license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
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cc-by-sa-4.0 | ['vietnamese', 'token-classification', 'pos', 'dependency-parsing'] | false | How to Use ```py from transformers import AutoTokenizer,AutoModelForTokenClassification,TokenClassificationPipeline tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-base-vietnamese-upos") model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/roberta-base-vietnamese-upos") pipeline=TokenCl... | daf8d813bb8462fc4e6cc5d3e8cdd7ed |
apache-2.0 | ['generated_from_trainer'] | false | my_awesome_model 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: 0.2361 - Accuracy: 0.9313 | 1e29bf7789a539772f94136c58ac6a49 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2339 | 1.0 | 1563 | 0.1924 | 0.9263 | | 0.1523 | 2.0 | 3126 | 0.2361 | 0.9313 | | d5eeaa80472b49b0cfb8725b18ec4c8d |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | `kan-bayashi/jsut_tts_train_fastspeech2_transformer_teacher_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.loss.ave` ♻️ Imported from https://zenodo.org/record/4433200/ This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/). | efbc29bea2fcf911a4178ae32506e81b |
cc-by-sa-4.0 | ['chinese', 'token-classification', 'pos', 'dependency-parsing'] | false | Model Description This is a RoBERTa model pre-trained on Chinese Wikipedia texts (both simplified and traditional) for POS-tagging and dependency-parsing (using `goeswith` for subwords), derived from [roberta-base-chinese-upos](https://huggingface.co/KoichiYasuoka/roberta-base-chinese-upos). | d42f3ffbcf5788e1f74a42d72f408329 |
cc-by-sa-4.0 | ['chinese', 'token-classification', 'pos', 'dependency-parsing'] | false | text = "+text+"\n" v=[(s,e) for s,e in w["offset_mapping"] if s<e] for i,(s,e) in enumerate(v,1): q=self.model.config.id2label[p[i,h[i]]].split("|") u+="\t".join([str(i),text[s:e],"_",q[0],"_","|".join(q[1:-1]),str(h[i]),q[-1],"_","_" if i<len(v) and e<v[i][0] else "SpaceAfter=No"])+"\n" return... | f7defa6805e02bb1a09887826833b39c |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-amazon-shoe-reviews-tensorboard 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: 0.9534 - Accuracy: 0.5779 - F1: [0.63189419 0.46645049 0.50381304 0.55843496 ... | 64e1e266c3d2fbb6fe1271eb118cb2ec |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:... | 2520acfc683116406718e46965f2e764 |
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: 0.8128 - Matthews Correlation: 0.5364 | 3f93dcdc3a6e172a478a04f8a46b62b9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5227 | 1.0 | 535 | 0.5220 | 0.4210 | | 0.3467 | 2.0 | 1070 | 0.5048 | 0.4882 | | 0.2... | 83db889c55c05d6352b496af3270a01c |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-wikihow_3epoch_b4_lr3e-4 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wikihow dataset. It achieves the following results on the evaluation set: - Loss: 2.2757 - Rouge1: 27.4024 - Rouge2: 10.7065 - Rougel: 23.3153 - Rougelsum: 26.7336 - Gen Len: 18.5506 | 48949e2b47b85c8085a7521c9f6ecde3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 2.8424 | 0.13 | 5000 | 2.5695 | 25.2232 | 8.7617 | 21.2019 | 24.4949 ... | b9e4b5080872a2c901a77166158b68ae |
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: 0.2185 - Accuracy: 0.9245 - F1: 0.9244 | a3d02a51645af3df16a721a25f4aad72 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.847 | 1.0 | 250 | 0.3228 | 0.8985 | 0.8951 | | 0.2543 | 2.0 | 500 | 0.2185 | 0.9245 | 0.9244 | | 38d0703922f3fbe4f47090a2a74b03c3 |
cc-by-sa-4.0 | ['long-documents'] | false | Model description [Longformer](https://arxiv.org/abs/2004.05150) is a transformer model for long documents. This version of Longformer presented in [An Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification (Chalkidis et al., 2022)](https://arxiv.org/abs/2210.05529). The mode... | 92fca1e2f988a431d96aba66dba5e277 |
cc-by-sa-4.0 | ['long-documents'] | false | Intended uses & limitations You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=longformer) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aime... | 6deb392357e83a7fc6912fbb07902351 |
cc-by-sa-4.0 | ['long-documents'] | false | How to use You can use this model directly with a pipeline for masked language modeling: ```python from transformers import pipeline mlm_model = pipeline('fill-mask', model='kiddothe2b/longformer-base-4096', trust_remote_code=True) mlm_model("Hello I'm a <mask> model.") ``` You can also fine-tune it for SequenceCla... | c71a52b4aa9527ac878c30985b7afec8 |
cc-by-sa-4.0 | ['long-documents'] | false | Training hyperparameters TThe following hyperparameters were used during training: - learning_rate: 0.0001 - 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_... | 423edbd55b54ef6b79880f77e7a585ba |
cc-by-sa-4.0 | ['long-documents'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.7067 | 0.2 | 10000 | 1.5923 | 0.6714 | | 1.6532 | 0.4 | 20000 | 1.5494 | 0.6784 | | 1.622 | 0.6 | 30000 | 1.5208 ... | c0075682166dcc2090c8b8ae5bb3bf35 |
cc-by-sa-4.0 | ['long-documents'] | false | Citing If you use HAT in your research, please cite: [An Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification](https://arxiv.org/abs/2210.05529). Ilias Chalkidis, Xiang Dai, Manos Fergadiotis, Prodromos Malakasiotis, and Desmond Elliott. 2022. arXiv:2210.05529 (Preprint). ```... | 768ca1461743c3126f3be9251ab867eb |
apache-2.0 | ['multiberts', 'multiberts-seed_3', 'multiberts-seed_3-step_1200k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 3, Step 1200k 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... | a05abbec4552a679bfee3fd6ab6a85cd |
apache-2.0 | ['multiberts', 'multiberts-seed_3', 'multiberts-seed_3-step_1200k'] | 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_3-step_1200k') model = TFBertModel.from_pretrained("google/multib... | 64665019a662cdfa07b83066024ec569 |
apache-2.0 | ['image-classification', 'timm'] | false | Model card for maxvit_rmlp_small_rw_224.sw_in1k A timm specific MaxViT (w/ a MLP Log-CPB (continuous log-coordinate relative position bias motivated by Swin-V2) image classification model. Trained in `timm` on ImageNet-1k by Ross Wightman. ImageNet-1k training done on TPUs thanks to support of the [TRC](https://site... | a9541d3dc2e1c1b84d739a3dc8cc4cdb |
apache-2.0 | ['image-classification', 'timm'] | false | Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 64.9 - GMACs: 10.7 - Activations (M): 49.3 - Image size: 224 x 224 - **Papers:** - MaxViT: Multi-Axis Vision Transformer: https://arxiv.org/abs/2204.01697 - Swin Transformer V2: Scaling Up Capacity and ... | 84d289e8bba54aeb2ec66ec893c67e9a |
apache-2.0 | ['image-classification', 'timm'] | false | Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model('maxvit_rmlp_small_rw_224.sw_in1k', pretrained=True) mo... | 1578b1a2e9d4c256a4f39f6557563c65 |
apache-2.0 | ['image-classification', 'timm'] | false | Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'maxvit_rmlp_small_rw_224.sw_in1k', pretrain... | 85e0d13fcec8c8cce3558ae1368e31b9 |
apache-2.0 | ['image-classification', 'timm'] | false | Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'maxvit_rmlp_small_rw_224.sw_in1k', pretrained=Tru... | 7ca6b12d3eeae79fddf8beeaf3e43e92 |
apache-2.0 | ['translation'] | false | ceb-eng * source group: Cebuano * target group: English * OPUS readme: [ceb-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ceb-eng/README.md) * model: transformer-align * source language(s): ceb * target language(s): eng * model: transformer-align * pre-processing: normalization + Sent... | c78fb5ed6ad44282d923b7412e0cf819 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: ceb-eng - source_languages: ceb - target_languages: eng - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ceb-eng/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['ceb', 'en'] - src_constituents: {'ceb'} - tgt_cons... | 344bcb6060dac43c28acf28fd40362ee |
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: 0.3016 - Accuracy: 0.8633 - F1: 0.8629 | ef6d6031b10ea0c5bee5c58eb4af718b |
cc-by-4.0 | ['audio', 'automatic-speech-recognition', 'icelandic', 'xlrs-53-icelandic', 'iceland', 'reykjavik', 'samromur'] | false | wav2vec2-large-xlsr-53-icelandic-ep10-1000h The "wav2vec2-large-xlsr-53-icelandic-ep10-1000h" is an acoustic model suitable for Automatic Speech Recognition in Icelandic. It is the result of fine-tuning the model "facebook/wav2vec2-large-xlsr-53" for 10 epochs with around 1000 hours of Icelandic data developed by the... | 31ebd08b0f8cacecc08feb4973af75de |
cc-by-4.0 | ['audio', 'automatic-speech-recognition', 'icelandic', 'xlrs-53-icelandic', 'iceland', 'reykjavik', 'samromur'] | false | Load the processor and model. MODEL_NAME="carlosdanielhernandezmena/wav2vec2-large-xlsr-53-icelandic-ep10-1000h" processor = Wav2Vec2Processor.from_pretrained(MODEL_NAME) model = Wav2Vec2ForCTC.from_pretrained(MODEL_NAME) | b6e647efd5632afac8a03035e348281a |
cc-by-4.0 | ['audio', 'automatic-speech-recognition', 'icelandic', 'xlrs-53-icelandic', 'iceland', 'reykjavik', 'samromur'] | false | BibTeX entry and citation info *When publishing results based on these models please refer to:* ```bibtex @misc{mena2022xlrs53icelandic, title={Acoustic Model in Icelandic: wav2vec2-large-xlsr-53-icelandic-ep10-1000h.}, author={Hernandez Mena, Carlos Daniel}, year={2022}, url={https://huggingf... | 2ea9d4154650d6a461cde8294b2320e5 |
apache-2.0 | ['tensorflowtts', 'audio', 'text-to-speech', 'mel-to-wav'] | false | Multi-band MelGAN trained on LJSpeech (En) This repository provides a pretrained [Multi-band MelGAN](https://arxiv.org/abs/2005.05106) trained on LJSpeech dataset (Eng). For a detail of the model, we encourage you to read more about [TensorFlowTTS](https://github.com/TensorSpeech/TensorFlowTTS). | 9a421b7607aaf3df05db67dbb8d0c272 |
apache-2.0 | ['tensorflowtts', 'audio', 'text-to-speech', 'mel-to-wav'] | false | Converting your Text to Wav ```python import soundfile as sf import numpy as np import tensorflow as tf from tensorflow_tts.inference import AutoProcessor from tensorflow_tts.inference import TFAutoModel processor = AutoProcessor.from_pretrained("tensorspeech/tts-tacotron2-ljspeech-en") tacotron2 = TFAutoModel.from... | 1580527cce47d6a3d492ef255c4de7e9 |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'Citrinet1024', 'NeMo', 'pytorch'] | false | Automatically instantiate the model ```python import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.ASRModel.from_pretrained("ypluit/stt_kr_citrinet1024_PublicCallCenter_1000H_0.22") ``` | 39e033c5a5f6283c77d37c9341c3450e |
afl-3.0 | [] | false | Clone this repo. In the /CIFAR100+CIFAR10_weights/CIFAR100+10_model/ directory, there are three weights for the three models trained on CIFAR100 + CIFAR10 dataset. The names of the weights can be found in my notebook respectively:
https://colab.research.google.com/drive/1zInKDML24y8eZTtElMrdxGZjaK4F-vTu?usp=sharing
... | 33a3159f31e5e5cc9d4f2749f892760b |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-utility-2-16-5-oos 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: 2.3728 - Accuracy: 0.3956 | 6253442d29a31dc7a5c0c7c2678bf31d |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-Welsh Fine-tuned facebook/wav2vec2-large-xlsr-53 on the Welsh Common Voice dataset. The data was augmented using standard augmentation approach. When using this model, make sure that your speech input is sampled at 16kHz. Test Result: 29.4% Usage The model can be used directly (without a languag... | ffd99af96500fdcf5155c8aae743e65d |
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): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:... | b738b2ae6004a4cd23476433ca059d06 |
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(lo... | 0b3f1f42bd45c98ce2c26c65a5c30f59 |
mit | ['generated_from_keras_callback'] | false | turkish-poem-generation This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 7.2815 - Validation Loss: 7.2658 - Epoch: 5 | 350ccbba51d64cf8ba92a1be93b30db5 |
mit | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps... | 6c3764313b4316919cb9e3ba356b5f4b |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 7.2815 | 7.2657 | 0 | | 7.2815 | 7.2659 | 1 | | 7.2817 | 7.2653 | 2 | | 7.2815 | 7.2657 | 3 | | 7.2816 | 7.2660 | 4 | | 7.2815 |... | 19d3fe4776d81fb5b36d86eed1cddb31 |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2r_en_xls-r_accent_us-8_england-2_s946 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure th... | e0b8d4a6dc40642b0555e43691fac726 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the germeval_14 dataset. It achieves the following results on the evaluation set: - Loss: 0.0744 - F1: 0.8588 | 8db13102a1565bc5950f5ee83fd90f68 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.1261 | 1.0 | 1000 | 0.0769 | 0.8335 | | 0.0555 | 2.0 | 2000 | 0.0679 | 0.8568 | | 0.0329 | 3.0 | 3000 | 0.0744 | 0.8588 | ... | fbf105ea93a677c660db70f96fe9d064 |
apache-2.0 | ['whisper-event'] | false | Whisper Gujarati Small This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Gujarati data available from multiple publicly available ASR corpuses. It has been fine-tuned as a part of the Whisper fine-tuning sprint. | ece69fbd5d33699a6a8cf3efdcb6c7a0 |
apache-2.0 | ['whisper-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.7e-05 - train_batch_size: 48 - eval_batch_size: 32 - seed: 22 - optimizer: adamw_bnb_8bit - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 4000 - training_steps: 20532 (terminated upon convergence. Initially s... | 192069e8f6f697d5833fb3c68c6abad2 |
apache-2.0 | [] | false | Named Entity Recognition (Token Classification Head) for Serbian / Croatian languges. Abbreviation|Description -|- O|Outside of a named entity B-MIS |Beginning of a miscellaneous entity right after another miscellaneous entity I-MIS | Miscellaneous entity B-PER |Beginning of a person’s name right after another person’... | 26e715196af1557fbfa96abbf7458c4a |
mit | [] | false | Tonal1 on Stable Diffusion This is the `<Tonal>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train yo... | 1145e1b1a8aebe6ed69469260e969741 |
apache-2.0 | ['object-detection'] | false | DETR (End-to-End Object Detection) model with ResNet-101 backbone (dilated C5 stage) 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 e... | f35e83808ad5cc00328ffb95e781b89b |
apache-2.0 | ['object-detection'] | false | How to use Here is how to use this model: ```python from transformers import DetrFeatureExtractor, DetrForObjectDetection 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 = DetrFeatureExtrac... | f1246613121cfddcc7987bd3ad5871e8 |
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: 0.2083 - Accuracy: 0.9245 - F1: 0.9248 | 6e18dd5921588cb63a16e88254f20b4d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.7794 | 1.0 | 250 | 0.2870 | 0.9115 | 0.9099 | | 0.2311 | 2.0 | 500 | 0.2083 | 0.9245 | 0.9248 | | 73e7f53d200455c9b3b19e9c579ce486 |
apache-2.0 | ['super-image', 'image-super-resolution'] | false | Model description The MDSR is a model that uses both deeper and wider architecture (32 ResBlocks and 256 channels) to improve performance. It uses both global and local skip connections, and up-scaling is done at the end of the network. It doesn't use batch normalization layers (input and output have similar distribut... | 2327d90b5814ca49699a3e5607db362a |
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 MdsrModel, ImageLoader from PIL import Image import requests url = 'https://pa... | dd5e431a741efad3f945fe172f3c2d2b |
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... | 88dde91d625ca96f595fe7349d0a212c |
apache-2.0 | ['super-image', 'image-super-resolution'] | false | BibTeX entry and citation info ```bibtex @article{ahn2018fast, title={Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network}, author={Ahn, Namhyuk and Kang, Byungkon and Sohn, Kyung-Ah}, journal={arXiv preprint arXiv:1803.08664}, year={2018} } ``` | a32f10ae02ff4a9401d7659bbc07b4a0 |
apache-2.0 | ['translation'] | false | eng-urd * source group: English * target group: Urdu * OPUS readme: [eng-urd](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-urd/README.md) * model: transformer-align * source language(s): eng * target language(s): urd * model: transformer-align * pre-processing: normalization + Sentenc... | 9286a82075acd3f75da19c452f1544b1 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: eng-urd - source_languages: eng - target_languages: urd - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-urd/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['en', 'ur'] - src_constituents: {'eng'} - tgt_const... | cc259fc0d722feccb9fc2ac44199a89d |
gpl-3.0 | ['image-classification', 'computer-vision', 'vision', 'yolo', 'yolov5'] | false | perform inference results = model(img) ``` - Finetune the model on your custom dataset: ```bash yolov5 classify train --img 128 --data mnist2560 --model fcakyon/yolov5n-cls-v7.0 --epochs 1 --device cpu ``` | 44b92d8d844be1d00ef62ac23d2474a6 |
apache-2.0 | ['sagemaker', 'ruperta', 'TextClassification', 'SentimentAnalysis'] | false | **A finetuned model for Sentiment analysis in Spanish**
This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container,
The base model is **RuPERTa-base (uncased)** which is a RoBERTa model trained on a uncased version of big Spanish corpus.
It was trained by mrm8488, Manuel Romero... | 30e9c7c2e035301084838ec83217f83b |
apache-2.0 | ['sagemaker', 'ruperta', 'TextClassification', 'SentimentAnalysis'] | false | Dataset
The dataset is a collection of movie reviews in Spanish, about 50,000 reviews. The dataset is balanced and provides every review in english, in spanish and the label in both languages.
Sizes of datasets:
- Train dataset: 42,500
- Validation dataset: 3,750
- Test dataset: 3,750
| 8e9eddd63d15370b8ba102c9b549fdc1 |
apache-2.0 | ['sagemaker', 'ruperta', 'TextClassification', 'SentimentAnalysis'] | false | Hyperparameters
{
"epochs": "4",
"train_batch_size": "32",
"eval_batch_size": "8",
"fp16": "true",
"learning_rate": "3e-05",
"model_name": "\"mrm8488/RuPERTa-base\"",
"sagemaker_container_log_level": "20",
"sagemaker_program": "\"train.py\"",
}
| 09494c7761425e87d2c29024ffcd3dda |
apache-2.0 | ['sagemaker', 'ruperta', 'TextClassification', 'SentimentAnalysis'] | false | Usage for Sentiment Analysis
```python
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("edumunozsala/RuPERTa_base_sentiment_analysis_es")
model = AutoModelForSequenceClassification.from_pretrained("edumunozsala/RuPERTa_base_sent... | ebd593cc93c08f3c690ca9bc45c0486e |
apache-2.0 | ['translation'] | false | bul-fra * source group: Bulgarian * target group: French * OPUS readme: [bul-fra](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/bul-fra/README.md) * model: transformer * source language(s): bul * target language(s): fra * model: transformer * pre-processing: normalization + SentencePiece (... | 4c375b5bd9ff39b065923cabf0aca3ac |
apache-2.0 | ['translation'] | false | System Info: - hf_name: bul-fra - source_languages: bul - target_languages: fra - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/bul-fra/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['bg', 'fr'] - src_constituents: {'bul', 'bul_Latn'} ... | c66ca7b7e41ff1a4f865897af1da9837 |
mit | ['generated_from_trainer'] | false | finetuned_gpt2-large_sst2_negation0.01_pretrainedTrue_epochs1 This model is a fine-tuned version of [gpt2-large](https://huggingface.co/gpt2-large) on the sst2 dataset. It achieves the following results on the evaluation set: - Loss: 2.8378 | db3d586ca4c5501185e43140feff6ef0 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-wnli-target-glue-qnli This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-wnli](https://huggingface.co/muhtasham/tiny-mlm-glue-wnli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4737 - Accuracy: 0.7794 | 0b1132b0ed3e1952a547494b158db649 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6298 | 0.15 | 500 | 0.5598 | 0.7249 | | 0.563 | 0.31 | 1000 | 0.5282 | 0.7435 | | 0.5386 | 0.46 | 1500 | 0.5010 | 0.... | 36044c3faa2570e8da4561d5a61764e6 |
apache-2.0 | ['stanza', 'token-classification'] | false | Stanza model for Upper_Sorbian (hsb) Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing. Find more about it in [our websi... | 1973559e60e6976b958829e6f097e172 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Small Hi - Sanchit Gandhi This model is a fine-tuned version of [Taqwa/whisper-small-hiTaqwa](https://huggingface.co/Taqwa/whisper-small-hiTaqwa) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.3353 - Wer: 35.7403 | 43515c2c6d851aa98642f54abe877aa4 |
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: 8 - 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: 500 - mixed_precisio... | f96b13189b1d3302bca63567d4a254d2 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0762 | 0.31 | 125 | 0.2818 | 33.3573 | | 0.0653 | 0.61 | 250 | 0.2930 | 33.9584 | | 0.062 | 0.92 | 375 | 0.3060 | 34.745... | 9fcb6226ad947cc11046ca1fb12b8f6c |
apache-2.0 | ['generated_from_keras_callback'] | false | TestZee/t5-base-finetuned-question-generation-data-t5-base This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.0855 - Validation Loss: 4.4354 - Train Rouge1: 27.4892 - Train Rouge2: 8.6370 - Tra... | dd8970c8a3c21d25d8883f3930a16362 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch | |:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:| | 4.0855 | 4.4354 | 27.4892 | 8.6370 ... | ef703ff6f697ba1d4d25d33997dcc3a6 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-2'] | false | MultiBERTs Seed 2 Checkpoint 1600k (uncased) Seed 2 intermediate checkpoint 1600k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in [this repository](https://github.com/g... | aa4c48c2f2fe32a2c2683e48ceeac4eb |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-2'] | false | How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('multiberts-seed-2-1600k') model = BertModel.from_pretrained("multiberts-seed-2-1600k") text = "Replace me by any text you'd lik... | 40758bd6ff9d89f83516a03f370ecf45 |
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: 1.1580 | 45495f67fffb5a412c335eb9e3d23f5c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2246 | 1.0 | 5533 | 1.1484 | | 0.9433 | 2.0 | 11066 | 1.1294 | | 0.7625 | 3.0 | 16599 | 1.1580 | | 2ef96ba988ab9a7618e4b09e11fe6ee3 |
apache-2.0 | ['generated_from_trainer'] | false | biomedical-roberta-finetuned-cantemist-test This model is a fine-tuned version of [PlanTL-GOB-ES/bsc-bio-ehr-es-cantemist](https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es-cantemist) on the cantemist-ner dataset. It achieves the following results on the evaluation set: - Loss: 0.0597 - F1: 0.8379 | d5c866ce81c242ab9c06ff6776218ffb |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.0015 | 1.0 | 607 | 0.0597 | 0.8379 | | 5c094dd1d2258618c2c9fbd830919145 |
apache-2.0 | ['automatic-speech-recognition', 'it'] | false | exp_w2v2t_it_no-pretraining_s615 Fine-tuned randomly initialized wav2vec2 model for speech recognition using the train split of [Common Voice 7.0 (it)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has bee... | 8550c383776ce92000fe6eaa075bc814 |
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: 0.7903 - Matthews Correlation: 0.5596 | c7c20f53ce857c05459f8fec1ee87dc0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5224 | 1.0 | 535 | 0.5373 | 0.3974 | | 0.3503 | 2.0 | 1070 | 0.5142 | 0.4942 | | 0.2... | 984086f6b0853185c7bcbd3224e8aa48 |
apache-2.0 | ['automatic-speech-recognition', 'zh-CN'] | false | exp_w2v2t_zh-cn_unispeech-ml_s515 Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) 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... | 141d17413ea1b09a38824a1cd1139b41 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'landscape', 'heywhale'] | false | DreamBooth model for the taolu concept trained by chenglu. This is a Stable Diffusion model fine-tuned on the taolu concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of taolu road** This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https:... | a6ebd3d1978383c2600ec58b8fd12d74 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'landscape', 'heywhale'] | false | Description This is a Stable Diffusion model fine-tuned on `road` images for the landscape theme. For the HF Dreambooth hackathon, from Hugging Face China Commuinity, Collabration with the HeyWhale platform. | d06317d2fcd6760515273d7d60dda550 |
apache-2.0 | ['classical chinese', 'literary chinese', 'ancient chinese', 'token-classification', 'pos', 'dependency-parsing'] | false | Model Description This is a RoBERTa model pre-trained on Classical Chinese texts for POS-tagging and dependency-parsing (using `goeswith` for subwords), derived from [roberta-classical-chinese-base-char](https://huggingface.co/KoichiYasuoka/roberta-classical-chinese-base-char) and [UD_Classical_Chinese-Kyoto](https:/... | 0d1d4cbec3bc40f26e00c2c5070840ab |
apache-2.0 | ['classical chinese', 'literary chinese', 'ancient chinese', 'token-classification', 'pos', 'dependency-parsing'] | false | text = "+text+"\n" v=[(s,e) for s,e in w["offset_mapping"] if s<e] for i,(s,e) in enumerate(v,1): q=self.model.config.id2label[p[i,h[i]]].split("|") u+="\t".join([str(i),text[s:e],"_",q[0],"_","|".join(q[1:-1]),str(h[i]),q[-1],"_","_" if i<len(v) and e<v[i][0] else "SpaceAfter=No"])+"\n" return... | bc408fec7f10d759a1a5a3e594039283 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-all This model is a fine-tuned version of [tkubotake/xlm-roberta-base-finetuned-panx-de](https://huggingface.co/tkubotake/xlm-roberta-base-finetuned-panx-de) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2290 - F1: 0.8629 | 537b782145a44511820b74378883d53d |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.1259 | 1.0 | 835 | 0.1879 | 0.8478 | | 0.078 | 2.0 | 1670 | 0.2121 | 0.8582 | | 0.0439 | 3.0 | 2505 | 0.2290 | 0.8629 | ... | 406796da698397f06b27a10fc79559c0 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'robust-speech-event', 'hf-asr-leaderboard'] | false | This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SV-SE dataset. It achieves the following results on the evaluation set: **Without LM**: - Wer: 0.2465 - Cer: 0.0717 **With LM**: - Wer: 0.1710 - C... | fd04990b53fc7f0c3de9ac5c6ac045a0 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.3224 | 1.37 | 500 | 3.2676 | 1.0 | | 2.9319 | 2.74 | 1000 | 2.9287 | 1.0000 | | 2.1173 | 4.11 | 1500 | 1.1478 | 0.878... | 9de6106d5ff6be65ec0fe130c652e5d1 |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-kitchen_and_dining-8-16-5-oos 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 | a57301ba70f0fff1f8f027a7961fe6cb |
apache-2.0 | [] | false | How to use the discriminator in `transformers` ```python from transformers import ElectraForPreTraining, ElectraTokenizerFast import torch discriminator = ElectraForPreTraining.from_pretrained("google/electra-small-discriminator") tokenizer = ElectraTokenizerFast.from_pretrained("google/electra-small-discriminator")... | 17451baf9cd08e75a1fd47abff597c60 |
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 conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0628 - Precision: 0.9254 - Recall: 0.9352 - F1: 0.9303 - Accuracy: 0.9835 | 5c600dc19e87829f0a91207f0aac5641 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2388 | 1.0 | 878 | 0.0723 | 0.9108 | 0.9186 | 0.9147 | 0.9798 | | 0.0526 | 2.0 |... | 171f24d0e4df47e68f43c0733afbc57c |
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 conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0731 - Precision: 0.9331 - Recall: 0.9432 - F1: 0.9381 - Accuracy: 0.9851 | d417d134e1d7723538b98a6ca57e2142 |
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