Datasets:

davanstrien

/

model_cards_with_readmes

like 1

susnato/bert-base-uncased-issues-128

susnato

bert

transformers

fill-mask

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 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 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### 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 ### 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 | ### Framework versions - Transformers 4.21.2 - Pytorch 1.13.0+cu117 - Datasets 2.7.1 - Tokenizers 0.12.1

0c36919bc38928cbfe31f404bdbe6315

FUXI/yuyan-11b

FUXI

text-generation

apache-2.0

['zh']

['text-generation', 'story-generation', 'pytorch', 'inference acceleration', 'gpt2', 'gpt3']

# 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. ## 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. ## 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. ``` # install pytorch pip install torch==1.8.1 # install pytorch # install fairseq unzip fairseq-0.12.2.zip cd fairseq-0.12.2 pip install. # install EET git clone https://github.com/NetEase-FuXi/EET.git cd EET pip install . # install transformers (EET requirements) pip install transformers==4.23 # 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 # 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。"]) ``` ## 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" } ``` ## Contact Us You can also contact us by email: xiyadong@corp.netease.com, dingjingzhen@corp.netease

e0e9f748f8efa87c14cb74d862837ee0

sanchit-gandhi/whisper-medium-es-4k-1e-7-bs-32

sanchit-gandhi

whisper

transformers

automatic-speech-recognition

apache-2.0

['es']

['facebook/multilingual_librispeech']

['hf-asr-leaderboard', 'generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 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 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### 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 ### 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 | ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.0 - Datasets 2.6.2.dev0 - Tokenizers 0.12.1

7430e5605e8ff17d8bcb630a664acd5b

sd-concepts-library/venice

sd-concepts-library

mit

[]

### 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`:        

ad6d3718973b8a107b34fa7c3e95a842

Krishadow/biobert-finetuned-ner

Krishadow

bert

transformers

token-classification

apache-2.0

['generated_from_keras_callback']

<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # 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 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### 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 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.1443 | 0.0597 | 0 | | 0.0450 | 0.0593 | 1 | ### Framework versions - Transformers 4.18.0 - TensorFlow 2.8.0 - Datasets 2.1.0 - Tokenizers 0.12.1

695315c94aaa7e69fc27cf0665f4099a

adsjklfsd/distilbert-base-uncased-finetuned-emotion

adsjklfsd

distilbert

transformers

text-classification

apache-2.0

['emotion']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 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 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### 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 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.10.1+cu113 - Datasets 2.9.0 - Tokenizers 0.13.2

981ca25bc509683a4189970673e54e90

hkunlp/instructor-xl

hkunlp

t5

sentence-transformers

sentence-similarity

apache-2.0

['en']

['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']

# 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! ## Quick start <hr /> ## Installation ```bash pip install InstructorEmbedding ``` ## 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) ``` ## Use cases <hr /> ## 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: &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;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. ## 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) ``` ## 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) ``` ## 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) ```

23c06254f67b8ad723affed51716e3f4

mchochowski/test-model

mchochowski

transformers

image-classification

apache-2.0

['imagenet']

['image-classification', 'resnet']

### 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) ### 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) ``` ### 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) ### 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 ```

0da925f8cdec0ab06a770d8ae3e5a813

abishanth/crpf_analysis_trail_1

abishanth

distilbert

transformers

text-classification

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 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 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

7294f44b996a90b477e343f6ae32cbc2

DrishtiSharma/whisper-large-v2-ml-700-steps

DrishtiSharma

whisper

transformers

automatic-speech-recognition

apache-2.0

['ml']

['mozilla-foundation/common_voice_11_0']

['whisper-event', 'generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 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 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0002 | 12.96 | 700 | 0.3159 | 28.2886 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu116 - Datasets 2.8.1.dev0 - Tokenizers 0.13.2

e1ae60f04f4fe80440a79b9a17d03737

vishwasgautam/HuBERT-base-libriSpeech-demo-colab

vishwasgautam

hubert

transformers

automatic-speech-recognition

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 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 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### 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 ### 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 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

508ce17af20be0246419d3a38f6463d1

wietsedv/xlm-roberta-base-ft-udpos28-cy

wietsedv

xlm-roberta

transformers

token-classification

apache-2.0

['cy']

['universal_dependencies']

['part-of-speech', 'token-classification']

# 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. ## 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") ```

8aa568fb46e55a8e84c605896eb885de

kingabzpro/wav2vec2-large-xls-r-300m-Indonesian

kingabzpro

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['id']

['mozilla-foundation/common_voice_7_0']

['automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 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 ### 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 ### 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 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0

2248a0490959902fdcd159557f83cf7e

AlekseyKorshuk/1.3b-dalio-principles-book

AlekseyKorshuk

opt

transformers

text-generation

other

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 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 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### 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 ### 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 | ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

d325a95dafb4aba8931b374c744c2d02

sasuke/bert-base-uncased-finetuned-sst2

sasuke

bert

transformers

text-classification

apache-2.0

['glue']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 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 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### 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 | ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0 - Datasets 2.2.2 - Tokenizers 0.12.1

f51121dce2febf1abda52abda04cce20

jgammack/MTL-roberta-base

jgammack

roberta

transformers

fill-mask

mit

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 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 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### 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 ### 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 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

1a42fb08f72de6f194875a6f0d6a7d67

jonatasgrosman/exp_w2v2t_th_wav2vec2_s664

jonatasgrosman

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['th']

['mozilla-foundation/common_voice_7_0']

['automatic-speech-recognition', 'th']

# 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.

f0b8c7bfbd1733bdfc143aec9d70d3f6

StarwingDigital/Oldjourney

StarwingDigital

diffusers

creativeml-openrail-m

['en']

['Text-to-image', 'Diffusers', 'stable-diffusion']

<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>

ce1c44f3f6793ffa1be68e8dd8177758

spacy/fr_core_news_lg

spacy

spacy

token-classification

lgpl-lr

['fr']

['spacy', 'token-classification']

### Details: https://spacy.io/models/fr#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) | ### 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> ### 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 |

5e2a6466e7c2ed2a03fe988d87d3486f

heziiiii/ddpm-butterflies-128

heziiiii

diffusers

apache-2.0

['en']

['huggan/smithsonian_butterflies_subset']

[]

<!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # ddpm-butterflies-128 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `huggan/smithsonian_butterflies_subset` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/heziiiii/ddpm-butterflies-128/tensorboard?#scalars)

1d9ac641223f45476fb771d4848b4b72

TheSkinnyRat/TI-EMB_elaina

TheSkinnyRat

creativeml-openrail-m

['stable-diffusion']

# Info > Trainer: [TheSkinnyRat](https://huggingface.co/TheSkinnyRat)\ > Type: Textual Inversion Embeddings # 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)) # Download > [Model download](https://huggingface.co/TheSkinnyRat/TI-EMB_elaina/tree/main) # Training > Model Used: [nai-wd.ckpt](https://huggingface.co/andite/training_models/tree/main)\ > Dataset: 13 images\ > Size: 512x512\ > Steps: 7500\ > N Steps: 500 # 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     

a6af81dbf17d0ce280f1fc326c07c815

annahaz/xlm-roberta-base-misogyny-sexism-tweets

annahaz

xlm-roberta

transformers

text-classification

mit

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 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 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### 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 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

904eb9c1e6f7faf20262fc071b839843

mattthew/technicolor-50s-diffusion

mattthew

cc-by-sa-4.0

[]

# 🌈 Technicolor-50s Diffusion ## Style Description - highly-saturated postcard-like colors, flat high-key lighting, strong rim-lighting, 40s and 50s lifestyle ## Sample Output (Raw Output)  _{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}  _{Use PNG block tool to view the prompts and settings used to product these images}     ## 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 ## 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! ## Training Process 20 images from movies filmed in technicolor, 200 photo-like classifiers, 6000 steps, using the Dreambooth Extension for Automatic1111.

484c28e3c6b7e38859a451398a2bbbec

Zlikwid/zlikwidv2

Zlikwid

diffusers

text-to-image

creativeml-openrail-m

['text-to-image', 'stable-diffusion']

### 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:

2b49a1e7bf066806e97791b1feec57df

mrsteyk/openchatgpt-neo-125m

mrsteyk

gpt_neo

transformers

text-generation

mit

['en']

['generated_from_trainer', 'text generation', 'pytorch', 'casual-lm']

# --- Disclaimer --- # "Neo is an incredibly cursed codebase, it should not be used by anyone" (C) co-founder of EleutherAI - Connor Leahy # !!! USE [openchatgpt-neox-125m](https://huggingface.co/mrsteyk/openchatgpt-neox-125m) INSTEAD !!! # --- Archived --- # 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 ## 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. ## 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`. ## 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. ## 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. ### 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 ### 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 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

363a38baa41437aac5164884c2768719

Siddu0406/gpt-2-model-2

Siddu0406

gpt2

transformers

text-generation

mit

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt-2-model-2 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### 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 ### Training results ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

f086e22089fd1f2875c22a8e19035a33

cammy/bart-large-cnn-100-lit-evalMA-NOpad2

cammy

bart

transformers

text2text-generation

mit

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 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 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### 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 ### 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 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.2 - Datasets 1.18.3 - Tokenizers 0.11.0

2904c5d06d606c80b84944cc19b49c00

Mehtap/whisper-base

Mehtap

whisper

transformers

automatic-speech-recognition

apache-2.0

['tr']

['hf-asr-leaderboard', 'generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 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 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### 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 ### 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 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.9.1+cu111 - Datasets 2.7.1 - Tokenizers 0.13.2

4de42494bf32c06be6b744bc961bde5e

fathyshalab/all-roberta-large-v1-meta-6-16-5

fathyshalab

roberta

transformers

text-classification

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 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 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.7721 | 1.0 | 1 | 2.6529 | 0.1889 | | 2.2569 | 2.0 | 2 | 2.5866 | 0.2333 | | 1.9837 | 3.0 | 3 | 2.5340 | 0.2644 | | 1.6425 | 4.0 | 4 | 2.4980 | 0.2756 | | 1.4612 | 5.0 | 5 | 2.4797 | 0.28 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

f263b49c8773eee9a7f13bdb4ac4bad4

Helsinki-NLP/opus-mt-fi-xh

Helsinki-NLP

marian

transformers

translation

apache-2.0

['translation']

### 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) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.fi.xh | 25.3 | 0.554 |

033987db784f4f88bd650c5730a9f0ac

lgris/bp500-base100k_voxpopuli

lgris

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['pt']

['common_voice', 'mls', 'cetuc', 'lapsbm', 'voxforge', 'tedx', 'sid']

['audio', 'speech', 'wav2vec2', 'pt', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch']

# 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). #### 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 | #### 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**| ## Demonstration ```python MODEL_NAME = "lgris/bp500-base100k_voxpopuli" ``` ### 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 ``` ### Helpers ```python chars_to_ignore_regex = '[\,\?\.\!\;\:\"]' # 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: # 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) ``` ### 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 ``` ### Download datasets ```python %%capture !gdown --id 1HFECzIizf-bmkQRLiQD0QVqcGtOG5upI !mkdir bp_dataset !unzip bp_dataset -d bp_dataset/ ``` ```python %cd bp_dataset ``` /content/bp_dataset ### Tests ```python stt = STT(MODEL_NAME) ``` #### 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 #### 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 #### 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 #### 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 #### 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 #### 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 #### 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 ### Tests with LM ```python !rm -rf ~/.cache !gdown --id 1GJIKseP5ZkTbllQVgOL98R4yYAcIySFP # trained with wikipedia stt = STT(MODEL_NAME, lm='pt-BR-wiki.word.4-gram.arpa') # !gdown --id 1dLFldy7eguPtyJj5OAlI4Emnx0BpFywg # trained with bp # stt = STT(MODEL_NAME, lm='pt-BR.word.4-gram.arpa') ``` ### 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 #### 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 #### 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 #### 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 #### 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 #### 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 #### 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: 0.12682088744588746

dfb57b0c3b85eeeade2b272e16bf346b

birgermoell/psst-fairseq-larger-rir

birgermoell

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['en']

['automatic-speech-recognition']

This model is trained on the PSST Challenge data, with a subset of TIMIT that was augmented using Room Impulse Response (RIR). A file containing the list of TIMIT IDs is in the repository (`timit-ids.txt`) The model was finetuned on [Wav2vec 2.0 Large, No finetuning](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec), and the results on the validation set were **PER:** 21\.0%, **FER:** 9\.2%.

330b8788ffa202e8ee922d131a574a4e

Mizuiro-sakura/deberta-v2-base-japanese-finetuned-ner

Mizuiro-sakura

deberta-v2

transformers

token-classification

mit

['ja']

['wikipedia', 'cc100', 'oscar']

['pytorch', 'deberta', 'deberta-v2', 'named entity recognition', 'named-entity-recognition', 'ner']

# このモデルはdeberta-v2-base-japaneseをファインチューニングして固有表現抽出（NER）に用いれるようにしたものです。 このモデルはdeberta-v2-base-japaneseを Wikipediaを用いた日本語の固有表現抽出データセット(ストックマーク社、https://github.com/stockmarkteam/ner-wikipedia-dataset )を用いてファインチューニングしたものです。 # This model is fine-tuned model for Named Entity Recognition (NER) which is based on deberta-v2-base-japanese This model is fine-tuned by using Wikipedia dataset. You could use this model for NER tasks. # How to use 使い方 transformersおよびpytorch、sentencepiece、Juman++をインストールしてください。 以下のコードを実行することで、固有表現抽出タスクを解かせることができます。 please execute this code. ```python from transformers import AutoTokenizer,pipeline, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained('Mizuiro-sakura/deberta-v2-base-japanese-finetuned-ner') model=AutoModelForTokenClassification.from_pretrained('Mizuiro-sakura/deberta-v2-base-japanese-finetuned-ner') # 学習済みモデルの読み込み text=('昨日は東京で買い物をした') ner=pipeline('ner', model=model, tokenizer=tokenizer) result=ner(text) print(result) ``` # モデルの精度 accuracy of model precision recall f1-score support その他の組織名 0.73 0.75 0.74 238 イベント名 0.81 0.81 0.81 215 人名 0.84 0.87 0.85 547 地名 0.83 0.83 0.83 446 政治的組織名 0.82 0.85 0.83 263 施設名 0.74 0.86 0.80 241 法人名 0.81 0.82 0.82 487 製品名 0.68 0.73 0.71 252 micro avg 0.79 0.82 0.81 2689 macro avg 0.78 0.81 0.80 2689 weighted avg 0.79 0.82 0.81 2689 # deberta-v2-base-japaneseとは？ 日本語Wikipedeia（3.2GB）および、cc100(85GB)、oscar(54GB)を用いて訓練されたモデルです。 京都大学黒橋研究室が公表されました。 # Model description This is a Japanese DeBERTa V2 base model pre-trained on Japanese Wikipedia, the Japanese portion of CC-100, and the Japanese portion of OSCAR. # Acknowledgments 謝辞 モデルを公開してくださった京都大学黒橋研究室には感謝いたします。 I would like to thank Kurohashi Lab at Kyoto University.

08052a58606c65d69b357d8b908b6794

stanfordnlp/stanza-swl

stanfordnlp

stanza

token-classification

apache-2.0

['swl']

['stanza', 'token-classification']

# Stanza model for Swedish_Sign_Language (swl) Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing. Find more about it in [our website](https://stanfordnlp.github.io/stanza) and our [GitHub repository](https://github.com/stanfordnlp/stanza). This card and repo were automatically prepared with `hugging_stanza.py` in the `stanfordnlp/huggingface-models` repo Last updated 2022-09-25 02:05:14.693

afd409c3b660a0f11d7a3218304d7115

muhtasham/small-mlm-glue-wnli

muhtasham

bert

transformers

fill-mask

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # small-mlm-glue-wnli This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1284 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - 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: constant - num_epochs: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 1.7452 | 6.25 | 500 | 1.2770 | | 0.9127 | 12.5 | 1000 | 0.8006 | | 0.6024 | 18.75 | 1500 | 0.5714 | | 0.3967 | 25.0 | 2000 | 0.6533 | | 0.3443 | 31.25 | 2500 | 0.3623 | | 0.2739 | 37.5 | 3000 | 0.3035 | | 0.2326 | 43.75 | 3500 | 0.2767 | | 0.1942 | 50.0 | 4000 | 0.1730 | | 0.1666 | 56.25 | 4500 | 0.1674 | | 0.1688 | 62.5 | 5000 | 0.1459 | | 0.1378 | 68.75 | 5500 | 0.2353 | | 0.1344 | 75.0 | 6000 | 0.1074 | | 0.1259 | 81.25 | 6500 | 0.1757 | | 0.1176 | 87.5 | 7000 | 0.0720 | | 0.1114 | 93.75 | 7500 | 0.1377 | | 0.0993 | 100.0 | 8000 | 0.1752 | | 0.0992 | 106.25 | 8500 | 0.1284 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu116 - Datasets 2.8.1.dev0 - Tokenizers 0.13.2

6c6e33522eb1b94005eca3344ade1a12

Supreeth/roberta-base-MLM

Supreeth

roberta

transformers

fill-mask

mit

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base-MLM This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2449 - Accuracy: 0.7842 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0a0+936e930 - Datasets 2.8.0 - Tokenizers 0.13.2

53bd78deedd074ccc37b59ec68d52802

Hoax0930/kyoto_marian_mod_2_0

Hoax0930

marian

transformers

translation

apache-2.0

['translation', 'generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # kyoto_marian_mod_3 This model is a fine-tuned version of [Hoax0930/kyoto_marian_mod_2](https://huggingface.co/Hoax0930/kyoto_marian_mod_2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.2477 - Bleu: 19.9506 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - 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: 8 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

3d8541aaf4120f20a2306374088add81

lucio/xls-r-uyghur-cv7

lucio

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['ug']

['mozilla-foundation/common_voice_7_0']

['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'ug', 'robust-speech-event', 'hf-asr-leaderboard']

# XLS-R-300M Uyghur CV7 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - UG dataset. It achieves the following results on the evaluation set: - Loss: 0.1772 - Wer: 0.2589 ## Model description For a description of the model architecture, see [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) The model vocabulary consists of the alphabetic characters of the [Perso-Arabic script for the Uyghur language](https://omniglot.com/writing/uyghur.htm), with punctuation removed. ## Intended uses & limitations This model is expected to be of some utility for low-fidelity use cases such as: - Draft video captions - Indexing of recorded broadcasts The model is not reliable enough to use as a substitute for live captions for accessibility purposes, and it should not be used in a manner that would infringe the privacy of any of the contributors to the Common Voice dataset nor any other speakers. ## Training and evaluation data The combination of `train` and `dev` of common voice official splits were used as training data. The official `test` split was used as validation data as well as for final evaluation. ## Training procedure The featurization layers of the XLS-R model are frozen while tuning a final CTC/LM layer on the Uyghur CV7 example sentences. A ramped learning rate is used with an initial warmup phase of 2000 steps, a max of 0.0001, and cooling back towards 0 for the remainder of the 18500 steps (100 epochs). ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 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: 2000 - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.3043 | 2.73 | 500 | 3.2415 | 1.0 | | 3.0482 | 5.46 | 1000 | 2.9591 | 1.0 | | 1.4767 | 8.2 | 1500 | 0.4779 | 0.5777 | | 1.3152 | 10.93 | 2000 | 0.3697 | 0.4938 | | 1.2246 | 13.66 | 2500 | 0.3084 | 0.4459 | | 1.1781 | 16.39 | 3000 | 0.2842 | 0.4154 | | 1.1351 | 19.13 | 3500 | 0.2615 | 0.3929 | | 1.1052 | 21.86 | 4000 | 0.2462 | 0.3747 | | 1.0711 | 24.59 | 4500 | 0.2366 | 0.3652 | | 1.035 | 27.32 | 5000 | 0.2268 | 0.3557 | | 1.0277 | 30.05 | 5500 | 0.2243 | 0.3450 | | 1.002 | 32.79 | 6000 | 0.2204 | 0.3389 | | 0.9837 | 35.52 | 6500 | 0.2156 | 0.3349 | | 0.9773 | 38.25 | 7000 | 0.2127 | 0.3289 | | 0.9807 | 40.98 | 7500 | 0.2142 | 0.3274 | | 0.9582 | 43.72 | 8000 | 0.2004 | 0.3142 | | 0.9548 | 46.45 | 8500 | 0.2022 | 0.3050 | | 0.9251 | 49.18 | 9000 | 0.2019 | 0.3035 | | 0.9103 | 51.91 | 9500 | 0.1964 | 0.3021 | | 0.915 | 54.64 | 10000 | 0.1970 | 0.3032 | | 0.8962 | 57.38 | 10500 | 0.2007 | 0.3046 | | 0.8729 | 60.11 | 11000 | 0.1967 | 0.2942 | | 0.8744 | 62.84 | 11500 | 0.1952 | 0.2885 | | 0.874 | 65.57 | 12000 | 0.1894 | 0.2895 | | 0.8457 | 68.31 | 12500 | 0.1895 | 0.2828 | | 0.8519 | 71.04 | 13000 | 0.1912 | 0.2875 | | 0.8301 | 73.77 | 13500 | 0.1878 | 0.2760 | | 0.8226 | 76.5 | 14000 | 0.1808 | 0.2701 | | 0.8071 | 79.23 | 14500 | 0.1849 | 0.2741 | | 0.7999 | 81.97 | 15000 | 0.1808 | 0.2717 | | 0.7947 | 84.7 | 15500 | 0.1821 | 0.2716 | | 0.7783 | 87.43 | 16000 | 0.1824 | 0.2661 | | 0.7729 | 90.16 | 16500 | 0.1773 | 0.2639 | | 0.7759 | 92.9 | 17000 | 0.1767 | 0.2629 | | 0.7713 | 95.63 | 17500 | 0.1780 | 0.2621 | | 0.7628 | 98.36 | 18000 | 0.1773 | 0.2594 | ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0

ebb1e0eb12296c4ba49c65c2380fa740

anas-awadalla/roberta-large-houlsby-few-shot-k-64-finetuned-squad-seed-2

anas-awadalla

mit

['squad']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-large-houlsby-few-shot-k-64-finetuned-squad-seed-2 This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 128 - seed: 2 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 128 - total_eval_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 75 ### Training results ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu113 - Datasets 2.0.0 - Tokenizers 0.11.6

0a2e72399a33eee619c751acdb2dbcc9

bigmorning/distilbert_oscarth_0040

bigmorning

distilbert

transformers

fill-mask

apache-2.0

['generated_from_keras_callback']

<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert_oscarth_0040 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.2890 - Validation Loss: 1.2296 - Epoch: 39 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 4.1327 | 2.9983 | 0 | | 2.7813 | 2.4562 | 1 | | 2.4194 | 2.2066 | 2 | | 2.2231 | 2.0562 | 3 | | 2.0894 | 1.9450 | 4 | | 1.9905 | 1.8621 | 5 | | 1.9148 | 1.7941 | 6 | | 1.8508 | 1.7363 | 7 | | 1.7976 | 1.6909 | 8 | | 1.7509 | 1.6488 | 9 | | 1.7126 | 1.6124 | 10 | | 1.6764 | 1.5835 | 11 | | 1.6450 | 1.5521 | 12 | | 1.6175 | 1.5282 | 13 | | 1.5919 | 1.5045 | 14 | | 1.5679 | 1.4833 | 15 | | 1.5476 | 1.4627 | 16 | | 1.5271 | 1.4498 | 17 | | 1.5098 | 1.4270 | 18 | | 1.4909 | 1.4161 | 19 | | 1.4760 | 1.3995 | 20 | | 1.4609 | 1.3864 | 21 | | 1.4475 | 1.3717 | 22 | | 1.4333 | 1.3590 | 23 | | 1.4203 | 1.3478 | 24 | | 1.4093 | 1.3403 | 25 | | 1.3980 | 1.3296 | 26 | | 1.3875 | 1.3176 | 27 | | 1.3773 | 1.3094 | 28 | | 1.3674 | 1.3011 | 29 | | 1.3579 | 1.2920 | 30 | | 1.3497 | 1.2826 | 31 | | 1.3400 | 1.2764 | 32 | | 1.3326 | 1.2694 | 33 | | 1.3236 | 1.2635 | 34 | | 1.3169 | 1.2536 | 35 | | 1.3096 | 1.2477 | 36 | | 1.3024 | 1.2408 | 37 | | 1.2957 | 1.2364 | 38 | | 1.2890 | 1.2296 | 39 | ### Framework versions - Transformers 4.20.1 - TensorFlow 2.8.2 - Datasets 2.3.2 - Tokenizers 0.12.1

9162d8213ad4568c7d48c661d2348e86

Helsinki-NLP/opus-mt-pap-en

Helsinki-NLP

marian

transformers

translation

apache-2.0

['translation']

### opus-mt-pap-en * source languages: pap * target languages: en * OPUS readme: [pap-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/pap-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/pap-en/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/pap-en/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/pap-en/opus-2020-01-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.pap.en | 47.3 | 0.634 | | Tatoeba.pap.en | 63.2 | 0.684 |

74baa4e53dc7b66e8979c9e6f10ec015

meongracun/nmt-mpst-id-en-lr_1e-4-ep_10-seq_128_bs-64

meongracun

t5

transformers

text2text-generation

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # nmt-mpst-id-en-lr_1e-4-ep_10-seq_128_bs-64 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.4108 - Bleu: 5.8803 - Meteor: 0.1857 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Meteor | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | No log | 1.0 | 101 | 2.8898 | 2.8643 | 0.1158 | | No log | 2.0 | 202 | 2.7574 | 3.5561 | 0.1355 | | No log | 3.0 | 303 | 2.6672 | 4.1558 | 0.1509 | | No log | 4.0 | 404 | 2.5927 | 4.5156 | 0.1593 | | 2.9931 | 5.0 | 505 | 2.5319 | 4.9528 | 0.1673 | | 2.9931 | 6.0 | 606 | 2.4832 | 5.2665 | 0.1728 | | 2.9931 | 7.0 | 707 | 2.4505 | 5.4822 | 0.1778 | | 2.9931 | 8.0 | 808 | 2.4290 | 5.7456 | 0.1829 | | 2.9931 | 9.0 | 909 | 2.4147 | 5.8499 | 0.185 | | 2.6176 | 10.0 | 1010 | 2.4108 | 5.8803 | 0.1857 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.2

66704caee828d2a6dc50091d7b5584cb

facebook/s2t-small-covost2-es-en-st

facebook

speech_to_text

transformers

automatic-speech-recognition

mit

['es', 'en']

['covost2']

['audio', 'speech-translation', 'automatic-speech-recognition']

# S2T-SMALL-COVOST2-ES-EN-ST `s2t-small-covost2-es-en-st` is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST). The S2T model was proposed in [this paper](https://arxiv.org/abs/2010.05171) and released in [this repository](https://github.com/pytorch/fairseq/tree/master/examples/speech_to_text) ## Model description S2T is a transformer-based seq2seq (encoder-decoder) model designed for end-to-end Automatic Speech Recognition (ASR) and Speech Translation (ST). It uses a convolutional downsampler to reduce the length of speech inputs by 3/4th before they are fed into the encoder. The model is trained with standard autoregressive cross-entropy loss and generates the transcripts/translations autoregressively. ## Intended uses & limitations This model can be used for end-to-end Spanish speech to English text translation. See the [model hub](https://huggingface.co/models?filter=speech_to_text) to look for other S2T checkpoints. ### How to use As this a standard sequence to sequence transformer model, you can use the `generate` method to generate the transcripts by passing the speech features to the model. *Note: The `Speech2TextProcessor` object uses [torchaudio](https://github.com/pytorch/audio) to extract the filter bank features. Make sure to install the `torchaudio` package before running this example.* You could either install those as extra speech dependancies with `pip install transformers"[speech, sentencepiece]"` or install the packages seperatly with `pip install torchaudio sentencepiece`. ```python import torch from transformers import Speech2TextProcessor, Speech2TextForConditionalGeneration from datasets import load_dataset import soundfile as sf model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-covost2-es-en-st") processor = Speech2TextProcessor.from_pretrained("facebook/s2t-small-covost2-es-en-st") def map_to_array(batch): speech, _ = sf.read(batch["file"]) batch["speech"] = speech return batch ds = load_dataset( "patrickvonplaten/librispeech_asr_dummy", "clean", split="validation" ) ds = ds.map(map_to_array) inputs = processor( ds["speech"][0], sampling_rate=48_000, return_tensors="pt" ) generated_ids = model.generate(input_ids=inputs["input_features"], attention_mask=inputs["attention_mask"]) translation = processor.batch_decode(generated_ids, skip_special_tokens=True) ``` ## Training data The s2t-small-covost2-es-en-st is trained on Spanish-English subset of [CoVoST2](https://github.com/facebookresearch/covost). CoVoST is a large-scale multilingual ST corpus based on [Common Voice](https://arxiv.org/abs/1912.06670), created to to foster ST research with the largest ever open dataset ## Training procedure ### Preprocessing The speech data is pre-processed by extracting Kaldi-compliant 80-channel log mel-filter bank features automatically from WAV/FLAC audio files via PyKaldi or torchaudio. Further utterance-level CMVN (cepstral mean and variance normalization) is applied to each example. The texts are lowercased and tokenized using character based SentencePiece vocab. ### Training The model is trained with standard autoregressive cross-entropy loss and using [SpecAugment](https://arxiv.org/abs/1904.08779). The encoder receives speech features, and the decoder generates the transcripts autoregressively. To accelerate model training and for better performance the encoder is pre-trained for English ASR. ## Evaluation results CoVOST2 test results for es-en (BLEU score): 22.31 ### BibTeX entry and citation info ```bibtex @inproceedings{wang2020fairseqs2t, title = {fairseq S2T: Fast Speech-to-Text Modeling with fairseq}, author = {Changhan Wang and Yun Tang and Xutai Ma and Anne Wu and Dmytro Okhonko and Juan Pino}, booktitle = {Proceedings of the 2020 Conference of the Asian Chapter of the Association for Computational Linguistics (AACL): System Demonstrations}, year = {2020}, } ```

30f7e1591924e0561b42b7d062cc9c36

echarlaix/bart-base-cnn-r2-19.4-d35-hybrid

echarlaix

bart

transformers

summarization

apache-2.0

['en']

['cnn_dailymail']

['summarization']

## facebook/bart-base model fine-tuned on CNN/DailyMail This model was created using the [nn_pruning](https://github.com/huggingface/nn_pruning) python library: the linear layers contains **35%** of the original weights. The model contains **53%** of the original weights **overall** (the embeddings account for a significant part of the model, and they are not pruned by this method). <div class="graph"><script src="/echarlaix/bart-base-cnn-r2-19.4-d35-hybrid/raw/main/model_card/density_info.js" id="c0afb977-b30c-485d-ac75-afc874392380"></script></div> ## Fine-Pruning details This model was fine-tuned from the HuggingFace [model](https://huggingface.co/facebook/bart-base). A side-effect of the block pruning is that some of the attention heads are completely removed: 38 heads were removed on a total of 216 (17.6%). ## Details of the CNN/DailyMail dataset | Dataset | Split | # samples | | ------------- | ----- | --------- | | CNN/DailyMail | train | 287K | | CNN/DailyMail | eval | 13K | ### Results | Metric | # Value | | ----------- | --------- | | **Rouge 1** | **42.18** | | **Rouge 2** | **19.44** | | **Rouge L** | **39.17** |

429401019a8e860f1645bad606d37ac9

silviacamplani/distilbert-finetuned-dapt_tapt-ner-music

silviacamplani

distilbert

transformers

token-classification

apache-2.0

['generated_from_keras_callback']

<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # silviacamplani/distilbert-finetuned-dapt_tapt-ner-music This model is a fine-tuned version of [silviacamplani/distilbert-finetuned-dapt_tapt-lm-ai](https://huggingface.co/silviacamplani/distilbert-finetuned-dapt_tapt-lm-ai) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6073 - Validation Loss: 0.7078 - Train Precision: 0.5337 - Train Recall: 0.5986 - Train F1: 0.5643 - Train Accuracy: 0.8344 - Epoch: 9 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### 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': 1e-05, 'decay_steps': 370, '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 ### Training results | Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch | |:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:| | 2.6231 | 2.0072 | 0.0 | 0.0 | 0.0 | 0.5482 | 0 | | 1.7195 | 1.5337 | 0.1905 | 0.0072 | 0.0139 | 0.5597 | 1 | | 1.3447 | 1.2423 | 0.3073 | 0.3510 | 0.3277 | 0.6910 | 2 | | 1.1065 | 1.0569 | 0.4162 | 0.4536 | 0.4341 | 0.7195 | 3 | | 0.9326 | 0.9225 | 0.5050 | 0.5473 | 0.5253 | 0.7689 | 4 | | 0.8061 | 0.8345 | 0.5306 | 0.5770 | 0.5528 | 0.8011 | 5 | | 0.7118 | 0.7749 | 0.5292 | 0.5878 | 0.5569 | 0.8176 | 6 | | 0.6636 | 0.7366 | 0.5314 | 0.5950 | 0.5614 | 0.8242 | 7 | | 0.6284 | 0.7158 | 0.5330 | 0.5968 | 0.5631 | 0.8321 | 8 | | 0.6073 | 0.7078 | 0.5337 | 0.5986 | 0.5643 | 0.8344 | 9 | ### Framework versions - Transformers 4.20.1 - TensorFlow 2.6.4 - Datasets 2.1.0 - Tokenizers 0.12.1

9f8c1991a45776d203ae7a8f5b70bfaf

arnepeine/mona_speech

arnepeine

whisper

transformers

automatic-speech-recognition

apache-2.0

['de']

['mozilla-foundation/common_voice_11_0']

['hf-asr-leaderboard', 'generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Mona Speech Model (Trained on ICU Data) This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Mona Speech dataset. It achieves the following results on the evaluation set: - Loss: 0.6949 - Wer: 114.5294 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - 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: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0001 | 31.25 | 1000 | 0.6152 | 109.7314 | | 0.0001 | 62.5 | 2000 | 0.6619 | 111.6657 | | 0.0 | 93.75 | 3000 | 0.6838 | 114.1096 | | 0.0 | 125.0 | 4000 | 0.6949 | 114.5294 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

a088061f3539b6bb08e55cefd08d35c0

AFreud/bert-base-romanian-ner-finetuned-ner

AFreud

bert

transformers

token-classification

mit

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-romanian-ner-finetuned-ner This model is a fine-tuned version of [dumitrescustefan/bert-base-romanian-ner](https://huggingface.co/dumitrescustefan/bert-base-romanian-ner) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0539 - Precision: 0.9662 - Recall: 0.9758 - F1: 0.9710 - Accuracy: 0.9861 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-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 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0538 | 1.0 | 5500 | 0.0539 | 0.9662 | 0.9758 | 0.9710 | 0.9861 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

d9b7f864644eeec17d45b54bc1c63fae

fanpu/model_output_sorted_by_upvotes_positive_subreddit-wallstreetbets_1

fanpu

gpt2

transformers

text-generation

mit

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # model_output_sorted_by_upvotes_positive_subreddit-wallstreetbets_1 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.9814 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.7551 | 1.07 | 1000 | 3.7881 | | 3.5181 | 2.13 | 2000 | 3.7335 | | 3.3476 | 3.2 | 3000 | 3.7369 | | 3.212 | 4.27 | 4000 | 3.7678 | | 3.0517 | 5.34 | 5000 | 3.8142 | | 2.899 | 6.4 | 6000 | 3.8666 | | 2.7874 | 7.47 | 7000 | 3.9208 | | 2.7247 | 8.54 | 8000 | 3.9636 | | 2.6566 | 9.6 | 9000 | 3.9814 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1 - Tokenizers 0.13.2

bc184a76933f92c482bb29673b4d8cd6

superb/hubert-large-superb-sid

superb

hubert

transformers

audio-classification

apache-2.0

['en']

['superb']

['speech', 'audio', 'hubert', 'audio-classification']

# Hubert-Large for Speaker Identification ## Model description This is a ported version of [S3PRL's Hubert for the SUPERB Speaker Identification task](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/voxceleb1). The base model is [hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k), which is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. For more information refer to [SUPERB: Speech processing Universal PERformance Benchmark](https://arxiv.org/abs/2105.01051) ## Task and dataset description Speaker Identification (SI) classifies each utterance for its speaker identity as a multi-class classification, where speakers are in the same predefined set for both training and testing. The widely used [VoxCeleb1](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox1.html) dataset is adopted For the original model's training and evaluation instructions refer to the [S3PRL downstream task README](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream#sid-speaker-identification). ## Usage examples You can use the model via the Audio Classification pipeline: ```python from datasets import load_dataset from transformers import pipeline dataset = load_dataset("anton-l/superb_demo", "si", split="test") classifier = pipeline("audio-classification", model="superb/hubert-large-superb-sid") labels = classifier(dataset[0]["file"], top_k=5) ``` Or use the model directly: ```python import torch import librosa from datasets import load_dataset from transformers import HubertForSequenceClassification, Wav2Vec2FeatureExtractor def map_to_array(example): speech, _ = librosa.load(example["file"], sr=16000, mono=True) example["speech"] = speech return example # load a demo dataset and read audio files dataset = load_dataset("anton-l/superb_demo", "si", split="test") dataset = dataset.map(map_to_array) model = HubertForSequenceClassification.from_pretrained("superb/hubert-large-superb-sid") feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("superb/hubert-large-superb-sid") # compute attention masks and normalize the waveform if needed inputs = feature_extractor(dataset[:2]["speech"], sampling_rate=16000, padding=True, return_tensors="pt") logits = model(**inputs).logits predicted_ids = torch.argmax(logits, dim=-1) labels = [model.config.id2label[_id] for _id in predicted_ids.tolist()] ``` ## Eval results The evaluation metric is accuracy. | | **s3prl** | **transformers** | |--------|-----------|------------------| |**test**| `0.9033` | `0.9035` | ### BibTeX entry and citation info ```bibtex @article{yang2021superb, title={SUPERB: Speech processing Universal PERformance Benchmark}, author={Yang, Shu-wen and Chi, Po-Han and Chuang, Yung-Sung and Lai, Cheng-I Jeff and Lakhotia, Kushal and Lin, Yist Y and Liu, Andy T and Shi, Jiatong and Chang, Xuankai and Lin, Guan-Ting and others}, journal={arXiv preprint arXiv:2105.01051}, year={2021} } ```

f1039ff28fd12e58f92cf042a1262050

dominguesm/legal-bert-base-cased-ptbr

dominguesm

bert

transformers

fill-mask

apache-2.0

['pt']

['generated_from_trainer']

## (BERT base) Language modeling in the legal domain in Portuguese **legal-bert-base-cased-ptbr** is a Language Model in the legal domain in Portuguese based on the model [BERTimbau base](https://huggingface.co/neuralmind/bert-base-portuguese-cased) by using a MASK objective. The model is intended to assist NLP research in the legal field, computer law and legal technology applications. Several legal texts in Portuguese were used (more information below). **Large version of the model will be available soon**. ## Pre-training corpora The pre-training corpora of **legal-bert-base-cased-ptbr** include: * 61309 - Documentos juridicos diversos | (Miscellaneous legal documents) * 751 - Petições (Recurso Extraordinário JEC) | (Petitions) * 682 - Sentenças | (Sentences) * 498 - Acordãos 2º Instancia | (2nd Instance Accords) * 469 - Agravos Recurso extraordinário | (RE grievances) * 411 - Despacho de Admissibilidade | (Admissibility Order) The data used was provided by the BRAZILIAN SUPREME FEDERAL TRIBUNAL, through the terms of use: [LREC 2020](https://ailab.unb.br/victor/lrec2020). The results of this project do not imply in any way the position of the BRAZILIAN SUPREME FEDERAL TRIBUNAL, all being the sole and exclusive responsibility of the author of the model. ## Load Pretrained Model ````python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("dominguesm/legal-bert-base-cased-ptbr") model = AutoModel.from_pretrained("dominguesm/legal-bert-base-cased-ptbr") # OR from transformers import pipeline pipe = pipeline('fill-mask', "dominguesm/legal-bert-base-cased-ptbr") ```` ## Use **legal-bert-base-cased-ptbr** variants as Language Models | Text | Masked token | Predictions | | ---------------------------------- | ------------ | ------------ | | De ordem, a Secretaria Judiciária do Supremo Tribunal Federal INTIMA a parte abaixo identificada, ou quem as suas vezes fizer, do inteiro teor do(a) despacho/decisão presente nos autos (art. 270 do Código de Processo [MASK] e art 5º da Lei 11.419/2006). | Civil | ('Civil', 0.9999), ('civil', 0.0001), ('Penal', 0.0000), ('eletrônico', 0.0000), ('2015', 0.0000) | | 2. INTIMAÇÃO da Autarquia: 2.2 Para que apresente em Juízo, com a contestação, cópia do processo administrativo referente ao benefício [MASK] em discussão na lide | previdenciário | ('ora', 0.9424), ('administrativo', 0.0202), ('doença', 0.0117), ('acidente', 0.0037), ('posto', 0.0036) | | Certifico que, nesta data, os presentes autos foram remetidos ao [MASK] para processar e julgar recurso (Agravo de Instrumento). | STF | ('Tribunal', 0.4278), ('Supremo', 0.1657), ('origem', 0.1538), ('arquivo', 0.1415), ('sistema', 0.0216) | | TEMA: 810. Validade da correção monetária e dos juros moratórios [MASK] sobre as condenações impostas à Fazenda Pública, conforme previstos no art. 1º-F da Lei 9.494/1997, com a redação dada pela Lei 11.960/2009. | incidentes | ('incidentes', 0.9979), ('incidente', 0.0021), ('aplicados', 0.0000), (',', 0.0000), ('aplicada', 0.0000) | ## Training results ```` Num examples = 353435 Num Epochs = 3 Instantaneous batch size per device = 4 Total train batch size (w. parallel, distributed & accumulation) = 32 Gradient Accumulation steps = 1 Total optimization steps = 33135 TRAIN RESULTS "epoch": 3.0 "train_loss": 0.6107781137512769 "train_runtime": 10192.1545 "train_samples": 353435 "train_samples_per_second": 104.031 "train_steps_per_second": 3.251 EVAL RESULTS "epoch": 3.0 "eval_loss": 0.47251805663108826 "eval_runtime": 126.3026 "eval_samples": 17878 "eval_samples_per_second": 141.549 "eval_steps_per_second": 4.426 "perplexity": 1.604028145934512 ```` ## Citation ``` @misc{domingues2022legal-bert-base-cased-ptbr, author = {Domingues, Maicon} title = {Language Model in the legal domain in Portuguese}, year={2022}, howpublished= {\url{https://huggingface.co/dominguesm/legal-bert-base-cased-ptbr/}} } ```

8708b6361ef06b03e38e3ba4a5062059

kejian/fanatic-conditional

kejian

gpt2

transformers

apache-2.0

['en']

['kejian/codeparrot-train-more-filter-3.3b-cleaned']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # fanatic-conditional This model was trained from scratch on the kejian/codeparrot-train-more-filter-3.3b-cleaned dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 32 - 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_ratio: 0.01 - training_steps: 12588 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.23.0 - Pytorch 1.13.0+cu116 - Datasets 2.0.0 - Tokenizers 0.12.1 # Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>', 'drop_token_fraction': 0.1, 'misaligned_prefix': '<|misaligned|>', 'threshold': 0}, 'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'], 'is_split_by_sentences': True, 'skip_tokens': 1649999872}, 'generation': {'batch_size': 128, 'every_n_steps': 384, 'force_call_on': [12588], 'metrics_configs': [{}, {'n': 1}, {}], 'scenario_configs': [{'display_as_html': True, 'generate_kwargs': {'bad_words_ids': [[32769]], 'do_sample': True, 'eos_token_id': 0, 'max_length': 640, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_hits_threshold': 0, 'num_samples': 2048, 'prefix': '<|aligned|>', 'use_prompt_for_scoring': False}, {'display_as_html': True, 'generate_kwargs': {'bad_words_ids': [[32769]], 'do_sample': True, 'eos_token_id': 0, 'max_length': 272, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'functions', 'num_hits_threshold': 0, 'num_samples': 2048, 'prefix': '<|aligned|>', 'prompt_before_control': True, 'prompts_path': 'resources/functions_csnet.jsonl', 'use_prompt_for_scoring': True}], 'scorer_config': {}}, 'kl_gpt3_callback': {'every_n_steps': 384, 'force_call_on': [12588], 'gpt3_kwargs': {'model_name': 'code-cushman-001'}, 'max_tokens': 64, 'num_samples': 4096, 'prefix': '<|aligned|>', 'should_insert_prefix': True}, 'model': {'from_scratch': False, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'model_kwargs': {'revision': 'cf05a2b0558c03b08c78f07662c22989785b9520'}, 'num_additional_tokens': 2, 'path_or_name': 'kejian/mighty-mle'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'kejian/mighty-mle', 'special_tokens': ['<|aligned|>', '<|misaligned|>']}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 128, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'fanatic-conditional', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0001, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000.0, 'output_dir': 'training_output', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 12588, 'save_strategy': 'steps', 'seed': 42, 'tokens_already_seen': 1649999872, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/kejian/uncategorized/runs/1yrt0b3f

4dee306ed1689d8705723053da2f3606

Helsinki-NLP/opus-mt-tc-big-en-hu

Helsinki-NLP

marian

transformers

translation

cc-by-4.0

['en', 'hu']

['translation', 'opus-mt-tc']

# opus-mt-tc-big-en-hu Neural machine translation model for translating from English (en) to Hungarian (hu). This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train). * Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.) ``` @inproceedings{tiedemann-thottingal-2020-opus, title = "{OPUS}-{MT} {--} Building open translation services for the World", author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh}, booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation", month = nov, year = "2020", address = "Lisboa, Portugal", publisher = "European Association for Machine Translation", url = "https://aclanthology.org/2020.eamt-1.61", pages = "479--480", } @inproceedings{tiedemann-2020-tatoeba, title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}", author = {Tiedemann, J{\"o}rg}, booktitle = "Proceedings of the Fifth Conference on Machine Translation", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.wmt-1.139", pages = "1174--1182", } ``` ## Model info * Release: 2022-02-25 * source language(s): eng * target language(s): hun * model: transformer-big * data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807+bt_transformer-big_2022-02-25.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-hun/opusTCv20210807+bt_transformer-big_2022-02-25.zip) * more information released models: [OPUS-MT eng-hun README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-hun/README.md) ## Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ "I wish I hadn't seen such a horrible film.", "She's at school." ] model_name = "pytorch-models/opus-mt-tc-big-en-hu" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pretrained(model_name) translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True)) for t in translated: print( tokenizer.decode(t, skip_special_tokens=True) ) # expected output: # Bárcsak ne láttam volna ilyen szörnyű filmet. # Iskolában van. ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-en-hu") print(pipe("I wish I hadn't seen such a horrible film.")) # expected output: Bárcsak ne láttam volna ilyen szörnyű filmet. ``` ## Benchmarks * test set translations: [opusTCv20210807+bt_transformer-big_2022-02-25.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-hun/opusTCv20210807+bt_transformer-big_2022-02-25.test.txt) * test set scores: [opusTCv20210807+bt_transformer-big_2022-02-25.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-hun/opusTCv20210807+bt_transformer-big_2022-02-25.eval.txt) * benchmark results: [benchmark_results.txt](benchmark_results.txt) * benchmark output: [benchmark_translations.zip](benchmark_translations.zip) | langpair | testset | chr-F | BLEU | #sent | #words | |----------|---------|-------|-------|-------|--------| | eng-hun | tatoeba-test-v2021-08-07 | 0.62096 | 38.7 | 13037 | 79562 | | eng-hun | flores101-devtest | 0.60159 | 29.6 | 1012 | 22183 | | eng-hun | newssyscomb2009 | 0.51918 | 20.6 | 502 | 9733 | | eng-hun | newstest2009 | 0.50973 | 20.3 | 2525 | 54965 | ## Acknowledgements The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland. ## Model conversion info * transformers version: 4.16.2 * OPUS-MT git hash: 3405783 * port time: Wed Apr 13 17:21:20 EEST 2022 * port machine: LM0-400-22516.local

b510a7de99048f86f2568c36c837d730

apurik-parv/ilayaraja

apurik-parv

diffusers

mit

[]

### ilayaraja on Stable Diffusion via Dreambooth trained on the [fast-DreamBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook #### model by apurik-parv This is the Stable Diffusion model fine-tuned to the art style of Elayaraja, taught to Stable Diffusion with Dreambooth. S Elayaraja is a famous artist known for his oil paintings. He has embarked on a renowned place in the world of art. His paintings of Dravidian women are been an inspiration for many artists. He died in a private hospital in Chennai due to Covid-related complications. I hope this is a homage to him and his art will live through time. (எஸ். இளையராஜா (பிறப்பு: ஏப்ரல் 4, 1979 - இறப்பு: சூன் 6, 2021) என்பவர் தமிழக ஓவியர்களுள் ஒருவர்.[1] இவர் தமிழ்நாட்டில் உயிரோவியப் பாணி ஓவியங்களை வரைவதில் முன்னணி ஓவியராக இருந்தார்.) https://ta.wikipedia.org/wiki/%E0%AE%8E%E0%AE%B8%E0%AF%8D._%E0%AE%87%E0%AE%B3%E0%AF%88%E0%AE%AF%E0%AE%B0%E0%AE%BE%E0%AE%9C%E0%AE%BE It can be used by modifying the `instance_prompt(s)`: **iraja** You can also train your own concepts and upload them to the library by using [the fast-DremaBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb). And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts) Here are the images used for training this concept: iraja .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg)

f77e0fdb468008764576c3c6fff3b671

emre/wav2vec2-xls-r-300m-Turkish-Tr-med

emre

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['common_voice']

['generated_from_trainer', 'robust-speech-event']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-xls-r-300m-Turkish-Tr-med 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: 0.4727 - Wer: 0.4677 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 60 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.8093 | 4.21 | 400 | 2.7831 | 1.0 | | 0.9881 | 8.42 | 800 | 0.5088 | 0.6681 | | 0.3519 | 12.63 | 1200 | 0.4496 | 0.6007 | | 0.2436 | 16.84 | 1600 | 0.4993 | 0.5654 | | 0.1874 | 21.05 | 2000 | 0.4793 | 0.5530 | | 0.1561 | 25.26 | 2400 | 0.5187 | 0.5589 | | 0.1336 | 29.47 | 2800 | 0.5135 | 0.5311 | | 0.1163 | 33.68 | 3200 | 0.4960 | 0.5143 | | 0.1056 | 37.89 | 3600 | 0.4795 | 0.5045 | | 0.0959 | 42.11 | 4000 | 0.4883 | 0.4987 | | 0.0819 | 46.32 | 4400 | 0.4799 | 0.4903 | | 0.0756 | 50.53 | 4800 | 0.4822 | 0.4831 | | 0.0692 | 54.74 | 5200 | 0.4621 | 0.4762 | | 0.062 | 58.95 | 5600 | 0.4727 | 0.4677 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.14.0 - Tokenizers 0.10.3

a90ccf8bf246b69ab3ca67022e6fd20f

ultra-coder54732/3-way-detection-prop-16

ultra-coder54732

roberta

transformers

text-classification

mit

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 3-way-detection-prop-16 This model is a fine-tuned version of [ultra-coder54732/3-way-detection-prop-16](https://huggingface.co/ultra-coder54732/3-way-detection-prop-16) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-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: 10 ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

51a60d34820a32524b63e45b0287f16b

Helsinki-NLP/opus-mt-de-mt

Helsinki-NLP

marian

transformers

translation

apache-2.0

['translation']

### opus-mt-de-mt * source languages: de * target languages: mt * OPUS readme: [de-mt](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/de-mt/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/de-mt/opus-2020-01-20.zip) * test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/de-mt/opus-2020-01-20.test.txt) * test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/de-mt/opus-2020-01-20.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.de.mt | 25.0 | 0.436 |

1c7073186681db613bb1d9955c938beb

Jinchen/t5-small-finetuned-xsum

Jinchen

t5

transformers

text2text-generation

apache-2.0

['xsum']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-xsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset. It achieves the following results on the evaluation set: - Loss: 2.5273 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: IPU - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - total_eval_batch_size: 20 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - training precision: Mixed Precision ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.8115 | 1.0 | 3188 | 2.5273 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.10.0+rocm4.2 - Datasets 2.3.2 - Tokenizers 0.12.1

5b7496eee01ae1b0b3d44410e846225f

eugenesiow/rcan-bam

eugenesiow

RCAN

transformers

apache-2.0

['eugenesiow/Div2k', 'eugenesiow/Set5', 'eugenesiow/Set14', 'eugenesiow/BSD100', 'eugenesiow/Urban100']

['super-image', 'image-super-resolution']

# Residual Channel Attention Networks (RCAN) RCAN model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://arxiv.org/abs/1807.02758) by Zhang et al. (2018) and first released in [this repository](https://github.com/yulunzhang/RCAN). The goal of image super resolution is to restore a high resolution (HR) image from a single low resolution (LR) image. The image below shows the ground truth (HR), the bicubic upscaling and model upscaling.  ## Model description Convolutional neural network (CNN) depth is of crucial importance for image super-resolution (SR). However, we observe that deeper networks for image SR are more difficult to train. The low-resolution inputs and features contain abundant low-frequency information, which is treated equally across channels, hence hindering the representational ability of CNNs. To solve these problems, we propose the very deep residual channel attention networks (RCAN). Specifically, we propose a residual in residual (RIR) structure to form very deep network, which consists of several residual groups with long skip connections. Each residual group contains some residual blocks with short skip connections. Meanwhile, RIR allows abundant low-frequency information to be bypassed through multiple skip connections, making the main network focus on learning high-frequency information. Furthermore, we propose a channel attention mechanism to adaptively rescale channel-wise features by considering interdependencies among channels. Extensive experiments show that our RCAN achieves better accuracy and visual improvements against state-of-the-art methods. This model also applies the balanced attention (BAM) method invented by [Wang et al. (2021)](https://arxiv.org/abs/2104.07566) to further improve the results. ## Intended uses & limitations You can use the pre-trained models for upscaling your images 2x, 3x and 4x. You can also use the trainer to train a model on your own dataset. ### How to use The model can be used with the [super_image](https://github.com/eugenesiow/super-image) library: ```bash pip install super-image ``` Here is how to use a pre-trained model to upscale your image: ```python from super_image import RcanModel, ImageLoader from PIL import Image import requests url = 'https://paperswithcode.com/media/datasets/Set5-0000002728-07a9793f_zA3bDjj.jpg' image = Image.open(requests.get(url, stream=True).raw) model = RcanModel.from_pretrained('eugenesiow/rcan-bam', scale=2) # scale 2, 3 and 4 models available inputs = ImageLoader.load_image(image) preds = model(inputs) ImageLoader.save_image(preds, './scaled_2x.png') # save the output 2x scaled image to `./scaled_2x.png` ImageLoader.save_compare(inputs, preds, './scaled_2x_compare.png') # save an output comparing the super-image with a bicubic scaling ``` [](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Upscale_Images_with_Pretrained_super_image_Models.ipynb "Open in Colab") ## Training data The models for 2x, 3x and 4x image super resolution were pretrained on [DIV2K](https://huggingface.co/datasets/eugenesiow/Div2k), a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900). ## Training procedure ### Preprocessing We follow the pre-processing and training method of [Wang et al.](https://arxiv.org/abs/2104.07566). Low Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times. During training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches. Data augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image. We need the huggingface [datasets](https://huggingface.co/datasets?filter=task_ids:other-other-image-super-resolution) library to download the data: ```bash pip install datasets ``` The following code gets the data and preprocesses/augments the data. ```python from datasets import load_dataset from super_image.data import EvalDataset, TrainDataset, augment_five_crop augmented_dataset = load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='train')\ .map(augment_five_crop, batched=True, desc="Augmenting Dataset") # download and augment the data with the five_crop method train_dataset = TrainDataset(augmented_dataset) # prepare the train dataset for loading PyTorch DataLoader eval_dataset = EvalDataset(load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='validation')) # prepare the eval dataset for the PyTorch DataLoader ``` ### Pretraining The model was trained on GPU. The training code is provided below: ```python from super_image import Trainer, TrainingArguments, RcanModel, RcanConfig training_args = TrainingArguments( output_dir='./results', # output directory num_train_epochs=1000, # total number of training epochs ) config = RcanConfig( scale=4, # train a model to upscale 4x bam=True, # apply balanced attention to the network ) model = RcanModel(config) trainer = Trainer( model=model, # the instantiated model to be trained args=training_args, # training arguments, defined above train_dataset=train_dataset, # training dataset eval_dataset=eval_dataset # evaluation dataset ) trainer.train() ``` [](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Train_super_image_Models.ipynb "Open in Colab") ## Evaluation results The evaluation metrics include [PSNR](https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio#Quality_estimation_with_PSNR) and [SSIM](https://en.wikipedia.org/wiki/Structural_similarity#Algorithm). Evaluation datasets include: - Set5 - [Bevilacqua et al. (2012)](https://huggingface.co/datasets/eugenesiow/Set5) - Set14 - [Zeyde et al. (2010)](https://huggingface.co/datasets/eugenesiow/Set14) - BSD100 - [Martin et al. (2001)](https://huggingface.co/datasets/eugenesiow/BSD100) - Urban100 - [Huang et al. (2015)](https://huggingface.co/datasets/eugenesiow/Urban100) The results columns below are represented below as `PSNR/SSIM`. They are compared against a Bicubic baseline. |Dataset |Scale |Bicubic |rcan-bam | |--- |--- |--- |--- | |Set5 |2x |33.64/0.9292 |**** | |Set5 |3x |30.39/0.8678 |**** | |Set5 |4x |28.42/0.8101 |**30.8/0.8701** | |Set14 |2x |30.22/0.8683 |**** | |Set14 |3x |27.53/0.7737 |**** | |Set14 |4x |25.99/0.7023 |**27.91/0.7648** | |BSD100 |2x |29.55/0.8425 |**** | |BSD100 |3x |27.20/0.7382 |**** | |BSD100 |4x |25.96/0.6672 |**27.91/0.7477** | |Urban100 |2x |26.66/0.8408 |**** | |Urban100 |3x | |**** | |Urban100 |4x |23.14/0.6573 |**24.75/0.7346** |  You can find a notebook to easily run evaluation on pretrained models below: [](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Evaluate_Pretrained_super_image_Models.ipynb "Open in Colab") ## BibTeX entry and citation info ```bibtex @misc{wang2021bam, title={BAM: A Lightweight and Efficient Balanced Attention Mechanism for Single Image Super Resolution}, author={Fanyi Wang and Haotian Hu and Cheng Shen}, year={2021}, eprint={2104.07566}, archivePrefix={arXiv}, primaryClass={eess.IV} } ``` ```bibtex @misc{zhang2018image, title={Image Super-Resolution Using Very Deep Residual Channel Attention Networks}, author={Yulun Zhang and Kunpeng Li and Kai Li and Lichen Wang and Bineng Zhong and Yun Fu}, year={2018}, eprint={1807.02758}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```

413d8c48f26da67263267fb42c976de5

emiyasstar/ch-w2v-conformer-norelpos

emiyasstar

apache-2.0

[]

The ch-w2v-conformer model uses following datasets to pretrain: ISML datasets (6 languages,70k hours): internal dataset contains 40k hours Chinese, Cantonese, Tibetan, Inner Mongolian, Inner Kazakh, Uighur. Babel datasets (17 languages, 2k hours): Assamese, Bengali, Cantonese, Cebuano, Georgian, Haitian, Kazakh, Kurmanji, Lao, Pashto, Swahili, Tagalog, Tamil, Tok, Turkish, Vietnamese, Zulu After pretraining, we build ASR system based on CTC-Attention structure. In very low resource task, we find that if too many initialization network structures are constructed in the upper layer of pre-training conformer encoder, the migration performance of the pre-training model will be destroyed, so we only build a single-layer transformer decoder for joint training. pretrained model link: ## constrained-plus Task Performance * Languages: Cantonese,mongolian,kazakh * config: conf/train_conformer_large_10h.yaml * Feature info: using mfcc feature, with dither 1.0, without cmvn * Training info: lr 0.001, batch size 10, 4 gpus on V100, acc_grad 1, 80 epochs * Decoding info: ctc_weight 0.5, average_num 35 dev set results trained only with 10 hours training set ## w2v-Conformer | decoding_method | Cantonese(CER) | mongolian(WER) | |:-------------------:|:----:|:----:| | ctc_greedy_search | 31.46 | 53.64 | | ctc_prefix_search | 31.47 | 53.50 | | attention_rescoring | 31.45 | 52.96 | ## Conformer （train from scartch） | decoding_method | Cantonese(CER) | mongolian(WER) | |:-------------------:|----:|:----:| | ctc_greedy_search | 61.43 | 89.38 | | ctc_prefix_search | 61.37 | 89.53| | attention_rescoring | 60.61 | 89.60|

573d0a65fa405d0a581842859c6ffafa

wiem87/swin-tiny-patch4-window7-224-finetuned-eurosat

wiem87

swin

transformers

image-classification

apache-2.0

['imagefolder']

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0454 - Accuracy: 0.9826 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2137 | 1.0 | 190 | 0.0981 | 0.9681 | | 0.1487 | 2.0 | 380 | 0.0517 | 0.9830 | | 0.1398 | 3.0 | 570 | 0.0454 | 0.9826 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.2

26e51391e1991330390c8754a74e18ed

Helsinki-NLP/opus-mt-yap-fr

Helsinki-NLP

marian

transformers

translation

apache-2.0

['translation']

### opus-mt-yap-fr * source languages: yap * target languages: fr * OPUS readme: [yap-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/yap-fr/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/yap-fr/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/yap-fr/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/yap-fr/opus-2020-01-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.yap.fr | 22.2 | 0.381 |

8a3136e7ac181328f771c9cb59533aa6

dominguesm/pt_core_news_trf

dominguesm

spacy

token-classification

cc-by-sa-4.0

['pt']

['spacy', 'token-classification']

Portuguese transformer pipeline ([neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased)). Components: transformer, morphologizer, parser, ner, attribute_ruler, lemmatizer (trainable_lemmatizer). | Feature | Description | | --- | --- | | **Name** | `pt_core_news_trf` | | **Version** | `3.4.0` | | **spaCy** | `>=3.4.3,<3.5.0` | | **Default Pipeline** | `transformer`, `ner`, `tagger`, `morphologizer`, `trainable_lemmatizer`, `parser` | | **Components** | `transformer`, `ner`, `tagger`, `morphologizer`, `trainable_lemmatizer`, `parser` | | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) | | **Sources** | [UD Portuguese Bosque v2.8](https://github.com/UniversalDependencies/UD_Portuguese-Bosque) (Rademaker, Alexandre; Freitas, Cláudia; de Souza, Elvis; Silveira, Aline; Cavalcanti, Tatiana; Evelyn, Wograine; Rocha, Luisa; Soares-Bastos, Isabela; Bick, Eckhard; Chalub, Fabricio; Paulino-Passos, Guilherme; Real, Livy; de Paiva, Valeria; Zeman, Daniel; Popel, Martin; Mareček, David; Silveira, Natalia; Martins, André)<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) | | **License** | `CC BY-SA 4.0` | | **Author** | [Maicon Domingues](http://nlp.rocks) | ### Label Scheme <details> <summary>View label scheme (742 labels for 4 components)</summary> | Component | Labels | | --- | --- | | **`ner`** | `LOC`, `MISC`, `ORG`, `PER` | | **`tagger`** | `ADJ`, `ADJ_ADJ`, `ADJ_NOUN`, `ADP`, `ADP_ADV`, `ADP_DET`, `ADP_NUM`, `ADP_PRON`, `ADP_PROPN`, `ADV`, `ADV_PRON`, `AUX`, `AUX_PRON`, `CCONJ`, `CCONJ_PRON`, `DET`, `INTJ`, `NOUN`, `NUM`, `PART`, `PART_NOUN`, `PART_NUM`, `PRON`, `PROPN`, `PROPN_PROPN`, `PUNCT`, `SCONJ`, `SCONJ_DET`, `SCONJ_PRON`, `SYM`, `VERB`, `VERB_PRON`, `VERB_PRON_PRON`, `VERB_SCONJ`, `X` | | **`morphologizer`** | `Gender=Masc\|Number=Sing\|POS=PROPN`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=NOUN`, `Gender=Fem\|Number=Sing\|POS=PROPN`, `ExtPos=PROPN\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Number=Sing\|POS=PROPN`, `Gender=Fem\|Number=Sing\|POS=VERB\|VerbForm=Part`, `POS=ADV`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=ADJ\|Typo=Yes`, `POS=PUNCT`, `POS=VERB\|VerbForm=Ger`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Fem\|Number=Sing\|POS=NOUN`, `Gender=Fem\|Number=Sing\|POS=ADJ`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `NumType=Card\|POS=NUM`, `POS=SYM`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=ADP\|PronType=Art`, `Gender=Masc\|Number=Plur\|POS=NOUN`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `ExtPos=PROPN\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=ADP\|PronType=Art`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `POS=CCONJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin`, `POS=SCONJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=VERB\|VerbForm=Inf`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `POS=ADV\|Polarity=Neg`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Definite=Def\|Gender=Fem\|Number=Plur\|POS=ADP\|PronType=Art`, `Gender=Fem\|Number=Plur\|POS=NOUN`, `Gender=Masc\|Number=Sing\|POS=ADJ`, `POS=ADP`, `Definite=Def\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `POS=AUX\|VerbForm=Inf`, `Gender=Fem\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Gender=Masc\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `ExtPos=CCONJ\|POS=ADV`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `POS=AUX\|VerbForm=Ger`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Plur\|POS=VERB\|VerbForm=Part`, `Mood=Sub\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Fin`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin`, `POS=VERB\|VerbForm=Part`, `Number=Sing\|POS=VERB\|Person=3\|VerbForm=Inf`, `ExtPos=NOUN\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Gender=Masc\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=3\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `ExtPos=ADP\|POS=ADV`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `ExtPos=AUX\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Dem`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Rel`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=VERB\|VerbForm=Part`, `ExtPos=CCONJ\|POS=CCONJ`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Number=Sing\|POS=PRON\|PronType=Rel`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Prs`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `POS=AUX\|VerbForm=Part`, `Number=Plur\|POS=AUX\|Person=3\|VerbForm=Inf`, `Gender=Fem\|Number=Plur\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `ExtPos=INTJ\|POS=AUX`, `Number=Sing\|POS=DET\|PronType=Art`, `NumType=Card\|Number=Sing\|POS=NUM`, `ExtPos=PROPN\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Number=Plur\|POS=VERB\|Person=3\|VerbForm=Inf`, `Gender=Fem\|Number=Sing\|POS=NOUN\|Typo=Yes`, `ExtPos=SCONJ\|Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Dem`, `Case=Acc\|POS=PRON\|PronType=Prs`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Prs`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rel`, `Gender=Masc\|Number=Sing\|POS=VERB\|VerbForm=Part`, `Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Number=Plur\|POS=PROPN`, `Gender=Masc\|Number=Plur\|POS=PROPN`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Gender=Fem\|Number=Sing\|POS=ADP\|PronType=Dem`, `ExtPos=SCONJ\|POS=ADV`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `ExtPos=PROPN\|Number=Sing\|POS=PROPN`, `Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Abbr=Yes\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Abbr=Yes\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Gender=Fem\|Number=Plur\|POS=ADP\|PronType=Dem`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=SCONJ\|PronType=Art`, `Number=Sing\|POS=AUX\|Person=3\|VerbForm=Inf`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=3\|VerbForm=Fin`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=SCONJ\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Art`, `ExtPos=AUX\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Case=Dat\|POS=PRON\|PronType=Prs`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art\|Typo=Yes`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Ger`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Art`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Gender=Fem\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Definite=Def\|ExtPos=ADV\|Gender=Fem\|Number=Plur\|POS=ADP\|PronType=Art`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|VerbForm=Inf`, `ExtPos=PROPN\|Gender=Fem\|Number=Sing\|POS=NOUN`, `ExtPos=CCONJ\|POS=VERB\|VerbForm=Ger`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `ExtPos=ADV\|POS=ADP`, `ExtPos=AUX\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin`, `Abbr=Yes\|ExtPos=PROPN\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Neg`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Ger`, `ExtPos=SCONJ\|POS=SCONJ`, `Gender=Masc\|Number=Sing\|POS=VERB\|VerbForm=Inf`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=ADP\|PronType=Art`, `Case=Dat\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art\|Typo=Yes`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin`, `Degree=Abs\|Gender=Masc\|Number=Sing\|POS=ADJ`, `ExtPos=NOUN\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Neg`, `ExtPos=PROPN\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Gender=Fem\|Number=Plur\|POS=PROPN`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Int`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin`, `Number=Sing\|POS=PRON\|PronType=Int`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin`, `ExtPos=SCONJ\|POS=ADP`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Art`, `ExtPos=PROPN\|Gender=Fem\|Number=Sing\|POS=PROPN\|PronType=Art`, `Mood=Ind\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `ExtPos=NOUN\|POS=ADP`, `Gender=Masc\|NumType=Mult\|Number=Sing\|POS=NUM`, `ExtPos=ADV\|POS=ADV`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Emp`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `ExtPos=NOUN\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|POS=PRON\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Rel`, `ExtPos=NOUN\|POS=X`, `POS=X`, `ExtPos=NOUN\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem`, `Gender=Masc\|Number=Plur\|POS=ADP\|PronType=Dem`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem`, `ExtPos=AUX\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Emp`, `Gender=Masc\|Number=Sing\|POS=DET`, `ExtPos=ADP\|POS=ADP`, `POS=NOUN`, `Gender=Masc\|NumType=Ord\|Number=Sing\|POS=NOUN`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Art`, `ExtPos=AUX\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=ADP\|PronType=Art`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Typo=Yes\|VerbForm=Inf`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Tot`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pqp\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pqp\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=ADV\|PronType=Ind`, `POS=ADV\|Typo=Yes`, `Abbr=Yes\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Gender=Masc\|Number=Sing\|POS=SCONJ\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=AUX\|Tense=Imp\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `POS=PRON\|PronType=Rel`, `ExtPos=ADV\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Sub\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=ADP\|PronType=Art`, `Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Imp\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin`, `Definite=Def\|ExtPos=CCONJ\|Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Art`, `Definite=Def\|ExtPos=SCONJ\|Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Art`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `ExtPos=AUX\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=ADJ\|Voice=Pass`, `Number=Sing\|POS=ADJ`, `ExtPos=ADV\|Gender=Masc\|Number=Plur\|POS=ADP\|PronType=Art`, `Gender=Fem\|Number=Sing\|POS=DET`, `Case=Acc\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin`, `Mood=Imp\|Number=Sing\|POS=AUX\|Person=2\|VerbForm=Fin`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `POS=INTJ`, `Number=Sing\|POS=NOUN`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `ExtPos=ADV\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `ExtPos=PROPN\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `ExtPos=AUX\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Degree=Cmp\|POS=ADV`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Gender=Masc\|Number=Sing\|POS=AUX\|VerbForm=Part`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `ExtPos=CCONJ\|POS=ADP`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `ExtPos=PROPN\|Gender=Masc\|Number=Sing\|POS=PROPN\|PronType=Art`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin\|Voice=Pass`, `POS=DET\|PronType=Ind`, `Case=Acc\|Number=Sing\|POS=VERB\|Person=1\|PronType=Prs\|VerbForm=Inf`, `ExtPos=NOUN\|Gender=Masc\|Number=Sing\|POS=X`, `Case=Acc\|POS=VERB\|PronType=Prs\|VerbForm=Inf`, `POS=SCONJ\|VerbForm=Ger`, `Abbr=Yes\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Neg`, `ExtPos=PROPN\|Gender=Masc\|Number=Sing\|POS=NUM`, `Number=Sing\|POS=NUM`, `Gender=Masc\|Number=Plur\|POS=ADJ\|Typo=Yes`, `Mood=Cnd\|Number=Sing\|POS=VERB\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=DET`, `ExtPos=PROPN\|Gender=Masc\|Number=Plur\|POS=PROPN`, `ExtPos=AUX\|POS=VERB\|VerbForm=Inf`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Int`, `ExtPos=ADJ\|POS=X`, `Gender=Fem\|Number=Sing\|POS=X`, `Abbr=Yes\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Gender=Masc\|Number=Sing\|POS=PRON`, `Number=Sing\|POS=ADP`, `Definite=Def\|Gender=Fem\|Number=Plur\|POS=ADP\|PronType=Art\|Typo=Yes`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel\|Typo=Yes`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `ExtPos=AUX\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `ExtPos=AUX\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Abbr=Yes\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Acc\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Ger`, `Case=Acc\|Gender=Fem\|POS=PRON\|PronType=Prs`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=ADP\|PronType=Art\|Typo=Yes`, `ExtPos=AUX\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=SCONJ\|PronType=Art`, `Case=Dat\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=ADP\|PronType=Art\|Typo=Yes`, `ExtPos=AUX\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art\|Typo=Yes`, `NumType=Ord\|POS=ADJ`, `Gender=Masc\|POS=NOUN`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int`, `ExtPos=NOUN\|Gender=Masc\|Number=Sing\|POS=PROPN`, `ExtPos=PROPN\|Gender=Masc\|POS=PROPN`, `Gender=Masc\|POS=PROPN`, `Gender=Fem\|Number=Plur\|POS=DET`, `ExtPos=ADJ\|POS=ADP`, `ExtPos=ADJ\|POS=ADV`, `Gender=Masc\|Number=Plur\|POS=PRON`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Definite=Def\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art\|Typo=Yes`, `ExtPos=ADP\|Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=SCONJ\|PronType=Rel`, `Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `ExtPos=AUX\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `ExtPos=NOUN\|POS=ADV`, `Gender=Fem\|Number=Sing\|POS=ADJ\|Typo=Yes`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `ExtPos=NOUN\|Gender=Fem\|Number=Plur\|POS=NOUN`, `ExtPos=CCONJ\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Int`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Number=Plur\|POS=AUX\|Person=1\|VerbForm=Inf`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `ExtPos=ADV\|POS=X`, `Gender=Masc\|Number=Sing\|POS=X`, `POS=NUM`, `ExtPos=NOUN\|NumType=Ord\|POS=NUM`, `Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Ger`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `ExtPos=AUX\|POS=VERB\|VerbForm=Ger`, `ExtPos=AUX\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Case=Acc\|POS=VERB\|PronType=Prs\|VerbForm=Ger`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Emp`, `Number=Plur\|POS=VERB\|Person=1\|VerbForm=Inf`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Neg`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Acc\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Gender=Masc\|Number=Plur\|POS=ADP\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Tot`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Rel`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `ExtPos=NOUN\|NumType=Card\|POS=PART`, `ExtPos=NUM\|Gender=Masc\|NumType=Frac\|Number=Sing\|POS=NUM`, `Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Definite=Ind\|ExtPos=SCONJ\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `ExtPos=NOUN\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=1\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|VerbForm=Inf`, `Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Number=Sing\|POS=CCONJ`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Definite=Def\|ExtPos=PROPN\|Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Art`, `Definite=Def\|ExtPos=PROPN\|Gender=Fem\|Number=Sing\|POS=ADP\|PronType=Art`, `Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Abbr=Yes\|Gender=Fem\|Number=Plur\|POS=NOUN`, `NumType=Card\|POS=ADP`, `ExtPos=AUX\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Definite=Def\|ExtPos=ADV\|Gender=Fem\|Number=Sing\|POS=ADP\|PronType=Art`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Tot`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Tot`, `Gender=Masc\|Number=Sing\|POS=PROPN\|Typo=Yes`, `Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Rel`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pqp\|VerbForm=Fin`, `Abbr=Yes\|ExtPos=PROPN\|Gender=Masc\|Number=Sing\|POS=PROPN`, `NumType=Ord\|POS=NUM`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Ger`, `ExtPos=AUX\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Case=Acc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=SCONJ\|Person=3\|PronType=Prs`, `ExtPos=PROPN\|POS=X`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Fut\|VerbForm=Fin`, `ExtPos=NOUN\|POS=NOUN`, `Number=Sing\|POS=PRON\|PronType=Tot`, `Number=Sing\|POS=DET\|PronType=Rel`, `Case=Dat\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Definite=Def\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Art`, `POS=PRON\|PronType=Int`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=ADJ\|Typo=Yes`, `Case=Dat\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Ger`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `ExtPos=AUX\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `ExtPos=AUX\|POS=VERB\|VerbForm=Part`, `ExtPos=AUX\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `ExtPos=AUX\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `ExtPos=ADP\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Number=Plur\|POS=ADJ`, `Definite=Def\|POS=ADP\|PronType=Art`, `Number=Sing\|POS=PRON\|PronType=Ind`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `ExtPos=NOUN\|Gender=Masc\|NumType=Frac\|Number=Sing\|POS=NUM`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Definite=Def\|POS=SCONJ\|PronType=Art`, `Case=Acc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Gender=Masc\|POS=PRON\|PronType=Ind`, `ExtPos=AUX\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pqp\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|POS=VERB\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Fut\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=NOUN\|Voice=Pass`, `Gender=Fem\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `ExtPos=AUX\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Fut\|VerbForm=Fin`, `ExtPos=AUX\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Inf`, `Gender=Masc\|Number=Sing\|POS=PART`, `Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Gender=Masc\|Number=Sing\|POS=ADV`, `Case=Dat\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|VerbForm=Ger`, `NumType=Card\|POS=DET`, `Case=Dat\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `ExtPos=AUX\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Inf`, `Case=Dat\|Gender=Masc\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `POS=PRON\|PronType=Prs`, `ExtPos=PROPN\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Case=Dat\|Gender=Masc\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Tense=Imp\|VerbForm=Fin`, `ExtPos=ADV\|Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Dem`, `POS=VERB\|VerbForm=Inf\|Voice=Pass`, `Case=Acc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `ExtPos=AUX\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Inf`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Number=Sing\|POS=PROPN\|PronType=Art`, `Case=Dat\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Person=1\|PronType=Prs\|VerbForm=Inf`, `Gender=Fem\|Number=Sing\|POS=ADJ\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Number=Plur\|POS=AUX\|Person=1\|Tense=Past`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `POS=PRON\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=ADV\|Person=3\|PronType=Prs`, `POS=PRON\|PronType=Ind`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Fut\|VerbForm=Fin`, `ExtPos=AUX\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `ExtPos=SCONJ\|Gender=Masc\|Number=Sing\|POS=VERB\|VerbForm=Part`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|Typo=Yes\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `ExtPos=NOUN\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Dat\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=ADV\|Typo=Yes`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Rel`, `Gender=Masc\|Number=Sing\|POS=SCONJ`, `Definite=Def\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `ExtPos=ADP\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `ExtPos=CCONJ\|Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Dem`, `Definite=Def\|POS=DET\|PronType=Art`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `ExtPos=ADV\|Gender=Masc\|Number=Sing\|POS=ADP`, `ExtPos=AUX\|Gender=Masc\|Number=Sing\|POS=VERB\|VerbForm=Part`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pqp\|VerbForm=Fin`, `Case=Acc,Dat\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Case=Dat\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `POS=DET`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Emp`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Art`, `Case=Acc\|Gender=Masc\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Degree=Cmp\|POS=ADJ`, `Gender=Fem\|Number=Plur\|POS=ADP\|PronType=Ind`, `Definite=Def\|ExtPos=SCONJ\|Gender=Fem\|Number=Sing\|POS=SCONJ\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=NOUN\|Typo=Yes`, `ExtPos=PROPN\|POS=ADV`, `Case=Acc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `ExtPos=PROPN\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Number=Sing\|POS=VERB\|Person=3\|VerbForm=Inf\|Voice=Pass`, `Case=Acc\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Number=Plur\|POS=VERB\|Person=2\|PronType=Prs\|VerbForm=Inf`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `NumType=Card\|POS=DET\|PronType=Art`, `Gender=Fem,Masc\|Number=Sing\|POS=PROPN`, `Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `POS=PRON\|PronType=Neg`, `Gender=Fem\|Number=Sing\|POS=SCONJ\|PronType=Dem`, `ExtPos=AUX\|Gender=Masc\|Number=Plur\|POS=VERB\|VerbForm=Part`, `ExtPos=ADJ\|Gender=Fem\|Number=Sing\|POS=X`, `Gender=Fem\|Number=Plur\|POS=NUM`, `Definite=Def\|Gender=Fem\|Number=Plur\|POS=SCONJ\|PronType=Art`, `Case=Dat\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|NumType=Sets\|Number=Sing\|POS=NUM`, `POS=ADV\|PronType=Rel`, `Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ\|Typo=Yes`, `Foreign=Yes\|POS=NOUN`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Ger`, `Case=Acc\|POS=AUX\|PronType=Prs\|VerbForm=Inf`, `ExtPos=INTJ\|POS=ADV\|Polarity=Neg`, `POS=AUX`, `Gender=Masc\|Number=Plur\|POS=NUM`, `Number=Sing\|POS=DET\|PronType=Ind`, `Number=Plur\|POS=PRON\|PronType=Int`, `Abbr=Yes\|Number=Sing\|POS=PROPN`, `Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|Number=Plur\|POS=ADP\|PronType=Ind`, `ExtPos=AUX\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art\|Typo=Yes`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `ExtPos=AUX\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=SCONJ\|PronType=Art\|Typo=Yes`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin\|Voice=Pass`, `ExtPos=NUM\|NumType=Mult\|POS=NUM`, `ExtPos=AUX\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|POS=VERB\|Tense=Imp\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `NumType=Card\|Number=Plur\|POS=NUM`, `ExtPos=AUX\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin`, `ExtPos=NUM\|NumType=Card\|POS=NUM`, `POS=VERB`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=SCONJ\|PronType=Rel`, `Case=Acc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=VERB\|Typo=Yes\|VerbForm=Part`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|Typo=Yes\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=ADV\|Polarity=Neg`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Case=Acc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Number=Sing\|POS=VERB\|Person=1\|VerbForm=Inf`, `ExtPos=NOUN\|Number=Sing\|POS=PROPN`, `ExtPos=ADP\|POS=DET`, `ExtPos=ADP\|Gender=Fem\|Number=Sing\|POS=ADP\|PronType=Art`, `Abbr=Yes\|ExtPos=PROPN\|Number=Sing\|POS=PROPN`, `ExtPos=AUX\|Gender=Fem\|Number=Sing\|POS=VERB\|VerbForm=Part`, `ExtPos=SCONJ\|Gender=Fem\|Number=Sing\|POS=ADV\|PronType=Ind`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Number=Plur\|POS=VERB\|Person=1\|PronType=Prs\|VerbForm=Inf`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Art`, `Case=Dat\|Gender=Fem\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Gender=Masc\|Number=Plur,Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `ExtPos=PROPN\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|PronType=Prs\|VerbForm=Inf`, `Number=Sing\|POS=DET\|PronType=Tot`, `NumType=Range\|POS=NUM`, `Case=Dat\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Mood=Sub\|POS=VERB\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|PronType=Rel`, `ExtPos=PROPN\|Gender=Masc\|Number=Plur\|POS=ADJ\|Typo=Yes`, `Definite=Def\|ExtPos=PROPN\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Dat\|Gender=Masc\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Fin`, `ExtPos=AUX\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Number=Sing\|POS=X`, `ExtPos=NOUN\|POS=PROPN`, `Gender=Masc\|Number=Sing\|POS=NUM`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Fut\|VerbForm=Fin`, `Abbr=Yes\|ExtPos=PROPN\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Ger`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Person=1\|PronType=Prs\|VerbForm=Inf`, `Case=Dat\|Gender=Masc\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Number=Sing\|POS=VERB\|Person=1\|VerbForm=Inf\|Voice=Pass`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=SCONJ\|PronType=Dem`, `ExtPos=SCONJ\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `NumType=Frac\|POS=NUM`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `POS=ADJ`, `Gender=Fem\|Number=Sing\|POS=ADP\|PronType=Ind`, `Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `ExtPos=AUX\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=ADV\|PronType=Rel`, `ExtPos=NOUN\|NumType=Card\|POS=NUM`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind\|Typo=Yes`, `Mood=Cnd\|POS=VERB\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin` | | **`parser`** | `ROOT`, `acl`, `acl:relcl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `cop`, `csubj`, `dep`, `det`, `discourse`, `expl`, `fixed`, `flat`, `flat:foreign`, `flat:name`, `iobj`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `obl:agent`, `parataxis`, `punct`, `xcomp` | </details> ### Accuracy | Type | Score | | --- | --- | | `ENTS_F` | 92.84 | | `ENTS_P` | 92.75 | | `ENTS_R` | 92.94 | | `TAG_ACC` | 97.82 | | `POS_ACC` | 97.81 | | `MORPH_ACC` | 96.11 | | `LEMMA_ACC` | 97.35 | | `DEP_UAS` | 92.84 | | `DEP_LAS` | 89.66 | | `SENTS_P` | 93.49 | | `SENTS_R` | 94.28 | | `SENTS_F` | 93.88 |

60d1f8cc178c5c1bd8a9459591a5da32

Fhrozen/test_an4

Fhrozen

espnet

automatic-speech-recognition

cc-by-4.0

['en']

['an4']

['espnet', 'audio', 'automatic-speech-recognition']

## ESPnet2 ASR model ### `Fhrozen/test_an4` This model was trained by Fhrozen using an4 recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```bash cd espnet git checkout b8df4c928e132acff78d196988bdb68a66987952 pip install -e . cd egs2/an4/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model Fhrozen/test_an4 ``` <!-- Generated by scripts/utils/show_asr_result.sh --> # RESULTS ## Environments - date: `Wed Oct 20 00:00:46 JST 2021` - python version: `3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0]` - espnet version: `espnet 0.10.4a1` - pytorch version: `pytorch 1.9.0` - Git hash: `b8df4c928e132acff78d196988bdb68a66987952` - Commit date: `Tue Oct 19 07:48:11 2021 -0400` ## asr_train_raw_en_bpe30 ### WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |inference_lm_lm_train_lm_en_bpe30_valid.loss.ave_asr_model_valid.acc.best/test|130|773|4.0|22.3|73.7|0.1|96.1|100.0| |inference_lm_lm_train_lm_en_bpe30_valid.loss.ave_asr_model_valid.acc.best/train_dev|100|591|2.7|21.8|75.5|0.0|97.3|100.0| ### CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |inference_lm_lm_train_lm_en_bpe30_valid.loss.ave_asr_model_valid.acc.best/test|130|2565|17.2|16.4|66.4|1.0|83.8|100.0| |inference_lm_lm_train_lm_en_bpe30_valid.loss.ave_asr_model_valid.acc.best/train_dev|100|1915|15.5|16.4|68.1|0.9|85.5|100.0| ### TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |inference_lm_lm_train_lm_en_bpe30_valid.loss.ave_asr_model_valid.acc.best/test|130|2695|21.1|15.6|63.3|0.9|79.9|100.0| |inference_lm_lm_train_lm_en_bpe30_valid.loss.ave_asr_model_valid.acc.best/train_dev|100|2015|19.4|15.6|65.0|0.9|81.5|100.0| ## ASR config <details><summary>expand</summary> ``` config: null print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_raw_en_bpe30 ngpu: 0 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist_rank: null local_rank: null dist_master_addr: null dist_master_port: null dist_launcher: null multiprocessing_distributed: false unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 40 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - train - loss - min - - valid - loss - min - - train - acc - max - - valid - acc - max keep_nbest_models: - 10 grad_clip: 5.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 1 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_tensorboard: true use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: null batch_size: 20 valid_batch_size: null batch_bins: 1000000 valid_batch_bins: null train_shape_file: - exp/asr_stats_raw_en_bpe30/train/speech_shape - exp/asr_stats_raw_en_bpe30/train/text_shape.bpe valid_shape_file: - exp/asr_stats_raw_en_bpe30/valid/speech_shape - exp/asr_stats_raw_en_bpe30/valid/text_shape.bpe batch_type: folded valid_batch_type: null fold_length: - 80000 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/train_nodev/wav.scp - speech - sound - - dump/raw/train_nodev/text - text - text valid_data_path_and_name_and_type: - - dump/raw/train_dev/wav.scp - speech - sound - - dump/raw/train_dev/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adadelta optim_conf: {} scheduler: null scheduler_conf: {} token_list: - <blank> - <unk> - ▁ - T - E - O - R - Y - A - H - U - S - I - F - B - L - P - D - G - M - C - V - X - J - K - Z - W - N - Q - <sos/eos> init: null input_size: null ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: true model_conf: ctc_weight: 0.5 ignore_id: -1 lsm_weight: 0.0 length_normalized_loss: false report_cer: true report_wer: true sym_space: <space> sym_blank: <blank> extract_feats_in_collect_stats: true use_preprocessor: true token_type: bpe bpemodel: data/en_token_list/bpe_unigram30/bpe.model non_linguistic_symbols: null cleaner: null g2p: null speech_volume_normalize: null rir_scp: null rir_apply_prob: 1.0 noise_scp: null noise_apply_prob: 1.0 noise_db_range: '13_15' frontend: default frontend_conf: fs: 16k specaug: null specaug_conf: {} normalize: global_mvn normalize_conf: stats_file: exp/asr_stats_raw_en_bpe30/train/feats_stats.npz preencoder: null preencoder_conf: {} encoder: rnn encoder_conf: {} postencoder: null postencoder_conf: {} decoder: rnn decoder_conf: {} required: - output_dir - token_list version: 0.10.4a1 distributed: false ``` </details> ## LM config <details><summary>expand</summary> ``` config: conf/train_lm.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/lm_train_lm_en_bpe30 ngpu: 0 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist_rank: null local_rank: null dist_master_addr: null dist_master_port: null dist_launcher: null multiprocessing_distributed: false unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 40 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - loss - min keep_nbest_models: 1 grad_clip: 5.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 1 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_tensorboard: true use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: null batch_size: 256 valid_batch_size: null batch_bins: 1000000 valid_batch_bins: null train_shape_file: - exp/lm_stats_en_bpe30/train/text_shape.bpe valid_shape_file: - exp/lm_stats_en_bpe30/valid/text_shape.bpe batch_type: folded valid_batch_type: null fold_length: - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/lm_train.txt - text - text valid_data_path_and_name_and_type: - - dump/raw/train_dev/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.1 scheduler: null scheduler_conf: {} token_list: - <blank> - <unk> - ▁ - T - E - O - R - Y - A - H - U - S - I - F - B - L - P - D - G - M - C - V - X - J - K - Z - W - N - Q - <sos/eos> init: null model_conf: ignore_id: 0 use_preprocessor: true token_type: bpe bpemodel: data/en_token_list/bpe_unigram30/bpe.model non_linguistic_symbols: null cleaner: null g2p: null lm: seq_rnn lm_conf: unit: 650 nlayers: 2 required: - output_dir - token_list version: 0.10.4a1 distributed: false ``` </details>

6fa244cd2fe0524b9b46c9c149349166

jonatasgrosman/exp_w2v2r_en_vp-100k_gender_male-8_female-2_s859

jonatasgrosman

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['en']

['mozilla-foundation/common_voice_7_0']

['automatic-speech-recognition', 'en']

# exp_w2v2r_en_vp-100k_gender_male-8_female-2_s859 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (en)](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.

4c59ea635d5a80ba0a6f5f36b5f7e61e

Sercan/whisper-small-tr

Sercan

whisper

transformers

automatic-speech-recognition

apache-2.0

['tr']

['mozilla-foundation/common_voice_11_0']

['whisper', 'generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Turkish This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 tr dataset. It achieves the following results on the evaluation set: - Loss: 0.2799 - Wer: 17.2753 - Cer: 4.5335 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:| | 0.1044 | 1.07 | 1000 | 0.2777 | 18.4046 | 4.8810 | | 0.0469 | 3.02 | 2000 | 0.2799 | 17.2753 | 4.5335 | | 0.014 | 4.09 | 3000 | 0.3202 | 18.0800 | 4.9039 | | 0.0039 | 6.04 | 4000 | 0.3326 | 18.2964 | 5.0192 | | 0.0022 | 7.11 | 5000 | 0.3453 | 18.0307 | 4.9470 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.1+cu117 - Datasets 2.8.1.dev0 - Tokenizers 0.13.2

7830a334a4304178ad0a98dfacd10674

gossminn/predict-perception-bertino-focus-object

gossminn

distilbert

transformers

text-classification

mit

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # predict-perception-bertino-focus-object This model is a fine-tuned version of [indigo-ai/BERTino](https://huggingface.co/indigo-ai/BERTino) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2766 - R2: 0.5460 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 20 - eval_batch_size: 8 - seed: 1996 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 47 ### Training results | Training Loss | Epoch | Step | Validation Loss | R2 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.4798 | 1.0 | 14 | 0.4519 | 0.2581 | | 0.2481 | 2.0 | 28 | 0.3042 | 0.5007 | | 0.12 | 3.0 | 42 | 0.3746 | 0.3851 | | 0.0969 | 4.0 | 56 | 0.3186 | 0.4770 | | 0.0907 | 5.0 | 70 | 0.3727 | 0.3882 | | 0.0673 | 6.0 | 84 | 0.2847 | 0.5327 | | 0.0457 | 7.0 | 98 | 0.3141 | 0.4844 | | 0.0431 | 8.0 | 112 | 0.3369 | 0.4470 | | 0.028 | 9.0 | 126 | 0.3039 | 0.5012 | | 0.0244 | 10.0 | 140 | 0.2964 | 0.5135 | | 0.0201 | 11.0 | 154 | 0.3072 | 0.4958 | | 0.0153 | 12.0 | 168 | 0.3049 | 0.4995 | | 0.0155 | 13.0 | 182 | 0.2924 | 0.5201 | | 0.015 | 14.0 | 196 | 0.2585 | 0.5757 | | 0.0181 | 15.0 | 210 | 0.3258 | 0.4652 | | 0.0136 | 16.0 | 224 | 0.3142 | 0.4842 | | 0.0105 | 17.0 | 238 | 0.2536 | 0.5837 | | 0.0104 | 18.0 | 252 | 0.2407 | 0.6050 | | 0.0107 | 19.0 | 266 | 0.2727 | 0.5524 | | 0.0084 | 20.0 | 280 | 0.3117 | 0.4883 | | 0.0102 | 21.0 | 294 | 0.2999 | 0.5078 | | 0.0074 | 22.0 | 308 | 0.3018 | 0.5047 | | 0.0068 | 23.0 | 322 | 0.2826 | 0.5361 | | 0.0054 | 24.0 | 336 | 0.2804 | 0.5398 | | 0.0044 | 25.0 | 350 | 0.2912 | 0.5220 | | 0.0048 | 26.0 | 364 | 0.2813 | 0.5382 | | 0.005 | 27.0 | 378 | 0.2933 | 0.5186 | | 0.0046 | 28.0 | 392 | 0.2820 | 0.5371 | | 0.004 | 29.0 | 406 | 0.2717 | 0.5541 | | 0.0054 | 30.0 | 420 | 0.2717 | 0.5540 | | 0.0042 | 31.0 | 434 | 0.2699 | 0.5570 | | 0.0033 | 32.0 | 448 | 0.2630 | 0.5684 | | 0.0038 | 33.0 | 462 | 0.2578 | 0.5767 | | 0.0032 | 34.0 | 476 | 0.2687 | 0.5589 | | 0.004 | 35.0 | 490 | 0.2737 | 0.5507 | | 0.0031 | 36.0 | 504 | 0.2753 | 0.5481 | | 0.0037 | 37.0 | 518 | 0.2819 | 0.5373 | | 0.0034 | 38.0 | 532 | 0.2759 | 0.5471 | | 0.0034 | 39.0 | 546 | 0.2835 | 0.5347 | | 0.0029 | 40.0 | 560 | 0.2814 | 0.5381 | | 0.0033 | 41.0 | 574 | 0.2801 | 0.5403 | | 0.0025 | 42.0 | 588 | 0.2759 | 0.5472 | | 0.0029 | 43.0 | 602 | 0.2790 | 0.5421 | | 0.0028 | 44.0 | 616 | 0.2801 | 0.5401 | | 0.003 | 45.0 | 630 | 0.2772 | 0.5451 | | 0.0028 | 46.0 | 644 | 0.2764 | 0.5463 | | 0.0026 | 47.0 | 658 | 0.2766 | 0.5460 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.2+cu113 - Datasets 1.18.3 - Tokenizers 0.11.0

01f35b10b8456e0c84c21e4d2755e9a0

sd-concepts-library/kaleido

sd-concepts-library

mit

[]

### kaleido on Stable Diffusion This is the `<kaleido>` 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 a `style`:     

4f37b5f7a89dc83181324353952275ad

jonatasgrosman/exp_w2v2t_pt_vp-100k_s69

jonatasgrosman

wav2vec2

transformers

automatic-speech-recognition

apache-2.0

['pt']

['mozilla-foundation/common_voice_7_0']

['automatic-speech-recognition', 'pt']

# exp_w2v2t_pt_vp-100k_s69 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pt)](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.

08f9f0d9d54585b72584d1536e9aa872

juancavallotti/t5-base-gec

juancavallotti

t5

transformers

text2text-generation

apache-2.0

['generated_from_trainer']

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-base-gec This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### 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: 5 ### Training results ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

bca2ae06405e129a9134222dadaaef78

Helsinki-NLP/opus-mt-fi-hil

Helsinki-NLP

marian

transformers

translation

apache-2.0

['translation']

### opus-mt-fi-hil * source languages: fi * target languages: hil * OPUS readme: [fi-hil](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-hil/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-24.zip](https://object.pouta.csc.fi/OPUS-MT-models/fi-hil/opus-2020-01-24.zip) * test set translations: [opus-2020-01-24.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-hil/opus-2020-01-24.test.txt) * test set scores: [opus-2020-01-24.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-hil/opus-2020-01-24.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.fi.hil | 38.7 | 0.610 |

0e1a3e6abfa9684a9a6b438b97c3811b

misterbrainley/ddpm-butterflies-128

misterbrainley

diffusers

apache-2.0

['en']

['huggan/smithsonian_butterflies_subset']

[]

<!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # ddpm-butterflies-128 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `huggan/smithsonian_butterflies_subset` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/misterbrainley/ddpm-butterflies-128/tensorboard?#scalars)

b8bbc6564e441ece0aaa4ae229682375

cmarkea/distilcamembert-base-nli

cmarkea

camembert