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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apache-2.0 | ['translation'] | false | ita-lit * source group: Italian * target group: Lithuanian * OPUS readme: [ita-lit](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ita-lit/README.md) * model: transformer-align * source language(s): ita * target language(s): lit * model: transformer-align * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/ita-lit/opus-2020-06-17.zip) * test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ita-lit/opus-2020-06-17.test.txt) * test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ita-lit/opus-2020-06-17.eval.txt) | 954d93285f5ead6d5267466d3db4e094 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: ita-lit - source_languages: ita - target_languages: lit - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ita-lit/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['it', 'lt'] - src_constituents: {'ita'} - tgt_constituents: {'lit'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/ita-lit/opus-2020-06-17.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/ita-lit/opus-2020-06-17.test.txt - src_alpha3: ita - tgt_alpha3: lit - short_pair: it-lt - chrF2_score: 0.652 - bleu: 38.1 - brevity_penalty: 0.9590000000000001 - ref_len: 1321.0 - src_name: Italian - tgt_name: Lithuanian - train_date: 2020-06-17 - src_alpha2: it - tgt_alpha2: lt - prefer_old: False - long_pair: ita-lit - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41 | 1dfd7b02165d60ceaa7d8a12a97f7cc2 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - 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 | 09653628607a71573405ad60abaa176f |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-issues-128 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0940 | ce364365263bfffd3c448c5e1d1afe1e |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 16 | c937e701766e4bd4fab87ded19ee5800 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.1003 | 1.0 | 291 | 1.6578 | | 1.6211 | 2.0 | 582 | 1.4140 | | 1.4964 | 3.0 | 873 | 1.3040 | | 1.41 | 4.0 | 1164 | 1.3011 | | 1.336 | 5.0 | 1455 | 1.3095 | | 1.2862 | 6.0 | 1746 | 1.3739 | | 1.2271 | 7.0 | 2037 | 1.2743 | | 1.2043 | 8.0 | 2328 | 1.2019 | | 1.1701 | 9.0 | 2619 | 1.2696 | | 1.1498 | 10.0 | 2910 | 1.2507 | | 1.1194 | 11.0 | 3201 | 1.1398 | | 1.1094 | 12.0 | 3492 | 1.1309 | | 1.0913 | 13.0 | 3783 | 1.0740 | | 1.0683 | 14.0 | 4074 | 1.1201 | | 1.0607 | 15.0 | 4365 | 1.1690 | | 1.0558 | 16.0 | 4656 | 1.0940 | | a498192fbc9d1d6bef762c21989a0e09 |
apache-2.0 | ['text-generation', 'story-generation', 'pytorch', 'inference acceleration', 'gpt2', 'gpt3'] | false | YuYan: Pre-training of Language Models for Story Generation YuYan is a series of Chinese language models with different size, developed by Fuxi AI lab, Netease.Inc. They are trained on a large Chinese novel dataset of high quality. YuYan is in the same family of decoder-only models like [GPT2 and GPT-3](https://arxiv.org/abs/2005.14165). As such, it was pretrained using the self-supervised causal language modedling objective. Because the training data is mainly the novel, the model is good at generating the next plot given the story context. | c5e78fcd8adc819d2f8e152e9ca29ee0 |
apache-2.0 | ['text-generation', 'story-generation', 'pytorch', 'inference acceleration', 'gpt2', 'gpt3'] | false | Model Inference Acceleration As the model size increases, the model inference time increases and more computational resources are required. Therefore, we developed our own transformer model inference acceleration framework, [EET](https://github.com/NetEase-FuXi/EET.git). More details are in [Easy and Efficient Transformer: Scalable Inference Solution For Large NLP Model](https://aclanthology.org/2022.naacl-industry.8/). We combine our language model with the EET inference framework to provide industrial-grade inference reasoning performance. | d50632c6e71abf460048a5746fcb0888 |
apache-2.0 | ['text-generation', 'story-generation', 'pytorch', 'inference acceleration', 'gpt2', 'gpt3'] | false | How to use Our model is trained based on the [fairseq](https://github.com/facebookresearch/fairseq). As a result, the inference and finetuning depend on it. For inference, we modify some parts of the original fairseq codes. Mainly > fairseq-0.12.2/fairseq/sequence_generator.py We integrate the EET with sequence_generator. We replace the eos token to a token unlikely to be sampled to ensure the generated text length. The repetition penalty trick is also modified. You can change the penalty strength by adjusting the value of `self.ban_weight`. Then, to keep the eos token in the final generated text, we change the line 75 `include_eos=False` to `include_eos=True` in > fairseq-0.12.2/fairseq/data/dictionary.py Finally, to pass in parameters in python scripts, we remove the line 67 ~ line 69 in >fairseq-0.12.2/fairseq/dataclass/utils.py Below are the install tutorial. ``` | 88e74866fb9d20ec925567cbbf478800 |
apache-2.0 | ['text-generation', 'story-generation', 'pytorch', 'inference acceleration', 'gpt2', 'gpt3'] | false | make a folder, move the dictionary file and model file into it. mkdir transformer_lm_gpt2_xxl mv dict.txt transformer_lm_gpt2_xxl/ mv checkpoint_best_part_*.pt transformer_lm_gpt2_xxl/ ``` `inference.py` is a script to provide a interface to initialize the EET object and sequence_generator. In addition, It includes some pre-process and post-process functions for text input and output. You can modify the script according to your needs. After the environment is ready, several lines of codes can realize the inference. ``` python from inference import Inference model_path = "transformer_lm_gpt2_xxl/checkpoint_best.pt" data_path = "transformer_lm_gpt2_xxl" eet_batch_size = 10 | 8b455d5313ead8520e340d4011754a39 |
apache-2.0 | ['text-generation', 'story-generation', 'pytorch', 'inference acceleration', 'gpt2', 'gpt3'] | false | max inference batch size, adjust according to cuda memory, 40GB memory is necessary inference = Inference(model_path, data_path, eet_batch_size) inp = "田园一听这话，轻挑的嘴角放了下来，两腿叉开，踱着方步，跨过汤婆子，一屁股坐在了老人面前。</s>刘萌和健军一左一右站在他身旁，像是王朝、马汉护着包公断案。" text = inference([inp] * 10, append_right_eos=True) ``` This interface supports batch inputs, so if you need to generate multiple results for one input, you can copy the input multiple times. The interface supports results generated for multiple different inputs, e.g. ```python text = inference(["四个月后，正是草长花秾的暮春季节。</s>令狐冲和盈盈新婚燕尔，携手共赴华山。","院子中传来急促的脚步声，他停下手中的招式，将开元刀插入刀鞘。"]) ``` | 387a4410c1afd9bdb6f105471d90b274 |
apache-2.0 | ['text-generation', 'story-generation', 'pytorch', 'inference acceleration', 'gpt2', 'gpt3'] | false | Citation If you find the technical report or resource is useful, please cite the following technical report in your paper. - https://aclanthology.org/2022.naacl-industry.8/ ``` @inproceedings{li-etal-2022-easy, title = "Easy and Efficient Transformer: Scalable Inference Solution For Large {NLP} Model", author = "Li, Gongzheng and Xi, Yadong and Ding, Jingzhen and Wang, Duan and Luo, Ziyang and Zhang, Rongsheng and Liu, Bai and Fan, Changjie and Mao, Xiaoxi and Zhao, Zeng", booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track", month = jul, year = "2022", address = "Hybrid: Seattle, Washington + Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.naacl-industry.8", doi = "10.18653/v1/2022.naacl-industry.8", pages = "62--68" } ``` | 4d3e7f8ad9c58c16bc24cf7851cbeb9f |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Small Es - Sanchit Gandhi This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Multilingual LibriSpeech dataset. It achieves the following results on the evaluation set: - Loss: 0.1694 - Wer: 7.3696 | 39c85c0ec7d0191b275119d8b9fe7db9 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-07 - train_batch_size: 2 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP | 95b1219e34e9e5367d9994db43db8f37 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.7733 | 0.25 | 1000 | 0.6193 | 17.9946 | | 0.2991 | 0.5 | 2000 | 0.3162 | 14.2555 | | 0.2929 | 0.75 | 3000 | 0.1799 | 7.7752 | | 0.3099 | 1.0 | 4000 | 0.1694 | 7.3696 | | a1ae3aa7f393ae88d919ffc323546be5 |
mit | [] | false | venice on Stable Diffusion This is the `<venice>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`:         | 236c0e82b8cbc48a92575423dceec925 |
apache-2.0 | ['generated_from_keras_callback'] | false | Krishadow/biobert-finetuned-ner 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: 0.0450 - Validation Loss: 0.0593 - Epoch: 1 | 191d1e5b3550851173195884d233c8fe |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 678, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 | 55b2b92d3d3c968ed30026f580fa80ae |
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 the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2226 - Accuracy: 0.9245 - F1: 0.9248 | 4b3ac479ee9333a6ab73693211e5d8dc |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8222 | 1.0 | 250 | 0.3162 | 0.9085 | 0.9063 | | 0.2501 | 2.0 | 500 | 0.2226 | 0.9245 | 0.9248 | | cbf1a95450fb13a190b08659375ea963 |
apache-2.0 | ['text-embedding', 'embeddings', 'information-retrieval', 'beir', 'text-classification', 'language-model', 'text-clustering', 'text-semantic-similarity', 'text-evaluation', 'prompt-retrieval', 'text-reranking', 'sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers', 't5', 'English', 'Sentence Similarity', 'natural_questions', 'ms_marco', 'fever', 'hotpot_qa', 'mteb'] | false | hkunlp/instructor-xl We introduce **Instructor**👨🏫, an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g., classification, retrieval, clustering, text evaluation, etc.) and domains (e.g., science, finance, etc.) ***by simply providing the task instruction, without any finetuning***. Instructor👨 achieves sota on 70 diverse embedding tasks! The model is easy to use with **our customized** `sentence-transformer` library. For more details, check out [our paper](https://arxiv.org/abs/2212.09741) and [project page](https://instructor-embedding.github.io/)! **************************** **Updates** **************************** * 01/21: We released a new [checkpoint](https://huggingface.co/hkunlp/instructor-xl) trained with hard negatives, which gives better performance. * 12/21: We released our [paper](https://arxiv.org/abs/2212.09741), [code](https://github.com/HKUNLP/instructor-embedding), [checkpoint](https://huggingface.co/hkunlp/instructor-xl) and [project page](https://instructor-embedding.github.io/)! Check them out! | 16aa25ee8f7161ee25892f7284863eae |
apache-2.0 | ['text-embedding', 'embeddings', 'information-retrieval', 'beir', 'text-classification', 'language-model', 'text-clustering', 'text-semantic-similarity', 'text-evaluation', 'prompt-retrieval', 'text-reranking', 'sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers', 't5', 'English', 'Sentence Similarity', 'natural_questions', 'ms_marco', 'fever', 'hotpot_qa', 'mteb'] | false | Compute your customized embeddings Then you can use the model like this to calculate domain-specific and task-aware embeddings: ```python from InstructorEmbedding import INSTRUCTOR model = INSTRUCTOR('hkunlp/instructor-xl') sentence = "3D ActionSLAM: wearable person tracking in multi-floor environments" instruction = "Represent the Science title:" embeddings = model.encode([[instruction,sentence]]) print(embeddings) ``` | 2349d7da6a2ab59ce132850f2c04b037 |
apache-2.0 | ['text-embedding', 'embeddings', 'information-retrieval', 'beir', 'text-classification', 'language-model', 'text-clustering', 'text-semantic-similarity', 'text-evaluation', 'prompt-retrieval', 'text-reranking', 'sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers', 't5', 'English', 'Sentence Similarity', 'natural_questions', 'ms_marco', 'fever', 'hotpot_qa', 'mteb'] | false | Calculate embeddings for your customized texts If you want to calculate customized embeddings for specific sentences, you may follow the unified template to write instructions: Represent the `domain` `text_type` for `task_objective`: * `domain` is optional, and it specifies the domain of the text, e.g., science, finance, medicine, etc. * `text_type` is required, and it specifies the encoding unit, e.g., sentence, document, paragraph, etc. * `task_objective` is optional, and it specifies the objective of embedding, e.g., retrieve a document, classify the sentence, etc. | 39d210c92caf070a45bdb0d384c08f15 |
apache-2.0 | ['text-embedding', 'embeddings', 'information-retrieval', 'beir', 'text-classification', 'language-model', 'text-clustering', 'text-semantic-similarity', 'text-evaluation', 'prompt-retrieval', 'text-reranking', 'sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers', 't5', 'English', 'Sentence Similarity', 'natural_questions', 'ms_marco', 'fever', 'hotpot_qa', 'mteb'] | false | Calculate Sentence similarities You can further use the model to compute similarities between two groups of sentences, with **customized embeddings**. ```python from sklearn.metrics.pairwise import cosine_similarity sentences_a = [['Represent the Science sentence: ','Parton energy loss in QCD matter'], ['Represent the Financial statement: ','The Federal Reserve on Wednesday raised its benchmark interest rate.']] sentences_b = [['Represent the Science sentence: ','The Chiral Phase Transition in Dissipative Dynamics'], ['Represent the Financial statement: ','The funds rose less than 0.5 per cent on Friday']] embeddings_a = model.encode(sentences_a) embeddings_b = model.encode(sentences_b) similarities = cosine_similarity(embeddings_a,embeddings_b) print(similarities) ``` | aec77e60880eb780f097d7060e494eda |
apache-2.0 | ['text-embedding', 'embeddings', 'information-retrieval', 'beir', 'text-classification', 'language-model', 'text-clustering', 'text-semantic-similarity', 'text-evaluation', 'prompt-retrieval', 'text-reranking', 'sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers', 't5', 'English', 'Sentence Similarity', 'natural_questions', 'ms_marco', 'fever', 'hotpot_qa', 'mteb'] | false | Information Retrieval You can also use **customized embeddings** for information retrieval. ```python import numpy as np from sklearn.metrics.pairwise import cosine_similarity query = [['Represent the Wikipedia question for retrieving supporting documents: ','where is the food stored in a yam plant']] corpus = [['Represent the Wikipedia document for retrieval: ','Capitalism has been dominant in the Western world since the end of feudalism, but most feel[who?] that the term "mixed economies" more precisely describes most contemporary economies, due to their containing both private-owned and state-owned enterprises. In capitalism, prices determine the demand-supply scale. For example, higher demand for certain goods and services lead to higher prices and lower demand for certain goods lead to lower prices.'], ['Represent the Wikipedia document for retrieval: ',"The disparate impact theory is especially controversial under the Fair Housing Act because the Act regulates many activities relating to housing, insurance, and mortgage loans—and some scholars have argued that the theory's use under the Fair Housing Act, combined with extensions of the Community Reinvestment Act, contributed to rise of sub-prime lending and the crash of the U.S. housing market and ensuing global economic recession"], ['Represent the Wikipedia document for retrieval: ','Disparate impact in United States labor law refers to practices in employment, housing, and other areas that adversely affect one group of people of a protected characteristic more than another, even though rules applied by employers or landlords are formally neutral. Although the protected classes vary by statute, most federal civil rights laws protect based on race, color, religion, national origin, and sex as protected traits, and some laws include disability status and other traits as well.']] query_embeddings = model.encode(query) corpus_embeddings = model.encode(corpus) similarities = cosine_similarity(query_embeddings,corpus_embeddings) retrieved_doc_id = np.argmax(similarities) print(retrieved_doc_id) ``` | 880529361433ded236eccc88ca31c1d4 |
apache-2.0 | ['text-embedding', 'embeddings', 'information-retrieval', 'beir', 'text-classification', 'language-model', 'text-clustering', 'text-semantic-similarity', 'text-evaluation', 'prompt-retrieval', 'text-reranking', 'sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers', 't5', 'English', 'Sentence Similarity', 'natural_questions', 'ms_marco', 'fever', 'hotpot_qa', 'mteb'] | false | Clustering Use **customized embeddings** for clustering texts in groups. ```python import sklearn.cluster sentences = [['Represent the Medicine sentence for clustering: ','Dynamical Scalar Degree of Freedom in Horava-Lifshitz Gravity'], ['Represent the Medicine sentence for clustering: ','Comparison of Atmospheric Neutrino Flux Calculations at Low Energies'], ['Represent the Medicine sentence for clustering: ','Fermion Bags in the Massive Gross-Neveu Model'], ['Represent the Medicine sentence for clustering: ',"QCD corrections to Associated t-tbar-H production at the Tevatron"], ['Represent the Medicine sentence for clustering: ','A New Analysis of the R Measurements: Resonance Parameters of the Higher, Vector States of Charmonium']] embeddings = model.encode(sentences) clustering_model = sklearn.cluster.MiniBatchKMeans(n_clusters=2) clustering_model.fit(embeddings) cluster_assignment = clustering_model.labels_ print(cluster_assignment) ``` | 2189294538e2e1c805c50da0f4c43aa6 |
apache-2.0 | ['image-classification', 'resnet'] | false | Model Description The ***ResNet50 v1.5*** model is a modified version of the [original ResNet50 v1 model](https://arxiv.org/abs/1512.03385). The difference between v1 and v1.5 is that, in the bottleneck blocks which requires downsampling, v1 has stride = 2 in the first 1x1 convolution, whereas v1.5 has stride = 2 in the 3x3 convolution. This difference makes ResNet50 v1.5 slightly more accurate (\~0.5% top1) than v1, but comes with a smallperformance drawback (\~5% imgs/sec). The model is initialized as described in [Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification](https://arxiv.org/pdf/1502.01852.pdf) This model is trained with mixed precision using Tensor Cores on Volta, Turing, and the NVIDIA Ampere GPU architectures. Therefore, researchers can get results over 2x faster than training without Tensor Cores, while experiencing the benefits of mixed precision training. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time. Note that the ResNet50 v1.5 model can be deployed for inference on the [NVIDIA Triton Inference Server](https://github.com/NVIDIA/trtis-inference-server) using TorchScript, ONNX Runtime or TensorRT as an execution backend. For details check [NGC](https://ngc.nvidia.com/catalog/resources/nvidia:resnet_for_triton_from_pytorch) | 9b647c9fcbee8d26b3f5d880fba3a2e0 |
apache-2.0 | ['image-classification', 'resnet'] | false | Example In the example below we will use the pretrained ***ResNet50 v1.5*** model to perform inference on ***image*** and present the result. To run the example you need some extra python packages installed. These are needed for preprocessing images and visualization. ```python !pip install validators matplotlib ``` ```python import torch from PIL import Image import torchvision.transforms as transforms import numpy as np import json import requests import matplotlib.pyplot as plt import warnings warnings.filterwarnings('ignore') %matplotlib inline device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") print(f'Using {device} for inference') ``` Load the model pretrained on IMAGENET dataset. ```python resnet50 = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_resnet50', pretrained=True) utils = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_convnets_processing_utils') resnet50.eval().to(device) ``` Prepare sample input data. ```python uris = [ 'http://images.cocodataset.org/test-stuff2017/000000024309.jpg', 'http://images.cocodataset.org/test-stuff2017/000000028117.jpg', 'http://images.cocodataset.org/test-stuff2017/000000006149.jpg', 'http://images.cocodataset.org/test-stuff2017/000000004954.jpg', ] batch = torch.cat( [utils.prepare_input_from_uri(uri) for uri in uris] ).to(device) ``` Run inference. Use `pick_n_best(predictions=output, n=topN)` helepr function to pick N most probably hypothesis according to the model. ```python with torch.no_grad(): output = torch.nn.functional.softmax(resnet50(batch), dim=1) results = utils.pick_n_best(predictions=output, n=5) ``` Display the result. ```python for uri, result in zip(uris, results): img = Image.open(requests.get(uri, stream=True).raw) img.thumbnail((256,256), Image.ANTIALIAS) plt.imshow(img) plt.show() print(result) ``` | 885ebd24389ce1fec126613023b6eb32 |
apache-2.0 | ['image-classification', 'resnet'] | false | Details For detailed information on model input and output, training recipies, inference and performance visit: [github](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Classification/ConvNets/resnet50v1.5) and/or [NGC](https://ngc.nvidia.com/catalog/resources/nvidia:resnet_50_v1_5_for_pytorch) | 617bd63da97767d5f6727ce64afae40c |
apache-2.0 | ['image-classification', 'resnet'] | false | References - [Original ResNet50 v1 paper](https://arxiv.org/abs/1512.03385) - [Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification](https://arxiv.org/pdf/1502.01852.pdf) - [model on github](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Classification/ConvNets/resnet50v1.5) - [model on NGC](https://ngc.nvidia.com/catalog/resources/nvidia:resnet_50_v1_5_for_pytorch) - [pretrained model on NGC](https://ngc.nvidia.com/catalog/models/nvidia:resnet50_pyt_amp) ```python ``` | 4c2af669a65b9ce8a3af7717881acbc5 |
apache-2.0 | ['generated_from_trainer'] | false | crpf_analysis_trail_1 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: 0.0301 - Accuracy: 0.9935 - F1: 0.8571 | 0d1e44c758b3e70b541470ec79282992 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Large V2 Malayalam- Drishti Sharma This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.3159 - Wer: 28.2886 | 772655f30f03ae777a45a94a90f05a2d |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 700 - mixed_precision_training: Native AMP | 864b3c6ea131cc150fdff62c26e4bb00 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0002 | 12.96 | 700 | 0.3159 | 28.2886 | | 0348bff6c303ee8c19a3f12463dc38af |
apache-2.0 | ['generated_from_trainer'] | false | HuBERT-base-libriSpeech-demo-colab This model is a fine-tuned version of [facebook/hubert-large-ls960-ft](https://huggingface.co/facebook/hubert-large-ls960-ft) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1456 - Wer: 0.2443 | 31488720c0e7d9e67eabd1f04df56e92 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP | 5d8e54516a9ceeab81aba1914987360c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 7.6395 | 13.51 | 500 | 3.1933 | 0.9930 | | 2.5994 | 27.03 | 1000 | 0.1456 | 0.2443 | | 09733c97c37a9fd5c05dc70da6a832ad |
apache-2.0 | ['part-of-speech', 'token-classification'] | false | XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Welsh This model is part of our paper called: - Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details. | c9d6ef04652302a295d191ed6d5f65df |
apache-2.0 | ['part-of-speech', 'token-classification'] | false | Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-cy") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-cy") ``` | eb46380b934b741c0d0b3edb3a5fa1fd |
apache-2.0 | ['automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event'] | false | wav2vec2-large-xls-r-300m-Indonesian This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.4087 - Wer: 0.2461 - Cer: 0.0666 | 36c95bb966c63d7234b811a5986c8f87 |
apache-2.0 | ['automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 400 - num_epochs: 50 - mixed_precision_training: Native AMP | a023fb7a90c484f3f3dc35ac7d280670 |
apache-2.0 | ['automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 5.0788 | 4.26 | 200 | 2.9389 | 1.0 | 1.0 | | 2.8288 | 8.51 | 400 | 2.2535 | 1.0 | 0.8004 | | 0.907 | 12.77 | 600 | 0.4558 | 0.4243 | 0.1095 | | 0.4071 | 17.02 | 800 | 0.4013 | 0.3468 | 0.0913 | | 0.3 | 21.28 | 1000 | 0.4167 | 0.3075 | 0.0816 | | 0.2544 | 25.53 | 1200 | 0.4132 | 0.2835 | 0.0762 | | 0.2145 | 29.79 | 1400 | 0.3878 | 0.2693 | 0.0729 | | 0.1923 | 34.04 | 1600 | 0.4023 | 0.2623 | 0.0702 | | 0.1681 | 38.3 | 1800 | 0.3984 | 0.2581 | 0.0686 | | 0.1598 | 42.55 | 2000 | 0.3982 | 0.2493 | 0.0663 | | 0.1464 | 46.81 | 2200 | 0.4087 | 0.2461 | 0.0666 | | e54ba32d6fe8f96f81a6c4831541f688 |
other | ['generated_from_trainer'] | false | 1.3b-dalio-principles-book This model is a fine-tuned version of [facebook/opt-1.3b](https://huggingface.co/facebook/opt-1.3b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.4512 - Accuracy: 0.4741 | 272aeb46240c6b4452abb7a6ea1ad1ca |
other | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 8 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 | d37d230fb9eaade521bc6a7f8c0ff3ed |
other | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.6914 | 0.14 | 1 | 2.6895 | 0.4477 | | 2.6897 | 0.29 | 2 | 2.6895 | 0.4477 | | 2.668 | 0.43 | 3 | 2.7031 | 0.4403 | | 2.7434 | 0.57 | 4 | 2.5918 | 0.4533 | | 2.6265 | 0.71 | 5 | 2.5410 | 0.4618 | | 2.5259 | 0.86 | 6 | 2.5156 | 0.4641 | | 2.5566 | 1.0 | 7 | 2.4902 | 0.4667 | | 2.2317 | 1.14 | 8 | 2.4766 | 0.4707 | | 2.2397 | 1.29 | 9 | 2.4727 | 0.4705 | | 2.0162 | 1.43 | 10 | 2.4766 | 0.4690 | | 2.0537 | 1.57 | 11 | 2.4805 | 0.4707 | | 2.1432 | 1.71 | 12 | 2.4707 | 0.4714 | | 2.0822 | 1.86 | 13 | 2.4570 | 0.4724 | | 1.9056 | 2.0 | 14 | 2.4512 | 0.4741 | | ab66341ef047d8c1c18aa8f9e3ba6d4e |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-finetuned-sst2 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.2982 - Accuracy: 0.9323 | e325b581530502487cbcf59b70da1ec0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.1817 | 1.0 | 4210 | 0.2920 | 0.9186 | | 0.1297 | 2.0 | 8420 | 0.3069 | 0.9209 | | 0.0978 | 3.0 | 12630 | 0.2982 | 0.9323 | | 0.062 | 4.0 | 16840 | 0.3278 | 0.9312 | | 0.0303 | 5.0 | 21050 | 0.3642 | 0.9323 | | 4030d077684f5dc8724f348e39d226d6 |
mit | ['generated_from_trainer'] | false | MTL-roberta-base This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4859 | 0aa88af4166a0fc5fe8666a0a15dc627 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 7 - eval_batch_size: 7 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 - mixed_precision_training: Native AMP | 4ce10d7bc5998d79135581a89e86e2f5 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.8338 | 1.0 | 98 | 1.6750 | | 1.7732 | 2.0 | 196 | 1.6229 | | 1.7208 | 3.0 | 294 | 1.6131 | | 1.6917 | 4.0 | 392 | 1.5936 | | 1.6579 | 5.0 | 490 | 1.6183 | | 1.6246 | 6.0 | 588 | 1.6015 | | 1.6215 | 7.0 | 686 | 1.5248 | | 1.5743 | 8.0 | 784 | 1.5454 | | 1.5621 | 9.0 | 882 | 1.5925 | | 1.5652 | 10.0 | 980 | 1.5213 | | 1.5615 | 11.0 | 1078 | 1.4845 | | 1.5349 | 12.0 | 1176 | 1.5443 | | 1.5165 | 13.0 | 1274 | 1.5304 | | 1.5164 | 14.0 | 1372 | 1.4773 | | 1.5293 | 15.0 | 1470 | 1.5537 | | 5dd491b2d58eeec729b9c0969dfe6734 |
apache-2.0 | ['automatic-speech-recognition', 'th'] | false | exp_w2v2t_th_wav2vec2_s664 Fine-tuned [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) for speech recognition on Thai 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 that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 33a6765de12d5faa33e63bba4de7210f |
creativeml-openrail-m | ['Text-to-image', 'Diffusers', 'stable-diffusion'] | false | <b>Oldjourney</b> Oldjourney is a finetuned Stable Diffusion 2.1 model trained on images from Midjourney 3 using Dreambooth. That older version of Midjourney was often messy and imprecise, but had a great artistic style. These two versions of Oldjourney can recreate the essence of that art style with added details, precision, and quality. The two models, Oldjourney Ultra and Oldjourney Lite, are very similar, but they have different strengths. Ultra is better at people, while Lite is better at painterly style images. Use the keyword <b>Oldjourney</b> to trigger the style, and set the resolution to 768 x 768 or greater. Examples and sample prompts below. This is a model for Stable Diffusion 2.1, so make sure to download the yaml files. <b>Rendered with Oldjourney Lite</b>  <b>Rendered with Oldjourney Ultra</b>  <b>Sample Prompts for Oldjourney Lite</b> <b>Sample 1</b> Oldjourney the legendary dream vortex and a dreamer, a boy laying on a bed in front of a vortex, ultrafine detailed painting, psychedelic art, watching the stars at night, pulled into the spiral vortex, videogame cover art, ... if only i could sleep, discord profile picture, time travel machine, photoshop render <b>Negative prompt:</b> pink, ugly, tiling, out of frame, body out of frame, blurry, blurred, grainy, cut off, draft, (cropped:1.2),(overexposure:1.2), (high contrast:1.2), (poorly drawn hands:1.2), (poorly drawn feet:1.2), (poorly drawn face:1.2), (too long neck:1:2), (extra limbs:1.2), (less than two arms:1.2), (less than two legs:1.2), disfigured, deformed,(bad anatomy:1.2), (watermark:1.2), (logo:1.2), (barcode:1.2), (UI:1.2), (signature:1.2), (text:1.2), (label:1.5), (error:1.2), (title:1.2), stickers, markings, speech bubbles, lines, cropped, low res, low quality, artifacts, low quality, worst quality, bad quality <i>Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 810775161, Size: 768x768, Model: Oldjourney Lite, ENSD: 1</i> <b>Sample 2</b> Oldjourney an image of a wizard with a glowing staff turned to the side, black background, light art, full of colors and rich detail, color grunge, profile picture 1024px, glowing liquid, high detailed colors, colorful fire, an old man, blacklight, discord profile picture <b>Negative prompt:</b> ugly, tiling, out of frame, body out of frame, blurry, blurred, grainy, cut off, draft, (cropped:1.2),(overexposure:1.2), (high contrast:1.2), (poorly drawn hands:1.2), (poorly drawn feet:1.2), (poorly drawn face:1.2), (too long neck:1:2), (extra limbs:1.2), (less than two arms:1.2), (less than two legs:1.2), disfigured, deformed,(bad anatomy:1.2), (watermark:1.2), (logo:1.2), (barcode:1.2), (UI:1.2), (signature:1.2), (text:1.2), (label:1.5), (error:1.2), (title:1.2), stickers, markings, speech bubbles, lines, cropped, low res, low quality, artifacts, low quality, worst quality, bad quality <i>Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 2371590421, Size: 768x768, Model: Oldjourney Lite, ENSD: 1</i> <b>Sample 3</b> Oldjourney a dog with a tiny top hat and steampunk goggles on its head and a steampunk collar, matte painting, insanely detailed, ultrafine details, hyperrealism <b>Negative prompt:</b> (cropped:1.2),(overexposure:1.2), (high contrast:1.2), (watermark:1.2), (logo:1.2), (barcode:1.2), (UI:1.2), (signature:1.2), (text:1.2), (label:1.5), (error:1.2), (title:1.2), stickers, markings, speech bubbles, lines, cropped, low res, low quality, artifacts, low quality, worst quality, bad quality <i>Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 3142299054, Size: 768x768, Model: Oldjourney Lite, ENSD: 1</i> <b>Sample Prompts for Oldjourney Ultra</b> <b>Sample 4</b> Oldjourney A woman facing the camera dancing aura of cosmic energy vortex of sparkling blue sand and glowing embers ((grunge)) smoke magical eerie noir lighting stars in the sky ethereal dream sandman surreal rembrandt artstation dark atmosphere 8k highly detailed atmospheric <b>Negative prompt:</b> ugly, tiling, (poorly drawn hands:1.2), (poorly drawn feet:1.2), (poorly drawn face:1.2), out of frame, extra limbs, less than two arms, less than two legs, disfigured, deformed, body out of frame, blurry, (bad anatomy:1.2), blurred, grainy, cut off, draft, (overexposure:1.2), (high contrast:1.2),(cropped:1.2), (watermark:1.2), (logo:1.2), (barcode:1.2), (UI:1.2), (signature:1.2), (text:1.2), (label:1.5), (error:1.2), (title:1.2), stickers, markings, speech bubbles, lines, cropped, low res, low quality, artifacts, low quality, worst quality, bad quality <i>Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 2676530026, Size: 768x768, Model: Oldjourney Ultra, ENSD: 1</i> <b>Sample 5</b> Oldjourney your fate revealed inside a crystal ball, crystal ball with swirling otherworldly fog reveals your fate, insanely detailed masterpiece Trending on Artstation 8k ray traced volumetric lighting ambient occlusion ultrafine details digital art painting <b>Negative prompt:</b> ugly, tiling, out of frame, body out of frame, blurry, blurred, grainy, cut off, draft, (cropped:1.2),(overexposure:1.2), (high contrast:1.2), (poorly drawn hands:1.2), (poorly drawn feet:1.2), (poorly drawn face:1.2), (too long neck:1:2), (extra limbs:1.2), (less than two arms:1.2), (less than two legs:1.2), disfigured, deformed,(bad anatomy:1.2), (watermark:1.2), (logo:1.2), (barcode:1.2), (UI:1.2), (signature:1.2), (text:1.2), (label:1.5), (error:1.2), (title:1.2), stickers, markings, speech bubbles, lines, cropped, low res, low quality, artifacts, low quality, worst quality, bad quality <i>Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 2555061923, Size: 768x768, Model: Oldjourney Ultra, ENSD: 1</i> <b>Sample 6</b> Oldjourney cosmic queen, ethereal woman with a crown on her head, head and shoulders portrait, fantasy art, star sky, star sky, face illuminated, sparkle, stars, cosmos, paticles <b>Negative prompt:</b> ugly, tiling, out of frame, body out of frame, blurry, blurred, grainy, cut off, draft, (cropped:1.2),(overexposure:1.2), (high contrast:1.2), (poorly drawn hands:1.2), (poorly drawn feet:1.2), (poorly drawn face:1.2), (too long neck:1:2), (extra limbs:1.2), (less than two arms:1.2), (less than two legs:1.2), disfigured, deformed,(bad anatomy:1.2), (watermark:1.2), (logo:1.2), (barcode:1.2), (UI:1.2), (signature:1.2), (text:1.2), (label:1.5), (error:1.2), (title:1.2), stickers, markings, speech bubbles, lines, cropped, low res, low quality, artifacts, low quality, worst quality, bad quality <i>Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 868461039, Face restoration: GFPGAN, Size: 768x768, Model: Oldjourney Ultra, ENSD: 1</i> | 625a915dd5ad77b35c7dd3cb1be1877d |
lgpl-lr | ['spacy', 'token-classification'] | false | fr_core_news_lg French pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler, lemmatizer. | Feature | Description | | --- | --- | | **Name** | `fr_core_news_lg` | | **Version** | `3.5.0` | | **spaCy** | `>=3.5.0,<3.6.0` | | **Default Pipeline** | `tok2vec`, `morphologizer`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` | | **Components** | `tok2vec`, `morphologizer`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner` | | **Vectors** | 500000 keys, 500000 unique vectors (300 dimensions) | | **Sources** | [UD French Sequoia v2.8](https://github.com/UniversalDependencies/UD_French-Sequoia) (Candito, Marie; Seddah, Djamé; Perrier, Guy; Guillaume, Bruno)<br />[WikiNER](https://figshare.com/articles/Learning_multilingual_named_entity_recognition_from_Wikipedia/5462500) (Joel Nothman, Nicky Ringland, Will Radford, Tara Murphy, James R Curran)<br />[spaCy lookups data](https://github.com/explosion/spacy-lookups-data) (Explosion)<br />[Explosion fastText Vectors (cbow, OSCAR Common Crawl + Wikipedia)](https://spacy.io) (Explosion) | | **License** | `LGPL-LR` | | **Author** | [Explosion](https://explosion.ai) | | a14649f49d8556c360c8898d589ae447 |
lgpl-lr | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (237 labels for 3 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=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `POS=SCONJ`, `POS=ADP`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `NumType=Ord\|POS=ADJ`, `Gender=Masc\|Number=Sing\|POS=NOUN`, `POS=PUNCT`, `Gender=Masc\|Number=Sing\|POS=PROPN`, `Number=Plur\|POS=ADJ`, `Gender=Masc\|Number=Plur\|POS=NOUN`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `POS=ADV`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Fem\|Number=Sing\|POS=PROPN`, `Definite=Def\|Number=Sing\|POS=DET\|PronType=Art`, `NumType=Card\|POS=NUM`, `Definite=Def\|Number=Plur\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Plur\|POS=ADJ`, `POS=CCONJ`, `Gender=Fem\|Number=Plur\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Gender=Fem\|Number=Plur\|POS=ADJ`, `POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `POS=PRON\|PronType=Rel`, `Number=Sing\|POS=DET\|Poss=Yes`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Art`, `Definite=Def\|Number=Plur\|POS=ADP\|PronType=Art`, `Definite=Ind\|Number=Plur\|POS=DET\|PronType=Art`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `POS=VERB\|VerbForm=Inf`, `Gender=Fem\|Number=Sing\|POS=ADJ`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3`, `Number=Plur\|POS=DET`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=ADJ`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `POS=ADV\|PronType=Int`, `POS=VERB\|Tense=Pres\|VerbForm=Part`, `Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Number=Plur\|POS=DET\|Poss=Yes`, `POS=AUX\|VerbForm=Inf`, `Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Gender=Masc\|POS=VERB\|Tense=Past\|VerbForm=Part`, `POS=ADV\|Polarity=Neg`, `Definite=Ind\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=3`, `POS=PRON\|Person=3\|Reflex=Yes`, `Gender=Masc\|POS=NOUN`, `POS=AUX\|Tense=Past\|VerbForm=Part`, `POS=PRON\|Person=3`, `Number=Plur\|POS=NOUN`, `NumType=Ord\|Number=Sing\|POS=ADJ`, `POS=VERB\|Tense=Past\|VerbForm=Part`, `POS=AUX\|Tense=Pres\|VerbForm=Part`, `Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Number=Sing\|POS=PRON\|Person=3`, `Number=Sing\|POS=NOUN`, `Gender=Masc\|Number=Plur\|POS=PRON\|Person=3`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Number=Plur\|POS=PROPN`, `Number=Sing\|POS=PROPN`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=DET`, `Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes`, `Gender=Masc\|POS=PRON`, `POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=PRON`, `Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Number=Sing\|POS=PRON`, `Number=Sing\|POS=PRON\|PronType=Dem`, `Mood=Ind\|POS=VERB\|VerbForm=Fin`, `Number=Plur\|POS=DET\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=PRON`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `POS=PRON`, `POS=NUM`, `Gender=Fem\|POS=NOUN`, `POS=SPACE`, `Gender=Fem\|Number=Plur\|POS=PRON`, `Number=Plur\|POS=PRON\|Person=3`, `Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Number=Sing\|POS=PRON\|Person=1`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=PRON`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `POS=INTJ`, `Number=Plur\|POS=PRON\|Person=2`, `NumType=Card\|POS=PRON`, `Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `NumType=Card\|POS=NOUN`, `POS=PRON\|PronType=Int`, `Gender=Fem\|Number=Plur\|POS=PRON\|Person=3`, `Gender=Fem\|Number=Sing\|POS=DET`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=DET`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `Gender=Masc\|Number=Plur\|POS=PROPN`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `Number=Sing\|POS=DET`, `Gender=Masc\|NumType=Card\|Number=Plur\|POS=NOUN`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Dem`, `Mood=Ind\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|POS=PRON`, `Gender=Masc\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Part`, `POS=X`, `POS=SYM`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int`, `POS=DET`, `Gender=Masc\|Number=Plur\|POS=PRON`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|POS=VERB\|Person=3\|VerbForm=Fin`, `Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Gender=Masc\|Number=Plur\|POS=DET`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Rel`, `Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rel`, `POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Gender=Fem\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin`, `Mood=Imp\|POS=VERB\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|Person=2\|Reflex=Yes`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|Person=1\|Reflex=Yes`, `Gender=Masc\|NumType=Card\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Number=Sing\|POS=PRON\|Person=1\|Reflex=Yes`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|POS=PROPN`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Imp\|VerbForm=Fin`, `Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Gender=Fem\|Number=Plur\|POS=PROPN`, `Gender=Masc\|NumType=Card\|POS=NUM` | | **`parser`** | `ROOT`, `acl`, `acl:relcl`, `advcl`, `advmod`, `amod`, `appos`, `aux:pass`, `aux:tense`, `case`, `cc`, `ccomp`, `conj`, `cop`, `dep`, `det`, `expl:comp`, `expl:pass`, `expl:subj`, `fixed`, `flat:foreign`, `flat:name`, `iobj`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl:agent`, `obl:arg`, `obl:mod`, `parataxis`, `punct`, `vocative`, `xcomp` | | **`ner`** | `LOC`, `MISC`, `ORG`, `PER` | </details> | cb07b6365c38cb87c31b1f6d9ab4ccea |
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` | 97.34 | | `MORPH_ACC` | 96.74 | | `MORPH_MICRO_P` | 98.91 | | `MORPH_MICRO_R` | 98.17 | | `MORPH_MICRO_F` | 98.54 | | `SENTS_P` | 85.92 | | `SENTS_R` | 89.26 | | `SENTS_F` | 87.35 | | `DEP_UAS` | 90.29 | | `DEP_LAS` | 86.54 | | `TAG_ACC` | 94.47 | | `LEMMA_ACC` | 91.36 | | `ENTS_P` | 83.99 | | `ENTS_R` | 83.87 | | `ENTS_F` | 83.93 | | c3369638e515005d47909d64def485b3 |
creativeml-openrail-m | ['stable-diffusion'] | false | Description > Elaina (イレイナ, Ireina) is the main protagonist of the Wandering Witch series. > She is a witch with the witch name of The Ashen Witch. > ([Fandom](https://wandering-witch.fandom.com/wiki/Elaina)) | da918abd4d45f99b5f6a8b8c757e1a6e |
creativeml-openrail-m | ['stable-diffusion'] | false | Preview > **Model:** [anything-v4.5-pruned.ckpt](https://huggingface.co/andite/anything-v4.0/tree/main)\ > **Model VAE:** [anything-v4.0.vae.pt](https://huggingface.co/andite/anything-v4.0/tree/main)\ > **Prompt:** masterpiece, best quality, EMB_elaina-7500\ > **Negative Prompt:** obese, (ugly:1.3), (duplicate:1.3), (morbid), (mutilated), out of frame, extra fingers, mutated hands, (poorly drawn hands), (poorly drawn face), (mutation:1.3), (deformed:1.3), (amputee:1.3), blurry, bad anatomy, bad proportions, (extra limbs), cloned face, (disfigured:1.3), gross proportions, (malformed limbs), (missing arms), (missing legs), (extra arms), (extra legs), mutated hands, (fused fingers), (too many fingers), (long neck:1.3), lowres, text, error, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, black and white, monochrome, censored,empty      | 4430c2776d6f455fa952e0e1075a7c2e |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-misogyny-sexism-tweets This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5009 - Accuracy: 0.796 - F1: 0.8132 - Precision: 0.75 - Recall: 0.888 - Mae: 0.204 - Tn: 352 - Fp: 148 - Fn: 56 - Tp: 444 | e500bf2b11ab1f4f0c8b1ef8cc9a1dec |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Mae | Tn | Fp | Fn | Tp | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-----:|:---:|:---:|:--:|:---:| | 0.4947 | 1.0 | 1646 | 0.4683 | 0.765 | 0.7866 | 0.7205 | 0.866 | 0.235 | 332 | 168 | 67 | 433 | | 0.4285 | 2.0 | 3292 | 0.4514 | 0.779 | 0.8004 | 0.7298 | 0.886 | 0.221 | 336 | 164 | 57 | 443 | | 0.3721 | 3.0 | 4938 | 0.4430 | 0.781 | 0.8060 | 0.7234 | 0.91 | 0.219 | 326 | 174 | 45 | 455 | | 0.3127 | 4.0 | 6584 | 0.5009 | 0.796 | 0.8132 | 0.75 | 0.888 | 0.204 | 352 | 148 | 56 | 444 | | 264fbf88d97eab69ef716fcce092a567 |
cc-by-sa-4.0 | [] | false | Sample Output (Raw Output)  <sub>tchnclr style, a closeup portrait of Brenda Song, happy beaming content, glitter, glittery Negative prompt: b&w, lowres, text, error, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, ugly Steps: 40, Sampler: Euler, CFG scale: 7, Seed: 1638627547, Size: 512x512, Model hash: ed87e89c, Variation seed: 3476746822, Variation seed strength: 0.2</sub>  <sub>Use PNG block tool to view the prompts and settings used to product these images</sub>     | 1e8cb745e6446a91e29fd40d197d69ba |
cc-by-sa-4.0 | [] | false | Recommended Usage - Your prompt must include "tchnclr style" - Use CFG of 7 or 8 for best results - The model was trained with and excels at closeup portraits of men and women - Try including "glitter" in your prompt! - Putting "b&w" as a negative prompt will help ensure color image | b08ad4de6f607d4696e4e04e97edda1e |
cc-by-sa-4.0 | [] | false | Known Limitations - It strongly tries to insert 40s and 50s hairstyles, clothing, and scenery - As you can see from the examples, you can insert some modernity and blend with other styles. But if your prompt insists on modern elements, the technicolor effect may disappear. - The model tends to turn men into women. It also likes to add hats! | 881a6f42c994818224ce8b75b961c26e |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | ZlikwidV2 Dreambooth model trained by Zlikwid with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept: | 777d43c1406d609e42d787a26addf1bc |
mit | ['generated_from_trainer', 'text generation', 'pytorch', 'casual-lm'] | false | openchatgpt-neo-r1 This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) on the openchatgpt safe-r1 dataset. It achieves the following results on the evaluation set: - Loss: 3.2156 - Accuracy: 0.8338 | 4c6af3f4c786077654dddf9572f01554 |
mit | ['generated_from_trainer', 'text generation', 'pytorch', 'casual-lm'] | false | Model description Finetune based on the inner workings of ChatGPT. I won't elaborate on that. You must have a faint idea of how prompt is made for it to spit anything that's not garbled mess. This is effectively a schizophrenic idea that met the light of day. Practically a collab of 3 students in a virtual shed. | aa2d764a4a8bde908cf17be25a1f7126 |
mit | ['generated_from_trainer', 'text generation', 'pytorch', 'casual-lm'] | false | Intended uses & limitations Intended uses & limitations fall in line with OpenAI's. Dataset used consists of safe texts (i.e. not highly sexual/erotica type stuff). NSFW version of the dataset is not planned to exist at the moment. Keep in mind that this is a 125m version of GPT-Neo. My 1050Ti Mobile couldn't even handle that without gradient thingmabobs. If anyone knows how to effectively finetune larger models on free colabs - feel free to let me know. Pile tokenizer also has one downside compared to native GPT-2/3 - `Assistant`. | 0138862e0a538919336101f456ed8801 |
mit | ['generated_from_trainer', 'text generation', 'pytorch', 'casual-lm'] | false | Training and evaluation data Data was split in ratio of 95%/5%. Preproccess included removing mentions of OpenAI wherever it was not deemed appropriete (GPT-2 has one of the appropriete mentions). Whole dataset consists of just shy off 3k input-output pairs. One input has multiple outputs (read as: one message has multiple variants of an answer). <<<1% (3 total) are curated lines (i.e. a huge mistake was spotted that needed corrections). Heavy bias on IT. | 4376573d0b8eba0ef8ae9f59c67c5b6e |
mit | ['generated_from_trainer', 'text generation', 'pytorch', 'casual-lm'] | false | Training procedure Input and output were straight up concatenated due to the nature of how ChatGPT works. Padding chosen was the same as the separator token, if that's not effective - please let me know as I am new to this stuff. | b91e5835f9d7733a1698af43b99cb65c |
mit | ['generated_from_trainer', 'text generation', 'pytorch', 'casual-lm'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 5 | 3cc22e64c41bf3ab8a07f1e6deefb652 |
mit | ['generated_from_trainer', 'text generation', 'pytorch', 'casual-lm'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 4.9203 | 1.0 | 1378 | 5.1668 | 0.7274 | | 4.1368 | 2.0 | 2756 | 4.3841 | 0.7563 | | 3.4554 | 3.0 | 4134 | 3.8068 | 0.7875 | | 2.7598 | 4.0 | 5512 | 3.3097 | 0.8303 | | 2.5879 | 5.0 | 6890 | 3.2156 | 0.8338 | | 0a8df8ccc1b6da106f1af0f61f2dfe81 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 5 - mixed_precision_training: Native AMP | 83a783feb1150dcac2fe398ad58d6d93 |
mit | ['generated_from_trainer'] | false | bart-large-cnn-100-lit-evalMA-NOpad2 This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2126 - Rouge1: 25.6196 - Rouge2: 7.2753 - Rougel: 18.0987 - Rougelsum: 20.8416 - Gen Len: 67.3 | c197c8230717e4f7a2559dd720e89b54 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP | 975ede807c7aa47b4a124912accc2b5e |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | No log | 1.0 | 100 | 1.0890 | 23.5493 | 8.9875 | 17.1471 | 20.1643 | 67.8 | | No log | 2.0 | 200 | 1.2126 | 25.6196 | 7.2753 | 18.0987 | 20.8416 | 67.3 | | ccc345c697fecb9d62e701eaa36a1381 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Base Turkish Whisper (BTW) This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Ermetal Meetings dataset. It achieves the following results on the evaluation set: - Loss: 0.0009 - Wer: 0.0 - Cer: 0.0 | 71faaf4593978a0907ad01f4cec6825e |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 1000 - mixed_precision_training: Native AMP | 3d3f4de01dace8b4a9f9302f5d3b3e6b |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 1.8786 | 6.63 | 100 | 1.3510 | 0.7866 | 0.6649 | | 0.4559 | 13.32 | 200 | 0.3395 | 0.3590 | 0.2157 | | 0.0793 | 19.95 | 300 | 0.0564 | 0.0996 | 0.0531 | | 0.0137 | 26.63 | 400 | 0.0120 | 0.0017 | 0.0017 | | 0.0042 | 33.32 | 500 | 0.0032 | 0.0 | 0.0 | | 0.0021 | 39.95 | 600 | 0.0018 | 0.0 | 0.0 | | 0.0014 | 46.63 | 700 | 0.0013 | 0.0 | 0.0 | | 0.0012 | 53.32 | 800 | 0.0011 | 0.0 | 0.0 | | 0.001 | 59.95 | 900 | 0.0010 | 0.0 | 0.0 | | 0.001 | 66.63 | 1000 | 0.0009 | 0.0 | 0.0 | | 585aeb820f82ec8a47205ab713b805c5 |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-meta-6-16-5 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: 2.4797 - Accuracy: 0.28 | 5474ea18202c3e8f14b7d5f3ec40d4db |
apache-2.0 | ['translation'] | false | opus-mt-fi-xh * source languages: fi * target languages: xh * OPUS readme: [fi-xh](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-xh/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/fi-xh/opus-2020-01-08.zip) * test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-xh/opus-2020-01-08.test.txt) * test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-xh/opus-2020-01-08.eval.txt) | 132cc2a2dacd029a8f9f38e383f95e2b |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | bp500-base100k_voxpopuli: Wav2vec 2.0 with Brazilian Portuguese (BP) Dataset This is a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the following datasets: - [CETUC](http://www02.smt.ufrj.br/~igor.quintanilha/alcaim.tar.gz): contains approximately 145 hours of Brazilian Portuguese speech distributed among 50 male and 50 female speakers, each pronouncing approximately 1,000 phonetically balanced sentences selected from the [CETEN-Folha](https://www.linguateca.pt/cetenfolha/) corpus. - [Common Voice 7.0](https://commonvoice.mozilla.org/pt): is a project proposed by Mozilla Foundation with the goal to create a wide open dataset in different languages. In this project, volunteers donate and validate speech using the [oficial site](https://commonvoice.mozilla.org/pt). - [Lapsbm](https://github.com/falabrasil/gitlab-resources): "Falabrasil - UFPA" is a dataset used by the Fala Brasil group to benchmark ASR systems in Brazilian Portuguese. Contains 35 speakers (10 females), each one pronouncing 20 unique sentences, totalling 700 utterances in Brazilian Portuguese. The audios were recorded in 22.05 kHz without environment control. - [Multilingual Librispeech (MLS)](https://arxiv.org/abs/2012.03411): a massive dataset available in many languages. The MLS is based on audiobook recordings in public domain like [LibriVox](https://librivox.org/). The dataset contains a total of 6k hours of transcribed data in many languages. The set in Portuguese [used in this work](http://www.openslr.org/94/) (mostly Brazilian variant) has approximately 284 hours of speech, obtained from 55 audiobooks read by 62 speakers. - [Multilingual TEDx](http://www.openslr.org/100): a collection of audio recordings from TEDx talks in 8 source languages. The Portuguese set (mostly Brazilian Portuguese variant) contains 164 hours of transcribed speech. - [Sidney](https://igormq.github.io/datasets/) (SID): contains 5,777 utterances recorded by 72 speakers (20 women) from 17 to 59 years old with fields such as place of birth, age, gender, education, and occupation; - [VoxForge](http://www.voxforge.org/): is a project with the goal to build open datasets for acoustic models. The corpus contains approximately 100 speakers and 4,130 utterances of Brazilian Portuguese, with sample rates varying from 16kHz to 44.1kHz. These datasets were combined to build a larger Brazilian Portuguese dataset. All data was used for training except Common Voice dev/test sets, that were used for validation/test respectively. We also made test sets for all the gathered datasets. | Dataset | Train | Valid | Test | |--------------------------------|-------:|------:|------:| | CETUC | 94.0h | -- | 5.4h | | Common Voice | 37.8h | 8.9h | 9.5h | | LaPS BM | 0.8h | -- | 0.1h | | MLS | 161.0h | -- | 3.7h | | Multilingual TEDx (Portuguese) | 148.9h | -- | 1.8h | | SID | 7.2h | -- | 1.0h | | VoxForge | 3.9h | -- | 0.1h | | Total | 453.6h | 8.9h | 21.6h | The original model was fine-tuned using [fairseq](https://github.com/pytorch/fairseq). This notebook uses a converted version of the original one. The link to the original fairseq model is available [here](https://drive.google.com/file/d/10iESR5AQxuxF5F7w3wLbpc_9YMsYbY9H/view?usp=sharing). | b2d284c992f359a82ed60e8e998cf7f9 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | Summary | | CETUC | CV | LaPS | MLS | SID | TEDx | VF | AVG | |----------------------|---------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------| | bp\_500-base100k_voxpopuli (demonstration below) | 0.142 | 0.201 | 0.052 | 0.224 | 0.102 | 0.317 | 0.048 | 0.155 | | bp\_500-base100k_voxpopuli + 4-gram (demonstration below) | 0.099 | 0.149 | 0.047 | 0.192 | 0.115 | 0.371 | 0.127 | 0.157 | | 2dc240bc28d8201312d024d163f2ae74 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | Transcription examples | Text | Transcription | |------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------| |qual o instagram dele|**qualo** **está** **gramedele**| |o capitão foi expulso do exército porque era doido|o **capitãl** foi **exposo** do exército porque era doido| |também por que não|também **porque** não| |não existe tempo como o presente|não existe tempo como *o* presente| |eu pulei para salvar rachel|eu pulei para salvar **haquel**| |augusto cezar passos marinho|augusto **cesa** **passoesmarinho**| | 5f70fc2c3d7a32e8831942f0c6cb134d |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | Imports and dependencies ```python %%capture !pip install torch==1.8.2+cu111 torchvision==0.9.2+cu111 torchaudio===0.8.2 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html !pip install datasets !pip install jiwer !pip install transformers !pip install soundfile !pip install pyctcdecode !pip install https://github.com/kpu/kenlm/archive/master.zip ``` ```python import jiwer import torchaudio from datasets import load_dataset, load_metric from transformers import ( Wav2Vec2ForCTC, Wav2Vec2Processor, ) from pyctcdecode import build_ctcdecoder import torch import re import sys ``` | abbb2aceffd97ccfcba395524cb4192c |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | noqa: W605 def map_to_array(batch): speech, _ = torchaudio.load(batch["path"]) batch["speech"] = speech.squeeze(0).numpy() batch["sampling_rate"] = 16_000 batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'") batch["target"] = batch["sentence"] return batch ``` ```python def calc_metrics(truths, hypos): wers = [] mers = [] wils = [] for t, h in zip(truths, hypos): try: wers.append(jiwer.wer(t, h)) mers.append(jiwer.mer(t, h)) wils.append(jiwer.wil(t, h)) except: | 8bb0eb3ea50eedc865fe12424393ed46 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | Empty string? pass wer = sum(wers)/len(wers) mer = sum(mers)/len(mers) wil = sum(wils)/len(wils) return wer, mer, wil ``` ```python def load_data(dataset): data_files = {'test': f'{dataset}/test.csv'} dataset = load_dataset('csv', data_files=data_files)["test"] return dataset.map(map_to_array) ``` | 0765a5c7d25f235ea8e8e1921a8c33b9 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | Model ```python class STT: def __init__(self, model_name, device='cuda' if torch.cuda.is_available() else 'cpu', lm=None): self.model_name = model_name self.model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device) self.processor = Wav2Vec2Processor.from_pretrained(model_name) self.vocab_dict = self.processor.tokenizer.get_vocab() self.sorted_dict = { k.lower(): v for k, v in sorted(self.vocab_dict.items(), key=lambda item: item[1]) } self.device = device self.lm = lm if self.lm: self.lm_decoder = build_ctcdecoder( list(self.sorted_dict.keys()), self.lm ) def batch_predict(self, batch): features = self.processor(batch["speech"], sampling_rate=batch["sampling_rate"][0], padding=True, return_tensors="pt") input_values = features.input_values.to(self.device) with torch.no_grad(): logits = self.model(input_values).logits if self.lm: logits = logits.cpu().numpy() batch["predicted"] = [] for sample_logits in logits: batch["predicted"].append(self.lm_decoder.decode(sample_logits)) else: pred_ids = torch.argmax(logits, dim=-1) batch["predicted"] = self.processor.batch_decode(pred_ids) return batch ``` | e2a51cdd539e1de6663f4725294ec531 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | Download datasets ```python %%capture !gdown --id 1HFECzIizf-bmkQRLiQD0QVqcGtOG5upI !mkdir bp_dataset !unzip bp_dataset -d bp_dataset/ ``` ```python %cd bp_dataset ``` /content/bp_dataset | 2d2c8550b14ca3d467eeee38aa9b2af4 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | CETUC ```python ds = load_data('cetuc_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CETUC WER:", wer) ``` CETUC WER: 0.1419179499917191 | fb658c44165a6c2856373894a40723b9 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | Common Voice ```python ds = load_data('commonvoice_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CV WER:", wer) ``` CV WER: 0.20079950312040154 | 2e5d6b981e76a6a4d4c42cb05411fff4 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | LaPS ```python ds = load_data('lapsbm_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Laps WER:", wer) ``` Laps WER: 0.052780934343434324 | b3ead9564b4f33183a1f73b30ccb9a08 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | MLS ```python ds = load_data('mls_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("MLS WER:", wer) ``` MLS WER: 0.22413887199364113 | a54c0cde962dbc8bc7a987ab2c7092ad |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | SID ```python ds = load_data('sid_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Sid WER:", wer) ``` Sid WER: 0.1019041538671034 | 3d955adf2bc8016bea655a82157dc6b7 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | TEDx ```python ds = load_data('tedx_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("TEDx WER:", wer) ``` TEDx WER: 0.31711268778273327 | 2fa243f8c8c366ecedf5ff317b85770f |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | VoxForge ```python ds = load_data('voxforge_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("VoxForge WER:", wer) ``` VoxForge WER: 0.04826433982683982 | 4ca44f9a7c96e799e5edcf97e9bdae55 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | Cetuc ```python ds = load_data('cetuc_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CETUC WER:", wer) ``` CETUC WER: 0.099518615112877 | 32e81e5e867c8e3addcfd4d73b99afe4 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | Common Voice ```python ds = load_data('commonvoice_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("CV WER:", wer) ``` CV WER: 0.1488912889506362 | 4552242e8ab84a5832b1db1b08c1202b |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | LaPS ```python ds = load_data('lapsbm_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Laps WER:", wer) ``` Laps WER: 0.047080176767676764 | 48b3272a11fdf5d34bd2ffea3580149e |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | MLS ```python ds = load_data('mls_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("MLS WER:", wer) ``` MLS WER: 0.19220291966887196 | 543054b466cb687d9790f024a6349bac |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | SID ```python ds = load_data('sid_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("Sid WER:", wer) ``` Sid WER: 0.11535498771650306 | 427d313619773d7e4de0eeb582fe00bb |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch'] | false | TEDx ```python ds = load_data('tedx_dataset') result = ds.map(stt.batch_predict, batched=True, batch_size=8) wer, mer, wil = calc_metrics(result["sentence"], result["predicted"]) print("TEDx WER:", wer) ``` TEDx WER: 0.3707890073539895 | eaad92fe548924209e7cbacfdbe22b71 |
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