license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
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mit | ['timelms', 'twitter'] | false | Example Masked Language Model ```python from transformers import pipeline, AutoTokenizer MODEL = "cardiffnlp/twitter-roberta-base-dec2020" fill_mask = pipeline("fill-mask", model=MODEL, tokenizer=MODEL) tokenizer = AutoTokenizer.from_pretrained(MODEL) def pprint(candidates, n): for i in range(n): token... | 861aadfa4ae29131c991b18508f2acb9 |
mit | ['timelms', 'twitter'] | false | naive approach for demonstration text = preprocess(text) encoded_input = tokenizer(text, return_tensors='pt') features = model(**encoded_input) features = features[0].detach().cpu().numpy() return np.mean(features[0], axis=0) MODEL = "cardiffnlp/twitter-roberta-base-dec2020" tokenizer = AutoTokenizer.fro... | ca632be532e126ce37b23eda517f00f5 |
mit | ['timelms', 'twitter'] | false | Example Feature Extraction ```python from transformers import AutoTokenizer, AutoModel, TFAutoModel import numpy as np MODEL = "cardiffnlp/twitter-roberta-base-dec2020" tokenizer = AutoTokenizer.from_pretrained(MODEL) text = "Good night 😊" text = preprocess(text) | c2f66cf21f9d8e48fc99fc2d8498b55f |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Galverse-Diffusion-wf-8888 Dreambooth model trained by jarvissan 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/TheLast... | 236063d0cfe0cc95a129b7f0af16f2b4 |
apache-2.0 | ['generated_from_trainer'] | false | bert-large-uncased-finetuned-infovqa This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 6.3170 | 4462db4beea571ff1649ebfdeb04e948 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 3.7861 | 0.12 | 1000 | 3.2778 | | 3.2186 | 0.23 | 2000 | 3.0658 | | 2.8504 | 0.35 | 3000 | 3.0456 | | 2.8621 | 0.46 | 4000 | 2.8758 ... | 7040ff2f6c8b862df1be4dcd1587e41a |
creativeml-openrail-m | ['stable-diffusion'] | false | Prompt: painting in the style tombartek   on the glue dataset. It achieves the following results on the evaluation set: - Loss: 1.2496 - Accuracy: 0.7581 | 4dc70c75993090b0029cfbb776009a1f |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 10 - eval_batch_size: 10 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | 76705f7061feac778d2253d75239e038 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 249 | 0.5914 | 0.6751 | | No log | 2.0 | 498 | 0.5843 | 0.7184 | | 0.5873 | 3.0 | 747 | 0.6925 | 0.... | 51d4f7540d7d36b72293febb43c9562c |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_data_aug_stsb_256 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 2.7922 - Pearson: 0.1559 - Spearmanr: 0.1663 - Combined Score: ... | 15b7b020f0a9e151d686facb8d431999 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:---------:|:--------------:| | 1.0735 | 1.0 | 2518 | 2.8241 | 0.1664 | 0.1853 | 0.1759 | | 0.6303 | 2.0 | 50... | 6dc5d63d8a1113c7f50f9f3d2e21329a |
gpl-3.0 | ['spacy', 'token-classification'] | false | es_core_news_md Spanish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler, lemmatizer. | Feature | Description | | --- | --- | | **Name** | `es_core_news_md` | | **Version** | `3.5.0` | | **spaCy** | `>=3.5.0,<3.6.0` | | **Default Pipeline** | `tok2vec`, `morphologiz... | 66bc3807b8ef9369757130ecaa63fc0f |
gpl-3.0 | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (468 labels for 3 components)</summary> | Component | Labels | | --- | --- | | **`morphologizer`** | `Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=NOUN`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Art... | c1a05c81050360562f1bd8f3f6985a83 |
gpl-3.0 | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_ACC` | 100.00 | | `TOKEN_P` | 99.89 | | `TOKEN_R` | 99.95 | | `TOKEN_F` | 99.92 | | `POS_ACC` | 98.48 | | `MORPH_ACC` | 98.02 | | `MORPH_MICRO_P` | 99.43 | | `MORPH_MICRO_R` | 98.85 | | `MORPH_MICRO_F` | 99.14 | | `SENTS_P` | 98.40 | | `SENTS_R` | 99.21 | | `SENTS_F` |... | 124f7805422b50a00a6b39dd1532b89d |
apache-2.0 | ['summarization', 'AraBERT', 'BERT', 'BERT2BERT', 'MSA', 'Arabic Text Summarization', 'Arabic News Title Generation', 'Arabic Paraphrasing', 'Summarization', 'generated_from_trainer', 'Transformers', 'PyTorch'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 | 4b3ff46a97a9599fbc2812065e3ed9e8 |
mit | ['generated_from_keras_callback'] | false | nandysoham16/Materialism-clustered This model is a fine-tuned version of [nandysoham16/7-clustered_aug](https://huggingface.co/nandysoham16/7-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0800 - Train End Logits Accuracy: 0.9931 - Train Start Logits Acc... | b409968f0aab7b6f735661368b0a3c53 |
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 | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | 3e510a7813f3cd98d7478886f24e1131 |
apache-2.0 | ['automatic-speech-recognition', 'de'] | false | exp_w2v2r_de_xls-r_accent_germany-10_austria-0_s886 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make s... | afaa47c256fd946d81cd2792d6587bbd |
apache-2.0 | ['translation'] | false | opus-mt-cs-de * source languages: cs * target languages: de * OPUS readme: [cs-de](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/cs-de/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://... | 7e8a9a49a7f0ca81d23c4968bd58a966 |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newssyscomb2009.cs.de | 22.0 | 0.525 | | news-test2008.cs.de | 21.1 | 0.520 | | newstest2009.cs.de | 22.2 | 0.525 | | newstest2010.cs.de | 22.1 | 0.527 | | newstest2011.cs.de | 21.6 | 0.515 | | newstest2012.cs.d... | 8e3b064c1d8b5c71d7408e94637cb863 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'hf-asr-leaderboard'] | false | Wav2Vec2-Large-XLSR-53-tamil Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Tamil using the [Common Voice](https://huggingface.co/datasets/common_voice) When using this model, make sure that your speech input is sampled at 16kHz. | a141b8231ae451844f9335c63b577bf3 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'hf-asr-leaderboard'] | false | Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "ta", split="test[:2%]") processor = Wav2Vec2Processor.from_pr... | aceddb2791eaabe7fa0d01b4ad3962ea |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'hf-asr-leaderboard'] | false | We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["spe... | afddb607ef9e9ad897de72f93f514cb1 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'hf-asr-leaderboard'] | false | Evaluation The model can be evaluated as follows on the {language} test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "ta", split="test") ... | 0b02416f9e4db3c2e7749e25402ffdd1 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'hf-asr-leaderboard'] | false | We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.a... | a0f6db17004e5c9092023618cf431a4d |
mit | ['roberta-base', 'roberta-base-epoch_50'] | false | RoBERTa, Intermediate Checkpoint - Epoch 50 This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692), trained on Wikipedia and the Book Corpus only. We train this model for almost 100K steps, corresponding to 83 epochs. We provide the 84 checkpoints (including the randomly ... | 45e6b760c77a17d12a3357ef3ab14878 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_cola_128 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.6077 - Matthews Correlation: 0.0 | 505492a68aa59ac9d724fd26b0b7d626 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.6155 | 1.0 | 67 | 0.6180 | 0.0 | | 0.6079 | 2.0 | 134 | 0.6180 | 0.0 | | 0.6... | c0e77399b28580108c4583d66829ba3f |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | Stable Diffusion v1-1 Model Card Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. For more information about how Stable Diffusion functions, please have a look at [🤗's Stable Diffusion with D🧨iffusers blog](https://huggingface.co/blog/stab... | fdd3286ee4538b0e133eb3e5b442d4a0 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | training). This weights here are intended to be used with the D🧨iffusers library. If you are looking for the weights to be loaded into the CompVis Stable Diffusion codebase, [come here](https://huggingface.co/CompVis/stable-diffusion-v-1-1-original) | 0f5d282d76adcc6593db062b79522163 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | Examples We recommend using [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run Stable Diffusion. ```bash pip install --upgrade diffusers transformers scipy ``` Run this command to log in with your HF Hub token if you haven't before: ```bash huggingface-cli login ``` Running the pipeline wit... | 7e8c101aa4e7f8bf29103369161b61ac |
mit | ['generated_from_trainer'] | false | Klassifizierung-RLT This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0616 - F1: 0.9852 | 0ff65f9b57dd7cf792fd791d1cc22950 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.828 | 1.0 | 292 | 0.2156 | 0.9447 | | 0.1491 | 2.0 | 584 | 0.0832 | 0.9805 | | 0.0695 | 3.0 | 876 | 0.0616 | 0.9852 | ... | d5e4dfb92efc0f06da3cb921c728fd69 |
creativeml-openrail-m | ['text-to-image'] | false | [](https://huggingface.co/spaces/MultiversexPeeps/ThePitchMee... | bbf84a778f86c80b49d4bca49623ccdf |
creativeml-openrail-m | ['text-to-image'] | false | The Pitch Meeting 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/huggingfac... | 68f9b93c1a8a06a5c80aa73cbaf7700a |
apache-2.0 | ['image-classification', 'timm'] | false | Model card for coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k A timm specific CoAtNet (w/ a MLP Log-CPB (continuous log-coordinate relative position bias motivated by Swin-V2) image classification model. Pretrained in `timm` on ImageNet-12k (a 11821 class subset of full ImageNet-22k) and fine-tuned on ImageNet-1k by Ross Wig... | d14d2f527634a9d5fc9e0b5f37b5790b |
apache-2.0 | ['image-classification', 'timm'] | false | Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 73.9 - GMACs: 15.2 - Activations (M): 54.8 - Image size: 224 x 224 - **Papers:** - CoAtNet: Marrying Convolution and Attention for All Data Sizes: https://arxiv.org/abs/2201.03545 - Swin Transformer V2:... | 63c5dab7222f2c8fc79f247ca779e17a |
apache-2.0 | ['image-classification', 'timm'] | false | Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model('coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k', pretrained=Tr... | 46c81ad4851496693172189bddecc818 |
apache-2.0 | ['image-classification', 'timm'] | false | Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k', pr... | 6b88f62b33a1b70d643db39078d5b06d |
apache-2.0 | ['image-classification', 'timm'] | false | Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k', pretrain... | 93c09b8a73c800b4cb380114eac0c466 |
apache-2.0 | ['generated_from_trainer'] | false | Vin7-P3 This model is a fine-tuned version of [HuyenNguyen/Vin6-P3](https://huggingface.co/HuyenNguyen/Vin6-P3) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2237 - Wer: 11.6272 | 2849415438651d6f0a09c261761b2691 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.247 | 0.51 | 200 | 0.2230 | 12.1483 | | 0.2463 | 1.03 | 400 | 0.2247 | 11.8044 | | 0.2187 | 1.54 | 600 | 0.2237 | 11.627... | 2484678614eb892931fce66f535e325c |
mit | ['computer vision', 'face alignment', 'facial landmark point', 'CNN', 'Knowledge Distillation', 'loss', 'CVIU', 'Tensor Flow'] | false | [](https://paperswithcode.com/sota/face-alignment-on-cofw?p=facial-landmark-points-detection-using) | 265d84d32f04363cd32022a3995033e8 |
mit | ['computer vision', 'face alignment', 'facial landmark point', 'CNN', 'Knowledge Distillation', 'loss', 'CVIU', 'Tensor Flow'] | false | Link to the paper: Google Scholar: https://scholar.google.com/citations?view_op=view_citation&hl=en&user=96lS6HIAAAAJ&citation_for_view=96lS6HIAAAAJ:zYLM7Y9cAGgC Elsevier: https://www.sciencedirect.com/science/article/pii/S1077314221001582 Arxiv: https://arxiv.org/abs/2111.07047 | c74b1f7afbda34eddd17c6f98514428c |
mit | ['computer vision', 'face alignment', 'facial landmark point', 'CNN', 'Knowledge Distillation', 'loss', 'CVIU', 'Tensor Flow'] | false | Link to the paperswithcode.com: https://paperswithcode.com/paper/facial-landmark-points-detection-using ```diff @@plaese STAR the repo if you like it.@@ ``` ``` Please cite this work as: @article{fard2022facial, title={Facial landmark points detection using knowledge distillation-based neural networks}, au... | 52c600554fe8259a58657a68120266d3 |
mit | ['computer vision', 'face alignment', 'facial landmark point', 'CNN', 'Knowledge Distillation', 'loss', 'CVIU', 'Tensor Flow'] | false | Introduction Facial landmark detection is a vital step for numerous facial image analysis applications. Although some deep learning-based methods have achieved good performances in this task, they are often not suitable for running on mobile devices. Such methods rely on networks with many parameters, which makes the ... | b9781ae334d61e1ba4019885a5ca5956 |
mit | ['computer vision', 'face alignment', 'facial landmark point', 'CNN', 'Knowledge Distillation', 'loss', 'CVIU', 'Tensor Flow'] | false | Architecture We train the Tough-Teacher, and the Tolerant-Teacher networks independently using the Hard-landmarks and the Soft-landmarks respectively utilizing the L2 loss:  Proposed KD-based architecture for training ... | fe33fe828b7e95c3e227b4c1a3f1b5d0 |
mit | ['computer vision', 'face alignment', 'facial landmark point', 'CNN', 'Knowledge Distillation', 'loss', 'CVIU', 'Tensor Flow'] | false | Evaluation Following are some samples in order to show the visual performance of KD-Loss on 300W, COFW and WFLW datasets: 300W:  COFW: ![KD_cofw_samples](https://github.com/aliprf/KD-Loss/blob/master/samples/KD_c... | 9f31c4c3fa721aa676ad17a34ec50e2e |
mit | ['computer vision', 'face alignment', 'facial landmark point', 'CNN', 'Knowledge Distillation', 'loss', 'CVIU', 'Tensor Flow'] | false | Using the pre-trained models You can test and use the preetrained models using the following codes which are available in the test.py: The pretrained student model are also located in "models/students". ``` cnn = CNNModel() model = cnn.get_model(arch=arch, input_tensor=None, output_len=self.output_len) ... | a6c4622a47df3780e6fa82060613031a |
mit | ['computer vision', 'face alignment', 'facial landmark point', 'CNN', 'Knowledge Distillation', 'loss', 'CVIU', 'Tensor Flow'] | false | Training Teacher Networks: The training implementation is located in teacher_trainer.py class. You can use the following code to start the training for the teacher networks: ``` '''train Teacher Networks''' trainer = TeacherTrainer(dataset_name=DatasetName.w300) trainer.train(arch='efficientNet',weight_pa... | 41132939ef2c0d857ed7e6d56604ad41 |
mit | ['computer vision', 'face alignment', 'facial landmark point', 'CNN', 'Knowledge Distillation', 'loss', 'CVIU', 'Tensor Flow'] | false | Training Student Networks: After Training the teacher networks, you can use the trained teachers to train the student network. The implemetation of training of the student network is provided in teacher_trainer.py . You can use the following code to start the training for the student networks: ``` st_trainer = Stud... | d513ba60e7b69d01ca31e30d37d070dc |
apache-2.0 | ['generated_from_trainer'] | false | t5_small_NCC_lm-finetuned-sv-frp-classifier-3 This model is a fine-tuned version of [north/t5_small_NCC_lm](https://huggingface.co/north/t5_small_NCC_lm) on the norwegian_parliament dataset. It achieves the following results on the evaluation set: - Loss: nan - Sequence Accuracy: 0.0 | ca60b4f7ef1e791ca20593401517a35d |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP | 0515193639d882c33067d3f4e1ea9c0c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Sequence Accuracy | |:-------------:|:-----:|:----:|:---------------:|:-----------------:| | No log | 1.0 | 113 | nan | 0.0 | | No log | 2.0 | 226 | nan | 0.0 | | No log |... | c7aa3313f3051e17c4cacf05c2404d06 |
lgpl-lr | ['spacy', 'token-classification'] | false | fr_dep_news_trf French transformer pipeline (camembert-base). Components: transformer, morphologizer, parser, attribute_ruler, lemmatizer. | Feature | Description | | --- | --- | | **Name** | `fr_dep_news_trf` | | **Version** | `3.5.0` | | **spaCy** | `>=3.5.0,<3.6.0` | | **Default Pipeline** | `transformer`, `morpho... | 94d42bbe30f5beeb945649dededb69e6 |
lgpl-lr | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (232 labels for 2 components)</summary> | Component | Labels | | --- | --- | | **`morphologizer`** | `POS=PROPN`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Gender=Fem\|Number=Sing\|POS=NOUN`, `Number=Plur\|POS=PRON\|Person=1`, `Mood=Ind\|Number=Sing\|POS=VE... | 14839a95b23d23716203a3bfa652a68b |
lgpl-lr | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_ACC` | 99.80 | | `TOKEN_P` | 98.44 | | `TOKEN_R` | 98.96 | | `TOKEN_F` | 98.70 | | `POS_ACC` | 98.71 | | `MORPH_ACC` | 97.84 | | `MORPH_MICRO_P` | 99.36 | | `MORPH_MICRO_R` | 99.01 | | `MORPH_MICRO_F` | 99.19 | | `SENTS_P` | 93.32 | | `SENTS_R` | 94.90 | | `SENTS_F` | ... | c7144e9b0b7adb40dcf438b5f799be58 |
cc-by-sa-4.0 | ['finance', 'financial'] | false | SEC-BERT
<img align="center" src="https://i.ibb.co/0yz81K9/sec-bert-logo.png" alt="SEC-BERT" width="400"/>
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SEC-BERT is a family of BERT models for the financial domain, intended to assist financial NLP research and FinTech applications.
SEC-BERT consists of the following mod... | 18acde27bc5b8e3c1645a03e34a1478c |
cc-by-sa-4.0 | ['finance', 'financial'] | false | Pre-training details
<div style="text-align: justify">
* We created a new vocabulary of 30k subwords by training a [BertWordPieceTokenizer](https://github.com/huggingface/tokenizers) from scratch on the pre-training corpus.
* We trained BERT using the official code provided in [Google BERT's GitHub repository](... | 0a37380a157271c25da28973f05f6083 |
cc-by-sa-4.0 | ['finance', 'financial'] | false | Load Pretrained Model
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("nlpaueb/sec-bert-base")
model = AutoModel.from_pretrained("nlpaueb/sec-bert-base")
```
| 99feae4a79db1f2e1ef391d8534d7c29 |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | t5-base-TEDxJP-0front-1body-10rear-order-RB This model is a fine-tuned version of [sonoisa/t5-base-japanese](https://huggingface.co/sonoisa/t5-base-japanese) on the te_dx_jp dataset. It achieves the following results on the evaluation set: - Loss: 0.4749 - Wer: 0.1754 - Mer: 0.1696 - Wil: 0.2575 - Wip: 0.7425 - Hits:... | 5d6208ed89984f73d49fa70621a75b98 |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Mer | Wil | Wip | Hits | Substitutions | Deletions | Insertions | Cer | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:------:|:-----:|:-------------:|:---------:|:----------:|:------:| | 0.637 ... | 0f38e0820ebef2a7b2e5ea31f850bb03 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-vanilla-target-glue-qqp This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4162 - Accuracy: 0.7951 - F1: 0.7610 | da5767edcf478bdfa5bb246195643393 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.5864 | 0.04 | 500 | 0.5228 | 0.7257 | 0.6710 | | 0.5173 | 0.09 | 1000 | 0.4944 | 0.7372 | 0.7000 | | 0.5005 |... | eb426072f6bb30da288f3968c374465d |
cc-by-4.0 | ['question generation'] | false | Model Card of `lmqg/bart-large-squadshifts-new_wiki-qg` This model is fine-tuned version of [lmqg/bart-large-squad](https://huggingface.co/lmqg/bart-large-squad) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: new_wiki) via [`lmqg`](https://... | 0df7c68f35d5f988158326e4f219d066 |
cc-by-4.0 | ['question generation'] | false | Overview - **Language model:** [lmqg/bart-large-squad](https://huggingface.co/lmqg/bart-large-squad) - **Language:** en - **Training data:** [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (new_wiki) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https:... | 95316646ed571dda6be8b30907aad1f5 |
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/bart-large-squadshifts-new_w... | 2fe03593442b216a8e6cd2e69d19e850 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/bart-large-squadshifts-new_wiki-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.new_wiki.json) | | Score | Type | Dataset ... | 86d2bb6a64bd986468b46baec72ef04d |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squadshifts - dataset_name: new_wiki - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: None - model: lmqg/bart-large-squad - max_length: 512 - max_length_output: 32 - ep... | b1cbfec0995dea65f9dbf35ed79906a5 |
apache-2.0 | ['image-classification', 'timm'] | false | Model card for levit_conv_192.fb_dist_in1k A LeViT image classification model using default linear mode (non-convolutional mode with nn.Linear and nn.BatchNorm1d). Pretrained on ImageNet-1k using distillation by paper authors. | 476d6921cdc62555e78b8f57de896aec |
apache-2.0 | ['image-classification', 'timm'] | false | Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 10.9 - GMACs: 0.7 - Activations (M): 3.2 - Image size: 224 x 224 - **Papers:** - LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference: https://arxiv.org/abs/2104.01136 - **Original:** ht... | 2f2c95745ccc415518c67ef6b67ea268 |
apache-2.0 | ['image-classification', 'timm'] | false | Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model('levit_conv_192.fb_dist_in1k', pretrained=True) model =... | bd2c878a5b671c64dcfc5cc8ab14f416 |
apache-2.0 | ['image-classification', 'timm'] | false | Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'levit_conv_192.fb_dist_in1k', pretrained=True, ... | 6904c8108c787b9b813fed6843140499 |
apache-2.0 | ['image-classification', 'timm'] | false | Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'levit_conv_192.fb_dist_in1k', pretrained=Tr... | b0a3c8efd80693987840a7ec4e9e3090 |
mit | ['generated_from_trainer'] | false | bart-large-cnn-1000-pad-early-lit This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4800 - Rouge1: 28.4538 - Rouge2: 13.5656 - Rougel: 22.2066 - Rougelsum: 25.3361 - ... | 87e8a1b711cf569d7146ce2891c12153 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 0.1556 | 1.0 | 1000 | 0.4383 | 29.1275 | 14.1415 | 22.5802 | 26.37 | 65... | c1c670364ed6b9251e526eb5902b33c4 |
apache-2.0 | ['translation'] | false | opus-mt-rw-en * source languages: rw * target languages: en * OPUS readme: [rw-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/rw-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | b1e9d8fc00f30e73db07b48ce6133cac |
apache-2.0 | ['automatic-speech-recognition', 'nl'] | false | exp_w2v2t_nl_unispeech-sat_s715 Fine-tuned [microsoft/unispeech-sat-large](https://huggingface.co/microsoft/unispeech-sat-large) 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 that your spee... | 101f1a5346ef82a279643e0fc9807936 |
apache-2.0 | ['CodeGPT-small-py', 'hearthstone'] | false | h0-1 This model is a fine-tuned version of [microsoft/CodeGPT-small-py](https://huggingface.co/microsoft/CodeGPT-small-py) on [hearthstone](https://huggingface.co/datasets/dvitel/hearthstone) dataset. [GitHub repo](https://github.com/dvitel/nlp-sem-parsing/blob/master/h0-1.py). It achieves the following results on th... | 8d51dcdc8a2759311b14a5d6e3247d4f |
apache-2.0 | ['CodeGPT-small-py', 'hearthstone'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Exact Match | Bleu | Codebleu | Chrf | |:-------------:|:------:|:-----:|:---------------:|:-----------:|:------:|:--------:|:-------:| | 0.2482 | 11.94 | 1600 | 0.2828 | 0.1364 | 0.9012 | 0.7012 | 92.2247 | | 0.0203 ... | 44c4c9df21d7b9fc93e311d7ea5f4845 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-POSITIVE_NEGATIVE_ONLY_BALANCED_CLASSES This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3710 - Accuracy: 0.8822 | 9bd13572a002d50701b5bda788d4a075 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sc... | 340bbea1bd7519c679a62c97c2674698 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7822 | 0.96 | 18 | 0.6874 | 0.7424 | | 0.5685 | 1.96 | 36 | 0.5974 | 0.7845 | | 0.45 | 2.96 | 54 | 0.4988 | 0.... | 04bef73573b1f032e829f31f67702891 |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | kyoto_marian_mod This model is a fine-tuned version of [Helsinki-NLP/opus-tatoeba-en-ja](https://huggingface.co/Helsinki-NLP/opus-tatoeba-en-ja) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5922 - Bleu: 16.5968 | 25f2224f8f34c6d595758720a66210a5 |
apache-2.0 | ['translation'] | false | it-he * source group: Italian * target group: Hebrew * OPUS readme: [ita-heb](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ita-heb/README.md) * model: transformer * source language(s): ita * target language(s): heb * model: transformer * pre-processing: normalization + SentencePiece (spm3... | 5e556c2756ec574d2a9631606a636cc1 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: it-he - source_languages: ita - target_languages: heb - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ita-heb/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['it', 'he'] - src_constituents: ('Italian', {'ita'}) ... | 2f03b3711cadf185c88365738a703337 |
apache-2.0 | ['generated_from_trainer'] | false | sd-panelization-v2 This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-large](https://huggingface.co/michiyasunaga/BioLinkBERT-large) on the source_data_nlp dataset. It achieves the following results on the evaluation set: - Loss: 0.0050 - Accuracy Score: 0.9982 - Precision: 0.9134 - Recall: 0.9495 - F1:... | 98549d5ef3ddcb340c786e2eb329988d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy Score | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------------:|:---------:|:------:|:------:| | 0.0048 | 1.0 | 431 | 0.0050 | 0.9982 | 0.9134 | 0.9495 | 0.9311 | | 12a6cd370ed4f2bdb4bb7d9eba62d010 |
other | ['opt', 'text-generation'] | false | transformers.generation_utils.GenerationMixin.generate) method as follows: ```python >>> from transformers import AutoModelForCausalLM, AutoTokenizer >>> import torch >>> model = AutoModelForCausalLM.from_pretrained("facebook/opt-13b", torch_dtype=torch.float16).cuda() >>> | 4a75369649c35e61f0dc2dd30c378b0d |
other | ['opt', 'text-generation'] | false | the fast tokenizer currently does not work correctly >>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-13b", use_fast=False) >>> prompt = "Hello, I'm am conscious and" >>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda() >>> generated_ids = model.generate(input_ids) >>> tokenizer.batc... | 8324cc07b6f178a30d53639de90b3703 |
other | ['opt', 'text-generation'] | false | the fast tokenizer currently does not work correctly >>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-13b", use_fast=False) >>> prompt = "Hello, I'm am conscious and" >>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda() >>> set_seed(32) >>> generated_ids = model.generate(input_ids, do_... | 7a0fe6bef319552e9d0d61f1bd5a4644 |
other | ['opt', 'text-generation'] | false | Limitations and bias As mentioned in Meta AI's model card, given that the training data used for this model contains a lot of unfiltered content from the internet, which is far from neutral the model is strongly biased : > Like other large language models for which the diversity (or lack thereof) of training > data... | 54083cee45d7589dd5d94b75d6d655b2 |
other | ['opt', 'text-generation'] | false | the fast tokenizer currently does not work correctly >>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-13b", use_fast=False) >>> prompt = "The woman worked as a" >>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda() >>> set_seed(32) >>> generated_ids = model.generate(input_ids, do_sample... | d82fb8cd6dcb83936b6b5364c10add00 |
other | ['opt', 'text-generation'] | false | the fast tokenizer currently does not work correctly >>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-13b", use_fast=False) >>> prompt = "The man worked as a" >>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda() >>> set_seed(32) >>> generated_ids = model.generate(input_ids, do_sample=T... | a5233c7a0d3326d252525c16c99100ab |
gpl-3.0 | ['segmentation'] | false | Model Details * **Developed by:** Haoli Yin * **Model type:** Atrous Spatial Pyramid Pooling (ASPP) model for Specular Reflection Segmentation in Endoscopic Images * **Language(s):** English * **License:** GPL 3.0 * **Model Description:** This is a model that can be used to create dense pixel-wise segmentation masks ... | 3905ad8cdd409ad3cefbbc910b16ecec |
gpl-3.0 | ['segmentation'] | false | Direct Use The model is intended to be used to generate dense pixel-wise segmentation maps of specular reflection regions found in endoscopy images. Intended uses exclude those described in the [Misuse and Out-of-Scope Use]( | 2ab567bae1942cbc635ce49666e7f0c7 |
gpl-3.0 | ['segmentation'] | false | Downstream Use The model could also be used for downstream use cases, including further research efforts, such as detecting specular reflection in other real-world scenarios. This application would require fine-tuning the model with domain-specific datasets. | 6777984a95d906db587295589f4e1c9a |
gpl-3.0 | ['segmentation'] | false | Limitations The performance of the model may degrade when applied on non-biological tissue images. There may also be edge cases causing the model to fail to detect specular reflection, especially if the specular reflection present is a different color than white. | 40100957eeba74f78bc717ef3f6962e2 |
gpl-3.0 | ['segmentation'] | false | Limitations and Bias Recommendations * Users (both direct and downstream) should be made aware of the biases and limitations. * Further work on this model should include methods for balanced representations of different types of specular reflections. | d7b92f3de6d3bb2717ef3045fcdda47f |
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