license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
mit | ['bridgetower'] | false | BibTeX entry and citation info ```bibtex @article{xu2022bridge, title={BridgeTower: Building Bridges Between Encoders in Vision-Language Representation Learning}, author={Xu, Xiao and Wu, Chenfei and Rosenman, Shachar and Lal, Vasudev and Che, Wanxiang and Duan, Nan}, journal={arXiv preprint arXiv:2206.08657}, ... | 65514f87b690f01c43b4b408ac5b86df |
apache-2.0 | ['translation'] | false | opus-mt-hil-fi * source languages: hil * target languages: fi * OPUS readme: [hil-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/hil-fi/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-24.zip](http... | 4693ca2e2d869eba080c8f652fbc666b |
apache-2.0 | ['generated_from_trainer'] | false | swadeshi_bhojpuriwav2vec2asr This model is a fine-tuned version of [theainerd/Wav2Vec2-large-xlsr-hindi](https://huggingface.co/theainerd/Wav2Vec2-large-xlsr-hindi) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2155 - Wer: 0.2931 | d93853b5ee49548cf352974f3b2d5e36 |
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 - 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... | a3fee83e1a24e395ee48342b2d33b88e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.6928 | 3.2 | 400 | 2.4820 | 0.9925 | | 1.6981 | 6.4 | 800 | 0.8053 | 0.6320 | | 0.975 | 9.6 | 1200 | 0.5420 | 0.4980 | |... | 751129884a9f434794ffd0092afe6877 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-turkish-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.3866 - Wer: 0.3363 | 26a15a3018163667498ca95573855b91 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.9949 | 3.67 | 400 | 0.7055 | 0.6984 | | 0.4192 | 7.34 | 800 | 0.4530 | 0.4711 | | 0.1987 | 11.01 | 1200 | 0.4319 | 0.4384 | |... | 478bcec219209cf762b07f7d959e1559 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-squad This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 0.9840 | 047c08389e7dd673e77c5a64b1c770e3 |
apache-2.0 | [] | false | GPT2 fine-tuned with COVID-19 question and answer pairs using Reinforcement Learning with Human Feedback (RLHF) and Proximal Policy Optimization (PPO). Uses PPO and TRL library to align the response based on BERTScore towards the expected response. You can ask the model any question related to COVID-19 in this forma... | eeef03c32be884895574b2ab31beceb5 |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0602 - Precision: 0.9293 - Recall: 0.9488 - F1: 0.9390 - Accuracy: 0.9864 | cde36bbf4ec4eaa02b4734150fd0f614 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0827 | 1.0 | 1756 | 0.0639 | 0.9167 | 0.9359 | 0.9262 | 0.9828 | | 0.0413 | 2.0 |... | cd5dee10725750cefa16cdc8075c279d |
apache-2.0 | [] | false | Overview Model included in a paper for modeling fine grained similarity between documents: **Title**: "Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity" **Authors**: Sheshera Mysore, Arman Cohan, Tom Hope **Paper**: https://arxiv.org/abs/2111.08366 **Github**: https://gith... | 7d81b902b83adda67837ed7c69c9fb6c |
apache-2.0 | [] | false | Model description This model is a BERT bi-encoder model trained for similarity of title-abstract pairs in biomedical scientific papers. The model is **initialized with the SPECTER encoder**. This model inputs the title and abstract of a paper and represents it with a single vector obtained by a scalar mix of the CLS ... | cfc9cc5a8ad58a81fdd5ac6bab3af8bb |
apache-2.0 | [] | false | Training procedure The model was trained with the Adam Optimizer and a learning rate of 1e-5 with 1000 warm-up steps followed by linear decay of the learning rate. The model training convergence is checked with the loss on a held out dev set consisting of co-cited paper pairs. | 40ab83464dae6f9911d61504c2192cf4 |
apache-2.0 | [] | false | Evaluation results The released model `aspire-biencoder-biomed-spec` (and `aspire-biencoder-biomed-spec-full`) is compared against `allenai/specter`. `aspire-biencoder-biomed-spec-full`<sup>*</sup> is the performance reported in our paper by averaging over 3 re-runs of the model. The released models `aspire-biencoder... | f6396449c6d2fe4cbc456b31601f88ce |
creativeml-openrail-m | ['stable-diffusion', 'prompt-generator', 'arxiv:2210.14140'] | false | Fast GPT2 PromptGen <style> .container { padding-left: 20px; border-left: 5px solid gray; } </style> <div class="container"> <p><strong><a href="https://huggingface.co/FredZhang7/anime-anything-promptgen-v2">Fast Anime PromptGen</a></strong> generates descriptive safebooru and danbooru tags for anime text-to-i... | 36766f0a3834a06946f1744b13e1c46b |
creativeml-openrail-m | ['stable-diffusion', 'prompt-generator', 'arxiv:2210.14140'] | false | Contrastive Search ```bash pip install --upgrade transformers ``` ```python from transformers import GPT2Tokenizer, GPT2LMHeadModel tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2') tokenizer.add_special_tokens({'pad_token': '[PAD]'}) model = GPT2LMHeadModel.from_pretrained('FredZhang7/distilgpt2-stable-diffus... | f28880664fd5c97dcdd2e1f954414123 |
creativeml-openrail-m | ['stable-diffusion', 'prompt-generator', 'arxiv:2210.14140'] | false | generate the result with contrastive search input_ids = tokenizer(prompt, return_tensors='pt').input_ids output = model.generate(input_ids, do_sample=True, temperature=temperature, top_k=top_k, max_length=max_length, num_return_sequences=num_return_sequences, repetition_penalty=repitition_penalty, penalty_alpha=0.6, n... | 39038c2fa6aa5dbdcc00f7e3eafa4b33 |
mit | ['generated_from_trainer'] | false | deberta-base-combined-squad1-aqa-newsqa This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8860 | 1b9ed67908636dda17b20c496676cfb7 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.8812 | 1.0 | 40819 | 0.8762 | | 0.6043 | 2.0 | 81638 | 0.8860 | | 3dc4dd1162b04d32a92b36196f691e4a |
apache-2.0 | ['generated_from_keras_callback'] | false | edgertej/poebert-checkpoint-finetuned-poetry-foundation-2 This model is a fine-tuned version of [edgertej/poebert-checkpoint-finetuned-poetry-foundation](https://huggingface.co/edgertej/poebert-checkpoint-finetuned-poetry-foundation) on an unknown dataset. It achieves the following results on the evaluation set: - Tr... | f8919ae25c1d2dac172dae63c6dfe91a |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 3.9003 | 3.6587 | 0 | | 3.8970 | 3.6169 | 1 | | 3.8653 | 3.5986 | 2 | | baab5f8e2b98768984af0537e2a50ead |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 292 | 0.6587 | 0.8082 | 0.8069 | | 0e38d88d75f6c3c1e583695d2dfc5a13 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-OTTO 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: 3.2745 | 5f67e398471528450c2c62e937d9b9a5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.7687 | 1.0 | 17 | 3.3507 | | 3.5069 | 2.0 | 34 | 3.2786 | | 3.4126 | 3.0 | 51 | 3.2575 | | ae105411b812835ff9cf0de0ce460e02 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8071 - Matthews Correlation: 0.5408 | 8d6f7a13b729965a36281ab207815769 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5233 | 1.0 | 535 | 0.5367 | 0.4301 | | 0.3486 | 2.0 | 1070 | 0.5107 | 0.4919 | | 0.2... | 205ec24d394f0c5b191130b3c96f0a9f |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | En-Af_update This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-af](https://huggingface.co/Helsinki-NLP/opus-mt-en-af) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8089 - Bleu: 45.1780 | cd64f8e6809b4471a8b68a66134f4df0 |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 1.4243 | 1.0 | 2553 | 1.8451 | 42.1314 | | 1.0987 | 2.0 | 5106 | 1.7509 | 44.0714 | | 0.9329 | 3.0 | 7659 | 1.7340 | 4... | ec09b16f0570b6de7c698271ed00251c |
mit | ['generated_from_trainer'] | false | bart-large-cnn-weaksup-1000-NOpad-early 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: 1.9082 - Rouge1: 26.9663 - Rouge2: 11.3027 - Rougel: 20.7327 - Rougelsum: 23.5... | 545ec6ea95a083699a35863a0ec81e63 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.4775 | 1.0 | 1000 | 1.6796 | 27.208 | 12.01 | 20.8401 | 24.1333 | 66... | 146718762cbbc09dff779c56be863be7 |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2r_es_vp-100k_gender_male-5_female-5_s358 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 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using t... | 7a346fe7df8eaccfef8b58ae6397757c |
apache-2.0 | ['automatic-speech-recognition', 'ja'] | false | exp_w2v2t_ja_vp-nl_s682 Fine-tuned [facebook/wav2vec2-large-nl-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-nl-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (ja)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | ee913fc6fce184e89a93f3337097e53a |
mit | ['generated_from_trainer'] | false | gpt2-gpt2-finetuned-mbti-0909 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 3.9795 - eval_runtime: 44.8441 - eval_samples_per_second: 38.69 - eval_steps_per_second: 4.839 - step: 0 | 90e6c167d33232a530fe24ae8d0919de |
mit | ['generated_from_trainer'] | false | roberta-base.CEBaB_confounding.uniform.absa.5-class.seed_42 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the OpenTable OPENTABLE-ABSA dataset. It achieves the following results on the evaluation set: - Loss: 0.3315 - Accuracy: 0.9025 - Macro-f1: 0.9009 - Weighted-macro-... | 0243728773d7c9de1814adcaeb401ce2 |
apache-2.0 | ['generated_from_trainer'] | false | flan-t5-base_en-no This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the bible_para en-no dataset. It achieves the following results on the evaluation set: - Loss: 0.8910 - Bleu: 28.7219 - Gen Len: 66.1081 | 856f2c90c6d2a6405e2093ce9fc7508f |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 64 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10.0 | 8ea045f2e1cdc85d5644d146d29b2267 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-en-to-it-hrs This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1558 - Bleu: 9.8991 - Gen Len: 51.8287 | 0414e0d0ef372973d5f5e0aa525d1b3e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:| | 2.0084 | 1.0 | 1125 | 2.8804 | 4.4102 | 67.6067 | | 1.7918 | 2.0 | 2250 | 2.7757 | 6.1959 | 58.0313 | | 1.6944 |... | 3d2cd123e7b7b0f17e27d6789f686882 |
cc-by-4.0 | ['spanish', 'roberta', 'vit'] | false | CLIP-Spanish CLIP Spanish is a CLIP-like model for Spanish language. It is composed of [BERTIN](https://huggingface.co/bertin-project/bertin-roberta-base-spanish) as a language encoder and the ViT-B/32 image encoder from [CLIP](https://huggingface.co/openai/clip-vit-base-patch32). The model is implemented in [Flax](h... | 39d425111e2e131dc160921662fb02fc |
cc-by-4.0 | ['spanish', 'roberta', 'vit'] | false | summary-timeline-calendar-6) - [Community Week README](https://github.com/huggingface/transformers/blob/master/examples/research_projects/jax-projects/README.md) - [Community Week thread](https://discuss.huggingface.co/t/bertin-pretrain-roberta-large-from-scratch-in-spanish/7125) - [Community Week channel](https://disc... | dcfb44c5b3ed5d5e54106dd2bbfcebdf |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-multilingual-cased-finetuned-misogyny-en-it This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0096 - Accuracy: 0.9985 - F1: 0.9984... | 83ad06f084f4250c1ed6e323abc4b949 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Mae | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:| | 0.3169 | 1.0 | 1006 | 0.3924 | 0.8154 | 0.8322 | 0.7388 | 0.9526 | 0.18... | 9831ef340f4b26fe28ef0619df28b634 |
apache-2.0 | ['generated_from_trainer'] | false | presentation_hate_42 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.8711 - F1: 0.7692 | 4caf417cceacce38b8676a341d419056 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.436235805743952e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 | 5f951735b6ebe4ffcff506eb3454f314 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5207 | 1.0 | 282 | 0.4815 | 0.7513 | | 0.3047 | 2.0 | 564 | 0.5557 | 0.7510 | | 0.2335 | 3.0 | 846 | 0.6627 | 0.7585 | |... | cb35bbe108be9247a3aad42ebfe16de9 |
apache-2.0 | ['bert', 'rte', 'glue', 'torchdistill'] | false | `bert-base-uncased` fine-tuned on RTE dataset, using [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_finetuning_and_submission.ipynb). The hyperparameters are the same as those... | 898f9a21c0d73e9facff184db066a495 |
apache-2.0 | [] | false | Vision-and-Language Transformer (ViLT), pre-trained only Vision-and-Language Transformer (ViLT) model pre-trained on GCC+SBU+COCO+VG (200k steps). It was introduced in the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Kim et al. and first ... | 7692b49215994cb767d0644aee3cde61 |
apache-2.0 | [] | false | How to use Here is how to use this model in PyTorch: ``` from transformers import ViltProcessor, ViltForMaskedLM import requests from PIL import Image import re url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) text = "a bunch of [MASK] laying on a... | a63041ff9daab5c25c9e309d48b78bd1 |
apache-2.0 | [] | false | gradually fill in the MASK tokens, one by one with torch.no_grad(): for i in range(tl): encoded = processor.tokenizer(inferred_token) input_ids = torch.tensor(encoded.input_ids).to(device) encoded = encoded["input_ids"][0][1:-1] outputs = model(input_ids=input_ids, pixel_values=pixe... | a6b06a88ebb9d6e3ed7451cd107afec9 |
apache-2.0 | [] | false | only take into account text features (minus CLS and SEP token) mlm_logits = mlm_logits[1 : input_ids.shape[1] - 1, :] mlm_values, mlm_ids = mlm_logits.softmax(dim=-1).max(dim=-1) | 0c5ae169846bbc461bf0a0e369b35c90 |
apache-2.0 | [] | false | only take into account text mlm_values[torch.tensor(encoded) != 103] = 0 select = mlm_values.argmax().item() encoded[select] = mlm_ids[select].item() inferred_token = [processor.decode(encoded)] selected_token = "" encoded = processor.tokenizer(inferred_token) processor.decode(encoded.... | 98aceff82fcc92269fa108f226e26f95 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-it 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: 0.2601 - F1: 0.8168 | 7f249fb7752ad3813c46f8ef0f374aca |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.8182 | 1.0 | 70 | 0.3477 | 0.7319 | | 0.3068 | 2.0 | 140 | 0.2838 | 0.7765 | | 0.193 | 3.0 | 210 | 0.2601 | 0.8168 | ... | d41ee0a0dde10088a78cd9aded4c9a37 |
apache-2.0 | ['generated_from_trainer'] | false | recipe-lr1e05-wd0.01-bs8 This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2779 - Rmse: 0.5272 - Mse: 0.2779 - Mae: 0.4281 | cfcd98e1b597cefd59bbb913ac85a0af |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 0.2766 | 1.0 | 2490 | 0.2740 | 0.5235 | 0.2740 | 0.4175 | | 0.2738 | 2.0 | 4980 | 0.2784 | 0.5277 | 0.2784 ... | a984bf8df90abed676e59bc55b62cfd6 |
apache-2.0 | ['generated_from_keras_callback'] | false | destillbert-statementsaboutfuture 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: | 25c1d1f505d7047c9dcf3e474ffa7d4a |
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-small') input_ids = torch.tensor([list("Life is like a box of chocolates.".encode("utf-8"))... | 43c38b2fdbcefac4620256764086e4cf |
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-small') tokenizer = AutoTokenizer.from_pretrained('google/byt5... | 130c82e41d573a78e3336639613ff867 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-mr 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: 0.1702 - F1: 0.8640 | 45afbcfa2daba3bd0a6162cd607c75b7 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.4802 | 1.0 | 209 | 0.2400 | 0.8111 | | 0.2005 | 2.0 | 418 | 0.2005 | 0.8390 | | 0.1301 | 3.0 | 627 | 0.1702 | 0.8640 | ... | 623507fa4aa72035c1b122efa211ee5e |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Low Poly Environment on Stable Diffusion via Dreambooth This the Stable Diffusion model fine-tuned the Low Poly Environment concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of lowpoly_environment** | 7233ac96ef81ca489530292cebcfaae0 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Run on [Mirage](https://app.mirageml.com) Run this model and explore text-to-3D on [Mirage](https://app.mirageml.com)! Here are is a sample output for this model:  | af1c6e5b869b225ceb470b870d08e53f |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Share your Results and Reach us on [Discord](https://discord.gg/9B2Pu2bEvj)! [](https://discord.gg/9B2Pu2bEvj) [Image Source](https://www.behance.net/gallery/76095417/Game-Environments?tracking_source=search_projects%7Cisom... | 86492eda44e7f359df60b0ec56b00df5 |
creativeml-openrail-m | ['text-to-image'] | false | 909b3788-7706-47df-a82a-285de5bc6917 Dreambooth model trained by tzvc 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/github... | 838272e1ad0285dccb81b20141e807aa |
apache-2.0 | ['translation'] | false | opus-mt-tw-fi * source languages: tw * target languages: fi * OPUS readme: [tw-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/tw-fi/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-24.zip](https://... | 5518dc867d896d510be58b95ae66326c |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased_fold_2_binary_v1 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: 1.8833 - F1: 0.7841 | 8d5d2f28024d4a9504516e758cae76fb |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 290 | 0.4060 | 0.8070 | | 0.3981 | 2.0 | 580 | 0.4534 | 0.8072 | | 0.3981 | 3.0 | 870 | 0.5460 | 0.7961 | |... | 80635d365e4d7415de59eea3c8d8f6eb |
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 the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1565 | fcdf2aaebd4aefa5f2d9d98c14e6996b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2059 | 1.0 | 5533 | 1.1450 | | 0.9519 | 2.0 | 11066 | 1.1236 | | 0.7477 | 3.0 | 16599 | 1.1565 | | 11ec9c6d407b11711225945d37a0c940 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-4'] | false | MultiBERTs Seed 4 Checkpoint 0k (uncased) Seed 4 intermediate checkpoint 0k 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/google-... | 54c47efed7259e5e9e11dd3f7c29d769 |
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-4-0k') model = BertModel.from_pretrained("multiberts-seed-4-0k") text = "Replace me by any text you'd like." en... | 9d6f1b3adaaff903e3125fc4edb45b06 |
cc0-1.0 | [] | false | Word2Vec model obtained by training the model of [1] on a dataset of 17,500 Italian news articles related to crime events [1] Di Gennaro G., Buonanno A., Di Girolamo A., Ospedale A., Palmieri F.A.N., Fedele G. (2021) An Analysis of Word2Vec for the Italian Language. In: Esposito A., Faundez-Zanuy M., Morabito F., Pas... | c546884c591718204d7e535b1899a086 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1000 | e41062f2397e47a748e9ff9f037c0bfd |
apache-2.0 | ['multiberts', 'multiberts-seed_3', 'multiberts-seed_3-step_1500k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 3, Step 1500k 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... | 19e72bf94e8ba1949e4a94dc79f6adf1 |
apache-2.0 | ['multiberts', 'multiberts-seed_3', 'multiberts-seed_3-step_1500k'] | 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_3-step_1500k') model = TFBertModel.from_pretrained("google/multib... | 26c046fa35b5bdb05910b4247086688a |
mit | ['generated_from_trainer'] | false | deberta-finetuned-ner-finetuned-ner This model is a fine-tuned version of [baptiste/deberta-finetuned-ner](https://huggingface.co/baptiste/deberta-finetuned-ner) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7964 - Precision: 0.6210 - Recall: 0.3188 - F1: 0.4213 - Accuracy: 0... | 92d46d2514ea1d2f5e50938e535a5a12 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 4 | 1.0763 | 0.5583 | 0.1387 | 0.2222 | 0.7916 | | No log | 2.0 |... | 9bb45d97fd1572cb458646222749146d |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'diffusers', 'Pop Art', 'Roy Lichenstein', '1970s'] | false | Roy PopArt Diffusion This is a SD1.5 model trained on pop arts made by the one and only Roy Lichenstein (And some other pop arts). The model can only really do portraits, and even then, to get decent looking results, you do have to tinker with the prompt/create more samples. Occasionally, it still makes realistic look... | f0e5b42d0604598595bf4b63f294dbf8 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'diffusers', 'Pop Art', 'Roy Lichenstein', '1970s'] | false | Gradio We support a [Gradio](https://github.com/gradio-app/gradio) Web UI to run Roy_PopArt_Diffusion: [ on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 1.7126 - Wer: 0.8198 | 1c1c7c1f1ac0193a507177af98885787 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sched... | a1eb2b0d4c8152b89bf491f1acbebc97 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:-----:|:---------------:|:------:| | 6.7419 | 2.38 | 200 | 3.1913 | 1.0 | | 3.0446 | 4.76 | 400 | 2.3247 | 1.0 | | 1.3163 | 7.14 | 600 | 1.2629 | ... | 1837bdd6003613395f8212a6fc2c4714 |
apache-2.0 | ['pytorch', 'image-to-text'] | false | Feature extraction ⛏️ This model has a separate Visualbackbone used to extract features. More info about: - the model: [michelecafagna26/vinvl_vg_x152c4](https://huggingface.co/michelecafagna26/vinvl_vg_x152c4) - the usage and installation [michelecafagna26/vinvl-visualbackbone](https://github.com/michelecafagna26/v... | 5c6e76bc61a90b1464e0f2df236c78db |
apache-2.0 | ['pytorch', 'image-to-text'] | false | Citations 🧾 Please consider citing the original project and the VinVL paper ```BibTeX @misc{han2021image, title={Image Scene Graph Generation (SGG) Benchmark}, author={Xiaotian Han and Jianwei Yang and Houdong Hu and Lei Zhang and Jianfeng Gao and Pengchuan Zhang}, year={2021}, eprint={210... | 7fcb6baafd87c5af8dd0c89cf258e6ff |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | albedotxt Dreambooth model trained by jkraasch 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-di... | f8b5fe8a632b1a8787587bc857a7a9d6 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | opus-mt-tc-big-lv-en Neural machine translation model for translating from Latvian (lv) to English (en). This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All model... | ff9606198200cc1d6a15e5b42c1a56b1 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Model info * Release: 2022-03-13 * source language(s): lav * target language(s): eng * model: transformer-big * data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807+bt_transformer-big_2022-03-13.zip](htt... | 0416e0d741773984cb9647988fbacd74 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ "Dienai ir divdesmit četras stundas.", "Jys lobs advokats." ] model_name = "pytorch-models/opus-mt-tc-big-lv-en" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pret... | 04d904192c326b6305fb6ca18baaef55 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Jys lobs lawyer. ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-lv-en") print(pipe("Dienai ir divdesmit četras stundas.")) | d6ec646e62ca2953f5fd10371aeef6c0 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Benchmarks * test set translations: [opusTCv20210807+bt_transformer-big_2022-03-13.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/lav-eng/opusTCv20210807+bt_transformer-big_2022-03-13.test.txt) * test set scores: [opusTCv20210807+bt_transformer-big_2022-03-13.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-... | 3f2891c613abdb870d7ab93a73bbe1e2 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | words | |----------|---------|-------|-------|-------|--------| | lav-eng | tatoeba-test-v2021-08-07 | 0.73884 | 59.2 | 1631 | 11213 | | lav-eng | flores101-devtest | 0.64246 | 37.2 | 1012 | 24721 | | lav-eng | newsdev2017 | 0.55467 | 30.8 | 2003 | 48175 | | lav-eng | newstest2017 | 0.48769 | 21.8 | 2001 | 47511 | | dfd4bc334d43b47548618ad39bd374b3 |
apache-2.0 | ['translation'] | false | vie-deu * source group: Vietnamese * target group: German * OPUS readme: [vie-deu](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/vie-deu/README.md) * model: transformer-align * source language(s): vie * target language(s): deu * model: transformer-align * pre-processing: normalization + Se... | 1c4c8aafd5a167b0542f66955a0e0743 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: vie-deu - source_languages: vie - target_languages: deu - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/vie-deu/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['vi', 'de'] - src_constituents: {'vie', 'vie_Hani'} ... | 8bcdf88d1965f88e01f860585195d1d9 |
apache-2.0 | ['Fake News Detection', 'Text Classification'] | false | distilroberta-base-finetuned-fake-news-detection This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on [this](https://huggingface.co/datasets/GonzaloA/fake_news) Fake News Detection Dataset, which has been constructed by combining multiple Fake News datasets from Kag... | ca60f8a129f6ad860f2316dd5915a978 |
apache-2.0 | ['Fake News Detection', 'Text Classification'] | false | Training and evaluation data All of the process to train this model is available in [this](https://github.com/vikram71198/Transformers/tree/main/Fake%20News%20Detection) repository. The dataset has been split into 24,353 examples for training & 8,117 examples for validation & testing each. | 00e47a90797ef7e209472341b734b46c |
apache-2.0 | ['Fake News Detection', 'Text Classification'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - optimizer: default AdamW Optimizer - num_epochs: 3 - warmup_steps: 500 - weight_decay: 0.01 - random seed: 42 I also trained for 3 full epochs on Colab's Tesla P100-... | a804b5ad14197b50d88ad3461e0a3d6b |
apache-2.0 | ['Fake News Detection', 'Text Classification'] | false | Training results | Epoch | Training Loss | Validation Loss | |:-------------:|:----:|:---------------:| | 1 | 0.099100 | 0.042086 | | 2 | 0.030200 | 0.028448 | | 3 | 0.017500 | 0.024397 | | dadf84c3c284ac5b2dc726118f4a327d |
apache-2.0 | ['Fake News Detection', 'Text Classification'] | false | Model in Action 🚀 ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch.nn as nn tokenizer = AutoTokenizer.from_pretrained("vikram71198/distilroberta-base-finetuned-fake-news-detection") model = AutoModelForSequenceClassification.from_pretrained("vikram71198/distilroberta-b... | 06244bcd2dba0584245b76711bdd7fdf |
apache-2.0 | ['Fake News Detection', 'Text Classification'] | false | Following the same truncation & padding strategy used while training encoded_input = tokenizer("Enter any news article to be classified. Can be a list of articles too.", truncation = True, padding = "max_length", max_length = 512, return_tensors='pt') output = model(**encoded_input)["logits"] | 153b9fef2ab5ff24ef81363141fd60a4 |
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