license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
cc-by-sa-4.0 | [] | false | BERT large Japanese (character-level tokenization with whole word masking, jawiki-20200831) This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language. This version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (... | f75e3a241693e23541b063cc4c9e8ed4 |
cc-by-sa-4.0 | [] | false | Training Data The models are trained on the Japanese version of Wikipedia. The training corpus is generated from the Wikipedia Cirrussearch dump file as of August 31, 2020. The generated corpus files are 4.0GB in total, containing approximately 30M sentences. We used the [MeCab](https://taku910.github.io/mecab/) mor... | 7cc05c2cc1b7223d62c37bafa1189317 |
cc-by-sa-4.0 | [] | false | Tokenization The texts are first tokenized by MeCab with the Unidic 2.1.2 dictionary and then split into characters. The vocabulary size is 6144. We used [`fugashi`](https://github.com/polm/fugashi) and [`unidic-lite`](https://github.com/polm/unidic-lite) packages for the tokenization. | 1e31f4bd7113903212ff6f7184feee37 |
cc-by-sa-4.0 | [] | false | Training The models are trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps. For training of the MLM (masked language modeling) objective, we introduced whole word masking in which all of the subword tokens corresponding to a single word (t... | 684cffa7267bc6c65d5d9fe523aa82f8 |
mit | ['generated_from_trainer'] | false | bertimbau-base-finetuned-lener-br-finetuned-brazilian_court_decisions This model is a fine-tuned version of [Luciano/bertimbau-base-finetuned-lener-br](https://huggingface.co/Luciano/bertimbau-base-finetuned-lener-br) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.8017 - Acc... | 200438748d51beecc9a37067f3956cb1 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 405 | 0.7790 | 0.6535 | | 0.8276 | 2.0 | 810 | 0.6739 | 0.7277 | | 0.5818 | 3.0 | 1215 | 0.8767 | 0.... | 75ed59fccd4f2094726db8d13708b6c4 |
apache-2.0 | ['multiberts', 'multiberts-seed_1', 'multiberts-seed_1-step_0k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 1, Step 0k MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different ra... | 410cd115be1dc585ea0c04eb4c2a9290 |
apache-2.0 | ['multiberts', 'multiberts-seed_1', 'multiberts-seed_1-step_0k'] | false | How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_1-step_0k') model = TFBertModel.from_pretrained("google/multibert... | fcd9f9872356709da346ac74670aaba0 |
agpl-3.0 | [] | false | Model is developed in support of the University of Belgrade doctoral dissertation "Composite pseudogrammars based on parallel language models of Serbian" by Mihailo Škorić. It generates semantically masked (lemmatized and without stopwords) sentences for Serbian. This small gpt-2 model was fine-tuned on several corp... | de68e83c7a84eae62323858d2013c7e7 |
mit | ['generated_from_trainer'] | false | refinement-finetuned-mnli-2 This model is a fine-tuned version of [mfreihaut/refinement-finetuned-mnli-1](https://huggingface.co/mfreihaut/refinement-finetuned-mnli-1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0242 | d6b42cc10f1c09f039ab06811bb787a6 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 | 1334107224676350dc25e6e1b3a62b60 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | No log | 1.0 | 303 | 0.3730 | | 1.1146 | 2.0 | 606 | 0.9860 | | 1.1146 | 3.0 | 909 | 0.7304 | | 1.0018 | 4.0 | 1212 | 0.6386 ... | 46fef9ac7a627d92040bd090e1a10e1f |
apache-2.0 | [] | false | ByT5 - xl ByT5 is a tokenizer-free version of [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) and generally follows the architecture of [MT5](https://huggingface.co/google/mt5-xl). ByT5 was only pre-trained on [mC4](https://www.tensorflow.org/datasets/catalog/c4 | 6bb3c208077257dc478ac94a9537ec39 |
apache-2.0 | [] | false | c4multilingual) excluding any supervised training with an average span-mask of 20 UTF-8 characters. Therefore, this model has to be fine-tuned before it is useable on a downstream task. ByT5 works especially well on noisy text data,*e.g.*, `google/byt5-xl` significantly outperforms [mt5-xl](https://huggingface.co/goog... | 13cb52b7ab1fa23af1167cf8fc2a0cbd |
apache-2.0 | [] | false | Example Inference ByT5 works on raw UTF-8 bytes and can be used without a tokenizer: ```python from transformers import T5ForConditionalGeneration import torch model = T5ForConditionalGeneration.from_pretrained('google/byt5-xl') input_ids = torch.tensor([list("Life is like a box of chocolates.".encode("utf-8"))]) ... | 7eb678a6edeec542a474b004aba4bc79 |
apache-2.0 | [] | false | forward pass ``` For batched inference & training it is however recommended using a tokenizer class for padding: ```python from transformers import T5ForConditionalGeneration, AutoTokenizer model = T5ForConditionalGeneration.from_pretrained('google/byt5-xl') tokenizer = AutoTokenizer.from_pretrained('google/byt5-xl... | 1db4e76409c93a49b6cab896c99a0b00 |
apache-2.0 | [] | false | Abstract Most widely-used pre-trained language models operate on sequences of tokens corresponding to word or subword units. Encoding text as a sequence of tokens requires a tokenizer, which is typically created as an independent artifact from the model. Token-free models that instead operate directly on raw text (by... | 2fed5267857cced2babf5dbe870abfab |
apache-2.0 | ['translation'] | false | ukr-ces * source group: Ukrainian * target group: Czech * OPUS readme: [ukr-ces](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ukr-ces/README.md) * model: transformer-align * source language(s): ukr * target language(s): ces * model: transformer-align * pre-processing: normalization + Sent... | 32c88943f4cd40945e99409168dfd6d9 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: ukr-ces - source_languages: ukr - target_languages: ces - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ukr-ces/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['uk', 'cs'] - src_constituents: {'ukr'} - tgt_const... | 0637664f08246109bac43f4510825201 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.3665 | 7fc221221651d765bf7e2fd144fd75c0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 52 | 3.5584 | | No log | 2.0 | 104 | 3.3937 | | No log | 3.0 | 156 | 3.3665 | | 9c266a4339a566e60464b100c74f70ca |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-cased-distilled-squad-finetuned-squad This model is a fine-tuned version of [distilbert-base-cased-distilled-squad](https://huggingface.co/distilbert-base-cased-distilled-squad) on the squad_v2_yash dataset. It achieves the following results on the evaluation set: - Loss: 0.0088 | 5c2196085e20d2bfb50952a8be2b3297 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 198 | 0.5409 | | No log | 2.0 | 396 | 0.3048 | | 0.9541 | 3.0 | 594 | 0.1764 | | 0.9541 | 4.0 | 792 | 0.1117 ... | 74f204b664ac8dcfaf8759d5a43b5706 |
mit | [] | false | Model Description A CLIP ViT-B/32 xlm roberta base model trained with the LAION-5B (https://laion.ai/blog/laion-5b/) using OpenCLIP (https://github.com/mlfoundations/open_clip). Model training done by Romain Beaumont on the [stability.ai](https://stability.ai/) cluster. | 672a9983df318969356c73b1e881cc3b |
mit | [] | false | Training Procedure Training with batch size 90k for 13B sample of laion5B, see https://wandb.ai/rom1504/open-clip/reports/xlm-roberta-base-B-32--VmlldzoyOTQ5OTE2 Model is B/32 on visual side, xlm roberta base initialized with pretrained weights on text side. | 395f53c7d88512b476c0a2b60a86aad3 |
mit | [] | false | Results The model achieves * imagenet 1k 62.33% (vs 62.9% for baseline) * mscoco 63.4% (vs 60.8% for baseline) * flickr30k 86.2% (vs 85.4% for baseline) A preliminary multilingual evaluation was run: 43% on imagenet1k italian (vs 21% for english B/32), 37% for imagenet1k japanese (vs 1% for english B/32 and 50% for ... | 760ecff25db6765251d763e65c0c006c |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | all-MiniLM-L12-v1 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. | 7e48ec7b9157d3031b613c05e31d5c5b |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | 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 sente... | ac70d5ffd82331e40e19f7000710a36b |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/all-MiniLM-L12-v1) ------ | 0d93b8ef1723b58cf2e1a7af58b5f06e |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Background The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised contrastive learning objective. We used the pretrained [`microsoft/MiniLM-L12-H384-uncased`](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) model and fine-tuned in on a 1B sentence... | 01532c7ca08649024d28fca69d5302f0 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Pre-training We use the pretrained [`microsoft/MiniLM-L12-H384-uncased`](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased). Please refer to the model card for more detailed information about the pre-training procedure. | 0a1f3e0aabcb5720e2dd0379e087e245 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Hyper parameters We trained ou model on a TPU v3-8. We train the model during 540k steps using a batch size of 1024 (128 per TPU core). We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with a 2e-5 learning rate. The full training script is accessible in... | caf65792d37227e6e7e2c63a20f24a31 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2036 - Accuracy: 0.9255 - F1: 0.9257 | 409984837d3d4484904cad501d3db873 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.788 | 1.0 | 250 | 0.2847 | 0.9135 | 0.9117 | | 0.2345 | 2.0 | 500 | 0.2036 | 0.9255 | 0.9257 | | e771694010640574c8772871a05972dd |
mit | ['vision', 'image-classification'] | false | NAT (mini variant) NAT-Mini trained on ImageNet-1K at 224x224 resolution. It was introduced in the paper [Neighborhood Attention Transformer](https://arxiv.org/abs/2204.07143) by Hassani et al. and first released in [this repository](https://github.com/SHI-Labs/Neighborhood-Attention-Transformer). | 17d5b9fc0d006a8f476d0bd2fdd38c5b |
mit | ['vision', 'image-classification'] | false | Example Here is how to use this model to classify an image from the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import AutoImageProcessor, NatForImageClassification from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = ... | 0f1f2621bd3f7e96d786dcf0420666d3 |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-utility-9-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3728 - Accuracy: 0.3956 | c0abb3ddb02d084656485697b19a6e0d |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch', 'hf-asr-leaderboard'] | false | Wav2vec 2.0 With Open Brazilian Portuguese Datasets v2 This a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the following datasets: - [CETUC](http://www02.smt.ufrj.br/~igor.quintanilha/alcaim.tar.gz): contains approximately 145 hours of Brazilian Portuguese speech distributed among 5... | 9b9f917c458789753eadf93d9d0590ee |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch', 'hf-asr-leaderboard'] | false | Imports and dependencies ```python %%capture !pip install datasets !pip install jiwer !pip install torchaudio !pip install transformers !pip install soundfile ``` ```python import torchaudio from datasets import load_dataset, load_metric from transformers import ( Wav2Vec2ForCTC, Wav2Vec2Processor, ) impor... | f86e69da07bd269342887c39c6a747ae |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch', 'hf-asr-leaderboard'] | false | noqa: W605 wer = load_metric("wer") device = "cuda" ``` ```python model_name = 'lgris/wav2vec2-large-xlsr-open-brazilian-portuguese-v2' model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device) processor = Wav2Vec2Processor.from_pretrained(model_name) ``` ```python def map_to_pred(batch): features = proces... | 8c3ca2d7744cef6f1785112c3a33fab5 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch', 'hf-asr-leaderboard'] | false | Test against Common Voice (In-domain) ```python dataset = load_dataset("common_voice", "pt", split="test", data_dir="./cv-corpus-6.1-2020-12-11") resampler = torchaudio.transforms.Resample(orig_freq=48_000, new_freq=16_000) def map_to_array(batch): speech, _ = torchaudio.load(batch["path"]) batch["speech"]... | 849cc0c098e2249b152f4a3125bd3dc7 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch', 'hf-asr-leaderboard'] | false | Test against [TEDx](http://www.openslr.org/100/) (Out-of-domain) ```python !gdown --id 1HJEnvthaGYwcV_whHEywgH2daIN4bQna !tar -xf tedx.tar.gz ``` ```python dataset = load_dataset('csv', data_files={'test': 'test.csv'})['test'] def map_to_array(batch): speech, _ = torchaudio.load(batch["path"]) batch["spee... | 5a44e6d63e272f29de22489f9ebcd29c |
apache-2.0 | ['generated_from_trainer'] | false | Article_500v2_NER_Model_3Epochs_UNAUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article500v2_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.1886 - Precision: 0.6510 - Recall: 0.7377 - F1: 0.6917 - Accuracy: ... | cf8c1a753e638e7335bc6c0e2c5d8e1d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 62 | 0.2863 | 0.4448 | 0.5990 | 0.5105 | 0.8927 | | No log | 2.0 |... | 0c34d33dd4a12b68cbb181af4c6631f9 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2175 - Accuracy: 0.9215 - F1: 0.9216 | 1422bdfcbcb2e9db402e392b6ebc3320 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.7814 | 1.0 | 250 | 0.3105 | 0.907 | 0.9046 | | 0.2401 | 2.0 | 500 | 0.2175 | 0.9215 | 0.9216 | | ce59e4c993174b834acba54feae0aae2 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased_finetuned_SPEECH_TEXT_CH_2_DISPLAY This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0863 - Accuracy: 0.7368 - F1: 0.7114 | ebf217cbf089b0570dcaa4baf7ae4e03 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 1.0362 | 1.0 | 19 | 0.9281 | 0.5789 | 0.4964 | | 0.9725 | 2.0 | 38 | 0.8906 | 0.6316 | 0.5707 | | 0.8712 |... | 275db6f9ed021aa1d825d075de16de84 |
apache-2.0 | ['generated_from_trainer'] | false | summarise_v4 This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the multi_news dataset. It achieves the following results on the evaluation set: - Loss: 2.5264 - Rouge2 Precision: 0.1349 - Rouge2 Recall: 0.1187 - Rouge2 Fmeasure: 0.1227 | 7292a952d24b7ab61497934bed4a0e64 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:| | 2.9616 | 0.08 | 10 | 2.8008 | 0.0552 | 0.1944 | 0.0844 ... | f25ba6d3dbb0f5797f82f236e9e91257 |
apache-2.0 | ['generated_from_trainer'] | false | t5-base-finetuned-weaksup-1000 This model is a fine-tuned version of [cammy/t5-base-finetuned-weaksup-1000](https://huggingface.co/cammy/t5-base-finetuned-weaksup-1000) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6699 - Rouge1: 22.2079 - Rouge2: 9.54 - Rougel: 19.9593 - R... | 2084f9a64f2be31d81499cdba20fd146 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 1.6257 | 1.0 | 1000 | 1.6699 | 22.2079 | 9.54 | 19.9593 | 20.2524 | 18.17... | 72be861b21cc571acc61c89a03139dae |
cc-by-4.0 | ['question generation'] | false | Model Card of `lmqg/t5-small-subjqa-books-qg` This model is fine-tuned version of [lmqg/t5-small-squad](https://huggingface.co/lmqg/t5-small-squad) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: books) via [`lmqg`](https://github.com/asahi417/lm-ques... | e14ab06f0afe801a4cea48d890729804 |
cc-by-4.0 | ['question generation'] | false | Overview - **Language model:** [lmqg/t5-small-squad](https://huggingface.co/lmqg/t5-small-squad) - **Language:** en - **Training data:** [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (books) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asah... | 93f3ac0e5368a3bb987ac510351259a7 |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/t5-small-subjqa-books-qg") o... | 5fe4d2162c2c3b2f1f4badd7e95068d9 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/t5-small-subjqa-books-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.books.json) | | Score | Type | Dataset | |:---... | 6ea70dd4f274f6e09ce048900aa90daa |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_subjqa - dataset_name: books - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: ['qg'] - model: lmqg/t5-small-squad - max_length: 512 - max_length_output: 32 - epoch: 2 ... | 9715858665f4b3504b6f9c30925b7c24 |
apache-2.0 | ['t5-lm-adapt'] | false | lm-adapted-t511lm100k) includes the following improvements compared to the original [T5 model](https://huggingface.co/t5-11b): - GEGLU activation in feed-forward hidden layer, rather than ReLU - see [here](https://arxiv.org/abs/2002.05202). - Dropout was turned off in pre-training (quality win). Dropout should be re-... | f71d17497551844f6d16a233fd246c0c |
apache-2.0 | ['translation'] | false | nor-dan * source group: Norwegian * target group: Danish * OPUS readme: [nor-dan](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nor-dan/README.md) * model: transformer-align * source language(s): nno nob * target language(s): dan * model: transformer-align * pre-processing: normalization +... | 43198825d00bd73510e51f4c333424f7 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: nor-dan - source_languages: nor - target_languages: dan - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nor-dan/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['no', 'da'] - src_constituents: {'nob', 'nno'} - tg... | f8289d255094dcaac85ea483baa92c6c |
apache-2.0 | ['generated_from_trainer'] | false | 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_sch... | c6a06a795a097eec84cba0d09e5a927d |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers'] | false | 介绍 - GuoFeng3 欢迎使用GuoFeng3模型 - (TIP:这个版本的名字进行了微调),这是一个中国华丽古风风格模型，也可以说是一个古风游戏角色模型，具有2.5D的质感。第三代大幅度减少上手难度，增加了场景元素与男性古风人物，除此之外为了模型能更好的适应其它TAG，还增加了其它风格的元素。这一代对脸和手的崩坏有一定的修复，同时素材大小也提高到了最长边1024。 -- Welcome to the GuoFeng3 model - (TIP: the name of this version has been fine-tuned). This is a Chinese gorgeous antique style... | f58544acaf7e4e6afb72616020e7808e |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers'] | false | 安装教程 - install 1. 将GuoFeng3.ckpt模型放入SD目录 - Put GuoFeng3.ckpt model into SD directory 2. 此模型自带VAE，如果你的程序不支持，请记得选择任意一个VAE文件，否则图形将为灰色 - This model comes with VAE. If your program does not support it, please remember to select any VAE file, otherwise the graphics will be gray | 2788e596a39d535e5cbb04b8ce6ca0ce |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers'] | false | 如何使用 - How to use **TIP：经过一天的测试，发现很多人物可能出现红眼问题，可以尝试在负面词添加red eyes。如果色彩艳丽可以尝试降低CFG - After a day of testing, we found that many characters may have red-eye problems. We can try to add red eyes to negative words。Try to reduce CFG if the color is bright** 简单：第三代大幅度减少上手难度 - Simple: the third generation greatly reduces t... | b5b48f025e16ce9e91414c00eaaa6d02 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers'] | false | 例图 - Examples (可在文件列表中找到原图，并放入WebUi查看关键词等信息) - (You can find the original image in the file list, and put WebUi to view keywords and other information) <img src=https://huggingface.co/xiaolxl/GuoFeng3/resolve/main/examples/e1.png> <img src=https://huggingface.co/xiaolxl/GuoFeng3/resolve/main/examples/e2.png> <img ... | 65c347d37db10ddfb80a6200d651c396 |
mit | [] | false | model by mangooo This your the Stable Diffusion model fine-tuned the edd concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **sks boy smiles** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.... | 714fb789270a3a87508a6aac29b47f12 |
apache-2.0 | ['Text2Text Generation', 'T5', 'chinese', 'sentencepiece'] | false | 简介 Brief Introduction 在Randeng-T5-784M的基础上，收集了100个左右的中文数据集，进行Text2Text统一范式的有监督任务预训练。 On the basis of Randeng-T5-784M, about 100 Chinese datasets were collected and pre-trained for the supervised task of Text2Text unified paradigm. 本模型在中文zero-shot榜单ZeroClue上取得了第三名（不包括人类）的成绩，在所有基于T5（encoder-decoder架构）的模型中排名第一。 This ... | 37db1f31a4f462f9ee8a3fb8a0cacb8f |
apache-2.0 | ['Text2Text Generation', 'T5', 'chinese', 'sentencepiece'] | false | 模型分类 Model Taxonomy | 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra | | :----: | :----: | :----: | :----: | :----: | :----: | | 通用 General | 自然语言转换 NLT | 燃灯 Randeng | MultiTask | 784M | 多任务-中文 MultiTask-Chinese | | 5843ccd80f0a91c6f29aad6081080d89 |
apache-2.0 | ['Text2Text Generation', 'T5', 'chinese', 'sentencepiece'] | false | 模型信息 Model Information 参考论文：[Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](http://jmlr.org/papers/v21/20-074.html) 基于[Randeng-T5-784M](https://huggingface.co/IDEA-CCNL/Randeng-T5-784M)，我们在收集的100+个中文领域的多任务数据集（从中采样了30w+个样本）上微调了它，得到了此多任务版本。这些多任务包括：情感分析，新闻分类，文本分类，意图识别，自然语言推理，多项选择，指代消... | 2187cd8d774b1bf2e84fb9d245f4eb21 |
apache-2.0 | ['Text2Text Generation', 'T5', 'chinese', 'sentencepiece'] | false | load tokenizer and model pretrained_model = "IDEA-CCNL/Randeng-T5-784M-MultiTask-Chinese" special_tokens = ["<extra_id_{}>".format(i) for i in range(100)] tokenizer = T5Tokenizer.from_pretrained( pretrained_model, do_lower_case=True, max_length=512, truncation=True, additional_special_tokens=spec... | aad79807e9f64eef4c9b0ce21766d1a6 |
apache-2.0 | ['Text2Text Generation', 'T5', 'chinese', 'sentencepiece'] | false | tokenize text = "新闻分类任务：【微软披露拓扑量子计算机计划！】这篇文章的类别是什么？故事/文化/娱乐/体育/财经/房产/汽车/教育/科技" encode_dict = tokenizer(text, max_length=512, padding='max_length',truncation=True) inputs = { "input_ids": torch.tensor([encode_dict['input_ids']]).long(), "attention_mask": torch.tensor([encode_dict['attention_mask']]).long(), } | 57ec77202d3d76476492157ad354546d |
apache-2.0 | ['Text2Text Generation', 'T5', 'chinese', 'sentencepiece'] | false | model output: 科技 ``` 除了分类任务，其他任务的数据构造例子如下： In addition to classification tasks, data construction examples of other tasks are as follows: ```python example_dict={ "文本分类":{"text_a":"钢琴块3别踩白块儿3钢琴块3是一款简洁的钢琴模拟软件,在Android平台上,类似的软件还是比较多的。","choices":["相机","影视娱乐","棋牌中心","新闻","财经","策略","休闲益智","教育"]}, '新闻分类':{"text_... | 1e8444ef760b41a07ac8760d0b0d88ce |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2r_fr_vp-100k_gender_male-5_female-5_s722 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using t... | 8be70ec5e27717cc2c4ce7710ca6cb4c |
apache-2.0 | ['translation'] | false | opus-mt-en-tl * source languages: en * target languages: tl * OPUS readme: [en-tl](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-tl/README.md) * dataset: opus+bt * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus+bt-2020-02-26.zip](ht... | 356b9125a7ce4db4a553527551512353 |
creativeml-openrail-m | ['text-to-image'] | false | happy textures model (happytex) Dreambooth model trained by gurbofrogman with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/git... | 7c0437a93f71b0e6b01342f8cf5d1735 |
apache-2.0 | ['vision', 'depth-estimation', 'generated_from_trainer'] | false | glpn-nyu-finetuned-diode-221221-110911 This model is a fine-tuned version of [vinvino02/glpn-nyu](https://huggingface.co/vinvino02/glpn-nyu) on the diode-subset dataset. It achieves the following results on the evaluation set: - Loss: 0.4188 - Mae: 0.4087 - Rmse: 0.6260 - Abs Rel: 0.3672 - Log Mae: 0.1626 - Log Rmse:... | 48dc591c32f63d5dee364bd877352040 |
apache-2.0 | ['vision', 'depth-estimation', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mae | Rmse | Abs Rel | Log Mae | Log Rmse | Delta1 | Delta2 | Delta3 | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:-------:|:--------:|:------:|:------:|:------:| | 0.5004 | 1.0 | 72 | 0.4287 ... | a861e20e68cd22b832aa1f09018404af |
apache-2.0 | ['embeddings', 'Speaker', 'Verification', 'Identification', 'pytorch', 'xvectors', 'TDNN', 'speechbrain', 'audio-classification'] | false | Speaker Verification with xvector embeddings on Voxceleb This repository provides all the necessary tools to extract speaker embeddings with a pretrained TDNN model using SpeechBrain. The system is trained on Voxceleb 1+ Voxceleb2 training data. For a better experience, we encourage you to learn more about [Speech... | 2cdddd9f177020f2b8e0336984944afd |
apache-2.0 | ['embeddings', 'Speaker', 'Verification', 'Identification', 'pytorch', 'xvectors', 'TDNN', 'speechbrain', 'audio-classification'] | false | Compute your speaker embeddings ```python import torchaudio from speechbrain.pretrained import EncoderClassifier classifier = EncoderClassifier.from_hparams(source="speechbrain/spkrec-xvect-voxceleb", savedir="pretrained_models/spkrec-xvect-voxceleb") signal, fs =torchaudio.load('tests/samples/ASR/spk1_snt1.wav') emb... | 7ea91be113982ceadae9e0c3d8aaffab |
apache-2.0 | ['embeddings', 'Speaker', 'Verification', 'Identification', 'pytorch', 'xvectors', 'TDNN', 'speechbrain', 'audio-classification'] | false | Training The model was trained with SpeechBrain (aa018540). To train it from scratch follows these steps: 1. Clone SpeechBrain: ```bash git clone https://github.com/speechbrain/speechbrain/ ``` 2. Install it: ``` cd speechbrain pip install -r requirements.txt pip install -e . ``` 3. Run Training: ``` cd recipes/VoxC... | b0de7d45a55051fd5804c14ad9f69580 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2676 - F1: 0.8449 | 520ff57765210f057c3badcc3c9c79cf |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5915 | 1.0 | 191 | 0.3285 | 0.7814 | | 0.2651 | 2.0 | 382 | 0.2707 | 0.8314 | | 0.174 | 3.0 | 573 | 0.2676 | 0.8449 | ... | 4a04c809cd590ea8ef6654493bec6b35 |
apache-2.0 | ['automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event'] | false | نموذج **صوت سيناء** للتعرف على الأصوات العربية الفصحى و تحويلها إلى نصوص 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_8_0 - AR dataset. It achieves the following results on the evaluation set: - Loss: 0... | ac9a2651054bf844cedbf6a3ba6b735e |
apache-2.0 | ['automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event'] | false | Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` ```bash python eval.py --model_id bakrianoo/sinai-voice-ar-stt --dataset mozilla-foundation/common_voice_8_0 --config ar --split test ``` | 37cba2ebf73f4b76119e8b89d2029242 |
apache-2.0 | ['automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event'] | false | Inference Without LM ```python from transformers import (Wav2Vec2Processor, Wav2Vec2ForCTC) import torchaudio import torch def speech_file_to_array_fn(voice_path, resampling_to=16000): speech_array, sampling_rate = torchaudio.load(voice_path) resampler = torchaudio.transforms.Resample(sampling_rate, resampli... | 626c2bd1140610e23d0570d61ba6c421 |
apache-2.0 | ['automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event'] | false | recognize the text in a sample sound file sound_path = './my_voice.mp3' sample, sr = speech_file_to_array_fn(sound_path) inputs = processor([sample], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values,).logits predicted_ids = torch.argmax(logits, di... | d713b1abddba03cafce772d24dab2c76 |
apache-2.0 | ['automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 32 - eval_batch_size: 10 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 256 - total_eval_batch_size: 80 - optimizer: Adam with betas=(0.9,0.999) and epsilo... | 4cf1deec0c075f531a1c9c8e8396a4f1 |
apache-2.0 | ['automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 1.354 | 0.64 | 1000 | 0.4109 | 0.4493 | | 0.5886 | 1.28 | 2000 | 0.2798 | 0.3099 | | 0.4977 | 1.92 | 3000 | 0.2387 | 0.267... | 8d39f424d7221781d9f36622b9c0bc15 |
apache-2.0 | ['generated_from_keras_callback'] | false | nandysoham/13-clustered This model is a fine-tuned version of [Rocketknight1/distilbert-base-uncased-finetuned-squad](https://huggingface.co/Rocketknight1/distilbert-base-uncased-finetuned-squad) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.9677 - Train End Logits Ac... | b553e0bc375cc10174edfd76f4d2614f |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 412, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_... | 8e13cee6d7d671c8112a8bf8b1e7916d |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | 5666b1f6182a6bb34bf78d4e3e2fc5b1 |
mit | [] | false | Kawaii Colors on Stable Diffusion This is the `<kawaii-colors-style>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. ... | b22fd1c89c46a9f1e0d82419221f61b1 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-2'] | false | MultiBERTs Seed 2 Checkpoint 900k (uncased) Seed 2 intermediate checkpoint 900k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in [this repository](https://github.com/goo... | 0ab94124e65eabfcae1046f0c77c19c0 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-2'] | false | How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('multiberts-seed-2-900k') model = BertModel.from_pretrained("multiberts-seed-2-900k") text = "Replace me by any text you'd like.... | ccceeec097fa65f7bfff99cb6054ce95 |
cc0-1.0 | ['bert', 'bluebert'] | false | Training data We provide [preprocessed PubMed texts](https://ftp.ncbi.nlm.nih.gov/pub/lu/Suppl/NCBI-BERT/pubmed_uncased_sentence_nltk.txt.tar.gz) that were used to pre-train the BlueBERT models. The corpus contains ~4000M words extracted from the [PubMed ASCII code version](https://www.ncbi.nlm.nih.gov/research/bion... | ec9790e2b525f59eaf2f2e5f0e060325 |
cc0-1.0 | ['bert', 'bluebert'] | false | Training procedure * lowercasing the text * removing speical chars `\x00`-`\x7F` * tokenizing the text using the [NLTK Treebank tokenizer](https://www.nltk.org/_modules/nltk/tokenize/treebank.html) Below is a code snippet for more details. ```python value = value.lower() value = re.sub(r'[\r\n]+', ' ', value) va... | 671292a46169fe7198647b53c7459255 |
cc0-1.0 | ['bert', 'bluebert'] | false | BibTeX entry and citation info ```bibtex @InProceedings{peng2019transfer, author = {Yifan Peng and Shankai Yan and Zhiyong Lu}, title = {Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets}, booktitle = {Proceedings of the 2019 Workshop... | 57a9afb62082488b8b638b5b4964ab5c |
cc0-1.0 | ['bert', 'bluebert'] | false | Acknowledgments This work was supported by the Intramural Research Programs of the National Institutes of Health, National Library of Medicine and Clinical Center. This work was supported by the National Library of Medicine of the National Institutes of Health under award number 4R00LM013001-01. We are also grateful... | d0b580a346fdb44c82615ddf51ca9911 |
cc0-1.0 | ['bert', 'bluebert'] | false | Disclaimer This tool shows the results of research conducted in the Computational Biology Branch, NCBI. The information produced on this website is not intended for direct diagnostic use or medical decision-making without review and oversight by a clinical professional. Individuals should not change their health beha... | 36bd9c4957c96e52c3ee6c0b57a5ffbe |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 1 - mixed_precision_tra... | bf9f4afd35305d65430af67972cfe2c8 |
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