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 | ['summarization', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-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: 3 - mixed_precision_training: Native AMP | 21e470e6572d2b19ce0e7e30e1fbf570 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | 1.223400 | 1 | 20437| 1.153162 | 1.0624 | 0.1351 | 1.0668 | 1.0740 | | 1.202900 | 2 | 40874... | 1d069a5cde96af2851da616d1492a1f8 |
apache-2.0 | ['image-classification', 'pytorch'] | false | Usage instructions ```python from PIL import Image from torchvision.transforms import Compose, ConvertImageDtype, Normalize, PILToTensor, Resize from torchvision.transforms.functional import InterpolationMode from holocron.models import model_from_hf_hub model = model_from_hf_hub("frgfm/darknet53").eval() img = Ima... | 1bf0d5021d866718e3969892f4a1333d |
apache-2.0 | ['image-classification', 'pytorch'] | false | Citation Original paper ```bibtex @article{DBLP:journals/corr/abs-1804-02767, author = {Joseph Redmon and Ali Farhadi}, title = {YOLOv3: An Incremental Improvement}, journal = {CoRR}, volume = {abs/1804.02767}, year = {2018}, url = {http://arxiv.org/abs/1804.02767}, ... | d4c120f7df53f4a1381e62e8493f6626 |
wtfpl | [] | false |  ![](https://huggingface.co/SDAddictsAnon/holosomnialandscape/... | 8267f7ef8048533f07aedb79cdc0ffe8 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'image-to-image', 'pixel-art'] | false | MODEL BY InternalMegaT How to use: **_pixlat_** "your prompt" Training on V1 - 3000 steps, 1024x1024, v1-5 Base, 210 images Uploaded on 11/27/22 Training on V2 - 3500 steps, 1024x1024, v1-5 Base, 10 images Uploaded on 12/1/22 Training on V3 - 3,835 steps, 1024x1024, v1-5 Base, 74 images Uploaded on 1/16/23 Exa... | 5219b6500f5acf784ed240a6d582c156 |
mit | [] | false | Training This was trained using [aitextgen](https://github.com/minimaxir/aitextgen), created by [Max Woolf](https://github.com/minimaxir), using the example notebook found [here](https://colab.research.google.com/drive/15qBZx5y9rdaQSyWpsreMDnTiZ5IlN0zD?usp=sharing). Using GPT-2's 124M model as the base, it was traine... | c9ad8eaa962f58f7c95c45f13f234940 |
mit | [] | false | Use This was created as a fun little project for the discord server and as such, should only be used for fun and not to harm people. This model must also follow the ethics guide of the tool that created it https://docs.aitextgen.io/ethics/ | 2dae88364050051b6ef099bca76c8f52 |
mit | [] | false | Description This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022) We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-... | 41d02ac4b769f8921934abe22ab8a872 |
mit | [] | false | Usage ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline model_name = 'm-vote-strict-epoch-2' tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased") full_model_path = f'MartinoMensio/racism-models-{model_name}' model = AutoModelForSequenceCl... | b2d513df213279d43a0c37d25ced52d2 |
apache-2.0 | ['multiberts', 'multiberts-seed_4', 'multiberts-seed_4-step_300k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 4, Step 300k 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 ... | 93dd201a8d7e0d17c67ac1068f9f38ab |
apache-2.0 | ['multiberts', 'multiberts-seed_4', 'multiberts-seed_4-step_300k'] | 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_4-step_300k') model = TFBertModel.from_pretrained("google/multibe... | ead105452983942059c0326977d23207 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 35.0 | a7a65d956319720368823ae45a2f5990 |
apache-2.0 | ['generated_from_trainer'] | false | mt5-base-finetuned-xsum-RAW_data_prep_2021_12_26___t22027_162754.csv__g_mt5_base_L5 This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: nan - Rouge1: 0.7722 - Rouge2: 0.0701 - Rougel: 0.... | 46b0e5b14818ce7ca8639681ee75de93 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:------:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | 0.0 | 1.0 | 131773 | nan | 0.7722 | 0.0701 | 0.772 | 0.7717 | 6.329... | 34c38e4a144f0918e384d13609d9e986 |
apache-2.0 | ['translation'] | false | opus-mt-lv-en * source languages: lv * target languages: en * OPUS readme: [lv-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/lv-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2019-12-18.zip](https://... | 6a5df6cf0f7e466b9313ee71b406b267 |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newsdev2017-enlv.lv.en | 29.9 | 0.587 | | newstest2017-enlv.lv.en | 22.1 | 0.526 | | Tatoeba.lv.en | 53.3 | 0.707 | | 361a13365cf326db4e84dc61e5d94f10 |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-BASE-FF6000 (Deep-Narrow version) T5-Efficient-BASE-FF6000 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* checkpoin... | f070838ebe1bc7740f116691947b78ae |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-base-ff6000** - is of model type **Base** with the following variations: - **ff** is **6000** It has **336.18** million parameters and thus requires *ca.* **1344.71 MB** of memory in full precision (*fp32*) or **672.36 MB** of memory in half precisi... | 1215151ed77e2e664723756663d8f589 |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | legal-bert-qa This model is a fine-tuned version of [nlpaueb/legal-bert-base-uncased](https://huggingface.co/nlpaueb/legal-bert-base-uncased) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 5.2974 | cdf24b77030d2e6000d92ab8d4b3f20f |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.5514 | 1.0 | 625 | 3.2106 | | 1.1372 | 2.0 | 1250 | 4.5593 | | 0.5365 | 3.0 | 1875 | 5.2974 | | ffdb68931e85eda9dcd84ce317a0d085 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | opus-mt-tc-big-tr-en Neural machine translation model for translating from Turkish (tr) to English (en). 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 model... | 3c8d81dcb8bbb73d73d8e6f06d8fb37d |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Model info * Release: 2022-03-17 * source language(s): tur * target language(s): eng * model: transformer-big * data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807+bt_transformer-big_2022-03-17.zip](htt... | fbfcab36b34795278b85a43f8d9871e2 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ "Allahsızlığı Yayma Kürsüsü başkanıydı.", "Tom'a ne olduğunu öğrenin." ] model_name = "pytorch-models/opus-mt-tc-big-tr-en" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTMode... | c7f5761efec81d2ef46716a951b0c36b |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Find out what happened to Tom. ``` 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-tr-en") print(pipe("Allahsızlığı Yayma Kürsüsü başkanıydı.")) | 9db3f6559d3ed28d137b5f7085936f86 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Benchmarks * test set translations: [opusTCv20210807+bt_transformer-big_2022-03-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/tur-eng/opusTCv20210807+bt_transformer-big_2022-03-17.test.txt) * test set scores: [opusTCv20210807+bt_transformer-big_2022-03-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-... | 8bf27616a5f6381757029c5181eef95a |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | words | |----------|---------|-------|-------|-------|--------| | tur-eng | tatoeba-test-v2021-08-07 | 0.71895 | 57.6 | 13907 | 109231 | | tur-eng | flores101-devtest | 0.64152 | 37.6 | 1012 | 24721 | | tur-eng | newsdev2016 | 0.58658 | 32.1 | 1001 | 21988 | | tur-eng | newstest2016 | 0.56960 | 29.3 | 3000 | 66175 | | ... | fd802bd151a8a0899306ae2185af7e59 |
mit | ['generated_from_trainer'] | false | deberta_finetune 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: - eval_loss: 0.3943 - eval_accuracy: 0.8673 - eval_runtime: 164.2323 - eval_samples_per_second: 29.178 - ... | c9523c2f8c41019282a7cc3eb22f9105 |
mit | ['generated_from_trainer'] | false | Model Recycling [Evaluation on 36 datasets](https://ibm.github.io/model-recycling/model_gain_chart?avg=0.47&mnli_lp=nan&20_newsgroup=-0.22&ag_news=-0.08&amazon_reviews_multi=0.62&anli=-0.22&boolq=1.36&cb=-1.79&cola=0.01&copa=9.60&dbpedia=0.23&esnli=-0.35&financial_phrasebank=4.11&imdb=-0.02&isear=0.37&mnli=-0.15&mrpc... | 2f13ee520e0ae31cfa3751a55f5bef96 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-marc-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.9177 - Mae: 0.4756 | 62eeb4bbcfa1908a0d8460871038aeab |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.136 | 1.0 | 235 | 0.9515 | 0.4756 | | 0.9724 | 2.0 | 470 | 0.9177 | 0.4756 | | a4c8d40de572dc8c9617a1d5c895a4a4 |
apache-2.0 | ['italian', 'sequence-to-sequence', 'newspaper', 'ilgiornale', 'repubblica', 'style-transfer'] | false | IT5 Large for News Headline Style Transfer (Repubblica to Il Giornale) 🗞️➡️🗞️ 🇮🇹 This repository contains the checkpoint for the [IT5 Large](https://huggingface.co/gsarti/it5-large) model fine-tuned on news headline style transfer in the Repubblica to Il Giornale direction on the Italian CHANGE-IT dataset as part... | 0ac32b3d3e9ebe189e7ddeac7a8ace4e |
apache-2.0 | ['italian', 'sequence-to-sequence', 'newspaper', 'ilgiornale', 'repubblica', 'style-transfer'] | false | Using the model The model is trained to generate an headline in the style of Il Giornale from the full body of an article written in the style of Repubblica. Model checkpoints are available for usage in Tensorflow, Pytorch and JAX. They can be used directly with pipelines as: ```python from transformers import pipel... | 23393bfffe25a188f0f250906f0f1287 |
apache-2.0 | ['onnx', 'exbert'] | false | ONNX export of bert-base-cased Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/abs/1810.04805) and first released in [this repository](https://github.com/google-research/bert). This model is case-sensitive: it makes a differenc... | d9cae7d19ea62afe7aba22b9bef2e44c |
apache-2.0 | ['text2text-generation', 'byt5', 'pytorch', 'qa'] | false | ByT5 small finetuned for Question Answering (QA) on SQUaD v1.1 Portuguese  Check our other QA models in Portuguese finetuned on SQUAD v1.1: - [Portuguese B... | 927275eb34685fbf85c208d968420752 |
apache-2.0 | ['text2text-generation', 'byt5', 'pytorch', 'qa'] | false | Introduction The model was trained on the dataset SQUAD v1.1 in portuguese from the [Deep Learning Brasil group](http://www.deeplearningbrasil.com.br/) on Google Colab from the language model [ByT5 small](https://huggingface.co/google/byt5-small) of Google. | 1400230df184596d2f99fce3d88f82bd |
apache-2.0 | ['text2text-generation', 'byt5', 'pytorch', 'qa'] | false | About ByT5 ByT5 is a tokenizer-free version of [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) and generally follows the architecture of [MT5](https://huggingface.co/google/mt5-small). ByT5 was only pre-trained on [mC4](https://www.tensorflow.org/datasets/catalog/c4 | 23e09b5122bc3a32ba34f98a56036cfa |
apache-2.0 | ['text2text-generation', 'byt5', 'pytorch', 'qa'] | false | c4multilingual) excluding any supervised training with an average span-mask of 20 UTF-8 characters. Therefore, this model has to be fine-tuned before it is useable on a downstream task. ByT5 works especially well on noisy text data,*e.g.*, `google/byt5-small` significantly outperforms [mt5-small](https://huggingface.c... | b70c5cd9667a055188901987eba0c8b1 |
apache-2.0 | ['text2text-generation', 'byt5', 'pytorch', 'qa'] | false | How to use the model... with Pipeline ```python import transformers from transformers import pipeline model_name = 'pierreguillou/byt5-small-qa-squad-v1.1-portuguese' nlp = pipeline("text2text-generation", model=model_name) | 9b4fda0e6087de8f9359de6dac75d3a3 |
apache-2.0 | ['text2text-generation', 'byt5', 'pytorch', 'qa'] | false | source: https://pt.wikipedia.org/wiki/Pandemia_de_COVID-19 input_text = r""" question: "Quando começou a pandemia de Covid-19 no mundo?" context: "A pandemia de COVID-19, também conhecida como pandemia de coronavírus, é uma pandemia em curso de COVID-19, uma doença respiratória aguda causada pelo coronavírus da síndr... | 8e65a648090fdf83552a64c131909cff |
apache-2.0 | ['text2text-generation', 'byt5', 'pytorch', 'qa'] | false | question: "Quando começou a pandemia de Covid-19 no mundo?" context: "A pandemia de COVID-19, também conhecida como pandemia de coronavírus, é uma pandemia em curso de COVID-19, uma doença respiratória aguda causada pelo coronavírus da síndrome respiratória aguda grave 2 (SARS-CoV-2). A doença foi identificada pela pr... | a6ef04c7323e43b6a2a5a0ae11708238 |
apache-2.0 | ['text2text-generation', 'byt5', 'pytorch', 'qa'] | false | How to use the model... with the Auto classes ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM model_name = 'pierreguillou/byt5-small-qa-squad-v1.1-portuguese' tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSeq2SeqLM.from_pretrained(model_name) | 781831ec705984f464093a5a2064c496 |
apache-2.0 | ['text2text-generation', 'byt5', 'pytorch', 'qa'] | false | question: "Quando começou a pandemia de Covid-19 no mundo?" context: "A pandemia de COVID-19, também conhecida como pandemia de coronavírus, é uma pandemia em curso de COVID-19, uma doença respiratória aguda causada pelo coronavírus da síndrome respiratória aguda grave 2 (SARS-CoV-2). A doença foi identificada pela pr... | 973e581117b8ed3a3cc11277c95a349e |
apache-2.0 | ['text2text-generation', 'byt5', 'pytorch', 'qa'] | false | Author Portuguese ByT5 small QA (Question Answering), finetuned on SQUAD v1.1 was trained and evaluated by [Pierre GUILLOU](https://www.linkedin.com/in/pierreguillou/) thanks to the Open Source code, platforms and advices of many organizations. In particular: [Google AI](https://huggingface.co/google), [Hugging Face]... | feb34117722939f34ba4a09a068ab9be |
apache-2.0 | ['text2text-generation', 'byt5', 'pytorch', 'qa'] | false | Citation If you use our work, please cite: ```bibtex @inproceedings{pierreguillou2021byt5smallsquadv11portuguese, title={Portuguese ByT5 small QA (Question Answering), finetuned on SQUAD v1.1}, author={Pierre Guillou}, year={2021} } ``` | da03c25ed924c204962ddadd19b28792 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'animal'] | false | DreamBooth model for the shbrcky concept trained by peterj on the peterj/shibaricky dataset. This is a Stable Diffusion model fine-tuned on the shbrcky concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of shbrcky dog** This model was created as part of the DreamBooth Hackathon 🔥.... | 1087a1ed3b9a727e591ad28090746163 |
apache-2.0 | ['finnish', 'gpt2'] | false | GPT-2 for Finnish Pretrained GPT-2 model on Finnish language using a causal language modeling (CLM) objective. GPT-2 was introduced in [this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) and first released at [this page](https://openai.com/b... | 7f79d356b8b6a3b58cf34ebd9dc9ec3e |
apache-2.0 | ['finnish', 'gpt2'] | false | Model description Finnish GPT-2 is a transformers model pretrained on a very large corpus of Finnish data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to ge... | 542c42150a6c89f8ee5153737aa32b0b |
apache-2.0 | ['finnish', 'gpt2'] | false | How to use You can use this model directly with a pipeline for text generation: ```python >>> from transformers import pipeline >>> generator = pipeline('text-generation', model='Finnish-NLP/gpt2-finnish') >>> generator("Tekstiä tuottava tekoäly on", max_length=30, num_return_sequences=5) [{'generated_text': 'Tekst... | 340148b86402b398a9d5a908d360c084 |
apache-2.0 | ['finnish', 'gpt2'] | false | Limitations and bias The training data used for this model contains a lot of unfiltered content from the internet, which is far from neutral. Therefore, the model can have biased predictions. This bias will also affect all fine-tuned versions of this model. As with all language models, it is hard to predict in advan... | cd4d8902cc693a490445a890732d17ed |
apache-2.0 | ['finnish', 'gpt2'] | false | Training data This Finnish GPT-2 model was pretrained on the combination of six datasets: - [mc4_fi_cleaned](https://huggingface.co/datasets/Finnish-NLP/mc4_fi_cleaned), the dataset mC4 is a multilingual colossal, cleaned version of Common Crawl's web crawl corpus. We used the Finnish subset of the mC4 dataset and fu... | bee56353dc42fe182225ba16add77463 |
apache-2.0 | ['finnish', 'gpt2'] | false | Preprocessing The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a vocabulary size of 50,257. The inputs are sequences of 512 consecutive tokens. | 6050f02f71857d9768241f52cd362ac8 |
apache-2.0 | ['finnish', 'gpt2'] | false | Pretraining The model was trained on TPUv3-8 VM, sponsored by the [Google TPU Research Cloud](https://sites.research.google/trc/about/), for 300k steps (a bit over 2 epochs, 256 batch size). The optimizer used was a second-order optimization method called [Distributed Shampoo](https://github.com/google-research/googl... | f8e02f94abb5d2a12af0cacbd21952a6 |
apache-2.0 | ['finnish', 'gpt2'] | false | Evaluation results Evaluation was done using the *validation* split of the [mc4_fi_cleaned](https://huggingface.co/datasets/Finnish-NLP/mc4_fi_cleaned) dataset with [Perplexity](https://huggingface.co/course/chapter7/3 | e2f94032711b5566da7f16ee107783d5 |
apache-2.0 | ['finnish', 'gpt2'] | false | perplexity-for-language-models) (smaller score the better) as the evaluation metric. As seen from the table below, this model (the first row of the table) loses to our bigger model variants. | | Perplexity | |------------------------------------------|------------| |Finnish-NLP... | ee5c2d794605a7af8b07833d93e9bba6 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-with-spanish-tweets-clf-cleaned-ds This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the dataset dataset. It achieves the following results on the evaluation set: - Loss: 1.1229 - Accuracy: 0.5556 - F1: 0.5578 - Precisi... | 4cd0eb4a2e08f1cffefe069e4d05c5d0 |
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: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4.0 | ed1543dac6ba0a857e8142319538fb3a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 1.0683 | 1.0 | 543 | 1.0019 | 0.4997 | 0.4041 | 0.4724 | 0.4488 | | 0.9372 | 2.0 |... | 7abaf43061fed4fbf44a51f3a2dbbc6a |
apache-2.0 | ['generated_from_trainer'] | false | distilled-mt5-small-0.6-5 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset. It achieves the following results on the evaluation set: - Loss: 5.9420 - Bleu: 0.0056 - Gen Len: 252.5743 | 9dfe3b3e9ff44ff2c4e77f3b3fc2db69 |
apache-2.0 | ['generated_from_trainer'] | false | edos-2023-baseline-bert-base-multilingual-uncased-label_vector This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6113 - F1: 0.2785 | 2daa679cdc4d58e68fea4d9eaab1eca2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.0125 | 1.18 | 100 | 1.8290 | 0.1089 | | 1.6698 | 2.35 | 200 | 1.6458 | 0.2223 | | 1.4812 | 3.53 | 300 | 1.6035 | 0.2463 | |... | 4dd8c1c31536ab0674a67251218eddb2 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | mpid-hassanblend-better-train Dreambooth model trained by tftgregrge 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/The... | e8a9a10d45ff3eda2a8430aaabbd2880 |
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: 0.0634 - Precision: 0.9327 - Recall: 0.9500 - F1: 0.9413 - Accuracy: 0.9861 | 67563c7b89c02456986f451bdc7c1b85 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0876 | 1.0 | 1756 | 0.0692 | 0.9127 | 0.9355 | 0.9240 | 0.9819 | | 0.0316 | 2.0 |... | e671935d43ec8985958fadcd0dfd192e |
apache-2.0 | ['vision', 'depth-estimation'] | false | DPT (large-sized model) Dense Prediction Transformer (DPT) model trained on 1.4 million images for monocular depth estimation. It was introduced in the paper [Vision Transformers for Dense Prediction](https://arxiv.org/abs/2103.13413) by Ranftl et al. and first released in [this repository](https://github.com/isl-org... | e282d2e34627167f5a28c4a71d3e0cb1 |
apache-2.0 | ['vision', 'depth-estimation'] | false | Model description DPT uses the Vision Transformer (ViT) as backbone and adds a neck + head on top for monocular depth estimation.  | 86032718f39ced26d1b3970ce0eacf2c |
apache-2.0 | ['vision', 'depth-estimation'] | false | Intended uses & limitations You can use the raw model for zero-shot monocular depth estimation. See the [model hub](https://huggingface.co/models?search=dpt) to look for fine-tuned versions on a task that interests you. | 111adeff9d6e82e1c6a0ea227507ddc5 |
apache-2.0 | ['vision', 'depth-estimation'] | false | How to use Here is how to use this model for zero-shot depth estimation on an image: ```python from transformers import DPTFeatureExtractor, DPTForDepthEstimation import torch import numpy as np from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(r... | 01f1d4188973cffb38a79ee12ba65c16 |
apache-2.0 | ['vision', 'depth-estimation'] | false | visualize the prediction output = prediction.squeeze().cpu().numpy() formatted = (output * 255 / np.max(output)).astype("uint8") depth = Image.fromarray(formatted) ``` For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/dpt). | f5aea4320ab8db97e3c43cb2cc9fabda |
apache-2.0 | ['generated_from_trainer'] | false | finetune-data-skills 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: 2.1058 | 643191a157b0f8bd5272e7fdb586a85a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.7239 | 1.0 | 3926 | 2.2459 | | 2.3113 | 2.0 | 7852 | 2.1255 | | 2.197 | 3.0 | 11778 | 2.0966 | | eb4ccc2316984f2544057b316da45660 |
apache-2.0 | ['generated_from_keras_callback'] | false | relevance-model This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta-wwm-ext) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3134 - Train Binary Accuracy: 0.8773 - Validation Loss: 0.3633 - Validation Binary Acc... | c40c88185e073a2c2307cca967bb5fe0 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Binary Accuracy | Validation Loss | Validation Binary Accuracy | Epoch | |:----------:|:---------------------:|:---------------:|:--------------------------:|:-----:| | 0.3980 | 0.8289 | 0.3739 | 0.8541 | 0 | | 0.3446 | 0.86... | effe88e0502a4d410092db6bdd440770 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 1 | 56211d4022b707cb39376b2053d19d49 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-marc-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.9569 - Mae: 0.5244 | 9450c3c1e2f1112f96a5bbbc0911b670 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.1386 | 1.0 | 235 | 1.0403 | 0.5122 | | 0.9591 | 2.0 | 470 | 0.9569 | 0.5244 | | 1d19e18970316a45d066ef6afde31d50 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.5393 | e28e4601f9db9ac79f94a7b43de9e58b |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2.5e-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: 3 | 1c0a74c3c94005d4af7c90153bc24e24 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.7511 | 1.0 | 557 | 1.7615 | | 1.4133 | 2.0 | 1114 | 1.5263 | | 1.0456 | 3.0 | 1671 | 1.5393 | | df9819b2b6d235c345ea364c1a5b78c0 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_logit_kd_qqp_256 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE QQP dataset. It achieves the following results on the evaluation set: - Loss: 0.6619 - Accuracy: 0.7949 - F1: 0.7224 - Combined Score: 0.7586 | 8d79c1bcf13720918278669a06cf36dc |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:| | 0.9454 | 1.0 | 2843 | 0.8257 | 0.7556 | 0.6563 | 0.7059 | | 0.8165 | 2.0 | 5686 | ... | 095f77c7fb0598a066d5380345b9563d |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-SMALL-DM2000 (Deep-Narrow version) T5-Efficient-SMALL-DM2000 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* checkpo... | bab0c165e78bcc62eaf6edc7452d9ebf |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-small-dm2000** - is of model type **Small** with the following variations: - **dm** is **2000** It has **242.04** million parameters and thus requires *ca.* **968.16 MB** of memory in full precision (*fp32*) or **484.08 MB** of memory in half precis... | 9f7ff9b1e9fe0e56a60d636f50602ca4 |
apache-2.0 | ['generated_from_trainer'] | false | 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: 2.4768 - Rouge1: 28.5758 - Rouge2: 7.9219 - Rougel: 22.5161 - Rougelsum: 22.5215 - Gen Len: 18.8288 | 0a3bb403844a67b0f89f69cd2ae9ed6a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 2.7504 | 1.0 | 6377 | 2.5012 | 28.218 | 7.6611 | 22.1733 | 22.1702 | 18... | 8e52e28731a28ea36c0e796d81fb6986 |
mit | ['conversational'] | false | This generation model is based on [sberbank-ai/rugpt3small_based_on_gpt2](https://huggingface.co/sberbank-ai/rugpt3small_based_on_gpt2). It's trained on large corpus of dialog data and can be used for buildning generative conversational agents The model was trained with context size 3 On a private validation set we ... | aaefc6c0c1084a0013d7f2bde522e7b8 |
apache-2.0 | ['vision', 'image-classification'] | false | Convolutional Vision Transformer (CvT) CvT-21 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [CvT: Introducing Convolutions to Vision Transformers](https://arxiv.org/abs/2103.15808) by Wu et al. and first released in [this repository](https://github.com/microsoft/CvT). Discla... | df3327ebc3247c8cc71058c85ff39844 |
apache-2.0 | ['vision', 'image-classification'] | false | Usage Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import AutoFeatureExtractor, CvtForImageClassification from PIL import Image import requests url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Im... | 29aeacbf3eed5f9f14523980be3777d1 |
mit | ['generated_from_trainer'] | false | deberta-v3-large__sst2__train-8-7 This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7037 - Accuracy: 0.5008 | 9f13e5f0cbfea97bf746d937c1153f45 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6864 | 1.0 | 3 | 0.7800 | 0.25 | | 0.6483 | 2.0 | 6 | 0.8067 | 0.25 | | 0.6028 | 3.0 | 9 | 0.8500 | 0.... | fb8c858ce7ff17202955a388075e4900 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.2928 - Accuracy: 0.906 - F1: 0.9073 | 3a7e8e94f9a06ff7ace67558817a3669 |
apache-2.0 | ['generated_from_keras_callback'] | false | distilroberta-base-uncased-squad This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.4145 - Validation Loss: 1.1534 - Epoch: 0 | 5911dac049289823598e151d228cec61 |
mit | [] | false | Stable Diffusion Artist Collaboration → Model 1 This is the `<model-1>` concept taught to stable diffusion via textual inversion training. Anyone is free to load this concept into the [stable conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_infer... | 9ee4a4d0c28d783bf0c20eb545bdffe0 |
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: 0.0615 - Precision: 0.9357 - Recall: 0.9509 - F1: 0.9432 - Accuracy: 0.9864 | be0c0de82c984fbcc3122dddb8977817 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0875 | 1.0 | 1756 | 0.0718 | 0.9252 | 0.9369 | 0.9310 | 0.9819 | | 0.0349 | 2.0 |... | 433756ee76c1d6128d110f4d7c9fa781 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 5.8501 | 1.0 | 1268 | 5.5777 | | 5.3422 | 2.0 | 2536 | 5.3277 | | 5.2058 | 3.0 | 3804 | 5.2545 | | b1c6a0d29a7c4f24d15a0aa066c0a64f |
apache-2.0 | ['pytorch', 'text-generation', 'causal-lm', 'rwkv'] | false | Model Description RWKV-4 169M is a L12-D768 causal language model trained on the Pile. See https://github.com/BlinkDL/RWKV-LM for details. Use https://github.com/BlinkDL/ChatRWKV to run it. ctx_len = 1024 n_layer = 12 n_embd = 768 Final checkpoint: RWKV-4-Pile-169M-20220807-8023.pth : Trained on the Pile for 332B ... | db7adfcc3043819a1e60cabfad076dd2 |
apache-2.0 | ['pytorch', 'text-generation', 'causal-lm', 'rwkv'] | false | Warning: 4 / 4a / 4b models ARE NOT compatible!!! Use RWKV-4 unless you know what you are doing. With tiny attention (--tiny_att_dim 256 --tiny_att_layer 9): RWKV-4a-Pile-170M-20221209-7955.pth * Pile loss 2.4702 * LAMBADA ppl 21.42, acc 38.23% * PIQA acc 63.76% * SC2016 acc 59.06% * Hellaswag acc_norm 32.40% RWKV-4... | faf5e4c020d2a55785c73b62e181763f |
mit | ['generated_from_trainer'] | false | finetuning-sentiment-model-10-samples_withGPU This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3893 - Accuracy: 0.8744 - F1: 0.8684 - Precision: 0.9126 - Recall: 0.8283 | fab26778ee78bd4d1f62257e00143bfb |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:| | 0.3631 | 1.0 | 7088 | 0.3622 | 0.8638 | 0.8519 | 0.9334 | 0.7835 | | 0.35 | 2.0 ... | 4faf707ce1ae42bc981840517163d51a |
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