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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apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Fscore | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:| | 0.8665 | 1.0 | 815 | 0.7583 | 0.7486 | 0.6417 | 0.6654 | | 0.5527 | 2.0 | 1630 | 0.9565 | 0.6848 ... | d34384a1adc3937efd2bb2ca34ca311f |
afl-3.0 | [] | false | Please cite as ``` @InProceedings{Spinde2021f, title = "Neural Media Bias Detection Using Distant Supervision With {BABE} - Bias Annotations By Experts", author = "Spinde, Timo and Plank, Manuel and Krieger, Jan-David and Ruas, Terry and Gipp, Bela and Aizawa, Akiko", boo... | f6f85a7f81456e95ba010063ccbd1126 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Usage Using this model becomes easy when you have [ConGen](https://github.com/KornWtp/ConGen) installed: ``` pip install -U git+https://github.com/KornWtp/ConGen.git ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence",... | d343e29809677e73e0794c2ef915d453 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Citing & Authors ```bibtex @inproceedings{limkonchotiwat-etal-2022-congen, title = "{ConGen}: Unsupervised Control and Generalization Distillation For Sentence Representation", author = "Limkonchotiwat, Peerat and Ponwitayarat, Wuttikorn and Lowphansirikul, Lalita and Udomcharoenchaikit, ... | 4454faf4395fdc42fd4f37e3dae92898 |
apache-2.0 | ['generated_from_keras_callback'] | false | TEdetection_distiBERT_mLM_V2_shuffleplus3 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: | 3981067a44918e94d922d588e4b64644 |
apache-2.0 | ['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... | 89e264870d3eaad84a066b7c80047a06 |
creativeml-openrail-m | ['coreml', 'stable-diffusion', 'text-to-image'] | false | -converting-models-to-core-ml).<br> Provide the model to an app such as [Mochi Diffusion](https://github.com/godly-devotion/MochiDiffusion) to generate images.<br> `split_einsum` version is compatible with all compute unit options including Neural Engine.<br> `original` version is only compatible with CPU & GPU option... | 1e7f48699e5ea2f088b95c299b1b64bf |
creativeml-openrail-m | ['coreml', 'stable-diffusion', 'text-to-image'] | false | Gradio We support a [Gradio](https://github.com/gradio-app/gradio) Web UI to run Analog-Diffusion: [Open in Spaces](https://huggingface.co/spaces/akhaliq/Analog-Diffusion)   **Authors**: Zongsheng Yue, Chen Change Loy **Abstract**: *While deep learning-based methods for blind face restoration have achieved unpre... | 2c462e9941fc65e1c05aff2692e2b00a |
other | ['pytorch', 'diffusers', 'face image enhancement'] | false | save the result ``` <!--For more in-detail information, please have a look at the [official inference example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb)--> | f2e2b60e2eec36011546e6cd1d04ef9e |
other | ['pytorch', 'diffusers', 'face image enhancement'] | false | Samples [<img src="assets/Solvay_conference.png" width="805px"/>](https://imgsli.com/MTM5NTgw) [<img src="assets/Hepburn.png" height="555px" width="400px"/>](https://imgsli.com/MTM5NTc5) [<img src="assets/oldimg_05.png" height="555px" width="400px"/>](https://imgsli.com/MTM5NTgy) <img src="cropped_faces/0368.png" hei... | ed8fd5b2bf530bb9084c02bbb7343688 |
apache-2.0 | ['generated_from_trainer'] | false | distilroberta-base-finetuned-wikitext2 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8340 | b16dea54697faf8d1f5987d3d20cf4e9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.0843 | 1.0 | 2406 | 1.9226 | | 1.9913 | 2.0 | 4812 | 1.8820 | | 1.9597 | 3.0 | 7218 | 1.8214 | | eb69c64f0f8dd886a80c00d41b8d97c6 |
other | ['vision', 'image-segmentation', 'generated_from_trainer'] | false | segformer-b0-finetuned-segments-sidewalk-oct-22 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set: - Loss: 0.9249 - Mean Iou: 0.1675 - Mean Accuracy: 0.2109 - Overall Accuracy: ... | cec6d32c915b7b026280435d5d9ef9b7 |
other | ['vision', 'image-segmentation', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 | 97fd24ab38ba0beb9e2b52a5042668c4 |
other | ['vision', 'image-segmentation', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Flat-road | Accuracy Flat-sidewalk | Accuracy Flat-crosswalk | Accuracy Flat-cyclinglane | Accuracy Flat-parkingdriveway | Accuracy Flat-railtrack | Accuracy Flat-curb | Accu... | dc944eec8cd446faeedc35477e254784 |
cc-by-4.0 | ['bert'] | false | bert-mlm-small A small-size BERT Language Model with an **MLM** pre-training objective. For more details about the pre-training objective and the pre-training hyperparameters, please refer to [How does the pre-training objective affect what large language models learn about linguistic properties?](https://aclantholog... | 772a32e10f82bc4be65baf74dc3e3617 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_qnli This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.6487 - Accuracy: 0.6094 | 918b1a7c23a19e99b6da2f142d8a8652 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6754 | 1.0 | 819 | 0.6491 | 0.6178 | | 0.6369 | 2.0 | 1638 | 0.6487 | 0.6094 | | 0.6125 | 3.0 | 2457 | 0.6555 | 0.... | e31bbd767689fa8324fea233d0ec403b |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-ks-2sec This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset. It achieves the following results on the evaluation set: - Loss: 0.0880 - Accuracy: 0.9822 | a2f51d718908863bf061e13f82645aed |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Accuracy | Validation Loss | |:-------------:|:-----:|:----:|:--------:|:---------------:| | 0.5003 | 1.0 | 399 | 0.9643 | 0.4284 | | 0.1868 | 2.0 | 798 | 0.9748 | 0.1628 | | 0.1413 | 3.0 | 1197 | 0.9796 | 0.1128 ... | 25abdebef0b4ecdec2fddb1c775a8ea3 |
apache-2.0 | ['generated_from_trainer'] | false | gpt2-finetuned-redditComments This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.8418 | 972733490094c5768691819b11daf229 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 | 817236509f0d2b81b074cda8392b4a83 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 3.9535 | 1.0 | 4320 | 3.8888 | | 3.8832 | 2.0 | 8640 | 3.8523 | | 3.8708 | 3.0 | 12960 | 3.8418 | | 7893f49c642ee3cb7a028045d76b2d7c |
creativeml-openrail-m | [] | false | Sample images:    + [d8ahazard dreambooth extension](https://github.com/d8ahazard/sd_dreambooth_extension) + [nitrosocke guide](https://github.com/nitrosocke/dreambooth-training-guide). 82 training images at 1e-6 learning rate for 8200 st... | b1746b06d7c15a8711e1ec50034f98dc |
cc-by-sa-4.0 | ['english', 'token-classification', 'pos', 'dependency-parsing'] | false | Model Description This is a RoBERTa model pre-trained with [UD_English](https://universaldependencies.org/en/) for POS-tagging and dependency-parsing, derived from [roberta-large](https://huggingface.co/roberta-large). Every word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal Part-Of-Speech)... | f261271bdeceb3154fdc4629ce08763b |
cc-by-sa-4.0 | ['english', 'token-classification', 'pos', 'dependency-parsing'] | false | How to Use ```py from transformers import AutoTokenizer,AutoModelForTokenClassification tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-large-english-upos") model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/roberta-large-english-upos") ``` or ```py import esupar nlp=esupar.load("Ko... | b63ed268fc85a49b9fe44d1a92c8ff89 |
cc-by-4.0 | ['generated_from_trainer'] | false | danish-bert-botxo-danish-finetuned-hatespeech This model is for a university project and is uploaded for sharing between students. It is training on a danish hate speech labeled training set. Feel free to use it, but as of now, we don't promise any good results ;-) This model is a fine-tuned version of [Maltehb/danis... | a6ad36807a7d3bdda897bf73985c232f |
cc-by-4.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4.0 | bdadd327521f67ecd133f366a24a2ade |
cc-by-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 315 | 0.3285 | | 0.2879 | 2.0 | 630 | 0.3288 | | 0.2879 | 3.0 | 945 | 0.3178 | | 0.1371 | 4.0 | 1260 | 0.3584 ... | 402b3d06562844d790a784bf3baa8a3d |
apache-2.0 | ['generated_from_keras_callback'] | false | ytsai25/bert-finetuned-ner This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0240 - Validation Loss: 0.0613 - Epoch: 2 | c5bcf89546c7f633fad926ceaebdc470 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.1218 | 0.0592 | 0 | | 0.0398 | 0.0602 | 1 | | 0.0240 | 0.0613 | 2 | | ce36b454147d1a928a803d1949bec325 |
mit | ['generated_from_trainer'] | false | distilbert-base-turkish-cased-sentiment This model is a fine-tuned version of [dbmdz/distilbert-base-turkish-cased](https://huggingface.co/dbmdz/distilbert-base-turkish-cased) on the [sepidmnorozy/Turkish_sentiment](https://huggingface.co/datasets/sepidmnorozy/Turkish_sentiment) dataset. It achieves the following res... | ca61c495a907e762d5444c0c6606f4f7 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'apache-2.0', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'PyTorch'] | false | Wav2Vec2-Large-XLSR-53-Portuguese Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Portuguese using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset. | cf7b96426940f106694dfd19632b715d |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'apache-2.0', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'PyTorch'] | 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", "pt", split="test[:2%]") processor = Wav2Vec2Processor.from_... | 6cdcef4f74e9208fa07ad44f45efe002 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'apache-2.0', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'PyTorch'] | false | Evaluation The model can be evaluated as follows on the Portuguese 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", "pt", split="test") ... | cf7b3fdbab375320fa88537bcd9e0c62 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'apache-2.0', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'PyTorch'] | 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... | 8d80fcdfba2d634790770fb9feb95d92 |
apache-2.0 | ['audio', 'speech', 'wav2vec2', 'pt', 'apache-2.0', 'portuguese-speech-corpus', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'PyTorch'] | false | Training The Common Voice `train`, `validation` datasets were used for training. The script used for training can be found at: https://github.com/joaoalvarenga/wav2vec2-large-xlsr-53-portuguese/blob/main/fine-tuning.py | cf55a255fadbfe7a3837a1426ec4bf0e |
mit | ['generated_from_trainer'] | false | xlm-sustainability-sentiment This model is a fine-tuned version of [Raccourci/fairguest-bert](https://huggingface.co/Raccourci/fairguest-bert) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2997 - F1: 0.9335 - Roc Auc: 0.9335 - Accuracy: 0.9335 | b7cf9c9dfbfe863be0c8b76b2c96c773 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_e... | 00fc47c1e07b33b0006457b8b5038832 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:| | No log | 0.98 | 15 | 0.5221 | 0.7173 | 0.7173 | 0.7173 | | No log | 1.98 | 30 | 0.3833 | 0.7365 ... | 8d5ff3c5d4de44f6940824ba95e6069c |
cc-by-sa-4.0 | ['finance'] | false | Model architecture The model architecture is the same as ELECTRA small in the [original ELECTRA implementation](https://github.com/google-research/electra); 12 layers, 256 dimensions of hidden states, and 4 attention heads. | 674a51a7f81eb19dd3a8463bcda2ee69 |
cc-by-sa-4.0 | ['finance'] | false | Training The models are trained with the same configuration as ELECTRA small in the [original ELECTRA paper](https://arxiv.org/abs/2003.10555) except size; 128 tokens per instance, 128 instances per batch, and 1M training steps. The size of the generator is the same of the discriminator. | c9cd0462ee18a8ade25bfcf57b0f0094 |
apache-2.0 | ['generated_from_trainer'] | false | closure_system_door_inne-bert-base-uncased This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7907 | d419fcc15aa3d0bcdae07456bc8ee0ff |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 7 - eval_batch_size: 7 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 | bece8220c37f0ef2aecb75e9b2455f26 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.7321 | 1.0 | 2 | 2.5801 | | 2.6039 | 2.0 | 4 | 2.0081 | | 2.4556 | 3.0 | 6 | 2.3329 | | 2.3587 | 4.0 | 8 | 2.4156 ... | af3327377228e08a096411a95273fb10 |
cc-by-4.0 | [] | false | Distiled-roberta-squad2 This is the *distilled* version of the [roberta-base-squad2-QA](https://huggingface.co/Shobhank-iiitdwd/Distiled-roberta-squad2-QA) model. This model has a comparable prediction quality and runs at twice the speed of the base model. | 30fa1480305cab1d7dc4c91c9ab04f5a |
cc-by-4.0 | [] | false | Hyperparameters ``` batch_size = 96 n_epochs = 4 base_LM_model = "Shobhank-iiitdwd/Distiled-roberta-squad2-QA" max_seq_len = 384 learning_rate = 3e-5 lr_schedule = LinearWarmup warmup_proportion = 0.2 doc_stride = 128 max_query_length = 64 distillation_loss_weight = 0.75 temperature = 1.5 teacher = "Shobhank-iiitdwd/... | b00221ef06ea05ba92f4c8fc5cae22d5 |
cc-by-4.0 | [] | false | Distillation This model was distilled using the TinyBERT approach.Firstly, we have performed intermediate layer distillation with roberta-base as the teacher which resulted in Distiles-roberta. Secondly, we have performed task-specific distillation with [roberta-base-squad2](https://huggingface.co/Shobhank-iiitdwd/rob... | 2e607729c7caac6ca1d9453884766e36 |
cc-by-4.0 | [] | false | Performance Evaluated on the SQuAD 2.0 dev set with the [official eval script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/). ``` "exact": 78.69114798281817, "f1": 81.9198998536977, "total": 11873, "HasAns_exact": 76.19770580296895, "HasAns_f1": 82.66446878592329, "Ha... | 7f9f0c035088538b8d89385279e9efaf |
creativeml-openrail-m | ['LoRa', 'embeddings'] | false | Lora Networks Still Exploring on this training process prompt: masterpiece, best_quality, clear details,1girl, cowboy_shot, simple_background with respective LoRa net     | 4ff48b4507272cb9b8d9a4552f9201ca |
creativeml-openrail-m | ['LoRa', 'embeddings'] | false | Lora characters and outfits using char-* and outfit-* togeather masterpiece, best_quality, clear details,1girl, reverse_outfit (pasties) (maebari) high_heels \<lora:outfit-reverseoutfit:1\>, (fullbody), looking_at_viewer, floor , \<lora:char-seia:0.9\>,  ---- | 7dfcbec80fb6cb3066c715fccc10e39f |
creativeml-openrail-m | ['LoRa', 'embeddings'] | false | Sample of shinymas/character embeddings generated with the same prompt with interchanging character phrase (char-X) prompt: masterpiece, best_quality, clear details, char-kogane ,shirt,1girl,upper body Negative prompt: fake_animal_ears, bad_prompt:0.8, (Cropped head), (Extra hands), (extra legs), (cropped), (missin... | ea741795c3e8047957a103007862dc8f |
creativeml-openrail-m | ['LoRa', 'embeddings'] | false | Sample of char-toru wearing various outfit embeddings generated with the same prompt with interchanging outfit phrase (char-X) prompt: masterpiece, best_quality, clear details, illustration of char-toru standing wearing outfit-null:1.1, ((full body)) , (smile),(solo), boots, floor, Negative prompt: fake_animal_ear... | 9ffa29f4b70655dcacd5a3336160b015 |
apache-2.0 | ['automatic-speech-recognition', 'et'] | false | exp_w2v2t_et_unispeech-sat_s108 Fine-tuned [microsoft/unispeech-sat-large](https://huggingface.co/microsoft/unispeech-sat-large) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your spee... | 9aee766b8a6a9c781d166f66499b066e |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-kor-11385-3 This model is a fine-tuned version of [teddy322/wav2vec2-large-xls-r-300m-kor-11385-2](https://huggingface.co/teddy322/wav2vec2-large-xls-r-300m-kor-11385-2) on the zeroth_korean_asr dataset. It achieves the following results on the evaluation set: - eval_loss: 0.2425 - eval_wer:... | 3a02942058acc8f8101559959e195953 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sche... | f51a1b0311ad57dbe79ed7fcf537eb5f |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | fusion-final Dreambooth model trained by valentinaw1sa4ajh 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/fa... | 7079b8b5970355aa37bbd810b87f9d09 |
apache-2.0 | ['generated_from_keras_callback'] | false | rhitabrat/bert-finetuned-squad This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.7887 - Epoch: 1 | 9290ac3f70e7358736274bb0837e73e7 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 7790, 'end_learning_ra... | 395c94d8a2269379d7d5a0b5a225b6f8 |
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 xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1354 - F1: 0.8621 | 8710f3f92c322db961fe1ff9199b4a54 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.254 | 1.0 | 525 | 0.1652 | 0.8254 | | 0.1293 | 2.0 | 1050 | 0.1431 | 0.8489 | | 0.0797 | 3.0 | 1575 | 0.1354 | 0.8621 | ... | 1c49febe209bcd49f5a418f936e88df7 |
cc-by-sa-4.0 | [] | false | BERT base Japanese (IPA dictionary) This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language. This version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by the WordPiece subword tokenization. The codes for th... | d687c558fec589d765a3aea7b450e42b |
apache-2.0 | ['generated_from_trainer'] | false | my_new_asr_model 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: 4.9912 - Wer: 0.9915 | 0863787f9c42bdafafd9c7ddc0a727c6 |
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: 1000 - mixed_precision_... | 8ab514fcf3f4549ecebe28a9d566ce07 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | No log | 200.0 | 200 | 3.2498 | 0.9972 | | No log | 400.0 | 400 | 4.1645 | 1.1339 | | 1.1325 | 600.0 | 600 | 4.7252 | 1.119... | f7d9047ac99950d96d658d2cae736e0e |
mit | ['huggingnft', 'nft', 'huggan', 'gan', 'image', 'images', 'unconditional-image-generation'] | false | Model description LightWeight GAN model for unconditional generation. NFT collection available [here](https://opensea.io/collection/dooggies). Dataset is available [here](https://huggingface.co/datasets/huggingnft/dooggies). Check Space: [link](https://huggingface.co/spaces/AlekseyKorshuk/huggingnft). Project rep... | 0fa04edcf07c2e522857df58f7e467f1 |
apache-2.0 | ['translation'] | false | opus-mt-fr-sl * source languages: fr * target languages: sl * OPUS readme: [fr-sl](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-sl/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | 99e34ce72b00fed1c03e1836ff32f9ff |
apache-2.0 | ['vision', 'image-classification'] | false | RegNetY 10B This gigantic model is a scale up [RegNetY](https://arxiv.org/abs/2003.13678) model trained on one bilion random images ad later finetuned on imagenet. Disclaimer: The team releasing RegNet did not write a model card for this model so this model card has been written by the Hugging Face team. | c57514d81aafc31ef206c4645c713c1e |
apache-2.0 | ['vision', 'image-classification'] | false | How to use Here is how to use this model: ```python >>> from transformers import AutoFeatureExtractor, RegNetForImageClassification >>> import torch >>> from datasets import load_dataset >>> dataset = load_dataset("huggingface/cats-image") >>> image = dataset["test"]["image"][0] >>> feature_extractor = AutoFeature... | 0898c68db258e9e7cadf595284153704 |
cc-by-4.0 | ['roberta', 'roberta-base', 'question-answering', 'qa', 'movies'] | false | roberta-base + Task Transfer (NER) --> Domain-Specific QA Objective: This is Roberta Base without any Domain Adaptive Pretraining --> Then trained for the NER task using MIT Movie Dataset --> Then a changed head to do the SQuAD Task. This makes a QA model capable of answering questions in the movie domain, with add... | 0f9cc8bc4b03b708dc7bd23eeedcf909 |
cc-by-4.0 | ['roberta', 'roberta-base', 'question-answering', 'qa', 'movies'] | false | Overview **Language model:** roberta-base **Language:** English **Downstream-task:** NER --> QA **Training data:** MIT Movie, SQuADv1 **Eval data:** MoviesQA (From https://github.com/ibm-aur-nlp/domain-specific-QA) **Infrastructure**: 4x Tesla v100 **Code:** See [example](https://github.com/adityaarunsin... | 5a95770e6c23a6c76e3aecf25bd3838e |
apache-2.0 | ['audio', 'automatic-speech-recognition'] | false | Changes & Notes 1. Document reproducible evaluation (below) to new transformer and datasets version. 2. Use batch size of 1 to reproduce results. 3. Validated with ```transformers v4.15.0```, ```datasets 1.18.0``` 4. You may need to manually install pypkg ```librosa```, ```jiwer``` | a34ab2b649e4eed77fd44fe0861b6e09 |
apache-2.0 | ['audio', 'automatic-speech-recognition'] | false | Evaluation This code snippet shows how to evaluate **facebook/wav2vec2-base-100h** on LibriSpeech's "clean" and "other" test data. ```python from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import soundfile as sf import torch from jiwer import wer librispeech_eval = lo... | b4204c021d0ed4a9f692a85521ad4a41 |
apache-2.0 | ['audio', 'automatic-speech-recognition'] | false | librispeech_eval = load_dataset("librispeech_asr", "other", split="test") model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-100h").to("cuda") processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-100h") def map_to_array(batch): | 1d30583731eaafc23df48cf238fc3296 |
apache-2.0 | ['audio', 'automatic-speech-recognition'] | false | batch["speech"] = speech batch["speech"] = batch['audio']['array'] return batch librispeech_eval = librispeech_eval.map(map_to_array) def map_to_pred(batch): input_values = processor(batch["speech"], return_tensors="pt", padding="longest").input_values with torch.no_grad(): logits = model(inp... | 81c6715a403d189a638b9cb8b148eb3d |
mit | [] | false | Training data
The training data contains around 2210 ebooks, mostly in the sci-fi and fantasy genres. The dataset is based on the same dataset used by GPT-Neo-2.7B-Picard, with 20% more data in various genres.
Some parts of the dataset have been prepended using the following text: `[Genre: <genre1>,<genre2>]`
| f42a2401dc04f186ab3c81982a9bb388 |
mit | [] | false | How to use
You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run:
```py
>>> from transformers import pipeline
>>> generator = pipeline('text-generation', model='KoboldAI/GPT-J-6B-Janeway')
>>> generator("Welcome Captain Janeway, I apolo... | 904b8bb2863e8cccc177aa61711d1d5c |
mit | [] | false | Limitations and Biases
The core functionality of GPT-J is taking a string of text and predicting the next token. While language models are widely used for tasks other than this, there are a lot of unknowns with this work. When prompting GPT-J it is important to remember that the statistically most likely next token... | 3e3237189724d828230abe4241b5e1ee |
mit | [] | false | BibTeX entry and citation info
The model uses the following model as base:
```bibtex
@misc{gpt-j,
author = {Wang, Ben and Komatsuzaki, Aran},
title = {{GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model}},
howpublished = {\url{https://github.com/kingoflolz/mesh-transformer-jax}},
year = 2021,... | 100f2c496f90c6290e0a9c3e809fd2b8 |
mit | [] | false | Acknowledgements
This project would not have been possible without compute generously provided by Google through the
[TPU Research Cloud](https://sites.research.google/trc/), as well as the Cloud TPU team for providing early access to the [Cloud TPU VM](https://cloud.google.com/blog/products/compute/introducing-cl... | 02dae082f6a5e42243ee6979423b0e3c |
apache-2.0 | ['generated_from_trainer'] | false | HateXplain-third-annotator This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.8016 - Accuracy: 0.5913 | fbb15b55319ffa7eebcbaccace643f7c |
apache-2.0 | ['summarization'] | false | BigBirdPegasus model (large) BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle. BigBird was intr... | 2b0a275597b18d5474d9eea74446f5d7 |
apache-2.0 | ['summarization'] | 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 BigBirdPegasusForConditionalGeneration, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("google/bigbird-pegasus-large-pubmed") | 37d849291c89d2fe0c48799757dfba39 |
apache-2.0 | ['summarization'] | false | you can change `attention_type` (encoder only) to full attention like this: model = BigBirdPegasusForConditionalGeneration.from_pretrained("google/bigbird-pegasus-large-pubmed", attention_type="original_full") | e63cdf8fade5a8cbff49ff7c0d1708d5 |
apache-2.0 | ['summarization'] | false | you can change `block_size` & `num_random_blocks` like this: model = BigBirdPegasusForConditionalGeneration.from_pretrained("google/bigbird-pegasus-large-pubmed", block_size=16, num_random_blocks=2) text = "Replace me by any text you'd like." inputs = tokenizer(text, return_tensors='pt') prediction = model.generate(*... | c91d1a95cad466027035345cc8f49839 |
apache-2.0 | ['summarization'] | false | Training Procedure This checkpoint is obtained after fine-tuning `BigBirdPegasusForConditionalGeneration` for **summarization** on **pubmed dataset** from [scientific_papers](https://huggingface.co/datasets/scientific_papers). | 0cffdad6c8ca030da76accb09c8d5f02 |
apache-2.0 | ['speech'] | false | Wav2Vec2-german model [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a... | 7df9380cee0ce34ae2bd5d2d9cc7ecfa |
apache-2.0 | ['speech'] | false | How to use `TODO: Update` ```python from transformers import FlaxWav2Vec2Processor, TFWav2Vec2Model model_id = "flax-community/wav2vec2-german" from datasets import load_dataset import soundfile as sf processor = Wav2Vec2Processor.from_pretrained(model_id) model = TFWav2Vec2Model.from_pretrained(model_id) def map... | abf085f5dbdb33f70d0c1fd876a51e48 |
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: 1.2104 | 5006942004404f620492811947ba2a7e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.3937 | 1.0 | 5533 | 1.2915 | | 1.1522 | 2.0 | 11066 | 1.2227 | | 1.0055 | 3.0 | 16599 | 1.2104 | | cb402e64ed9374d48ea3f361cd646c37 |
apache-2.0 | [] | false | bert-base-ur-cased We are sharing smaller versions of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) that handle a custom number of languages. Unlike [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased), our versions give exactly the sam... | 51dce4315f74c841ced701d84de42499 |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/bert-base-ur-cased") model = AutoModel.from_pretrained("Geotrend/bert-base-ur-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github repo](https:/... | ed4a1852579a98e2b56ebe4d23956861 |
apache-2.0 | ['generated_from_trainer'] | false | try1 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 None dataset. It achieves the following results on the evaluation set: - Loss: 0.6966 - Precision: 0.4569 - Recall: 0.4569 - F1: 0.4569 - Pf1: 0.0597 - Accuracy: 0.4569 | 056a121c6836960228ecce8cf1a0137e |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - 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 - num_ep... | 77107df664ac6a6748a5f0aa57273c77 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Pf1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:------:|:--------:| | 0.704 | 1.72 | 100 | 0.6920 | 0.5409 | 0.5409 | 0.5409 | 0.6979 | 0.5409 ... | c8b3a59ea064ee4458398aaca18febc7 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 0.01 | 7337d8526fcafc2b309c4229b5ecee9e |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | marian-finetuned-kde4-en-to-fr This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset. It achieves the following results on the evaluation set: - Loss: 0.8560 - Bleu: 52.8311 | 55c6bb02313a2403f8e50adb4b66ae19 |
apache-2.0 | ['automatic-speech-recognition', 'pt'] | false | exp_w2v2t_pt_no-pretraining_s84 Fine-tuned randomly initialized wav2vec2 model for speech recognition using the train split of [Common Voice 7.0 (pt)](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 been... | 7e89779d95d53ba66aa696575a3940f4 |
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