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 | ['longformer', 'longformer-scico'] | false | Longformer for SciCo This model is the `unified` model discussed in the paper [SciCo: Hierarchical Cross-Document Coreference for Scientific Concepts (AKBC 2021)](https://openreview.net/forum?id=OFLbgUP04nC) that formulates the task of hierarchical cross-document coreference resolution (H-CDCR) as a multiclass proble... | ee42024b63cf8c95ad990341f154d36d |
apache-2.0 | ['longformer', 'longformer-scico'] | false | global attention to the </m> token globs = torch.cat((start, end)) value = torch.ones(globs.shape[0]) global_attention_mask.index_put_(tuple(globs.t()), value) return global_attention_mask m1 = "In this paper we present the results of an experiment in <m> automatic concept and definition extractio... | 9a2055633a6dc63beca8b549ac7b7e59 |
apache-2.0 | ['longformer', 'longformer-scico'] | false | tensor([[0.0818, 0.0023, 0.0019, 0.9139]]) -- m1 is a child of m2 ``` **Note:** There is a slight difference between this model and the original model presented in the [paper](https://openreview.net/forum?id=OFLbgUP04nC). The original model includes a single linear layer on top of the `<s>` token (equivalent to `[CLS... | beddc925a00b83c9d6a3787f2f5b3062 |
apache-2.0 | ['longformer', 'longformer-scico'] | false | Citation ```python @inproceedings{ cattan2021scico, title={SciCo: Hierarchical Cross-Document Coreference for Scientific Concepts}, author={Arie Cattan and Sophie Johnson and Daniel S Weld and Ido Dagan and Iz Beltagy and Doug Downey and Tom Hope}, booktitle={3rd Conference on Automated Knowledge Base... | 48a4a4f06c792c28740fc79481617fdb |
mit | [] | false | model by daniel16 This your the Stable Diffusion model fine-tuned the Josemiel concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of Josemiel** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.resea... | 5fe9653dd75bdeb3cc2910e53b45f37f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 447 | 0.0023 | 0.9994 | | 263e0a6d9699b585ba4df27b685e365f |
apache-2.0 | ['automatic-speech-recognition', 'et'] | false | exp_w2v2t_et_r-wav2vec2_s957 Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speec... | 2fa5033316b1583a95baeaa3fcfe95d0 |
mit | [] | false | Among-Us-Logic-AI-Characters on Stable Diffusion via Dreambooth trained on the [fast-DreamBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook | c8d2a893c7b4968afb18cedc22e2fa08 |
mit | [] | false | Model by Laughify This your the Stable Diffusion model fine-tuned the Among-Us-Logic-AI-Characters concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt(s)`: nmaguosilgco You can also train your own concepts and upload them to the library by using [the fast-DremaBooth.ip... | 5b3f06cc74b1f9e65af7688a3b7abe35 |
apache-2.0 | ['generated_from_trainer'] | false | PSST medium Scrambled This model is a fine-tuned version of [openai/whisper-medium.en](https://huggingface.co/openai/whisper-medium.en) on the Santa Barbara Corpus of Spoken American English dataset. | 965947144e17f1ce503d3efcdcbba3fd |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 8 - 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 - lr_sche... | a597043da6146fe4870b7edd13ea4902 |
apache-2.0 | ['translation'] | false | eng-aav * source group: English * target group: Austro-Asiatic languages * OPUS readme: [eng-aav](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-aav/README.md) * model: transformer * source language(s): eng * target language(s): hoc hoc_Latn kha khm khm_Latn mnw vie vie_Hani * model: tr... | cf2d8711b90e6e5aadff0cb5caf307bc |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.eng-hoc.eng.hoc | 0.1 | 0.033 | | Tatoeba-test.eng-kha.eng.kha | 0.4 | 0.043 | | Tatoeba-test.eng-khm.eng.khm | 0.2 | 0.242 | | Tatoeba-test.eng-mnw.eng.mnw | 0.8 | 0.003 | | Tatoeba-test.eng.multi |... | 56db41429f23b2ac8b31bb2c14162e10 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: eng-aav - source_languages: eng - target_languages: aav - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-aav/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['en', 'vi', 'km', 'aav'] - src_constituents: {'eng'}... | ecea63d841ff41147c0c252cc47f1d99 |
apache-2.0 | ['generated_from_trainer'] | false | electra-small-finetuned-amazon-review This model is a fine-tuned version of [google/electra-small-discriminator](https://huggingface.co/google/electra-small-discriminator) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 1.0560 - Accuracy: 0.5504 - F1: 0.5458 - Pre... | beea5e8ab83ccc21a09bdbdeebaac3e9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 1.2172 | 1.0 | 1000 | 1.1014 | 0.5216 | 0.4902 | 0.4954 | 0.5216 | | 1.0027 | 2.0 |... | b572a5a555b0a8dbf2d5ac523d5a5d0f |
apache-2.0 | ['generated_from_trainer'] | false | distilroberta-base-finetuned-wikitext2 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8512 | 76376a1bb04c0be486efd2690e467a44 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.084 | 1.0 | 2406 | 1.9229 | | 1.9999 | 2.0 | 4812 | 1.8832 | | 1.9616 | 3.0 | 7218 | 1.8173 | | 1127ef09675b2f2ebcbe55c30ceacfa7 |
mit | ['generated_from_trainer'] | false | distilcamembert-cae-no-thinking This model is a fine-tuned version of [cmarkea/distilcamembert-base](https://huggingface.co/cmarkea/distilcamembert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5464 - Precision: 0.7959 - Recall: 0.7848 - F1: 0.7869 | ba045f9a980cae9c01fd6d893616553a |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:| | 1.1607 | 1.0 | 40 | 0.9958 | 0.6444 | 0.4684 | 0.3248 | | 1.0099 | 2.0 | 80 | 0.9761 | 0.6090 ... | 78d97204df86f1dfa1dc68f1a436e27a |
apache-2.0 | ['generated_from_trainer'] | false | BERT_MC_OpenBookQA This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.8077 - Accuracy: 0.654 | fa86f2d9fdad0adaf5f614089020d808 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.8972 | 1.61 | 500 | 0.9912 | 0.636 | | 0.2906 | 3.23 | 1000 | 1.4448 | 0.654 | | 0.07 | 4.84 | 1500 | 1.8077 | 0.... | 24b20f147cffbae2d636a44c0149b907 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-finetuned-cola This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8989 - Matthews Correlation: 0.5774 | ac64f8b77a1e4081c537146497f3e3ff |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.4832 | 1.0 | 535 | 0.5599 | 0.4693 | | 0.2869 | 2.0 | 1070 | 0.4750 | 0.5771 | | 0.1... | 546649b752185564c057dcc90e7d6b65 |
mit | ['generated_from_keras_callback'] | false | ishaankul67/Pub-clustered This model is a fine-tuned version of [nandysoham16/16-clustered_aug](https://huggingface.co/nandysoham16/16-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3332 - Train End Logits Accuracy: 0.9028 - Train Start Logits Accuracy: ... | 7cc9e36636bd0814592b706fa6fab149 |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | 56aff7f8c28f95fba6df4a194cc02e5b |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-banking-1-16-5 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: 2.4479 - Accuracy: 0.2301 | fc50bb4a9695845ce672928823c70924 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.716 | 1.0 | 1 | 2.6641 | 0.1327 | | 2.1674 | 2.0 | 2 | 2.5852 | 0.1858 | | 1.7169 | 3.0 | 3 | 2.5202 | 0.... | 3d774a792f948fa4d4608b717792fbb4 |
mit | [] | false | Bert muppet 2 on Stable Diffusion This is the `<bert-muppet>` 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 ... | 5006bb7831adb1fff6ad145cad9541a0 |
cc-by-sa-4.0 | ['japanese', 'token-classification', 'pos', 'dependency-parsing'] | false | Model Description This is a DeBERTa(V2) model pre-trained on 青空文庫 texts for POS-tagging and dependency-parsing, derived from [deberta-large-japanese-aozora](https://huggingface.co/KoichiYasuoka/deberta-large-japanese-aozora). Every long-unit-word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universa... | 7680eeab5960325b121520e92a2bc387 |
cc-by-sa-4.0 | ['japanese', 'token-classification', 'pos', 'dependency-parsing'] | false | How to Use ```py import torch from transformers import AutoTokenizer,AutoModelForTokenClassification tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-large-japanese-luw-upos") model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/deberta-large-japanese-luw-upos") s="国境の長いトンネルを抜けると雪国であった。"... | 164a7d46802e276a6d904a2ff45a1c0d |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-cased-finetuned-basil 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: - Loss: 1.2272 | 246e85e5009829337795cca852b8b321 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.8527 | 1.0 | 800 | 1.4425 | | 1.4878 | 2.0 | 1600 | 1.2740 | | 1.3776 | 3.0 | 2400 | 1.2273 | | de72506c0e7be7d9517573d6afff3cc0 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 256 - eval_batch_size: 256 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP | 2c7f719726ecf1ff72d9b71f95207871 |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | modeversion1_m7_e4 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the sudo-s/herbier_mesuem7 dataset. It achieves the following results on the evaluation set: - Loss: 0.0902 - Accuracy: 0.9731 | cf82e1e2601b0f356f5b42e0bb07b477 |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 4.073 | 0.06 | 100 | 3.9370 | 0.1768 | | 3.4186 | 0.12 | 200 | 3.2721 | 0.2590 | | 2.6745 | 0.18 | 300 | 2.6465 | 0.... | ca7fc453fd534a5f028d0b8b2f922e73 |
apache-2.0 | ['generated_from_trainer'] | false | bert-sentiment-analysis-model-40k-samples This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.2669 - Accuracy: 0.9276 - F1: 0.9624 | 68986bbc221455676da47a8fdb33c451 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | samantha Dreambooth model trained by Spiltcokesf 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-... | 146492372e3050a31f6453cb1fcc5196 |
apache-2.0 | ['automatic-speech-recognition', 'nl'] | false | exp_w2v2t_nl_vp-100k_s899 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 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure th... | 2ff2f217e963788a652f757d86375c0f |
cc-by-4.0 | [] | false | Intended Use This model is ready to be used for entity recognition. It is capable of tagging the 6 entity types from [ACE 2005](https://www.ldc.upenn.edu/sites/www.ldc.upenn.edu/files/english-entities-guidelines-v6.6.pdf) - Person (PER) - ORG - GPE - LOC - VEH - FAC Due to the fine-tuning domain, it is expected to wo... | a1288ebcdee28523b0cbce2246944e9f |
mit | [] | false | обученный rubert от sberbank-ai/ruBert-base. размер выборки - 4. Эпохи - 2. ```python from transformers import pipeline qa_pipeline = pipeline( "question-answering", model="Den4ikAI/rubert-large-squad", tokenizer="Den4ikAI/rubert-large-squad" ) predictions = qa_pipeline({ 'context': "Пушкин родился 6... | f6593da30b0d2c20cf11f95c46f87a4e |
mit | ['generated_from_trainer'] | false | zealous_almeida This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekk... | 1af74f5b0e3cc8544a93e321c0372371 |
mit | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', ... | fb1a656379253e4298a90a86db397b59 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Sample pictures of this concept: .png) .png)  given in [ARC Challenge](https://leaderboard.allenai.org/arc/submissions/public) | 4bab99195b3a30044598fee14c4f57b5 |
mit | [] | false | How to use ```python import datasets from transformers import RobertaTokenizer from transformers import RobertaForMultipleChoice tokenizer = RobertaTokenizer.from_pretrained( "LIAMF-USP/aristo-roberta") model = RobertaForMultipleChoice.from_pretrained( "LIAMF-USP/aristo-roberta") dataset = datasets.load_dataset( ... | 3f7744881f82180dfedb801035ec2b46 |
mit | [] | false | fill in the banks questions question_option = question.replace("_", ending) else: question_option = question + " " + ending inputs = tokenizer( context, question_option, add_special_tokens=True, max_length=MAX_SEQ_LENGTH, padding="max_length", ... | ab4826915071d2f45bec5309ceb77119 |
mit | [] | false | necessary to check if "attention_mask" in choices_inputs[0] else None ) example_encoded = { "example_id": example_id, "input_ids": input_ids, "attention_mask": attention_mask, "token_type_ids": token_type_ids, "label": label } output = model(**example_encoded) ``` | 3ed7e1a3b3b359f4a3fbc763106ce446 |
mit | [] | false | Training data the Training data was the same as proposed [here](https://leaderboard.allenai.org/arc/submission/blcotvl7rrltlue6bsv0) The only diferrence was the hypeparameters of RACE fine tuned model, which were reported [here](https://huggingface.co/LIAMF-USP/roberta-large-finetuned-race | e714bff8e3b11230cdd0c1fd215358b3 |
mit | [] | false | Training procedure It was necessary to preprocess the data with a method that is exemplified for a single instance in the _How to use_ section. The used hyperparameters were the following: | Hyperparameter | Value | |:----:|:----:| | adam_beta1 | 0.9 | | adam_beta2 | 0.98 |... | d222acbc02a35aba63277475f05de558 |
mit | [] | false | Garcon the cat on Stable Diffusion This is the `<garcon-the-cat>` 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 ... | 13427b60c6863e6e8baa72895d07abc3 |
afl-3.0 | [] | false | Model Description We release all models introduced in our [paper](https://arxiv.org/pdf/2206.11147.pdf), covering 13 different application scenarios. Each model contains 11 billion parameters. | Model | Description | Recommended Application | ----------- | ----------- |----------- | | rst-all-11b ... | 6fee60892462f62e70c9b9a586c23dcf |
mit | ['generated_from_trainer'] | false | deberta-base-finetuned-cola This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.5812 - Matthews Correlation: 0.6332 | 1a78aecd7d01ae6c416ea141831909ab |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.4826 | 1.0 | 535 | 0.5277 | 0.5443 | | 0.28 | 2.0 | 1070 | 0.4723 | 0.6331 | | 0.1... | a17c6e8b6be07b2982988b554a33d9cc |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-xls-r-bengali_v1 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: 3.2973 - Wer: 1.0 | 3a4e80968f0f0d0bac5a29d17e1a992b |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - 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_sche... | c62f742b1190a748ecb4af9eb7e6f085 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 8.7896 | 0.8 | 500 | 3.8455 | 1.0 | | 3.3871 | 1.6 | 1000 | 3.2862 | 1.0 | | 3.3302 | 2.4 | 1500 | 3.3086 | 1.0 | | 3.3259 ... | a05c1bf19b03d6d7e8944151ce2bcaca |
mit | [] | false | Running synthesizer Example for the synthesizing command: ``` tts --text "Гепарды жывуць у адкрытых і прасторных месцах, дзе ёсць шмат здабычы." \ --config_path ${PATH_TO_FILE}/config.json \ --model_path ${PATH_TO_FILE}/model.pth \ --out_path ${PATH_TO_FILE}/output.wav \ --vocoder_path ${PATH_TO_FILE}... | 5885d183a4eeb93272cdd872a1c409b7 |
other | ['art'] | false | モデル説明 (model explanation) - [YaguruMagiku](https://huggingface.co/Toooajk/YaguruMagiku/blob/main/YaguruMagiku-v3-Anybased/YaguruMagiku-v3.1-AnyBased.ckpt) 0.6 : [AbyssOrangeMix2_sfw](https://huggingface.co/WarriorMama777/OrangeMixs/blob/main/Models/AbyssOrangeMix2/AbyssOrangeMix2_sfw.ckpt) 0.4 - **マージ元のルーツにNAIリークが含まれる... | e3ef33b76b7e46128b67437826587b7a |
other | ['art'] | false | サンプル画像 (sample images) ``art by yaguru magiku``プロンプトを適切な強さで追加することで、YaguruMagikuスタイルの顔を出力できる。 Add the propmpt ``art by yaguru magiku`` with a proper strength to get the face in the style of YaguruMagiku.  on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2168 - Accuracy: 0.925 - F1: 0.9247 | fefdad42000d849c606dcf84d5db154e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8435 | 1.0 | 250 | 0.3160 | 0.9065 | 0.9045 | | 0.2457 | 2.0 | 500 | 0.2168 | 0.925 | 0.9247 | | 24cd713c158b34554a3943a061963271 |
apache-2.0 | ['vision, language', 'pretrained model', 'image-to-text'] | false | How to use ```python from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("KETI-AIR/veld-base", trust_remote_code=True) model = AutoModel.from_pretrained("KETI-AIR/veld-base", trust_remote_code=True) ``` You can use AutoTokenizer and AutoFeatureExtractor instead AutoProcessor.... | 7f50dfd1a493904b3040063e989e48d7 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion-medium 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: 0.3555 - Accuracy: 0.8491 - F1: 0.8491 | adf2e50943361158b361cac4d8d8cf58 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 100 - eval_batch_size: 100 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 | 72154065d46aace4a51e8a203df1a41b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.3886 | 1.0 | 1400 | 0.3562 | 0.844 | 0.8440 | | 0.3194 | 2.0 | 2800 | 0.3555 | 0.8491 | 0.8491 | | 2696691a31122ab6cea5021e0fc87da6 |
creativeml-openrail-m | ['text-to-image'] | false | hilleli Dreambooth model trained by tzvc with the v1-5 base model Sample pictures of: sdcid (use that on your prompt)  notebook. You can also train your ... | 2d02d4a033635ba1f1a4cb53867e5a94 |
mit | ['ja', 'japanese', 'gpt', 'text-generation', 'lm', 'nlp'] | false | How to use the model *NOTE:* Use `T5Tokenizer` to initiate the tokenizer. ~~~~ import torch from transformers import T5Tokenizer, AutoModelForCausalLM tokenizer = T5Tokenizer.from_pretrained("rinna/japanese-gpt-1b") model = AutoModelForCausalLM.from_pretrained("rinna/japanese-gpt-1b") if torch.cuda.is_available():... | 3384f0dc3b86cb0b85324b13058e948d |
mit | ['ja', 'japanese', 'gpt', 'text-generation', 'lm', 'nlp'] | false | sample output: 西田幾多郎は、その主著の「善の研究」などで、人間の内面に自然とその根源があると指摘し、その根源的な性格は、この西田哲学を象徴しているとして、カントの「純粋理性批判」と「判断力批判」を対比して捉えます。それは、「人が理性的存在であるかぎりにおいて、人はその当人に固有な道徳的に自覚された善悪の基準を持っている」とするもので、この理性的な善悪の観念を否定するのがカントの ~~~~ | 8c78b17578b8caeef91acfb439dd338f |
mit | ['ja', 'japanese', 'gpt', 'text-generation', 'lm', 'nlp'] | false | Training The model was trained on [Japanese C4](https://huggingface.co/datasets/allenai/c4), [Japanese CC-100](http://data.statmt.org/cc-100/ja.txt.xz) and [Japanese Wikipedia](https://dumps.wikimedia.org/other/cirrussearch) to optimize a traditional language modelling objective. It reaches around 14 perplexity on a c... | dc5777d1be45ecb10c180770583153e4 |
mit | ['ja', 'japanese', 'gpt', 'text-generation', 'lm', 'nlp'] | false | Tokenization The model uses a [sentencepiece](https://github.com/google/sentencepiece)-based tokenizer. The vocabulary was first trained on a selected subset from the training data using the official sentencepiece training script, and then augmented with emojis and symbols. | 72d461ee42c4b244a463413d82de95ec |
mit | [] | false | Hubris-Oshri on Stable Diffusion This is the `<Hubris>` 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 t... | a7fcb08b79b092270765b4deef9d7408 |
mit | ['Text Generation'] | false | GPT-Rapgenerator The Rapgenerator is trained for [nullsechsroy](https://genius.com/artists/Nullsechsroy) on an english [GPT2](https://huggingface.co/transformers/model_doc/gpt2.html) that is converted to a german [GerPT2](https://github.com/bminixhofer/gerpt2). | 07f95f69ea16c924eaa6cf784eb43776 |
mit | ['Text Generation'] | false | Usage ``` from transformers import pipeline, AutoTokenizer,AutoModelForCausalLM german_gpt_model = "stefan-it/german-gpt2-larger" rap_model = AutoModelForCausalLM.from_pretrained("Bachstelze/Rapgenerator") tokenizer = AutoTokenizer.from_pretrained(german_gpt_model) rap_pipe = pipeline('text-generation', ... | d0f3c6fe688dc91883c6f8264bcf76c1 |
mit | ['Text Generation'] | false | create a new title with Deluxe as rap-feature title_line = rap_pipe("[Title_nullsechsroy feat. Deluxe_")[0]['generated_text'] raw_title_line= title_line[33 : int(title_line.index("]"))] ``` We used the [genius](https://docs.genius.com/ | 78646b0c234f55cb0a930263965274d1 |
mit | ['Text Generation'] | false | /songs-h2) songlyrics from the following artists: ['Ace Tee', 'Aligatoah', 'AnnenMayKantereit', 'Apache 207', 'Azad', 'Badmómzjay', 'Bausa', 'Blumentopf', 'Blumio', 'Capital Bra', 'Casper', 'Celo & Abdi', 'Cro', 'Dardan', 'Dendemann', 'Die P', 'Dondon', 'Dynamite Deluxe', 'Edgar Wasser', 'Eko Fresh', 'Farid Bang', 'Fa... | 2e66133ef3188de46b14f100b093e566 |
mit | ['Text Generation'] | false | Example song structure ``` [Title_nullsechsroy_Goodies] [Part 1_nullsechsroy_Goodies] Soulja Boy – „Pretty Boy Swag“ Heute bei ihr, aber morgen schon weg, ja .. [Hook_nullsechsroy_Goodies] Ich hab' Jungs in der Trap, ich hab' Jungs an der Uni (Ahh) ... [Part 2_nullsechsroy_Goodies] Ja, Soulja Boy – „Pretty Boy Swag“ ... | ce059eed55c89b1f55f357b28df67d9d |
apache-2.0 | ['setfit', 'sentence-transformers', 'text-classification'] | false | fathyshalab/domain_transfer_clinic_credit_cards-massive_lists-roberta-large-v1-2-93 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... | 764640165ec90eddd0f4d38a16fab578 |
apache-2.0 | ['text', 'token-classification', 'en-atc', 'en', 'generated_from_trainer', 'bert', 'bertraffic'] | false | bert-base-token-classification-for-atc-en-uwb-atcc This model allow to detect speaker roles and speaker changes based on text. Normally, this task is done on the acoustic level. However, we propose to perform this task on the text level. We solve this challenge by performing speaker role and change detection with a B... | 3abf13770a90cd923c9459abb4d661b9 |
apache-2.0 | ['text', 'token-classification', 'en-atc', 'en', 'generated_from_trainer', 'bert', 'bertraffic'] | false | Intended uses & limitations This model was fine-tuned on air traffic control data. We don't expect that it keeps the same performance on some others datasets where BERT was pre-trained or fine-tuned. | d97b6ab9970894b423e0ac6983eb7642 |
apache-2.0 | ['text', 'token-classification', 'en-atc', 'en', 'generated_from_trainer', 'bert', 'bertraffic'] | false | Training and evaluation data See Table 3 (page 5) in our paper:[BERTraffic: BERT-based Joint Speaker Role and Speaker Change Detection for Air Traffic Control Communications](https://arxiv.org/abs/2110.05781).. We described there the data used to fine-tune or model for speaker role and speaker change detection. - W... | e1dc07d53fc2cd2e937922927d4e1115 |
apache-2.0 | ['text', 'token-classification', 'en-atc', 'en', 'generated_from_trainer', 'bert', 'bertraffic'] | false | Writing your own inference script The snippet of code: ```python from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Jzuluaga/bert-base-token-classification-for-atc-en-uwb-atcc") model = AutoModelForTokenClassification.from_pretrained("Jzuluag... | 5a85b925c3ac8749ada465301758f93a |
apache-2.0 | ['text', 'token-classification', 'en-atc', 'en', 'generated_from_trainer', 'bert', 'bertraffic'] | false | Process text sample (from UWB-ATCC) from transformers import pipeline nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple") nlp("lining up runway three one csa five bravo b easy five three kilo romeo contact ruzyne ground one two one decimal nine good bye) [{'entity_group': 'pilot',... | f5620586c821257fa2442e7a6e4e7ea6 |
apache-2.0 | ['text', 'token-classification', 'en-atc', 'en', 'generated_from_trainer', 'bert', 'bertraffic'] | false | Cite us If you use this code for your research, please cite our paper with: ``` @article{zuluaga2022bertraffic, title={BERTraffic: BERT-based Joint Speaker Role and Speaker Change Detection for Air Traffic Control Communications}, author={Zuluaga-Gomez, Juan and Sarfjoo, Seyyed Saeed and Prasad, Amrutha and othe... | c185ad04055a651cd8e30b5084e11d4a |
apache-2.0 | ['text', 'token-classification', 'en-atc', 'en', 'generated_from_trainer', 'bert', 'bertraffic'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 64 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - training_steps: 10000 | 33cdb7e0db0c7aa38f7f6cddb56956a0 |
apache-2.0 | ['text', 'token-classification', 'en-atc', 'en', 'generated_from_trainer', 'bert', 'bertraffic'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 0.03 | 500 | 0.2282 | 0.6818 | 0.7001 | 0.6908 | 0.9246 | | 0.3487 | 0.06... | c19f2366722932673ef197080a5504aa |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.2954 - Accuracy: 0.86 - F1: 0.8600 | 1009c16d0470493249c055cf1c131aae |
gpl-3.0 | ['generated_from_trainer'] | false | bert-tagalog-base-uncased-WWM-ner-v1 This model is a fine-tuned version of [jcblaise/bert-tagalog-base-uncased-WWM](https://huggingface.co/jcblaise/bert-tagalog-base-uncased-WWM) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2838 - Precision: 0.9280 - Recall: 0.9153 - F1: 0.9... | a7c5fe91f53427460a1a7c5794b428b0 |
gpl-3.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 205 | 0.4812 | 0.6267 | 0.6367 | 0.6317 | 0.8502 | | No log | 2.0 |... | 92dbd27b8d3ab19147248352ebc750b5 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-wiki 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: 1.7509 | d1e620e39d6eda43e93d87387d8b80ca |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.9294 | 1.0 | 2319 | 1.7732 | | 1.8219 | 2.0 | 4638 | 1.7363 | | 1.7957 | 3.0 | 6957 | 1.7454 | | 61cc19a0cfd5f06bda781d7fd20ab330 |
apache-2.0 | ['t5', 'pytorch', 'zh', 'Text2Text-Generation'] | false | T5 for Chinese Couplet(t5-chinese-couplet) Model T5中文对联生成模型 `t5-chinese-couplet` evaluate couplet test data： The overall performance of T5 on couplet **test**: |prefix|input_text|target_text|pred| |:-- |:--- |:--- |:-- | |对联：|春回大地，对对黄莺鸣暖树|日照神州，群群紫燕衔新泥|福至人间,家家紫燕舞和风| 在Couplet测试集上生成结果满足字数相同、词性对齐、词面对齐、形似要求，而语义对仗工整和平仄合... | 0225fa940aaf0b73cf435cd097d8e64d |
apache-2.0 | ['t5', 'pytorch', 'zh', 'Text2Text-Generation'] | false | Usage 本项目开源在文本生成项目：[textgen](https://github.com/shibing624/textgen)，可支持T5模型，通过如下命令调用： Install package: ```shell pip install -U textgen ``` ```python from textgen import T5Model model = T5Model("t5", "shibing624/t5-chinese-couplet") r = model.predict(["对联：丹枫江冷人初去"]) print(r) | e50be63296d6caabee5d2be2c9963e0a |
apache-2.0 | ['t5', 'pytorch', 'zh', 'Text2Text-Generation'] | false | Usage (HuggingFace Transformers) Without [textgen](https://github.com/shibing624/textgen), you can use the model like this: First, you pass your input through the transformer model, then you get the generated sentence. Install package: ``` pip install transformers ``` ```python from transformers import T5ForCondi... | 3d3691ddeda33181f1834e84c41c8ce1 |
apache-2.0 | ['t5', 'pytorch', 'zh', 'Text2Text-Generation'] | false | 中文对联数据集 - 数据：[对联github](https://github.com/wb14123/couplet-dataset)、[清洗过的对联github](https://github.com/v-zich/couplet-clean-dataset) - 相关内容 - [Huggingface](https://huggingface.co/) - LangZhou Chinese [MengZi T5 pretrained Model](https://huggingface.co/Langboat/mengzi-t5-base) and [paper](https://arxiv.org/pdf/2110... | 72230893a3bd77cbd367368e191b438b |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sentence-transformers/nli-bert-large This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. | 6cd324903f4afdac80848d97f961449c |
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... | ffd665253641f0280082bc58621f27f3 |
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/nli-bert-large) | 380b23cf298e80eac09eda4bc5727315 |
apache-2.0 | ['translation'] | false | tur-ara * source group: Turkish * target group: Arabic * OPUS readme: [tur-ara](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/tur-ara/README.md) * model: transformer * source language(s): tur * target language(s): apc_Latn ara ara_Latn arq_Latn * model: transformer * pre-processing: normal... | a40fd4792d201bc7602d800cd4619f2c |
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