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 | ['whisper-event', 'hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0105 | 4.24 | 1000 | 0.1973 | 12.6130 | | 0.0016 | 8.47 | 2000 | 0.2198 | 11.8985 | | 0.0004 | 12.71 | 3000 | 0.2310 | 11.454... | 07173f68a1314e309fb2eac2bc58c80d |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sentence-transformers/stsb-distilroberta-base-v2 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. | 90eac81fa0320a9b6eb2ec869981a917 |
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... | 23aa58d760fc36390b1e7f4b3fecea85 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/stsb-distilroberta-base-v2') model = AutoModel.from_pretrained('sentence-transformers/stsb-distilroberta-base-v2') | 7095307ed2c1d38f45956220c9c75115 |
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/stsb-distilroberta-base-v2) | 265c2c0ad40a5718ee008a1ef45e9e93 |
mit | ['generated_from_trainer'] | false | smalldata-pysentimiento-robertuito-eng-only-sentiment-single-finetuned-memes This model is a fine-tuned version of [jayantapaul888/twitter-data-microsoft-xtremedistil-l6-h256-uncased-sentiment-finetuned-memes](https://huggingface.co/jayantapaul888/twitter-data-microsoft-xtremedistil-l6-h256-uncased-sentiment-finetune... | c3c72a071781ff43f2659a1d0c3a85ac |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | No log | 1.0 | 378 | 0.3505 | 0.8466 | 0.8687 | 0.8600 | 0.8608 | | 0.4239 | 2.0 |... | 0978afe76edaf9ea78a2b7988d043564 |
apache-2.0 | ['image-segmentation', 'vision'] | false | DETR (End-to-End Object Detection) model with ResNet-101 backbone DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 panoptic (118k annotated images). It was introduced in the paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by Carion et al. and first released in... | 8931a807cb44be192c2fe2b83a72b888 |
apache-2.0 | ['image-segmentation', 'vision'] | false | Model description The DETR model is an encoder-decoder transformer with a convolutional backbone. Two heads are added on top of the decoder outputs in order to perform object detection: a linear layer for the class labels and a MLP (multi-layer perceptron) for the bounding boxes. The model uses so-called object queri... | 74b43ad0c2d21b6a37a8a75012e32b93 |
apache-2.0 | ['image-segmentation', 'vision'] | false | How to use Here is how to use this model: ```python from transformers import DetrFeatureExtractor, DetrForSegmentation from PIL import Image import requests url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Image.open(requests.get(url, stream=True).raw) feature_extractor = DetrFeatureExtractor... | 5e8be4f6ec9787584ec21e4742f0d3e4 |
apache-2.0 | ['image-segmentation', 'vision'] | false | use the `post_process_panoptic` method of `DetrFeatureExtractor` to convert to COCO format processed_sizes = torch.as_tensor(inputs["pixel_values"].shape[-2:]).unsqueeze(0) result = feature_extractor.post_process_panoptic(outputs, processed_sizes)[0] | fe64847d75e67edf9f8563179ed6ff51 |
apache-2.0 | ['image-segmentation', 'vision'] | false | Preprocessing The exact details of preprocessing of images during training/validation can be found [here](https://github.com/facebookresearch/detr/blob/master/datasets/coco_panoptic.py). Images are resized/rescaled such that the shortest side is at least 800 pixels and the largest side at most 1333 pixels, and norm... | b429252807a11bcc78f0f19b4ff568df |
apache-2.0 | ['image-segmentation', 'vision'] | false | Evaluation results This model achieves the following results on COCO 2017 validation: a box AP (average precision) of **40.1**, a segmentation AP (average precision) of **33** and a PQ (panoptic quality) of **45.1**. For more details regarding evaluation results, we refer to table 5 of the original paper. | f101f5987815d73d70ecc7cf483d7bc2 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'animal'] | false | DreamBooth model for the kndrtycr concept trained by alikanakar on the alikanakar/sd_finetune_toy_car dataset. This is a Stable Diffusion model fine-tuned on the kndrtycr concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of kndrtycr toy** This model was created as part of the Drea... | 0056b5203e83dfe0402e74ccd4cb8480 |
mit | ['generated_from_trainer'] | false | berturk-128k-keyword-discriminator This model is a fine-tuned version of [dbmdz/bert-base-turkish-128k-cased](https://huggingface.co/dbmdz/bert-base-turkish-128k-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3828 - Precision: 0.6791 - Recall: 0.7234 - Accuracy: 0.929... | 7c5c29e7a97bc472d8230a13e070775f |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1 | Ent/precision | Ent/accuracy | Ent/f1 | Con/precision | Con/accuracy | Con/f1 | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:--------:|:------:|:-------------:|:------------:|:----... | c226ce4eb741bbccd6f587f0dafea315 |
apache-2.0 | ['generated_from_trainer'] | false | initial-dq-model This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1677 - Precision: 0.7763 - Recall: 0.9380 - F1: 0.8495 - Accuracy: 0.9423 | ac3b0f6dd05dc12c942c3f0eb6305de2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2251 | 1.0 | 1220 | 0.1768 | 0.7481 | 0.9264 | 0.8277 | 0.9378 | | 0.186 | 2.0 |... | 72e8796785320461dccbef1f6935cd72 |
apache-2.0 | ['generated_from_keras_callback'] | false | pranavkrishna/bert_amazon-finetuned-imdb 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: - Train Loss: 3.0874 - Validation Loss: 2.6529 - Epoch: 0 | 6216190956358807a792af4f36fd0dde |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps... | 53b3f2769f11ad8291dfeed687647659 |
apache-2.0 | ['generated_from_trainer'] | false | VANBase-finetuned-brs-finetuned-brs This model is a fine-tuned version of [Visual-Attention-Network/van-base](https://huggingface.co/Visual-Attention-Network/van-base) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.7056 - Accuracy: 0.5882 - F1: 0.6957 - Precision (ppv):... | 3b813442e6b7c0c92bf2a03d65c802da |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - 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_schedu... | 799358473e053c586d3037134013cb99 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision (ppv) | Recall (sensitivity) | Specificity | Npv | Auc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------------:|:--------------------:|:-----------:|:------:|:------:| | 0.6589 ... | 363d0f55925029a1de12ab96e97a17e0 |
apache-2.0 | ['translation'] | false | opus-mt-es-ase * source languages: es * target languages: ase * OPUS readme: [es-ase](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-ase/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](http... | b1be7df693f71d36fcce21c82c9ba4c3 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion 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: - Loss: 0.2234 - Accuracy: 0.9265 - F1: 0.9265 | beaa9eca8d3c333e91075649a9b030fc |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8759 | 1.0 | 250 | 0.3343 | 0.9035 | 0.8999 | | 0.2637 | 2.0 | 500 | 0.2234 | 0.9265 | 0.9265 | | 253d7ecd1842b2a96387a8e87662eddd |
mit | [] | false | Cyberpunk-Lucy on Stable Diffusion This is the `<cyberpunk-lucy>` 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 ... | ff2233abe23164c43c6dd9c68634bcb9 |
apache-2.0 | ['generated_from_trainer'] | false | youtube-bert 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: 2.4771 | 2210ff8ad00cb77874386a3f0490ae9a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.691 | 1.0 | 1077 | 2.5445 | | 2.5768 | 2.0 | 2154 | 2.5226 | | 2.5227 | 3.0 | 3231 | 2.5027 | | e2e12af506c086025edbf4b10acab1ad |
apache-2.0 | ['generated_from_trainer'] | false | Tagged_Uni_500v8_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_uni500v8_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.2501 - Precision: 0.7046 - Recall: 0.6968 - F1: 0.7007 - Accura... | e709231e006e60e53ef836fc53e8fde8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 169 | 0.2800 | 0.5648 | 0.5035 | 0.5324 | 0.9043 | | No log | 2.0 |... | b8e632d496e1f68766be1367c1df69eb |
cc-by-sa-4.0 | ['vietnamese', 'token-classification', 'pos', 'dependency-parsing'] | false | Model Description This is a PhoBERT model pre-trained on Vietnamese texts for POS-tagging and dependency-parsing (using `goeswith` for subwords), derived from [phobert-large](https://huggingface.co/vinai/phobert-large). | 1c8cb2799f0464554fbee6e2aaa15077 |
cc-by-sa-4.0 | ['vietnamese', 'token-classification', 'pos', 'dependency-parsing'] | false | text = "+text+"\n" q=[self.model.config.id2label[p[i,j]].split("|") for i,j in enumerate(h)] t=[i.replace("_"," ") for i in t] if len(t)!=len(v)-2: t=[z.pop(0) if i==self.tokenizer.unk_token else i.replace("_"," ") for i in self.tokenizer.convert_ids_to_tokens(v[1:-1])] for i,j in reversed(list... | 842d547110f9da4ba41e7e8b1ddaceec |
apache-2.0 | ['token-classification'] | false | distilroberta-base-ner-conll2003 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the conll2003 dataset. eval F1-Score: **95,29** (CoNLL-03) test F1-Score: **90,74** (CoNLL-03) eval F1-Score: **95,29** (CoNLL++ / CoNLL-03 corrected) test F1-Score: **92,... | f911bf7f2db0513aa13bdd3cda03a747 |
apache-2.0 | ['token-classification'] | false | Model Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("philschmid/distilroberta-base-ner-conll2003") model = AutoModelForTokenClassification.from_pretrained("philschmid/distilroberta-base-ner-conll2003... | e43ccc466e677950027cdb893f55dd6f |
apache-2.0 | ['token-classification'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4.9902376275441704e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6.0 - mixed_precision_training: Native ... | 5cc77273bc7c0a55543fc2992b8381fb |
apache-2.0 | ['token-classification'] | false | CoNNL2003 It achieves the following results on the evaluation set: - Loss: 0.0583 - Precision: 0.9493 - Recall: 0.9566 - F1: 0.9529 - Accuracy: 0.9883 It achieves the following results on the test set: - Loss: 0.2025 - Precision: 0.8999 - Recall: 0.915 - F1: 0.9074 - Accuracy: 0.9741 | 314b3ea736fce63873108bf65cfdcc8c |
apache-2.0 | ['token-classification'] | false | CoNNL++ / CoNLL2003 corrected It achieves the following results on the evaluation set: - Loss: 0.0567 - Precision: 0.9493 - Recall: 0.9566 - F1: 0.9529 - Accuracy: 0.9883 It achieves the following results on the test set: - Loss: 0.1359 - Precision: 0.92 - Recall: 0.9245 - F1: 0.9223 - Accuracy: 0.9785 | 5eac220b3ad0439e71d68f74e65d78ae |
cc-by-4.0 | ['question generation'] | false | Model Card of `lmqg/t5-small-squadshifts-nyt-qg` This model is fine-tuned version of [lmqg/t5-small-squad](https://huggingface.co/lmqg/t5-small-squad) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: nyt) via [`lmqg`](https://github.com/asahi... | 88a12a480a218df95f3fdce84d35a730 |
cc-by-4.0 | ['question generation'] | false | Overview - **Language model:** [lmqg/t5-small-squad](https://huggingface.co/lmqg/t5-small-squad) - **Language:** en - **Training data:** [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (nyt) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.... | 43a2cd2969907a51c701926ac88e7e8a |
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", "lmqg/t5-small-squadshifts-nyt-qg"... | 0c144b0172efcedc83e1e2640ebd0afc |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/t5-small-squadshifts-nyt-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.nyt.json) | | Score | Type | Dataset ... | ae6493b4a743dcc06f47ee6413967387 |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squadshifts - dataset_name: nyt - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: ['qg'] - model: lmqg/t5-small-squad - max_length: 512 - max_length_output: 32 - epoch: ... | ff3831027681c87feebadd32a3a70450 |
mit | ['Explain code', 'Code Summarization', 'Summarization'] | false | Model description Gemini is a transformer based on Google's T5 model. The model is pre-trained on approximately 800k code/description pairs and then fine-tuned on 10k higher-level explanations that were synthetically generated. Gemini is capable of summarization/explaining short to medium code snippets in: - Python ... | be6abfee8703aa5daa7e269820a8e6c5 |
mit | ['Explain code', 'Code Summarization', 'Summarization'] | false | Intended uses Gemini without any additional fine-tuning is capable of explaining code in a sentence or two and typically performs best in Python and Javascript. We recommend using Gemini for either simple code explanation, documentation or producing more synthetic data to improve its explanations. | c592a90dcd2696b1dbbf22920f55c92e |
mit | ['Explain code', 'Code Summarization', 'Summarization'] | false | How to use You can use this model directly with a pipeline for Text2Text generation, as shown below: ```python from transformers import pipeline, set_seed summarizer = pipeline('text2text-generation', model='describeai/gemini') code = "print('hello world!')" response = summarizer(code, max_length=100, num_beams=3)... | 2a1211b609b782783cf4dd87c7b15cc7 |
mit | ['Explain code', 'Code Summarization', 'Summarization'] | false | Limitations Typically, Gemini may produce overly simplistic descriptions that don't encompass the entire code snippet. We suspect with more training data, this could be circumvented and will produce better results. | 87c7bb9c4f7b4a274c7969a8ed2a13cd |
mit | ['Explain code', 'Code Summarization', 'Summarization'] | false | About Us A Describe.ai, we are focused on building Artificial Intelligence systems that can understand language as well as humans. While a long path, we plan to contribute our findings to our API to the Open Source community. | 1e2314f8bc92467e61451fbe4c2e5ccd |
apache-2.0 | ['whisper-event', 'generated_from_trainer', 'hf-asr-leaderboard', 'pashto', 'ps'] | false | Whisper Small Pashto This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the google/fleurs ps_af dataset. It achieves the following results on the evaluation set: - Loss: 1.1800 - Wer: 63.1053 | c95dcc4f16bea300fd2480f1d35a6161 |
apache-2.0 | ['whisper-event', 'generated_from_trainer', 'hf-asr-leaderboard', 'pashto', 'ps'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-07 - train_batch_size: 32 - eval_batch_size: 16 - 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_sch... | af8fa9461dd560a1855962f193378a5d |
apache-2.0 | ['whisper-event', 'generated_from_trainer', 'hf-asr-leaderboard', 'pashto', 'ps'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:--------:| | 2.0871 | 14.29 | 100 | 2.0102 | 230.2739 | | 1.465 | 28.57 | 200 | 1.4969 | 137.2427 | | 1.1617 | 42.86 | 300 | 1.2716 ... | 5fa2906a145d5cebf512ac211549d9a0 |
apache-2.0 | ['bert', 'qqp', 'glue', 'kd', 'torchdistill'] | false | `bert-base-uncased` fine-tuned on QQP dataset, using fine-tuned `bert-large-uncased` as a teacher model, [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_kd_and_submission.ipynb) ... | d9bae4091d7a4ca5187d9808f6781b1d |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-imdb-target-imdb This model is a fine-tuned version of [muhtasham/tiny-mlm-imdb](https://huggingface.co/muhtasham/tiny-mlm-imdb) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.2699 - Accuracy: 0.8895 - F1: 0.9415 | 814bf72d01a6b6892f507ba534b480ad |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.5432 | 0.64 | 500 | 0.3567 | 0.8578 | 0.9235 | | 0.366 | 1.28 | 1000 | 0.3687 | 0.8414 | 0.9138 | | 0.32 |... | 8c104ee6588985a43a121e297181445f |
creativeml-openrail-m | ['text-to-image'] | false | Duskfall's Final Fantasy Pt2 Dreambooth model trained by Duskfallcrew 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... | 1ec0f82704f8bdcfcb95ce676d06fe2b |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | all-roberta-large-v1 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. | 1edcf223f19db013fadd2d8e0217acc2 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | 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 sente... | 2576d6ab6c0be65c483f799a9931a76c |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-roberta-large-v1') model = AutoModel.from_pretrained('sentence-transformers/all-roberta-large-v1') | d4cb10b2f2b1b4ed56ea32fc5da8afbb |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | 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/all-roberta-large-v1) ------ | 3a38762243fc0ffafba704e12550fdb1 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Background The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised contrastive learning objective. We used the pretrained [`roberta-large`](https://huggingface.co/roberta-large) model and fine-tuned in on a 1B sentence pairs dataset. We use a contrastive lea... | 895c3f59ca2dc66b194e5736f2a9cc52 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Hyper parameters We trained ou model on a TPU v3-8. We train the model during 400k steps using a batch size of 256 (32 per TPU core). We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with a 2e-5 learning rate. The full training script is accessible in t... | 2c8f22bdc0ead3ca2038d18819eb169a |
apache-2.0 | ['speech'] | false | Data2Vec-Audio-Large-100h [Facebook's Data2Vec](https://ai.facebook.com/research/data2vec-a-general-framework-for-self-supervised-learning-in-speech-vision-and-language/) The large model pretrained and fine-tuned on 100 hours of Librispeech on 16kHz sampled speech audio. When using the model make sure that your spee... | 4fd9bcfeee5ddadebab63b692bdc27f6 |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-SMALL-KV16 (Deep-Narrow version) T5-Efficient-SMALL-KV16 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* checkpoint ... | 3a5d73241932b6c5c1cf0e2cfec8e81b |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-small-kv16** - is of model type **Small** with the following variations: - **kv** is **16** It has **46.37** million parameters and thus requires *ca.* **185.46 MB** of memory in full precision (*fp32*) or **92.73 MB** of memory in half precision (*... | a04e8c7722ca4313ea98a080df7df0f8 |
apache-2.0 | ['protein language model', 'generated_from_trainer'] | false | tape-fluorescence-prediction-RITA_s This model is a fine-tuned version of [lightonai/RITA_s](https://huggingface.co/lightonai/RITA_s) on the cradle-bio/tape-fluorescence dataset. It achieves the following results on the evaluation set: - Loss: 0.5855 - Spearmanr: 0.2955 | 7b7ab5f0ad4d4a1a4644d6e3afa616eb |
apache-2.0 | ['protein language model', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 128 - total_train_batch_size: 4096 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - nu... | aa524897054736a1f5f2589a008e7c41 |
apache-2.0 | ['protein language model', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Spearmanr | |:-------------:|:-----:|:----:|:---------------:|:---------:| | 4.3595 | 0.85 | 4 | 0.7057 | 0.0940 | | 0.8654 | 1.85 | 8 | 0.6873 | 0.1280 | | 0.8292 | 2.85 | 12 | 0.6835 ... | a6ba8fb54bdf52c6624a1f8f36529771 |
apache-2.0 | ['generated_from_keras_callback'] | false | silviacamplani/distilbert-finetuned-ner-music 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: - Train Loss: 0.6767 - Validation Loss: 0.7802 - Train Precision: 0.5256 - Train... | b8b0ebe7d325b5ae4df9ad4db1bbf9ac |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch | |:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:| | 2.6671 | 2.0032 | 0.0 | 0.0 | 0.0 | 0.5482 | 0 ... | ffcb9a26b533ef93b54d8cc74eadce43 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_logit_kd_qqp_384 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QQP dataset. It achieves the following results on the evaluation set: - Loss: 0.6771 - Accuracy: 0.6454 - F1: 0.0788 - Combined Score: 0.3621 | 07343c9a1d74cede3d29a588645bb391 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:| | 0.7984 | 1.0 | 1422 | 0.7600 | 0.6318 | 0.0 | 0.3159 | | 0.7388 | 2.0 | 2844 | ... | cd1001b35c4257b2f449cb9a40c6b093 |
apache-2.0 | ['translation'] | false | hbs-epo * source group: Serbo-Croatian * target group: Esperanto * OPUS readme: [hbs-epo](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/hbs-epo/README.md) * model: transformer-align * source language(s): bos_Latn hrv srp_Cyrl srp_Latn * target language(s): epo * model: transformer-align * ... | b6110099a237d4110867e059835480de |
apache-2.0 | ['translation'] | false | System Info: - hf_name: hbs-epo - source_languages: hbs - target_languages: epo - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/hbs-epo/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['sh', 'eo'] - src_constituents: {'hrv', 'srp_Cyrl', ... | ad8b5d38d826f900c6ca59ffd168de12 |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2t_en_vp-it_s859 Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure th... | f7b902a9dc3079e6fe7c43996068a39f |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-ksponspeech This model is a fine-tuned version of [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: - **WER(Word Error Rate)** for Third party test data : 0.373 **For improving WER:** - Numeric / ... | 3c3426544fe6c237c40a45a4fa5c198e |
apache-2.0 | ['generated_from_trainer'] | false | Model description Korean Wav2vec with Ksponspeech dataset. This model was trained by two dataset : - Train1 : https://huggingface.co/datasets/Taeham/wav2vec2-ksponspeech-train (1 ~ 20000th data in Ksponspeech) - Train2 : https://huggingface.co/datasets/Taeham/wav2vec2-ksponspeech-train2 (20100 ~ 40100th data in K... | 1e61ffabf84200603e0524ea56499dcd |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_tra... | fec4567bff90508b426e087172dcf354 |
apache-2.0 | ['generated_from_trainer'] | false | opus-mt-en-ar-evaluated-en-to-ar-1000instancesopus-leaningRate2e-05-batchSize8-11epoch-3 This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ar](https://huggingface.co/Helsinki-NLP/opus-mt-en-ar) on the opus100 dataset. It achieves the following results on the evaluation set: - Loss: 0.1421 - Bleu: 21.3028... | 7ac57cbed3124c49ccce4b1159fcd1cd |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Meteor | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:| | 1.0508 | 1.0 | 100 | 0.1413 | 27.9009 | 0.1416 | 8.85 | | 0.1253 | 2.0 | 200 | 0.1372 | 23.11 | ... | 5dbbc9dbcc5d3f42af2afb8715c1fdf0 |
apache-2.0 | ['generated_from_keras_callback'] | false | celera_relevance This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta-wwm-ext) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3072 - Train Sparse Categorical Accuracy: 0.8813 - Validation Loss: 0.4371 - Validati... | 4500f98d0d025ac8f127e43d8cd10358 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Sparse Categorical Accuracy | Validation Loss | Validation Sparse Categorical Accuracy | Epoch | |:----------:|:---------------------------------:|:---------------:|:--------------------------------------:|:-----:| | 0.4060 | 0.8274 | 0.3665 ... | 41da7e2355d805a8d7d216d216180f0f |
cc-by-sa-4.0 | ['english', 'token-classification', 'pos', 'dependency-parsing'] | false | Model Description This is an XLM-RoBERTa model pre-trained with [UD_English-EWT](https://github.com/UniversalDependencies/UD_English-EWT) for POS-tagging and dependency-parsing, derived from [xlm-roberta-base](https://huggingface.co/xlm-roberta-base). Every word is tagged by [UPOS](https://universaldependencies.org/u... | f01b88ab79aced27fedc5ee659fc991f |
cc-by-sa-4.0 | ['english', 'token-classification', 'pos', 'dependency-parsing'] | false | How to Use ```py from transformers import AutoTokenizer,AutoModelForTokenClassification tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/xlm-roberta-base-english-upos") model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/xlm-roberta-base-english-upos") ``` or ```py import esupar nlp=esupar.lo... | 476f01116d51e99c0e1aee77e01c6558 |
mit | [] | false | Description A fine-tuned multi-label classification model that detects 9 [WHO-ICF](https://www.who.int/standards/classifications/international-classification-of-functioning-disability-and-health) domains in clinical text in Dutch. The model is based on a pre-trained Dutch medical language model ([link to be added]())... | cc6e16deb3681bd2df9ecdd2f780f80f |
mit | [] | false | ICF domains The model can detect 9 domains, which were chosen due to their relevance to recovery from COVID-19: ICF code | Domain | name in repo ---|---|--- b440 | Respiration functions | ADM b140 | Attention functions | ATT d840-d859 | Work and employment | BER b1300 | Energy level | ENR d550 | Eating | ETN d450 | W... | 1b3b41c1a092199b269ba45f3ebccd02 |
mit | [] | false | How to use To generate predictions with the model, use the [Simple Transformers](https://simpletransformers.ai/) library: ``` from simpletransformers.classification import MultiLabelClassificationModel model = MultiLabelClassificationModel( 'roberta', 'CLTL/icf-domains', use_cuda=False, ) example = 'Nu s... | b21df2799b93ed220db5ce5b3ee712da |
mit | [] | false | Sentence-level | | ADM | ATT | BER | ENR | ETN | FAC | INS | MBW | STM |---|---|---|---|---|---|---|---|---|--- precision | 0.98 | 0.98 | 0.56 | 0.96 | 0.92 | 0.84 | 0.89 | 0.79 | 0.70 recall | 0.49 | 0.41 | 0.29 | 0.57 | 0.49 | 0.71 | 0.26 | 0.62 | 0.75 F1-score | 0.66 | 0.58 | 0.35 | 0.72 | 0.63 | 0.76 | 0.41 | 0.70... | 97beefbf93fb6f791958fc0d09919c72 |
mit | [] | false | Note-level | | ADM | ATT | BER | ENR | ETN | FAC | INS | MBW | STM |---|---|---|---|---|---|---|---|---|--- precision | 1.0 | 1.0 | 0.66 | 0.96 | 0.95 | 0.84 | 0.95 | 0.87 | 0.80 recall | 0.89 | 0.56 | 0.44 | 0.70 | 0.72 | 0.89 | 0.46 | 0.87 | 0.87 F1-score | 0.94 | 0.71 | 0.50 | 0.81 | 0.82 | 0.86 | 0.61 | 0.87 | 0.8... | 8a1625dd2867b6839e4475977e824076 |
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 an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3517 | da8c5283c98a24fd90171ba88b109538 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2094 | 1.0 | 8235 | 1.2174 | | 0.9515 | 2.0 | 16470 | 1.1923 | | 0.7687 | 3.0 | 24705 | 1.3517 | | 352a99a1ac7740629247d93e5d881e66 |
cc | [] | false | ParchArt is an embedding for Stable Diffusion 2.0+ Parchart should be considered a talented artist who has imbibed far too much absinthe. He works primarily in ink on whatever scraps of parchment he can find and occasionally pulls out some watercolor paints. He loves to write on his work so you'll see a lot of random a... | 4e1228ed98c58c4f31b139a8da7573ca |
mit | [] | false | model by alxdfy This your the Stable Diffusion model fine-tuned the noggles_glasses_600 concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of a person wearing sks glasses** You can also train your own concepts and upload them to the library by using [this ... | 32a6590f635b082ded809c565fa65f50 |
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 | 63 | 3.5640 | 14.382 | 3.9092 | 10.6947 | 12.6762 | 19.0 ... | d0624f71034bf75172f98a25ce415ea9 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_data_aug_qqp This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QQP dataset. It achieves the following results on the evaluation set: - Loss: 0.6240 - Accuracy: 0.8026 - F1: 0.7392 - Combined Score: 0.7709 | 5ab4551ebfd54d19b69c37e3eb5309f3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:--------------:| | 0.2706 | 1.0 | 29671 | 0.6240 | 0.8026 | 0.7392 | 0.7709 | | 0.0776 | 2.0 | 59342... | dff73c6f87b9ac7399cff49438871062 |
agpl-3.0 | ['token classification'] | false | Model description This model is a [RoBERTa base model](https://huggingface.co/roberta-base) that was further trained using a masked language modeling task on a compendium of english scientific textual examples from the life sciences using the [BioLang dataset](https://huggingface.co/datasets/EMBO/biolang). It was the... | ffcf49e645210ef672803bb5fe119cf3 |
agpl-3.0 | ['token classification'] | false | How to use The intended use of this model is for 'parsing' figure legends into sub-fragments corresponding to individual panels as used in SourceData annotations (https://sourcedata.embo.org). To have a quick check of the model: ```python from transformers import pipeline, RobertaTokenizerFast, RobertaForTokenClas... | e2af19632be277e2e077545af42ceae7 |
agpl-3.0 | ['token classification'] | false | Training procedure The training was run on an NVIDIA DGX Station with 4XTesla V100 GPUs. Training code is available at https://github.com/source-data/soda-roberta - Model fine-tuned: EMBO/bio-lm - Tokenizer vocab size: 50265 - Training data: EMBO/sd-nlp - Dataset configuration: PANELIZATION - TTraining with 2175 ex... | 8f228f0563ab9c58d9305f247b3a9423 |
agpl-3.0 | ['token classification'] | false | Eval results Testing on 1802 examples from test set with `sklearn.metrics`: ``` precision recall f1-score support PANEL_START 0.89 0.95 0.92 5427 micro avg 0.89 0.95 0.92 5... | b14c05c4e3cc672856d8344c7a091629 |
apache-2.0 | ['generated_from_trainer'] | false | MediumVin2 This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3370 - Wer: 100.0 | f3b0fd4b2b88ad8125128400fac3bca9 |
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