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 | ['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': 468, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': ... | 9173c1ce38d9ae5351dd2ecb14e7b047 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 1.9989 | 1.6524 | 0 | | 1.3489 | 1.6702 | 1 | | 1.0422 | 1.7343 | 2 | | 37e7fc52e23ae5f174d885b5a10640f7 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-parth This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.9468 - Rouge1: 26.5826 - Rouge2: 21.7867 - Rougel: 25.1629 - Rougelsum: 26.2364 - Gen Len: 16.9 | 90966657f56fe7b0100657a3b17fbfe6 |
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 | 4 | 3.3692 | 25.2983 | 20.639 | 24.0087 | 25.0732 | 16... | a9438f971434508be47962dec1bbcb25 |
apache-2.0 | ['generated_from_trainer'] | false | roberta-base-bne-finetuned_personality_multi_3 This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1145 - Accuracy: 0.4847 | 68abb7d459c94ff483e353e070a6251e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.2498 | 1.0 | 63 | 2.2799 | 0.2236 | | 2.3044 | 2.0 | 126 | 2.1644 | 0.2980 | | 1.9017 | 3.0 | 189 | 1.9934 | 0.... | 2dca4f2064f8ce76e42ef1c6577b435f |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer'] | false | xls-r-300m-hi 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 - HI dataset. It achieves the following results on the evaluation set: - Loss: 0.7522 - Wer: 1.0091 | 170ca6ab206a6bbc43d049afbe177916 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.0417 | 2.59 | 500 | 5.1484 | 1.0 | | 3.3722 | 5.18 | 1000 | 3.3380 | 1.0001 | | 1.9752 | 7.77 | 1500 | 1.3910 | 1.0074 | |... | 1fa81713ad1ba4b1aada3ddf66a8f919 |
apache-2.0 | ['generated_from_trainer'] | false | In Transformers ```python from transformers import pipeline,AutoTokenizer model_name = "distilbert-base-uncased-finetuned-sst-2-english" tokenizer = AutoTokenizer.from_pretrained(model_name) text = "I feel happy today!" inputs = tokenizer(text,return_tensors="pt",padding=True, truncation=True) { 'input_ids': te... | 4a280b509212c9ac4f0bc893e114381e |
apache-2.0 | ['generated_from_trainer'] | false | BertTokenizerFast tokenizer = BertTokenizerFast.from_pretrained(model_name) inputs_for_BertTokenizer = tokenizer(text, return_tensors="pt",padding=False, truncation=True, max_length=512, stride=256) { 'input_ids': tensor([[ 101, 100, 11297, 9200, 11262, 106, 102]]), 'token_type_ids': tensor([[0, 0, 0, 0... | 4b5e17231a9101ffaf6f9fb8c948abc4 |
apache-2.0 | ['generated_from_trainer'] | false | BartTokenizerFast tokenizer = BartTokenizerFast.from_pretrained("facebook/bart-base") inputs_for_BartTokenizerFast= tokenizer(text, return_tensors="pt",padding=False, truncation=True, max_length=512, stride=256) { 'input_ids': tensor([[ 0, 100, 619, 1372, 452, 328, 2]]), 'attention_mask': tensor([[1, 1... | 8014204c9ffe750d06fa638e6dc14334 |
apache-2.0 | ['generated_from_trainer'] | false | Model from transformers import AutoModel model_name = "distilbert-base-uncased-finetuned-sst-2-english" model = AutoModel.from_pretrained(model_name) outputs = model(**inputs) print(outputs.last_hidden_state.shape) { torch.Size([1, 7, 768]) } from transformers import AutoModelForSequenceClassification model_n... | 380f6322a01cb2a9130c4f09a556d201 |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'Transducer', 'Conformer', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard'] | false | datasets) This model transcribes speech into lowercase Esperanto alphabet including spaces and apostroph. The model was obtained by finetuning from English SSL-pretrained model on Mozilla Common Voice Esperanto 11.0 dataset. It is a non-autoregressive "large" variant of Conformer [1], with around 120 million paramete... | c97ba410bf3cbbc0e984ba635f2ff641 |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'Transducer', 'Conformer', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard'] | false | Transcribing many audio files ```shell python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py pretrained_name="nvidia/stt_eo_conformer_transducer_large" audio_dir="<DIRECTORY CONTAINING AUDIO FILES>" ``` | 58ad7640766da70487c5d4fee3319c22 |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'Transducer', 'Conformer', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard'] | false | Training The NeMo toolkit [3] was used for finetuning from English SSL model for over several hundred epochs. The model is finetuning with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_transducer/speech_to_text_rnnt_bpe.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/m... | d730d0a4aab0a47923ce8b7d8b27a6d7 |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'Transducer', 'Conformer', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard'] | false | Performance The list of the available models in this collection is shown in the following table. Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding. | Version | Tokenizer | Vocabulary Size | Dev WER| Test WER| Train Dataset | |---------|-----------------... | ce9905c0709641e68199969adbc9c8df |
mit | [] | false | wheelchair on Stable Diffusion This is the `<wheelchair>` 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... | 0b55b55c7dc8956fab12c2303d64d3b8 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 12 - eval_batch_size: 8 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 | 7917aae0c20892652503fd5658cd0372 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper-Small (el) for Transcription This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 el dataset. It achieves the following results on the evaluation set: - Loss: 0.4805 - Wer: 20.6352 | c0b16779fdfe50b21df1815c2de7c57f |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training procedure The script used has been uploaded in the files of this space The command to run it was: ``` python ./run_speech_recognition_seq2seq_streaming.py \ --model_name_or_path "openai/whisper-small" \ --model_revision "main" \ --do_train T... | d5e1bc8a230ffc1716e6d9ad9fd03ffd |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0024 | 18.01 | 1000 | 0.4246 | 21.0438 | | 0.0003 | 37.01 | 2000 | 0.4805 | 20.6352 | | 0.0001 | 56.01 | 3000 | 0.5102 | 20.839... | 53eec5b9115f6102069eca83f2a94297 |
apache-2.0 | ['Text2Text Generation', 'T5', 'chinese', 'sentencepiece'] | false | 简介 Brief Introduction 在Randeng-T5-77M的基础上，收集了100个左右的中文数据集，进行Text2Text统一范式的有监督任务预训练。 On the basis of Randeng-T5-77M, about 100 Chinese datasets were collected and pre-trained for the supervised task of Text2Text unified paradigm. | 3e8ab41c3412eb1238f9ab3d2dbf6d39 |
apache-2.0 | ['Text2Text Generation', 'T5', 'chinese', 'sentencepiece'] | 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 | 自然语言转换 NLT | 燃灯 Randeng | MultiTask | 77M | 多任务-中文 MultiTask-Chinese | | c8352ca981c2fcec24a91e59e8cc27ad |
apache-2.0 | ['Text2Text Generation', 'T5', 'chinese', 'sentencepiece'] | false | 模型信息 Model Information 参考论文：[Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](http://jmlr.org/papers/v21/20-074.html) 基于[Randeng-T5-77M](https://huggingface.co/IDEA-CCNL/Randeng-T5-77M)，我们在收集的100+个中文领域的多任务数据集（从中采样了30w+个样本）上微调了它，得到了此多任务版本。这些多任务包括：情感分析，新闻分类，文本分类，意图识别，自然语言推理，多项选择，指代消解，... | 0fe246ac8b0e55f97835e6ed62bd1db1 |
apache-2.0 | ['Text2Text Generation', 'T5', 'chinese', 'sentencepiece'] | false | load tokenizer and model pretrained_model = "IDEA-CCNL/Randeng-T5-77M-MultiTask-Chinese" special_tokens = ["<extra_id_{}>".format(i) for i in range(100)] tokenizer = T5Tokenizer.from_pretrained( pretrained_model, do_lower_case=True, max_length=512, truncation=True, additional_special_tokens=speci... | ac4ba834ff4c221ee97fd6df6f88d564 |
apache-2.0 | ['Text2Text Generation', 'T5', 'chinese', 'sentencepiece'] | false | tokenize text = "情感分析任务：【房间还是比较舒适的,酒店服务良好】这篇文章的情感态度是什么？正面/负面" encode_dict = tokenizer(text, max_length=512, padding='max_length',truncation=True) inputs = { "input_ids": torch.tensor([encode_dict['input_ids']]).long(), "attention_mask": torch.tensor([encode_dict['attention_mask']]).long(), } | a1fd52b0dd26468f88a1117629563417 |
apache-2.0 | ['Text2Text Generation', 'T5', 'chinese', 'sentencepiece'] | false | generate answer logits = model.generate( input_ids = inputs['input_ids'], max_length=100, early_stopping=True, ) logits=logits[:,1:] predict_label = [tokenizer.decode(i,skip_special_tokens=True) for i in logits] print(predict_label) | 81ab1dd66f8acab47df456e982fc0d99 |
apache-2.0 | ['Text2Text Generation', 'T5', 'chinese', 'sentencepiece'] | false | model output: 正面 ``` 除了分类任务，其他任务的数据构造例子如下： In addition to classification tasks, data construction examples of other tasks are as follows: ```python example_dict={ "文本分类":{"text_a":"钢琴块3别踩白块儿3钢琴块3是一款简洁的钢琴模拟软件,在Android平台上,类似的软件还是比较多的。","choices":["相机","影视娱乐","棋牌中心","新闻","财经","策略","休闲益智","教育"]}, '新闻分类':{"text_... | d4d604382c36eb7a9ead4d6fcfc97057 |
apache-2.0 | ['Text2Text Generation', 'T5', 'chinese', 'sentencepiece'] | false | 构造prompt的过程中，verbalizer这个占位key的内容，是通过 "/".join(choices) 拼接起来 dataset2instruction = { "情感分析": { "prompt": "{}任务：【{}】这篇文章的情感态度是什么？{}", "keys_order": ["subtask_type","text_a", "verbalizer"], "data_type": "classification", }, "文本分类": { "prompt": "{}任务：【{}】这篇文章的类别是什么？{}", ... | d7a9fee8904783d07e7b56e851477c18 |
apache-2.0 | ['Text2Text Generation', 'T5', 'chinese', 'sentencepiece'] | false | -------------------- "自然语言推理": { "prompt": "{}任务：【{}】和【{}】，以上两句话的逻辑关系是什么？{}", "keys_order": ["subtask_type","text_a", "text_b", "verbalizer"], "data_type": "classification", }, "语义匹配": { "prompt": "{}任务：【{}】和【{}】，以上两句话的内容是否相似？{}", "keys_order": ["subtask_type","text_... | 6b43b7718a25ef5c497a9f386ab40bea |
apache-2.0 | ['Text2Text Generation', 'T5', 'chinese', 'sentencepiece'] | false | ----------------------- "指代消解": { "prompt": "{}任务：文章【{}】中{}{}", "keys_order": ["subtask_type","text_a", "question", "verbalizer"], "data_type": "classification", }, "多项选择": { "prompt": "{}任务：阅读文章【{}】问题【{}】？{}", "keys_order": ["subtask_type","text_a", "question", "ver... | b8110de9506af38f3a80a338d4bd6b38 |
apache-2.0 | ['Text2Text Generation', 'T5', 'chinese', 'sentencepiece'] | false | ------------------------ "抽取式阅读理解": { "prompt": "{}任务：阅读文章【{}】问题【{}】的答案是什么？", "keys_order": ["subtask_type","text_a", "question"], "data_type": "mrc", }, "实体识别": { "prompt": "{}任务：找出【{}】这篇文章中所有【{}】类型的实体？", "keys_order": ["subtask_type","text_a", "question"], ... | 35e3fd73b02be4c123cb366a8d0eeb0d |
apache-2.0 | ['Text2Text Generation', 'T5', 'chinese', 'sentencepiece'] | false | ------------------------ "关键词抽取": { "prompt": "{}任务：【{}】这篇文章的关键词是什么？", "keys_order": ["subtask_type","text_a"], "data_type": "keys", }, "关键词识别":{ "prompt": "{}任务：阅读文章【{}】问题【{}】{}", "keys_order": ["subtask_type","text_a","question","verbalizer"], "data_type": ... | da2aeb3d97cc51ee6c4224a4c05388ff |
apache-2.0 | ['Text2Text Generation', 'T5', 'chinese', 'sentencepiece'] | false | print(sample) sample["instruction"] = template["prompt"].format(*[ sample[k] for k in template["keys_order"] ]) print(sample["instruction"]) return sample["instruction"] ``` | ae71f9ae82f146a07672e4894e78cd85 |
apache-2.0 | ['Text2Text Generation', 'T5', 'chinese', 'sentencepiece'] | false | 预训练或微调 prtrain or finetune 如果您对于怎么预训练Randeng-T5模型或者想在自己的下游任务中微调Randeng模型，欢迎使用[Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM/)项目，这里提供了完整的示例： - [预训练](https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen/examples/pretrain_t5) - [微调](https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshe... | de624f0a414b6fc21a4a5e9a228ef125 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-finetuned-ks 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: 0.0862 - Accuracy: 0.9835 | 11054c2c56be61deeb0a3405f8296df3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6609 | 1.0 | 399 | 0.5366 | 0.9662 | | 0.29 | 2.0 | 798 | 0.1719 | 0.9776 | | 0.184 | 3.0 | 1197 | 0.1134 | 0.... | 19118904c11c469b0fcf81821fa936fe |
apache-2.0 | ['generated_from_trainer'] | false | Tagged_Uni_500v6_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_uni500v6_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.2386 - Precision: 0.6992 - Recall: 0.6987 - F1: 0.6989 - Accura... | ee958b2a285397505bad3c04ea51a6a2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 182 | 0.2452 | 0.5956 | 0.5432 | 0.5682 | 0.9189 | | No log | 2.0 |... | bf04077574393bf952662875c6af5333 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-xls-r-tf-left-right-trainer This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 0.0090 - eval_wer: 0.0037 - eval_runtime: 11.2686 - eval_samples_per... | 2c5b965040b73866a1fbf1605c1bf690 |
mit | [] | false | plant style on Stable Diffusion This is the `<plant>` 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 tra... | 63ac1b299ebe1937ea38ce17ec4cd909 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3291 - Accuracy: 0.8733 - F1: 0.8758 | 9c6153707acd3432dad9118aec1f9d4f |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetune 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: 0.0149 - Precision: 0.8458 - Recall: 0.8060 - F1: 0.8255 - Accuracy: 0.9954 | 0f83b94d12a7364c937e7abef426679a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 48 | 0.0556 | 0.5372 | 0.1902 | 0.2809 | 0.9838 | | No log | 2.0 |... | e82f0a92af1ac19afd54863681832494 |
apache-2.0 | ['generated_from_trainer'] | false | med_v1_M04_1e-05 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.4848 - Wer: 1.0 - Cer: 1.0 | 0f211d8787c8be9978d675a8923041f5 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 20 - 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: 18400 - num_epochs: 2000 - mixed_precision... | 36c9cad820fa34f51c21793ceea73b49 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:------:|:------:|:---------------:|:---:|:---:| | 38.6628 | 200.0 | 36800 | 3.4943 | 1.0 | 1.0 | | 3.1727 | 400.0 | 73600 | 3.4369 | 1.0 | 1.0 | | 3.0757 | 600.0 | 110400 | 3... | 138ce987a9ad094af2f2e868951b604f |
apache-2.0 | ['automatic-speech-recognition', 'et'] | false | exp_w2v2t_et_wav2vec2_s635 Fine-tuned [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) 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 speech inpu... | 48c61d7adb63c78a93ea4e92df3b9c07 |
apache-2.0 | ['translation'] | false | ita-ukr * source group: Italian * target group: Ukrainian * OPUS readme: [ita-ukr](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ita-ukr/README.md) * model: transformer-align * source language(s): ita * target language(s): ukr * model: transformer-align * pre-processing: normalization + Se... | 4c3bbb8ab10ea986cc1dc3b3f7eb8ce0 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: ita-ukr - source_languages: ita - target_languages: ukr - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ita-ukr/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['it', 'uk'] - src_constituents: {'ita'} - tgt_const... | 1f074ec5ae9f1a897dccc375c6e6c6fc |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-cola-4 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: 0.0011 - Matthews Correlation: 1.0 | 3c2dc028701e99370d0f596e1dd3880a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | No log | 1.0 | 104 | 0.0243 | 1.0 | | No log | 2.0 | 208 | 0.0074 | 1.0 | | No ... | 30f663af4979dfcd0c2be3ee13ac9406 |
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: 0.2152 - Accuracy: 0.927 - F1: 0.9270 | 8300e3054543cdba961ecd9b608a6f8f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8354 | 1.0 | 250 | 0.3134 | 0.9065 | 0.9050 | | 0.2478 | 2.0 | 500 | 0.2152 | 0.927 | 0.9270 | | ee4faaecfed8c17e76b092e761e8d5ff |
apache-2.0 | ['translation'] | false | opus-mt-fi-ha * source languages: fi * target languages: ha * OPUS readme: [fi-ha](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-ha/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-24.zip](https://... | 06d238909c4934f9f2b1b54e3796b363 |
apache-2.0 | ['ASR', 'CTC', 'Attention', 'Conformer', 'pytorch', 'speechbrain'] | false | Conformer for KsponSpeech (with Transformer LM) This repository provides all the necessary tools to perform automatic speech recognition from an end-to-end system pretrained on KsponSpeech (Kr) within SpeechBrain. For a better experience, we encourage you to learn more about [SpeechBrain](https://speechbrain.github.i... | a2c975fd4777560ff067dd12e419ca9e |
apache-2.0 | ['ASR', 'CTC', 'Attention', 'Conformer', 'pytorch', 'speechbrain'] | false | Pipeline description This ASR system is composed of 3 different but linked blocks: - Tokenizer (unigram) that transforms words into subword units and trained with the train transcriptions of KsponSpeech. - Neural language model (Transformer LM) trained on the train transcriptions of KsponSpeech - Acoustic model made ... | ee7464c24d189d7e525e875c67c7814f |
apache-2.0 | ['ASR', 'CTC', 'Attention', 'Conformer', 'pytorch', 'speechbrain'] | false | Install SpeechBrain First of all, please install SpeechBrain with the following command: ``` !pip install git+https://github.com/speechbrain/speechbrain.git ``` Please notice that we encourage you to read our tutorials and learn more about [SpeechBrain](https://speechbrain.github.io). | 2f08768716399ef86dd677e30f98f613 |
apache-2.0 | ['ASR', 'CTC', 'Attention', 'Conformer', 'pytorch', 'speechbrain'] | false | Transcribing your own audio files (in Korean) ```python from speechbrain.pretrained import EncoderDecoderASR asr_model = EncoderDecoderASR.from_hparams(source="ddwkim/asr-conformer-transformerlm-ksponspeech", savedir="pretrained_models/asr-conformer-transformerlm-ksponspeech", run_opts={"device":"cuda"}) asr_model.tr... | ab758ef121c20cfcf82f9760c47b1f40 |
apache-2.0 | ['ASR', 'CTC', 'Attention', 'Conformer', 'pytorch', 'speechbrain'] | false | Training The model was trained with SpeechBrain (Commit hash: 'c762107'). To train it from scratch follow these steps: 1. Clone SpeechBrain: ```bash git clone https://github.com/speechbrain/speechbrain/ ``` 2. Install it: ```bash cd speechbrain pip install -r requirements.txt pip install . ``` 3. Run Training: ```bas... | 15543f961427175a83381b63f4c32446 |
apache-2.0 | ['ASR', 'CTC', 'Attention', 'Conformer', 'pytorch', 'speechbrain'] | false | **Citing SpeechBrain** Please, cite SpeechBrain if you use it for your research or business. ```bibtex @misc{speechbrain, title={{SpeechBrain}: A General-Purpose Speech Toolkit}, author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan a... | 3334d1558579fa2bb39fd67ed3d3daa3 |
apache-2.0 | ['ASR', 'CTC', 'Attention', 'Conformer', 'pytorch', 'speechbrain'] | false | Citing the model ```bibtex @misc{returnzero, title = {ReturnZero Conformer Korean ASR model}, author = {Dongwon Kim and Dongwoo Kim and Jeongkyu Roh}, year = {2021}, howpublished = {\url{https://huggingface.co/ddwkim/asr-conformer-transformerlm-ksponspeech}}, } ``` | 04feb0b4acb8dcede1898753bd64083f |
apache-2.0 | ['ASR', 'CTC', 'Attention', 'Conformer', 'pytorch', 'speechbrain'] | false | Citing KsponSpeech dataset ```bibtex @Article{app10196936, AUTHOR = {Bang, Jeong-Uk and Yun, Seung and Kim, Seung-Hi and Choi, Mu-Yeol and Lee, Min-Kyu and Kim, Yeo-Jeong and Kim, Dong-Hyun and Park, Jun and Lee, Young-Jik and Kim, Sang-Hun}, TITLE = {KsponSpeech: Korean Spontaneous Speech Corpus for Automatic Speech ... | 888759489986c2033099e258bb554480 |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-SMALL-EL16-DL2 (Deep-Narrow version) T5-Efficient-SMALL-EL16-DL2 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* che... | f83d9f1eaec09740ca4805acd4ef4ffc |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-small-el16-dl2** - is of model type **Small** with the following variations: - **el** is **16** - **dl** is **2** It has **75.21** million parameters and thus requires *ca.* **300.83 MB** of memory in full precision (*fp32*) or **150.42 MB** of memo... | 76a4cb7ee3e1583469d78f2f7457443a |
apache-2.0 | ['translation'] | false | eng-zho * source group: English * target group: Chinese * OPUS readme: [eng-zho](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-zho/README.md) * model: transformer * source language(s): eng * target language(s): cjy_Hans cjy_Hant cmn cmn_Hans cmn_Hant gan lzh lzh_Hans nan wuu yue yue_Ha... | 7bf0bd6c1b1f93767c726517d2119577 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: eng-zho - source_languages: eng - target_languages: zho - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-zho/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['en', 'zh'] - src_constituents: {'eng'} - tgt_const... | 43231f165ba6064a3c37b6f56a3303d5 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | danny Dreambooth model trained by raw-vitor 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-stable-diffu... | 60cebbf06cc342290dcaa5e62d4b5b2b |
apache-2.0 | ['automatic-speech-recognition', 'id'] | false | exp_w2v2t_id_no-pretraining_s861 Fine-tuned randomly initialized wav2vec2 model for speech recognition using the train split of [Common Voice 7.0 (id)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has bee... | dcd6d3f58f9b574c5cd3794631649f1e |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-1'] | false | MultiBERTs Seed 1 Checkpoint 100k (uncased) Seed 1 intermediate checkpoint 100k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in [this repository](https://github.com/goo... | 880f769b364e34338780e88a69eadf52 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-1'] | false | How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('multiberts-seed-1-100k') model = BertModel.from_pretrained("multiberts-seed-1-100k") text = "Replace me by any text you'd like.... | 25dfb3a7c32de5bc77239d32941e60e2 |
apache-2.0 | ['image-classification', 'vision', 'generated_from_trainer'] | false | pedestrian_age_recognition_local This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.5004 - Accuracy: 0.8073 | 2afc024add64588f4e4e441e95fa7708 |
apache-2.0 | ['image-classification', 'vision', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.8849 | 1.0 | 2008 | 0.7939 | 0.6807 | | 0.9836 | 2.0 | 4016 | 0.6694 | 0.7336 | | 0.8128 | 3.0 | 6024 | 0.5768 ... | 48e450d5c77c93c24a049d317bc2fc52 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | openai/whisper-small-Assamese This model is a fine-tuned version of [kpriyanshu256/whisper-small-as-500-64-1e-05-bn](https://huggingface.co/kpriyanshu256/whisper-small-as-500-64-1e-05-bn) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.4463 - Wer: 32.7197 | 8b8c99097b3d8eb4dd1adea1e12889d2 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sche... | 14ad02b76ff1cbfe5c77a150eea03c5f |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.2654 | 3.04 | 50 | 0.2905 | 33.8026 | | 0.0643 | 7.04 | 100 | 0.3321 | 31.7813 | | 0.0089 | 11.03 | 150 | 0.4060 | 32.015... | fccf738ad306b58eb25a8a5487fa6681 |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-LARGE-KV32 (Deep-Narrow version) T5-Efficient-LARGE-KV32 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* checkpoint ... | 983cea62cb26f259d28b879534eebabb |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-large-kv32** - is of model type **Large** with the following variations: - **kv** is **32** It has **586.73** million parameters and thus requires *ca.* **2346.92 MB** of memory in full precision (*fp32*) or **1173.46 MB** of memory in half precisio... | f6a5ecf6fd54471d2aa78d04d372257b |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | joaopviana_v2 Dreambooth model trained by JP2004 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-stable-... | 9b91c90340df116c2fb3a8af81739817 |
apache-2.0 | ['generated_from_trainer'] | false | sentiment-model-sample This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.5280 - Accuracy: 0.9395 | 26ac00233d00c1efaa1c0e66b0566121 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base_toy_train_data_masked_audio_10ms 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: 1.2477 - Wer: 0.7145 | 85006e389a7a2fcfb230f9b28531055b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.1337 | 1.05 | 250 | 3.4081 | 0.9982 | | 3.0792 | 2.1 | 500 | 3.2446 | 0.9982 | | 2.0577 | 3.15 | 750 | 1.5839 | 0.9492 | |... | d94339ab2bbc058de2172ac74d743289 |
apache-2.0 | ['generated_from_trainer'] | false | mt5-small_summarization This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.1774 - Rouge1: 18.2118 - Rouge2: 6.6244 - Rougel: 15.4682 - Rougelsum: 15.3942 | c897a471c603d8803d1e0c5461dc2fd5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:| | 17.7253 | 1.0 | 50 | 7.6921 | 6.677 | 1.1111 | 6.5586 | 6.6861 | | 9.8457 | 2.0 |... | f7a1afa87cd74963a4f814a2b519dba1 |
apache-2.0 | ['automatic-speech-recognition', 'common_voice', 'generated_from_trainer', 'hf-asr-leaderboard', 'robust-speech-event', 'tr'] | false | This model is a fine-tuned version of [cahya/wav2vec2-base-turkish-artificial-cv](https://huggingface.co/cahya/wav2vec2-base-turkish-artificial-cv) on the COMMON_VOICE - TR dataset. It achieves the following results on the evaluation set: | | Dataset | WER | CER | |---|---------... | b64df90ff3c49d70ec0bea9c1c0d4952 |
apache-2.0 | ['automatic-speech-recognition', 'common_voice', 'generated_from_trainer', 'hf-asr-leaderboard', 'robust-speech-event', 'tr'] | false | Training and evaluation data The following datasets were used for finetuning: - [Common Voice 7.0 TR](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) 'train', 'validation' and 'other' split were used for training. - [Media Speech](https://www.openslr.org/108/) - [Magic Hub](https://magichub.com... | 1effc6642881669e8a6c40c14afbe380 |
apache-2.0 | ['automatic-speech-recognition', 'common_voice', 'generated_from_trainer', 'hf-asr-leaderboard', 'robust-speech-event', 'tr'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-06 - train_batch_size: 6 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 24 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | 0cae3db58428f64d8909050c6b553d95 |
apache-2.0 | ['automatic-speech-recognition', 'common_voice', 'generated_from_trainer', 'hf-asr-leaderboard', 'robust-speech-event', 'tr'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.1224 | 3.45 | 500 | 0.1641 | 0.1396 | | a5a00da9941124fbaa7e6a23d67c4dfc |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-4'] | false | MultiBERTs Seed 4 Checkpoint 600k (uncased) Seed 4 intermediate checkpoint 600k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in [this repository](https://github.com/goo... | 1d1b559879225214212ccbb1682979ac |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-4'] | false | How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('multiberts-seed-4-600k') model = BertModel.from_pretrained("multiberts-seed-4-600k") text = "Replace me by any text you'd like.... | 93d4505fd3488785fa54c6db4cde6ad8 |
apache-2.0 | ['automatic-speech-recognition', 'openslr', 'robust-speech-event', 'km', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the openslr dataset. It achieves the following results on the evaluation set: - Loss: 0.3281 - Wer: 0.3462 | 136451c999c6d4e6ac2a599f844e8b77 |
apache-2.0 | ['automatic-speech-recognition', 'openslr', 'robust-speech-event', 'km', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | Installation Install the following libraries on top of HuggingFace Transformers for the supports of language model. ``` pip install pyctcdecode pip install https://github.com/kpu/kenlm/archive/master.zip ``` | 0474b111cdcb3239ccc5377ceb2ea252 |
apache-2.0 | ['automatic-speech-recognition', 'openslr', 'robust-speech-event', 'km', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | Process raw audio output = pipe("sound_file.wav", chunk_length_s=10, stride_length_s=(4, 2)) ``` **Approach 2:** More custom way to predict phonemes. ```python from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC import librosa import torch | 96d4b0c48caf62bfb8ee752599fc8769 |
apache-2.0 | ['automatic-speech-recognition', 'openslr', 'robust-speech-event', 'km', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | Read and process the input speech_array, sampling_rate = librosa.load("sound_file.wav", sr=16_000) inputs = processor(speech_array, sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch... | 34f642ebfb3aca5c77aefd22be7eb8af |
apache-2.0 | ['automatic-speech-recognition', 'openslr', 'robust-speech-event', 'km', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | Intended uses & limitations The data used for this model is only around 4 hours of recordings. - We split into 80/10/10. Hence, the training hour is 3.2 hours, which is very very small. - Yet, its performance is not too bad. Quite interesting for such small dataset, actually. You can try it out. - Its limitation is:... | 6bb374c34d7206872268be0a7e206731 |
apache-2.0 | ['automatic-speech-recognition', 'openslr', 'robust-speech-event', 'km', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - 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_sched... | 6b5fe1c7bab8b8704857b8e66ab22871 |
apache-2.0 | ['automatic-speech-recognition', 'openslr', 'robust-speech-event', 'km', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.0795 | 5.47 | 400 | 4.4121 | 1.0 | | 3.5658 | 10.95 | 800 | 3.5203 | 1.0 | | 3.3689 | 16.43 | 1200 | 2.8984 | 0.9996 | |... | 3bef21cd510c00f3ef9bbf5c8bb95a04 |
mit | ['msmarco', 'miniLM', 'pytorch', 'tensorflow', 'pt', 'pt-br'] | false | Introduction mMiniLM-L6-v2-pt-msmarco-v1 is a multilingual miniLM-based model finetuned on a Portuguese translated version of MS MARCO passage dataset. In the version v1, the Portuguese dataset was translated using [Helsinki](https://huggingface.co/Helsinki-NLP) NMT model. Further information about the dataset or the ... | 85abc93f658d867c4b454a541ecdf9ed |
mit | ['msmarco', 'miniLM', 'pytorch', 'tensorflow', 'pt', 'pt-br'] | false | Usage ```python from transformers import AutoTokenizer, AutoModel model_name = 'unicamp-dl/mMiniLM-L6-v2-pt-msmarco-v1' tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModel.from_pretrained(model_name) ``` | 4133e0e2108b514fcbbff4a4d4f583c1 |
mit | ['msmarco', 'miniLM', 'pytorch', 'tensorflow', 'pt', 'pt-br'] | false | Citation If you use mMiniLM-L6-v2-pt-msmarco-v1, please cite: @misc{bonifacio2021mmarco, title={mMARCO: A Multilingual Version of MS MARCO Passage Ranking Dataset}, author={Luiz Henrique Bonifacio and Vitor Jeronymo and Hugo Queiroz Abonizio and Israel Campiotti and Marzieh Fadaee and and Roberto Lo... | 974566936136b8d1ef94826ebebc075a |
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