license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
cc-by-sa-4.0 | ['belarusian', 'masked-lm'] | false | How to Use ```py from transformers import AutoTokenizer,AutoModelForMaskedLM tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-small-belarusian") model=AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/roberta-small-belarusian") ``` | 771d6a370662319561a8a0ac446e0c27 |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | t5-base-TEDxJP-0front-1body-5rear This model is a fine-tuned version of [sonoisa/t5-base-japanese](https://huggingface.co/sonoisa/t5-base-japanese) on the te_dx_jp dataset. It achieves the following results on the evaluation set: - Loss: 0.4695 - Wer: 0.1761 - Mer: 0.1701 - Wil: 0.2587 - Wip: 0.7413 - Hits: 55488 - S... | d9f70dc18985e94cbda44eaa806d2036 |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Mer | Wil | Wip | Hits | Substitutions | Deletions | Insertions | Cer | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:------:|:-----:|:-------------:|:---------:|:----------:|:------:| | 0.6479 ... | c8833acb47009fb261148ea9c1f597c7 |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | en_nso_ukuxhumana_model This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-nso](https://huggingface.co/Helsinki-NLP/opus-mt-en-nso) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.8482 - Bleu (before training): 12.2324 - Bleu: 18.9287 | d2db7becca4934d1f9c6ee5f818ba797 |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - 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 | 3bc805f79764dcae063836853a92b7cb |
mit | [] | false | Jamie Hewlett Style on Stable Diffusion This is the `<hewlett>` 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 ca... | b43dbc698a5935b0da8256c97df41433 |
mit | ['generated_from_trainer'] | false | roberta-news-classifier This model is a fine-tuned version of [russellc/roberta-news-classifier](https://huggingface.co/russellc/roberta-news-classifier) on the custom(Kaggle) dataset. It achieves the following results on the evaluation set: - Loss: 0.1043 - Accuracy: 0.9786 - F1: 0.9786 - Precision: 0.9786 - Recall:... | 061ff7ebef2f536ed71c11482450d20f |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 5 | 3ad54ab179699438455f78dc1cb2f51e |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 0.1327 | 1.0 | 123 | 0.1043 | 0.9786 | 0.9786 | 0.9786 | 0.9786 | | 0.1103 | 2.0 |... | 1ce14bebe0ce7b197818b7584606fd80 |
mit | ['generated_from_trainer'] | false | Evaluation results ***** Running Prediction ***** Num examples = 980 Batch size = 64 precision recall f1-score support dunya 0.99 0.96 0.97 147 ekonomi 0.96 0.96 0.96 141 kultur 0.97 0.99 0.... | 7b2f18e31204abab92fa82b5964da4e1 |
apache-2.0 | ['generated_from_keras_callback'] | false | kasrahabib/all-MiniLM-L6-v2-finetunned-fnfreq-clf-promise This model is a fine-tuned version of [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1935 - Validation Loss... | 3811190979770c6dcc7a81c401d66a5c |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Epoch | |:----------:|:---------------:|:---------------:|:------------:|:--------:|:-----:| | 0.6719 | 0.6246 | 0.592 | 1.0 | 0.7437 | 0 | | 0.5484 | 0.3982 | 0.8961 ... | a6b9c04113c13e6c26d40ce0df6768a4 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased__subj__train-8-2 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: 0.3081 - Accuracy: 0.8755 | 2180efbcd763dfbb32d798cc8cd15916 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7146 | 1.0 | 3 | 0.6798 | 0.75 | | 0.6737 | 2.0 | 6 | 0.6847 | 0.75 | | 0.6519 | 3.0 | 9 | 0.6783 | 0.... | 9a1cbfe765da285e41fd8fb256a5a584 |
unknown | [] | false | Token: su_mdl Class: style Example: 1girl, grin, solo, female focus, smile, sparkling eyes, shiny hair, su_mdl style I get good results using these negative prompts: bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, si... | 3e3dabc538124ec279d51384ce8b335c |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small PL This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.3739 - Wer: 8.5898 | 3bc4f806bae10c87e8542d4601d0c82e |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.0474 | 1.1 | 1000 | 0.2561 | 9.4612 | | 0.0119 | 3.09 | 2000 | 0.2901 | 8.9726 | | 0.0045 | 5.08 | 3000 | 0.3151 | 8.8870 | |... | 3a3acfc6d18f084e1b5381bc50794767 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Evaluation results When tested on diffrent polish ASR datasets (splits: test), this model achieves the following results: | Dataset | WER | WER unnormalized | CER | MER | |:-----------------:|:-----:|:----------------:|:-----:|:-----:| |common_voice_11_0 | 8.85 | 21.75 | 2.63 | 8.76 | | c06c6c98f84661d39dea96f5093eab2b |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-cased-finetuned-squad This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1755 | 450e50952b230ab7a3627251a45f8831 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2357 | 1.0 | 5546 | 1.1985 | | 0.9525 | 2.0 | 11092 | 1.1285 | | 0.744 | 3.0 | 16638 | 1.1755 | | ac7b8ca835e8ee256b3cb03d62728873 |
mit | ['roberta-base', 'roberta-base-epoch_49'] | false | RoBERTa, Intermediate Checkpoint - Epoch 49 This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692), trained on Wikipedia and the Book Corpus only. We train this model for almost 100K steps, corresponding to 83 epochs. We provide the 84 checkpoints (including the randomly ... | ac01f7e02cfc7bc372ad12a2551d0bb0 |
apache-2.0 | ['generated_from_trainer'] | false | electra-base-discriminator-yelp-mlm This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the yelp_review_full yelp_review_full dataset. It achieves the following results on the evaluation set: - Loss: 1.5550 - Accuracy: 0.6783 | c54efa2fb8bcc50772d7ebf049026be3 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 | 7882e9d41dc5ad590aba1b3abf51fbbf |
creativeml-openrail-m | [] | false | model by no3 This your waifu-diffusion v1.4 model fine-tuned kate concept taught to waifu-diffusion v1.4 with Dreambooth. It can be used by modifying the `instance_prompt`: **sks kate girl** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/... | 77baf94bb69b44e8fde98640ae1c809e |
creativeml-openrail-m | [] | false | note If you want to to use in UI like [AUTOMATIC1111](https://github.com/AUTOMATIC1111/stable-diffusion-webui) or any UI that's uses .ckpt files just download one or more file from here for your convenience. [kateA4-wd-1.4-beta1.ckpt](https://huggingface.co/no3/kate-wd-1.4-beta1/resolve/main/kateA4-wd-1.4-beta1.ckpt)... | 07b4dbbca1f598e4dba015b5ac781b1a |
['mit'] | ['BERT', 'MNLI', 'NLI', 'transformer', 'pre-training'] | false | The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the [official Google BERT repository](https://github.com/google-research/bert). This is one of the smaller pre-trained BERT variants, together with [bert-small](https://huggingface.co/prajjwal1/bert-small) and ... | 308c977078ef8e83ebb18c4734524a7e |
apache-2.0 | ['generated_from_trainer'] | false | openai/whisper-base.en This model is a fine-tuned version of [openai/whisper-base.en](https://huggingface.co/openai/whisper-base.en) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6446 - Wer: 16.4580 | 8028de23e2032eb514d29e85613b89fc |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.3205 | 4.02 | 1000 | 0.4080 | 14.5116 | | 0.1568 | 8.04 | 2000 | 0.4672 | 15.3758 | | 0.035 | 13.01 | 3000 | 0.5696 | 15.973... | d8bc87b6867c1117b255a27f91e944c9 |
mit | [] | false | Pokemon Conquest Sprites on Stable Diffusion This is the `<poke-conquest>` 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) noteb... | 6cbb4174b8e8b143a1bb2a1b703b4a62 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Estonian Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Estonian 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. | ba5b0214300411de3c6e37cd6a74c34b |
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", "et", split="test[:2%]") processor = Wav2Vec2Processor.from_p... | 6faeb24b7781b202c31368d9d5301e17 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Estonian test data of Common Voice. ```python import torch import torchaudio import urllib.request import tarfile import pandas as pd from tqdm.auto import tqdm from datasets import load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor | 431f582c91db1a7580d0927ca55ca42c |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Download the raw data instead of using HF datasets to save disk space data_url = "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-6.1-2020-12-11/et.tar.gz" filestream = urllib.request.urlopen(data_url) data_file = tarfile.open(fileobj=filestream, mode="r|gz") data_file.e... | 02313ec149cbb7acbd0342d575416973 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | remove repeated spaces sent = " ".join(sent.split()) return sent targets = [] preds = [] for i, row in tqdm(cv_test.iterrows(), total=cv_test.shape[0]): row["sentence"] = clean_sentence(row["sentence"]) speech_array, sampling_rate = torchaudio.load(clips_path + row["path"]) resampler = torchaudio... | 62463be32e77ce9ac4980b9bfd396267 |
apache-2.0 | ['translation'] | false | opus-mt-crs-de * source languages: crs * target languages: de * OPUS readme: [crs-de](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/crs-de/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](http... | efd8e556df269b22c7932a95aeb40abc |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 1984 - distributed_type: IPU - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) ... | 58abb56ae6d254cbf612ed4cc4127ab8 |
mit | ['ja', 'japanese', 'gpt2', 'text-generation', 'lm', 'nlp'] | false | japanese-gpt2-xsmall  This repository provides an extra-small-sized Japanese GPT-2 model. The model was trained using code from Github repository [rinnakk/japanese-pretrained-models](https://github.com/rinnakk/japanese-pretrained-models) by [rinna Co., Ltd.](https://corp.rinna.co.jp/) | 57ff68a9b900ba5f81fdaa1855913ceb |
mit | ['ja', 'japanese', 'gpt2', 'text-generation', 'lm', 'nlp'] | false | Training The model was trained on [Japanese CC-100](http://data.statmt.org/cc-100/ja.txt.xz) and [Japanese Wikipedia](https://dumps.wikimedia.org/other/cirrussearch) to optimize a traditional language modelling objective on 8\\*V100 GPUs for around 4 days. It reaches around 28 perplexity on a chosen validation set fro... | d6cc0652a02ad8b5bdde867392c16358 |
mit | [] | false | kogatan_shiny on Stable Diffusion This is the `kogatan` 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 t... | 47a3d1f4b568f961244d2f1a52ab0a56 |
['mit'] | ['paraphrase-generation', 'multilingual', 'nlp', 'indicnlp'] | false | MultiIndicParaphraseGenerationSS
This repository contains the [IndicBARTSS](https://huggingface.co/ai4bharat/IndicBARTSS) checkpoint finetuned on the 11 languages of [IndicParaphrase](https://huggingface.co/datasets/ai4bharat/IndicParaphrase) dataset. For finetuning details,
see the [paper](https://arxiv.org/abs/2... | 1ab9092cfaccec7daf63f49ac89f8358 |
['mit'] | ['paraphrase-generation', 'multilingual', 'nlp', 'indicnlp'] | false | Using this model in `transformers`
```
from transformers import MBartForConditionalGeneration, AutoModelForSeq2SeqLM
from transformers import AlbertTokenizer, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("ai4bharat/MultiIndicParaphraseGenerationSS", do_lower_case=False, use_fast=False, keep_accents=Tr... | 0a902cee0f9f28012fd21e6e0e3ea0f0 |
['mit'] | ['paraphrase-generation', 'multilingual', 'nlp', 'indicnlp'] | false | Or use tokenizer = AlbertTokenizer.from_pretrained("ai4bharat/MultiIndicParaphraseGenerationSS", do_lower_case=False, use_fast=False, keep_accents=True)
model = AutoModelForSeq2SeqLM.from_pretrained("ai4bharat/MultiIndicParaphraseGenerationSS")
| 50837e473deeb042f1875a05c79aa8c7 |
['mit'] | ['paraphrase-generation', 'multilingual', 'nlp', 'indicnlp'] | false | Some initial mapping
bos_id = tokenizer._convert_token_to_id_with_added_voc("<s>")
eos_id = tokenizer._convert_token_to_id_with_added_voc("</s>")
pad_id = tokenizer._convert_token_to_id_with_added_voc("<pad>")
| fc58edd19cef1add8049b8e3dca7088f |
['mit'] | ['paraphrase-generation', 'multilingual', 'nlp', 'indicnlp'] | false | First tokenize the input. The format below is how IndicBART was trained so the input should be "Sentence </s> <2xx>" where xx is the language code. Similarly, the output should be "<2yy> Sentence </s>".
inp = tokenizer("दिल्ली यूनिवर्सिटी देश की प्रसिद्ध यूनिवर्सिटी में से एक है. </s> <2hi>", add_special_tokens=False... | 78d33912e23a7e6cc7fc611da99c7f89 |
['mit'] | ['paraphrase-generation', 'multilingual', 'nlp', 'indicnlp'] | false | For generation. Pardon the messiness. Note the decoder_start_token_id.
model_output=model.generate(inp, use_cache=True,no_repeat_ngram_size=3,encoder_no_repeat_ngram_size=3, num_beams=4, max_length=20, min_length=1, early_stopping=True, pad_token_id=pad_id, bos_token_id=bos_id, eos_token_id=eos_id, decoder_start_to... | 0766351c1454eef51397436b6b5b2fea |
['mit'] | ['paraphrase-generation', 'multilingual', 'nlp', 'indicnlp'] | false | Benchmarks
Scores on the `IndicParaphrase` test sets are as follows:
Language | BLEU / Self-BLEU / iBLEU
---------|----------------------------
as | 1.19 / 1.64 / 0.34
bn | 10.04 / 1.08 / 6.70
gu | 18.69 / 1.62 / 12.60
hi | 25.05 / 1.75 / 17.01
kn | 13.14 / 1.89 / 8.63
ml | 8.71 / 1.36 / 5.69
mr | 18.50 ... | 8e8c5c48547a244751a3427788f98a6c |
['mit'] | ['paraphrase-generation', 'multilingual', 'nlp', 'indicnlp'] | false | Citation
If you use this model, please cite the following paper:
```
@inproceedings{Kumar2022IndicNLGSM,
title={IndicNLG Suite: Multilingual Datasets for Diverse NLG Tasks in Indic Languages},
author={Aman Kumar and Himani Shrotriya and Prachi Sahu and Raj Dabre and Ratish Puduppully and Anoop Kunchukuttan ... | 74ee614293c8d16e98e2007b8a2aaf74 |
mit | [] | false | model by alxdfy This your the Stable Diffusion model fine-tuned the noggles_render_1k concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a render of sks** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.re... | 6ef6c6b366adb0c62c5236e0690f2a35 |
other | ['vision', 'image-segmentation', 'generated_from_trainer'] | false | trashbot This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the mraottth/all_locations_pooled dataset. It achieves the following results on the evaluation set: - Loss: 0.0191 - Mean Iou: 0.3997 - Mean Accuracy: 0.7995 - Overall Accuracy: 0.7995 - Accuracy Unlabeled: nan - A... | 6bca330c9b15e3336399c57db22989dd |
other | ['vision', 'image-segmentation', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 3 - eval_batch_size: 3 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 | 5e54f323d584435f858dbaa1e7af1586 |
other | ['vision', 'image-segmentation', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Trash | Iou Unlabeled | Iou Trash | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:--------------:|:---... | 2418b47dc43d171602fdd5e106a8426f |
other | [] | false | This model was trained for evaluating linguistic acceptability and grammaticality. The finetuning was carried out based off [the camembert-base model](https://huggingface.co/camembert/camembert-base). Label_1 means ACCEPTABLE - the sentence is perfectly understandable by native speakers and has no serious grammatic an... | 377b25e960d8e7a1d12540d30962be80 |
apache-2.0 | ['generated_from_trainer'] | false | emotion_trained_final This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.9349 - F1: 0.7469 | b102cdb9d317892bee4820d12cb67fa9 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.502523631581398e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 | dcc2bb18bec82d8ccddf706ca6f299b0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.9013 | 1.0 | 815 | 0.7822 | 0.6470 | | 0.5008 | 2.0 | 1630 | 0.7142 | 0.7419 | | 0.3684 | 3.0 | 2445 | 0.8621 | 0.7443 | |... | 7e6faa3d6c8940431b8b6b38c7e594f7 |
apache-2.0 | ['generated_from_trainer'] | false | violation-classification-bantai-vit-v80ep 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 image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.1974 - Accuracy: 0.9560 | 6d8f1ab2e3b8073c4ecd631d462ec2bf |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 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_sc... | f6a28aa4469b60431c74a1c79efd6b1b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.797 | 4.95 | 500 | 0.3926 | 0.8715 | | 0.3095 | 9.9 | 1000 | 0.2597 | 0.9107 | | 0.1726 | 14.85 | 1500 | 0.2157 | 0.... | 3d0fb39060027276d5c635011347ace2 |
apache-2.0 | ['translation'] | false | opus-mt-sv-th * source languages: sv * target languages: th * OPUS readme: [sv-th](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-th/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | df8a1112eba8a4b1386a377b3edc6058 |
apache-2.0 | ['translation'] | false | opus-mt-fi-ig * source languages: fi * target languages: ig * OPUS readme: [fi-ig](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-ig/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://... | 80a770eb9ad295a9638127b18fd1d6fc |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 | b1302db048a438d2254a4bb8da26b434 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-mnli This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.9722 | cf8b0b37ab53b8e0e82ae8ce71037cc0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.4196 | 0.4 | 500 | 3.9829 | | 4.3712 | 0.8 | 1000 | 4.0000 | | 4.3439 | 1.2 | 1500 | 3.9642 | | 4.2725 | 1.6 | 2000 | 3.9736 ... | 92853921ce18503ae6abc01bf1131c1c |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | FamilyPortrait Dreambooth model trained by yuanzheng 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-sta... | 8f46631f2419021f254e4eedf300b781 |
apache-2.0 | ['text2text-generation', 'reading-comprehension'] | false | Usage with pipeline ```python from transformers import pipeline context = "A kedd hajnalban elhunyt Somló Tamásról emlékezett meg zenésztársa, Presser Gábor. Somló Tamás nagyszerű egyénisége, énekhangja és éneklési stílusa egészen egyedülálló volt' – fogalmazott. '1968 lehetett, amikor először találkoztunk, gyakorla... | 4d5b5f034fc4efbe32ee7892a10631b9 |
apache-2.0 | ['text2text-generation', 'reading-comprehension'] | false | Citation If you use this model, please cite the following paper: ``` @article {yang-ligeti-rc, title = {Building machine reading comprehension model from scratch}, journal = {Annales Mathematicae et Informaticae}, year = {2023}, author = {Yang, Zijian Győző and Ligeti-Nagy, Noémi}, pages = {accetped} } ``` | 00e032252683bc57b1ab3929ba96dd71 |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-TINY-EL8 (Deep-Narrow version) T5-Efficient-TINY-EL8 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 and ... | 7b75bcc8a1e719783bf641e4e5d25b6a |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-tiny-el8** - is of model type **Tiny** with the following variations: - **el** is **8** It has **27.14** million parameters and thus requires *ca.* **108.55 MB** of memory in full precision (*fp32*) or **54.28 MB** of memory in half precision (*fp16... | 0a3e4086064a504bb2676ce9bb08f0fb |
mit | ['question generation'] | false | german-qg-t5-drink600 This model is fine-tuned in question generation in German. The expected answer must be highlighted with <hl> token. It is based on [german-qg-t5-quad](https://huggingface.co/dehio/german-qg-t5-quad) and further pre-trained on drink related questions. | 7ee603e3dc20ddeeb0ec4ca2608827e7 |
mit | ['question generation'] | false | Model description The model is based on [german-qg-t5-quad](https://huggingface.co/dehio/german-qg-t5-quad), which was pre-trained on [GermanQUAD](https://www.deepset.ai/germanquad). We further pre-trained it on questions annotated on drink receipts from [Mixology](https://mixology.eu/) ("drink600"). We have not yet... | eb54137e75f9cfd645a8e3d2f6d20778 |
mit | ['question generation'] | false | Evaluation It achieves a **BLEU-4 score of 29.80** on the drink600 test set (n=120) and **11.30** on the GermanQUAD test set. Thus, fine-tuning on drink600 did not affect performance on GermanQuAD. In comparison, *german-qg-t5-quad* achieves a BLEU-4 score of **10.76** on the drink600 test set. | cc000ad1b932af2e714f4f6705d7512c |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-credit_cards-9-16-5 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: 2.3376 - Accuracy: 0.3186 | 8c4fa414344fbe87281b3a8d40f07a9d |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sentence-transformers/distiluse-base-multilingual-cased-v2 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search. | 8db849b2f4fcf44b750ab9dcbf790b71 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sen... | 5740101b13b0df2812d624a7162ec4ce |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/distiluse-base-multilingual-cased-v2) | 6b5f69bc11ff1d7b9654743d6e6d4aa5 |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | vit-classify-manipulations-ft 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 vit-classify-manipulations-v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.2798 - Accuracy: 0.8694 | bc7be73ce95cf85fd17f34472cda8d62 |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - 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 - num_epochs: 100 - mixed_precision_training: Native AMP | 1d2131bdec94f1dd3e1b3cebf4ecb57f |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.2882 | 0.66 | 100 | 0.2873 | 0.8806 | | 0.3869 | 1.32 | 200 | 0.2798 | 0.8694 | | 0.2345 | 1.99 | 300 | 0.3074 ... | a03a2a13af557b65a26765416d7d5e14 |
apache-2.0 | ['generated_from_trainer'] | false | injury-report-distilgpt2-test This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.5243 | 0869477c695af695bb7c000ca05de49d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 380 | 3.6525 | | 3.9116 | 2.0 | 760 | 3.5507 | | 3.6015 | 3.0 | 1140 | 3.5243 | | bbde90973704d89685708a1b20237f24 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-vanilla-target-glue-qnli This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4624 - Accuracy: 0.7825 | 1ecef71d0423d6f198552ded481f0055 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6082 | 0.15 | 500 | 0.5375 | 0.7362 | | 0.5378 | 0.31 | 1000 | 0.5192 | 0.7459 | | 0.5161 | 0.46 | 1500 | 0.4967 | 0.... | ea30ebb5e820b6543e2f68861988b804 |
apache-2.0 | ['generated_from_trainer'] | false | base-mlm-imdb-target-tweet This model is a fine-tuned version of [muhtasham/base-mlm-imdb](https://huggingface.co/muhtasham/base-mlm-imdb) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 1.7516 - Accuracy: 0.7754 - F1: 0.7789 | cfedf3f6927f0f59e53978a008dfc829 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.3412 | 4.9 | 500 | 1.0525 | 0.7888 | 0.7891 | | 0.0365 | 9.8 | 1000 | 1.4590 | 0.7540 | 0.7572 | | 0.0127 |... | c54ce4063b56e562292676d2e29ab480 |
apache-2.0 | ['summarisation', 'generated_from_trainer'] | false | bert-small2bert-small-finetuned-cnn_daily_mail-summarization-finetuned-bbc-news This model is a fine-tuned version of [mrm8488/bert-small2bert-small-finetuned-cnn_daily_mail-summarization](https://huggingface.co/mrm8488/bert-small2bert-small-finetuned-cnn_daily_mail-summarization) on an unknown dataset. It achieves t... | 99a9af7f6c9618f0cd5877ca35aaaf5f |
apache-2.0 | ['summarisation', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:| | 0.8246 | 1.0 | 223 | 0.7050 | 55.7882 | 42.9793 | 38.4511 | 54.3125 | | 0.6414 | 2.0 ... | a2aea8dffa167fae3cb7ee1e01fe9c65 |
mit | [] | false | Uganda Labor Market Interview Text Classification This model is a fine-tuned [Roberta base model](https://huggingface.co/roberta-base) using text transcripts of interviews between Vocational Training Institutes (VTI) students and their successful alumni in Uganda on the subject of the labor market. | 57ce15765704829ff3bdf3dc416799d4 |
mit | [] | false | Model description There are 6 categories in total. In the training data, a sentence can get classified as more than one topic. I classify a sentence using the following criteria: info: information about the job market, working conditions, salaries, and what to expect at work. Also alumn's and student's current sit... | 170a86d2bd35fb44b28eb8dbaee52e57 |
mit | [] | false | How to use You can use this model directly with a pipeline for text classification: ```python >>> from transformers import pipeline >>> pipe = pipeline("text-classification", model= "wanghao2023/uganda-labor-market-interview-text-classification", tokenizer = "wanghao2023/uganda-labor-market-interview-text-classifica... | 3f115ca57a558f2ed58948abe4e8e8a8 |
mit | [] | false | Limitations and bias The classification of a sentence is heavily based on the context. For example, "be patient" can be classified as tip and/or strategy and/or motivation depending on which occasion the alumna asks the students to be patient. If the alumna asks the student to be patient during the interview, it's st... | d3f757914258007c5450b050923e5965 |
mit | [] | false | Evaluation results This model achieves the following results when tested on the validation dataset (multilabel, threshold = 0.3). There is a huge room for improvement but it performs much better than a dice roll at least: | F1 | Roc Auc | Accuracy | |:----:|:----:|:----:| | 0.655779 | 0.799979 | 0.552670 | | a7ba97ccbe3d437be9688ae54bfbacf3 |
openrail++ | ['stable-diffusion', 'text-to-image'] | false | Lazurite  - [lazurite-v1-050.safetensors](https://huggingface.co/p1atdev/ore-o/blob/main/lazurite-v1-050.safetensors) (0.5 weight) - [lazurite-v1-050.ckpt](https://huggingface.co/p1atdev/ore-o/blob/main/lazurite-v1-050.ckpt... | 8190f4f956adf48c501c2dc0498bf5e4 |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | t5-base-TEDxJP-0front-1body-10rear This model is a fine-tuned version of [sonoisa/t5-base-japanese](https://huggingface.co/sonoisa/t5-base-japanese) on the te_dx_jp dataset. It achieves the following results on the evaluation set: - Loss: 0.4646 - Wer: 0.1756 - Mer: 0.1698 - Wil: 0.2581 - Wip: 0.7419 - Hits: 55450 - ... | 59ba502a58e1ecfb5527fc311579715b |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Mer | Wil | Wip | Hits | Substitutions | Deletions | Insertions | Cer | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:------:|:-----:|:-------------:|:---------:|:----------:|:------:| | 0.6456 ... | 0327774eb47275dc244ef02a9e6840e7 |
apache-2.0 | ['generated_from_keras_callback'] | false | ssavla2/bert-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: 0.0243 - Validation Loss: 0.0603 - Epoch: 2 | a206bf091ebe22879fa5ab6172c58a57 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.1199 | 0.0570 | 0 | | 0.0399 | 0.0586 | 1 | | 0.0243 | 0.0603 | 2 | | 3a65a39538daf0f03a03d5878b88caba |
mit | ['generated_from_keras_callback'] | false | europython-imdb This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1279 - Train Accuracy: 0.9548 - Validation Loss: 0.1595 - Validation Accuracy: 0.9418 - Ep... | 97de423137eda2466b65c10fa0afd61d |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.2073 | 0.9203 | 0.1486 | 0.9435 | 0 | | 0.1279 | 0.9548 | 0.1595 | 0.9418 ... | fd0bdfbeadc73b2f0edd510c403e909f |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-auto_and_commute-1-16-5 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: 2.2614 - Accuracy: 0.4289 | 243d8e51c15947b95f96e64ba6a8fdc3 |
apache-2.0 | ['translation'] | false | opus-mt-fi-sg * source languages: fi * target languages: sg * OPUS readme: [fi-sg](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-sg/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-24.zip](https://... | 358037d03ffe741eb3f932d8722b4b4b |
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