license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
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apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 318 | 3.1622 | 0.7468 | | 3.6918 | 2.0 | 636 | 1.5555 | 0.8565 | | 3.6918 | 3.0 | 954 | 0.7728 | 0.... | bcd7e1a0df789bf9fa31f605f6d64e07 |
cc-by-4.0 | ['espnet', 'audio', 'speech-translation'] | false | `espnet/brianyan918_iwslt22_dialect_train_st_conformer_ctc0.3_lr2e-3_warmup15k_newspecaug` This model was trained by Brian Yan using iwslt22_dialect recipe in [espnet](https://github.com/espnet/espnet/). | b48438dd93e8f4ae89ba4493378c939c |
cc-by-4.0 | ['espnet', 'audio', 'speech-translation'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout 77fce65312877a132bbae01917ad26b74f6e2e14 pip install -e . cd egs2/iwslt22_dialect/st1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/brianyan918_iwslt22_dialect_train_st_conformer_ctc0.3_lr2e-3_warmup15k_newspecaug ``` <!-- Generat... | 45bb512c949dd0b9db6f89702d7bd6ec |
cc-by-4.0 | ['espnet', 'audio', 'speech-translation'] | false | Environments - date: `Tue Feb 8 12:54:12 EST 2022` - python version: `3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0]` - espnet version: `espnet 0.10.7a1` - pytorch version: `pytorch 1.8.1` - Git hash: `77fce65312877a132bbae01917ad26b74f6e2e14` - Commit date: `Tue Feb 8 10:48:10 2022 -0500` | 6a43a13dd3175dbda1b2256cdac5af85 |
cc-by-4.0 | ['espnet', 'audio', 'speech-translation'] | false | ST config <details><summary>expand</summary> ``` config: conf/tuning/train_st_conformer_ctc0.3_lr2e-3_warmup15k_newspecaug.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/st_train_st_conformer_ctc0.3_lr2e-3_warmup15k_newspecaug_raw_bpe_tc1000_sp ngpu: 1 seed: 0 num_wor... | 56048213e8c945cb3ed4a61d8986b4ab |
apache-2.0 | ['generated_from_keras_callback'] | false | MaryaAI/opus-mt-ar-en-finetunedQAdata-v1-ar-to-en This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ar-en](https://huggingface.co/Helsinki-NLP/opus-mt-ar-en) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0053 - Validation Loss: 8.2764 - Epoch: 14 | 75339c5bd6ec9c4b081270849a80c857 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.0090 | 12.3530 | 0 | | 0.0134 | 11.3018 | 1 | | 0.0110 | 10.5958 | 2 | | 0.0083 | 9.7381 | 3 | | 0.0068 | 8.9434 | 4 | | 0.0080 |... | 2c26827187272eb957244ba9a7bfcab1 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-xls-r-300m-ar-6 This model is a fine-tuned version of [MeshalAlamr/wav2vec2-xls-r-300m-ar-6](https://huggingface.co/MeshalAlamr/wav2vec2-xls-r-300m-ar-6) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 78.2951 - Wer: 0.2040 | ecf83d98bb614c37b135129ffba642a9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 85 | 75.3576 | 0.2131 | | No log | 2.0 | 170 | 75.3215 | 0.2150 | | No log | 3.0 | 255 | 75.5332 | 0.2201 | |... | e49e1ff92445dfbd309a017b9eb71e4e |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-text2log-compute-metrics-v5-400 This model is a fine-tuned version of [mrm8488/t5-small-finetuned-text2log](https://huggingface.co/mrm8488/t5-small-finetuned-text2log) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5820 - Bleu: 30.1378 - Gen Len: 18.568 | c0e08fa05480f518cb06b89f6f7d085f |
apache-2.0 | ['generated_from_trainer'] | false | 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: 150 - mixed_precision_training: Native AMP | 6d332de0f0c6d98f9c784d6358ce3442 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | No log | 1.0 | 23 | 1.5732 | 8.4784 | 17.3373 | | No log | 2.0 | 46 | 1.1400 | 22.8492 | 18.8284 | | No log |... | ccddc1da2ef849a85814692dcb5e2e4d |
apache-2.0 | [] | false | Introduction The research for social science texts in Chinese needs the support natural language processing tools. The pre-trained language model has greatly improved the accuracy of text mining in general texts. At present, there is an urgent need for a pre-trained language model specifically for the automatic pro... | 751035e8cfc2b915546df31db2544504 |
apache-2.0 | [] | false | Huggingface Transformers The `from_pretrained` method based on [Huggingface Transformers](https://github.com/huggingface/transformers) can directly obtain CSSCI_ABS_BERT, CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm models online. - CSSCI_ABS_BERT ```python from transformers import AutoTokenizer, AutoModel toke... | b8a410095fa9b4b17a5f858f05c71247 |
apache-2.0 | [] | false | From Huggingface - Download directly through Huggingface's official website. - [KM4STfulltext/CSSCI_ABS_BERT](https://huggingface.co/KM4STfulltext/CSSCI_ABS_BERT) - [KM4STfulltext/CSSCI_ABS_roberta](https://huggingface.co/KM4STfulltext/CSSCI_ABS_roberta) - [KM4STfulltext/CSSCI_ABS_roberta_wwm](https://huggingface.c... | 0c06d19bcc8f8e73f3f7f36967a4735c |
apache-2.0 | [] | false | Evaluation & Results - We useCSSCI_ABS_BERT, CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm to perform Text Classificationon different social science research corpus. The experimental results are as follows. | 0d31f913557fcb262cb94576b7409bd0 |
apache-2.0 | [] | false | Movement recognition experiments for data analysis and knowledge discovery abstract | Tag | bert-base-Chinese | chinese-roberta-wwm,ext | CSSCI_ABS_BERT | CSSCI_ABS_roberta | CSSCI_ABS_roberta_wwm | support | | ------------ | ----------------- | ----------------------- | -------------- | ----------------- | ... | a075d2c599e15da3b1d7bf0985707d79 |
apache-2.0 | [] | false | Chinese literary entity recognition | Tag | bert-base-Chinese | chinese-roberta-wwm,ext | CSSCI_ABS_BERT | CSSCI_ABS_roberta | CSSCI_ABS_roberta_wwm | support | | ------------ | ----------------- | ----------------------- | -------------- | ----------------- | --------------------- | ------- | | Abstract ... | c0ed37da74db6334a4307bda9c942d70 |
apache-2.0 | [] | false | Cited - If our content is helpful for your research work, please quote our research in your article. - If you want to quote our research, you can use this url [S-T-Full-Text-Knowledge-Mining/CSSCI-BERT (github.com)](https://github.com/S-T-Full-Text-Knowledge-Mining/CSSCI-BERT) as an alternative before our paper is p... | 0c3eeeea55884eae5a1547135cfc6b07 |
apache-2.0 | [] | false | Acknowledgment - CSSCI_ABS_BERT was trained based on [BERT-Base-Chinese]([google-research/bert: TensorFlow code and pre-trained models for BERT (github.com)](https://github.com/google-research/bert)). - CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm was trained based on [RoBERTa-wwm-ext, Chinese]([ymcui/Chinese-BERT-ww... | b683013dd2b6be7965f173e37d79c463 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | StableDiffusion_finetuning_cat_emoticon_style Dreambooth model trained by jha2ee 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.co... | f3f8fa49aea5be77a9c8245323bc50f2 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased_fold_4_ternary_v1 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: 1.9355 - F1: 0.7891 | 4b4cc268977a983ccf6c63d881277969 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 289 | 0.5637 | 0.7485 | | 0.5729 | 2.0 | 578 | 0.5305 | 0.7805 | | 0.5729 | 3.0 | 867 | 0.6948 | 0.7670 | |... | 510d8d4f751d8ebe0a686cddf750c12d |
mit | [] | false | [Project Page](https://sites.google.com/view/cspnet) | [Paper](https://arxiv.org/abs/2106.05779) | [Code](https://github.com/rahulvenkk/csp-net) If you find our code or paper useful, please cite as @InProceedings{Venkatesh_2021_ICCV, author = {Venkatesh, Rahul and Karmali, Tejan and Sharma, Sarthak an... | 035d7f2724c8daf33e37b4997b4a66ea |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased.CEBaB_confounding.price_food_ambiance_negative.sa.5-class.seed_44 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the OpenTable OPENTABLE dataset. It achieves the following results on the evaluation set: - Loss: 0.7475 - Accuracy: 0.6934 - Macro... | 59d0585eb097348e0f29ecfe7dbf8663 |
apache-2.0 | ['generated_from_trainer'] | false | distilBERT-fresh_10epoch 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: 0.0234 - Precision: 0.0 - Recall: 0.0 - F1: 0.0 - Accuracy: 0.9935 | 04d73df0cfb45434168c4683095d9603 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:| | No log | 1.0 | 174 | 0.1913 | 0.0 | 0.0 | 0.0 | 0.9312 | | No log | 2.0 | 348 | 0... | f2e00f3964fd4b2d01343982074b9f0e |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Portuguese Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Portuguese using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset. When using this model, make sure that your speech input is sampled at 16kHz. | 36e1f692c29ceb83fdec3eebc9f28111 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "pt", split="test[:2%]") processor = Wav2Vec2Processor.from_p... | ff9c6b3aae305cf85680d3c60f8862b4 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Portuguese test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "pt", split="test") ... | 53f2afde8341e539db1fe5dab26c7b71 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = t... | 2b31622836de73d18527e85ef05da4eb |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Training The Common Voice `train` and `validation` datasets were used for training. The script used for training can be found [here](https://github.com/jqueguiner/wav2vec2-sprint/blob/main/run_common_voice.py). The parameters passed were: ```bash | 6a0a86917768be1146e288a5dc935a98 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | !/usr/bin/env bash python run_common_voice.py \ --model_name_or_path="facebook/wav2vec2-large-xlsr-53" \ --dataset_config_name="pt" \ --output_dir=/workspace/output_models/pt/wav2vec2-large-xlsr-pt \ --cache_dir=/workspace/data \ --overwrite_output_dir \ --num_train_epochs="30" \ --per_devic... | 3fc9d6173456105d29f6a25c501b0504 |
['apache-2.0', 'bsd-3-clause'] | ['summarization', 'summary', 'booksum', 'long-document', 'long-form'] | false | Model description A fine-tuned version of [google/long-t5-tglobal-base](https://huggingface.co/google/long-t5-tglobal-base) on the `booksum` dataset: - 30+ epochs of fine-tuning from the base model on V100/A100 GPUs - Training used 16384 token input / 1024 max output Read the paper by Guo et al. here: [LongT5: Effi... | d777c0500f5ab409a2553ca0d39df2bc |
['apache-2.0', 'bsd-3-clause'] | ['summarization', 'summary', 'booksum', 'long-document', 'long-form'] | false | How-To in Python Install/update transformers `pip install -U transformers` Summarize text with pipeline: ```python import torch from transformers import pipeline summarizer = pipeline( "summarization", "pszemraj/long-t5-tglobal-base-16384-book-summary", device=0 if torch.cuda.is_available() else -1, ) ... | 886209fee43427847ac99710c343bc5d |
['apache-2.0', 'bsd-3-clause'] | ['summarization', 'summary', 'booksum', 'long-document', 'long-form'] | false | Intended uses & limitations - The current checkpoint is fairly well converged but will be updated if further improvements can be made. - Compare performance to [LED-base](https://huggingface.co/pszemraj/led-base-book-summary) trained on the same dataset (API gen parameters are the same). - while this model seems ... | 4665882698aa51f4e9a8b576e0c2c4ee |
['apache-2.0', 'bsd-3-clause'] | ['summarization', 'summary', 'booksum', 'long-document', 'long-form'] | false | Training and evaluation data `kmfoda/booksum` dataset on HuggingFace - read [the original paper here](https://arxiv.org/abs/2105.08209). Summaries longer than 1024 LongT5 tokens were filtered out to prevent the model from learning to generate "partial" summaries. | 30e5b0c2fdea06f64dbf133a20a2f62e |
['apache-2.0', 'bsd-3-clause'] | ['summarization', 'summary', 'booksum', 'long-document', 'long-form'] | false | How to run inference over a very long (30k+ tokens) document in batches? See `summarize.py` in [the code for my hf space Document Summarization](https://huggingface.co/spaces/pszemraj/document-summarization/blob/main/summarize.py) :) You can also use the same code to split a document into batches of 4096, etc., and ... | 8766209afafdaf6045720df647ce57b6 |
['apache-2.0', 'bsd-3-clause'] | ['summarization', 'summary', 'booksum', 'long-document', 'long-form'] | false | How to fine-tune further? See [train with a script](https://huggingface.co/docs/transformers/run_scripts) and [the summarization scripts](https://github.com/huggingface/transformers/tree/main/examples/pytorch/summarization). This model was originally tuned on Google Colab with a heavily modified variant of the [long... | 34e9290dae7e4a219474cf3722840fb0 |
['apache-2.0', 'bsd-3-clause'] | ['summarization', 'summary', 'booksum', 'long-document', 'long-form'] | false | Training hyperparameters _NOTE: early checkpoints of this model were trained on a "smaller" subsection of the dataset as it was filtered for summaries of **1024 characters**. This was subsequently caught and adjusted to **1024 tokens** and then trained further for 10+ epochs._ The following hyperparameters were used... | 070a089be2c1775ff401840797ca3ffe |
apache-2.0 | ['translation'] | false | epo-afr * source group: Esperanto * target group: Afrikaans * OPUS readme: [epo-afr](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/epo-afr/README.md) * model: transformer-align * source language(s): epo * target language(s): afr * model: transformer-align * pre-processing: normalization + ... | b343f36067296a002780aca7ee66836c |
apache-2.0 | ['translation'] | false | System Info: - hf_name: epo-afr - source_languages: epo - target_languages: afr - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/epo-afr/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['eo', 'af'] - src_constituents: {'epo'} - tgt_const... | 1a6f9ddba3a885b93af1c6c475538d3b |
apache-2.0 | ['audio-classification', 'speechbrain', 'embeddings', 'Language', 'Identification', 'pytorch', 'ECAPA-TDNN', 'TDNN', 'VoxLingua107'] | false | Model description This is a spoken language recognition model trained on the VoxLingua107 dataset using SpeechBrain. The model uses the ECAPA-TDNN architecture that has previously been used for speaker recognition. However, it uses more fully connected hidden layers after the embedding layer, and cross-entropy loss w... | 1cb4902b51fc1797224f5f7e8bb386a7 |
apache-2.0 | ['audio-classification', 'speechbrain', 'embeddings', 'Language', 'Identification', 'pytorch', 'ECAPA-TDNN', 'TDNN', 'VoxLingua107'] | false | How to use ```python import torchaudio from speechbrain.pretrained import EncoderClassifier language_id = EncoderClassifier.from_hparams(source="speechbrain/lang-id-voxlingua107-ecapa", savedir="tmp") | 951e8f428aa57fa5058e8bc295e657fd |
apache-2.0 | ['generated_from_trainer'] | false | generateur-bucolique This model is a fine-tuned version of [asi/gpt-fr-cased-small](https://huggingface.co/asi/gpt-fr-cased-small) on the "romans champêtres" dataset (four books). These four novels written by George Sand (*La mare au diable*, *La petite fadette*, *Les maîtres sonneurs*, *François le champi*) are ch... | 0bfe0cd9f1795c7524d1b0c1c89c9433 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The parameters are the same as in [Rey Farhan' notebook (Easy GPT2 fine-tuning with Hugging Face and PyTorch)](https://reyfarhan.com/posts/easy-gpt2-finetuning-huggingface) The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 8 - eval_batch_siz... | 209c0c440d3c2723754727e6c5508aa8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 495 | 2.1639 | | 2.3543 | 2.0 | 990 | 2.1599 | | 1.6869 | 3.0 | 1485 | 2.4279 | | 1.0753 | 4.0 | 1980 | 2.8547 ... | 1ea978f861c7ca999197aee7b8c2c9ce |
apache-2.0 | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'], 'is_split_by_sentences': True}, 'generation': {'batch_size': 128, 'metrics_configs': [{}, {'n': 1}, {}], 'scenario_configs': [{'display_as_html': True, ... | f9b4b5b45d1ee9e38a5bd31c49b5b545 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-zindi_tweets 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: 0.3203 - Accuracy: 0.9168 - F1: 0.9168 | 8bb95d8d9320615f6f1ea7abe263e2eb |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.4224 | 1.0 | 67 | 0.2924 | 0.8894 | 0.8893 | | 0.2096 | 2.0 | 134 | 0.2632 | 0.9055 | 0.9055 | | 0.1329 |... | 711ea1a5ab13094f3f15d1f1feb116ce |
creativeml-openrail-m | ['text-to-image'] | false | training params ```json { "pretrained_model_name_or_path": "runwayml/stable-diffusion-v1-5", "instance_data_dir": "./2cabda5b-4e53-40e9-8fcf-cdba5ea5bd6c/instance_data", "class_data_dir": "./class_data/a-portrait-of-a-person", "output_dir": "./2cabda5b-4e53-40e9-8fcf-cdba5ea5bd6c/", "train_text_enc... | 7ee56824b6895bf9352f488b20a6d37d |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-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: 1 | e43a0793fd938233e0ec758be69bac85 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 213 | 0.3717 | 0.6279 | 0.3707 | 0.4662 | 0.9481 | | 66c8e1284d9bdb2527c465f2ee0e42a2 |
apache-2.0 | ['text-classification', 'generated_from_trainer'] | false | categorizacion_comercios_v_0.0.4 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the datasetX dataset. It achieves the following results on the evaluation set: - Loss: 0.4303 - Accuracy: 0.8786 | 70456bd1aa7894c540b970c02198a881 |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'architecture', 'diffusers'] | false | Model Trained to create a Midjourney architecture buildings style. Used 120 images 768x768 Prompts example: "a photo of a futuristic house with a organic facade, roots, waves, many windows, by midjourneyi" "a photo of a parametric building midjourneyi, waves, roots, veins, many windows, in a street." "a photo of ... | f953b20155ec35d5c492f30af19b3fab |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'architecture', 'diffusers'] | false | Examples <img src="https://s3.amazonaws.com/moonup/production/uploads/1672217149393-632079ef9f7a9f2208c6582a.jpeg" style="max-width: 200px;" width="100%"/> <img src="https://s3.amazonaws.com/moonup/production/uploads/1672217145915-632079ef9f7a9f2208c6582a.jpeg" style="max-width: 200px;" width="100%"/> <img src="https... | 0ba4ad1a378369de4766467042b3ab7b |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | kyoto_marian_mod_5 This model is a fine-tuned version of [Hoax0930/kyoto_marian_mod_4](https://huggingface.co/Hoax0930/kyoto_marian_mod_4) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.9144 - Bleu: 20.1999 | a90d36588e82f2a2ed43ff9ba2104ae5 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-multilingual-cased-finetuned-with-spanish-tweets-clf-cleaned-ds This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the dataset dataset. It achieves the following results on the evaluation set: - Loss: 1.5095 - Accura... | c6c7f850e0a474a256c494b39b3df5b9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 1.018 | 1.0 | 543 | 0.9421 | 0.5536 | 0.4949 | 0.5347 | 0.5146 | | 0.8079 | 2.0 |... | 547b7492d4cde2832d3e7f271f9b88e0 |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-SMALL-EL16 (Deep-Narrow version) T5-Efficient-SMALL-EL16 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* checkpoint ... | 3c4c59dd74c45a15c4a6165229fc4a65 |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-small-el16** - is of model type **Small** with the following variations: - **el** is **16** It has **92.0** million parameters and thus requires *ca.* **367.99 MB** of memory in full precision (*fp32*) or **183.99 MB** of memory in half precision (*... | 04f6e48fc6443628ed0e9baf5ece064a |
apache-2.0 | ['multiberts', 'multiberts-seed_3', 'multiberts-seed_3-step_180k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 3, Step 180k MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different ... | f1f3830d9937b4c0ed05208cfd16ed0f |
apache-2.0 | ['multiberts', 'multiberts-seed_3', 'multiberts-seed_3-step_180k'] | false | How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_3-step_180k') model = TFBertModel.from_pretrained("google/multibe... | b65f8b2a1183ae9e0461a1466bd2fb01 |
bsd-3-clause | ['flair', 'token-classification', 'sequence-tagger-model'] | false | Biomedical Entity Recognition in Bahasa Indonesia Summary: - Trained using manually annotated data from alodokter.com (online health QA platform) using UMLS guideline (see https://rdcu.be/cNxV3) - Recognize disorders (DISO) and anatomy (ANAT) entities - Achieve best F1 macro score 0.81 - Based on XLM-Roberta. So, cr... | 83d75051f6023318ff4af574155523d5 |
bsd-3-clause | ['flair', 'token-classification', 'sequence-tagger-model'] | false | CITATION This work is done with generous support from Safitri Juanita, Dr. Diana Purwitasari and Dr. Mauridhi Hery Purnomo from Institut Teknologi Sepuluh Nopember, Indonesia. Citation for academic purpose will be provided later. For demo, please go to the HF space demo: https://huggingface.co/spaces/abid/id-bioner-... | a285c81e65e2190f98c28fb0f22fc57b |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epoch... | 98f8f7290b7957758b640a1434a7a89f |
apache-2.0 | ['EBK-BERT'] | false | BK-BERT Event Knowledge-Based BERT (EBK-BERT) leverages knowledge extracted from events-related sentences to mask words that are significant to the events detection task. This approach aims to produce a language model that enhances the performance of the down-stream event detection task, which is later trained du... | bf0dca8232e62dbe0d95ba8aa982919c |
apache-2.0 | ['EBK-BERT'] | false | Pre-training Data The pre-training data consists of news articles from the 1.5 billion words corpus by (El-Khair, 2016). Due to computation limitations, we only use articles from Alittihad, Riyadh, Almasrya- lyoum, and Alqabas, which amount to 10GB of text and about 8M sentences after splitting the articles to approxi... | bd7bd59c259f786aea5737f4e5353ece |
apache-2.0 | ['EBK-BERT'] | false | Pretraining As previous studies have shown, contextual representation models that are pre-trained using top Personnel Transaction Contact Nature Movement Life Justice Conflict business the MLM training task benefit from masking the most significant words, using whole word masking. To select the most significant word... | 30414735024f51cbfc0f50dcc3758139 |
apache-2.0 | ['EBK-BERT'] | false | Fine-tuning data Tweets are collected from well-known Arabic news accounts, which are: Al-Arabiya, Sabq, CNN Arabic, and BBC Arabic. These accounts belong to television channels and online newspapers, where they use Twitter to broadcast news related to real-world events. The first collection process tracks tweets f... | 4c4a5ff83f373c998c8775e756e01997 |
apache-2.0 | ['EBK-BERT'] | false | Evaluation results When fine-tuned on down-stream event detection task, this model achieves the following results: . Every long-unit-word is tagged by [UPOS](https://universaldependencies.org/u/po... | f10273db6140894af33fd2938e3ed3b3 |
cc-by-sa-4.0 | ['japanese', 'token-classification', 'pos', 'wikipedia', 'dependency-parsing'] | false | How to Use ```py import torch from transformers import AutoTokenizer,AutoModelForTokenClassification tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/bert-base-japanese-luw-upos") model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/bert-base-japanese-luw-upos") s="国境の長いトンネルを抜けると雪国であった。" p=[mode... | f216f351d92c6923b74df5b97a3a9410 |
apache-2.0 | ['translation'] | false | art-eng * source group: Artificial languages * target group: English * OPUS readme: [art-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/art-eng/README.md) * model: transformer * source language(s): afh_Latn avk_Latn dws_Latn epo ido ido_Latn ile_Latn ina_Latn jbo jbo_Cyrl jbo_Latn ldn_... | 0b417683711da6eaa9bf696dbed60b43 |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.afh-eng.afh.eng | 1.2 | 0.099 | | Tatoeba-test.avk-eng.avk.eng | 0.4 | 0.105 | | Tatoeba-test.dws-eng.dws.eng | 1.6 | 0.076 | | Tatoeba-test.epo-eng.epo.eng | 34.6 | 0.530 | | Tatoeba-test.ido-eng.ido... | 48da5907205d7bd64716c852f6cb8004 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: art-eng - source_languages: art - target_languages: eng - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/art-eng/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['eo', 'io', 'art', 'en'] - src_constituents: {'sjn_L... | e9e18778d65b88eabc4ec6637eb588c3 |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2t_en_vp-100k_s421 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make s... | b3ae6b43aaf2c00e016b8194b37a3534 |
apache-2.0 | ['generated_from_trainer'] | false | platzi-vit-model-omar-espejel This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0367 - Accuracy: 0.9850 | 4bd3918578bddbd5d160e731694756f6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.149 | 3.85 | 500 | 0.0367 | 0.9850 | | bd8b17fc9fdc47fe7ae468db28919054 |
mit | [] | false | model by hiero This your the Stable Diffusion model fine-tuned the angus mcbride style concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **angus mcbride style** You can also train your own concepts and upload them to the library by using [this notebook](https://col... | e5f9e32b577a2bad209bd825f34face6 |
apache-2.0 | ['translation'] | false | opus-mt-es-swc * source languages: es * target languages: swc * OPUS readme: [es-swc](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-swc/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | 5b0131dd20daef6978959b525d2bb9be |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0627 - Precision: 0.9355 - Recall: 0.9524 - F1: 0.9439 - Accuracy: 0.9862 | 07b4c02eb884dcdef7caf0ca4de7df4b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0842 | 1.0 | 1756 | 0.0662 | 0.9195 | 0.9396 | 0.9294 | 0.9839 | | 0.0384 | 2.0 |... | 3aa60c0df78deb542e5e6a2f0a381965 |
mit | ['ja', 'japanese', 'gpt2', 'text-generation', 'lm', 'nlp'] | false | 日本語 gpt2 蒸留モデル このモデルは[rinna/japanese-gpt2-meduim](https://huggingface.co/rinna/japanese-gpt2-medium)を教師として蒸留したものです。 蒸留には、HuggigFace Transformersの[コード](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation)をベースとし、[りんなの訓練コード](https://github.com/rinnakk/japanese-pretrained-models)... | 412eda4c9ac59710061175654c7d7b01 |
mit | ['ja', 'japanese', 'gpt2', 'text-generation', 'lm', 'nlp'] | false | Japanese GPT-2 model This model is a dillated model from [rinna/japanese-gpt2-medium](https://huggingface.co/rinna/japanese-gpt2-medium). To train, I combined HuggingFace Transformers [code](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation) and [rinna gpt2 train code](http... | a3d41549d85f90e263dd98a98e2ec0bd |
apache-2.0 | ['translation'] | false | ukr-slv * source group: Ukrainian * target group: Slovenian * OPUS readme: [ukr-slv](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ukr-slv/README.md) * model: transformer-align * source language(s): ukr * target language(s): slv * model: transformer-align * pre-processing: normalization + ... | c6412980241a78436a5d68a522d3b401 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: ukr-slv - source_languages: ukr - target_languages: slv - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ukr-slv/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['uk', 'sl'] - src_constituents: {'ukr'} - tgt_const... | d6e84d203f96550399c58878271d4754 |
other | ['vision', 'image-segmentation'] | false | Mask2Former Mask2Former model trained on ADE20k panoptic segmentation (large-sized version, Swin backbone). It was introduced in the paper [Masked-attention Mask Transformer for Universal Image Segmentation ](https://arxiv.org/abs/2112.01527) and first released in [this repository](https://github.com/facebookresearch... | e7bf8328f03bb9c994c01266e5996126 |
other | ['vision', 'image-segmentation'] | false | load Mask2Former fine-tuned on ADE20k panoptic segmentation processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-large-ade-panoptic") model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-large-ade-panoptic") url = "http://images.cocodataset.org/val2017/0000000397... | e947872531cf0f1ba510d4423a081be6 |
mit | ['generated_from_trainer'] | false | donut-base-Medical_Handwritten_Prescriptions_Information_Extraction This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset. | 2c997815505be9243e9f289a896ec469 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - 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 - num_epochs: 6 - mixed_precision_training: Native AMP | 6477b2fe12ac83ddb0da96418ebd6ce9 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout c3569453a408fd4ff4173d9c1d2062c88d1fc060 pip install -e . cd egs2/librispeech/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model pyf98/librispeech_conformer ``` <!-- Generated by scripts/utils/show_asr_result.sh --> | d33d3767d0af312b199db00f866871d1 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Mon Mar 7 12:26:10 EST 2022` - python version: `3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0]` - espnet version: `espnet 0.10.7a1` - pytorch version: `pytorch 1.10.1` - Git hash: `c3569453a408fd4ff4173d9c1d2062c88d1fc060` - Commit date: `Sun Mar 6 23:58:36 2022 -0500` | 61d82e413639f16e2e00491b76a4165c |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |beam60_ctc0.2/dev_clean|2703|54402|98.0|1.8|0.2|0.2|2.2|27.2| |beam60_ctc0.2/dev_other|2864|50948|95.1|4.4|0.5|0.5|5.4|43.3| |beam60_ctc0.2/test_clean|2620|52576|97.9|1.9|0.2|0.3|2.4|28.8| |beam60_ctc0.2/test_other|2939|52343|95.2... | 4d661c4332299a4dfb30d82d108718f3 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |beam60_ctc0.2/dev_clean|2703|288456|99.4|0.3|0.3|0.2|0.8|27.2| |beam60_ctc0.2/dev_other|2864|265951|98.1|1.1|0.8|0.6|2.5|43.3| |beam60_ctc0.2/test_clean|2620|281530|99.4|0.3|0.3|0.2|0.8|28.8| |beam60_ctc0.2/test_other|2939|272758|... | 52fe5289a24627bbedd0652034469ce3 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |beam60_ctc0.2/dev_clean|2703|68010|97.5|1.8|0.7|0.3|2.9|27.2| |beam60_ctc0.2/dev_other|2864|63110|94.1|4.4|1.6|0.9|6.8|43.3| |beam60_ctc0.2/test_clean|2620|65818|97.4|1.8|0.8|0.3|2.9|28.8| |beam60_ctc0.2/test_other|2939|65101|94.1... | 68416d5615f13400a0d3b261430beffe |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_conformer8.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_conformer8_raw_en_bpe5000_sp ngpu: 1 seed: 0 num_workers: 4 num_att_plot: 3 dist_backend: nccl dist_init_method: env... | 5f6559a5a223a271a4d1d96dcd79170f |
apache-2.0 | ['generated_from_trainer'] | false | t5-dialogue-summarization This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the samsum dataset. dataset: type: {summarization} name: {samsum} | f0bcbf0dc80d97d387149825875961fb |
apache-2.0 | ['speech'] | false | Wav2Vec2-Base [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downst... | 35906261c772356e571a41c362956aa3 |
apache-2.0 | ['generated_from_keras_callback'] | false | TestZee/t5-small-baseline_summary_zee_v1.0 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.3722 - Validation Loss: 2.1596 - Train Rouge1: 21.6350 - Train Rouge2: 8.9453 - Train Rougel: 17.... | 527592ac6ef225cc93ca65b49145e917 |
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