license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
apache-2.0 | ['finnish', 't5', 't5x', 'seq2seq'] | false | How to use Here is how to use this model in PyTorch: ```python from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("Finnish-NLP/t5-base-nl36-finnish") model = T5ForConditionalGeneration.from_pretrained("Finnish-NLP/t5-base-nl36-finnish") ``` and in TensorFlow: ... | 0369c991ff12423ae99edb819184eebf |
apache-2.0 | ['finnish', 't5', 't5x', 'seq2seq'] | false | Pretraining The model was trained on TPUv3-8 VM, sponsored by the [Google TPU Research Cloud](https://sites.research.google/trc/about/), for 1M steps with a batch size of 64 (in total 33B tokens). The optimizer used was a AdaFactor with learning rate warmup for 10K steps with a constant learning rate of 1e-2, and the... | 31fd9221fe00babd91086d9eb2c731d2 |
apache-2.0 | ['finnish', 't5', 't5x', 'seq2seq'] | false | Evaluation results Evaluation was done by fine-tuning the model on a downstream text classification task with two different labeled Finnish datasets: [Yle News](https://github.com/spyysalo/yle-corpus) and [Eduskunta](https://github.com/aajanki/eduskunta-vkk). Classification fine-tuning was done with a sequence length... | f9149d3eb84dd1c41a81dec3654f67d3 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Nalisten-Likeness-1 Dreambooth model trained by nalisten1 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/fas... | cd740dba3f59dce3a85edd3f0e1d002d |
apache-2.0 | ['generated_from_trainer'] | false | whisper-medium-toi This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.8215 - Wer: 59.6163 | 08998c09dcf71e6071e5cf9ae362f17e |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precisio... | 5235b977a82767059168d66a1811e9fd |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.4727 | 1.47 | 500 | 2.0656 | 70.8002 | | 0.2033 | 2.95 | 1000 | 2.0971 | 67.6416 | | 0.0658 | 4.42 | 1500 | 2.3894 | 62.026... | 929b8a0ee39eb639012a40e6523b72ac |
mit | [] | false | ConvBERT small 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. | 715e092b42f9b3280587eb4ce7153d9a |
mit | [] | false | Metrics on evaluation set ``` disc_accuracy = 0.95163906 disc_auc = 0.9405496 disc_loss = 0.13658184 disc_precision = 0.80829453 disc_recall = 0.49316448 global_step = 1000000 loss = 9.12079 masked_lm_accuracy = 0.53505784 masked_lm_loss = 2.3028736 sampled_masked_lm_accuracy = 0.44047198 ``` | a4f91280fdf03fccb979c5a76e147c66 |
mit | [] | false | Usage ```python from transformers import AutoModel, AutoTokenizer model_name = "mrm8488/convbert-small-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](ht... | 6d13c4a6fe4785a5dcdbaac405eb9389 |
apache-2.0 | ['generated_from_trainer'] | false | codeparrot-ds-sample-gpt-small-neo-10epoch1 This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5696 | b9d678fae2a7b0d5d6e7048ecc5a31d4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 3.5639 | 0.94 | 1000 | 2.9253 | | 2.3253 | 1.88 | 2000 | 2.4563 | | 1.8494 | 2.82 | 3000 | 2.2655 | | 1.5133 | 3.77 | 4000 | 2.1635 ... | d1ca9d35146761b91f98c4383f6280c1 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Large Punjabi - Drishti Sharma This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.2846 - Wer: 19.7125 | 55bba59dfdc9d91bcddc43a3fb80a6ad |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-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: 100 - training_steps: 1000 - mixed_precisio... | f8d0fa338b94a538a0894bfcf47d5a14 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0004 | 8.26 | 1000 | 0.2846 | 19.7125 | | db89dada1cff0b26c96a93b006da7c2b |
apache-2.0 | ['generated_from_trainer', 'fnet-bert-base-comparison'] | false | bert-base-cased-finetuned-rte This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 0.7260 - Accuracy: 0.6715 The model was fine-tuned to compare [google/fnet-base](https://huggingface.... | f22846ed9220e415027f5c26e8dab3b0 |
apache-2.0 | ['generated_from_trainer', 'fnet-bert-base-comparison'] | false | !/usr/bin/bash python ../run_glue.py \\n --model_name_or_path bert-base-cased \\n --task_name rte \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir bert-base-cased-finetuned-rte \\n --push_to_hub \\n ... | 06ebb05cb9e53c19ec04f66bb71f09cd |
apache-2.0 | ['generated_from_trainer', 'fnet-bert-base-comparison'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6915 | 1.0 | 156 | 0.6491 | 0.6606 | | 0.55 | 2.0 | 312 | 0.6737 | 0.6570 | | 0.3955 | 3.0 | 468 | 0.7260 | 0.... | 4d2ba7fd72917b0027c2ad043f57c8dd |
apache-2.0 | ['generated_from_keras_callback'] | false | JustAdvanceTechonology/bert-fine-tuned-medical-insurance-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0269 - Validation Loss: 0.0551 - Epoch: 2 | 8e3af3458e234e9a2bdef2c2bdcb8867 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.1775 | 0.0646 | 0 | | 0.0454 | 0.0580 | 1 | | 0.0269 | 0.0551 | 2 | | 6f0572de3cbc660c89e628193b43d7eb |
apache-2.0 | ['generated_from_trainer'] | false | sst2 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.3521 - Accuracy: 0.9335 | fbd19fc4ddd447c40949ad88eeae108e |
apache-2.0 | ['Source Separation', 'Speech Separation', 'Audio Source Separation', 'WSJ0-3Mix', 'SepFormer', 'Transformer', 'audio-to-audio', 'audio-source-separation', 'speechbrain'] | false | SepFormer trained on WSJ0-3Mix This repository provides all the necessary tools to perform audio source separation with a [SepFormer](https://arxiv.org/abs/2010.13154v2) model, implemented with SpeechBrain, and pretrained on WSJ0-3Mix dataset. For a better experience we encourage you to learn more about [SpeechBrain... | 927f24c9aeab9ec8b7ef477a0220c1f0 |
apache-2.0 | ['Source Separation', 'Speech Separation', 'Audio Source Separation', 'WSJ0-3Mix', 'SepFormer', 'Transformer', 'audio-to-audio', 'audio-source-separation', 'speechbrain'] | false | Perform source separation on your own audio file ```python from speechbrain.pretrained import SepformerSeparation as separator import torchaudio model = separator.from_hparams(source="speechbrain/sepformer-wsj03mix", savedir='pretrained_models/sepformer-wsj03mix') est_sources = model.separate_file(path='speechbrain... | 5403c11bf23658754d993102da120203 |
apache-2.0 | ['Source Separation', 'Speech Separation', 'Audio Source Separation', 'WSJ0-3Mix', 'SepFormer', 'Transformer', 'audio-to-audio', 'audio-source-separation', 'speechbrain'] | false | Training The model was trained with SpeechBrain (fc2eabb7). 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/WSJ0... | e11e4f8947ff7acb83284579c8596d3c |
apache-2.0 | ['translation'] | false | opus-mt-fi-uk * source languages: fi * target languages: uk * OPUS readme: [fi-uk](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-uk/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://... | 6f895bc0c781bec3bf0191a4c00d1ebc |
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 | 探索 Exploration | 周文王 Zhouwenwang | 待定 TBD | 1.3B | 中文 Chinese | | ee0475583d95d802422c0b8654692340 |
apache-2.0 | [] | false | 模型信息 Model Information IDEA研究院认知计算中心联合追一科技有限公司提出的具有新结构的大模型。该模型在预训练阶段时考虑统一LM和MLM的任务，这让其同时具备生成和理解的能力，并且增加了旋转位置编码技术。目前已有13亿参数的Zhouwenwang-Unified-1.3B大模型，是中文领域中可以同时做LM和MLM任务的最大的模型。我们后续会持续在模型规模、知识融入、监督辅助任务等方向不断优化。 A large-scale model (Zhouwenwang-Unified-1.3B) with a new structure proposed by IDEA CCNL and Zhuiyi Techno... | b3984baeb185d96f138901f5a5163e2c |
apache-2.0 | [] | false | 下游任务 Performance 下游中文任务的得分（没有做任何数据增强）。 Scores on downstream chinese tasks (without any data augmentation) | 模型 Model | afqmc | tnews | iflytek | ocnli | cmnli | wsc | csl | | :--------: | :-----: | :----: | :-----: | :----: | :----: | :----: | :----: | | roberta-wwm-ext-large | 0.7514 | ... | 5de7952751201a0dff1b06da901f2066 |
apache-2.0 | [] | false | 使用 Usage 因为[transformers](https://github.com/huggingface/transformers)库中是没有 Zhouwenwang-Unified-1.3B相关的模型结构的，所以你可以在我们的[Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)中找到并且运行代码。 Since there is no structure of Zhouwenwang-Unified-1.3B in [transformers library](https://github.com/huggingface/transformers... | 8173c4ca148f64aff75d8a28f1e87fb7 |
apache-2.0 | [] | false | 加载模型 Loading Models ```python from fengshen import RoFormerModel from fengshen import RoFormerConfig from transformers import BertTokenizer tokenizer = BertTokenizer.from_pretrained("IDEA-CCNL/Zhouwenwang-Unified-1.3B") config = RoFormerConfig.from_pretrained("IDEA-CCNL/Zhouwenwang-Unified-1.3B") model = RoForme... | 6444705e905d42136f945c09de789642 |
apache-2.0 | [] | false | 使用示例 Usage Examples 你可以使用该模型进行续写任务。 You can use the model for continuation writing tasks. ```python from fengshen import RoFormerModel from transformers import AutoTokenizer import torch import numpy as np sentence = '清华大学位于' max_length = 32 tokenizer = AutoTokenizer.from_pretrained("IDEA-CCNL/Zhouwenwang-Unified... | d3760a41c34436227c61fe916e23318a |
apache-2.0 | ['generated_from_trainer'] | false | bart-finetuned-conala-3 This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on an CoNaLa. It achieves the following results on the evaluation set: - Loss: 1.8253 - Rouge1: 47.4345 - Rouge2: 23.8936 - Rougel: 45.317 - Rougelsum: 45.4339 - Bleu: 0.0657 - Gen Len: 58.0... | 37bd53cf64cd70fd9f54f1e094687ecf |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:------:|:-------:| | No log | 0.08 | 50 | 2.7823 | 35.8458 | 12.1898 | 33.746... | 525b8b8f8782390caf65d18d2f55b11a |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | Model Card of `lmqg/flan-t5-small-squad-qg-ae` This model is fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) for question generation and answer extraction jointly on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https:/... | f5d8e045c0034126bd4414af2863548f |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | model prediction question_answer_pairs = model.generate_qa("William Turner was an English painter who specialised in watercolour landscapes") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/flan-t5-small-squad-qg-ae") | 24d8a516a8ff89f10cde5436193be27b |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/flan-t5-small-squad-qg-ae/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squad.default.json) | | Score | Type | Dataset | |:--... | e9e4febea4bc2c63e697f228b0fc3044 |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squad - dataset_name: default - input_types: ['paragraph_answer', 'paragraph_sentence'] - output_types: ['question', 'answer'] - prefix_types: ['qg', 'ae'] - model: google/flan-t5-small - max_length: 51... | efc5fa33b627823317c480bbaea2f15c |
cc-by-2.0 | ['text2image', 'prompting'] | false | This is a GPT-2 model fine-tuned on the [succinctly/midjourney-prompts](https://huggingface.co/datasets/succinctly/midjourney-prompts) dataset, which contains 250k text prompts that users issued to the [Midjourney](https://www.midjourney.com/) text-to-image service over a month period. For more details on how this dat... | be1d30839019fa2cbf902e6765559490 |
cc-by-2.0 | ['text2image', 'prompting'] | false | advanced-text-weights) (e.g. `hot dog::1.5 food::-1` is likely to produce the image of an animal instead of a frankfurter). When using this model, please attribute credit to [Succinctly AI](https://succinctly.ai). | 1154c6418cb3615febec792942756f21 |
apache-2.0 | ['setfit', 'sentence-transformers', 'text-classification'] | false | fathyshalab/domain_transfer_clinic_credit_cards-massive_social-roberta-large-v1-1-5 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... | 17408c2e7af780fa8230b35cd3c0c29c |
mit | [] | false | Daycare Attendant Sun FNAF on Stable Diffusion This is the `<biblic-sun-fnaf>` 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) n... | b1c6a02501c44f65402132bd6b6e9a2c |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned_9th 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: 0.2826 - Accuracy: 0.4462 | e30dc749427e77c0c67529f0b894f985 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2357 | 1.0 | 569 | 0.2277 | 0.3474 | | 0.2237 | 2.0 | 1138 | 0.2316 | 0.3474 | | 0.1847 | 3.0 | 1707 | 0.2456 | 0.... | 1d2606a2a952ec0976b82e6980851ea4 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - l... | 3ff37a95bd0040aead1ba5d1ae09e2ba |
mit | ['generated_from_keras_callback'] | false | juro95/xlm-roberta-finetuned-ner-2 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0997 - Validation Loss: 0.1174 - Epoch: 2 | 6a58acebbf6fce6c5ac5e149eeed2257 |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.2954 | 0.1690 | 0 | | 0.1468 | 0.1274 | 1 | | 0.0997 | 0.1174 | 2 | | 92d8cb828cd1e54a3f08b560493ec319 |
creativeml-openrail-m | [] | false | This model was trained based off of https://huggingface.co/runwayml/stable-diffusion-v1-5 for 15000 steps using 2.5k images from https://dune.fandom.com/wiki/Dune_Wiki "bene gesserit"  "dune" ![dun... | 38e9cee92d738c5e3bfbe3d0d8632b72 |
mit | ['generated_from_trainer'] | false | hau_xlmr This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7674 | c832b24b4b123ae2b91d91baa85fb732 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 5 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 2 - total_train_batch_size: 10 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_sch... | 1344c17625e559aa44a9c7e5fda1302c |
apache-2.0 | ['translation'] | false | bel-spa * source group: Belarusian * target group: Spanish * OPUS readme: [bel-spa](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/bel-spa/README.md) * model: transformer-align * source language(s): bel bel_Latn * target language(s): spa * model: transformer-align * pre-processing: normaliz... | 0f3cd19e79d02a1f99b58363583e519e |
apache-2.0 | ['translation'] | false | System Info: - hf_name: bel-spa - source_languages: bel - target_languages: spa - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/bel-spa/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['be', 'es'] - src_constituents: {'bel', 'bel_Latn'} ... | c93ed05cbfe87f2a78e0feb6e5232f7d |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout c173c30930631731e6836c274a591ad571749741 pip install -e . cd egs2/ljspeech/tts1 ./run.sh --skip_data_prep false --skip_train true --download_model imdanboy/jets ``` | c76b9bce94b726a1a80c0752ed5e83fe |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'as', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | wav2vec2-large-xls-r-300m-as-v9 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: 1.1679 - Wer: 0.5761 | bce0fd2552e66fd4b116ea23254de691 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'as', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | Evaluation Command 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-as-v9 --dataset mozilla-foundation/common_voice_8_0 --config as --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data Assamese (... | 6312708406e6b857f453e829784d8e4d |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'as', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000111 - 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_s... | 26f990ac7ad23e93f2f73de0d23eab4e |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'as', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 8.3852 | 10.51 | 200 | 3.6402 | 1.0 | | 3.5374 | 21.05 | 400 | 3.3894 | 1.0 | | 2.8645 | 31.56 | 600 | 1.3143 | 0.830... | 52eb5179fcaccd0847c9b4d56216248b |
apache-2.0 | ['generated_from_trainer'] | false | test This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 2.2724 - F1: 0.1240 | f4e9d18d394c2bc54119f50b3b3925de |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 0.001 | 36713a87062c8699bf3c648e208b97a3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 0.0 | 1 | 2.2724 | 0.1240 | | 8c8460fc2729489757a88a2758f81d0e |
apache-2.0 | ['image-classification', 'timm'] | false | Model card for convnext_base.clip_laiona_augreg_ft_in1k_384 A ConvNeXt image classification model. CLIP image tower weights pretrained in [OpenCLIP](https://github.com/mlfoundations/open_clip) on LAION and fine-tuned on ImageNet-1k in `timm` by Ross Wightman. Please see related OpenCLIP model cards for more details ... | 6265994b1b46cfabacc1bbd7834fd5ac |
apache-2.0 | ['image-classification', 'timm'] | false | Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 88.6 - GMACs: 45.2 - Activations (M): 84.5 - Image size: 384 x 384 - **Papers:** - LAION-5B: An open large-scale dataset for training next generation image-text models: https://arxiv.org/abs/2210.08402 ... | 6d212c9994ffd4038a587706ec3fe443 |
apache-2.0 | ['image-classification', 'timm'] | false | Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model('convnext_base.clip_laiona_augreg_ft_in1k_384', pretrai... | 8e1d11566e7f8c37f70844403f6f9cf5 |
apache-2.0 | ['image-classification', 'timm'] | false | Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'convnext_base.clip_laiona_augreg_ft_in1k_384', ... | 9ecf56195e0cc99233108623e44082e4 |
apache-2.0 | ['image-classification', 'timm'] | false | Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'convnext_base.clip_laiona_augreg_ft_in1k_384', pr... | a5551a7b4d93059a116499f266babd6e |
mit | ['generated_from_trainer'] | false | BiBert-MultiTask-2 This model is a fine-tuned version of [nlptown/bert-base-multilingual-uncased-sentiment](https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment) on the None dataset. | a8a7c60ed0fd55e0277d94260a945365 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_ep... | d67f1336ee93470e2caccf212a4d9ff0 |
apache-2.0 | ['generated_from_trainer'] | false | 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: 1 | 1332fdb2f5b30ffcb3520fd878bc08fa |
apache-2.0 | ['generated_from_trainer'] | false | DistilBERT-POWO_MGH_Life_Form_Finetuned 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: 0.3845 | 7c9f96282cd2c0b4f3a6371cc18588ff |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.5891 | 1.0 | 914 | 0.4130 | | 0.4207 | 2.0 | 1828 | 0.3868 | | 0.3722 | 3.0 | 2742 | 0.3845 | | 0671655d7e7f69628b1fa86a86e05a95 |
mit | ['deberta-v1', 'fill-mask'] | false | DeBERTa: Decoding-enhanced BERT with Disentangled Attention [DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. With those two improvements, DeBERTa out perform RoBERTa on a majority of NLU tasks with 80GB training data. Please che... | ad36f973e609aa2657fa8dbcd5f6ce99 |
mit | ['deberta-v1', 'fill-mask'] | false | Notes. - <sup>1</sup> Following RoBERTa, for RTE, MRPC, STS-B, we fine-tune the tasks based on [DeBERTa-Large-MNLI](https://huggingface.co/microsoft/deberta-large-mnli), [DeBERTa-XLarge-MNLI](https://huggingface.co/microsoft/deberta-xlarge-mnli), [DeBERTa-V2-XLarge-MNLI](https://huggingface.co/microsoft/deberta-v2-xl... | 8721eefea3377c1e3a263131ff43be6c |
cc-by-4.0 | [] | false | Hyperparameters ``` batch_size = 96 n_epochs = 2 base_LM_model = "roberta-base" max_seq_len = 386 learning_rate = 3e-5 lr_schedule = LinearWarmup warmup_proportion = 0.2 doc_stride=128 max_query_length=64 ``` The distilled model has a comparable prediction quality and runs at twice the speed of the base model. | d92c8983ade281dbd172ea02de6b3989 |
cc-by-4.0 | [] | false | In Haystack Haystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in [Haystack](https://github.com/deepset-ai/haystack/): ```python reader = FARMReader(model_name_or_path="Shobhank-iiitdwd/RoBERTA-rrQA") | 6fef050b03ccf70fc847c199f2d22f0c |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Arabic Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Arabic using the `train` splits of [Common Voice](https://huggingface.co/datasets/common_voice) and [Arabic Speech Corpus](https://huggingface.co/datasets/arabic_speech_corpus). When u... | dc173440b65dc3e5c96a094e510e65e0 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Usage The model can be used directly (without a language model) as follows: ```python %%capture !pip install datasets !pip install transformers==4.4.0 !pip install torchaudio !pip install jiwer !pip install tnkeeh import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForC... | acfd4caca46689da38f562047ce067ab |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the audio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["spee... | 0648b29a0356be3460f0de194158b655 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Arabic test data of Common Voice: ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re | a5370137b98e4760afc967b5023e58fb |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | creating a dictionary with all diacritics dict = { 'ِ': '', 'ُ': '', 'ٓ': '', 'ٰ': '', 'ْ': '', 'ٌ': '', 'ٍ': '', 'ً': '', 'ّ': '', 'َ': '', '~': '', ',': '', 'ـ': '', '—': '', '.': '', '!': '', '-': '', ';': '', ':': '', '\'': '', '"': '', '☭': '', '«': '', '»': '', '؛': '', 'ـ': '', '_': '', '،': '', '“': ''... | 9f2dac269225e11b62219875aa72a8ba |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | For each match, look-up corresponding value in dictionary batch["sentence"] = regex.sub(lambda mo: dict[mo.string[mo.start():mo.end()]], batch["sentence"]) return batch test_dataset = load_dataset("common_voice", "ar", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("moh... | a1c0702b3dec18304288aa91414007f5 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the audio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) test_dataset = test_dataset.map(remov... | 1710c0dde057e3a2773fb5f9d1368958 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the audio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch... | 848f84d7c320c80539dcb6e861dc6a21 |
mit | ['summarization', 'bart'] | false | kobart-news - This model is a [kobart](https://huggingface.co/hyunwoongko/kobart) fine-tuned on the [문서요약 텍스트/신문기사](https://aihub.or.kr/aidata/8054) using [Ainize Teachable-NLP](https://ainize.ai/teachable-nlp). | 0074e129ff5cdc167ed0e33779f65b1d |
mit | ['summarization', 'bart'] | false | Encode Input Text input_text = '국내 전반적인 경기침체로 상가 건물주의 수익도 전국적인 감소세를 보이고 있는 것으로 나타났다. 수익형 부동산 연구개발기업 상가정보연구소는 한국감정원 통계를 분석한 결과 전국 중대형 상가 순영업소득(부동산에서 발생하는 임대수입, 기타수입에서 제반 경비를 공제한 순소득)이 1분기 ㎡당 3만4200원에서 3분기 2만5800원으로 감소했다고 17일 밝혔다. 수도권, 세종시, 지방광역시에서 순영업소득이 가장 많이 감소한 지역은 3분기 1만3100원을 기록한 울산으로, 1분기 1만9100원 대비 31.4% 감소했다. 이... | d26d6cf0a1bac19645d45cf8c395b330 |
mit | ['summarization', 'bart'] | false | Generate Summary Text Ids summary_text_ids = model.generate( input_ids=input_ids, bos_token_id=model.config.bos_token_id, eos_token_id=model.config.eos_token_id, length_penalty=2.0, max_length=142, min_length=56, num_beams=4, ) | 27e9a9fb451ec668181b3f74c52bb978 |
mit | ['summarization', 'bart'] | false | API and Demo You can experience this model through [ainize-api](https://ainize.ai/gkswjdzz/summarize-torchserve?branch=main) and [ainize-demo](https://main-summarize-torchserve-gkswjdzz.endpoint.ainize.ai/). | 26e694f548dab8214a2c664a92f4a214 |
mit | [] | false | Kinda-sus on Stable Diffusion This is the `<amogus>` 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. You can also trai... | f1aa30b4ec53592dc36b1c9ae1b68b8a |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-small_talk-7-16-5 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: 2.3566 - Accuracy: 0.3855 | 75f0371e29161e826d538d0cd007bdff |
apache-2.0 | ['translation'] | false | isl-ita * source group: Icelandic * target group: Italian * OPUS readme: [isl-ita](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/isl-ita/README.md) * model: transformer-align * source language(s): isl * target language(s): ita * model: transformer-align * pre-processing: normalization + Se... | 35c38828201797703381869c728c4c54 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: isl-ita - source_languages: isl - target_languages: ita - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/isl-ita/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['is', 'it'] - src_constituents: {'isl'} - tgt_const... | 0a0a7e8a545e499f3f485d191c583fe7 |
mit | ['generated_from_trainer'] | false | roberta-large-ner-conllpp-v1 This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the conllpp dataset. It achieves the following results on the evaluation set: - Loss: nan - Precision: 0.9581 - Recall: 0.9586 - F1: 0.9584 - Accuracy: 0.9629 | 8ff3a1ea884905ff08cf727abceafae9 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.3602 | 1.0 | 878 | nan | 0.9450 | 0.9484 | 0.9467 | 0.9541 | | 0.1101 | 2.0 |... | 0d9ad8634db742afaea60bf743f5e40c |
apache-2.0 | ['generated_from_trainer'] | false | finetuned-base_base This model is a fine-tuned version of [google/bert_uncased_L-12_H-768_A-12](https://huggingface.co/google/bert_uncased_L-12_H-768_A-12) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3594 - Accuracy: 0.9094 - F1: 0.9525 | b04c8d81f2155997e6a5dbfdd43b59ab |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 50 - eval_batch_size: 50 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 200 | c0419f9514386dcc4a72646f3c674e94 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.2414 | 1.0 | 500 | 0.1796 | 0.9343 | 0.9660 | | 0.1235 | 2.0 | 1000 | 0.2042 | 0.9311 | 0.9643 | | 0.0633 |... | 70a2f5d246483fa69be78f2741705ecf |
apache-2.0 | ['translation'] | false | opus-mt-fr-hr * source languages: fr * target languages: hr * OPUS readme: [fr-hr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-hr/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | e54350bb104ac332becbaebd7867d075 |
apache-2.0 | ['spacy', 'token-classification'] | false | DaCy small transformer DaCy is a Danish language processing framework with state-of-the-art pipelines as well as functionality for analysing Danish pipelines. DaCy's largest pipeline has achieved State-of-the-Art performance on Named entity recognition, part-of-speech tagging and dependency parsing for Danish on the... | 274410f6b3f58e9ad0620d082f1c1c68 |
apache-2.0 | ['spacy', 'token-classification'] | false | danish-dependency-treebank-dane) (Rasmus Hvingelby, Amalie B. Pauli, Maria Barrett, Christina Rosted, Lasse M. Lidegaard, Anders Søgaard)<br />[Maltehb/-l-ctra-danish-electra-small-cased](https://huggingface.co/Maltehb/-l-ctra-danish-electra-small-cased) (Malte Højmark-Bertelsen) | | **License** | `Apache-2.0 License` ... | eff6ec1b287e17879f058dba484c27be |
apache-2.0 | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (192 labels for 3 components)</summary> | Component | Labels | | --- | --- | | **`morphologizer`** | `AdpType=Prep\|POS=ADP`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=NOUN`, `Mood=Ind\|POS=AUX\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `POS=PROPN`, `Definite=Ind\|Num... | d3928d4c0c77cc696c1e63dc3cfd014e |
apache-2.0 | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `POS_ACC` | 95.83 | | `MORPH_ACC` | 95.70 | | `DEP_UAS` | 84.92 | | `DEP_LAS` | 81.76 | | `SENTS_P` | 86.04 | | `SENTS_R` | 87.41 | | `SENTS_F` | 86.72 | | `LEMMA_ACC` | 84.91 | | `ENTS_F` | 82.32 | | `ENTS_P` | 81.72 | | `ENTS_R` | 82.92 | | `TRANSFORMER_LOSS` | 41746686.63 ... | 24c05467d88d4d110549dfaa7bc1ec9d |
apache-2.0 | ['spacy', 'token-classification'] | false | Bias and Robustness Besides the validation done by SpaCy on the DaNE testset, DaCy also provides a series of augmentations to the DaNE test set to see how well the models deal with these types of augmentations. The can be seen as behavioural probes akinn to the NLP checklist. | 904cc9d03ca7b424bcf523145d7b55ad |
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