license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
mit | [] | false | cgdonny1 on Stable Diffusion This is the `<donny1>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train... | c67109600b78b97c5953e152ad7fade7 |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-SMALL-FF3000 (Deep-Narrow version) T5-Efficient-SMALL-FF3000 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... | c43e52723c86117266b9cc92ecf5049f |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-small-ff3000** - is of model type **Small** with the following variations: - **ff** is **3000** It has **73.1** million parameters and thus requires *ca.* **292.42 MB** of memory in full precision (*fp32*) or **146.21 MB** of memory in half precisio... | 1237eca9f3e3e3d01480f39dcf0f8c35 |
mit | ['generated_from_trainer'] | false | recipe-roberta-upper-tIs This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7904 | e309995afaba02abf09ddab1632ff17f |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 256 - eval_batch_size: 256 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP | d823105bde40ed97402d19619a2e644e |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2671 | 1.0 | 1281 | 1.0554 | | 1.0995 | 2.0 | 2562 | 0.9832 | | 1.0339 | 3.0 | 3843 | 0.9389 | | 0.9925 | 4.0 | 5124 | 0.9095 ... | 5ebfa9758df2c388921cc163f75aee33 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-0'] | false | MultiBERTs Seed 0 Checkpoint 300k (uncased) Seed 0 intermediate checkpoint 300k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in [this repository](https://github.com/goo... | 7647dd23d326e0a72a64acfb4cc25c29 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-0'] | false | How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('multiberts-seed-0-300k') model = BertModel.from_pretrained("multiberts-seed-0-300k") text = "Replace me by any text you'd like.... | 0d5a40eef3a0689d6ae99e2877dc7123 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 6.5562 | 1.0 | 2249 | 6.4689 | | 6.1912 | 2.0 | 4498 | 6.2003 | | 6.0155 | 3.0 | 6747 | 6.1099 | | ed7fd7ae3927cec769dbce8a951326d8 |
apache-2.0 | ['generated_from_trainer'] | false | summarizer This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the multi_news dataset. It achieves the following results on the evaluation set: - Loss: 2.7745 - Rouge1: 0.1434 - Rouge2: 0.0448 - Rougel: 0.1097 - Rougelsum: 0.1097 - Gen Len: 18.9968 | 43b68a7d2f342c68036b41354b927e50 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | No log | 1.0 | 352 | 2.8572 | 0.1386 | 0.0423 | 0.106 | 0.106 | 18.9968 | |... | c8faab1e71da4a34c4b1cd6376426880 |
apache-2.0 | ['generated_from_trainer'] | false | distilroberta-base-Massive-intent This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the massive dataset. It achieves the following results on the evaluation set: - Loss: 0.6618 - Accuracy: 0.8938 | 1532b70f27e9b00ba83edf8e559c6e4d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.41 | 1.0 | 720 | 0.6742 | 0.8288 | | 0.4978 | 2.0 | 1440 | 0.5150 | 0.8751 | | 0.3009 | 3.0 | 2160 | 0.5705 | 0.... | d42b534fa108f377f91a5984b97739f9 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4409 - Wer: 0.3484 | 8aef4e239ce6e23635220687ceef43ae |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.3824 | 1.0 | 500 | 1.4646 | 1.0050 | | 0.8251 | 2.01 | 1000 | 0.5555 | 0.5319 | | 0.4265 | 3.01 | 1500 | 0.4303 | 0.4456 | |... | 4fb80b24837c37a687a3d9080238e378 |
apache-2.0 | ['translation'] | false | opus-mt-en-hu * source languages: en * target languages: hu * OPUS readme: [en-hu](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-hu/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2019-12-18.zip](https://... | a1380083e2407a18a092efbf383ddf35 |
apache-2.0 | ['image-classification', 'timm'] | false | Model card for levit_128.fb_dist_in1k A LeViT image classification model using convolutional mode (using nn.Conv2d and nn.BatchNorm2d). Pretrained on ImageNet-1k using distillation by paper authors. | d6de44697f50bea22a27cc044b5acf29 |
apache-2.0 | ['image-classification', 'timm'] | false | Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model('levit_128.fb_dist_in1k', pretrained=True) model = mode... | aaa75400691ef6c267f2e27a9294b3e4 |
apache-2.0 | ['image-classification', 'timm'] | false | Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'levit_128.fb_dist_in1k', pretrained=True, num... | 7df0212604919d99fef06a403d881268 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-multilingual-uncased-en-de-oct-15 This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0331 - F1: 0.9558 | dfd0b994f3a999468074445a91cac92b |
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: 3 | d1404bbe9abd02885bfc1436e945c268 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.064 | 1.0 | 4312 | 0.0367 | 0.9358 | | 0.0258 | 2.0 | 8624 | 0.0317 | 0.9499 | | 0.0126 | 3.0 | 12936 | 0.0331 | 0.955... | c277360c5dd4f67173bcaad02b31cc48 |
apache-2.0 | ['speech', 'xls_r', 'xls_r_pretrained'] | false | Wav2Vec2-XLS-R-2B [Facebook's Wav2Vec2 XLS-R](https://ai.facebook.com/blog/xls-r-self-supervised-speech-processing-for-128-languages) counting **2 billion** parameters.  XLS-R is Facebook AI's large-scale multilingu... | 1a8a59a7fc06d40feae48b7f4349abd3 |
apache-2.0 | ['speech', 'xls_r', 'xls_r_pretrained'] | false | Usage See [this google colab](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Fine_Tune_XLS_R_on_Common_Voice.ipynb) for more information on how to fine-tune the model. You can find other pretrained XLS-R models with different numbers of parameters: * [300M parameters version](https:... | 11b6a3edeb41645649674daea6e97308 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-distilled-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.2656 - Accuracy: 0.9503 | 5c66bd1efce453651cf3bb9e71b33525 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-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: 9 | a954b8dcb53a8d42568f24fa925fe0f1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 3.1212 | 1.0 | 1271 | 1.2698 | 0.8558 | | 0.6441 | 2.0 | 2542 | 0.3528 | 0.9326 | | 0.149 | 3.0 | 3813 | 0.2512 ... | fb3fed87ccf6a4b706246b356f578679 |
apache-2.0 | ['gpt2', 'turkish', 'aiwriter', 'finetuned'] | false | Model description This model is enhanced version of gpt2-small-turkish finetuned version. In addition to 28-10-2020 Wikipedia Turkish article dump this model is trained with more than 400 classic novels and plays in Turkish (Including Dostoyevski, Shaekspeare, Dumas) Base work has been done on Pierre Guillou tutoria... | cc0a143563333082617bbd67c56f6230 |
apache-2.0 | ['gpt2', 'turkish', 'aiwriter', 'finetuned'] | false | Install ```python from transformers import AutoTokenizer, AutoModelWithLMHead import torch tokenizer = AutoTokenizer.from_pretrained("gorkemgoknar/gpt2-turkish-writer") model = AutoModelWithLMHead.from_pretrained("gorkemgoknar/gpt2-turkish-writer") | 36b94a6a587a161e4740cbb64e4b5875 |
apache-2.0 | ['gpt2', 'turkish', 'aiwriter', 'finetuned'] | false | Limitations and bias The training data used for this model come from Turkish Wikipedia and books. We know it contains a lot of unfiltered content from the internet, which is far from neutral. Also not much pre-processing was done on books hence chapter names and page numbers can be seen on some cases. This is a work ... | 089a2f775bd725073d12b6c479c385f1 |
apache-2.0 | ['gpt2', 'turkish', 'aiwriter', 'finetuned'] | false | Eval results | epoch |train_loss |valid_loss |accuracy |perplexity |time | | ----- | -------- |--------- | ---------- | --------- | ----- | |0 |4.497828 |4.549605 |0.277328 |94.595070 |2:09:58| |1 |4.503929 |4.519456 |0.275071 |91.785645 |2:04:30| |2 |3.612716 |3.921146 |0.344802 |50.458256 |2:03:2... | 6e409a970864527311ea1cdcfc79456c |
apache-2.0 | ['translation'] | false | deu-tgl * source group: German * target group: Tagalog * OPUS readme: [deu-tgl](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/deu-tgl/README.md) * model: transformer-align * source language(s): deu * target language(s): tgl_Latn * model: transformer-align * pre-processing: normalization + ... | 2295abc2676e87b6a52a40b0fa22729f |
apache-2.0 | ['translation'] | false | System Info: - hf_name: deu-tgl - source_languages: deu - target_languages: tgl - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/deu-tgl/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['de', 'tl'] - src_constituents: {'deu'} - tgt_const... | 379f4525799d870858afdce0557f3824 |
cc-by-4.0 | ['question generation'] | false | Model Card of `lmqg/bart-base-subjqa-restaurants-qg` This model is fine-tuned version of [lmqg/bart-base-squad](https://huggingface.co/lmqg/bart-base-squad) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: restaurants) via [`lmqg`](https://github.com/a... | 58fb7bde48d634a84437ef5d611e96f3 |
cc-by-4.0 | ['question generation'] | false | Overview - **Language model:** [lmqg/bart-base-squad](https://huggingface.co/lmqg/bart-base-squad) - **Language:** en - **Training data:** [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (restaurants) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.... | 5a2753c32ca4293dec99a9e5ad3acc8e |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/bart-base-subjqa-restaurants... | cb6bc2c871f65f531171cc9009cba7c2 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/bart-base-subjqa-restaurants-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.restaurants.json) | | Score | Type | Dataset ... | b1114b41bef6338acdfefd7c9ee314ce |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_subjqa - dataset_name: restaurants - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: None - model: lmqg/bart-base-squad - max_length: 512 - max_length_output: 32 - epoch... | 8fc2e75f4733cdb7d7af3c7c66390690 |
apache-2.0 | ['translation'] | false | opus-mt-nso-en * source languages: nso * target languages: en * OPUS readme: [nso-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/nso-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | 4c95b201d44b775ebe460889e386dc56 |
mit | ['summarization', 'generated_from_trainer'] | false | bart-large-cnn-finetuned-random-sample-80 This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.2424 - Rouge1: 0.3273 - Rouge2: 0.0961 - Rougel: 0.2088 - Rougelsum: 0.2886 ... | 6dfd37318b6b6713e358b62125356270 |
mit | ['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: 2 | b67a73ece707867ee27df349cb4011ce |
mit | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | 3.3302 | 1.0 | 40 | 3.1922 | 0.2965 | 0.0789 | 0.1837 | 0.2565 | | 1.9373 | 2.0 | 80 ... | 75a6857ec96f50379e5bad60593e8304 |
unknown | [] | false | Example prompts `mcdonald restaurant lego set`: <img src="https://huggingface.co/cyburn/lego_set/resolve/main/1.jpg" alt="Picture." width="500"/> `lego set crow, skull`: <img src="https://huggingface.co/cyburn/lego_set/resolve/main/2.jpg" alt="Picture." width="500"/> | 32375979422b6b6dccd4ee6b49ae98e6 |
cc-by-sa-4.0 | ['translation', 'wmt20'] | false | Fairseq Ro-En NMT WMT20 MLQE This repository contains the Romanian-English model trained with the [fairseq toolkit](https://github.com/pytorch/fairseq) that was used to produce translations used in the WMT20 shared task on quality estimation (QE) on the [MLQE dataset](https://github.com/facebookresearch/mlqe). The c... | 1ed09ff057783c3770ca4a0a764cbfb2 |
mit | [] | false | model by soulpawa This your the Stable Diffusion model fine-tuned the beard oil big sur concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of sks beard oil** You can also train your own concepts and upload them to the library by using [this notebook](https... | b448bf5623e00cf61e08b4275a7b58d5 |
apache-2.0 | ['generated_from_keras_callback'] | false | Deysi/mt5-small-sumarizacion-textos-bilingual This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.1454 - Validation Loss: 3.3754 - Epoch: 7 | a1fdc1f1acf718d481bd607acf5ae051 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 10.2282 | 4.6664 | 0 | | 6.0978 | 3.8777 | 1 | | 5.2791 | 3.6299 | 2 | | 4.8386 | 3.5296 | 3 | | 4.5569 | 3.4565 | 4 | | 4.3616 |... | 92fec67a54f03ed209fee42c55172189 |
apache-2.0 | ['generated_from_trainer'] | false | fin_sentiment 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: 0.5405 - Accuracy: 0.7758 | a4bb0483edca59ca03b64117bba687ff |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 125 | 0.5405 | 0.7758 | | fe9c386127e97f8efa15b4fdda95416c |
apache-2.0 | ['translation'] | false | opus-mt-sv-ZH * source languages: sv * target languages: cmn,cn,yue,ze_zh,zh_cn,zh_CN,zh_HK,zh_tw,zh_TW,zh_yue,zhs,zht,zh * OPUS readme: [sv-cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+z... | 56a075be445ba574dc93bb9f446bfd68 |
apache-2.0 | ['generated_from_keras_callback'] | false | distilbert-base-uncased-finetuned-cyber This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: | a547a626f6845d1663db8b7b8e08bcf9 |
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: 1.1564 | 6406937629a510d963de4bb22c508d97 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.224 | 1.0 | 5533 | 1.1571 | | 0.9572 | 2.0 | 11066 | 1.1265 | | 0.7554 | 3.0 | 16599 | 1.1564 | | 69cb3eed0409b62e1af17c2db1833c3b |
apache-2.0 | ['generated_from_trainer'] | false | openai/whisper-medium This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5021 - Wer: 11.9280 | 8d3f6dd7afa7e176f5c59790f9650ce0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.3007 | 1.01 | 500 | 0.3027 | 12.4842 | | 0.0962 | 2.02 | 1000 | 0.3147 | 11.9450 | | 0.0409 | 3.03 | 1500 | 0.3528 | 12.943... | 53af047b2fe243dc6e225cd0bbebc872 |
creativeml-openrail-m | ['text-to-image'] | false | Nasa-space-V2-768 Dreambooth model trained by Fireman4740 with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v2-768 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingfa... | 7336f2ab27c400de9a72dd85dd31a5c4 |
mit | ['t5', 'pytorch', 'pt', 'pt-br', 'summarization', 'abstractive summarization'] | false | Introduction PTT5 Summ is a fine-tuned [PTT5](https://github.com/unicamp-dl/PTT5) model to perform Abstractive Summarization in Brazilian Portuguese texts. This model was fine-tuned on the datasets: [WikiLingua](https://github.com/esdurmus/Wikilingua), [XL-Sum](https://github.com/csebuetnlp/xl-sum), [TeMário](http://w... | 9838aa8e949f9b09abc4825b44dc2b06 |
mit | ['t5', 'pytorch', 'pt', 'pt-br', 'summarization', 'abstractive summarization'] | false | Available models | Model | Dataset used in fine-tuning| | :-: | :-: | | [phpaiola/ptt5-base-summ-wikil... | 655a329f078d03b32c39939c4c537cbe |
mit | ['t5', 'pytorch', 'pt', 'pt-br', 'summarization', 'abstractive summarization'] | false | PyTorch model from transformers import T5Model, T5ForConditionalGeneration token_name = 'unicamp-dl/ptt5-base-portuguese-vocab' model_name = 'phpaiola/ptt5-base-summ-xlsum' tokenizer = T5Tokenizer.from_pretrained(token_name ) model_pt = T5ForConditionalGeneration.from_pretrained(model_name) text = ''' “A tendência... | 2a6ce5f8846f1d1f5fbf59bb59faf327 |
mit | ['t5', 'pytorch', 'pt', 'pt-br', 'summarization', 'abstractive summarization'] | false | Citation @aInProceedings{ptt5summ_bracis, author="Paiola, Pedro H. and de Rosa, Gustavo H. and Papa, Jo{\~a}o P.", editor="Xavier-Junior, Jo{\~a}o Carlos and Rios, Ricardo Ara{\'u}jo", title="Deep Learning-Based Abstractive Summarization for Brazilian Portuguese Texts", ... | 14567f217a42285386a6c4fc9d7cffee |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Model * Name: Whisper Large-v2 Swahili * Description: Whisper weights for speech-to-text task, fine-tuned and evaluated on normalized data. * Dataset: - Train and validation splits for Swahili subsets of [Common Voice 11.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0). - Train, validation ... | c666670f1e79c0465122f10be3a56aa2 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Usage To use these weights in HuggingFace's `transformers` library, you can do the following: ```python from transformers import WhisperForConditionalGeneration model = WhisperForConditionalGeneration.from_pretrained("hedronstone/whisper-large-v2-sw") ``` | 12ff98e68cb81e1f7b8e2dcfab3c245c |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-MINI-NL8 (Deep-Narrow version) T5-Efficient-MINI-NL8 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 ... | 458370f325b8e1eedf90b39456e8a60c |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-mini-nl8** - is of model type **Mini** with the following variations: - **nl** is **8** It has **50.12** million parameters and thus requires *ca.* **200.49 MB** of memory in full precision (*fp32*) or **100.24 MB** of memory in half precision (*fp1... | 0045c75ec6258c4294249bcedd66e872 |
cc-by-4.0 | ['generated_from_trainer'] | false | bertin-roberta-base-spanish-finetuned-recores This model is a fine-tuned version of [bertin-project/bertin-roberta-base-spanish](https://huggingface.co/bertin-project/bertin-roberta-base-spanish) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.2985 - Accuracy: 0.3581 | fb23d762e60e26a1c5b389273ff231ac |
cc-by-4.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | a106399c98f14bb263b6e11a8522be6b |
cc-by-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.6065 | 1.0 | 1047 | 1.5944 | 0.2948 | | 1.4913 | 2.0 | 2094 | 2.4456 | 0.3581 | | 0.7893 | 3.0 | 3141 | 3.4247 | 0.... | 3d71ba83bf6fa8a682ace6290b83fc57 |
apache-2.0 | ['image-classification'] | false | Data-efficient Image Transformer (base-sized model) Data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper [Training data-efficient image transformers & distillation through attention](https:... | baef05f3eb16bad7535a93bb7171220a |
apache-2.0 | ['image-classification'] | false | Model description This model is actually a more efficiently trained Vision Transformer (ViT). The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pre-trained and fine-tuned on a large collection of images in a supervised fashion, namely ImageNet-1k, at a resolution of 224x224 pixels. Images are... | d84091f20a884c2e70224c004890c53f |
apache-2.0 | ['image-classification'] | false | Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=facebook/deit) to look for fine-tuned versions on a task that interests you. | 121013b176fdaca69ad639cda134a461 |
apache-2.0 | ['image-classification'] | false | How to use Since this model is a more efficiently trained ViT model, you can plug it into ViTModel or ViTForImageClassification. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the mo... | 6583dc4d8edf242308874e6870911a25 |
apache-2.0 | ['image-classification'] | false | model predicts one of the 1000 ImageNet classes predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx]) ``` Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon. | 015935bcf9179e6902eefc6c2ca81b68 |
apache-2.0 | ['image-classification'] | false | Preprocessing The exact details of preprocessing of images during training/validation can be found [here](https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py | 74f2560ac8c2785263aa4582e3c72821 |
apache-2.0 | ['image-classification'] | false | L78). At inference time, images are resized/rescaled to the same resolution (256x256), center-cropped at 224x224 and normalized across the RGB channels with the ImageNet mean and standard deviation. | 8274d56678440f32ac05e8e5bb56aee4 |
apache-2.0 | ['image-classification'] | false | Pretraining The model was trained on a single 8-GPU node for 3 days. Training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper. | 6454234593bb28e6e691b5e500ca1370 |
apache-2.0 | ['image-classification'] | false | params | URL | |---------------------------------------|-------------------------|-------------------------|----------|------------------------------------------------------------------| | DeiT-tiny | 72.2 | 91.... | 9fce874b006b677f5f76fbe06112632b |
apache-2.0 | ['image-classification'] | false | BibTeX entry and citation info ```bibtex @misc{touvron2021training, title={Training data-efficient image transformers & distillation through attention}, author={Hugo Touvron and Matthieu Cord and Matthijs Douze and Francisco Massa and Alexandre Sablayrolles and Hervé Jégou}, year={2021}, epri... | 4fb13e9c2e4829a9eec8aa6fd6697227 |
apache-2.0 | ['translation'] | false | glg-por * source group: Galician * target group: Portuguese * OPUS readme: [glg-por](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/glg-por/README.md) * model: transformer-align * source language(s): glg * target language(s): por * model: transformer-align * pre-processing: normalization + ... | 380264cc7efa45b3f22b66daf308bd1d |
apache-2.0 | ['translation'] | false | System Info: - hf_name: glg-por - source_languages: glg - target_languages: por - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/glg-por/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['gl', 'pt'] - src_constituents: {'glg'} - tgt_const... | 396fb06f6bb9bb5754f7e43ac89cce50 |
apache-2.0 | ['generated_from_trainer'] | false | mbert-fine-tune-ner_fr This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the wikiann dataset. It achieves the following results on the evaluation set: - Loss: 0.1887 - Precision: 0.9000 - Recall: 0.9078 - F1: 0.9038 - Accuracy: 0.9491 | 2c782f0c0e7e4ef4d2650f183733dff3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2929 | 1.0 | 1250 | 0.2028 | 0.8782 | 0.8938 | 0.8860 | 0.9417 | | 0.1355 | 2.0 |... | 4b81a9891972040445b172bc6afad1ee |
apache-2.0 | ['generated_from_trainer'] | false | navid_test_bert This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8149 - Matthews Correlation: 0.5834 | 9f8e21c7f68485f5525e6d15d266c315 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.4598 | 1.0 | 1069 | 0.4919 | 0.5314 | | 0.3228 | 2.0 | 2138 | 0.6362 | 0.5701 | | 0.1... | 21b589fe640f545055909c09ba514e38 |
apache-2.0 | ['generated_from_trainer'] | false | stsb This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 0.3978 - Pearson: 0.9090 - Spearmanr: 0.9051 - Combined Score: 0.9071 | 1450e300b1bb3d277ef422db4b8dfb8f |
mit | ['text-classification'] | false | Model Description Distilbert model fine-tuned on depression comments for the task of depression classification. - **Developed by:** [jambran](https://github.com/jambran) - **Shared by [Optional]:** [jambran](https://github.com/jambran) - **Model type:** Language model - **Language(s) (NLP):** en - **License:** mit ... | bdc58829bf96e40a21e060e183fe3afd |
mit | ['text-classification'] | false | Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> This project was done for fun on the side. It is not intended to diagnose, treat, or make decisions. | 2c21c43ef05d5bfada3de691d6bc07d1 |
mit | ['text-classification'] | false | Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> <!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." --> ```python from transformers import ( AutoTokenizer, ) from t... | f8a69705b4511142f02de20982b3794b |
mit | ['text-classification'] | false | Downstream Use [Optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> <!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." --> | 4694d7e70c273abaf34295121d2a5424 |
mit | ['text-classification'] | false | Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> <!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." --> Any real-life, production use case is out-of-scope for this ... | 104d1f5049c8bb0dd8a369241e2df403 |
mit | ['text-classification'] | false | Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.... | 4dd56f69ad64f088926a2dbd0723c196 |
mit | ['text-classification'] | false | Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> Dataset originally from [Depression: Reddit Dataset](https://www.kaggle.com/datasets/infamouscoder/d... | be6e283869c88974b93833ee434078f6 |
apache-2.0 | ['automatic-speech-recognition', 'zh-CN'] | false | exp_w2v2t_zh-cn_xls-r_s847 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (zh-CN)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech i... | edcd5f6ad5085fea5b273ab149734ec9 |
mit | [] | false | manga char nov 23 on Stable Diffusion This is the `<char-nov23>` 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 c... | 9460e38bb2489d6807fdb05524da882e |
mit | [] | false | Raichu on Stable Diffusion This is the `<raichu>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train y... | d6e89b4c05a7dca5c1343d63f72a3e57 |
apache-2.0 | [] | false | distilbert-base-en-el-cased We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages. Our versions give exactly the same representations produced by the original model which preserves the original accur... | 4b315d0bb425c18f973971055bdd65a8 |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-en-el-cased") model = AutoModel.from_pretrained("Geotrend/distilbert-base-en-el-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Gi... | f72c834c3ff466589654b39c20dc8bb3 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8697 - Matthews Correlation: 0.5599 | 7c1e37ec4396d956336333f2d2a50968 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5223 | 1.0 | 535 | 0.5444 | 0.4309 | | 0.3457 | 2.0 | 1070 | 0.5213 | 0.5021 | | 0.2... | b4173729a721e5c24fea11f18f64e961 |
apache-2.0 | ['image-classification'] | false | VAN-Large
VAN is trained on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper [Visual Attention Network](https://arxiv.org/abs/2202.09741) and first released in [here](https://github.com/Visual-Attention-Network).
| e50dc4e7bfb808218781349cb5e8d385 |
apache-2.0 | ['image-classification'] | false | Description
While originally designed for natural language processing (NLP) tasks, the self-attention mechanism has recently taken various computer vision areas by storm. However, the 2D nature of images brings three challenges for applying self-attention in computer vision. (1) Treating images as 1D sequences negl... | 5aa2cf2fbda5f66edf9a853a2346f8e0 |
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