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 | [] | false | Abstractive Summarization ``` from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("bloomberg/KeyBART") model = AutoModelForSeq2SeqLM.from_pretrained("bloomberg/KeyBART") from datasets import load_dataset dataset = load_dataset("cnn_dailymail") ``` Reported Result... | 2c6eccd0256e4c488174549eebf2f6fd |
apache-2.0 | [] | false | Zero-shot settings ``` from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("bloomberg/KeyBART") model = AutoModelForSeq2SeqLM.from_pretrained("bloomberg/KeyBART") ``` Alternatively use the Hosted Inference API console provided in https://huggingface.co/bloomberg/Ke... | d9d5ef877bbd6dc77ee932600f26a489 |
apache-2.0 | [] | false | Citation Please cite this work using the following BibTeX entry: ``` @inproceedings{kulkarni-etal-2022-learning, title = "Learning Rich Representation of Keyphrases from Text", author = "Kulkarni, Mayank and Mahata, Debanjan and Arora, Ravneet and Bhowmik, Rajarshi", booktitle = "Fin... | 98d908b18eb430b062e69fc50b9d94ed |
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 an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3916 - Accuracy: 0.886 - F1: 0.8818 | 1748b3764781ee7f0ce811238b85c300 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 125 | 0.6487 | 0.7875 | 0.7547 | | 0.8271 | 2.0 | 250 | 0.3916 | 0.886 | 0.8818 | | facad25e2d9404325294a3145f029f38 |
apache-2.0 | ['automatic-speech-recognition', 'pl'] | false | exp_w2v2t_pl_vp-sv_s507 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 (pl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | 40c3f13725c31aea245d79371c80fe7f |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | `kan-bayashi/jsut_tts_train_conformer_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.loss.ave` ♻️ Imported from https://zenodo.org/record/4436448/ This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/). | 2bb79a04369401f5e2ed978187238463 |
mit | [] | false | TomCat on Stable Diffusion This is the `<tom-cat>` 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 ... | bd062e5810d5592e159e4f84a80b51ba |
apache-2.0 | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'], 'is_split_by_sentences': True, 'skip_tokens': 1649999872}, 'generation': {'batch_size': 128, 'every_n_steps': 512, 'force_call_on': [12588], 'metrics_con... | e333af3254555477d2178e9a6d56b9b2 |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | t5-base-TEDxJP-9front-1body-9rear This model is a fine-tuned version of [sonoisa/t5-base-japanese](https://huggingface.co/sonoisa/t5-base-japanese) on the te_dx_jp dataset. It achieves the following results on the evaluation set: - Loss: 0.4361 - Wer: 0.1687 - Mer: 0.1630 - Wil: 0.2486 - Wip: 0.7514 - Hits: 55941 - S... | e8945cecc42ecb2ee46b588ef39038eb |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Mer | Wil | Wip | Hits | Substitutions | Deletions | Insertions | Cer | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:------:|:-----:|:-------------:|:---------:|:----------:|:------:| | 0.6124 ... | 1e81a7b3b96cc33afec7dc1224dc8fb7 |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | `kan-bayashi/libritts_tts_train_xvector_conformer_fastspeech2_transformer_teacher_raw_phn_tacotron_g2p_en_no_space_train.loss` ♻️ Imported from https://zenodo.org/record/4418754/ This model was trained by kan-bayashi using libritts/tts1 recipe in [espnet](https://github.com/espnet/espnet/). | aa72eaa912069fb94663514f737dde4b |
apache-2.0 | ['translation'] | false | opus-mt-en-uk * source languages: en * target languages: uk * OPUS readme: [en-uk](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-uk/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://... | 58df04f0df3967eff1390f90dc644046 |
apache-2.0 | ['generated_from_keras_callback'] | false | vinitharaj/distilbert-base-uncased-finetuned-squad 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: 4.5718 - Validation Loss: 4.2502 - Epoch: 1 | 2b436880f03affdc4802853ac9947132 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 46, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1... | 0ab7f1750d3114d53594f0946ee6b959 |
cc-by-4.0 | ['question generation'] | false | Model Card of `research-backup/bart-large-squad-qg-no-paragraph` This model is fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.c... | 80ff387a8cac2c5bc64f0e81d0c9a799 |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "research-backup/bart-large-squad-... | eb4c46fdf1fee432880478c4383b4585 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/bart-large-squad-qg-no-paragraph/raw/main/eval/metric.first.sentence.sentence_answer.question.lmqg_qg_squad.default.json) | | Score | Type | Dataset ... | b5fc329a2b8d2d14f316404bc0159a50 |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squad - dataset_name: default - input_types: ['sentence_answer'] - output_types: ['question'] - prefix_types: None - model: facebook/bart-large - max_length: 128 - max_length_output: 32 - epoch: 8 - ... | a0ab6b27bf54ff530a7b131d1b5f9135 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'wildcard'] | false | DreamBooth model for the Wall-E-01 concept trained by DiamondYin. This is a Stable Diffusion model fine-tuned on the Wall-E-01 concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of Wall-E-01 robot** This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisat... | 4a1005c0d0a4dfed85745d802984c88c |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'wildcard'] | false | Description This is a Stable Diffusion model fine-tuned on `robot` images for the wildcard theme, for the Hugging Face DreamBooth Hackathon, from the HF CN Community, corporated with the HeyWhale. The production cost of WALL-E is $180 million. It tells about a lonely robot designed to clean up the polluted earth. ... | 4e53a1f8f1719e05be20863052cbc322 |
apache-2.0 | ['visual-question-answering'] | false | Vision-and-Language Transformer (ViLT), fine-tuned on VQAv2 Vision-and-Language Transformer (ViLT) model fine-tuned on [VQAv2](https://visualqa.org/). It was introduced in the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Kim et al. and fi... | 98b2712482e5be52f53da76ad87a7191 |
apache-2.0 | ['visual-question-answering'] | false | prepare image + question url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) text = "How many cats are there?" processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa") model = ViltForQuestionAnswering.from_pretrained("dandelin/vilt-... | 24581b87b6fd0780850700fde7e684d7 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | elyyoorrcchh Dreambooth model trained by Jorgitosch 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... | c4d83c0f0522684f8e77ea248b0c481d |
apache-2.0 | ['generated_from_trainer'] | false | tiny-bert-sst2-distilled This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 1.7305 - Accuracy: 0.8326 | c201e2c6bcf063e59b14beca31f2c2eb |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0007199555649276667 - train_batch_size: 1024 - eval_batch_size: 1024 - seed: 33 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7 - mixed_precision_training: Native... | f241b02c269a5e2e4ac3304dac4844a7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.77 | 1.0 | 66 | 1.6939 | 0.8165 | | 0.729 | 2.0 | 132 | 1.5090 | 0.8326 | | 0.5242 | 3.0 | 198 | 1.5369 | 0.... | 8915c5808b513b940bf866b3b4180031 |
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: 0.2256 - Accuracy: 0.923 - F1: 0.9226 | a015fee0b5f9bfc7b883c76ba035549c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.807 | 1.0 | 250 | 0.3202 | 0.8995 | 0.8968 | | 0.2491 | 2.0 | 500 | 0.2256 | 0.923 | 0.9226 | | e055cdeeb9f409506af3c6918b18318c |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | wav2vec2-large-xlsr-53-hebrew Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the several downloaded youtube samples. When using this model, make sure that your speech input is sampled at 16kHz. | 648607ce519fb796e524fbe9f400bbbd |
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", "he", split="test[:2%]") | 71c6e4824a7e110faaac5e042e710785 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | there is no common dataset for Hebrew, please, paste your data processor = Wav2Vec2Processor.from_pretrained("imvladikon/wav2vec2-large-xlsr-53-hebrew") model = Wav2Vec2ForCTC.from_pretrained("imvladikon/wav2vec2-large-xlsr-53-hebrew") resampler = torchaudio.transforms.Resample(48_000, 16_000) | 9835dc581f23f33991658c00fbae4a2e |
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"][:2... | 2431ac0838c846fb4417b857026a4638 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on some Hebrew test data ```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", "he", split="test") | 7c9c3b22824ab766433c4e525751d9a4 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | there is no common dataset for Hebrew, please, paste your data wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("imvladikon/wav2vec2-large-xlsr-53-hebrew") model = Wav2Vec2ForCTC.from_pretrained("imvladikon/wav2vec2-large-xlsr-53-hebrew").to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\... | 09baa3bd6f3b0e517d7653dfd7212370 |
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(logi... | aca635f7a6f2068815c2d1d343d1fcbf |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_data_aug_wnli This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 2.4231 - Accuracy: 0.0845 | cc1aa466f007d2fccc9280a79c2456b4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6166 | 1.0 | 218 | 2.4231 | 0.0845 | | 0.4183 | 2.0 | 436 | 4.2000 | 0.0986 | | 0.3033 | 3.0 | 654 | 5.7862 | 0.... | 0fb4a15bff8d96bebce09ffaa1d785e9 |
apache-2.0 | ['generated_from_trainer'] | false | recipe-distilroberta-Is 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: 4.7427 | b45905973709f100bc9cfd6c2e008edc |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 25 - mixed_precision_training: Native AMP | 86da6716478087d9d24266a08c562bd9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 19.6191 | 1.0 | 2135 | 10.5217 | | 8.6838 | 2.0 | 4270 | 7.3017 | | 6.884 | 3.0 | 6405 | 6.4445 | | 6.2953 | 4.0 | 8540 | 6.0610 ... | ddaa7a7249345a61a4ef3206c68fefae |
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: 0.2232 - Accuracy: 0.9215 - F1: 0.9218 | 01cf06fcdf35d5f8352b24f0a3877412 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8098 | 1.0 | 250 | 0.3138 | 0.9025 | 0.9001 | | 0.2429 | 2.0 | 500 | 0.2232 | 0.9215 | 0.9218 | | 6f252d64376ce59d8b826a58d80167f3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 125 | 0.5730 | 0.7840 | | 86a78ba74aeeb83b12633f6b2ea24f51 |
creativeml-openrail-m | ['text-to-image'] | false | SksUminaoshiSimabu Dreambooth model trained by Hirokusa 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-... | ce2da44e00f43313b5917931c87342e9 |
mit | ['pytorch', 'diffusers', 'unconditional-image-generation', 'diffusion-models-class'] | false | Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. In this run I just ran each cell of the NB to understand what is going on. Experimentation to follow 🙏 | 0d0270bf23ae06d7c6ad38e95cb8c5d3 |
apache-2.0 | ['generated_from_trainer'] | false | mdeberta-targin-final This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5637 - Accuracy: 0.7091 - Precision: 0.6841 - Recall: 0.6557 - F1: 0.6617 ... | 336359cb9473ce866eac3df78b1c8697 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | No log | 1.0 | 296 | 0.6001 | 0.6435 | 0.6344 | 0.5087 | 0.4156 | | 0.6011 | 2.0 |... | 06c4d63f61a350c2d5c52266f114081e |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Telugu Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Telugu using the [OpenSLR SLR66](http://openslr.org/66/) dataset. When using this model, make sure that your speech input is sampled at 16kHz. | 72a22b81b24341099195b2ac7c8b441b |
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 import pandas as pd | 5808b28679e656984b7f78bd3ddae7c0 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation notebook contains the procedure to download the data df = pd.read_csv("/content/te/test.tsv", sep="\t") df["path"] = "/content/te/clips/" + df["path"] test_dataset = Dataset.from_pandas(df) processor = Wav2Vec2Processor.from_pretrained("anuragshas/wav2vec2-large-xlsr-53-telugu") model = Wav2Vec2ForCTC.from_... | db710b7fc05409a6d1250ab4336bfef1 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation ```python import torch import torchaudio from datasets import Dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re from sklearn.model_selection import train_test_split import pandas as pd | 187c3acea47e5c6b76b7159a8b6f72a4 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation notebook contains the procedure to download the data df = pd.read_csv("/content/te/test.tsv", sep="\t") df["path"] = "/content/te/clips/" + df["path"] test_dataset = Dataset.from_pandas(df) wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("anuragshas/wav2vec2-large-xlsr-53-telugu") mod... | a73f51f624cf21d6bf6242bff208968a |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Use your custom normalizer text = text.replace("\\n","\n") text = ' '.join(text.split()) text = re.sub(r'''([a-z]+)''','',text,flags=re.IGNORECASE) text = re.sub(r'''%'''," శాతం ", text) text = re.sub(r'''(/|-|_)'''," ", text) text = re.sub("ై","ై", text) text = text.strip() return tex... | a961eee82f284eceb106ac26f6ac981d |
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... | fe421ae70d2a3455b87a0b88104f4649 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Training 70% of the OpenSLR Telugu dataset was used for training. Train Split of annotations is [here](https://www.dropbox.com/s/xqc0wtour7f9h4c/train.tsv) Test Split of annotations is [here](https://www.dropbox.com/s/qw1uy63oj4qdiu4/test.tsv) Training Data Preparation notebook can be found [here](https://colab.res... | 47e124b603e8a46f27f7b3eee38ae55c |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'landscape'] | false | DreamBooth model for the stupa concept trained by Someman on the Someman/boudhastupa dataset. This is a Stable Diffusion model fine-tuned on the concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of boudhanath stupa** This model was created as part of the DreamBooth Hackathon 🔥. ... | 2b360235c820f80b93b06bd4cb6b381a |
cc-by-4.0 | ['generated_from_trainer'] | false | bert-base-cased-squad2-coffee20230113 This model is a fine-tuned version of [deepset/bert-base-cased-squad2](https://huggingface.co/deepset/bert-base-cased-squad2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.3859 | 45905d8a96318d3e062ddbc7306b9002 |
cc-by-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 88 | 2.0785 | | 2.2589 | 2.0 | 176 | 1.9542 | | 1.4038 | 3.0 | 264 | 1.7714 | | 0.9533 | 4.0 | 352 | 2.2673 ... | 4df6cade815d2752b4f2d6fd52f88cf9 |
apache-2.0 | ['multiberts', 'multiberts-seed_3', 'multiberts-seed_3-step_2000k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 3, Step 2000k 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... | ae3c14cd85fc991487b12ccf3d13db28 |
apache-2.0 | ['multiberts', 'multiberts-seed_3', 'multiberts-seed_3-step_2000k'] | 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_2000k') model = TFBertModel.from_pretrained("google/multib... | a1803a30e7a8e154acd1e72066943a7b |
gpl-3.0 | ['audio', 'music'] | false | This model encodes audio files into vectors of 100 dimensions. It was trained on 240,000 Spotify playlists and on 30 second samples of over 4 million songs. The details can be found [here](https://github.com/teticio/Deej-AI). To encode an audio first install the package with ``` pip install audiodiffusion ``` and th... | 5a4c3f4092735b860afcb277e1e71e2b |
apache-2.0 | ['generated_from_trainer'] | false | distilled-mt5-small-0.0-0.5 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset. It achieves the following results on the evaluation set: - Loss: 2.8994 - Bleu: 7.5838 - Gen Len: 45.058 | 5ff66ac0612d1b8be0e8ef212c2dd4b8 |
mit | ['torch'] | false | How to use Here is how to use this model in PyTorch: ```python >>> from transformers import EncoderDecoderModel, XLMRobertaTokenizer >>> >>> model_id = "rmihaylov/roberta2roberta-shared-nmt-bg" >>> model = EncoderDecoderModel.from_pretrained(model_id) >>> model.encoder.pooler = None >>> tokenizer = XLMRobertaTokeniz... | 3accb120bf654ddaabc8055a9ce6a585 |
mit | ['torch'] | false | How to use Here is how to use this model in PyTorch: ```python >>> from transformers import PegasusForConditionalGeneration, AutoTokenizer >>> >>> model_id = "rmihaylov/pegasus-base-cnn-dailymail-bg" >>> model = PegasusForConditionalGeneration.from_pretrained(model_id) >>> tokenizer = AutoTokenizer.from_pretrained(m... | 107a2b605d610ffaf1473bbbc4659a3a |
mit | ['generated_from_trainer'] | false | kobart_4_5.6e-5_datav2_min30_lp5.0_temperature1.0 This model is a fine-tuned version of [gogamza/kobart-base-v2](https://huggingface.co/gogamza/kobart-base-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.9891 - Rouge1: 35.4597 - Rouge2: 12.0824 - Rougel: 23.0161 - Bleu1: 29... | 2477f6399900a357a2175bbbff8a3e9d |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 4 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 | 6e22a32ba6c049a45550a885560f288e |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Bleu1 | Bleu2 | Bleu3 | Bleu4 | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:-------:|:-------:|:------:|:-------:| | 2.3968 | 0.47 | 5000 | 2.909... | 59fdcef48c83b7ee85d8c52f2cc4590a |
creativeml-openrail-m | ['coreml', 'stable-diffusion', 'text-to-image'] | false | Epic Diffusion: Source(s): [Hugging Face](https://huggingface.co/johnslegers/epic-diffusion) - [CivitAI](https://civitai.com/models/3855/epic-diffusion) Why Epic Diffusion Epîc Diffusion is a general purpose model based on Stable Diffusion 1.x intended to replace the official SD releases as your default model. It is ... | d825adc245724214697cc54b48bfcd20 |
afl-3.0 | [] | false | This model is actually very accurate for this rerank products given one query, intuitively inspired by information retrieval techniques. In 2019, Nils Reimers and Iryna Gurevych introduced a new transformers model called Sentence-BERT, Sentence Embeddings using Siamese BERT-Networks. The model is introduced by this pa... | 0c7cd2e1dff8e0b5100f173af153853a |
afl-3.0 | [] | false | Download and Use ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LiYuan/Amazon-Cup-Cross-Encoder-Regression") model = AutoModelForSequenceClassification.from_pretrained("LiYuan/Amazon-Cup-Cross-Encoder-Regression") ``` As we can observ... | caf331bce3240732225d2faef8a09f01 |
apache-2.0 | ['automatic-speech-recognition', 'it'] | false | exp_w2v2t_it_hubert_s722 Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) 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... | 921850125eb25f760976ea393633fa31 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'robust-speech-event'] | false | This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the KRESNIK/ZEROTH_KOREAN - CLEAN dataset. It achieves the following results on the evaluation set: - Loss: 0.0639 - Wer: 0.0449 | 53c714c7985258635f2c9a1758ca1256 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 4.603 | 0.72 | 500 | 4.6572 | 0.9985 | | 2.6314 | 1.44 | 1000 | 2.0424 | 0.9256 | | 2.2708 | 2.16 | 1500 | 0.9889 | 0.698... | 43804daf83b54a1d00a04807e9cd37bc |
apache-2.0 | ['translation'] | false | opus-mt-es-cs * source languages: es * target languages: cs * OPUS readme: [es-cs](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-cs/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | b83911c98602ebc3a04d51973a998484 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-wikihow_3epoch_v2 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: 2.2758 - Rouge1: 27.48 - Rouge2: 10.7621 - Rougel: 23.4136 - Rougelsum: 26.7923 - Gen Len: 18.5424 | 02328779394a4d29c58639e3dda93d98 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 2.8423 | 0.13 | 5000 | 2.5715 | 25.2685 | 8.6964 | 21.229 | 24.5773 ... | 391ca9fcf950b57c7b4b9165555b54c1 |
apache-2.0 | ['generated_from_trainer'] | false | olm-bert-tiny-december-2022-target-glue-qqp This model is a fine-tuned version of [muhtasham/olm-bert-tiny-december-2022](https://huggingface.co/muhtasham/olm-bert-tiny-december-2022) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5217 - Accuracy: 0.7433 - F1: 0.6048 | 495a0515c52e8d4f8ef615cd46d488ca |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.6283 | 0.04 | 500 | 0.5955 | 0.6795 | 0.5186 | | 0.5875 | 0.09 | 1000 | 0.5763 | 0.6972 | 0.5596 | | 0.5791 |... | 9d36768531ad96e2ef9815a7601e8b69 |
mit | ['bart'] | false | Model Description [**BART**](https://arxiv.org/pdf/1910.13461.pdf)(**B**idirectional and **A**uto-**R**egressive **T**ransformers)는 입력 텍스트 일부에 노이즈를 추가하여 이를 다시 원문으로 복구하는 `autoencoder`의 형태로 학습이 됩니다. 한국어 BART(이하 **KoBART**) 는 논문에서 사용된 `Text Infilling` 노이즈 함수를 사용하여 **40GB** 이상의 한국어 텍스트에 대해서 학습한 한국어 `encoder-decoder` 언어 ... | 43898082e8d5eb682223da09fcf85929 |
mit | ['bart'] | false | Bias, Risks, and Limitations Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may incl... | d5e927b10c2aa10428a6e82980bb1f4c |
mit | ['bart'] | false | of Sentences | |-------|---------------:| | Korean Wiki | 5M | | Other corpus | 0.27B | 한국어 위키 백과 이외, 뉴스, 책, [모두의 말뭉치 v1.0(대화, 뉴스, ...)](https://corpus.korean.go.kr/), [청와대 국민청원](https://github.com/akngs/petitions) 등의 다양한 데이터가 모델 학습에 사용되었습니다. `vocab` 사이즈는 30,000 이며 대화에 자주 쓰이는 아래와 같은 이모티콘, 이모지 등을 추가하여 ... | 9b271b1630d1622034e48640f12f94bf |
mit | ['bart'] | false | of heads | ffn_dim | hidden_dims | |--------------|:----:|:-------:|--------:|--------:|--------:|--------------:| | `KoBART-base` | 124M | Encoder | 6 | 16 | 3072 | 768 | | | | Decoder | 6 | 16 | 3072 | 768 | | d529e9711364a6f6f75261afed92bf69 |
mit | ['bart'] | false | Results NSMC - acc. : 0.901 The model authors also note in the [GitHub Repo](https://github.com/haven-jeon/KoBART): | | [NSMC](https://github.com/e9t/nsmc)(acc) | [KorSTS](https://github.com/kakaobrain/KorNLUDatasets)(spearman) | [Question Pair](https://github.com/aisolab/nlp_classification/tree/master/BERT_p... | 13588b6d9367bca2ad086eac9a206f8a |
mit | ['bart'] | false | How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> ```python from transformers import PreTrainedTokenizerFast, BartModel tokenizer = PreTrainedTokenizerFast.from_pretrained('gogamza/kobart-base-v2') model = BartModel.from_pretrained... | 69b66baaf98a0e0de2d3d428944b8138 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_data_aug_wnli_384 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 1.7589 - Accuracy: 0.1268 | 61b0cf7be740c23579d2f9bee2edbcd2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Accuracy | Validation Loss | |:-------------:|:-----:|:----:|:--------:|:---------------:| | 0.6429 | 1.0 | 218 | 0.1268 | 1.7589 | | 0.4693 | 2.0 | 436 | 3.1597 | 0.1127 | | 0.3905 | 3.0 | 654 | 4.0613 | 0.0704 ... | 3b842d26b5d90fa127fcdcd778ac411e |
apache-2.0 | ['automatic-speech-recognition', 'collectivat/tv3_parla', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'projecte-aina/parlament_parla', 'robust-speech-event'] | false | wav2vec2-xls-r-300m-ca-lm 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 - CA, the [tv3_parla](https://huggingface.co/datasets/collectivat/tv3_parla) and [parlament_parla](https://huggingface.co/datas... | 4804998615b0b0b04305fc63b374198f |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - 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: 5 | 86407266f2ba34af43a95a3a00e37a62 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.5695 | 0.39 | 500 | 0.0591 | | 0.0606 | 0.77 | 1000 | 0.0588 | | 0.0575 | 1.16 | 1500 | 0.0588 | | 0.0551 | 1.55 | 2000 | 0.0586 ... | 618f3406f4954b946abc5c143b754eb0 |
apache-2.0 | ['generated_from_trainer'] | false | test This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2778 - Accuracy: 0.9335 - F1:: 0.9337 | 11f8e6ee1404ff79a58b8921bbab9e21 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1: | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 250 | 0.3285 | 0.9285 | 0.9291 | | No log | 2.0 | 500 | 0.2778 | 0.9335 | 0.9337 | | 46178b6329e9b3440db8b4793902540f |
mit | [] | false | PolicyBERTa-7d This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on data from the [Manifesto Project](https://manifesto-project.wzb.eu/). It was inspired by the model from [Laurer (2020)](https://huggingface.co/MoritzLaurer/policy-distilbert-7d). It achieves the following resu... | 36816339ad3ee2512c64355292c6e000 |
mit | [] | false | Model description This model was trained on 115,943 manually annotated sentences to classify text into one of seven political categories: "external relations", "freedom and democracy", "political system", "economy", "welfare and quality of life", "fabric of society" and "social groups". | 9dc22d6c03f4ffcfb3870e62b6425508 |
mit | [] | false | Intended uses & limitations The model output reproduces the limitations of the dataset in terms of country coverage, time span, domain definitions and potential biases of the annotators - as any supervised machine learning model would. Applying the model to other types of data (other types of texts, countries etc.) w... | 753aa62920ea3b8d5ce1025949a172e9 |
mit | [] | false | Training and evaluation data PolicyBERTa-7d was trained on the English-speaking subset of the [Manifesto Project Dataset (MPDS2021a)](https://manifesto-project.wzb.eu/datasets). The model was trained on 115,943 sentences from 163 political manifestos in 7 English-speaking countries (Australia, Canada, Ireland, New Ze... | 636e5ef480aca2e15a32d083895e5628 |
mit | [] | false | Tain data Train data was higly imbalanced. | Label | Description | Count | |------------|--------------|--------| | 0 | external relations | 7,640 | | 1 | freedom and democracy | 5,880 | | 2 | political system | 11,234 | | 3 | economy | 29,218 | | 4 | we... | db4ae762cfae63277a1b7986799a4927 |
mit | [] | false | Validation data The validation was created by chance. | Label | Description | Count | |------------|--------------|--------| | 0 | external relations | 1,345 | | 1 | freedom and democracy | 1,043 | | 2 | political system | 2,038 | | 3 | economy | 5,140 | | 4 ... | be5c21b003eec250543e66a1f0eb2903 |
mit | [] | false | Test data The test dataset contains ten canadian manifestos between 2004 and 2008. | Label | Description | Count | |------------|--------------|--------| | 0 | external relations | 824 | | 1 | freedom and democracy | 296 | | 2 | political system | 1,041 | | 3 | e... | 7e4efdcca67c39d331f9a26c72cd060e |
mit | [] | false | Training hyperparameters The following hyperparameters were used during training: ``` training_args = TrainingArguments( warmup_steps=0, weight_decay=0.1, learning_rate=1e-05, fp16 = True, evaluation_strategy="epoch", num_train_epochs=5, per_device_train_batch_size=16, overwrite_outpu... | de94d971a296d826fd4f0587a1b37fa7 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.