license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
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mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1643 - F1: 0.8626 | ca121a4eae0310beb8663a1792d91ca8 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2891 | 1.0 | 715 | 0.1780 | 0.8288 | | 0.1472 | 2.0 | 1430 | 0.1633 | 0.8488 | | 0.0948 | 3.0 | 2145 | 0.1643 | 0.8626 | ... | cc2c9239b9ab17505a9ed9ba50946de5 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 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. | ef3016d43cc7238130c1dbfe4535ab86 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | 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 sen... | 966944db9a40059695316a4074662c40 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2') model = AutoModel.from_pretrained('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2') | 1bd1b8eb39380a28f48aa2dfe2c756f5 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | 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/paraphrase-multilingual-MiniLM-L12-v2) | f05ae462f3a8b4ac7fa630803a829da1 |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-race This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3863 - Accuracy: 0.2982 | fac78fa95aa08905862326fe1e2365ea |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_schedu... | 690e6fa2059bfa00c60a81117813d6c7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.3936 | 0.25 | 3100 | 1.3863 | 0.2418 | | 1.3768 | 0.51 | 6200 | 1.3863 | 0.2483 | | 1.3954 | 0.76 | 9300 | 1.3863 | 0.... | c22c304f253495929750c619246fef62 |
cc-by-sa-4.0 | ['roberta', 'tagalog', 'filipino'] | false | RoBERTa Tagalog Large Tagalog RoBERTa trained as an improvement over our previous Tagalog pretrained Transformers. Trained with TLUnified, a newer, larger, more topically-varied pretraining corpus for Filipino. This model is part of a larger research project. We open-source the model to allow greater usage within the ... | 3afdf6efb4af7ede4488571660fd9b97 |
cc-by-sa-4.0 | ['roberta', 'tagalog', 'filipino'] | false | Citations All model details and training setups can be found in our papers. If you use our model or find it useful in your projects, please cite our work: ``` @article{cruz2021improving, title={Improving Large-scale Language Models and Resources for Filipino}, author={Jan Christian Blaise Cruz and Charibeth Cheng... | 99b50a6f52e75f2f3544d78c8c6a8d75 |
apache-2.0 | [] | 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 | | :----: | :----: | :----: | :----: | :----: | :----: | | 特殊 Special | 领域 Domain | 余元 Yuyuan | GPT2 | 3.5B | - | | 8971834b5e10ac527612d7f15d1aac75 |
apache-2.0 | [] | false | 模型信息 Model Information 我们采用与Wenzhong-GPT2-3.5B相同的架构，在50GB的医学(PubMed)语料库上进行预训练。我们使用了32个NVIDIA A100显卡大约7天。我们的Yuyuan-GPT2-3.5B是医疗领域最大的开源的GPT2模型。进一步地，模型可以通过计算困惑度（PPL）来判断事实。为了完成问答功能，我们将短语模式从疑问的形式转换为了陈述句。 We adopt the same architecture as Wenzhong-GPT2-3.5B to be pre-trained on 50 GB medical (PubMed) corpus. We use 32 NVI... | 97763a1ff1303ca0acd2e0ee24b78bed |
apache-2.0 | [] | false | 加载模型 Loading Models ```python from transformers import GPT2Tokenizer, GPT2Model tokenizer = GPT2Tokenizer.from_pretrained('IDEA-CCNL/Yuyuan-GPT2-3.5B') model = GPT2Model.from_pretrained('IDEA-CCNL/Yuyuan-GPT2-3.5B') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') outp... | 3b5e32557f8079f3afc41b501ecb7cf6 |
apache-2.0 | [] | false | 使用示例 Usage Examples ```python from transformers import pipeline, set_seed set_seed(55) generator = pipeline('text-generation', model='IDEA-CCNL/Yuyuan-GPT2-3.5B') generator("Diabetics should not eat", max_length=30, num_return_sequences=1) ``` | d56d8aaa85590c228b131664bfe66eaa |
apache-2.0 | ['generated_from_trainer'] | false | tiny-vanilla-target-glue-mnli This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8100 - Accuracy: 0.6375 | 78f55b87c1e299d008242506a8267cb4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.0866 | 0.04 | 500 | 1.0515 | 0.4557 | | 1.0101 | 0.08 | 1000 | 0.9526 | 0.5612 | | 0.9599 | 0.12 | 1500 | 0.9195 ... | 8b3746444cda50d4da61734b334e107c |
['apache-2.0'] | [] | false | Romanian paraphrase  Fine-tune t5-base-paraphrase-ro model for paraphrase. Since there is no Romanian dataset for paraphrasing, I had to create my own [dataset](https://huggingface.co/datasets/BlackKakapo/paraphrase-ro-v2). The dataset contains ~30k exa... | e85a4c9e742dbc5e2fd9adf61a574b49 |
['apache-2.0'] | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("BlackKakapo/t5-base-paraphrase-ro-v2") model = AutoModelForSeq2SeqLM.from_pretrained("BlackKakapo/t5-base-paraphrase-ro-v2") ``` | 2c8f00116d513bf35bb19c28ce74ab60 |
['apache-2.0'] | [] | false | Or ```python from transformers import T5ForConditionalGeneration, T5TokenizerFast model = T5ForConditionalGeneration.from_pretrained("BlackKakapo/t5-base-paraphrase-ro-v2") tokenizer = T5TokenizerFast.from_pretrained("BlackKakapo/t5-base-paraphrase-ro-v2") ``` | 49c9ec7e92143d48abfc1ecb6f220c03 |
['apache-2.0'] | [] | false | Generate ```python text = "Într-un interviu pentru Radio Europa Liberă România, acesta a menționat că Bucureștiul este pregătit oricând și ar dura doar o oră de la solicitare, până când gazele ar ajunge la Chișinău." encoding = tokenizer.encode_plus(text, pad_to_max_length=True, return_tensors="pt") input_ids, atten... | cfbc3a92449b22d92611777b4442e7ff |
['apache-2.0'] | [] | false | Output ```out ['Într-un interviu cu Radio Europa Liberă România, el a spus că Bucureștiul este pregătit în orice moment și ar dura doar o oră de la cererea până când gazele ar ajunge la Chișinău.'] ``` | 6d899333d21ba25f464d411291b27534 |
apache-2.0 | ['generated_from_trainer'] | false | wa2vec2-large-xls-r-colab_turkish 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: 0.3941 - Wer: 0.3812 | 01b29ce4ebb57472062731e62aad5b96 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.0265 | 3.67 | 400 | 0.7368 | 0.8192 | | 0.4253 | 7.34 | 800 | 0.4467 | 0.5111 | | 0.1902 | 11.01 | 1200 | 0.4423 | 0.4723 | |... | 9d26ffda28da174b4c06ef192c18dc04 |
mit | ['generated_from_trainer'] | false | bart-large-cnn-finetuned-weaksup-1000-pad-early-new1 This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4948 - Rouge1: 28.1465 - Rouge2: 13.4076 - Rougel: 22.2763 - Ro... | 8a18cdda28269d04bbd78d78d2899327 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP | 92f28aa29115f0e06d9c9b6c21b0ba68 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 0.156 | 1.0 | 1000 | 0.4377 | 27.8782 | 13.1274 | 21.2329 | 24.6465 | 66... | 61004b59886756ed281904c60ae97523 |
cc-by-sa-4.0 | ['korean', 'token-classification', 'pos', 'dependency-parsing'] | false | Model Description This is a RoBERTa model pre-trained on Korean texts for POS-tagging and dependency-parsing (using `goeswith` for subwords), derived from [roberta-large-korean-hanja](https://huggingface.co/KoichiYasuoka/roberta-large-korean-hanja). | d62d152583ab85af827f850d1b5369c1 |
cc-by-sa-4.0 | ['korean', 'token-classification', 'pos', 'dependency-parsing'] | false | text = "+text+"\n" v=[(s,e) for s,e in w["offset_mapping"] if s<e] for i,(s,e) in enumerate(v,1): q=self.model.config.id2label[p[i,h[i]]].split("|") u+="\t".join([str(i),text[s:e],"_",q[0],"_","|".join(q[1:-1]),str(h[i]),q[-1],"_","_" if i<len(v) and e<v[i][0] else "SpaceAfter=No"])+"\n" return... | b315f6083c791a3c83af38dac7bb7a97 |
apache-2.0 | ['automatic-speech-recognition', 'common_voice', 'generated_from_trainer'] | false | 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 - SV-SE dataset. It achieves the following results on the evaluation set: - Loss: 0.5528 - Wer: 0.3811 | 8e037042fbed6b946c4318b2378c97f4 |
apache-2.0 | ['automatic-speech-recognition', 'common_voice', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 0.74 | 100 | 3.4444 | 1.0 | | No log | 1.47 | 200 | 2.9421 | 1.0 | | No log | 2.21 | 300 | 2.2802 | 1.0137 | |... | dc3a5da503da8c606395f04c07df5aa9 |
apache-2.0 | ['generated_from_trainer'] | false | 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: 0.4548 - Wer: 0.3373 | 1adccbe9e34e9e917c7f949e857c2b8a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.3291 | 4.0 | 500 | 1.0403 | 0.7174 | | 0.5336 | 8.0 | 1000 | 0.4744 | 0.4489 | | 0.2155 | 12.0 | 1500 | 0.4476 | 0.3832 | |... | f100d3f8914d2f85a5111bed54151867 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1408 - F1: 0.8646 | 40245690e0cbfa5a161e61f96b647630 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2626 | 1.0 | 525 | 0.1807 | 0.8067 | | 0.1307 | 2.0 | 1050 | 0.1388 | 0.8526 | | 0.0829 | 3.0 | 1575 | 0.1408 | 0.8646 | ... | d5beef31cef8186795bf5ab28d5229ce |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-4'] | false | MultiBERTs Seed 4 Checkpoint 1900k (uncased) Seed 4 intermediate checkpoint 1900k 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/g... | 9fd08a4e73c6f5426da296b6c5b7ce82 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-4'] | 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-4-1900k') model = BertModel.from_pretrained("multiberts-seed-4-1900k") text = "Replace me by any text you'd lik... | e0364203057a9099ec548f35fc639a5b |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-demo-sagemaker 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: 0.4713 - Wer: 0.3381 | 25514404c0e03096558426ad66b2dd9d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.4274 | 4.0 | 500 | 1.2279 | 0.8902 | | 0.5778 | 8.0 | 1000 | 0.4838 | 0.4488 | | 0.2244 | 12.0 | 1500 | 0.4813 | 0.3793 | |... | 12ec15af0cff6aae2b54f1df03fa9920 |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-ner-ontonotes This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the ontonotes5 dataset. It achieves the following results on the evaluation set: - Loss: 0.1503 - Precision: 0.8567 - Recall: 0.8842 - F1: 0.8702 - Accuracy: 0.9755 | e1fabecba296eb531392cb450780423d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0842 | 1.0 | 7491 | 0.0950 | 0.8524 | 0.8715 | 0.8618 | 0.9745 | | 0.0523 | 2.0 ... | 7aa6ccaae1ea4b7dd231c439c682bd1c |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | mpid-bkdbj Dreambooth model trained by tftgregrge with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable... | 2342c31b20b95174e1dbd280cb565e74 |
mit | [] | false | ConvBERT base pre-trained on large_spanish_corpus The ConvBERT architecture is presented in the ["ConvBERT: Improving BERT with Span-based Dynamic Convolution"](https://arxiv.org/abs/2008.02496) paper. | f4c6a5533b7af5fa715a77eab4f08058 |
mit | [] | false | Metrics on evaluation set ``` disc_accuracy = 0.9488542 disc_auc = 0.8833056 disc_loss = 0.15933733 disc_precision = 0.79224133 disc_recall = 0.27443287 global_step = 1000000 loss = 9.658503 masked_lm_accuracy = 0.6177698 masked_lm_loss = 1.7050561 sampled_masked_lm_accuracy = 0.5379228 ``` | 5ffb2311ebdb23cd389d86c4865fa432 |
mit | [] | false | Usage ```python from transformers import AutoModel, AutoTokenizer model_name = "mrm8488/convbert-base-spanish" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModel.from_pretrained(model_name) ``` > Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) with the support of [Narrativa](htt... | 739e088b52d161be13a1fbd3273e9274 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small Marathi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 mr dataset. It achieves the following results on the evaluation set: - Loss: 0.4888 - Wer: 19.71 | b32a8dc5840a16cb9909070530b933cc |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1518 - F1: 0.8616 | 2db41add4098f7373fa42d4381a6c30a |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epoc... | d4fb93e7a3a5ff135d25fe936af8c1b9 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 786 | 0.1926 | 0.8138 | | No log | 2.0 | 1572 | 0.1580 | 0.8493 | | No log | 3.0 | 2358 | 0.1518 | 0.8616 | ... | eb5254f51bf336fd80c401e20c10962e |
mit | ['roberta-base', 'roberta-base-epoch_5'] | false | RoBERTa, Intermediate Checkpoint - Epoch 5 This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692), trained on Wikipedia and the Book Corpus only. We train this model for almost 100K steps, corresponding to 83 epochs. We provide the 84 checkpoints (including the randomly i... | c51eac59307e443d901b1fb5de48ab8e |
odbl | [] | false | This is a diffusion model fine-tuned with [QRsst2](https://huggingface.co/datasets/gojiteji/QRsst2). This model generates a QR code from text. Please clone this repository and replace [LambdaLabsML's example's inference code ](https://github.com/LambdaLabsML/examples/blob/767e1101b0125202871812ec7e1b5c46aa9c8d95/stab... | 958db1271cff04d230a09a41de69a97f |
apache-2.0 | ['translation'] | false | opus-mt-de-lt * source languages: de * target languages: lt * OPUS readme: [de-lt](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/de-lt/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://... | 6e4c2b37c316b685134e11813b7a1709 |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2r_es_xls-r_age_teens-8_sixties-2_s287 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure th... | 69e33cd0dd72e1d7f1a14fbc80defb13 |
apache-2.0 | ['generated_from_trainer'] | false | electra-base-discriminator-finetuned-cola This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.6367 - Matthews Correlation: 0.6824 | a0bb728409bf941e9d810fc9a77eaedd |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.4139 | 1.0 | 535 | 0.4137 | 0.6381 | | 0.2452 | 2.0 | 1070 | 0.4887 | 0.6504 | | 0.1... | 97931c6682e8818fc41d37f03e3e9025 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | `pyf98/chime4_e_branchformer_e10` This model was trained by Yifan Peng using chime4 recipe in [espnet](https://github.com/espnet/espnet/). References: - [E-Branchformer: Branchformer with Enhanced merging for speech recognition (SLT 2022)](https://arxiv.org/abs/2210.00077) - [Branchformer: Parallel MLP-Attention Arc... | 691b358baa026d12b3c4088b2b126a39 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Demo: How to use in ESPnet2 Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html) if you haven't done that already. ```bash cd espnet git checkout ad91279f0108d54bd22abe29671b376f048822c5 pip install -e . cd egs2/chime4/asr1 ./run.sh --skip_data_prep false --skip_train true... | 8a34fad224f62b2134231627cfa9d19f |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Wed Dec 28 15:49:24 EST 2022` - python version: `3.9.15 (main, Nov 24 2022, 14:31:59) [GCC 11.2.0]` - espnet version: `espnet 202211` - pytorch version: `pytorch 1.12.1` - Git hash: `f9a8009aef6ff9ba192a78c19b619ae4a9f3b9d2` - Commit date: `Wed Dec 28 00:30:54 2022 -0500` | 2a3b163d7e5109cfdcceebe14f642ab1 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_lm_lm_train_lm_transformer_en_char_valid.loss.ave_asr_model_valid.acc.ave/dt05_real_beamformit_5mics|1640|27119|93.7|5.0|1.2|0.6|6.8|52.5| |decode_asr_lm_lm_train_lm_transformer_en_char_valid.loss.ave_asr_model_valid.ac... | 9fb5b893ff27c36a645a819a9a750996 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_lm_lm_train_lm_transformer_en_char_valid.loss.ave_asr_model_valid.acc.ave/dt05_real_beamformit_5mics|1640|160390|97.4|1.3|1.3|0.7|3.3|52.5| |decode_asr_lm_lm_train_lm_transformer_en_char_valid.loss.ave_asr_model_valid.a... | 872759444f2f60688118d8d214001718 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_e_branchformer_e10_mlp1024_linear1024_macaron_lr1e-3_warmup25k.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_e_branchformer_e10_mlp1024_linear1024_macaron_lr1e-3_warmup25k_r... | 8eb0c748f3665b6bf56d6bc318b2a8e4 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-training-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2215 - Matthews Correlation: 0.8777 | 6cadaa30b77074e653a5762322235d71 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | No log | 1.0 | 113 | 0.2954 | 0.7090 | | No log | 2.0 | 226 | 0.2212 | 0.8232 | | No ... | 8b5dc3bcc093040ed093fdb04d541d9a |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-thai-test 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: - eval_loss: 0.7728 - eval_wer: 0.9490 - eval_runtime: 678.2819 - eval_sa... | d649c4f1056efaa2f24df42236def02e |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sche... | 16f0992ef254c7f7eaf1ba3ca6f208c2 |
unlicense | ['PyTorch', 'Transformers', 'gpt2'] | false | Задача Incomplete Utterance Restoration Генеративная модель на основе [sberbank-ai/rugpt3large_based_on_gpt2](https://huggingface.co/sberbank-ai/rugpt3large_based_on_gpt2) для восстановления полного текста реплик в диалоге из контекста. Допустим, последние 2 строки диалога имеют вид: ``` - Как тебя зовут? - Джульет... | f2c472b05996cf30b24b278013fb4d37 |
unlicense | ['PyTorch', 'Transformers', 'gpt2'] | false | обрабатываемые-ситуации) и в [этом документе](https://huggingface.co/inkoziev/rugpt_interpreter/blob/main/%D0%92%D0%BE%D1%81%D1%81%D1%82%D0%B0%D0%BD%D0%BE%D0%B2%D0%BB%D0%B5%D0%BD%D0%B8%D0%B5%20%D0%BF%D0%BE%D0%BB%D0%BD%D1%8B%D1%85%20%D1%80%D0%B5%D0%BF%D0%BB%D0%B8%D0%BA%20%D0%B2%20%D0%B4%D0%B8%D0%B0%D0%BB%D0%BE%D0%B3%D0%... | 81bdadcff8448d36221ff166867449d9 |
unlicense | ['PyTorch', 'Transformers', 'gpt2'] | false | Пример использования Данная модель работает в прототипе [диалоговой системы](https://github.com/Koziev/chatbot). Она не требует для работы никакой "обвязки", пре- или постпроцессинга, помимо стандартных для моделей семейства GPT, поэтому использовать ее очень просто: ``` import torch from transformers import AutoTok... | 1812f4520215844f433a06f5202e9c6e |
unlicense | ['PyTorch', 'Transformers', 'gpt2'] | false | """ encoded_prompt = tokenizer.encode(input_text, add_special_tokens=False, return_tensors="pt").to(device) output_sequences = model.generate(input_ids=encoded_prompt, max_length=100, num_return_sequences=1, pad_token_id=tokenizer.pad_token_id) text = tokenizer.decode(output_sequences[0].tolist(), clean_up_tokenizat... | a0033e0edc79821e0fcdd6bf44f79206 |
unlicense | ['PyTorch', 'Transformers', 'gpt2'] | false | Формат входных данных На вход модели подается результат токенизации для текста, составленного из 2 или 3 последних реплик диалога. Первым токеном должен быть ```<s>```. Каждая реплика должна начинаться префиксом "- ". Реплики разделяются символом перевода строки. К последней реплике, которая будет раскрываться, добав... | 8c6cdeac8ffea8724e86aef85e08c21a |
unlicense | ['PyTorch', 'Transformers', 'gpt2'] | false | Обрабатываемые ситуации Модель разрабатывается с прицелом на использование в [чатботе](https://github.com/Koziev/chatbot). Она поддерживает некоторые типичные ситуации в читчате, которые перечислены далее. В примерах после символа ⇒ идет эталонная раскрытая реплика, которую должна сгенерировать модель. [Эллипсисы](... | 67c4a638e2f0b31f201a5a0da8516a09 |
unlicense | ['PyTorch', 'Transformers', 'gpt2'] | false | %D0%93%D1%8D%D0%BF%D0%BF%D0%B8%D0%BD%D0%B3_(en:Gapping)): ``` - Ты кошек любишь? - Их – нет ⇒ я не люблю кошек ``` Сложный гэппинг: ``` - В 25 лет вы получаете пенсию? - Не я - отец. ⇒ Я не получаю пенсию. Отец получает пенсию ``` Восстановление необязательного местоименного подлежащего (см. [pro drop](https://... | f5d2189358feef112f76dbecad664b8a |
unlicense | ['PyTorch', 'Transformers', 'gpt2'] | false | Пример работы в чате Первый столбец содержит реплики диалога [модели читчата](https://huggingface.co/inkoziev/rugpt_chitchat) с собой, второй столбец - результат раскрытия реплик моделью интерпретатора: ``` - Добрый вечер, бро! | - Чё, будем общаться? | М... | 6f09f26d20aeaf9c66660ab8c5c43005 |
unlicense | ['PyTorch', 'Transformers', 'gpt2'] | false | Citation: ``` @MISC{rugpt_interpreter, author = {Ilya Koziev}, title = {Incomplete Utterance Restoration in Russian Chit-Chat conversations}, url = {https://huggingface.co/inkoziev/rugpt_interpreter}, year = 2022 } ``` | 0a12140d818b3bc88eb39578ae75fd37 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.961395091713594e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 27 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 | 09c4315b7f94bb158d3837582a8faab6 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | scottpilgrim Dreambooth model trained by spooncats with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stabl... | 20677320f4250c0d70685de5a8741b0b |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | `kan-bayashi/jsut_vits_accent_with_pause` ♻️ Imported from https://zenodo.org/record/5414980/ This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/). | 2234042f72177a3a2bfecd9b1990ec21 |
mit | ['classification', 'similarity'] | false | Aspect-based Document Similarity for Research Papers A `scibert-scivocab-uncased` model fine-tuned on the ACL Anthology corpus as in [Aspect-based Document Similarity for Research Papers](https://arxiv.org/abs/2010.06395). <img src="https://raw.githubusercontent.com/malteos/aspect-document-similarity/master/docrel.p... | 008a313ed0da93516527f65f007f949c |
mit | ['classification', 'similarity'] | false | Demo <a href="https://colab.research.google.com/github/malteos/aspect-document-similarity/blob/master/demo.ipynb"><img src="https://camo.githubusercontent.com/52feade06f2fecbf006889a904d221e6a730c194/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt=... | a4f46f73f1134393f452c81b5ffbd524 |
apache-2.0 | ['generated_from_trainer'] | false | t5-base-adv-top_v2 This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the top_v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.0336 - Exact Match: 0.8540 | e1102868b6ee0cc4eef661fa9417b6cb |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 64 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - traini... | 8a49e6842c1bd1b44ab3ace7519ff88a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Exact Match | |:-------------:|:-----:|:----:|:---------------:|:-----------:| | 1.4252 | 0.21 | 200 | 0.3381 | 0.1505 | | 0.4478 | 0.41 | 400 | 0.0673 | 0.3914 | | 0.38 | 0.62 | 600 | 0.0533 ... | e5711222bd32d88e9ae39ceb4a1a26f4 |
mit | ['generated_from_trainer'] | false | BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-ContaminationQAmodel_PubmedBERT This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on an unknown dataset. It achieves the followi... | 0f1f6e5e9ec2254a4cdf0a2cf976df73 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 22 | 3.9518 | | No log | 2.0 | 44 | 3.2703 | | No log | 3.0 | 66 | 2.9308 | | No log | 4.0 | 88 | 2.7806 ... | f609ca3f0fc402eb8a93273f4ea0c052 |
apache-2.0 | [] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(0.95, 0.999), weight_decay=1e-06 and epsilon=1e-08 - lr_scheduler: cosine - lr_warmup_steps: 500 - ema_... | df9f358cfa0d90f4c712e984937f91f1 |
apache-2.0 | ['setfit', 'sentence-transformers', 'text-classification'] | false | fathyshalab/massive_social-roberta-large-v1-1 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrast... | e3e598436493c0fd1566b3fed1fabe77 |
apache-2.0 | ['text aggregation', 'summarization'] | false | Model description This is a T5 Large fine-tuned for crowdsourced text aggregation tasks. The model takes multiple performers' responses and yields a single aggregated response. This approach was introduced for the first time during [VLDB 2021 Crowd Science Challenge](https://crowdscience.ai/challenges/vldb21) and ori... | 2954f68c98ea08699fb42da0b19b3bf2 |
apache-2.0 | ['text aggregation', 'summarization'] | false | How to use ```python from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, AutoConfig mname = "toloka/t5-large-for-text-aggregation" tokenizer = AutoTokenizer.from_pretrained(mname) model = AutoModelForSeq2SeqLM.from_pretrained(mname) input = "samplee text | sampl text | sample textt" input_ids = tokenizer.... | ce3ecd99e62fc1e3c9f7561f714fef3d |
apache-2.0 | ['text aggregation', 'summarization'] | false | Training data Pretrained weights were taken from the [original](https://huggingface.co/t5-large) T5 Large model by Google. For more details on the T5 architecture and training procedure see https://arxiv.org/abs/1910.10683 Model was fine-tuned on `train-clean`, `dev-clean` and `dev-other` parts of the [CrowdSpeech](... | 45717466d89cb82b0b4b7e6f59966d63 |
apache-2.0 | ['text aggregation', 'summarization'] | false | Training procedure The model was fine-tuned for eight epochs directly following the HuggingFace summarization training [example](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization). | a32b743a60fa6c9639ac225ece6f4e13 |
apache-2.0 | ['text aggregation', 'summarization'] | false | BibTeX entry and citation info ```bibtex @inproceedings{Pletenev:21, author = {Pletenev, Sergey}, title = {{Noisy Text Sequences Aggregation as a Summarization Subtask}}, year = {2021}, booktitle = {Proceedings of the 2nd Crowd Science Workshop: Trust, Ethics, and Excellence in Crowdsourced Data Ma... | afb6f543b34d08468d554bfa57b52da9 |
apache-2.0 | ['generated_from_trainer'] | false | distilroberta-base-finetuned-assignment2 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: 0.5976 | c7f30edb2241b1b239fb7d5e21228544 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 52 | 0.6602 | | No log | 2.0 | 104 | 0.5939 | | No log | 3.0 | 156 | 0.6450 | | c8e3cffed0d248a887018b168fed63a4 |
apache-2.0 | ['generated_from_trainer'] | false | small-vanilla-target-glue-mnli-linear-probe This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0612 - Accuracy: 0.4363 | ad07e7d8157f97cd90d111492e273f52 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.1093 | 0.04 | 500 | 1.0875 | 0.3914 | | 1.089 | 0.08 | 1000 | 1.0814 | 0.3988 | | 1.0811 | 0.12 | 1500 | 1.0760 | 0.... | 6e8f496fa752d819179eedd75b77330a |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | rraacchhiissbb Dreambooth model trained by gababas with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stabl... | 6c3c20cb19e26f0e0e001fdfbd72416b |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-colab0 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: 1.1808 - Wer: 0.7734 | 19fb0cbbedd368b58579fc358e85c91e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.8077 | 7.04 | 500 | 3.1554 | 1.0 | | 2.8549 | 14.08 | 1000 | 2.0683 | 1.0846 | | 1.3297 | 21.13 | 1500 | 1.2084 | 0.7984 | |... | 6547ac089bae229693cfc4ba3f4e5442 |
apache-2.0 | ['generated_from_trainer'] | false | wnli_bert-base-uncased_81_v2 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.6991 - Accuracy: 0.4507 | 46d09b608f3e817c58cfe3e559b2ec2f |
apache-2.0 | ['generated_from_trainer'] | false | wspr-sm-ar This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4515 - Wer: 72.6173 | 6d929f7ae7e7f2dcab6f49b585b91d32 |
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