license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 200 - mixed_precision_training: Native AMP | da27bc0be245220344fbe456f98d7033 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | No log | 1.0 | 14 | 1.8310 | 1.6355 | 11.0 | | No log | 2.0 | 28 | 1.0849 | 18.3378 | 18.8661 | | No log |... | 021317f7e7a6b4b3d75579a39cea2d01 |
apache-2.0 | [] | false | How to use Here is how to use this model to inspect a log. Given text must be parsed as like: `"path: <path>; ref:<referrer>; ua:<user agent>;"` ```python >>> from transformers import pipeline >>> inspector = pipeline('text-classification', model="u-haru/log-inspector") >>> inspector('path: /cgi-bin/kerbynet?Secti... | 20ff17f67d3a10ad13eb05e96b83a3d4 |
gpl-3.0 | ['spacy', 'token-classification'] | false | Basic Spacy BioNER pipeline, with a RoBERTa-based model [bsc-bio-ehr-es] (https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es) and a dataset, CANTEMIST, annotated with tumour morphology entities. For further information, check the [official website](https://temu.bsc.es/cantemist/). Visit our [GitHub repository](https... | 511ecdf8ce2dc00c952761281f9fb964 |
mit | ['vision', 'image-to-text'] | false | GIT (GenerativeImage2Text), base-sized, fine-tuned on MSRVTT-QA GIT (short for GenerativeImage2Text) model, base-sized version, fine-tuned on MSRVTT-QA. It was introduced in the paper [GIT: A Generative Image-to-text Transformer for Vision and Language](https://arxiv.org/abs/2205.14100) by Wang et al. and first relea... | 31c6d626cc85fb4fc8604c8dae530c5e |
mit | ['vision', 'image-to-text'] | false | Intended uses & limitations You can use the raw model for video question answering (QA). See the [model hub](https://huggingface.co/models?search=microsoft/git) to look for fine-tuned versions on a task that interests you. | 35308e3346873676740daa6777e941a0 |
mit | ['vision', 'image-to-text'] | false | Training data From the paper: > We collect 0.8B image-text pairs for pre-training, which include COCO (Lin et al., 2014), Conceptual Captions (CC3M) (Sharma et al., 2018), SBU (Ordonez et al., 2011), Visual Genome (VG) (Krishna et al., 2016), Conceptual Captions (CC12M) (Changpinyo et al., 2021), ALT200M (Hu et al.,... | 67691c79916b7a514ea76fed7018e409 |
apache-2.0 | ['generated_from_trainer', 'fnet-bert-base-comparison'] | false | bert-base-cased-finetuned-sst2 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.3649 - Accuracy: 0.9232 The model was fine-tuned to compare [google/fnet-base](https://huggingfac... | 5f0ab859c8fc4963c9d6b246794493ce |
apache-2.0 | ['generated_from_trainer', 'fnet-bert-base-comparison'] | false | !/usr/bin/bash python ../run_glue.py \\n --model_name_or_path bert-base-cased \\n --task_name sst2 \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir bert-base-cased-finetuned-sst2 \\n --push_to_hub \\... | d8d60e84c43e019259a33936b2860194 |
apache-2.0 | ['generated_from_trainer', 'fnet-bert-base-comparison'] | false | Training results | Training Loss | Epoch | Step | Accuracy | Validation Loss | |:-------------:|:-----:|:-----:|:--------:|:---------------:| | 0.233 | 1.0 | 4210 | 0.9174 | 0.2841 | | 0.1261 | 2.0 | 8420 | 0.9278 | 0.3310 | | 0.0768 | 3.0 | 12630 | 0.9232 | 0.36... | e3942a6722efd410253f08502399b1b1 |
creativeml-openrail-m | ['text-to-image'] | false | khujli Dreambooth model trained by smjain with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/... | d9fcdd19697b39cc8015ecb2a48b245c |
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: 0.8265 - Matthews Correlation: 0.5671 | 31729e16d95e19093b20cbfae450fbdc |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5216 | 1.0 | 535 | 0.5536 | 0.4041 | | 0.3481 | 2.0 | 1070 | 0.5242 | 0.5206 | | 0.2... | e7d1711af87f274f2a37a9a8a7078b09 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | LegoBlocksStyle Dreambooth model trained by sivar with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable... | ad4d8c3339bd894f65df7495dd3708d0 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-casedfinetuned-fake-news-detection This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the [Fake and Reals News](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset) dataset. It achieves the following results on the eval... | 5d9cf5f230d44625198a4f3221a3ee29 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:| | No log | 1.0 | 1684 | 0.0021 | 0.9998 | 0.9998 | | No log | 2.0 | 3368 | 0.0019 | 0.9998 | 0.9998 | | fc212785791d030280ae16a01069c6e8 |
apache-2.0 | ['audio-classification', 'generated_from_trainer'] | false | wav2vec2-base-ks-linear_lrX1000 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset. It achieves the following results on the evaluation set: - Loss: 0.5661 - Accuracy: 0.8325 | cdfc14e4a3c7579444cf69142a872dea |
apache-2.0 | ['audio-classification', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.03 - train_batch_size: 256 - eval_batch_size: 256 - seed: 0 - gradient_accumulation_steps: 4 - total_train_batch_size: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_s... | fb0952d622b064aba663565086e06820 |
apache-2.0 | ['audio-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7558 | 1.0 | 50 | 1.0584 | 0.6462 | | 0.5971 | 2.0 | 100 | 0.7816 | 0.7510 | | 0.5382 | 3.0 | 150 | 0.7870 | 0.... | bbd877fdfd1872d30b4a1ec856233809 |
mit | [] | false | model by thesun1094224 This your the Stable Diffusion model fine-tuned the paolo-bonolis concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of sks paolo bonolis** You can also train your own concepts and upload them to the library by using [this notebook](... | b0d22e0b3504a8061f16062559359496 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'animal'] | false | DreamBooth model for the catty concept trained by Harukanaa. This is a Stable Diffusion model fine-tuned on the catty concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of catty pet** This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https... | 285fca57afafd92c8a9c71b25d16b381 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'animal'] | false | Description This is a Stable Diffusion model fine-tuned on `pet` images for the animal theme, for the Hugging Face DreamBooth Hackathon, from the HF CN Community, This is just my sleppy cat cattty, enjoy. | 59b446ac66dd5ac09e7c133c78da4bfd |
apache-2.0 | ['generated_from_trainer'] | false | Model description This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the Custom Domain-Specific dataset. It achieves the following results on the evaluation set: - Loss: 1.2337 | 4758c9f9b2da65ffecc0f67980d22851 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epo... | 8339e429a9b88f67306139ed6011cf76 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.6162 | 0.34 | 100 | 1.8890 | | 1.9995 | 0.67 | 200 | 1.6871 | | 1.8697 | 1.01 | 300 | 1.6146 | | 1.7682 | 1.34 | 400 | 1.5530 ... | a564dbb684d68e2e74bfd39175ef8f78 |
apache-2.0 | ['catalan', 'qa'] | false | Model description The **roberta-base-ca-cased-qa** is a Question Answering (QA) model for the Catalan language fine-tuned from the roberta-base-ca model, a [RoBERTa](https://arxiv.org/abs/1907.11692) base model pre-trained on a medium-size corpus collected from publicly available corpora and crawlers. | 8db73ac4bb9ae83d4bb11c2967f8bbdb |
apache-2.0 | ['catalan', 'qa'] | false | Intended uses and limitations **roberta-base-ca-cased-qa** model can be used for extractive question answering. The model is limited by its training dataset and may not generalize well for all use cases. | defa55590aea8515895de31258d27316 |
apache-2.0 | ['catalan', 'qa'] | false | How to use Here is how to use this model: ```python from transformers import pipeline nlp = pipeline("question-answering", model="projecte-aina/roberta-base-ca-cased-qa") text = "Quan va començar el Super3?" context = "El Super3 o Club Super3 és un univers infantil català creat a partir d'un programa emès per Televi... | fd7d3833e2c4d596792c74103083760a |
apache-2.0 | ['catalan', 'qa'] | false | Training data We used the QA dataset in Catalan called [CatalanQA](https://huggingface.co/datasets/projecte-aina/catalanqa) for training and evaluation, and the [XQuAD-ca](https://huggingface.co/datasets/projecte-aina/xquad-ca) test set for evaluation. | 205f7cc51c498cfc68a05d4aeaa018b3 |
apache-2.0 | ['catalan', 'qa'] | false | Evaluation results We evaluated the _roberta-base-ca-cased-qa_ on the CatalanQA and XQuAD-ca test sets against standard multilingual and monolingual baselines: | Model | ViquiQuAD (F1/EM) | XQuAD-ca (F1/EM) | | ------------|:-------------:| -----:| | roberta-base-ca-cased-qa | **86.99/73.25** | **67.81/49.4... | d3308feaf4f4b3dc2f2e30270dc47fc9 |
apache-2.0 | ['catalan', 'qa'] | false | Citation Information If you use any of these resources (datasets or models) in your work, please cite our latest paper: ```bibtex @inproceedings{armengol-estape-etal-2021-multilingual, title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}a... | 370aa2f95714307904f447570ccb9b19 |
apache-2.0 | ['part-of-speech', 'token-classification'] | false | XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Serbian This model is part of our paper called: - Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details. | 19fec7065367ee747428a63a920a189f |
apache-2.0 | ['part-of-speech', 'token-classification'] | false | Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-sr") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-sr") ``` | 33b36510a3107e19d6fd25593d87d303 |
apache-2.0 | ['automatic-speech-recognition', 'nl'] | false | exp_w2v2t_nl_vp-es_s476 Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | 0f5f25f5ce66e3b2d1c13f368cad87d9 |
apache-2.0 | ['generated_from_keras_callback'] | false | distilgpt_oscarth_0080 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.8143 - Validation Loss: 2.7051 - Epoch: 79 | c1301bc6f20f954615d48f0744ec2015 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 5.6021 | 4.5759 | 0 | | 4.4536 | 4.1235 | 1 | | 4.1386 | 3.9013 | 2 | | 3.9546 | 3.7563 | 3 | | 3.8255 | 3.6477 | 4 | | 3.7271 |... | 09f51af93d936d4cd9e6c7c9da13e4ac |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Avatar-Generator Dreambooth model trained by shorya24 with [buildspace's DreamBooth](https://colab.research.google.com/github/buildspace/diffusers/blob/main/examples/dreambooth/DreamBooth_Stable_Diffusion.ipynb) notebook Build your own using the [AI Avatar project](https://buildspace.so/builds/ai-avatar)! To get st... | 43bcb459c88ac7d884349cb90fefd417 |
apache-2.0 | ['super-image', 'image-super-resolution'] | false | Pixel Attention Network (PAN) PAN model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper [Efficient Image Super-Resolution Using Pixel Attention](https://arxiv.org/abs/2010.01073) by Zhao et al. (2020) a... | f6477a890cfde7a8053b911c36b2bc80 |
apache-2.0 | ['super-image', 'image-super-resolution'] | false | Model description The PAN model proposes a a lightweight convolutional neural network for image super resolution. Pixel attention (PA) is similar to channel attention and spatial attention in formulation. PA however produces 3D attention maps instead of a 1D attention vector or a 2D map. This attention scheme introduc... | 5db8740ef63c1ae97b61b044244abc57 |
apache-2.0 | ['super-image', 'image-super-resolution'] | false | How to use The model can be used with the [super_image](https://github.com/eugenesiow/super-image) library: ```bash pip install super-image ``` Here is how to use a pre-trained model to upscale your image: ```python from super_image import PanModel, ImageLoader from PIL import Image import requests url = 'https://pap... | b3f5df124ccfaa7d3d14b0880fbe3e31 |
apache-2.0 | ['super-image', 'image-super-resolution'] | false | Pretraining The model was trained on GPU. The training code is provided below: ```python from super_image import Trainer, TrainingArguments, PanModel, PanConfig training_args = TrainingArguments( output_dir='./results', | fcb96cbcad7a9d38f646feaea8b91f52 |
apache-2.0 | ['super-image', 'image-super-resolution'] | false | Algorithm). Evaluation datasets include: - Set5 - [Bevilacqua et al. (2012)](https://huggingface.co/datasets/eugenesiow/Set5) - Set14 - [Zeyde et al. (2010)](https://huggingface.co/datasets/eugenesiow/Set14) - BSD100 - [Martin et al. (2001)](https://huggingface.co/datasets/eugenesiow/BSD100) - Urban100 - [Huang et al... | 85e27ed8e8ba867702949b7e15b6c315 |
apache-2.0 | ['super-image', 'image-super-resolution'] | false | BibTeX entry and citation info ```bibtex @misc{zhao2020efficient, title={Efficient Image Super-Resolution Using Pixel Attention}, author={Hengyuan Zhao and Xiangtao Kong and Jingwen He and Yu Qiao and Chao Dong}, year={2020}, eprint={2010.01073}, archivePrefix={arXiv}, primaryClass... | f769703741f85e64b7f5c2d951641016 |
apache-2.0 | ['generated_from_trainer'] | false | relation-distilbert-em 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: 0.7801 | 2d3311a776609da357cc018ea6acafbc |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 | 88346e12a2506ed0923f3ec898e9ca31 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.6947 | 1.0 | 2812 | 0.7616 | | 0.6946 | 2.0 | 5624 | 0.7740 | | 0.6944 | 3.0 | 8436 | 0.7801 | | e7a78f7ef544215940fc2412f5808bcf |
apache-2.0 | ['pytorch', 'text-generation', 'causal-lm', 'rwkv'] | false | Model Description RWKV-4 430M is a L24-D1024 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 = 24 n_embd = 1024 Final checkpoint: RWKV-4-Pile-430M-20220808-8066.pth : Trained on the Pile for 333... | a7ecd5e148246e782721e743b335f90c |
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 512 --tiny_att_layer 18): RWKV-4a-Pile-433M-20221223-8039.pth * Pile loss 2.2394 * LAMBADA ppl 10.54, acc 50.20% * PIQA acc 68.12% * SC2016 acc 63.55% * Hellaswag acc_norm 40.82% | b163b51cc64674ddd6603d1473466842 |
apache-2.0 | ['finnish', 'roberta'] | false | NOTE: We have trained newer and better Finnish RoBERTa large model which can be found from different repository: [https://huggingface.co/Finnish-NLP/roberta-large-finnish](https://huggingface.co/Finnish-NLP/roberta-large-finnish). Our future Finnish models will be available at the [Finnish-NLP](https://huggingface.co/... | 7964fb60cdf7aa4e67b56c17f6026dab |
apache-2.0 | ['finnish', 'roberta'] | false | RoBERTa large model for Finnish Pretrained model on Finnish language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/abs/1907.11692) and first released in [this repository](https://github.com/pytorch/fairseq/tree/master/examples/roberta). This model is case-sensit... | ba79c1ba601e5495f05c5c4e3950dc19 |
apache-2.0 | ['finnish', 'roberta'] | false | Model description RoBERTa is a transformers model pretrained on a 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 generate inpu... | 592a4bfc147828991d33376c9939ad22 |
apache-2.0 | ['finnish', 'roberta'] | false | How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='flax-community/RoBERTa-large-finnish') >>> unmasker("Moikka olen <mask> kielimalli.") [{'sequence': 'Moikka olen uusi kielimalli.', '... | 6c180af06e12abb3a96ef228434c8043 |
apache-2.0 | ['finnish', 'roberta'] | false | Training data This Finnish RoBERTa model was pretrained on the combination of two datasets: - [mc4](https://huggingface.co/datasets/mc4), 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 - [Yle Finnish News Archive](http://ur... | 8bfaa731fe0b333d670dbb8ac728fc1b |
apache-2.0 | ['finnish', 'roberta'] | false | Pretraining The model was trained on TPUv3-8 VM, sponsored by the Hugging Face JAX/Flax community week event, for 2 epochs with a sequence length of 128 and continuing for one more epoch with a sequence length of 512. The optimizer used is Adafactor with a learning rate of 2e-4, \\(\beta_{1} = 0.9\\), \\(\beta_{2} = ... | 0de9859c0895bf1cf169ed90cc9738d9 |
apache-2.0 | ['finnish', 'roberta'] | false | Evaluation results Evaluation was done by fine-tuning the model on downstream text classification task with two different labeled datasets: [Yle News](https://github.com/spyysalo/yle-corpus) and [Eduskunta](https://github.com/aajanki/eduskunta-vkk). Yle News classification fine-tuning was done with two different sequ... | fe3fbd5190eed1713e217496c5c4f749 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | openai/whisper-medium-Assamese 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: 1.0992 - Wer: 58.3649 | e92f20513e6416a159df0fc44d9ba25f |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sche... | edb5b86491d024ed9eda1e7368351d18 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0841 | 1.13 | 600 | 1.0992 | 58.3649 | | 03da2eeac300cdb63960773c3619232d |
apache-2.0 | ['generated_from_trainer'] | false | Roberta-wwm-ext-large-qa This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext-large](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large) on the cmrc2018 dataset. It achieves the following results on the evaluation set: - Loss: 1.1028 | 4b9d206a2aa91b13030fa8dfa35a6207 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 | 9341767bfe3f3b3a03f0402188966ff6 |
apache-2.0 | ['generated_from_trainer'] | false | new_classifer_epoch10 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: - Loss: 0.0837 - Accuracy: 0.9867 | 3b40e1edfdec14225c0b3911b6f616e5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.0524 | 1.0 | 4248 | 0.0628 | 0.9790 | | 0.0251 | 2.0 | 8496 | 0.0496 | 0.9848 | | 0.0153 | 3.0 | 12744 | 0.0857 ... | 2474706aa53398a3870927514111a89d |
creativeml-openrail-m | ['art'] | false |  Cornflower is a comprehensive painting model based on StableDiffusion, trained with specific styles of illustration and merged with multiple models, which is theoretically somewhat different from real-life human painters. **Since the Cornflower model... | 4e5a00fce5a03bd2b52a98cbd3236d60 |
creativeml-openrail-m | ['art'] | false | How to install? **'cornflower_v7.safetensors'** and **vae file** are placed in the Stable Diffusion model directory. The .pt files in **'embeddings'** folder are placed in the embeddings directory. **'cornflower_v7_phantom.pt'** in hypernetwork folder is placed in the Hypernetworks model directory. | b88f193ed0e9ffae53bf9e6edcce515f |
creativeml-openrail-m | ['art'] | false | How to use? After the installation is complete, open webui and switch checkpoint to 'cornflower_v7.safetensors', Hypernetwork to 'cornflower_v7_phantom'. The following parameters are recommended, and the sampler recommends DPM2 a Karras. Steps: 20, Sampler: DPM2 a Karras, CFG scale: 7, Size: 640x960, Clip skip: 2, E... | 8bef0231168686c47a6ee458f9ca2d8f |
apache-2.0 | [] | false | (English) GPT2-small-spanish: a Language Model for Spanish text generation (and more NLP tasks...) GPT2-small-spanish is a state-of-the-art language model for Spanish based on the GPT-2 small model. It was trained on Spanish Wikipedia using **Transfer Learning and Fine-tuning techniques**. The training took around 7... | 6f6efe908f5d6af77cee6247c425bdfe |
apache-2.0 | [] | false | Limitations and bias (Copied from original GPorTuguese-2 model)The training data used for this model come from Spanish Wikipedia. We know it contains a lot of unfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their model card: > Because large-scale language m... | 85998005dc114bf6307052f5e24b5987 |
apache-2.0 | [] | false | Authors The model was trained and evaluated by [Josué Obregon](https://www.linkedin.com/in/josue-obregon/) and [Berny Carrera](https://www.linkedin.com/in/bernycarrera/), founders of [Datificate](https://datificate.com), a space for learning Machine Learning in Spanish. The training was possible thanks to the computi... | a1d033054296e02ac8503c7b2f3f7949 |
apache-2.0 | [] | false | (Español) GPT2-small-spanish: un modelo de lenguaje para generación de texto en Español (y algunas otras tareas de NLP...) GPT2-small-spanish es un modelo de lenguaje de vanguardia en Español basado en el modelo pequeño GPT-2. Fué entrenado con la Wikipedia en Español usando **técnicas de Aprendizaje por Transferen... | 77d20c2b3a0a826557ed32320f4e9b1d |
apache-2.0 | [] | false | Limitaciones y sesgos (Copiado del modelo original GPorTuguese-2 model)Los datos de entrenamiento provienen de la Wikipedia en Español. Se sabe que contiene bastante contenido no filtrado del internet, lo cual está lejos de ser neutral. Esto es señalado por el equipo desarrollador de openAI en su propia tarjeta de mo... | 68dd3119048aa1582746046944982c8f |
apache-2.0 | [] | false | Autores El modelo fue entreando y evaluado por [Josué Obregon](https://www.linkedin.com/in/josue-obregon/) y [Berny Carrera](https://www.linkedin.com/in/bernycarrera/), fundadores de [Datificate](https://datificate.com), un espacio para aprender Machine Learning en Español. El entrenamiento fue posible gracias al po... | e2599e5e365750d205bc086c9c13c114 |
apache-2.0 | ['multiberts', 'multiberts-seed_1', 'multiberts-seed_1-step_1600k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1600k 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... | 7f864bd3548933e12dc2a57de0394b87 |
apache-2.0 | ['multiberts', 'multiberts-seed_1', 'multiberts-seed_1-step_1600k'] | 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_1-step_1600k') model = TFBertModel.from_pretrained("google/multib... | 29c5576aa10233d11a9939a9142d12f2 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-multilingual-uncased-oct-8 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: 0.0411 - F1: 0.9359 | 7b624f5895196603111850e2d917e392 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 | e38d4e74fe999a42c53c80254d8eeed8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.0899 | 1.0 | 1078 | 0.0464 | 0.9132 | | 0.0344 | 2.0 | 2156 | 0.0412 | 0.9287 | | 0.0208 | 3.0 | 3234 | 0.0411 | 0.9359 | ... | cfc256310a1d793f4504df865f633979 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-1b-irish-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 1.0795 - Wer: 46.91 | 7799a17deef0e06ff807cb1f97e1560d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.6902 | 12.12 | 400 | 1.1158 | 0.5959 | | 0.2988 | 24.24 | 800 | 1.1375 | 0.5094 | | 9f5157bb81b9102d63400efb62133d36 |
apache-2.0 | ['generated_from_trainer'] | false | distilroberta-base-finetuned-wikitext2 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7037 | ff687d5ba1b6c48f5306777917ed102a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 251 | 1.7837 | | 2.0311 | 2.0 | 502 | 1.7330 | | 2.0311 | 3.0 | 753 | 1.7085 | | 6b2a359169c24630c71e9c713da6d315 |
cc-by-4.0 | ['answer extraction'] | false | Model Card of `lmqg/mt5-small-frquad-ae` This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for answer extraction on the [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation)... | fde59da7db840ea604c3c59d06063810 |
cc-by-4.0 | ['answer extraction'] | false | Overview - **Language model:** [google/mt5-small](https://huggingface.co/google/mt5-small) - **Language:** fr - **Training data:** [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417... | caee48d280b307a1fbf28926ceb08e74 |
cc-by-4.0 | ['answer extraction'] | false | model prediction answers = model.generate_a("Créateur » (Maker), lui aussi au singulier, « le Suprême Berger » (The Great Shepherd) ; de l'autre, des réminiscences de la théologie de l'Antiquité : le tonnerre, voix de Jupiter, « Et souvent ta voix gronde en un tonnerre terrifiant », etc.") ``` - With `transformers` ... | aed70c70d8444239290906d4100f6fa5 |
cc-by-4.0 | ['answer extraction'] | false | Evaluation - ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-frquad-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_frquad.default.json) | | Score | Type | Dataset | |:---... | 018a96fdc4f84be9c8c5a762f106641f |
cc-by-4.0 | ['answer extraction'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_frquad - dataset_name: default - input_types: ['paragraph_sentence'] - output_types: ['answer'] - prefix_types: None - model: google/mt5-small - max_length: 512 - max_length_output: 32 - epoch: 23 - ... | 09e90cd99bc6378d8274a5ceafe80456 |
gpl-3.0 | ['object-detection', 'computer-vision', 'yolov6', 'yolo'] | false | Yolov6 Inference ```python from yolov6 import YOLOV6 model = YOLOV6(weights='kadirnar/yolov6s6-v3.0', device='cuda:0', hf_model=True) model.classes = None model.conf = 0.25 model.iou = 0.45 model.show = False model.save = True pred = model.predict(source='data/images',yaml='data/coco.yaml', img_size=640) ``` | c7226d452673a5f2a41c98ea04fa302a |
apache-2.0 | ['vision'] | false | Vision Transformer (large-sized model) pre-trained with MSN Vision Transformer (ViT) model pre-trained using the MSN method. It was introduced in the paper [Masked Siamese Networks for Label-Efficient Learning](https://arxiv.org/abs/2204.07141) by Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian... | b925dafdc81fdf7a3175b84b3d1a36d0 |
apache-2.0 | ['vision'] | false | How to use Here is how to use this backbone encoder: ```python from transformers import AutoFeatureExtractor, ViTMSNModel import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) feature_extractor = Auto... | 1f1221702251cbded659bdd1314f9ce7 |
mit | ['Twitter', 'COVID-19', 'text-classification', 'pytorch', 'tensorflow', 'bert'] | false | Model description This model provides a zero-shot classifier to be used in cases where it is not possible to finetune CT-BERT on a specific task, due to lack of labelled data. The technique is based on [Yin et al.](https://arxiv.org/abs/1909.00161). The article describes a very clever way of using pre-trained MNLI mo... | c32ac4a36329b604670c3d4787ec8929 |
mit | ['Twitter', 'COVID-19', 'text-classification', 'pytorch', 'tensorflow', 'bert'] | false | Usage Please note that how you formulate the question can give slightly different results. Collecting a training set and finetuning on this, will most likely give you better accuracy. The easiest way to try this out is by using the Hugging Face pipeline. This uses the default Enlish template where it puts the text "T... | 77a238080820f1d321ba9022d8b3e0e6 |
mit | ['Twitter', 'COVID-19', 'text-classification', 'pytorch', 'tensorflow', 'bert'] | false | References ```bibtex @article{muller2020covid, title={COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter}, author={M{\"u}ller, Martin and Salath{\'e}, Marcel and Kummervold, Per E}, journal={arXiv preprint arXiv:2005.07503}, year={2020} } ``` or ``` Martin Müller, Ma... | 83008d588debc1ab9822f31674f6891c |
mit | ['indobert', 'indobenchmark', 'indonlu'] | false | IndoBERT-Lite Large Model (phase2 - uncased) [IndoBERT](https://arxiv.org/abs/2009.05387) is a state-of-the-art language model for Indonesian based on the BERT model. The pretrained model is trained using a masked language modeling (MLM) objective and next sentence prediction (NSP) objective. | a256d94226efb60ca2cb9a84fe3adb25 |
mit | ['indobert', 'indobenchmark', 'indonlu'] | false | params | Arch. | Training data | |--------------------------------|--------------------------------|-------|-----------------------------------| | `indobenchmark/indobert-base-p1` | 124.5M | Base | Indo4B (23.43 GB of text) | | `indobenchmark/indobert-base-p2` | ... | 06ad79abf2db5421c55c7c57e252e6c8 |
mit | ['indobert', 'indobenchmark', 'indonlu'] | false | Load model and tokenizer ```python from transformers import BertTokenizer, AutoModel tokenizer = BertTokenizer.from_pretrained("indobenchmark/indobert-lite-large-p2") model = AutoModel.from_pretrained("indobenchmark/indobert-lite-large-p2") ``` | df87b8ac3f181dd269e3cdbfb0da5d9c |
mit | ['indobert', 'indobenchmark', 'indonlu'] | false | Authors <b>IndoBERT</b> was trained and evaluated by Bryan Wilie\*, Karissa Vincentio\*, Genta Indra Winata\*, Samuel Cahyawijaya\*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti. | 2b4975da09ab55e6316c0814433ad729 |
mit | ['indobert', 'indobenchmark', 'indonlu'] | false | Citation If you use our work, please cite: ```bibtex @inproceedings{wilie2020indonlu, title={IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding}, author={Bryan Wilie and Karissa Vincentio and Genta Indra Winata and Samuel Cahyawijaya and X. Li and Zhi Yuan Lim and S. Soleman... | cdc11ddc9db797454c1164e6dcaf1114 |
apache-2.0 | ['translation'] | false | opus-mt-sn-fr * source languages: sn * target languages: fr * OPUS readme: [sn-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sn-fr/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | ee969e6269ddf5fe62f77b79a0fbe63d |
apache-2.0 | ['text-to-image'] | false | This is a Stable Diffusion (v1.5) model fine-tuned on the concept of my dog, Tessa, usiung the Dreambooth method: https://dreambooth.github.io/ To use the model, try modifying the basic prompt: \"**a photo of \<tessa\> dog**\". The model was fine-tuned for 1200 steps with a learning rate of 2e-6, using 15 images of T... | 0b69bd93448da8aefd85f7a106a99ce8 |
mit | [] | false | wire-angels on Stable Diffusion This is the `<wire-angels>` 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 al... | ba9658d178537cc3bfaaef728685b702 |
apache-2.0 | ['generated_from_trainer'] | false | finetuned_sentence_itr0_0.0002_all_27_02_2022-22_30_53 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3825 - Accuracy... | 06b3daf47e5bdcaff89234dd0be872ca |
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