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 | ['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", "ta", split="test[:2%]") processor = Wav2Vec2Processor.from_pretr... | 5238471ec91af8d283c904a86515b10d |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Tamil 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", "ta", split="test") wer = loa... | 6d89fd9b299063b17051c7bfbd173d56 |
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 pred_ids = torch.a... | 0dd564f652edc5e20909cd447ff5d33d |
apache-2.0 | ['generated_from_trainer'] | false | my_awesome_food_model 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 food101 dataset. It achieves the following results on the evaluation set: - Loss: 1.0616 - Accuracy: 0.7962 | f12d9c26ad6ffb343bd813415ea766dd |
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: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | 5987b5548bd78d659b89fdb6cd4215f2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.9969 | 1.0 | 947 | 1.9538 | 0.7321 | | 1.1907 | 2.0 | 1894 | 1.2216 | 0.7806 | | 0.9433 | 3.0 | 2841 | 1.0616 | 0.... | 73aa73c37db1ea3449255ba8544f534e |
apache-2.0 | ['multiberts', 'multiberts-seed_21'] | false | MultiBERTs - Seed 21 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 random seeds, which causes variatio... | b96bd10203a76f991f2eaba8dffe4a92 |
apache-2.0 | ['multiberts', 'multiberts-seed_21'] | 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_21') model = TFBertModel.from_pretrained("google/multiberts-seed_... | 72684afb5cfbba025eb865fe96f00d2a |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-ner 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: 0.3610 - Precision: 0.8259 - Recall: 0.7483 - F1: 0.7852 - Accuracy: 0.9283 | 040b1b8ced6a524dfc4346120d766579 |
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: 4 | bbdab40f43adad4da4daef7cecba9f69 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 234 | 0.2604 | 0.8277 | 0.7477 | 0.7856 | 0.9292 | | No log | 2.0 |... | 54460416948a377fd749ef631678b3e8 |
apache-2.0 | ['automatic-speech-recognition', 'nl'] | false | exp_w2v2t_nl_vp-fr_s417 Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-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... | f426bea88325f2ab056155188050da49 |
apache-2.0 | ['automatic-speech-recognition', 'zh-CN'] | false | exp_w2v2t_zh-cn_vp-sv_s331 Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) 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 th... | 09a108e7acb8de33e65999916c149629 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.6751 - Accuracy: 0.6567 - F1: 0.6555 | 439208fb80b0481a9fd1ec96dfad3781 |
apache-2.0 | ['generated_from_trainer'] | false | librispeech-5h-supervised This model is a fine-tuned version of [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2041 - Wer: 0.0624 | 114a454c1708f096e70b10c36431b612 |
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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 100 - mixed_precision_... | 6266f8ce4982321cd94476e6d08bc507 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.7758 | 11.11 | 1000 | 0.3120 | 0.2337 | | 0.1238 | 22.22 | 2000 | 0.1651 | 0.0826 | | 0.0383 | 33.33 | 3000 | 0.1667 | 0.0712 | |... | 763ebb09efdbde7ffcfdc526c531b231 |
creativeml-openrail-m | ['text-to-image'] | false | oracleclyde-custom Dreambooth model trained by oracleclyde with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v2-1-768 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggin... | 2ff547509de0ecef63d8652d355606a4 |
apache-2.0 | ['generated_from_trainer'] | false | vit-base-patch16-224-finetuned-eurosat This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0363 - Accuracy: 0.9889 | 5657a39570b4b83a90210c14e17e5d8a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1667 | 1.0 | 190 | 0.0731 | 0.9756 | | 0.115 | 2.0 | 380 | 0.0426 | 0.9878 | | 0.0903 | 3.0 | 570 | 0.0363 | 0.... | 98aaf1b43b5490ae14219b75906e2312 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-korean-w1 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1406 - Cer: 0.0393 | c6443547ae6df2ea35599febf98cb483 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - 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: 1000 - num_epochs: 3 - mixed_precision_tra... | 16eb0e9659d541fe89090fdfe9dcd857 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 24.537 | 0.56 | 800 | 3.0461 | 0.9274 | | 1.9309 | 1.13 | 1600 | 0.7723 | 0.2168 | | 0.7595 | 1.69 | 2400 | 0.3197 | 0.0916 | |... | 4a5c1983862c10aa3820402ddba01c7a |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_... | 8d607ada1c4e32e6563b90bbaee22e18 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.495 | 0.16 | 500 | 3.3883 | 1.0 | | 2.9095 | 0.32 | 1000 | 2.9152 | 1.0000 | | 1.8434 | 0.49 | 1500 | 1.0473 | 0.7446 | |... | 54370e1306a52e9b974730a165915455 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_7` with split `test` ```bash python eval.py --model_id Plim/xls-r-300m-fr --dataset mozilla-foundation/common_voice_7_0 --config fr --split test ``` 2. To evaluate on `speech-recognition-community-v2/dev_data` ```bash python eval.py --model_id P... | 63634c8af08799a784c4005cd48c9e0b |
apache-2.0 | [] | false | 模型分类 Model Taxonomy | 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra | | :----: | :----: | :----: | :----: | :----: | :----: | | 通用 General | 自然语言生成 NLG| 闻仲 Wenzhong | GPT2 | 3.5B | 中文 Chinese | | 1f5c814f26f6f229ed9ce445cdfbdc26 |
apache-2.0 | [] | false | 模型信息 Model Information 为了可以获得一个强大的单向语言模型，我们采用GPT模型结构，并且应用于中文语料上。具体地，这个模型拥有30层解码器和35亿参数，这比原本的GPT2-XL还要大。我们在100G的中文语料上预训练，这消耗了32个NVIDIA A100显卡大约28小时。据我们所知，它是目前最大的中文的GPT模型。 To obtain a robust unidirectional language model, we adopt the GPT model structure and apply it to the Chinese corpus. Specifically, this model has... | 6da135d11f213a5e1c233a3934c89bed |
apache-2.0 | [] | false | 加载模型 Loading Models ```python from transformers import GPT2Tokenizer, GPT2Model tokenizer = GPT2Tokenizer.from_pretrained('IDEA-CCNL/Wenzhong-GPT2-3.5B') model = GPT2Model.from_pretrained('IDEA-CCNL/Wenzhong-GPT2-3.5B') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') ... | ad8a53c31bd1e2d11b91e9e9135eb1de |
apache-2.0 | [] | false | 使用示例 Usage Examples ```python from transformers import pipeline, set_seed set_seed(55) generator = pipeline('text-generation', model='IDEA-CCNL/Wenzhong-GPT2-3.5B') generator("北京位于", max_length=30, num_return_sequences=1) ``` | b3d4a0d088a3460c97663a3c510a0f2b |
apache-2.0 | ['object-detection', 'vision'] | false | DETR (End-to-End Object Detection) model with ResNet-50 backbone DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by Carion et al. and first rele... | 3c12736dda55e4cc203d35806a66cc9d |
apache-2.0 | ['object-detection', 'vision'] | false | How to use Here is how to use this model: ```python from transformers import DetrImageProcessor, DetrForObjectDetection 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) processor = DetrImageProce... | 7369f19751b461f8e7f2bc4dd95f151b |
apache-2.0 | ['object-detection', 'vision'] | false | let's only keep detections with score > 0.9 target_sizes = torch.tensor([image.size[::-1]]) results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0] for score, label, box in zip(results["scores"], results["labels"], results["boxes"]): box = [round(i, 2) for i in box.... | 3cfc29e62036c370cb7b2b1cae6c8e64 |
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: 0.2204 - Accuracy: 0.9245 - F1: 0.9244 | 31b6307b869f96fdb152cc40e9c5ecb1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8209 | 1.0 | 250 | 0.3154 | 0.91 | 0.9081 | | 0.2531 | 2.0 | 500 | 0.2204 | 0.9245 | 0.9244 | | 412956656667e88a04bdf459e216b6fa |
mit | [] | false | Isabell Schulte pviii - 1 image - Style on Stable Diffusion This is the `<isabell-schulte-p8-1-style>` 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_conceptua... | 7c80abc72c957accc21cf59a27fc0365 |
mit | ['huggan', 'gan'] | false | BibTeX entry and citation info ```bibtex @inproceedings{xia2021tedigan, title={TediGAN: Text-Guided Diverse Face Image Generation and Manipulation}, author={Xia, Weihao and Yang, Yujiu and Xue, Jing-Hao and Wu, Baoyuan}, booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year={2021... | fa207aad958dd78efbb0fb6e5fe50ae1 |
apache-2.0 | ['generated_from_trainer'] | false | sagemaker-distilbert-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: 0.2590 - Accuracy: 0.915 | ad4c03088c55eef921a4c355fd781695 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.9292 | 1.0 | 500 | 0.2590 | 0.915 | | 465cf22c44174ed91bcb52db271f65de |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-BASE-NL4 (Deep-Narrow version) T5-Efficient-BASE-NL4 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 ... | 05962ef0849223b3c085f09c99ea8ab0 |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-base-nl4** - is of model type **Base** with the following variations: - **nl** is **4** It has **90.76** million parameters and thus requires *ca.* **363.05 MB** of memory in full precision (*fp32*) or **181.52 MB** of memory in half precision (*fp1... | 265e9638da0e769b1901253287b36e69 |
apache-2.0 | ['generated_from_trainer'] | false | bert-all-squad_all_translated This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5261 | 1aac44a456f3690ecffb65d1bc11c3e7 |
apache-2.0 | [] | false | Graphcore/wav2vec2-ctc-base-ipu Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Grap... | ad064d3fef3d8b297f50d67e10533600 |
apache-2.0 | [] | false | Intended uses & limitations This model contains just the `IPUConfig` files for running the Wav2Vec2ForCTC base model (e.g. [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base)) on Graphcore IPUs. **This model contains no model weights, only an IPUConfig.** | 84a7f81eb9b0242414fd421a80a1ffe1 |
apache-2.0 | ['generated_from_trainer'] | false | finetuned_distilgpt2_sst2_negation0.0_pretrainedFalse_epochs0 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the sst2 dataset. It achieves the following results on the evaluation set: - eval_loss: 4.6217 - eval_runtime: 0.9358 - eval_samples_per_second: 196.632 - eval_steps_p... | 353e4df59eb4df54d4547cb18444dc52 |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-kitchen_and_dining-3-16-5 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: 2.3560 - Accuracy: 0.2692 | 0dafb0bcd01e3aabf0d28c63e846c45f |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 | 1eed1ef0f4e3bce88c12a67572cb82ca |
gpl-3.0 | [] | false | Pre-trained word embeddings using the text of published clinical case reports. These embeddings use 300 dimensions and were trained using the GloVe algorithm on published clinical case reports found in the [PMC Open Access Subset](https://www.ncbi.nlm.nih.gov/pmc/tools/openftlist/). See the paper here: https://pubmed.... | e6ce8999f1185d2d8de30cf48edb2be4 |
apache-2.0 | ['generated_from_trainer'] | false | whisper-base-ar-quran This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0839 - Wer: 5.7544 | 2cf0bcbe98ea471562c90c40456444f4 |
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 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 128 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon... | 0510593b8bd3a42eb1317252c27406ce |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.1092 | 0.05 | 250 | 0.1969 | 13.3890 | | 0.0361 | 0.1 | 500 | 0.1583 | 10.6375 | | 0.0192 | 0.15 | 750 | 0.1109 | 8.8468... | 8418e68aea89673298465a70119676c1 |
apache-2.0 | ['MyanBERTa', 'Myanmar', 'BERT', 'RoBERTa'] | false | Model description This model is a BERT based Myanmar pre-trained language model. MyanBERTa was pre-trained for 528K steps on a word segmented Myanmar dataset consisting of 5,992,299 sentences (136M words). As the tokenizer, byte-leve BPE tokenizer of 30,522 subword units which is learned after word segmentation is ap... | 0b781928640d2894f30e1b061246822d |
apache-2.0 | ['translation'] | false | opus-mt-de-en * source languages: de * target languages: en * OPUS readme: [de-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/de-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-02-26.zip](https://... | 30e7e005fa41d5fa07ff04811d4966d1 |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newssyscomb2009.de.en | 29.4 | 0.557 | | news-test2008.de.en | 27.8 | 0.548 | | newstest2009.de.en | 26.8 | 0.543 | | newstest2010.de.en | 30.2 | 0.584 | | newstest2011.de.en | 27.4 | 0.556 | | newstest2012.de.e... | a35856f53de9fd2dba3c6a4802e13f9b |
apache-2.0 | ['translation'] | false | opus-mt-fi-swc * source languages: fi * target languages: swc * OPUS readme: [fi-swc](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-swc/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-24.zip](http... | a7c79d36716306e0da4e2a5f902b3b94 |
creativeml-openrail-m | ['text-to-image'] | false | Chantum Test q Dreambooth model trained by Balthamos with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v2-1-768 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/... | 92ffc9831703a58be0cfa22606ed6780 |
apache-2.0 | ['generated_from_trainer'] | false | swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.7828 - Accuracy: 0.2857 | 477e2c000127267f12bd015f354da6c5 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 200 - eval_batch_size: 200 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 800 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_... | 68b097045825e9e1fdcd61382eaf8067 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 1 | 0.7828 | 0.2857 | | No log | 2.0 | 2 | 0.8606 | 0.1429 | | No log | 3.0 | 3 | 0.8619 | 0.... | 45d6bd5a3bb4e507d3c05186771a88c2 |
apache-2.0 | ['generated_from_trainer'] | false | SAE-bert-base-uncased This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the [jgammack/SAE-door-abstracts](https://huggingface.co/datasets/jgammack/SAE-door-abstracts) dataset. It achieves the following results on the evaluation set: - Loss: 2.1256 | 09a47cb13cfc8e412b3a04a23e006bce |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.5967 | 1.0 | 80 | 2.3409 | | 2.4881 | 2.0 | 160 | 2.2707 | | 2.3567 | 3.0 | 240 | 2.3134 | | 2.3413 | 4.0 | 320 | 2.2592 ... | ebbfd2968d5c7389d9b4cfeda7803e88 |
apache-2.0 | [] | false | Basic info model based [Salesforce/codegen-350M-mono](https://huggingface.co/Salesforce/codegen-350M-mono) fine-tuned with data [codeparrot/github-code-clean](https://huggingface.co/datasets/codeparrot/github-code-clean) data filter by python | b3e30fd539511a97570a1c5e9eb2f2a6 |
apache-2.0 | [] | false | Usage ```python from transformers import AutoTokenizer, AutoModelForCausalLM model_type = 'kdf/python-docstring-generation' tokenizer = AutoTokenizer.from_pretrained(model_type) model = AutoModelForCausalLM.from_pretrained(model_type) inputs = tokenizer('''<|endoftext|> def load_excel(path): return pd.read_exce... | d65beafa7c018169e92aa6f2ea011053 |
apache-2.0 | [] | false | docstring """''', return_tensors='pt') doc_max_length = 128 generated_ids = model.generate( **inputs, max_length=inputs.input_ids.shape[1] + doc_max_length, do_sample=False, return_dict_in_generate=True, num_return_sequences=1, output_scores=True, pad_token_id=50256, eos_token_id=5025... | 5128b1414119be332065b5b86dce304e |
apache-2.0 | [] | false | docstring """ Calculate numbers add. Args: a: the first number to add b: the second number to add Return: The result of a + b """ <|endoftext|> def load_excel(path): return pd.read_excel(path) | 022d01903a648773d3ec661e5ad1e488 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-4 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: 3.1442 - Wer: 1.0 | e2c4e47af125429f5986d5719cc2a6b6 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - 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: 400 - num_epochs: 30 | 7cd75bee8132316ab50e5fe888a611f7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 5.1303 | 1.37 | 200 | 3.2783 | 1.0 | | 2.8798 | 2.74 | 400 | 3.1233 | 1.0 | | 2.8586 | 4.11 | 600 | 3.1612 | 1.0 | | 2.8613 ... | 3a8947533d0f7669a0244ceba4669c2b |
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: 0.2175 - Accuracy: 0.9285 - F1: 0.9285 | 844d2bfc35e9c80cec19cf9feed3c6f5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8486 | 1.0 | 250 | 0.3234 | 0.896 | 0.8913 | | 0.257 | 2.0 | 500 | 0.2175 | 0.9285 | 0.9285 | | 00563c0034469192c098172fa4a672af |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Spanish Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Spanish using the [Common Voice](https://huggingface.co/datasets/common_voice). When using this model, make sure that your speech input is sampled at 16kHz. | 1a3063e5434c68d60076fccb2ec4e386 |
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", "es", split="test[:2%]"). processor = Wav2Vec2Processor.f... | 49e6d5a81f727533eac7b7364c203807 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Ukrainian 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", "es", split="test... | 12f47c05024c8545a443597d3343dc08 |
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_dataset... | a88220ae0de19524680e571e17220f6c |
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 pred_ids = torch.argmax... | c92dd206242aebbe35e078aa06cb05e2 |
apache-2.0 | ['fill-mask', 'transformers', 'en', 'ko'] | false | albert-small-kor-v1 - albert-small 한국어 scratch 모델 - [ai_hub 웹데이터 기반 한국어 말뭉치 데이터](https://aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=624) (약 52M Text) 말뭉치로 SOP, MLM 훈련시킨 모델 - vocab: 30,000개 (SentencePiece) | 51a744f8a7fa8350f2ccc5812c0a4e86 |
apache-2.0 | ['fill-mask', 'transformers', 'en', 'ko'] | false | MASK 예시 ```python from transformers import AutoTokenizer, AutoModel, AlbertForMaskedLM import torch import torch.nn.functional as F tokenizer = AutoTokenizer.from_pretrained('bongsoo/albert-small-kor-v1', do_lower_case=True) model = AlbertForMaskedLM.from_pretrained('bongsoo/albert-small-kor-v1') text = ['한국 수도는 [MASK... | 8991e611fba748a83e02cbc6a02b7e67 |
apache-2.0 | ['fill-mask', 'transformers', 'en', 'ko'] | false | => **해당 [MASK] 안덱스 값 mask_idx 에서는 아래 출력하는데 사용됨 mask_idx = token_str.index('[MASK]') mask_idx_list.append(mask_idx) for idx, mask_idx in enumerate(mask_idx_list): logits_pred=torch.argmax(F.softmax(logits[idx]), dim=1) mask_logits_idx = int(logits_pred[mask_idx]) | a28c05a874a1331ec3ab79f858c9056f |
apache-2.0 | ['fill-mask', 'transformers', 'en', 'ko'] | false | 결과 출력 print('\n') print('*Input: {}'.format(text[idx])) print('*[MASK] : {} ({})'.format(mask_logits_token, mask_logits_idx)) ``` - 결과 ``` *Input: 한국 수도는 [MASK] 이다 *[MASK] : ▁서울 (80) *Input: 프랑스 수도는 [MASK]이다 *[MASK] : ▁불과 (1682) *Input: 충무공 이순신은 [MASK]에 최고의 장수였다 *[MASK] : ▁우리 (184) ``` | fd29bd2bf63425d21ed8e3670ad7c2f2 |
apache-2.0 | ['fill-mask', 'transformers', 'en', 'ko'] | false | Training **MLM(Masked Langeuage Model) 훈련** - 모델 : Bert-base - 말뭉치 : [ai_hub 웹데이터 기반 한국어 말뭉치 데이터](https://aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=624) (약 52M Text) - HyperParameter : **lr = 1e-4 , weigth_decay=0.01, batch_size = 128, token_max_len = 128,epoch = 8** - Voc... | 517756c4c7a538fa609081c56b196ecb |
apache-2.0 | ['fill-mask', 'transformers', 'en', 'ko'] | false | Model Config ``` { "architectures": [ "AlbertForPreTraining" ], "attention_probs_dropout_prob": 0, "bos_token_id": 2, "classifier_dropout_prob": 0.1, "embedding_size": 128, "eos_token_id": 3, "hidden_act": "gelu_new", "hidden_dropout_prob": 0, "hidden_size": 768, "initializer_range": 0.02, ... | 609eae23f0ca7d6743d34607f19859d6 |
openrail | [] | false | Contains: Shirayuki General - general / artistic model Shirayuki Anime - anime model Example settings: Negative prompt: lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blur... | 5f80bf094462acd4391eb70857745353 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | opus-mt-tc-big-bg-en Neural machine translation model for translating from Bulgarian (bg) to English (en). This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All mod... | 6c8145a6808756161da5265f8ffa0857 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Model info * Release: 2022-03-09 * source language(s): bul * target language(s): eng * model: transformer-big * data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807+bt_transformer-big_2022-03-09.zip](htt... | 7611551381ab19b3c14c15dfc5d41f46 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ "2001 е годината, с която започва 21-ви век.", "Това е Copacabana!" ] model_name = "pytorch-models/opus-mt-tc-big-bg-en" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.f... | 14bcb6a9db6a0ff97b3d2e8c28ce0ce1 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | It's Copacabana! ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-bg-en") print(pipe("2001 е годината, с която започва 21-ви век.")) | cac59efdb14617c8c386ee3d01b2728a |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Benchmarks * test set translations: [opusTCv20210807+bt_transformer-big_2022-03-09.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/bul-eng/opusTCv20210807+bt_transformer-big_2022-03-09.test.txt) * test set scores: [opusTCv20210807+bt_transformer-big_2022-03-09.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-... | f48ad92fc3478d3890ea44d5b9067914 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | words | |----------|---------|-------|-------|-------|--------| | bul-eng | tatoeba-test-v2021-08-07 | 0.73687 | 60.5 | 10000 | 71872 | | bul-eng | flores101-devtest | 0.67938 | 42.9 | 1012 | 24721 | | 34f4384511a43aca34283479b2d58d59 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | rbto3v5 Dreambooth model trained by rudzinskimaciej 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-stab... | 63e848d26ec51f5c63382069f6084a98 |
apache-2.0 | ['generated_from_trainer'] | false | finetuned-ner 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: 0.2041 - Precision: 0.8442 - Recall: 0.8460 - F1: 0.8451 - Accuracy: 0.9398 | f1b6b120f99f06e553e3c20d24e094ad |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1913 | 1.0 | 16472 | 0.1915 | 0.8375 | 0.8414 | 0.8395 | 0.9376 | | 0.1544 | 2.0 ... | edd704e88b7a46b99bdef1b9c9b9039f |
apache-2.0 | ['translation'] | false | opus-mt-uk-en * source languages: uk * target languages: en * OPUS readme: [uk-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/uk-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | 92aef8cba133059e1946aef162b55a75 |
creativeml-openrail-m | ['text-to-image'] | false | Sample pictures of reddog pharmacy catalog:  on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.4136 - Matthews Correlation: 0.5992 | 9d9a707978ae7979c88bd052ba0df271 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.4952 | 1.0 | 67 | 0.4678 | 0.5145 | | 0.2907 | 2.0 | 134 | 0.4136 | 0.5992 | | 0.1... | db40e67d6cf6826df305c1086aeb915d |
apache-2.0 | ['generated_from_trainer'] | false | mBERTnews This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1136 - Accuracy: 0.9739 - F1: 0.9732 | ea0dd4cd778cde10fddcebefe011772d |
apache-2.0 | ['generated_from_trainer'] | false | vc-bantai-vit-withoutAMBI This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the image_folder dataset. It achieves the following results on the evaluation set: - eval_loss: 0.2578 - eval_accuracy: 0.9533 - eval_runtime: 31.6691 - eval... | a761830fc1e56ea57fdd508df251ce06 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sc... | 45854441df61d5368bed9bab4e287f14 |
apache-2.0 | ['generated_from_keras_callback'] | false | ms29315/distilbert-base-uncased-finetuned-cola 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: - Train Loss: 0.3100 - Validation Loss: 0.5090 - Epoch: 0 | 298b40d4e35244f81654cdf1b0fbb34b |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | mt5-base-finetuned-v1 This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the Persian News dataset. It achieves the following results on the evaluation set: - Loss: 1.087988 - Rouge1: 1.2887 - Rouge2: 0.1861 - Rougel: 1.2862 - Rougelsum: 1.2818 | bd0ebc77e694de602c01a53aba0daf89 |
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