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 results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.692 | 0.15 | 500 | 0.6882 | 0.5574 | | 0.6777 | 0.31 | 1000 | 0.6637 | 0.6059 | | 0.667 | 0.46 | 1500 | 0.6568 | 0.... | 4b986941f52037c120e12a9782749790 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | openai/whisper-large-v2 This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the common_voice_11_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.5984 - Wer: 18.3045 | 366571baed26530762188a1b6e04ac7e |
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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 1500 - mixed_precisio... | adc82daffa977a6d1867d36ec90aa9eb |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0002 | 24.01 | 1500 | 0.5984 | 18.3045 | | 6dc3d872f5de1bff93e8f8e048f2813d |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-home-6-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3789 - Accuracy: 0.3356 | 58cddbe7fdb80ca7d1417db0e729a96b |
apache-2.0 | ['translation'] | false | opus-mt-en-ny * source languages: en * target languages: ny * OPUS readme: [en-ny](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-ny/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://... | de2937e86223e3e2731adf0bcb48a32b |
apache-2.0 | ['t5-small', 'text2text-generation', 'natural language understanding', 'conversational system', 'task-oriented dialog'] | false | t5-small-nlu-tm1-context3 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on [Taskmaster-1](https://huggingface.co/datasets/ConvLab/tm1) with context window size == 3. Refer to [ConvLab-3](https://github.com/ConvLab/ConvLab-3) for model description and usage. | c488497a053b5459926def180c14af81 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-vios-google-colab This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5647 - Wer: 0.4970 | 32759410b34c922c95dbe1ed3b5cd17b |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | af383d35a378892a4697d8d2b8bd26c8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 7.7292 | 2.0 | 500 | 3.4159 | 1.0 | | 3.0762 | 4.0 | 1000 | 1.3005 | 0.9615 | | 0.8812 | 6.0 | 1500 | 0.4664 | 0.4740 | |... | 640922ddcc1b69ba44b2083f8725b5c6 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.3821 - Accuracy: 0.896 - F1: 0.8928 | eb2a2e133d27eebdd476edc4dc72a3cc |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 125 | 0.6029 | 0.7985 | 0.7597 | | 0.7905 | 2.0 | 250 | 0.3821 | 0.896 | 0.8928 | | 7903e24acfa504c33b21befd39ef7f25 |
apache-2.0 | ['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 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_schedu... | d4e9c62cdbc2f7bcd07eb8d2d195b057 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | No log | 1.13 | 8 | 2.1362 | 43.7647 | | 6d589972942b617339e5ad8bedf04ef8 |
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: 1.1639 | 65fb1811025e806698df2cc76604c456 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2291 | 1.0 | 5533 | 1.1581 | | 0.9553 | 2.0 | 11066 | 1.1249 | | 0.7767 | 3.0 | 16599 | 1.1639 | | 9fb3fc85c4ce7985f0468d7809139cd5 |
apache-2.0 | ['generated_from_trainer'] | false | w2v2 This model is a fine-tuned version of [facebook/wav2vec2-large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8860 - Wer: 0.2817 | 953748fcf47a789d15fc547dbfc820a1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.5664 | 3.07 | 500 | 3.0411 | 0.9997 | | 2.5607 | 6.13 | 1000 | 1.0770 | 0.3660 | | 0.9959 | 9.2 | 1500 | 0.8815 | 0.3017 | |... | 34c03aec2141f73a20e0eac69b746229 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | wav2vec2-large-xlsr-53-Georgian Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Georgian using the [Common Voice](https://huggingface.co/datasets/common_voice) When using this model, make sure that your speech input is sampled at 16kHz. | d1826b586e5d081cd69c85eeb449c9bd |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "ka", split="test[:2%]") processor = Wav2Vec2Processor.fr... | 2a130cd018be5c1081f4a67ed6e79e87 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Georgian test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "ka", split="test"... | 53405759496ed01e04e6bc151e167f3f |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_d... | dcb27c4a5034ae62d3ce5bdd0010ed2e |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pre... | 2d625d385359dbbb2dda73e46a631387 |
apache-2.0 | ['translation'] | false | opus-mt-fi-to * source languages: fi * target languages: to * OPUS readme: [fi-to](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-to/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-24.zip](https://... | f172ae2e23955b8ddb21d6d29efd33fc |
mit | ['generated_from_keras_callback'] | false | esm2_t12_35M_UR50D-finetuned-cytosol-membrane-classification This model is a fine-tuned version of [facebook/esm2_t12_35M_UR50D](https://huggingface.co/facebook/esm2_t12_35M_UR50D) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1009 - Train Accuracy: 0.9684 - Validatio... | db1f12a6870ed3761c1ee89486f63aae |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.2464 | 0.9228 | 0.1954 | 0.9417 | 0 | | 0.1428 | 0.9565 | 0.1831 | 0.9345 ... | fcb549f90e43aacc2943371ba4224e68 |
mit | ['exbert'] | false | Overview **Language model:** deepset/roberta-base-squad2-distilled **Language:** English **Training data:** SQuAD 2.0 training set **Eval data:** SQuAD 2.0 dev set **Infrastructure**: 4x V100 GPU **Published**: Dec 8th, 2021 | af38e48cb5cae5db696f3e4911eb4db6 |
mit | ['roberta-base', 'roberta-base-epoch_57'] | false | RoBERTa, Intermediate Checkpoint - Epoch 57 This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692), trained on Wikipedia and the Book Corpus only. We train this model for almost 100K steps, corresponding to 83 epochs. We provide the 84 checkpoints (including the randomly ... | 573c9aa384596bfeaadafbcbfed96236 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2141 - Accuracy: 0.922 - F1: 0.9220 | 43af6f8de5477a053dd008ef5bf9c868 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8134 | 1.0 | 250 | 0.3017 | 0.9105 | 0.9087 | | 0.2455 | 2.0 | 500 | 0.2141 | 0.922 | 0.9220 | | e6ef19c9a68cfb51050dd35068d8b05a |
apache-2.0 | ['generated_from_trainer'] | false | english-filipino-wav2vec2-l-xls-r-test This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) on the filipino_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.5795 - Wer: 0.3996 | 21fb46d240b322c7567a836690227540 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.0751 | 2.09 | 400 | 2.4744 | 0.9804 | | 0.7852 | 4.19 | 800 | 0.5836 | 0.5620 | | 0.3751 | 6.28 | 1200 | 0.4873 | 0.4658 | |... | c50c9d1b98042c7d3433f8140abf39de |
apache-2.0 | ['summarization', 'urdu', 'ur', 'mt5', 'Abstractive Summarization', 'generated_from_trainer'] | false | mt5-base-finetuned-urdu This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on Urdu subset the xlsum dataset. It achieves the following results on the evaluation set: - Loss: 2.8954 - Rouge-1: 28.84 - Rouge-2: 13.87 - Rouge-l: 25.63 - Gen Len: 19.0 - Bertscore: 71.31 | 3d39015143fa3bc36414f2aac29a2a83 |
apache-2.0 | ['summarization', 'urdu', 'ur', 'mt5', 'Abstractive Summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:| | 3.6205 | 1.0 | 2114 | 3.0871 | 26.45 | 11.4 | 23.26 | 19.0 | 7... | 427620accdb79d09572eb4fe0c6369b3 |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-BASE-DM512 (Deep-Narrow version) T5-Efficient-BASE-DM512 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 ... | 876acfe517660f66e1e98251634545ef |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-base-dm512** - is of model type **Base** with the following variations: - **dm** is **512** It has **148.63** million parameters and thus requires *ca.* **594.52 MB** of memory in full precision (*fp32*) or **297.26 MB** of memory in half precision ... | eb0076bfa334aa72c27cb9771f3029d6 |
apache-2.0 | ['generated_from_trainer'] | false | test 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: 0.3003 - Accuracy: 0.88 - F1: 0.88 | d188f99b9039940e8762715bd4372110 |
mit | [] | false | Trained on amateur photographs of chickens from Reddit. Include "chkn" in a prompt to use.  ![22227-1353605590-Floc... | 44a527343aea938a08cf6f029c8d1bd7 |
mit | [] | false | model by Xmuzz This your the Stable Diffusion model fine-tuned the xordixx concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **xordizz** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/g... | 129a0ce345785e40eea0f0ed6da84f2a |
cc-by-4.0 | ['question generation'] | false | Model Card of `lmqg/mt5-small-squad-qg` This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generat... | 49cbe1e13cab7bd8ce5ae6f7fb1b1e25 |
cc-by-4.0 | ['question generation'] | false | Overview - **Language model:** [google/mt5-small](https://huggingface.co/google/mt5-small) - **Language:** en - **Training data:** [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/l... | 6eae25cc484f4cfc32c939caf2fdd9a6 |
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/mt5-small-squad-qg") output ... | 5b8be73adcd568ef90655037f5e352bb |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-squad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squad.default.json) | | Score | Type | Dataset | |:---------... | 0cfb1e71b7eab03a2633bf6e9224089b |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squad - dataset_name: default - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: None - model: google/mt5-small - max_length: 512 - max_length_output: 32 - epoch: 15 - b... | 16c51afd0c6ee8b1312014d3e494c07f |
mit | ['generated_from_trainer'] | false | my-lilt-en-funsd This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the funsd-layoutlmv3 dataset. It achieves the following results on the evaluation set: - Loss: 1.7942 - Answer: {'precision': 0.8597914252607184, 'recall': 0.90820073... | 837b13e0c78882095eec146ea84874ca |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question ... | aafba09ce0c77f3b325872a868b847c3 |
mit | ['generated_from_trainer'] | false | roberta_large-ner-conll2003_0818_v0 This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.1793 - Precision: 0.9064 - Recall: 0.9333 - F1: 0.9197 - Accuracy: 0.9796 | 9a2ada70ac4ad8db5b6bae9af1312b76 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0273 | 1.0 | 878 | 0.0500 | 0.9338 | 0.9588 | 0.9461 | 0.9894 | | 0.0154 | 2.0 |... | 3edb44d5207b601adf72b27eac9fc03c |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-mlm-pubmed This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8008 - Rouge2 Precision: 0.6071 - Rouge2 Recall: 0.4566 - Rouge2 Fmeasure: 0.5079 | 45106ddc9f11e80b511e8affd3f1437f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:| | 0.914 | 0.75 | 500 | 0.8691 | 0.5901 | 0.4357 | 0.48... | 3b1da4aca7297cd473f6823111c5cbfb |
gpl-3.0 | ['object-detection', 'yolo', 'autogenerated-modelcard'] | false | Model Description <!-- Provide a longer summary of what this model is. --> YOLOv6 is a single-stage object detection framework dedicated to industrial applications, with hardware-friendly efficient design and high performance. - **Developed by:** [More Information Needed] - **Shared by [Optional]:** [@nateraw](http... | f27eeebbd0e4db5b7ee8fb2f5e7e8f0b |
mit | [] | false | If you cannot load a model, please create a model here. ``` import torch import torch.nn as nn class Janken(nn.Module): def __init__(self): super().__init__() self.fc1=nn.Linear(2,20,bias=False) self.fc2=nn.Linear(20,60,bias=False) self.fc3=nn.Linear(60,70,bias=False) self.fc... | ce96df4736a0caea111f32a3263cad4e |
mit | ['audio', 'speech-translation', 'automatic-speech-recognition'] | false | S2T-SMALL-COVOST2-CA-EN-ST `s2t-small-covost2-ca-en-st` is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST). The S2T model was proposed in [this paper](https://arxiv.org/abs/2010.05171) and released in [this repository](https://github.com/pytorch/fairseq/tree/master/examples/spe... | c17534840ab4cf11467a7ced7ddf3a2b |
mit | ['audio', 'speech-translation', 'automatic-speech-recognition'] | false | Intended uses & limitations This model can be used for end-to-end Catalan speech to English text translation. See the [model hub](https://huggingface.co/models?filter=speech_to_text) to look for other S2T checkpoints. | 4cf1215cf2c062d563c1229878aef2d0 |
mit | ['audio', 'speech-translation', 'automatic-speech-recognition'] | false | How to use As this a standard sequence to sequence transformer model, you can use the `generate` method to generate the transcripts by passing the speech features to the model. *Note: The `Speech2TextProcessor` object uses [torchaudio](https://github.com/pytorch/audio) to extract the filter bank features. Make sure... | 2a2c6c8266f0424ba0bcbd740df71328 |
mit | ['audio', 'speech-translation', 'automatic-speech-recognition'] | false | Training data The s2t-small-covost2-ca-en-st is trained on Catalan-English subset of [CoVoST2](https://github.com/facebookresearch/covost). CoVoST is a large-scale multilingual ST corpus based on [Common Voice](https://arxiv.org/abs/1912.06670), created to to foster ST research with the largest ever open dataset | b1e9a2b33cf6e1f438ed747dfb116ab9 |
apache-2.0 | ['super-image', 'image-super-resolution'] | false | Enhanced Deep Residual Networks for Single Image Super-Resolution (EDSR) EDSR 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 [Enhanced Deep Residual Networks for Single Image Super-Resolution](ht... | 50fb33b85e6db5c80f4f6892df7168c3 |
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 EdsrModel, ImageLoader from PIL import Image import requests url = 'https://pa... | 4391fee36b7430f02772a8702673ad71 |
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... | 8ad36aa48d1454bf88debca274c4ec20 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 9.2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sc... | 42cffdc441ea499bb95d8e1589767f89 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 3.67 | 0.32 | 5000 | 3.4705 | | 3.573 | 0.63 | 10000 | 3.3747 | | 3.5075 | 0.95 | 15000 | 3.3154 | | 3.4486 | 1.26 | 20000 | 3.2704 ... | c0b1c7ec2bcb02abb2494c3744b4b182 |
apache-2.0 | ['generated_from_trainer'] | false | bert-nlp-project-ft-imdb This model is a fine-tuned version of [jestemleon/bert-nlp-project-imdb](https://huggingface.co/jestemleon/bert-nlp-project-imdb) on the [steciuk/imdb](https://huggingface.co/datasets/steciuk/imdb) dataset. It achieves the following results on the evaluation set: - Loss: 0.2429 - Accuracy: 0.... | 17d08a312c62827562a38293257184bc |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.2603 | 0.38 | 750 | 0.1922 | 0.9293 | 0.9293 | | 0.2021 | 0.75 | 1500 | 0.1633 | 0.9463 | 0.9446 | | 0.1706 |... | e856f76eebd36f55d53776236ae7ff27 |
apache-2.0 | ['chinese', 'classical chinese', 'literary chinese', 'ancient chinese', 'bert', 'pytorch'] | false | Model description  This is a RoBERTa model pre-trained on Classical Chinese. You can fine-tune GuwenBERT for downstream tasks, such as sentence breaking, punctuation, named entity recognition, and so on. ... | 1d6806a082aca6a516e757c6791e530d |
apache-2.0 | ['chinese', 'classical chinese', 'literary chinese', 'ancient chinese', 'bert', 'pytorch'] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ethanyt/guwenbert-large") model = AutoModel.from_pretrained("ethanyt/guwenbert-large") ``` | ecbfee50cad950d91aa61bbd9896fe7a |
apache-2.0 | ['chinese', 'classical chinese', 'literary chinese', 'ancient chinese', 'bert', 'pytorch'] | false | Training data The training data is daizhige dataset (殆知阁古代文献) which is contains of 15,694 books in Classical Chinese, covering Buddhism, Confucianism, Medicine, History, Zi, Yi, Yizang, Shizang, Taoism, and Jizang. 76% of them are punctuated. The total number of characters is 1.7B (1,743,337,673). All traditional Ch... | 7001d34b0fa76adf55b1ef23d08e8feb |
apache-2.0 | ['chinese', 'classical chinese', 'literary chinese', 'ancient chinese', 'bert', 'pytorch'] | false | Training procedure The models are initialized with `hfl/chinese-roberta-wwm-ext-large` and then pre-trained with a 2-step strategy. In the first step, the model learns MLM with only word embeddings updated during training, until convergence. In the second step, all parameters are updated during training. The models ... | d8f0778abef921f32d7a2f664658f398 |
apache-2.0 | ['chinese', 'classical chinese', 'literary chinese', 'ancient chinese', 'bert', 'pytorch'] | false | 2) with a batch size of 2,048 and a sequence length of 512. The optimizer used is Adam with a learning rate of 1e-4, adam-betas of (0.9,0.98), adam-eps of 1e-6, a weight decay of 0.01, learning rate warmup for 5K steps, and linear decay of learning rate after. | f20d1eb33c9f34588454b34d253b3e4b |
apache-2.0 | ['chinese', 'classical chinese', 'literary chinese', 'ancient chinese', 'bert', 'pytorch'] | false | "Gulian Cup" Ancient Books Named Entity Recognition Evaluation Second place in the competition. Detailed test results: | NE Type | Precision | Recall | F1 | |:----------:|:-----------:|:------:|:-----:| | Book Name | 77.50 | 73.73 | 75.57 | | Other Name | 85.85 | 89.32 | 87.55 | | Micro Avg. |... | 456f79dd18b780e61c32a1ee39353f97 |
apache-2.0 | ['chinese', 'classical chinese', 'literary chinese', 'ancient chinese', 'bert', 'pytorch'] | false | About Us We are from [Datahammer](https://datahammer.net), Beijing Institute of Technology. For more cooperation, please contact email: ethanyt [at] qq.com > Created with ❤️ by Tan Yan [](https://github.com/Ethan-yt) and Zewen Chi... | c326234a729ee3e780480f6497a4071e |
apache-2.0 | ['generated_from_trainer'] | false | roberta-base-bne-finetuned-amazon_reviews_multi This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.2313 - Accuracy: 0.9337 | 88ebd355271a76904fcb44d36e115cf1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1864 | 1.0 | 1250 | 0.2209 | 0.9317 | | 0.1063 | 2.0 | 2500 | 0.2313 | 0.9337 | | 8616dd477ed0e04a64006e9865c29db9 |
creativeml-openrail-m | ['text-to-image', 'diffusers', 'lora'] | false | cat-toy-z Dreambooth LoRA model trained by multimodalart 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/... | e2de6bb6de351d8840da045d842bd8f0 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2294 - Accuracy: 0.924 - F1: 0.9240 | ae043bbf175ce8682634a0b86b3c48fd |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 250 | 0.3316 | 0.9025 | 0.8985 | | No log | 2.0 | 500 | 0.2294 | 0.924 | 0.9240 | | 4cce011e15a34294c82dca159f4425b9 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-finetuned This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9895 | 69bbe08da16dd08a4a902aaa7116734a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.2103 | 1.0 | 10024 | 2.0834 | | 2.1146 | 2.0 | 20048 | 2.0387 | | 2.0721 | 3.0 | 30072 | 2.0095 | | 0653fe228af193ec619b74b998e23e84 |
mit | ['vision', 'image-classification'] | false | DiNAT (base variant) DiNAT-Base trained on ImageNet-1K at 224x224 resolution. It was introduced in the paper [Dilated Neighborhood Attention Transformer](https://arxiv.org/abs/2209.15001) by Hassani et al. and first released in [this repository](https://github.com/SHI-Labs/Neighborhood-Attention-Transformer). | 33e642ab3317bb3130c560490f205e63 |
mit | ['vision', 'image-classification'] | false | Example Here is how to use this model to classify an image from the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import AutoImageProcessor, DinatForImageClassification from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image ... | 3ae5835fd39e50b6bba7f84ee5cb6d57 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Small Fa - BuzzyBuzzy This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.5968 - Wer: 34.5206 | d96cd5812bb3b1f6e1068d4f124fbe67 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-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 - lr_scheduler_warmup_steps: 500 - training_steps: 10000 - mixed_preci... | 582e853eac821dc780d21b5f27642790 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 0.0091 | 0.86 | 2000 | 0.5627 | 37.7340 | | 0.0077 | 1.72 | 4000 | 0.5761 | 36.5033 | | 0.0028 | 2.58 | 6000 | 0.5851 | 3... | c04a6d50478a5611c7682d09ce5ea7ca |
apache-2.0 | ['translation'] | false | spa-nor * source group: Spanish * target group: Norwegian * OPUS readme: [spa-nor](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/spa-nor/README.md) * model: transformer-align * source language(s): spa * target language(s): nno nob * model: transformer-align * pre-processing: normalization ... | b3d3aeb544acb4922aba3cf744758df8 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: spa-nor - source_languages: spa - target_languages: nor - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/spa-nor/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['es', 'no'] - src_constituents: {'spa'} - tgt_const... | fd6debeeda79292b87777fefb204c698 |
openrail | [] | false | The training get transformers: ``` git clone https://github.com/huggingface/transformers cd transformers ``` Prepare an initialized opt-1.3 model: ``` cat << EOT > prep-fp32.py from transformers import AutoConfig, AutoModel, AutoTokenizer import torch mname = "facebook/opt-1.3b" config = AutoConfig.from_pretraine... | 46719ef6e2cb89417c684de3c0cc503c |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1675 - Accuracy: 0.9325 - F1: 0.9327 | 5d79b0211b0b27e667f882a70f58a45c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.29 | 1.0 | 250 | 0.1896 | 0.9265 | 0.9255 | | 0.1557 | 2.0 | 500 | 0.1675 | 0.9325 | 0.9327 | | 685f35651f6263d70ea3489fcec4425f |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'animal'] | false | DreamBooth model for the dashdash concept trained by jiaenyue. This is a Stable Diffusion model fine-tuned on the dashdash concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of dashdash cat** This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation pag... | e8a4bd216e44f5fa41b1dc936184ac32 |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'image-to-image'] | false | Stable Diffusion Image Variations Model Card This version of Stable Diffusion has been fine tuned from [CompVis/stable-diffusion-v1-3-original](https://huggingface.co/CompVis/stable-diffusion-v-1-3-original) to accept CLIP image embedding rather than text embeddings. This allows the creation of "image variations" sim... | 7c104df67482bfb09175c89102013238 |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'image-to-image'] | false | Example First clone [Lambda Diffusers](https://github.com/LambdaLabsML/lambda-diffusers) and install any requirements (in a virtual environment in the example below): ```bash git clone https://github.com/LambdaLabsML/lambda-diffusers.git cd lambda-diffusers python -m venv .venv source .venv/bin/activate pip install ... | bb25f9c4f89142c9c4b4b3c84890d1c7 |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'image-to-image'] | false | Training **Training Data** The model developers used the following dataset for training the model: - LAION-2B (en) and subsets thereof (see next section) **Training Procedure** This model is fine tuned from Stable Diffusion v1-3 where the text encoder has been replaced with an image encoder. The training procedure ... | d4bd8618442f6243a81426828851814a |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'image-to-image'] | false | Safety Module The intended use of this model is with the [Safety Checker](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) in Diffusers. This checker works by checking model outputs against known hard-coded NSFW concepts. The concepts are intentionally hi... | 3c6e20a5504352bbcc7e093e89b97de7 |
apache-2.0 | [] | false | Example Usage ```python from transformers import AutoTokenizer, T5ForConditionalGeneration tokenizer = AutoTokenizer.from_pretrained("laituan245/molt5-large", model_max_length=512) model = T5ForConditionalGeneration.from_pretrained('laituan245/molt5-large') ``` | 28b5d7801ba27db613ffa22e5f939707 |
apache-2.0 | ['generated_from_keras_callback'] | false | xander71988/t5-small-finetuned-facet-contract-type This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1701 - Validation Loss: 0.1605 - Epoch: 6 | 41ea0504fdaf49d0c8bc05913be4431e |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 7000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay... | f831bddfd7d2d13f2c641c69c6d8e80d |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.8446 | 0.3244 | 0 | | 0.2976 | 0.1945 | 1 | | 0.2240 | 0.1686 | 2 | | 0.1970 | 0.1763 | 3 | | 0.1866 | 0.1548 | 4 | | 0.1793 |... | dfd79b3be086bfd5fad919edbdff4753 |
apache-2.0 | ['translation'] | false | opus-mt-fr-gil * source languages: fr * target languages: gil * OPUS readme: [fr-gil](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-gil/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](http... | 343ce953ffe8891b0ee35968a5b0b152 |
apache-2.0 | ['image-classification', 'vision', 'generated_from_trainer'] | false | vit-base-beans This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0769 - Accuracy: 0.9850 | fc1b1a0741098629c1c315dca35bf6a2 |
apache-2.0 | ['image-classification', 'vision', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Accuracy | Validation Loss | |:-------------:|:-----:|:----:|:--------:|:---------------:| | 0.2572 | 1.0 | 130 | 0.9624 | 0.2294 | | 0.1531 | 2.0 | 260 | 0.9699 | 0.1501 | | 0.0817 | 3.0 | 390 | 0.9850 | 0.0896 ... | 83c608826c8bdda5b4e8b67455d2581b |
cc-by-sa-4.0 | ['vietnamese', 'token-classification', 'pos', 'dependency-parsing'] | false | Model Description This is a RoBERTa model pre-trained on Vietnamese texts for POS-tagging and dependency-parsing, derived from [roberta-base-vietnamese](https://huggingface.co/KoichiYasuoka/roberta-base-vietnamese). Every word is tagged by [UPOS](https://universaldependencies.org/u/pos/)(Universal Part-Of-Speech). | 795e8ceaeec557b8b06b7d71ae9f7379 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.