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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mit | [] | false | 사용 예시 ```python from transformers import pipeline model_name = "heegyu/kogpt-j-350m" pipe = pipeline('text-generation', model=model_name) print(pipe("안녕하세요", repetition_penalty=1.2, do_sample=True, eos_token_id=1, early_stopping=True, max_new_tokens=128)) print(pipe("오늘 정부 발표에 따르면, ", repetition_penalty=1.2, do_samp... | 68e929a91cb07692d3cd7a44da12e507 |
mit | ['AMRBART'] | false | AMRBART (base-sized model) AMRBART model is continually pre-trained on the English text and AMR Graphs based on the BART model. It was introduced in the paper: [Graph Pre-training for AMR Parsing and Generation](https://arxiv.org/pdf/2203.07836.pdf) by bai et al. in ACL 2022 and first released in [this repository](ht... | 1aa37b4f7e6dcdfc7a1f632452ecf595 |
mit | ['AMRBART'] | false | Model description AMRBART follows the BART model which uses a transformer encoder-encoder architecture. AMRBART is pre-trained with 6 tasks: + learning to reconstruct the text based on the corrupted text. + learning to reconstruct AMR graphs based on the corrupted AMR graph. + learning to reconstruct the text based ... | b8a6dbc271e7c132534f0eac4fc4c417 |
mit | ['AMRBART'] | false | Training data The AMRBART model is pre-trained on [AMR3.0](https://catalog.ldc.upenn.edu/LDC2020T02), a dataset consisting of 55,635 training instances and [English Gigaword](https://catalog.ldc.upenn.edu/LDC2003T05) (we randomly sampled 200,000 sentences). | 180acc8f72c492c10971834cd58cb677 |
mit | ['AMRBART'] | false | How to use Here is how to initialize this model in PyTorch: ```python from transformers import BartForConditionalGeneration model = BartForConditionalGeneration.from_pretrained("xfbai/AMRBART-base") ``` Please refer to [this repository](https://github.com/muyeby/AMRBART) for tokenizer initialization and data preproce... | f52c7a133b78e5a002f2b7f22404b4f6 |
mit | ['generated_from_trainer'] | false | pedantic_wright This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk3-250... | 6ee70b8ec0110837a001d9ff8b68533e |
mit | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['tomekkorbak/pii-pile-chunk3-0-50000', 'tomekkorbak/pii-pile-chunk3-50000-100000', 'tomekkorbak/pii-pile-chunk3-100000-150000', 'tomekkorbak/pii-pile-chunk3-150000-200000', 'tom... | 17622e13a1b090400aae6f9f7f06875e |
cc-by-4.0 | ['danish', 'bert', 'sentiment', 'text-classification', 'Maltehb/danish-bert-botxo', 'Helsinki-NLP/opus-mt-en-da', 'go-emotion', 'Certainly'] | false | Danish-Bert-GoÆmotion Danish Go-Emotions classifier. [Maltehb/danish-bert-botxo](https://huggingface.co/Maltehb/danish-bert-botxo) (uncased) finetuned on a translation of the [go_emotions](https://huggingface.co/datasets/go_emotions) dataset using [Helsinki-NLP/opus-mt-en-da](https://huggingface.co/Helsinki-NLP/opus-... | dde48a96d779f28725aad3dfc6e3f97b |
cc-by-4.0 | ['danish', 'bert', 'sentiment', 'text-classification', 'Maltehb/danish-bert-botxo', 'Helsinki-NLP/opus-mt-en-da', 'go-emotion', 'Certainly'] | false | Training - Translating the training data with MT: [Notebook](https://colab.research.google.com/github/RJuro/Da-HyggeBERT-finetuning/blob/main/HyggeBERT_translation_en_da.ipynb) - Fine-tuning danish-bert-botxo: coming soon... | 13d249ddacd6c1103713807eedce69f8 |
cc-by-4.0 | ['danish', 'bert', 'sentiment', 'text-classification', 'Maltehb/danish-bert-botxo', 'Helsinki-NLP/opus-mt-en-da', 'go-emotion', 'Certainly'] | false | Using the model with `transformers` Easiest use with `transformers` and `pipeline`: ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline model = AutoModelForSequenceClassification.from_pretrained('RJuro/Da-HyggeBERT') tokenizer = AutoTokenizer.from_pretrained('RJuro/Da-Hygge... | a39b756f5ed164b8a463404504807215 |
cc-by-4.0 | ['danish', 'bert', 'sentiment', 'text-classification', 'Maltehb/danish-bert-botxo', 'Helsinki-NLP/opus-mt-en-da', 'go-emotion', 'Certainly'] | false | Using the model with `simpletransformers` ```python from simpletransformers.classification import MultiLabelClassificationModel model = MultiLabelClassificationModel('bert', 'RJuro/Da-HyggeBERT') predictions, raw_outputs = model.predict(df['text']) ``` | b796ee8622d46c85194bf4a9ce76f607 |
apache-2.0 | ['generated_from_trainer'] | false | BertMultiHateSpeech This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7496 - Accuracy: 0.74 - F1: 0.4841 | 141da59b58e841de585218c375bedb9b |
cc-by-4.0 | ['answer extraction'] | false | Model Card of `lmqg/mt5-base-dequad-ae` This model is fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) for answer extraction on the [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). ... | 2693b656d8aed2156c99103687874ca6 |
cc-by-4.0 | ['answer extraction'] | false | Overview - **Language model:** [google/mt5-base](https://huggingface.co/google/mt5-base) - **Language:** de - **Training data:** [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/l... | 6e90e5bd13f95a5af7edeacf7444285c |
cc-by-4.0 | ['answer extraction'] | false | model prediction answers = model.generate_a("das erste weltweit errichtete Hermann Brehmer 1855 im niederschlesischen ''Görbersdorf'' (heute Sokołowsko, Polen).") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/mt5-base-dequad-ae") output = pipe("S... | 7f4a9e80db0b470c699d3197f8196af8 |
cc-by-4.0 | ['answer extraction'] | false | Evaluation - ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/mt5-base-dequad-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_dequad.default.json) | | Score | Type | Dataset | |:----... | cca1bd37b07584105593c5e108319db1 |
cc-by-4.0 | ['answer extraction'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_dequad - dataset_name: default - input_types: ['paragraph_sentence'] - output_types: ['answer'] - prefix_types: None - model: google/mt5-base - max_length: 512 - max_length_output: 32 - epoch: 15 - b... | c85e67c92f2ae82a7c9fa25fb05864b3 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: IPU - gradient_accumulation_steps: 64 - total_train_batch_size: 128 - total_eval_batch_size: 5 - optimizer: Adam with betas=(0.9,0.999) and... | d48dd88f01a42781fea94170d3030b52 |
other | ['generated_from_trainer'] | false | 125m-dalio-book-handwritten-io-constant-1e-6-v2 This model is a fine-tuned version of [facebook/opt-125m](https://huggingface.co/facebook/opt-125m) on the AlekseyKorshuk/dalio-book-handwritten-io-sorted-v2 dataset. It achieves the following results on the evaluation set: - Loss: 3.0859 - Accuracy: 0.2336 - Perplexity... | c67134929cbd61ac928f921b0c898108 |
other | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Perplexity | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:| | 3.3352 | 0.01 | 1 | 3.1738 | 0.2305 | 23.8988 | | 3.3091 | 0.03 | 2 | 3.1738 | 0.2305 | 23.8988 | |... | 3c7044cc67adb80781d0472b93140e48 |
mit | [] | false | model by NobuLuis This your the Stable Diffusion model fine-tuned the andynsane concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of sks andynsane** You can also train your own concepts and upload them to the library by using [this notebook](https://colab... | ea0fc0edccb8b92c252db1da1cbd35b6 |
apache-2.0 | ['generated_from_trainer'] | false | distilled-indobert-classification This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the indonlu dataset. It achieves the following results on the evaluation set: - Loss: 0.6015 - Accuracy: 0.9016 - F1: 0.9015 | a86c37328173d418c1551b5be9c44036 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 33 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | af52699ecdfa774c266b342eeb249e58 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 1.0427 | 1.0 | 688 | 0.6306 | 0.8683 | 0.8684 | | 0.5332 | 2.0 | 1376 | 0.5621 | 0.8794 | 0.8779 | | 0.3021 |... | 2927cec7686f29daf0dbefa47c60e0f2 |
apache-2.0 | ['generated_from_trainer'] | false | 02_model 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: 0.5219 - Accuracy: 0.7412 - F1: 0.7625 | a1151f8d934823ef8ccfa1abb91ce731 |
apache-2.0 | ['bert', 'mrpc', 'glue', 'kd', 'torchdistill'] | false | `bert-base-uncased` fine-tuned on MRPC dataset, using fine-tuned `bert-large-uncased` as a teacher model, [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_kd_and_submission.ipynb)... | b081bf023b231b38819b0cf0208e8aaa |
mit | ['medical'] | false | BioGPT Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain. Among the two main branches of pre-trained language models in the general language domain, i.e. BERT (and its variants) and GPT (and its variants), t... | d0822b0999a6a3c0ea9cb5a8c3aed61c |
mit | ['medical'] | false | Citation If you find BioGPT useful in your research, please cite the following paper: ```latex @article{10.1093/bib/bbac409, author = {Luo, Renqian and Sun, Liai and Xia, Yingce and Qin, Tao and Zhang, Sheng and Poon, Hoifung and Liu, Tie-Yan}, title = "{BioGPT: generative pre-trained transformer for biomedi... | 1652973c3d97b8e93b679074a958b3dd |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-Sundanese Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the [OpenSLR High quality TTS data for Sundanese](https://openslr.org/44/). When using this model, make sure that your speech input is sampled at 16kHz. | d55446d92d442f3dcd6f171236ee2997 |
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, load_metric, Dataset from datasets.utils.download_manager import DownloadManager from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor from pathlib import Path im... | 51b5802ea94677b25b8430e3992ed77e |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | df = df.sample(frac=1, random_state=1).reset_index(drop=True) dataset = Dataset.from_pandas(df) dataset = dataset.remove_columns('__index_level_0__') return dataset.train_test_split(test_size=0.1, seed=1) dataset = load_dataset_sundanese() test_dataset = dataset['test'] processor = Wav2Vec2Proce... | 271365121b88d01c26bf05ffad3e9da4 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the audio 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[:2]["... | 0000eefd89a5b5ab0163985b8e55a499 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows or using the [notebook](https://github.com/cahya-wirawan/indonesian-speech-recognition/blob/main/XLSR_Wav2Vec2_for_Indonesian_Evaluation-Sundanese.ipynb). ```python import torch import torchaudio from datasets import load_dataset, load_metric, Dataset from transformer... | 1547cc4929faaa9a476228f178e50020 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | df = df.sample(frac=1, random_state=1).reset_index(drop=True) dataset = Dataset.from_pandas(df) dataset = dataset.remove_columns('__index_level_0__') return dataset.train_test_split(test_size=0.1, seed=1) dataset = load_dataset_sundanese() test_dataset = dataset['test'] wer = load_metric("wer") ... | 51f20afb478b2f59db8432caf1c1de6f |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the audio 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... | 67a3e0c8d206f31c2c177ec81fc135e3 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Training [OpenSLR High quality TTS data for Sundanese](https://openslr.org/44/) was used for training. The script used for training can be found [here](https://github.com/cahya-wirawan/indonesian-speech-recognition/blob/main/XLSR_Wav2Vec2_for_Indonesian_Evaluation-Sundanese.ipynb) and to [evaluate it](https://github... | d4fa5552a2f9f14d9a325ba6e4ba3503 |
mit | ['generated_from_trainer'] | false | bert-base-german-cased-finetuned-subj_v6_7Epoch_v3 This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2732 - Precision: 0.7654 - Recall: 0.7829 - F1: 0.7740 - Accuracy: ... | 04429c8c9c6b9910607c778980e45681 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 33 | 0.3281 | 0.6656 | 0.5914 | 0.6263 | 0.8623 | | No log | 2.0 |... | 14eb8ca37c68741ef0013c96fc186fb5 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Small Basque - Xabi Ezpeleta This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.2666 - Wer: 23.9965 | 2640ee1155f247fe781ad635c88b25db |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.2635 | 0.92 | 1000 | 0.3264 | 31.9754 | | 0.1492 | 1.84 | 2000 | 0.2668 | 25.7403 | | 0.0707 | 2.76 | 3000 | 0.2595 | 24.485... | 1e329aff793d37385ea33867c1f90379 |
apache-2.0 | ['generated_from_trainer'] | false | distilled-mt5-small-0.05-0.5 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: 2.8399 - Bleu: 7.0815 - Gen Len: 43.6583 | c63c0a9e7f8afb0e46a536b028f3b277 |
mit | [] | false | SpaceBERT This is one of the 3 further pre-trained models from the SpaceTransformers family presented in [SpaceTransformers: Language Modeling for Space Systems](https://ieeexplore.ieee.org/document/9548078). The original Git repo is [strath-ace/smart-nlp](https://github.com/strath-ace/smart-nlp). The further pre-tr... | df77a266c37e5c4492685b516de57904 |
mit | ['generated_from_keras_callback'] | false | juro95/xlm-roberta-finetuned-ner-0.6-ratio-and-samples This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0415 - Validation Loss: 0.0722 - Epoch: 3 | b2f9e1ec504e8dba114d7346d0764a9d |
mit | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 105112, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay... | 48162c3a95b64ff0fdb3b2b8341cddaf |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.2391 | 0.1212 | 0 | | 0.1048 | 0.0862 | 1 | | 0.0644 | 0.0734 | 2 | | 0.0415 | 0.0722 | 3 | | 7f156863ab84b5bcbf342a6420c90b52 |
apache-2.0 | ['generated_from_trainer'] | false | led-base-16384-100-MDS This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.1425 - Rouge1: 16.7324 - Rouge2: 5.8501 - Rougel: 13.908 - Rougelsum: 13.8469 - Gen Len: 20.0 | 757845fcea295dc99cf83a34bc50c800 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epoch... | ff4842a53f719ab29f54314031793315 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | No log | 1.0 | 25 | 3.6187 | 15.1426 | 4.2468 | 13.4488 | 13.38 | 20.0 ... | fb658851eab366719048e0f62810b750 |
apache-2.0 | ['Text', 'Sentence Similarity', 'Sentence-Embedding', 'camembert-base'] | false | Pre-trained sentence embedding models are the state-of-the-art of Sentence Embeddings for French. Model is Fine-tuned using pre-trained [facebook/camembert-base](https://huggingface.co/camembert/camembert-base) and [Siamese BERT-Networks with 'sentences-transformers'](https://www.sbert.net/) on dataset [stsb](https://... | 3a55e6b7332c8f1ef99270076aadc153 |
apache-2.0 | ['Text', 'Sentence Similarity', 'Sentence-Embedding', 'camembert-base'] | false | Usage The model can be used directly (without a language model) as follows: ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer("dangvantuan/sentence-camembert-base") sentences = ["Un avion est en train de décoller.", "Un homme joue d'une grande flûte.", "... | 524a03d875854b13ef65efe2bde0207e |
apache-2.0 | ['Text', 'Sentence Similarity', 'Sentence-Embedding', 'camembert-base'] | false | Evaluation The model can be evaluated as follows on the French test data of stsb. ```python from sentence_transformers import SentenceTransformer from sentence_transformers.readers import InputExample from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator from datasets import load_dataset def conve... | 8e0539147e08ddc4b6905a646a153503 |
apache-2.0 | ['Text', 'Sentence Similarity', 'Sentence-Embedding', 'camembert-base'] | false | Normalize score to range 0 ... 1 inp_example = InputExample(texts=[df['sentence1'], df['sentence2']], label=score) dataset_samples.append(inp_example) return dataset_samples | c0d8f9930d155949daeae9eaadeb8f62 |
apache-2.0 | ['Text', 'Sentence Similarity', 'Sentence-Embedding', 'camembert-base'] | false | For Test set: test_samples = convert_dataset(df_test) test_evaluator = EmbeddingSimilarityEvaluator.from_input_examples(test_samples, name='sts-test') test_evaluator(model, output_path="./") ``` **Test Result**: The performance is measured using Pearson and Spearman correlation: - On dev | Model | Pearson correla... | 6a71e38925af1f49deae0240e967c170 |
apache-2.0 | ['Text', 'Sentence Similarity', 'Sentence-Embedding', 'camembert-base'] | false | params | | ------------- | ------------- | ------------- |------------- | | [dangvantuan/sentence-camembert-base](https://huggingface.co/dangvantuan/sentence-camembert-base)| 86.73 |86.54 | 110M | | [distiluse-base-multilingual-cased](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased) | 79... | fd7ec1f92ad645f50c668a980207226f |
apache-2.0 | ['Text', 'Sentence Similarity', 'Sentence-Embedding', 'camembert-base'] | false | Citation @article{reimers2019sentence, title={Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks}, author={Nils Reimers, Iryna Gurevych}, journal={https://arxiv.org/abs/1908.10084}, year={2019} } @article{martin2020camembert, title={CamemBERT: a Tasty French Language Mode}, a... | f11b9316a0885e579740b4420c3e92a1 |
apache-2.0 | ['translation'] | false | opus-mt-sv-war * source languages: sv * target languages: war * OPUS readme: [sv-war](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-war/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | 49159a949473c7e383f8282717c02d0d |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-moaiz_exp2 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.1884 - Wer: 1.0 | 25b47c0f5e938d4a1ed1060359927510 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0004 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_t... | 78b6c5c0676e018b836f2ffd0ac4db6d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 4.15 | 13.89 | 500 | 3.2020 | 1.0 | | 3.1522 | 27.78 | 1000 | 3.1884 | 1.0 | | 371307b763941dcd124b30e8308cd851 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-finetuned-stop-classification This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the audiofolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1647 - Accuracy: 0.9470 | 6c0b7138fbb78156438741a8b4085842 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sc... | 7cfbf723aa19406ec897feba89f50bce |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.671 | 0.98 | 26 | 0.5553 | 0.8347 | | 0.3525 | 1.98 | 52 | 0.2647 | 0.9163 | | 0.291 | 2.98 | 78 | 0.2474 | 0.... | 0080cc33d769d9d2f4374e9b8d21b45f |
apache-2.0 | ['summarization', 'arabic', 'ar', 'mt5', 'Abstractive Summarization', 'generated_from_trainer'] | false | mt5-base-arabic This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on arabic subset on the xlsum dataset. It achieves the following results on the evaluation set: - Loss: 3.2742 - Rouge-1: 22.86 - Rouge-2: 10.31 - Rouge-l: 20.85 - Gen Len: 19.0 - Bertscore: 71.52 | fde481a2bc19ee88dd34347c75524509 |
apache-2.0 | ['summarization', 'arabic', 'ar', 'mt5', 'Abstractive Summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:| | 4.2331 | 1.0 | 1172 | 3.5051 | 18.54 | 6.63 | 16.77 | 19.0 | 70.2... | 5b9475bfcc38a69259aa3a2ccff242f1 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3100 - Precision: 0.9309 - Recall: 0.9435 - F1: 0.9371 - Accuracy: 0.9294 | 3e02181c68c362a5f79183e58174a97e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 234 | 0.2362 | 0.9356 | 0.9484 | 0.9420 | 0.9335 | | No log | 2.0 |... | 4af184c104044d0a25a6df64f57d207f |
mit | [] | false | gpt2-wechsel-uyghur Model trained with WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models. See the code here: https://github.com/CPJKU/wechsel And the paper here: https://arxiv.org/abs/2112.06598 | a32e01c6671f079c28d45f3518d0e84d |
mit | [] | false | Citation Please cite WECHSEL as ``` @misc{minixhofer2021wechsel, title={WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models}, author={Benjamin Minixhofer and Fabian Paischer and Navid Rekabsaz}, year={2021}, eprint={2112.06598}, ... | 83fd7a44f2a6de2dcae44b6505c07c21 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'hy'] | 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 - HY-AM dataset. It achieves the following results on the evaluation set: - Loss: 0.5891 - Wer: 0.6569 **Note**: If you aim for best performance use [t... | 386078c2eaa1524ea73218573a319d4e |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'hy'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_s... | f9c242dbe7ebe3f80a3c64790a93d1bf |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'hy'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 9.167 | 16.67 | 100 | 3.5599 | 1.0 | | 3.2645 | 33.33 | 200 | 3.1771 | 1.0 | | 3.1509 | 50.0 | 300 | 3.1321 | 1.0 ... | abf5fe33067350868b28109795b2df49 |
creativeml-openrail-m | ['anime', 'manga', 'manhwa', 'webtoon'] | false | <h1>The goal of this repo is to</h1> <ul> <li>Capturing webtoon character's unique characteristics</li> <li>Get varieties of poses, gestures and actions without damaging too many characteristics</li> </ul> <h3>For the LoRA inference</h3> <ul> <li>Current LoRA checkpoints are under development. Instruction will ... | 4218f868be89025710d72f31cae4c3aa |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased__hate_speech_offensive__train-32-7 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: 0.8210 - Accuracy: 0.6305 | a70ed8cbb67bbd40478e6ab96e392e20 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.0989 | 1.0 | 19 | 1.0655 | 0.4 | | 1.0102 | 2.0 | 38 | 0.9927 | 0.6 | | 0.8063 | 3.0 | 57 | 0.9117 | 0.... | d19a48fbd0a7ed867cf3497fa9a1212b |
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: 0.3041 - Accuracy: 0.87 - F1: 0.8696 | ab1b9b4d33cef9cd9d7548fccb5dc8d3 |
apache-2.0 | ['setfit', 'sentence-transformers', 'text-classification'] | false | fathyshalab/domain_transfer_clinic_credit_cards-massive_qa-roberta-large-v1-2-71 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](h... | ab6db79a52dcb7e5acb537f0a4809678 |
apache-2.0 | ['classification', 'zero-shot'] | false | Erlangshen-UniMC-Albert-235M-English - Main Page:[Fengshenbang](https://fengshenbang-lm.com/) - Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen/examples/unimc/) - Docs: [Fengshenbang-Docs](https://fengshenbang-doc.readthedocs.io/) - API: [Fengshen-OpenAPI](https://fengshenba... | 8b1c5596f4a08016a37f414c570a4d40 |
apache-2.0 | ['classification', 'zero-shot'] | false | 模型分类 Model Taxonomy | 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra | | :----: | :----: | :----: | :----: | :----: | :----: | | 通用 General | 自然语言理解 NLU | 二郎神 Erlangshen | Albert | 235M | English | | 4e6dc51d261d7cf4ba7e52fdd1d1e61a |
apache-2.0 | ['classification', 'zero-shot'] | false | 模型信息 Model Information 我们为零样本学习者提出了一种与输入无关的新范式，从某种意义上说，它与任何格式兼容并适用于一系列语言任务，例如文本分类、常识推理、共指解析、情感分析。我们的方法将零样本学习转化为多项选择任务，避免常用的大型生成模型（如 FLAN）中的问题。它不仅增加了模型的泛化能力，而且显着减少了对参数的需求。我们证明了这种方法可以在通用语言基准上取得最先进的性能，并在自然语言推理和文本分类等任务上产生令人满意的结果。更多详细信息可以参考我们的[论文](https://arxiv.org/abs/2210.08590)或者[GitHub](https://github.com/IDEA-CCNL/Fe... | 9dc6a328582aa4ad8ed6bbcf6626bb28 |
apache-2.0 | ['classification', 'zero-shot'] | false | 下游效果 Performance **Zero-Shot Classification** | Model | T0 11B | GLaM 60B | FLAN 137B | PaLM 540B | UniMC 235M | |---------|--------|----------|-----------|-----------|------------| | ANLI R1 | 43.6 | 40.9 | 47.7 | 48.4 | 52 | | ANLI R2 | 38.7 | 38.2 | 43.9 | 44.2 | 44.4 ... | 83d9dfabf29d90117e137ad6bfaf371c |
apache-2.0 | ['classification', 'zero-shot'] | false | 使用 Usage ```python3 import argparse from fengshen.pipelines.multiplechoice import UniMCPipelines total_parser = argparse.ArgumentParser("TASK NAME") total_parser = UniMCPipelines.piplines_args(total_parser) args = total_parser.parse_args() pretrained_model_path = 'IDEA-CCNL/Erlangshen-UniMC-Albert-235M-English'... | a815d56eacf7ddea619e30b1f0a75cf9 |
mit | [] | false | XGLM-4.5B XGLM-4.5B is a multilingual autoregressive language model (with 4.5 billion parameters) trained on a balanced corpus of a diverse set of 134 languages. It was introduced in the paper [Few-shot Learning with Multilingual Language Models](https://arxiv.org/abs/2112.10668) by Xi Victoria Lin\*, Todor Mihaylov,... | 3352c1a9a3eafe3c70bdf53d029e8891 |
mit | [] | false | Example (COPA) The following snippet shows how to evaluate our models (GPT-3 style, zero-shot) on the Choice of Plausible Alternatives (COPA) task, using examples in English, Chinese and Hindi. ```python import torch import torch.nn.functional as F from transformers import XGLMTokenizer, XGLMForCausalLM tokenizer =... | c7e7e7e18dcea1603ebb0c9feb0a41d2 |
apache-2.0 | ['generated_from_trainer'] | false | rte This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 0.8396 - Accuracy: 0.6679 | 1334cd76bbeac05a749d9bca28f4de9c |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10.0 | 2ce6f338aaa92a91f52fe068cb274fac |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-recipe-ar This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0529 - F1: 0.9856 | aab6b5c4cbbe01776c3bb3a800bb7c0b |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.4605 | 1.0 | 74 | 0.1084 | 0.9609 | | 0.1105 | 2.0 | 148 | 0.0563 | 0.9809 | | 0.0696 | 3.0 | 222 | 0.0500 | 0.9851 | |... | 103c45a7820582c7a16f75ae9fb4c170 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the {language} 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", "id", split="test") wer ... | 1a65b64be9adb04c60d4c4faf00b62bd |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) resampler = torchaudio.transforms.Resample(sampling_rate, 16_000) batch["speech"] = resamp... | d70b5c09b1e5207607636d65a8d00773 |
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(log... | c568a38326085d7aabd99012ff9bbc07 |
apache-2.0 | ['translation'] | false | he-es * source group: Hebrew * target group: Spanish * OPUS readme: [heb-spa](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/heb-spa/README.md) * model: transformer * source language(s): heb * target language(s): spa * model: transformer * pre-processing: normalization + SentencePiece (spm3... | 99ba415a06119a9f297b88cc8d810371 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: he-es - source_languages: heb - target_languages: spa - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/heb-spa/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['he', 'es'] - src_constituents: ('Hebrew', {'heb'}) -... | a71078aeb16c6da5e02569a76f9edf9d |
apache-2.0 | ['summarization', 'translation'] | false | PreTraining The model was pre-trained on a on a **multi-task mixture of unsupervised (1.) and supervised tasks (2.)**. Thereby, the following datasets were being used for (1.) and (2.): 1. **Datasets used for Unsupervised denoising objective**: - [C4](https://huggingface.co/datasets/c4) - [Wiki-DPR](https://hugging... | 0df8cfd10467a3c551d6ce7702ec7e26 |
apache-2.0 | ['summarization', 'translation'] | false | Paper For more information, please take a look at the original paper. Paper: [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf) Authors: *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li,... | 460c5db4f45e821afed5890c91633a40 |
apache-2.0 | ['translation'] | false | zho-fin * source group: Chinese * target group: Finnish * OPUS readme: [zho-fin](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zho-fin/README.md) * model: transformer-align * source language(s): cmn_Bopo cmn_Hani cmn_Latn nan_Hani yue yue_Hani * target language(s): fin * model: transformer... | 5e276053ef60d2586f3df4ab6ac7898d |
apache-2.0 | ['translation'] | false | System Info: - hf_name: zho-fin - source_languages: zho - target_languages: fin - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zho-fin/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['zh', 'fi'] - src_constituents: {'cmn_Hans', 'nan', ... | a57b599eac3ef9249a1e77f1ae61d583 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 7.9484 | 0.19 | 500 | 7.8474 | | 7.7968 | 0.39 | 1000 | 7.7020 | | 7.6992 | 0.58 | 1500 | 7.6949 | | 7.656 | 0.77 | 2000 | 7.6922 ... | 8dca02ab5229f2c5f6f347b7bb543611 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-cola 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 None dataset. It achieves the following results on the evaluation set: - Loss: nan | a0d6c719fea59986962760ed47408d24 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.2428 | 0.47 | 500 | 3.7383 | | 4.0764 | 0.94 | 1000 | 3.6771 | | 3.8781 | 1.4 | 1500 | 3.5846 | | 3.8168 | 1.87 | 2000 | 3.6091 ... | 0a7fdf4cad50aa9436363bd268369e4c |
mit | ['generated_from_trainer'] | false | roberta-base.CEBaB_confounding.observational.sa.5-class.seed_42 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the OpenTable OPENTABLE dataset. It achieves the following results on the evaluation set: - Loss: 0.7697 - Accuracy: 0.7191 - Macro-f1: 0.7025 - Weighted-macro-f... | 9714f4d26f1d69d80e5941b0fd8f8819 |
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