Iuliia and Sarfjoo, Saeed and others}, journal={IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar}, year={2022} } ``` and, ``` @article{zuluaga2022bertraffic, title={BERTraffic: BERT-based Joint Speaker Role and Speaker Change Detection for Air Traffic Control Communications}, author={Zuluaga-Gomez, Juan and Sarfjoo, Seyyed Saeed and Prasad, Amrutha and others}, journal={IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar}, year={2022} } ``` and, ``` @article{zuluaga2022atco2, title={ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications}, author={Zuluaga-Gomez, Juan and Vesel{\`y}, Karel and Sz{\"o}ke, Igor and Motlicek, Petr and others}, journal={arXiv preprint arXiv:2211.04054}, year={2022} } ``` ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 24 - eval_batch_size: 12 - 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: 10000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| No log \| 1.06 \| 500 \| 2.9016 \| 0.9995 \| \| 2.877 \| 2.12 \| 1000 \| 0.9812 \| 0.3485 \| \| 2.877 \| 3.18 \| 1500 \| 0.7842 \| 0.2732 \| \| 0.7834 \| 4.25 \| 2000 \| 0.6962 \| 0.2192 \| \| 0.7834 \| 5.31 \| 2500 \| 0.6527 \| 0.2042 \| \| 0.6084 \| 6.37 \| 3000 \| 0.6220 \| 0.1972 \| \| 0.6084 \| 7.43 \| 3500 \| 0.6442 \| 0.1934 \| \| 0.5147 \| 8.49 \| 4000 \| 0.6793 \| 0.1950 \| \| 0.5147 \| 9.55 \| 4500 \| 0.6432 \| 0.1920 \| \| 0.4566 \| 10.62 \| 5000 \| 0.6605 \| 0.1853 \| \| 0.4566 \| 11.68 \| 5500 \| 0.6393 \| 0.1866 \| \| 0.4155 \| 12.74 \| 6000 \| 0.6918 \| 0.1803 \| \| 0.4155 \| 13.8 \| 6500 \| 0.6514 \| 0.1791 \| \| 0.372 \| 14.86 \| 7000 \| 0.7010 \| 0.1851 \| \| 0.372 \| 15.92 \| 7500 \| 0.6824 \| 0.1786 \| \| 0.3368 \| 16.99 \| 8000 \| 0.6895 \| 0.1780 \| \| 0.3368 \| 18.05 \| 8500 \| 0.7150 \| 0.1759 \| \| 0.3244 \| 19.11 \| 9000 \| 0.7141 \| 0.1759 \| \| 0.3244 \| 20.17 \| 9500 \| 0.7225 \| 0.1756 \| \| 0.2981 \| 21.23 \| 10000 \| 0.7287 \| 0.1756 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.6.1 - Tokenizers 0.13.2
MaxKazak/ruBert-base-russian-emotion-detection	MaxKazak	2023-09-11T14:27:43Z	13,789	4	transformers	[ "transformers", "pytorch", "safetensors", "bert", "text-classification", "sentiment", "emotion-classification", "multilabel", "multiclass", "ru", "dataset:Djacon/ru_goemotions", "base_model:ai-forever/ruBert-base", "base_model:finetune:ai-forever/ruBert-base", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-05-28T15:25:35Z	--- language: - ru license: apache-2.0 tags: - sentiment - emotion-classification - multilabel - multiclass datasets: - Djacon/ru_goemotions metrics: - accuracy widget: - text: Очень рад тебя видеть! - text: Как дела? - text: Мне немного отвратно это делать - text: Я испытал мурашки от страха - text: Нет ничего радостного в этих горьких новостях - text: Ого, неожидал тебя здесь увидеть! - text: Фу ну и мерзость - text: Мне неприятно общение с тобой base_model: ai-forever/ruBert-base model-index: - name: ruBert-base-russian-emotions-classifier-goEmotions results: - task: type: multilabel-text-classification name: Multilabel Text Classification dataset: name: ru_goemotions type: Djacon/ru_goemotions args: ru metrics: - type: roc_auc value: 92% name: multilabel ROC AUC --- # ruBert-base-russian-emotions-classifier-goEmotions This model is a fine-tuned version of [ai-forever/ruBert-base](https://huggingface.co/ai-forever/ruBert-base) on [Djacon/ru_goemotions](https://huggingface.co/datasets/Djacon/ru_goemotions). It achieves the following results on the evaluation set (2nd epoch): - Loss: 0.2088 - AUC: 0.9240 The quality of the predicted probabilities on the test dataset is the following: \| label \| joy \| interest \| surpise \| sadness \| anger \| disgust \| fear \| guilt \| neutral \| average \| \|----------\|--------\|----------\|---------\|---------\|--------\|---------\|--------\|--------\|---------\|---------\| \| AUC \| 0.9369 \| 0.9213 \| 0.9325 \| 0.8791 \| 0.8374 \| 0.9041 \| 0.9470 \| 0.9758 \| 0.8518 \| 0.9095 \| \| F1-micro \| 0.9528 \| 0.9157 \| 0.9697 \| 0.9284 \| 0.8690 \| 0.9658 \| 0.9851 \| 0.9875 \| 0.7654 \| 0.9266 \| \| F1-macro \| 0.8369 \| 0.7922 \| 0.7561 \| 0.7392 \| 0.7351 \| 0.7356 \| 0.8176 \| 0.8247 \| 0.7650 \| 0.7781 \| ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| AUC \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.1755 \| 1.0 \| 1685 \| 0.1717 \| 0.9220 \| \| 0.1391 \| 2.0 \| 3370 \| 0.1757 \| 0.9240 \| \| 0.0899 \| 3.0 \| 5055 \| 0.2088 \| 0.9106 \| ### Framework versions - Transformers 4.24.0 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.11.0
osieosie/bloom-mnli-8bit-7b-bnb-seed65	osieosie	2023-09-11T14:13:28Z	0	0	peft	[ "peft", "region:us" ]	null	2023-09-11T14:13:27Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0.dev0
checkiejan/flan-t5-prefix-25-9-2	checkiejan	2023-09-11T14:10:12Z	0	0	peft	[ "peft", "region:us" ]	null	2023-09-11T14:10:08Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0
ldos/text_shortening_model_v29	ldos	2023-09-11T14:05:28Z	4	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-09-11T13:17:46Z	--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: text_shortening_model_v29 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. --> # text_shortening_model_v29 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.6052 - Rouge1: 0.5112 - Rouge2: 0.2802 - Rougel: 0.4539 - Rougelsum: 0.4538 - Bert precision: 0.8765 - Bert recall: 0.8742 - Average word count: 8.8438 - Max word count: 16 - Min word count: 4 - Average token count: 13.4174 - % shortened texts with length > 12: 8.7087 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Bert precision \| Bert recall \| Average word count \| Max word count \| Min word count \| Average token count \| % shortened texts with length > 12 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:--------------:\|:-----------:\|:------------------:\|:--------------:\|:--------------:\|:-------------------:\|:----------------------------------:\| \| 1.9361 \| 1.0 \| 145 \| 1.4858 \| 0.4996 \| 0.2801 \| 0.4497 \| 0.4507 \| 0.8753 \| 0.8723 \| 8.7808 \| 16 \| 3 \| 13.2372 \| 7.2072 \| \| 1.4692 \| 2.0 \| 290 \| 1.3868 \| 0.5013 \| 0.2812 \| 0.4477 \| 0.4485 \| 0.8736 \| 0.8731 \| 9.0601 \| 16 \| 3 \| 13.7147 \| 13.2132 \| \| 1.2301 \| 3.0 \| 435 \| 1.3641 \| 0.5294 \| 0.307 \| 0.4735 \| 0.474 \| 0.8785 \| 0.8799 \| 9.0961 \| 16 \| 4 \| 13.7327 \| 16.8168 \| \| 1.049 \| 4.0 \| 580 \| 1.3702 \| 0.524 \| 0.2979 \| 0.4705 \| 0.4706 \| 0.8782 \| 0.8788 \| 9.1081 \| 16 \| 4 \| 13.6066 \| 13.8138 \| \| 0.9261 \| 5.0 \| 725 \| 1.3843 \| 0.5424 \| 0.3166 \| 0.489 \| 0.4886 \| 0.8829 \| 0.8833 \| 8.9219 \| 17 \| 4 \| 13.6907 \| 8.4084 \| \| 0.8067 \| 6.0 \| 870 \| 1.4039 \| 0.5269 \| 0.3011 \| 0.4682 \| 0.4684 \| 0.8777 \| 0.878 \| 9.2252 \| 17 \| 4 \| 13.973 \| 13.2132 \| \| 0.7133 \| 7.0 \| 1015 \| 1.5083 \| 0.5168 \| 0.3022 \| 0.4618 \| 0.4613 \| 0.8791 \| 0.8758 \| 8.7447 \| 17 \| 4 \| 13.4655 \| 10.2102 \| \| 0.6428 \| 8.0 \| 1160 \| 1.4856 \| 0.5184 \| 0.2907 \| 0.4624 \| 0.4617 \| 0.8804 \| 0.8754 \| 8.5976 \| 16 \| 3 \| 13.0571 \| 9.009 \| \| 0.5741 \| 9.0 \| 1305 \| 1.5332 \| 0.5231 \| 0.3003 \| 0.4669 \| 0.4673 \| 0.8809 \| 0.8791 \| 8.8829 \| 17 \| 4 \| 13.5706 \| 7.5075 \| \| 0.5231 \| 10.0 \| 1450 \| 1.5603 \| 0.53 \| 0.3032 \| 0.4725 \| 0.4727 \| 0.8843 \| 0.8775 \| 8.4625 \| 17 \| 4 \| 13.033 \| 5.7057 \| \| 0.4607 \| 11.0 \| 1595 \| 1.6079 \| 0.5118 \| 0.2821 \| 0.4583 \| 0.4577 \| 0.8777 \| 0.8715 \| 8.3453 \| 16 \| 4 \| 13.012 \| 6.9069 \| \| 0.4136 \| 12.0 \| 1740 \| 1.7147 \| 0.5136 \| 0.2849 \| 0.4558 \| 0.4556 \| 0.8776 \| 0.8734 \| 8.7297 \| 16 \| 3 \| 13.3874 \| 9.3093 \| \| 0.3829 \| 13.0 \| 1885 \| 1.7425 \| 0.5182 \| 0.287 \| 0.459 \| 0.4591 \| 0.8792 \| 0.8746 \| 8.7207 \| 17 \| 4 \| 13.3934 \| 8.1081 \| \| 0.3366 \| 14.0 \| 2030 \| 1.7518 \| 0.5171 \| 0.2871 \| 0.4564 \| 0.4557 \| 0.8796 \| 0.8735 \| 8.5195 \| 16 \| 4 \| 13.0811 \| 5.4054 \| \| 0.3076 \| 15.0 \| 2175 \| 1.8555 \| 0.5139 \| 0.2891 \| 0.4581 \| 0.4581 \| 0.879 \| 0.8754 \| 8.7658 \| 16 \| 4 \| 13.2973 \| 9.9099 \| \| 0.2908 \| 16.0 \| 2320 \| 1.8983 \| 0.5239 \| 0.3011 \| 0.4654 \| 0.4651 \| 0.8799 \| 0.8794 \| 8.979 \| 16 \| 4 \| 13.6547 \| 12.012 \| \| 0.2606 \| 17.0 \| 2465 \| 1.9211 \| 0.5158 \| 0.2875 \| 0.4538 \| 0.4542 \| 0.8774 \| 0.8739 \| 8.7868 \| 17 \| 2 \| 13.5736 \| 12.012 \| \| 0.2477 \| 18.0 \| 2610 \| 1.9208 \| 0.51 \| 0.2872 \| 0.4515 \| 0.4517 \| 0.8774 \| 0.8733 \| 8.6577 \| 17 \| 4 \| 13.3093 \| 10.8108 \| \| 0.2195 \| 19.0 \| 2755 \| 1.9720 \| 0.5112 \| 0.2838 \| 0.456 \| 0.4559 \| 0.8775 \| 0.8754 \| 8.8799 \| 17 \| 3 \| 13.4835 \| 10.8108 \| \| 0.1998 \| 20.0 \| 2900 \| 1.9987 \| 0.511 \| 0.2817 \| 0.4526 \| 0.4525 \| 0.8783 \| 0.8751 \| 8.7838 \| 17 \| 3 \| 13.4955 \| 9.9099 \| \| 0.1936 \| 21.0 \| 3045 \| 2.0389 \| 0.5066 \| 0.2818 \| 0.4482 \| 0.4485 \| 0.8762 \| 0.8722 \| 8.6186 \| 17 \| 4 \| 13.1231 \| 9.009 \| \| 0.1813 \| 22.0 \| 3190 \| 2.0735 \| 0.5078 \| 0.29 \| 0.4556 \| 0.4562 \| 0.8772 \| 0.8754 \| 8.8198 \| 17 \| 4 \| 13.4895 \| 9.3093 \| \| 0.1726 \| 23.0 \| 3335 \| 2.0743 \| 0.5108 \| 0.2901 \| 0.458 \| 0.4581 \| 0.8795 \| 0.8736 \| 8.4775 \| 17 \| 2 \| 13.0931 \| 9.009 \| \| 0.164 \| 24.0 \| 3480 \| 2.1380 \| 0.5077 \| 0.2887 \| 0.4578 \| 0.4565 \| 0.878 \| 0.8727 \| 8.4474 \| 17 \| 4 \| 13.003 \| 5.7057 \| \| 0.1506 \| 25.0 \| 3625 \| 2.1435 \| 0.5005 \| 0.2725 \| 0.4456 \| 0.4452 \| 0.8748 \| 0.8717 \| 8.6637 \| 17 \| 4 \| 13.2943 \| 6.6066 \| \| 0.1402 \| 26.0 \| 3770 \| 2.1956 \| 0.5114 \| 0.2899 \| 0.4577 \| 0.4571 \| 0.8769 \| 0.8753 \| 8.8709 \| 17 \| 4 \| 13.3544 \| 9.3093 \| \| 0.138 \| 27.0 \| 3915 \| 2.2175 \| 0.5079 \| 0.2824 \| 0.4544 \| 0.4548 \| 0.8772 \| 0.8739 \| 8.6847 \| 17 \| 4 \| 13.3423 \| 8.4084 \| \| 0.1313 \| 28.0 \| 4060 \| 2.2267 \| 0.5048 \| 0.2793 \| 0.4483 \| 0.448 \| 0.8747 \| 0.8717 \| 8.6817 \| 17 \| 4 \| 13.2733 \| 9.009 \| \| 0.122 \| 29.0 \| 4205 \| 2.2464 \| 0.5105 \| 0.2813 \| 0.4544 \| 0.4548 \| 0.8746 \| 0.8736 \| 8.9099 \| 18 \| 4 \| 13.4595 \| 10.5105 \| \| 0.1195 \| 30.0 \| 4350 \| 2.2419 \| 0.5124 \| 0.2922 \| 0.461 \| 0.4609 \| 0.8768 \| 0.8733 \| 8.6637 \| 16 \| 4 \| 13.2883 \| 7.5075 \| \| 0.1131 \| 31.0 \| 4495 \| 2.2243 \| 0.5215 \| 0.3025 \| 0.4702 \| 0.4698 \| 0.8802 \| 0.878 \| 8.7117 \| 16 \| 4 \| 13.3814 \| 9.3093 \| \| 0.1102 \| 32.0 \| 4640 \| 2.2847 \| 0.5078 \| 0.2826 \| 0.4567 \| 0.4559 \| 0.8788 \| 0.8729 \| 8.3904 \| 18 \| 4 \| 12.9099 \| 6.3063 \| \| 0.1105 \| 33.0 \| 4785 \| 2.2545 \| 0.5049 \| 0.2759 \| 0.4489 \| 0.4484 \| 0.8762 \| 0.8729 \| 8.6667 \| 18 \| 4 \| 13.1952 \| 9.009 \| \| 0.099 \| 34.0 \| 4930 \| 2.2819 \| 0.5207 \| 0.296 \| 0.4662 \| 0.4665 \| 0.8814 \| 0.8775 \| 8.6186 \| 17 \| 4 \| 13.1952 \| 8.1081 \| \| 0.1018 \| 35.0 \| 5075 \| 2.2901 \| 0.5133 \| 0.2812 \| 0.4597 \| 0.4597 \| 0.8777 \| 0.8743 \| 8.7237 \| 17 \| 4 \| 13.3243 \| 10.8108 \| \| 0.0992 \| 36.0 \| 5220 \| 2.3349 \| 0.5011 \| 0.272 \| 0.4442 \| 0.4439 \| 0.8738 \| 0.8722 \| 8.9129 \| 16 \| 2 \| 13.5856 \| 11.1111 \| \| 0.0921 \| 37.0 \| 5365 \| 2.3193 \| 0.506 \| 0.2816 \| 0.4539 \| 0.4539 \| 0.8776 \| 0.8739 \| 8.7658 \| 16 \| 4 \| 13.3093 \| 8.7087 \| \| 0.0936 \| 38.0 \| 5510 \| 2.3404 \| 0.5101 \| 0.2815 \| 0.4565 \| 0.4566 \| 0.8768 \| 0.8754 \| 8.8168 \| 16 \| 4 \| 13.4535 \| 10.5105 \| \| 0.0833 \| 39.0 \| 5655 \| 2.3583 \| 0.5026 \| 0.2818 \| 0.4512 \| 0.4509 \| 0.8749 \| 0.8743 \| 8.8709 \| 16 \| 3 \| 13.4955 \| 9.3093 \| \| 0.0869 \| 40.0 \| 5800 \| 2.3443 \| 0.5091 \| 0.2855 \| 0.4521 \| 0.4521 \| 0.8769 \| 0.8743 \| 8.8378 \| 16 \| 4 \| 13.4474 \| 11.4114 \| \| 0.0783 \| 41.0 \| 5945 \| 2.3609 \| 0.5045 \| 0.2851 \| 0.4519 \| 0.4513 \| 0.8784 \| 0.8738 \| 8.5946 \| 16 \| 4 \| 13.1261 \| 7.8078 \| \| 0.08 \| 42.0 \| 6090 \| 2.4229 \| 0.5053 \| 0.2774 \| 0.4508 \| 0.4506 \| 0.8769 \| 0.8743 \| 8.6667 \| 16 \| 4 \| 13.2853 \| 8.4084 \| \| 0.0792 \| 43.0 \| 6235 \| 2.3731 \| 0.5156 \| 0.2877 \| 0.4618 \| 0.4619 \| 0.8775 \| 0.8771 \| 8.955 \| 16 \| 4 \| 13.6937 \| 8.7087 \| \| 0.075 \| 44.0 \| 6380 \| 2.4058 \| 0.5119 \| 0.286 \| 0.453 \| 0.4535 \| 0.8761 \| 0.8762 \| 8.976 \| 17 \| 3 \| 13.7387 \| 12.012 \| \| 0.0754 \| 45.0 \| 6525 \| 2.3808 \| 0.5142 \| 0.2894 \| 0.4584 \| 0.4583 \| 0.8772 \| 0.8765 \| 8.967 \| 16 \| 4 \| 13.6096 \| 12.3123 \| \| 0.0713 \| 46.0 \| 6670 \| 2.3949 \| 0.5093 \| 0.2841 \| 0.4566 \| 0.4568 \| 0.8758 \| 0.8748 \| 8.8559 \| 16 \| 4 \| 13.4775 \| 9.9099 \| \| 0.066 \| 47.0 \| 6815 \| 2.4103 \| 0.5094 \| 0.2798 \| 0.4551 \| 0.4553 \| 0.8763 \| 0.8753 \| 8.9009 \| 16 \| 4 \| 13.4655 \| 10.2102 \| \| 0.0684 \| 48.0 \| 6960 \| 2.4284 \| 0.5021 \| 0.2763 \| 0.4476 \| 0.4465 \| 0.8754 \| 0.8733 \| 8.6727 \| 16 \| 4 \| 13.2162 \| 8.7087 \| \| 0.0656 \| 49.0 \| 7105 \| 2.4512 \| 0.5137 \| 0.289 \| 0.4584 \| 0.4583 \| 0.8763 \| 0.8748 \| 8.8378 \| 16 \| 4 \| 13.4174 \| 9.6096 \| \| 0.0664 \| 50.0 \| 7250 \| 2.4427 \| 0.5106 \| 0.2789 \| 0.4507 \| 0.4501 \| 0.8761 \| 0.8747 \| 8.7327 \| 16 \| 4 \| 13.5255 \| 8.4084 \| \| 0.0628 \| 51.0 \| 7395 \| 2.4792 \| 0.5069 \| 0.2802 \| 0.4527 \| 0.453 \| 0.8775 \| 0.8751 \| 8.7417 \| 16 \| 2 \| 13.3063 \| 8.7087 \| \| 0.0662 \| 52.0 \| 7540 \| 2.4619 \| 0.5103 \| 0.281 \| 0.4567 \| 0.4567 \| 0.8776 \| 0.874 \| 8.6216 \| 16 \| 3 \| 13.1772 \| 9.009 \| \| 0.0633 \| 53.0 \| 7685 \| 2.4705 \| 0.5053 \| 0.2785 \| 0.4489 \| 0.449 \| 0.8761 \| 0.8735 \| 8.7447 \| 16 \| 4 \| 13.3874 \| 8.7087 \| \| 0.0592 \| 54.0 \| 7830 \| 2.4978 \| 0.5133 \| 0.2813 \| 0.452 \| 0.4528 \| 0.8769 \| 0.8746 \| 8.8438 \| 16 \| 4 \| 13.4354 \| 9.6096 \| \| 0.0577 \| 55.0 \| 7975 \| 2.4823 \| 0.5063 \| 0.2793 \| 0.448 \| 0.4488 \| 0.8758 \| 0.8721 \| 8.6036 \| 16 \| 4 \| 13.1111 \| 6.9069 \| \| 0.0609 \| 56.0 \| 8120 \| 2.4779 \| 0.5133 \| 0.2797 \| 0.4539 \| 0.4544 \| 0.8764 \| 0.8756 \| 8.97 \| 16 \| 3 \| 13.5976 \| 10.5105 \| \| 0.0539 \| 57.0 \| 8265 \| 2.5132 \| 0.5096 \| 0.2778 \| 0.453 \| 0.4536 \| 0.877 \| 0.8734 \| 8.7117 \| 16 \| 4 \| 13.3003 \| 7.2072 \| \| 0.0564 \| 58.0 \| 8410 \| 2.4783 \| 0.517 \| 0.2872 \| 0.4622 \| 0.4625 \| 0.8778 \| 0.8759 \| 8.9159 \| 16 \| 4 \| 13.5556 \| 11.4114 \| \| 0.0543 \| 59.0 \| 8555 \| 2.5184 \| 0.5071 \| 0.2788 \| 0.4515 \| 0.4513 \| 0.8766 \| 0.8734 \| 8.7177 \| 16 \| 4 \| 13.2583 \| 9.009 \| \| 0.0518 \| 60.0 \| 8700 \| 2.4945 \| 0.5049 \| 0.2754 \| 0.4529 \| 0.4529 \| 0.8755 \| 0.8749 \| 8.9459 \| 16 \| 4 \| 13.6787 \| 10.8108 \| \| 0.0541 \| 61.0 \| 8845 \| 2.5282 \| 0.4983 \| 0.2693 \| 0.4414 \| 0.4403 \| 0.8723 \| 0.8726 \| 8.973 \| 16 \| 4 \| 13.6667 \| 11.1111 \| \| 0.0532 \| 62.0 \| 8990 \| 2.5237 \| 0.5007 \| 0.2712 \| 0.4464 \| 0.4456 \| 0.8741 \| 0.8744 \| 9.0541 \| 16 \| 4 \| 13.7477 \| 11.1111 \| \| 0.0514 \| 63.0 \| 9135 \| 2.5247 \| 0.5041 \| 0.2784 \| 0.4525 \| 0.452 \| 0.8768 \| 0.8735 \| 8.7898 \| 16 \| 4 \| 13.4144 \| 8.7087 \| \| 0.0516 \| 64.0 \| 9280 \| 2.5289 \| 0.5065 \| 0.2826 \| 0.4517 \| 0.4515 \| 0.8753 \| 0.8745 \| 9.042 \| 16 \| 4 \| 13.6907 \| 11.1111 \| \| 0.0504 \| 65.0 \| 9425 \| 2.5002 \| 0.5055 \| 0.2826 \| 0.4565 \| 0.4562 \| 0.877 \| 0.8724 \| 8.6727 \| 16 \| 4 \| 13.3123 \| 7.5075 \| \| 0.0479 \| 66.0 \| 9570 \| 2.5361 \| 0.503 \| 0.2783 \| 0.4529 \| 0.4532 \| 0.8756 \| 0.874 \| 8.8529 \| 16 \| 4 \| 13.4865 \| 8.1081 \| \| 0.0515 \| 67.0 \| 9715 \| 2.5260 \| 0.5043 \| 0.2758 \| 0.451 \| 0.4512 \| 0.874 \| 0.8748 \| 9.0661 \| 17 \| 4 \| 13.7808 \| 10.5105 \| \| 0.0544 \| 68.0 \| 9860 \| 2.5213 \| 0.5051 \| 0.2846 \| 0.4543 \| 0.4545 \| 0.8754 \| 0.8739 \| 8.9219 \| 16 \| 3 \| 13.5586 \| 10.5105 \| \| 0.0445 \| 69.0 \| 10005 \| 2.5543 \| 0.5097 \| 0.2859 \| 0.4573 \| 0.4577 \| 0.878 \| 0.8748 \| 8.6937 \| 16 \| 3 \| 13.3363 \| 9.009 \| \| 0.0484 \| 70.0 \| 10150 \| 2.5472 \| 0.5028 \| 0.2791 \| 0.4502 \| 0.4503 \| 0.8757 \| 0.8736 \| 8.8078 \| 16 \| 3 \| 13.4264 \| 7.5075 \| \| 0.0437 \| 71.0 \| 10295 \| 2.5621 \| 0.5089 \| 0.2851 \| 0.4553 \| 0.4556 \| 0.8765 \| 0.8742 \| 8.8408 \| 16 \| 4 \| 13.5105 \| 8.7087 \| \| 0.0473 \| 72.0 \| 10440 \| 2.5503 \| 0.5087 \| 0.2818 \| 0.4558 \| 0.4555 \| 0.8771 \| 0.8743 \| 8.8559 \| 16 \| 4 \| 13.4204 \| 8.7087 \| \| 0.0472 \| 73.0 \| 10585 \| 2.5726 \| 0.5168 \| 0.2866 \| 0.4571 \| 0.4577 \| 0.8775 \| 0.8761 \| 8.9039 \| 17 \| 4 \| 13.5285 \| 9.6096 \| \| 0.041 \| 74.0 \| 10730 \| 2.5982 \| 0.5137 \| 0.2895 \| 0.4594 \| 0.4601 \| 0.8769 \| 0.8757 \| 8.8709 \| 16 \| 4 \| 13.4805 \| 9.3093 \| \| 0.0409 \| 75.0 \| 10875 \| 2.5589 \| 0.5058 \| 0.2824 \| 0.4553 \| 0.4554 \| 0.8766 \| 0.8746 \| 8.7898 \| 16 \| 4 \| 13.3033 \| 8.7087 \| \| 0.0441 \| 76.0 \| 11020 \| 2.5642 \| 0.501 \| 0.2791 \| 0.452 \| 0.4521 \| 0.8763 \| 0.8717 \| 8.5225 \| 16 \| 4 \| 13.048 \| 6.006 \| \| 0.0427 \| 77.0 \| 11165 \| 2.5522 \| 0.5102 \| 0.2864 \| 0.4573 \| 0.4579 \| 0.8784 \| 0.8749 \| 8.7207 \| 17 \| 4 \| 13.3183 \| 7.5075 \| \| 0.0449 \| 78.0 \| 11310 \| 2.5454 \| 0.5071 \| 0.2846 \| 0.4567 \| 0.4561 \| 0.8775 \| 0.875 \| 8.7658 \| 16 \| 4 \| 13.2523 \| 7.5075 \| \| 0.0397 \| 79.0 \| 11455 \| 2.5598 \| 0.5111 \| 0.2863 \| 0.4566 \| 0.4569 \| 0.8781 \| 0.8752 \| 8.7267 \| 16 \| 4 \| 13.2973 \| 7.2072 \| \| 0.046 \| 80.0 \| 11600 \| 2.5171 \| 0.5063 \| 0.2838 \| 0.4541 \| 0.4541 \| 0.8768 \| 0.8734 \| 8.6456 \| 16 \| 4 \| 13.2492 \| 6.6066 \| \| 0.0403 \| 81.0 \| 11745 \| 2.5398 \| 0.5154 \| 0.2872 \| 0.4584 \| 0.4584 \| 0.8774 \| 0.876 \| 8.9489 \| 18 \| 4 \| 13.4955 \| 8.7087 \| \| 0.0407 \| 82.0 \| 11890 \| 2.5526 \| 0.5178 \| 0.2904 \| 0.4631 \| 0.4632 \| 0.8789 \| 0.8769 \| 8.8589 \| 18 \| 4 \| 13.4354 \| 7.5075 \| \| 0.0414 \| 83.0 \| 12035 \| 2.5718 \| 0.5154 \| 0.2876 \| 0.4604 \| 0.4609 \| 0.8783 \| 0.8749 \| 8.7808 \| 17 \| 4 \| 13.3303 \| 7.5075 \| \| 0.0406 \| 84.0 \| 12180 \| 2.5673 \| 0.5138 \| 0.2861 \| 0.4581 \| 0.4587 \| 0.8773 \| 0.8758 \| 8.8949 \| 17 \| 4 \| 13.4895 \| 8.1081 \| \| 0.037 \| 85.0 \| 12325 \| 2.5770 \| 0.511 \| 0.2873 \| 0.4575 \| 0.4573 \| 0.8775 \| 0.876 \| 8.8559 \| 16 \| 4 \| 13.4384 \| 8.4084 \| \| 0.0404 \| 86.0 \| 12470 \| 2.5786 \| 0.5145 \| 0.2848 \| 0.4578 \| 0.4581 \| 0.8774 \| 0.8754 \| 8.8649 \| 16 \| 4 \| 13.4865 \| 8.7087 \| \| 0.0364 \| 87.0 \| 12615 \| 2.5822 \| 0.5089 \| 0.2791 \| 0.454 \| 0.4539 \| 0.8761 \| 0.8743 \| 8.8288 \| 17 \| 4 \| 13.4174 \| 7.8078 \| \| 0.0365 \| 88.0 \| 12760 \| 2.5821 \| 0.5105 \| 0.2806 \| 0.4555 \| 0.4559 \| 0.8779 \| 0.8752 \| 8.7838 \| 16 \| 4 \| 13.3634 \| 7.8078 \| \| 0.0359 \| 89.0 \| 12905 \| 2.5798 \| 0.5121 \| 0.2787 \| 0.4546 \| 0.4549 \| 0.8771 \| 0.8753 \| 8.8799 \| 16 \| 4 \| 13.4835 \| 8.4084 \| \| 0.0349 \| 90.0 \| 13050 \| 2.5960 \| 0.5109 \| 0.2788 \| 0.4533 \| 0.454 \| 0.8775 \| 0.8747 \| 8.8108 \| 16 \| 4 \| 13.3874 \| 9.009 \| \| 0.035 \| 91.0 \| 13195 \| 2.5979 \| 0.5072 \| 0.2778 \| 0.454 \| 0.4539 \| 0.8764 \| 0.8743 \| 8.8589 \| 16 \| 4 \| 13.3964 \| 9.6096 \| \| 0.0355 \| 92.0 \| 13340 \| 2.6016 \| 0.5101 \| 0.2795 \| 0.4544 \| 0.4548 \| 0.8767 \| 0.8743 \| 8.8589 \| 16 \| 4 \| 13.4505 \| 9.009 \| \| 0.0352 \| 93.0 \| 13485 \| 2.6036 \| 0.5107 \| 0.2814 \| 0.455 \| 0.4554 \| 0.8772 \| 0.8747 \| 8.8619 \| 16 \| 4 \| 13.4294 \| 9.009 \| \| 0.0338 \| 94.0 \| 13630 \| 2.6016 \| 0.5065 \| 0.2771 \| 0.4512 \| 0.4514 \| 0.8758 \| 0.8741 \| 8.9249 \| 16 \| 4 \| 13.5165 \| 9.3093 \| \| 0.0359 \| 95.0 \| 13775 \| 2.6044 \| 0.5071 \| 0.2761 \| 0.4496 \| 0.4501 \| 0.8755 \| 0.8733 \| 8.8559 \| 16 \| 4 \| 13.4264 \| 9.6096 \| \| 0.0349 \| 96.0 \| 13920 \| 2.5986 \| 0.5072 \| 0.277 \| 0.4523 \| 0.4524 \| 0.8756 \| 0.8736 \| 8.8679 \| 16 \| 4 \| 13.4655 \| 9.6096 \| \| 0.0358 \| 97.0 \| 14065 \| 2.5994 \| 0.5068 \| 0.276 \| 0.4498 \| 0.4502 \| 0.8749 \| 0.8733 \| 8.8589 \| 16 \| 4 \| 13.4685 \| 8.7087 \| \| 0.0338 \| 98.0 \| 14210 \| 2.6041 \| 0.5105 \| 0.2805 \| 0.4536 \| 0.4535 \| 0.8761 \| 0.8741 \| 8.8498 \| 16 \| 4 \| 13.4444 \| 8.7087 \| \| 0.0359 \| 99.0 \| 14355 \| 2.6051 \| 0.5095 \| 0.2774 \| 0.452 \| 0.4522 \| 0.876 \| 0.8738 \| 8.8529 \| 16 \| 4 \| 13.4174 \| 9.009 \| \| 0.0357 \| 100.0 \| 14500 \| 2.6052 \| 0.5112 \| 0.2802 \| 0.4539 \| 0.4538 \| 0.8765 \| 0.8742 \| 8.8438 \| 16 \| 4 \| 13.4174 \| 8.7087 \| ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
checkiejan/flan-t5-prefix-25-7-2	checkiejan	2023-09-11T13:58:54Z	0	0	peft	[ "peft", "region:us" ]	null	2023-09-11T13:58:50Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0
Tensoic/Llama-2-7B-alpaca-2k-test-merged	Tensoic	2023-09-11T13:52:02Z	6	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "dataset:mhenrichsen/alpaca_2k_test", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-09-07T17:32:33Z	--- datasets: - mhenrichsen/alpaca_2k_test --- We fine tune base `Llama-2-7b-hf` on the `henrichsen/alpaca_2k_test` dataset using peft-LORA. Find adapters at: https://huggingface.co/Tensoic/Llama-2-7B-alpaca-2k-test Visit us at: https://tensoic.com ## Training Setup: ``` Number of GPUs: 8x NVIDIA V100 GPUs GPU Memory: 32GB each (SXM2 form factor) ``` ## Training Configuration: ```yaml base_model: meta-llama/Llama-2-7b-hf base_model_config: meta-llama/Llama-2-7b-hf model_type: LlamaForCausalLM tokenizer_type: LlamaTokenizer is_llama_derived_model: true load_in_8bit: true load_in_4bit: false strict: false datasets: - path: mhenrichsen/alpaca_2k_test type: alpaca dataset_prepared_path: last_run_prepared val_set_size: 0.01 output_dir: ./lora-out sequence_len: 4096 sample_packing: false pad_to_sequence_len: true adapter: lora lora_model_dir: lora_r: 32 lora_alpha: 16 lora_dropout: 0.05 lora_target_linear: true lora_fan_in_fan_out: wandb_project: wandb_entity: wandb_watch: wandb_run_id: wandb_log_model: gradient_accumulation_steps: 4 micro_batch_size: 2 num_epochs: 3 optimizer: adamw_bnb_8bit lr_scheduler: cosine learning_rate: 0.0002 train_on_inputs: false group_by_length: false bf16: false fp16: true tf32: false gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: true flash_attention: false warmup_steps: 10 eval_steps: 20 save_steps: debug: deepspeed: weight_decay: 0.0 fsdp: fsdp_config: special_tokens: bos_token: "<s>" eos_token: "</s>" unk_token: "<unk>" ``` ``` The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ```
bigmorning/whisper_4_with_init_sun_syl_wd_0__0090	bigmorning	2023-09-11T13:49:34Z	59	0	transformers	[ "transformers", "tf", "whisper", "automatic-speech-recognition", "generated_from_keras_callback", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-09-11T13:49:26Z	--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_keras_callback model-index: - name: whisper_4_with_init_sun_syl_wd_0__0090 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. --> # whisper_4_with_init_sun_syl_wd_0__0090 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0943 - Train Accuracy: 0.0356 - Train Wermet: 0.0118 - Train Wermet Syl: 0.0159 - Validation Loss: 1.2876 - Validation Accuracy: 0.0208 - Validation Wermet: 0.3252 - Validation Wermet Syl: 0.2884 - Epoch: 89 ## 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': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0} - training_precision: float32 ### Training results \| Train Loss \| Train Accuracy \| Train Wermet \| Train Wermet Syl \| Validation Loss \| Validation Accuracy \| Validation Wermet \| Validation Wermet Syl \| Epoch \| \|:----------:\|:--------------:\|:------------:\|:----------------:\|:---------------:\|:-------------------:\|:-----------------:\|:---------------------:\|:-----:\| \| 5.3409 \| 0.0111 \| 1.3547 \| 1.2898 \| 3.9789 \| 0.0114 \| 0.9710 \| 0.9563 \| 0 \| \| 4.7143 \| 0.0116 \| 0.8622 \| 0.8228 \| 3.9404 \| 0.0113 \| 0.9823 \| 0.9735 \| 1 \| \| 4.6752 \| 0.0117 \| 0.8472 \| 0.8057 \| 3.9081 \| 0.0114 \| 0.9579 \| 0.9359 \| 2 \| \| 4.6500 \| 0.0117 \| 0.8382 \| 0.7945 \| 3.8820 \| 0.0115 \| 0.9213 \| 0.8856 \| 3 \| \| 4.6282 \| 0.0118 \| 0.8286 \| 0.7805 \| 3.8738 \| 0.0114 \| 0.9433 \| 0.9119 \| 4 \| \| 4.6095 \| 0.0118 \| 0.8190 \| 0.7696 \| 3.8630 \| 0.0115 \| 0.9117 \| 0.8698 \| 5 \| \| 4.5875 \| 0.0119 \| 0.7976 \| 0.7465 \| 3.8341 \| 0.0116 \| 0.8976 \| 0.8552 \| 6 \| \| 4.5682 \| 0.0120 \| 0.7753 \| 0.7227 \| 3.8277 \| 0.0116 \| 0.9014 \| 0.8653 \| 7 \| \| 4.5376 \| 0.0121 \| 0.7528 \| 0.7005 \| 3.7844 \| 0.0118 \| 0.8332 \| 0.7815 \| 8 \| \| 4.5060 \| 0.0122 \| 0.7392 \| 0.6844 \| 3.7537 \| 0.0118 \| 0.8578 \| 0.8152 \| 9 \| \| 4.4580 \| 0.0124 \| 0.7221 \| 0.6694 \| 3.7038 \| 0.0120 \| 0.8190 \| 0.7679 \| 10 \| \| 4.3989 \| 0.0125 \| 0.7156 \| 0.6636 \| 3.6169 \| 0.0122 \| 0.7979 \| 0.7429 \| 11 \| \| 4.3056 \| 0.0128 \| 0.7069 \| 0.6557 \| 3.5098 \| 0.0125 \| 0.7924 \| 0.7396 \| 12 \| \| 4.1673 \| 0.0132 \| 0.7054 \| 0.6584 \| 3.3542 \| 0.0128 \| 0.7759 \| 0.7240 \| 13 \| \| 3.9762 \| 0.0138 \| 0.6987 \| 0.6559 \| 3.1318 \| 0.0133 \| 0.7644 \| 0.7231 \| 14 \| \| 3.7385 \| 0.0145 \| 0.6835 \| 0.6448 \| 2.9144 \| 0.0138 \| 0.7392 \| 0.6955 \| 15 \| \| 3.5040 \| 0.0152 \| 0.6644 \| 0.6298 \| 2.7413 \| 0.0142 \| 0.7019 \| 0.6548 \| 16 \| \| 3.2728 \| 0.0160 \| 0.6408 \| 0.6101 \| 2.5183 \| 0.0149 \| 0.6798 \| 0.6363 \| 17 \| \| 3.0657 \| 0.0167 \| 0.6188 \| 0.5912 \| 2.3594 \| 0.0153 \| 0.6528 \| 0.6103 \| 18 \| \| 2.8703 \| 0.0174 \| 0.5936 \| 0.5685 \| 2.2644 \| 0.0156 \| 0.6310 \| 0.5925 \| 19 \| \| 2.6850 \| 0.0181 \| 0.5680 \| 0.5453 \| 2.1296 \| 0.0160 \| 0.6040 \| 0.5652 \| 20 \| \| 2.5227 \| 0.0188 \| 0.5423 \| 0.5215 \| 2.0019 \| 0.0165 \| 0.5793 \| 0.5403 \| 21 \| \| 2.3878 \| 0.0194 \| 0.5199 \| 0.5015 \| 1.8996 \| 0.0169 \| 0.5592 \| 0.5229 \| 22 \| \| 2.2437 \| 0.0201 \| 0.4959 \| 0.4788 \| 1.8141 \| 0.0172 \| 0.5414 \| 0.5045 \| 23 \| \| 2.1205 \| 0.0207 \| 0.4752 \| 0.4607 \| 1.7245 \| 0.0175 \| 0.5208 \| 0.4838 \| 24 \| \| 1.9919 \| 0.0213 \| 0.4533 \| 0.4390 \| 1.6673 \| 0.0178 \| 0.5026 \| 0.4659 \| 25 \| \| 1.9140 \| 0.0217 \| 0.4355 \| 0.4216 \| 1.6041 \| 0.0181 \| 0.4873 \| 0.4512 \| 26 \| \| 1.8225 \| 0.0222 \| 0.4184 \| 0.4052 \| 1.6271 \| 0.0179 \| 0.4852 \| 0.4511 \| 27 \| \| 1.7265 \| 0.0227 \| 0.4016 \| 0.3895 \| 1.5219 \| 0.0184 \| 0.4635 \| 0.4275 \| 28 \| \| 1.6240 \| 0.0233 \| 0.3833 \| 0.3729 \| 1.4718 \| 0.0186 \| 0.4515 \| 0.4170 \| 29 \| \| 1.5610 \| 0.0236 \| 0.3697 \| 0.3588 \| 1.4404 \| 0.0188 \| 0.4407 \| 0.4056 \| 30 \| \| 1.4719 \| 0.0242 \| 0.3540 \| 0.3449 \| 1.4125 \| 0.0189 \| 0.4310 \| 0.3961 \| 31 \| \| 1.4152 \| 0.0245 \| 0.3421 \| 0.3339 \| 1.3655 \| 0.0191 \| 0.4234 \| 0.3881 \| 32 \| \| 1.3546 \| 0.0249 \| 0.3277 \| 0.3195 \| 1.3419 \| 0.0192 \| 0.4156 \| 0.3816 \| 33 \| \| 1.2565 \| 0.0256 \| 0.3135 \| 0.3060 \| 1.3172 \| 0.0194 \| 0.4065 \| 0.3722 \| 34 \| \| 1.2135 \| 0.0258 \| 0.3026 \| 0.2958 \| 1.3019 \| 0.0194 \| 0.4006 \| 0.3662 \| 35 \| \| 1.1739 \| 0.0261 \| 0.2923 \| 0.2861 \| 1.3843 \| 0.0190 \| 0.3951 \| 0.3587 \| 36 \| \| 1.0950 \| 0.0267 \| 0.2782 \| 0.2733 \| 1.2665 \| 0.0197 \| 0.3883 \| 0.3541 \| 37 \| \| 1.0435 \| 0.0271 \| 0.2673 \| 0.2631 \| 1.2567 \| 0.0197 \| 0.3837 \| 0.3497 \| 38 \| \| 0.9922 \| 0.0275 \| 0.2580 \| 0.2542 \| 1.2566 \| 0.0197 \| 0.3801 \| 0.3444 \| 39 \| \| 0.9387 \| 0.0279 \| 0.2464 \| 0.2438 \| 1.2441 \| 0.0198 \| 0.3767 \| 0.3423 \| 40 \| \| 0.9345 \| 0.0278 \| 0.2393 \| 0.2373 \| 1.2221 \| 0.0199 \| 0.3682 \| 0.3336 \| 41 \| \| 0.8574 \| 0.0285 \| 0.2268 \| 0.2255 \| 1.2258 \| 0.0199 \| 0.3680 \| 0.3338 \| 42 \| \| 0.8275 \| 0.0287 \| 0.2183 \| 0.2180 \| 1.2044 \| 0.0201 \| 0.3628 \| 0.3290 \| 43 \| \| 0.8201 \| 0.0288 \| 0.2114 \| 0.2108 \| 1.2056 \| 0.0201 \| 0.3601 \| 0.3270 \| 44 \| \| 0.7684 \| 0.0292 \| 0.2020 \| 0.2029 \| 1.1879 \| 0.0202 \| 0.3553 \| 0.3215 \| 45 \| \| 0.7262 \| 0.0295 \| 0.1938 \| 0.1947 \| 1.2263 \| 0.0200 \| 0.3537 \| 0.3177 \| 46 \| \| 0.7286 \| 0.0295 \| 0.1876 \| 0.1898 \| 1.1772 \| 0.0203 \| 0.3485 \| 0.3135 \| 47 \| \| 0.6807 \| 0.0300 \| 0.1775 \| 0.1797 \| 1.1761 \| 0.0203 \| 0.3490 \| 0.3155 \| 48 \| \| 0.6609 \| 0.0301 \| 0.1713 \| 0.1742 \| 1.1853 \| 0.0203 \| 0.3484 \| 0.3153 \| 49 \| \| 0.6062 \| 0.0306 \| 0.1615 \| 0.1653 \| 1.1660 \| 0.0204 \| 0.3432 \| 0.3090 \| 50 \| \| 0.5755 \| 0.0309 \| 0.1547 \| 0.1584 \| 1.1698 \| 0.0204 \| 0.3428 \| 0.3089 \| 51 \| \| 0.5600 \| 0.0310 \| 0.1482 \| 0.1524 \| 1.1667 \| 0.0204 \| 0.3398 \| 0.3058 \| 52 \| \| 0.5715 \| 0.0308 \| 0.1449 \| 0.1496 \| 1.1614 \| 0.0205 \| 0.3381 \| 0.3036 \| 53 \| \| 0.5247 \| 0.0313 \| 0.1358 \| 0.1411 \| 1.1639 \| 0.0205 \| 0.3359 \| 0.3025 \| 54 \| \| 0.5085 \| 0.0315 \| 0.1301 \| 0.1358 \| 1.2420 \| 0.0202 \| 0.3412 \| 0.3064 \| 55 \| \| 0.4827 \| 0.0317 \| 0.1239 \| 0.1295 \| 1.1677 \| 0.0205 \| 0.3349 \| 0.3009 \| 56 \| \| 0.4848 \| 0.0317 \| 0.1207 \| 0.1280 \| 1.1653 \| 0.0205 \| 0.3326 \| 0.2991 \| 57 \| \| 0.4323 \| 0.0322 \| 0.1109 \| 0.1185 \| 1.1602 \| 0.0206 \| 0.3299 \| 0.2953 \| 58 \| \| 0.4183 \| 0.0323 \| 0.1057 \| 0.1133 \| 1.1622 \| 0.0206 \| 0.3307 \| 0.2962 \| 59 \| \| 0.4329 \| 0.0322 \| 0.1028 \| 0.1100 \| 1.1714 \| 0.0206 \| 0.3300 \| 0.2950 \| 60 \| \| 0.3962 \| 0.0326 \| 0.0964 \| 0.1045 \| 1.1726 \| 0.0206 \| 0.3311 \| 0.2967 \| 61 \| \| 0.3642 \| 0.0329 \| 0.0898 \| 0.0973 \| 1.1699 \| 0.0206 \| 0.3289 \| 0.2936 \| 62 \| \| 0.3786 \| 0.0327 \| 0.0884 \| 0.0963 \| 1.1734 \| 0.0206 \| 0.3279 \| 0.2929 \| 63 \| \| 0.3698 \| 0.0328 \| 0.0842 \| 0.0925 \| 1.1728 \| 0.0207 \| 0.3282 \| 0.2932 \| 64 \| \| 0.3219 \| 0.0333 \| 0.0765 \| 0.0850 \| 1.1830 \| 0.0207 \| 0.3258 \| 0.2907 \| 65 \| \| 0.3035 \| 0.0335 \| 0.0725 \| 0.0811 \| 1.1840 \| 0.0207 \| 0.3261 \| 0.2904 \| 66 \| \| 0.3522 \| 0.0330 \| 0.0745 \| 0.0826 \| 1.2107 \| 0.0206 \| 0.3299 \| 0.2955 \| 67 \| \| 0.3001 \| 0.0335 \| 0.0663 \| 0.0749 \| 1.1810 \| 0.0207 \| 0.3264 \| 0.2909 \| 68 \| \| 0.2729 \| 0.0338 \| 0.0595 \| 0.0677 \| 1.1911 \| 0.0207 \| 0.3247 \| 0.2886 \| 69 \| \| 0.2696 \| 0.0338 \| 0.0572 \| 0.0654 \| 1.1950 \| 0.0207 \| 0.3260 \| 0.2905 \| 70 \| \| 0.2840 \| 0.0337 \| 0.0563 \| 0.0648 \| 1.2094 \| 0.0207 \| 0.3250 \| 0.2887 \| 71 \| \| 0.2319 \| 0.0342 \| 0.0484 \| 0.0569 \| 1.2107 \| 0.0207 \| 0.3250 \| 0.2878 \| 72 \| \| 0.2371 \| 0.0342 \| 0.0464 \| 0.0541 \| 1.2059 \| 0.0207 \| 0.3240 \| 0.2880 \| 73 \| \| 0.2666 \| 0.0338 \| 0.0486 \| 0.0575 \| 1.2036 \| 0.0207 \| 0.3241 \| 0.2887 \| 74 \| \| 0.2443 \| 0.0340 \| 0.0442 \| 0.0522 \| 1.2106 \| 0.0207 \| 0.3241 \| 0.2877 \| 75 \| \| 0.2118 \| 0.0344 \| 0.0380 \| 0.0456 \| 1.2172 \| 0.0207 \| 0.3240 \| 0.2871 \| 76 \| \| 0.1997 \| 0.0346 \| 0.0354 \| 0.0428 \| 1.2247 \| 0.0208 \| 0.3219 \| 0.2852 \| 77 \| \| 0.2461 \| 0.0341 \| 0.0386 \| 0.0466 \| 1.2257 \| 0.0207 \| 0.3240 \| 0.2874 \| 78 \| \| 0.2367 \| 0.0342 \| 0.0364 \| 0.0431 \| 1.2173 \| 0.0208 \| 0.3234 \| 0.2870 \| 79 \| \| 0.1857 \| 0.0347 \| 0.0294 \| 0.0365 \| 1.2287 \| 0.0208 \| 0.3244 \| 0.2876 \| 80 \| \| 0.1504 \| 0.0351 \| 0.0244 \| 0.0314 \| 1.2425 \| 0.0207 \| 0.3238 \| 0.2871 \| 81 \| \| 0.1438 \| 0.0352 \| 0.0227 \| 0.0287 \| 1.2495 \| 0.0208 \| 0.3222 \| 0.2861 \| 82 \| \| 0.1545 \| 0.0350 \| 0.0232 \| 0.0288 \| 1.2612 \| 0.0207 \| 0.3257 \| 0.2898 \| 83 \| \| 0.2122 \| 0.0345 \| 0.0284 \| 0.0346 \| 1.2518 \| 0.0208 \| 0.3241 \| 0.2884 \| 84 \| \| 0.1685 \| 0.0349 \| 0.0222 \| 0.0278 \| 1.2466 \| 0.0208 \| 0.3231 \| 0.2868 \| 85 \| \| 0.1371 \| 0.0352 \| 0.0181 \| 0.0236 \| 1.2606 \| 0.0208 \| 0.3239 \| 0.2869 \| 86 \| \| 0.1357 \| 0.0352 \| 0.0171 \| 0.0216 \| 1.2675 \| 0.0208 \| 0.3240 \| 0.2874 \| 87 \| \| 0.1022 \| 0.0356 \| 0.0132 \| 0.0172 \| 1.2887 \| 0.0208 \| 0.3233 \| 0.2875 \| 88 \| \| 0.0943 \| 0.0356 \| 0.0118 \| 0.0159 \| 1.2876 \| 0.0208 \| 0.3252 \| 0.2884 \| 89 \| ### Framework versions - Transformers 4.34.0.dev0 - TensorFlow 2.13.0 - Tokenizers 0.13.3
RickyIG/image_classification	RickyIG	2023-09-11T13:48:48Z	215	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:food101", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-09-11T13:39:57Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - food101 metrics: - accuracy model-index: - name: image_classification results: - task: name: Image Classification type: image-classification dataset: name: food101 type: food101 config: default split: train[:5000] args: default metrics: - name: Accuracy type: accuracy value: 0.886 --- <!-- 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. --> # image_classification This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the food101 dataset. It achieves the following results on the evaluation set: - Loss: 1.6283 - Accuracy: 0.886 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 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: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.7254 \| 0.99 \| 62 \| 2.5418 \| 0.819 \| \| 1.8131 \| 2.0 \| 125 \| 1.8025 \| 0.852 \| \| 1.5991 \| 2.98 \| 186 \| 1.6367 \| 0.889 \| ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
facebook/mask2former-swin-base-ade-semantic	facebook	2023-09-11T13:46:21Z	1,503	0	transformers	[ "transformers", "pytorch", "safetensors", "mask2former", "vision", "image-segmentation", "dataset:coco", "arxiv:2112.01527", "arxiv:2107.06278", "license:other", "endpoints_compatible", "region:us" ]	image-segmentation	2023-01-05T12:23:05Z	--- license: other tags: - vision - image-segmentation datasets: - coco widget: - src: http://images.cocodataset.org/val2017/000000039769.jpg example_title: Cats - src: http://images.cocodataset.org/val2017/000000039770.jpg example_title: Castle --- # Mask2Former Mask2Former model trained on ADE20k semantic segmentation (base-sized version, Swin backbone). It was introduced in the paper [Masked-attention Mask Transformer for Universal Image Segmentation ](https://arxiv.org/abs/2112.01527) and first released in [this repository](https://github.com/facebookresearch/Mask2Former/). Disclaimer: The team releasing Mask2Former did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description Mask2Former addresses instance, semantic and panoptic segmentation with the same paradigm: by predicting a set of masks and corresponding labels. Hence, all 3 tasks are treated as if they were instance segmentation. Mask2Former outperforms the previous SOTA, [MaskFormer](https://arxiv.org/abs/2107.06278) both in terms of performance an efficiency by (i) replacing the pixel decoder with a more advanced multi-scale deformable attention Transformer, (ii) adopting a Transformer decoder with masked attention to boost performance without without introducing additional computation and (iii) improving training efficiency by calculating the loss on subsampled points instead of whole masks. ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/mask2former_architecture.png) ## Intended uses & limitations You can use this particular checkpoint for panoptic segmentation. See the [model hub](https://huggingface.co/models?search=mask2former) to look for other fine-tuned versions on a task that interests you. ### How to use Here is how to use this model: ```python import requests import torch from PIL import Image from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation # load Mask2Former fine-tuned on ADE20k semantic segmentation processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-base-ade-semantic") model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-base-ade-semantic") url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) inputs = processor(images=image, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs) # model predicts class_queries_logits of shape `(batch_size, num_queries)` # and masks_queries_logits of shape `(batch_size, num_queries, height, width)` class_queries_logits = outputs.class_queries_logits masks_queries_logits = outputs.masks_queries_logits # you can pass them to processor for postprocessing predicted_semantic_map = processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0] # we refer to the demo notebooks for visualization (see "Resources" section in the Mask2Former docs) ``` For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/mask2former).
davanstrien/detr-resnet-50_find_tuned_beyond_words	davanstrien	2023-09-11T13:45:54Z	165	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "detr", "object-detection", "generated_from_trainer", "dataset:beyond_words_23", "base_model:facebook/detr-resnet-50", "base_model:finetune:facebook/detr-resnet-50", "license:apache-2.0", "endpoints_compatible", "region:us" ]	object-detection	2023-02-27T22:50:50Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - beyond_words_23 base_model: facebook/detr-resnet-50 model-index: - name: detr-resnet-50_find_tuned_beyond_words results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr-resnet-50_find_tuned_beyond_words This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the beyond_words_23 dataset. It achieves the following results on the evaluation set: - Loss: 0.9310 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - 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: 200 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.7439 \| 0.56 \| 100 \| 2.2690 \| \| 1.7644 \| 1.12 \| 200 \| 1.5053 \| \| 1.557 \| 1.69 \| 300 \| 1.3136 \| \| 1.3207 \| 2.25 \| 400 \| 1.2063 \| \| 1.3705 \| 2.81 \| 500 \| 1.2007 \| \| 1.1924 \| 3.37 \| 600 \| 1.2704 \| \| 1.2604 \| 3.93 \| 700 \| 1.1784 \| \| 1.1982 \| 4.49 \| 800 \| 1.1167 \| \| 1.1912 \| 5.06 \| 900 \| 1.1562 \| \| 1.1206 \| 5.62 \| 1000 \| 1.2124 \| \| 1.1344 \| 6.18 \| 1100 \| 1.0622 \| \| 1.1388 \| 6.74 \| 1200 \| 1.0425 \| \| 1.0124 \| 7.3 \| 1300 \| 0.9908 \| \| 1.0776 \| 7.87 \| 1400 \| 1.1182 \| \| 0.9614 \| 8.43 \| 1500 \| 0.9967 \| \| 1.0136 \| 8.99 \| 1600 \| 0.8933 \| \| 1.0206 \| 9.55 \| 1700 \| 0.9354 \| \| 0.9529 \| 10.11 \| 1800 \| 0.9751 \| \| 1.0126 \| 10.67 \| 1900 \| 0.9310 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.0 - Tokenizers 0.13.2
flyswot/test	flyswot	2023-09-11T13:45:41Z	248	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:image_folder", "base_model:facebook/deit-tiny-patch16-224", "base_model:finetune:facebook/deit-tiny-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-05-01T17:30:58Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - image_folder metrics: - f1 base_model: facebook/deit-tiny-patch16-224 model-index: - name: test results: - task: type: image-classification name: Image Classification dataset: name: image_folder type: image_folder args: default metrics: - type: f1 value: 0.12404601272248332 name: F1 --- <!-- 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. --> # test This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 2.2724 - F1: 0.1240 ## 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: 0.001 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 0.0 \| 1 \| 2.2724 \| 0.1240 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0 - Datasets 2.0.0 - Tokenizers 0.11.6
davanstrien/convnext_flyswot	davanstrien	2023-09-11T13:44:59Z	248	0	transformers	[ "transformers", "pytorch", "safetensors", "convnext", "image-classification", "generated_from_trainer", "dataset:image_folder", "base_model:facebook/convnext-base-224-22k", "base_model:finetune:facebook/convnext-base-224-22k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - image_folder metrics: - f1 base_model: facebook/convnext-base-224-22k model-index: - name: convnext_flyswot results: - task: type: image-classification name: Image Classification dataset: name: image_folder type: image_folder args: default metrics: - type: f1 value: 0.959245529738118 name: F1 --- <!-- 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. --> # convnext_flyswot This model is a fine-tuned version of [facebook/convnext-base-224-22k](https://huggingface.co/facebook/convnext-base-224-22k) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.1441 - F1: 0.9592 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 666 - 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 \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 52 \| 0.6833 \| 0.7484 \| \| No log \| 2.0 \| 104 \| 0.3666 \| 0.8750 \| \| No log \| 3.0 \| 156 \| 0.2090 \| 0.9321 \| \| No log \| 4.0 \| 208 \| 0.1478 \| 0.9449 \| \| No log \| 5.0 \| 260 \| 0.1002 \| 0.9518 \| \| No log \| 6.0 \| 312 \| 0.1053 \| 0.9506 \| \| No log \| 7.0 \| 364 \| 0.1182 \| 0.9616 \| \| No log \| 8.0 \| 416 \| 0.1102 \| 0.9592 \| \| No log \| 9.0 \| 468 \| 0.1262 \| 0.9616 \| \| 0.203 \| 10.0 \| 520 \| 0.1286 \| 0.9616 \| \| 0.203 \| 11.0 \| 572 \| 0.1355 \| 0.9592 \| \| 0.203 \| 12.0 \| 624 \| 0.1299 \| 0.9592 \| \| 0.203 \| 13.0 \| 676 \| 0.1154 \| 0.9592 \| \| 0.203 \| 14.0 \| 728 \| 0.1385 \| 0.9580 \| \| 0.203 \| 15.0 \| 780 \| 0.1330 \| 0.9592 \| \| 0.203 \| 16.0 \| 832 \| 0.1390 \| 0.9592 \| \| 0.203 \| 17.0 \| 884 \| 0.1386 \| 0.9592 \| \| 0.203 \| 18.0 \| 936 \| 0.1390 \| 0.9592 \| \| 0.203 \| 19.0 \| 988 \| 0.1409 \| 0.9592 \| \| 0.0006 \| 20.0 \| 1040 \| 0.1411 \| 0.9592 \| \| 0.0006 \| 21.0 \| 1092 \| 0.1413 \| 0.9592 \| \| 0.0006 \| 22.0 \| 1144 \| 0.1415 \| 0.9592 \| \| 0.0006 \| 23.0 \| 1196 \| 0.1426 \| 0.9592 \| \| 0.0006 \| 24.0 \| 1248 \| 0.1435 \| 0.9592 \| \| 0.0006 \| 25.0 \| 1300 \| 0.1438 \| 0.9592 \| \| 0.0006 \| 26.0 \| 1352 \| 0.1434 \| 0.9592 \| \| 0.0006 \| 27.0 \| 1404 \| 0.1437 \| 0.9592 \| \| 0.0006 \| 28.0 \| 1456 \| 0.1441 \| 0.9592 \| \| 0.0002 \| 29.0 \| 1508 \| 0.1440 \| 0.9592 \| \| 0.0002 \| 30.0 \| 1560 \| 0.1441 \| 0.9592 \| ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.6
davanstrien/flyswot_iiif	davanstrien	2023-09-11T13:44:35Z	238	0	transformers	[ "transformers", "pytorch", "convnext", "image-classification", "generated_from_trainer", "base_model:facebook/convnext-base-224-22k", "base_model:finetune:facebook/convnext-base-224-22k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 base_model: facebook/convnext-base-224-22k model-index: - name: flyswot_iiif 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. --> # flyswot_iiif This model is a fine-tuned version of [facebook/convnext-base-224-22k](https://huggingface.co/facebook/convnext-base-224-22k) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.1280 - F1: 0.0034 ## 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: 666 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 - mixed_precision_training: Native AMP - label_smoothing_factor: 0.1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 8.5184 \| 0.26 \| 500 \| 7.9280 \| 0.0005 \| \| 7.7409 \| 0.52 \| 1000 \| 7.5824 \| 0.0007 \| \| 7.4649 \| 0.78 \| 1500 \| 7.3841 \| 0.0010 \| \| 7.3285 \| 1.04 \| 2000 \| 7.2652 \| 0.0012 \| \| 7.1404 \| 1.3 \| 2500 \| 7.1559 \| 0.0014 \| \| 7.0322 \| 1.56 \| 3000 \| 7.0551 \| 0.0016 \| \| 6.9197 \| 1.82 \| 3500 \| 6.9449 \| 0.0019 \| \| 6.7822 \| 2.09 \| 4000 \| 6.8773 \| 0.0018 \| \| 6.6506 \| 2.35 \| 4500 \| 6.7980 \| 0.0020 \| \| 6.5811 \| 2.61 \| 5000 \| 6.7382 \| 0.0022 \| \| 6.538 \| 2.87 \| 5500 \| 6.6582 \| 0.0022 \| \| 6.4136 \| 3.13 \| 6000 \| 6.6013 \| 0.0024 \| \| 6.3325 \| 3.39 \| 6500 \| 6.5369 \| 0.0024 \| \| 6.2566 \| 3.65 \| 7000 \| 6.4875 \| 0.0025 \| \| 6.2285 \| 3.91 \| 7500 \| 6.4342 \| 0.0027 \| \| 6.1281 \| 4.17 \| 8000 \| 6.4066 \| 0.0027 \| \| 6.0762 \| 4.43 \| 8500 \| 6.3674 \| 0.0027 \| \| 6.0309 \| 4.69 \| 9000 \| 6.3336 \| 0.0027 \| \| 6.0123 \| 4.95 \| 9500 \| 6.2932 \| 0.0030 \| \| 5.9089 \| 5.21 \| 10000 \| 6.2835 \| 0.0029 \| \| 5.8901 \| 5.47 \| 10500 \| 6.2481 \| 0.0030 \| \| 5.86 \| 5.74 \| 11000 \| 6.2295 \| 0.0030 \| \| 5.8586 \| 6.0 \| 11500 \| 6.2068 \| 0.0033 \| \| 5.7768 \| 6.26 \| 12000 \| 6.1937 \| 0.0031 \| \| 5.7591 \| 6.52 \| 12500 \| 6.1916 \| 0.0032 \| \| 5.7443 \| 6.78 \| 13000 \| 6.1579 \| 0.0033 \| \| 5.7125 \| 7.04 \| 13500 \| 6.1478 \| 0.0033 \| \| 5.6751 \| 7.3 \| 14000 \| 6.1379 \| 0.0035 \| \| 5.6648 \| 7.56 \| 14500 \| 6.1304 \| 0.0035 \| \| 5.6644 \| 7.82 \| 15000 \| 6.1280 \| 0.0034 \| ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.6
davanstrien/flyswot_test	davanstrien	2023-09-11T13:44:08Z	157	0	transformers	[ "transformers", "pytorch", "convnext", "image-classification", "generated_from_trainer", "dataset:image_folder", "base_model:facebook/convnext-base-224-22k", "base_model:finetune:facebook/convnext-base-224-22k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - image_folder base_model: facebook/convnext-base-224-22k model-index: - name: flyswot_test results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # flyswot_test This model is a fine-tuned version of [facebook/convnext-base-224-22k](https://huggingface.co/facebook/convnext-base-224-22k) on the image_folder dataset. It achieves the following results on the evaluation set: - eval_loss: 0.1518 - eval_f1: 0.9595 - eval_runtime: 5.9337 - eval_samples_per_second: 69.603 - eval_steps_per_second: 2.191 - epoch: 7.0 - step: 364 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 666 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 40 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.6
davanstrien/iiif_manuscript_vit	davanstrien	2023-09-11T13:44:01Z	251	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 base_model: google/vit-base-patch16-224-in21k model-index: - name: iiif_manuscript_vit 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. --> # iiif_manuscript_vit This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5684 - F1: 0.5996 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP - label_smoothing_factor: 0.1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 0.5639 \| 1.0 \| 2269 \| 0.5822 \| 0.5516 \| \| 0.5834 \| 2.0 \| 4538 \| 0.5825 \| 0.5346 \| \| 0.5778 \| 3.0 \| 6807 \| 0.5794 \| 0.6034 \| \| 0.5735 \| 4.0 \| 9076 \| 0.5742 \| 0.5713 \| \| 0.5731 \| 5.0 \| 11345 \| 0.5745 \| 0.6008 \| \| 0.5701 \| 6.0 \| 13614 \| 0.5729 \| 0.5499 \| \| 0.5696 \| 7.0 \| 15883 \| 0.5717 \| 0.5952 \| \| 0.5683 \| 8.0 \| 18152 \| 0.5680 \| 0.6005 \| \| 0.5648 \| 9.0 \| 20421 \| 0.5679 \| 0.5967 \| \| 0.564 \| 10.0 \| 22690 \| 0.5684 \| 0.5996 \| ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0
davanstrien/vit-base-patch16-224-in21k-base-manuscripts	davanstrien	2023-09-11T13:43:35Z	34	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "masked-image-modeling", "generated_from_trainer", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2022-03-10T07:44:17Z	--- license: apache-2.0 tags: - masked-image-modeling - generated_from_trainer base_model: google/vit-base-patch16-224-in21k model-index: - name: vit-base-patch16-224-in21k-base-manuscripts 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-base-manuscripts This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the davanstrien/iiif_manuscripts_label_ge_50 dataset. It achieves the following results on the evaluation set: - Loss: 0.5210 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 1333 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.5198 \| 1.0 \| 32 \| 0.5208 \| ### Framework versions - Transformers 4.18.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.18.4 - Tokenizers 0.11.6
davanstrien/test_mae_flysheet	davanstrien	2023-09-11T13:43:28Z	64	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit_mae", "pretraining", "masked-auto-encoding", "generated_from_trainer", "dataset:image_folder", "base_model:facebook/vit-mae-base", "base_model:finetune:facebook/vit-mae-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2022-03-13T15:30:34Z	--- license: apache-2.0 tags: - masked-auto-encoding - generated_from_trainer datasets: - image_folder base_model: facebook/vit-mae-base model-index: - name: test_mae_flysheet 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. --> # test_mae_flysheet This model is a fine-tuned version of [facebook/vit-mae-base](https://huggingface.co/facebook/vit-mae-base) on the davanstrien/flysheet dataset. It achieves the following results on the evaluation set: - Loss: 0.2675 ## 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: 3.75e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.284 \| 1.0 \| 28 \| 2.2812 \| \| 2.137 \| 2.0 \| 56 \| 2.0288 \| \| 1.6016 \| 3.0 \| 84 \| 1.2437 \| \| 0.8055 \| 4.0 \| 112 \| 0.7419 \| \| 0.5304 \| 5.0 \| 140 \| 0.5151 \| \| 0.4873 \| 6.0 \| 168 \| 0.4884 \| \| 0.442 \| 7.0 \| 196 \| 0.4441 \| \| 0.4039 \| 8.0 \| 224 \| 0.4159 \| \| 0.3866 \| 9.0 \| 252 \| 0.3975 \| \| 0.391 \| 10.0 \| 280 \| 0.3869 \| \| 0.3549 \| 11.0 \| 308 \| 0.3801 \| \| 0.3462 \| 12.0 \| 336 \| 0.3577 \| \| 0.3402 \| 13.0 \| 364 \| 0.3519 \| \| 0.3357 \| 14.0 \| 392 \| 0.3447 \| \| 0.3474 \| 15.0 \| 420 \| 0.3369 \| \| 0.3254 \| 16.0 \| 448 \| 0.3386 \| \| 0.3033 \| 17.0 \| 476 \| 0.3294 \| \| 0.3047 \| 18.0 \| 504 \| 0.3274 \| \| 0.3103 \| 19.0 \| 532 \| 0.3209 \| \| 0.3067 \| 20.0 \| 560 \| 0.3186 \| \| 0.2959 \| 21.0 \| 588 \| 0.3190 \| \| 0.2899 \| 22.0 \| 616 \| 0.3147 \| \| 0.2872 \| 23.0 \| 644 \| 0.3082 \| \| 0.2956 \| 24.0 \| 672 \| 0.3070 \| \| 0.2865 \| 25.0 \| 700 \| 0.3072 \| \| 0.2947 \| 26.0 \| 728 \| 0.3072 \| \| 0.2811 \| 27.0 \| 756 \| 0.3131 \| \| 0.2935 \| 28.0 \| 784 \| 0.3069 \| \| 0.2814 \| 29.0 \| 812 \| 0.3043 \| \| 0.2753 \| 30.0 \| 840 \| 0.2984 \| \| 0.2823 \| 31.0 \| 868 \| 0.2995 \| \| 0.2962 \| 32.0 \| 896 \| 0.3012 \| \| 0.2869 \| 33.0 \| 924 \| 0.3050 \| \| 0.2833 \| 34.0 \| 952 \| 0.2960 \| \| 0.2892 \| 35.0 \| 980 \| 0.3039 \| \| 0.2764 \| 36.0 \| 1008 \| 0.3010 \| \| 0.2807 \| 37.0 \| 1036 \| 0.2998 \| \| 0.2843 \| 38.0 \| 1064 \| 0.2989 \| \| 0.2808 \| 39.0 \| 1092 \| 0.2970 \| \| 0.2862 \| 40.0 \| 1120 \| 0.2940 \| \| 0.2601 \| 41.0 \| 1148 \| 0.2952 \| \| 0.2742 \| 42.0 \| 1176 \| 0.2940 \| \| 0.2791 \| 43.0 \| 1204 \| 0.2997 \| \| 0.2759 \| 44.0 \| 1232 \| 0.2951 \| \| 0.2819 \| 45.0 \| 1260 \| 0.2896 \| \| 0.287 \| 46.0 \| 1288 \| 0.2938 \| \| 0.2711 \| 47.0 \| 1316 \| 0.2973 \| \| 0.2782 \| 48.0 \| 1344 \| 0.2946 \| \| 0.2674 \| 49.0 \| 1372 \| 0.2913 \| \| 0.268 \| 50.0 \| 1400 \| 0.2944 \| \| 0.2624 \| 51.0 \| 1428 \| 0.2940 \| \| 0.2842 \| 52.0 \| 1456 \| 0.2978 \| \| 0.2753 \| 53.0 \| 1484 \| 0.2951 \| \| 0.2733 \| 54.0 \| 1512 \| 0.2880 \| \| 0.2782 \| 55.0 \| 1540 \| 0.2969 \| \| 0.2789 \| 56.0 \| 1568 \| 0.2919 \| \| 0.2815 \| 57.0 \| 1596 \| 0.2916 \| \| 0.2629 \| 58.0 \| 1624 \| 0.2947 \| \| 0.2716 \| 59.0 \| 1652 \| 0.2828 \| \| 0.2623 \| 60.0 \| 1680 \| 0.2924 \| \| 0.2773 \| 61.0 \| 1708 \| 0.2765 \| \| 0.268 \| 62.0 \| 1736 \| 0.2754 \| \| 0.2839 \| 63.0 \| 1764 \| 0.2744 \| \| 0.2684 \| 64.0 \| 1792 \| 0.2744 \| \| 0.2865 \| 65.0 \| 1820 \| 0.2716 \| \| 0.2845 \| 66.0 \| 1848 \| 0.2769 \| \| 0.2663 \| 67.0 \| 1876 \| 0.2754 \| \| 0.269 \| 68.0 \| 1904 \| 0.2737 \| \| 0.2681 \| 69.0 \| 1932 \| 0.2697 \| \| 0.2748 \| 70.0 \| 1960 \| 0.2779 \| \| 0.2769 \| 71.0 \| 1988 \| 0.2728 \| \| 0.2805 \| 72.0 \| 2016 \| 0.2729 \| \| 0.2771 \| 73.0 \| 2044 \| 0.2728 \| \| 0.2717 \| 74.0 \| 2072 \| 0.2749 \| \| 0.267 \| 75.0 \| 2100 \| 0.2732 \| \| 0.2812 \| 76.0 \| 2128 \| 0.2743 \| \| 0.2749 \| 77.0 \| 2156 \| 0.2739 \| \| 0.2746 \| 78.0 \| 2184 \| 0.2730 \| \| 0.2707 \| 79.0 \| 2212 \| 0.2743 \| \| 0.2644 \| 80.0 \| 2240 \| 0.2740 \| \| 0.2691 \| 81.0 \| 2268 \| 0.2727 \| \| 0.2679 \| 82.0 \| 2296 \| 0.2771 \| \| 0.2748 \| 83.0 \| 2324 \| 0.2744 \| \| 0.2744 \| 84.0 \| 2352 \| 0.2703 \| \| 0.2715 \| 85.0 \| 2380 \| 0.2733 \| \| 0.2682 \| 86.0 \| 2408 \| 0.2715 \| \| 0.2641 \| 87.0 \| 2436 \| 0.2722 \| \| 0.274 \| 88.0 \| 2464 \| 0.2748 \| \| 0.2669 \| 89.0 \| 2492 \| 0.2753 \| \| 0.2707 \| 90.0 \| 2520 \| 0.2724 \| \| 0.2755 \| 91.0 \| 2548 \| 0.2703 \| \| 0.2769 \| 92.0 \| 2576 \| 0.2737 \| \| 0.2659 \| 93.0 \| 2604 \| 0.2721 \| \| 0.2674 \| 94.0 \| 2632 \| 0.2763 \| \| 0.2723 \| 95.0 \| 2660 \| 0.2723 \| \| 0.2723 \| 96.0 \| 2688 \| 0.2744 \| \| 0.272 \| 97.0 \| 2716 \| 0.2686 \| \| 0.27 \| 98.0 \| 2744 \| 0.2728 \| \| 0.2721 \| 99.0 \| 2772 \| 0.2743 \| \| 0.2692 \| 100.0 \| 2800 \| 0.2748 \| ### Framework versions - Transformers 4.18.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.18.4 - Tokenizers 0.11.6
davanstrien/convnext-tiny-224-leicester_binary	davanstrien	2023-09-11T13:43:16Z	190	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "convnext", "image-classification", "vision", "generated_from_trainer", "base_model:facebook/convnext-tiny-224", "base_model:finetune:facebook/convnext-tiny-224", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-12-06T16:45:11Z	--- license: apache-2.0 tags: - image-classification - vision - generated_from_trainer metrics: - precision - recall - f1 - accuracy base_model: facebook/convnext-tiny-224 model-index: - name: convnext-tiny-224-leicester_binary 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. --> # convnext-tiny-224-leicester_binary This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the davanstrien/leicester_loaded_annotations_binary dataset. It achieves the following results on the evaluation set: - Loss: 0.4213 - Precision: 0.4583 - Recall: 0.5 - F1: 0.4783 - Accuracy: 0.9167 ## 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: 128 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 7 \| 0.4213 \| 0.4583 \| 0.5 \| 0.4783 \| 0.9167 \| ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
kartiks26/Llama2-7B	kartiks26	2023-09-11T13:41:59Z	0	0	peft	[ "peft", "region:us" ]	null	2023-09-11T13:39:06Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.5.0
rohitsroch/hybrid_utt-clusterrank_bart-base_samsum_sum	rohitsroch	2023-09-11T13:38:47Z	114	0	transformers	[ "transformers", "pytorch", "safetensors", "bart", "text2text-generation", "dialogue-summarization", "en", "dataset:samsum", "base_model:facebook/bart-base", "base_model:finetune:facebook/bart-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-06-12T21:55:09Z	--- language: - en license: apache-2.0 tags: - dialogue-summarization datasets: - samsum model_index: - name: hybrid_utt-clusterrank_bart-base_samsum_sum results: - task: name: Summarization type: summarization base_model: facebook/bart-base --- ## Paper ## [Domain Adapted Abstractive Summarization of Dialogue using Transfer Learning](https://dl.acm.org/doi/10.1145/3508546.3508640) Authors: Rohit Sroch ## Abstract Recently, the abstractive dialogue summarization task has been gaining a lot of attention from researchers. Also, unlike news articles and documents with well-structured text, dialogue differs in the sense that it often comes from two or more interlocutors, exchanging information with each other and having an inherent hierarchical structure based on the sequence of utterances by different speakers. This paper proposes a simple but effective hybrid approach that consists of two modules and uses transfer learning by leveraging pretrained language models (PLMs) to generate an abstractive summary. The first module highlights important utterances, capturing the utterance level relationship by adapting an auto-encoding model like BERT based on the unsupervised or supervised method. And then, the second module generates a concise abstractive summary by adapting encoder-decoder models like T5, BART, and PEGASUS. Experiment results on benchmark datasets show that our approach achieves a state-of-the-art performance by adapting to dialogue scenarios and can also be helpful in low-resource settings for domain adaptation. Rohit Sroch. 2021. Domain Adapted Abstractive Summarization of Dialogue using Transfer Learning. In 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence (ACAI'21). Association for Computing Machinery, New York, NY, USA, Article 94, 1–6. https://doi.org/10.1145/3508546.3508640 # hybrid_utt-clusterrank_bart-base_samsum_sum This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on SAMSum dataset for dialogue summarization task. ## Model description More information needed ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-5 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 - label_smoothing_factor: 0.1 ### Results on Test Set - predict_gen_len = 23.9048 - predict_rouge1 = 47.355 - predict_rouge2 = 22.4593 - predict_rougeL = 38.694 - predict_rougeLsum = 42.98 - predict_samples = 819 - predict_samples_per_second = 9.279 - predict_steps_per_second = 2.322 ### Framework versions - Transformers>=4.8.0 - Pytorch>=1.6.0 - Datasets>=1.10.2 - Tokenizers>=0.10.3 If you use this model, please cite the following paper: ``` @inproceedings{10.1145/3508546.3508640, author = {Sroch, Rohit}, title = {Domain Adapted Abstractive Summarization of Dialogue Using Transfer Learning}, year = {2021}, isbn = {9781450385053}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3508546.3508640}, doi = {10.1145/3508546.3508640}, articleno = {94}, numpages = {6}, keywords = {encoder-decoder, T5, abstractive summary, PEGASUS, BART, dialogue summarization, PLMs, BERT}, location = {Sanya, China}, series = {ACAI'21} } ```
HiTZ/A2T_RoBERTa_SMFA_TACRED-re	HiTZ	2023-09-11T13:35:34Z	117	0	transformers	[ "transformers", "pytorch", "safetensors", "roberta", "text-classification", "zero-shot-classification", "dataset:snli", "dataset:anli", "dataset:multi_nli", "dataset:multi_nli_mismatch", "dataset:fever", "arxiv:2104.14690", "arxiv:2203.13602", "autotrain_compatible", "endpoints_compatible", "region:us" ]	zero-shot-classification	2022-05-02T12:52:23Z	--- pipeline_tag: zero-shot-classification datasets: - snli - anli - multi_nli - multi_nli_mismatch - fever --- # A2T Entailment model Important: These pretrained entailment models are intended to be used with the [Ask2Transformers](https://github.com/osainz59/Ask2Transformers) library but are also fully compatible with the `ZeroShotTextClassificationPipeline` from [Transformers](https://github.com/huggingface/Transformers). Textual Entailment (or Natural Language Inference) has turned out to be a good choice for zero-shot text classification problems [(Yin et al., 2019](https://aclanthology.org/D19-1404/); [Wang et al., 2021](https://arxiv.org/abs/2104.14690); [Sainz and Rigau, 2021)](https://aclanthology.org/2021.gwc-1.6/). Recent research addressed Information Extraction problems with the same idea [(Lyu et al., 2021](https://aclanthology.org/2021.acl-short.42/); [Sainz et al., 2021](https://aclanthology.org/2021.emnlp-main.92/); [Sainz et al., 2022a](), [Sainz et al., 2022b)](https://arxiv.org/abs/2203.13602). The A2T entailment models are first trained with NLI datasets such as MNLI [(Williams et al., 2018)](), SNLI [(Bowman et al., 2015)]() or/and ANLI [(Nie et al., 2020)]() and then fine-tuned to specific tasks that were previously converted to textual entailment format. For more information please, take a look to the [Ask2Transformers](https://github.com/osainz59/Ask2Transformers) library or the following published papers: - [Label Verbalization and Entailment for Effective Zero and Few-Shot Relation Extraction (Sainz et al., EMNLP 2021)](https://aclanthology.org/2021.emnlp-main.92/) - [Textual Entailment for Event Argument Extraction: Zero- and Few-Shot with Multi-Source Learning (Sainz et al., Findings of NAACL-HLT 2022)]() ## About the model The model name describes the configuration used for training as follows: <!-- $$\text{HiTZ/A2T\_[pretrained\_model]\_[NLI\_datasets]\_[finetune\_datasets]}$$ --> <h3 align="center">HiTZ/A2T_[pretrained_model]_[NLI_datasets]_[finetune_datasets]</h3> - `pretrained_model`: The checkpoint used for initialization. For example: RoBERTa<sub>large</sub>. - `NLI_datasets`: The NLI datasets used for pivot training. - `S`: Standford Natural Language Inference (SNLI) dataset. - `M`: Multi Natural Language Inference (MNLI) dataset. - `F`: Fever-nli dataset. - `A`: Adversarial Natural Language Inference (ANLI) dataset. - `finetune_datasets`: The datasets used for fine tuning the entailment model. Note that for more than 1 dataset the training was performed sequentially. For example: ACE-arg. Some models like `HiTZ/A2T_RoBERTa_SMFA_ACE-arg` have been trained marking some information between square brackets (`'[['` and `']]'`) like the event trigger span. Make sure you follow the same preprocessing in order to obtain the best results. ## Cite If you use this model, consider citing the following publications: ```bibtex @inproceedings{sainz-etal-2021-label, title = "Label Verbalization and Entailment for Effective Zero and Few-Shot Relation Extraction", author = "Sainz, Oscar and Lopez de Lacalle, Oier and Labaka, Gorka and Barrena, Ander and Agirre, Eneko", booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.92", doi = "10.18653/v1/2021.emnlp-main.92", pages = "1199--1212", } ```
bigmorning/whisper_4_with_init_sun_syl_wd_0__0085	bigmorning	2023-09-11T13:34:24Z	59	0	transformers	[ "transformers", "tf", "whisper", "automatic-speech-recognition", "generated_from_keras_callback", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-09-11T13:34:17Z	--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_keras_callback model-index: - name: whisper_4_with_init_sun_syl_wd_0__0085 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. --> # whisper_4_with_init_sun_syl_wd_0__0085 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2122 - Train Accuracy: 0.0345 - Train Wermet: 0.0284 - Train Wermet Syl: 0.0346 - Validation Loss: 1.2518 - Validation Accuracy: 0.0208 - Validation Wermet: 0.3241 - Validation Wermet Syl: 0.2884 - Epoch: 84 ## 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': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0} - training_precision: float32 ### Training results \| Train Loss \| Train Accuracy \| Train Wermet \| Train Wermet Syl \| Validation Loss \| Validation Accuracy \| Validation Wermet \| Validation Wermet Syl \| Epoch \| \|:----------:\|:--------------:\|:------------:\|:----------------:\|:---------------:\|:-------------------:\|:-----------------:\|:---------------------:\|:-----:\| \| 5.3409 \| 0.0111 \| 1.3547 \| 1.2898 \| 3.9789 \| 0.0114 \| 0.9710 \| 0.9563 \| 0 \| \| 4.7143 \| 0.0116 \| 0.8622 \| 0.8228 \| 3.9404 \| 0.0113 \| 0.9823 \| 0.9735 \| 1 \| \| 4.6752 \| 0.0117 \| 0.8472 \| 0.8057 \| 3.9081 \| 0.0114 \| 0.9579 \| 0.9359 \| 2 \| \| 4.6500 \| 0.0117 \| 0.8382 \| 0.7945 \| 3.8820 \| 0.0115 \| 0.9213 \| 0.8856 \| 3 \| \| 4.6282 \| 0.0118 \| 0.8286 \| 0.7805 \| 3.8738 \| 0.0114 \| 0.9433 \| 0.9119 \| 4 \| \| 4.6095 \| 0.0118 \| 0.8190 \| 0.7696 \| 3.8630 \| 0.0115 \| 0.9117 \| 0.8698 \| 5 \| \| 4.5875 \| 0.0119 \| 0.7976 \| 0.7465 \| 3.8341 \| 0.0116 \| 0.8976 \| 0.8552 \| 6 \| \| 4.5682 \| 0.0120 \| 0.7753 \| 0.7227 \| 3.8277 \| 0.0116 \| 0.9014 \| 0.8653 \| 7 \| \| 4.5376 \| 0.0121 \| 0.7528 \| 0.7005 \| 3.7844 \| 0.0118 \| 0.8332 \| 0.7815 \| 8 \| \| 4.5060 \| 0.0122 \| 0.7392 \| 0.6844 \| 3.7537 \| 0.0118 \| 0.8578 \| 0.8152 \| 9 \| \| 4.4580 \| 0.0124 \| 0.7221 \| 0.6694 \| 3.7038 \| 0.0120 \| 0.8190 \| 0.7679 \| 10 \| \| 4.3989 \| 0.0125 \| 0.7156 \| 0.6636 \| 3.6169 \| 0.0122 \| 0.7979 \| 0.7429 \| 11 \| \| 4.3056 \| 0.0128 \| 0.7069 \| 0.6557 \| 3.5098 \| 0.0125 \| 0.7924 \| 0.7396 \| 12 \| \| 4.1673 \| 0.0132 \| 0.7054 \| 0.6584 \| 3.3542 \| 0.0128 \| 0.7759 \| 0.7240 \| 13 \| \| 3.9762 \| 0.0138 \| 0.6987 \| 0.6559 \| 3.1318 \| 0.0133 \| 0.7644 \| 0.7231 \| 14 \| \| 3.7385 \| 0.0145 \| 0.6835 \| 0.6448 \| 2.9144 \| 0.0138 \| 0.7392 \| 0.6955 \| 15 \| \| 3.5040 \| 0.0152 \| 0.6644 \| 0.6298 \| 2.7413 \| 0.0142 \| 0.7019 \| 0.6548 \| 16 \| \| 3.2728 \| 0.0160 \| 0.6408 \| 0.6101 \| 2.5183 \| 0.0149 \| 0.6798 \| 0.6363 \| 17 \| \| 3.0657 \| 0.0167 \| 0.6188 \| 0.5912 \| 2.3594 \| 0.0153 \| 0.6528 \| 0.6103 \| 18 \| \| 2.8703 \| 0.0174 \| 0.5936 \| 0.5685 \| 2.2644 \| 0.0156 \| 0.6310 \| 0.5925 \| 19 \| \| 2.6850 \| 0.0181 \| 0.5680 \| 0.5453 \| 2.1296 \| 0.0160 \| 0.6040 \| 0.5652 \| 20 \| \| 2.5227 \| 0.0188 \| 0.5423 \| 0.5215 \| 2.0019 \| 0.0165 \| 0.5793 \| 0.5403 \| 21 \| \| 2.3878 \| 0.0194 \| 0.5199 \| 0.5015 \| 1.8996 \| 0.0169 \| 0.5592 \| 0.5229 \| 22 \| \| 2.2437 \| 0.0201 \| 0.4959 \| 0.4788 \| 1.8141 \| 0.0172 \| 0.5414 \| 0.5045 \| 23 \| \| 2.1205 \| 0.0207 \| 0.4752 \| 0.4607 \| 1.7245 \| 0.0175 \| 0.5208 \| 0.4838 \| 24 \| \| 1.9919 \| 0.0213 \| 0.4533 \| 0.4390 \| 1.6673 \| 0.0178 \| 0.5026 \| 0.4659 \| 25 \| \| 1.9140 \| 0.0217 \| 0.4355 \| 0.4216 \| 1.6041 \| 0.0181 \| 0.4873 \| 0.4512 \| 26 \| \| 1.8225 \| 0.0222 \| 0.4184 \| 0.4052 \| 1.6271 \| 0.0179 \| 0.4852 \| 0.4511 \| 27 \| \| 1.7265 \| 0.0227 \| 0.4016 \| 0.3895 \| 1.5219 \| 0.0184 \| 0.4635 \| 0.4275 \| 28 \| \| 1.6240 \| 0.0233 \| 0.3833 \| 0.3729 \| 1.4718 \| 0.0186 \| 0.4515 \| 0.4170 \| 29 \| \| 1.5610 \| 0.0236 \| 0.3697 \| 0.3588 \| 1.4404 \| 0.0188 \| 0.4407 \| 0.4056 \| 30 \| \| 1.4719 \| 0.0242 \| 0.3540 \| 0.3449 \| 1.4125 \| 0.0189 \| 0.4310 \| 0.3961 \| 31 \| \| 1.4152 \| 0.0245 \| 0.3421 \| 0.3339 \| 1.3655 \| 0.0191 \| 0.4234 \| 0.3881 \| 32 \| \| 1.3546 \| 0.0249 \| 0.3277 \| 0.3195 \| 1.3419 \| 0.0192 \| 0.4156 \| 0.3816 \| 33 \| \| 1.2565 \| 0.0256 \| 0.3135 \| 0.3060 \| 1.3172 \| 0.0194 \| 0.4065 \| 0.3722 \| 34 \| \| 1.2135 \| 0.0258 \| 0.3026 \| 0.2958 \| 1.3019 \| 0.0194 \| 0.4006 \| 0.3662 \| 35 \| \| 1.1739 \| 0.0261 \| 0.2923 \| 0.2861 \| 1.3843 \| 0.0190 \| 0.3951 \| 0.3587 \| 36 \| \| 1.0950 \| 0.0267 \| 0.2782 \| 0.2733 \| 1.2665 \| 0.0197 \| 0.3883 \| 0.3541 \| 37 \| \| 1.0435 \| 0.0271 \| 0.2673 \| 0.2631 \| 1.2567 \| 0.0197 \| 0.3837 \| 0.3497 \| 38 \| \| 0.9922 \| 0.0275 \| 0.2580 \| 0.2542 \| 1.2566 \| 0.0197 \| 0.3801 \| 0.3444 \| 39 \| \| 0.9387 \| 0.0279 \| 0.2464 \| 0.2438 \| 1.2441 \| 0.0198 \| 0.3767 \| 0.3423 \| 40 \| \| 0.9345 \| 0.0278 \| 0.2393 \| 0.2373 \| 1.2221 \| 0.0199 \| 0.3682 \| 0.3336 \| 41 \| \| 0.8574 \| 0.0285 \| 0.2268 \| 0.2255 \| 1.2258 \| 0.0199 \| 0.3680 \| 0.3338 \| 42 \| \| 0.8275 \| 0.0287 \| 0.2183 \| 0.2180 \| 1.2044 \| 0.0201 \| 0.3628 \| 0.3290 \| 43 \| \| 0.8201 \| 0.0288 \| 0.2114 \| 0.2108 \| 1.2056 \| 0.0201 \| 0.3601 \| 0.3270 \| 44 \| \| 0.7684 \| 0.0292 \| 0.2020 \| 0.2029 \| 1.1879 \| 0.0202 \| 0.3553 \| 0.3215 \| 45 \| \| 0.7262 \| 0.0295 \| 0.1938 \| 0.1947 \| 1.2263 \| 0.0200 \| 0.3537 \| 0.3177 \| 46 \| \| 0.7286 \| 0.0295 \| 0.1876 \| 0.1898 \| 1.1772 \| 0.0203 \| 0.3485 \| 0.3135 \| 47 \| \| 0.6807 \| 0.0300 \| 0.1775 \| 0.1797 \| 1.1761 \| 0.0203 \| 0.3490 \| 0.3155 \| 48 \| \| 0.6609 \| 0.0301 \| 0.1713 \| 0.1742 \| 1.1853 \| 0.0203 \| 0.3484 \| 0.3153 \| 49 \| \| 0.6062 \| 0.0306 \| 0.1615 \| 0.1653 \| 1.1660 \| 0.0204 \| 0.3432 \| 0.3090 \| 50 \| \| 0.5755 \| 0.0309 \| 0.1547 \| 0.1584 \| 1.1698 \| 0.0204 \| 0.3428 \| 0.3089 \| 51 \| \| 0.5600 \| 0.0310 \| 0.1482 \| 0.1524 \| 1.1667 \| 0.0204 \| 0.3398 \| 0.3058 \| 52 \| \| 0.5715 \| 0.0308 \| 0.1449 \| 0.1496 \| 1.1614 \| 0.0205 \| 0.3381 \| 0.3036 \| 53 \| \| 0.5247 \| 0.0313 \| 0.1358 \| 0.1411 \| 1.1639 \| 0.0205 \| 0.3359 \| 0.3025 \| 54 \| \| 0.5085 \| 0.0315 \| 0.1301 \| 0.1358 \| 1.2420 \| 0.0202 \| 0.3412 \| 0.3064 \| 55 \| \| 0.4827 \| 0.0317 \| 0.1239 \| 0.1295 \| 1.1677 \| 0.0205 \| 0.3349 \| 0.3009 \| 56 \| \| 0.4848 \| 0.0317 \| 0.1207 \| 0.1280 \| 1.1653 \| 0.0205 \| 0.3326 \| 0.2991 \| 57 \| \| 0.4323 \| 0.0322 \| 0.1109 \| 0.1185 \| 1.1602 \| 0.0206 \| 0.3299 \| 0.2953 \| 58 \| \| 0.4183 \| 0.0323 \| 0.1057 \| 0.1133 \| 1.1622 \| 0.0206 \| 0.3307 \| 0.2962 \| 59 \| \| 0.4329 \| 0.0322 \| 0.1028 \| 0.1100 \| 1.1714 \| 0.0206 \| 0.3300 \| 0.2950 \| 60 \| \| 0.3962 \| 0.0326 \| 0.0964 \| 0.1045 \| 1.1726 \| 0.0206 \| 0.3311 \| 0.2967 \| 61 \| \| 0.3642 \| 0.0329 \| 0.0898 \| 0.0973 \| 1.1699 \| 0.0206 \| 0.3289 \| 0.2936 \| 62 \| \| 0.3786 \| 0.0327 \| 0.0884 \| 0.0963 \| 1.1734 \| 0.0206 \| 0.3279 \| 0.2929 \| 63 \| \| 0.3698 \| 0.0328 \| 0.0842 \| 0.0925 \| 1.1728 \| 0.0207 \| 0.3282 \| 0.2932 \| 64 \| \| 0.3219 \| 0.0333 \| 0.0765 \| 0.0850 \| 1.1830 \| 0.0207 \| 0.3258 \| 0.2907 \| 65 \| \| 0.3035 \| 0.0335 \| 0.0725 \| 0.0811 \| 1.1840 \| 0.0207 \| 0.3261 \| 0.2904 \| 66 \| \| 0.3522 \| 0.0330 \| 0.0745 \| 0.0826 \| 1.2107 \| 0.0206 \| 0.3299 \| 0.2955 \| 67 \| \| 0.3001 \| 0.0335 \| 0.0663 \| 0.0749 \| 1.1810 \| 0.0207 \| 0.3264 \| 0.2909 \| 68 \| \| 0.2729 \| 0.0338 \| 0.0595 \| 0.0677 \| 1.1911 \| 0.0207 \| 0.3247 \| 0.2886 \| 69 \| \| 0.2696 \| 0.0338 \| 0.0572 \| 0.0654 \| 1.1950 \| 0.0207 \| 0.3260 \| 0.2905 \| 70 \| \| 0.2840 \| 0.0337 \| 0.0563 \| 0.0648 \| 1.2094 \| 0.0207 \| 0.3250 \| 0.2887 \| 71 \| \| 0.2319 \| 0.0342 \| 0.0484 \| 0.0569 \| 1.2107 \| 0.0207 \| 0.3250 \| 0.2878 \| 72 \| \| 0.2371 \| 0.0342 \| 0.0464 \| 0.0541 \| 1.2059 \| 0.0207 \| 0.3240 \| 0.2880 \| 73 \| \| 0.2666 \| 0.0338 \| 0.0486 \| 0.0575 \| 1.2036 \| 0.0207 \| 0.3241 \| 0.2887 \| 74 \| \| 0.2443 \| 0.0340 \| 0.0442 \| 0.0522 \| 1.2106 \| 0.0207 \| 0.3241 \| 0.2877 \| 75 \| \| 0.2118 \| 0.0344 \| 0.0380 \| 0.0456 \| 1.2172 \| 0.0207 \| 0.3240 \| 0.2871 \| 76 \| \| 0.1997 \| 0.0346 \| 0.0354 \| 0.0428 \| 1.2247 \| 0.0208 \| 0.3219 \| 0.2852 \| 77 \| \| 0.2461 \| 0.0341 \| 0.0386 \| 0.0466 \| 1.2257 \| 0.0207 \| 0.3240 \| 0.2874 \| 78 \| \| 0.2367 \| 0.0342 \| 0.0364 \| 0.0431 \| 1.2173 \| 0.0208 \| 0.3234 \| 0.2870 \| 79 \| \| 0.1857 \| 0.0347 \| 0.0294 \| 0.0365 \| 1.2287 \| 0.0208 \| 0.3244 \| 0.2876 \| 80 \| \| 0.1504 \| 0.0351 \| 0.0244 \| 0.0314 \| 1.2425 \| 0.0207 \| 0.3238 \| 0.2871 \| 81 \| \| 0.1438 \| 0.0352 \| 0.0227 \| 0.0287 \| 1.2495 \| 0.0208 \| 0.3222 \| 0.2861 \| 82 \| \| 0.1545 \| 0.0350 \| 0.0232 \| 0.0288 \| 1.2612 \| 0.0207 \| 0.3257 \| 0.2898 \| 83 \| \| 0.2122 \| 0.0345 \| 0.0284 \| 0.0346 \| 1.2518 \| 0.0208 \| 0.3241 \| 0.2884 \| 84 \| ### Framework versions - Transformers 4.34.0.dev0 - TensorFlow 2.13.0 - Tokenizers 0.13.3
ixa-ehu/roberta-eus-euscrawl-large-cased	ixa-ehu	2023-09-11T13:33:15Z	114	3	transformers	[ "transformers", "pytorch", "safetensors", "roberta", "fill-mask", "basque", "eu", "arxiv:2203.08111", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-16T09:55:25Z	--- language: eu license: cc-by-nc-4.0 tags: - basque - roberta --- # Roberta-eus Euscrawl large cased This is a RoBERTa model for Basque model presented in [Does corpus quality really matter for low-resource languages?](https://arxiv.org/abs/2203.08111). There are several models for Basque using the RoBERTa architecture, using different corpora: - roberta-eus-euscrawl-base-cased: Basque RoBERTa model trained on Euscrawl, a corpus created using tailored crawling from Basque sites. EusCrawl contains 12,528k documents and 423M tokens. - roberta-eus-euscrawl-large-cased: RoBERTa large trained on EusCrawl. - roberta-eus-mC4-base-cased: Basque RoBERTa model trained on the Basque portion of mc4 dataset. - roberta-eus-CC100-base-cased: Basque RoBERTa model trained on Basque portion of cc100 dataset. The models have been tested on five different downstream tasks for Basque: Topic classification, Sentiment analysis, Stance detection, Named Entity Recognition (NER), and Question Answering (refer to the [paper](https://arxiv.org/abs/2203.08111) for more details). See summary of results below: \| Model \| Topic class. \| Sentiment \| Stance det. \| NER \| QA \| Average \| \|----------------------------------\|--------------\|-----------\|-------------\|----------\|----------\|----------\| \| roberta-eus-euscrawl-base-cased \| 76.2 \| 77.7 \| 57.4 \| 86.8 \| 34.6 \| 66.5 \| \| roberta-eus-euscrawl-large-cased \| 77.6 \| 78.8 \| 62.9 \| 87.2 \| 38.3 \| 69.0 \| \| roberta-eus-mC4-base-cased \| 75.3 \| 80.4 \| 59.1 \| 86.0 \| 35.2 \| 67.2 \| \| roberta-eus-CC100-base-cased \| 76.2 \| 78.8 \| 63.4 \| 85.2 \| 35.8 \| 67.9 \| If you use any of these models, please cite the following paper: ``` @misc{artetxe2022euscrawl, title={Does corpus quality really matter for low-resource languages?}, author={Mikel Artetxe, Itziar Aldabe, Rodrigo Agerri, Olatz Perez-de-Viñaspre, Aitor Soroa}, year={2022}, eprint={2203.08111}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
kensvin/audio_classification	kensvin	2023-09-11T13:31:00Z	162	0	transformers	[ "transformers", "pytorch", "wav2vec2", "audio-classification", "generated_from_trainer", "dataset:minds14", "base_model:facebook/wav2vec2-base", "base_model:finetune:facebook/wav2vec2-base", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	audio-classification	2023-09-11T13:27:41Z	--- license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer datasets: - minds14 metrics: - accuracy model-index: - name: audio_classification results: - task: name: Audio Classification type: audio-classification dataset: name: minds14 type: minds14 config: en-US split: train args: en-US metrics: - name: Accuracy type: accuracy value: 0.07079646017699115 --- <!-- 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. --> # audio_classification This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the minds14 dataset. It achieves the following results on the evaluation set: - Loss: 2.6513 - Accuracy: 0.0708 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 0.8 \| 3 \| 2.6439 \| 0.0531 \| \| No log \| 1.87 \| 7 \| 2.6446 \| 0.0708 \| \| 2.6349 \| 2.93 \| 11 \| 2.6484 \| 0.0885 \| \| 2.6349 \| 4.0 \| 15 \| 2.6497 \| 0.0885 \| \| 2.6349 \| 4.8 \| 18 \| 2.6509 \| 0.0796 \| \| 2.6233 \| 5.87 \| 22 \| 2.6513 \| 0.0708 \| \| 2.6233 \| 6.93 \| 26 \| 2.6515 \| 0.0708 \| \| 2.612 \| 8.0 \| 30 \| 2.6513 \| 0.0708 \| ### Framework versions - Transformers 4.33.1 - Pytorch 1.13.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3
ixa-ehu/SciBERT-SQuAD-QuAC	ixa-ehu	2023-09-11T13:30:44Z	262	4	transformers	[ "transformers", "pytorch", "safetensors", "bert", "question-answering", "en", "arxiv:1808.07036", "endpoints_compatible", "region:us" ]	question-answering	2022-03-02T23:29:05Z	--- language: en --- # SciBERT-SQuAD-QuAC This is the [SciBERT language representation model](https://huggingface.co/allenai/scibert_scivocab_uncased) fine tuned for Question Answering. SciBERT is a pre-trained language model based on BERT that has been trained on a large corpus of scientific text. When fine tuning for Question Answering we combined [SQuAD2.0](https://www.aclweb.org/anthology/P18-2124/) and [QuAC](https://arxiv.org/abs/1808.07036) datasets. If using this model, please cite the following paper: ``` @inproceedings{otegi-etal-2020-automatic, title = "Automatic Evaluation vs. User Preference in Neural Textual {Q}uestion{A}nswering over {COVID}-19 Scientific Literature", author = "Otegi, Arantxa and Campos, Jon Ander and Azkune, Gorka and Soroa, Aitor and Agirre, Eneko", booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020", month = dec, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.nlpcovid19-2.15", doi = "10.18653/v1/2020.nlpcovid19-2.15", } ```
saattrupdan/wav2vec2-xls-r-300m-ftspeech	saattrupdan	2023-09-11T13:27:55Z	115,130	0	transformers	[ "transformers", "pytorch", "safetensors", "wav2vec2", "automatic-speech-recognition", "da", "dataset:ftspeech", "base_model:facebook/wav2vec2-xls-r-300m", "base_model:finetune:facebook/wav2vec2-xls-r-300m", "license:other", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-04T14:53:05Z	--- language: - da license: other datasets: - ftspeech metrics: - wer tasks: - automatic-speech-recognition base_model: facebook/wav2vec2-xls-r-300m model-index: - name: wav2vec2-xls-r-300m-ftspeech results: - task: type: automatic-speech-recognition dataset: name: Danish Common Voice 8.0 type: mozilla-foundation/common_voice_8_0 args: da metrics: - type: wer value: 17.91 - task: type: automatic-speech-recognition dataset: name: Alvenir ASR test dataset type: Alvenir/alvenir_asr_da_eval metrics: - type: wer value: 13.84 --- # XLS-R-300m-FTSpeech ## Model description This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the [FTSpeech dataset](https://ftspeech.github.io/), being a dataset of 1,800 hours of transcribed speeches from the Danish parliament. ## Performance The model achieves the following WER scores (lower is better): \| Dataset \| WER without LM \| WER with 5-gram LM \| \| :---: \| ---: \| ---: \| \| [Danish part of Common Voice 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0/viewer/da/train) \| 20.48 \| 17.91 \| \| [Alvenir test set](https://huggingface.co/datasets/Alvenir/alvenir_asr_da_eval) \| 15.46 \| 13.84 \| ## License The use of this model needs to adhere to [this license from the Danish Parliament](https://www.ft.dk/da/aktuelt/tv-fra-folketinget/deling-og-rettigheder).
sanchit-gandhi/whisper-small-dv	sanchit-gandhi	2023-09-11T13:25:29Z	210	1	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dv", "dataset:mozilla-foundation/common_voice_13_0", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-06-27T14:43:10Z	--- language: - dv license: apache-2.0 tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_13_0 metrics: - wer base_model: openai/whisper-small model-index: - name: Whisper Small Dv - Sanchit Gandhi results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: Common Voice 13 type: mozilla-foundation/common_voice_13_0 config: dv split: test args: dv metrics: - type: wer value: 14.066140417985187 name: Wer --- <!-- 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 Dv - Sanchit Gandhi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 13 dataset. It achieves the following results on the evaluation set: - Loss: 0.1727 - Wer Ortho: 63.8972 - Wer: 14.0661 ## 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: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer Ortho \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:-------:\| \| 0.136 \| 1.63 \| 500 \| 0.1727 \| 63.8972 \| 14.0661 \| ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1.dev0 - Tokenizers 0.13.3
nickmuchi/distilroberta-finetuned-financial-text-classification	nickmuchi	2023-09-11T13:23:38Z	1,773	15	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "financial-sentiment-analysis", "sentiment-analysis", "sentence_50agree", "generated_from_trainer", "sentiment", "finance", "en", "dataset:financial_phrasebank", "dataset:Kaggle_Self_label", "dataset:nickmuchi/financial-classification", "base_model:distilbert/distilroberta-base", "base_model:finetune:distilbert/distilroberta-base", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- language: en license: apache-2.0 tags: - financial-sentiment-analysis - sentiment-analysis - sentence_50agree - generated_from_trainer - sentiment - finance datasets: - financial_phrasebank - Kaggle_Self_label - nickmuchi/financial-classification metrics: - f1 widget: - text: The USD rallied by 10% last night example_title: Bullish Sentiment - text: Covid-19 cases have been increasing over the past few months impacting earnings for global firms example_title: Bearish Sentiment - text: the USD has been trending lower example_title: Mildly Bearish Sentiment base_model: distilroberta-base model-index: - name: distilroberta-finetuned-finclass results: - task: type: text-classification name: Text Classification dataset: name: financial_phrasebank type: finance args: sentence_50agree metrics: - type: F1 value: 0.8835 name: F1 - type: accuracy value: 0.89 name: accuracy --- # distilroberta-finetuned-financial-text-classification This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the sentence_50Agree [financial-phrasebank + Kaggle Dataset](https://huggingface.co/datasets/nickmuchi/financial-classification), a dataset consisting of 4840 Financial News categorised by sentiment (negative, neutral, positive). The Kaggle dataset includes Covid-19 sentiment data and can be found here: [sentiment-classification-selflabel-dataset](https://www.kaggle.com/percyzheng/sentiment-classification-selflabel-dataset). It achieves the following results on the evaluation set: - Loss: 0.4463 - F1: 0.8835 ## Model description Model determines the financial sentiment of given text. Given the unbalanced distribution of the class labels, the weights were adjusted to pay attention to the less sampled labels which should increase overall performance. The Covid dataset was added in order to enrich the model, given most models have not been trained on the impact of Covid-19 on earnings or markets. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.7309 \| 1.0 \| 72 \| 0.3671 \| 0.8441 \| \| 0.3757 \| 2.0 \| 144 \| 0.3199 \| 0.8709 \| \| 0.3054 \| 3.0 \| 216 \| 0.3096 \| 0.8678 \| \| 0.2229 \| 4.0 \| 288 \| 0.3776 \| 0.8390 \| \| 0.1744 \| 5.0 \| 360 \| 0.3678 \| 0.8723 \| \| 0.1436 \| 6.0 \| 432 \| 0.3728 \| 0.8758 \| \| 0.1044 \| 7.0 \| 504 \| 0.4116 \| 0.8744 \| \| 0.0931 \| 8.0 \| 576 \| 0.4148 \| 0.8761 \| \| 0.0683 \| 9.0 \| 648 \| 0.4423 \| 0.8837 \| \| 0.0611 \| 10.0 \| 720 \| 0.4463 \| 0.8835 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.18.0 - Tokenizers 0.10.3
nielsr/swin-tiny-patch4-window7-224-finetuned-cifar10	nielsr	2023-09-11T13:16:37Z	221	0	transformers	[ "transformers", "pytorch", "tensorboard", "swin", "image-classification", "generated_from_trainer", "dataset:image_folder", "base_model:microsoft/swin-tiny-patch4-window7-224", "base_model:finetune:microsoft/swin-tiny-patch4-window7-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-04-11T11:59:54Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - image_folder metrics: - accuracy base_model: microsoft/swin-tiny-patch4-window7-224 model-index: - name: swin-tiny-patch4-window7-224-finetuned-cifar10 results: - task: type: image-classification name: Image Classification dataset: name: image_folder type: image_folder args: default metrics: - type: accuracy value: 0.9788888888888889 name: Accuracy --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-tiny-patch4-window7-224-finetuned-cifar10 This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.0690 - Accuracy: 0.9789 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2446 \| 1.0 \| 190 \| 0.1128 \| 0.9659 \| \| 0.1722 \| 2.0 \| 380 \| 0.1034 \| 0.9663 \| \| 0.1355 \| 3.0 \| 570 \| 0.0690 \| 0.9789 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
osieosie/bloom-mnli-8bit-7b-bnb-seed87	osieosie	2023-09-11T13:16:15Z	0	0	peft	[ "peft", "region:us" ]	null	2023-09-11T13:16:14Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0.dev0
vesteinn/IceBERT-ner	vesteinn	2023-09-11T13:14:09Z	186	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "roberta", "token-classification", "generated_from_trainer", "dataset:mim_gold_ner", "base_model:vesteinn/IceBERT", "base_model:finetune:vesteinn/IceBERT", "license:gpl-3.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- license: gpl-3.0 tags: - generated_from_trainer datasets: - mim_gold_ner metrics: - precision - recall - f1 - accuracy widget: - text: Systurnar Guðrún og Monique átu einar á McDonalds og horfðu á Stöð 2, þar glitti í Bruce Willis leika í Die Hard 2. base_model: vesteinn/IceBERT model-index: - name: IceBERT-finetuned-ner results: - task: type: token-classification name: Token Classification dataset: name: mim_gold_ner type: mim_gold_ner args: mim-gold-ner metrics: - type: precision value: 0.9351994710160899 name: Precision - type: recall value: 0.9440427188786294 name: Recall - type: f1 value: 0.9396002878813043 name: F1 - type: accuracy value: 0.9920330921021648 name: Accuracy --- <!-- 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. --> # IceBERT-finetuned-ner This model is a fine-tuned version of [vesteinn/IceBERT](https://huggingface.co/vesteinn/IceBERT) on the mim_gold_ner dataset. It achieves the following results on the evaluation set: - Loss: 0.0347 - Precision: 0.9352 - Recall: 0.9440 - F1: 0.9396 - Accuracy: 0.9920 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.0568 \| 1.0 \| 2929 \| 0.0386 \| 0.9114 \| 0.9162 \| 0.9138 \| 0.9897 \| \| 0.0325 \| 2.0 \| 5858 \| 0.0325 \| 0.9300 \| 0.9363 \| 0.9331 \| 0.9912 \| \| 0.0184 \| 3.0 \| 8787 \| 0.0347 \| 0.9352 \| 0.9440 \| 0.9396 \| 0.9920 \| ### Framework versions - Transformers 4.11.0 - Pytorch 1.9.0+cu102 - Datasets 1.12.1 - Tokenizers 0.10.3
ArifYZ/dutch-sentences-model	ArifYZ	2023-09-11T13:11:40Z	0	0	null	[ "region:us" ]	null	2023-09-11T13:04:55Z	Model for embedding Dutch Sentences
HamZurger/Reinforce-CartPole_v2	HamZurger	2023-09-11T13:00:21Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-09-11T13:00:13Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole_v2 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
tuikhar/naga	tuikhar	2023-09-11T12:57:49Z	0	0	null	[ "arxiv:1910.09700", "region:us" ]	null	2023-09-11T12:57:09Z	--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. 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JasperLS/gelectra-base-injection-pt_v1	JasperLS	2023-09-11T12:55:08Z	114	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "electra", "text-classification", "generated_from_trainer", "base_model:deepset/gelectra-base", "base_model:finetune:deepset/gelectra-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-04-06T12:31:06Z	--- license: mit tags: - generated_from_trainer metrics: - accuracy base_model: deepset/gelectra-base model-index: - name: gelectra-base-injection-pt_v1 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. --> # gelectra-base-injection-pt_v1 DEPRECATED - PLEASE USE NEWER GELECTRA OR DEBERTA VERSION This model is a fine-tuned version of [deepset/gelectra-base](https://huggingface.co/deepset/gelectra-base) on a closed prompt injection dataset. It achieves the following results on the evaluation set: - Loss: 0.0163 - Accuracy: 1.0 ## Model description The model classifies prompts as injections or legitimate questions. ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 45 \| 0.2042 \| 0.9211 \| \| No log \| 2.0 \| 90 \| 0.0247 \| 1.0 \| \| No log \| 3.0 \| 135 \| 0.0163 \| 1.0 \| ### Framework versions - Transformers 4.27.4 - Pytorch 2.0.0+cu118 - Tokenizers 0.13.3
abelkrw/audio_classification	abelkrw	2023-09-11T12:53:59Z	162	0	transformers	[ "transformers", "pytorch", "wav2vec2", "audio-classification", "generated_from_trainer", "dataset:minds14", "base_model:facebook/wav2vec2-base", "base_model:finetune:facebook/wav2vec2-base", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	audio-classification	2023-09-11T12:50:42Z	--- license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer datasets: - minds14 metrics: - accuracy model-index: - name: audio_classification results: - task: name: Audio Classification type: audio-classification dataset: name: minds14 type: minds14 config: en-US split: train args: en-US metrics: - name: Accuracy type: accuracy value: 0.07079646017699115 --- <!-- 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. --> # audio_classification This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the minds14 dataset. It achieves the following results on the evaluation set: - Loss: 2.6569 - Accuracy: 0.0708 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 0.8 \| 3 \| 2.6456 \| 0.0265 \| \| No log \| 1.87 \| 7 \| 2.6512 \| 0.0442 \| \| 2.6372 \| 2.93 \| 11 \| 2.6509 \| 0.0619 \| \| 2.6372 \| 4.0 \| 15 \| 2.6541 \| 0.0708 \| \| 2.6372 \| 4.8 \| 18 \| 2.6554 \| 0.0708 \| \| 2.6217 \| 5.87 \| 22 \| 2.6561 \| 0.0708 \| \| 2.6217 \| 6.93 \| 26 \| 2.6564 \| 0.0708 \| \| 2.6141 \| 8.0 \| 30 \| 2.6569 \| 0.0708 \| ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
esperesa/xlm-roberta-base-finetuned-panx-de	esperesa	2023-09-11T12:53:29Z	106	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	2023-09-11T12:43:57Z	--- 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: 0.8653353814644136 --- <!-- 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: 0.1339 - F1: 0.8653 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2583 \| 1.0 \| 525 \| 0.1596 \| 0.8231 \| \| 0.1262 \| 2.0 \| 1050 \| 0.1395 \| 0.8468 \| \| 0.0824 \| 3.0 \| 1575 \| 0.1339 \| 0.8653 \| ### Framework versions - Transformers 4.16.2 - Pytorch 2.0.1+cu118 - Datasets 1.16.1 - Tokenizers 0.14.0
bigmorning/whisper_4_with_init_sun_syl_wd_0__0070	bigmorning	2023-09-11T12:48:55Z	59	0	transformers	[ "transformers", "tf", "whisper", "automatic-speech-recognition", "generated_from_keras_callback", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-09-11T12:48:46Z	--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_keras_callback model-index: - name: whisper_4_with_init_sun_syl_wd_0__0070 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. --> # whisper_4_with_init_sun_syl_wd_0__0070 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2729 - Train Accuracy: 0.0338 - Train Wermet: 0.0595 - Train Wermet Syl: 0.0677 - Validation Loss: 1.1911 - Validation Accuracy: 0.0207 - Validation Wermet: 0.3247 - Validation Wermet Syl: 0.2886 - Epoch: 69 ## 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': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0} - training_precision: float32 ### Training results \| Train Loss \| Train Accuracy \| Train Wermet \| Train Wermet Syl \| Validation Loss \| Validation Accuracy \| Validation Wermet \| Validation Wermet Syl \| Epoch \| \|:----------:\|:--------------:\|:------------:\|:----------------:\|:---------------:\|:-------------------:\|:-----------------:\|:---------------------:\|:-----:\| \| 5.3409 \| 0.0111 \| 1.3547 \| 1.2898 \| 3.9789 \| 0.0114 \| 0.9710 \| 0.9563 \| 0 \| \| 4.7143 \| 0.0116 \| 0.8622 \| 0.8228 \| 3.9404 \| 0.0113 \| 0.9823 \| 0.9735 \| 1 \| \| 4.6752 \| 0.0117 \| 0.8472 \| 0.8057 \| 3.9081 \| 0.0114 \| 0.9579 \| 0.9359 \| 2 \| \| 4.6500 \| 0.0117 \| 0.8382 \| 0.7945 \| 3.8820 \| 0.0115 \| 0.9213 \| 0.8856 \| 3 \| \| 4.6282 \| 0.0118 \| 0.8286 \| 0.7805 \| 3.8738 \| 0.0114 \| 0.9433 \| 0.9119 \| 4 \| \| 4.6095 \| 0.0118 \| 0.8190 \| 0.7696 \| 3.8630 \| 0.0115 \| 0.9117 \| 0.8698 \| 5 \| \| 4.5875 \| 0.0119 \| 0.7976 \| 0.7465 \| 3.8341 \| 0.0116 \| 0.8976 \| 0.8552 \| 6 \| \| 4.5682 \| 0.0120 \| 0.7753 \| 0.7227 \| 3.8277 \| 0.0116 \| 0.9014 \| 0.8653 \| 7 \| \| 4.5376 \| 0.0121 \| 0.7528 \| 0.7005 \| 3.7844 \| 0.0118 \| 0.8332 \| 0.7815 \| 8 \| \| 4.5060 \| 0.0122 \| 0.7392 \| 0.6844 \| 3.7537 \| 0.0118 \| 0.8578 \| 0.8152 \| 9 \| \| 4.4580 \| 0.0124 \| 0.7221 \| 0.6694 \| 3.7038 \| 0.0120 \| 0.8190 \| 0.7679 \| 10 \| \| 4.3989 \| 0.0125 \| 0.7156 \| 0.6636 \| 3.6169 \| 0.0122 \| 0.7979 \| 0.7429 \| 11 \| \| 4.3056 \| 0.0128 \| 0.7069 \| 0.6557 \| 3.5098 \| 0.0125 \| 0.7924 \| 0.7396 \| 12 \| \| 4.1673 \| 0.0132 \| 0.7054 \| 0.6584 \| 3.3542 \| 0.0128 \| 0.7759 \| 0.7240 \| 13 \| \| 3.9762 \| 0.0138 \| 0.6987 \| 0.6559 \| 3.1318 \| 0.0133 \| 0.7644 \| 0.7231 \| 14 \| \| 3.7385 \| 0.0145 \| 0.6835 \| 0.6448 \| 2.9144 \| 0.0138 \| 0.7392 \| 0.6955 \| 15 \| \| 3.5040 \| 0.0152 \| 0.6644 \| 0.6298 \| 2.7413 \| 0.0142 \| 0.7019 \| 0.6548 \| 16 \| \| 3.2728 \| 0.0160 \| 0.6408 \| 0.6101 \| 2.5183 \| 0.0149 \| 0.6798 \| 0.6363 \| 17 \| \| 3.0657 \| 0.0167 \| 0.6188 \| 0.5912 \| 2.3594 \| 0.0153 \| 0.6528 \| 0.6103 \| 18 \| \| 2.8703 \| 0.0174 \| 0.5936 \| 0.5685 \| 2.2644 \| 0.0156 \| 0.6310 \| 0.5925 \| 19 \| \| 2.6850 \| 0.0181 \| 0.5680 \| 0.5453 \| 2.1296 \| 0.0160 \| 0.6040 \| 0.5652 \| 20 \| \| 2.5227 \| 0.0188 \| 0.5423 \| 0.5215 \| 2.0019 \| 0.0165 \| 0.5793 \| 0.5403 \| 21 \| \| 2.3878 \| 0.0194 \| 0.5199 \| 0.5015 \| 1.8996 \| 0.0169 \| 0.5592 \| 0.5229 \| 22 \| \| 2.2437 \| 0.0201 \| 0.4959 \| 0.4788 \| 1.8141 \| 0.0172 \| 0.5414 \| 0.5045 \| 23 \| \| 2.1205 \| 0.0207 \| 0.4752 \| 0.4607 \| 1.7245 \| 0.0175 \| 0.5208 \| 0.4838 \| 24 \| \| 1.9919 \| 0.0213 \| 0.4533 \| 0.4390 \| 1.6673 \| 0.0178 \| 0.5026 \| 0.4659 \| 25 \| \| 1.9140 \| 0.0217 \| 0.4355 \| 0.4216 \| 1.6041 \| 0.0181 \| 0.4873 \| 0.4512 \| 26 \| \| 1.8225 \| 0.0222 \| 0.4184 \| 0.4052 \| 1.6271 \| 0.0179 \| 0.4852 \| 0.4511 \| 27 \| \| 1.7265 \| 0.0227 \| 0.4016 \| 0.3895 \| 1.5219 \| 0.0184 \| 0.4635 \| 0.4275 \| 28 \| \| 1.6240 \| 0.0233 \| 0.3833 \| 0.3729 \| 1.4718 \| 0.0186 \| 0.4515 \| 0.4170 \| 29 \| \| 1.5610 \| 0.0236 \| 0.3697 \| 0.3588 \| 1.4404 \| 0.0188 \| 0.4407 \| 0.4056 \| 30 \| \| 1.4719 \| 0.0242 \| 0.3540 \| 0.3449 \| 1.4125 \| 0.0189 \| 0.4310 \| 0.3961 \| 31 \| \| 1.4152 \| 0.0245 \| 0.3421 \| 0.3339 \| 1.3655 \| 0.0191 \| 0.4234 \| 0.3881 \| 32 \| \| 1.3546 \| 0.0249 \| 0.3277 \| 0.3195 \| 1.3419 \| 0.0192 \| 0.4156 \| 0.3816 \| 33 \| \| 1.2565 \| 0.0256 \| 0.3135 \| 0.3060 \| 1.3172 \| 0.0194 \| 0.4065 \| 0.3722 \| 34 \| \| 1.2135 \| 0.0258 \| 0.3026 \| 0.2958 \| 1.3019 \| 0.0194 \| 0.4006 \| 0.3662 \| 35 \| \| 1.1739 \| 0.0261 \| 0.2923 \| 0.2861 \| 1.3843 \| 0.0190 \| 0.3951 \| 0.3587 \| 36 \| \| 1.0950 \| 0.0267 \| 0.2782 \| 0.2733 \| 1.2665 \| 0.0197 \| 0.3883 \| 0.3541 \| 37 \| \| 1.0435 \| 0.0271 \| 0.2673 \| 0.2631 \| 1.2567 \| 0.0197 \| 0.3837 \| 0.3497 \| 38 \| \| 0.9922 \| 0.0275 \| 0.2580 \| 0.2542 \| 1.2566 \| 0.0197 \| 0.3801 \| 0.3444 \| 39 \| \| 0.9387 \| 0.0279 \| 0.2464 \| 0.2438 \| 1.2441 \| 0.0198 \| 0.3767 \| 0.3423 \| 40 \| \| 0.9345 \| 0.0278 \| 0.2393 \| 0.2373 \| 1.2221 \| 0.0199 \| 0.3682 \| 0.3336 \| 41 \| \| 0.8574 \| 0.0285 \| 0.2268 \| 0.2255 \| 1.2258 \| 0.0199 \| 0.3680 \| 0.3338 \| 42 \| \| 0.8275 \| 0.0287 \| 0.2183 \| 0.2180 \| 1.2044 \| 0.0201 \| 0.3628 \| 0.3290 \| 43 \| \| 0.8201 \| 0.0288 \| 0.2114 \| 0.2108 \| 1.2056 \| 0.0201 \| 0.3601 \| 0.3270 \| 44 \| \| 0.7684 \| 0.0292 \| 0.2020 \| 0.2029 \| 1.1879 \| 0.0202 \| 0.3553 \| 0.3215 \| 45 \| \| 0.7262 \| 0.0295 \| 0.1938 \| 0.1947 \| 1.2263 \| 0.0200 \| 0.3537 \| 0.3177 \| 46 \| \| 0.7286 \| 0.0295 \| 0.1876 \| 0.1898 \| 1.1772 \| 0.0203 \| 0.3485 \| 0.3135 \| 47 \| \| 0.6807 \| 0.0300 \| 0.1775 \| 0.1797 \| 1.1761 \| 0.0203 \| 0.3490 \| 0.3155 \| 48 \| \| 0.6609 \| 0.0301 \| 0.1713 \| 0.1742 \| 1.1853 \| 0.0203 \| 0.3484 \| 0.3153 \| 49 \| \| 0.6062 \| 0.0306 \| 0.1615 \| 0.1653 \| 1.1660 \| 0.0204 \| 0.3432 \| 0.3090 \| 50 \| \| 0.5755 \| 0.0309 \| 0.1547 \| 0.1584 \| 1.1698 \| 0.0204 \| 0.3428 \| 0.3089 \| 51 \| \| 0.5600 \| 0.0310 \| 0.1482 \| 0.1524 \| 1.1667 \| 0.0204 \| 0.3398 \| 0.3058 \| 52 \| \| 0.5715 \| 0.0308 \| 0.1449 \| 0.1496 \| 1.1614 \| 0.0205 \| 0.3381 \| 0.3036 \| 53 \| \| 0.5247 \| 0.0313 \| 0.1358 \| 0.1411 \| 1.1639 \| 0.0205 \| 0.3359 \| 0.3025 \| 54 \| \| 0.5085 \| 0.0315 \| 0.1301 \| 0.1358 \| 1.2420 \| 0.0202 \| 0.3412 \| 0.3064 \| 55 \| \| 0.4827 \| 0.0317 \| 0.1239 \| 0.1295 \| 1.1677 \| 0.0205 \| 0.3349 \| 0.3009 \| 56 \| \| 0.4848 \| 0.0317 \| 0.1207 \| 0.1280 \| 1.1653 \| 0.0205 \| 0.3326 \| 0.2991 \| 57 \| \| 0.4323 \| 0.0322 \| 0.1109 \| 0.1185 \| 1.1602 \| 0.0206 \| 0.3299 \| 0.2953 \| 58 \| \| 0.4183 \| 0.0323 \| 0.1057 \| 0.1133 \| 1.1622 \| 0.0206 \| 0.3307 \| 0.2962 \| 59 \| \| 0.4329 \| 0.0322 \| 0.1028 \| 0.1100 \| 1.1714 \| 0.0206 \| 0.3300 \| 0.2950 \| 60 \| \| 0.3962 \| 0.0326 \| 0.0964 \| 0.1045 \| 1.1726 \| 0.0206 \| 0.3311 \| 0.2967 \| 61 \| \| 0.3642 \| 0.0329 \| 0.0898 \| 0.0973 \| 1.1699 \| 0.0206 \| 0.3289 \| 0.2936 \| 62 \| \| 0.3786 \| 0.0327 \| 0.0884 \| 0.0963 \| 1.1734 \| 0.0206 \| 0.3279 \| 0.2929 \| 63 \| \| 0.3698 \| 0.0328 \| 0.0842 \| 0.0925 \| 1.1728 \| 0.0207 \| 0.3282 \| 0.2932 \| 64 \| \| 0.3219 \| 0.0333 \| 0.0765 \| 0.0850 \| 1.1830 \| 0.0207 \| 0.3258 \| 0.2907 \| 65 \| \| 0.3035 \| 0.0335 \| 0.0725 \| 0.0811 \| 1.1840 \| 0.0207 \| 0.3261 \| 0.2904 \| 66 \| \| 0.3522 \| 0.0330 \| 0.0745 \| 0.0826 \| 1.2107 \| 0.0206 \| 0.3299 \| 0.2955 \| 67 \| \| 0.3001 \| 0.0335 \| 0.0663 \| 0.0749 \| 1.1810 \| 0.0207 \| 0.3264 \| 0.2909 \| 68 \| \| 0.2729 \| 0.0338 \| 0.0595 \| 0.0677 \| 1.1911 \| 0.0207 \| 0.3247 \| 0.2886 \| 69 \| ### Framework versions - Transformers 4.34.0.dev0 - TensorFlow 2.13.0 - Tokenizers 0.13.3
kaitchup/Llama-2-7b-gptq-2bit	kaitchup	2023-09-11T12:48:38Z	160	1	transformers	[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "2-bit", "gptq", "region:us" ]	text-generation	2023-08-29T11:19:52Z	--- license: apache-2.0 language: - en --- # Model Card for Model ID This is Meta's Llama 2 7B quantized in 2-bit using AutoGPTQ from Hugging Face Transformers. ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [The Kaitchup](https://kaitchup.substack.com/) - Model type: Causal (Llama 2) - Language(s) (NLP): English - License: [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0), [Llama 2 license agreement](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) ### Model Sources The method and code used to quantize the model are explained here: [Quantize and Fine-tune LLMs with GPTQ Using Transformers and TRL](https://kaitchup.substack.com/p/quantize-and-fine-tune-llms-with) ## Uses This model is pre-trained and not fine-tuned. You may fine-tune it with PEFT using adapters. Note that the 2-bit quantization significantly decreases the performance of Llama 2. ## Other versions - [kaitchup/Llama-2-7b-gptq-4bit](https://huggingface.co/kaitchup/Llama-2-7b-gptq-4bit) - [kaitchup/Llama-2-7b-gptq-3bit](https://huggingface.co/kaitchup/Llama-2-7b-gptq-3bit) ## Model Card Contact [The Kaitchup](https://kaitchup.substack.com/)
ChristianMDahl/segFormer-b3-horizontal-vertical	ChristianMDahl	2023-09-11T12:45:44Z	2	0	transformers	[ "transformers", "tf", "segformer", "generated_from_keras_callback", "base_model:nvidia/mit-b3", "base_model:finetune:nvidia/mit-b3", "license:other", "endpoints_compatible", "region:us" ]	null	2023-06-13T19:07:57Z	--- license: other tags: - generated_from_keras_callback base_model: nvidia/mit-b3 model-index: - name: ChristianMDahl/segFormer-b3-horizontal-vertical 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. --> # ChristianMDahl/segFormer-b3-horizontal-vertical This model is a fine-tuned version of [nvidia/mit-b3](https://huggingface.co/nvidia/mit-b3) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1671 - Validation Loss: 0.2320 - Epoch: 19 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': 6e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 0.3203 \| 0.2831 \| 0 \| \| 0.2822 \| 0.2688 \| 1 \| \| 0.2662 \| 0.2578 \| 2 \| \| 0.2526 \| 0.2484 \| 3 \| \| 0.2396 \| 0.2442 \| 4 \| \| 0.2288 \| 0.2416 \| 5 \| \| 0.2195 \| 0.2381 \| 6 \| \| 0.2121 \| 0.2361 \| 7 \| \| 0.2058 \| 0.2314 \| 8 \| \| 0.1999 \| 0.2277 \| 9 \| \| 0.1952 \| 0.2287 \| 10 \| \| 0.1912 \| 0.2221 \| 11 \| \| 0.1869 \| 0.2205 \| 12 \| \| 0.1835 \| 0.2226 \| 13 \| \| 0.1804 \| 0.2209 \| 14 \| \| 0.1775 \| 0.2181 \| 15 \| \| 0.1745 \| 0.2206 \| 16 \| \| 0.1721 \| 0.2179 \| 17 \| \| 0.1693 \| 0.2199 \| 18 \| \| 0.1671 \| 0.2320 \| 19 \| ### Framework versions - Transformers 4.29.2 - TensorFlow 2.10.1 - Tokenizers 0.13.3
ahsan-mavros/error-test	ahsan-mavros	2023-09-11T12:42:32Z	105	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "base_model:google/flan-t5-base", "base_model:finetune:google/flan-t5-base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-09-11T12:41:35Z	--- license: apache-2.0 base_model: google/flan-t5-base tags: - generated_from_trainer metrics: - rouge model-index: - name: error-test results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # error-test This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0649 - Rouge1: 98.8411 - Rouge2: 95.5257 - Rougel: 98.8389 - Rougelsum: 98.8411 - Gen Len: 5.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - 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 \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 0.0675 \| 1.0 \| 2500 \| 0.0649 \| 98.8411 \| 95.5257 \| 98.8389 \| 98.8411 \| 5.0 \| ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.0 - Datasets 2.14.5 - Tokenizers 0.13.3
baebee/llama2-qlora-finetunined-french	baebee	2023-09-11T12:40:30Z	0	0	peft	[ "peft", "region:us" ]	null	2023-09-11T12:40:24Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.0.dev0
baebee/Starlight-13b	baebee	2023-09-11T12:39:07Z	0	0	peft	[ "peft", "region:us" ]	null	2023-09-11T12:38:59Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.0.dev0
ImhotepAI/yoruba-tts	ImhotepAI	2023-09-11T12:38:50Z	84	1	transformers	[ "transformers", "pytorch", "speecht5", "text-to-audio", "text-to-speech", "yo", "dataset:openslr", "dataset:mozilla-foundation/common_voice_13_0", "dataset:Lagos-NWU_Yoruba_Speech_Corpus", "license:cc-by-nc-sa-4.0", "endpoints_compatible", "region:us" ]	text-to-speech	2023-09-09T11:08:20Z	--- license: cc-by-nc-sa-4.0 datasets: - openslr - mozilla-foundation/common_voice_13_0 - Lagos-NWU_Yoruba_Speech_Corpus language: - yo library_name: transformers pipeline_tag: text-to-speech --- ```python # Load model directly from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan from huggingface_hub import hf_hub_download import torch processor = SpeechT5Processor.from_pretrained("imhotepai/yoruba-tts") model = SpeechT5ForTextToSpeech.from_pretrained("imhotepai/yoruba-tts") dir_= hf_hub_download(repo_id="imhotepai/yoruba-tts", filename="speaker_embeddings.pt") speaker_embeddings= torch.load(dir_) text='Báwó ni'.lower() inputs = processor(text=text, return_tensors="pt") vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan") speech = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder) # Audio in notebook from IPython.display import Audio Audio(speech.numpy(), rate=16000) ```
thusken/nb-bert-large-user-needs	thusken	2023-09-11T12:36:25Z	193	2	transformers	[ "transformers", "pytorch", "safetensors", "bert", "text-classification", "generated_from_trainer", "no", "nb", "nn", "base_model:NbAiLab/nb-bert-large", "base_model:finetune:NbAiLab/nb-bert-large", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-08-11T11:15:43Z	--- language: - 'no' - nb - nn license: cc-by-4.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall widget: - text: Fløyfjelltunnelen på E39 retning sentrum er åpen for fri ferdsel. - text: Slik kan du redusere strømregningen din pipeline_tag: text-classification base_model: NbAiLab/nb-bert-large model-index: - name: nb-bert-large-user-needs 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. --> # nb-bert-large-user-needs This model is a fine-tuned version of [NbAiLab/nb-bert-large](https://huggingface.co/NbAiLab/nb-bert-large) on a dataset of 2000 articles from Bergens Tidende, published between 06/01/2020 and 02/02/2020. These articles are labelled as one of six classes / user needs, as introduced by the [BBC in 2017](https://www.linkedin.com/pulse/five-lessons-i-learned-while-digitally-changing-bbc-world-shishkin/). It achieves the following results on the evaluation set: - Loss: 1.0102 - Accuracy: 0.8900 - F1: 0.8859 - Precision: 0.8883 - Recall: 0.8900 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 8 - eval_batch_size: 64 - 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: 25 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| No log \| 1.0 \| 195 \| 0.6790 \| 0.8082 \| 0.7567 \| 0.7679 \| 0.8082 \| \| No log \| 2.0 \| 390 \| 0.5577 \| 0.8465 \| 0.8392 \| 0.8364 \| 0.8465 \| \| 0.8651 \| 3.0 \| 585 \| 0.5494 \| 0.8338 \| 0.8191 \| 0.8145 \| 0.8338 \| \| 0.8651 \| 4.0 \| 780 \| 0.5453 \| 0.8517 \| 0.8386 \| 0.8293 \| 0.8517 \| \| 0.8651 \| 5.0 \| 975 \| 0.8855 \| 0.8491 \| 0.8298 \| 0.8444 \| 0.8491 \| \| 0.3707 \| 6.0 \| 1170 \| 0.7282 \| 0.8645 \| 0.8526 \| 0.8581 \| 0.8645 \| \| 0.3707 \| 7.0 \| 1365 \| 0.8797 \| 0.8619 \| 0.8537 \| 0.8573 \| 0.8619 \| \| 0.1092 \| 8.0 \| 1560 \| 0.9120 \| 0.8491 \| 0.8520 \| 0.8579 \| 0.8491 \| \| 0.1092 \| 9.0 \| 1755 \| 1.0700 \| 0.8696 \| 0.8615 \| 0.8669 \| 0.8696 \| \| 0.1092 \| 10.0 \| 1950 \| 1.0599 \| 0.8670 \| 0.8654 \| 0.8701 \| 0.8670 \| \| 0.0355 \| 11.0 \| 2145 \| 1.0808 \| 0.8670 \| 0.8656 \| 0.8685 \| 0.8670 \| \| 0.0355 \| 12.0 \| 2340 \| 1.0102 \| 0.8900 \| 0.8859 \| 0.8883 \| 0.8900 \| \| 0.0002 \| 13.0 \| 2535 \| 1.0236 \| 0.8849 \| 0.8812 \| 0.8824 \| 0.8849 \| \| 0.0002 \| 14.0 \| 2730 \| 1.0358 \| 0.8875 \| 0.8833 \| 0.8841 \| 0.8875 \| \| 0.0002 \| 15.0 \| 2925 \| 1.0476 \| 0.8875 \| 0.8833 \| 0.8841 \| 0.8875 \| \| 0.0001 \| 16.0 \| 3120 \| 1.0559 \| 0.8798 \| 0.8764 \| 0.8776 \| 0.8798 \| \| 0.0001 \| 17.0 \| 3315 \| 1.0648 \| 0.8798 \| 0.8754 \| 0.8765 \| 0.8798 \| \| 0.0001 \| 18.0 \| 3510 \| 1.0720 \| 0.8798 \| 0.8754 \| 0.8765 \| 0.8798 \| \| 0.0001 \| 19.0 \| 3705 \| 1.0796 \| 0.8824 \| 0.8775 \| 0.8783 \| 0.8824 \| \| 0.0001 \| 20.0 \| 3900 \| 1.0862 \| 0.8798 \| 0.8739 \| 0.8745 \| 0.8798 \| \| 0.0 \| 21.0 \| 4095 \| 1.0917 \| 0.8798 \| 0.8739 \| 0.8745 \| 0.8798 \| \| 0.0 \| 22.0 \| 4290 \| 1.0973 \| 0.8798 \| 0.8739 \| 0.8745 \| 0.8798 \| \| 0.0 \| 23.0 \| 4485 \| 1.1007 \| 0.8798 \| 0.8739 \| 0.8745 \| 0.8798 \| \| 0.0 \| 24.0 \| 4680 \| 1.1029 \| 0.8798 \| 0.8739 \| 0.8745 \| 0.8798 \| \| 0.0 \| 25.0 \| 4875 \| 1.1037 \| 0.8798 \| 0.8739 \| 0.8745 \| 0.8798 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.2+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
ldos/text_shortening_model_v27	ldos	2023-09-11T12:35:54Z	3	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-09-11T11:48:09Z	--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: text_shortening_model_v27 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. --> # text_shortening_model_v27 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.1933 - Rouge1: 0.4266 - Rouge2: 0.2061 - Rougel: 0.38 - Rougelsum: 0.3804 - Bert precision: 0.8628 - Bert recall: 0.8555 - Average word count: 8.003 - Max word count: 16 - Min word count: 3 - Average token count: 12.3784 - % shortened texts with length > 12: 3.003 ## 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: 0.005 - 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: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Bert precision \| Bert recall \| Average word count \| Max word count \| Min word count \| Average token count \| % shortened texts with length > 12 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:--------------:\|:-----------:\|:------------------:\|:--------------:\|:--------------:\|:-------------------:\|:----------------------------------:\| \| 2.4306 \| 1.0 \| 145 \| 1.8708 \| 0.4779 \| 0.2499 \| 0.4349 \| 0.4355 \| 0.8758 \| 0.866 \| 7.9099 \| 16 \| 3 \| 12.3093 \| 5.1051 \| \| 1.7537 \| 2.0 \| 290 \| 1.8412 \| 0.4532 \| 0.2437 \| 0.4165 \| 0.4174 \| 0.8687 \| 0.8604 \| 8.4775 \| 19 \| 3 \| 12.8859 \| 6.9069 \| \| 1.4338 \| 3.0 \| 435 \| 1.7898 \| 0.4365 \| 0.219 \| 0.4002 \| 0.4007 \| 0.868 \| 0.856 \| 7.6637 \| 14 \| 3 \| 11.8919 \| 2.1021 \| \| 1.2645 \| 4.0 \| 580 \| 1.8826 \| 0.4609 \| 0.238 \| 0.4158 \| 0.4159 \| 0.8711 \| 0.8637 \| 8.4655 \| 16 \| 4 \| 12.8228 \| 6.006 \| \| 1.1208 \| 5.0 \| 725 \| 1.9741 \| 0.4389 \| 0.2351 \| 0.4038 \| 0.4051 \| 0.8719 \| 0.8568 \| 7.5886 \| 18 \| 3 \| 12.1231 \| 2.4024 \| \| 1.0057 \| 6.0 \| 870 \| 1.9700 \| 0.4658 \| 0.2526 \| 0.4275 \| 0.4276 \| 0.8728 \| 0.8646 \| 8.0841 \| 19 \| 2 \| 12.3634 \| 7.8078 \| \| 0.973 \| 7.0 \| 1015 \| 2.0594 \| 0.4488 \| 0.2358 \| 0.4085 \| 0.4093 \| 0.8735 \| 0.8591 \| 7.3063 \| 14 \| 4 \| 11.6757 \| 0.9009 \| \| 0.9018 \| 8.0 \| 1160 \| 2.0945 \| 0.4362 \| 0.2229 \| 0.4006 \| 0.4005 \| 0.8654 \| 0.8568 \| 8.1411 \| 19 \| 4 \| 12.5435 \| 8.4084 \| \| 0.8608 \| 9.0 \| 1305 \| 2.1088 \| 0.4096 \| 0.1926 \| 0.372 \| 0.372 \| 0.8603 \| 0.8514 \| 8.0661 \| 19 \| 2 \| 12.7297 \| 3.6036 \| \| 0.8243 \| 10.0 \| 1450 \| 2.2384 \| 0.4237 \| 0.2089 \| 0.3876 \| 0.3891 \| 0.8688 \| 0.8548 \| 7.4775 \| 18 \| 3 \| 11.8228 \| 2.1021 \| \| 0.7966 \| 11.0 \| 1595 \| 2.2565 \| 0.418 \| 0.2104 \| 0.3823 \| 0.3824 \| 0.8673 \| 0.847 \| 7.2402 \| 19 \| 2 \| 11.4024 \| 2.4024 \| \| 0.7687 \| 12.0 \| 1740 \| 2.3329 \| 0.4238 \| 0.2061 \| 0.3819 \| 0.383 \| 0.8649 \| 0.8518 \| 8.0721 \| 19 \| 2 \| 12.4715 \| 6.006 \| \| 0.7548 \| 13.0 \| 1885 \| 2.2799 \| 0.4253 \| 0.2129 \| 0.3822 \| 0.3835 \| 0.8642 \| 0.8532 \| 7.9069 \| 17 \| 4 \| 12.2733 \| 4.2042 \| \| 0.7301 \| 14.0 \| 2030 \| 2.4219 \| 0.4066 \| 0.1904 \| 0.3715 \| 0.3728 \| 0.8629 \| 0.8478 \| 7.4324 \| 18 \| 4 \| 11.6697 \| 3.6036 \| \| 0.7011 \| 15.0 \| 2175 \| 2.3663 \| 0.4463 \| 0.2222 \| 0.4042 \| 0.4052 \| 0.8655 \| 0.8606 \| 8.3634 \| 16 \| 4 \| 12.955 \| 6.9069 \| \| 0.6667 \| 16.0 \| 2320 \| 2.5128 \| 0.4238 \| 0.1918 \| 0.3835 \| 0.3843 \| 0.8631 \| 0.8522 \| 7.6456 \| 15 \| 3 \| 12.0841 \| 2.4024 \| \| 0.6854 \| 17.0 \| 2465 \| 2.3646 \| 0.4202 \| 0.2011 \| 0.3774 \| 0.3776 \| 0.861 \| 0.8543 \| 8.3664 \| 17 \| 2 \| 13.033 \| 8.4084 \| \| 0.648 \| 18.0 \| 2610 \| 2.5636 \| 0.4159 \| 0.2074 \| 0.3753 \| 0.3751 \| 0.8562 \| 0.8525 \| 8.5135 \| 19 \| 4 \| 13.024 \| 6.006 \| \| 0.6346 \| 19.0 \| 2755 \| 2.5641 \| 0.4173 \| 0.1937 \| 0.3732 \| 0.3735 \| 0.8592 \| 0.8549 \| 8.8078 \| 19 \| 3 \| 13.0931 \| 12.3123 \| \| 0.6223 \| 20.0 \| 2900 \| 2.5289 \| 0.4268 \| 0.2164 \| 0.3904 \| 0.3897 \| 0.8617 \| 0.8574 \| 8.2372 \| 17 \| 4 \| 12.9099 \| 5.4054 \| \| 0.6127 \| 21.0 \| 3045 \| 2.4946 \| 0.427 \| 0.2022 \| 0.3844 \| 0.3842 \| 0.8645 \| 0.8575 \| 8.0511 \| 16 \| 3 \| 12.8108 \| 5.7057 \| \| 0.6209 \| 22.0 \| 3190 \| 2.6277 \| 0.3987 \| 0.1934 \| 0.3657 \| 0.3657 \| 0.8584 \| 0.8506 \| 7.8859 \| 18 \| 3 \| 12.1742 \| 5.4054 \| \| 0.5752 \| 23.0 \| 3335 \| 2.7998 \| 0.4019 \| 0.1954 \| 0.3648 \| 0.3646 \| 0.8576 \| 0.8511 \| 8.3904 \| 17 \| 3 \| 12.7057 \| 7.5075 \| \| 0.5588 \| 24.0 \| 3480 \| 2.6732 \| 0.4039 \| 0.1948 \| 0.3649 \| 0.3652 \| 0.8594 \| 0.8492 \| 7.8829 \| 15 \| 3 \| 12.0901 \| 6.006 \| \| 0.5641 \| 25.0 \| 3625 \| 2.6012 \| 0.419 \| 0.2091 \| 0.376 \| 0.3765 \| 0.8588 \| 0.8523 \| 8.03 \| 16 \| 3 \| 12.2763 \| 3.003 \| \| 0.5525 \| 26.0 \| 3770 \| 2.6587 \| 0.418 \| 0.1929 \| 0.3722 \| 0.3726 \| 0.8577 \| 0.8545 \| 8.5345 \| 17 \| 4 \| 13.0961 \| 8.1081 \| \| 0.5372 \| 27.0 \| 3915 \| 2.7572 \| 0.4104 \| 0.1895 \| 0.366 \| 0.3671 \| 0.8583 \| 0.8495 \| 7.8949 \| 17 \| 3 \| 12.1862 \| 4.8048 \| \| 0.5105 \| 28.0 \| 4060 \| 2.7023 \| 0.4319 \| 0.2127 \| 0.3884 \| 0.3891 \| 0.8636 \| 0.8571 \| 8.2553 \| 16 \| 3 \| 12.5495 \| 6.6066 \| \| 0.5026 \| 29.0 \| 4205 \| 2.6991 \| 0.4252 \| 0.2222 \| 0.3899 \| 0.3903 \| 0.867 \| 0.8543 \| 7.7898 \| 19 \| 4 \| 12.2643 \| 4.2042 \| \| 0.4956 \| 30.0 \| 4350 \| 2.7064 \| 0.4066 \| 0.1974 \| 0.3726 \| 0.3735 \| 0.8568 \| 0.8523 \| 8.4985 \| 18 \| 3 \| 13.021 \| 8.7087 \| \| 0.5064 \| 31.0 \| 4495 \| 2.7564 \| 0.4159 \| 0.205 \| 0.3763 \| 0.3765 \| 0.8613 \| 0.8523 \| 7.6877 \| 16 \| 3 \| 12.3393 \| 3.003 \| \| 0.4932 \| 32.0 \| 4640 \| 2.6909 \| 0.394 \| 0.1866 \| 0.3564 \| 0.3573 \| 0.8574 \| 0.8496 \| 7.8378 \| 16 \| 2 \| 12.4715 \| 3.6036 \| \| 0.4757 \| 33.0 \| 4785 \| 2.7851 \| 0.4117 \| 0.1932 \| 0.3719 \| 0.3728 \| 0.8582 \| 0.8534 \| 8.5946 \| 18 \| 3 \| 12.973 \| 8.1081 \| \| 0.4753 \| 34.0 \| 4930 \| 2.7823 \| 0.3814 \| 0.1747 \| 0.3466 \| 0.3464 \| 0.8555 \| 0.8459 \| 7.7357 \| 18 \| 3 \| 12.0721 \| 3.3033 \| \| 0.4603 \| 35.0 \| 5075 \| 2.7607 \| 0.4135 \| 0.2003 \| 0.3777 \| 0.3781 \| 0.8616 \| 0.8538 \| 8.0601 \| 19 \| 3 \| 12.3183 \| 5.4054 \| \| 0.4645 \| 36.0 \| 5220 \| 2.8364 \| 0.4073 \| 0.1957 \| 0.3643 \| 0.3652 \| 0.8544 \| 0.8524 \| 8.8529 \| 19 \| 2 \| 13.1982 \| 12.012 \| \| 0.4377 \| 37.0 \| 5365 \| 2.7809 \| 0.3965 \| 0.192 \| 0.357 \| 0.3573 \| 0.858 \| 0.8442 \| 7.4384 \| 19 \| 2 \| 11.5495 \| 2.4024 \| \| 0.4287 \| 38.0 \| 5510 \| 2.7801 \| 0.4191 \| 0.1984 \| 0.3774 \| 0.3779 \| 0.8593 \| 0.8533 \| 8.2462 \| 16 \| 2 \| 12.5015 \| 6.3063 \| \| 0.4295 \| 39.0 \| 5655 \| 2.7206 \| 0.4281 \| 0.2104 \| 0.3851 \| 0.3861 \| 0.8634 \| 0.856 \| 8.1922 \| 16 \| 4 \| 12.5826 \| 5.7057 \| \| 0.4121 \| 40.0 \| 5800 \| 2.8363 \| 0.4049 \| 0.1916 \| 0.3614 \| 0.3624 \| 0.8553 \| 0.8516 \| 8.5435 \| 19 \| 4 \| 12.7928 \| 9.6096 \| \| 0.3893 \| 41.0 \| 5945 \| 2.7785 \| 0.4255 \| 0.2086 \| 0.3858 \| 0.3864 \| 0.8601 \| 0.8574 \| 8.3964 \| 17 \| 4 \| 13.0541 \| 4.5045 \| \| 0.3786 \| 42.0 \| 6090 \| 2.8752 \| 0.3908 \| 0.1775 \| 0.3497 \| 0.3509 \| 0.851 \| 0.8463 \| 8.2853 \| 17 \| 2 \| 12.8679 \| 7.8078 \| \| 0.3703 \| 43.0 \| 6235 \| 2.8799 \| 0.4148 \| 0.1894 \| 0.3719 \| 0.3727 \| 0.8606 \| 0.8519 \| 8.1502 \| 18 \| 3 \| 12.4745 \| 3.9039 \| \| 0.3636 \| 44.0 \| 6380 \| 2.8542 \| 0.4043 \| 0.1922 \| 0.3631 \| 0.3635 \| 0.8554 \| 0.8504 \| 8.2883 \| 19 \| 4 \| 12.7147 \| 4.5045 \| \| 0.3438 \| 45.0 \| 6525 \| 2.8282 \| 0.4218 \| 0.2022 \| 0.3792 \| 0.3802 \| 0.861 \| 0.8528 \| 8.2072 \| 16 \| 4 \| 12.6486 \| 6.3063 \| \| 0.3511 \| 46.0 \| 6670 \| 2.9184 \| 0.405 \| 0.1934 \| 0.3652 \| 0.3658 \| 0.8572 \| 0.8487 \| 8.2372 \| 18 \| 3 \| 12.5526 \| 7.5075 \| \| 0.3453 \| 47.0 \| 6815 \| 2.8649 \| 0.4064 \| 0.1956 \| 0.3681 \| 0.3686 \| 0.8601 \| 0.8508 \| 8.0871 \| 16 \| 3 \| 12.3604 \| 5.7057 \| \| 0.3299 \| 48.0 \| 6960 \| 2.9183 \| 0.4266 \| 0.202 \| 0.3777 \| 0.3787 \| 0.8591 \| 0.8578 \| 8.6216 \| 17 \| 4 \| 13.2402 \| 9.009 \| \| 0.3132 \| 49.0 \| 7105 \| 2.9077 \| 0.4242 \| 0.2021 \| 0.3784 \| 0.3793 \| 0.861 \| 0.8562 \| 8.4354 \| 19 \| 4 \| 12.6877 \| 5.1051 \| \| 0.3031 \| 50.0 \| 7250 \| 2.9042 \| 0.4177 \| 0.1977 \| 0.3741 \| 0.3752 \| 0.8584 \| 0.8522 \| 8.006 \| 15 \| 4 \| 12.4565 \| 2.7027 \| \| 0.2974 \| 51.0 \| 7395 \| 2.8820 \| 0.4318 \| 0.2087 \| 0.3849 \| 0.3854 \| 0.8605 \| 0.857 \| 8.2613 \| 16 \| 3 \| 12.8769 \| 6.9069 \| \| 0.2873 \| 52.0 \| 7540 \| 2.8622 \| 0.4194 \| 0.2023 \| 0.3786 \| 0.3782 \| 0.8626 \| 0.8542 \| 8.021 \| 18 \| 3 \| 12.3243 \| 3.003 \| \| 0.2718 \| 53.0 \| 7685 \| 2.8665 \| 0.4128 \| 0.2043 \| 0.3716 \| 0.3717 \| 0.8592 \| 0.8541 \| 8.2643 \| 16 \| 3 \| 12.8348 \| 6.006 \| \| 0.2598 \| 54.0 \| 7830 \| 2.9774 \| 0.4177 \| 0.1983 \| 0.3794 \| 0.3797 \| 0.8612 \| 0.8511 \| 7.8709 \| 19 \| 3 \| 12.048 \| 4.2042 \| \| 0.2532 \| 55.0 \| 7975 \| 2.8569 \| 0.4111 \| 0.1959 \| 0.3717 \| 0.3723 \| 0.8612 \| 0.8531 \| 7.9399 \| 16 \| 3 \| 12.5315 \| 3.6036 \| \| 0.2363 \| 56.0 \| 8120 \| 2.9634 \| 0.4253 \| 0.2111 \| 0.385 \| 0.386 \| 0.8657 \| 0.8543 \| 7.8438 \| 14 \| 3 \| 12.3153 \| 3.003 \| \| 0.2323 \| 57.0 \| 8265 \| 2.9573 \| 0.418 \| 0.1924 \| 0.3771 \| 0.3781 \| 0.8573 \| 0.854 \| 8.4234 \| 16 \| 3 \| 13.1261 \| 6.3063 \| \| 0.2223 \| 58.0 \| 8410 \| 2.9307 \| 0.4276 \| 0.2079 \| 0.3847 \| 0.3854 \| 0.8651 \| 0.8545 \| 7.7688 \| 16 \| 3 \| 11.97 \| 2.1021 \| \| 0.2101 \| 59.0 \| 8555 \| 2.9953 \| 0.4114 \| 0.1928 \| 0.3673 \| 0.3681 \| 0.8562 \| 0.8502 \| 8.3814 \| 19 \| 4 \| 12.7087 \| 5.7057 \| \| 0.2069 \| 60.0 \| 8700 \| 2.9768 \| 0.4154 \| 0.1921 \| 0.3718 \| 0.3725 \| 0.8619 \| 0.8538 \| 7.97 \| 16 \| 4 \| 12.2072 \| 3.9039 \| \| 0.1971 \| 61.0 \| 8845 \| 2.9218 \| 0.4276 \| 0.2046 \| 0.3847 \| 0.3854 \| 0.8609 \| 0.8568 \| 8.4414 \| 18 \| 4 \| 12.8949 \| 6.3063 \| \| 0.1873 \| 62.0 \| 8990 \| 2.9857 \| 0.4068 \| 0.191 \| 0.3606 \| 0.3609 \| 0.8559 \| 0.8496 \| 8.2583 \| 16 \| 4 \| 12.5646 \| 5.1051 \| \| 0.1815 \| 63.0 \| 9135 \| 2.8995 \| 0.417 \| 0.1981 \| 0.3722 \| 0.3723 \| 0.8624 \| 0.8528 \| 8.003 \| 15 \| 4 \| 12.2583 \| 5.7057 \| \| 0.1807 \| 64.0 \| 9280 \| 2.9644 \| 0.415 \| 0.1933 \| 0.3694 \| 0.3693 \| 0.8585 \| 0.8541 \| 8.4024 \| 17 \| 3 \| 12.7688 \| 7.5075 \| \| 0.1729 \| 65.0 \| 9425 \| 2.9640 \| 0.4138 \| 0.1965 \| 0.3692 \| 0.3698 \| 0.8576 \| 0.8515 \| 8.042 \| 16 \| 3 \| 12.6036 \| 4.2042 \| \| 0.1609 \| 66.0 \| 9570 \| 2.9912 \| 0.4255 \| 0.2051 \| 0.3816 \| 0.3826 \| 0.8632 \| 0.8554 \| 8.0751 \| 16 \| 4 \| 12.2733 \| 5.1051 \| \| 0.1621 \| 67.0 \| 9715 \| 3.0527 \| 0.4249 \| 0.2033 \| 0.3786 \| 0.3793 \| 0.862 \| 0.8544 \| 8.0631 \| 15 \| 2 \| 12.4925 \| 3.3033 \| \| 0.1468 \| 68.0 \| 9860 \| 3.0214 \| 0.4274 \| 0.2053 \| 0.3822 \| 0.3824 \| 0.861 \| 0.8552 \| 8.4204 \| 18 \| 4 \| 12.7447 \| 7.8078 \| \| 0.1334 \| 69.0 \| 10005 \| 3.1114 \| 0.4116 \| 0.1911 \| 0.3698 \| 0.3695 \| 0.8601 \| 0.8515 \| 7.9099 \| 14 \| 3 \| 12.0961 \| 3.9039 \| \| 0.1261 \| 70.0 \| 10150 \| 2.9442 \| 0.4226 \| 0.2032 \| 0.3783 \| 0.3785 \| 0.8625 \| 0.854 \| 8.033 \| 16 \| 3 \| 12.4384 \| 4.5045 \| \| 0.1137 \| 71.0 \| 10295 \| 3.0685 \| 0.422 \| 0.2035 \| 0.375 \| 0.3757 \| 0.8621 \| 0.8543 \| 8.0541 \| 16 \| 2 \| 12.3904 \| 3.9039 \| \| 0.1078 \| 72.0 \| 10440 \| 2.9759 \| 0.4198 \| 0.1981 \| 0.3759 \| 0.3767 \| 0.8602 \| 0.8544 \| 8.1712 \| 16 \| 2 \| 12.7297 \| 4.5045 \| \| 0.1074 \| 73.0 \| 10585 \| 2.9892 \| 0.4226 \| 0.2082 \| 0.3835 \| 0.3841 \| 0.8621 \| 0.8556 \| 8.0661 \| 14 \| 2 \| 12.5195 \| 4.2042 \| \| 0.105 \| 74.0 \| 10730 \| 3.0216 \| 0.427 \| 0.1997 \| 0.379 \| 0.3801 \| 0.8611 \| 0.8562 \| 8.3093 \| 17 \| 3 \| 12.8108 \| 5.1051 \| \| 0.0944 \| 75.0 \| 10875 \| 3.0108 \| 0.4169 \| 0.1956 \| 0.3714 \| 0.3721 \| 0.8582 \| 0.8523 \| 8.1231 \| 14 \| 4 \| 12.7568 \| 3.003 \| \| 0.0871 \| 76.0 \| 11020 \| 3.0794 \| 0.4246 \| 0.2007 \| 0.3739 \| 0.3756 \| 0.8593 \| 0.8556 \| 8.3063 \| 14 \| 4 \| 12.7598 \| 4.8048 \| \| 0.0739 \| 77.0 \| 11165 \| 3.0940 \| 0.4205 \| 0.1974 \| 0.3776 \| 0.3792 \| 0.8629 \| 0.8532 \| 7.9189 \| 15 \| 2 \| 12.0961 \| 3.003 \| \| 0.066 \| 78.0 \| 11310 \| 3.0764 \| 0.4234 \| 0.201 \| 0.3781 \| 0.3785 \| 0.8603 \| 0.8559 \| 8.2913 \| 16 \| 3 \| 12.8198 \| 4.8048 \| \| 0.0641 \| 79.0 \| 11455 \| 3.0736 \| 0.4299 \| 0.2067 \| 0.3831 \| 0.3835 \| 0.8622 \| 0.8568 \| 8.018 \| 15 \| 4 \| 12.4835 \| 3.003 \| \| 0.0633 \| 80.0 \| 11600 \| 3.0852 \| 0.4191 \| 0.2007 \| 0.3741 \| 0.3741 \| 0.86 \| 0.8537 \| 8.1742 \| 19 \| 3 \| 12.5556 \| 4.8048 \| \| 0.0625 \| 81.0 \| 11745 \| 3.0450 \| 0.4153 \| 0.1989 \| 0.3734 \| 0.374 \| 0.8583 \| 0.8524 \| 8.1321 \| 16 \| 4 \| 12.5826 \| 3.9039 \| \| 0.0624 \| 82.0 \| 11890 \| 3.1202 \| 0.4286 \| 0.209 \| 0.385 \| 0.3851 \| 0.8642 \| 0.8557 \| 8.0 \| 16 \| 4 \| 12.3003 \| 3.003 \| \| 0.0593 \| 83.0 \| 12035 \| 3.0514 \| 0.4319 \| 0.2159 \| 0.3887 \| 0.3899 \| 0.8653 \| 0.8587 \| 8.0601 \| 14 \| 4 \| 12.4805 \| 1.8018 \| \| 0.0562 \| 84.0 \| 12180 \| 3.0821 \| 0.4362 \| 0.2166 \| 0.3924 \| 0.3925 \| 0.8656 \| 0.8576 \| 8.1051 \| 15 \| 4 \| 12.5736 \| 4.5045 \| \| 0.0586 \| 85.0 \| 12325 \| 3.0843 \| 0.4297 \| 0.2061 \| 0.3861 \| 0.3865 \| 0.8649 \| 0.856 \| 8.1051 \| 15 \| 3 \| 12.3964 \| 5.1051 \| \| 0.0528 \| 86.0 \| 12470 \| 3.0610 \| 0.4209 \| 0.2034 \| 0.3752 \| 0.3755 \| 0.8606 \| 0.8542 \| 8.2162 \| 16 \| 4 \| 12.6817 \| 5.1051 \| \| 0.0478 \| 87.0 \| 12615 \| 3.0935 \| 0.4244 \| 0.2076 \| 0.382 \| 0.3815 \| 0.8596 \| 0.8553 \| 8.3243 \| 15 \| 2 \| 12.9009 \| 6.006 \| \| 0.0431 \| 88.0 \| 12760 \| 3.0865 \| 0.429 \| 0.2092 \| 0.3847 \| 0.3843 \| 0.8645 \| 0.855 \| 7.964 \| 15 \| 4 \| 12.2312 \| 3.003 \| \| 0.0453 \| 89.0 \| 12905 \| 3.0960 \| 0.4147 \| 0.1984 \| 0.3718 \| 0.3722 \| 0.8619 \| 0.8528 \| 7.9219 \| 14 \| 3 \| 12.2973 \| 3.3033 \| \| 0.0429 \| 90.0 \| 13050 \| 3.1163 \| 0.4237 \| 0.205 \| 0.3776 \| 0.3776 \| 0.8622 \| 0.8552 \| 8.1231 \| 16 \| 4 \| 12.4985 \| 3.003 \| \| 0.0381 \| 91.0 \| 13195 \| 3.0962 \| 0.427 \| 0.2089 \| 0.3814 \| 0.3817 \| 0.8624 \| 0.8547 \| 8.006 \| 14 \| 4 \| 12.3664 \| 2.4024 \| \| 0.0374 \| 92.0 \| 13340 \| 3.1022 \| 0.4275 \| 0.2031 \| 0.3818 \| 0.3823 \| 0.8636 \| 0.8574 \| 8.2042 \| 15 \| 3 \| 12.5646 \| 4.2042 \| \| 0.0357 \| 93.0 \| 13485 \| 3.1479 \| 0.4282 \| 0.2089 \| 0.3855 \| 0.3865 \| 0.8637 \| 0.8559 \| 8.009 \| 17 \| 3 \| 12.2492 \| 3.003 \| \| 0.0329 \| 94.0 \| 13630 \| 3.1188 \| 0.4311 \| 0.2086 \| 0.3858 \| 0.3861 \| 0.8646 \| 0.8559 \| 7.8949 \| 15 \| 3 \| 12.2703 \| 2.4024 \| \| 0.0307 \| 95.0 \| 13775 \| 3.1409 \| 0.4284 \| 0.2099 \| 0.3825 \| 0.3828 \| 0.8633 \| 0.8562 \| 7.994 \| 17 \| 3 \| 12.3153 \| 2.4024 \| \| 0.0291 \| 96.0 \| 13920 \| 3.1605 \| 0.4292 \| 0.2074 \| 0.3831 \| 0.3833 \| 0.8635 \| 0.8554 \| 7.8979 \| 14 \| 4 \| 12.3243 \| 1.5015 \| \| 0.0299 \| 97.0 \| 14065 \| 3.1838 \| 0.4274 \| 0.2022 \| 0.3791 \| 0.3792 \| 0.863 \| 0.8552 \| 7.9489 \| 16 \| 4 \| 12.3303 \| 2.1021 \| \| 0.0264 \| 98.0 \| 14210 \| 3.1810 \| 0.4224 \| 0.201 \| 0.3762 \| 0.3773 \| 0.8624 \| 0.8544 \| 7.9309 \| 16 \| 3 \| 12.2372 \| 2.4024 \| \| 0.0257 \| 99.0 \| 14355 \| 3.1893 \| 0.4241 \| 0.2056 \| 0.3785 \| 0.3796 \| 0.8624 \| 0.855 \| 7.985 \| 16 \| 3 \| 12.3874 \| 2.4024 \| \| 0.0244 \| 100.0 \| 14500 \| 3.1933 \| 0.4266 \| 0.2061 \| 0.38 \| 0.3804 \| 0.8628 \| 0.8555 \| 8.003 \| 16 \| 3 \| 12.3784 \| 3.003 \| ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
davanstrien/deberta-v3-base_fine_tuned_food_ner	davanstrien	2023-09-11T12:33:57Z	154	10	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "deberta-v2", "token-classification", "generated_from_trainer", "base_model:microsoft/deberta-v3-base", "base_model:finetune:microsoft/deberta-v3-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-08-03T14:39:17Z	--- license: mit tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy base_model: microsoft/deberta-v3-base model-index: - name: deberta-v3-base_fine_tuned_food_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. --> # deberta-v3-base_fine_tuned_food_ner This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4164 - Precision: 0.9268 - Recall: 0.9446 - F1: 0.9356 - Accuracy: 0.9197 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 40 \| 0.8425 \| 0.8323 \| 0.8323 \| 0.8323 \| 0.8073 \| \| No log \| 2.0 \| 80 \| 0.5533 \| 0.8703 \| 0.8941 \| 0.8820 \| 0.8731 \| \| No log \| 3.0 \| 120 \| 0.4855 \| 0.8771 \| 0.9109 \| 0.8937 \| 0.8797 \| \| No log \| 4.0 \| 160 \| 0.4238 \| 0.8949 \| 0.9222 \| 0.9083 \| 0.8964 \| \| No log \| 5.0 \| 200 \| 0.4176 \| 0.9048 \| 0.9302 \| 0.9173 \| 0.9008 \| \| No log \| 6.0 \| 240 \| 0.4127 \| 0.9065 \| 0.9342 \| 0.9202 \| 0.9004 \| \| No log \| 7.0 \| 280 \| 0.4409 \| 0.9294 \| 0.9302 \| 0.9298 \| 0.9043 \| \| No log \| 8.0 \| 320 \| 0.3971 \| 0.9129 \| 0.9334 \| 0.9230 \| 0.9061 \| \| No log \| 9.0 \| 360 \| 0.3941 \| 0.9112 \| 0.9390 \| 0.9249 \| 0.9061 \| \| No log \| 10.0 \| 400 \| 0.4069 \| 0.9233 \| 0.9366 \| 0.9299 \| 0.9148 \| \| No log \| 11.0 \| 440 \| 0.4039 \| 0.9213 \| 0.9390 \| 0.9300 \| 0.9162 \| \| No log \| 12.0 \| 480 \| 0.4000 \| 0.9126 \| 0.9470 \| 0.9295 \| 0.9113 \| \| 0.3799 \| 13.0 \| 520 \| 0.4126 \| 0.9323 \| 0.9390 \| 0.9356 \| 0.9179 \| \| 0.3799 \| 14.0 \| 560 \| 0.4076 \| 0.9272 \| 0.9398 \| 0.9334 \| 0.9140 \| \| 0.3799 \| 15.0 \| 600 \| 0.4129 \| 0.9317 \| 0.9414 \| 0.9365 \| 0.9188 \| \| 0.3799 \| 16.0 \| 640 \| 0.4000 \| 0.9239 \| 0.9446 \| 0.9341 \| 0.9162 \| \| 0.3799 \| 17.0 \| 680 \| 0.4098 \| 0.9267 \| 0.9438 \| 0.9352 \| 0.9179 \| \| 0.3799 \| 18.0 \| 720 \| 0.4110 \| 0.9232 \| 0.9454 \| 0.9342 \| 0.9188 \| \| 0.3799 \| 19.0 \| 760 \| 0.4202 \| 0.9275 \| 0.9446 \| 0.9360 \| 0.9183 \| \| 0.3799 \| 20.0 \| 800 \| 0.4164 \| 0.9268 \| 0.9446 \| 0.9356 \| 0.9197 \| ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
bigmorning/whisper_4_with_init_sun_syl_wd_0__0065	bigmorning	2023-09-11T12:33:45Z	59	0	transformers	[ "transformers", "tf", "whisper", "automatic-speech-recognition", "generated_from_keras_callback", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-09-11T12:33:31Z	--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_keras_callback model-index: - name: whisper_4_with_init_sun_syl_wd_0__0065 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. --> # whisper_4_with_init_sun_syl_wd_0__0065 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3698 - Train Accuracy: 0.0328 - Train Wermet: 0.0842 - Train Wermet Syl: 0.0925 - Validation Loss: 1.1728 - Validation Accuracy: 0.0207 - Validation Wermet: 0.3282 - Validation Wermet Syl: 0.2932 - Epoch: 64 ## 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': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0} - training_precision: float32 ### Training results \| Train Loss \| Train Accuracy \| Train Wermet \| Train Wermet Syl \| Validation Loss \| Validation Accuracy \| Validation Wermet \| Validation Wermet Syl \| Epoch \| \|:----------:\|:--------------:\|:------------:\|:----------------:\|:---------------:\|:-------------------:\|:-----------------:\|:---------------------:\|:-----:\| \| 5.3409 \| 0.0111 \| 1.3547 \| 1.2898 \| 3.9789 \| 0.0114 \| 0.9710 \| 0.9563 \| 0 \| \| 4.7143 \| 0.0116 \| 0.8622 \| 0.8228 \| 3.9404 \| 0.0113 \| 0.9823 \| 0.9735 \| 1 \| \| 4.6752 \| 0.0117 \| 0.8472 \| 0.8057 \| 3.9081 \| 0.0114 \| 0.9579 \| 0.9359 \| 2 \| \| 4.6500 \| 0.0117 \| 0.8382 \| 0.7945 \| 3.8820 \| 0.0115 \| 0.9213 \| 0.8856 \| 3 \| \| 4.6282 \| 0.0118 \| 0.8286 \| 0.7805 \| 3.8738 \| 0.0114 \| 0.9433 \| 0.9119 \| 4 \| \| 4.6095 \| 0.0118 \| 0.8190 \| 0.7696 \| 3.8630 \| 0.0115 \| 0.9117 \| 0.8698 \| 5 \| \| 4.5875 \| 0.0119 \| 0.7976 \| 0.7465 \| 3.8341 \| 0.0116 \| 0.8976 \| 0.8552 \| 6 \| \| 4.5682 \| 0.0120 \| 0.7753 \| 0.7227 \| 3.8277 \| 0.0116 \| 0.9014 \| 0.8653 \| 7 \| \| 4.5376 \| 0.0121 \| 0.7528 \| 0.7005 \| 3.7844 \| 0.0118 \| 0.8332 \| 0.7815 \| 8 \| \| 4.5060 \| 0.0122 \| 0.7392 \| 0.6844 \| 3.7537 \| 0.0118 \| 0.8578 \| 0.8152 \| 9 \| \| 4.4580 \| 0.0124 \| 0.7221 \| 0.6694 \| 3.7038 \| 0.0120 \| 0.8190 \| 0.7679 \| 10 \| \| 4.3989 \| 0.0125 \| 0.7156 \| 0.6636 \| 3.6169 \| 0.0122 \| 0.7979 \| 0.7429 \| 11 \| \| 4.3056 \| 0.0128 \| 0.7069 \| 0.6557 \| 3.5098 \| 0.0125 \| 0.7924 \| 0.7396 \| 12 \| \| 4.1673 \| 0.0132 \| 0.7054 \| 0.6584 \| 3.3542 \| 0.0128 \| 0.7759 \| 0.7240 \| 13 \| \| 3.9762 \| 0.0138 \| 0.6987 \| 0.6559 \| 3.1318 \| 0.0133 \| 0.7644 \| 0.7231 \| 14 \| \| 3.7385 \| 0.0145 \| 0.6835 \| 0.6448 \| 2.9144 \| 0.0138 \| 0.7392 \| 0.6955 \| 15 \| \| 3.5040 \| 0.0152 \| 0.6644 \| 0.6298 \| 2.7413 \| 0.0142 \| 0.7019 \| 0.6548 \| 16 \| \| 3.2728 \| 0.0160 \| 0.6408 \| 0.6101 \| 2.5183 \| 0.0149 \| 0.6798 \| 0.6363 \| 17 \| \| 3.0657 \| 0.0167 \| 0.6188 \| 0.5912 \| 2.3594 \| 0.0153 \| 0.6528 \| 0.6103 \| 18 \| \| 2.8703 \| 0.0174 \| 0.5936 \| 0.5685 \| 2.2644 \| 0.0156 \| 0.6310 \| 0.5925 \| 19 \| \| 2.6850 \| 0.0181 \| 0.5680 \| 0.5453 \| 2.1296 \| 0.0160 \| 0.6040 \| 0.5652 \| 20 \| \| 2.5227 \| 0.0188 \| 0.5423 \| 0.5215 \| 2.0019 \| 0.0165 \| 0.5793 \| 0.5403 \| 21 \| \| 2.3878 \| 0.0194 \| 0.5199 \| 0.5015 \| 1.8996 \| 0.0169 \| 0.5592 \| 0.5229 \| 22 \| \| 2.2437 \| 0.0201 \| 0.4959 \| 0.4788 \| 1.8141 \| 0.0172 \| 0.5414 \| 0.5045 \| 23 \| \| 2.1205 \| 0.0207 \| 0.4752 \| 0.4607 \| 1.7245 \| 0.0175 \| 0.5208 \| 0.4838 \| 24 \| \| 1.9919 \| 0.0213 \| 0.4533 \| 0.4390 \| 1.6673 \| 0.0178 \| 0.5026 \| 0.4659 \| 25 \| \| 1.9140 \| 0.0217 \| 0.4355 \| 0.4216 \| 1.6041 \| 0.0181 \| 0.4873 \| 0.4512 \| 26 \| \| 1.8225 \| 0.0222 \| 0.4184 \| 0.4052 \| 1.6271 \| 0.0179 \| 0.4852 \| 0.4511 \| 27 \| \| 1.7265 \| 0.0227 \| 0.4016 \| 0.3895 \| 1.5219 \| 0.0184 \| 0.4635 \| 0.4275 \| 28 \| \| 1.6240 \| 0.0233 \| 0.3833 \| 0.3729 \| 1.4718 \| 0.0186 \| 0.4515 \| 0.4170 \| 29 \| \| 1.5610 \| 0.0236 \| 0.3697 \| 0.3588 \| 1.4404 \| 0.0188 \| 0.4407 \| 0.4056 \| 30 \| \| 1.4719 \| 0.0242 \| 0.3540 \| 0.3449 \| 1.4125 \| 0.0189 \| 0.4310 \| 0.3961 \| 31 \| \| 1.4152 \| 0.0245 \| 0.3421 \| 0.3339 \| 1.3655 \| 0.0191 \| 0.4234 \| 0.3881 \| 32 \| \| 1.3546 \| 0.0249 \| 0.3277 \| 0.3195 \| 1.3419 \| 0.0192 \| 0.4156 \| 0.3816 \| 33 \| \| 1.2565 \| 0.0256 \| 0.3135 \| 0.3060 \| 1.3172 \| 0.0194 \| 0.4065 \| 0.3722 \| 34 \| \| 1.2135 \| 0.0258 \| 0.3026 \| 0.2958 \| 1.3019 \| 0.0194 \| 0.4006 \| 0.3662 \| 35 \| \| 1.1739 \| 0.0261 \| 0.2923 \| 0.2861 \| 1.3843 \| 0.0190 \| 0.3951 \| 0.3587 \| 36 \| \| 1.0950 \| 0.0267 \| 0.2782 \| 0.2733 \| 1.2665 \| 0.0197 \| 0.3883 \| 0.3541 \| 37 \| \| 1.0435 \| 0.0271 \| 0.2673 \| 0.2631 \| 1.2567 \| 0.0197 \| 0.3837 \| 0.3497 \| 38 \| \| 0.9922 \| 0.0275 \| 0.2580 \| 0.2542 \| 1.2566 \| 0.0197 \| 0.3801 \| 0.3444 \| 39 \| \| 0.9387 \| 0.0279 \| 0.2464 \| 0.2438 \| 1.2441 \| 0.0198 \| 0.3767 \| 0.3423 \| 40 \| \| 0.9345 \| 0.0278 \| 0.2393 \| 0.2373 \| 1.2221 \| 0.0199 \| 0.3682 \| 0.3336 \| 41 \| \| 0.8574 \| 0.0285 \| 0.2268 \| 0.2255 \| 1.2258 \| 0.0199 \| 0.3680 \| 0.3338 \| 42 \| \| 0.8275 \| 0.0287 \| 0.2183 \| 0.2180 \| 1.2044 \| 0.0201 \| 0.3628 \| 0.3290 \| 43 \| \| 0.8201 \| 0.0288 \| 0.2114 \| 0.2108 \| 1.2056 \| 0.0201 \| 0.3601 \| 0.3270 \| 44 \| \| 0.7684 \| 0.0292 \| 0.2020 \| 0.2029 \| 1.1879 \| 0.0202 \| 0.3553 \| 0.3215 \| 45 \| \| 0.7262 \| 0.0295 \| 0.1938 \| 0.1947 \| 1.2263 \| 0.0200 \| 0.3537 \| 0.3177 \| 46 \| \| 0.7286 \| 0.0295 \| 0.1876 \| 0.1898 \| 1.1772 \| 0.0203 \| 0.3485 \| 0.3135 \| 47 \| \| 0.6807 \| 0.0300 \| 0.1775 \| 0.1797 \| 1.1761 \| 0.0203 \| 0.3490 \| 0.3155 \| 48 \| \| 0.6609 \| 0.0301 \| 0.1713 \| 0.1742 \| 1.1853 \| 0.0203 \| 0.3484 \| 0.3153 \| 49 \| \| 0.6062 \| 0.0306 \| 0.1615 \| 0.1653 \| 1.1660 \| 0.0204 \| 0.3432 \| 0.3090 \| 50 \| \| 0.5755 \| 0.0309 \| 0.1547 \| 0.1584 \| 1.1698 \| 0.0204 \| 0.3428 \| 0.3089 \| 51 \| \| 0.5600 \| 0.0310 \| 0.1482 \| 0.1524 \| 1.1667 \| 0.0204 \| 0.3398 \| 0.3058 \| 52 \| \| 0.5715 \| 0.0308 \| 0.1449 \| 0.1496 \| 1.1614 \| 0.0205 \| 0.3381 \| 0.3036 \| 53 \| \| 0.5247 \| 0.0313 \| 0.1358 \| 0.1411 \| 1.1639 \| 0.0205 \| 0.3359 \| 0.3025 \| 54 \| \| 0.5085 \| 0.0315 \| 0.1301 \| 0.1358 \| 1.2420 \| 0.0202 \| 0.3412 \| 0.3064 \| 55 \| \| 0.4827 \| 0.0317 \| 0.1239 \| 0.1295 \| 1.1677 \| 0.0205 \| 0.3349 \| 0.3009 \| 56 \| \| 0.4848 \| 0.0317 \| 0.1207 \| 0.1280 \| 1.1653 \| 0.0205 \| 0.3326 \| 0.2991 \| 57 \| \| 0.4323 \| 0.0322 \| 0.1109 \| 0.1185 \| 1.1602 \| 0.0206 \| 0.3299 \| 0.2953 \| 58 \| \| 0.4183 \| 0.0323 \| 0.1057 \| 0.1133 \| 1.1622 \| 0.0206 \| 0.3307 \| 0.2962 \| 59 \| \| 0.4329 \| 0.0322 \| 0.1028 \| 0.1100 \| 1.1714 \| 0.0206 \| 0.3300 \| 0.2950 \| 60 \| \| 0.3962 \| 0.0326 \| 0.0964 \| 0.1045 \| 1.1726 \| 0.0206 \| 0.3311 \| 0.2967 \| 61 \| \| 0.3642 \| 0.0329 \| 0.0898 \| 0.0973 \| 1.1699 \| 0.0206 \| 0.3289 \| 0.2936 \| 62 \| \| 0.3786 \| 0.0327 \| 0.0884 \| 0.0963 \| 1.1734 \| 0.0206 \| 0.3279 \| 0.2929 \| 63 \| \| 0.3698 \| 0.0328 \| 0.0842 \| 0.0925 \| 1.1728 \| 0.0207 \| 0.3282 \| 0.2932 \| 64 \| ### Framework versions - Transformers 4.34.0.dev0 - TensorFlow 2.13.0 - Tokenizers 0.13.3
flyswot/flyswot	flyswot	2023-09-11T12:33:38Z	229	0	transformers	[ "transformers", "pytorch", "convnext", "image-classification", "generated_from_trainer", "base_model:flyswot/convnext-tiny-224_flyswot", "base_model:finetune:flyswot/convnext-tiny-224_flyswot", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-04-06T15:56:05Z	--- tags: - generated_from_trainer base_model: flyswot/convnext-tiny-224_flyswot model-index: - name: flyswot 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. --> # flyswot This model is a fine-tuned version of [flyswot/convnext-tiny-224_flyswot](https://huggingface.co/flyswot/convnext-tiny-224_flyswot) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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: 0.1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 0.1 \| 23 \| 0.0894 \| 0.9941 \| ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
stefaniftime/tmpnk87cy75	stefaniftime	2023-09-11T12:22:58Z	196	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:daily_dialog", "base_model:microsoft/DialoGPT-medium", "base_model:finetune:microsoft/DialoGPT-medium", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-09-11T12:20:13Z	--- license: mit base_model: microsoft/DialoGPT-medium tags: - generated_from_trainer datasets: - daily_dialog model-index: - name: tmpnk87cy75 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. --> # tmpnk87cy75 This model is a fine-tuned version of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) on the daily_dialog dataset. It achieves the following results on the evaluation set: - eval_loss: 1.7442 - eval_runtime: 12.5801 - eval_samples_per_second: 79.49 - eval_steps_per_second: 2.544 - epoch: 9.35 - step: 6500 ## 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: 32 - 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: 10 ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
Jzuluaga/bert-base-ner-atc-en-atco2-1h	Jzuluaga	2023-09-11T12:20:42Z	135	7	transformers	[ "transformers", "pytorch", "safetensors", "bert", "token-classification", "text", "en-atc", "en", "generated_from_trainer", "ner-for-atc", "dataset:Jzuluaga/atco2_corpus_1h", "arxiv:2211.04054", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-12-05T10:09:41Z	--- language: en license: apache-2.0 tags: - text - token-classification - en-atc - en - generated_from_trainer - bert - ner-for-atc datasets: - Jzuluaga/atco2_corpus_1h metrics: - Precision - Recall - Accuracy - F1 widget: - text: csa two nine six startup approved mike current qnh one zero one eight time check one seven - text: swiss four eight seven november runway three one cleared for takeoff wind one three zero degrees seven knots - text: lufthansa five yankee victor runway one three clear to land wind zero seven zero degrees - text: austrian seven one zulu hello to you reduce one six zero knots - text: sky travel one nine two approaching holding point three one ready for departure base_model: bert-base-uncased model-index: - name: bert-base-ner-atc-en-atco2-1h results: - task: type: token-classification name: ner dataset: name: ATCO2 corpus (Air Traffic Control Communications) type: Jzuluaga/atco2_corpus_1h config: test split: test metrics: - type: F1 value: 0.94 name: TEST F1 (callsign) verified: false - type: F1 value: 0.74 name: TEST F1 (command) verified: false - type: F1 value: 0.81 name: TEST F1 (value) verified: false --- # bert-base-ner-atc-en-atco2-1h This model allow to perform named-entity recognition (NER) on air traffic control communications data. We solve this challenge by performing token classification (NER) with a BERT model. We fine-tune a pretrained BERT model on the ner task. For instance, if you have the following transcripts/gold annotations: - Utterance: lufthansa three two five cleared to land runway three four left Could you tell what are the main entities in the communication? The desired output is shown below: - Named-entity module output: [call] lufthansa three two five [/call] [cmd] cleared to land [/cmd] [val] runway three four left [/val] This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the [atco2_corpus_1h](https://huggingface.co/datasets/Jzuluaga/atco2_corpus_1h). <a href="https://github.com/idiap/atco2-corpus"> <img alt="GitHub" src="https://img.shields.io/badge/GitHub-Open%20source-green\"> </a> It achieves the following results on the development set: - Loss: 1.4282 - Precision: 0.6195 - Recall: 0.7071 - F1: 0.6604 - Accuracy: 0.8182 Paper: [ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications](https://arxiv.org/abs/2211.04054) Authors: Juan Zuluaga-Gomez, Karel Veselý, Igor Szöke, Petr Motlicek, Martin Kocour, Mickael Rigault, Khalid Choukri, Amrutha Prasad and others Abstract: Personal assistants, automatic speech recognizers and dialogue understanding systems are becoming more critical in our interconnected digital world. A clear example is air traffic control (ATC) communications. ATC aims at guiding aircraft and controlling the airspace in a safe and optimal manner. These voice-based dialogues are carried between an air traffic controller (ATCO) and pilots via very-high frequency radio channels. In order to incorporate these novel technologies into ATC (low-resource domain), large-scale annotated datasets are required to develop the data-driven AI systems. Two examples are automatic speech recognition (ASR) and natural language understanding (NLU). In this paper, we introduce the ATCO2 corpus, a dataset that aims at fostering research on the challenging ATC field, which has lagged behind due to lack of annotated data. The ATCO2 corpus covers 1) data collection and pre-processing, 2) pseudo-annotations of speech data, and 3) extraction of ATC-related named entities. The ATCO2 corpus is split into three subsets. 1) ATCO2-test-set corpus contains 4 hours of ATC speech with manual transcripts and a subset with gold annotations for named-entity recognition (callsign, command, value). 2) The ATCO2-PL-set corpus consists of 5281 hours of unlabeled ATC data enriched with automatic transcripts from an in-domain speech recognizer, contextual information, speaker turn information, signal-to-noise ratio estimate and English language detection score per sample. Both available for purchase through ELDA at this http URL. 3) The ATCO2-test-set-1h corpus is a one-hour subset from the original test set corpus, that we are offering for free at this url: https://www.atco2.org/data. We expect the ATCO2 corpus will foster research on robust ASR and NLU not only in the field of ATC communications but also in the general research community. Code — GitHub repository: https://github.com/idiap/atco2-corpus ## Intended uses & limitations This model was fine-tuned on air traffic control data. We don't expect that it keeps the same performance on some others datasets where BERT was pre-trained or fine-tuned. ## Training and evaluation data See Table 6 (page 18) in our paper: [ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications](https://arxiv.org/abs/2211.04054). We described there the data used to fine-tune our NER model. - We use the ATCO2 corpus to fine-tune this model. You can download a free sample here: https://www.atco2.org/data - However, do not worry, we have prepared a script in our repository for preparing this databases: - Dataset preparation folder: https://github.com/idiap/atco2-corpus/tree/main/data/databases/atco2_test_set_1h/data_prepare_atco2_corpus_other.sh - Get the data in the format required by HuggingFace: speaker_role/data_preparation/prepare_spkid_atco2_corpus_test_set_1h.sh ## Writing your own inference script The snippet of code: ```python from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-ner-atc-en-atco2-1h") model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-ner-atc-en-atco2-1h") ##### Process text sample from transformers import pipeline nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="first") nlp("lufthansa three two five cleared to land runway three four left") # output: [{'entity_group': 'callsign', 'score': 0.8753265, 'word': 'lufthansa three two five', 'start': 0, 'end': 24}, {'entity_group': 'command', 'score': 0.99988264, 'word': 'cleared to land', 'start': 25, 'end': 40}, {'entity_group': 'value', 'score': 0.9999145, 'word': 'runway three four left', 'start': 41, 'end': 63}] ``` # Cite us If you use this code for your research, please cite our paper with: ``` @article{zuluaga2022bertraffic, title={BERTraffic: BERT-based Joint Speaker Role and Speaker Change Detection for Air Traffic Control Communications}, author={Zuluaga-Gomez, Juan and Sarfjoo, Seyyed Saeed and Prasad, Amrutha and others}, journal={IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar}, year={2022} } ``` and, ``` @article{zuluaga2022how, title={How Does Pre-trained Wav2Vec2. 0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications}, author={Zuluaga-Gomez, Juan and Prasad, Amrutha and Nigmatulina, Iuliia and Sarfjoo, Saeed and others}, journal={IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar}, year={2022} } ``` and, ``` @article{zuluaga2022atco2, title={ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications}, author={Zuluaga-Gomez, Juan and Vesel{\`y}, Karel and Sz{\"o}ke, Igor and Motlicek, Petr and others}, journal={arXiv preprint arXiv:2211.04054}, year={2022} } ``` ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 3000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 125.0 \| 500 \| 0.8692 \| 0.6396 \| 0.7172 \| 0.6762 \| 0.8307 \| \| 0.2158 \| 250.0 \| 1000 \| 1.0074 \| 0.5702 \| 0.6970 \| 0.6273 \| 0.8245 \| \| 0.2158 \| 375.0 \| 1500 \| 1.3560 \| 0.6577 \| 0.7374 \| 0.6952 \| 0.8119 \| \| 0.0184 \| 500.0 \| 2000 \| 1.3393 \| 0.6182 \| 0.6869 \| 0.6507 \| 0.8056 \| \| 0.0184 \| 625.0 \| 2500 \| 1.3528 \| 0.6087 \| 0.7071 \| 0.6542 \| 0.8213 \| \| 0.0175 \| 750.0 \| 3000 \| 1.4282 \| 0.6195 \| 0.7071 \| 0.6604 \| 0.8182 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.7.0 - Tokenizers 0.13.2
bigmorning/whisper_4_with_init_sun_syl_wd_0__0060	bigmorning	2023-09-11T12:18:27Z	59	0	transformers	[ "transformers", "tf", "whisper", "automatic-speech-recognition", "generated_from_keras_callback", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-09-11T12:18:20Z	--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_keras_callback model-index: - name: whisper_4_with_init_sun_syl_wd_0__0060 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. --> # whisper_4_with_init_sun_syl_wd_0__0060 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.4183 - Train Accuracy: 0.0323 - Train Wermet: 0.1057 - Train Wermet Syl: 0.1133 - Validation Loss: 1.1622 - Validation Accuracy: 0.0206 - Validation Wermet: 0.3307 - Validation Wermet Syl: 0.2962 - Epoch: 59 ## 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': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0} - training_precision: float32 ### Training results \| Train Loss \| Train Accuracy \| Train Wermet \| Train Wermet Syl \| Validation Loss \| Validation Accuracy \| Validation Wermet \| Validation Wermet Syl \| Epoch \| \|:----------:\|:--------------:\|:------------:\|:----------------:\|:---------------:\|:-------------------:\|:-----------------:\|:---------------------:\|:-----:\| \| 5.3409 \| 0.0111 \| 1.3547 \| 1.2898 \| 3.9789 \| 0.0114 \| 0.9710 \| 0.9563 \| 0 \| \| 4.7143 \| 0.0116 \| 0.8622 \| 0.8228 \| 3.9404 \| 0.0113 \| 0.9823 \| 0.9735 \| 1 \| \| 4.6752 \| 0.0117 \| 0.8472 \| 0.8057 \| 3.9081 \| 0.0114 \| 0.9579 \| 0.9359 \| 2 \| \| 4.6500 \| 0.0117 \| 0.8382 \| 0.7945 \| 3.8820 \| 0.0115 \| 0.9213 \| 0.8856 \| 3 \| \| 4.6282 \| 0.0118 \| 0.8286 \| 0.7805 \| 3.8738 \| 0.0114 \| 0.9433 \| 0.9119 \| 4 \| \| 4.6095 \| 0.0118 \| 0.8190 \| 0.7696 \| 3.8630 \| 0.0115 \| 0.9117 \| 0.8698 \| 5 \| \| 4.5875 \| 0.0119 \| 0.7976 \| 0.7465 \| 3.8341 \| 0.0116 \| 0.8976 \| 0.8552 \| 6 \| \| 4.5682 \| 0.0120 \| 0.7753 \| 0.7227 \| 3.8277 \| 0.0116 \| 0.9014 \| 0.8653 \| 7 \| \| 4.5376 \| 0.0121 \| 0.7528 \| 0.7005 \| 3.7844 \| 0.0118 \| 0.8332 \| 0.7815 \| 8 \| \| 4.5060 \| 0.0122 \| 0.7392 \| 0.6844 \| 3.7537 \| 0.0118 \| 0.8578 \| 0.8152 \| 9 \| \| 4.4580 \| 0.0124 \| 0.7221 \| 0.6694 \| 3.7038 \| 0.0120 \| 0.8190 \| 0.7679 \| 10 \| \| 4.3989 \| 0.0125 \| 0.7156 \| 0.6636 \| 3.6169 \| 0.0122 \| 0.7979 \| 0.7429 \| 11 \| \| 4.3056 \| 0.0128 \| 0.7069 \| 0.6557 \| 3.5098 \| 0.0125 \| 0.7924 \| 0.7396 \| 12 \| \| 4.1673 \| 0.0132 \| 0.7054 \| 0.6584 \| 3.3542 \| 0.0128 \| 0.7759 \| 0.7240 \| 13 \| \| 3.9762 \| 0.0138 \| 0.6987 \| 0.6559 \| 3.1318 \| 0.0133 \| 0.7644 \| 0.7231 \| 14 \| \| 3.7385 \| 0.0145 \| 0.6835 \| 0.6448 \| 2.9144 \| 0.0138 \| 0.7392 \| 0.6955 \| 15 \| \| 3.5040 \| 0.0152 \| 0.6644 \| 0.6298 \| 2.7413 \| 0.0142 \| 0.7019 \| 0.6548 \| 16 \| \| 3.2728 \| 0.0160 \| 0.6408 \| 0.6101 \| 2.5183 \| 0.0149 \| 0.6798 \| 0.6363 \| 17 \| \| 3.0657 \| 0.0167 \| 0.6188 \| 0.5912 \| 2.3594 \| 0.0153 \| 0.6528 \| 0.6103 \| 18 \| \| 2.8703 \| 0.0174 \| 0.5936 \| 0.5685 \| 2.2644 \| 0.0156 \| 0.6310 \| 0.5925 \| 19 \| \| 2.6850 \| 0.0181 \| 0.5680 \| 0.5453 \| 2.1296 \| 0.0160 \| 0.6040 \| 0.5652 \| 20 \| \| 2.5227 \| 0.0188 \| 0.5423 \| 0.5215 \| 2.0019 \| 0.0165 \| 0.5793 \| 0.5403 \| 21 \| \| 2.3878 \| 0.0194 \| 0.5199 \| 0.5015 \| 1.8996 \| 0.0169 \| 0.5592 \| 0.5229 \| 22 \| \| 2.2437 \| 0.0201 \| 0.4959 \| 0.4788 \| 1.8141 \| 0.0172 \| 0.5414 \| 0.5045 \| 23 \| \| 2.1205 \| 0.0207 \| 0.4752 \| 0.4607 \| 1.7245 \| 0.0175 \| 0.5208 \| 0.4838 \| 24 \| \| 1.9919 \| 0.0213 \| 0.4533 \| 0.4390 \| 1.6673 \| 0.0178 \| 0.5026 \| 0.4659 \| 25 \| \| 1.9140 \| 0.0217 \| 0.4355 \| 0.4216 \| 1.6041 \| 0.0181 \| 0.4873 \| 0.4512 \| 26 \| \| 1.8225 \| 0.0222 \| 0.4184 \| 0.4052 \| 1.6271 \| 0.0179 \| 0.4852 \| 0.4511 \| 27 \| \| 1.7265 \| 0.0227 \| 0.4016 \| 0.3895 \| 1.5219 \| 0.0184 \| 0.4635 \| 0.4275 \| 28 \| \| 1.6240 \| 0.0233 \| 0.3833 \| 0.3729 \| 1.4718 \| 0.0186 \| 0.4515 \| 0.4170 \| 29 \| \| 1.5610 \| 0.0236 \| 0.3697 \| 0.3588 \| 1.4404 \| 0.0188 \| 0.4407 \| 0.4056 \| 30 \| \| 1.4719 \| 0.0242 \| 0.3540 \| 0.3449 \| 1.4125 \| 0.0189 \| 0.4310 \| 0.3961 \| 31 \| \| 1.4152 \| 0.0245 \| 0.3421 \| 0.3339 \| 1.3655 \| 0.0191 \| 0.4234 \| 0.3881 \| 32 \| \| 1.3546 \| 0.0249 \| 0.3277 \| 0.3195 \| 1.3419 \| 0.0192 \| 0.4156 \| 0.3816 \| 33 \| \| 1.2565 \| 0.0256 \| 0.3135 \| 0.3060 \| 1.3172 \| 0.0194 \| 0.4065 \| 0.3722 \| 34 \| \| 1.2135 \| 0.0258 \| 0.3026 \| 0.2958 \| 1.3019 \| 0.0194 \| 0.4006 \| 0.3662 \| 35 \| \| 1.1739 \| 0.0261 \| 0.2923 \| 0.2861 \| 1.3843 \| 0.0190 \| 0.3951 \| 0.3587 \| 36 \| \| 1.0950 \| 0.0267 \| 0.2782 \| 0.2733 \| 1.2665 \| 0.0197 \| 0.3883 \| 0.3541 \| 37 \| \| 1.0435 \| 0.0271 \| 0.2673 \| 0.2631 \| 1.2567 \| 0.0197 \| 0.3837 \| 0.3497 \| 38 \| \| 0.9922 \| 0.0275 \| 0.2580 \| 0.2542 \| 1.2566 \| 0.0197 \| 0.3801 \| 0.3444 \| 39 \| \| 0.9387 \| 0.0279 \| 0.2464 \| 0.2438 \| 1.2441 \| 0.0198 \| 0.3767 \| 0.3423 \| 40 \| \| 0.9345 \| 0.0278 \| 0.2393 \| 0.2373 \| 1.2221 \| 0.0199 \| 0.3682 \| 0.3336 \| 41 \| \| 0.8574 \| 0.0285 \| 0.2268 \| 0.2255 \| 1.2258 \| 0.0199 \| 0.3680 \| 0.3338 \| 42 \| \| 0.8275 \| 0.0287 \| 0.2183 \| 0.2180 \| 1.2044 \| 0.0201 \| 0.3628 \| 0.3290 \| 43 \| \| 0.8201 \| 0.0288 \| 0.2114 \| 0.2108 \| 1.2056 \| 0.0201 \| 0.3601 \| 0.3270 \| 44 \| \| 0.7684 \| 0.0292 \| 0.2020 \| 0.2029 \| 1.1879 \| 0.0202 \| 0.3553 \| 0.3215 \| 45 \| \| 0.7262 \| 0.0295 \| 0.1938 \| 0.1947 \| 1.2263 \| 0.0200 \| 0.3537 \| 0.3177 \| 46 \| \| 0.7286 \| 0.0295 \| 0.1876 \| 0.1898 \| 1.1772 \| 0.0203 \| 0.3485 \| 0.3135 \| 47 \| \| 0.6807 \| 0.0300 \| 0.1775 \| 0.1797 \| 1.1761 \| 0.0203 \| 0.3490 \| 0.3155 \| 48 \| \| 0.6609 \| 0.0301 \| 0.1713 \| 0.1742 \| 1.1853 \| 0.0203 \| 0.3484 \| 0.3153 \| 49 \| \| 0.6062 \| 0.0306 \| 0.1615 \| 0.1653 \| 1.1660 \| 0.0204 \| 0.3432 \| 0.3090 \| 50 \| \| 0.5755 \| 0.0309 \| 0.1547 \| 0.1584 \| 1.1698 \| 0.0204 \| 0.3428 \| 0.3089 \| 51 \| \| 0.5600 \| 0.0310 \| 0.1482 \| 0.1524 \| 1.1667 \| 0.0204 \| 0.3398 \| 0.3058 \| 52 \| \| 0.5715 \| 0.0308 \| 0.1449 \| 0.1496 \| 1.1614 \| 0.0205 \| 0.3381 \| 0.3036 \| 53 \| \| 0.5247 \| 0.0313 \| 0.1358 \| 0.1411 \| 1.1639 \| 0.0205 \| 0.3359 \| 0.3025 \| 54 \| \| 0.5085 \| 0.0315 \| 0.1301 \| 0.1358 \| 1.2420 \| 0.0202 \| 0.3412 \| 0.3064 \| 55 \| \| 0.4827 \| 0.0317 \| 0.1239 \| 0.1295 \| 1.1677 \| 0.0205 \| 0.3349 \| 0.3009 \| 56 \| \| 0.4848 \| 0.0317 \| 0.1207 \| 0.1280 \| 1.1653 \| 0.0205 \| 0.3326 \| 0.2991 \| 57 \| \| 0.4323 \| 0.0322 \| 0.1109 \| 0.1185 \| 1.1602 \| 0.0206 \| 0.3299 \| 0.2953 \| 58 \| \| 0.4183 \| 0.0323 \| 0.1057 \| 0.1133 \| 1.1622 \| 0.0206 \| 0.3307 \| 0.2962 \| 59 \| ### Framework versions - Transformers 4.34.0.dev0 - TensorFlow 2.13.0 - Tokenizers 0.13.3
Jukaboo/Llama2_7B_chat_dialogsum_ft_adapters_v2400	Jukaboo	2023-09-11T12:14:06Z	0	0	null	[ "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-chat-hf", "base_model:finetune:meta-llama/Llama-2-7b-chat-hf", "region:us" ]	null	2023-09-11T11:56:50Z	--- base_model: meta-llama/Llama-2-7b-chat-hf tags: - generated_from_trainer model-index: - name: Llama2_7B_chat_dialogsum_ft_adapters_v2400 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. --> # Llama2_7B_chat_dialogsum_ft_adapters_v2400 This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
tyzp-INC/bench2-all-MiniLM-L6-v2-tuned-stratified	tyzp-INC	2023-09-11T12:09:48Z	6	0	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]	text-classification	2023-09-10T13:38:56Z	--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # tyzp-INC/bench2-all-MiniLM-L6-v2-tuned-stratified This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("tyzp-INC/bench2-all-MiniLM-L6-v2-tuned-stratified") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```
NbAiLab/notram-bert-norwegian-cased-080321	NbAiLab	2023-09-11T12:08:33Z	128	1	transformers	[ "transformers", "pytorch", "tf", "safetensors", "bert", "norwegian", "fill-mask", "no", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:04Z	--- language: no license: cc-by-4.0 tags: - norwegian - bert pipeline_tag: fill-mask widget: - text: På biblioteket kan du [MASK] en bok. - text: Dette er et [MASK] eksempel. - text: Av og til kan en språkmodell gi et [MASK] resultat. - text: Som ansat får du [MASK] for at bidrage til borgernes adgang til dansk kulturarv, til forskning og til samfundets demokratiske udvikling. --- ## Results \|Model \| NoRec \| NorNe-NB\| NorNe-NN \| NorDial \| DaNe \| Da-Angry-Tweets \| \|:-----------\|------------:\|------------:\|------------:\|------------:\|------------:\|------------:\| \|roberta-base (English) \| 51.77 \| 79.01/79.53\| 79.79/83.02 \| 67.18\| 75.44/78.07 \| 55.51 \| \|mBERT-cased \| 63.91 \| 83.72/86.12\| 83.05/87.12 \| 66.23\| 80.00/81.43 \| 57.67 \| \|nb-bert-base \| 75.60 \|91.98/92.95 \|90.93/94.06\|69.39\| 81.95/84.83\| 64.18\| \|notram-bert-norwegian-cased \| 72.47 \| 91.77/93.12\|89.79/93.70\| 78.55\| 83.69/86.55\| 64.19 \| \|notram-bert-norwegian-uncased \| 73.47 \| 89.28/91.61 \|87.23/90.23 \|74.21 \| 80.29/82.31\| 61.18\| \|notram-bert-norwegian-cased-pod \| 76.18 \| 91.24/92.24\| 90.88/93.21\| 76.21\| 81.82/84.99\| 62.16 \| \|nb-roberta-base \| 68.77 \|87.99/89.43 \| 85.43/88.66\| 76.34\| 75.91/77.94\| 61.50 \| \|nb-roberta-base-scandinavian \| 67.88 \| 87.73/89.14\| 87.39/90.92\| 74.81\| 76.22/78.66 \| 63.37 \| \|nb-roberta-base-v2-200k \| 46.87 \| 85.57/87.04\| - \| 64.99\| - \| - \| \|test_long_w5 200k\| 60.48 \| 88.00/90:00 \| 83.93/88.45 \| 68.41 \|75.22/78.50\| 57.95 \| \|test_long_w5_roberta_tokenizer 200k\| 63.51\| 86.28/87.77\| 84.95/88.61 \| 69.86 \| 71.31/74.27 \| 59.96 \| \|test_long_w5_roberta_tokenizer 400k\| 59.76 \|87.39/89.06 \| 85.16/89.01 \| 71.46 \| 72.39/75.65\| 39.73 \| \|test_long_w5_dataset 400k\| 66.80 \| 86.52/88.55 \| 82.81/86.76 \| 66.94 \| 71.47/74.20\| 55.25 \| \|test_long_w5_dataset 600k\| 67.37 \| 89.98/90.95 \| 84.53/88.37 \| 66.84 \| 75.14/76.50\| 57.47 \| \|roberta-jan-128_ncc - 400k - 128\| 67.79 \| 91.45/92.33 \| 86.41/90.19 \| 67.20 \| 81.00/82.39\| 59.65 \| \|roberta-jan-128_ncc - 1000k - 128\| 68.17 \| 89.34/90.74 \| 86.89/89.87 \| 68.41 \| 80.30/82.17\| 61.63 \|
NbAiLab/nb-bert-large	NbAiLab	2023-09-11T12:08:15Z	1,099	13	transformers	[ "transformers", "pytorch", "tf", "jax", "safetensors", "bert", "norwegian", "fill-mask", "no", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:04Z	--- language: no license: cc-by-4.0 tags: - norwegian - bert thumbnail: nblogo_3.png pipeline_tag: fill-mask widget: - text: På biblioteket kan du låne en [MASK]. --- - Release 1.0beta (April 29, 2021) # NB-BERT-large (beta) ## Description NB-BERT-large is a general BERT-large model built on the large digital collection at the National Library of Norway. This model is trained from scratch on a wide variety of Norwegian text (both bokmål and nynorsk) from the last 200 years using a monolingual Norwegian vocabulary. ## Intended use & limitations The 1.0 version of the model is general, and should be fine-tuned for any particular use. Some fine-tuning sets may be found on Github, see * https://github.com/NBAiLab/notram ## Training data The model is trained on a wide variety of text. The training set is described on * https://github.com/NBAiLab/notram ## More information For more information on the model, see https://github.com/NBAiLab/notram
CyberHarem/u_olga_marie_fgo	CyberHarem	2023-09-11T12:07:45Z	0	0	null	[ "art", "text-to-image", "dataset:CyberHarem/u_olga_marie_fgo", "license:mit", "region:us" ]	text-to-image	2023-08-09T00:05:10Z	--- license: mit datasets: - CyberHarem/u_olga_marie_fgo pipeline_tag: text-to-image tags: - art --- # Lora of u_olga_marie_fgo This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 5100, you need to download `5100/u_olga_marie_fgo.pt` as the embedding and `5100/u_olga_marie_fgo.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. The best step we recommend is 5100, with the score of 0.942. The trigger words are: 1. `u_olga_marie_fgo` 2. `long_hair, braid, white_hair, horns, hair_between_eyes, jewelry, smile, earrings, ascot, yellow_eyes, red_ascot, breasts, open_mouth, single_horn` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: \| Steps \| Score \| Download \| pattern_1 \| bikini \| bondage \| free \| maid \| miko \| nude \| nude2 \| suit \| yukata \| \|:---------\|:----------\|:------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------\|:--------------------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-----------------------------------------------\|:------------------------------------------------\|:-------------------------------------\|:-----------------------------------------\| \| 5100 \| 0.942 \| [Download](5100/u_olga_marie_fgo.zip) \| ![pattern_1-5100](5100/previews/pattern_1.png) \| ![bikini-5100](5100/previews/bikini.png) \| [<NSFW, click to see>](5100/previews/bondage.png) \| ![free-5100](5100/previews/free.png) \| ![maid-5100](5100/previews/maid.png) \| ![miko-5100](5100/previews/miko.png) \| [<NSFW, click to see>](5100/previews/nude.png) \| [<NSFW, click to see>](5100/previews/nude2.png) \| ![suit-5100](5100/previews/suit.png) \| ![yukata-5100](5100/previews/yukata.png) \| \| 4760 \| 0.879 \| [Download](4760/u_olga_marie_fgo.zip) \| ![pattern_1-4760](4760/previews/pattern_1.png) \| ![bikini-4760](4760/previews/bikini.png) \| [<NSFW, click to see>](4760/previews/bondage.png) \| ![free-4760](4760/previews/free.png) \| ![maid-4760](4760/previews/maid.png) \| ![miko-4760](4760/previews/miko.png) \| [<NSFW, click to see>](4760/previews/nude.png) \| [<NSFW, click to see>](4760/previews/nude2.png) \| ![suit-4760](4760/previews/suit.png) \| ![yukata-4760](4760/previews/yukata.png) \| \| 4420 \| 0.902 \| [Download](4420/u_olga_marie_fgo.zip) \| ![pattern_1-4420](4420/previews/pattern_1.png) \| ![bikini-4420](4420/previews/bikini.png) \| [<NSFW, click to see>](4420/previews/bondage.png) \| ![free-4420](4420/previews/free.png) \| ![maid-4420](4420/previews/maid.png) \| ![miko-4420](4420/previews/miko.png) \| [<NSFW, click to see>](4420/previews/nude.png) \| [<NSFW, click to see>](4420/previews/nude2.png) \| ![suit-4420](4420/previews/suit.png) \| ![yukata-4420](4420/previews/yukata.png) \| \| 4080 \| 0.910 \| [Download](4080/u_olga_marie_fgo.zip) \| ![pattern_1-4080](4080/previews/pattern_1.png) \| ![bikini-4080](4080/previews/bikini.png) \| [<NSFW, click to see>](4080/previews/bondage.png) \| ![free-4080](4080/previews/free.png) \| ![maid-4080](4080/previews/maid.png) \| ![miko-4080](4080/previews/miko.png) \| [<NSFW, click to see>](4080/previews/nude.png) \| [<NSFW, click to see>](4080/previews/nude2.png) \| ![suit-4080](4080/previews/suit.png) \| ![yukata-4080](4080/previews/yukata.png) \| \| 3740 \| 0.905 \| [Download](3740/u_olga_marie_fgo.zip) \| ![pattern_1-3740](3740/previews/pattern_1.png) \| ![bikini-3740](3740/previews/bikini.png) \| [<NSFW, click to see>](3740/previews/bondage.png) \| ![free-3740](3740/previews/free.png) \| ![maid-3740](3740/previews/maid.png) \| ![miko-3740](3740/previews/miko.png) \| [<NSFW, click to see>](3740/previews/nude.png) \| [<NSFW, click to see>](3740/previews/nude2.png) \| ![suit-3740](3740/previews/suit.png) \| ![yukata-3740](3740/previews/yukata.png) \| \| 3400 \| 0.914 \| [Download](3400/u_olga_marie_fgo.zip) \| ![pattern_1-3400](3400/previews/pattern_1.png) \| ![bikini-3400](3400/previews/bikini.png) \| [<NSFW, click to see>](3400/previews/bondage.png) \| ![free-3400](3400/previews/free.png) \| ![maid-3400](3400/previews/maid.png) \| ![miko-3400](3400/previews/miko.png) \| [<NSFW, click to see>](3400/previews/nude.png) \| [<NSFW, click to see>](3400/previews/nude2.png) \| ![suit-3400](3400/previews/suit.png) \| ![yukata-3400](3400/previews/yukata.png) \| \| 3060 \| 0.908 \| [Download](3060/u_olga_marie_fgo.zip) \| ![pattern_1-3060](3060/previews/pattern_1.png) \| ![bikini-3060](3060/previews/bikini.png) \| [<NSFW, click to see>](3060/previews/bondage.png) \| ![free-3060](3060/previews/free.png) \| ![maid-3060](3060/previews/maid.png) \| ![miko-3060](3060/previews/miko.png) \| [<NSFW, click to see>](3060/previews/nude.png) \| [<NSFW, click to see>](3060/previews/nude2.png) \| ![suit-3060](3060/previews/suit.png) \| ![yukata-3060](3060/previews/yukata.png) \| \| 2720 \| 0.889 \| [Download](2720/u_olga_marie_fgo.zip) \| ![pattern_1-2720](2720/previews/pattern_1.png) \| ![bikini-2720](2720/previews/bikini.png) \| [<NSFW, click to see>](2720/previews/bondage.png) \| ![free-2720](2720/previews/free.png) \| ![maid-2720](2720/previews/maid.png) \| ![miko-2720](2720/previews/miko.png) \| [<NSFW, click to see>](2720/previews/nude.png) \| [<NSFW, click to see>](2720/previews/nude2.png) \| ![suit-2720](2720/previews/suit.png) \| ![yukata-2720](2720/previews/yukata.png) \| \| 2380 \| 0.886 \| [Download](2380/u_olga_marie_fgo.zip) \| ![pattern_1-2380](2380/previews/pattern_1.png) \| ![bikini-2380](2380/previews/bikini.png) \| [<NSFW, click to see>](2380/previews/bondage.png) \| ![free-2380](2380/previews/free.png) \| ![maid-2380](2380/previews/maid.png) \| ![miko-2380](2380/previews/miko.png) \| [<NSFW, click to see>](2380/previews/nude.png) \| [<NSFW, click to see>](2380/previews/nude2.png) \| ![suit-2380](2380/previews/suit.png) \| ![yukata-2380](2380/previews/yukata.png) \| \| 2040 \| 0.905 \| [Download](2040/u_olga_marie_fgo.zip) \| ![pattern_1-2040](2040/previews/pattern_1.png) \| ![bikini-2040](2040/previews/bikini.png) \| [<NSFW, click to see>](2040/previews/bondage.png) \| ![free-2040](2040/previews/free.png) \| ![maid-2040](2040/previews/maid.png) \| ![miko-2040](2040/previews/miko.png) \| [<NSFW, click to see>](2040/previews/nude.png) \| [<NSFW, click to see>](2040/previews/nude2.png) \| ![suit-2040](2040/previews/suit.png) \| ![yukata-2040](2040/previews/yukata.png) \| \| 1700 \| 0.812 \| [Download](1700/u_olga_marie_fgo.zip) \| ![pattern_1-1700](1700/previews/pattern_1.png) \| ![bikini-1700](1700/previews/bikini.png) \| [<NSFW, click to see>](1700/previews/bondage.png) \| ![free-1700](1700/previews/free.png) \| ![maid-1700](1700/previews/maid.png) \| ![miko-1700](1700/previews/miko.png) \| [<NSFW, click to see>](1700/previews/nude.png) \| [<NSFW, click to see>](1700/previews/nude2.png) \| ![suit-1700](1700/previews/suit.png) \| ![yukata-1700](1700/previews/yukata.png) \| \| 1360 \| 0.889 \| [Download](1360/u_olga_marie_fgo.zip) \| ![pattern_1-1360](1360/previews/pattern_1.png) \| ![bikini-1360](1360/previews/bikini.png) \| [<NSFW, click to see>](1360/previews/bondage.png) \| ![free-1360](1360/previews/free.png) \| ![maid-1360](1360/previews/maid.png) \| ![miko-1360](1360/previews/miko.png) \| [<NSFW, click to see>](1360/previews/nude.png) \| [<NSFW, click to see>](1360/previews/nude2.png) \| ![suit-1360](1360/previews/suit.png) \| ![yukata-1360](1360/previews/yukata.png) \| \| 1020 \| 0.820 \| [Download](1020/u_olga_marie_fgo.zip) \| ![pattern_1-1020](1020/previews/pattern_1.png) \| ![bikini-1020](1020/previews/bikini.png) \| [<NSFW, click to see>](1020/previews/bondage.png) \| ![free-1020](1020/previews/free.png) \| ![maid-1020](1020/previews/maid.png) \| ![miko-1020](1020/previews/miko.png) \| [<NSFW, click to see>](1020/previews/nude.png) \| [<NSFW, click to see>](1020/previews/nude2.png) \| ![suit-1020](1020/previews/suit.png) \| ![yukata-1020](1020/previews/yukata.png) \| \| 680 \| 0.809 \| [Download](680/u_olga_marie_fgo.zip) \| ![pattern_1-680](680/previews/pattern_1.png) \| ![bikini-680](680/previews/bikini.png) \| [<NSFW, click to see>](680/previews/bondage.png) \| ![free-680](680/previews/free.png) \| ![maid-680](680/previews/maid.png) \| ![miko-680](680/previews/miko.png) \| [<NSFW, click to see>](680/previews/nude.png) \| [<NSFW, click to see>](680/previews/nude2.png) \| ![suit-680](680/previews/suit.png) \| ![yukata-680](680/previews/yukata.png) \| \| 340 \| 0.658 \| [Download](340/u_olga_marie_fgo.zip) \| ![pattern_1-340](340/previews/pattern_1.png) \| ![bikini-340](340/previews/bikini.png) \| [<NSFW, click to see>](340/previews/bondage.png) \| ![free-340](340/previews/free.png) \| ![maid-340](340/previews/maid.png) \| ![miko-340](340/previews/miko.png) \| [<NSFW, click to see>](340/previews/nude.png) \| [<NSFW, click to see>](340/previews/nude2.png) \| ![suit-340](340/previews/suit.png) \| ![yukata-340](340/previews/yukata.png) \|
bigmorning/whisper_4_with_init_sun_syl_wd_0__0055	bigmorning	2023-09-11T12:03:22Z	59	0	transformers	[ "transformers", "tf", "whisper", "automatic-speech-recognition", "generated_from_keras_callback", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-09-11T12:03:14Z	--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_keras_callback model-index: - name: whisper_4_with_init_sun_syl_wd_0__0055 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. --> # whisper_4_with_init_sun_syl_wd_0__0055 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5247 - Train Accuracy: 0.0313 - Train Wermet: 0.1358 - Train Wermet Syl: 0.1411 - Validation Loss: 1.1639 - Validation Accuracy: 0.0205 - Validation Wermet: 0.3359 - Validation Wermet Syl: 0.3025 - Epoch: 54 ## 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': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0} - training_precision: float32 ### Training results \| Train Loss \| Train Accuracy \| Train Wermet \| Train Wermet Syl \| Validation Loss \| Validation Accuracy \| Validation Wermet \| Validation Wermet Syl \| Epoch \| \|:----------:\|:--------------:\|:------------:\|:----------------:\|:---------------:\|:-------------------:\|:-----------------:\|:---------------------:\|:-----:\| \| 5.3409 \| 0.0111 \| 1.3547 \| 1.2898 \| 3.9789 \| 0.0114 \| 0.9710 \| 0.9563 \| 0 \| \| 4.7143 \| 0.0116 \| 0.8622 \| 0.8228 \| 3.9404 \| 0.0113 \| 0.9823 \| 0.9735 \| 1 \| \| 4.6752 \| 0.0117 \| 0.8472 \| 0.8057 \| 3.9081 \| 0.0114 \| 0.9579 \| 0.9359 \| 2 \| \| 4.6500 \| 0.0117 \| 0.8382 \| 0.7945 \| 3.8820 \| 0.0115 \| 0.9213 \| 0.8856 \| 3 \| \| 4.6282 \| 0.0118 \| 0.8286 \| 0.7805 \| 3.8738 \| 0.0114 \| 0.9433 \| 0.9119 \| 4 \| \| 4.6095 \| 0.0118 \| 0.8190 \| 0.7696 \| 3.8630 \| 0.0115 \| 0.9117 \| 0.8698 \| 5 \| \| 4.5875 \| 0.0119 \| 0.7976 \| 0.7465 \| 3.8341 \| 0.0116 \| 0.8976 \| 0.8552 \| 6 \| \| 4.5682 \| 0.0120 \| 0.7753 \| 0.7227 \| 3.8277 \| 0.0116 \| 0.9014 \| 0.8653 \| 7 \| \| 4.5376 \| 0.0121 \| 0.7528 \| 0.7005 \| 3.7844 \| 0.0118 \| 0.8332 \| 0.7815 \| 8 \| \| 4.5060 \| 0.0122 \| 0.7392 \| 0.6844 \| 3.7537 \| 0.0118 \| 0.8578 \| 0.8152 \| 9 \| \| 4.4580 \| 0.0124 \| 0.7221 \| 0.6694 \| 3.7038 \| 0.0120 \| 0.8190 \| 0.7679 \| 10 \| \| 4.3989 \| 0.0125 \| 0.7156 \| 0.6636 \| 3.6169 \| 0.0122 \| 0.7979 \| 0.7429 \| 11 \| \| 4.3056 \| 0.0128 \| 0.7069 \| 0.6557 \| 3.5098 \| 0.0125 \| 0.7924 \| 0.7396 \| 12 \| \| 4.1673 \| 0.0132 \| 0.7054 \| 0.6584 \| 3.3542 \| 0.0128 \| 0.7759 \| 0.7240 \| 13 \| \| 3.9762 \| 0.0138 \| 0.6987 \| 0.6559 \| 3.1318 \| 0.0133 \| 0.7644 \| 0.7231 \| 14 \| \| 3.7385 \| 0.0145 \| 0.6835 \| 0.6448 \| 2.9144 \| 0.0138 \| 0.7392 \| 0.6955 \| 15 \| \| 3.5040 \| 0.0152 \| 0.6644 \| 0.6298 \| 2.7413 \| 0.0142 \| 0.7019 \| 0.6548 \| 16 \| \| 3.2728 \| 0.0160 \| 0.6408 \| 0.6101 \| 2.5183 \| 0.0149 \| 0.6798 \| 0.6363 \| 17 \| \| 3.0657 \| 0.0167 \| 0.6188 \| 0.5912 \| 2.3594 \| 0.0153 \| 0.6528 \| 0.6103 \| 18 \| \| 2.8703 \| 0.0174 \| 0.5936 \| 0.5685 \| 2.2644 \| 0.0156 \| 0.6310 \| 0.5925 \| 19 \| \| 2.6850 \| 0.0181 \| 0.5680 \| 0.5453 \| 2.1296 \| 0.0160 \| 0.6040 \| 0.5652 \| 20 \| \| 2.5227 \| 0.0188 \| 0.5423 \| 0.5215 \| 2.0019 \| 0.0165 \| 0.5793 \| 0.5403 \| 21 \| \| 2.3878 \| 0.0194 \| 0.5199 \| 0.5015 \| 1.8996 \| 0.0169 \| 0.5592 \| 0.5229 \| 22 \| \| 2.2437 \| 0.0201 \| 0.4959 \| 0.4788 \| 1.8141 \| 0.0172 \| 0.5414 \| 0.5045 \| 23 \| \| 2.1205 \| 0.0207 \| 0.4752 \| 0.4607 \| 1.7245 \| 0.0175 \| 0.5208 \| 0.4838 \| 24 \| \| 1.9919 \| 0.0213 \| 0.4533 \| 0.4390 \| 1.6673 \| 0.0178 \| 0.5026 \| 0.4659 \| 25 \| \| 1.9140 \| 0.0217 \| 0.4355 \| 0.4216 \| 1.6041 \| 0.0181 \| 0.4873 \| 0.4512 \| 26 \| \| 1.8225 \| 0.0222 \| 0.4184 \| 0.4052 \| 1.6271 \| 0.0179 \| 0.4852 \| 0.4511 \| 27 \| \| 1.7265 \| 0.0227 \| 0.4016 \| 0.3895 \| 1.5219 \| 0.0184 \| 0.4635 \| 0.4275 \| 28 \| \| 1.6240 \| 0.0233 \| 0.3833 \| 0.3729 \| 1.4718 \| 0.0186 \| 0.4515 \| 0.4170 \| 29 \| \| 1.5610 \| 0.0236 \| 0.3697 \| 0.3588 \| 1.4404 \| 0.0188 \| 0.4407 \| 0.4056 \| 30 \| \| 1.4719 \| 0.0242 \| 0.3540 \| 0.3449 \| 1.4125 \| 0.0189 \| 0.4310 \| 0.3961 \| 31 \| \| 1.4152 \| 0.0245 \| 0.3421 \| 0.3339 \| 1.3655 \| 0.0191 \| 0.4234 \| 0.3881 \| 32 \| \| 1.3546 \| 0.0249 \| 0.3277 \| 0.3195 \| 1.3419 \| 0.0192 \| 0.4156 \| 0.3816 \| 33 \| \| 1.2565 \| 0.0256 \| 0.3135 \| 0.3060 \| 1.3172 \| 0.0194 \| 0.4065 \| 0.3722 \| 34 \| \| 1.2135 \| 0.0258 \| 0.3026 \| 0.2958 \| 1.3019 \| 0.0194 \| 0.4006 \| 0.3662 \| 35 \| \| 1.1739 \| 0.0261 \| 0.2923 \| 0.2861 \| 1.3843 \| 0.0190 \| 0.3951 \| 0.3587 \| 36 \| \| 1.0950 \| 0.0267 \| 0.2782 \| 0.2733 \| 1.2665 \| 0.0197 \| 0.3883 \| 0.3541 \| 37 \| \| 1.0435 \| 0.0271 \| 0.2673 \| 0.2631 \| 1.2567 \| 0.0197 \| 0.3837 \| 0.3497 \| 38 \| \| 0.9922 \| 0.0275 \| 0.2580 \| 0.2542 \| 1.2566 \| 0.0197 \| 0.3801 \| 0.3444 \| 39 \| \| 0.9387 \| 0.0279 \| 0.2464 \| 0.2438 \| 1.2441 \| 0.0198 \| 0.3767 \| 0.3423 \| 40 \| \| 0.9345 \| 0.0278 \| 0.2393 \| 0.2373 \| 1.2221 \| 0.0199 \| 0.3682 \| 0.3336 \| 41 \| \| 0.8574 \| 0.0285 \| 0.2268 \| 0.2255 \| 1.2258 \| 0.0199 \| 0.3680 \| 0.3338 \| 42 \| \| 0.8275 \| 0.0287 \| 0.2183 \| 0.2180 \| 1.2044 \| 0.0201 \| 0.3628 \| 0.3290 \| 43 \| \| 0.8201 \| 0.0288 \| 0.2114 \| 0.2108 \| 1.2056 \| 0.0201 \| 0.3601 \| 0.3270 \| 44 \| \| 0.7684 \| 0.0292 \| 0.2020 \| 0.2029 \| 1.1879 \| 0.0202 \| 0.3553 \| 0.3215 \| 45 \| \| 0.7262 \| 0.0295 \| 0.1938 \| 0.1947 \| 1.2263 \| 0.0200 \| 0.3537 \| 0.3177 \| 46 \| \| 0.7286 \| 0.0295 \| 0.1876 \| 0.1898 \| 1.1772 \| 0.0203 \| 0.3485 \| 0.3135 \| 47 \| \| 0.6807 \| 0.0300 \| 0.1775 \| 0.1797 \| 1.1761 \| 0.0203 \| 0.3490 \| 0.3155 \| 48 \| \| 0.6609 \| 0.0301 \| 0.1713 \| 0.1742 \| 1.1853 \| 0.0203 \| 0.3484 \| 0.3153 \| 49 \| \| 0.6062 \| 0.0306 \| 0.1615 \| 0.1653 \| 1.1660 \| 0.0204 \| 0.3432 \| 0.3090 \| 50 \| \| 0.5755 \| 0.0309 \| 0.1547 \| 0.1584 \| 1.1698 \| 0.0204 \| 0.3428 \| 0.3089 \| 51 \| \| 0.5600 \| 0.0310 \| 0.1482 \| 0.1524 \| 1.1667 \| 0.0204 \| 0.3398 \| 0.3058 \| 52 \| \| 0.5715 \| 0.0308 \| 0.1449 \| 0.1496 \| 1.1614 \| 0.0205 \| 0.3381 \| 0.3036 \| 53 \| \| 0.5247 \| 0.0313 \| 0.1358 \| 0.1411 \| 1.1639 \| 0.0205 \| 0.3359 \| 0.3025 \| 54 \| ### Framework versions - Transformers 4.34.0.dev0 - TensorFlow 2.13.0 - Tokenizers 0.13.3
mesolitica/llama-13b-hf-16384-fpf	mesolitica	2023-09-11T11:57:07Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "ms", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-08-30T14:14:39Z	--- language: - ms --- # Full Parameter Finetuning 13B 16384 context length Llama2 on Malaysian text README at https://github.com/huseinzol05/malaya/tree/5.1/session/llama2#full-parameter-finetuning WandB, https://wandb.ai/mesolitica/fpf-Llama-2-13b-16k-hf?workspace=user-husein-mesolitica

tommyadams/finetuned_falconb6

tommyadams

2023-09-11T17:28:55Z

0

null

[ "generated_from_trainer", "base_model:TinyLlama/TinyLlama-1.1B-step-50K-105b", "base_model:finetune:TinyLlama/TinyLlama-1.1B-step-50K-105b", "license:apache-2.0", "region:us" ]

null

2023-09-10T22:00:12Z

--- license: apache-2.0 base_model: PY007/TinyLlama-1.1B-step-50K-105b tags: - generated_from_trainer model-index: - name: finetuned_falconb6 results: [] ---  # finetuned_falconb6 This model is a fine-tuned version of [PY007/TinyLlama-1.1B-step-50K-105b](https://huggingface.co/PY007/TinyLlama-1.1B-step-50K-105b) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - training_steps: 3 ### Training results ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3

bigmorning/whisper_4_with_init_sun_syl_wd_0_lr_en2_0010

bigmorning

2023-09-11T17:15:58Z

59

0

transformers

[ "transformers", "tf", "whisper", "automatic-speech-recognition", "generated_from_keras_callback", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-09-11T17:15:49Z

--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_keras_callback model-index: - name: whisper_4_with_init_sun_syl_wd_0_lr_en2_0010 results: [] ---  # whisper_4_with_init_sun_syl_wd_0_lr_en2_0010 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.8685 - Train Accuracy: 0.0113 - Train Wermet: 0.9890 - Train Wermet Syl: 0.9897 - Validation Loss: 4.1857 - Validation Accuracy: 0.0113 - Validation Wermet: 0.9851 - Validation Wermet Syl: 0.9843 - Epoch: 9 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 0.01, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Train Wermet | Train Wermet Syl | Validation Loss | Validation Accuracy | Validation Wermet | Validation Wermet Syl | Epoch | |:----------:|:--------------:|:------------:|:----------------:|:---------------:|:-------------------:|:-----------------:|:---------------------:|:-----:| | 39.6121 | 0.0057 | 33.2649 | 25.5768 | 4.5339 | 0.0113 | 0.9851 | 0.9843 | 0 | | 5.3698 | 0.0107 | 12.0116 | 9.0545 | 4.3408 | 0.0112 | 0.9919 | 0.9915 | 1 | | 5.1979 | 0.0109 | 9.4008 | 7.1909 | 4.2108 | 0.0113 | 0.9851 | 0.9843 | 2 | | 5.0669 | 0.0110 | 7.0382 | 5.3339 | 4.1662 | 0.0113 | 0.9851 | 0.9843 | 3 | | 4.9546 | 0.0111 | 4.8506 | 3.7351 | 4.3022 | 0.0112 | 0.9870 | 0.9854 | 4 | | 4.9453 | 0.0111 | 3.9228 | 3.1750 | 4.1194 | 0.0113 | 0.9851 | 0.9843 | 5 | | 4.9123 | 0.0112 | 2.2402 | 1.9643 | 4.1865 | 0.0112 | 1.0000 | 1.0000 | 6 | | 4.8957 | 0.0112 | 1.7673 | 1.5892 | 4.1150 | 0.0112 | 1.0000 | 0.9999 | 7 | | 4.8959 | 0.0112 | 2.2166 | 1.9601 | 4.1185 | 0.0113 | 0.9851 | 0.9843 | 8 | | 4.8685 | 0.0113 | 0.9890 | 0.9897 | 4.1857 | 0.0113 | 0.9851 | 0.9843 | 9 | ### Framework versions - Transformers 4.34.0.dev0 - TensorFlow 2.13.0 - Tokenizers 0.13.3

yugant13/fav-cricketer

yugant13

2023-09-11T17:10:18Z

0

null

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

text-to-image

2023-09-11T17:09:29Z

--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### fav-cricketer Dreambooth model trained by yugant13 following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: GoX19932gAS Sample pictures of this concept: ![0](https://huggingface.co/yugant13/fav-cricketer/resolve/main/sample_images/xzg_(2).jpg)

mindchain/llama2-adapter_AAA110

mindchain

2023-09-11T17:03:43Z

2

0

peft

[ "peft", "region:us" ]

null

2023-09-11T17:03:39Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.0.dev0

turing-motors/heron-chat-git-Llama-2-7b-v0

turing-motors

2023-09-11T16:53:31Z

24

0

transformers

[ "transformers", "pytorch", "git_llama", "text-generation", "heron", "vision", "image-captioning", "VQA", "image-to-text", "en", "arxiv:2205.14100", "arxiv:2307.09288", "license:cc-by-nc-4.0", "autotrain_compatible", "region:us" ]

image-to-text

2023-09-07T10:55:05Z

--- language: - en tags: - heron - vision - image-captioning - VQA pipeline_tag: image-to-text license: - cc-by-nc-4.0 inference: false --- # Heron GIT Llama 2 Fast 7B ![heron](./heron_image.png) ## Model Details Heron GIT Llama 2 7B is a vision-language model that can converse about input images.<br> This model was trained using [the heron library](https://github.com/turingmotors/heron). Please refer to the code for details. ## Usage Follow [the installation guide](https://github.com/turingmotors/heron/#1-clone-this-repository). ```python import requests from PIL import Image import torch from transformers import AutoProcessor from heron.models.git_llm.git_llama import GitLlamaConfig, GitLlamaForCausalLM device_id = 0 # prepare a pretrained model model = GitLlamaForCausalLM.from_pretrained( 'turing-motors/heron-chat-git-Llama-2-7b-v0', torch_dtype=torch.float16 ) model.eval() model.to(f"cuda:{device_id}") # prepare a processor processor = AutoProcessor.from_pretrained('turing-motors/heron-chat-git-Llama-2-7b-v0') # prepare inputs url = "https://www.barnorama.com/wp-content/uploads/2016/12/03-Confusing-Pictures.jpg" image = Image.open(requests.get(url, stream=True).raw) text = f"##human: What is this picture?\n##gpt: " # do preprocessing inputs = processor( text, image, return_tensors="pt", truncation=True, ) inputs = {k: v.to(f"cuda:{device_id}") for k, v in inputs.items()} # set eos token eos_token_id_list = [ processor.tokenizer.pad_token_id, processor.tokenizer.eos_token_id, ] # do inference with torch.no_grad(): out = model.generate(**inputs, max_length=256, do_sample=False, temperature=0., eos_token_id=eos_token_id_list) # print result print(processor.tokenizer.batch_decode(out)[0]) ``` ## Model Details * **Developed by**: [Turing Inc.](https://www.turing-motors.com/) * **Adaptor type**: [GIT](https://arxiv.org/abs/2205.14100) * **Lamguage Model**: [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) * **Language(s)**: English ### Training This model was initially trained with the Adaptor using Coco Captions in M3IT. In the second phase, it was fine-tuned with M3IT. Finally, it was trained by instruction tuning with LLaVA-Instruct-150K. ### Training Dataset - [LLaVA-Instruct-150K](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K) - [M3IT](https://huggingface.co/datasets/MMInstruction/M3IT) ## Use and Limitations ### Intended Use This model is intended for use in chat-like applications and for research purposes. ### Limitations The model may produce inaccurate or false information, and its accuracy is not guaranteed. It is still in the research and development stage. ## How to cite ```bibtex @misc{GitLlama2, url = {[https://huggingface.co/turing-motors/heron-chat-git-Llama-2-7b-v0](https://huggingface.co/turing-motors/heron-chat-git-Llama-2-7b-v0)}, title = {Heron GIT Llama 2 7B}, author = {Yuichi Inoue, Kotaro Tanahashi, and Yu Yamaguchi} } ``` ## Citations ```bibtex @misc{touvron2023llama, title={Llama 2: Open Foundation and Fine-Tuned Chat Models}, author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov and Soumya Batra and Prajjwal Bhargava and Shruti Bhosale and Dan Bikel and Lukas Blecher and Cristian Canton Ferrer and Moya Chen and Guillem Cucurull and David Esiobu and Jude Fernandes and Jeremy Fu and Wenyin Fu and Brian Fuller and Cynthia Gao and Vedanuj Goswami and Naman Goyal and Anthony Hartshorn and Saghar Hosseini and Rui Hou and Hakan Inan and Marcin Kardas and Viktor Kerkez and Madian Khabsa and Isabel Kloumann and Artem Korenev and Punit Singh Koura and Marie-Anne Lachaux and Thibaut Lavril and Jenya Lee and Diana Liskovich and Yinghai Lu and Yuning Mao and Xavier Martinet and Todor Mihaylov and Pushkar Mishra and Igor Molybog and Yixin Nie and Andrew Poulton and Jeremy Reizenstein and Rashi Rungta and Kalyan Saladi and Alan Schelten and Ruan Silva and Eric Michael Smith and Ranjan Subramanian and Xiaoqing Ellen Tan and Binh Tang and Ross Taylor and Adina Williams and Jian Xiang Kuan and Puxin Xu and Zheng Yan and Iliyan Zarov and Yuchen Zhang and Angela Fan and Melanie Kambadur and Sharan Narang and Aurelien Rodriguez and Robert Stojnic and Sergey Edunov and Thomas Scialom}, year={2023}, eprint={2307.09288}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` --- license: cc-by-nc-4.0 ---

iven5880/distilbert-base-uncased-finetuned-imdb

iven5880

2023-09-11T16:34:41Z

108

0

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "distilbert", "fill-mask", "generated_from_trainer", "dataset:imdb", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-09-08T01:39:44Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb base_model: distilbert-base-uncased model-index: - name: distilbert-base-uncased-finetuned-imdb results: [] ---  # distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 2.4442 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.6985 | 1.0 | 157 | 2.5612 | | 2.562 | 2.0 | 314 | 2.4226 | | 2.5316 | 3.0 | 471 | 2.4218 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.0 - Datasets 2.14.5 - Tokenizers 0.13.2

ldos/text_shortening_model_v31

ldos

2023-09-11T16:05:54Z

51

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-09-11T15:08:02Z

--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: text_shortening_model_v31 results: [] ---  # text_shortening_model_v31 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.7416 - Rouge1: 0.4961 - Rouge2: 0.2712 - Rougel: 0.4388 - Rougelsum: 0.4386 - Bert precision: 0.8749 - Bert recall: 0.8711 - Average word count: 8.5135 - Max word count: 16 - Min word count: 3 - Average token count: 13.1592 - % shortened texts with length > 12: 10.2102 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bert precision | Bert recall | Average word count | Max word count | Min word count | Average token count | % shortened texts with length > 12 | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:--------------:|:-----------:|:------------------:|:--------------:|:--------------:|:-------------------:|:----------------------------------:| | 1.1978 | 1.0 | 145 | 1.5250 | 0.4953 | 0.2842 | 0.4528 | 0.4524 | 0.8806 | 0.8681 | 7.8919 | 18 | 3 | 12.4234 | 4.2042 | | 1.0092 | 2.0 | 290 | 1.4421 | 0.5257 | 0.3053 | 0.4698 | 0.4689 | 0.875 | 0.8809 | 9.6006 | 18 | 4 | 14.3574 | 19.2192 | | 0.8932 | 3.0 | 435 | 1.4060 | 0.5266 | 0.3045 | 0.4728 | 0.472 | 0.8766 | 0.8776 | 9.0841 | 18 | 4 | 13.6366 | 14.7147 | | 0.79 | 4.0 | 580 | 1.4022 | 0.5329 | 0.3136 | 0.4714 | 0.4714 | 0.8802 | 0.8797 | 8.952 | 16 | 4 | 13.6036 | 12.9129 | | 0.7506 | 5.0 | 725 | 1.4514 | 0.5145 | 0.2935 | 0.4485 | 0.4485 | 0.8745 | 0.8726 | 8.97 | 18 | 4 | 13.6096 | 12.012 | | 0.6981 | 6.0 | 870 | 1.4602 | 0.5146 | 0.2914 | 0.4566 | 0.4559 | 0.8778 | 0.8762 | 8.958 | 18 | 3 | 13.5195 | 15.3153 | | 0.6426 | 7.0 | 1015 | 1.4745 | 0.5196 | 0.2973 | 0.4596 | 0.4593 | 0.8759 | 0.8788 | 9.1802 | 16 | 4 | 13.9159 | 14.1141 | | 0.6251 | 8.0 | 1160 | 1.5026 | 0.5217 | 0.2965 | 0.461 | 0.4611 | 0.8802 | 0.8775 | 8.8198 | 16 | 4 | 13.3393 | 12.012 | | 0.5901 | 9.0 | 1305 | 1.5890 | 0.5156 | 0.2967 | 0.4606 | 0.4609 | 0.8773 | 0.876 | 8.7718 | 17 | 3 | 13.4655 | 9.6096 | | 0.5544 | 10.0 | 1450 | 1.6294 | 0.5172 | 0.287 | 0.4562 | 0.4559 | 0.8779 | 0.876 | 8.7688 | 18 | 4 | 13.5195 | 11.7117 | | 0.5354 | 11.0 | 1595 | 1.6805 | 0.5169 | 0.2871 | 0.457 | 0.4571 | 0.8768 | 0.8774 | 8.994 | 17 | 4 | 13.6486 | 14.1141 | | 0.5103 | 12.0 | 1740 | 1.7334 | 0.5121 | 0.2824 | 0.4556 | 0.455 | 0.8785 | 0.8745 | 8.5465 | 16 | 3 | 13.1021 | 8.1081 | | 0.4796 | 13.0 | 1885 | 1.7767 | 0.499 | 0.2763 | 0.442 | 0.4418 | 0.8754 | 0.8739 | 8.6396 | 17 | 4 | 13.3183 | 11.4114 | | 0.4825 | 14.0 | 2030 | 1.8319 | 0.5114 | 0.2849 | 0.4497 | 0.4501 | 0.8746 | 0.8758 | 8.994 | 17 | 4 | 13.6667 | 12.9129 | | 0.4572 | 15.0 | 2175 | 1.8613 | 0.5129 | 0.2884 | 0.4546 | 0.4549 | 0.8785 | 0.8757 | 8.6877 | 17 | 3 | 13.3784 | 10.5105 | | 0.4489 | 16.0 | 2320 | 1.8790 | 0.5144 | 0.2829 | 0.4533 | 0.4536 | 0.8777 | 0.8754 | 8.8078 | 16 | 3 | 13.4955 | 13.2132 | | 0.4211 | 17.0 | 2465 | 1.9604 | 0.4936 | 0.2641 | 0.4322 | 0.4326 | 0.8735 | 0.8696 | 8.4985 | 17 | 3 | 13.1892 | 9.009 | | 0.4246 | 18.0 | 2610 | 2.0639 | 0.4951 | 0.2634 | 0.4331 | 0.4334 | 0.8721 | 0.8703 | 8.7538 | 16 | 4 | 13.3453 | 12.6126 | | 0.4063 | 19.0 | 2755 | 2.0587 | 0.499 | 0.2685 | 0.4378 | 0.4383 | 0.8741 | 0.8707 | 8.5916 | 16 | 3 | 13.3003 | 9.9099 | | 0.3912 | 20.0 | 2900 | 2.1089 | 0.5068 | 0.2727 | 0.4471 | 0.4469 | 0.8764 | 0.8744 | 8.7538 | 18 | 3 | 13.4625 | 11.1111 | | 0.3855 | 21.0 | 3045 | 2.1048 | 0.5022 | 0.2704 | 0.4473 | 0.4478 | 0.875 | 0.8728 | 8.6847 | 16 | 4 | 13.3483 | 9.3093 | | 0.3808 | 22.0 | 3190 | 2.1804 | 0.4977 | 0.2722 | 0.4414 | 0.4412 | 0.875 | 0.8711 | 8.5315 | 17 | 4 | 13.0631 | 10.8108 | | 0.3851 | 23.0 | 3335 | 2.1740 | 0.4993 | 0.2696 | 0.4442 | 0.4443 | 0.8742 | 0.8719 | 8.5676 | 15 | 3 | 13.2252 | 9.009 | | 0.3741 | 24.0 | 3480 | 2.1872 | 0.4921 | 0.2683 | 0.4365 | 0.4369 | 0.8728 | 0.8692 | 8.5195 | 17 | 3 | 13.2192 | 8.4084 | | 0.3604 | 25.0 | 3625 | 2.2617 | 0.4988 | 0.2681 | 0.4421 | 0.4426 | 0.8747 | 0.8705 | 8.5255 | 17 | 3 | 13.2492 | 8.1081 | | 0.3676 | 26.0 | 3770 | 2.2561 | 0.4931 | 0.2603 | 0.4328 | 0.4331 | 0.874 | 0.8711 | 8.6276 | 15 | 3 | 13.3363 | 11.7117 | | 0.3799 | 27.0 | 3915 | 2.2404 | 0.4912 | 0.2652 | 0.4329 | 0.433 | 0.8729 | 0.8702 | 8.6517 | 17 | 3 | 13.4414 | 8.1081 | | 0.3617 | 28.0 | 4060 | 2.2728 | 0.4983 | 0.2704 | 0.4424 | 0.4427 | 0.8756 | 0.8734 | 8.7568 | 17 | 3 | 13.5225 | 11.4114 | | 0.3588 | 29.0 | 4205 | 2.2695 | 0.4904 | 0.2601 | 0.4331 | 0.4328 | 0.8743 | 0.87 | 8.4775 | 18 | 3 | 13.1592 | 9.009 | | 0.3567 | 30.0 | 4350 | 2.3006 | 0.4993 | 0.2693 | 0.4419 | 0.4417 | 0.8747 | 0.8737 | 8.8529 | 17 | 3 | 13.5976 | 12.012 | | 0.3573 | 31.0 | 4495 | 2.3257 | 0.4979 | 0.2669 | 0.4378 | 0.4379 | 0.8743 | 0.8735 | 8.9069 | 18 | 3 | 13.6697 | 12.9129 | | 0.3471 | 32.0 | 4640 | 2.3513 | 0.4989 | 0.2723 | 0.441 | 0.4405 | 0.8758 | 0.8728 | 8.6246 | 17 | 3 | 13.3063 | 10.8108 | | 0.3591 | 33.0 | 4785 | 2.3467 | 0.4972 | 0.2751 | 0.4415 | 0.4413 | 0.8742 | 0.8727 | 8.8078 | 17 | 3 | 13.5616 | 10.5105 | | 0.3401 | 34.0 | 4930 | 2.4229 | 0.4854 | 0.2661 | 0.4313 | 0.4318 | 0.8737 | 0.8701 | 8.5826 | 17 | 3 | 13.2673 | 8.7087 | | 0.3476 | 35.0 | 5075 | 2.3804 | 0.4895 | 0.2602 | 0.4322 | 0.4326 | 0.874 | 0.8712 | 8.6577 | 17 | 3 | 13.2883 | 9.3093 | | 0.3473 | 36.0 | 5220 | 2.4242 | 0.4938 | 0.2689 | 0.438 | 0.4387 | 0.8745 | 0.8713 | 8.5976 | 17 | 3 | 13.2432 | 9.3093 | | 0.3415 | 37.0 | 5365 | 2.3836 | 0.4943 | 0.2617 | 0.4351 | 0.4351 | 0.8751 | 0.8711 | 8.4054 | 17 | 3 | 13.0571 | 8.1081 | | 0.3549 | 38.0 | 5510 | 2.4110 | 0.501 | 0.2696 | 0.4402 | 0.4406 | 0.8765 | 0.8713 | 8.2282 | 17 | 3 | 12.9459 | 6.6066 | | 0.3432 | 39.0 | 5655 | 2.4016 | 0.4999 | 0.27 | 0.4387 | 0.4393 | 0.8751 | 0.8712 | 8.5285 | 17 | 3 | 13.2402 | 8.4084 | | 0.3387 | 40.0 | 5800 | 2.4546 | 0.4986 | 0.2718 | 0.4417 | 0.4422 | 0.8742 | 0.871 | 8.5766 | 17 | 3 | 13.2312 | 9.3093 | | 0.3351 | 41.0 | 5945 | 2.4478 | 0.4981 | 0.2714 | 0.4367 | 0.4372 | 0.8756 | 0.8722 | 8.4775 | 15 | 3 | 13.1411 | 8.7087 | | 0.3366 | 42.0 | 6090 | 2.4447 | 0.4961 | 0.2703 | 0.4359 | 0.437 | 0.8746 | 0.8699 | 8.4745 | 16 | 3 | 13.1231 | 9.3093 | | 0.3324 | 43.0 | 6235 | 2.4974 | 0.4989 | 0.2809 | 0.4428 | 0.4432 | 0.8747 | 0.873 | 8.7147 | 16 | 3 | 13.4565 | 10.5105 | | 0.3306 | 44.0 | 6380 | 2.4938 | 0.4902 | 0.2657 | 0.4301 | 0.4306 | 0.8733 | 0.8692 | 8.4925 | 15 | 3 | 13.1622 | 8.4084 | | 0.3388 | 45.0 | 6525 | 2.5098 | 0.4788 | 0.2616 | 0.4246 | 0.4245 | 0.8734 | 0.8662 | 8.2162 | 16 | 3 | 12.7538 | 8.1081 | | 0.346 | 46.0 | 6670 | 2.4595 | 0.4987 | 0.2689 | 0.438 | 0.4389 | 0.875 | 0.8718 | 8.5676 | 16 | 3 | 13.2252 | 9.9099 | | 0.3401 | 47.0 | 6815 | 2.5098 | 0.4934 | 0.2653 | 0.4353 | 0.4356 | 0.8744 | 0.87 | 8.3934 | 15 | 3 | 13.048 | 8.1081 | | 0.3271 | 48.0 | 6960 | 2.5204 | 0.4951 | 0.2674 | 0.4373 | 0.4372 | 0.8749 | 0.8703 | 8.4625 | 16 | 3 | 13.024 | 9.009 | | 0.3267 | 49.0 | 7105 | 2.5291 | 0.4887 | 0.2605 | 0.428 | 0.4284 | 0.8728 | 0.8702 | 8.7057 | 18 | 3 | 13.3363 | 11.1111 | | 0.3382 | 50.0 | 7250 | 2.5422 | 0.4899 | 0.2666 | 0.4354 | 0.4356 | 0.8755 | 0.8707 | 8.4505 | 16 | 3 | 13.0931 | 8.1081 | | 0.3255 | 51.0 | 7395 | 2.5254 | 0.4921 | 0.2634 | 0.4346 | 0.4352 | 0.8738 | 0.8691 | 8.4535 | 16 | 3 | 13.027 | 10.2102 | | 0.32 | 52.0 | 7540 | 2.5460 | 0.4991 | 0.2727 | 0.4423 | 0.4421 | 0.8745 | 0.873 | 8.8919 | 16 | 3 | 13.5736 | 11.7117 | | 0.3154 | 53.0 | 7685 | 2.5446 | 0.5027 | 0.2712 | 0.4463 | 0.4463 | 0.8768 | 0.8734 | 8.6426 | 16 | 3 | 13.2973 | 11.1111 | | 0.3293 | 54.0 | 7830 | 2.5378 | 0.4928 | 0.2669 | 0.4352 | 0.4354 | 0.8736 | 0.869 | 8.5225 | 16 | 3 | 13.1291 | 10.2102 | | 0.3231 | 55.0 | 7975 | 2.5905 | 0.4949 | 0.2678 | 0.4378 | 0.4375 | 0.8743 | 0.8714 | 8.6426 | 15 | 3 | 13.3003 | 9.009 | | 0.3239 | 56.0 | 8120 | 2.5884 | 0.4969 | 0.2697 | 0.4399 | 0.4399 | 0.8737 | 0.8712 | 8.6697 | 16 | 3 | 13.3754 | 10.5105 | | 0.3174 | 57.0 | 8265 | 2.5500 | 0.4958 | 0.267 | 0.4389 | 0.4386 | 0.8739 | 0.8715 | 8.7327 | 16 | 4 | 13.3844 | 10.5105 | | 0.3209 | 58.0 | 8410 | 2.5804 | 0.4989 | 0.2706 | 0.442 | 0.4426 | 0.8751 | 0.8717 | 8.5766 | 15 | 3 | 13.1952 | 9.3093 | | 0.3297 | 59.0 | 8555 | 2.5909 | 0.494 | 0.2622 | 0.4343 | 0.4338 | 0.8733 | 0.8698 | 8.5976 | 16 | 3 | 13.1652 | 11.7117 | | 0.3226 | 60.0 | 8700 | 2.5857 | 0.4976 | 0.2639 | 0.4377 | 0.438 | 0.8753 | 0.8701 | 8.3904 | 17 | 3 | 12.973 | 7.8078 | | 0.3241 | 61.0 | 8845 | 2.5824 | 0.5011 | 0.2698 | 0.4428 | 0.4436 | 0.8764 | 0.8725 | 8.5345 | 16 | 3 | 13.1502 | 10.5105 | | 0.3201 | 62.0 | 8990 | 2.6156 | 0.4968 | 0.2673 | 0.4371 | 0.4372 | 0.8755 | 0.8702 | 8.3904 | 16 | 3 | 12.979 | 6.9069 | | 0.3234 | 63.0 | 9135 | 2.6374 | 0.4945 | 0.2677 | 0.4387 | 0.4388 | 0.8744 | 0.8693 | 8.4444 | 17 | 3 | 12.958 | 8.1081 | | 0.3246 | 64.0 | 9280 | 2.6338 | 0.4912 | 0.2672 | 0.4396 | 0.4402 | 0.8738 | 0.8698 | 8.4955 | 17 | 3 | 13.1021 | 8.1081 | | 0.3188 | 65.0 | 9425 | 2.6206 | 0.4999 | 0.2739 | 0.4443 | 0.4444 | 0.8763 | 0.8726 | 8.6006 | 17 | 3 | 13.2042 | 10.5105 | | 0.3186 | 66.0 | 9570 | 2.6499 | 0.5007 | 0.2771 | 0.4462 | 0.4463 | 0.8765 | 0.8729 | 8.5375 | 17 | 3 | 13.2162 | 9.3093 | | 0.319 | 67.0 | 9715 | 2.6488 | 0.5023 | 0.2715 | 0.4452 | 0.4454 | 0.8761 | 0.8736 | 8.6817 | 17 | 3 | 13.3904 | 10.2102 | | 0.3328 | 68.0 | 9860 | 2.6238 | 0.5002 | 0.2696 | 0.4408 | 0.4411 | 0.8755 | 0.8717 | 8.5075 | 17 | 3 | 13.1081 | 9.009 | | 0.3068 | 69.0 | 10005 | 2.6525 | 0.4971 | 0.2684 | 0.4391 | 0.4397 | 0.8755 | 0.8712 | 8.5045 | 17 | 3 | 13.1411 | 11.4114 | | 0.3192 | 70.0 | 10150 | 2.6494 | 0.4976 | 0.2722 | 0.4395 | 0.4405 | 0.8762 | 0.8714 | 8.3964 | 17 | 3 | 13.033 | 8.4084 | | 0.3232 | 71.0 | 10295 | 2.6642 | 0.4976 | 0.2717 | 0.4412 | 0.4411 | 0.8756 | 0.8717 | 8.5075 | 17 | 3 | 13.1622 | 9.9099 | | 0.3084 | 72.0 | 10440 | 2.6596 | 0.4931 | 0.2669 | 0.4352 | 0.4354 | 0.8734 | 0.8696 | 8.4865 | 17 | 3 | 13.1682 | 9.009 | | 0.313 | 73.0 | 10585 | 2.6551 | 0.4942 | 0.2699 | 0.4363 | 0.4368 | 0.8742 | 0.8699 | 8.4715 | 16 | 3 | 13.1201 | 9.6096 | | 0.3194 | 74.0 | 10730 | 2.6769 | 0.4962 | 0.2689 | 0.4388 | 0.4389 | 0.874 | 0.8715 | 8.5976 | 17 | 3 | 13.2763 | 10.5105 | | 0.3143 | 75.0 | 10875 | 2.6860 | 0.493 | 0.2652 | 0.4335 | 0.4343 | 0.8734 | 0.8702 | 8.5706 | 16 | 3 | 13.2462 | 9.3093 | | 0.3209 | 76.0 | 11020 | 2.6777 | 0.4893 | 0.2592 | 0.4325 | 0.4324 | 0.8726 | 0.869 | 8.5225 | 16 | 3 | 13.2012 | 9.3093 | | 0.3078 | 77.0 | 11165 | 2.6797 | 0.4877 | 0.261 | 0.4321 | 0.4323 | 0.8724 | 0.8693 | 8.5796 | 16 | 3 | 13.2402 | 9.6096 | | 0.3192 | 78.0 | 11310 | 2.6812 | 0.495 | 0.2677 | 0.4382 | 0.4383 | 0.8739 | 0.871 | 8.5706 | 18 | 3 | 13.2523 | 10.8108 | | 0.3147 | 79.0 | 11455 | 2.6777 | 0.495 | 0.2693 | 0.4371 | 0.4374 | 0.874 | 0.8707 | 8.5015 | 16 | 3 | 13.1471 | 9.3093 | | 0.3049 | 80.0 | 11600 | 2.6767 | 0.4917 | 0.2647 | 0.4344 | 0.4346 | 0.8723 | 0.8696 | 8.5616 | 16 | 3 | 13.2162 | 9.9099 | | 0.3191 | 81.0 | 11745 | 2.6932 | 0.4929 | 0.2683 | 0.4392 | 0.4392 | 0.8737 | 0.8707 | 8.5676 | 16 | 3 | 13.2342 | 9.6096 | | 0.3073 | 82.0 | 11890 | 2.7036 | 0.4959 | 0.2699 | 0.4389 | 0.4393 | 0.8738 | 0.8722 | 8.6547 | 17 | 3 | 13.3964 | 10.2102 | | 0.3129 | 83.0 | 12035 | 2.6941 | 0.4918 | 0.2657 | 0.4341 | 0.434 | 0.8742 | 0.8703 | 8.4985 | 16 | 3 | 13.1411 | 9.3093 | | 0.3308 | 84.0 | 12180 | 2.6968 | 0.4927 | 0.2659 | 0.4335 | 0.4337 | 0.8737 | 0.8698 | 8.4955 | 16 | 3 | 13.1652 | 9.3093 | | 0.3221 | 85.0 | 12325 | 2.6966 | 0.4903 | 0.2606 | 0.4306 | 0.4306 | 0.8726 | 0.8698 | 8.5766 | 16 | 3 | 13.2823 | 9.6096 | | 0.3085 | 86.0 | 12470 | 2.7123 | 0.4862 | 0.2608 | 0.4288 | 0.4286 | 0.8723 | 0.8688 | 8.4595 | 16 | 3 | 13.0901 | 8.7087 | | 0.3281 | 87.0 | 12615 | 2.7101 | 0.4918 | 0.2638 | 0.4322 | 0.4328 | 0.8731 | 0.8695 | 8.4775 | 16 | 3 | 13.1291 | 9.009 | | 0.3183 | 88.0 | 12760 | 2.7102 | 0.4902 | 0.2649 | 0.4294 | 0.4301 | 0.873 | 0.8688 | 8.4955 | 16 | 3 | 13.0901 | 9.6096 | | 0.3063 | 89.0 | 12905 | 2.7198 | 0.4934 | 0.2676 | 0.4338 | 0.4344 | 0.8734 | 0.8692 | 8.4565 | 17 | 3 | 13.0751 | 9.009 | | 0.3123 | 90.0 | 13050 | 2.7228 | 0.492 | 0.2676 | 0.4338 | 0.4343 | 0.8732 | 0.8692 | 8.4535 | 17 | 3 | 13.0931 | 9.3093 | | 0.3163 | 91.0 | 13195 | 2.7264 | 0.4953 | 0.2702 | 0.4357 | 0.4358 | 0.874 | 0.8693 | 8.4625 | 17 | 3 | 13.033 | 9.3093 | | 0.3085 | 92.0 | 13340 | 2.7236 | 0.4934 | 0.2702 | 0.4369 | 0.4369 | 0.8738 | 0.8695 | 8.4925 | 17 | 3 | 13.0721 | 9.9099 | | 0.3257 | 93.0 | 13485 | 2.7202 | 0.4953 | 0.2706 | 0.4368 | 0.4368 | 0.8746 | 0.8699 | 8.4595 | 16 | 3 | 13.0571 | 10.2102 | | 0.3092 | 94.0 | 13630 | 2.7261 | 0.4988 | 0.2748 | 0.4415 | 0.4419 | 0.8755 | 0.8708 | 8.4535 | 16 | 3 | 13.0751 | 9.9099 | | 0.3187 | 95.0 | 13775 | 2.7248 | 0.4968 | 0.2727 | 0.4383 | 0.4389 | 0.8751 | 0.8709 | 8.5075 | 16 | 3 | 13.1321 | 9.9099 | | 0.3155 | 96.0 | 13920 | 2.7335 | 0.4962 | 0.2686 | 0.4372 | 0.4373 | 0.8749 | 0.8712 | 8.5135 | 16 | 3 | 13.1772 | 10.2102 | | 0.3271 | 97.0 | 14065 | 2.7384 | 0.4971 | 0.2721 | 0.4396 | 0.4397 | 0.8749 | 0.8711 | 8.5135 | 16 | 3 | 13.1832 | 10.5105 | | 0.3096 | 98.0 | 14210 | 2.7400 | 0.496 | 0.2712 | 0.4386 | 0.4385 | 0.8748 | 0.8711 | 8.5225 | 16 | 3 | 13.1682 | 10.2102 | | 0.3116 | 99.0 | 14355 | 2.7411 | 0.4961 | 0.2712 | 0.4388 | 0.4386 | 0.8749 | 0.8711 | 8.5135 | 16 | 3 | 13.1592 | 10.2102 | | 0.3102 | 100.0 | 14500 | 2.7416 | 0.4961 | 0.2712 | 0.4388 | 0.4386 | 0.8749 | 0.8711 | 8.5135 | 16 | 3 | 13.1592 | 10.2102 | ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

michelecafagna26/vinvl-base-finetuned-hl-actions-image-captioning

michelecafagna26

2023-09-11T16:03:21Z

9

0

pytorch

[ "pytorch", "bert", "image-to-text", "en", "dataset:michelecafagna26/hl", "arxiv:2302.12189", "arxiv:2107.12604", "license:apache-2.0", "region:us" ]

image-to-text

2023-09-11T15:10:26Z

--- license: apache-2.0 datasets: - michelecafagna26/hl language: - en metrics: - sacrebleu - rouge - meteor - spice - cider library_name: pytorch tags: - pytorch - image-to-text --- # Model Card: VinVL for Captioning 🖼️ [Microsoft's VinVL](https://github.com/microsoft/Oscar) base fine-tuned on [HL dataset](https://arxiv.org/abs/2302.12189?context=cs.CL) for **action description generation** downstream task. # Model fine-tuning 🏋️‍ The model has been finetuned for 10 epochs on the action captions of the [HL dataset](https://arxiv.org/abs/2302.12189?context=cs.CL) (available on 🤗 HUB: [michelecafagna26/hl](https://huggingface.co/datasets/michelecafagna26/hl)) # Test set metrics 📈 Obtained with beam size 5 and max length 20 | Bleu-1 | Bleu-2 | Bleu-3 | Bleu-4 | METEOR | ROUGE-L | CIDEr | SPICE | |--------|--------|--------|--------|--------|---------|-------|-------| | 0.74 | 0.62 | 0.50 | 0.40 | 0.31 | 0.65 | 1.73 | 0.21 | # Usage and Installation: More info about how to install and use this model can be found here: [michelecafagna26/VinVL ](https://github.com/michelecafagna26/VinVL) # Feature extraction ⛏️ This model has a separate Visualbackbone used to extract features. More info about: - the model: [michelecafagna26/vinvl_vg_x152c4](https://huggingface.co/michelecafagna26/vinvl_vg_x152c4) - the usage: [michelecafagna26/vinvl-visualbackbone](https://github.com/michelecafagna26/vinvl-visualbackbone) # Quick start: 🚀 ```python from transformers.pytorch_transformers import BertConfig, BertTokenizer from oscar.modeling.modeling_bert import BertForImageCaptioning from oscar.wrappers import OscarTensorizer ckpt = "path/to/the/checkpoint" device = "cuda" if torch.cuda.is_available() else "cpu" # original code config = BertConfig.from_pretrained(ckpt) tokenizer = BertTokenizer.from_pretrained(ckpt) model = BertForImageCaptioning.from_pretrained(ckpt, config=config).to(device) # This takes care of the preprocessing tensorizer = OscarTensorizer(tokenizer=tokenizer, device=device) # numpy-arrays with shape (1, num_boxes, feat_size) # feat_size is 2054 by default in VinVL visual_features = torch.from_numpy(feat_obj).to(device).unsqueeze(0) # labels are usually extracted by the features extractor labels = [['boat', 'boat', 'boat', 'bottom', 'bush', 'coat', 'deck', 'deck', 'deck', 'dock', 'hair', 'jacket']] inputs = tensorizer.encode(visual_features, labels=labels) outputs = model(**inputs) pred = tensorizer.decode(outputs) # the output looks like this: # pred = {0: [{'caption': 'He is sailing', 'conf': 0.7070220112800598]} ``` # Citations 🧾 HL Dataset paper: ```BibTeX @inproceedings{cafagna2023hl, title={{HL} {D}ataset: {V}isually-grounded {D}escription of {S}cenes, {A}ctions and {R}ationales}, author={Cafagna, Michele and van Deemter, Kees and Gatt, Albert}, booktitle={Proceedings of the 16th International Natural Language Generation Conference (INLG'23)}, address = {Prague, Czech Republic}, year={2023} } ``` Please consider citing the original project and the VinVL paper ```BibTeX @misc{han2021image, title={Image Scene Graph Generation (SGG) Benchmark}, author={Xiaotian Han and Jianwei Yang and Houdong Hu and Lei Zhang and Jianfeng Gao and Pengchuan Zhang}, year={2021}, eprint={2107.12604}, archivePrefix={arXiv}, primaryClass={cs.CV} } @inproceedings{zhang2021vinvl, title={Vinvl: Revisiting visual representations in vision-language models}, author={Zhang, Pengchuan and Li, Xiujun and Hu, Xiaowei and Yang, Jianwei and Zhang, Lei and Wang, Lijuan and Choi, Yejin and Gao, Jianfeng}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={5579--5588}, year={2021} } ```

Atulit23/flan-t5-base-indian-constitution

Atulit23

2023-09-11T15:55:07Z

102

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "base_model:google/flan-t5-base", "base_model:finetune:google/flan-t5-base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-09-11T15:54:25Z

--- license: apache-2.0 base_model: google/flan-t5-base tags: - generated_from_trainer metrics: - rouge model-index: - name: flan-t5-base-indian-constitution results: [] ---  # flan-t5-base-indian-constitution This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0008 - Rouge1: 29.7093 - Rouge2: 28.4336 - Rougel: 29.6229 - Rougelsum: 29.5617 - Gen Len: 18.9651 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 344 | 0.0009 | 29.7093 | 28.4336 | 29.6229 | 29.5617 | 18.9651 | | 0.0021 | 2.0 | 688 | 0.0008 | 29.7093 | 28.4336 | 29.6229 | 29.5617 | 18.9651 | | 0.0013 | 3.0 | 1032 | 0.0008 | 29.7093 | 28.4336 | 29.6229 | 29.5617 | 18.9651 | ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

FasterDecoding/medusa-vicuna-33b-v1.3

FasterDecoding

2023-09-11T15:53:39Z

40

4

transformers

[ "transformers", "endpoints_compatible", "region:us" ]

null

2023-09-10T02:52:22Z

<div align="center"><img src="https://github.com/FasterDecoding/Medusa/blob/main/assets/logo.png?raw=true" alt="Medusa" width="100" align="center"></div> <div align="center"><h1> Medusa: Simple Framework for Accelerating LLM Generation with Multiple Decoding Heads</h1></div> <p align="center"> | <a href="https://sites.google.com/view/ medusa-llm"><b>Blog</b></a> | <a href="https://github.com/FasterDecoding/Medusa"><b>Codebase</b></a> | </p> --- ## Installation ### Method 1: With pip ```bash pip install medusa-llm ``` ### Method 2: From source ```bash git clone https://github.com/FasterDecoding/Medusa.git cd Medusa pip install -e . ``` ### Model Weights | Size | Chat Command | Hugging Face Repo | | ---- | --------------------------------------------- | --------------------------------------------------------------------- | | 7B | `python -m medusa.inference.cli --model FasterDecoding/medusa-vicuna-7b-v1.3` | [FasterDecoding/medusa-vicuna-33b-v1.3](https://huggingface.co/FasterDecoding/medusa-vicuna-7b-v1.3) | | 13B | `python -m medusa.inference.cli --model FasterDecoding/medusa-vicuna-13b-v1.3` | [FasterDecoding/medusa-vicuna-13b-v1.3](https://huggingface.co/FasterDecoding/medusa-vicuna-13b-v1.3) | | 33B | `python -m medusa.inference.cli --model FasterDecoding/medusa-vicuna-33b-v1.3` | [FasterDecoding/medusa-vicuna-33b-v1.3](https://huggingface.co/FasterDecoding/medusa-vicuna-33b-v1.3) | ### Inference We currently support inference in the single GPU and batch size 1 setting, which is the most common setup for local model hosting. We are actively working to extend Medusa's capabilities by integrating it into other inference frameworks, please don't hesitate to reach out if you are interested in contributing to this effort. You can use the following command for lauching a CLI interface: ```bash python -m medusa.inference.cli --model [path of medusa model] ``` You can also pass `--load-in-8bit` or `--load-in-4bit` to load the base model in quantized format.

FasterDecoding/medusa-vicuna-13b-v1.3

FasterDecoding

2023-09-11T15:53:15Z

63

5

transformers

[ "transformers", "endpoints_compatible", "region:us" ]

null

2023-09-10T02:47:47Z

<div align="center"><img src="https://github.com/FasterDecoding/Medusa/blob/main/assets/logo.png?raw=true" alt="Medusa" width="100" align="center"></div> <div align="center"><h1> Medusa: Simple Framework for Accelerating LLM Generation with Multiple Decoding Heads</h1></div> <p align="center"> | <a href="https://sites.google.com/view/ medusa-llm"><b>Blog</b></a> | <a href="https://github.com/FasterDecoding/Medusa"><b>Codebase</b></a> | </p> --- ## Installation ### Method 1: With pip ```bash pip install medusa-llm ``` ### Method 2: From source ```bash git clone https://github.com/FasterDecoding/Medusa.git cd Medusa pip install -e . ``` ### Model Weights | Size | Chat Command | Hugging Face Repo | | ---- | --------------------------------------------- | --------------------------------------------------------------------- | | 7B | `python -m medusa.inference.cli --model FasterDecoding/medusa-vicuna-7b-v1.3` | [FasterDecoding/medusa-vicuna-33b-v1.3](https://huggingface.co/FasterDecoding/medusa-vicuna-7b-v1.3) | | 13B | `python -m medusa.inference.cli --model FasterDecoding/medusa-vicuna-13b-v1.3` | [FasterDecoding/medusa-vicuna-13b-v1.3](https://huggingface.co/FasterDecoding/medusa-vicuna-13b-v1.3) | | 33B | `python -m medusa.inference.cli --model FasterDecoding/medusa-vicuna-33b-v1.3` | [FasterDecoding/medusa-vicuna-33b-v1.3](https://huggingface.co/FasterDecoding/medusa-vicuna-33b-v1.3) | ### Inference We currently support inference in the single GPU and batch size 1 setting, which is the most common setup for local model hosting. We are actively working to extend Medusa's capabilities by integrating it into other inference frameworks, please don't hesitate to reach out if you are interested in contributing to this effort. You can use the following command for lauching a CLI interface: ```bash python -m medusa.inference.cli --model [path of medusa model] ``` You can also pass `--load-in-8bit` or `--load-in-4bit` to load the base model in quantized format.

geralt/MechDistilGPT2

geralt

2023-09-11T15:49:22Z

137

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "Causal Language modeling", "CLM", "arxiv:2105.09680", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-03-02T23:29:05Z

--- tags: - Causal Language modeling - text-generation - CLM model_index: - name: MechDistilGPT2 results: - task: name: Causal Language modeling type: Causal Language modeling --- # MechDistilGPT2 ## Table of Contents - [Model Details](#model-details) - [Uses](#uses) - [Risks, Limitations and Biases](#risks-limitations-and-biases) - [Training](#training) - [Environmental Impact](#environmental-impact) - [How to Get Started With the Model](#how-to-get-started-with-the-model) ## Model Details - **Model Description:** This model is fine-tuned on text scraped from 100+ Mechanical/Automotive pdf books. - **Developed by:** [Ashwin](https://huggingface.co/geralt) - **Model Type:** Causal Language modeling - **Language(s):** English - **License:** [More Information Needed] - **Parent Model:** See the [DistilGPT2model](https://huggingface.co/distilgpt2) for more information about the Distilled-GPT2 base model. - **Resources for more information:** - [Research Paper](https://arxiv.org/abs/2105.09680) - [GitHub Repo](https://github.com/huggingface/notebooks/blob/master/examples/language_modeling.ipynb) ## Uses #### Direct Use The model can be used for tasks including topic classification, Causal Language modeling and text generation #### Misuse and Out-of-scope Use The model should not be used to intentionally create hostile or alienating environments for people. In addition, the model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. ## Risks, Limitations and Biases **CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.** Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). ## Training #### Training Data This model is fine-tuned on text scraped from 100+ Mechanical/Automotive pdf books. #### Training Procedure ###### Fine-Tuning * Default Training Args * Epochs = 3 * Training set = 200k sentences * Validation set = 40k sentences ###### Framework versions * Transformers 4.7.0.dev0 * Pytorch 1.8.1+cu111 * Datasets 1.6.2 * Tokenizers 0.10.2 # Environmental Impact Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More information needed] - **Hours used:** [More information needed] - **Cloud Provider:** [More information needed] - **Compute Region:** [More information needed"] - **Carbon Emitted:** [More information needed] ## How to Get Started With the Model ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("geralt/MechDistilGPT2") model = AutoModelForCausalLM.from_pretrained("geralt/MechDistilGPT2") ```

PabloSuaLap/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es-retrained-pabloV3

PabloSuaLap

2023-09-11T15:44:00Z

63

0

transformers

[ "transformers", "tf", "tensorboard", "bert", "question-answering", "generated_from_keras_callback", "base_model:mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es", "base_model:finetune:mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2023-08-17T18:06:48Z

--- license: apache-2.0 tags: - generated_from_keras_callback base_model: mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es model-index: - name: P4B10/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es-retrained-pabloV3 results: [] ---  # P4B10/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es-retrained-pabloV3 This model is a fine-tuned version of [mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es](https://huggingface.co/mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.7249 - Train End Logits Accuracy: 0.1667 - Train Start Logits Accuracy: 0.1667 - Validation Loss: 3.2576 - Validation End Logits Accuracy: 0.0 - Validation Start Logits Accuracy: 0.8333 - Epoch: 1 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 4.7073 | 0.1667 | 0.1667 | 3.5715 | 0.0 | 0.8333 | 0 | | 3.7249 | 0.1667 | 0.1667 | 3.2576 | 0.0 | 0.8333 | 1 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.0 - Tokenizers 0.13.2

RyyyT/q-Taxi-v3

RyyyT

2023-09-11T15:39:52Z

0

null

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

reinforcement-learning

2023-09-11T15:38:05Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="RyyyT/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"]) ```

ProomptEngineer/cute-animals-style

ProomptEngineer

2023-09-11T15:38:10Z

48

4

diffusers

[ "diffusers", "text-to-image", "stable-diffusion", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:other", "region:us" ]

text-to-image

2023-09-11T15:38:06Z

--- license: other tags: - text-to-image - stable-diffusion - lora - diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: PE_CuteAnimals widget: - text: PE_CuteAnimals --- # Cute Animals [Style] ![Image 0](2172186.jpeg) <p>lora to make cute animal illustrations</p><p>Weights of 0.8-1</p><h2 id="heading-7">If you want to donate:</h2><h2 id="heading-8"><a target="_blank" rel="ugc" href="https://ko-fi.com/proomptengineer">https://ko-fi.com/proomptengineer</a></h2><p></p> ## Image examples for the model: ![Image 1](2172182.jpeg) ![Image 2](2172183.jpeg) ![Image 3](2172184.jpeg) ![Image 4](2172187.jpeg) ![Image 5](2172185.jpeg) ![Image 6](2172189.jpeg) ![Image 7](2172188.jpeg) ![Image 8](2172190.jpeg) ![Image 9](2172191.jpeg)

Lethargus/Taxi-v3

Lethargus

2023-09-11T15:37:23Z

0

null

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

reinforcement-learning

2023-09-11T15:32:40Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage model = load_from_hub(repo_id="Lethargus/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"])

ProomptEngineer/pe-habsburg-diffusion-style-big-chin

ProomptEngineer

2023-09-11T15:34:56Z

17

1

diffusers

[ "diffusers", "text-to-image", "stable-diffusion", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:other", "region:us" ]

text-to-image

2023-09-11T15:34:53Z

--- license: other tags: - text-to-image - stable-diffusion - lora - diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: PEHabsburg widget: - text: PEHabsburg --- # PE Habsburg Diffusion [Style] [Big Chin] ![Image 0](2186263.jpeg) <p>Add some habsburg to your images!</p><p>weights 1-1.4</p><h2 id="heading-7">If you want to donate:</h2><h2 id="heading-8"><a target="_blank" rel="ugc" href="https://ko-fi.com/proomptengineer">https://ko-fi.com/proomptengineer</a></h2> ## Image examples for the model: ![Image 1](2186265.jpeg) ![Image 2](2186232.jpeg) ![Image 3](2186236.jpeg) ![Image 4](2186228.jpeg) ![Image 5](2186231.jpeg) ![Image 6](2186234.jpeg) ![Image 7](2186242.jpeg) ![Image 8](2186248.jpeg) ![Image 9](2186250.jpeg)

ProomptEngineer/pe-shitty-fanart

ProomptEngineer

2023-09-11T15:29:56Z

99

7

diffusers

[ "diffusers", "text-to-image", "stable-diffusion", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:other", "region:us" ]

text-to-image

2023-09-11T15:29:53Z

--- license: other tags: - text-to-image - stable-diffusion - lora - diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: PETerribleFanArt widget: - text: PETerribleFanArt --- # PE Shitty FanArt ![Image 0](2028526.jpeg) <h2 id="heading-7">Sick of perfect AI Images? Then use this Lora to make some terrible FanArt!</h2><h2 id="heading-8">Weights 0.8-1</h2><h2 id="heading-63">If you want to donate:</h2><h2 id="heading-64"><a target="_blank" rel="ugc" href="https://ko-fi.com/proomptengineer">https://ko-fi.com/proomptengineer</a></h2><p></p> ## Image examples for the model: ![Image 1](2028530.jpeg) ![Image 2](2028528.jpeg) ![Image 3](2028517.jpeg) ![Image 4](2028518.jpeg) ![Image 5](2028519.jpeg) ![Image 6](2028522.jpeg) ![Image 7](2028520.jpeg) ![Image 8](2028532.jpeg) ![Image 9](2028533.jpeg)

saattrupdan/xlmr-base-texas-squad-da

saattrupdan

2023-09-11T15:29:54Z

133

5

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "xlm-roberta", "question-answering", "generated_from_trainer", "da", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "endpoints_compatible", "region:us" ]

question-answering

2022-03-02T23:29:05Z

--- language: - da license: mit tags: - generated_from_trainer widget: - text: Hvem handler artiklen om? context: 'Forfatter og musiker Flemming Quist Møller er død i en alder af 79 år. Den folkekære kunstner faldt om ved morgenbordet med en blodprop i hjertet i mandags. Det kunne forfatterens søn, Carl Quist-Møller, bekræfte over for TV 2 Lorry.- Han faldt om i det hus i Taarbæk, hvor han er vokset op og også har boet de sidste år af sit liv. Han blev lagt i koma på Rigshospitalet. Her har vi siddet omkring ham i en uge, siger Carl Quist-Møller til mediet.MindeordI mange år var Flemming Quist Møller en del af bandet Bazaar sammen med Peter Bastian, Anders Koppel og Mehmet Ozan.Anders Koppel er tydeligt rørt over vennens død, da Ekstra Bladet rækker ud til ham mandag aften.- Det er en stor del af mit liv, der er forsvundet med Flemmings liv, det er klart. Vi har spillet sammen i 37 år, siger han og fortsætter:- Jeg vil mest huske ham for hans ukonventionelle tilgang til alting. Flemming havde et meget stærkt blik for det autentiske og ærlige. Han var ikke bundet af normer -tværtimod, hvis han så en norm, hvor noget skulle gøres på en bestemt måde, så flygtede han eller prøvede at springe det i stumper og stykker.Ifølge den danske musiker og komponist er netop følgende ord rammende for Flemming Quist Møller: Original, vidende, kompromisløs og humoristisk.' base_model: xlm-roberta-base model-index: - name: xlmr-base-texas-squad-da results: [] --- # TExAS-SQuAD-da This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the TExAS-SQuAD-da dataset. It achieves the following results on the evaluation set: - Exact match: 63.96% - F1-score: 68.40% In comparison, the `jacobshein/danish-bert-botxo-qa-squad` model achieves 30.37% EM and 37.15% F1. ## 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 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.6438 | 1.0 | 4183 | 1.4711 | | 1.4079 | 2.0 | 8366 | 1.4356 | | 1.2532 | 3.0 | 12549 | 1.4509 | ### Framework versions - Transformers 4.12.2 - Pytorch 1.8.1+cu101 - Datasets 1.12.1 - Tokenizers 0.10.3

mehta-rohan/car-bike-diff

mehta-rohan

2023-09-11T15:25:14Z

0

fastai

[ "fastai", "image_classification", "en", "region:us" ]

null

2023-09-11T12:11:40Z

--- language: - en library_name: fastai tags: - image_classification --- This is my first model Starting the AI/ML journey

esperesa/xlm-roberta-base-finetuned-panx-all

esperesa

2023-09-11T15:23:31Z

126

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-09-11T15:03:15Z

--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-all results: [] ---  # xlm-roberta-base-finetuned-panx-all This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1828 - F1: 0.8519 ## 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: 24 - eval_batch_size: 24 - 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2947 | 1.0 | 739 | 0.1879 | 0.8175 | | 0.152 | 2.0 | 1478 | 0.1853 | 0.8385 | | 0.0974 | 3.0 | 2217 | 0.1828 | 0.8519 | ### Framework versions - Transformers 4.16.2 - Pytorch 2.0.1+cu118 - Datasets 1.16.1 - Tokenizers 0.14.0

ProomptEngineer/pe-ice-sculpture-style

ProomptEngineer

2023-09-11T15:23:17Z

31

2

diffusers

[ "diffusers", "text-to-image", "stable-diffusion", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:other", "region:us" ]

text-to-image

2023-09-11T15:23:14Z

--- license: other tags: - text-to-image - stable-diffusion - lora - diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: PEIceSculpture widget: - text: PEIceSculpture --- # PE Ice Sculpture [Style] ![Image 0](2249636.jpeg) <p>make beautiful images in the style of ice sculpture...</p><p>weights 0.8-1</p><h2 id="heading-63">If you want to donate:</h2><h2 id="heading-64"><a target="_blank" rel="ugc" href="https://ko-fi.com/proomptengineer">https://ko-fi.com/proomptengineer</a></h2> ## Image examples for the model: ![Image 1](2249584.jpeg) ![Image 2](2249589.jpeg) ![Image 3](2249590.jpeg) ![Image 4](2249591.jpeg) ![Image 5](2249592.jpeg) ![Image 6](2249585.jpeg) ![Image 7](2249594.jpeg) ![Image 8](2249986.jpeg) ![Image 9](2250115.jpeg)

Prot10/swinv2-base-patch4-window8-256-for-pre_evaluation

Prot10

2023-09-11T15:22:30Z

4

0

transformers

[ "transformers", "pytorch", "swinv2", "image-classification", "generated_from_trainer", "base_model:microsoft/swinv2-base-patch4-window8-256", "base_model:finetune:microsoft/swinv2-base-patch4-window8-256", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-08-30T11:21:06Z

--- license: apache-2.0 base_model: microsoft/swinv2-base-patch4-window8-256 tags: - generated_from_trainer metrics: - accuracy model-index: - name: swinv2-base-patch4-window8-256-for-pre_evaluation results: [] ---  # swinv2-base-patch4-window8-256-for-pre_evaluation This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window8-256](https://huggingface.co/microsoft/swinv2-base-patch4-window8-256) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4873 - Accuracy: 0.4106 ## 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: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.6064 | 1.0 | 16 | 1.5189 | 0.3073 | | 1.5058 | 2.0 | 32 | 1.5056 | 0.3073 | | 1.5176 | 3.0 | 48 | 1.5176 | 0.2961 | | 1.4883 | 4.0 | 64 | 1.5130 | 0.3073 | | 1.4446 | 5.0 | 80 | 1.4540 | 0.3296 | | 1.4568 | 6.0 | 96 | 1.5154 | 0.3156 | | 1.4106 | 7.0 | 112 | 1.4272 | 0.3883 | | 1.3804 | 8.0 | 128 | 1.4185 | 0.3743 | | 1.3725 | 9.0 | 144 | 1.3943 | 0.3911 | | 1.3441 | 10.0 | 160 | 1.4510 | 0.4022 | | 1.3335 | 11.0 | 176 | 1.4337 | 0.3827 | | 1.3055 | 12.0 | 192 | 1.4633 | 0.3855 | | 1.3303 | 13.0 | 208 | 1.4674 | 0.3883 | | 1.2882 | 14.0 | 224 | 1.4388 | 0.3911 | | 1.2362 | 15.0 | 240 | 1.4676 | 0.3855 | | 1.2572 | 16.0 | 256 | 1.4805 | 0.3799 | | 1.2164 | 17.0 | 272 | 1.4717 | 0.3939 | | 1.221 | 18.0 | 288 | 1.4354 | 0.4078 | | 1.1713 | 19.0 | 304 | 1.4836 | 0.4078 | | 1.18 | 20.0 | 320 | 1.4873 | 0.4106 | | 1.1349 | 21.0 | 336 | 1.4853 | 0.3855 | | 1.1138 | 22.0 | 352 | 1.4927 | 0.3966 | | 1.1402 | 23.0 | 368 | 1.4672 | 0.3994 | | 1.1183 | 24.0 | 384 | 1.5033 | 0.4022 | | 1.0834 | 25.0 | 400 | 1.5448 | 0.3855 | | 1.0515 | 26.0 | 416 | 1.5131 | 0.3939 | | 1.0745 | 27.0 | 432 | 1.5314 | 0.3827 | | 1.0332 | 28.0 | 448 | 1.5474 | 0.3939 | | 1.0679 | 29.0 | 464 | 1.5327 | 0.3855 | | 1.0295 | 30.0 | 480 | 1.5402 | 0.3855 | ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

ProomptEngineer/pe-snow-sculpture-style

ProomptEngineer

2023-09-11T15:22:04Z

28

1

diffusers

[ "diffusers", "text-to-image", "stable-diffusion", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:other", "region:us" ]

text-to-image

2023-09-11T15:21:55Z

--- license: other tags: - text-to-image - stable-diffusion - lora - diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: PESnowSculpture widget: - text: PESnowSculpture --- # PE Snow Sculpture [Style] ![Image 0](2249928.jpeg) <p>make some snow sculptures...</p><p>weights 0.8-1</p><h2 id="heading-63">If you want to donate:</h2><h2 id="heading-64"><a target="_blank" rel="ugc" href="https://ko-fi.com/proomptengineer">https://ko-fi.com/proomptengineer</a></h2> ## Image examples for the model: ![Image 1](2249926.jpeg) ![Image 2](2249923.jpeg) ![Image 3](2249921.jpeg) ![Image 4](2249927.jpeg) ![Image 5](2249922.jpeg) ![Image 6](2249925.jpeg) ![Image 7](2249934.jpeg) ![Image 8](2249924.jpeg) ![Image 9](2249930.jpeg)

ProomptEngineer/pe-anime-background-landscapes-style

ProomptEngineer

2023-09-11T15:20:28Z

88

10

diffusers

[ "diffusers", "text-to-image", "stable-diffusion", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:other", "region:us" ]

text-to-image

2023-09-11T15:20:24Z

--- license: other tags: - text-to-image - stable-diffusion - lora - diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: PEAnimeBG widget: - text: PEAnimeBG --- # PE Anime Background / Landscapes [Style] ![Image 0](2266542.jpeg) <p>Lora to make landscapes or backgrounds in anime style...</p><p>weights 0.8-1</p><h2 id="heading-63">If you want to donate:</h2><h2 id="heading-64"><a target="_blank" rel="ugc" href="https://ko-fi.com/proomptengineer">https://ko-fi.com/proomptengineer</a></h2> ## Image examples for the model: ![Image 1](2266568.jpeg) ![Image 2](2266530.jpeg) ![Image 3](2266540.jpeg) ![Image 4](2266554.jpeg) ![Image 5](2266541.jpeg) ![Image 6](2266560.jpeg) ![Image 7](2266571.jpeg) ![Image 8](2266566.jpeg) ![Image 9](2266567.jpeg)

esperesa/xlm-roberta-base-finetuned-panx-en

esperesa

2023-09-11T15:10:24Z

105

0

transformers

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

token-classification

2023-09-11T15:03:09Z

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-en results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.en metrics: - name: F1 type: f1 value: 0.6837988826815643 ---  # xlm-roberta-base-finetuned-panx-en 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: 0.3984 - F1: 0.6838 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.1357 | 1.0 | 50 | 0.5871 | 0.4590 | | 0.5236 | 2.0 | 100 | 0.4412 | 0.6478 | | 0.3765 | 3.0 | 150 | 0.3984 | 0.6838 | ### Framework versions - Transformers 4.16.2 - Pytorch 2.0.1+cu118 - Datasets 1.16.1 - Tokenizers 0.14.0

irenepap/t5-small-asqa-ob

irenepap

2023-09-11T15:09:52Z

116

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "dataset:din0s/asqa", "base_model:google/t5-small-ssm-nq", "base_model:finetune:google/t5-small-ssm-nq", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-09-28T14:00:43Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: din0s/asqa metrics: - rouge base_model: google/t5-small-ssm-nq model-index: - name: t5-small-asqa-ob results: [] ---  # t5-small-asqa-ob This model is a fine-tuned version of [google/t5-small-ssm-nq](https://huggingface.co/google/t5-small-ssm-nq) on the [ASQA](https://huggingface.co/datasets/din0s/asqa) dataset without context (closed book). It achieves the following results on the evaluation set: - Loss: 2.8099 - Rouge1: 0.1493 - Rouge2: 0.0837 - Rougel: 0.1272 - Rougelsum: 0.1270 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | 3.8208 | 1.0 | 710 | 2.7856 | 0.1267 | 0.0644 | 0.1086 | 0.1084 | | 3.0532 | 2.0 | 1420 | 2.6247 | 0.1321 | 0.0721 | 0.1145 | 0.1144 | | 2.5656 | 3.0 | 2130 | 2.5062 | 0.1399 | 0.0773 | 0.1213 | 0.1213 | | 2.3806 | 4.0 | 2840 | 2.5004 | 0.1431 | 0.0805 | 0.1243 | 0.1241 | | 2.157 | 5.0 | 3550 | 2.5008 | 0.1455 | 0.0808 | 0.1255 | 0.1254 | | 2.0458 | 6.0 | 4260 | 2.5313 | 0.1510 | 0.0846 | 0.1303 | 0.1301 | | 1.914 | 7.0 | 4970 | 2.5298 | 0.1585 | 0.0885 | 0.1361 | 0.1358 | | 1.7479 | 8.0 | 5680 | 2.5832 | 0.1508 | 0.0844 | 0.1292 | 0.1291 | | 1.6875 | 9.0 | 6390 | 2.5928 | 0.1493 | 0.0834 | 0.1281 | 0.1279 | | 1.574 | 10.0 | 7100 | 2.6364 | 0.1591 | 0.0885 | 0.1364 | 0.1363 | | 1.4554 | 11.0 | 7810 | 2.6978 | 0.1513 | 0.0849 | 0.1295 | 0.1295 | | 1.3909 | 12.0 | 8520 | 2.8099 | 0.1493 | 0.0837 | 0.1272 | 0.1270 | ### Framework versions - Transformers 4.23.0.dev0 - Pytorch 1.12.1+cu102 - Datasets 2.5.1 - Tokenizers 0.12.1

moonlightnexus/realize

moonlightnexus

2023-09-11T15:07:50Z

37

1

diffusers

[ "diffusers", "text-to-image", "en", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-09-11T09:26:08Z

--- license: other language: - en library_name: diffusers pipeline_tag: text-to-image ---

danbochman/ccxl

danbochman

2023-09-11T15:07:42Z

40

1

diffusers

[ "diffusers", "safetensors", "text-to-image", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]

text-to-image

2023-09-11T09:49:13Z

--- library_name: diffusers pipeline_tag: text-to-image --- This is a `diffusers` compatible version of the [Crystal Clear XL model](https://civitai.com/models/122822/crystal-clear-xl) from Team Crystal Clear.

checkiejan/flan-t5-prefix-30-10-2

checkiejan

2023-09-11T15:06:10Z

0

peft

[ "peft", "region:us" ]

null

2023-09-11T15:06:06Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0

ldos/text_shortening_model_v30

ldos

2023-09-11T15:05:21Z

8

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-09-11T14:06:20Z

--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: text_shortening_model_v30 results: [] ---  # text_shortening_model_v30 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.6784 - Rouge1: 0.4871 - Rouge2: 0.2579 - Rougel: 0.428 - Rougelsum: 0.4272 - Bert precision: 0.8743 - Bert recall: 0.8706 - Average word count: 8.4775 - Max word count: 17 - Min word count: 3 - Average token count: 12.9249 - % shortened texts with length > 12: 9.3093 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bert precision | Bert recall | Average word count | Max word count | Min word count | Average token count | % shortened texts with length > 12 | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:--------------:|:-----------:|:------------------:|:--------------:|:--------------:|:-------------------:|:----------------------------------:| | 1.2044 | 1.0 | 145 | 1.6064 | 0.5052 | 0.2865 | 0.4472 | 0.448 | 0.8751 | 0.8756 | 8.8979 | 17 | 3 | 13.4024 | 12.6126 | | 1.0041 | 2.0 | 290 | 1.4900 | 0.5154 | 0.2921 | 0.4554 | 0.4542 | 0.8735 | 0.878 | 9.3724 | 17 | 3 | 13.8529 | 17.7177 | | 0.8935 | 3.0 | 435 | 1.4617 | 0.5181 | 0.2968 | 0.4607 | 0.4622 | 0.8751 | 0.8818 | 9.4024 | 16 | 4 | 14.1171 | 17.1171 | | 0.8028 | 4.0 | 580 | 1.4744 | 0.5103 | 0.2966 | 0.4497 | 0.4496 | 0.8797 | 0.8725 | 8.1982 | 17 | 4 | 12.5706 | 8.1081 | | 0.7395 | 5.0 | 725 | 1.4797 | 0.5121 | 0.3016 | 0.4548 | 0.4554 | 0.8796 | 0.8761 | 8.4985 | 16 | 3 | 12.985 | 10.8108 | | 0.6986 | 6.0 | 870 | 1.5154 | 0.5218 | 0.2987 | 0.4554 | 0.4542 | 0.8808 | 0.879 | 8.7297 | 16 | 4 | 13.0691 | 14.1141 | | 0.6527 | 7.0 | 1015 | 1.5347 | 0.5083 | 0.2876 | 0.4494 | 0.4485 | 0.8797 | 0.8763 | 8.5526 | 16 | 4 | 13.012 | 11.4114 | | 0.588 | 8.0 | 1160 | 1.5578 | 0.4984 | 0.2752 | 0.4403 | 0.4399 | 0.8746 | 0.8728 | 8.6336 | 17 | 4 | 13.006 | 10.8108 | | 0.5705 | 9.0 | 1305 | 1.6569 | 0.5152 | 0.2902 | 0.4544 | 0.454 | 0.8803 | 0.8764 | 8.5135 | 16 | 4 | 13.1592 | 9.9099 | | 0.5601 | 10.0 | 1450 | 1.6651 | 0.5246 | 0.2837 | 0.4572 | 0.4579 | 0.8777 | 0.8807 | 8.979 | 16 | 4 | 13.6607 | 12.012 | | 0.523 | 11.0 | 1595 | 1.7085 | 0.5149 | 0.2854 | 0.4508 | 0.4507 | 0.879 | 0.8789 | 8.7718 | 17 | 4 | 13.2613 | 10.8108 | | 0.5032 | 12.0 | 1740 | 1.7886 | 0.5107 | 0.2817 | 0.4457 | 0.4457 | 0.8778 | 0.8772 | 8.8378 | 17 | 4 | 13.4204 | 11.7117 | | 0.4872 | 13.0 | 1885 | 1.8073 | 0.5097 | 0.2808 | 0.4439 | 0.4441 | 0.8786 | 0.8758 | 8.6306 | 16 | 4 | 13.1562 | 9.6096 | | 0.4703 | 14.0 | 2030 | 1.8436 | 0.5059 | 0.2754 | 0.4456 | 0.4457 | 0.8769 | 0.8756 | 8.6817 | 17 | 4 | 13.1471 | 9.9099 | | 0.4598 | 15.0 | 2175 | 1.9150 | 0.5148 | 0.2794 | 0.4532 | 0.4532 | 0.8798 | 0.8775 | 8.6907 | 18 | 4 | 13.1021 | 11.4114 | | 0.4385 | 16.0 | 2320 | 1.9319 | 0.4966 | 0.2666 | 0.4402 | 0.4406 | 0.8771 | 0.8724 | 8.2703 | 16 | 4 | 12.7237 | 7.8078 | | 0.4306 | 17.0 | 2465 | 1.9821 | 0.5041 | 0.2763 | 0.4449 | 0.4448 | 0.8788 | 0.8752 | 8.5105 | 16 | 4 | 13.0541 | 9.3093 | | 0.4154 | 18.0 | 2610 | 2.0345 | 0.5066 | 0.2746 | 0.4467 | 0.4461 | 0.8796 | 0.8732 | 8.1922 | 16 | 3 | 12.6186 | 7.8078 | | 0.3995 | 19.0 | 2755 | 2.0671 | 0.4954 | 0.2707 | 0.4411 | 0.4416 | 0.8773 | 0.8721 | 8.4505 | 17 | 4 | 12.8468 | 8.7087 | | 0.4053 | 20.0 | 2900 | 2.1265 | 0.4975 | 0.2704 | 0.4365 | 0.4364 | 0.8767 | 0.873 | 8.5075 | 17 | 3 | 13.0571 | 9.009 | | 0.3812 | 21.0 | 3045 | 2.2077 | 0.5011 | 0.2733 | 0.4406 | 0.4411 | 0.8764 | 0.8756 | 8.7958 | 17 | 3 | 13.4084 | 12.012 | | 0.3856 | 22.0 | 3190 | 2.2043 | 0.4956 | 0.2603 | 0.4358 | 0.4361 | 0.8775 | 0.8729 | 8.2913 | 17 | 3 | 12.8078 | 8.7087 | | 0.3805 | 23.0 | 3335 | 2.2201 | 0.5015 | 0.2698 | 0.4421 | 0.4427 | 0.8789 | 0.8728 | 8.2402 | 17 | 3 | 12.5856 | 8.1081 | | 0.3741 | 24.0 | 3480 | 2.2269 | 0.5029 | 0.2652 | 0.4412 | 0.4413 | 0.8767 | 0.8743 | 8.5856 | 16 | 4 | 13.039 | 10.2102 | | 0.3697 | 25.0 | 3625 | 2.2596 | 0.4956 | 0.2674 | 0.436 | 0.4359 | 0.8765 | 0.8728 | 8.4895 | 17 | 4 | 12.9129 | 9.9099 | | 0.3663 | 26.0 | 3770 | 2.2506 | 0.4891 | 0.2572 | 0.432 | 0.432 | 0.8749 | 0.8716 | 8.4865 | 17 | 4 | 12.8498 | 6.9069 | | 0.3409 | 27.0 | 3915 | 2.2893 | 0.4958 | 0.2635 | 0.4328 | 0.4327 | 0.8772 | 0.8727 | 8.3994 | 17 | 3 | 12.8228 | 9.6096 | | 0.3524 | 28.0 | 4060 | 2.3127 | 0.4907 | 0.2597 | 0.4322 | 0.4329 | 0.8751 | 0.8712 | 8.4084 | 16 | 4 | 12.7718 | 8.1081 | | 0.3379 | 29.0 | 4205 | 2.3167 | 0.4958 | 0.2674 | 0.4374 | 0.4368 | 0.8772 | 0.8737 | 8.4234 | 16 | 4 | 12.8138 | 7.2072 | | 0.3472 | 30.0 | 4350 | 2.3157 | 0.4987 | 0.2713 | 0.4415 | 0.4403 | 0.8788 | 0.8736 | 8.3634 | 17 | 3 | 12.6517 | 7.2072 | | 0.3353 | 31.0 | 4495 | 2.3506 | 0.4991 | 0.2631 | 0.4375 | 0.436 | 0.8764 | 0.8744 | 8.6396 | 17 | 4 | 13.1502 | 9.6096 | | 0.3466 | 32.0 | 4640 | 2.3594 | 0.4897 | 0.2593 | 0.4307 | 0.4301 | 0.8777 | 0.8711 | 8.1712 | 16 | 4 | 12.6126 | 5.4054 | | 0.3406 | 33.0 | 4785 | 2.3632 | 0.495 | 0.2746 | 0.4401 | 0.4397 | 0.8772 | 0.8732 | 8.5556 | 16 | 4 | 13.027 | 8.4084 | | 0.3382 | 34.0 | 4930 | 2.3505 | 0.4856 | 0.261 | 0.4306 | 0.4295 | 0.8758 | 0.8693 | 8.2733 | 17 | 3 | 12.6366 | 7.5075 | | 0.3392 | 35.0 | 5075 | 2.3665 | 0.4972 | 0.2719 | 0.4376 | 0.4372 | 0.8764 | 0.8741 | 8.6847 | 17 | 4 | 13.1532 | 9.3093 | | 0.3465 | 36.0 | 5220 | 2.3837 | 0.4981 | 0.2722 | 0.441 | 0.4411 | 0.876 | 0.8738 | 8.6607 | 17 | 4 | 13.1982 | 12.3123 | | 0.3377 | 37.0 | 5365 | 2.3984 | 0.4832 | 0.2623 | 0.4294 | 0.4285 | 0.8737 | 0.8697 | 8.5225 | 17 | 4 | 12.9399 | 10.5105 | | 0.3523 | 38.0 | 5510 | 2.3843 | 0.495 | 0.2671 | 0.438 | 0.4368 | 0.8754 | 0.873 | 8.5886 | 17 | 3 | 13.1111 | 7.2072 | | 0.3261 | 39.0 | 5655 | 2.4337 | 0.4948 | 0.2666 | 0.4378 | 0.4369 | 0.8771 | 0.8726 | 8.4655 | 17 | 4 | 12.8919 | 9.009 | | 0.3262 | 40.0 | 5800 | 2.4149 | 0.4971 | 0.2691 | 0.438 | 0.4375 | 0.8772 | 0.8717 | 8.4505 | 16 | 4 | 12.9249 | 8.1081 | | 0.3307 | 41.0 | 5945 | 2.4352 | 0.4834 | 0.2585 | 0.4261 | 0.4256 | 0.8746 | 0.8697 | 8.4024 | 17 | 3 | 12.8859 | 9.6096 | | 0.3226 | 42.0 | 6090 | 2.4241 | 0.488 | 0.2584 | 0.4318 | 0.4315 | 0.8756 | 0.8706 | 8.4444 | 17 | 3 | 12.8288 | 8.7087 | | 0.34 | 43.0 | 6235 | 2.4485 | 0.4891 | 0.2589 | 0.4326 | 0.432 | 0.8758 | 0.8705 | 8.3243 | 17 | 4 | 12.7898 | 6.6066 | | 0.3425 | 44.0 | 6380 | 2.4457 | 0.4865 | 0.26 | 0.4293 | 0.4287 | 0.8733 | 0.8713 | 8.6336 | 16 | 3 | 13.1922 | 9.6096 | | 0.3201 | 45.0 | 6525 | 2.4535 | 0.4811 | 0.2473 | 0.4243 | 0.4237 | 0.8751 | 0.8697 | 8.3093 | 17 | 3 | 12.7748 | 8.4084 | | 0.3094 | 46.0 | 6670 | 2.4918 | 0.4916 | 0.2614 | 0.4351 | 0.4342 | 0.8758 | 0.8726 | 8.5706 | 17 | 3 | 13.039 | 10.2102 | | 0.3262 | 47.0 | 6815 | 2.4839 | 0.4822 | 0.255 | 0.425 | 0.4237 | 0.8719 | 0.869 | 8.5375 | 17 | 4 | 12.976 | 9.009 | | 0.3186 | 48.0 | 6960 | 2.4966 | 0.486 | 0.2492 | 0.4276 | 0.4264 | 0.8738 | 0.8707 | 8.4745 | 17 | 3 | 12.955 | 6.6066 | | 0.3231 | 49.0 | 7105 | 2.4978 | 0.4889 | 0.2661 | 0.4343 | 0.434 | 0.8767 | 0.871 | 8.4505 | 17 | 3 | 12.8468 | 9.009 | | 0.3294 | 50.0 | 7250 | 2.4731 | 0.4916 | 0.2683 | 0.4374 | 0.4373 | 0.877 | 0.8726 | 8.4955 | 17 | 4 | 12.9369 | 9.3093 | | 0.3172 | 51.0 | 7395 | 2.4922 | 0.4861 | 0.2573 | 0.4314 | 0.431 | 0.8759 | 0.87 | 8.3003 | 17 | 4 | 12.6907 | 7.8078 | | 0.3247 | 52.0 | 7540 | 2.5044 | 0.4802 | 0.2495 | 0.4281 | 0.4282 | 0.8737 | 0.8698 | 8.4715 | 17 | 4 | 12.9009 | 8.1081 | | 0.3132 | 53.0 | 7685 | 2.5168 | 0.4832 | 0.2558 | 0.4273 | 0.4268 | 0.8736 | 0.8703 | 8.5706 | 17 | 3 | 12.967 | 9.3093 | | 0.3285 | 54.0 | 7830 | 2.5296 | 0.4882 | 0.26 | 0.4323 | 0.4319 | 0.8754 | 0.8724 | 8.5495 | 17 | 3 | 13.0541 | 8.7087 | | 0.3111 | 55.0 | 7975 | 2.5529 | 0.4829 | 0.2561 | 0.4268 | 0.4262 | 0.874 | 0.8694 | 8.4474 | 17 | 3 | 12.9339 | 7.2072 | | 0.3194 | 56.0 | 8120 | 2.5903 | 0.49 | 0.2614 | 0.4337 | 0.4329 | 0.8747 | 0.8719 | 8.5946 | 17 | 3 | 13.0931 | 8.1081 | | 0.3144 | 57.0 | 8265 | 2.5787 | 0.4859 | 0.2593 | 0.4315 | 0.4303 | 0.8739 | 0.8698 | 8.5195 | 17 | 4 | 12.8679 | 8.4084 | | 0.2972 | 58.0 | 8410 | 2.5759 | 0.4848 | 0.2565 | 0.4291 | 0.4279 | 0.8738 | 0.8697 | 8.5165 | 17 | 3 | 12.9219 | 8.1081 | | 0.3209 | 59.0 | 8555 | 2.5609 | 0.4792 | 0.246 | 0.4212 | 0.4201 | 0.8723 | 0.8678 | 8.4114 | 17 | 3 | 12.8799 | 6.9069 | | 0.3148 | 60.0 | 8700 | 2.5758 | 0.481 | 0.2454 | 0.4243 | 0.4231 | 0.874 | 0.8688 | 8.3664 | 16 | 3 | 12.7628 | 7.5075 | | 0.3026 | 61.0 | 8845 | 2.5819 | 0.4804 | 0.2555 | 0.4231 | 0.4231 | 0.8738 | 0.8689 | 8.4204 | 17 | 3 | 12.7628 | 8.4084 | | 0.3074 | 62.0 | 8990 | 2.5882 | 0.4893 | 0.2627 | 0.431 | 0.4303 | 0.8753 | 0.8715 | 8.4895 | 17 | 3 | 12.8889 | 8.7087 | | 0.3013 | 63.0 | 9135 | 2.5865 | 0.4835 | 0.2599 | 0.426 | 0.4251 | 0.8743 | 0.8707 | 8.4865 | 17 | 4 | 12.964 | 8.7087 | | 0.3274 | 64.0 | 9280 | 2.5957 | 0.4928 | 0.2649 | 0.436 | 0.4353 | 0.8738 | 0.8734 | 8.8018 | 17 | 3 | 13.2823 | 11.4114 | | 0.2928 | 65.0 | 9425 | 2.5846 | 0.4888 | 0.2653 | 0.4365 | 0.4356 | 0.8763 | 0.8713 | 8.2973 | 17 | 3 | 12.6637 | 8.1081 | | 0.3261 | 66.0 | 9570 | 2.5704 | 0.4901 | 0.267 | 0.4386 | 0.4374 | 0.8759 | 0.871 | 8.3303 | 17 | 4 | 12.7838 | 6.6066 | | 0.3153 | 67.0 | 9715 | 2.6023 | 0.4897 | 0.2611 | 0.4311 | 0.4301 | 0.8749 | 0.872 | 8.6426 | 17 | 3 | 13.0691 | 10.8108 | | 0.3185 | 68.0 | 9860 | 2.5831 | 0.4862 | 0.2579 | 0.4257 | 0.4247 | 0.8735 | 0.8718 | 8.6486 | 17 | 4 | 13.1441 | 12.012 | | 0.3054 | 69.0 | 10005 | 2.5949 | 0.4831 | 0.2575 | 0.4247 | 0.4239 | 0.8728 | 0.87 | 8.5405 | 17 | 4 | 13.036 | 9.9099 | | 0.3006 | 70.0 | 10150 | 2.5822 | 0.4853 | 0.252 | 0.4255 | 0.4243 | 0.8735 | 0.87 | 8.5495 | 17 | 3 | 13.0 | 10.5105 | | 0.3092 | 71.0 | 10295 | 2.5743 | 0.4903 | 0.2595 | 0.432 | 0.4315 | 0.8759 | 0.8719 | 8.4474 | 17 | 3 | 12.8559 | 8.7087 | | 0.2928 | 72.0 | 10440 | 2.5905 | 0.4918 | 0.2665 | 0.4356 | 0.4347 | 0.876 | 0.8724 | 8.4474 | 17 | 4 | 12.8679 | 8.4084 | | 0.3021 | 73.0 | 10585 | 2.6171 | 0.4957 | 0.266 | 0.4368 | 0.4354 | 0.8764 | 0.873 | 8.5676 | 17 | 3 | 12.964 | 11.1111 | | 0.3047 | 74.0 | 10730 | 2.6233 | 0.492 | 0.2655 | 0.4341 | 0.4328 | 0.8753 | 0.8715 | 8.5736 | 17 | 3 | 12.952 | 10.5105 | | 0.3043 | 75.0 | 10875 | 2.6405 | 0.4887 | 0.2623 | 0.4318 | 0.4309 | 0.8756 | 0.8704 | 8.4895 | 17 | 3 | 12.8679 | 9.9099 | | 0.305 | 76.0 | 11020 | 2.6171 | 0.4942 | 0.2687 | 0.4381 | 0.4372 | 0.8766 | 0.8724 | 8.5586 | 17 | 3 | 12.9369 | 10.8108 | | 0.3127 | 77.0 | 11165 | 2.6289 | 0.4959 | 0.2646 | 0.4366 | 0.4357 | 0.8767 | 0.8731 | 8.5766 | 17 | 3 | 13.006 | 12.012 | | 0.2945 | 78.0 | 11310 | 2.6453 | 0.4881 | 0.2589 | 0.4272 | 0.4261 | 0.8753 | 0.8711 | 8.5375 | 17 | 3 | 12.8739 | 9.3093 | | 0.2844 | 79.0 | 11455 | 2.6543 | 0.4895 | 0.2565 | 0.4294 | 0.4288 | 0.8753 | 0.8718 | 8.5616 | 17 | 3 | 12.997 | 11.7117 | | 0.3188 | 80.0 | 11600 | 2.6556 | 0.4919 | 0.2677 | 0.4328 | 0.4318 | 0.8756 | 0.8712 | 8.5345 | 17 | 3 | 12.973 | 9.9099 | | 0.2857 | 81.0 | 11745 | 2.6696 | 0.4914 | 0.2666 | 0.434 | 0.4332 | 0.8761 | 0.8717 | 8.4595 | 17 | 3 | 12.8829 | 10.5105 | | 0.3091 | 82.0 | 11890 | 2.6577 | 0.4986 | 0.2718 | 0.4397 | 0.4388 | 0.8766 | 0.8741 | 8.6276 | 17 | 3 | 13.1441 | 10.8108 | | 0.3115 | 83.0 | 12035 | 2.6720 | 0.4944 | 0.266 | 0.4364 | 0.4351 | 0.8766 | 0.8725 | 8.4925 | 17 | 3 | 12.9309 | 9.3093 | | 0.2947 | 84.0 | 12180 | 2.6490 | 0.4955 | 0.2628 | 0.4347 | 0.4343 | 0.8767 | 0.873 | 8.4985 | 17 | 3 | 13.018 | 7.5075 | | 0.312 | 85.0 | 12325 | 2.6425 | 0.4928 | 0.2689 | 0.4364 | 0.4358 | 0.8763 | 0.8728 | 8.5766 | 17 | 3 | 13.0631 | 9.9099 | | 0.3081 | 86.0 | 12470 | 2.6314 | 0.4904 | 0.2648 | 0.4327 | 0.432 | 0.875 | 0.8722 | 8.6246 | 17 | 3 | 13.1411 | 10.5105 | | 0.3043 | 87.0 | 12615 | 2.6485 | 0.4863 | 0.259 | 0.4273 | 0.4259 | 0.8736 | 0.8709 | 8.5736 | 17 | 3 | 13.0901 | 9.6096 | | 0.3034 | 88.0 | 12760 | 2.6402 | 0.4867 | 0.2604 | 0.4279 | 0.4274 | 0.8739 | 0.871 | 8.5706 | 17 | 3 | 13.0751 | 8.1081 | | 0.3058 | 89.0 | 12905 | 2.6573 | 0.4926 | 0.2638 | 0.4348 | 0.4339 | 0.8762 | 0.872 | 8.4805 | 17 | 3 | 12.955 | 7.8078 | | 0.2909 | 90.0 | 13050 | 2.6654 | 0.4955 | 0.2679 | 0.4357 | 0.4342 | 0.8756 | 0.8729 | 8.6817 | 17 | 3 | 13.1802 | 10.2102 | | 0.3082 | 91.0 | 13195 | 2.6757 | 0.4942 | 0.2671 | 0.4362 | 0.4349 | 0.8756 | 0.8724 | 8.5796 | 17 | 3 | 13.0721 | 9.6096 | | 0.3016 | 92.0 | 13340 | 2.6791 | 0.4933 | 0.2657 | 0.4351 | 0.4345 | 0.875 | 0.8722 | 8.6336 | 17 | 3 | 13.1441 | 9.9099 | | 0.2993 | 93.0 | 13485 | 2.6814 | 0.493 | 0.2658 | 0.433 | 0.4318 | 0.8747 | 0.8726 | 8.6997 | 17 | 3 | 13.2462 | 11.1111 | | 0.3022 | 94.0 | 13630 | 2.6698 | 0.4929 | 0.2638 | 0.4334 | 0.4324 | 0.8751 | 0.8723 | 8.5976 | 17 | 3 | 13.0961 | 9.3093 | | 0.2921 | 95.0 | 13775 | 2.6665 | 0.4867 | 0.2586 | 0.4294 | 0.4284 | 0.8744 | 0.8709 | 8.4955 | 17 | 3 | 12.988 | 8.4084 | | 0.3034 | 96.0 | 13920 | 2.6704 | 0.4854 | 0.2574 | 0.4275 | 0.4266 | 0.8742 | 0.8704 | 8.4805 | 17 | 3 | 12.9429 | 8.7087 | | 0.3063 | 97.0 | 14065 | 2.6749 | 0.4863 | 0.2576 | 0.4275 | 0.4266 | 0.8743 | 0.8707 | 8.4805 | 17 | 3 | 12.9369 | 8.7087 | | 0.2984 | 98.0 | 14210 | 2.6772 | 0.4858 | 0.258 | 0.4274 | 0.4264 | 0.8739 | 0.8704 | 8.5105 | 17 | 3 | 12.97 | 9.6096 | | 0.2942 | 99.0 | 14355 | 2.6784 | 0.4872 | 0.2595 | 0.4279 | 0.427 | 0.874 | 0.8704 | 8.5075 | 17 | 3 | 12.967 | 9.6096 | | 0.2866 | 100.0 | 14500 | 2.6784 | 0.4871 | 0.2579 | 0.428 | 0.4272 | 0.8743 | 0.8706 | 8.4775 | 17 | 3 | 12.9249 | 9.3093 | ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

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

hanlforever

2023-09-11T15:00:13Z

105

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-09-11T13:40:18Z

--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr results: [] ---  # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1650 - F1: 0.8562 ## 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: 24 - eval_batch_size: 24 - 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2884 | 1.0 | 715 | 0.1855 | 0.8234 | | 0.1452 | 2.0 | 1430 | 0.1642 | 0.8458 | | 0.094 | 3.0 | 2145 | 0.1650 | 0.8562 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.11.0

jroberts/my_awesome_pokemon_model_resnet18

jroberts

2023-09-11T14:57:50Z

270

0

transformers

[ "transformers", "pytorch", "safetensors", "resnet", "image-classification", "generated_from_trainer", "dataset:pokemon-classification", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-05-19T14:09:42Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - pokemon-classification metrics: - accuracy model-index: - name: my_awesome_pokemon_model_resnet18 results: - task: name: Image Classification type: image-classification dataset: name: pokemon-classification type: pokemon-classification config: full split: validation args: full metrics: - name: Accuracy type: accuracy value: 0.01079136690647482 ---  # my_awesome_pokemon_model_resnet18 This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on the pokemon-classification dataset. It achieves the following results on the evaluation set: - Loss: 6.8019 - Accuracy: 0.0108 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 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: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 4.275 | 1.0 | 76 | 6.1680 | 0.0014 | | 3.3896 | 1.99 | 152 | 6.6421 | 0.0115 | | 3.0563 | 2.99 | 228 | 6.8019 | 0.0108 | ### Framework versions - Transformers 4.29.1 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.13.3

venetis/distilbert-base-uncased_finetuned_disaster_tweets

venetis

2023-09-11T14:46:19Z

107

0

transformers

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

text-classification

2023-01-10T20:42:40Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 base_model: distilbert-base-uncased model-index: - name: distilbert-base-uncased_finetuned_disaster_tweets results: [] ---  # distilbert-base-uncased_finetuned_disaster_tweets This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4007 - Accuracy: 0.8399 - F1: 0.8384 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.4594 | 1.0 | 191 | 0.4059 | 0.8163 | 0.8164 | | 0.3399 | 2.0 | 382 | 0.3905 | 0.8346 | 0.8333 | | 0.2859 | 3.0 | 573 | 0.4007 | 0.8399 | 0.8384 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

gyesibiney/Distilbert-movie-review-sentiment-classifier-2

gyesibiney

2023-09-11T14:45:58Z

106

0

transformers

[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-09-10T18:57:28Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: Distilbert-capstone_1 results: [] ---  # Distilbert-capstone_1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4272 - Accuracy: 0.9251 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2895 | 1.0 | 623 | 0.2569 | 0.8930 | | 0.1635 | 2.0 | 1246 | 0.2479 | 0.9171 | | 0.0911 | 3.0 | 1869 | 0.3438 | 0.9207 | | 0.053 | 4.0 | 2492 | 0.3986 | 0.9223 | | 0.011 | 5.0 | 3115 | 0.4272 | 0.9251 | ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

AIYIYA/my_tt

AIYIYA

2023-09-11T14:42:38Z

61

0

transformers

[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-09-11T14:04:56Z

--- tags: - generated_from_keras_callback model-index: - name: AIYIYA/my_tt results: [] ---  # AIYIYA/my_tt This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.0110 - Validation Loss: 1.1941 - Train Accuracy: 0.5185 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 20, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | 1.8538 | 1.2004 | 0.5185 | 0 | | 1.0820 | 1.1683 | 0.5185 | 1 | | 1.0110 | 1.1941 | 0.5185 | 2 | ### Framework versions - Transformers 4.33.1 - TensorFlow 2.13.0 - Datasets 2.14.5 - Tokenizers 0.13.3

jasoneden/bloom560m-squad-helloworld

jasoneden

2023-09-11T14:42:14Z

86

8

transformers

[ "transformers", "pytorch", "safetensors", "bloom", "question-answering", "generated_from_trainer", "dataset:squad_v2", "base_model:bigscience/bloom-560m", "base_model:finetune:bigscience/bloom-560m", "license:bigscience-bloom-rail-1.0", "text-generation-inference", "endpoints_compatible", "region:us" ]

question-answering

2022-10-25T18:46:33Z

--- license: bigscience-bloom-rail-1.0 tags: - generated_from_trainer datasets: - squad_v2 base_model: bigscience/bloom-560m model-index: - name: debug_bloom_squad results: [] ---  # POC - BLOOM for QuestionAnswering, tuned on squad_v2 This model is a fine-tuned version of [bigscience/bloom-560m](https://huggingface.co/bigscience/bloom-560m) on the squad_v2 dataset. It is intended for a proof of concept, and perhaps to serve as a starting point for others trying to do the same thing. Ongoing discussion surrounding this effort: https://huggingface.co/bigscience/bloom/discussions/46#633c57b2ccce04161f82e6c2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 6 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.24.0.dev0 - Pytorch 1.12.1+cu102 - Datasets 2.6.1 - Tokenizers 0.13.1

jncraton/LaMini-GPT-774M-ct2-int8

jncraton

2023-09-11T14:38:50Z

13

0

transformers

[ "transformers", "text-generation", "en", "arxiv:2304.14402", "base_model:openai-community/gpt2-large", "base_model:finetune:openai-community/gpt2-large", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]

text-generation

2023-06-24T21:16:48Z

--- language: - en license: cc-by-nc-4.0 pipeline_tag: text-generation widget: - text: 'Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: how can I become more healthy? ### Response:' example_title: example base_model: gpt2-large ---  <p align="center" width="100%"> <a><img src="https://raw.githubusercontent.com/mbzuai-nlp/lamini-lm/main/images/lamini.png" alt="Title" style="width: 100%; min-width: 300px; display: block; margin: auto;"></a> </p> # LaMini-GPT-774M [![Model License](https://img.shields.io/badge/Model%20License-CC%20By%20NC%204.0-red.svg)]() This model is one of our LaMini-LM model series in paper "[LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions](https://github.com/mbzuai-nlp/lamini-lm)". This model is a fine-tuned version of [gpt2-large](https://huggingface.co/gpt2-large) on [LaMini-instruction dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction) that contains 2.58M samples for instruction fine-tuning. For more information about our dataset, please refer to our [project repository](https://github.com/mbzuai-nlp/lamini-lm/). You can view other models of LaMini-LM series as follows. Models with ✩ are those with the best overall performance given their size/architecture, hence we recommend using them. More details can be seen in our paper. <table> <thead> <tr> <th>Base model</th> <th colspan="4">LaMini-LM series (#parameters)</th> </tr> </thead> <tbody> <tr> <td>T5</td> <td><a href="https://huggingface.co/MBZUAI/lamini-t5-61m" target="_blank" rel="noopener noreferrer">LaMini-T5-61M</a></td> <td><a href="https://huggingface.co/MBZUAI/lamini-t5-223m" target="_blank" rel="noopener noreferrer">LaMini-T5-223M</a></td> <td><a href="https://huggingface.co/MBZUAI/lamini-t5-738m" target="_blank" rel="noopener noreferrer">LaMini-T5-738M</a></td> <td></td> </tr> <tr> <td>Flan-T5</td> <td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-77m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-77M</a>✩</td> <td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-248m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-248M</a>✩</td> <td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-783m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-783M</a>✩</td> <td></td> </tr> <tr> <td>Cerebras-GPT</td> <td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-111m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-111M</a></td> <td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-256m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-256M</a></td> <td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-590m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-590M</a></td> <td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-1.3b" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-1.3B</a></td> </tr> <tr> <td>GPT-2</td> <td><a href="https://huggingface.co/MBZUAI/lamini-gpt-124m" target="_blank" rel="noopener noreferrer">LaMini-GPT-124M</a>✩</td> <td><a href="https://huggingface.co/MBZUAI/lamini-gpt-774m" target="_blank" rel="noopener noreferrer">LaMini-GPT-774M</a>✩</td> <td><a href="https://huggingface.co/MBZUAI/lamini-gpt-1.5b" target="_blank" rel="noopener noreferrer">LaMini-GPT-1.5B</a>✩</td> <td></td> </tr> <tr> <td>GPT-Neo</td> <td><a href="https://huggingface.co/MBZUAI/lamini-neo-125m" target="_blank" rel="noopener noreferrer">LaMini-Neo-125M</a></td> <td><a href="https://huggingface.co/MBZUAI/lamini-neo-1.3b" target="_blank" rel="noopener noreferrer">LaMini-Neo-1.3B</a></td> <td></td> <td></td> </tr> <tr> <td>GPT-J</td> <td colspan="4">coming soon</td> </tr> <tr> <td>LLaMA</td> <td colspan="4">coming soon</td> </tr> </tbody> </table> ## Use ### Intended use We recommend using the model to respond to human instructions written in natural language. Since this decoder-only model is fine-tuned with wrapper text, we suggest using the same wrapper text to achieve the best performance. See the example on the right or the code below. We now show you how to load and use our model using HuggingFace `pipeline()`. ```python # pip install -q transformers from transformers import pipeline checkpoint = "{model_name}" model = pipeline('text-generation', model = checkpoint) instruction = 'Please let me know your thoughts on the given place and why you think it deserves to be visited: \n"Barcelona, Spain"' input_prompt = f"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:" generated_text = model(input_prompt, max_length=512, do_sample=True)[0]['generated_text'] print("Response", generated_text) ``` ## Training Procedure <p align="center" width="100%"> <a><img src="https://raw.githubusercontent.com/mbzuai-nlp/lamini-lm/main/images/lamini-pipeline.drawio.png" alt="Title" style="width: 100%; min-width: 250px; display: block; margin: auto;"></a> </p> We initialize with [gpt2-large](https://huggingface.co/gpt2-large) and fine-tune it on our [LaMini-instruction dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction). Its total number of parameters is 774M. ### Training Hyperparameters ## Evaluation We conducted two sets of evaluations: automatic evaluation on downstream NLP tasks and human evaluation on user-oriented instructions. For more detail, please refer to our [paper](). ## Limitations More information needed # Citation ```bibtex @article{lamini-lm, author = {Minghao Wu and Abdul Waheed and Chiyu Zhang and Muhammad Abdul-Mageed and Alham Fikri Aji }, title = {LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions}, journal = {CoRR}, volume = {abs/2304.14402}, year = {2023}, url = {https://arxiv.org/abs/2304.14402}, eprinttype = {arXiv}, eprint = {2304.14402} } ```

jncraton/LaMini-GPT-124M-ct2-int8

jncraton

2023-09-11T14:38:27Z

563

0

transformers

[ "transformers", "text-generation", "en", "arxiv:2304.14402", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]

text-generation

2023-06-24T22:21:05Z

--- language: - en license: cc-by-nc-4.0 pipeline_tag: text-generation widget: - text: 'Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: how can I become more healthy? ### Response:' example_title: example base_model: gpt2 ---  <p align="center" width="100%"> <a><img src="https://raw.githubusercontent.com/mbzuai-nlp/lamini-lm/main/images/lamini.png" alt="Title" style="width: 100%; min-width: 300px; display: block; margin: auto;"></a> </p> # LaMini-GPT-124M [![Model License](https://img.shields.io/badge/Model%20License-CC%20By%20NC%204.0-red.svg)]() This model is one of our LaMini-LM model series in paper "[LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions](https://github.com/mbzuai-nlp/lamini-lm)". This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on [LaMini-instruction dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction) that contains 2.58M samples for instruction fine-tuning. For more information about our dataset, please refer to our [project repository](https://github.com/mbzuai-nlp/lamini-lm/). You can view other models of LaMini-LM series as follows. Models with ✩ are those with the best overall performance given their size/architecture, hence we recommend using them. More details can be seen in our paper. <table> <thead> <tr> <th>Base model</th> <th colspan="4">LaMini-LM series (#parameters)</th> </tr> </thead> <tbody> <tr> <td>T5</td> <td><a href="https://huggingface.co/MBZUAI/lamini-t5-61m" target="_blank" rel="noopener noreferrer">LaMini-T5-61M</a></td> <td><a href="https://huggingface.co/MBZUAI/lamini-t5-223m" target="_blank" rel="noopener noreferrer">LaMini-T5-223M</a></td> <td><a href="https://huggingface.co/MBZUAI/lamini-t5-738m" target="_blank" rel="noopener noreferrer">LaMini-T5-738M</a></td> <td></td> </tr> <tr> <td>Flan-T5</td> <td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-77m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-77M</a>✩</td> <td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-248m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-248M</a>✩</td> <td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-783m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-783M</a>✩</td> <td></td> </tr> <tr> <td>Cerebras-GPT</td> <td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-111m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-111M</a></td> <td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-256m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-256M</a></td> <td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-590m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-590M</a></td> <td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-1.3b" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-1.3B</a></td> </tr> <tr> <td>GPT-2</td> <td><a href="https://huggingface.co/MBZUAI/lamini-gpt-124m" target="_blank" rel="noopener noreferrer">LaMini-GPT-124M</a>✩</td> <td><a href="https://huggingface.co/MBZUAI/lamini-gpt-774m" target="_blank" rel="noopener noreferrer">LaMini-GPT-774M</a>✩</td> <td><a href="https://huggingface.co/MBZUAI/lamini-gpt-1.5b" target="_blank" rel="noopener noreferrer">LaMini-GPT-1.5B</a>✩</td> <td></td> </tr> <tr> <td>GPT-Neo</td> <td><a href="https://huggingface.co/MBZUAI/lamini-neo-125m" target="_blank" rel="noopener noreferrer">LaMini-Neo-125M</a></td> <td><a href="https://huggingface.co/MBZUAI/lamini-neo-1.3b" target="_blank" rel="noopener noreferrer">LaMini-Neo-1.3B</a></td> <td></td> <td></td> </tr> <tr> <td>GPT-J</td> <td colspan="4">coming soon</td> </tr> <tr> <td>LLaMA</td> <td colspan="4">coming soon</td> </tr> </tbody> </table> ## Use ### Intended use We recommend using the model to respond to human instructions written in natural language. Since this decoder-only model is fine-tuned with wrapper text, we suggest using the same wrapper text to achieve the best performance. See the example on the right or the code below. We now show you how to load and use our model using HuggingFace `pipeline()`. ```python # pip install -q transformers from transformers import pipeline checkpoint = "{model_name}" model = pipeline('text-generation', model = checkpoint) instruction = 'Please let me know your thoughts on the given place and why you think it deserves to be visited: \n"Barcelona, Spain"' input_prompt = f"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:" generated_text = model(input_prompt, max_length=512, do_sample=True)[0]['generated_text'] print("Response", generated_text) ``` ## Training Procedure <p align="center" width="100%"> <a><img src="https://raw.githubusercontent.com/mbzuai-nlp/lamini-lm/main/images/lamini-pipeline.drawio.png" alt="Title" style="width: 100%; min-width: 250px; display: block; margin: auto;"></a> </p> We initialize with [gpt2](https://huggingface.co/gpt2) and fine-tune it on our [LaMini-instruction dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction). Its total number of parameters is 124M. ### Training Hyperparameters ## Evaluation We conducted two sets of evaluations: automatic evaluation on downstream NLP tasks and human evaluation on user-oriented instructions. For more detail, please refer to our [paper](). ## Limitations More information needed # Citation ```bibtex @article{lamini-lm, author = {Minghao Wu and Abdul Waheed and Chiyu Zhang and Muhammad Abdul-Mageed and Alham Fikri Aji }, title = {LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions}, journal = {CoRR}, volume = {abs/2304.14402}, year = {2023}, url = {https://arxiv.org/abs/2304.14402}, eprinttype = {arXiv}, eprint = {2304.14402} } ```

jncraton/LaMini-Flan-T5-248M-ct2-int8

jncraton

2023-09-11T14:37:41Z

232

0

transformers

[ "transformers", "generated_from_trainer", "instruction fine-tuning", "text2text-generation", "en", "arxiv:2304.14402", "base_model:google/flan-t5-base", "base_model:finetune:google/flan-t5-base", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]

text2text-generation

2023-06-04T21:36:33Z

--- language: - en license: cc-by-nc-4.0 tags: - generated_from_trainer - instruction fine-tuning pipeline_tag: text2text-generation widget: - text: how can I become more healthy? example_title: example base_model: google/flan-t5-base model-index: - name: flan-t5-small-distil-v2 results: [] ---  <p align="center" width="100%"> <a><img src="https://raw.githubusercontent.com/mbzuai-nlp/lamini-lm/main/images/lamini.png" alt="Title" style="width: 100%; min-width: 300px; display: block; margin: auto;"></a> </p> # LaMini-Flan-T5-248M [![Model License](https://img.shields.io/badge/Model%20License-CC%20By%20NC%204.0-red.svg)]() This model is one of our LaMini-LM model series in paper "[LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions](https://github.com/mbzuai-nlp/lamini-lm)". This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on [LaMini-instruction dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction) that contains 2.58M samples for instruction fine-tuning. For more information about our dataset, please refer to our [project repository](https://github.com/mbzuai-nlp/lamini-lm/). You can view other models of LaMini-LM series as follows. Models with ✩ are those with the best overall performance given their size/architecture, hence we recommend using them. More details can be seen in our paper. <table> <thead> <tr> <th>Base model</th> <th colspan="4">LaMini-LM series (#parameters)</th> </tr> </thead> <tbody> <tr> <td>T5</td> <td><a href="https://huggingface.co/MBZUAI/lamini-t5-61m" target="_blank" rel="noopener noreferrer">LaMini-T5-61M</a></td> <td><a href="https://huggingface.co/MBZUAI/lamini-t5-223m" target="_blank" rel="noopener noreferrer">LaMini-T5-223M</a></td> <td><a href="https://huggingface.co/MBZUAI/lamini-t5-738m" target="_blank" rel="noopener noreferrer">LaMini-T5-738M</a></td> <td></td> </tr> <tr> <td>Flan-T5</td> <td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-77m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-77M</a>✩</td> <td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-248m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-248M</a>✩</td> <td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-783m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-783M</a>✩</td> <td></td> </tr> <tr> <td>Cerebras-GPT</td> <td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-111m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-111M</a></td> <td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-256m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-256M</a></td> <td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-590m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-590M</a></td> <td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-1.3b" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-1.3B</a></td> </tr> <tr> <td>GPT-2</td> <td><a href="https://huggingface.co/MBZUAI/lamini-gpt-124m" target="_blank" rel="noopener noreferrer">LaMini-GPT-124M</a>✩</td> <td><a href="https://huggingface.co/MBZUAI/lamini-gpt-774m" target="_blank" rel="noopener noreferrer">LaMini-GPT-774M</a>✩</td> <td><a href="https://huggingface.co/MBZUAI/lamini-gpt-1.5b" target="_blank" rel="noopener noreferrer">LaMini-GPT-1.5B</a>✩</td> <td></td> </tr> <tr> <td>GPT-Neo</td> <td><a href="https://huggingface.co/MBZUAI/lamini-neo-125m" target="_blank" rel="noopener noreferrer">LaMini-Neo-125M</a></td> <td><a href="https://huggingface.co/MBZUAI/lamini-neo-1.3b" target="_blank" rel="noopener noreferrer">LaMini-Neo-1.3B</a></td> <td></td> <td></td> </tr> <tr> <td>GPT-J</td> <td colspan="4">coming soon</td> </tr> <tr> <td>LLaMA</td> <td colspan="4">coming soon</td> </tr> </tbody> </table> ## Use ### Intended use We recommend using the model to response to human instructions written in natural language. We now show you how to load and use our model using HuggingFace `pipeline()`. ```python # pip install -q transformers from transformers import pipeline checkpoint = "{model_name}" model = pipeline('text2text-generation', model = checkpoint) input_prompt = 'Please let me know your thoughts on the given place and why you think it deserves to be visited: \n"Barcelona, Spain"' generated_text = model(input_prompt, max_length=512, do_sample=True)[0]['generated_text'] print("Response", generated_text) ``` ## Training Procedure <p align="center" width="100%"> <a><img src="https://raw.githubusercontent.com/mbzuai-nlp/lamini-lm/main/images/lamini-pipeline.drawio.png" alt="Title" style="width: 100%; min-width: 250px; display: block; margin: auto;"></a> </p> We initialize with [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) and fine-tune it on our [LaMini-instruction dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction). Its total number of parameters is 248M. ### Training Hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ## Evaluation We conducted two sets of evaluations: automatic evaluation on downstream NLP tasks and human evaluation on user-oriented instructions. For more detail, please refer to our [paper](). ## Limitations More information needed # Citation ```bibtex @article{lamini-lm, author = {Minghao Wu and Abdul Waheed and Chiyu Zhang and Muhammad Abdul-Mageed and Alham Fikri Aji }, title = {LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions}, journal = {CoRR}, volume = {abs/2304.14402}, year = {2023}, url = {https://arxiv.org/abs/2304.14402}, eprinttype = {arXiv}, eprint = {2304.14402} } ```

Jzuluaga/wav2vec2-large-960h-lv60-self-en-atc-uwb-atcc

Jzuluaga

2023-09-11T14:30:11Z

96

3

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "audio", "en-atc", "en", "generated_from_trainer", "dataset:Jzuluaga/uwb_atcc", "arxiv:2203.16822", "arxiv:2211.04054", "base_model:facebook/wav2vec2-large-960h-lv60-self", "base_model:finetune:facebook/wav2vec2-large-960h-lv60-self", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-11-30T07:59:57Z

--- language: en license: apache-2.0 tags: - audio - automatic-speech-recognition - en-atc - en - generated_from_trainer datasets: - Jzuluaga/uwb_atcc metrics: - wer base_model: facebook/wav2vec2-large-960h-lv60-self model-index: - name: wav2vec2-large-960h-lv60-self-en-atc-uwb-atcc results: - task: type: automatic-speech-recognition name: Speech Recognition dataset: name: UWB-ATCC dataset (Air Traffic Control Communications) type: Jzuluaga/uwb_atcc config: test split: test metrics: - type: wer value: 17.2 name: TEST WER verified: false - type: wer value: 13.72 name: TEST WER (+LM) verified: false - task: type: automatic-speech-recognition name: Speech Recognition dataset: name: ATCOSIM corpus (Air Traffic Control Communications) type: Jzuluaga/atcosim_corpus config: test split: test metrics: - type: wer value: 15.31 name: TEST WER verified: false - type: wer value: 11.88 name: TEST WER (+LM) verified: false --- # wav2vec2-large-960h-lv60-self-en-atc-uwb-atcc This model is a fine-tuned version of [facebook/wav2vec2-large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self) on the [UWB-ATCC corpus](https://huggingface.co/datasets/Jzuluaga/uwb_atcc). <a href="https://colab.research.google.com/github/idiap/w2v2-air-traffic/blob/main/src/eval_xlsr_atc_model.ipynb"> <img alt="GitHub" src="https://colab.research.google.com/assets/colab-badge.svg\"> </a> <a href="https://github.com/idiap/w2v2-air-traffic"> <img alt="GitHub" src="https://img.shields.io/badge/GitHub-Open%20source-green\"> </a> It achieves the following results on the evaluation set: - Loss: 0.7287 - Wer: 0.1756 Paper: [How Does Pre-trained Wav2Vec 2.0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications](https://arxiv.org/abs/2203.16822). Authors: Juan Zuluaga-Gomez, Amrutha Prasad, Iuliia Nigmatulina, Saeed Sarfjoo, Petr Motlicek, Matthias Kleinert, Hartmut Helmke, Oliver Ohneiser, Qingran Zhan Abstract: Recent work on self-supervised pre-training focus</b> on leveraging large-scale unlabeled speech data to build robust end-to-end (E2E)acoustic models (AM) that can be later fine-tuned on downstream tasks e.g., automatic speech recognition (ASR). Yet, few works investigated the impact on performance when the data properties substantially differ between the pre-training and fine-tuning phases, termed domain shift. We target this scenario by analyzing the robustness of Wav2Vec 2.0 and XLS-R models on downstream ASR for a completely unseen domain, air traffic control (ATC) communications. We benchmark these two models on several open-source and challenging ATC databases with signal-to-noise ratio between 5 and 20 dB. Relative word error rate (WER) reductions between 20% to 40% are obtained in comparison to hybrid-based ASR baselines by only fine-tuning E2E acoustic models with a smaller fraction of labeled data. We analyze WERs on the low-resource scenario and gender bias carried by one ATC dataset. Code — GitHub repository: https://github.com/idiap/w2v2-air-traffic ## Usage You can use our Google Colab notebook to run and evaluate our model: https://github.com/idiap/w2v2-air-traffic/blob/master/src/eval_xlsr_atc_model.ipynb ## Intended uses & limitations This model was fine-tuned on air traffic control data. We don't expect that it keeps the same performance on some others datasets, e.g., LibriSpeech or CommonVoice. ## Training and evaluation data See Table 1 (page 3) in our paper: [How Does Pre-trained Wav2Vec 2.0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications](https://arxiv.org/abs/2203.16822). We described there the partitions of how to use our model. - We use the UWB-ATCC corpus to fine-tune this model. You can download the raw data here: https://lindat.mff.cuni.cz/repository/xmlui/handle/11858/00-097C-0000-0001-CCA1-0 - However, do not worry, we have prepared the database in `Datasets format`. Here, [UWB-ATCC corpus on HuggingFace](https://huggingface.co/datasets/Jzuluaga/uwb_atcc). You can scroll and check the train/test partitions, and even listen to some audios. - If you want to prepare a database in HuggingFace format, you can follow the data loader script in: [data_loader_atc.py](https://huggingface.co/datasets/Jzuluaga/uwb_atcc/blob/main/atc_data_loader.py). - ## Writing your own inference script If you use language model, you need to install the KenLM bindings with: ```bash conda activate your_environment pip install https://github.com/kpu/kenlm/archive/master.zip ``` The snippet of code: ```python from datasets import load_dataset, load_metric, Audio import torch from transformers import AutoModelForCTC, Wav2Vec2Processor, Wav2Vec2ProcessorWithLM import torchaudio.functional as F USE_LM = False DATASET_ID = "Jzuluaga/uwb_atcc" MODEL_ID = "Jzuluaga/wav2vec2-large-960h-lv60-self-en-atc-uwb-atcc" # 1. Load the dataset # we only load the 'test' partition, however, if you want to load the 'train' partition, you can change it accordingly uwb_atcc_corpus_test = load_dataset(DATASET_ID, "test", split="test") # 2. Load the model model = AutoModelForCTC.from_pretrained(MODEL_ID) # 3. Load the processors, we offer support with LM, which should yield better resutls if USE_LM: processor = Wav2Vec2ProcessorWithLM.from_pretrained(MODEL_ID) else: processor = Wav2Vec2Processor.from_pretrained(MODEL_ID) # 4. Format the test sample sample = next(iter(uwb_atcc_corpus_test)) file_sampling_rate = sample['audio']['sampling_rate'] # resample if neccessary if file_sampling_rate != 16000: resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), file_sampling_rate, 16000).numpy() else: resampled_audio = torch.tensor(sample["audio"]["array"]).numpy() input_values = processor(resampled_audio, return_tensors="pt").input_values # 5. Run the forward pass in the model with torch.no_grad(): logits = model(input_values).logits # get the transcription with processor if USE_LM: transcription = processor.batch_decode(logits.numpy()).text else: pred_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(pred_ids) # print the output print(transcription) ``` # Cite us If you use this code for your research, please cite our paper with: ``` @article{zuluaga2022how, title={How Does Pre-trained Wav2Vec2. 0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications}, author={Zuluaga-Gomez, Juan and Prasad, Amrutha and Nigmatulina, Iuliia and Sarfjoo, Saeed and others}, journal={IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar}, year={2022} } ``` and, ``` @article{zuluaga2022bertraffic, title={BERTraffic: BERT-based Joint Speaker Role and Speaker Change Detection for Air Traffic Control Communications}, author={Zuluaga-Gomez, Juan and Sarfjoo, Seyyed Saeed and Prasad, Amrutha and others}, journal={IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar}, year={2022} } ``` and, ``` @article{zuluaga2022atco2, title={ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications}, author={Zuluaga-Gomez, Juan and Vesel{\`y}, Karel and Sz{\"o}ke, Igor and Motlicek, Petr and others}, journal={arXiv preprint arXiv:2211.04054}, year={2022} } ``` ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 24 - eval_batch_size: 12 - 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: 10000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | No log | 1.06 | 500 | 2.9016 | 0.9995 | | 2.877 | 2.12 | 1000 | 0.9812 | 0.3485 | | 2.877 | 3.18 | 1500 | 0.7842 | 0.2732 | | 0.7834 | 4.25 | 2000 | 0.6962 | 0.2192 | | 0.7834 | 5.31 | 2500 | 0.6527 | 0.2042 | | 0.6084 | 6.37 | 3000 | 0.6220 | 0.1972 | | 0.6084 | 7.43 | 3500 | 0.6442 | 0.1934 | | 0.5147 | 8.49 | 4000 | 0.6793 | 0.1950 | | 0.5147 | 9.55 | 4500 | 0.6432 | 0.1920 | | 0.4566 | 10.62 | 5000 | 0.6605 | 0.1853 | | 0.4566 | 11.68 | 5500 | 0.6393 | 0.1866 | | 0.4155 | 12.74 | 6000 | 0.6918 | 0.1803 | | 0.4155 | 13.8 | 6500 | 0.6514 | 0.1791 | | 0.372 | 14.86 | 7000 | 0.7010 | 0.1851 | | 0.372 | 15.92 | 7500 | 0.6824 | 0.1786 | | 0.3368 | 16.99 | 8000 | 0.6895 | 0.1780 | | 0.3368 | 18.05 | 8500 | 0.7150 | 0.1759 | | 0.3244 | 19.11 | 9000 | 0.7141 | 0.1759 | | 0.3244 | 20.17 | 9500 | 0.7225 | 0.1756 | | 0.2981 | 21.23 | 10000 | 0.7287 | 0.1756 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.6.1 - Tokenizers 0.13.2

MaxKazak/ruBert-base-russian-emotion-detection

MaxKazak

2023-09-11T14:27:43Z

13,789

4

transformers

[ "transformers", "pytorch", "safetensors", "bert", "text-classification", "sentiment", "emotion-classification", "multilabel", "multiclass", "ru", "dataset:Djacon/ru_goemotions", "base_model:ai-forever/ruBert-base", "base_model:finetune:ai-forever/ruBert-base", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-05-28T15:25:35Z

--- language: - ru license: apache-2.0 tags: - sentiment - emotion-classification - multilabel - multiclass datasets: - Djacon/ru_goemotions metrics: - accuracy widget: - text: Очень рад тебя видеть! - text: Как дела? - text: Мне немного отвратно это делать - text: Я испытал мурашки от страха - text: Нет ничего радостного в этих горьких новостях - text: Ого, неожидал тебя здесь увидеть! - text: Фу ну и мерзость - text: Мне неприятно общение с тобой base_model: ai-forever/ruBert-base model-index: - name: ruBert-base-russian-emotions-classifier-goEmotions results: - task: type: multilabel-text-classification name: Multilabel Text Classification dataset: name: ru_goemotions type: Djacon/ru_goemotions args: ru metrics: - type: roc_auc value: 92% name: multilabel ROC AUC --- # ruBert-base-russian-emotions-classifier-goEmotions This model is a fine-tuned version of [ai-forever/ruBert-base](https://huggingface.co/ai-forever/ruBert-base) on [Djacon/ru_goemotions](https://huggingface.co/datasets/Djacon/ru_goemotions). It achieves the following results on the evaluation set (2nd epoch): - Loss: 0.2088 - AUC: 0.9240 The quality of the predicted probabilities on the test dataset is the following: | label | joy | interest | surpise | sadness | anger | disgust | fear | guilt | neutral | average | |----------|--------|----------|---------|---------|--------|---------|--------|--------|---------|---------| | AUC | 0.9369 | 0.9213 | 0.9325 | 0.8791 | 0.8374 | 0.9041 | 0.9470 | 0.9758 | 0.8518 | 0.9095 | | F1-micro | 0.9528 | 0.9157 | 0.9697 | 0.9284 | 0.8690 | 0.9658 | 0.9851 | 0.9875 | 0.7654 | 0.9266 | | F1-macro | 0.8369 | 0.7922 | 0.7561 | 0.7392 | 0.7351 | 0.7356 | 0.8176 | 0.8247 | 0.7650 | 0.7781 | ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | AUC | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1755 | 1.0 | 1685 | 0.1717 | 0.9220 | | 0.1391 | 2.0 | 3370 | 0.1757 | 0.9240 | | 0.0899 | 3.0 | 5055 | 0.2088 | 0.9106 | ### Framework versions - Transformers 4.24.0 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.11.0

osieosie/bloom-mnli-8bit-7b-bnb-seed65

osieosie

2023-09-11T14:13:28Z

0

peft

[ "peft", "region:us" ]

null

2023-09-11T14:13:27Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0.dev0

checkiejan/flan-t5-prefix-25-9-2

checkiejan

2023-09-11T14:10:12Z

0

peft

[ "peft", "region:us" ]

null

2023-09-11T14:10:08Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0

ldos/text_shortening_model_v29

ldos

2023-09-11T14:05:28Z

4

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-09-11T13:17:46Z

--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: text_shortening_model_v29 results: [] ---  # text_shortening_model_v29 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.6052 - Rouge1: 0.5112 - Rouge2: 0.2802 - Rougel: 0.4539 - Rougelsum: 0.4538 - Bert precision: 0.8765 - Bert recall: 0.8742 - Average word count: 8.8438 - Max word count: 16 - Min word count: 4 - Average token count: 13.4174 - % shortened texts with length > 12: 8.7087 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bert precision | Bert recall | Average word count | Max word count | Min word count | Average token count | % shortened texts with length > 12 | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:--------------:|:-----------:|:------------------:|:--------------:|:--------------:|:-------------------:|:----------------------------------:| | 1.9361 | 1.0 | 145 | 1.4858 | 0.4996 | 0.2801 | 0.4497 | 0.4507 | 0.8753 | 0.8723 | 8.7808 | 16 | 3 | 13.2372 | 7.2072 | | 1.4692 | 2.0 | 290 | 1.3868 | 0.5013 | 0.2812 | 0.4477 | 0.4485 | 0.8736 | 0.8731 | 9.0601 | 16 | 3 | 13.7147 | 13.2132 | | 1.2301 | 3.0 | 435 | 1.3641 | 0.5294 | 0.307 | 0.4735 | 0.474 | 0.8785 | 0.8799 | 9.0961 | 16 | 4 | 13.7327 | 16.8168 | | 1.049 | 4.0 | 580 | 1.3702 | 0.524 | 0.2979 | 0.4705 | 0.4706 | 0.8782 | 0.8788 | 9.1081 | 16 | 4 | 13.6066 | 13.8138 | | 0.9261 | 5.0 | 725 | 1.3843 | 0.5424 | 0.3166 | 0.489 | 0.4886 | 0.8829 | 0.8833 | 8.9219 | 17 | 4 | 13.6907 | 8.4084 | | 0.8067 | 6.0 | 870 | 1.4039 | 0.5269 | 0.3011 | 0.4682 | 0.4684 | 0.8777 | 0.878 | 9.2252 | 17 | 4 | 13.973 | 13.2132 | | 0.7133 | 7.0 | 1015 | 1.5083 | 0.5168 | 0.3022 | 0.4618 | 0.4613 | 0.8791 | 0.8758 | 8.7447 | 17 | 4 | 13.4655 | 10.2102 | | 0.6428 | 8.0 | 1160 | 1.4856 | 0.5184 | 0.2907 | 0.4624 | 0.4617 | 0.8804 | 0.8754 | 8.5976 | 16 | 3 | 13.0571 | 9.009 | | 0.5741 | 9.0 | 1305 | 1.5332 | 0.5231 | 0.3003 | 0.4669 | 0.4673 | 0.8809 | 0.8791 | 8.8829 | 17 | 4 | 13.5706 | 7.5075 | | 0.5231 | 10.0 | 1450 | 1.5603 | 0.53 | 0.3032 | 0.4725 | 0.4727 | 0.8843 | 0.8775 | 8.4625 | 17 | 4 | 13.033 | 5.7057 | | 0.4607 | 11.0 | 1595 | 1.6079 | 0.5118 | 0.2821 | 0.4583 | 0.4577 | 0.8777 | 0.8715 | 8.3453 | 16 | 4 | 13.012 | 6.9069 | | 0.4136 | 12.0 | 1740 | 1.7147 | 0.5136 | 0.2849 | 0.4558 | 0.4556 | 0.8776 | 0.8734 | 8.7297 | 16 | 3 | 13.3874 | 9.3093 | | 0.3829 | 13.0 | 1885 | 1.7425 | 0.5182 | 0.287 | 0.459 | 0.4591 | 0.8792 | 0.8746 | 8.7207 | 17 | 4 | 13.3934 | 8.1081 | | 0.3366 | 14.0 | 2030 | 1.7518 | 0.5171 | 0.2871 | 0.4564 | 0.4557 | 0.8796 | 0.8735 | 8.5195 | 16 | 4 | 13.0811 | 5.4054 | | 0.3076 | 15.0 | 2175 | 1.8555 | 0.5139 | 0.2891 | 0.4581 | 0.4581 | 0.879 | 0.8754 | 8.7658 | 16 | 4 | 13.2973 | 9.9099 | | 0.2908 | 16.0 | 2320 | 1.8983 | 0.5239 | 0.3011 | 0.4654 | 0.4651 | 0.8799 | 0.8794 | 8.979 | 16 | 4 | 13.6547 | 12.012 | | 0.2606 | 17.0 | 2465 | 1.9211 | 0.5158 | 0.2875 | 0.4538 | 0.4542 | 0.8774 | 0.8739 | 8.7868 | 17 | 2 | 13.5736 | 12.012 | | 0.2477 | 18.0 | 2610 | 1.9208 | 0.51 | 0.2872 | 0.4515 | 0.4517 | 0.8774 | 0.8733 | 8.6577 | 17 | 4 | 13.3093 | 10.8108 | | 0.2195 | 19.0 | 2755 | 1.9720 | 0.5112 | 0.2838 | 0.456 | 0.4559 | 0.8775 | 0.8754 | 8.8799 | 17 | 3 | 13.4835 | 10.8108 | | 0.1998 | 20.0 | 2900 | 1.9987 | 0.511 | 0.2817 | 0.4526 | 0.4525 | 0.8783 | 0.8751 | 8.7838 | 17 | 3 | 13.4955 | 9.9099 | | 0.1936 | 21.0 | 3045 | 2.0389 | 0.5066 | 0.2818 | 0.4482 | 0.4485 | 0.8762 | 0.8722 | 8.6186 | 17 | 4 | 13.1231 | 9.009 | | 0.1813 | 22.0 | 3190 | 2.0735 | 0.5078 | 0.29 | 0.4556 | 0.4562 | 0.8772 | 0.8754 | 8.8198 | 17 | 4 | 13.4895 | 9.3093 | | 0.1726 | 23.0 | 3335 | 2.0743 | 0.5108 | 0.2901 | 0.458 | 0.4581 | 0.8795 | 0.8736 | 8.4775 | 17 | 2 | 13.0931 | 9.009 | | 0.164 | 24.0 | 3480 | 2.1380 | 0.5077 | 0.2887 | 0.4578 | 0.4565 | 0.878 | 0.8727 | 8.4474 | 17 | 4 | 13.003 | 5.7057 | | 0.1506 | 25.0 | 3625 | 2.1435 | 0.5005 | 0.2725 | 0.4456 | 0.4452 | 0.8748 | 0.8717 | 8.6637 | 17 | 4 | 13.2943 | 6.6066 | | 0.1402 | 26.0 | 3770 | 2.1956 | 0.5114 | 0.2899 | 0.4577 | 0.4571 | 0.8769 | 0.8753 | 8.8709 | 17 | 4 | 13.3544 | 9.3093 | | 0.138 | 27.0 | 3915 | 2.2175 | 0.5079 | 0.2824 | 0.4544 | 0.4548 | 0.8772 | 0.8739 | 8.6847 | 17 | 4 | 13.3423 | 8.4084 | | 0.1313 | 28.0 | 4060 | 2.2267 | 0.5048 | 0.2793 | 0.4483 | 0.448 | 0.8747 | 0.8717 | 8.6817 | 17 | 4 | 13.2733 | 9.009 | | 0.122 | 29.0 | 4205 | 2.2464 | 0.5105 | 0.2813 | 0.4544 | 0.4548 | 0.8746 | 0.8736 | 8.9099 | 18 | 4 | 13.4595 | 10.5105 | | 0.1195 | 30.0 | 4350 | 2.2419 | 0.5124 | 0.2922 | 0.461 | 0.4609 | 0.8768 | 0.8733 | 8.6637 | 16 | 4 | 13.2883 | 7.5075 | | 0.1131 | 31.0 | 4495 | 2.2243 | 0.5215 | 0.3025 | 0.4702 | 0.4698 | 0.8802 | 0.878 | 8.7117 | 16 | 4 | 13.3814 | 9.3093 | | 0.1102 | 32.0 | 4640 | 2.2847 | 0.5078 | 0.2826 | 0.4567 | 0.4559 | 0.8788 | 0.8729 | 8.3904 | 18 | 4 | 12.9099 | 6.3063 | | 0.1105 | 33.0 | 4785 | 2.2545 | 0.5049 | 0.2759 | 0.4489 | 0.4484 | 0.8762 | 0.8729 | 8.6667 | 18 | 4 | 13.1952 | 9.009 | | 0.099 | 34.0 | 4930 | 2.2819 | 0.5207 | 0.296 | 0.4662 | 0.4665 | 0.8814 | 0.8775 | 8.6186 | 17 | 4 | 13.1952 | 8.1081 | | 0.1018 | 35.0 | 5075 | 2.2901 | 0.5133 | 0.2812 | 0.4597 | 0.4597 | 0.8777 | 0.8743 | 8.7237 | 17 | 4 | 13.3243 | 10.8108 | | 0.0992 | 36.0 | 5220 | 2.3349 | 0.5011 | 0.272 | 0.4442 | 0.4439 | 0.8738 | 0.8722 | 8.9129 | 16 | 2 | 13.5856 | 11.1111 | | 0.0921 | 37.0 | 5365 | 2.3193 | 0.506 | 0.2816 | 0.4539 | 0.4539 | 0.8776 | 0.8739 | 8.7658 | 16 | 4 | 13.3093 | 8.7087 | | 0.0936 | 38.0 | 5510 | 2.3404 | 0.5101 | 0.2815 | 0.4565 | 0.4566 | 0.8768 | 0.8754 | 8.8168 | 16 | 4 | 13.4535 | 10.5105 | | 0.0833 | 39.0 | 5655 | 2.3583 | 0.5026 | 0.2818 | 0.4512 | 0.4509 | 0.8749 | 0.8743 | 8.8709 | 16 | 3 | 13.4955 | 9.3093 | | 0.0869 | 40.0 | 5800 | 2.3443 | 0.5091 | 0.2855 | 0.4521 | 0.4521 | 0.8769 | 0.8743 | 8.8378 | 16 | 4 | 13.4474 | 11.4114 | | 0.0783 | 41.0 | 5945 | 2.3609 | 0.5045 | 0.2851 | 0.4519 | 0.4513 | 0.8784 | 0.8738 | 8.5946 | 16 | 4 | 13.1261 | 7.8078 | | 0.08 | 42.0 | 6090 | 2.4229 | 0.5053 | 0.2774 | 0.4508 | 0.4506 | 0.8769 | 0.8743 | 8.6667 | 16 | 4 | 13.2853 | 8.4084 | | 0.0792 | 43.0 | 6235 | 2.3731 | 0.5156 | 0.2877 | 0.4618 | 0.4619 | 0.8775 | 0.8771 | 8.955 | 16 | 4 | 13.6937 | 8.7087 | | 0.075 | 44.0 | 6380 | 2.4058 | 0.5119 | 0.286 | 0.453 | 0.4535 | 0.8761 | 0.8762 | 8.976 | 17 | 3 | 13.7387 | 12.012 | | 0.0754 | 45.0 | 6525 | 2.3808 | 0.5142 | 0.2894 | 0.4584 | 0.4583 | 0.8772 | 0.8765 | 8.967 | 16 | 4 | 13.6096 | 12.3123 | | 0.0713 | 46.0 | 6670 | 2.3949 | 0.5093 | 0.2841 | 0.4566 | 0.4568 | 0.8758 | 0.8748 | 8.8559 | 16 | 4 | 13.4775 | 9.9099 | | 0.066 | 47.0 | 6815 | 2.4103 | 0.5094 | 0.2798 | 0.4551 | 0.4553 | 0.8763 | 0.8753 | 8.9009 | 16 | 4 | 13.4655 | 10.2102 | | 0.0684 | 48.0 | 6960 | 2.4284 | 0.5021 | 0.2763 | 0.4476 | 0.4465 | 0.8754 | 0.8733 | 8.6727 | 16 | 4 | 13.2162 | 8.7087 | | 0.0656 | 49.0 | 7105 | 2.4512 | 0.5137 | 0.289 | 0.4584 | 0.4583 | 0.8763 | 0.8748 | 8.8378 | 16 | 4 | 13.4174 | 9.6096 | | 0.0664 | 50.0 | 7250 | 2.4427 | 0.5106 | 0.2789 | 0.4507 | 0.4501 | 0.8761 | 0.8747 | 8.7327 | 16 | 4 | 13.5255 | 8.4084 | | 0.0628 | 51.0 | 7395 | 2.4792 | 0.5069 | 0.2802 | 0.4527 | 0.453 | 0.8775 | 0.8751 | 8.7417 | 16 | 2 | 13.3063 | 8.7087 | | 0.0662 | 52.0 | 7540 | 2.4619 | 0.5103 | 0.281 | 0.4567 | 0.4567 | 0.8776 | 0.874 | 8.6216 | 16 | 3 | 13.1772 | 9.009 | | 0.0633 | 53.0 | 7685 | 2.4705 | 0.5053 | 0.2785 | 0.4489 | 0.449 | 0.8761 | 0.8735 | 8.7447 | 16 | 4 | 13.3874 | 8.7087 | | 0.0592 | 54.0 | 7830 | 2.4978 | 0.5133 | 0.2813 | 0.452 | 0.4528 | 0.8769 | 0.8746 | 8.8438 | 16 | 4 | 13.4354 | 9.6096 | | 0.0577 | 55.0 | 7975 | 2.4823 | 0.5063 | 0.2793 | 0.448 | 0.4488 | 0.8758 | 0.8721 | 8.6036 | 16 | 4 | 13.1111 | 6.9069 | | 0.0609 | 56.0 | 8120 | 2.4779 | 0.5133 | 0.2797 | 0.4539 | 0.4544 | 0.8764 | 0.8756 | 8.97 | 16 | 3 | 13.5976 | 10.5105 | | 0.0539 | 57.0 | 8265 | 2.5132 | 0.5096 | 0.2778 | 0.453 | 0.4536 | 0.877 | 0.8734 | 8.7117 | 16 | 4 | 13.3003 | 7.2072 | | 0.0564 | 58.0 | 8410 | 2.4783 | 0.517 | 0.2872 | 0.4622 | 0.4625 | 0.8778 | 0.8759 | 8.9159 | 16 | 4 | 13.5556 | 11.4114 | | 0.0543 | 59.0 | 8555 | 2.5184 | 0.5071 | 0.2788 | 0.4515 | 0.4513 | 0.8766 | 0.8734 | 8.7177 | 16 | 4 | 13.2583 | 9.009 | | 0.0518 | 60.0 | 8700 | 2.4945 | 0.5049 | 0.2754 | 0.4529 | 0.4529 | 0.8755 | 0.8749 | 8.9459 | 16 | 4 | 13.6787 | 10.8108 | | 0.0541 | 61.0 | 8845 | 2.5282 | 0.4983 | 0.2693 | 0.4414 | 0.4403 | 0.8723 | 0.8726 | 8.973 | 16 | 4 | 13.6667 | 11.1111 | | 0.0532 | 62.0 | 8990 | 2.5237 | 0.5007 | 0.2712 | 0.4464 | 0.4456 | 0.8741 | 0.8744 | 9.0541 | 16 | 4 | 13.7477 | 11.1111 | | 0.0514 | 63.0 | 9135 | 2.5247 | 0.5041 | 0.2784 | 0.4525 | 0.452 | 0.8768 | 0.8735 | 8.7898 | 16 | 4 | 13.4144 | 8.7087 | | 0.0516 | 64.0 | 9280 | 2.5289 | 0.5065 | 0.2826 | 0.4517 | 0.4515 | 0.8753 | 0.8745 | 9.042 | 16 | 4 | 13.6907 | 11.1111 | | 0.0504 | 65.0 | 9425 | 2.5002 | 0.5055 | 0.2826 | 0.4565 | 0.4562 | 0.877 | 0.8724 | 8.6727 | 16 | 4 | 13.3123 | 7.5075 | | 0.0479 | 66.0 | 9570 | 2.5361 | 0.503 | 0.2783 | 0.4529 | 0.4532 | 0.8756 | 0.874 | 8.8529 | 16 | 4 | 13.4865 | 8.1081 | | 0.0515 | 67.0 | 9715 | 2.5260 | 0.5043 | 0.2758 | 0.451 | 0.4512 | 0.874 | 0.8748 | 9.0661 | 17 | 4 | 13.7808 | 10.5105 | | 0.0544 | 68.0 | 9860 | 2.5213 | 0.5051 | 0.2846 | 0.4543 | 0.4545 | 0.8754 | 0.8739 | 8.9219 | 16 | 3 | 13.5586 | 10.5105 | | 0.0445 | 69.0 | 10005 | 2.5543 | 0.5097 | 0.2859 | 0.4573 | 0.4577 | 0.878 | 0.8748 | 8.6937 | 16 | 3 | 13.3363 | 9.009 | | 0.0484 | 70.0 | 10150 | 2.5472 | 0.5028 | 0.2791 | 0.4502 | 0.4503 | 0.8757 | 0.8736 | 8.8078 | 16 | 3 | 13.4264 | 7.5075 | | 0.0437 | 71.0 | 10295 | 2.5621 | 0.5089 | 0.2851 | 0.4553 | 0.4556 | 0.8765 | 0.8742 | 8.8408 | 16 | 4 | 13.5105 | 8.7087 | | 0.0473 | 72.0 | 10440 | 2.5503 | 0.5087 | 0.2818 | 0.4558 | 0.4555 | 0.8771 | 0.8743 | 8.8559 | 16 | 4 | 13.4204 | 8.7087 | | 0.0472 | 73.0 | 10585 | 2.5726 | 0.5168 | 0.2866 | 0.4571 | 0.4577 | 0.8775 | 0.8761 | 8.9039 | 17 | 4 | 13.5285 | 9.6096 | | 0.041 | 74.0 | 10730 | 2.5982 | 0.5137 | 0.2895 | 0.4594 | 0.4601 | 0.8769 | 0.8757 | 8.8709 | 16 | 4 | 13.4805 | 9.3093 | | 0.0409 | 75.0 | 10875 | 2.5589 | 0.5058 | 0.2824 | 0.4553 | 0.4554 | 0.8766 | 0.8746 | 8.7898 | 16 | 4 | 13.3033 | 8.7087 | | 0.0441 | 76.0 | 11020 | 2.5642 | 0.501 | 0.2791 | 0.452 | 0.4521 | 0.8763 | 0.8717 | 8.5225 | 16 | 4 | 13.048 | 6.006 | | 0.0427 | 77.0 | 11165 | 2.5522 | 0.5102 | 0.2864 | 0.4573 | 0.4579 | 0.8784 | 0.8749 | 8.7207 | 17 | 4 | 13.3183 | 7.5075 | | 0.0449 | 78.0 | 11310 | 2.5454 | 0.5071 | 0.2846 | 0.4567 | 0.4561 | 0.8775 | 0.875 | 8.7658 | 16 | 4 | 13.2523 | 7.5075 | | 0.0397 | 79.0 | 11455 | 2.5598 | 0.5111 | 0.2863 | 0.4566 | 0.4569 | 0.8781 | 0.8752 | 8.7267 | 16 | 4 | 13.2973 | 7.2072 | | 0.046 | 80.0 | 11600 | 2.5171 | 0.5063 | 0.2838 | 0.4541 | 0.4541 | 0.8768 | 0.8734 | 8.6456 | 16 | 4 | 13.2492 | 6.6066 | | 0.0403 | 81.0 | 11745 | 2.5398 | 0.5154 | 0.2872 | 0.4584 | 0.4584 | 0.8774 | 0.876 | 8.9489 | 18 | 4 | 13.4955 | 8.7087 | | 0.0407 | 82.0 | 11890 | 2.5526 | 0.5178 | 0.2904 | 0.4631 | 0.4632 | 0.8789 | 0.8769 | 8.8589 | 18 | 4 | 13.4354 | 7.5075 | | 0.0414 | 83.0 | 12035 | 2.5718 | 0.5154 | 0.2876 | 0.4604 | 0.4609 | 0.8783 | 0.8749 | 8.7808 | 17 | 4 | 13.3303 | 7.5075 | | 0.0406 | 84.0 | 12180 | 2.5673 | 0.5138 | 0.2861 | 0.4581 | 0.4587 | 0.8773 | 0.8758 | 8.8949 | 17 | 4 | 13.4895 | 8.1081 | | 0.037 | 85.0 | 12325 | 2.5770 | 0.511 | 0.2873 | 0.4575 | 0.4573 | 0.8775 | 0.876 | 8.8559 | 16 | 4 | 13.4384 | 8.4084 | | 0.0404 | 86.0 | 12470 | 2.5786 | 0.5145 | 0.2848 | 0.4578 | 0.4581 | 0.8774 | 0.8754 | 8.8649 | 16 | 4 | 13.4865 | 8.7087 | | 0.0364 | 87.0 | 12615 | 2.5822 | 0.5089 | 0.2791 | 0.454 | 0.4539 | 0.8761 | 0.8743 | 8.8288 | 17 | 4 | 13.4174 | 7.8078 | | 0.0365 | 88.0 | 12760 | 2.5821 | 0.5105 | 0.2806 | 0.4555 | 0.4559 | 0.8779 | 0.8752 | 8.7838 | 16 | 4 | 13.3634 | 7.8078 | | 0.0359 | 89.0 | 12905 | 2.5798 | 0.5121 | 0.2787 | 0.4546 | 0.4549 | 0.8771 | 0.8753 | 8.8799 | 16 | 4 | 13.4835 | 8.4084 | | 0.0349 | 90.0 | 13050 | 2.5960 | 0.5109 | 0.2788 | 0.4533 | 0.454 | 0.8775 | 0.8747 | 8.8108 | 16 | 4 | 13.3874 | 9.009 | | 0.035 | 91.0 | 13195 | 2.5979 | 0.5072 | 0.2778 | 0.454 | 0.4539 | 0.8764 | 0.8743 | 8.8589 | 16 | 4 | 13.3964 | 9.6096 | | 0.0355 | 92.0 | 13340 | 2.6016 | 0.5101 | 0.2795 | 0.4544 | 0.4548 | 0.8767 | 0.8743 | 8.8589 | 16 | 4 | 13.4505 | 9.009 | | 0.0352 | 93.0 | 13485 | 2.6036 | 0.5107 | 0.2814 | 0.455 | 0.4554 | 0.8772 | 0.8747 | 8.8619 | 16 | 4 | 13.4294 | 9.009 | | 0.0338 | 94.0 | 13630 | 2.6016 | 0.5065 | 0.2771 | 0.4512 | 0.4514 | 0.8758 | 0.8741 | 8.9249 | 16 | 4 | 13.5165 | 9.3093 | | 0.0359 | 95.0 | 13775 | 2.6044 | 0.5071 | 0.2761 | 0.4496 | 0.4501 | 0.8755 | 0.8733 | 8.8559 | 16 | 4 | 13.4264 | 9.6096 | | 0.0349 | 96.0 | 13920 | 2.5986 | 0.5072 | 0.277 | 0.4523 | 0.4524 | 0.8756 | 0.8736 | 8.8679 | 16 | 4 | 13.4655 | 9.6096 | | 0.0358 | 97.0 | 14065 | 2.5994 | 0.5068 | 0.276 | 0.4498 | 0.4502 | 0.8749 | 0.8733 | 8.8589 | 16 | 4 | 13.4685 | 8.7087 | | 0.0338 | 98.0 | 14210 | 2.6041 | 0.5105 | 0.2805 | 0.4536 | 0.4535 | 0.8761 | 0.8741 | 8.8498 | 16 | 4 | 13.4444 | 8.7087 | | 0.0359 | 99.0 | 14355 | 2.6051 | 0.5095 | 0.2774 | 0.452 | 0.4522 | 0.876 | 0.8738 | 8.8529 | 16 | 4 | 13.4174 | 9.009 | | 0.0357 | 100.0 | 14500 | 2.6052 | 0.5112 | 0.2802 | 0.4539 | 0.4538 | 0.8765 | 0.8742 | 8.8438 | 16 | 4 | 13.4174 | 8.7087 | ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

checkiejan/flan-t5-prefix-25-7-2

checkiejan

2023-09-11T13:58:54Z

0

peft

[ "peft", "region:us" ]

null

2023-09-11T13:58:50Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0

Tensoic/Llama-2-7B-alpaca-2k-test-merged

Tensoic

2023-09-11T13:52:02Z

6

0

transformers

[ "transformers", "pytorch", "llama", "text-generation", "dataset:mhenrichsen/alpaca_2k_test", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-09-07T17:32:33Z

--- datasets: - mhenrichsen/alpaca_2k_test --- We fine tune base `Llama-2-7b-hf` on the `henrichsen/alpaca_2k_test` dataset using peft-LORA. Find adapters at: https://huggingface.co/Tensoic/Llama-2-7B-alpaca-2k-test Visit us at: https://tensoic.com ## Training Setup: ``` Number of GPUs: 8x NVIDIA V100 GPUs GPU Memory: 32GB each (SXM2 form factor) ``` ## Training Configuration: ```yaml base_model: meta-llama/Llama-2-7b-hf base_model_config: meta-llama/Llama-2-7b-hf model_type: LlamaForCausalLM tokenizer_type: LlamaTokenizer is_llama_derived_model: true load_in_8bit: true load_in_4bit: false strict: false datasets: - path: mhenrichsen/alpaca_2k_test type: alpaca dataset_prepared_path: last_run_prepared val_set_size: 0.01 output_dir: ./lora-out sequence_len: 4096 sample_packing: false pad_to_sequence_len: true adapter: lora lora_model_dir: lora_r: 32 lora_alpha: 16 lora_dropout: 0.05 lora_target_linear: true lora_fan_in_fan_out: wandb_project: wandb_entity: wandb_watch: wandb_run_id: wandb_log_model: gradient_accumulation_steps: 4 micro_batch_size: 2 num_epochs: 3 optimizer: adamw_bnb_8bit lr_scheduler: cosine learning_rate: 0.0002 train_on_inputs: false group_by_length: false bf16: false fp16: true tf32: false gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: true flash_attention: false warmup_steps: 10 eval_steps: 20 save_steps: debug: deepspeed: weight_decay: 0.0 fsdp: fsdp_config: special_tokens: bos_token: "<s>" eos_token: "</s>" unk_token: "<unk>" ``` ``` The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ```

bigmorning/whisper_4_with_init_sun_syl_wd_0__0090

bigmorning

2023-09-11T13:49:34Z

59

0

transformers

[ "transformers", "tf", "whisper", "automatic-speech-recognition", "generated_from_keras_callback", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-09-11T13:49:26Z

--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_keras_callback model-index: - name: whisper_4_with_init_sun_syl_wd_0__0090 results: [] ---  # whisper_4_with_init_sun_syl_wd_0__0090 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0943 - Train Accuracy: 0.0356 - Train Wermet: 0.0118 - Train Wermet Syl: 0.0159 - Validation Loss: 1.2876 - Validation Accuracy: 0.0208 - Validation Wermet: 0.3252 - Validation Wermet Syl: 0.2884 - Epoch: 89 ## 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': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Train Wermet | Train Wermet Syl | Validation Loss | Validation Accuracy | Validation Wermet | Validation Wermet Syl | Epoch | |:----------:|:--------------:|:------------:|:----------------:|:---------------:|:-------------------:|:-----------------:|:---------------------:|:-----:| | 5.3409 | 0.0111 | 1.3547 | 1.2898 | 3.9789 | 0.0114 | 0.9710 | 0.9563 | 0 | | 4.7143 | 0.0116 | 0.8622 | 0.8228 | 3.9404 | 0.0113 | 0.9823 | 0.9735 | 1 | | 4.6752 | 0.0117 | 0.8472 | 0.8057 | 3.9081 | 0.0114 | 0.9579 | 0.9359 | 2 | | 4.6500 | 0.0117 | 0.8382 | 0.7945 | 3.8820 | 0.0115 | 0.9213 | 0.8856 | 3 | | 4.6282 | 0.0118 | 0.8286 | 0.7805 | 3.8738 | 0.0114 | 0.9433 | 0.9119 | 4 | | 4.6095 | 0.0118 | 0.8190 | 0.7696 | 3.8630 | 0.0115 | 0.9117 | 0.8698 | 5 | | 4.5875 | 0.0119 | 0.7976 | 0.7465 | 3.8341 | 0.0116 | 0.8976 | 0.8552 | 6 | | 4.5682 | 0.0120 | 0.7753 | 0.7227 | 3.8277 | 0.0116 | 0.9014 | 0.8653 | 7 | | 4.5376 | 0.0121 | 0.7528 | 0.7005 | 3.7844 | 0.0118 | 0.8332 | 0.7815 | 8 | | 4.5060 | 0.0122 | 0.7392 | 0.6844 | 3.7537 | 0.0118 | 0.8578 | 0.8152 | 9 | | 4.4580 | 0.0124 | 0.7221 | 0.6694 | 3.7038 | 0.0120 | 0.8190 | 0.7679 | 10 | | 4.3989 | 0.0125 | 0.7156 | 0.6636 | 3.6169 | 0.0122 | 0.7979 | 0.7429 | 11 | | 4.3056 | 0.0128 | 0.7069 | 0.6557 | 3.5098 | 0.0125 | 0.7924 | 0.7396 | 12 | | 4.1673 | 0.0132 | 0.7054 | 0.6584 | 3.3542 | 0.0128 | 0.7759 | 0.7240 | 13 | | 3.9762 | 0.0138 | 0.6987 | 0.6559 | 3.1318 | 0.0133 | 0.7644 | 0.7231 | 14 | | 3.7385 | 0.0145 | 0.6835 | 0.6448 | 2.9144 | 0.0138 | 0.7392 | 0.6955 | 15 | | 3.5040 | 0.0152 | 0.6644 | 0.6298 | 2.7413 | 0.0142 | 0.7019 | 0.6548 | 16 | | 3.2728 | 0.0160 | 0.6408 | 0.6101 | 2.5183 | 0.0149 | 0.6798 | 0.6363 | 17 | | 3.0657 | 0.0167 | 0.6188 | 0.5912 | 2.3594 | 0.0153 | 0.6528 | 0.6103 | 18 | | 2.8703 | 0.0174 | 0.5936 | 0.5685 | 2.2644 | 0.0156 | 0.6310 | 0.5925 | 19 | | 2.6850 | 0.0181 | 0.5680 | 0.5453 | 2.1296 | 0.0160 | 0.6040 | 0.5652 | 20 | | 2.5227 | 0.0188 | 0.5423 | 0.5215 | 2.0019 | 0.0165 | 0.5793 | 0.5403 | 21 | | 2.3878 | 0.0194 | 0.5199 | 0.5015 | 1.8996 | 0.0169 | 0.5592 | 0.5229 | 22 | | 2.2437 | 0.0201 | 0.4959 | 0.4788 | 1.8141 | 0.0172 | 0.5414 | 0.5045 | 23 | | 2.1205 | 0.0207 | 0.4752 | 0.4607 | 1.7245 | 0.0175 | 0.5208 | 0.4838 | 24 | | 1.9919 | 0.0213 | 0.4533 | 0.4390 | 1.6673 | 0.0178 | 0.5026 | 0.4659 | 25 | | 1.9140 | 0.0217 | 0.4355 | 0.4216 | 1.6041 | 0.0181 | 0.4873 | 0.4512 | 26 | | 1.8225 | 0.0222 | 0.4184 | 0.4052 | 1.6271 | 0.0179 | 0.4852 | 0.4511 | 27 | | 1.7265 | 0.0227 | 0.4016 | 0.3895 | 1.5219 | 0.0184 | 0.4635 | 0.4275 | 28 | | 1.6240 | 0.0233 | 0.3833 | 0.3729 | 1.4718 | 0.0186 | 0.4515 | 0.4170 | 29 | | 1.5610 | 0.0236 | 0.3697 | 0.3588 | 1.4404 | 0.0188 | 0.4407 | 0.4056 | 30 | | 1.4719 | 0.0242 | 0.3540 | 0.3449 | 1.4125 | 0.0189 | 0.4310 | 0.3961 | 31 | | 1.4152 | 0.0245 | 0.3421 | 0.3339 | 1.3655 | 0.0191 | 0.4234 | 0.3881 | 32 | | 1.3546 | 0.0249 | 0.3277 | 0.3195 | 1.3419 | 0.0192 | 0.4156 | 0.3816 | 33 | | 1.2565 | 0.0256 | 0.3135 | 0.3060 | 1.3172 | 0.0194 | 0.4065 | 0.3722 | 34 | | 1.2135 | 0.0258 | 0.3026 | 0.2958 | 1.3019 | 0.0194 | 0.4006 | 0.3662 | 35 | | 1.1739 | 0.0261 | 0.2923 | 0.2861 | 1.3843 | 0.0190 | 0.3951 | 0.3587 | 36 | | 1.0950 | 0.0267 | 0.2782 | 0.2733 | 1.2665 | 0.0197 | 0.3883 | 0.3541 | 37 | | 1.0435 | 0.0271 | 0.2673 | 0.2631 | 1.2567 | 0.0197 | 0.3837 | 0.3497 | 38 | | 0.9922 | 0.0275 | 0.2580 | 0.2542 | 1.2566 | 0.0197 | 0.3801 | 0.3444 | 39 | | 0.9387 | 0.0279 | 0.2464 | 0.2438 | 1.2441 | 0.0198 | 0.3767 | 0.3423 | 40 | | 0.9345 | 0.0278 | 0.2393 | 0.2373 | 1.2221 | 0.0199 | 0.3682 | 0.3336 | 41 | | 0.8574 | 0.0285 | 0.2268 | 0.2255 | 1.2258 | 0.0199 | 0.3680 | 0.3338 | 42 | | 0.8275 | 0.0287 | 0.2183 | 0.2180 | 1.2044 | 0.0201 | 0.3628 | 0.3290 | 43 | | 0.8201 | 0.0288 | 0.2114 | 0.2108 | 1.2056 | 0.0201 | 0.3601 | 0.3270 | 44 | | 0.7684 | 0.0292 | 0.2020 | 0.2029 | 1.1879 | 0.0202 | 0.3553 | 0.3215 | 45 | | 0.7262 | 0.0295 | 0.1938 | 0.1947 | 1.2263 | 0.0200 | 0.3537 | 0.3177 | 46 | | 0.7286 | 0.0295 | 0.1876 | 0.1898 | 1.1772 | 0.0203 | 0.3485 | 0.3135 | 47 | | 0.6807 | 0.0300 | 0.1775 | 0.1797 | 1.1761 | 0.0203 | 0.3490 | 0.3155 | 48 | | 0.6609 | 0.0301 | 0.1713 | 0.1742 | 1.1853 | 0.0203 | 0.3484 | 0.3153 | 49 | | 0.6062 | 0.0306 | 0.1615 | 0.1653 | 1.1660 | 0.0204 | 0.3432 | 0.3090 | 50 | | 0.5755 | 0.0309 | 0.1547 | 0.1584 | 1.1698 | 0.0204 | 0.3428 | 0.3089 | 51 | | 0.5600 | 0.0310 | 0.1482 | 0.1524 | 1.1667 | 0.0204 | 0.3398 | 0.3058 | 52 | | 0.5715 | 0.0308 | 0.1449 | 0.1496 | 1.1614 | 0.0205 | 0.3381 | 0.3036 | 53 | | 0.5247 | 0.0313 | 0.1358 | 0.1411 | 1.1639 | 0.0205 | 0.3359 | 0.3025 | 54 | | 0.5085 | 0.0315 | 0.1301 | 0.1358 | 1.2420 | 0.0202 | 0.3412 | 0.3064 | 55 | | 0.4827 | 0.0317 | 0.1239 | 0.1295 | 1.1677 | 0.0205 | 0.3349 | 0.3009 | 56 | | 0.4848 | 0.0317 | 0.1207 | 0.1280 | 1.1653 | 0.0205 | 0.3326 | 0.2991 | 57 | | 0.4323 | 0.0322 | 0.1109 | 0.1185 | 1.1602 | 0.0206 | 0.3299 | 0.2953 | 58 | | 0.4183 | 0.0323 | 0.1057 | 0.1133 | 1.1622 | 0.0206 | 0.3307 | 0.2962 | 59 | | 0.4329 | 0.0322 | 0.1028 | 0.1100 | 1.1714 | 0.0206 | 0.3300 | 0.2950 | 60 | | 0.3962 | 0.0326 | 0.0964 | 0.1045 | 1.1726 | 0.0206 | 0.3311 | 0.2967 | 61 | | 0.3642 | 0.0329 | 0.0898 | 0.0973 | 1.1699 | 0.0206 | 0.3289 | 0.2936 | 62 | | 0.3786 | 0.0327 | 0.0884 | 0.0963 | 1.1734 | 0.0206 | 0.3279 | 0.2929 | 63 | | 0.3698 | 0.0328 | 0.0842 | 0.0925 | 1.1728 | 0.0207 | 0.3282 | 0.2932 | 64 | | 0.3219 | 0.0333 | 0.0765 | 0.0850 | 1.1830 | 0.0207 | 0.3258 | 0.2907 | 65 | | 0.3035 | 0.0335 | 0.0725 | 0.0811 | 1.1840 | 0.0207 | 0.3261 | 0.2904 | 66 | | 0.3522 | 0.0330 | 0.0745 | 0.0826 | 1.2107 | 0.0206 | 0.3299 | 0.2955 | 67 | | 0.3001 | 0.0335 | 0.0663 | 0.0749 | 1.1810 | 0.0207 | 0.3264 | 0.2909 | 68 | | 0.2729 | 0.0338 | 0.0595 | 0.0677 | 1.1911 | 0.0207 | 0.3247 | 0.2886 | 69 | | 0.2696 | 0.0338 | 0.0572 | 0.0654 | 1.1950 | 0.0207 | 0.3260 | 0.2905 | 70 | | 0.2840 | 0.0337 | 0.0563 | 0.0648 | 1.2094 | 0.0207 | 0.3250 | 0.2887 | 71 | | 0.2319 | 0.0342 | 0.0484 | 0.0569 | 1.2107 | 0.0207 | 0.3250 | 0.2878 | 72 | | 0.2371 | 0.0342 | 0.0464 | 0.0541 | 1.2059 | 0.0207 | 0.3240 | 0.2880 | 73 | | 0.2666 | 0.0338 | 0.0486 | 0.0575 | 1.2036 | 0.0207 | 0.3241 | 0.2887 | 74 | | 0.2443 | 0.0340 | 0.0442 | 0.0522 | 1.2106 | 0.0207 | 0.3241 | 0.2877 | 75 | | 0.2118 | 0.0344 | 0.0380 | 0.0456 | 1.2172 | 0.0207 | 0.3240 | 0.2871 | 76 | | 0.1997 | 0.0346 | 0.0354 | 0.0428 | 1.2247 | 0.0208 | 0.3219 | 0.2852 | 77 | | 0.2461 | 0.0341 | 0.0386 | 0.0466 | 1.2257 | 0.0207 | 0.3240 | 0.2874 | 78 | | 0.2367 | 0.0342 | 0.0364 | 0.0431 | 1.2173 | 0.0208 | 0.3234 | 0.2870 | 79 | | 0.1857 | 0.0347 | 0.0294 | 0.0365 | 1.2287 | 0.0208 | 0.3244 | 0.2876 | 80 | | 0.1504 | 0.0351 | 0.0244 | 0.0314 | 1.2425 | 0.0207 | 0.3238 | 0.2871 | 81 | | 0.1438 | 0.0352 | 0.0227 | 0.0287 | 1.2495 | 0.0208 | 0.3222 | 0.2861 | 82 | | 0.1545 | 0.0350 | 0.0232 | 0.0288 | 1.2612 | 0.0207 | 0.3257 | 0.2898 | 83 | | 0.2122 | 0.0345 | 0.0284 | 0.0346 | 1.2518 | 0.0208 | 0.3241 | 0.2884 | 84 | | 0.1685 | 0.0349 | 0.0222 | 0.0278 | 1.2466 | 0.0208 | 0.3231 | 0.2868 | 85 | | 0.1371 | 0.0352 | 0.0181 | 0.0236 | 1.2606 | 0.0208 | 0.3239 | 0.2869 | 86 | | 0.1357 | 0.0352 | 0.0171 | 0.0216 | 1.2675 | 0.0208 | 0.3240 | 0.2874 | 87 | | 0.1022 | 0.0356 | 0.0132 | 0.0172 | 1.2887 | 0.0208 | 0.3233 | 0.2875 | 88 | | 0.0943 | 0.0356 | 0.0118 | 0.0159 | 1.2876 | 0.0208 | 0.3252 | 0.2884 | 89 | ### Framework versions - Transformers 4.34.0.dev0 - TensorFlow 2.13.0 - Tokenizers 0.13.3

RickyIG/image_classification

RickyIG

2023-09-11T13:48:48Z

215

0

transformers

[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:food101", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-09-11T13:39:57Z

--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - food101 metrics: - accuracy model-index: - name: image_classification results: - task: name: Image Classification type: image-classification dataset: name: food101 type: food101 config: default split: train[:5000] args: default metrics: - name: Accuracy type: accuracy value: 0.886 ---  # image_classification This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the food101 dataset. It achieves the following results on the evaluation set: - Loss: 1.6283 - Accuracy: 0.886 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 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: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.7254 | 0.99 | 62 | 2.5418 | 0.819 | | 1.8131 | 2.0 | 125 | 1.8025 | 0.852 | | 1.5991 | 2.98 | 186 | 1.6367 | 0.889 | ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

facebook/mask2former-swin-base-ade-semantic

facebook

2023-09-11T13:46:21Z

1,503

0

transformers

[ "transformers", "pytorch", "safetensors", "mask2former", "vision", "image-segmentation", "dataset:coco", "arxiv:2112.01527", "arxiv:2107.06278", "license:other", "endpoints_compatible", "region:us" ]

image-segmentation

2023-01-05T12:23:05Z

--- license: other tags: - vision - image-segmentation datasets: - coco widget: - src: http://images.cocodataset.org/val2017/000000039769.jpg example_title: Cats - src: http://images.cocodataset.org/val2017/000000039770.jpg example_title: Castle --- # Mask2Former Mask2Former model trained on ADE20k semantic segmentation (base-sized version, Swin backbone). It was introduced in the paper [Masked-attention Mask Transformer for Universal Image Segmentation ](https://arxiv.org/abs/2112.01527) and first released in [this repository](https://github.com/facebookresearch/Mask2Former/). Disclaimer: The team releasing Mask2Former did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description Mask2Former addresses instance, semantic and panoptic segmentation with the same paradigm: by predicting a set of masks and corresponding labels. Hence, all 3 tasks are treated as if they were instance segmentation. Mask2Former outperforms the previous SOTA, [MaskFormer](https://arxiv.org/abs/2107.06278) both in terms of performance an efficiency by (i) replacing the pixel decoder with a more advanced multi-scale deformable attention Transformer, (ii) adopting a Transformer decoder with masked attention to boost performance without without introducing additional computation and (iii) improving training efficiency by calculating the loss on subsampled points instead of whole masks. ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/mask2former_architecture.png) ## Intended uses & limitations You can use this particular checkpoint for panoptic segmentation. See the [model hub](https://huggingface.co/models?search=mask2former) to look for other fine-tuned versions on a task that interests you. ### How to use Here is how to use this model: ```python import requests import torch from PIL import Image from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation # load Mask2Former fine-tuned on ADE20k semantic segmentation processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-base-ade-semantic") model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-base-ade-semantic") url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) inputs = processor(images=image, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs) # model predicts class_queries_logits of shape `(batch_size, num_queries)` # and masks_queries_logits of shape `(batch_size, num_queries, height, width)` class_queries_logits = outputs.class_queries_logits masks_queries_logits = outputs.masks_queries_logits # you can pass them to processor for postprocessing predicted_semantic_map = processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0] # we refer to the demo notebooks for visualization (see "Resources" section in the Mask2Former docs) ``` For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/mask2former).

davanstrien/detr-resnet-50_find_tuned_beyond_words

davanstrien

2023-09-11T13:45:54Z

165

0

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "detr", "object-detection", "generated_from_trainer", "dataset:beyond_words_23", "base_model:facebook/detr-resnet-50", "base_model:finetune:facebook/detr-resnet-50", "license:apache-2.0", "endpoints_compatible", "region:us" ]

object-detection

2023-02-27T22:50:50Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - beyond_words_23 base_model: facebook/detr-resnet-50 model-index: - name: detr-resnet-50_find_tuned_beyond_words results: [] ---  # detr-resnet-50_find_tuned_beyond_words This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the beyond_words_23 dataset. It achieves the following results on the evaluation set: - Loss: 0.9310 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - 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: 200 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.7439 | 0.56 | 100 | 2.2690 | | 1.7644 | 1.12 | 200 | 1.5053 | | 1.557 | 1.69 | 300 | 1.3136 | | 1.3207 | 2.25 | 400 | 1.2063 | | 1.3705 | 2.81 | 500 | 1.2007 | | 1.1924 | 3.37 | 600 | 1.2704 | | 1.2604 | 3.93 | 700 | 1.1784 | | 1.1982 | 4.49 | 800 | 1.1167 | | 1.1912 | 5.06 | 900 | 1.1562 | | 1.1206 | 5.62 | 1000 | 1.2124 | | 1.1344 | 6.18 | 1100 | 1.0622 | | 1.1388 | 6.74 | 1200 | 1.0425 | | 1.0124 | 7.3 | 1300 | 0.9908 | | 1.0776 | 7.87 | 1400 | 1.1182 | | 0.9614 | 8.43 | 1500 | 0.9967 | | 1.0136 | 8.99 | 1600 | 0.8933 | | 1.0206 | 9.55 | 1700 | 0.9354 | | 0.9529 | 10.11 | 1800 | 0.9751 | | 1.0126 | 10.67 | 1900 | 0.9310 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.0 - Tokenizers 0.13.2

flyswot/test

flyswot

2023-09-11T13:45:41Z

248

0

transformers

[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:image_folder", "base_model:facebook/deit-tiny-patch16-224", "base_model:finetune:facebook/deit-tiny-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2022-05-01T17:30:58Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - image_folder metrics: - f1 base_model: facebook/deit-tiny-patch16-224 model-index: - name: test results: - task: type: image-classification name: Image Classification dataset: name: image_folder type: image_folder args: default metrics: - type: f1 value: 0.12404601272248332 name: F1 ---  # test This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 2.2724 - F1: 0.1240 ## 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: 0.001 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 0.0 | 1 | 2.2724 | 0.1240 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0 - Datasets 2.0.0 - Tokenizers 0.11.6

davanstrien/convnext_flyswot

davanstrien

2023-09-11T13:44:59Z

248

0

transformers

[ "transformers", "pytorch", "safetensors", "convnext", "image-classification", "generated_from_trainer", "dataset:image_folder", "base_model:facebook/convnext-base-224-22k", "base_model:finetune:facebook/convnext-base-224-22k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2022-03-02T23:29:05Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - image_folder metrics: - f1 base_model: facebook/convnext-base-224-22k model-index: - name: convnext_flyswot results: - task: type: image-classification name: Image Classification dataset: name: image_folder type: image_folder args: default metrics: - type: f1 value: 0.959245529738118 name: F1 ---  # convnext_flyswot This model is a fine-tuned version of [facebook/convnext-base-224-22k](https://huggingface.co/facebook/convnext-base-224-22k) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.1441 - F1: 0.9592 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 666 - 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 | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 52 | 0.6833 | 0.7484 | | No log | 2.0 | 104 | 0.3666 | 0.8750 | | No log | 3.0 | 156 | 0.2090 | 0.9321 | | No log | 4.0 | 208 | 0.1478 | 0.9449 | | No log | 5.0 | 260 | 0.1002 | 0.9518 | | No log | 6.0 | 312 | 0.1053 | 0.9506 | | No log | 7.0 | 364 | 0.1182 | 0.9616 | | No log | 8.0 | 416 | 0.1102 | 0.9592 | | No log | 9.0 | 468 | 0.1262 | 0.9616 | | 0.203 | 10.0 | 520 | 0.1286 | 0.9616 | | 0.203 | 11.0 | 572 | 0.1355 | 0.9592 | | 0.203 | 12.0 | 624 | 0.1299 | 0.9592 | | 0.203 | 13.0 | 676 | 0.1154 | 0.9592 | | 0.203 | 14.0 | 728 | 0.1385 | 0.9580 | | 0.203 | 15.0 | 780 | 0.1330 | 0.9592 | | 0.203 | 16.0 | 832 | 0.1390 | 0.9592 | | 0.203 | 17.0 | 884 | 0.1386 | 0.9592 | | 0.203 | 18.0 | 936 | 0.1390 | 0.9592 | | 0.203 | 19.0 | 988 | 0.1409 | 0.9592 | | 0.0006 | 20.0 | 1040 | 0.1411 | 0.9592 | | 0.0006 | 21.0 | 1092 | 0.1413 | 0.9592 | | 0.0006 | 22.0 | 1144 | 0.1415 | 0.9592 | | 0.0006 | 23.0 | 1196 | 0.1426 | 0.9592 | | 0.0006 | 24.0 | 1248 | 0.1435 | 0.9592 | | 0.0006 | 25.0 | 1300 | 0.1438 | 0.9592 | | 0.0006 | 26.0 | 1352 | 0.1434 | 0.9592 | | 0.0006 | 27.0 | 1404 | 0.1437 | 0.9592 | | 0.0006 | 28.0 | 1456 | 0.1441 | 0.9592 | | 0.0002 | 29.0 | 1508 | 0.1440 | 0.9592 | | 0.0002 | 30.0 | 1560 | 0.1441 | 0.9592 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.6

davanstrien/flyswot_iiif

davanstrien

2023-09-11T13:44:35Z

238

0

transformers

[ "transformers", "pytorch", "convnext", "image-classification", "generated_from_trainer", "base_model:facebook/convnext-base-224-22k", "base_model:finetune:facebook/convnext-base-224-22k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2022-03-02T23:29:05Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 base_model: facebook/convnext-base-224-22k model-index: - name: flyswot_iiif results: [] ---  # flyswot_iiif This model is a fine-tuned version of [facebook/convnext-base-224-22k](https://huggingface.co/facebook/convnext-base-224-22k) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.1280 - F1: 0.0034 ## 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: 666 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 - mixed_precision_training: Native AMP - label_smoothing_factor: 0.1 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 8.5184 | 0.26 | 500 | 7.9280 | 0.0005 | | 7.7409 | 0.52 | 1000 | 7.5824 | 0.0007 | | 7.4649 | 0.78 | 1500 | 7.3841 | 0.0010 | | 7.3285 | 1.04 | 2000 | 7.2652 | 0.0012 | | 7.1404 | 1.3 | 2500 | 7.1559 | 0.0014 | | 7.0322 | 1.56 | 3000 | 7.0551 | 0.0016 | | 6.9197 | 1.82 | 3500 | 6.9449 | 0.0019 | | 6.7822 | 2.09 | 4000 | 6.8773 | 0.0018 | | 6.6506 | 2.35 | 4500 | 6.7980 | 0.0020 | | 6.5811 | 2.61 | 5000 | 6.7382 | 0.0022 | | 6.538 | 2.87 | 5500 | 6.6582 | 0.0022 | | 6.4136 | 3.13 | 6000 | 6.6013 | 0.0024 | | 6.3325 | 3.39 | 6500 | 6.5369 | 0.0024 | | 6.2566 | 3.65 | 7000 | 6.4875 | 0.0025 | | 6.2285 | 3.91 | 7500 | 6.4342 | 0.0027 | | 6.1281 | 4.17 | 8000 | 6.4066 | 0.0027 | | 6.0762 | 4.43 | 8500 | 6.3674 | 0.0027 | | 6.0309 | 4.69 | 9000 | 6.3336 | 0.0027 | | 6.0123 | 4.95 | 9500 | 6.2932 | 0.0030 | | 5.9089 | 5.21 | 10000 | 6.2835 | 0.0029 | | 5.8901 | 5.47 | 10500 | 6.2481 | 0.0030 | | 5.86 | 5.74 | 11000 | 6.2295 | 0.0030 | | 5.8586 | 6.0 | 11500 | 6.2068 | 0.0033 | | 5.7768 | 6.26 | 12000 | 6.1937 | 0.0031 | | 5.7591 | 6.52 | 12500 | 6.1916 | 0.0032 | | 5.7443 | 6.78 | 13000 | 6.1579 | 0.0033 | | 5.7125 | 7.04 | 13500 | 6.1478 | 0.0033 | | 5.6751 | 7.3 | 14000 | 6.1379 | 0.0035 | | 5.6648 | 7.56 | 14500 | 6.1304 | 0.0035 | | 5.6644 | 7.82 | 15000 | 6.1280 | 0.0034 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.6

davanstrien/flyswot_test

davanstrien

2023-09-11T13:44:08Z

157

0

transformers

[ "transformers", "pytorch", "convnext", "image-classification", "generated_from_trainer", "dataset:image_folder", "base_model:facebook/convnext-base-224-22k", "base_model:finetune:facebook/convnext-base-224-22k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2022-03-02T23:29:05Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - image_folder base_model: facebook/convnext-base-224-22k model-index: - name: flyswot_test results: [] ---  # flyswot_test This model is a fine-tuned version of [facebook/convnext-base-224-22k](https://huggingface.co/facebook/convnext-base-224-22k) on the image_folder dataset. It achieves the following results on the evaluation set: - eval_loss: 0.1518 - eval_f1: 0.9595 - eval_runtime: 5.9337 - eval_samples_per_second: 69.603 - eval_steps_per_second: 2.191 - epoch: 7.0 - step: 364 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 666 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 40 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.6

davanstrien/iiif_manuscript_vit

davanstrien

2023-09-11T13:44:01Z

251

0

transformers

[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2022-03-02T23:29:05Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 base_model: google/vit-base-patch16-224-in21k model-index: - name: iiif_manuscript_vit results: [] ---  # iiif_manuscript_vit This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5684 - F1: 0.5996 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP - label_smoothing_factor: 0.1 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.5639 | 1.0 | 2269 | 0.5822 | 0.5516 | | 0.5834 | 2.0 | 4538 | 0.5825 | 0.5346 | | 0.5778 | 3.0 | 6807 | 0.5794 | 0.6034 | | 0.5735 | 4.0 | 9076 | 0.5742 | 0.5713 | | 0.5731 | 5.0 | 11345 | 0.5745 | 0.6008 | | 0.5701 | 6.0 | 13614 | 0.5729 | 0.5499 | | 0.5696 | 7.0 | 15883 | 0.5717 | 0.5952 | | 0.5683 | 8.0 | 18152 | 0.5680 | 0.6005 | | 0.5648 | 9.0 | 20421 | 0.5679 | 0.5967 | | 0.564 | 10.0 | 22690 | 0.5684 | 0.5996 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

davanstrien/vit-base-patch16-224-in21k-base-manuscripts

davanstrien

2023-09-11T13:43:35Z

34

0

transformers

[ "transformers", "pytorch", "tensorboard", "vit", "masked-image-modeling", "generated_from_trainer", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2022-03-10T07:44:17Z

--- license: apache-2.0 tags: - masked-image-modeling - generated_from_trainer base_model: google/vit-base-patch16-224-in21k model-index: - name: vit-base-patch16-224-in21k-base-manuscripts results: [] ---  # vit-base-patch16-224-in21k-base-manuscripts This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the davanstrien/iiif_manuscripts_label_ge_50 dataset. It achieves the following results on the evaluation set: - Loss: 0.5210 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 1333 - 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 | |:-------------:|:-----:|:----:|:---------------:| | 0.5198 | 1.0 | 32 | 0.5208 | ### Framework versions - Transformers 4.18.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.18.4 - Tokenizers 0.11.6

davanstrien/test_mae_flysheet

davanstrien

2023-09-11T13:43:28Z

64

0

transformers

[ "transformers", "pytorch", "tensorboard", "vit_mae", "pretraining", "masked-auto-encoding", "generated_from_trainer", "dataset:image_folder", "base_model:facebook/vit-mae-base", "base_model:finetune:facebook/vit-mae-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2022-03-13T15:30:34Z

--- license: apache-2.0 tags: - masked-auto-encoding - generated_from_trainer datasets: - image_folder base_model: facebook/vit-mae-base model-index: - name: test_mae_flysheet results: [] ---  # test_mae_flysheet This model is a fine-tuned version of [facebook/vit-mae-base](https://huggingface.co/facebook/vit-mae-base) on the davanstrien/flysheet dataset. It achieves the following results on the evaluation set: - Loss: 0.2675 ## 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: 3.75e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.284 | 1.0 | 28 | 2.2812 | | 2.137 | 2.0 | 56 | 2.0288 | | 1.6016 | 3.0 | 84 | 1.2437 | | 0.8055 | 4.0 | 112 | 0.7419 | | 0.5304 | 5.0 | 140 | 0.5151 | | 0.4873 | 6.0 | 168 | 0.4884 | | 0.442 | 7.0 | 196 | 0.4441 | | 0.4039 | 8.0 | 224 | 0.4159 | | 0.3866 | 9.0 | 252 | 0.3975 | | 0.391 | 10.0 | 280 | 0.3869 | | 0.3549 | 11.0 | 308 | 0.3801 | | 0.3462 | 12.0 | 336 | 0.3577 | | 0.3402 | 13.0 | 364 | 0.3519 | | 0.3357 | 14.0 | 392 | 0.3447 | | 0.3474 | 15.0 | 420 | 0.3369 | | 0.3254 | 16.0 | 448 | 0.3386 | | 0.3033 | 17.0 | 476 | 0.3294 | | 0.3047 | 18.0 | 504 | 0.3274 | | 0.3103 | 19.0 | 532 | 0.3209 | | 0.3067 | 20.0 | 560 | 0.3186 | | 0.2959 | 21.0 | 588 | 0.3190 | | 0.2899 | 22.0 | 616 | 0.3147 | | 0.2872 | 23.0 | 644 | 0.3082 | | 0.2956 | 24.0 | 672 | 0.3070 | | 0.2865 | 25.0 | 700 | 0.3072 | | 0.2947 | 26.0 | 728 | 0.3072 | | 0.2811 | 27.0 | 756 | 0.3131 | | 0.2935 | 28.0 | 784 | 0.3069 | | 0.2814 | 29.0 | 812 | 0.3043 | | 0.2753 | 30.0 | 840 | 0.2984 | | 0.2823 | 31.0 | 868 | 0.2995 | | 0.2962 | 32.0 | 896 | 0.3012 | | 0.2869 | 33.0 | 924 | 0.3050 | | 0.2833 | 34.0 | 952 | 0.2960 | | 0.2892 | 35.0 | 980 | 0.3039 | | 0.2764 | 36.0 | 1008 | 0.3010 | | 0.2807 | 37.0 | 1036 | 0.2998 | | 0.2843 | 38.0 | 1064 | 0.2989 | | 0.2808 | 39.0 | 1092 | 0.2970 | | 0.2862 | 40.0 | 1120 | 0.2940 | | 0.2601 | 41.0 | 1148 | 0.2952 | | 0.2742 | 42.0 | 1176 | 0.2940 | | 0.2791 | 43.0 | 1204 | 0.2997 | | 0.2759 | 44.0 | 1232 | 0.2951 | | 0.2819 | 45.0 | 1260 | 0.2896 | | 0.287 | 46.0 | 1288 | 0.2938 | | 0.2711 | 47.0 | 1316 | 0.2973 | | 0.2782 | 48.0 | 1344 | 0.2946 | | 0.2674 | 49.0 | 1372 | 0.2913 | | 0.268 | 50.0 | 1400 | 0.2944 | | 0.2624 | 51.0 | 1428 | 0.2940 | | 0.2842 | 52.0 | 1456 | 0.2978 | | 0.2753 | 53.0 | 1484 | 0.2951 | | 0.2733 | 54.0 | 1512 | 0.2880 | | 0.2782 | 55.0 | 1540 | 0.2969 | | 0.2789 | 56.0 | 1568 | 0.2919 | | 0.2815 | 57.0 | 1596 | 0.2916 | | 0.2629 | 58.0 | 1624 | 0.2947 | | 0.2716 | 59.0 | 1652 | 0.2828 | | 0.2623 | 60.0 | 1680 | 0.2924 | | 0.2773 | 61.0 | 1708 | 0.2765 | | 0.268 | 62.0 | 1736 | 0.2754 | | 0.2839 | 63.0 | 1764 | 0.2744 | | 0.2684 | 64.0 | 1792 | 0.2744 | | 0.2865 | 65.0 | 1820 | 0.2716 | | 0.2845 | 66.0 | 1848 | 0.2769 | | 0.2663 | 67.0 | 1876 | 0.2754 | | 0.269 | 68.0 | 1904 | 0.2737 | | 0.2681 | 69.0 | 1932 | 0.2697 | | 0.2748 | 70.0 | 1960 | 0.2779 | | 0.2769 | 71.0 | 1988 | 0.2728 | | 0.2805 | 72.0 | 2016 | 0.2729 | | 0.2771 | 73.0 | 2044 | 0.2728 | | 0.2717 | 74.0 | 2072 | 0.2749 | | 0.267 | 75.0 | 2100 | 0.2732 | | 0.2812 | 76.0 | 2128 | 0.2743 | | 0.2749 | 77.0 | 2156 | 0.2739 | | 0.2746 | 78.0 | 2184 | 0.2730 | | 0.2707 | 79.0 | 2212 | 0.2743 | | 0.2644 | 80.0 | 2240 | 0.2740 | | 0.2691 | 81.0 | 2268 | 0.2727 | | 0.2679 | 82.0 | 2296 | 0.2771 | | 0.2748 | 83.0 | 2324 | 0.2744 | | 0.2744 | 84.0 | 2352 | 0.2703 | | 0.2715 | 85.0 | 2380 | 0.2733 | | 0.2682 | 86.0 | 2408 | 0.2715 | | 0.2641 | 87.0 | 2436 | 0.2722 | | 0.274 | 88.0 | 2464 | 0.2748 | | 0.2669 | 89.0 | 2492 | 0.2753 | | 0.2707 | 90.0 | 2520 | 0.2724 | | 0.2755 | 91.0 | 2548 | 0.2703 | | 0.2769 | 92.0 | 2576 | 0.2737 | | 0.2659 | 93.0 | 2604 | 0.2721 | | 0.2674 | 94.0 | 2632 | 0.2763 | | 0.2723 | 95.0 | 2660 | 0.2723 | | 0.2723 | 96.0 | 2688 | 0.2744 | | 0.272 | 97.0 | 2716 | 0.2686 | | 0.27 | 98.0 | 2744 | 0.2728 | | 0.2721 | 99.0 | 2772 | 0.2743 | | 0.2692 | 100.0 | 2800 | 0.2748 | ### Framework versions - Transformers 4.18.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.18.4 - Tokenizers 0.11.6

davanstrien/convnext-tiny-224-leicester_binary

davanstrien

2023-09-11T13:43:16Z

190

0

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "convnext", "image-classification", "vision", "generated_from_trainer", "base_model:facebook/convnext-tiny-224", "base_model:finetune:facebook/convnext-tiny-224", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2022-12-06T16:45:11Z

--- license: apache-2.0 tags: - image-classification - vision - generated_from_trainer metrics: - precision - recall - f1 - accuracy base_model: facebook/convnext-tiny-224 model-index: - name: convnext-tiny-224-leicester_binary results: [] ---  # convnext-tiny-224-leicester_binary This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the davanstrien/leicester_loaded_annotations_binary dataset. It achieves the following results on the evaluation set: - Loss: 0.4213 - Precision: 0.4583 - Recall: 0.5 - F1: 0.4783 - Accuracy: 0.9167 ## 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: 128 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 7 | 0.4213 | 0.4583 | 0.5 | 0.4783 | 0.9167 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

kartiks26/Llama2-7B

kartiks26

2023-09-11T13:41:59Z

0

peft

[ "peft", "region:us" ]

null

2023-09-11T13:39:06Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.5.0

rohitsroch/hybrid_utt-clusterrank_bart-base_samsum_sum

rohitsroch

2023-09-11T13:38:47Z

114

0

transformers

[ "transformers", "pytorch", "safetensors", "bart", "text2text-generation", "dialogue-summarization", "en", "dataset:samsum", "base_model:facebook/bart-base", "base_model:finetune:facebook/bart-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-06-12T21:55:09Z

--- language: - en license: apache-2.0 tags: - dialogue-summarization datasets: - samsum model_index: - name: hybrid_utt-clusterrank_bart-base_samsum_sum results: - task: name: Summarization type: summarization base_model: facebook/bart-base --- ## Paper ## [Domain Adapted Abstractive Summarization of Dialogue using Transfer Learning](https://dl.acm.org/doi/10.1145/3508546.3508640) Authors: *Rohit Sroch* ## Abstract Recently, the abstractive dialogue summarization task has been gaining a lot of attention from researchers. Also, unlike news articles and documents with well-structured text, dialogue differs in the sense that it often comes from two or more interlocutors, exchanging information with each other and having an inherent hierarchical structure based on the sequence of utterances by different speakers. This paper proposes a simple but effective hybrid approach that consists of two modules and uses transfer learning by leveraging pretrained language models (PLMs) to generate an abstractive summary. The first module highlights important utterances, capturing the utterance level relationship by adapting an auto-encoding model like BERT based on the unsupervised or supervised method. And then, the second module generates a concise abstractive summary by adapting encoder-decoder models like T5, BART, and PEGASUS. Experiment results on benchmark datasets show that our approach achieves a state-of-the-art performance by adapting to dialogue scenarios and can also be helpful in low-resource settings for domain adaptation. *Rohit Sroch. 2021. Domain Adapted Abstractive Summarization of Dialogue using Transfer Learning. In 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence (ACAI'21). Association for Computing Machinery, New York, NY, USA, Article 94, 1–6. https://doi.org/10.1145/3508546.3508640* # hybrid_utt-clusterrank_bart-base_samsum_sum This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on SAMSum dataset for dialogue summarization task. ## Model description More information needed ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-5 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 - label_smoothing_factor: 0.1 ### Results on Test Set - predict_gen_len = 23.9048 - predict_rouge1 = **47.355** - predict_rouge2 = **22.4593** - predict_rougeL = **38.694** - predict_rougeLsum = **42.98** - predict_samples = 819 - predict_samples_per_second = 9.279 - predict_steps_per_second = 2.322 ### Framework versions - Transformers>=4.8.0 - Pytorch>=1.6.0 - Datasets>=1.10.2 - Tokenizers>=0.10.3 If you use this model, please cite the following paper: ``` @inproceedings{10.1145/3508546.3508640, author = {Sroch, Rohit}, title = {Domain Adapted Abstractive Summarization of Dialogue Using Transfer Learning}, year = {2021}, isbn = {9781450385053}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3508546.3508640}, doi = {10.1145/3508546.3508640}, articleno = {94}, numpages = {6}, keywords = {encoder-decoder, T5, abstractive summary, PEGASUS, BART, dialogue summarization, PLMs, BERT}, location = {Sanya, China}, series = {ACAI'21} } ```

HiTZ/A2T_RoBERTa_SMFA_TACRED-re

HiTZ

2023-09-11T13:35:34Z

117

0

transformers

[ "transformers", "pytorch", "safetensors", "roberta", "text-classification", "zero-shot-classification", "dataset:snli", "dataset:anli", "dataset:multi_nli", "dataset:multi_nli_mismatch", "dataset:fever", "arxiv:2104.14690", "arxiv:2203.13602", "autotrain_compatible", "endpoints_compatible", "region:us" ]

zero-shot-classification

2022-05-02T12:52:23Z

--- pipeline_tag: zero-shot-classification datasets: - snli - anli - multi_nli - multi_nli_mismatch - fever --- # A2T Entailment model **Important:** These pretrained entailment models are intended to be used with the [Ask2Transformers](https://github.com/osainz59/Ask2Transformers) library but are also fully compatible with the `ZeroShotTextClassificationPipeline` from [Transformers](https://github.com/huggingface/Transformers). Textual Entailment (or Natural Language Inference) has turned out to be a good choice for zero-shot text classification problems [(Yin et al., 2019](https://aclanthology.org/D19-1404/); [Wang et al., 2021](https://arxiv.org/abs/2104.14690); [Sainz and Rigau, 2021)](https://aclanthology.org/2021.gwc-1.6/). Recent research addressed Information Extraction problems with the same idea [(Lyu et al., 2021](https://aclanthology.org/2021.acl-short.42/); [Sainz et al., 2021](https://aclanthology.org/2021.emnlp-main.92/); [Sainz et al., 2022a](), [Sainz et al., 2022b)](https://arxiv.org/abs/2203.13602). The A2T entailment models are first trained with NLI datasets such as MNLI [(Williams et al., 2018)](), SNLI [(Bowman et al., 2015)]() or/and ANLI [(Nie et al., 2020)]() and then fine-tuned to specific tasks that were previously converted to textual entailment format. For more information please, take a look to the [Ask2Transformers](https://github.com/osainz59/Ask2Transformers) library or the following published papers: - [Label Verbalization and Entailment for Effective Zero and Few-Shot Relation Extraction (Sainz et al., EMNLP 2021)](https://aclanthology.org/2021.emnlp-main.92/) - [Textual Entailment for Event Argument Extraction: Zero- and Few-Shot with Multi-Source Learning (Sainz et al., Findings of NAACL-HLT 2022)]() ## About the model The model name describes the configuration used for training as follows:  <h3 align="center">HiTZ/A2T_[pretrained_model]_[NLI_datasets]_[finetune_datasets]</h3> - `pretrained_model`: The checkpoint used for initialization. For example: RoBERTa<sub>large</sub>. - `NLI_datasets`: The NLI datasets used for pivot training. - `S`: Standford Natural Language Inference (SNLI) dataset. - `M`: Multi Natural Language Inference (MNLI) dataset. - `F`: Fever-nli dataset. - `A`: Adversarial Natural Language Inference (ANLI) dataset. - `finetune_datasets`: The datasets used for fine tuning the entailment model. Note that for more than 1 dataset the training was performed sequentially. For example: ACE-arg. Some models like `HiTZ/A2T_RoBERTa_SMFA_ACE-arg` have been trained marking some information between square brackets (`'[['` and `']]'`) like the event trigger span. Make sure you follow the same preprocessing in order to obtain the best results. ## Cite If you use this model, consider citing the following publications: ```bibtex @inproceedings{sainz-etal-2021-label, title = "Label Verbalization and Entailment for Effective Zero and Few-Shot Relation Extraction", author = "Sainz, Oscar and Lopez de Lacalle, Oier and Labaka, Gorka and Barrena, Ander and Agirre, Eneko", booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.92", doi = "10.18653/v1/2021.emnlp-main.92", pages = "1199--1212", } ```

bigmorning/whisper_4_with_init_sun_syl_wd_0__0085

bigmorning

2023-09-11T13:34:24Z

59

0

transformers

[ "transformers", "tf", "whisper", "automatic-speech-recognition", "generated_from_keras_callback", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-09-11T13:34:17Z

--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_keras_callback model-index: - name: whisper_4_with_init_sun_syl_wd_0__0085 results: [] ---  # whisper_4_with_init_sun_syl_wd_0__0085 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2122 - Train Accuracy: 0.0345 - Train Wermet: 0.0284 - Train Wermet Syl: 0.0346 - Validation Loss: 1.2518 - Validation Accuracy: 0.0208 - Validation Wermet: 0.3241 - Validation Wermet Syl: 0.2884 - Epoch: 84 ## 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': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Train Wermet | Train Wermet Syl | Validation Loss | Validation Accuracy | Validation Wermet | Validation Wermet Syl | Epoch | |:----------:|:--------------:|:------------:|:----------------:|:---------------:|:-------------------:|:-----------------:|:---------------------:|:-----:| | 5.3409 | 0.0111 | 1.3547 | 1.2898 | 3.9789 | 0.0114 | 0.9710 | 0.9563 | 0 | | 4.7143 | 0.0116 | 0.8622 | 0.8228 | 3.9404 | 0.0113 | 0.9823 | 0.9735 | 1 | | 4.6752 | 0.0117 | 0.8472 | 0.8057 | 3.9081 | 0.0114 | 0.9579 | 0.9359 | 2 | | 4.6500 | 0.0117 | 0.8382 | 0.7945 | 3.8820 | 0.0115 | 0.9213 | 0.8856 | 3 | | 4.6282 | 0.0118 | 0.8286 | 0.7805 | 3.8738 | 0.0114 | 0.9433 | 0.9119 | 4 | | 4.6095 | 0.0118 | 0.8190 | 0.7696 | 3.8630 | 0.0115 | 0.9117 | 0.8698 | 5 | | 4.5875 | 0.0119 | 0.7976 | 0.7465 | 3.8341 | 0.0116 | 0.8976 | 0.8552 | 6 | | 4.5682 | 0.0120 | 0.7753 | 0.7227 | 3.8277 | 0.0116 | 0.9014 | 0.8653 | 7 | | 4.5376 | 0.0121 | 0.7528 | 0.7005 | 3.7844 | 0.0118 | 0.8332 | 0.7815 | 8 | | 4.5060 | 0.0122 | 0.7392 | 0.6844 | 3.7537 | 0.0118 | 0.8578 | 0.8152 | 9 | | 4.4580 | 0.0124 | 0.7221 | 0.6694 | 3.7038 | 0.0120 | 0.8190 | 0.7679 | 10 | | 4.3989 | 0.0125 | 0.7156 | 0.6636 | 3.6169 | 0.0122 | 0.7979 | 0.7429 | 11 | | 4.3056 | 0.0128 | 0.7069 | 0.6557 | 3.5098 | 0.0125 | 0.7924 | 0.7396 | 12 | | 4.1673 | 0.0132 | 0.7054 | 0.6584 | 3.3542 | 0.0128 | 0.7759 | 0.7240 | 13 | | 3.9762 | 0.0138 | 0.6987 | 0.6559 | 3.1318 | 0.0133 | 0.7644 | 0.7231 | 14 | | 3.7385 | 0.0145 | 0.6835 | 0.6448 | 2.9144 | 0.0138 | 0.7392 | 0.6955 | 15 | | 3.5040 | 0.0152 | 0.6644 | 0.6298 | 2.7413 | 0.0142 | 0.7019 | 0.6548 | 16 | | 3.2728 | 0.0160 | 0.6408 | 0.6101 | 2.5183 | 0.0149 | 0.6798 | 0.6363 | 17 | | 3.0657 | 0.0167 | 0.6188 | 0.5912 | 2.3594 | 0.0153 | 0.6528 | 0.6103 | 18 | | 2.8703 | 0.0174 | 0.5936 | 0.5685 | 2.2644 | 0.0156 | 0.6310 | 0.5925 | 19 | | 2.6850 | 0.0181 | 0.5680 | 0.5453 | 2.1296 | 0.0160 | 0.6040 | 0.5652 | 20 | | 2.5227 | 0.0188 | 0.5423 | 0.5215 | 2.0019 | 0.0165 | 0.5793 | 0.5403 | 21 | | 2.3878 | 0.0194 | 0.5199 | 0.5015 | 1.8996 | 0.0169 | 0.5592 | 0.5229 | 22 | | 2.2437 | 0.0201 | 0.4959 | 0.4788 | 1.8141 | 0.0172 | 0.5414 | 0.5045 | 23 | | 2.1205 | 0.0207 | 0.4752 | 0.4607 | 1.7245 | 0.0175 | 0.5208 | 0.4838 | 24 | | 1.9919 | 0.0213 | 0.4533 | 0.4390 | 1.6673 | 0.0178 | 0.5026 | 0.4659 | 25 | | 1.9140 | 0.0217 | 0.4355 | 0.4216 | 1.6041 | 0.0181 | 0.4873 | 0.4512 | 26 | | 1.8225 | 0.0222 | 0.4184 | 0.4052 | 1.6271 | 0.0179 | 0.4852 | 0.4511 | 27 | | 1.7265 | 0.0227 | 0.4016 | 0.3895 | 1.5219 | 0.0184 | 0.4635 | 0.4275 | 28 | | 1.6240 | 0.0233 | 0.3833 | 0.3729 | 1.4718 | 0.0186 | 0.4515 | 0.4170 | 29 | | 1.5610 | 0.0236 | 0.3697 | 0.3588 | 1.4404 | 0.0188 | 0.4407 | 0.4056 | 30 | | 1.4719 | 0.0242 | 0.3540 | 0.3449 | 1.4125 | 0.0189 | 0.4310 | 0.3961 | 31 | | 1.4152 | 0.0245 | 0.3421 | 0.3339 | 1.3655 | 0.0191 | 0.4234 | 0.3881 | 32 | | 1.3546 | 0.0249 | 0.3277 | 0.3195 | 1.3419 | 0.0192 | 0.4156 | 0.3816 | 33 | | 1.2565 | 0.0256 | 0.3135 | 0.3060 | 1.3172 | 0.0194 | 0.4065 | 0.3722 | 34 | | 1.2135 | 0.0258 | 0.3026 | 0.2958 | 1.3019 | 0.0194 | 0.4006 | 0.3662 | 35 | | 1.1739 | 0.0261 | 0.2923 | 0.2861 | 1.3843 | 0.0190 | 0.3951 | 0.3587 | 36 | | 1.0950 | 0.0267 | 0.2782 | 0.2733 | 1.2665 | 0.0197 | 0.3883 | 0.3541 | 37 | | 1.0435 | 0.0271 | 0.2673 | 0.2631 | 1.2567 | 0.0197 | 0.3837 | 0.3497 | 38 | | 0.9922 | 0.0275 | 0.2580 | 0.2542 | 1.2566 | 0.0197 | 0.3801 | 0.3444 | 39 | | 0.9387 | 0.0279 | 0.2464 | 0.2438 | 1.2441 | 0.0198 | 0.3767 | 0.3423 | 40 | | 0.9345 | 0.0278 | 0.2393 | 0.2373 | 1.2221 | 0.0199 | 0.3682 | 0.3336 | 41 | | 0.8574 | 0.0285 | 0.2268 | 0.2255 | 1.2258 | 0.0199 | 0.3680 | 0.3338 | 42 | | 0.8275 | 0.0287 | 0.2183 | 0.2180 | 1.2044 | 0.0201 | 0.3628 | 0.3290 | 43 | | 0.8201 | 0.0288 | 0.2114 | 0.2108 | 1.2056 | 0.0201 | 0.3601 | 0.3270 | 44 | | 0.7684 | 0.0292 | 0.2020 | 0.2029 | 1.1879 | 0.0202 | 0.3553 | 0.3215 | 45 | | 0.7262 | 0.0295 | 0.1938 | 0.1947 | 1.2263 | 0.0200 | 0.3537 | 0.3177 | 46 | | 0.7286 | 0.0295 | 0.1876 | 0.1898 | 1.1772 | 0.0203 | 0.3485 | 0.3135 | 47 | | 0.6807 | 0.0300 | 0.1775 | 0.1797 | 1.1761 | 0.0203 | 0.3490 | 0.3155 | 48 | | 0.6609 | 0.0301 | 0.1713 | 0.1742 | 1.1853 | 0.0203 | 0.3484 | 0.3153 | 49 | | 0.6062 | 0.0306 | 0.1615 | 0.1653 | 1.1660 | 0.0204 | 0.3432 | 0.3090 | 50 | | 0.5755 | 0.0309 | 0.1547 | 0.1584 | 1.1698 | 0.0204 | 0.3428 | 0.3089 | 51 | | 0.5600 | 0.0310 | 0.1482 | 0.1524 | 1.1667 | 0.0204 | 0.3398 | 0.3058 | 52 | | 0.5715 | 0.0308 | 0.1449 | 0.1496 | 1.1614 | 0.0205 | 0.3381 | 0.3036 | 53 | | 0.5247 | 0.0313 | 0.1358 | 0.1411 | 1.1639 | 0.0205 | 0.3359 | 0.3025 | 54 | | 0.5085 | 0.0315 | 0.1301 | 0.1358 | 1.2420 | 0.0202 | 0.3412 | 0.3064 | 55 | | 0.4827 | 0.0317 | 0.1239 | 0.1295 | 1.1677 | 0.0205 | 0.3349 | 0.3009 | 56 | | 0.4848 | 0.0317 | 0.1207 | 0.1280 | 1.1653 | 0.0205 | 0.3326 | 0.2991 | 57 | | 0.4323 | 0.0322 | 0.1109 | 0.1185 | 1.1602 | 0.0206 | 0.3299 | 0.2953 | 58 | | 0.4183 | 0.0323 | 0.1057 | 0.1133 | 1.1622 | 0.0206 | 0.3307 | 0.2962 | 59 | | 0.4329 | 0.0322 | 0.1028 | 0.1100 | 1.1714 | 0.0206 | 0.3300 | 0.2950 | 60 | | 0.3962 | 0.0326 | 0.0964 | 0.1045 | 1.1726 | 0.0206 | 0.3311 | 0.2967 | 61 | | 0.3642 | 0.0329 | 0.0898 | 0.0973 | 1.1699 | 0.0206 | 0.3289 | 0.2936 | 62 | | 0.3786 | 0.0327 | 0.0884 | 0.0963 | 1.1734 | 0.0206 | 0.3279 | 0.2929 | 63 | | 0.3698 | 0.0328 | 0.0842 | 0.0925 | 1.1728 | 0.0207 | 0.3282 | 0.2932 | 64 | | 0.3219 | 0.0333 | 0.0765 | 0.0850 | 1.1830 | 0.0207 | 0.3258 | 0.2907 | 65 | | 0.3035 | 0.0335 | 0.0725 | 0.0811 | 1.1840 | 0.0207 | 0.3261 | 0.2904 | 66 | | 0.3522 | 0.0330 | 0.0745 | 0.0826 | 1.2107 | 0.0206 | 0.3299 | 0.2955 | 67 | | 0.3001 | 0.0335 | 0.0663 | 0.0749 | 1.1810 | 0.0207 | 0.3264 | 0.2909 | 68 | | 0.2729 | 0.0338 | 0.0595 | 0.0677 | 1.1911 | 0.0207 | 0.3247 | 0.2886 | 69 | | 0.2696 | 0.0338 | 0.0572 | 0.0654 | 1.1950 | 0.0207 | 0.3260 | 0.2905 | 70 | | 0.2840 | 0.0337 | 0.0563 | 0.0648 | 1.2094 | 0.0207 | 0.3250 | 0.2887 | 71 | | 0.2319 | 0.0342 | 0.0484 | 0.0569 | 1.2107 | 0.0207 | 0.3250 | 0.2878 | 72 | | 0.2371 | 0.0342 | 0.0464 | 0.0541 | 1.2059 | 0.0207 | 0.3240 | 0.2880 | 73 | | 0.2666 | 0.0338 | 0.0486 | 0.0575 | 1.2036 | 0.0207 | 0.3241 | 0.2887 | 74 | | 0.2443 | 0.0340 | 0.0442 | 0.0522 | 1.2106 | 0.0207 | 0.3241 | 0.2877 | 75 | | 0.2118 | 0.0344 | 0.0380 | 0.0456 | 1.2172 | 0.0207 | 0.3240 | 0.2871 | 76 | | 0.1997 | 0.0346 | 0.0354 | 0.0428 | 1.2247 | 0.0208 | 0.3219 | 0.2852 | 77 | | 0.2461 | 0.0341 | 0.0386 | 0.0466 | 1.2257 | 0.0207 | 0.3240 | 0.2874 | 78 | | 0.2367 | 0.0342 | 0.0364 | 0.0431 | 1.2173 | 0.0208 | 0.3234 | 0.2870 | 79 | | 0.1857 | 0.0347 | 0.0294 | 0.0365 | 1.2287 | 0.0208 | 0.3244 | 0.2876 | 80 | | 0.1504 | 0.0351 | 0.0244 | 0.0314 | 1.2425 | 0.0207 | 0.3238 | 0.2871 | 81 | | 0.1438 | 0.0352 | 0.0227 | 0.0287 | 1.2495 | 0.0208 | 0.3222 | 0.2861 | 82 | | 0.1545 | 0.0350 | 0.0232 | 0.0288 | 1.2612 | 0.0207 | 0.3257 | 0.2898 | 83 | | 0.2122 | 0.0345 | 0.0284 | 0.0346 | 1.2518 | 0.0208 | 0.3241 | 0.2884 | 84 | ### Framework versions - Transformers 4.34.0.dev0 - TensorFlow 2.13.0 - Tokenizers 0.13.3

ixa-ehu/roberta-eus-euscrawl-large-cased

ixa-ehu

2023-09-11T13:33:15Z

114

3

transformers

[ "transformers", "pytorch", "safetensors", "roberta", "fill-mask", "basque", "eu", "arxiv:2203.08111", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-03-16T09:55:25Z

--- language: eu license: cc-by-nc-4.0 tags: - basque - roberta --- # Roberta-eus Euscrawl large cased This is a RoBERTa model for Basque model presented in [Does corpus quality really matter for low-resource languages?](https://arxiv.org/abs/2203.08111). There are several models for Basque using the RoBERTa architecture, using different corpora: - roberta-eus-euscrawl-base-cased: Basque RoBERTa model trained on Euscrawl, a corpus created using tailored crawling from Basque sites. EusCrawl contains 12,528k documents and 423M tokens. - roberta-eus-euscrawl-large-cased: RoBERTa large trained on EusCrawl. - roberta-eus-mC4-base-cased: Basque RoBERTa model trained on the Basque portion of mc4 dataset. - roberta-eus-CC100-base-cased: Basque RoBERTa model trained on Basque portion of cc100 dataset. The models have been tested on five different downstream tasks for Basque: Topic classification, Sentiment analysis, Stance detection, Named Entity Recognition (NER), and Question Answering (refer to the [paper](https://arxiv.org/abs/2203.08111) for more details). See summary of results below: | Model | Topic class. | Sentiment | Stance det. | NER | QA | Average | |----------------------------------|--------------|-----------|-------------|----------|----------|----------| | roberta-eus-euscrawl-base-cased | 76.2 | 77.7 | 57.4 | 86.8 | 34.6 | 66.5 | | roberta-eus-euscrawl-large-cased | **77.6** | 78.8 | 62.9 | **87.2** | **38.3** | **69.0** | | roberta-eus-mC4-base-cased | 75.3 | **80.4** | 59.1 | 86.0 | 35.2 | 67.2 | | roberta-eus-CC100-base-cased | 76.2 | 78.8 | **63.4** | 85.2 | 35.8 | 67.9 | If you use any of these models, please cite the following paper: ``` @misc{artetxe2022euscrawl, title={Does corpus quality really matter for low-resource languages?}, author={Mikel Artetxe, Itziar Aldabe, Rodrigo Agerri, Olatz Perez-de-Viñaspre, Aitor Soroa}, year={2022}, eprint={2203.08111}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

kensvin/audio_classification

kensvin

2023-09-11T13:31:00Z

162

0

transformers

[ "transformers", "pytorch", "wav2vec2", "audio-classification", "generated_from_trainer", "dataset:minds14", "base_model:facebook/wav2vec2-base", "base_model:finetune:facebook/wav2vec2-base", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

audio-classification

2023-09-11T13:27:41Z

--- license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer datasets: - minds14 metrics: - accuracy model-index: - name: audio_classification results: - task: name: Audio Classification type: audio-classification dataset: name: minds14 type: minds14 config: en-US split: train args: en-US metrics: - name: Accuracy type: accuracy value: 0.07079646017699115 ---  # audio_classification This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the minds14 dataset. It achieves the following results on the evaluation set: - Loss: 2.6513 - Accuracy: 0.0708 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 0.8 | 3 | 2.6439 | 0.0531 | | No log | 1.87 | 7 | 2.6446 | 0.0708 | | 2.6349 | 2.93 | 11 | 2.6484 | 0.0885 | | 2.6349 | 4.0 | 15 | 2.6497 | 0.0885 | | 2.6349 | 4.8 | 18 | 2.6509 | 0.0796 | | 2.6233 | 5.87 | 22 | 2.6513 | 0.0708 | | 2.6233 | 6.93 | 26 | 2.6515 | 0.0708 | | 2.612 | 8.0 | 30 | 2.6513 | 0.0708 | ### Framework versions - Transformers 4.33.1 - Pytorch 1.13.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3

ixa-ehu/SciBERT-SQuAD-QuAC

ixa-ehu

2023-09-11T13:30:44Z

262

4

transformers

[ "transformers", "pytorch", "safetensors", "bert", "question-answering", "en", "arxiv:1808.07036", "endpoints_compatible", "region:us" ]

question-answering

2022-03-02T23:29:05Z

--- language: en --- # SciBERT-SQuAD-QuAC This is the [SciBERT language representation model](https://huggingface.co/allenai/scibert_scivocab_uncased) fine tuned for Question Answering. SciBERT is a pre-trained language model based on BERT that has been trained on a large corpus of scientific text. When fine tuning for Question Answering we combined [SQuAD2.0](https://www.aclweb.org/anthology/P18-2124/) and [QuAC](https://arxiv.org/abs/1808.07036) datasets. If using this model, please cite the following paper: ``` @inproceedings{otegi-etal-2020-automatic, title = "Automatic Evaluation vs. User Preference in Neural Textual {Q}uestion{A}nswering over {COVID}-19 Scientific Literature", author = "Otegi, Arantxa and Campos, Jon Ander and Azkune, Gorka and Soroa, Aitor and Agirre, Eneko", booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020", month = dec, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.nlpcovid19-2.15", doi = "10.18653/v1/2020.nlpcovid19-2.15", } ```

saattrupdan/wav2vec2-xls-r-300m-ftspeech

saattrupdan

2023-09-11T13:27:55Z

115,130

0

transformers

[ "transformers", "pytorch", "safetensors", "wav2vec2", "automatic-speech-recognition", "da", "dataset:ftspeech", "base_model:facebook/wav2vec2-xls-r-300m", "base_model:finetune:facebook/wav2vec2-xls-r-300m", "license:other", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-03-04T14:53:05Z

--- language: - da license: other datasets: - ftspeech metrics: - wer tasks: - automatic-speech-recognition base_model: facebook/wav2vec2-xls-r-300m model-index: - name: wav2vec2-xls-r-300m-ftspeech results: - task: type: automatic-speech-recognition dataset: name: Danish Common Voice 8.0 type: mozilla-foundation/common_voice_8_0 args: da metrics: - type: wer value: 17.91 - task: type: automatic-speech-recognition dataset: name: Alvenir ASR test dataset type: Alvenir/alvenir_asr_da_eval metrics: - type: wer value: 13.84 --- # XLS-R-300m-FTSpeech ## Model description This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the [FTSpeech dataset](https://ftspeech.github.io/), being a dataset of 1,800 hours of transcribed speeches from the Danish parliament. ## Performance The model achieves the following WER scores (lower is better): | **Dataset** | **WER without LM** | **WER with 5-gram LM** | | :---: | ---: | ---: | | [Danish part of Common Voice 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0/viewer/da/train) | 20.48 | 17.91 | | [Alvenir test set](https://huggingface.co/datasets/Alvenir/alvenir_asr_da_eval) | 15.46 | 13.84 | ## License The use of this model needs to adhere to [this license from the Danish Parliament](https://www.ft.dk/da/aktuelt/tv-fra-folketinget/deling-og-rettigheder).

sanchit-gandhi/whisper-small-dv

sanchit-gandhi

2023-09-11T13:25:29Z

210

1

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dv", "dataset:mozilla-foundation/common_voice_13_0", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-06-27T14:43:10Z

--- language: - dv license: apache-2.0 tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_13_0 metrics: - wer base_model: openai/whisper-small model-index: - name: Whisper Small Dv - Sanchit Gandhi results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: Common Voice 13 type: mozilla-foundation/common_voice_13_0 config: dv split: test args: dv metrics: - type: wer value: 14.066140417985187 name: Wer ---  # Whisper Small Dv - Sanchit Gandhi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 13 dataset. It achieves the following results on the evaluation set: - Loss: 0.1727 - Wer Ortho: 63.8972 - Wer: 14.0661 ## 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: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 500 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer | |:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:| | 0.136 | 1.63 | 500 | 0.1727 | 63.8972 | 14.0661 | ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1.dev0 - Tokenizers 0.13.3

nickmuchi/distilroberta-finetuned-financial-text-classification

nickmuchi

2023-09-11T13:23:38Z

1,773

15

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "financial-sentiment-analysis", "sentiment-analysis", "sentence_50agree", "generated_from_trainer", "sentiment", "finance", "en", "dataset:financial_phrasebank", "dataset:Kaggle_Self_label", "dataset:nickmuchi/financial-classification", "base_model:distilbert/distilroberta-base", "base_model:finetune:distilbert/distilroberta-base", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-03-02T23:29:05Z

--- language: en license: apache-2.0 tags: - financial-sentiment-analysis - sentiment-analysis - sentence_50agree - generated_from_trainer - sentiment - finance datasets: - financial_phrasebank - Kaggle_Self_label - nickmuchi/financial-classification metrics: - f1 widget: - text: The USD rallied by 10% last night example_title: Bullish Sentiment - text: Covid-19 cases have been increasing over the past few months impacting earnings for global firms example_title: Bearish Sentiment - text: the USD has been trending lower example_title: Mildly Bearish Sentiment base_model: distilroberta-base model-index: - name: distilroberta-finetuned-finclass results: - task: type: text-classification name: Text Classification dataset: name: financial_phrasebank type: finance args: sentence_50agree metrics: - type: F1 value: 0.8835 name: F1 - type: accuracy value: 0.89 name: accuracy --- # distilroberta-finetuned-financial-text-classification This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the sentence_50Agree [financial-phrasebank + Kaggle Dataset](https://huggingface.co/datasets/nickmuchi/financial-classification), a dataset consisting of 4840 Financial News categorised by sentiment (negative, neutral, positive). The Kaggle dataset includes Covid-19 sentiment data and can be found here: [sentiment-classification-selflabel-dataset](https://www.kaggle.com/percyzheng/sentiment-classification-selflabel-dataset). It achieves the following results on the evaluation set: - Loss: 0.4463 - F1: 0.8835 ## Model description Model determines the financial sentiment of given text. Given the unbalanced distribution of the class labels, the weights were adjusted to pay attention to the less sampled labels which should increase overall performance. The Covid dataset was added in order to enrich the model, given most models have not been trained on the impact of Covid-19 on earnings or markets. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.7309 | 1.0 | 72 | 0.3671 | 0.8441 | | 0.3757 | 2.0 | 144 | 0.3199 | 0.8709 | | 0.3054 | 3.0 | 216 | 0.3096 | 0.8678 | | 0.2229 | 4.0 | 288 | 0.3776 | 0.8390 | | 0.1744 | 5.0 | 360 | 0.3678 | 0.8723 | | 0.1436 | 6.0 | 432 | 0.3728 | 0.8758 | | 0.1044 | 7.0 | 504 | 0.4116 | 0.8744 | | 0.0931 | 8.0 | 576 | 0.4148 | 0.8761 | | 0.0683 | 9.0 | 648 | 0.4423 | 0.8837 | | 0.0611 | 10.0 | 720 | 0.4463 | 0.8835 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.18.0 - Tokenizers 0.10.3

nielsr/swin-tiny-patch4-window7-224-finetuned-cifar10

nielsr

2023-09-11T13:16:37Z

221

0

transformers

[ "transformers", "pytorch", "tensorboard", "swin", "image-classification", "generated_from_trainer", "dataset:image_folder", "base_model:microsoft/swin-tiny-patch4-window7-224", "base_model:finetune:microsoft/swin-tiny-patch4-window7-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2022-04-11T11:59:54Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - image_folder metrics: - accuracy base_model: microsoft/swin-tiny-patch4-window7-224 model-index: - name: swin-tiny-patch4-window7-224-finetuned-cifar10 results: - task: type: image-classification name: Image Classification dataset: name: image_folder type: image_folder args: default metrics: - type: accuracy value: 0.9788888888888889 name: Accuracy ---  # swin-tiny-patch4-window7-224-finetuned-cifar10 This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.0690 - Accuracy: 0.9789 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2446 | 1.0 | 190 | 0.1128 | 0.9659 | | 0.1722 | 2.0 | 380 | 0.1034 | 0.9663 | | 0.1355 | 3.0 | 570 | 0.0690 | 0.9789 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

osieosie/bloom-mnli-8bit-7b-bnb-seed87

osieosie

2023-09-11T13:16:15Z

0

peft

[ "peft", "region:us" ]

null

2023-09-11T13:16:14Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0.dev0

vesteinn/IceBERT-ner

vesteinn

2023-09-11T13:14:09Z

186

0

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "roberta", "token-classification", "generated_from_trainer", "dataset:mim_gold_ner", "base_model:vesteinn/IceBERT", "base_model:finetune:vesteinn/IceBERT", "license:gpl-3.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-03-02T23:29:05Z

--- license: gpl-3.0 tags: - generated_from_trainer datasets: - mim_gold_ner metrics: - precision - recall - f1 - accuracy widget: - text: Systurnar Guðrún og Monique átu einar á McDonalds og horfðu á Stöð 2, þar glitti í Bruce Willis leika í Die Hard 2. base_model: vesteinn/IceBERT model-index: - name: IceBERT-finetuned-ner results: - task: type: token-classification name: Token Classification dataset: name: mim_gold_ner type: mim_gold_ner args: mim-gold-ner metrics: - type: precision value: 0.9351994710160899 name: Precision - type: recall value: 0.9440427188786294 name: Recall - type: f1 value: 0.9396002878813043 name: F1 - type: accuracy value: 0.9920330921021648 name: Accuracy ---  # IceBERT-finetuned-ner This model is a fine-tuned version of [vesteinn/IceBERT](https://huggingface.co/vesteinn/IceBERT) on the mim_gold_ner dataset. It achieves the following results on the evaluation set: - Loss: 0.0347 - Precision: 0.9352 - Recall: 0.9440 - F1: 0.9396 - Accuracy: 0.9920 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0568 | 1.0 | 2929 | 0.0386 | 0.9114 | 0.9162 | 0.9138 | 0.9897 | | 0.0325 | 2.0 | 5858 | 0.0325 | 0.9300 | 0.9363 | 0.9331 | 0.9912 | | 0.0184 | 3.0 | 8787 | 0.0347 | 0.9352 | 0.9440 | 0.9396 | 0.9920 | ### Framework versions - Transformers 4.11.0 - Pytorch 1.9.0+cu102 - Datasets 1.12.1 - Tokenizers 0.10.3

ArifYZ/dutch-sentences-model

ArifYZ

2023-09-11T13:11:40Z

0

null

[ "region:us" ]

null

2023-09-11T13:04:55Z

Model for embedding Dutch Sentences

HamZurger/Reinforce-CartPole_v2

HamZurger

2023-09-11T13:00:21Z

0

null

[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2023-09-11T13:00:13Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole_v2 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

tuikhar/naga

tuikhar

2023-09-11T12:57:49Z

0

null

[ "arxiv:1910.09700", "region:us" ]

null

2023-09-11T12:57:09Z

--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Model Card for Model ID  This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

JasperLS/gelectra-base-injection-pt_v1

JasperLS

2023-09-11T12:55:08Z

114

0

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "electra", "text-classification", "generated_from_trainer", "base_model:deepset/gelectra-base", "base_model:finetune:deepset/gelectra-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-04-06T12:31:06Z

--- license: mit tags: - generated_from_trainer metrics: - accuracy base_model: deepset/gelectra-base model-index: - name: gelectra-base-injection-pt_v1 results: [] ---  # gelectra-base-injection-pt_v1 DEPRECATED - PLEASE USE NEWER GELECTRA OR DEBERTA VERSION This model is a fine-tuned version of [deepset/gelectra-base](https://huggingface.co/deepset/gelectra-base) on a closed prompt injection dataset. It achieves the following results on the evaluation set: - Loss: 0.0163 - Accuracy: 1.0 ## Model description The model classifies prompts as injections or legitimate questions. ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 45 | 0.2042 | 0.9211 | | No log | 2.0 | 90 | 0.0247 | 1.0 | | No log | 3.0 | 135 | 0.0163 | 1.0 | ### Framework versions - Transformers 4.27.4 - Pytorch 2.0.0+cu118 - Tokenizers 0.13.3

abelkrw/audio_classification

abelkrw

2023-09-11T12:53:59Z

162

0

transformers

[ "transformers", "pytorch", "wav2vec2", "audio-classification", "generated_from_trainer", "dataset:minds14", "base_model:facebook/wav2vec2-base", "base_model:finetune:facebook/wav2vec2-base", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

audio-classification

2023-09-11T12:50:42Z

--- license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer datasets: - minds14 metrics: - accuracy model-index: - name: audio_classification results: - task: name: Audio Classification type: audio-classification dataset: name: minds14 type: minds14 config: en-US split: train args: en-US metrics: - name: Accuracy type: accuracy value: 0.07079646017699115 ---  # audio_classification This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the minds14 dataset. It achieves the following results on the evaluation set: - Loss: 2.6569 - Accuracy: 0.0708 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 0.8 | 3 | 2.6456 | 0.0265 | | No log | 1.87 | 7 | 2.6512 | 0.0442 | | 2.6372 | 2.93 | 11 | 2.6509 | 0.0619 | | 2.6372 | 4.0 | 15 | 2.6541 | 0.0708 | | 2.6372 | 4.8 | 18 | 2.6554 | 0.0708 | | 2.6217 | 5.87 | 22 | 2.6561 | 0.0708 | | 2.6217 | 6.93 | 26 | 2.6564 | 0.0708 | | 2.6141 | 8.0 | 30 | 2.6569 | 0.0708 | ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

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

esperesa

2023-09-11T12:53:29Z

106

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

2023-09-11T12:43:57Z

--- 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: 0.8653353814644136 ---  # 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: 0.1339 - F1: 0.8653 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2583 | 1.0 | 525 | 0.1596 | 0.8231 | | 0.1262 | 2.0 | 1050 | 0.1395 | 0.8468 | | 0.0824 | 3.0 | 1575 | 0.1339 | 0.8653 | ### Framework versions - Transformers 4.16.2 - Pytorch 2.0.1+cu118 - Datasets 1.16.1 - Tokenizers 0.14.0

bigmorning/whisper_4_with_init_sun_syl_wd_0__0070

bigmorning

2023-09-11T12:48:55Z

59

0

transformers

[ "transformers", "tf", "whisper", "automatic-speech-recognition", "generated_from_keras_callback", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-09-11T12:48:46Z

--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_keras_callback model-index: - name: whisper_4_with_init_sun_syl_wd_0__0070 results: [] ---  # whisper_4_with_init_sun_syl_wd_0__0070 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2729 - Train Accuracy: 0.0338 - Train Wermet: 0.0595 - Train Wermet Syl: 0.0677 - Validation Loss: 1.1911 - Validation Accuracy: 0.0207 - Validation Wermet: 0.3247 - Validation Wermet Syl: 0.2886 - Epoch: 69 ## 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': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Train Wermet | Train Wermet Syl | Validation Loss | Validation Accuracy | Validation Wermet | Validation Wermet Syl | Epoch | |:----------:|:--------------:|:------------:|:----------------:|:---------------:|:-------------------:|:-----------------:|:---------------------:|:-----:| | 5.3409 | 0.0111 | 1.3547 | 1.2898 | 3.9789 | 0.0114 | 0.9710 | 0.9563 | 0 | | 4.7143 | 0.0116 | 0.8622 | 0.8228 | 3.9404 | 0.0113 | 0.9823 | 0.9735 | 1 | | 4.6752 | 0.0117 | 0.8472 | 0.8057 | 3.9081 | 0.0114 | 0.9579 | 0.9359 | 2 | | 4.6500 | 0.0117 | 0.8382 | 0.7945 | 3.8820 | 0.0115 | 0.9213 | 0.8856 | 3 | | 4.6282 | 0.0118 | 0.8286 | 0.7805 | 3.8738 | 0.0114 | 0.9433 | 0.9119 | 4 | | 4.6095 | 0.0118 | 0.8190 | 0.7696 | 3.8630 | 0.0115 | 0.9117 | 0.8698 | 5 | | 4.5875 | 0.0119 | 0.7976 | 0.7465 | 3.8341 | 0.0116 | 0.8976 | 0.8552 | 6 | | 4.5682 | 0.0120 | 0.7753 | 0.7227 | 3.8277 | 0.0116 | 0.9014 | 0.8653 | 7 | | 4.5376 | 0.0121 | 0.7528 | 0.7005 | 3.7844 | 0.0118 | 0.8332 | 0.7815 | 8 | | 4.5060 | 0.0122 | 0.7392 | 0.6844 | 3.7537 | 0.0118 | 0.8578 | 0.8152 | 9 | | 4.4580 | 0.0124 | 0.7221 | 0.6694 | 3.7038 | 0.0120 | 0.8190 | 0.7679 | 10 | | 4.3989 | 0.0125 | 0.7156 | 0.6636 | 3.6169 | 0.0122 | 0.7979 | 0.7429 | 11 | | 4.3056 | 0.0128 | 0.7069 | 0.6557 | 3.5098 | 0.0125 | 0.7924 | 0.7396 | 12 | | 4.1673 | 0.0132 | 0.7054 | 0.6584 | 3.3542 | 0.0128 | 0.7759 | 0.7240 | 13 | | 3.9762 | 0.0138 | 0.6987 | 0.6559 | 3.1318 | 0.0133 | 0.7644 | 0.7231 | 14 | | 3.7385 | 0.0145 | 0.6835 | 0.6448 | 2.9144 | 0.0138 | 0.7392 | 0.6955 | 15 | | 3.5040 | 0.0152 | 0.6644 | 0.6298 | 2.7413 | 0.0142 | 0.7019 | 0.6548 | 16 | | 3.2728 | 0.0160 | 0.6408 | 0.6101 | 2.5183 | 0.0149 | 0.6798 | 0.6363 | 17 | | 3.0657 | 0.0167 | 0.6188 | 0.5912 | 2.3594 | 0.0153 | 0.6528 | 0.6103 | 18 | | 2.8703 | 0.0174 | 0.5936 | 0.5685 | 2.2644 | 0.0156 | 0.6310 | 0.5925 | 19 | | 2.6850 | 0.0181 | 0.5680 | 0.5453 | 2.1296 | 0.0160 | 0.6040 | 0.5652 | 20 | | 2.5227 | 0.0188 | 0.5423 | 0.5215 | 2.0019 | 0.0165 | 0.5793 | 0.5403 | 21 | | 2.3878 | 0.0194 | 0.5199 | 0.5015 | 1.8996 | 0.0169 | 0.5592 | 0.5229 | 22 | | 2.2437 | 0.0201 | 0.4959 | 0.4788 | 1.8141 | 0.0172 | 0.5414 | 0.5045 | 23 | | 2.1205 | 0.0207 | 0.4752 | 0.4607 | 1.7245 | 0.0175 | 0.5208 | 0.4838 | 24 | | 1.9919 | 0.0213 | 0.4533 | 0.4390 | 1.6673 | 0.0178 | 0.5026 | 0.4659 | 25 | | 1.9140 | 0.0217 | 0.4355 | 0.4216 | 1.6041 | 0.0181 | 0.4873 | 0.4512 | 26 | | 1.8225 | 0.0222 | 0.4184 | 0.4052 | 1.6271 | 0.0179 | 0.4852 | 0.4511 | 27 | | 1.7265 | 0.0227 | 0.4016 | 0.3895 | 1.5219 | 0.0184 | 0.4635 | 0.4275 | 28 | | 1.6240 | 0.0233 | 0.3833 | 0.3729 | 1.4718 | 0.0186 | 0.4515 | 0.4170 | 29 | | 1.5610 | 0.0236 | 0.3697 | 0.3588 | 1.4404 | 0.0188 | 0.4407 | 0.4056 | 30 | | 1.4719 | 0.0242 | 0.3540 | 0.3449 | 1.4125 | 0.0189 | 0.4310 | 0.3961 | 31 | | 1.4152 | 0.0245 | 0.3421 | 0.3339 | 1.3655 | 0.0191 | 0.4234 | 0.3881 | 32 | | 1.3546 | 0.0249 | 0.3277 | 0.3195 | 1.3419 | 0.0192 | 0.4156 | 0.3816 | 33 | | 1.2565 | 0.0256 | 0.3135 | 0.3060 | 1.3172 | 0.0194 | 0.4065 | 0.3722 | 34 | | 1.2135 | 0.0258 | 0.3026 | 0.2958 | 1.3019 | 0.0194 | 0.4006 | 0.3662 | 35 | | 1.1739 | 0.0261 | 0.2923 | 0.2861 | 1.3843 | 0.0190 | 0.3951 | 0.3587 | 36 | | 1.0950 | 0.0267 | 0.2782 | 0.2733 | 1.2665 | 0.0197 | 0.3883 | 0.3541 | 37 | | 1.0435 | 0.0271 | 0.2673 | 0.2631 | 1.2567 | 0.0197 | 0.3837 | 0.3497 | 38 | | 0.9922 | 0.0275 | 0.2580 | 0.2542 | 1.2566 | 0.0197 | 0.3801 | 0.3444 | 39 | | 0.9387 | 0.0279 | 0.2464 | 0.2438 | 1.2441 | 0.0198 | 0.3767 | 0.3423 | 40 | | 0.9345 | 0.0278 | 0.2393 | 0.2373 | 1.2221 | 0.0199 | 0.3682 | 0.3336 | 41 | | 0.8574 | 0.0285 | 0.2268 | 0.2255 | 1.2258 | 0.0199 | 0.3680 | 0.3338 | 42 | | 0.8275 | 0.0287 | 0.2183 | 0.2180 | 1.2044 | 0.0201 | 0.3628 | 0.3290 | 43 | | 0.8201 | 0.0288 | 0.2114 | 0.2108 | 1.2056 | 0.0201 | 0.3601 | 0.3270 | 44 | | 0.7684 | 0.0292 | 0.2020 | 0.2029 | 1.1879 | 0.0202 | 0.3553 | 0.3215 | 45 | | 0.7262 | 0.0295 | 0.1938 | 0.1947 | 1.2263 | 0.0200 | 0.3537 | 0.3177 | 46 | | 0.7286 | 0.0295 | 0.1876 | 0.1898 | 1.1772 | 0.0203 | 0.3485 | 0.3135 | 47 | | 0.6807 | 0.0300 | 0.1775 | 0.1797 | 1.1761 | 0.0203 | 0.3490 | 0.3155 | 48 | | 0.6609 | 0.0301 | 0.1713 | 0.1742 | 1.1853 | 0.0203 | 0.3484 | 0.3153 | 49 | | 0.6062 | 0.0306 | 0.1615 | 0.1653 | 1.1660 | 0.0204 | 0.3432 | 0.3090 | 50 | | 0.5755 | 0.0309 | 0.1547 | 0.1584 | 1.1698 | 0.0204 | 0.3428 | 0.3089 | 51 | | 0.5600 | 0.0310 | 0.1482 | 0.1524 | 1.1667 | 0.0204 | 0.3398 | 0.3058 | 52 | | 0.5715 | 0.0308 | 0.1449 | 0.1496 | 1.1614 | 0.0205 | 0.3381 | 0.3036 | 53 | | 0.5247 | 0.0313 | 0.1358 | 0.1411 | 1.1639 | 0.0205 | 0.3359 | 0.3025 | 54 | | 0.5085 | 0.0315 | 0.1301 | 0.1358 | 1.2420 | 0.0202 | 0.3412 | 0.3064 | 55 | | 0.4827 | 0.0317 | 0.1239 | 0.1295 | 1.1677 | 0.0205 | 0.3349 | 0.3009 | 56 | | 0.4848 | 0.0317 | 0.1207 | 0.1280 | 1.1653 | 0.0205 | 0.3326 | 0.2991 | 57 | | 0.4323 | 0.0322 | 0.1109 | 0.1185 | 1.1602 | 0.0206 | 0.3299 | 0.2953 | 58 | | 0.4183 | 0.0323 | 0.1057 | 0.1133 | 1.1622 | 0.0206 | 0.3307 | 0.2962 | 59 | | 0.4329 | 0.0322 | 0.1028 | 0.1100 | 1.1714 | 0.0206 | 0.3300 | 0.2950 | 60 | | 0.3962 | 0.0326 | 0.0964 | 0.1045 | 1.1726 | 0.0206 | 0.3311 | 0.2967 | 61 | | 0.3642 | 0.0329 | 0.0898 | 0.0973 | 1.1699 | 0.0206 | 0.3289 | 0.2936 | 62 | | 0.3786 | 0.0327 | 0.0884 | 0.0963 | 1.1734 | 0.0206 | 0.3279 | 0.2929 | 63 | | 0.3698 | 0.0328 | 0.0842 | 0.0925 | 1.1728 | 0.0207 | 0.3282 | 0.2932 | 64 | | 0.3219 | 0.0333 | 0.0765 | 0.0850 | 1.1830 | 0.0207 | 0.3258 | 0.2907 | 65 | | 0.3035 | 0.0335 | 0.0725 | 0.0811 | 1.1840 | 0.0207 | 0.3261 | 0.2904 | 66 | | 0.3522 | 0.0330 | 0.0745 | 0.0826 | 1.2107 | 0.0206 | 0.3299 | 0.2955 | 67 | | 0.3001 | 0.0335 | 0.0663 | 0.0749 | 1.1810 | 0.0207 | 0.3264 | 0.2909 | 68 | | 0.2729 | 0.0338 | 0.0595 | 0.0677 | 1.1911 | 0.0207 | 0.3247 | 0.2886 | 69 | ### Framework versions - Transformers 4.34.0.dev0 - TensorFlow 2.13.0 - Tokenizers 0.13.3

kaitchup/Llama-2-7b-gptq-2bit

kaitchup

2023-09-11T12:48:38Z

160

1

transformers

[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "2-bit", "gptq", "region:us" ]

text-generation

2023-08-29T11:19:52Z

--- license: apache-2.0 language: - en --- # Model Card for Model ID This is Meta's Llama 2 7B quantized in 2-bit using AutoGPTQ from Hugging Face Transformers. ## Model Details ### Model Description  - **Developed by:** [The Kaitchup](https://kaitchup.substack.com/) - **Model type:** Causal (Llama 2) - **Language(s) (NLP):** English - **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0), [Llama 2 license agreement](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) ### Model Sources The method and code used to quantize the model are explained here: [Quantize and Fine-tune LLMs with GPTQ Using Transformers and TRL](https://kaitchup.substack.com/p/quantize-and-fine-tune-llms-with) ## Uses This model is pre-trained and not fine-tuned. You may fine-tune it with PEFT using adapters. Note that the 2-bit quantization significantly decreases the performance of Llama 2. ## Other versions - [kaitchup/Llama-2-7b-gptq-4bit](https://huggingface.co/kaitchup/Llama-2-7b-gptq-4bit) - [kaitchup/Llama-2-7b-gptq-3bit](https://huggingface.co/kaitchup/Llama-2-7b-gptq-3bit) ## Model Card Contact [The Kaitchup](https://kaitchup.substack.com/)

ChristianMDahl/segFormer-b3-horizontal-vertical

ChristianMDahl

2023-09-11T12:45:44Z

2

0

transformers

[ "transformers", "tf", "segformer", "generated_from_keras_callback", "base_model:nvidia/mit-b3", "base_model:finetune:nvidia/mit-b3", "license:other", "endpoints_compatible", "region:us" ]

null

2023-06-13T19:07:57Z

--- license: other tags: - generated_from_keras_callback base_model: nvidia/mit-b3 model-index: - name: ChristianMDahl/segFormer-b3-horizontal-vertical results: [] ---  # ChristianMDahl/segFormer-b3-horizontal-vertical This model is a fine-tuned version of [nvidia/mit-b3](https://huggingface.co/nvidia/mit-b3) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1671 - Validation Loss: 0.2320 - Epoch: 19 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': 6e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.3203 | 0.2831 | 0 | | 0.2822 | 0.2688 | 1 | | 0.2662 | 0.2578 | 2 | | 0.2526 | 0.2484 | 3 | | 0.2396 | 0.2442 | 4 | | 0.2288 | 0.2416 | 5 | | 0.2195 | 0.2381 | 6 | | 0.2121 | 0.2361 | 7 | | 0.2058 | 0.2314 | 8 | | 0.1999 | 0.2277 | 9 | | 0.1952 | 0.2287 | 10 | | 0.1912 | 0.2221 | 11 | | 0.1869 | 0.2205 | 12 | | 0.1835 | 0.2226 | 13 | | 0.1804 | 0.2209 | 14 | | 0.1775 | 0.2181 | 15 | | 0.1745 | 0.2206 | 16 | | 0.1721 | 0.2179 | 17 | | 0.1693 | 0.2199 | 18 | | 0.1671 | 0.2320 | 19 | ### Framework versions - Transformers 4.29.2 - TensorFlow 2.10.1 - Tokenizers 0.13.3

ahsan-mavros/error-test

ahsan-mavros

2023-09-11T12:42:32Z

105

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "base_model:google/flan-t5-base", "base_model:finetune:google/flan-t5-base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-09-11T12:41:35Z

--- license: apache-2.0 base_model: google/flan-t5-base tags: - generated_from_trainer metrics: - rouge model-index: - name: error-test results: [] ---  # error-test This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0649 - Rouge1: 98.8411 - Rouge2: 95.5257 - Rougel: 98.8389 - Rougelsum: 98.8411 - Gen Len: 5.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 0.0675 | 1.0 | 2500 | 0.0649 | 98.8411 | 95.5257 | 98.8389 | 98.8411 | 5.0 | ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.0 - Datasets 2.14.5 - Tokenizers 0.13.3

baebee/llama2-qlora-finetunined-french

baebee

2023-09-11T12:40:30Z

0

peft

[ "peft", "region:us" ]

null

2023-09-11T12:40:24Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.0.dev0

baebee/Starlight-13b

baebee

2023-09-11T12:39:07Z

0

peft

[ "peft", "region:us" ]

null

2023-09-11T12:38:59Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.0.dev0

ImhotepAI/yoruba-tts

ImhotepAI

2023-09-11T12:38:50Z

84

1

transformers

[ "transformers", "pytorch", "speecht5", "text-to-audio", "text-to-speech", "yo", "dataset:openslr", "dataset:mozilla-foundation/common_voice_13_0", "dataset:Lagos-NWU_Yoruba_Speech_Corpus", "license:cc-by-nc-sa-4.0", "endpoints_compatible", "region:us" ]

text-to-speech

2023-09-09T11:08:20Z

--- license: cc-by-nc-sa-4.0 datasets: - openslr - mozilla-foundation/common_voice_13_0 - Lagos-NWU_Yoruba_Speech_Corpus language: - yo library_name: transformers pipeline_tag: text-to-speech --- ```python # Load model directly from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan from huggingface_hub import hf_hub_download import torch processor = SpeechT5Processor.from_pretrained("imhotepai/yoruba-tts") model = SpeechT5ForTextToSpeech.from_pretrained("imhotepai/yoruba-tts") dir_= hf_hub_download(repo_id="imhotepai/yoruba-tts", filename="speaker_embeddings.pt") speaker_embeddings= torch.load(dir_) text='Báwó ni'.lower() inputs = processor(text=text, return_tensors="pt") vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan") speech = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder) # Audio in notebook from IPython.display import Audio Audio(speech.numpy(), rate=16000) ```

thusken/nb-bert-large-user-needs

thusken

2023-09-11T12:36:25Z

193

2

transformers

[ "transformers", "pytorch", "safetensors", "bert", "text-classification", "generated_from_trainer", "no", "nb", "nn", "base_model:NbAiLab/nb-bert-large", "base_model:finetune:NbAiLab/nb-bert-large", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-08-11T11:15:43Z

--- language: - 'no' - nb - nn license: cc-by-4.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall widget: - text: Fløyfjelltunnelen på E39 retning sentrum er åpen for fri ferdsel. - text: Slik kan du redusere strømregningen din pipeline_tag: text-classification base_model: NbAiLab/nb-bert-large model-index: - name: nb-bert-large-user-needs results: [] ---  # nb-bert-large-user-needs This model is a fine-tuned version of [NbAiLab/nb-bert-large](https://huggingface.co/NbAiLab/nb-bert-large) on a dataset of 2000 articles from Bergens Tidende, published between 06/01/2020 and 02/02/2020. These articles are labelled as one of six classes / user needs, as introduced by the [BBC in 2017](https://www.linkedin.com/pulse/five-lessons-i-learned-while-digitally-changing-bbc-world-shishkin/). It achieves the following results on the evaluation set: - Loss: 1.0102 - Accuracy: 0.8900 - F1: 0.8859 - Precision: 0.8883 - Recall: 0.8900 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 8 - eval_batch_size: 64 - 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: 25 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | No log | 1.0 | 195 | 0.6790 | 0.8082 | 0.7567 | 0.7679 | 0.8082 | | No log | 2.0 | 390 | 0.5577 | 0.8465 | 0.8392 | 0.8364 | 0.8465 | | 0.8651 | 3.0 | 585 | 0.5494 | 0.8338 | 0.8191 | 0.8145 | 0.8338 | | 0.8651 | 4.0 | 780 | 0.5453 | 0.8517 | 0.8386 | 0.8293 | 0.8517 | | 0.8651 | 5.0 | 975 | 0.8855 | 0.8491 | 0.8298 | 0.8444 | 0.8491 | | 0.3707 | 6.0 | 1170 | 0.7282 | 0.8645 | 0.8526 | 0.8581 | 0.8645 | | 0.3707 | 7.0 | 1365 | 0.8797 | 0.8619 | 0.8537 | 0.8573 | 0.8619 | | 0.1092 | 8.0 | 1560 | 0.9120 | 0.8491 | 0.8520 | 0.8579 | 0.8491 | | 0.1092 | 9.0 | 1755 | 1.0700 | 0.8696 | 0.8615 | 0.8669 | 0.8696 | | 0.1092 | 10.0 | 1950 | 1.0599 | 0.8670 | 0.8654 | 0.8701 | 0.8670 | | 0.0355 | 11.0 | 2145 | 1.0808 | 0.8670 | 0.8656 | 0.8685 | 0.8670 | | 0.0355 | 12.0 | 2340 | 1.0102 | 0.8900 | 0.8859 | 0.8883 | 0.8900 | | 0.0002 | 13.0 | 2535 | 1.0236 | 0.8849 | 0.8812 | 0.8824 | 0.8849 | | 0.0002 | 14.0 | 2730 | 1.0358 | 0.8875 | 0.8833 | 0.8841 | 0.8875 | | 0.0002 | 15.0 | 2925 | 1.0476 | 0.8875 | 0.8833 | 0.8841 | 0.8875 | | 0.0001 | 16.0 | 3120 | 1.0559 | 0.8798 | 0.8764 | 0.8776 | 0.8798 | | 0.0001 | 17.0 | 3315 | 1.0648 | 0.8798 | 0.8754 | 0.8765 | 0.8798 | | 0.0001 | 18.0 | 3510 | 1.0720 | 0.8798 | 0.8754 | 0.8765 | 0.8798 | | 0.0001 | 19.0 | 3705 | 1.0796 | 0.8824 | 0.8775 | 0.8783 | 0.8824 | | 0.0001 | 20.0 | 3900 | 1.0862 | 0.8798 | 0.8739 | 0.8745 | 0.8798 | | 0.0 | 21.0 | 4095 | 1.0917 | 0.8798 | 0.8739 | 0.8745 | 0.8798 | | 0.0 | 22.0 | 4290 | 1.0973 | 0.8798 | 0.8739 | 0.8745 | 0.8798 | | 0.0 | 23.0 | 4485 | 1.1007 | 0.8798 | 0.8739 | 0.8745 | 0.8798 | | 0.0 | 24.0 | 4680 | 1.1029 | 0.8798 | 0.8739 | 0.8745 | 0.8798 | | 0.0 | 25.0 | 4875 | 1.1037 | 0.8798 | 0.8739 | 0.8745 | 0.8798 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.2+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

ldos/text_shortening_model_v27

ldos

2023-09-11T12:35:54Z

3

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-09-11T11:48:09Z

--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: text_shortening_model_v27 results: [] ---  # text_shortening_model_v27 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.1933 - Rouge1: 0.4266 - Rouge2: 0.2061 - Rougel: 0.38 - Rougelsum: 0.3804 - Bert precision: 0.8628 - Bert recall: 0.8555 - Average word count: 8.003 - Max word count: 16 - Min word count: 3 - Average token count: 12.3784 - % shortened texts with length > 12: 3.003 ## 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: 0.005 - 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: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bert precision | Bert recall | Average word count | Max word count | Min word count | Average token count | % shortened texts with length > 12 | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:--------------:|:-----------:|:------------------:|:--------------:|:--------------:|:-------------------:|:----------------------------------:| | 2.4306 | 1.0 | 145 | 1.8708 | 0.4779 | 0.2499 | 0.4349 | 0.4355 | 0.8758 | 0.866 | 7.9099 | 16 | 3 | 12.3093 | 5.1051 | | 1.7537 | 2.0 | 290 | 1.8412 | 0.4532 | 0.2437 | 0.4165 | 0.4174 | 0.8687 | 0.8604 | 8.4775 | 19 | 3 | 12.8859 | 6.9069 | | 1.4338 | 3.0 | 435 | 1.7898 | 0.4365 | 0.219 | 0.4002 | 0.4007 | 0.868 | 0.856 | 7.6637 | 14 | 3 | 11.8919 | 2.1021 | | 1.2645 | 4.0 | 580 | 1.8826 | 0.4609 | 0.238 | 0.4158 | 0.4159 | 0.8711 | 0.8637 | 8.4655 | 16 | 4 | 12.8228 | 6.006 | | 1.1208 | 5.0 | 725 | 1.9741 | 0.4389 | 0.2351 | 0.4038 | 0.4051 | 0.8719 | 0.8568 | 7.5886 | 18 | 3 | 12.1231 | 2.4024 | | 1.0057 | 6.0 | 870 | 1.9700 | 0.4658 | 0.2526 | 0.4275 | 0.4276 | 0.8728 | 0.8646 | 8.0841 | 19 | 2 | 12.3634 | 7.8078 | | 0.973 | 7.0 | 1015 | 2.0594 | 0.4488 | 0.2358 | 0.4085 | 0.4093 | 0.8735 | 0.8591 | 7.3063 | 14 | 4 | 11.6757 | 0.9009 | | 0.9018 | 8.0 | 1160 | 2.0945 | 0.4362 | 0.2229 | 0.4006 | 0.4005 | 0.8654 | 0.8568 | 8.1411 | 19 | 4 | 12.5435 | 8.4084 | | 0.8608 | 9.0 | 1305 | 2.1088 | 0.4096 | 0.1926 | 0.372 | 0.372 | 0.8603 | 0.8514 | 8.0661 | 19 | 2 | 12.7297 | 3.6036 | | 0.8243 | 10.0 | 1450 | 2.2384 | 0.4237 | 0.2089 | 0.3876 | 0.3891 | 0.8688 | 0.8548 | 7.4775 | 18 | 3 | 11.8228 | 2.1021 | | 0.7966 | 11.0 | 1595 | 2.2565 | 0.418 | 0.2104 | 0.3823 | 0.3824 | 0.8673 | 0.847 | 7.2402 | 19 | 2 | 11.4024 | 2.4024 | | 0.7687 | 12.0 | 1740 | 2.3329 | 0.4238 | 0.2061 | 0.3819 | 0.383 | 0.8649 | 0.8518 | 8.0721 | 19 | 2 | 12.4715 | 6.006 | | 0.7548 | 13.0 | 1885 | 2.2799 | 0.4253 | 0.2129 | 0.3822 | 0.3835 | 0.8642 | 0.8532 | 7.9069 | 17 | 4 | 12.2733 | 4.2042 | | 0.7301 | 14.0 | 2030 | 2.4219 | 0.4066 | 0.1904 | 0.3715 | 0.3728 | 0.8629 | 0.8478 | 7.4324 | 18 | 4 | 11.6697 | 3.6036 | | 0.7011 | 15.0 | 2175 | 2.3663 | 0.4463 | 0.2222 | 0.4042 | 0.4052 | 0.8655 | 0.8606 | 8.3634 | 16 | 4 | 12.955 | 6.9069 | | 0.6667 | 16.0 | 2320 | 2.5128 | 0.4238 | 0.1918 | 0.3835 | 0.3843 | 0.8631 | 0.8522 | 7.6456 | 15 | 3 | 12.0841 | 2.4024 | | 0.6854 | 17.0 | 2465 | 2.3646 | 0.4202 | 0.2011 | 0.3774 | 0.3776 | 0.861 | 0.8543 | 8.3664 | 17 | 2 | 13.033 | 8.4084 | | 0.648 | 18.0 | 2610 | 2.5636 | 0.4159 | 0.2074 | 0.3753 | 0.3751 | 0.8562 | 0.8525 | 8.5135 | 19 | 4 | 13.024 | 6.006 | | 0.6346 | 19.0 | 2755 | 2.5641 | 0.4173 | 0.1937 | 0.3732 | 0.3735 | 0.8592 | 0.8549 | 8.8078 | 19 | 3 | 13.0931 | 12.3123 | | 0.6223 | 20.0 | 2900 | 2.5289 | 0.4268 | 0.2164 | 0.3904 | 0.3897 | 0.8617 | 0.8574 | 8.2372 | 17 | 4 | 12.9099 | 5.4054 | | 0.6127 | 21.0 | 3045 | 2.4946 | 0.427 | 0.2022 | 0.3844 | 0.3842 | 0.8645 | 0.8575 | 8.0511 | 16 | 3 | 12.8108 | 5.7057 | | 0.6209 | 22.0 | 3190 | 2.6277 | 0.3987 | 0.1934 | 0.3657 | 0.3657 | 0.8584 | 0.8506 | 7.8859 | 18 | 3 | 12.1742 | 5.4054 | | 0.5752 | 23.0 | 3335 | 2.7998 | 0.4019 | 0.1954 | 0.3648 | 0.3646 | 0.8576 | 0.8511 | 8.3904 | 17 | 3 | 12.7057 | 7.5075 | | 0.5588 | 24.0 | 3480 | 2.6732 | 0.4039 | 0.1948 | 0.3649 | 0.3652 | 0.8594 | 0.8492 | 7.8829 | 15 | 3 | 12.0901 | 6.006 | | 0.5641 | 25.0 | 3625 | 2.6012 | 0.419 | 0.2091 | 0.376 | 0.3765 | 0.8588 | 0.8523 | 8.03 | 16 | 3 | 12.2763 | 3.003 | | 0.5525 | 26.0 | 3770 | 2.6587 | 0.418 | 0.1929 | 0.3722 | 0.3726 | 0.8577 | 0.8545 | 8.5345 | 17 | 4 | 13.0961 | 8.1081 | | 0.5372 | 27.0 | 3915 | 2.7572 | 0.4104 | 0.1895 | 0.366 | 0.3671 | 0.8583 | 0.8495 | 7.8949 | 17 | 3 | 12.1862 | 4.8048 | | 0.5105 | 28.0 | 4060 | 2.7023 | 0.4319 | 0.2127 | 0.3884 | 0.3891 | 0.8636 | 0.8571 | 8.2553 | 16 | 3 | 12.5495 | 6.6066 | | 0.5026 | 29.0 | 4205 | 2.6991 | 0.4252 | 0.2222 | 0.3899 | 0.3903 | 0.867 | 0.8543 | 7.7898 | 19 | 4 | 12.2643 | 4.2042 | | 0.4956 | 30.0 | 4350 | 2.7064 | 0.4066 | 0.1974 | 0.3726 | 0.3735 | 0.8568 | 0.8523 | 8.4985 | 18 | 3 | 13.021 | 8.7087 | | 0.5064 | 31.0 | 4495 | 2.7564 | 0.4159 | 0.205 | 0.3763 | 0.3765 | 0.8613 | 0.8523 | 7.6877 | 16 | 3 | 12.3393 | 3.003 | | 0.4932 | 32.0 | 4640 | 2.6909 | 0.394 | 0.1866 | 0.3564 | 0.3573 | 0.8574 | 0.8496 | 7.8378 | 16 | 2 | 12.4715 | 3.6036 | | 0.4757 | 33.0 | 4785 | 2.7851 | 0.4117 | 0.1932 | 0.3719 | 0.3728 | 0.8582 | 0.8534 | 8.5946 | 18 | 3 | 12.973 | 8.1081 | | 0.4753 | 34.0 | 4930 | 2.7823 | 0.3814 | 0.1747 | 0.3466 | 0.3464 | 0.8555 | 0.8459 | 7.7357 | 18 | 3 | 12.0721 | 3.3033 | | 0.4603 | 35.0 | 5075 | 2.7607 | 0.4135 | 0.2003 | 0.3777 | 0.3781 | 0.8616 | 0.8538 | 8.0601 | 19 | 3 | 12.3183 | 5.4054 | | 0.4645 | 36.0 | 5220 | 2.8364 | 0.4073 | 0.1957 | 0.3643 | 0.3652 | 0.8544 | 0.8524 | 8.8529 | 19 | 2 | 13.1982 | 12.012 | | 0.4377 | 37.0 | 5365 | 2.7809 | 0.3965 | 0.192 | 0.357 | 0.3573 | 0.858 | 0.8442 | 7.4384 | 19 | 2 | 11.5495 | 2.4024 | | 0.4287 | 38.0 | 5510 | 2.7801 | 0.4191 | 0.1984 | 0.3774 | 0.3779 | 0.8593 | 0.8533 | 8.2462 | 16 | 2 | 12.5015 | 6.3063 | | 0.4295 | 39.0 | 5655 | 2.7206 | 0.4281 | 0.2104 | 0.3851 | 0.3861 | 0.8634 | 0.856 | 8.1922 | 16 | 4 | 12.5826 | 5.7057 | | 0.4121 | 40.0 | 5800 | 2.8363 | 0.4049 | 0.1916 | 0.3614 | 0.3624 | 0.8553 | 0.8516 | 8.5435 | 19 | 4 | 12.7928 | 9.6096 | | 0.3893 | 41.0 | 5945 | 2.7785 | 0.4255 | 0.2086 | 0.3858 | 0.3864 | 0.8601 | 0.8574 | 8.3964 | 17 | 4 | 13.0541 | 4.5045 | | 0.3786 | 42.0 | 6090 | 2.8752 | 0.3908 | 0.1775 | 0.3497 | 0.3509 | 0.851 | 0.8463 | 8.2853 | 17 | 2 | 12.8679 | 7.8078 | | 0.3703 | 43.0 | 6235 | 2.8799 | 0.4148 | 0.1894 | 0.3719 | 0.3727 | 0.8606 | 0.8519 | 8.1502 | 18 | 3 | 12.4745 | 3.9039 | | 0.3636 | 44.0 | 6380 | 2.8542 | 0.4043 | 0.1922 | 0.3631 | 0.3635 | 0.8554 | 0.8504 | 8.2883 | 19 | 4 | 12.7147 | 4.5045 | | 0.3438 | 45.0 | 6525 | 2.8282 | 0.4218 | 0.2022 | 0.3792 | 0.3802 | 0.861 | 0.8528 | 8.2072 | 16 | 4 | 12.6486 | 6.3063 | | 0.3511 | 46.0 | 6670 | 2.9184 | 0.405 | 0.1934 | 0.3652 | 0.3658 | 0.8572 | 0.8487 | 8.2372 | 18 | 3 | 12.5526 | 7.5075 | | 0.3453 | 47.0 | 6815 | 2.8649 | 0.4064 | 0.1956 | 0.3681 | 0.3686 | 0.8601 | 0.8508 | 8.0871 | 16 | 3 | 12.3604 | 5.7057 | | 0.3299 | 48.0 | 6960 | 2.9183 | 0.4266 | 0.202 | 0.3777 | 0.3787 | 0.8591 | 0.8578 | 8.6216 | 17 | 4 | 13.2402 | 9.009 | | 0.3132 | 49.0 | 7105 | 2.9077 | 0.4242 | 0.2021 | 0.3784 | 0.3793 | 0.861 | 0.8562 | 8.4354 | 19 | 4 | 12.6877 | 5.1051 | | 0.3031 | 50.0 | 7250 | 2.9042 | 0.4177 | 0.1977 | 0.3741 | 0.3752 | 0.8584 | 0.8522 | 8.006 | 15 | 4 | 12.4565 | 2.7027 | | 0.2974 | 51.0 | 7395 | 2.8820 | 0.4318 | 0.2087 | 0.3849 | 0.3854 | 0.8605 | 0.857 | 8.2613 | 16 | 3 | 12.8769 | 6.9069 | | 0.2873 | 52.0 | 7540 | 2.8622 | 0.4194 | 0.2023 | 0.3786 | 0.3782 | 0.8626 | 0.8542 | 8.021 | 18 | 3 | 12.3243 | 3.003 | | 0.2718 | 53.0 | 7685 | 2.8665 | 0.4128 | 0.2043 | 0.3716 | 0.3717 | 0.8592 | 0.8541 | 8.2643 | 16 | 3 | 12.8348 | 6.006 | | 0.2598 | 54.0 | 7830 | 2.9774 | 0.4177 | 0.1983 | 0.3794 | 0.3797 | 0.8612 | 0.8511 | 7.8709 | 19 | 3 | 12.048 | 4.2042 | | 0.2532 | 55.0 | 7975 | 2.8569 | 0.4111 | 0.1959 | 0.3717 | 0.3723 | 0.8612 | 0.8531 | 7.9399 | 16 | 3 | 12.5315 | 3.6036 | | 0.2363 | 56.0 | 8120 | 2.9634 | 0.4253 | 0.2111 | 0.385 | 0.386 | 0.8657 | 0.8543 | 7.8438 | 14 | 3 | 12.3153 | 3.003 | | 0.2323 | 57.0 | 8265 | 2.9573 | 0.418 | 0.1924 | 0.3771 | 0.3781 | 0.8573 | 0.854 | 8.4234 | 16 | 3 | 13.1261 | 6.3063 | | 0.2223 | 58.0 | 8410 | 2.9307 | 0.4276 | 0.2079 | 0.3847 | 0.3854 | 0.8651 | 0.8545 | 7.7688 | 16 | 3 | 11.97 | 2.1021 | | 0.2101 | 59.0 | 8555 | 2.9953 | 0.4114 | 0.1928 | 0.3673 | 0.3681 | 0.8562 | 0.8502 | 8.3814 | 19 | 4 | 12.7087 | 5.7057 | | 0.2069 | 60.0 | 8700 | 2.9768 | 0.4154 | 0.1921 | 0.3718 | 0.3725 | 0.8619 | 0.8538 | 7.97 | 16 | 4 | 12.2072 | 3.9039 | | 0.1971 | 61.0 | 8845 | 2.9218 | 0.4276 | 0.2046 | 0.3847 | 0.3854 | 0.8609 | 0.8568 | 8.4414 | 18 | 4 | 12.8949 | 6.3063 | | 0.1873 | 62.0 | 8990 | 2.9857 | 0.4068 | 0.191 | 0.3606 | 0.3609 | 0.8559 | 0.8496 | 8.2583 | 16 | 4 | 12.5646 | 5.1051 | | 0.1815 | 63.0 | 9135 | 2.8995 | 0.417 | 0.1981 | 0.3722 | 0.3723 | 0.8624 | 0.8528 | 8.003 | 15 | 4 | 12.2583 | 5.7057 | | 0.1807 | 64.0 | 9280 | 2.9644 | 0.415 | 0.1933 | 0.3694 | 0.3693 | 0.8585 | 0.8541 | 8.4024 | 17 | 3 | 12.7688 | 7.5075 | | 0.1729 | 65.0 | 9425 | 2.9640 | 0.4138 | 0.1965 | 0.3692 | 0.3698 | 0.8576 | 0.8515 | 8.042 | 16 | 3 | 12.6036 | 4.2042 | | 0.1609 | 66.0 | 9570 | 2.9912 | 0.4255 | 0.2051 | 0.3816 | 0.3826 | 0.8632 | 0.8554 | 8.0751 | 16 | 4 | 12.2733 | 5.1051 | | 0.1621 | 67.0 | 9715 | 3.0527 | 0.4249 | 0.2033 | 0.3786 | 0.3793 | 0.862 | 0.8544 | 8.0631 | 15 | 2 | 12.4925 | 3.3033 | | 0.1468 | 68.0 | 9860 | 3.0214 | 0.4274 | 0.2053 | 0.3822 | 0.3824 | 0.861 | 0.8552 | 8.4204 | 18 | 4 | 12.7447 | 7.8078 | | 0.1334 | 69.0 | 10005 | 3.1114 | 0.4116 | 0.1911 | 0.3698 | 0.3695 | 0.8601 | 0.8515 | 7.9099 | 14 | 3 | 12.0961 | 3.9039 | | 0.1261 | 70.0 | 10150 | 2.9442 | 0.4226 | 0.2032 | 0.3783 | 0.3785 | 0.8625 | 0.854 | 8.033 | 16 | 3 | 12.4384 | 4.5045 | | 0.1137 | 71.0 | 10295 | 3.0685 | 0.422 | 0.2035 | 0.375 | 0.3757 | 0.8621 | 0.8543 | 8.0541 | 16 | 2 | 12.3904 | 3.9039 | | 0.1078 | 72.0 | 10440 | 2.9759 | 0.4198 | 0.1981 | 0.3759 | 0.3767 | 0.8602 | 0.8544 | 8.1712 | 16 | 2 | 12.7297 | 4.5045 | | 0.1074 | 73.0 | 10585 | 2.9892 | 0.4226 | 0.2082 | 0.3835 | 0.3841 | 0.8621 | 0.8556 | 8.0661 | 14 | 2 | 12.5195 | 4.2042 | | 0.105 | 74.0 | 10730 | 3.0216 | 0.427 | 0.1997 | 0.379 | 0.3801 | 0.8611 | 0.8562 | 8.3093 | 17 | 3 | 12.8108 | 5.1051 | | 0.0944 | 75.0 | 10875 | 3.0108 | 0.4169 | 0.1956 | 0.3714 | 0.3721 | 0.8582 | 0.8523 | 8.1231 | 14 | 4 | 12.7568 | 3.003 | | 0.0871 | 76.0 | 11020 | 3.0794 | 0.4246 | 0.2007 | 0.3739 | 0.3756 | 0.8593 | 0.8556 | 8.3063 | 14 | 4 | 12.7598 | 4.8048 | | 0.0739 | 77.0 | 11165 | 3.0940 | 0.4205 | 0.1974 | 0.3776 | 0.3792 | 0.8629 | 0.8532 | 7.9189 | 15 | 2 | 12.0961 | 3.003 | | 0.066 | 78.0 | 11310 | 3.0764 | 0.4234 | 0.201 | 0.3781 | 0.3785 | 0.8603 | 0.8559 | 8.2913 | 16 | 3 | 12.8198 | 4.8048 | | 0.0641 | 79.0 | 11455 | 3.0736 | 0.4299 | 0.2067 | 0.3831 | 0.3835 | 0.8622 | 0.8568 | 8.018 | 15 | 4 | 12.4835 | 3.003 | | 0.0633 | 80.0 | 11600 | 3.0852 | 0.4191 | 0.2007 | 0.3741 | 0.3741 | 0.86 | 0.8537 | 8.1742 | 19 | 3 | 12.5556 | 4.8048 | | 0.0625 | 81.0 | 11745 | 3.0450 | 0.4153 | 0.1989 | 0.3734 | 0.374 | 0.8583 | 0.8524 | 8.1321 | 16 | 4 | 12.5826 | 3.9039 | | 0.0624 | 82.0 | 11890 | 3.1202 | 0.4286 | 0.209 | 0.385 | 0.3851 | 0.8642 | 0.8557 | 8.0 | 16 | 4 | 12.3003 | 3.003 | | 0.0593 | 83.0 | 12035 | 3.0514 | 0.4319 | 0.2159 | 0.3887 | 0.3899 | 0.8653 | 0.8587 | 8.0601 | 14 | 4 | 12.4805 | 1.8018 | | 0.0562 | 84.0 | 12180 | 3.0821 | 0.4362 | 0.2166 | 0.3924 | 0.3925 | 0.8656 | 0.8576 | 8.1051 | 15 | 4 | 12.5736 | 4.5045 | | 0.0586 | 85.0 | 12325 | 3.0843 | 0.4297 | 0.2061 | 0.3861 | 0.3865 | 0.8649 | 0.856 | 8.1051 | 15 | 3 | 12.3964 | 5.1051 | | 0.0528 | 86.0 | 12470 | 3.0610 | 0.4209 | 0.2034 | 0.3752 | 0.3755 | 0.8606 | 0.8542 | 8.2162 | 16 | 4 | 12.6817 | 5.1051 | | 0.0478 | 87.0 | 12615 | 3.0935 | 0.4244 | 0.2076 | 0.382 | 0.3815 | 0.8596 | 0.8553 | 8.3243 | 15 | 2 | 12.9009 | 6.006 | | 0.0431 | 88.0 | 12760 | 3.0865 | 0.429 | 0.2092 | 0.3847 | 0.3843 | 0.8645 | 0.855 | 7.964 | 15 | 4 | 12.2312 | 3.003 | | 0.0453 | 89.0 | 12905 | 3.0960 | 0.4147 | 0.1984 | 0.3718 | 0.3722 | 0.8619 | 0.8528 | 7.9219 | 14 | 3 | 12.2973 | 3.3033 | | 0.0429 | 90.0 | 13050 | 3.1163 | 0.4237 | 0.205 | 0.3776 | 0.3776 | 0.8622 | 0.8552 | 8.1231 | 16 | 4 | 12.4985 | 3.003 | | 0.0381 | 91.0 | 13195 | 3.0962 | 0.427 | 0.2089 | 0.3814 | 0.3817 | 0.8624 | 0.8547 | 8.006 | 14 | 4 | 12.3664 | 2.4024 | | 0.0374 | 92.0 | 13340 | 3.1022 | 0.4275 | 0.2031 | 0.3818 | 0.3823 | 0.8636 | 0.8574 | 8.2042 | 15 | 3 | 12.5646 | 4.2042 | | 0.0357 | 93.0 | 13485 | 3.1479 | 0.4282 | 0.2089 | 0.3855 | 0.3865 | 0.8637 | 0.8559 | 8.009 | 17 | 3 | 12.2492 | 3.003 | | 0.0329 | 94.0 | 13630 | 3.1188 | 0.4311 | 0.2086 | 0.3858 | 0.3861 | 0.8646 | 0.8559 | 7.8949 | 15 | 3 | 12.2703 | 2.4024 | | 0.0307 | 95.0 | 13775 | 3.1409 | 0.4284 | 0.2099 | 0.3825 | 0.3828 | 0.8633 | 0.8562 | 7.994 | 17 | 3 | 12.3153 | 2.4024 | | 0.0291 | 96.0 | 13920 | 3.1605 | 0.4292 | 0.2074 | 0.3831 | 0.3833 | 0.8635 | 0.8554 | 7.8979 | 14 | 4 | 12.3243 | 1.5015 | | 0.0299 | 97.0 | 14065 | 3.1838 | 0.4274 | 0.2022 | 0.3791 | 0.3792 | 0.863 | 0.8552 | 7.9489 | 16 | 4 | 12.3303 | 2.1021 | | 0.0264 | 98.0 | 14210 | 3.1810 | 0.4224 | 0.201 | 0.3762 | 0.3773 | 0.8624 | 0.8544 | 7.9309 | 16 | 3 | 12.2372 | 2.4024 | | 0.0257 | 99.0 | 14355 | 3.1893 | 0.4241 | 0.2056 | 0.3785 | 0.3796 | 0.8624 | 0.855 | 7.985 | 16 | 3 | 12.3874 | 2.4024 | | 0.0244 | 100.0 | 14500 | 3.1933 | 0.4266 | 0.2061 | 0.38 | 0.3804 | 0.8628 | 0.8555 | 8.003 | 16 | 3 | 12.3784 | 3.003 | ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

davanstrien/deberta-v3-base_fine_tuned_food_ner

davanstrien

2023-09-11T12:33:57Z

154

10

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "deberta-v2", "token-classification", "generated_from_trainer", "base_model:microsoft/deberta-v3-base", "base_model:finetune:microsoft/deberta-v3-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-08-03T14:39:17Z

--- license: mit tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy base_model: microsoft/deberta-v3-base model-index: - name: deberta-v3-base_fine_tuned_food_ner results: [] ---  # deberta-v3-base_fine_tuned_food_ner This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4164 - Precision: 0.9268 - Recall: 0.9446 - F1: 0.9356 - Accuracy: 0.9197 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 40 | 0.8425 | 0.8323 | 0.8323 | 0.8323 | 0.8073 | | No log | 2.0 | 80 | 0.5533 | 0.8703 | 0.8941 | 0.8820 | 0.8731 | | No log | 3.0 | 120 | 0.4855 | 0.8771 | 0.9109 | 0.8937 | 0.8797 | | No log | 4.0 | 160 | 0.4238 | 0.8949 | 0.9222 | 0.9083 | 0.8964 | | No log | 5.0 | 200 | 0.4176 | 0.9048 | 0.9302 | 0.9173 | 0.9008 | | No log | 6.0 | 240 | 0.4127 | 0.9065 | 0.9342 | 0.9202 | 0.9004 | | No log | 7.0 | 280 | 0.4409 | 0.9294 | 0.9302 | 0.9298 | 0.9043 | | No log | 8.0 | 320 | 0.3971 | 0.9129 | 0.9334 | 0.9230 | 0.9061 | | No log | 9.0 | 360 | 0.3941 | 0.9112 | 0.9390 | 0.9249 | 0.9061 | | No log | 10.0 | 400 | 0.4069 | 0.9233 | 0.9366 | 0.9299 | 0.9148 | | No log | 11.0 | 440 | 0.4039 | 0.9213 | 0.9390 | 0.9300 | 0.9162 | | No log | 12.0 | 480 | 0.4000 | 0.9126 | 0.9470 | 0.9295 | 0.9113 | | 0.3799 | 13.0 | 520 | 0.4126 | 0.9323 | 0.9390 | 0.9356 | 0.9179 | | 0.3799 | 14.0 | 560 | 0.4076 | 0.9272 | 0.9398 | 0.9334 | 0.9140 | | 0.3799 | 15.0 | 600 | 0.4129 | 0.9317 | 0.9414 | 0.9365 | 0.9188 | | 0.3799 | 16.0 | 640 | 0.4000 | 0.9239 | 0.9446 | 0.9341 | 0.9162 | | 0.3799 | 17.0 | 680 | 0.4098 | 0.9267 | 0.9438 | 0.9352 | 0.9179 | | 0.3799 | 18.0 | 720 | 0.4110 | 0.9232 | 0.9454 | 0.9342 | 0.9188 | | 0.3799 | 19.0 | 760 | 0.4202 | 0.9275 | 0.9446 | 0.9360 | 0.9183 | | 0.3799 | 20.0 | 800 | 0.4164 | 0.9268 | 0.9446 | 0.9356 | 0.9197 | ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

bigmorning/whisper_4_with_init_sun_syl_wd_0__0065

bigmorning

2023-09-11T12:33:45Z

59

0

transformers

[ "transformers", "tf", "whisper", "automatic-speech-recognition", "generated_from_keras_callback", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-09-11T12:33:31Z

--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_keras_callback model-index: - name: whisper_4_with_init_sun_syl_wd_0__0065 results: [] ---  # whisper_4_with_init_sun_syl_wd_0__0065 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3698 - Train Accuracy: 0.0328 - Train Wermet: 0.0842 - Train Wermet Syl: 0.0925 - Validation Loss: 1.1728 - Validation Accuracy: 0.0207 - Validation Wermet: 0.3282 - Validation Wermet Syl: 0.2932 - Epoch: 64 ## 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': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Train Wermet | Train Wermet Syl | Validation Loss | Validation Accuracy | Validation Wermet | Validation Wermet Syl | Epoch | |:----------:|:--------------:|:------------:|:----------------:|:---------------:|:-------------------:|:-----------------:|:---------------------:|:-----:| | 5.3409 | 0.0111 | 1.3547 | 1.2898 | 3.9789 | 0.0114 | 0.9710 | 0.9563 | 0 | | 4.7143 | 0.0116 | 0.8622 | 0.8228 | 3.9404 | 0.0113 | 0.9823 | 0.9735 | 1 | | 4.6752 | 0.0117 | 0.8472 | 0.8057 | 3.9081 | 0.0114 | 0.9579 | 0.9359 | 2 | | 4.6500 | 0.0117 | 0.8382 | 0.7945 | 3.8820 | 0.0115 | 0.9213 | 0.8856 | 3 | | 4.6282 | 0.0118 | 0.8286 | 0.7805 | 3.8738 | 0.0114 | 0.9433 | 0.9119 | 4 | | 4.6095 | 0.0118 | 0.8190 | 0.7696 | 3.8630 | 0.0115 | 0.9117 | 0.8698 | 5 | | 4.5875 | 0.0119 | 0.7976 | 0.7465 | 3.8341 | 0.0116 | 0.8976 | 0.8552 | 6 | | 4.5682 | 0.0120 | 0.7753 | 0.7227 | 3.8277 | 0.0116 | 0.9014 | 0.8653 | 7 | | 4.5376 | 0.0121 | 0.7528 | 0.7005 | 3.7844 | 0.0118 | 0.8332 | 0.7815 | 8 | | 4.5060 | 0.0122 | 0.7392 | 0.6844 | 3.7537 | 0.0118 | 0.8578 | 0.8152 | 9 | | 4.4580 | 0.0124 | 0.7221 | 0.6694 | 3.7038 | 0.0120 | 0.8190 | 0.7679 | 10 | | 4.3989 | 0.0125 | 0.7156 | 0.6636 | 3.6169 | 0.0122 | 0.7979 | 0.7429 | 11 | | 4.3056 | 0.0128 | 0.7069 | 0.6557 | 3.5098 | 0.0125 | 0.7924 | 0.7396 | 12 | | 4.1673 | 0.0132 | 0.7054 | 0.6584 | 3.3542 | 0.0128 | 0.7759 | 0.7240 | 13 | | 3.9762 | 0.0138 | 0.6987 | 0.6559 | 3.1318 | 0.0133 | 0.7644 | 0.7231 | 14 | | 3.7385 | 0.0145 | 0.6835 | 0.6448 | 2.9144 | 0.0138 | 0.7392 | 0.6955 | 15 | | 3.5040 | 0.0152 | 0.6644 | 0.6298 | 2.7413 | 0.0142 | 0.7019 | 0.6548 | 16 | | 3.2728 | 0.0160 | 0.6408 | 0.6101 | 2.5183 | 0.0149 | 0.6798 | 0.6363 | 17 | | 3.0657 | 0.0167 | 0.6188 | 0.5912 | 2.3594 | 0.0153 | 0.6528 | 0.6103 | 18 | | 2.8703 | 0.0174 | 0.5936 | 0.5685 | 2.2644 | 0.0156 | 0.6310 | 0.5925 | 19 | | 2.6850 | 0.0181 | 0.5680 | 0.5453 | 2.1296 | 0.0160 | 0.6040 | 0.5652 | 20 | | 2.5227 | 0.0188 | 0.5423 | 0.5215 | 2.0019 | 0.0165 | 0.5793 | 0.5403 | 21 | | 2.3878 | 0.0194 | 0.5199 | 0.5015 | 1.8996 | 0.0169 | 0.5592 | 0.5229 | 22 | | 2.2437 | 0.0201 | 0.4959 | 0.4788 | 1.8141 | 0.0172 | 0.5414 | 0.5045 | 23 | | 2.1205 | 0.0207 | 0.4752 | 0.4607 | 1.7245 | 0.0175 | 0.5208 | 0.4838 | 24 | | 1.9919 | 0.0213 | 0.4533 | 0.4390 | 1.6673 | 0.0178 | 0.5026 | 0.4659 | 25 | | 1.9140 | 0.0217 | 0.4355 | 0.4216 | 1.6041 | 0.0181 | 0.4873 | 0.4512 | 26 | | 1.8225 | 0.0222 | 0.4184 | 0.4052 | 1.6271 | 0.0179 | 0.4852 | 0.4511 | 27 | | 1.7265 | 0.0227 | 0.4016 | 0.3895 | 1.5219 | 0.0184 | 0.4635 | 0.4275 | 28 | | 1.6240 | 0.0233 | 0.3833 | 0.3729 | 1.4718 | 0.0186 | 0.4515 | 0.4170 | 29 | | 1.5610 | 0.0236 | 0.3697 | 0.3588 | 1.4404 | 0.0188 | 0.4407 | 0.4056 | 30 | | 1.4719 | 0.0242 | 0.3540 | 0.3449 | 1.4125 | 0.0189 | 0.4310 | 0.3961 | 31 | | 1.4152 | 0.0245 | 0.3421 | 0.3339 | 1.3655 | 0.0191 | 0.4234 | 0.3881 | 32 | | 1.3546 | 0.0249 | 0.3277 | 0.3195 | 1.3419 | 0.0192 | 0.4156 | 0.3816 | 33 | | 1.2565 | 0.0256 | 0.3135 | 0.3060 | 1.3172 | 0.0194 | 0.4065 | 0.3722 | 34 | | 1.2135 | 0.0258 | 0.3026 | 0.2958 | 1.3019 | 0.0194 | 0.4006 | 0.3662 | 35 | | 1.1739 | 0.0261 | 0.2923 | 0.2861 | 1.3843 | 0.0190 | 0.3951 | 0.3587 | 36 | | 1.0950 | 0.0267 | 0.2782 | 0.2733 | 1.2665 | 0.0197 | 0.3883 | 0.3541 | 37 | | 1.0435 | 0.0271 | 0.2673 | 0.2631 | 1.2567 | 0.0197 | 0.3837 | 0.3497 | 38 | | 0.9922 | 0.0275 | 0.2580 | 0.2542 | 1.2566 | 0.0197 | 0.3801 | 0.3444 | 39 | | 0.9387 | 0.0279 | 0.2464 | 0.2438 | 1.2441 | 0.0198 | 0.3767 | 0.3423 | 40 | | 0.9345 | 0.0278 | 0.2393 | 0.2373 | 1.2221 | 0.0199 | 0.3682 | 0.3336 | 41 | | 0.8574 | 0.0285 | 0.2268 | 0.2255 | 1.2258 | 0.0199 | 0.3680 | 0.3338 | 42 | | 0.8275 | 0.0287 | 0.2183 | 0.2180 | 1.2044 | 0.0201 | 0.3628 | 0.3290 | 43 | | 0.8201 | 0.0288 | 0.2114 | 0.2108 | 1.2056 | 0.0201 | 0.3601 | 0.3270 | 44 | | 0.7684 | 0.0292 | 0.2020 | 0.2029 | 1.1879 | 0.0202 | 0.3553 | 0.3215 | 45 | | 0.7262 | 0.0295 | 0.1938 | 0.1947 | 1.2263 | 0.0200 | 0.3537 | 0.3177 | 46 | | 0.7286 | 0.0295 | 0.1876 | 0.1898 | 1.1772 | 0.0203 | 0.3485 | 0.3135 | 47 | | 0.6807 | 0.0300 | 0.1775 | 0.1797 | 1.1761 | 0.0203 | 0.3490 | 0.3155 | 48 | | 0.6609 | 0.0301 | 0.1713 | 0.1742 | 1.1853 | 0.0203 | 0.3484 | 0.3153 | 49 | | 0.6062 | 0.0306 | 0.1615 | 0.1653 | 1.1660 | 0.0204 | 0.3432 | 0.3090 | 50 | | 0.5755 | 0.0309 | 0.1547 | 0.1584 | 1.1698 | 0.0204 | 0.3428 | 0.3089 | 51 | | 0.5600 | 0.0310 | 0.1482 | 0.1524 | 1.1667 | 0.0204 | 0.3398 | 0.3058 | 52 | | 0.5715 | 0.0308 | 0.1449 | 0.1496 | 1.1614 | 0.0205 | 0.3381 | 0.3036 | 53 | | 0.5247 | 0.0313 | 0.1358 | 0.1411 | 1.1639 | 0.0205 | 0.3359 | 0.3025 | 54 | | 0.5085 | 0.0315 | 0.1301 | 0.1358 | 1.2420 | 0.0202 | 0.3412 | 0.3064 | 55 | | 0.4827 | 0.0317 | 0.1239 | 0.1295 | 1.1677 | 0.0205 | 0.3349 | 0.3009 | 56 | | 0.4848 | 0.0317 | 0.1207 | 0.1280 | 1.1653 | 0.0205 | 0.3326 | 0.2991 | 57 | | 0.4323 | 0.0322 | 0.1109 | 0.1185 | 1.1602 | 0.0206 | 0.3299 | 0.2953 | 58 | | 0.4183 | 0.0323 | 0.1057 | 0.1133 | 1.1622 | 0.0206 | 0.3307 | 0.2962 | 59 | | 0.4329 | 0.0322 | 0.1028 | 0.1100 | 1.1714 | 0.0206 | 0.3300 | 0.2950 | 60 | | 0.3962 | 0.0326 | 0.0964 | 0.1045 | 1.1726 | 0.0206 | 0.3311 | 0.2967 | 61 | | 0.3642 | 0.0329 | 0.0898 | 0.0973 | 1.1699 | 0.0206 | 0.3289 | 0.2936 | 62 | | 0.3786 | 0.0327 | 0.0884 | 0.0963 | 1.1734 | 0.0206 | 0.3279 | 0.2929 | 63 | | 0.3698 | 0.0328 | 0.0842 | 0.0925 | 1.1728 | 0.0207 | 0.3282 | 0.2932 | 64 | ### Framework versions - Transformers 4.34.0.dev0 - TensorFlow 2.13.0 - Tokenizers 0.13.3

flyswot/flyswot

flyswot

2023-09-11T12:33:38Z

229

0

transformers

[ "transformers", "pytorch", "convnext", "image-classification", "generated_from_trainer", "base_model:flyswot/convnext-tiny-224_flyswot", "base_model:finetune:flyswot/convnext-tiny-224_flyswot", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2022-04-06T15:56:05Z

--- tags: - generated_from_trainer base_model: flyswot/convnext-tiny-224_flyswot model-index: - name: flyswot results: [] ---  # flyswot This model is a fine-tuned version of [flyswot/convnext-tiny-224_flyswot](https://huggingface.co/flyswot/convnext-tiny-224_flyswot) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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: 0.1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 0.1 | 23 | 0.0894 | 0.9941 | ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

stefaniftime/tmpnk87cy75

stefaniftime

2023-09-11T12:22:58Z

196

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:daily_dialog", "base_model:microsoft/DialoGPT-medium", "base_model:finetune:microsoft/DialoGPT-medium", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-09-11T12:20:13Z

--- license: mit base_model: microsoft/DialoGPT-medium tags: - generated_from_trainer datasets: - daily_dialog model-index: - name: tmpnk87cy75 results: [] ---  # tmpnk87cy75 This model is a fine-tuned version of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) on the daily_dialog dataset. It achieves the following results on the evaluation set: - eval_loss: 1.7442 - eval_runtime: 12.5801 - eval_samples_per_second: 79.49 - eval_steps_per_second: 2.544 - epoch: 9.35 - step: 6500 ## 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: 32 - 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: 10 ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

Jzuluaga/bert-base-ner-atc-en-atco2-1h

Jzuluaga

2023-09-11T12:20:42Z

135

7

transformers

[ "transformers", "pytorch", "safetensors", "bert", "token-classification", "text", "en-atc", "en", "generated_from_trainer", "ner-for-atc", "dataset:Jzuluaga/atco2_corpus_1h", "arxiv:2211.04054", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-12-05T10:09:41Z

--- language: en license: apache-2.0 tags: - text - token-classification - en-atc - en - generated_from_trainer - bert - ner-for-atc datasets: - Jzuluaga/atco2_corpus_1h metrics: - Precision - Recall - Accuracy - F1 widget: - text: csa two nine six startup approved mike current qnh one zero one eight time check one seven - text: swiss four eight seven november runway three one cleared for takeoff wind one three zero degrees seven knots - text: lufthansa five yankee victor runway one three clear to land wind zero seven zero degrees - text: austrian seven one zulu hello to you reduce one six zero knots - text: sky travel one nine two approaching holding point three one ready for departure base_model: bert-base-uncased model-index: - name: bert-base-ner-atc-en-atco2-1h results: - task: type: token-classification name: ner dataset: name: ATCO2 corpus (Air Traffic Control Communications) type: Jzuluaga/atco2_corpus_1h config: test split: test metrics: - type: F1 value: 0.94 name: TEST F1 (callsign) verified: false - type: F1 value: 0.74 name: TEST F1 (command) verified: false - type: F1 value: 0.81 name: TEST F1 (value) verified: false --- # bert-base-ner-atc-en-atco2-1h This model allow to perform named-entity recognition (NER) on air traffic control communications data. We solve this challenge by performing token classification (NER) with a BERT model. We fine-tune a pretrained BERT model on the ner task. For instance, if you have the following transcripts/gold annotations: - **Utterance**: lufthansa three two five cleared to land runway three four left Could you tell what are the main entities in the communication? The desired output is shown below: - **Named-entity module output**: [call] lufthansa three two five [/call] [cmd] cleared to land [/cmd] [val] runway three four left [/val] This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the [atco2_corpus_1h](https://huggingface.co/datasets/Jzuluaga/atco2_corpus_1h). <a href="https://github.com/idiap/atco2-corpus"> <img alt="GitHub" src="https://img.shields.io/badge/GitHub-Open%20source-green\"> </a> It achieves the following results on the development set: - Loss: 1.4282 - Precision: 0.6195 - Recall: 0.7071 - F1: 0.6604 - Accuracy: 0.8182 **Paper**: [ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications](https://arxiv.org/abs/2211.04054) Authors: Juan Zuluaga-Gomez, Karel Veselý, Igor Szöke, Petr Motlicek, Martin Kocour, Mickael Rigault, Khalid Choukri, Amrutha Prasad and others Abstract: Personal assistants, automatic speech recognizers and dialogue understanding systems are becoming more critical in our interconnected digital world. A clear example is air traffic control (ATC) communications. ATC aims at guiding aircraft and controlling the airspace in a safe and optimal manner. These voice-based dialogues are carried between an air traffic controller (ATCO) and pilots via very-high frequency radio channels. In order to incorporate these novel technologies into ATC (low-resource domain), large-scale annotated datasets are required to develop the data-driven AI systems. Two examples are automatic speech recognition (ASR) and natural language understanding (NLU). In this paper, we introduce the ATCO2 corpus, a dataset that aims at fostering research on the challenging ATC field, which has lagged behind due to lack of annotated data. The ATCO2 corpus covers 1) data collection and pre-processing, 2) pseudo-annotations of speech data, and 3) extraction of ATC-related named entities. The ATCO2 corpus is split into three subsets. 1) ATCO2-test-set corpus contains 4 hours of ATC speech with manual transcripts and a subset with gold annotations for named-entity recognition (callsign, command, value). 2) The ATCO2-PL-set corpus consists of 5281 hours of unlabeled ATC data enriched with automatic transcripts from an in-domain speech recognizer, contextual information, speaker turn information, signal-to-noise ratio estimate and English language detection score per sample. Both available for purchase through ELDA at this http URL. 3) The ATCO2-test-set-1h corpus is a one-hour subset from the original test set corpus, that we are offering for free at this url: https://www.atco2.org/data. We expect the ATCO2 corpus will foster research on robust ASR and NLU not only in the field of ATC communications but also in the general research community. Code — GitHub repository: https://github.com/idiap/atco2-corpus ## Intended uses & limitations This model was fine-tuned on air traffic control data. We don't expect that it keeps the same performance on some others datasets where BERT was pre-trained or fine-tuned. ## Training and evaluation data See Table 6 (page 18) in our paper: [ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications](https://arxiv.org/abs/2211.04054). We described there the data used to fine-tune our NER model. - We use the ATCO2 corpus to fine-tune this model. You can download a free sample here: https://www.atco2.org/data - However, do not worry, we have prepared a script in our repository for preparing this databases: - Dataset preparation folder: https://github.com/idiap/atco2-corpus/tree/main/data/databases/atco2_test_set_1h/data_prepare_atco2_corpus_other.sh - Get the data in the format required by HuggingFace: speaker_role/data_preparation/prepare_spkid_atco2_corpus_test_set_1h.sh ## Writing your own inference script The snippet of code: ```python from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-ner-atc-en-atco2-1h") model = AutoModelForTokenClassification.from_pretrained("Jzuluaga/bert-base-ner-atc-en-atco2-1h") ##### Process text sample from transformers import pipeline nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="first") nlp("lufthansa three two five cleared to land runway three four left") # output: [{'entity_group': 'callsign', 'score': 0.8753265, 'word': 'lufthansa three two five', 'start': 0, 'end': 24}, {'entity_group': 'command', 'score': 0.99988264, 'word': 'cleared to land', 'start': 25, 'end': 40}, {'entity_group': 'value', 'score': 0.9999145, 'word': 'runway three four left', 'start': 41, 'end': 63}] ``` # Cite us If you use this code for your research, please cite our paper with: ``` @article{zuluaga2022bertraffic, title={BERTraffic: BERT-based Joint Speaker Role and Speaker Change Detection for Air Traffic Control Communications}, author={Zuluaga-Gomez, Juan and Sarfjoo, Seyyed Saeed and Prasad, Amrutha and others}, journal={IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar}, year={2022} } ``` and, ``` @article{zuluaga2022how, title={How Does Pre-trained Wav2Vec2. 0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications}, author={Zuluaga-Gomez, Juan and Prasad, Amrutha and Nigmatulina, Iuliia and Sarfjoo, Saeed and others}, journal={IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar}, year={2022} } ``` and, ``` @article{zuluaga2022atco2, title={ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications}, author={Zuluaga-Gomez, Juan and Vesel{\`y}, Karel and Sz{\"o}ke, Igor and Motlicek, Petr and others}, journal={arXiv preprint arXiv:2211.04054}, year={2022} } ``` ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 3000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 125.0 | 500 | 0.8692 | 0.6396 | 0.7172 | 0.6762 | 0.8307 | | 0.2158 | 250.0 | 1000 | 1.0074 | 0.5702 | 0.6970 | 0.6273 | 0.8245 | | 0.2158 | 375.0 | 1500 | 1.3560 | 0.6577 | 0.7374 | 0.6952 | 0.8119 | | 0.0184 | 500.0 | 2000 | 1.3393 | 0.6182 | 0.6869 | 0.6507 | 0.8056 | | 0.0184 | 625.0 | 2500 | 1.3528 | 0.6087 | 0.7071 | 0.6542 | 0.8213 | | 0.0175 | 750.0 | 3000 | 1.4282 | 0.6195 | 0.7071 | 0.6604 | 0.8182 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.7.0 - Tokenizers 0.13.2

bigmorning/whisper_4_with_init_sun_syl_wd_0__0060

bigmorning

2023-09-11T12:18:27Z

59

0

transformers

[ "transformers", "tf", "whisper", "automatic-speech-recognition", "generated_from_keras_callback", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-09-11T12:18:20Z

--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_keras_callback model-index: - name: whisper_4_with_init_sun_syl_wd_0__0060 results: [] ---  # whisper_4_with_init_sun_syl_wd_0__0060 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.4183 - Train Accuracy: 0.0323 - Train Wermet: 0.1057 - Train Wermet Syl: 0.1133 - Validation Loss: 1.1622 - Validation Accuracy: 0.0206 - Validation Wermet: 0.3307 - Validation Wermet Syl: 0.2962 - Epoch: 59 ## 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': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Train Wermet | Train Wermet Syl | Validation Loss | Validation Accuracy | Validation Wermet | Validation Wermet Syl | Epoch | |:----------:|:--------------:|:------------:|:----------------:|:---------------:|:-------------------:|:-----------------:|:---------------------:|:-----:| | 5.3409 | 0.0111 | 1.3547 | 1.2898 | 3.9789 | 0.0114 | 0.9710 | 0.9563 | 0 | | 4.7143 | 0.0116 | 0.8622 | 0.8228 | 3.9404 | 0.0113 | 0.9823 | 0.9735 | 1 | | 4.6752 | 0.0117 | 0.8472 | 0.8057 | 3.9081 | 0.0114 | 0.9579 | 0.9359 | 2 | | 4.6500 | 0.0117 | 0.8382 | 0.7945 | 3.8820 | 0.0115 | 0.9213 | 0.8856 | 3 | | 4.6282 | 0.0118 | 0.8286 | 0.7805 | 3.8738 | 0.0114 | 0.9433 | 0.9119 | 4 | | 4.6095 | 0.0118 | 0.8190 | 0.7696 | 3.8630 | 0.0115 | 0.9117 | 0.8698 | 5 | | 4.5875 | 0.0119 | 0.7976 | 0.7465 | 3.8341 | 0.0116 | 0.8976 | 0.8552 | 6 | | 4.5682 | 0.0120 | 0.7753 | 0.7227 | 3.8277 | 0.0116 | 0.9014 | 0.8653 | 7 | | 4.5376 | 0.0121 | 0.7528 | 0.7005 | 3.7844 | 0.0118 | 0.8332 | 0.7815 | 8 | | 4.5060 | 0.0122 | 0.7392 | 0.6844 | 3.7537 | 0.0118 | 0.8578 | 0.8152 | 9 | | 4.4580 | 0.0124 | 0.7221 | 0.6694 | 3.7038 | 0.0120 | 0.8190 | 0.7679 | 10 | | 4.3989 | 0.0125 | 0.7156 | 0.6636 | 3.6169 | 0.0122 | 0.7979 | 0.7429 | 11 | | 4.3056 | 0.0128 | 0.7069 | 0.6557 | 3.5098 | 0.0125 | 0.7924 | 0.7396 | 12 | | 4.1673 | 0.0132 | 0.7054 | 0.6584 | 3.3542 | 0.0128 | 0.7759 | 0.7240 | 13 | | 3.9762 | 0.0138 | 0.6987 | 0.6559 | 3.1318 | 0.0133 | 0.7644 | 0.7231 | 14 | | 3.7385 | 0.0145 | 0.6835 | 0.6448 | 2.9144 | 0.0138 | 0.7392 | 0.6955 | 15 | | 3.5040 | 0.0152 | 0.6644 | 0.6298 | 2.7413 | 0.0142 | 0.7019 | 0.6548 | 16 | | 3.2728 | 0.0160 | 0.6408 | 0.6101 | 2.5183 | 0.0149 | 0.6798 | 0.6363 | 17 | | 3.0657 | 0.0167 | 0.6188 | 0.5912 | 2.3594 | 0.0153 | 0.6528 | 0.6103 | 18 | | 2.8703 | 0.0174 | 0.5936 | 0.5685 | 2.2644 | 0.0156 | 0.6310 | 0.5925 | 19 | | 2.6850 | 0.0181 | 0.5680 | 0.5453 | 2.1296 | 0.0160 | 0.6040 | 0.5652 | 20 | | 2.5227 | 0.0188 | 0.5423 | 0.5215 | 2.0019 | 0.0165 | 0.5793 | 0.5403 | 21 | | 2.3878 | 0.0194 | 0.5199 | 0.5015 | 1.8996 | 0.0169 | 0.5592 | 0.5229 | 22 | | 2.2437 | 0.0201 | 0.4959 | 0.4788 | 1.8141 | 0.0172 | 0.5414 | 0.5045 | 23 | | 2.1205 | 0.0207 | 0.4752 | 0.4607 | 1.7245 | 0.0175 | 0.5208 | 0.4838 | 24 | | 1.9919 | 0.0213 | 0.4533 | 0.4390 | 1.6673 | 0.0178 | 0.5026 | 0.4659 | 25 | | 1.9140 | 0.0217 | 0.4355 | 0.4216 | 1.6041 | 0.0181 | 0.4873 | 0.4512 | 26 | | 1.8225 | 0.0222 | 0.4184 | 0.4052 | 1.6271 | 0.0179 | 0.4852 | 0.4511 | 27 | | 1.7265 | 0.0227 | 0.4016 | 0.3895 | 1.5219 | 0.0184 | 0.4635 | 0.4275 | 28 | | 1.6240 | 0.0233 | 0.3833 | 0.3729 | 1.4718 | 0.0186 | 0.4515 | 0.4170 | 29 | | 1.5610 | 0.0236 | 0.3697 | 0.3588 | 1.4404 | 0.0188 | 0.4407 | 0.4056 | 30 | | 1.4719 | 0.0242 | 0.3540 | 0.3449 | 1.4125 | 0.0189 | 0.4310 | 0.3961 | 31 | | 1.4152 | 0.0245 | 0.3421 | 0.3339 | 1.3655 | 0.0191 | 0.4234 | 0.3881 | 32 | | 1.3546 | 0.0249 | 0.3277 | 0.3195 | 1.3419 | 0.0192 | 0.4156 | 0.3816 | 33 | | 1.2565 | 0.0256 | 0.3135 | 0.3060 | 1.3172 | 0.0194 | 0.4065 | 0.3722 | 34 | | 1.2135 | 0.0258 | 0.3026 | 0.2958 | 1.3019 | 0.0194 | 0.4006 | 0.3662 | 35 | | 1.1739 | 0.0261 | 0.2923 | 0.2861 | 1.3843 | 0.0190 | 0.3951 | 0.3587 | 36 | | 1.0950 | 0.0267 | 0.2782 | 0.2733 | 1.2665 | 0.0197 | 0.3883 | 0.3541 | 37 | | 1.0435 | 0.0271 | 0.2673 | 0.2631 | 1.2567 | 0.0197 | 0.3837 | 0.3497 | 38 | | 0.9922 | 0.0275 | 0.2580 | 0.2542 | 1.2566 | 0.0197 | 0.3801 | 0.3444 | 39 | | 0.9387 | 0.0279 | 0.2464 | 0.2438 | 1.2441 | 0.0198 | 0.3767 | 0.3423 | 40 | | 0.9345 | 0.0278 | 0.2393 | 0.2373 | 1.2221 | 0.0199 | 0.3682 | 0.3336 | 41 | | 0.8574 | 0.0285 | 0.2268 | 0.2255 | 1.2258 | 0.0199 | 0.3680 | 0.3338 | 42 | | 0.8275 | 0.0287 | 0.2183 | 0.2180 | 1.2044 | 0.0201 | 0.3628 | 0.3290 | 43 | | 0.8201 | 0.0288 | 0.2114 | 0.2108 | 1.2056 | 0.0201 | 0.3601 | 0.3270 | 44 | | 0.7684 | 0.0292 | 0.2020 | 0.2029 | 1.1879 | 0.0202 | 0.3553 | 0.3215 | 45 | | 0.7262 | 0.0295 | 0.1938 | 0.1947 | 1.2263 | 0.0200 | 0.3537 | 0.3177 | 46 | | 0.7286 | 0.0295 | 0.1876 | 0.1898 | 1.1772 | 0.0203 | 0.3485 | 0.3135 | 47 | | 0.6807 | 0.0300 | 0.1775 | 0.1797 | 1.1761 | 0.0203 | 0.3490 | 0.3155 | 48 | | 0.6609 | 0.0301 | 0.1713 | 0.1742 | 1.1853 | 0.0203 | 0.3484 | 0.3153 | 49 | | 0.6062 | 0.0306 | 0.1615 | 0.1653 | 1.1660 | 0.0204 | 0.3432 | 0.3090 | 50 | | 0.5755 | 0.0309 | 0.1547 | 0.1584 | 1.1698 | 0.0204 | 0.3428 | 0.3089 | 51 | | 0.5600 | 0.0310 | 0.1482 | 0.1524 | 1.1667 | 0.0204 | 0.3398 | 0.3058 | 52 | | 0.5715 | 0.0308 | 0.1449 | 0.1496 | 1.1614 | 0.0205 | 0.3381 | 0.3036 | 53 | | 0.5247 | 0.0313 | 0.1358 | 0.1411 | 1.1639 | 0.0205 | 0.3359 | 0.3025 | 54 | | 0.5085 | 0.0315 | 0.1301 | 0.1358 | 1.2420 | 0.0202 | 0.3412 | 0.3064 | 55 | | 0.4827 | 0.0317 | 0.1239 | 0.1295 | 1.1677 | 0.0205 | 0.3349 | 0.3009 | 56 | | 0.4848 | 0.0317 | 0.1207 | 0.1280 | 1.1653 | 0.0205 | 0.3326 | 0.2991 | 57 | | 0.4323 | 0.0322 | 0.1109 | 0.1185 | 1.1602 | 0.0206 | 0.3299 | 0.2953 | 58 | | 0.4183 | 0.0323 | 0.1057 | 0.1133 | 1.1622 | 0.0206 | 0.3307 | 0.2962 | 59 | ### Framework versions - Transformers 4.34.0.dev0 - TensorFlow 2.13.0 - Tokenizers 0.13.3

Jukaboo/Llama2_7B_chat_dialogsum_ft_adapters_v2400

Jukaboo

2023-09-11T12:14:06Z

0

null

[ "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-chat-hf", "base_model:finetune:meta-llama/Llama-2-7b-chat-hf", "region:us" ]

null

2023-09-11T11:56:50Z

--- base_model: meta-llama/Llama-2-7b-chat-hf tags: - generated_from_trainer model-index: - name: Llama2_7B_chat_dialogsum_ft_adapters_v2400 results: [] ---  # Llama2_7B_chat_dialogsum_ft_adapters_v2400 This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

tyzp-INC/bench2-all-MiniLM-L6-v2-tuned-stratified

tyzp-INC

2023-09-11T12:09:48Z

6

0

sentence-transformers

[ "sentence-transformers", "pytorch", "bert", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]

text-classification

2023-09-10T13:38:56Z

--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # tyzp-INC/bench2-all-MiniLM-L6-v2-tuned-stratified This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("tyzp-INC/bench2-all-MiniLM-L6-v2-tuned-stratified") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

NbAiLab/notram-bert-norwegian-cased-080321

NbAiLab

2023-09-11T12:08:33Z

128

1

transformers

[ "transformers", "pytorch", "tf", "safetensors", "bert", "norwegian", "fill-mask", "no", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]

fill-mask

2022-03-02T23:29:04Z

--- language: no license: cc-by-4.0 tags: - norwegian - bert pipeline_tag: fill-mask widget: - text: På biblioteket kan du [MASK] en bok. - text: Dette er et [MASK] eksempel. - text: Av og til kan en språkmodell gi et [MASK] resultat. - text: Som ansat får du [MASK] for at bidrage til borgernes adgang til dansk kulturarv, til forskning og til samfundets demokratiske udvikling. --- ## Results |**Model** | **NoRec** | **NorNe-NB**| **NorNe-NN** | **NorDial** | **DaNe** | **Da-Angry-Tweets** | |:-----------|------------:|------------:|------------:|------------:|------------:|------------:| |roberta-base (English) | 51.77 | 79.01/79.53| 79.79/83.02 | 67.18| 75.44/78.07 | 55.51 | |mBERT-cased | 63.91 | 83.72/86.12| 83.05/87.12 | 66.23| 80.00/81.43 | 57.67 | |nb-bert-base | 75.60 |**91.98**/**92.95** |**90.93**/**94.06**|69.39| 81.95/84.83| 64.18| |notram-bert-norwegian-cased | 72.47 | 91.77/93.12|89.79/93.70| **78.55**| **83.69**/**86.55**| **64.19** | |notram-bert-norwegian-uncased | 73.47 | 89.28/91.61 |87.23/90.23 |74.21 | 80.29/82.31| 61.18| |notram-bert-norwegian-cased-pod | **76.18** | 91.24/92.24| 90.88/93.21| 76.21| 81.82/84.99| 62.16 | |nb-roberta-base | 68.77 |87.99/89.43 | 85.43/88.66| 76.34| 75.91/77.94| 61.50 | |nb-roberta-base-scandinavian | 67.88 | 87.73/89.14| 87.39/90.92| 74.81| 76.22/78.66 | 63.37 | |nb-roberta-base-v2-200k | 46.87 | 85.57/87.04| - | 64.99| - | - | |test_long_w5 200k| 60.48 | 88.00/90:00 | 83.93/88.45 | 68.41 |75.22/78.50| 57.95 | |test_long_w5_roberta_tokenizer 200k| 63.51| 86.28/87.77| 84.95/88.61 | 69.86 | 71.31/74.27 | 59.96 | |test_long_w5_roberta_tokenizer 400k| 59.76 |87.39/89.06 | 85.16/89.01 | 71.46 | 72.39/75.65| 39.73 | |test_long_w5_dataset 400k| 66.80 | 86.52/88.55 | 82.81/86.76 | 66.94 | 71.47/74.20| 55.25 | |test_long_w5_dataset 600k| 67.37 | 89.98/90.95 | 84.53/88.37 | 66.84 | 75.14/76.50| 57.47 | |roberta-jan-128_ncc - 400k - 128| 67.79 | 91.45/92.33 | 86.41/90.19 | 67.20 | 81.00/82.39| 59.65 | |roberta-jan-128_ncc - 1000k - 128| 68.17 | 89.34/90.74 | 86.89/89.87 | 68.41 | 80.30/82.17| 61.63 |

NbAiLab/nb-bert-large

NbAiLab

2023-09-11T12:08:15Z

1,099

13

transformers

[ "transformers", "pytorch", "tf", "jax", "safetensors", "bert", "norwegian", "fill-mask", "no", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]

fill-mask

2022-03-02T23:29:04Z

--- language: no license: cc-by-4.0 tags: - norwegian - bert thumbnail: nblogo_3.png pipeline_tag: fill-mask widget: - text: På biblioteket kan du låne en [MASK]. --- - **Release 1.0beta** (April 29, 2021) # NB-BERT-large (beta) ## Description NB-BERT-large is a general BERT-large model built on the large digital collection at the National Library of Norway. This model is trained from scratch on a wide variety of Norwegian text (both bokmål and nynorsk) from the last 200 years using a monolingual Norwegian vocabulary. ## Intended use & limitations The 1.0 version of the model is general, and should be fine-tuned for any particular use. Some fine-tuning sets may be found on Github, see * https://github.com/NBAiLab/notram ## Training data The model is trained on a wide variety of text. The training set is described on * https://github.com/NBAiLab/notram ## More information For more information on the model, see https://github.com/NBAiLab/notram

CyberHarem/u_olga_marie_fgo

CyberHarem

2023-09-11T12:07:45Z

0

null

[ "art", "text-to-image", "dataset:CyberHarem/u_olga_marie_fgo", "license:mit", "region:us" ]

text-to-image

2023-08-09T00:05:10Z

--- license: mit datasets: - CyberHarem/u_olga_marie_fgo pipeline_tag: text-to-image tags: - art --- # Lora of u_olga_marie_fgo This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 5100, you need to download `5100/u_olga_marie_fgo.pt` as the embedding and `5100/u_olga_marie_fgo.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 5100**, with the score of 0.942. The trigger words are: 1. `u_olga_marie_fgo` 2. `long_hair, braid, white_hair, horns, hair_between_eyes, jewelry, smile, earrings, ascot, yellow_eyes, red_ascot, breasts, open_mouth, single_horn` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | **5100** | **0.942** | [**Download**](5100/u_olga_marie_fgo.zip) | ![pattern_1-5100](5100/previews/pattern_1.png) | ![bikini-5100](5100/previews/bikini.png) | [<NSFW, click to see>](5100/previews/bondage.png) | ![free-5100](5100/previews/free.png) | ![maid-5100](5100/previews/maid.png) | ![miko-5100](5100/previews/miko.png) | [<NSFW, click to see>](5100/previews/nude.png) | [<NSFW, click to see>](5100/previews/nude2.png) | ![suit-5100](5100/previews/suit.png) | ![yukata-5100](5100/previews/yukata.png) | | 4760 | 0.879 | [Download](4760/u_olga_marie_fgo.zip) | ![pattern_1-4760](4760/previews/pattern_1.png) | ![bikini-4760](4760/previews/bikini.png) | [<NSFW, click to see>](4760/previews/bondage.png) | ![free-4760](4760/previews/free.png) | ![maid-4760](4760/previews/maid.png) | ![miko-4760](4760/previews/miko.png) | [<NSFW, click to see>](4760/previews/nude.png) | [<NSFW, click to see>](4760/previews/nude2.png) | ![suit-4760](4760/previews/suit.png) | ![yukata-4760](4760/previews/yukata.png) | | 4420 | 0.902 | [Download](4420/u_olga_marie_fgo.zip) | ![pattern_1-4420](4420/previews/pattern_1.png) | ![bikini-4420](4420/previews/bikini.png) | [<NSFW, click to see>](4420/previews/bondage.png) | ![free-4420](4420/previews/free.png) | ![maid-4420](4420/previews/maid.png) | ![miko-4420](4420/previews/miko.png) | [<NSFW, click to see>](4420/previews/nude.png) | [<NSFW, click to see>](4420/previews/nude2.png) | ![suit-4420](4420/previews/suit.png) | ![yukata-4420](4420/previews/yukata.png) | | 4080 | 0.910 | [Download](4080/u_olga_marie_fgo.zip) | ![pattern_1-4080](4080/previews/pattern_1.png) | ![bikini-4080](4080/previews/bikini.png) | [<NSFW, click to see>](4080/previews/bondage.png) | ![free-4080](4080/previews/free.png) | ![maid-4080](4080/previews/maid.png) | ![miko-4080](4080/previews/miko.png) | [<NSFW, click to see>](4080/previews/nude.png) | [<NSFW, click to see>](4080/previews/nude2.png) | ![suit-4080](4080/previews/suit.png) | ![yukata-4080](4080/previews/yukata.png) | | 3740 | 0.905 | [Download](3740/u_olga_marie_fgo.zip) | ![pattern_1-3740](3740/previews/pattern_1.png) | ![bikini-3740](3740/previews/bikini.png) | [<NSFW, click to see>](3740/previews/bondage.png) | ![free-3740](3740/previews/free.png) | ![maid-3740](3740/previews/maid.png) | ![miko-3740](3740/previews/miko.png) | [<NSFW, click to see>](3740/previews/nude.png) | [<NSFW, click to see>](3740/previews/nude2.png) | ![suit-3740](3740/previews/suit.png) | ![yukata-3740](3740/previews/yukata.png) | | 3400 | 0.914 | [Download](3400/u_olga_marie_fgo.zip) | ![pattern_1-3400](3400/previews/pattern_1.png) | ![bikini-3400](3400/previews/bikini.png) | [<NSFW, click to see>](3400/previews/bondage.png) | ![free-3400](3400/previews/free.png) | ![maid-3400](3400/previews/maid.png) | ![miko-3400](3400/previews/miko.png) | [<NSFW, click to see>](3400/previews/nude.png) | [<NSFW, click to see>](3400/previews/nude2.png) | ![suit-3400](3400/previews/suit.png) | ![yukata-3400](3400/previews/yukata.png) | | 3060 | 0.908 | [Download](3060/u_olga_marie_fgo.zip) | ![pattern_1-3060](3060/previews/pattern_1.png) | ![bikini-3060](3060/previews/bikini.png) | [<NSFW, click to see>](3060/previews/bondage.png) | ![free-3060](3060/previews/free.png) | ![maid-3060](3060/previews/maid.png) | ![miko-3060](3060/previews/miko.png) | [<NSFW, click to see>](3060/previews/nude.png) | [<NSFW, click to see>](3060/previews/nude2.png) | ![suit-3060](3060/previews/suit.png) | ![yukata-3060](3060/previews/yukata.png) | | 2720 | 0.889 | [Download](2720/u_olga_marie_fgo.zip) | ![pattern_1-2720](2720/previews/pattern_1.png) | ![bikini-2720](2720/previews/bikini.png) | [<NSFW, click to see>](2720/previews/bondage.png) | ![free-2720](2720/previews/free.png) | ![maid-2720](2720/previews/maid.png) | ![miko-2720](2720/previews/miko.png) | [<NSFW, click to see>](2720/previews/nude.png) | [<NSFW, click to see>](2720/previews/nude2.png) | ![suit-2720](2720/previews/suit.png) | ![yukata-2720](2720/previews/yukata.png) | | 2380 | 0.886 | [Download](2380/u_olga_marie_fgo.zip) | ![pattern_1-2380](2380/previews/pattern_1.png) | ![bikini-2380](2380/previews/bikini.png) | [<NSFW, click to see>](2380/previews/bondage.png) | ![free-2380](2380/previews/free.png) | ![maid-2380](2380/previews/maid.png) | ![miko-2380](2380/previews/miko.png) | [<NSFW, click to see>](2380/previews/nude.png) | [<NSFW, click to see>](2380/previews/nude2.png) | ![suit-2380](2380/previews/suit.png) | ![yukata-2380](2380/previews/yukata.png) | | 2040 | 0.905 | [Download](2040/u_olga_marie_fgo.zip) | ![pattern_1-2040](2040/previews/pattern_1.png) | ![bikini-2040](2040/previews/bikini.png) | [<NSFW, click to see>](2040/previews/bondage.png) | ![free-2040](2040/previews/free.png) | ![maid-2040](2040/previews/maid.png) | ![miko-2040](2040/previews/miko.png) | [<NSFW, click to see>](2040/previews/nude.png) | [<NSFW, click to see>](2040/previews/nude2.png) | ![suit-2040](2040/previews/suit.png) | ![yukata-2040](2040/previews/yukata.png) | | 1700 | 0.812 | [Download](1700/u_olga_marie_fgo.zip) | ![pattern_1-1700](1700/previews/pattern_1.png) | ![bikini-1700](1700/previews/bikini.png) | [<NSFW, click to see>](1700/previews/bondage.png) | ![free-1700](1700/previews/free.png) | ![maid-1700](1700/previews/maid.png) | ![miko-1700](1700/previews/miko.png) | [<NSFW, click to see>](1700/previews/nude.png) | [<NSFW, click to see>](1700/previews/nude2.png) | ![suit-1700](1700/previews/suit.png) | ![yukata-1700](1700/previews/yukata.png) | | 1360 | 0.889 | [Download](1360/u_olga_marie_fgo.zip) | ![pattern_1-1360](1360/previews/pattern_1.png) | ![bikini-1360](1360/previews/bikini.png) | [<NSFW, click to see>](1360/previews/bondage.png) | ![free-1360](1360/previews/free.png) | ![maid-1360](1360/previews/maid.png) | ![miko-1360](1360/previews/miko.png) | [<NSFW, click to see>](1360/previews/nude.png) | [<NSFW, click to see>](1360/previews/nude2.png) | ![suit-1360](1360/previews/suit.png) | ![yukata-1360](1360/previews/yukata.png) | | 1020 | 0.820 | [Download](1020/u_olga_marie_fgo.zip) | ![pattern_1-1020](1020/previews/pattern_1.png) | ![bikini-1020](1020/previews/bikini.png) | [<NSFW, click to see>](1020/previews/bondage.png) | ![free-1020](1020/previews/free.png) | ![maid-1020](1020/previews/maid.png) | ![miko-1020](1020/previews/miko.png) | [<NSFW, click to see>](1020/previews/nude.png) | [<NSFW, click to see>](1020/previews/nude2.png) | ![suit-1020](1020/previews/suit.png) | ![yukata-1020](1020/previews/yukata.png) | | 680 | 0.809 | [Download](680/u_olga_marie_fgo.zip) | ![pattern_1-680](680/previews/pattern_1.png) | ![bikini-680](680/previews/bikini.png) | [<NSFW, click to see>](680/previews/bondage.png) | ![free-680](680/previews/free.png) | ![maid-680](680/previews/maid.png) | ![miko-680](680/previews/miko.png) | [<NSFW, click to see>](680/previews/nude.png) | [<NSFW, click to see>](680/previews/nude2.png) | ![suit-680](680/previews/suit.png) | ![yukata-680](680/previews/yukata.png) | | 340 | 0.658 | [Download](340/u_olga_marie_fgo.zip) | ![pattern_1-340](340/previews/pattern_1.png) | ![bikini-340](340/previews/bikini.png) | [<NSFW, click to see>](340/previews/bondage.png) | ![free-340](340/previews/free.png) | ![maid-340](340/previews/maid.png) | ![miko-340](340/previews/miko.png) | [<NSFW, click to see>](340/previews/nude.png) | [<NSFW, click to see>](340/previews/nude2.png) | ![suit-340](340/previews/suit.png) | ![yukata-340](340/previews/yukata.png) |

bigmorning/whisper_4_with_init_sun_syl_wd_0__0055

bigmorning

2023-09-11T12:03:22Z

59

0

transformers

[ "transformers", "tf", "whisper", "automatic-speech-recognition", "generated_from_keras_callback", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-09-11T12:03:14Z

--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_keras_callback model-index: - name: whisper_4_with_init_sun_syl_wd_0__0055 results: [] ---  # whisper_4_with_init_sun_syl_wd_0__0055 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5247 - Train Accuracy: 0.0313 - Train Wermet: 0.1358 - Train Wermet Syl: 0.1411 - Validation Loss: 1.1639 - Validation Accuracy: 0.0205 - Validation Wermet: 0.3359 - Validation Wermet Syl: 0.3025 - Epoch: 54 ## 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': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Train Wermet | Train Wermet Syl | Validation Loss | Validation Accuracy | Validation Wermet | Validation Wermet Syl | Epoch | |:----------:|:--------------:|:------------:|:----------------:|:---------------:|:-------------------:|:-----------------:|:---------------------:|:-----:| | 5.3409 | 0.0111 | 1.3547 | 1.2898 | 3.9789 | 0.0114 | 0.9710 | 0.9563 | 0 | | 4.7143 | 0.0116 | 0.8622 | 0.8228 | 3.9404 | 0.0113 | 0.9823 | 0.9735 | 1 | | 4.6752 | 0.0117 | 0.8472 | 0.8057 | 3.9081 | 0.0114 | 0.9579 | 0.9359 | 2 | | 4.6500 | 0.0117 | 0.8382 | 0.7945 | 3.8820 | 0.0115 | 0.9213 | 0.8856 | 3 | | 4.6282 | 0.0118 | 0.8286 | 0.7805 | 3.8738 | 0.0114 | 0.9433 | 0.9119 | 4 | | 4.6095 | 0.0118 | 0.8190 | 0.7696 | 3.8630 | 0.0115 | 0.9117 | 0.8698 | 5 | | 4.5875 | 0.0119 | 0.7976 | 0.7465 | 3.8341 | 0.0116 | 0.8976 | 0.8552 | 6 | | 4.5682 | 0.0120 | 0.7753 | 0.7227 | 3.8277 | 0.0116 | 0.9014 | 0.8653 | 7 | | 4.5376 | 0.0121 | 0.7528 | 0.7005 | 3.7844 | 0.0118 | 0.8332 | 0.7815 | 8 | | 4.5060 | 0.0122 | 0.7392 | 0.6844 | 3.7537 | 0.0118 | 0.8578 | 0.8152 | 9 | | 4.4580 | 0.0124 | 0.7221 | 0.6694 | 3.7038 | 0.0120 | 0.8190 | 0.7679 | 10 | | 4.3989 | 0.0125 | 0.7156 | 0.6636 | 3.6169 | 0.0122 | 0.7979 | 0.7429 | 11 | | 4.3056 | 0.0128 | 0.7069 | 0.6557 | 3.5098 | 0.0125 | 0.7924 | 0.7396 | 12 | | 4.1673 | 0.0132 | 0.7054 | 0.6584 | 3.3542 | 0.0128 | 0.7759 | 0.7240 | 13 | | 3.9762 | 0.0138 | 0.6987 | 0.6559 | 3.1318 | 0.0133 | 0.7644 | 0.7231 | 14 | | 3.7385 | 0.0145 | 0.6835 | 0.6448 | 2.9144 | 0.0138 | 0.7392 | 0.6955 | 15 | | 3.5040 | 0.0152 | 0.6644 | 0.6298 | 2.7413 | 0.0142 | 0.7019 | 0.6548 | 16 | | 3.2728 | 0.0160 | 0.6408 | 0.6101 | 2.5183 | 0.0149 | 0.6798 | 0.6363 | 17 | | 3.0657 | 0.0167 | 0.6188 | 0.5912 | 2.3594 | 0.0153 | 0.6528 | 0.6103 | 18 | | 2.8703 | 0.0174 | 0.5936 | 0.5685 | 2.2644 | 0.0156 | 0.6310 | 0.5925 | 19 | | 2.6850 | 0.0181 | 0.5680 | 0.5453 | 2.1296 | 0.0160 | 0.6040 | 0.5652 | 20 | | 2.5227 | 0.0188 | 0.5423 | 0.5215 | 2.0019 | 0.0165 | 0.5793 | 0.5403 | 21 | | 2.3878 | 0.0194 | 0.5199 | 0.5015 | 1.8996 | 0.0169 | 0.5592 | 0.5229 | 22 | | 2.2437 | 0.0201 | 0.4959 | 0.4788 | 1.8141 | 0.0172 | 0.5414 | 0.5045 | 23 | | 2.1205 | 0.0207 | 0.4752 | 0.4607 | 1.7245 | 0.0175 | 0.5208 | 0.4838 | 24 | | 1.9919 | 0.0213 | 0.4533 | 0.4390 | 1.6673 | 0.0178 | 0.5026 | 0.4659 | 25 | | 1.9140 | 0.0217 | 0.4355 | 0.4216 | 1.6041 | 0.0181 | 0.4873 | 0.4512 | 26 | | 1.8225 | 0.0222 | 0.4184 | 0.4052 | 1.6271 | 0.0179 | 0.4852 | 0.4511 | 27 | | 1.7265 | 0.0227 | 0.4016 | 0.3895 | 1.5219 | 0.0184 | 0.4635 | 0.4275 | 28 | | 1.6240 | 0.0233 | 0.3833 | 0.3729 | 1.4718 | 0.0186 | 0.4515 | 0.4170 | 29 | | 1.5610 | 0.0236 | 0.3697 | 0.3588 | 1.4404 | 0.0188 | 0.4407 | 0.4056 | 30 | | 1.4719 | 0.0242 | 0.3540 | 0.3449 | 1.4125 | 0.0189 | 0.4310 | 0.3961 | 31 | | 1.4152 | 0.0245 | 0.3421 | 0.3339 | 1.3655 | 0.0191 | 0.4234 | 0.3881 | 32 | | 1.3546 | 0.0249 | 0.3277 | 0.3195 | 1.3419 | 0.0192 | 0.4156 | 0.3816 | 33 | | 1.2565 | 0.0256 | 0.3135 | 0.3060 | 1.3172 | 0.0194 | 0.4065 | 0.3722 | 34 | | 1.2135 | 0.0258 | 0.3026 | 0.2958 | 1.3019 | 0.0194 | 0.4006 | 0.3662 | 35 | | 1.1739 | 0.0261 | 0.2923 | 0.2861 | 1.3843 | 0.0190 | 0.3951 | 0.3587 | 36 | | 1.0950 | 0.0267 | 0.2782 | 0.2733 | 1.2665 | 0.0197 | 0.3883 | 0.3541 | 37 | | 1.0435 | 0.0271 | 0.2673 | 0.2631 | 1.2567 | 0.0197 | 0.3837 | 0.3497 | 38 | | 0.9922 | 0.0275 | 0.2580 | 0.2542 | 1.2566 | 0.0197 | 0.3801 | 0.3444 | 39 | | 0.9387 | 0.0279 | 0.2464 | 0.2438 | 1.2441 | 0.0198 | 0.3767 | 0.3423 | 40 | | 0.9345 | 0.0278 | 0.2393 | 0.2373 | 1.2221 | 0.0199 | 0.3682 | 0.3336 | 41 | | 0.8574 | 0.0285 | 0.2268 | 0.2255 | 1.2258 | 0.0199 | 0.3680 | 0.3338 | 42 | | 0.8275 | 0.0287 | 0.2183 | 0.2180 | 1.2044 | 0.0201 | 0.3628 | 0.3290 | 43 | | 0.8201 | 0.0288 | 0.2114 | 0.2108 | 1.2056 | 0.0201 | 0.3601 | 0.3270 | 44 | | 0.7684 | 0.0292 | 0.2020 | 0.2029 | 1.1879 | 0.0202 | 0.3553 | 0.3215 | 45 | | 0.7262 | 0.0295 | 0.1938 | 0.1947 | 1.2263 | 0.0200 | 0.3537 | 0.3177 | 46 | | 0.7286 | 0.0295 | 0.1876 | 0.1898 | 1.1772 | 0.0203 | 0.3485 | 0.3135 | 47 | | 0.6807 | 0.0300 | 0.1775 | 0.1797 | 1.1761 | 0.0203 | 0.3490 | 0.3155 | 48 | | 0.6609 | 0.0301 | 0.1713 | 0.1742 | 1.1853 | 0.0203 | 0.3484 | 0.3153 | 49 | | 0.6062 | 0.0306 | 0.1615 | 0.1653 | 1.1660 | 0.0204 | 0.3432 | 0.3090 | 50 | | 0.5755 | 0.0309 | 0.1547 | 0.1584 | 1.1698 | 0.0204 | 0.3428 | 0.3089 | 51 | | 0.5600 | 0.0310 | 0.1482 | 0.1524 | 1.1667 | 0.0204 | 0.3398 | 0.3058 | 52 | | 0.5715 | 0.0308 | 0.1449 | 0.1496 | 1.1614 | 0.0205 | 0.3381 | 0.3036 | 53 | | 0.5247 | 0.0313 | 0.1358 | 0.1411 | 1.1639 | 0.0205 | 0.3359 | 0.3025 | 54 | ### Framework versions - Transformers 4.34.0.dev0 - TensorFlow 2.13.0 - Tokenizers 0.13.3

mesolitica/llama-13b-hf-16384-fpf

mesolitica

2023-09-11T11:57:07Z

6

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "ms", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-08-30T14:14:39Z

--- language: - ms --- # Full Parameter Finetuning 13B 16384 context length Llama2 on Malaysian text README at https://github.com/huseinzol05/malaya/tree/5.1/session/llama2#full-parameter-finetuning WandB, https://wandb.ai/mesolitica/fpf-Llama-2-13b-16k-hf?workspace=user-husein-mesolitica