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train · 796k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-06-03 12:30:42	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 466 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-03 12:30:29	card stringlengths 11 1.01M
toasthans/Facebook_and_Twitter_Ohne_HPS	toasthans	2021-12-23T14:55:46Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- license: mit tags: - generated_from_trainer metrics: - accuracy model-index: - name: Facebook_and_Twitter_Ohne_HPS 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. --> # Facebook_and_Twitter_Ohne_HPS This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9218 - Accuracy: 0.8512 ## Model description More information needed ## Intended uses & 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: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.4364 \| 1.0 \| 713 \| 0.4107 \| 0.8302 \| \| 0.2843 \| 2.0 \| 1426 \| 0.4316 \| 0.8495 \| \| 0.0869 \| 3.0 \| 2139 \| 0.7700 \| 0.8558 \| \| 0.0443 \| 4.0 \| 2852 \| 0.9218 \| 0.8512 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
Monsia/test-model-lg-data	Monsia	2021-12-23T14:03:38Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:04Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: test-model-lg-data 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-model-lg-data This model is a fine-tuned version of [Monsia/test-model-lg-data](https://huggingface.co/Monsia/test-model-lg-data) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.3354 - Wer: 0.4150 ## Model description More information needed ## Intended uses & 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: 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 - lr_scheduler_warmup_steps: 200 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.0236 \| 0.67 \| 100 \| 0.4048 \| 0.4222 \| \| 0.0304 \| 1.35 \| 200 \| 0.4266 \| 0.4809 \| \| 0.0545 \| 2.03 \| 300 \| 0.4309 \| 0.4735 \| \| 0.0415 \| 2.7 \| 400 \| 0.4269 \| 0.4595 \| \| 0.033 \| 3.38 \| 500 \| 0.4085 \| 0.4537 \| \| 0.0328 \| 4.05 \| 600 \| 0.3642 \| 0.4224 \| \| 0.0414 \| 4.73 \| 700 \| 0.3354 \| 0.4150 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.13.3 - Tokenizers 0.10.3
redbloodyknife/DialoGPT-medium-shayo	redbloodyknife	2021-12-23T12:17:05Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- tags: - conversational --- #Shayo Bot by Shogun #Ai Chatbot Testing based on GPT2 and DialoGPT-Medium by Microsoft #shoguπ#9999
toasthans/Facebook_Mit_HPS_5_Epoch	toasthans	2021-12-23T08:27:55Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- license: mit tags: - generated_from_trainer metrics: - accuracy model-index: - name: Facebook_Mit_HPS_5_Epoch 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. --> # Facebook_Mit_HPS_5_Epoch This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4774 - Accuracy: 0.9315 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.546392051994155e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 5 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 292 \| 0.2181 \| 0.9264 \| \| 0.2411 \| 2.0 \| 584 \| 0.2571 \| 0.9289 \| \| 0.2411 \| 3.0 \| 876 \| 0.5712 \| 0.8947 \| \| 0.0558 \| 4.0 \| 1168 \| 0.4675 \| 0.9332 \| \| 0.0558 \| 5.0 \| 1460 \| 0.4774 \| 0.9315 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
YYJ/KunquChat	YYJ	2021-12-23T07:21:17Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	# 经典昆曲欣赏期末作业 ## KunquChat Author: 1900012921 俞跃江
BigSalmon/InformalToFormalLincolnDistilledGPT2	BigSalmon	2021-12-23T03:39:15Z	10	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:04Z	Informal to Formal: ``` from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincolnDistilledGPT2") model = AutoModelWithLMHead.from_pretrained("BigSalmon/InformalToFormalLincolnDistilledGPT2") ``` ``` https://huggingface.co/spaces/BigSalmon/GPT2 (The model for this space changes over time) ``` ``` https://huggingface.co/spaces/BigSalmon/GPT2_Most_Probable (The model for this space changes over time) ``` ``` How To Make Prompt: informal english: i am very ready to do that just that. Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end. Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task. informal english: space is huge and needs to be explored. Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless. Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration. informal english: ````
Ayham/albert_gpt2_summarization_cnndm	Ayham	2021-12-23T01:36:49Z	14	0	transformers	[ "transformers", "pytorch", "tensorboard", "encoder-decoder", "text2text-generation", "generated_from_trainer", "dataset:cnn_dailymail", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:04Z	--- tags: - generated_from_trainer datasets: - cnn_dailymail model-index: - name: albert_large_gpt2_summarization_cnndm results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # albert_large_gpt2_summarization_cnndm This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
alecmullen/autonlp-group-classification-441411446	alecmullen	2021-12-22T23:03:27Z	4	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "autonlp", "en", "dataset:alecmullen/autonlp-data-group-classification", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- tags: autonlp language: en widget: - text: "I love AutoNLP 🤗" datasets: - alecmullen/autonlp-data-group-classification co2_eq_emissions: 0.4362732160754736 --- # Model Trained Using AutoNLP - Problem type: Multi-class Classification - Model ID: 441411446 - CO2 Emissions (in grams): 0.4362732160754736 ## Validation Metrics - Loss: 0.7598486542701721 - Accuracy: 0.8222222222222222 - Macro F1: 0.2912091747693842 - Micro F1: 0.8222222222222222 - Weighted F1: 0.7707160863181806 - Macro Precision: 0.29631463146314635 - Micro Precision: 0.8222222222222222 - Weighted Precision: 0.7341339689524508 - Macro Recall: 0.30174603174603176 - Micro Recall: 0.8222222222222222 - Weighted Recall: 0.8222222222222222 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/alecmullen/autonlp-group-classification-441411446 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("alecmullen/autonlp-group-classification-441411446", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("alecmullen/autonlp-group-classification-441411446", use_auth_token=True) inputs = tokenizer("I love AutoNLP", return_tensors="pt") outputs = model(**inputs) ```
BigSalmon/InformalToFormalLincoln14	BigSalmon	2021-12-22T22:40:51Z	10	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:04Z	Informal to Formal: ``` from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln14") model = AutoModelWithLMHead.from_pretrained("BigSalmon/InformalToFormalLincoln14") ``` ``` https://huggingface.co/spaces/BigSalmon/GPT2 (The model for this space changes over time) ``` ``` https://huggingface.co/spaces/BigSalmon/GPT2_Most_Probable (The model for this space changes over time) ``` ``` How To Make Prompt: informal english: i am very ready to do that just that. Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end. Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task. informal english: space is huge and needs to be explored. Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless. Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration. informal english: ````
s3h/opus-mt-ar-en-finetuned-src-to-trg-testing	s3h	2021-12-22T20:20:22Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "marian", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - bleu model-index: - name: opus-mt-ar-en-finetuned-src-to-trg-testing 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. --> # opus-mt-ar-en-finetuned-src-to-trg-testing This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ar-en](https://huggingface.co/Helsinki-NLP/opus-mt-ar-en) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.3973 - Bleu: 0.1939 - Gen Len: 37.6364 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Apex, opt level O1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-------:\| \| No log \| 1.0 \| 5 \| 3.4353 \| 0.1994 \| 36.6364 \| \| No log \| 2.0 \| 10 \| 3.4015 \| 0.1994 \| 36.0909 \| \| No log \| 3.0 \| 15 \| 3.3973 \| 0.1939 \| 37.6364 \| ### Framework versions - Transformers 4.14.1 - Pytorch 1.5.0 - Datasets 1.17.0 - Tokenizers 0.10.3
SajjadAyoubi/clip-fa-text	SajjadAyoubi	2021-12-22T19:02:56Z	1,578	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "arxiv:2103.00020", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-03-02T23:29:04Z	# CLIPfa: Connecting Farsi Text and Images OpenAI released [`the paper Learning Transferable Visual Models From Natural Language Supervision`](https://arxiv.org/abs/2103.00020) in which they present the CLIP (Contrastive Language–Image Pre-training) model. This model is trained to connect text and images, by matching their corresponding vector representations using a contrastive learning objective. CLIP consists of two separate models, a vision encoder and a text encoder. These were trained on 400 Million images and corresponding captions. We have trained a Farsi (Persian) version of OpenAI's CLIP on a dataset of 400,000 (image, text) pairs. We used [`Farahani's RoBERTa-fa`](https://huggingface.co/m3hrdadfi/roberta-zwnj-wnli-mean-tokens) as the text encoder and [‍‍`ViT‍`](https://huggingface.co/openai/clip-vit-base-patch32) as the vision encoder from Original CLIP and finetuned them. - It should be noted that only 400K pairs were used for this training, whereas 4 million pairs were used for the Original CLIP. Also, the training took 30 days across 592 GPUs powered by the V100 chip. ## How to use? Both models generate vectors with 768 dimensions. ```python from transformers import CLIPVisionModel, RobertaModel, AutoTokenizer, CLIPFeatureExtractor # download pre-trained models vision_encoder = CLIPVisionModel.from_pretrained('SajjadAyoubi/clip-fa-vision') preprocessor = CLIPFeatureExtractor.from_pretrained('SajjadAyoubi/clip-fa-vision') text_encoder = RobertaModel.from_pretrained('SajjadAyoubi/clip-fa-text') tokenizer = AutoTokenizer.from_pretrained('SajjadAyoubi/clip-fa-text') # define input image and input text text = 'something' image = PIL.Image.open('my_favorite_image.jpg') # compute embeddings text_embedding = text_encoder(tokenizer(text, return_tensors='pt')).pooler_output image_embedding = vision_encoder(preprocessor(image, return_tensors='pt')).pooler_output text_embedding.shape == image_embedding.shape ``` ## Demo: The followings are just some use cases of CLIPfa on 25K [`Unsplash images`](https://github.com/unsplash/datasets) - use `pip install -q git+https://github.com/sajjjadayobi/clipfa.git` ```python from clipfa import CLIPDemo demo = CLIPDemo(vision_encoder, text_encoder, tokenizer) demo.compute_text_embeddings(['گاو' ,'اسب' ,'ماهی']) demo.compute_image_embeddings(test_df.image_path.to_list()) ``` ## Online Demo: [CLIPfa at Huggingface🤗 spaces](https://huggingface.co/spaces/SajjadAyoubi/CLIPfa-Demo) We used a small set of images (25K) to keep this app almost real-time, but it's obvious that the quality of image search depends heavily on the size of the image database. > Made with ❤️ in my basement🤫
gngpostalsrvc/BERiTmodel2	gngpostalsrvc	2021-12-22T17:25:25Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "fill-mask", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:05Z	--- license: mit tags: - generated_from_trainer model-index: - name: BERiTmodel2 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. --> # BERiTmodel2 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.1508 ## Model description More information needed ## Intended uses & 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: cosine - lr_scheduler_warmup_steps: 280 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 3.1924 \| 1.0 \| 2854 \| 3.4329 \| \| 3.0936 \| 2.0 \| 5708 \| 3.5036 \| \| 2.9998 \| 3.0 \| 8562 \| 3.1906 \| \| 2.9064 \| 4.0 \| 11416 \| 3.4867 \| \| 2.8493 \| 5.0 \| 14270 \| 3.2027 \| \| 2.7538 \| 6.0 \| 17124 \| 2.9772 \| \| 2.7273 \| 7.0 \| 19978 \| 2.9950 \| \| 2.7399 \| 8.0 \| 22832 \| 2.9690 \| \| 2.67 \| 9.0 \| 25686 \| 3.0311 \| \| 2.6388 \| 10.0 \| 28540 \| 3.1508 \| ### Framework versions - Transformers 4.14.1 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
deepparag/DumBot-Beta	deepparag	2021-12-22T16:32:40Z	6	0	transformers	[ "transformers", "pytorch", "gpt_neo", "text-generation", "conversational", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- thumbnail: https://cdn.discordapp.com/app-icons/870239976690970625/c02cae78ae105f07969cfd8f8ea3d0a0.png tags: - conversational license: mit --- An generative AI made using [microsoft/DialoGPT-small](https://huggingface.co/microsoft/DialoGPT-small). Trained on: https://www.kaggle.com/Cornell-University/movie-dialog-corpus https://www.kaggle.com/jef1056/discord-data Important: The AI can be a bit weird at times as it is still undergoing training! At times it send stuff using :<random_wierd_words>: as they are discord emotes. It also send random @RandomName as it is trying to ping people. This works well on discord but on the web not so much but it is easy enough to remove such stuff using [re.sub](https://docs.python.org/3/library/re.html#re.sub) Issues: The AI like with all conversation AI lacks a character, it changes its name way too often. This can be solved using an AIML chatbot to give it a stable character! [Live Demo](https://dumbot-331213.uc.r.appspot.com/) Example: ```python from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("deepparag/DumBot") model = AutoModelWithLMHead.from_pretrained("deepparag/DumBot") # Let's chat for 4 lines for step in range(4): # encode the new user input, add the eos_token and return a tensor in Pytorch new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt') # print(new_user_input_ids) # append the new user input tokens to the chat history bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids # generated a response while limiting the total chat history to 1000 tokens, chat_history_ids = model.generate( bot_input_ids, max_length=200, pad_token_id=tokenizer.eos_token_id, no_repeat_ngram_size=4, do_sample=True, top_k=100, top_p=0.7, temperature=0.8 ) # pretty print last ouput tokens from bot print("DumBot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True))) ```
NbAiLabArchive/test_w5	NbAiLabArchive	2021-12-22T16:11:11Z	4	0	transformers	[ "transformers", "pytorch", "jax", "tensorboard", "roberta", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:04Z	Just for performing some experiments. Do not use.
huggingartists/100-gecs	huggingartists	2021-12-22T15:23:59Z	103	0	transformers	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingartists", "lyrics", "lm-head", "causal-lm", "en", "dataset:huggingartists/100-gecs", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en datasets: - huggingartists/100-gecs tags: - huggingartists - lyrics - lm-head - causal-lm widget: - text: "I am" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/9fd98af9a817af8cd78636f71895b6ad.500x500x1.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">100 gecs</div> <a href="https://genius.com/artists/100-gecs"> <div style="text-align: center; font-size: 14px;">@100-gecs</div> </a> </div> I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists). Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)! ## How does it work? To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist). ## Training data The model was trained on lyrics from 100 gecs. Dataset is available [here](https://huggingface.co/datasets/huggingartists/100-gecs). And can be used with: ```python from datasets import load_dataset dataset = load_dataset("huggingartists/100-gecs") ``` [Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3c9j4tvq/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on 100 gecs's lyrics. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1v0ffa4e) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1v0ffa4e/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingartists/100-gecs') generator("I am", num_return_sequences=5) ``` Or with Transformers library: ```python from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("huggingartists/100-gecs") model = AutoModelWithLMHead.from_pretrained("huggingartists/100-gecs") ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Aleksey Korshuk [![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk) [![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk) [![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)
dtomas/roberta-base-bne-irony	dtomas	2021-12-22T13:55:36Z	8	1	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "irony", "sarcasm", "spanish", "es", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- language: - es tags: - irony - sarcasm - spanish widget: - text: "¡Cómo disfruto peleándome con los Transformers!" example_title: "Ironic" - text: "Madrid es la capital de España" example_title: "Non ironic" --- # RoBERTa base finetuned for Spanish irony detection ## Model description Model to perform irony detection in Spanish. This is a finetuned version of the [RoBERTa-base-bne model](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) on the [IroSvA](https://www.autoritas.net/IroSvA2019/) corpus. Only the Spanish from Spain variant was used in the training process. It comprises 2,400 tweets labeled as ironic/non-ironic.
CheonggyeMountain-Sherpa/kogpt-trinity-punct-wrapper	CheonggyeMountain-Sherpa	2021-12-22T09:29:39Z	1	0	null	[ "gpt2", "ko", "license:cc-by-nc-sa-4.0", "region:us" ]	null	2022-03-02T23:29:04Z	--- language: - ko tags: - gpt2 license: cc-by-nc-sa-4.0 --- ## Model based on [Ko-GPT-Trinity 1.2B (v0.5)](https://huggingface.co/skt/ko-gpt-trinity-1.2B-v0.5) ## Example ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained( "CheonggyeMountain-Sherpa/kogpt-trinity-punct-wrapper", revision="punct_wrapper-related_words-overfit", # or punct_wrapper-related_words-minevalloss bos_token="<s>", eos_token="</s>", unk_token="<unk>", pad_token="<pad>", mask_token="<mask>", ) model = AutoModelForCausalLM.from_pretrained( "CheonggyeMountain-Sherpa/kogpt-trinity-punct-wrapper", revision="punct_wrapper-related_words-overfit", # or punct_wrapper-related_words-minevalloss pad_token_id=tokenizer.eos_token_id, ).to(device="cuda") model.eval() prompt = "석양이 보이는 경치" wrapped_prompt = f"@{prompt}@<usr>\n" with torch.no_grad(): tokens = tokenizer.encode(wrapped_prompt, return_tensors="pt").to(device="cuda") gen_tokens = model.generate( tokens, max_length=64, repetition_penalty=2.0, pad_token_id=tokenizer.pad_token_id, eos_token_id=tokenizer.eos_token_id, bos_token_id=tokenizer.bos_token_id, top_k=16, top_p=0.8, ) generated = tokenizer.decode(gen_tokens[0][len(tokens[0]):]) print(generated) # 해가 지고 있을 무렵 # 나는 석양을 보러 간다 # 붉은 하늘과 하얀 구름이 나를 반겨줄 것 같아서리 # 하지만 내가 본 해는 저물어만 가고 # 구름마저 자취를 감춘 어둠만이 남아있을 뿐이네 # 내가 탄 배는 보이지도 않고 ```
ayameRushia/bert-base-indonesian-1.5G-sentiment-analysis-smsa	ayameRushia	2021-12-22T08:52:47Z	78,229	14	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "id", "dataset:indonlu", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- license: mit tags: - generated_from_trainer datasets: - indonlu metrics: - accuracy model-index: - name: bert-base-indonesian-1.5G-finetuned-sentiment-analysis-smsa results: - task: name: Text Classification type: text-classification dataset: name: indonlu type: indonlu args: smsa metrics: - name: Accuracy type: accuracy value: 0.9373015873015873 language: id widget: - text: "Saya mengapresiasi usaha anda" --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-indonesian-1.5G-finetuned-sentiment-analysis-smsa This model is a fine-tuned version of [cahya/bert-base-indonesian-1.5G](https://huggingface.co/cahya/bert-base-indonesian-1.5G) on the indonlu dataset. It achieves the following results on the evaluation set: - Loss: 0.3390 - Accuracy: 0.9373 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2864 \| 1.0 \| 688 \| 0.2154 \| 0.9286 \| \| 0.1648 \| 2.0 \| 1376 \| 0.2238 \| 0.9357 \| \| 0.0759 \| 3.0 \| 2064 \| 0.3351 \| 0.9365 \| \| 0.044 \| 4.0 \| 2752 \| 0.3390 \| 0.9373 \| \| 0.0308 \| 5.0 \| 3440 \| 0.4346 \| 0.9365 \| \| 0.0113 \| 6.0 \| 4128 \| 0.4708 \| 0.9365 \| \| 0.006 \| 7.0 \| 4816 \| 0.5533 \| 0.9325 \| \| 0.0047 \| 8.0 \| 5504 \| 0.5888 \| 0.9310 \| \| 0.0001 \| 9.0 \| 6192 \| 0.5961 \| 0.9333 \| \| 0.0 \| 10.0 \| 6880 \| 0.5992 \| 0.9357 \| ### Framework versions - Transformers 4.14.1 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
hrdipto/wav2vec2-base-timit-demo-colab	hrdipto	2021-12-22T08:25:34Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-demo-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4241 - Wer: 0.3381 ## Model description More information needed ## Intended uses & 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: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 3.7749 \| 4.0 \| 500 \| 2.0639 \| 1.0018 \| \| 0.9252 \| 8.0 \| 1000 \| 0.4853 \| 0.4821 \| \| 0.3076 \| 12.0 \| 1500 \| 0.4507 \| 0.4044 \| \| 0.1732 \| 16.0 \| 2000 \| 0.4315 \| 0.3688 \| \| 0.1269 \| 20.0 \| 2500 \| 0.4481 \| 0.3559 \| \| 0.1087 \| 24.0 \| 3000 \| 0.4354 \| 0.3464 \| \| 0.0832 \| 28.0 \| 3500 \| 0.4241 \| 0.3381 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
dpasch01/finetune-clm-employment	dpasch01	2021-12-22T07:59:51Z	161	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: finetune-clm-employment results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetune-clm-employment This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8445 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 2.3283 \| 1.0 \| 3989 \| 1.9578 \| \| 2.0824 \| 2.0 \| 7978 \| 1.9013 \| \| 1.9936 \| 3.0 \| 11967 \| 1.8625 \| ### Framework versions - Transformers 4.14.1 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
hrdipto/wav2vec2-xls-r-timit-tokenizer-base	hrdipto	2021-12-22T07:19:26Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-xls-r-timit-tokenizer-base results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-xls-r-timit-tokenizer-base This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.0828 - Wer: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - 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 - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---:\| \| 3.3134 \| 4.03 \| 500 \| 3.0814 \| 1.0 \| \| 2.9668 \| 8.06 \| 1000 \| 3.0437 \| 1.0 \| \| 2.9604 \| 12.1 \| 1500 \| 3.0337 \| 1.0 \| \| 2.9619 \| 16.13 \| 2000 \| 3.0487 \| 1.0 \| \| 2.9588 \| 20.16 \| 2500 \| 3.0859 \| 1.0 \| \| 2.957 \| 24.19 \| 3000 \| 3.0921 \| 1.0 \| \| 2.9555 \| 28.22 \| 3500 \| 3.0828 \| 1.0 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
csukuangfj/icefall-asr-librispeech-transducer-bpe-500-2021-12-17	csukuangfj	2021-12-22T04:24:10Z	0	1	null	[ "region:us" ]	null	2022-03-02T23:29:05Z	# Introduction ## How to clone this repo ``` sudo apt-get install git-lfs git clone https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-bpe-500-2021-12-17 cd icefall-asr-librispeech-transducer-bpe-500-2021-12-17 git lfs pull ``` Catuion: You have to run `git lfs pull`. Otherwise, you will be SAD later. The model in this repo is trained using the commit `cb04c8a7509425ab45fae888b0ca71bbbd23f0de`. You can use ``` git clone https://github.com/k2-fsa/icefall cd icefall git checkout cb04c8a7509425ab45fae888b0ca71bbbd23f0de ``` to download `icefall`. You can find the model information by visiting <https://github.com/k2-fsa/icefall/blob/cb04c8a7509425ab45fae888b0ca71bbbd23f0de/egs/librispeech/ASR/transducer/train.py#L196> In short, the encoder is a Conformer model with 8 heads, 12 encoder layers, 512-dim attention, 2048-dim feedforward; the decoder contains a 1024-dim embedding layer, plus a 4-layer LSTM with hidden size 512. ----- ## Description This repo provides pre-trained RNN-T Conformer model for the librispeech dataset using [icefall][icefall]. The commands for training are: ``` cd egs/librispeech/ASR/ ./prepare.sh export CUDA_VISIBLE_DEVICES="0,1,2,3" ./transducer/train.py \ --world-size 4 \ --num-epochs 30 \ --start-epoch 0 \ --exp-dir transducer/exp-lr-2.5-full \ --full-libri 1 \ --max-duration 250 \ --lr-factor 2.5 ``` The command for decoding is: ``` epoch=26 avg=12 ./transducer/decode.py \ --epoch $epoch \ --avg $avg \ --exp-dir transducer/exp-lr-2.5-full \ --bpe-model ./data/lang_bpe_500/bpe.model \ --max-duration 100 ``` You can find the decoding log for the above command in this repo: [log/log-decode-epoch-26-avg-12-2021-12-17-09-33-04](log/log-decode-epoch-26-avg-12-2021-12-17-09-33-04). The best WER using greedy search is: \| \| test-clean \| test-other \| \|-----\|------------\|------------\| \| WER \| 3.16 \| 7.71 \| # File description - [log][log], this directory contains the decoding log and decoding results - [test_wavs][test_wavs], this directory contains wave files for testing the pre-trained model - [data][data], this directory contains files generated by [prepare.sh][prepare] - [exp][exp], this directory contains only one file: `preprained.pt` `exp/pretrained.pt` is generated by the following command: ``` ./transducer/export.py \ --epoch 26 \ --avg 12 \ --bpe-model data/lang_bpe_500/bpe.model \ --exp-dir transducer/exp-lr-2.5-full ``` HINT: To use `pre-trained.pt` to compute the WER for test-clean and test-other, just do the following: ``` cp icefall-asr-librispeech-transducer-bpe-500-2021-12-17/exp/pretrained.pt \ /path/to/icefall/egs/librispeech/ASR/transducer/exp/epoch-999.pt ``` and pass `--epoch 999 --avg 1` to `transducer/decode.py`. [icefall]: https://github.com/k2-fsa/icefall [prepare]: https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/prepare.sh [exp]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-bpe-500-2021-12-17/tree/main/exp [data]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-bpe-500-2021-12-17/tree/main/data [test_wavs]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-bpe-500-2021-12-17/tree/main/test_wavs [log]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-bpe-500-2021-12-17/tree/main/log [icefall]: https://github.com/k2-fsa/icefall
huggingtweets/_luisinhobr-beckvencido	huggingtweets	2021-12-22T02:57:34Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: http://www.huggingtweets.com/_luisinhobr-beckvencido/1640141850327/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1470914400764715012/YO9XqA0n_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1390224220643278850/LcIZLss-_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">agrummgit ag😜 & luisfer nando</div> <div style="text-align: center; font-size: 14px;">@_luisinhobr-beckvencido</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from agrummgit ag😜 & luisfer nando. \| Data \| agrummgit ag😜 \| luisfer nando \| \| --- \| --- \| --- \| \| Tweets downloaded \| 3226 \| 2366 \| \| Retweets \| 379 \| 367 \| \| Short tweets \| 672 \| 503 \| \| Tweets kept \| 2175 \| 1496 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/34idoh6o/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @_luisinhobr-beckvencido's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1w6ipjqa) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1w6ipjqa/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/_luisinhobr-beckvencido') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
tingtingyuli/wav2vec2-base-timit-demo-colab	tingtingyuli	2021-12-21T22:26:02Z	14	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-demo-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4371 - Wer: 0.3402 ## Model description More information needed ## Intended uses & 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: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 3.6515 \| 4.0 \| 500 \| 1.9481 \| 0.9825 \| \| 0.8007 \| 8.0 \| 1000 \| 0.4364 \| 0.4424 \| \| 0.2559 \| 12.0 \| 1500 \| 0.4188 \| 0.3848 \| \| 0.1483 \| 16.0 \| 2000 \| 0.4466 \| 0.3524 \| \| 0.1151 \| 20.0 \| 2500 \| 0.4492 \| 0.3519 \| \| 0.0971 \| 24.0 \| 3000 \| 0.4568 \| 0.3453 \| \| 0.0765 \| 28.0 \| 3500 \| 0.4371 \| 0.3402 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
iliketurtles/distilgpt2-finetuned-wikitext2	iliketurtles	2021-12-21T19:51:47Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilgpt2-finetuned-wikitext2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilgpt2-finetuned-wikitext2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.6424 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.7608 \| 1.0 \| 2334 \| 3.6655 \| \| 3.6335 \| 2.0 \| 4668 \| 3.6455 \| \| 3.6066 \| 3.0 \| 7002 \| 3.6424 \| ### Framework versions - Transformers 4.13.0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
akashsivanandan/wav2vec2-large-xls-r-300m-tamil-colab	akashsivanandan	2021-12-21T18:26:28Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-tamil-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-tamil-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.8072 - Wer: 0.6531 ## Model description More information needed ## Intended uses & 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: 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 - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 11.0967 \| 1.0 \| 118 \| 4.6437 \| 1.0 \| \| 3.4973 \| 2.0 \| 236 \| 3.2588 \| 1.0 \| \| 3.1305 \| 3.0 \| 354 \| 2.6566 \| 1.0 \| \| 1.2931 \| 4.0 \| 472 \| 0.9156 \| 0.9944 \| \| 0.6851 \| 5.0 \| 590 \| 0.7474 \| 0.8598 \| \| 0.525 \| 6.0 \| 708 \| 0.6649 \| 0.7995 \| \| 0.4325 \| 7.0 \| 826 \| 0.6740 \| 0.7752 \| \| 0.3766 \| 8.0 \| 944 \| 0.6220 \| 0.7628 \| \| 0.3256 \| 9.0 \| 1062 \| 0.6316 \| 0.7322 \| \| 0.2802 \| 10.0 \| 1180 \| 0.6442 \| 0.7305 \| \| 0.2575 \| 11.0 \| 1298 \| 0.6885 \| 0.7280 \| \| 0.2248 \| 12.0 \| 1416 \| 0.6702 \| 0.7197 \| \| 0.2089 \| 13.0 \| 1534 \| 0.6781 \| 0.7173 \| \| 0.1893 \| 14.0 \| 1652 \| 0.6981 \| 0.7049 \| \| 0.1652 \| 15.0 \| 1770 \| 0.7154 \| 0.7436 \| \| 0.1643 \| 16.0 \| 1888 \| 0.6798 \| 0.7023 \| \| 0.1472 \| 17.0 \| 2006 \| 0.7381 \| 0.6947 \| \| 0.1372 \| 18.0 \| 2124 \| 0.7240 \| 0.7065 \| \| 0.1318 \| 19.0 \| 2242 \| 0.7305 \| 0.6714 \| \| 0.1211 \| 20.0 \| 2360 \| 0.7288 \| 0.6597 \| \| 0.1178 \| 21.0 \| 2478 \| 0.7417 \| 0.6699 \| \| 0.1118 \| 22.0 \| 2596 \| 0.7476 \| 0.6753 \| \| 0.1016 \| 23.0 \| 2714 \| 0.7973 \| 0.6647 \| \| 0.0998 \| 24.0 \| 2832 \| 0.8027 \| 0.6633 \| \| 0.0917 \| 25.0 \| 2950 \| 0.8045 \| 0.6680 \| \| 0.0907 \| 26.0 \| 3068 \| 0.7884 \| 0.6565 \| \| 0.0835 \| 27.0 \| 3186 \| 0.8009 \| 0.6622 \| \| 0.0749 \| 28.0 \| 3304 \| 0.8123 \| 0.6536 \| \| 0.0755 \| 29.0 \| 3422 \| 0.8006 \| 0.6555 \| \| 0.074 \| 30.0 \| 3540 \| 0.8072 \| 0.6531 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
espnet/ftshijt_espnet2_asr_totonac_transformer	espnet	2021-12-21T16:10:01Z	1	0	espnet	[ "espnet", "audio", "automatic-speech-recognition", "dataset:totonac", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- tags: - espnet - audio - automatic-speech-recognition language: noinfo datasets: - totonac license: cc-by-4.0 --- ## ESPnet2 ASR model ### `espnet/ftshijt_espnet2_asr_totonac_transformer` This model was trained by ftshijt using totonac recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```bash cd espnet pip install -e . cd els/totonac/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/ftshijt_espnet2_asr_totonac_transformer ``` <!-- Generated by scripts/utils/show_asr_result.sh --> # RESULTS ## Environments - date: `Sun Nov 7 09:22:09 EST 2021` - python version: `3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0]` - espnet version: `espnet 0.10.4a1` - pytorch version: `pytorch 1.9.0` - Git hash: `` - Commit date: `` ## asr_train_asr_transformer_specaug_raw_bpe250_sp ### WER \|dataset\|Snt\|Wrd\|Corr\|Sub\|Del\|Ins\|Err\|S.Err\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\| \|decode_asr_lm_lm_train_bpe250_valid.loss.ave_asr_model_valid.acc.best/dev\|530\|3547\|59.8\|32.9\|7.3\|6.5\|46.7\|87.4\| \|decode_asr_lm_lm_train_bpe250_valid.loss.ave_asr_model_valid.acc.best/test\|704\|5018\|55.5\|35.7\|8.8\|6.1\|50.6\|92.0\| ### CER \|dataset\|Snt\|Wrd\|Corr\|Sub\|Del\|Ins\|Err\|S.Err\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\| \|decode_asr_lm_lm_train_bpe250_valid.loss.ave_asr_model_valid.acc.best/dev\|530\|22510\|88.1\|4.4\|7.4\|3.9\|15.8\|87.4\| \|decode_asr_lm_lm_train_bpe250_valid.loss.ave_asr_model_valid.acc.best/test\|704\|32990\|86.9\|4.3\|8.8\|4.0\|17.1\|92.0\| ### TER \|dataset\|Snt\|Wrd\|Corr\|Sub\|Del\|Ins\|Err\|S.Err\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\| \|decode_asr_lm_lm_train_bpe250_valid.loss.ave_asr_model_valid.acc.best/dev\|530\|9360\|70.3\|15.8\|13.8\|4.3\|34.0\|87.4\| \|decode_asr_lm_lm_train_bpe250_valid.loss.ave_asr_model_valid.acc.best/test\|704\|13835\|70.5\|16.0\|13.6\|4.4\|33.9\|92.0\| ## ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_transformer_specaug.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_transformer_specaug_raw_bpe250_sp ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist_rank: null local_rank: 0 dist_master_addr: null dist_master_port: null dist_launcher: null multiprocessing_distributed: false unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 100 patience: 15 val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - acc - max keep_nbest_models: 10 grad_clip: 5 grad_clip_type: 2.0 grad_noise: false accum_grad: 2 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_tensorboard: true use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: null batch_size: 32 valid_batch_size: null batch_bins: 1000000 valid_batch_bins: null train_shape_file: - exp/asr_stats_raw_bpe250_sp/train/speech_shape - exp/asr_stats_raw_bpe250_sp/train/text_shape.bpe valid_shape_file: - exp/asr_stats_raw_bpe250_sp/valid/speech_shape - exp/asr_stats_raw_bpe250_sp/valid/text_shape.bpe batch_type: folded valid_batch_type: null fold_length: - 80000 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - /tmp/jiatong-7359.okvPvI3Z/raw/train_sp/wav.scp - speech - kaldi_ark - - /tmp/jiatong-7359.okvPvI3Z/raw/train_sp/text - text - text valid_data_path_and_name_and_type: - - /tmp/jiatong-7359.okvPvI3Z/raw/dev/wav.scp - speech - kaldi_ark - - /tmp/jiatong-7359.okvPvI3Z/raw/dev/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 1.0 scheduler: noamlr scheduler_conf: warmup_steps: 4000 token_list: - <blank> - <unk> - ':' - ▁N - NI - N - ▁IYMA - ▁NA - NA - ▁WA - WA - ▁ - '''' - KA - ▁MA - MA - T - ▁XA - TA - NCHU - WI - ▁LI - ▁NI - PA - YI - ▁PUS - K - ▁PI - ▁X - S - ▁TA - YA - ▁LA - Q - QA - TI - ▁KA - QO - W - ▁KAH - ▁PALA - H - X - XA - ▁KI - A - LH - I - LA - ▁CHA - ▁A - ▁XLI - ▁LHI - U - ▁K - KANI - KU - Y - ▁LU - Á - ▁CHU - O - KI - ▁KIWI - NTLA - ▁TLA - M - ▁TAWA - ▁TI - ▁S - WANI - CHA - LHI - LI - ▁TU - ▁PALHA - Í - ▁CHANÁ - ▁KILHWAMPA - KÁN - ▁WAYMA - E - SA - ▁E - ▁LHU - LHA - PU - ▁LHA - ▁PA - ▁LAK - ▁ANTA - ▁KITI - NCHÚ - SI - TLA - PI - ▁KINI - CHI - ▁PEROH - ▁PU - QÓ - QALHCHIWINA - TU - ▁TLHA - ▁WI - NÁ - ▁KAN - ▁NAYI - CH - 'NO' - ▁U - TSA - MÁ - NQO - ▁ANA - ▁LIKWA - ▁XTA - J - ▁QALH - TO - TÁ - ▁USA - ▁PORQUE - ▁MI - L - ▁TAWÁ - XI - LHAQAPASA - P - CHIWI - WÁ - NTI - ▁JKA - Ú - NTLHA - R - TSI - C - STA - ▁LH - LHU - MPI - ▁I - ▁NILH - ▁KATSI - ▁LHAK - MAKLHAKASKI - ▁WANIKÁN - ▁WIXI - ▁TSI - KÚ - NÍ - ▁PAKS - NU - TLHA - YÁ - KUCHAN - XAQATLI - ▁MAX - ▁LAQAPASA - ▁LAQ - QALH - KATSI - Ó - LAQAPASA - ▁J - ▁QAMA - NTU - MI - KIWI - ▁KIN - ▁XANAT - ▁CHI - JA - ▁IY - ▁TSU - MAKLAKAS - ▁MAQA - LÁ - ▁KATSIYA - ▁TLANKA - ▁STAK - ▁XLA - ▁LHIKWA - ▁SQA - ▁P - TAHNA - ▁TLAQ - ▁JKATSI - MAKLAKASKINKA - YÁW - WATIYA - CHÁ - ▁IPORQUEI - ▁AKXNI - TSU - ▁TSINÓ - ▁STAKA - ▁AKXNÍ - LAKATA - KATSÍ - ▁XALHAK - TLAWAYA - SPUT - ▁XATAWA - QALHCHIWI - PÁ - JU - ▁XAXANAT - ▁PÉREZ - ▁AKTSU - ▁JKI - NTÚ - ▁KATSIYÁ - ▁IESTEI - LAQAPASÁ - ▁MASKI - ▁LAQSQATÁ - ▁TLHANKA - ▁WANIKANI - ▁LÓPEZ - MAKLAKASKINKÁN - ▁ANTÁ - ▁TACHIWÍ - ▁SEBAST - ▁CANO - ▁XKUTNI - ▁UKXILH - TANKAH - LAKASKINQO - LAKAPASTAK - ▁XCHACHAT - TAKAWANÍ - ▁TLÁ - ▁TSINOH - KAXTLAWA - ▁NÚÑEZ - ▁XLAKASKINKA - ▁WÁTIYA - ONCE - Z - É - D - Ñ - V - F - G - '1' - B - <sos/eos> init: xavier_uniform input_size: null ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: true model_conf: ctc_weight: 0.3 lsm_weight: 0.1 length_normalized_loss: false use_preprocessor: true token_type: bpe bpemodel: data/token_list/bpe_unigram250/bpe.model non_linguistic_symbols: null cleaner: null g2p: null speech_volume_normalize: null rir_scp: null rir_apply_prob: 1.0 noise_scp: null noise_apply_prob: 1.0 noise_db_range: '13_15' frontend: default frontend_conf: fs: 16k specaug: specaug specaug_conf: apply_time_warp: true time_warp_window: 5 time_warp_mode: bicubic apply_freq_mask: true freq_mask_width_range: - 0 - 30 num_freq_mask: 2 apply_time_mask: true time_mask_width_range: - 0 - 40 num_time_mask: 2 normalize: global_mvn normalize_conf: stats_file: exp/asr_stats_raw_bpe250_sp/train/feats_stats.npz preencoder: null preencoder_conf: {} encoder: transformer encoder_conf: input_layer: conv2d num_blocks: 12 linear_units: 2048 dropout_rate: 0.1 output_size: 256 attention_heads: 4 attention_dropout_rate: 0.0 postencoder: null postencoder_conf: {} decoder: transformer decoder_conf: input_layer: embed num_blocks: 6 linear_units: 2048 dropout_rate: 0.1 required: - output_dir - token_list version: 0.10.4a1 distributed: false ``` </details> ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
espnet/ftshijt_espnet2_asr_puebla_nahuatl_transfer	espnet	2021-12-21T15:43:26Z	4	0	espnet	[ "espnet", "audio", "automatic-speech-recognition", "dataset:puebla_nahuatl", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- tags: - espnet - audio - automatic-speech-recognition language: noinfo datasets: - puebla_nahuatl license: cc-by-4.0 --- ## ESPnet2 ASR model ### `espnet/ftshijt_espnet2_asr_puebla_nahuatl_transfer` This model was trained by ftshijt using puebla_nahuatl recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```bash cd espnet pip install -e . cd els/puebla_nahuatl/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/ftshijt_espnet2_asr_puebla_nahuatl_transfer ``` <!-- Generated by scripts/utils/show_asr_result.sh --> # RESULTS ## Environments - date: `Sun Nov 7 18:16:55 EST 2021` - python version: `3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0]` - espnet version: `espnet 0.10.4a1` - pytorch version: `pytorch 1.9.0` - Git hash: `` - Commit date: `` ## asr_train_asr_transformer_hubert_raw_bpe500_sp ### WER \|dataset\|Snt\|Wrd\|Corr\|Sub\|Del\|Ins\|Err\|S.Err\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\| \|decode_asr_lm_lm_train_bpe500_valid.loss.ave_asr_model_valid.acc.best/test\|10576\|90532\|77.0\|17.0\|6.0\|3.6\|26.6\|74.0\| ### CER \|dataset\|Snt\|Wrd\|Corr\|Sub\|Del\|Ins\|Err\|S.Err\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\| \|decode_asr_lm_lm_train_bpe500_valid.loss.ave_asr_model_valid.acc.best/test\|10576\|590273\|92.2\|2.1\|5.7\|3.0\|10.8\|74.0\| ### TER \|dataset\|Snt\|Wrd\|Corr\|Sub\|Del\|Ins\|Err\|S.Err\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\| \|decode_asr_lm_lm_train_bpe500_valid.loss.ave_asr_model_valid.acc.best/test\|10576\|242435\|86.0\|7.3\|6.8\|3.5\|17.5\|74.0\| ## ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_transformer_hubert.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_transformer_hubert_raw_bpe500_sp ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist_rank: null local_rank: 0 dist_master_addr: null dist_master_port: null dist_launcher: null multiprocessing_distributed: false unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 100 patience: 15 val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - acc - max keep_nbest_models: 10 grad_clip: 5 grad_clip_type: 2.0 grad_noise: false accum_grad: 2 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_tensorboard: true use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: null batch_size: 32 valid_batch_size: null batch_bins: 1000000 valid_batch_bins: null train_shape_file: - exp/asr_stats_raw_bpe500_sp/train/speech_shape - exp/asr_stats_raw_bpe500_sp/train/text_shape.bpe valid_shape_file: - exp/asr_stats_raw_bpe500_sp/valid/speech_shape - exp/asr_stats_raw_bpe500_sp/valid/text_shape.bpe batch_type: folded valid_batch_type: null fold_length: - 80000 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - /tmp/jiatong-150390.uytFFbyG/raw/train_sp/wav.scp - speech - kaldi_ark - - /tmp/jiatong-150390.uytFFbyG/raw/train_sp/text - text - text valid_data_path_and_name_and_type: - - /tmp/jiatong-150390.uytFFbyG/raw/dev/wav.scp - speech - kaldi_ark - - /tmp/jiatong-150390.uytFFbyG/raw/dev/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 1.0 scheduler: noamlr scheduler_conf: warmup_steps: 25000 token_list: - <blank> - <unk> - ':' - N - ▁A - ▁WA - ▁KE - ▁YO - ▁NE - ▁SE - H - MO - WA - '''' - ▁NO - ▁I - ▁N - S - ▁KI - K - ▁ - MAH - KA - TA - L - ▁POS - PA - ▁KA - ▁TA - ▁MO - T - ▁YEHWA - I - MEH - ▁YA - ▁DE - MA - A - ▁TE - TI - TSI - NI - CHI - ▁PERO - KI - LI - TO - WI - ▁PARA - KO - E - ▁O - ▁IKA - TE - O - W - ▁NEH - ▁NOCHI - CH - ▁TI - ▁TIK - LO - ▁SAH - ▁MAH - NA - LA - ▁OMPA - ▁IHKÓ - YA - ▁NI - ▁PORQUE - ▁MA - YO - ▁TEIN - LIA - ▁E - MPA - ▁NIKA - X - YAH - ▁KWALTSI - SA - TSA - ▁MOCHI - ▁NIK - ▁WE - ▁TO - TSÍ - ▁SEMI - ▁KITA - WAK - KWI - MI - ▁MM - ▁XO - ▁SEKI - JÓ - AH - ▁KOMO - R - NE - ▁OK - ▁KWALI - ▁CHI - ▁YEH - ▁NELI - SE - PO - WAH - PI - ME - KWA - ▁PA - ▁ONKAK - KE - ▁YE - ▁T - LTIK - ▁TEHWA - TAH - ▁TIKI - ▁QUE - ▁NIKI - PE - ▁IWKI - XI - TOK - ▁TAMAN - ▁KO - TSO - LE - RA - SI - WÍ - MAN - ▁TIMO - 'NO' - SO - ▁MIAK - U - ▁TEH - ▁KICHI - ▁XA - WE - ▁KOW - KEH - NÍ - LIK - ▁ITECH - TIH - ▁PE - ▁KIPIA - ▁CUANDO - ▁KWALTIA - ▁HASTA - LOWA - ▁ENTÓ - ▁NA - XO - RO - TIA - ▁NIKITA - CHIHCHI - ▁SEPA - ▁MAHYÁ - ▁PAHTI - ▁K - LIAH - ▁SAYOH - MATI - ▁PI - TS - ▁MÁS - XMATI - KAH - ▁XI - M - ▁ESTE - HKO - KOWIT - MIKI - CHO - ▁TAK - Á - ▁KILIAH - CHIO - ▁KIHTOWA - ▁KITE - NEKI - ▁ME - XA - ▁TEL - B - ▁KOWIT - ▁ATA - TIK - ▁EKINTSI - ▁IMA - ▁KWA - ▁OSO - ▁NEHJÓ - ▁ITEYO - Y - SKEH - ▁ISTA - ▁NIKILIA - LIH - ▁TIKWI - ▁PANÉ - KOWA - ▁OX - TEKI - ▁SA - NTE - ▁KIKWI - TSITSI - NOH - AHSI - ▁IXO - WIA - LTSI - ▁KIMA - C - ▁WEHWEI - ▁TEPITSI - ▁IHK - ▁XIWIT - YI - LIS - ▁CA - XMATTOK - SÁ - ▁MOTA - RE - ▁TIKIHTO - ▁MI - ▁X - D - ▁SAN - WIH - ▁WEHKA - KWE - CHA - ▁SI - KTIK - ▁YETOK - ▁MOKA - NEMI - LILIA - ▁¿ - TIW - ▁KIHTOWAH - LTI - Ó - MASÁ - ▁POR - ▁TIKITA - KETSA - ▁IWA - METS - YOH - ▁TAKWA - HKEH - ▁KIKWIH - ▁KIKWA - NIA - ▁ACHI - ▁KIKWAH - ▁KACHI - ▁PO - ▁IGUAL - NAL - ▁PILI - ▁NIMAN - YE - ▁NIKMATI - WIAH - ▁KIPA - ▁M - J - ▁KWI - ▁WI - WAYA - Z - ▁KITEKI - G - ▁' - ▁IHKO - CE - ▁TONI - ▁TSIKITSI - P - DO - TOKEH - NIK - ▁TIKILIAH - ▁KOWTAH - ▁TAI - ▁TATA - TIAH - CA - PIL - CHOWA - ▁KIMATI - ▁TAMA - XKA - XIWIT - TOS - KILIT - ILWI - SKI - YEH - DA - WAYO - ▁TAPA - ▁NIMO - CHIT - ▁NIMITS - ▁KINA - PAHTI - RI - ▁BUENO - ▁ESKI - WAYAH - PANO - KOW - WEYAK - LPAN - LTIA - ▁KITO - CO - ▁TINE - KIH - JO - ▁KATKA - ▁TIKTA - PAHTIA - ▁XIWTSI - ▁CHIKA - ▁KANAH - ▁KOYO - MPI - ▁IXIWYO - IHTIK - ▁KWE - ▁XIW - WILIA - XTIK - ▁VE - ▁TIKMATI - ▁KOKOLIS - LKWI - ▁AHKO - MEKAT - ▁TIKMA - ▁NIMITSILIA - ▁MITS - XTA - ▁CO - ▁KOMA - ▁KOMOHKÓ - F - ▁OKSEKI - ▁TEISÁ - ▁ESO - ▁IKOWYO - ▁ES - TOHTO - XTI - ▁TSI - ▁TIKO - PIHPI - ▁OKSÉ - ▁WEHKAPAN - KALAKI - ▁WEL - ▁MIGUEL - TEKITI - ▁TOKNI - ROWA - ▁MOSKALTIA - Í - XOKO - ▁TIKCHI - ▁EHE - ▁KWO - LPI - HTOK - TSTI - TÍ - ▁TEIHSÁ - KILO - ▁PUES - SKIA - HTIW - LILIAH - ▁IHWA - ▁KOSTIK - ▁TIKIHTOWAH - ▁CHA - ▁COMO - ▁KIMANA - CU - TAMAN - WITS - ▁KOKO - ILPIA - ▁NIMONO - ▁WELI - ▁NIKWI - WTOK - ▁KINEKI - KOKOH - ▁P - LTIAH - XKO - ▁ONKAYA - TAPOWI - MATTOK - ▁MISMO - ▁NIKIHTO - ▁NIKMATTOK - MESKIA - ▁SOH - KWOWIT - XTIA - WELITA - ▁DESPUÉS - ▁IXWA - ZA - TSAPOT - SKAL - ▁SIEMPRE - TINEMI - Ñ - ▁ESKIA - NELOWA - ▁TZINACAPAN - ▁DI - XIWYO - ▁AHA - ▁AHWIA - É - ▁KIKWIAH - MATTOKEH - ▁ACHTO - XTILIA - TAPAL - ▁KIHTO - TEHTE - ▁PORIN - ▁TSOPE - ▁KAHFE - GU - ▁NIMITSTAHTANI - ▁TAHTA - ▁KOWTATI - ISWAT - ▁TIKPIA - ▁KOMEKAT - TIOWIH - ▁TIMONOHNO - ▁TIEMPO - WEHKA - QUI - ▁TIHTI - ▁XOXOKTIK - ▁TAXKAL - EHE - ▁AJÁ - NANAKAT - NIWKI - ▁CI - ▁ITSMOL - ▁NIKPIA - TEKPA - ▁BO - ▁TASOHKA - Ú - ¡ - '8' - '9' - '0' - '1' - '2' - ¿ - Ò - '4' - À - '7' - '5' - '3' - ́ - V - ̈ - Ï - '6' - Q - Ì - <sos/eos> init: xavier_uniform input_size: null ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: true model_conf: ctc_weight: 0.3 lsm_weight: 0.1 length_normalized_loss: false extract_feats_in_collect_stats: false use_preprocessor: true token_type: bpe bpemodel: data/token_list/bpe_unigram500/bpe.model non_linguistic_symbols: null cleaner: null g2p: null speech_volume_normalize: null rir_scp: null rir_apply_prob: 1.0 noise_scp: null noise_apply_prob: 1.0 noise_db_range: '13_15' frontend: s3prl frontend_conf: frontend_conf: upstream: hubert_large_ll60k download_dir: ./hub multilayer_feature: true fs: 16k specaug: specaug specaug_conf: apply_time_warp: true time_warp_window: 5 time_warp_mode: bicubic apply_freq_mask: true freq_mask_width_range: - 0 - 30 num_freq_mask: 2 apply_time_mask: true time_mask_width_range: - 0 - 40 num_time_mask: 2 normalize: utterance_mvn normalize_conf: {} preencoder: linear preencoder_conf: input_size: 1024 output_size: 80 encoder: transformer encoder_conf: input_layer: conv2d num_blocks: 12 linear_units: 2048 dropout_rate: 0.1 output_size: 256 attention_heads: 4 attention_dropout_rate: 0.0 postencoder: null postencoder_conf: {} decoder: transformer decoder_conf: input_layer: embed num_blocks: 6 linear_units: 2048 dropout_rate: 0.1 required: - output_dir - token_list version: 0.10.4a1 distributed: false ``` </details> ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
huggingtweets/_luisinhobr-nomesdegato-nomesdj	huggingtweets	2021-12-21T14:04:49Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: http://www.huggingtweets.com/_luisinhobr-nomesdegato-nomesdj/1640095484918/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1390224220643278850/LcIZLss-_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1175884636624510976/KtBI_1GE_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1245550936807874560/j_zCtKSJ_400x400.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">luisfer nando & nomes foda de dj & nomes de gato</div> <div style="text-align: center; font-size: 14px;">@_luisinhobr-nomesdegato-nomesdj</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from luisfer nando & nomes foda de dj & nomes de gato. \| Data \| luisfer nando \| nomes foda de dj \| nomes de gato \| \| --- \| --- \| --- \| --- \| \| Tweets downloaded \| 2357 \| 3250 \| 3211 \| \| Retweets \| 365 \| 6 \| 69 \| \| Short tweets \| 503 \| 632 \| 1710 \| \| Tweets kept \| 1489 \| 2612 \| 1432 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1mwm543c/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @_luisinhobr-nomesdegato-nomesdj's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3nbxg8c7) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3nbxg8c7/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/_luisinhobr-nomesdegato-nomesdj') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
hrdipto/wav2vec2-xls-r-timit-tokenizer	hrdipto	2021-12-21T11:49:30Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-xls-r-timit-tokenizer results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-xls-r-timit-tokenizer This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4285 - Wer: 0.3662 ## Model description More information needed ## Intended uses & 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: 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 - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 2.1571 \| 4.03 \| 500 \| 0.5235 \| 0.5098 \| \| 0.2001 \| 8.06 \| 1000 \| 0.4172 \| 0.4375 \| \| 0.0968 \| 12.1 \| 1500 \| 0.4562 \| 0.4016 \| \| 0.0607 \| 16.13 \| 2000 \| 0.4640 \| 0.4050 \| \| 0.0409 \| 20.16 \| 2500 \| 0.4688 \| 0.3914 \| \| 0.0273 \| 24.19 \| 3000 \| 0.4414 \| 0.3763 \| \| 0.0181 \| 28.22 \| 3500 \| 0.4285 \| 0.3662 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
bhavikardeshna/multilingual-bert-base-cased-english	bhavikardeshna	2021-12-21T11:42:34Z	4	0	transformers	[ "transformers", "pytorch", "bert", "question-answering", "arxiv:2112.09866", "endpoints_compatible", "region:us" ]	question-answering	2022-03-02T23:29:05Z	# BibTeX entry and citation info ``` @misc{pandya2021cascading, title={Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages}, author={Hariom A. Pandya and Bhavik Ardeshna and Dr. Brijesh S. Bhatt}, year={2021}, eprint={2112.09866}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
bhavikardeshna/multilingual-bert-base-cased-chinese	bhavikardeshna	2021-12-21T11:41:47Z	6	2	transformers	[ "transformers", "pytorch", "bert", "question-answering", "arxiv:2112.09866", "endpoints_compatible", "region:us" ]	question-answering	2022-03-02T23:29:05Z	# BibTeX entry and citation info ``` @misc{pandya2021cascading, title={Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages}, author={Hariom A. Pandya and Bhavik Ardeshna and Dr. Brijesh S. Bhatt}, year={2021}, eprint={2112.09866}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
bhavikardeshna/multilingual-bert-base-cased-arabic	bhavikardeshna	2021-12-21T11:41:30Z	27	0	transformers	[ "transformers", "pytorch", "bert", "question-answering", "arxiv:2112.09866", "endpoints_compatible", "region:us" ]	question-answering	2022-03-02T23:29:05Z	# BibTeX entry and citation info ``` @misc{pandya2021cascading, title={Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages}, author={Hariom A. Pandya and Bhavik Ardeshna and Dr. Brijesh S. Bhatt}, year={2021}, eprint={2112.09866}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
bhavikardeshna/xlm-roberta-base-arabic	bhavikardeshna	2021-12-21T11:41:04Z	27	1	transformers	[ "transformers", "pytorch", "xlm-roberta", "question-answering", "arxiv:2112.09866", "endpoints_compatible", "region:us" ]	question-answering	2022-03-02T23:29:05Z	# BibTeX entry and citation info ``` @misc{pandya2021cascading, title={Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages}, author={Hariom A. Pandya and Bhavik Ardeshna and Dr. Brijesh S. Bhatt}, year={2021}, eprint={2112.09866}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
bhavikardeshna/xlm-roberta-base-german	bhavikardeshna	2021-12-21T11:40:35Z	7	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "question-answering", "arxiv:2112.09866", "endpoints_compatible", "region:us" ]	question-answering	2022-03-02T23:29:05Z	# BibTeX entry and citation info ``` @misc{pandya2021cascading, title={Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages}, author={Hariom A. Pandya and Bhavik Ardeshna and Dr. Brijesh S. Bhatt}, year={2021}, eprint={2112.09866}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
bhavikardeshna/xlm-roberta-base-spanish	bhavikardeshna	2021-12-21T11:39:52Z	6	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "question-answering", "arxiv:2112.09866", "endpoints_compatible", "region:us" ]	question-answering	2022-03-02T23:29:05Z	# BibTeX entry and citation info ``` @misc{pandya2021cascading, title={Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages}, author={Hariom A. Pandya and Bhavik Ardeshna and Dr. Brijesh S. Bhatt}, year={2021}, eprint={2112.09866}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
patrickvonplaten/xls-r-300m-it-phoneme	patrickvonplaten	2021-12-21T11:15:39Z	16	1	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "mozilla-foundation/common_voice_3_0", "generated_from_trainer", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- tags: - automatic-speech-recognition - mozilla-foundation/common_voice_3_0 - generated_from_trainer model-index: - name: xls-r-300m-it-phoneme 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. --> # xls-r-300m-it-phoneme This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the mozilla-foundation/common_voice_3_0 - IT dataset. It achieves the following results on the evaluation set: - Loss: 0.3899 - Wer: 0.0770 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000075 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 32 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 150 - mixed_precision_training: Native AMP ### Training results See Training Metrics Tab. ### Framework versions - Transformers 4.15.0.dev0 - Pytorch 1.9.0+cu111 - Datasets 1.16.2.dev0 - Tokenizers 0.10.3
kwang1993/wav2vec2-base-timit-demo	kwang1993	2021-12-21T04:54:44Z	3	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	https://huggingface.co/blog/fine-tune-wav2vec2-english Use the processor from https://huggingface.co/facebook/wav2vec2-base
vuiseng9/pegasus-billsum	vuiseng9	2021-12-21T01:41:33Z	3	0	transformers	[ "transformers", "pytorch", "pegasus", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	This model is developed with transformers v4.13 with minor patch in this [fork](https://github.com/vuiseng9/transformers/tree/pegasus-v4p13). # Setup ```bash git clone https://github.com/vuiseng9/transformers cd transformers git checkout pegasus-v4p13 && git reset --hard 41eeb07 # installation, set summarization dependency # . . . ``` # Train ```bash #!/usr/bin/env bash export CUDA_VISIBLE_DEVICES=0,1,2,3 NEPOCH=10 RUNID=pegasus-billsum-${NEPOCH}eph-run1 OUTDIR=/data1/vchua/pegasus-hf4p13/pegasus/${RUNID} mkdir -p $OUTDIR nohup python run_summarization.py \ --model_name_or_path google/pegasus-large \ --dataset_name billsum \ --do_train \ --adafactor \ --learning_rate 2e-4 \ --label_smoothing_factor 0.1 \ --num_train_epochs $NEPOCH \ --per_device_train_batch_size 2 \ --do_eval \ --per_device_eval_batch_size 2 \ --num_beams 8 \ --max_source_length 1024 \ --max_target_length 256 \ --evaluation_strategy steps \ --eval_steps 1000 \ --save_strategy steps \ --save_steps 2000 \ --logging_steps 1 \ --overwrite_output_dir \ --run_name $RUNID \ --output_dir $OUTDIR > $OUTDIR/run.log 2>&1 & ``` # Eval ```bash #!/usr/bin/env bash export CUDA_VISIBLE_DEVICES=3 DT=$(date +%F_%H-%M) RUNID=pegasus-billsum-${DT} OUTDIR=/data1/vchua/pegasus-hf4p13/pegasus-test/${RUNID} mkdir -p $OUTDIR nohup python run_summarization.py \ --model_name_or_path vuiseng9/pegasus-billsum \ --dataset_name billsum \ --max_source_length 1024 \ --max_target_length 256 \ --do_predict \ --per_device_eval_batch_size 8 \ --predict_with_generate \ --num_beams 8 \ --overwrite_output_dir \ --run_name $RUNID \ --output_dir $OUTDIR > $OUTDIR/run.log 2>&1 & ``` Although fine-tuning is carried out for 10 epochs, this model is the checkpoint (@12000 steps, 6.6epoch, 210mins) with lowest eval loss during training. Test/predict with this checkpoint should give results below. ``` *** predict metrics *** predict_gen_len = 179.7363 predict_loss = 1.2452 predict_rouge1 = 56.8657 predict_rouge2 = 38.6531 predict_rougeL = 44.8399 predict_rougeLsum = 51.6266 predict_runtime = 1:19:28.20 predict_samples = 3269 predict_samples_per_second = 0.686 predict_steps_per_second = 0.086 ```
patrickvonplaten/wavlm-libri-clean-100h-base-plus	patrickvonplaten	2021-12-20T12:59:01Z	14,635	3	transformers	[ "transformers", "pytorch", "tensorboard", "wavlm", "automatic-speech-recognition", "librispeech_asr", "generated_from_trainer", "wavlm_libri_finetune", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- tags: - automatic-speech-recognition - librispeech_asr - generated_from_trainer - wavlm_libri_finetune model-index: - name: wavlm-libri-clean-100h-base-plus results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wavlm-libri-clean-100h-base-plus This model is a fine-tuned version of [microsoft/wavlm-base-plus](https://huggingface.co/microsoft/wavlm-base-plus) on the LIBRISPEECH_ASR - CLEAN dataset. It achieves the following results on the evaluation set: - Loss: 0.0819 - Wer: 0.0683 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 32 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 2.8877 \| 0.34 \| 300 \| 2.8649 \| 1.0 \| \| 0.2852 \| 0.67 \| 600 \| 0.2196 \| 0.1830 \| \| 0.1198 \| 1.01 \| 900 \| 0.1438 \| 0.1273 \| \| 0.0906 \| 1.35 \| 1200 \| 0.1145 \| 0.1035 \| \| 0.0729 \| 1.68 \| 1500 \| 0.1055 \| 0.0955 \| \| 0.0605 \| 2.02 \| 1800 \| 0.0936 \| 0.0859 \| \| 0.0402 \| 2.35 \| 2100 \| 0.0885 \| 0.0746 \| \| 0.0421 \| 2.69 \| 2400 \| 0.0848 \| 0.0700 \| ### Framework versions - Transformers 4.15.0.dev0 - Pytorch 1.9.0+cu111 - Datasets 1.16.2.dev0 - Tokenizers 0.10.3
patrickvonplaten/wav2vec2-common_voice-tr-demo	patrickvonplaten	2021-12-20T12:54:39Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "speech-recognition", "common_voice", "generated_from_trainer", "tr", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- language: - tr license: apache-2.0 tags: - speech-recognition - common_voice - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-common_voice-tr-demo results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-common_voice-tr-demo This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the COMMON_VOICE - TR dataset. It achieves the following results on the evaluation set: - Loss: 0.3856 - Wer: 0.3556 ## Model description More information needed ## Intended uses & 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: 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 - lr_scheduler_warmup_steps: 500 - num_epochs: 15.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 3.7391 \| 0.92 \| 100 \| 3.5760 \| 1.0 \| \| 2.927 \| 1.83 \| 200 \| 3.0796 \| 0.9999 \| \| 0.9009 \| 2.75 \| 300 \| 0.9278 \| 0.8226 \| \| 0.6529 \| 3.67 \| 400 \| 0.5926 \| 0.6367 \| \| 0.3623 \| 4.59 \| 500 \| 0.5372 \| 0.5692 \| \| 0.2888 \| 5.5 \| 600 \| 0.4407 \| 0.4838 \| \| 0.285 \| 6.42 \| 700 \| 0.4341 \| 0.4694 \| \| 0.0842 \| 7.34 \| 800 \| 0.4153 \| 0.4302 \| \| 0.1415 \| 8.26 \| 900 \| 0.4317 \| 0.4136 \| \| 0.1552 \| 9.17 \| 1000 \| 0.4145 \| 0.4013 \| \| 0.1184 \| 10.09 \| 1100 \| 0.4115 \| 0.3844 \| \| 0.0556 \| 11.01 \| 1200 \| 0.4182 \| 0.3862 \| \| 0.0851 \| 11.93 \| 1300 \| 0.3985 \| 0.3688 \| \| 0.0961 \| 12.84 \| 1400 \| 0.4030 \| 0.3665 \| \| 0.0596 \| 13.76 \| 1500 \| 0.3880 \| 0.3631 \| \| 0.0917 \| 14.68 \| 1600 \| 0.3878 \| 0.3582 \| ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0+cu111 - Datasets 1.12.1 - Tokenizers 0.10.3
patrickvonplaten/wav2vec2-librispeech-clean-100h-demo-dist	patrickvonplaten	2021-12-20T12:53:43Z	87	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "speech-recognition", "librispeech_asr", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - speech-recognition - librispeech_asr - generated_from_trainer model-index: - name: wav2vec2-librispeech-clean-100h-demo-dist results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-librispeech-clean-100h-demo-dist This model is a fine-tuned version of [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) on the LIBRISPEECH_ASR - CLEAN dataset. It achieves the following results on the evaluation set: - Loss: 0.0572 - Wer: 0.0417 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 32 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 3.399 \| 0.11 \| 100 \| 3.6153 \| 1.0 \| \| 2.8892 \| 0.22 \| 200 \| 2.8963 \| 1.0 \| \| 2.8284 \| 0.34 \| 300 \| 2.8574 \| 1.0 \| \| 0.7347 \| 0.45 \| 400 \| 0.6158 \| 0.4850 \| \| 0.1138 \| 0.56 \| 500 \| 0.2038 \| 0.1560 \| \| 0.248 \| 0.67 \| 600 \| 0.1274 \| 0.1024 \| \| 0.2586 \| 0.78 \| 700 \| 0.1108 \| 0.0876 \| \| 0.0733 \| 0.9 \| 800 \| 0.0936 \| 0.0762 \| \| 0.044 \| 1.01 \| 900 \| 0.0834 \| 0.0662 \| \| 0.0393 \| 1.12 \| 1000 \| 0.0792 \| 0.0622 \| \| 0.0941 \| 1.23 \| 1100 \| 0.0769 \| 0.0627 \| \| 0.036 \| 1.35 \| 1200 \| 0.0731 \| 0.0603 \| \| 0.0768 \| 1.46 \| 1300 \| 0.0713 \| 0.0559 \| \| 0.0518 \| 1.57 \| 1400 \| 0.0686 \| 0.0537 \| \| 0.0815 \| 1.68 \| 1500 \| 0.0639 \| 0.0515 \| \| 0.0603 \| 1.79 \| 1600 \| 0.0636 \| 0.0500 \| \| 0.056 \| 1.91 \| 1700 \| 0.0609 \| 0.0480 \| \| 0.0265 \| 2.02 \| 1800 \| 0.0621 \| 0.0465 \| \| 0.0496 \| 2.13 \| 1900 \| 0.0607 \| 0.0449 \| \| 0.0436 \| 2.24 \| 2000 \| 0.0591 \| 0.0446 \| \| 0.0421 \| 2.35 \| 2100 \| 0.0590 \| 0.0428 \| \| 0.0641 \| 2.47 \| 2200 \| 0.0603 \| 0.0443 \| \| 0.0466 \| 2.58 \| 2300 \| 0.0580 \| 0.0429 \| \| 0.0132 \| 2.69 \| 2400 \| 0.0574 \| 0.0423 \| \| 0.0073 \| 2.8 \| 2500 \| 0.0586 \| 0.0417 \| \| 0.0021 \| 2.91 \| 2600 \| 0.0574 \| 0.0412 \| ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0+cu111 - Datasets 1.12.1 - Tokenizers 0.10.3
patrickvonplaten/hubert-librispeech-clean-100h-demo-dist	patrickvonplaten	2021-12-20T12:53:35Z	10	1	transformers	[ "transformers", "pytorch", "tensorboard", "hubert", "automatic-speech-recognition", "speech-recognition", "librispeech_asr", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - speech-recognition - librispeech_asr - generated_from_trainer model-index: - name: hubert-librispeech-clean-100h-demo-dist results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hubert-librispeech-clean-100h-demo-dist This model is a fine-tuned version of [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) on the LIBRISPEECH_ASR - CLEAN dataset. It achieves the following results on the evaluation set: - Loss: 0.0984 - Wer: 0.0883 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 32 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 2.9031 \| 0.11 \| 100 \| 2.9220 \| 1.0 \| \| 2.6437 \| 0.22 \| 200 \| 2.6268 \| 1.0 \| \| 0.3934 \| 0.34 \| 300 \| 0.4860 \| 0.4182 \| \| 0.3531 \| 0.45 \| 400 \| 0.3088 \| 0.2894 \| \| 0.2255 \| 0.56 \| 500 \| 0.2568 \| 0.2426 \| \| 0.3379 \| 0.67 \| 600 \| 0.2073 \| 0.2011 \| \| 0.2419 \| 0.78 \| 700 \| 0.1849 \| 0.1838 \| \| 0.2128 \| 0.9 \| 800 \| 0.1662 \| 0.1690 \| \| 0.1341 \| 1.01 \| 900 \| 0.1600 \| 0.1541 \| \| 0.0946 \| 1.12 \| 1000 \| 0.1431 \| 0.1404 \| \| 0.1643 \| 1.23 \| 1100 \| 0.1373 \| 0.1304 \| \| 0.0663 \| 1.35 \| 1200 \| 0.1293 \| 0.1307 \| \| 0.162 \| 1.46 \| 1300 \| 0.1247 \| 0.1266 \| \| 0.1433 \| 1.57 \| 1400 \| 0.1246 \| 0.1262 \| \| 0.1581 \| 1.68 \| 1500 \| 0.1219 \| 0.1154 \| \| 0.1036 \| 1.79 \| 1600 \| 0.1127 \| 0.1081 \| \| 0.1352 \| 1.91 \| 1700 \| 0.1087 \| 0.1040 \| \| 0.0471 \| 2.02 \| 1800 \| 0.1085 \| 0.1005 \| \| 0.0945 \| 2.13 \| 1900 \| 0.1066 \| 0.0973 \| \| 0.0843 \| 2.24 \| 2000 \| 0.1102 \| 0.0964 \| \| 0.0774 \| 2.35 \| 2100 \| 0.1079 \| 0.0940 \| \| 0.0952 \| 2.47 \| 2200 \| 0.1056 \| 0.0927 \| \| 0.0635 \| 2.58 \| 2300 \| 0.1026 \| 0.0920 \| \| 0.0665 \| 2.69 \| 2400 \| 0.1012 \| 0.0905 \| \| 0.034 \| 2.8 \| 2500 \| 0.1009 \| 0.0900 \| \| 0.0251 \| 2.91 \| 2600 \| 0.0993 \| 0.0883 \| ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0+cu111 - Datasets 1.12.1 - Tokenizers 0.10.3
abhishek/autonlp-prodigy-10-3362554	abhishek	2021-12-20T11:11:03Z	6	2	transformers	[ "transformers", "pytorch", "bert", "token-classification", "autonlp", "en", "dataset:abhishek/autonlp-data-prodigy-10", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- tags: autonlp language: en widget: - text: "I love AutoNLP 🤗" datasets: - abhishek/autonlp-data-prodigy-10 co2_eq_emissions: 5.340540212393564 --- # Model Trained Using AutoNLP - Problem type: Entity Extraction - Model ID: 3362554 - CO2 Emissions (in grams): 5.340540212393564 ## Validation Metrics - Loss: 0.14167872071266174 - Accuracy: 0.9587076867229332 - Precision: 0.7351351351351352 - Recall: 0.7923728813559322 - F1: 0.7626816212082591 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/abhishek/autonlp-prodigy-10-3362554 ``` Or Python API: ``` from transformers import AutoModelForTokenClassification, AutoTokenizer model = AutoModelForTokenClassification.from_pretrained("abhishek/autonlp-prodigy-10-3362554", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("abhishek/autonlp-prodigy-10-3362554", use_auth_token=True) inputs = tokenizer("I love AutoNLP", return_tensors="pt") outputs = model(**inputs) ```
juliusco/biobert-base-cased-v1.1-squad-finetuned-covbiobert	juliusco	2021-12-20T07:58:26Z	5	0	transformers	[ "transformers", "pytorch", "bert", "question-answering", "generated_from_trainer", "dataset:covid_qa_deepset", "endpoints_compatible", "region:us" ]	question-answering	2022-03-02T23:29:05Z	--- tags: - generated_from_trainer datasets: - covid_qa_deepset model-index: - name: biobert-base-cased-v1.1-squad-finetuned-covbiobert 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. --> # biobert-base-cased-v1.1-squad-finetuned-covbiobert This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.1-squad](https://huggingface.co/dmis-lab/biobert-base-cased-v1.1-squad) on the covid_qa_deepset dataset. It achieves the following results on the evaluation set: - Loss: 0.3959 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 486 \| 0.3787 \| \| 0.161 \| 2.0 \| 972 \| 0.3959 \| ### Framework versions - Transformers 4.13.0 - Pytorch 1.10.0+cu102 - Datasets 1.16.1 - Tokenizers 0.10.3
Amalq/roberta-base-finetuned-schizophreniaReddit2	Amalq	2021-12-20T05:41:28Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "fill-mask", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:04Z	--- license: mit tags: - generated_from_trainer model-index: - name: roberta-base-finetuned-schizophreniaReddit2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base-finetuned-schizophreniaReddit2 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7785 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 490 \| 1.8093 \| \| 1.9343 \| 2.0 \| 980 \| 1.7996 \| \| 1.8856 \| 3.0 \| 1470 \| 1.7966 \| \| 1.8552 \| 4.0 \| 1960 \| 1.7844 \| \| 1.8267 \| 5.0 \| 2450 \| 1.7839 \| ### Framework versions - Transformers 4.14.1 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
addy88/wav2vec2-assamese-stt	addy88	2021-12-19T16:55:56Z	3	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	## Usage The model can be used directly (without a language model) as follows: ```python import soundfile as sf import torch from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import argparse def parse_transcription(wav_file): # load pretrained model processor = Wav2Vec2Processor.from_pretrained("addy88/addy88/wav2vec2-assamese-stt") model = Wav2Vec2ForCTC.from_pretrained("addy88/addy88/wav2vec2-assamese-stt") # load audio audio_input, sample_rate = sf.read(wav_file) # pad input values and return pt tensor input_values = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values # INFERENCE # retrieve logits & take argmax logits = model(input_values).logits predicted_ids = torch.argmax(logits, dim=-1) # transcribe transcription = processor.decode(predicted_ids[0], skip_special_tokens=True) print(transcription) ```
addy88/wav2vec2-bengali-stt	addy88	2021-12-19T16:52:02Z	4	3	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	## Usage The model can be used directly (without a language model) as follows: ```python import soundfile as sf import torch from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import argparse def parse_transcription(wav_file): # load pretrained model processor = Wav2Vec2Processor.from_pretrained("addy88/wav2vec2-bengali-stt") model = Wav2Vec2ForCTC.from_pretrained("addy88/wav2vec2-bengali-stt") # load audio audio_input, sample_rate = sf.read(wav_file) # pad input values and return pt tensor input_values = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values # INFERENCE # retrieve logits & take argmax logits = model(input_values).logits predicted_ids = torch.argmax(logits, dim=-1) # transcribe transcription = processor.decode(predicted_ids[0], skip_special_tokens=True) print(transcription) ```
addy88/wav2vec2-bhojpuri-stt	addy88	2021-12-19T16:48:06Z	4	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	## Usage The model can be used directly (without a language model) as follows: ```python import soundfile as sf import torch from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import argparse def parse_transcription(wav_file): # load pretrained model processor = Wav2Vec2Processor.from_pretrained("addy88/wav2vec2-bhojpuri-stt") model = Wav2Vec2ForCTC.from_pretrained("addy88/wav2vec2-bhojpuri-stt") # load audio audio_input, sample_rate = sf.read(wav_file) # pad input values and return pt tensor input_values = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values # INFERENCE # retrieve logits & take argmax logits = model(input_values).logits predicted_ids = torch.argmax(logits, dim=-1) # transcribe transcription = processor.decode(predicted_ids[0], skip_special_tokens=True) print(transcription) ```
addy88/wav2vec2-marathi-stt	addy88	2021-12-19T16:31:22Z	21	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	## Usage The model can be used directly (without a language model) as follows: ```python import soundfile as sf import torch from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import argparse def parse_transcription(wav_file): # load pretrained model processor = Wav2Vec2Processor.from_pretrained("addy88/wav2vec2-marathi-stt") model = Wav2Vec2ForCTC.from_pretrained("addy88/wav2vec2-marathi-stt") # load audio audio_input, sample_rate = sf.read(wav_file) # pad input values and return pt tensor input_values = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values # INFERENCE # retrieve logits & take argmax logits = model(input_values).logits predicted_ids = torch.argmax(logits, dim=-1) # transcribe transcription = processor.decode(predicted_ids[0], skip_special_tokens=True) print(transcription) ```
addy88/wav2vec2-rajsthani-stt	addy88	2021-12-19T15:52:16Z	5	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	## Usage The model can be used directly (without a language model) as follows: ```python import soundfile as sf import torch from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import argparse def parse_transcription(wav_file): # load pretrained model processor = Wav2Vec2Processor.from_pretrained("addy88/wav2vec2-rajsthani-stt") model = Wav2Vec2ForCTC.from_pretrained("addy88/wav2vec2-rajsthani-stt") # load audio audio_input, sample_rate = sf.read(wav_file) # pad input values and return pt tensor input_values = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values # INFERENCE # retrieve logits & take argmax logits = model(input_values).logits predicted_ids = torch.argmax(logits, dim=-1) # transcribe transcription = processor.decode(predicted_ids[0], skip_special_tokens=True) print(transcription) ```
addy88/wav2vec2-nepali-stt	addy88	2021-12-19T15:36:06Z	4	1	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	## Usage The model can be used directly (without a language model) as follows: ```python import soundfile as sf import torch from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import argparse def parse_transcription(wav_file): # load pretrained model processor = Wav2Vec2Processor.from_pretrained("addy88/wav2vec2-nepali-stt") model = Wav2Vec2ForCTC.from_pretrained("addy88/wav2vec2-nepali-stt") # load audio audio_input, sample_rate = sf.read(wav_file) # pad input values and return pt tensor input_values = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values # INFERENCE # retrieve logits & take argmax logits = model(input_values).logits predicted_ids = torch.argmax(logits, dim=-1) # transcribe transcription = processor.decode(predicted_ids[0], skip_special_tokens=True) print(transcription) ```
Ayham/bert_gpt2_summarization_cnndm_new	Ayham	2021-12-19T15:09:12Z	7	0	transformers	[ "transformers", "pytorch", "tensorboard", "encoder-decoder", "text2text-generation", "generated_from_trainer", "dataset:cnn_dailymail", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:04Z	--- tags: - generated_from_trainer datasets: - cnn_dailymail model-index: - name: bert_gpt2_summarization_cnndm_new results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert_gpt2_summarization_cnndm_new This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
addy88/wav2vec2-english-stt	addy88	2021-12-19T15:08:42Z	17	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	## Usage The model can be used directly (without a language model) as follows: ```python import soundfile as sf import torch from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import argparse def parse_transcription(wav_file): # load pretrained model processor = Wav2Vec2Processor.from_pretrained("addy88/wav2vec2-english-stt") model = Wav2Vec2ForCTC.from_pretrained("addy88/wav2vec2-english-stt") # load audio audio_input, sample_rate = sf.read(wav_file) # pad input values and return pt tensor input_values = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values # INFERENCE # retrieve logits & take argmax logits = model(input_values).logits predicted_ids = torch.argmax(logits, dim=-1) # transcribe transcription = processor.decode(predicted_ids[0], skip_special_tokens=True) print(transcription) ```
addy88/wav2vec2-kannada-stt	addy88	2021-12-19T13:35:26Z	248	1	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	## Usage The model can be used directly (without a language model) as follows: ```python import soundfile as sf import torch from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import argparse def parse_transcription(wav_file): # load pretrained model processor = Wav2Vec2Processor.from_pretrained("addy88/wav2vec2-kannada-stt") model = Wav2Vec2ForCTC.from_pretrained("addy88/wav2vec2-kannada-stt") # load audio audio_input, sample_rate = sf.read(wav_file) # pad input values and return pt tensor input_values = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values # INFERENCE # retrieve logits & take argmax logits = model(input_values).logits predicted_ids = torch.argmax(logits, dim=-1) # transcribe transcription = processor.decode(predicted_ids[0], skip_special_tokens=True) print(transcription) ```
rlagusrlagus123/XTC4096	rlagusrlagus123	2021-12-19T11:19:34Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- tags: - conversational --- --- #12 epochs, each batch size 4, gradient accumulation steps 1, tail 4096. #THIS SEEMS TO BE THE OPTIMAL SETUP.
rlagusrlagus123/XTC20000	rlagusrlagus123	2021-12-19T11:00:28Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- tags: - conversational --- --- #12 epochs, each batch size 2, gradient accumulation steps 2, tail 20000
NbAiLabArchive/test_w5_long_roberta_tokenizer	NbAiLabArchive	2021-12-19T10:36:40Z	41	0	transformers	[ "transformers", "pytorch", "jax", "tensorboard", "roberta", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:04Z	Just for performing some experiments. Do not use.
haotieu/en-vi-mt-model	haotieu	2021-12-19T10:17:03Z	14	1	transformers	[ "transformers", "pytorch", "marian", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	# Helsinki-NLP/opus-mt-en-vi - This model is a fine-tune checkpoint of [Helsinki-NLP/opus-mt-en-vi](https://huggingface.co/Helsinki-NLP/opus-mt-en-vi). - This model reaches BLEU score = 33.086 on the test set of IWSLT'15 English-Vietnamese data. # Fine-tuning hyper-parameters - learning_rate = 1e-4 - batch_size = 4 - num_train_epochs = 3.0
Langame/gpt2-waiting	Langame	2021-12-19T09:02:26Z	11	1	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "en", "dataset:waiting-messages", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:04Z	--- language: - en # Example: en license: mit # Example: apache-2.0 or any license from https://hf.co/docs/hub/model-repos#list-of-license-identifiers tags: - text-generation datasets: - waiting-messages # Example: common_voice. Use dataset id from https://hf.co/datasets widget: - text: 'List of funny waiting messages:' example_title: 'Funny waiting messages' --- # Langame/gpt2-waiting This fine-tuned model can generate funny waiting messages. [Langame](https://langa.me) uses these within its platform 😛.
Ayham/roberta_gpt2_summarization_cnn_dailymail	Ayham	2021-12-19T06:58:26Z	14	1	transformers	[ "transformers", "pytorch", "tensorboard", "encoder-decoder", "text2text-generation", "generated_from_trainer", "dataset:cnn_dailymail", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:04Z	--- tags: - generated_from_trainer datasets: - cnn_dailymail model-index: - name: roberta_gpt2_summarization_cnn_dailymail results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta_gpt2_summarization_cnn_dailymail This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail dataset. ## Model description This model uses RoBerta encoder and GPT2 decoder and fine-tuned on the summarization task. It got Rouge scores as follows: Rouge1= 35.886 Rouge2= 16.292 RougeL= 23.499 ## Intended uses & limitations To use its API: from transformers import RobertaTokenizerFast, GPT2Tokenizer, EncoderDecoderModel model = EncoderDecoderModel.from_pretrained("Ayham/roberta_gpt2_summarization_cnn_dailymail") input_tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base') output_tokenizer = GPT2Tokenizer.from_pretrained("gpt2") article = """Your Input Text""" input_ids = input_tokenizer(article, return_tensors="pt").input_ids output_ids = model.generate(input_ids) print(output_tokenizer.decode(output_ids[0], skip_special_tokens=True)) More information needed More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
Ayham/roberta_gpt2_summarization_xsum	Ayham	2021-12-19T06:35:43Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "encoder-decoder", "text2text-generation", "generated_from_trainer", "dataset:xsum", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:04Z	--- tags: - generated_from_trainer datasets: - xsum model-index: - name: roberta_gpt2_summarization_xsum results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta_gpt2_summarization_xsum This model is a fine-tuned version of [](https://huggingface.co/) on the xsum dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
Ayham/xlnet_gpt2_summarization_xsum	Ayham	2021-12-19T04:50:11Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "encoder-decoder", "text2text-generation", "generated_from_trainer", "dataset:xsum", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:04Z	--- tags: - generated_from_trainer datasets: - xsum model-index: - name: xlnet_gpt2_summarization_xsum 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. --> # xlnet_gpt2_summarization_xsum This model is a fine-tuned version of [](https://huggingface.co/) on the xsum dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
zaccharieramzi/UNet-fastmri	zaccharieramzi	2021-12-19T02:05:48Z	0	0	null	[ "region:us" ]	null	2022-03-02T23:29:05Z	# UNet-fastmri --- tags: - TensorFlow - MRI reconstruction - MRI datasets: - fastMRI --- This model can be used to reconstruct single coil fastMRI data with an acceleration factor of 4. ## Model description For more details, see https://www.mdpi.com/2076-3417/10/5/1816. This section is WIP. ## Intended uses and limitations This model can be used to reconstruct single coil knee data from Siemens scanner at acceleration factor 4. It cannot be used on multi-coil data. ## How to use This model can be loaded using the following repo: https://github.com/zaccharieramzi/fastmri-reproducible-benchmark. After cloning the repo, `git clone https://github.com/zaccharieramzi/fastmri-reproducible-benchmark`, you can install the package via `pip install fastmri-reproducible-benchmark`. The framework is TensorFlow. You can initialize and load the model weights as follows: ```python from fastmri_recon.models.functional_models.unet import unet model = unet(n_layers=4, layers_n_channels=[16, 32, 64, 128], layers_n_non_lins=2,) model.load_weights('UNet-fastmri/model_weights.h5') ``` Using the model is then as simple as: ```python model(zero_filled_recon) ``` ## Limitations and bias The limitations and bias of this model have not been properly investigated. ## Training data This model was trained using the [fastMRI dataset](https://fastmri.org/dataset/). ## Training procedure The training procedure is described in https://www.mdpi.com/2076-3417/10/5/1816 for brain data. This section is WIP. ## Evaluation results This model was evaluated using the [fastMRI dataset](https://fastmri.org/dataset/). \| Contrast \| PD \| PDFS \| \|----------\|-------\|--------\| \| PSNR \| 33.64 \| 29.89 \| \| SSIM \| 0.807 \| 0.6334 \| ## Bibtex entry ``` @article{ramzi2020benchmarking, title={Benchmarking MRI reconstruction neural networks on large public datasets}, author={Ramzi, Zaccharie and Ciuciu, Philippe and Starck, Jean-Luc}, journal={Applied Sciences}, volume={10}, number={5}, pages={1816}, year={2020}, publisher={Multidisciplinary Digital Publishing Institute} } ```
zaccharieramzi/KIKI-net-OASIS	zaccharieramzi	2021-12-19T01:59:51Z	0	0	null	[ "region:us" ]	null	2022-03-02T23:29:05Z	# KIKI-net-OASIS --- tags: - TensorFlow - MRI reconstruction - MRI datasets: - OASIS --- This model can be used to reconstruct single coil OASIS data with an acceleration factor of 4. ## Model description For more details, see https://www.mdpi.com/2076-3417/10/5/1816. This section is WIP. ## Intended uses and limitations This model can be used to reconstruct single coil brain retrospective data from the OASIS database at acceleration factor 4. It cannot be used on multi-coil data. ## How to use This model can be loaded using the following repo: https://github.com/zaccharieramzi/fastmri-reproducible-benchmark. After cloning the repo, `git clone https://github.com/zaccharieramzi/fastmri-reproducible-benchmark`, you can install the package via `pip install fastmri-reproducible-benchmark`. The framework is TensorFlow. You can initialize and load the model weights as follows: ```python from fastmri_recon.models.functional_models.kiki_sep import full_kiki_net from fastmri_recon.models.utils.non_linearities import lrelu model = full_kiki_net(n_convs=16, n_filters=48, activation=lrelu) model.load_weights('model_weights.h5') ``` Using the model is then as simple as: ```python model([ kspace, # shape: [n_slices, n_rows, n_cols, 1] mask, # shape: [n_slices, n_rows, n_cols] ]) ``` ## Limitations and bias The limitations and bias of this model have not been properly investigated. ## Training data This model was trained using the [OASIS dataset](https://www.oasis-brains.org/). ## Training procedure The training procedure is described in https://www.mdpi.com/2076-3417/10/5/1816 for brain data. This section is WIP. ## Evaluation results This model was evaluated using the [OASIS dataset](https://www.oasis-brains.org/). - PSNR: 30.08 - SSIM: 0.853 ## Bibtex entry ``` @article{ramzi2020benchmarking, title={Benchmarking MRI reconstruction neural networks on large public datasets}, author={Ramzi, Zaccharie and Ciuciu, Philippe and Starck, Jean-Luc}, journal={Applied Sciences}, volume={10}, number={5}, pages={1816}, year={2020}, publisher={Multidisciplinary Digital Publishing Institute} } ```
zaccharieramzi/NCPDNet-singlecoil-spiral	zaccharieramzi	2021-12-19T00:47:15Z	0	0	null	[ "region:us" ]	null	2022-03-02T23:29:05Z	# NCPDNet-singlecoil-spiral --- tags: - TensorFlow - MRI reconstruction - MRI datasets: - fastMRI --- This is a non-Cartesian MRI reconstruction model for spiral trajectories at acceleration factor 4. The model uses 10 iterations and a small vanilla CNN. ## Model description For more details, see https://hal.inria.fr/hal-03188997. This section is WIP. ## Intended uses and limitations This model can be used to reconstruct knee data from Siemens scanner at acceleration factor 4 in a spiral acquisition setting. ## How to use This model can be loaded using the following repo: https://github.com/zaccharieramzi/fastmri-reproducible-benchmark. After cloning the repo, `git clone https://github.com/zaccharieramzi/fastmri-reproducible-benchmark`, you can install the package via `pip install fastmri-reproducible-benchmark`. The framework is TensorFlow. You can initialize and load the model weights as follows: ```python import tensorflow as tf from fastmri_recon.models.subclassed_models.ncpdnet import NCPDNet model = NCPDNet( im_size=(640, 400), dcomp=True, ) kspace_shape = 1 inputs = [ tf.zeros([1, 1, kspace_shape, 1], dtype=tf.complex64), tf.zeros([1, 2, kspace_shape], dtype=tf.float32), (tf.constant([320]), tf.ones([1, kspace_shape], dtype=tf.float32)), ] model(inputs) model.load_weights('model_weights.h5') ``` Using the model is then as simple as: ```python model([ kspace, # shape: [n_slices, 1, n_kspace_samples, 1] traj, # shape: [n_slices, 1, 2, n_kspace_samples] ( output_shape, # shape: [n_slices, 1] dcomp, # shape: [n_slices, n_kspace_samples] ) ]) ``` ## Limitations and bias The limitations and bias of this model have not been properly investigated. ## Training data This model was trained using the [fastMRI dataset](https://fastmri.org/dataset/). ## Training procedure The training procedure is described in https://hal.inria.fr/hal-03188997. This section is WIP. ## Evaluation results On the fastMRI validation dataset: - PSNR: 33.08 - SSIM: 0.7534 ## Bibtex entry ``` @unpublished{ramzi:hal-03188997, TITLE = {{NC-PDNet: a Density-Compensated Unrolled Network for 2D and 3D non-Cartesian MRI Reconstruction}}, AUTHOR = {Ramzi, Zaccharie and G R, Chaithya and Starck, Jean-Luc and Ciuciu, Philippe}, YEAR = {2021}, MONTH = Sep, } ```
tasosk/bert-base-uncased-airlines	tasosk	2021-12-18T20:20:24Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: bert-base-uncased-airlines results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-uncased-airlines This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3458 - Accuracy: 0.9021 - F1: 0.9022 ## Model description More information needed ## Intended uses & 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-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 405 \| 0.3230 \| 0.8754 \| 0.8750 \| \| 0.4658 \| 2.0 \| 810 \| 0.2738 \| 0.8986 \| 0.8985 \| \| 0.2473 \| 3.0 \| 1215 \| 0.2944 \| 0.9110 \| 0.9111 \| \| 0.2498 \| 4.0 \| 1620 \| 0.3322 \| 0.8950 \| 0.8949 \| \| 0.2174 \| 5.0 \| 2025 \| 0.3342 \| 0.9021 \| 0.9021 \| \| 0.2174 \| 6.0 \| 2430 \| 0.3526 \| 0.8986 \| 0.8985 \| \| 0.2055 \| 7.0 \| 2835 \| 0.3458 \| 0.9021 \| 0.9022 \| ### Framework versions - Transformers 4.14.1 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
IlyaGusev/rut5_base_headline_gen_telegram	IlyaGusev	2021-12-18T19:27:52Z	13,204	8	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "summarization", "ru", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	summarization	2022-03-02T23:29:04Z	--- language: - ru tags: - summarization license: apache-2.0 widget: - text: "Комиссия Совета Федерации по информационной политике и взаимодействию со СМИ совместно с заинтересованными ведомствами думает над разработкой национального законодательства в области налогообложения глобальных интернет-компаний, таких как Google и Facebook. Об этом сообщил ТАСС председатель комиссии Алексей Пушков. «В настоящее время по линии ОЭСР [Организация экономического сотрудничества и развития] ведется разработка международной конвенции, однако работа над ней еще не завершена. В этих условиях мы исходим из того, что самая разумная позиция - начать разработку национального законодательства, не дожидаясь конвенции», — пояснил сенатор. Пушков отметил, что по такому пути пошли еще несколько стран, в числе которых Франция, Австралия и Турция. По его словам, в России важно задействовать в этой работе Минфин, ФНС, МИД РФ и Роскомнадзор. «Интернет-платформы не фигурируют у нас сейчас как отдельный объект налогообложения. Когда они откроют в России свои представительства в рамках закона о «приземлении», возникнет вопрос: как их официальное присутствие на территории России, которого сейчас нет, будет соотноситься с нашим налоговым режимом. Мы сейчас продумываем, как установить эту взаимосвязь», — сказал Пушков, добавляя, что вопрос внесения изменений в российское законодательство в части налогообложения крупных IT-компаний находится «на первой стадии изучения». Сам сенатор выступает за введение прогрессивной ставки налога в зависимости от прибыли IT-компаний на территории страны. При этом, подчеркнул он, одна из задач национальной системы налогообложения будет заключаться в подсчете налогооблагаемой базы. Сейчас крупные ИТ-компании самостоятельно отчитываются о своей прибыли. Однако России нужна собственная система подсчета их доходов, которая позволит определить их «реальную налогооблагаемую базу», считает Пушков. (https://www.gazeta.ru/tech/news/2021/12/17/n_17024239.shtml)" example_title: "Новость про налоги в IT" - text: "Первую многоножку, у которой более тысячи ног, обнаружили в австралийских пещерах биологи, изучавшие там подземные воды. Предыдущей рекордсменкой по количеству ног была 700-ногая многоножка. Новый вид имеет длинное тонкое тело, похожее на нить, и большое количество конечностей, по-видимому, дает преимущества для быстрого перемещения и проникновения в труднодоступные места — ученые полагают, такая многоножка может спокойно перемещаться по трещинам в камнях. Австралия известна своими огромными и жутковатыми животными вроде 25-сантиметровых пауков. Теперь список пугающих членистоногих пополнился самой «многоногой» в мире многоножкой, у которой более тысячи ног. Необычное животное обнаружила группа исследователей из Австралии и США в пещерах на западе страны. Подробнее многоножку ученые описали в статье в журнале Scientific Reports. Исследователи занимались оценкой воздействия подземных вод на окружающую среду в зоне добычи полезных ископаемых на западе страны, когда наткнулись на новый вид многоножек. В отличие от большинства сородичей, живущих на поверхности, эти многоножки обитали в пещерах на глубине до 60 метров. Новый вид исследователи назвали Eumillipes persephone, в честь Персефоны — древнегреческой богини подземного мира. У многоножки оказалось 1306 ног — больше, чем у любого другого известного вида. Предыдущей рекордсменкой была калифорнийская Illacme plenipes, у которой насчитывалось до 750 ног. «Эти животные были настолько уникальны, — говорит биолог Бруно Бузатто. — Как только я понял, какой длины они были... Стало ясно, что это что-то совершенно новое». У Е. persephone нитевидное тело длиной около 9,5 см и шириной всего миллиметр, состоящее из 330 сегментов, короткие ноги и конусообразная голова. Как и другие животные, живущие в постоянной темноте, эти многоножки бледны и слепы. Энтомолог Пол Марек сравнивает ее с белой нитью, выдернутой из рубашки. Чтобы посчитать количество ног, ученым пришлось сначала снять многоножку в высоком разрешении, а затем закрашивать на фото каждый десяток ног другим цветом. (https://www.gazeta.ru/science/2021/12/17_a_14325355.shtml)" example_title: "Новость про многоножку" - text: "Высота башни составляет 324 метра (1063 фута), примерно такая же высота, как у 81-этажного здания, и самое высокое сооружение в Париже. Его основание квадратно, размером 125 метров (410 футов) с любой стороны. Во время строительства Эйфелева башня превзошла монумент Вашингтона, став самым высоким искусственным сооружением в мире, и этот титул она удерживала в течение 41 года до завершения строительство здания Крайслер в Нью-Йорке в 1930 году. Это первое сооружение которое достигло высоты 300 метров. Из-за добавления вещательной антенны на вершине башни в 1957 году она сейчас выше здания Крайслер на 5,2 метра (17 футов). За исключением передатчиков, Эйфелева башня является второй самой высокой отдельно стоящей структурой во Франции после виадука Мийо." example_title: "Википедия" --- # RuT5TelegramHeadlines ## Model description Based on [rut5-base](https://huggingface.co/cointegrated/rut5-base) model ## Intended uses & limitations #### How to use ```python from transformers import AutoTokenizer, T5ForConditionalGeneration model_name = "IlyaGusev/rut5_base_headline_gen_telegram" tokenizer = AutoTokenizer.from_pretrained(model_name) model = T5ForConditionalGeneration.from_pretrained(model_name) article_text = "..." input_ids = tokenizer( [article_text], max_length=600, add_special_tokens=True, padding="max_length", truncation=True, return_tensors="pt" )["input_ids"] output_ids = model.generate( input_ids=input_ids )[0] headline = tokenizer.decode(output_ids, skip_special_tokens=True) print(headline) ``` ## Training data - Dataset: [ru_all_split.tar.gz](https://www.dropbox.com/s/ykqk49a8avlmnaf/ru_all_split.tar.gz) ## Training procedure - Training script: [train.py](https://github.com/IlyaGusev/summarus/blob/master/external/hf_scripts/train.py)
tasosk/distilbert-base-uncased-airlines	tasosk	2021-12-18T19:25:39Z	8	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-airlines 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-airlines This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tasosk/airlines dataset. It achieves the following results on the evaluation set: - Loss: 0.3174 - Accuracy: 0.9288 - F1: 0.9289 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 203 \| 0.2281 \| 0.9164 \| 0.9164 \| \| No log \| 2.0 \| 406 \| 0.2676 \| 0.9164 \| 0.9164 \| \| 0.2314 \| 3.0 \| 609 \| 0.3117 \| 0.9217 \| 0.9217 \| \| 0.2314 \| 4.0 \| 812 \| 0.3175 \| 0.9270 \| 0.9271 \| \| 0.08 \| 5.0 \| 1015 \| 0.3174 \| 0.9288 \| 0.9289 \| ### Framework versions - Transformers 4.14.1 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
zaccharieramzi/UPDNet-knee-af8	zaccharieramzi	2021-12-18T18:08:29Z	0	0	null	[ "arxiv:2010.07290", "region:us" ]	null	2022-03-02T23:29:05Z	# UPDNet-knee-af8 --- tags: - TensorFlow - MRI reconstruction - MRI datasets: - fastMRI --- This model was used to achieve the 9th highest submission in terms of PSNR on the fastMRI dataset (see https://fastmri.org/leaderboards/) (0.2dB behind the 2nd submission). It is a base model for acceleration factor 8. The model uses 25 iterations and a medium-ca-prelu U-net, and a medium sensitivity maps refiner. ## Model description For more details, see https://arxiv.org/abs/2010.07290. This section is WIP. ## Intended uses and limitations This model can be used to reconstruct knee data from Siemens scanner at acceleration factor 8. ## How to use This model can be loaded using the following repo: https://github.com/zaccharieramzi/fastmri-reproducible-benchmark. After cloning the repo, `git clone https://github.com/zaccharieramzi/fastmri-reproducible-benchmark`, you can install the package via `pip install fastmri-reproducible-benchmark`. The framework is TensorFlow. You can initialize and load the model weights as follows: ```python import tensorflow as tf from fastmri_recon.models.subclassed_models.updnet import UPDNet model = UPDNet( multicoil=True, n_dual=1, primal_only=True, n_layers=4, n_iter=25, channel_attention_kwargs={'dense': True}, refine_smaps=True, non_linearity='prelu', layers_n_channels=[16 * 2**i for i in range(4)], ) kspace_size = [1, 1, 320, 320] inputs = [ tf.zeros(kspace_size + [1], dtype=tf.complex64), # kspace tf.zeros(kspace_size, dtype=tf.complex64), # mask tf.zeros(kspace_size, dtype=tf.complex64), # smaps ] model(inputs) model.load_weights('model_weights.h5') ``` Using the model is then as simple as: ```python model([ kspace, # shape: [n_slices, n_coils, n_rows, n_cols, 1] mask, # shape: [n_slices, n_coils, n_rows, n_cols] smaps, # shape: [n_slices, n_coils, n_rows, n_cols] ]) ``` ## Limitations and bias The limitations and bias of this model have not been properly investigated. ## Training data This model was trained using the [fastMRI dataset](https://fastmri.org/dataset/). ## Training procedure The training procedure is described in https://arxiv.org/abs/2010.07290. This section is WIP. ## Evaluation results No evaluation available outside the one from the fastMRI leaderboard (id: `updnet_v3`). ## Bibtex entry ``` @inproceedings{Ramzi2020d, archivePrefix = {arXiv}, arxivId = {2010.07290}, author = {Ramzi, Zaccharie and Ciuciu, Philippe and Starck, Jean-Luc}, booktitle = {ISMRM}, eprint = {2010.07290}, pages = {1--4}, title = {{XPDNet for MRI Reconstruction: an application to the 2020 fastMRI challenge}}, url = {http://arxiv.org/abs/2010.07290}, year = {2021} } ```
zaccharieramzi/UPDNet-knee-af4	zaccharieramzi	2021-12-18T18:08:04Z	0	0	null	[ "arxiv:2010.07290", "region:us" ]	null	2022-03-02T23:29:05Z	# UPDNet-knee-af4 --- tags: - TensorFlow - MRI reconstruction - MRI datasets: - fastMRI --- This model was used to achieve the 9th highest submission in terms of PSNR on the fastMRI dataset (see https://fastmri.org/leaderboards/) (0.2dB behind the 2nd submission). It is a base model for acceleration factor 4. The model uses 25 iterations and a medium-ca-prelu U-net, and a medium sensitivity maps refiner. ## Model description For more details, see https://arxiv.org/abs/2010.07290. This section is WIP. ## Intended uses and limitations This model can be used to reconstruct knee data from Siemens scanner at acceleration factor 4. ## How to use This model can be loaded using the following repo: https://github.com/zaccharieramzi/fastmri-reproducible-benchmark. After cloning the repo, `git clone https://github.com/zaccharieramzi/fastmri-reproducible-benchmark`, you can install the package via `pip install fastmri-reproducible-benchmark`. The framework is TensorFlow. You can initialize and load the model weights as follows: ```python import tensorflow as tf from fastmri_recon.models.subclassed_models.updnet import UPDNet model = UPDNet( multicoil=True, n_dual=1, primal_only=True, n_layers=4, n_iter=25, channel_attention_kwargs={'dense': True}, refine_smaps=True, non_linearity='prelu', layers_n_channels=[16 * 2**i for i in range(4)], ) kspace_size = [1, 1, 320, 320] inputs = [ tf.zeros(kspace_size + [1], dtype=tf.complex64), # kspace tf.zeros(kspace_size, dtype=tf.complex64), # mask tf.zeros(kspace_size, dtype=tf.complex64), # smaps ] model(inputs) model.load_weights('model_weights.h5') ``` Using the model is then as simple as: ```python model([ kspace, # shape: [n_slices, n_coils, n_rows, n_cols, 1] mask, # shape: [n_slices, n_coils, n_rows, n_cols] smaps, # shape: [n_slices, n_coils, n_rows, n_cols] ]) ``` ## Limitations and bias The limitations and bias of this model have not been properly investigated. ## Training data This model was trained using the [fastMRI dataset](https://fastmri.org/dataset/). ## Training procedure The training procedure is described in https://arxiv.org/abs/2010.07290. This section is WIP. ## Evaluation results No evaluation available outside the one from the fastMRI leaderboard (id: `updnet_v3`). ## Bibtex entry ``` @inproceedings{Ramzi2020d, archivePrefix = {arXiv}, arxivId = {2010.07290}, author = {Ramzi, Zaccharie and Ciuciu, Philippe and Starck, Jean-Luc}, booktitle = {ISMRM}, eprint = {2010.07290}, pages = {1--4}, title = {{XPDNet for MRI Reconstruction: an application to the 2020 fastMRI challenge}}, url = {http://arxiv.org/abs/2010.07290}, year = {2021} } ```
jcsilva/wav2vec2-base-timit-demo-colab	jcsilva	2021-12-18T13:45:19Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-demo-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7665 - Wer: 0.6956 ## Model description More information needed ## Intended uses & 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: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 3.052 \| 0.8 \| 100 \| 3.0167 \| 1.0 \| \| 2.7436 \| 1.6 \| 200 \| 1.9369 \| 1.0006 \| \| 1.4182 \| 2.4 \| 300 \| 0.7665 \| 0.6956 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
jiho0304/bad-korean-tokenizer	jiho0304	2021-12-18T04:17:15Z	6	0	transformers	[ "transformers", "electra", "pretraining", "endpoints_compatible", "region:us" ]	null	2022-03-02T23:29:05Z	KcELECTRA([https://github.com/Beomi/KcELECTRA](https://github.com/Beomi/KcELECTRA))의 Tokenizer에서 [UNK]로 대체되는 토큰들을 추가했습니다.
microsoft/unispeech-sat-large-sd	microsoft	2021-12-17T18:42:36Z	72	1	transformers	[ "transformers", "pytorch", "unispeech-sat", "audio-frame-classification", "speech", "en", "arxiv:1912.07875", "arxiv:2106.06909", "arxiv:2101.00390", "arxiv:2110.05752", "endpoints_compatible", "region:us" ]	null	2022-03-02T23:29:05Z	--- language: - en datasets: tags: - speech --- # UniSpeech-SAT-Large for Speaker Diarization [Microsoft's UniSpeech](https://www.microsoft.com/en-us/research/publication/unispeech-unified-speech-representation-learning-with-labeled-and-unlabeled-data/) The model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz. The model was pre-trained on: - 60,000 hours of [Libri-Light](https://arxiv.org/abs/1912.07875) - 10,000 hours of [GigaSpeech](https://arxiv.org/abs/2106.06909) - 24,000 hours of [VoxPopuli](https://arxiv.org/abs/2101.00390) [Paper: UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE PRE-TRAINING](https://arxiv.org/abs/2110.05752) Authors: Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu Abstract Self-supervised learning (SSL) is a long-standing goal for speech processing, since it utilizes large-scale unlabeled data and avoids extensive human labeling. Recent years witness great successes in applying self-supervised learning in speech recognition, while limited exploration was attempted in applying SSL for modeling speaker characteristics. In this paper, we aim to improve the existing SSL framework for speaker representation learning. Two methods are introduced for enhancing the unsupervised speaker information extraction. First, we apply the multi-task learning to the current SSL framework, where we integrate the utterance-wise contrastive loss with the SSL objective function. Second, for better speaker discrimination, we propose an utterance mixing strategy for data augmentation, where additional overlapped utterances are created unsupervisely and incorporate during training. We integrate the proposed methods into the HuBERT framework. Experiment results on SUPERB benchmark show that the proposed system achieves state-of-the-art performance in universal representation learning, especially for speaker identification oriented tasks. An ablation study is performed verifying the efficacy of each proposed method. Finally, we scale up training dataset to 94 thousand hours public audio data and achieve further performance improvement in all SUPERB tasks.. The original model can be found under https://github.com/microsoft/UniSpeech/tree/main/UniSpeech-SAT. # Fine-tuning details The model is fine-tuned on the [LibriMix dataset](https://github.com/JorisCos/LibriMix) using just a linear layer for mapping the network outputs. # Usage ## Speaker Diarization ```python from transformers import Wav2Vec2FeatureExtractor, UniSpeechSatForAudioFrameClassification from datasets import load_dataset import torch dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation") feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained('microsoft/unispeech-sat-large-sd') model = UniSpeechSatForAudioFrameClassification.from_pretrained('microsoft/unispeech-sat-large-sd') # audio file is decoded on the fly inputs = feature_extractor(dataset[0]["audio"]["array"], return_tensors="pt") logits = model(**inputs).logits probabilities = torch.sigmoid(logits[0]) # labels is a one-hot array of shape (num_frames, num_speakers) labels = (probabilities > 0.5).long() ``` # License The official license can be found [here](https://github.com/microsoft/UniSpeech/blob/main/LICENSE) ![design](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/UniSpeechSAT.png)
Eyvaz/wav2vec2-base-russian-modified-kaggle	Eyvaz	2021-12-17T18:39:50Z	5	1	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:04Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: name: wav2vec2-base-russian-modified-kaggle --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-russian-modified-kaggle This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) 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: 0.0001 - train_batch_size: 12 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 24 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.1 - Datasets 1.13.3 - Tokenizers 0.10.3
microsoft/unispeech-sat-base-sd	microsoft	2021-12-17T18:39:23Z	38	0	transformers	[ "transformers", "pytorch", "unispeech-sat", "audio-frame-classification", "speech", "en", "dataset:librispeech_asr", "arxiv:2110.05752", "endpoints_compatible", "region:us" ]	null	2022-03-02T23:29:05Z	--- language: - en datasets: - librispeech_asr tags: - speech --- # UniSpeech-SAT-Base for Speaker Diarization [Microsoft's UniSpeech](https://www.microsoft.com/en-us/research/publication/unispeech-unified-speech-representation-learning-with-labeled-and-unlabeled-data/) The model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz. The model was pre-trained on: - 960 hours of [LibriSpeech](https://huggingface.co/datasets/librispeech_asr) [Paper: UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE PRE-TRAINING](https://arxiv.org/abs/2110.05752) Authors: Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu Abstract Self-supervised learning (SSL) is a long-standing goal for speech processing, since it utilizes large-scale unlabeled data and avoids extensive human labeling. Recent years witness great successes in applying self-supervised learning in speech recognition, while limited exploration was attempted in applying SSL for modeling speaker characteristics. In this paper, we aim to improve the existing SSL framework for speaker representation learning. Two methods are introduced for enhancing the unsupervised speaker information extraction. First, we apply the multi-task learning to the current SSL framework, where we integrate the utterance-wise contrastive loss with the SSL objective function. Second, for better speaker discrimination, we propose an utterance mixing strategy for data augmentation, where additional overlapped utterances are created unsupervisely and incorporate during training. We integrate the proposed methods into the HuBERT framework. Experiment results on SUPERB benchmark show that the proposed system achieves state-of-the-art performance in universal representation learning, especially for speaker identification oriented tasks. An ablation study is performed verifying the efficacy of each proposed method. Finally, we scale up training dataset to 94 thousand hours public audio data and achieve further performance improvement in all SUPERB tasks.. The original model can be found under https://github.com/microsoft/UniSpeech/tree/main/UniSpeech-SAT. # Fine-tuning details The model is fine-tuned on the [LibriMix dataset](https://github.com/JorisCos/LibriMix) using just a linear layer for mapping the network outputs. # Usage ## Speaker Diarization ```python from transformers import Wav2Vec2FeatureExtractor, UniSpeechSatForAudioFrameClassification from datasets import load_dataset import torch dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation") feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained('microsoft/unispeech-sat-base-sd') model = UniSpeechSatForAudioFrameClassification.from_pretrained('microsoft/unispeech-sat-base-sd') # audio file is decoded on the fly inputs = feature_extractor(dataset[0]["audio"]["array"], return_tensors="pt") logits = model(**inputs).logits probabilities = torch.sigmoid(logits[0]) # labels is a one-hot array of shape (num_frames, num_speakers) labels = (probabilities > 0.5).long() ``` # License The official license can be found [here](https://github.com/microsoft/UniSpeech/blob/main/LICENSE) ![design](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/UniSpeechSAT.png)
microsoft/unispeech-sat-base-sv	microsoft	2021-12-17T18:11:05Z	200	0	transformers	[ "transformers", "pytorch", "unispeech-sat", "audio-xvector", "speech", "en", "dataset:librispeech_asr", "arxiv:2110.05752", "endpoints_compatible", "region:us" ]	null	2022-03-02T23:29:05Z	--- language: - en datasets: - librispeech_asr tags: - speech --- # UniSpeech-SAT-Base for Speaker Verification [Microsoft's UniSpeech](https://www.microsoft.com/en-us/research/publication/unispeech-unified-speech-representation-learning-with-labeled-and-unlabeled-data/) The model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz. The model was pre-trained on: - 960 hours of [LibriSpeech](https://huggingface.co/datasets/librispeech_asr) [Paper: UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE PRE-TRAINING](https://arxiv.org/abs/2110.05752) Authors: Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu Abstract Self-supervised learning (SSL) is a long-standing goal for speech processing, since it utilizes large-scale unlabeled data and avoids extensive human labeling. Recent years witness great successes in applying self-supervised learning in speech recognition, while limited exploration was attempted in applying SSL for modeling speaker characteristics. In this paper, we aim to improve the existing SSL framework for speaker representation learning. Two methods are introduced for enhancing the unsupervised speaker information extraction. First, we apply the multi-task learning to the current SSL framework, where we integrate the utterance-wise contrastive loss with the SSL objective function. Second, for better speaker discrimination, we propose an utterance mixing strategy for data augmentation, where additional overlapped utterances are created unsupervisely and incorporate during training. We integrate the proposed methods into the HuBERT framework. Experiment results on SUPERB benchmark show that the proposed system achieves state-of-the-art performance in universal representation learning, especially for speaker identification oriented tasks. An ablation study is performed verifying the efficacy of each proposed method. Finally, we scale up training dataset to 94 thousand hours public audio data and achieve further performance improvement in all SUPERB tasks.. The original model can be found under https://github.com/microsoft/UniSpeech/tree/main/UniSpeech-SAT. # Fine-tuning details The model is fine-tuned on the [VoxCeleb1 dataset](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox1.html) using an X-Vector head with an Additive Margin Softmax loss [X-Vectors: Robust DNN Embeddings for Speaker Recognition](https://www.danielpovey.com/files/2018_icassp_xvectors.pdf) # Usage ## Speaker Verification ```python from transformers import Wav2Vec2FeatureExtractor, UniSpeechSatForXVector from datasets import load_dataset import torch dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation") feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained('microsoft/unispeech-sat-base-sv') model = UniSpeechSatForXVector.from_pretrained('microsoft/unispeech-sat-base-sv') # audio files are decoded on the fly inputs = feature_extractor(dataset[:2]["audio"]["array"], return_tensors="pt") embeddings = model(**inputs).embeddings embeddings = torch.nn.functional.normalize(embeddings, dim=-1).cpu() # the resulting embeddings can be used for cosine similarity-based retrieval cosine_sim = torch.nn.CosineSimilarity(dim=-1) similarity = cosine_sim(embeddings[0], embeddings[1]) threshold = 0.86 # the optimal threshold is dataset-dependent if similarity < threshold: print("Speakers are not the same!") ``` # License The official license can be found [here](https://github.com/microsoft/UniSpeech/blob/main/LICENSE) ![design](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/UniSpeechSAT.png)
butchland/bert-finetuned-ner	butchland	2021-12-17T15:53:25Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "dataset:conll2003", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 args: conll2003 metrics: - name: Precision type: precision value: 0.9389679126695336 - name: Recall type: recall value: 0.9554022214742511 - name: F1 type: f1 value: 0.9471137804471137 - name: Accuracy type: accuracy value: 0.9873138282215812 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0586 - Precision: 0.9390 - Recall: 0.9554 - F1: 0.9471 - Accuracy: 0.9873 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.0877 \| 1.0 \| 1756 \| 0.0662 \| 0.9081 \| 0.9344 \| 0.9210 \| 0.9827 \| \| 0.0376 \| 2.0 \| 3512 \| 0.0599 \| 0.9362 \| 0.9502 \| 0.9431 \| 0.9862 \| \| 0.0209 \| 3.0 \| 5268 \| 0.0586 \| 0.9390 \| 0.9554 \| 0.9471 \| 0.9873 \| ### Framework versions - Transformers 4.14.1 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
osanseviero/fastai_cat_vs_dog_fork2	osanseviero	2021-12-17T14:27:39Z	33	0	generic	[ "generic", "image-classification", "region:us" ]	image-classification	2022-03-02T23:29:05Z	--- tags: - image-classification library_name: generic --- # Dog vs Cat Image Classification with FastAI CNN Training is based in FastAI [Quick Start](https://docs.fast.ai/quick_start.html). Example training ## Training The model was trained as follows ```python path = untar_data(URLs.PETS)/'images' def is_cat(x): return x[0].isupper() dls = ImageDataLoaders.from_name_func( path, get_image_files(path), valid_pct=0.2, seed=42, label_func=is_cat, item_tfms=Resize(224)) learn = cnn_learner(dls, resnet34, metrics=error_rate) learn.fine_tune(1) ```
Rocketknight1/gbert-base-germaner	Rocketknight1	2021-12-17T14:04:59Z	5	1	transformers	[ "transformers", "tf", "tensorboard", "bert", "token-classification", "generated_from_keras_callback", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:04Z	--- license: mit tags: - generated_from_keras_callback model-index: - name: Rocketknight1/gbert-base-germaner 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. --> # Rocketknight1/gbert-base-germaner This model is a fine-tuned version of [deepset/gbert-base](https://huggingface.co/deepset/gbert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0340 - Validation Loss: 0.0881 - 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: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 4176, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 0.1345 \| 0.0865 \| 0 \| \| 0.0550 \| 0.0878 \| 1 \| \| 0.0340 \| 0.0881 \| 2 \| ### Framework versions - Transformers 4.15.0.dev0 - TensorFlow 2.6.0 - Datasets 1.16.2.dev0 - Tokenizers 0.10.3
llange/xlm-roberta-large-spanish-clinical	llange	2021-12-17T10:27:39Z	3	1	transformers	[ "transformers", "pytorch", "xlm-roberta", "fill-mask", "arxiv:2112.08754", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:05Z	# CLIN-X-ES: a pre-trained language model for the Spanish clinical domain Details on the model, the pre-training corpus and the downstream task performance are given in the paper: "CLIN-X: pre-trained language models and a study on cross-task transfer for concept extraction in the clinical domain" by Lukas Lange, Heike Adel, Jannik Strötgen and Dietrich Klakow. The paper can be found [here](https://arxiv.org/abs/2112.08754). In case of questions, please contact the authors as listed on the paper. Please cite the above paper when reporting, reproducing or extending the results. @misc{lange-etal-2021-clin-x, author = {Lukas Lange and Heike Adel and Jannik Str{\"{o}}tgen and Dietrich Klakow}, title = {CLIN-X: pre-trained language models and a study on cross-task transfer for concept extraction in the clinical domain}, year={2021}, eprint={2112.08754}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2112.08754} } ## Training details The model is based on the multilingual XLM-R transformer `(xlm-roberta-large)`, which was trained on 100 languages and showed superior performance in many different tasks across languages and can even outperform monolingual models in certain settings (Conneau et al. 2020). Even though XLM-R was pre-trained on 53GB of Spanish documents, this was only 2% of the overall training data. To steer this model towards the Spanish clinical domain, we sample documents from the Scielo archive (https://scielo.org/) and the MeSpEn resources (Villegas et al. 2018). The resulting corpus has a size of 790MB and is highly specific for the clinical domain. We initialize CLIN-X using the pre-trained XLM-R weights and train masked language modeling (MLM) on the Spanish clinical corpus for 3 epochs which roughly corresponds to 32k steps. This allows researchers and practitioners to address the Spanish clinical domain with an out-of-the-box tailored model. ## Results for Spanish concept extraction We apply CLIN-X-ES to five Spanish concept extraction tasks from the clinical domain in a standard sequence labeling architecture similar to Devlin et al. 2019 and compare to a Spanish BERT model called BETO. In addition, we perform experiments with an improved architecture `(+ OurArchitecture)` as described in the paper linked above. The code for our model architecture can be found [here](https://github.com/boschresearch/clin_x). \| \| Cantemist \| Meddocan \| Meddoprof (NER) \| Meddoprof (CLASS) \| Pharmaconer \| \|------------------------------------------\|-----------\|----------\|-----------------\|-------------------\|-------------\| \| BETO (Spanish BERT) \| 81.30 \| 96.81 \| 79.19 \| 74.59 \| 87.70 \| \| CLIN-X (ES) \| 83.22 \| 97.08 \| 79.54 \| 76.95 \| 90.05 \| \| CLIN-X (ES) + OurArchitecture \| 88.24 \| 98.00 \| 81.68 \| 80.54 \| 92.27 \| ### Results for English concept extraction As the CLIN-X-ES model is based on XLM-R, the model is still multilingual and we demonstrate the positive impact of cross-language domain adaptation by applying this model to five different English sequence labeling tasks from i2b2. We found that further transfer from related concept extraction is particularly helpful in this cross-language setting. For a detailed description of the transfer process and all other models, we refer to our paper. \| \| i2b2 2006 \| i2b2 2010 \| i2b2 2012 (Concept) \| i2b2 2012 (Time) \| i2b2 2014 \| \|------------------------------------------\|-----------\|-----------\|---------------\|---------------\|-----------\| \| BERT \| 94.80 \| 85.25 \| 76.51 \| 75.28 \| 94.86 \| \| ClinicalBERT \| 94.8 \| 87.8 \| 78.9 \| 76.6 \| 93.0 \| \| CLIN-X (ES) \| 95.49 \| 87.94 \| 79.58 \| 77.57 \| 96.80 \| \| CLIN-X (ES) + OurArchitecture \| 98.30 \| 89.10 \| 80.42 \| 78.48 \| 97.62 \| \| CLIN-X (ES) + OurArchitecture + Transfer \| 89.50 \| 89.74 \| 80.93 \| 79.60 \| 97.46 \| ## Purpose of the project This software is a research prototype, solely developed for and published as part of the publication cited above. It will neither be maintained nor monitored in any way. ## License The CLIN-X models are open-sourced under the CC-BY 4.0 license. See the [LICENSE](LICENSE) file for details.
digio/Twitter4SSE	digio	2021-12-17T09:01:29Z	17	7	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "Pytorch", "Sentence Transformers", "Transformers", "sentence-similarity", "en", "arxiv:2110.02030", "license:apache-2.0", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:05Z	--- language: - en pipeline_tag: sentence-similarity tags: - Pytorch - Sentence Transformers - Transformers license: "apache-2.0" --- # Twitter4SSE This model maps texts to 768 dimensional dense embeddings that encode semantic similarity. It was trained with Multiple Negatives Ranking Loss (MNRL) on a Twitter dataset. It was initialized from [BERTweet](https://huggingface.co/vinai/bertweet-base) and trained with [Sentence-transformers](https://www.sbert.net/). ## Usage The model is easier to use with sentence-trainsformers library ``` pip install -U sentence-transformers ``` ``` from sentence_transformers import SentenceTransformer sentences = ["This is the first tweet", "This is the second tweet"] model = SentenceTransformer('digio/Twitter4SSE') embeddings = model.encode(sentences) print(embeddings) ``` Without sentence-transfomer library, please refer to [this repository](https://huggingface.co/sentence-transformers) for detailed instructions on how to use Sentence Transformers on Huggingface. ## Citing & Authors The official paper [Exploiting Twitter as Source of Large Corpora of Weakly Similar Pairs for Semantic Sentence Embeddings](https://arxiv.org/abs/2110.02030) will be presented at EMNLP 2021. Further details will be available soon. ``` @inproceedings{di-giovanni-brambilla-2021-exploiting, title = "Exploiting {T}witter as Source of Large Corpora of Weakly Similar Pairs for Semantic Sentence Embeddings", author = "Di Giovanni, Marco and Brambilla, Marco", 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.780", pages = "9902--9910", } ``` The official code is available on [GitHub](https://github.com/marco-digio/Twitter4SSE)
jamescalam/bert-stsb-gold	jamescalam	2021-12-17T08:57:06Z	2	0	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:05Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # Gold-only BERT STSb This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. It is used as a demo model within the [NLP for Semantic Search course](https://www.pinecone.io/learn/nlp), for the chapter on [In-domain Data Augmentation with BERT](https://www.pinecone.io/learn/data-augmentation/). ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('bert-stsb-gold') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('bert-stsb-gold') model = AutoModel.from_pretrained('bert-stsb-gold') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 360 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 36, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ```
jamescalam/bert-stsb-cross-encoder	jamescalam	2021-12-17T08:54:27Z	1,081	1	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "text-classification", "sentence-similarity", "transformers", "cross-encoder", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:05Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - transformers - cross-encoder --- # Augmented SBERT STSb This is a [sentence-transformers](https://www.SBERT.net) cross encoder model. It is used as a demo model within the [NLP for Semantic Search course](https://www.pinecone.io/learn/nlp), for the chapter on [In-domain Data Augmentation with BERT](https://www.pinecone.io/learn/data-augmentation/).
jamescalam/bert-stsb-aug	jamescalam	2021-12-17T08:52:21Z	4	1	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:05Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # Augmented SBERT STSb This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. It is used as a demo model within the [NLP for Semantic Search course](https://www.pinecone.io/learn/nlp), for the chapter on [In-domain Data Augmentation with BERT](https://www.pinecone.io/learn/data-augmentation/). ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('bert-stsb-aug') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('bert-stsb-aug') model = AutoModel.from_pretrained('bert-stsb-aug') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 2059 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 308, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ```
huggingtweets/bladeefan91	huggingtweets	2021-12-17T07:39:20Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: http://www.huggingtweets.com/bladeefan91/1639726754777/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1470642032851009537/LWrcZk48_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">sweetie p1e</div> <div style="text-align: center; font-size: 14px;">@bladeefan91</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from sweetie p1e. \| Data \| sweetie p1e \| \| --- \| --- \| \| Tweets downloaded \| 2249 \| \| Retweets \| 351 \| \| Short tweets \| 547 \| \| Tweets kept \| 1351 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/cacbnxbr/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @bladeefan91's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2kupw7ab) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2kupw7ab/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/bladeefan91') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
nvidia/qdqbert-base-uncased	nvidia	2021-12-17T06:31:27Z	0	1	null	[ "region:us" ]	null	2022-03-02T23:29:05Z	<!--- Copyright 2021 NVIDIA Corporation. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> # QDQBERT base model (uncased) ## Model description [QDQBERT](https://huggingface.co/docs/transformers/model_doc/qdqbert) model inserts fake quantization operations (pair of QuantizeLinear/DequantizeLinear operators) to (i) linear layer inputs and weights, (ii) matmul inputs, (iii) residual add inputs, in BERT model. QDQBERT model can be loaded from any checkpoint of HuggingFace BERT model (for example bert-base-uncased), and perform Quantization Aware Training/Post Training Quantization. In this model card, qdqbert-base-uncased corresponds to the bert-base-uncased model with QuantizeLinear/DequantizeLinear ops (Q/DQ nodes). Similarly, one can also use the QDQBERT model for qdqbert-large-cased corresponding to bert-large-cased, etc. ## How to run QDQBERT using Transformers ### Prerequisites QDQBERT requires the dependency of [Pytorch Quantization Toolkit](https://github.com/NVIDIA/TensorRT/tree/main/tools/pytorch-quantization). To install Pytorch Quantization Toolkit, run ``` pip install pytorch-quantization --extra-index-url https://pypi.ngc.nvidia.com ``` ### Set default quantizers QDQBERT model inserts Q/DQ nodes to BERT by TensorQuantizer in Pytorch Quantization Toolkit. TensorQuantizer is the module for quantizing tensors, with QuantDescriptor defining how the tensor should be quantized. Refer to [Pytorch Quantization Toolkit userguide](https://docs.nvidia.com/deeplearning/tensorrt/pytorch-quantization-toolkit/docs/userguide.html) for more details. Before creating QDQBERT model, one has to set the default QuantDescriptor defining default tensor quantizers. Example: ```python import pytorch_quantization.nn as quant_nn from pytorch_quantization.tensor_quant import QuantDescriptor # The default tensor quantizer is set to use Max calibration method input_desc = QuantDescriptor(num_bits=8, calib_method="max") # The default tensor quantizer is set to be per-channel quantization for weights weight_desc = QuantDescriptor(num_bits=8, axis=((0,))) quant_nn.QuantLinear.set_default_quant_desc_input(input_desc) quant_nn.QuantLinear.set_default_quant_desc_weight(weight_desc) ``` ### Calibration Calibration is the terminology of passing data samples to the quantizer and deciding the best scaling factors for tensors. After setting up the tensor quantizers, one can use the following example to calibrate the model: ```python # Find the TensorQuantizer and enable calibration for name, module in model.named_modules(): if name.endswith('_input_quantizer'): module.enable_calib() module.disable_quant() # Use full precision data to calibrate # Feeding data samples model(x) # ... # Finalize calibration for name, module in model.named_modules(): if name.endswith('_input_quantizer'): module.load_calib_amax() module.enable_quant() # If running on GPU, it needs to call .cuda() again because new tensors will be created by calibration process model.cuda() # Keep running the quantized model # ... ``` ### Export to ONNX The goal of exporting to ONNX is to deploy inference by [TensorRT](https://developer.nvidia.com/tensorrt). Fake quantization will be broken into a pair of QuantizeLinear/DequantizeLinear ONNX ops. After setting the static member TensorQuantizer to use Pytorch’s own fake quantization functions, fake quantized model can be exported to ONNX, follow the instructions in [torch.onnx](https://pytorch.org/docs/stable/onnx.html). Example: ```python from pytorch_quantization.nn import TensorQuantizer TensorQuantizer.use_fb_fake_quant = True # Load the calibrated model ... # ONNX export torch.onnx.export(...) ``` ## Complete example A complete example of using QDQBERT model to perform Quatization Aware Training and Post Training Quantization for SQUAD task can be found at [transformers/examples/research_projects/quantization-qdqbert](https://github.com/huggingface/transformers/tree/master/examples/research_projects/quantization-qdqbert)
HenryAI/KerasBERTv1	HenryAI	2021-12-17T03:20:18Z	6	7	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:04Z	Thanks for checking this out! <br /> This video explains the ideas behind KerasBERT (still very much a work in progress) https://www.youtube.com/watch?v=J3P8WLAELqk
baffo32/t5-base-ptmap	baffo32	2021-12-16T23:38:12Z	16	0	transformers	[ "transformers", "pytorch", "tf", "jax", "rust", "t5", "text2text-generation", "summarization", "translation", "en", "fr", "ro", "de", "dataset:c4", "arxiv:1910.10683", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	translation	2022-03-02T23:29:05Z	--- language: - en - fr - ro - de datasets: - c4 tags: - summarization - translation license: apache-2.0 --- [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) Pretraining Dataset: [C4](https://huggingface.co/datasets/c4) Other Community Checkpoints: [here](https://huggingface.co/models?search=t5) Paper: [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf) Authors: Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu ## Abstract Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpus”, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code. ![model image](https://camo.githubusercontent.com/623b4dea0b653f2ad3f36c71ebfe749a677ac0a1/68747470733a2f2f6d69726f2e6d656469756d2e636f6d2f6d61782f343030362f312a44304a31674e51663876727255704b657944387750412e706e67)
airKlizz/mt5-small-wikinewssum-test	airKlizz	2021-12-16T16:18:08Z	8	0	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "summarization", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - summarization - generated_from_trainer metrics: - rouge model-index: - name: mt5-small-wikinewssum-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. --> # mt5-small-wikinewssum-test This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.9354 - Rouge1: 6.8433 - Rouge2: 2.5498 - Rougel: 5.6114 - Rougelsum: 6.353 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\| \| No log \| 1.0 \| 661 \| 3.2810 \| 6.4161 \| 2.403 \| 5.3674 \| 6.0329 \| \| No log \| 2.0 \| 1322 \| 3.1515 \| 6.9291 \| 2.6826 \| 5.6839 \| 6.4359 \| \| No log \| 3.0 \| 1983 \| 3.0565 \| 6.7939 \| 2.6113 \| 5.6133 \| 6.3126 \| \| No log \| 4.0 \| 2644 \| 2.9815 \| 6.0279 \| 2.1637 \| 4.9892 \| 5.5962 \| \| No log \| 5.0 \| 3305 \| 2.9645 \| 6.3926 \| 2.339 \| 5.2716 \| 5.9443 \| \| 3.9937 \| 6.0 \| 3966 \| 2.9476 \| 6.4739 \| 2.3615 \| 5.3473 \| 6.0089 \| \| 3.9937 \| 7.0 \| 4627 \| 2.9405 \| 6.615 \| 2.4309 \| 5.4493 \| 6.1445 \| \| 3.9937 \| 8.0 \| 5288 \| 2.9354 \| 6.8433 \| 2.5498 \| 5.6114 \| 6.353 \| ### Framework versions - Transformers 4.13.0 - Pytorch 1.10.1 - Datasets 1.16.1 - Tokenizers 0.10.3
lewtun/xlm-roberta-base-finetuned-marc-en-hslu	lewtun	2021-12-16T14:55:28Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "text-classification", "generated_from_trainer", "dataset:amazon_reviews_multi", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- license: mit tags: - generated_from_trainer datasets: - amazon_reviews_multi model-index: - name: xlm-roberta-base-finetuned-marc-en-hslu 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-marc-en-hslu This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.8826 - Mae: 0.5 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mae \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 1.1121 \| 1.0 \| 235 \| 0.9400 \| 0.5732 \| \| 0.9487 \| 2.0 \| 470 \| 0.8826 \| 0.5 \| ### Framework versions - Transformers 4.14.1 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
mateocolina/xlm-roberta-base-finetuned-marc-en	mateocolina	2021-12-16T14:39:14Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "text-classification", "generated_from_trainer", "dataset:amazon_reviews_multi", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- license: mit tags: - generated_from_trainer datasets: - amazon_reviews_multi model-index: - name: xlm-roberta-base-finetuned-marc-en 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-marc-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.9276 - Mae: 0.5366 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mae \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 1.0992 \| 1.0 \| 235 \| 0.9340 \| 0.5122 \| \| 0.945 \| 2.0 \| 470 \| 0.9276 \| 0.5366 \| ### Framework versions - Transformers 4.14.1 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
Giannipinelli/xlm-roberta-base-finetuned-marc-en	Giannipinelli	2021-12-16T14:34:58Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "text-classification", "generated_from_trainer", "dataset:amazon_reviews_multi", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:04Z	--- license: mit tags: - generated_from_trainer datasets: - amazon_reviews_multi model-index: - name: xlm-roberta-base-finetuned-marc-en 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-marc-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.9161 - Mae: 0.4634 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mae \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 1.1217 \| 1.0 \| 235 \| 0.9396 \| 0.4878 \| \| 0.9574 \| 2.0 \| 470 \| 0.9161 \| 0.4634 \| ### Framework versions - Transformers 4.14.1 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
TomO/xlm-roberta-base-finetuned-marc-en	TomO	2021-12-16T14:31:13Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "text-classification", "generated_from_trainer", "dataset:amazon_reviews_multi", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- license: mit tags: - generated_from_trainer datasets: - amazon_reviews_multi model-index: - name: xlm-roberta-base-finetuned-marc-en 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-marc-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.9237 - Mae: 0.5122 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mae \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 1.1089 \| 1.0 \| 235 \| 0.9380 \| 0.4878 \| \| 0.9546 \| 2.0 \| 470 \| 0.9237 \| 0.5122 \| ### Framework versions - Transformers 4.14.1 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
rafiulrumy/wav2vec2-large-xlsr-53-demo-colab	rafiulrumy	2021-12-16T05:09:16Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xlsr-53-demo-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xlsr-53-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 6.7860 - Wer: 1.1067 ## Model description More information needed ## Intended uses & 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: 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 - lr_scheduler_warmup_steps: 500 - num_epochs: 1000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:------:\| \| 8.2273 \| 44.42 \| 400 \| 3.3544 \| 1.0 \| \| 0.9228 \| 88.84 \| 800 \| 4.7054 \| 1.1601 \| \| 0.1423 \| 133.32 \| 1200 \| 5.9489 \| 1.1578 \| \| 0.0751 \| 177.74 \| 1600 \| 5.5939 \| 1.1717 \| \| 0.0554 \| 222.21 \| 2000 \| 6.1230 \| 1.1717 \| \| 0.0356 \| 266.63 \| 2400 \| 6.2845 \| 1.1613 \| \| 0.0288 \| 311.11 \| 2800 \| 6.6109 \| 1.2100 \| \| 0.0223 \| 355.53 \| 3200 \| 6.5605 \| 1.1299 \| \| 0.0197 \| 399.95 \| 3600 \| 7.1242 \| 1.1682 \| \| 0.0171 \| 444.42 \| 4000 \| 7.2452 \| 1.1578 \| \| 0.0149 \| 488.84 \| 4400 \| 7.4048 \| 1.0684 \| \| 0.0118 \| 533.32 \| 4800 \| 6.6227 \| 1.1172 \| \| 0.011 \| 577.74 \| 5200 \| 6.7909 \| 1.1566 \| \| 0.0095 \| 622.21 \| 5600 \| 6.8088 \| 1.1102 \| \| 0.0077 \| 666.63 \| 6000 \| 7.4451 \| 1.1311 \| \| 0.0062 \| 711.11 \| 6400 \| 6.8486 \| 1.0777 \| \| 0.0051 \| 755.53 \| 6800 \| 6.8812 \| 1.1241 \| \| 0.0051 \| 799.95 \| 7200 \| 6.9987 \| 1.1450 \| \| 0.0041 \| 844.42 \| 7600 \| 7.3048 \| 1.1323 \| \| 0.0044 \| 888.84 \| 8000 \| 6.6644 \| 1.1125 \| \| 0.0031 \| 933.32 \| 8400 \| 6.6298 \| 1.1148 \| \| 0.0027 \| 977.74 \| 8800 \| 6.7860 \| 1.1067 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.14.0 - Tokenizers 0.10.3
huggingtweets/ai_hexcrawl	huggingtweets	2021-12-15T19:46:29Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: http://www.huggingtweets.com/ai_hexcrawl/1639597537705/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1467327234365181953/gFho8YCv_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">AI Hexcrawl</div> <div style="text-align: center; font-size: 14px;">@ai_hexcrawl</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from AI Hexcrawl. \| Data \| AI Hexcrawl \| \| --- \| --- \| \| Tweets downloaded \| 1164 \| \| Retweets \| 42 \| \| Short tweets \| 2 \| \| Tweets kept \| 1120 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/vdxugbwr/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @ai_hexcrawl's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/r9ejkubu) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/r9ejkubu/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/ai_hexcrawl') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
shainahub/covid_qa_distillbert	shainahub	2021-12-15T19:10:48Z	20	1	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:covid_qa_deepset", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - covid_qa_deepset metrics: - squad_v2 # Example: wer. Use metric id from https://hf.co/metrics widget: - text: "What is COVID-19?" context: "Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The first known case was identified in Wuhan, China, in December 2019.[7] The disease has since spread worldwide, leading to an ongoing pandemic." - text: "Where was COVID-19 first discovered?" context: "The first known infections from SARS-CoV-2 were discovered in Wuhan, China. The original source of viral transmission to humans remains unclear, as does whether the virus became pathogenic before or after the spillover event." - text: "What is Post-COVID syndrome?" context: "Long COVID, also known as post-COVID-19 syndrome, post-acute sequelae of COVID-19 (PASC), or chronic COVID syndrome (CCS) is a condition characterized by long-term sequelae appearing or persisting after the typical convalescence period of COVID-19. Long COVID can affect nearly every organ system, with sequelae including respiratory system disorders, nervous system and neurocognitive disorders, mental health disorders, metabolic disorders, cardiovascular disorders, gastrointestinal disorders, malaise, fatigue, musculoskeletal pain, and anemia. A wide range of symptoms are commonly reported, including fatigue, headaches, shortness of breath, anosmia (loss of smell), parosmia (distorted smell), muscle weakness, low fever and cognitive dysfunction." --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the covid_qa_deepset dataset. It achieves the following results on the evaluation set: - Loss: 0.0976 ## Model description More information needed ## Intended uses & 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 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 0.2502 \| 1.0 \| 3880 \| 0.1824 \| \| 0.2007 \| 2.0 \| 7760 \| 0.1250 \| \| 0.1338 \| 3.0 \| 11640 \| 0.0976 \| ### Framework versions - Transformers 4.13.0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
Ayham/xlnet_gpt2_summarization_cnn_dailymail	Ayham	2021-12-15T18:08:27Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "encoder-decoder", "text2text-generation", "generated_from_trainer", "dataset:cnn_dailymail", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:04Z	--- tags: - generated_from_trainer datasets: - cnn_dailymail model-index: - name: xlnet_gpt2_summarization_cnn_dailymail 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. --> # xlnet_gpt2_summarization_cnn_dailymail This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
nguyenvulebinh/spelling-oov	nguyenvulebinh	2021-12-15T17:00:58Z	672	1	transformers	[ "transformers", "pytorch", "encoder-decoder", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	```python from transformers import EncoderDecoderModel from importlib.machinery import SourceFileLoader from transformers.file_utils import cached_path, hf_bucket_url import torch import os ## Load model & tokenizer cache_dir='./cache' model_name='nguyenvulebinh/spelling-oov' def download_tokenizer_files(): resources = ['envibert_tokenizer.py', 'dict.txt', 'sentencepiece.bpe.model'] for item in resources: if not os.path.exists(os.path.join(cache_dir, item)): tmp_file = hf_bucket_url(model_name, filename=item) tmp_file = cached_path(tmp_file,cache_dir=cache_dir) os.rename(tmp_file, os.path.join(cache_dir, item)) download_tokenizer_files() spell_tokenizer = SourceFileLoader("envibert.tokenizer",os.path.join(cache_dir,'envibert_tokenizer.py')).load_module().RobertaTokenizer(cache_dir) spell_model = EncoderDecoderModel.from_pretrained(model_name) def oov_spelling(word, num_candidate=1): result = [] inputs = spell_tokenizer([word.lower()]) input_ids = inputs['input_ids'] attention_mask = inputs['attention_mask'] inputs = { "input_ids": torch.tensor(input_ids), "attention_mask": torch.tensor(attention_mask) } outputs = spell_model.generate(**inputs, num_return_sequences=num_candidate) for output in outputs.cpu().detach().numpy().tolist(): result.append(spell_tokenizer.sp_model.DecodePieces(spell_tokenizer.decode(output, skip_special_tokens=True).split())) return result oov_spelling('spacespeaker') # output: ['x pây x pếch cơ'] ```
Jeska/VaccinChatSentenceClassifierDutch_fromBERTje2_DAdialogQonly09	Jeska	2021-12-15T16:50:47Z	16	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:04Z	--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: VaccinChatSentenceClassifierDutch_fromBERTje2_DAdialogQonly09 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. --> # VaccinChatSentenceClassifierDutch_fromBERTje2_DAdialogQonly09 This model is a fine-tuned version of [outputDAQonly09/](https://huggingface.co/outputDAQonly09/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4978 - Accuracy: 0.9031 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 330 \| 3.9692 \| 0.2249 \| \| 4.3672 \| 2.0 \| 660 \| 3.1312 \| 0.4031 \| \| 4.3672 \| 3.0 \| 990 \| 2.5068 \| 0.5658 \| \| 3.1495 \| 4.0 \| 1320 \| 2.0300 \| 0.6600 \| \| 2.2491 \| 5.0 \| 1650 \| 1.6517 \| 0.7450 \| \| 2.2491 \| 6.0 \| 1980 \| 1.3604 \| 0.7943 \| \| 1.622 \| 7.0 \| 2310 \| 1.1328 \| 0.8327 \| \| 1.1252 \| 8.0 \| 2640 \| 0.9484 \| 0.8611 \| \| 1.1252 \| 9.0 \| 2970 \| 0.8212 \| 0.8757 \| \| 0.7969 \| 10.0 \| 3300 \| 0.7243 \| 0.8830 \| \| 0.5348 \| 11.0 \| 3630 \| 0.6597 \| 0.8867 \| \| 0.5348 \| 12.0 \| 3960 \| 0.5983 \| 0.8857 \| \| 0.3744 \| 13.0 \| 4290 \| 0.5635 \| 0.8976 \| \| 0.2564 \| 14.0 \| 4620 \| 0.5437 \| 0.8985 \| \| 0.2564 \| 15.0 \| 4950 \| 0.5124 \| 0.9013 \| \| 0.1862 \| 16.0 \| 5280 \| 0.5074 \| 0.9022 \| \| 0.1349 \| 17.0 \| 5610 \| 0.5028 \| 0.9049 \| \| 0.1349 \| 18.0 \| 5940 \| 0.4876 \| 0.9077 \| \| 0.0979 \| 19.0 \| 6270 \| 0.4971 \| 0.9049 \| \| 0.0763 \| 20.0 \| 6600 \| 0.4941 \| 0.9022 \| \| 0.0763 \| 21.0 \| 6930 \| 0.4957 \| 0.9049 \| \| 0.0602 \| 22.0 \| 7260 \| 0.4989 \| 0.9049 \| \| 0.0504 \| 23.0 \| 7590 \| 0.4959 \| 0.9040 \| \| 0.0504 \| 24.0 \| 7920 \| 0.4944 \| 0.9031 \| \| 0.0422 \| 25.0 \| 8250 \| 0.4985 \| 0.9040 \| \| 0.0379 \| 26.0 \| 8580 \| 0.4970 \| 0.9049 \| \| 0.0379 \| 27.0 \| 8910 \| 0.4949 \| 0.9040 \| \| 0.0351 \| 28.0 \| 9240 \| 0.4971 \| 0.9040 \| \| 0.0321 \| 29.0 \| 9570 \| 0.4967 \| 0.9031 \| \| 0.0321 \| 30.0 \| 9900 \| 0.4978 \| 0.9031 \| ### Framework versions - Transformers 4.13.0.dev0 - Pytorch 1.10.0 - Datasets 1.16.1 - Tokenizers 0.10.3

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