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 | ['pytorch', 'causal-lm'] | false | Training data GPT-J 6B was pretrained on the [Pile](pile.eleuther.ai), a large scale curated dataset created by EleutherAI for the purpose of training this model. After the pre-training, it's finetuned on our Japanese storytelling dataset. Check our blog post for more details. | 79072a2a0d7896cdc9022f7cd09f1614 |
apache-2.0 | ['pytorch', 'causal-lm'] | false | How to use ``` from transformers import AutoTokenizer, AutoModelForCausalLM import torch tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B") model = AutoModelForCausalLM.from_pretrained("NovelAI/genji-jp", torch_dtype=torch.float16, low_cpu_mem_usage=True).eval().cuda() text = '''あらすじ：あなたは異世界に転生してしまいました... | 1096cbfd08cfca6eb1c397f50ec91e72 |
apache-2.0 | ['pytorch', 'causal-lm'] | false | Acknowledgements This project was possible because of the compute provided by the [TPU Research Cloud](https://sites.research.google/trc/) Thanks [EleutherAI](https://eleuther.ai/) for pretraining the GPT-J 6B model. Thanks to everyone who contributed to this project! - [Finetune](https://github.com/finetuneanon) ... | fe88b939cb4b65cdc5de36720144ddd2 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-xls-r-300m_phoneme-mfa_korean This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on a phonetically balanced native Korean read-speech corpus. | 75b86462f9cbf391a00ae95c1dfe0b25 |
apache-2.0 | ['generated_from_trainer'] | false | Training and Evaluation Data Training Data - Data Name: Phonetically Balanced Native Korean Read-speech Corpus - Num. of Samples: 54,000 - Audio Length: 108 Hours Evaluation Data - Data Name: Phonetically Balanced Native Korean Read-speech Corpus - Num. of Samples: 6,000 - Audio Length: 12 Hours | 19e00de90a17d1dbc9e66c9dda61e801 |
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... | a1bb489dce320f719f91ce7ce37819e3 |
apache-2.0 | ['generated_from_trainer'] | false | Experimental Results Official implementation of the paper (in review) Major error patterns of L2 Korean speech from five different L1s: Chinese (ZH), Vietnamese (VI), Japanese (JP), Thai (TH), English (EN)  | 1608902c02bcff756be756deb422d622 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Tiny Dutch This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.7024 - Wer: 42.0655 | fca5e3220ca2266c49bb524d6f03607e |
mit | ['conversational'] | false | THIS AI IS OUTDATED. See [Aeona](https://huggingface.co/deepparag/Aeona) An generative AI made using [microsoft/DialoGPT-small](https://huggingface.co/microsoft/DialoGPT-small). Trained on: https://www.kaggle.com/Cornell-University/movie-dialog-corpus https://www.kaggle.com/jef1056/discord-data [L... | 3775b335cb35cde7b2eb2a82300d5964 |
cc-by-4.0 | ['spanish', 'roberta'] | false | This is a **RoBERTa-base** model trained from scratch in Spanish. The training dataset is [mc4](https://huggingface.co/datasets/bertin-project/mc4-es-sampled ) subsampling documents to a total of about 50 million examples. Sampling is random. This model continued training from [sequence length 128](https://huggingfac... | 915b274e6baa63d22925737b11fab313 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Fine-tuned XLSR-53 large model for speech recognition in Hungarian Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Hungarian using the train and validation splits of [Common Voice 6.1](https://huggingface.co/datasets/common_voice) and [CSS10](https://github.com/... | fd5a3939795acfea442244b624d99a12 |
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... Using the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) library: ```python from huggingsound import SpeechRecognitionModel model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-large-xlsr-53-hungarian") audio_paths = [... | 01657bf9319a0ff73473ae4fc1ed316a |
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 = librosa.load(batch["path"], sr=16_000) batch["speech"] = speech_array batch["sentence"] = batch["sentence"].upper() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs =... | 1594f50f8a0439549b4b19bdd9472619 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Hungarian test data of Common Voice. ```python import torch import re import librosa from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor LANG_ID = "hu" MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-hung... | 7b69ae3dd6c9af1071e1669fd48ed673 |
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(DEVICE), attention_mask=inputs.attention_mask.to(DEVICE)).logits pred_ids = torch... | e851569cbe26eb5a62532aa8f272d42b |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Citation If you want to cite this model you can use this: ```bibtex @misc{grosman2021xlsr53-large-hungarian, title={Fine-tuned {XLSR}-53 large model for speech recognition in {H}ungarian}, author={Grosman, Jonatas}, howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-hungarian}}, y... | 23292a64dec425f2720fd550bc8a0f38 |
apache-2.0 | ['generated_from_trainer'] | false | VN_ja-en_helsinki This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ja-en](https://huggingface.co/Helsinki-NLP/opus-mt-ja-en) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.2409 - BLEU: 15.28 | 905a86b025471c3e3f6d94c67ff1d774 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP | bcac4d8767f7395f9c1a62dc412d2134 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.6165 | 0.19 | 2000 | 2.6734 | | 2.3805 | 0.39 | 4000 | 2.6047 | | 2.2793 | 0.58 | 6000 | 2.5461 | | 2.2028 | 0.78 | 8000 | 2.5127 ... | 4e8ad9b782864011cfc8d2569df13614 |
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: 0.0614 - Precision: 0.9310 - Recall: 0.9498 - F1: 0.9404 - Accuracy: 0.9857 | 3d559cf69c55abf56679281e46a1b20c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0875 | 1.0 | 1756 | 0.0639 | 0.9167 | 0.9387 | 0.9276 | 0.9833 | | 0.0332 | 2.0 |... | 5d9e34ec1e924e14a406c0b3cdfa94ea |
apache-2.0 | ['translation'] | false | opus-mt-sv-fi * source languages: sv * target languages: fi * OPUS readme: [sv-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-fi/README.md) * dataset: opus+bt * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus+bt-2020-04-07.zip](ht... | f5d7b2846ef38788d619332f4c56f02d |
apache-2.0 | ['automatic-speech-recognition', 'pt'] | false | exp_w2v2t_pt_xlsr-53_s454 Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech... | 6f2a53b524ac571e8a7bb872d8c54db5 |
apache-2.0 | ['finnish', 'roberta'] | false | RoBERTa large model trained with WECHSEL method for Finnish Pretrained RoBERTa model on Finnish language using a masked language modeling (MLM) objective with WECHSEL method. RoBERTa was introduced in [this paper](https://arxiv.org/abs/1907.11692) and first released in [this repository](https://github.com/pytorch/fai... | cecd2e32edcdb908f581144781a5c614 |
apache-2.0 | ['finnish', 'roberta'] | false | WECHSEL method Using the WECHSEL method, we first took the pretrained English [roberta-large](https://huggingface.co/roberta-large) model, changed its tokenizer with our Finnish tokenizer and initialized model's token embeddings such that they are close to semantically similar English tokens by utilizing multilingual... | b6891fbfe314b5cd3cdac3fd472036ee |
apache-2.0 | ['finnish', 'roberta'] | false | How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='Finnish-NLP/roberta-large-wechsel-finnish') >>> unmasker("Moikka olen <mask> kielimalli.") [{'sequence': 'Moikka olen hyvä kielimalli.'... | 5b6aa0c0531614071777315c60833655 |
apache-2.0 | ['finnish', 'roberta'] | false | Training data This Finnish RoBERTa model was pretrained on the combination of five datasets: - [mc4_fi_cleaned](https://huggingface.co/datasets/Finnish-NLP/mc4_fi_cleaned), the dataset mC4 is a multilingual colossal, cleaned version of Common Crawl's web crawl corpus. We used the Finnish subset of the mC4 dataset and... | db5c6b3c990c735a159ff8c2fd0b7136 |
apache-2.0 | ['finnish', 'roberta'] | false | Pretraining The model was trained on TPUv3-8 VM, sponsored by the [Google TPU Research Cloud](https://sites.research.google/trc/about/), for 270k steps (a bit over 1 epoch, 512 batch size) with a sequence length of 128 and continuing for 180k steps (batch size 64) with a sequence length of 512. The optimizer used was... | 408e424ff782e270ed2e79a50be1debd |
apache-2.0 | ['finnish', 'roberta'] | false | Evaluation results Evaluation was done by fine-tuning the model on downstream text classification task with two different labeled datasets: [Yle News](https://github.com/spyysalo/yle-corpus) and [Eduskunta](https://github.com/aajanki/eduskunta-vkk). Yle News classification fine-tuning was done with two different sequ... | fd30857e4dedebeb44facf15d0cdb51d |
apache-2.0 | ['finnish', 'roberta'] | false | Team Members - Aapo Tanskanen, [Hugging Face profile](https://huggingface.co/aapot), [LinkedIn profile](https://www.linkedin.com/in/aapotanskanen/) - Rasmus Toivanen [Hugging Face profile](https://huggingface.co/RASMUS), [LinkedIn profile](https://www.linkedin.com/in/rasmustoivanen/) Feel free to contact us for more... | 353a71cf3b26f31041c65d3a11e24384 |
apache-2.0 | ['generated_from_keras_callback'] | false | javilonso/classificationPolEsp2 This model is a fine-tuned version of [PlanTL-GOB-ES/gpt2-base-bne](https://huggingface.co/PlanTL-GOB-ES/gpt2-base-bne) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1229 - Validation Loss: 0.8172 - Epoch: 2 | a1dc31baebbd0e8e1c5d1b6f7c747858 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 17958, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay'... | cb470e1c154a9a64f31828b80b641d55 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.6246 | 0.5679 | 0 | | 0.4198 | 0.6097 | 1 | | 0.1229 | 0.8172 | 2 | | 10c6d8f2ad0765b9735b0aa1620b0d13 |
mit | [] | false | InsideWhale on Stable Diffusion This is the `<InsideWhale>` 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 al... | be1c8c95bcee8ca1ca50e6181d73c6cb |
apache-2.0 | ['summarization'] | 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 BigBirdPegasusForConditionalGeneration, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("google/bigbird-pegasus-large-bigpatent") | dbc643d676782b59dad96ddd66e2c1dc |
apache-2.0 | ['summarization'] | false | you can change `attention_type` (encoder only) to full attention like this: model = BigBirdPegasusForConditionalGeneration.from_pretrained("google/bigbird-pegasus-large-bigpatent", attention_type="original_full") | 2503b6f004d4434e23fdcac065d1bac4 |
apache-2.0 | ['summarization'] | false | you can change `block_size` & `num_random_blocks` like this: model = BigBirdPegasusForConditionalGeneration.from_pretrained("google/bigbird-pegasus-large-bigpatent", block_size=16, num_random_blocks=2) text = "Replace me by any text you'd like." inputs = tokenizer(text, return_tensors='pt') prediction = model.generat... | c145bccb391141bcc80e9a75f712102c |
other | ['generated_from_trainer'] | false | finetuned-distilbert-news-article-catgorization This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the adult_content dataset. It achieves the following results on the evaluation set: - Loss: 0.0065 - F1_score(weighted): 0.90 | 14d064bb7c4926077c362803100a827e |
other | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-5 - train_batch_size: 5 - eval_batch_size: 5 - seed: 17 - optimizer: AdamW(lr=1e-5 and epsilon=1e-08) - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 0 - num_epochs: 2 | 31279bbcae9645099bbd8d153f70e7a4 |
other | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Validation Loss | f1 score | |:-------------:|:-----:|:---------------: |:------:| | 0.1414 | 1.0 | 0.4585 | 0.9058 | | 0.1410 | 2.0 | 0.4584 | 0.9058 | | afdc65a5e70b761015046d1db957f340 |
apache-2.0 | ['generated_from_keras_callback'] | false | annaeze/lab9_1 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: - Train Loss: 0.0230 - Validation Loss: 0.0572 - Epoch: 2 | 3835e52ccf1a95909105743b9ab596df |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.1174 | 0.0596 | 0 | | 0.0391 | 0.0529 | 1 | | 0.0230 | 0.0572 | 2 | | f91856522d5c62701af4b1e03eaee0bb |
apache-2.0 | [] | false | BERT multilingual base model (cased) Pretrained model on the top 104 languages with the largest Wikipedia using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/abs/1810.04805) and first released in [this repository](https://github.com/google-research/bert). This model i... | 34971bbad56a1a89515004b7d009c591 |
apache-2.0 | [] | false | Model description BERT is a transformers model pretrained on a large corpus of multilingual data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate in... | c746fb08c2142034ae2c8d01c2029223 |
apache-2.0 | [] | false | How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='bert-base-multilingual-cased') >>> unmasker("Hello I'm a [MASK] model.") [{'sequence': "[CLS] Hello I'm a model model. [SEP]", 'score... | a7f91a7cc832793ff4f83b44266417c2 |
apache-2.0 | [] | false | Training data The BERT model was pretrained on the 104 languages with the largest Wikipedias. You can find the complete list [here](https://github.com/google-research/bert/blob/master/multilingual.md | 2cb3c7c20be6262f1e00e0ec3ea5abee |
apache-2.0 | [] | false | Preprocessing The texts are lowercased and tokenized using WordPiece and a shared vocabulary size of 110,000. The languages with a larger Wikipedia are under-sampled and the ones with lower resources are oversampled. For languages like Chinese, Japanese Kanji and Korean Hanja that don't have space, a CJK Unicode bloc... | 376b2bb9d8fc8cf536d1d880ea01ae7f |
apache-2.0 | ['generated_from_trainer'] | false | UrduAudio2Text 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: 1.4978 - Wer: 0.8376 | 85fb942b1fcdf9a5a5b8fcc73d17e2f7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.5558 | 15.98 | 400 | 1.4978 | 0.8376 | | 8c216a479c5829acb56cccf0edbb6732 |
apache-2.0 | ['monai', 'medical'] | false | Model Overview This model is trained using the runner-up [1] awarded pipeline of the "Medical Segmentation Decathlon Challenge 2018" using the UNet architecture [2] with 32 training images and 9 validation images. | 9fe7b153d77dbfc9de17a7a1160de599 |
apache-2.0 | ['monai', 'medical'] | false | commands example Execute training: ``` python -m monai.bundle run training --meta_file configs/metadata.json --config_file configs/train.json --logging_file configs/logging.conf ``` Override the `train` config to execute multi-GPU training: ``` torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run... | 483328602e4e11c4e8bafbd474dfa041 |
apache-2.0 | ['monai', 'medical'] | false | References [1] Xia, Yingda, et al. "3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training." arXiv preprint arXiv:1811.12506 (2018). https://arxiv.org/abs/1811.12506. [2] Kerfoot E., Clough J., Oksuz I., Lee J., King A.P., Schnabel J.A. (2019) Left-Ventricle Quantification Using Residual U-Net. In:... | 27f5dca9b3a83135a3099d127011e863 |
apache-2.0 | ['generated_from_trainer'] | false | Use this model to detect Twitter users' profiles related to healthcare. User profile classification may be useful when searching for health information on Twitter. For a certain health topic, tweets from physicians or organizations (e.g. ```Board-certified dermatologist```) may be more reliable than undefined or vague... | 03b4f20be45809a1704ea55aac372037 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0616 - Precision: 0.9265 - Recall: 0.9361 - F1: 0.9313 - Accuracy: 0.9837 | 66a9b2eda032996199900f228f4aa9dd |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2437 | 1.0 | 878 | 0.0745 | 0.9144 | 0.9173 | 0.9158 | 0.9799 | | 0.0518 | 2.0 |... | ad990d9f0d629be54ac02ca78cb91fa5 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-policies This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the policies dataset. It achieves the following results on the evaluation set: - Loss: 0.0193 | 14fe6560b72f47e25fd2ef8c6aaa38ff |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.4208 | 1.0 | 759 | 0.0183 | | 0.0115 | 2.0 | 1518 | 0.0202 | | 0.0048 | 3.0 | 2277 | 0.0193 | | 70322d00117b9b738db0aecc9a5336d6 |
apache-2.0 | ['generated_from_trainer'] | false | aa This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 15.9757 - Wer: 1.0 | 68779a3f16dde36200910b445f14fd37 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | 517af4f4b0fb43d92a4a1e589a86ec82 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 14.5628 | 3.33 | 20 | 16.1808 | 1.0 | | 14.5379 | 6.67 | 40 | 16.1005 | 1.0 | | 14.3379 | 10.0 | 60 | 15.9757 | 1.0 | | 373758c08e913fcba590d46f82c71122 |
mit | ['generated_from_trainer'] | false | roberta-large-md-conllpp-v3 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: 0.0564 - Precision: 0.9980 - Recall: 0.9951 - F1: 0.9965 - Accuracy: 0.9943 | 777ebae082be738d194209323c988c8f |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0945 | 1.0 | 878 | 0.0317 | 0.9975 | 0.9950 | 0.9963 | 0.9939 | | 0.0175 | 2.0 |... | bd7c284aeafb6611cfba7c910cf96348 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | mmarco-sentence-BERTino This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. It was trained on [mmarco](https://huggingface.co/datasets/unicamp-dl/mmarco/viewer/italian/train... | bb5ad3f224ad68f864d31ff240871173 |
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 = ["Questo è un esempio di fr... | 2dc92e9e6d264dada5eb67ff09fcf1d0 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTo... | 0ef165af21f8f9b0fa682c4b060f68f0 |
apache-2.0 | ['mobile', 'vison', 'image-classification'] | false | Model Details EfficientFormer-L3, developed by [Snap Research](https://github.com/snap-research), is one of three EfficientFormer models. The EfficientFormer models were released as part of an effort to prove that properly designed transformers can reach extremely low latency on mobile devices while maintaining high... | 6eae00a5e55ceb229399d29be5ce56ba |
apache-2.0 | ['mobile', 'vison', 'image-classification'] | false | Load preprocessor and pretrained model model_name = "huggingface/efficientformer-l3-300" processor = EfficientFormerImageProcessor.from_pretrained(model_name) model = EfficientFormerForImageClassificationWithTeacher.from_pretrained(model_name) | fb2125fe496335d47993fcff6abdd1f2 |
apache-2.0 | ['generated_from_trainer'] | false | funnel-transformer-xlarge_ner_wikiann This model is a fine-tuned version of [funnel-transformer/xlarge](https://huggingface.co/funnel-transformer/xlarge) on the wikiann dataset. It achieves the following results on the evaluation set: - Loss: 0.4023 - Precision: 0.8522 - Recall: 0.8634 - F1: 0.8577 - Accuracy: 0.9358... | 3703317fee31481929a15ee1795769e0 |
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: cosine - num_epochs: 5 | 0d3284741cbb314becf07e86c32779a9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.3193 | 1.0 | 5000 | 0.3116 | 0.8239 | 0.8296 | 0.8267 | 0.9260 | | 0.2836 | 2.0 ... | 90bb7988f28e651d9f346371159f613f |
cc-by-4.0 | ['question generation'] | false | Model Card of `research-backup/t5-base-squadshifts-vanilla-reddit-qg` This model is fine-tuned version of [t5-base](https://huggingface.co/t5-base) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: reddit) via [`lmqg`](https://github.com/asahi... | b317f34498cf4a888958bc2f97b2df44 |
cc-by-4.0 | ['question generation'] | false | Overview - **Language model:** [t5-base](https://huggingface.co/t5-base) - **Language:** en - **Training data:** [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (reddit) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-quest... | 8ecda5ba142b0a72e5e48f7b38028add |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "research-backup/t5-base-squadshif... | 22b5bdfe473e5b5271ebf976a96de631 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/t5-base-squadshifts-vanilla-reddit-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.reddit.json) | | Score | Type | Dataset ... | 8959ab5b6d6a44b26da340b28b1adbaa |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squadshifts - dataset_name: reddit - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: ['qg'] - model: t5-base - max_length: 512 - max_length_output: 32 - epoch: 5 - batc... | d6b6e0579ac32e4d13dfdfe617271110 |
mit | ['generated_from_trainer'] | false | xlmr-finetuned-ner This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the wikiann dataset. It achieves the following results on the evaluation set: - Loss: 0.1395 - Precision: 0.9044 - Recall: 0.9137 - F1: 0.9090 - Accuracy: 0.9649 | 4895dd3c52b07ee0af0bca5e797acc01 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.4215 | 1.0 | 938 | 0.1650 | 0.8822 | 0.8781 | 0.8802 | 0.9529 | | 0.1559 | 2.0 |... | 2f72b41152969f6a8bb41895136febab |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-cola-custom-tokenizer-target-glue-stsb This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-cola-custom-tokenizer](https://huggingface.co/muhtasham/tiny-mlm-glue-cola-custom-tokenizer) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3195 - Pearson: 0.674... | 1be1754b0240b91cb72457f4489a4fe8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:| | 3.3068 | 2.78 | 500 | 1.9896 | 0.3651 | 0.3938 | | 1.4915 | 5.56 | 1000 | 1.3053 | 0.6677 | 0.6745 | | 1.2119 ... | 4ae26ce176f35217883ba1b93a043da7 |
creativeml-openrail-m | [] | false |  [*EMBEDDING DOWNLOAD LINK*](https://huggingface.co/joachimsallstrom/Glitch-Embedding/resolve/main/glitch.pt) – Glitch is a finetuned embedding inspired by 80s and 90s VHS tape aesthetics (trained on SD 2.1 768 ema pruned). With it ... | 25288636aefd67e0c251c0abca0919cf |
creativeml-openrail-m | [] | false | Install instructions and usage 1. Place either the [*glitch.pt*](https://huggingface.co/joachimsallstrom/Glitch-Embedding/resolve/main/glitch.pt) or [*glitch.png*](https://huggingface.co/joachimsallstrom/Glitch-Embedding/resolve/main/glitch.png) file in the embeddings folder of your Automatic1111 installation. 3. Tri... | 7482c4dbe6336be2fedca8cd26e063ce |
creativeml-openrail-m | [] | false | Example prompts and settings TV/movie still:<br> **glitch, close-up portrait of Millie Bobby Brown as Eleven, Stranger Things 1 9 8 2 movie still, Mitchell FC 65 Camera 35 mm, heavy grain**<br> Negative prompt: **ugly cartoon drawing, blurry, blurry, blurry, blurry**<br> _Steps: 30, Sampler: DPM++ SDE Karras, CFG sca... | 01758c2383015c8689eee2dc1ddf412f |
creativeml-openrail-m | [] | false | Credit Thanks to [*masslevel*](https://twitter.com/masslevel?s=21&t=_O7DiffGgoNtZD33jECV_g) who has contributed with a large number of images and knowledge on prompt settings. Thanks also to Klinter for providing the gif animation. | ed3cb7dd39ccb53acdacdb92df3212e0 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Large-v2 Nepali This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the mozilla-foundation/common_voice_11_0 ne-NP dataset. It achieves the following results on the evaluation set: - Loss: 1.5723 - Wer: 56.0976 | 707e84e35f8a1c5c40840f19ee100140 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0 | 999.0 | 1000 | 1.5723 | 56.0976 | | fda1e38178b0e24aa95d477e5bf16173 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-misinfo-model-1000-Zhaohui 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: 0.7352 - Accuracy: 0.8226 - F1: 0.8571 | 819d2e62d7cb3a3f65de6bfd28f9adab |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-xsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1681 - Rouge1: 60.7249 - Rouge2: 36.0768 - Rougel: 57.6761 - Rougelsum: 57.8618 - Gen Len: 17.9 | 47fdc5f5906264bc10dd10bf5d1b1005 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100 - mixed_precision_training: Native AMP | 7d6c0a42eb1e7a0134ee415aae5c4ed2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 2 | 2.7817 | 13.2305 | 4.2105 | 11.0476 | 11.2063 | 13... | f3fc7b493b76db82dd4f67f6f2923462 |
apache-2.0 | ['generated_from_trainer'] | false | roberta-base-bne-finetuned-amazon_reviews_multi This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.2291 - Accuracy: 0.9343 | 7dc89f4faea1225cb2aca94645aa2f5b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1909 | 1.0 | 1250 | 0.1784 | 0.9295 | | 0.1013 | 2.0 | 2500 | 0.2291 | 0.9343 | | 8ddb13f62d7aaedb61e3f39c6ced864a |
apache-2.0 | ['generated_from_trainer'] | false | swin-tiny-patch4-window7-224-finetuned-3e This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1065 - Accuracy: 0.9606 | f8fcb9ba4260cb637ca0c23650fef83d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4549 | 1.0 | 527 | 0.2910 | 0.8857 | | 0.2838 | 2.0 | 1054 | 0.1524 | 0.9410 | | 0.254 | 3.0 | 1581 | 0.1065 | 0.... | c58df9931be931f9f78fb34ef6c1221a |
cc | ['generated_from_trainer'] | false | racism-finetuned-detests-prueba This model is a fine-tuned version of [davidmasip/racism](https://huggingface.co/davidmasip/racism) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3034 - F1: 0.6222 | 153da09b3fb9c34a1bcc18b29ffa827b |
cc | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-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: linear - num_epochs: 10 | 64a1b15408f6200630aecbe627fc2126 |
cc | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.0001 | 1.0 | 49 | 1.0331 | 0.6667 | | 0.0 | 2.0 | 98 | 1.2473 | 0.5992 | | 0.0 | 3.0 | 147 | 1.2280 | 0.6227 | |... | 3e51cfbcb8fbee95818f085f469bccf2 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-cola-from-scratch-custom-tokenizer-expand-vocab-target-glue-cola This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-cola-from-scratch-custom-tokenizer-expand-vocab](https://huggingface.co/muhtasham/tiny-mlm-glue-cola-from-scratch-custom-tokenizer-expand-vocab) on the None dataset. It achieve... | 3daad064a3d9ebd642a6928ca6acbc3c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.6103 | 1.87 | 500 | 0.6214 | 0.0 | | 0.6073 | 3.73 | 1000 | 0.6197 | 0.0 | | 0.6... | 9bbcfac9fd6950731ea9e3224cfc7f23 |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-travel-2-16-5 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: 2.1384 - Accuracy: 0.4289 | de5b84aaee6ec5b137dd4f1cec669600 |
apache-2.0 | [] | false | doc2query/all-with_prefix-t5-base-v1
This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on T5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)).
It can be used for:
- **Document expansion**: You generate for your paragraphs 20-40... | 6ff0cbad0177c8f7c9515d0544b92270 |
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