license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1 class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
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cc-by-4.0 | [] | false | Training data The model is trained on the parallel FreEM dataset [FreEM_norm corpus](https://freem-corpora.github.io/corpora/norm/), consisting of 17,930 training sentences and 2,443 development sentences (used for model selection). | bc8e2047fc5b1c2d7a32d8942f43be8d |
cc-by-4.0 | [] | false | Preprocessing Texts are normalised (in terms of apostrophes, quotes and spaces), before being tokenised with SentencePiece and a vocabulary size of 1000. The inputs are of the form: ``` Sentence in Early Modern French </s> ``` where `</s>` is the end-of-sentence (eos) token. | 1736f9f5938e39aaeb99e07b5833e38a |
cc-by-4.0 | [] | false | Training The model was trained using [Fairseq](https://github.com/facebookresearch/fairseq) and ported to HuggingFace using an adapted version of [Stas's scripts for FSMT models](https://huggingface.co/blog/porting-fsmt). | 6ecf3905d56f59faa068ece32d20feaa |
cc-by-4.0 | [] | false | BibTex entry and citation info <a name="cite"></a> Rachel Bawden, Jonathan Poinhos, Eleni Kogkitsidou, Philippe Gambette, Benoît Sagot and Simon Gabay. 2022. [Automatic Normalisation of Early Modern French](http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.358.pdf). In Proceedings of the 13th Language Resources and Evaluation Conference. European Language Resources Association. Marseille, France.] Bibtex: ``` @inproceedings{bawden-etal-2022-automatic, title = {{Automatic Normalisation of Early Modern French}}, author = {Bawden, Rachel and Poinhos, Jonathan and Kogkitsidou, Eleni and Gambette, Philippe and Sagot, Beno{\^i}t and Gabay, Simon}, url = {https://hal.inria.fr/hal-03540226}, booktitle = {Proceedings of the 13th Language Resources and Evaluation Conference}, publisher = {European Language Resources Association}, year = {2022}, address = {Marseille, France}, pages = {3354--3366}, url = {http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.358.pdf} } ``` And to reference the FreEM-norm dataset used in the experiments: Simon Gabay. (2022). FreEM-corpora/FreEMnorm: FreEM norm Parallel corpus (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.5865428 ``` @software{simon_gabay_2022_5865428, author = {Simon Gabay}, title = {{FreEM-corpora/FreEMnorm: FreEM norm Parallel corpus}}, month = jan, year = 2022, publisher = {Zenodo}, version = {1.0.0}, doi = {10.5281/zenodo.5865428}, url = {https://doi.org/10.5281/zenodo.5865428} } | 193e1b8d894311f70005965e1ea0bd59 |
apache-2.0 | ['translation'] | false | deu-afr * source group: German * target group: Afrikaans * OPUS readme: [deu-afr](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/deu-afr/README.md) * model: transformer-align * source language(s): deu * target language(s): afr * model: transformer-align * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/deu-afr/opus-2020-06-17.zip) * test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/deu-afr/opus-2020-06-17.test.txt) * test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/deu-afr/opus-2020-06-17.eval.txt) | 58372b640a3d8e22031f61b57ddd289f |
apache-2.0 | ['translation'] | false | System Info: - hf_name: deu-afr - source_languages: deu - target_languages: afr - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/deu-afr/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['de', 'af'] - src_constituents: {'deu'} - tgt_constituents: {'afr'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/deu-afr/opus-2020-06-17.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/deu-afr/opus-2020-06-17.test.txt - src_alpha3: deu - tgt_alpha3: afr - short_pair: de-af - chrF2_score: 0.69 - bleu: 51.3 - brevity_penalty: 1.0 - ref_len: 9507.0 - src_name: German - tgt_name: Afrikaans - train_date: 2020-06-17 - src_alpha2: de - tgt_alpha2: af - prefer_old: False - long_pair: deu-afr - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41 | dce9f4003b005004e2b6143fde5edfb8 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | rbto_v3 Dreambooth model trained by rudzinskimaciej 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-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb) Sample pictures of this concept: | c9ee32717ddd07912aeea481fb9ad5f6 |
apache-2.0 | [] | false | Digikala Digikala user comments provided by [Open Data Mining Program (ODMP)](https://www.digikala.com/opendata/). This dataset contains 62,321 user comments with three labels: | Label | | bc44d98feece5ee290be23522a26a75e |
apache-2.0 | [] | false | | |:---------------:|:------:| | no_idea | 10394 | | not_recommended | 15885 | | recommended | 36042 | **Download** You can download the dataset from [here](https://www.digikala.com/opendata/) | 64345b16ddd393dec0f0d5a0a566bf12 |
apache-2.0 | [] | false | Results The following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures. | Dataset | ParsBERT v2 | ParsBERT v1 | mBERT | DeepSentiPers | |:------------------------:|:-----------:|:-----------:|:-----:|:-------------:| | Digikala User Comments | 81.72 | 81.74* | 80.74 | - | | 937508b6a0602e2478389e632dc644c5 |
mit | ['autotrain', 'vision', 'image-classification'] | false | Dataset Info This was trained on scraped pfp images from Mastodon, with some non-pfp images thrown in for "balancing" (i.e ensuring pokemon, kemonomimi (catgirls/foxgirls/etc), and normal animals weren't classified as 'furry') **Furry images**: 551 **Non-furry images**: 641 | aca5004b6a9c4f368e3f3c8a691f3da4 |
mit | ['autotrain', 'vision', 'image-classification'] | false | Disclaimer Please do not ruin this by using this to harass anyone. This is *not* intended to be used for targeted harrassement, and I will explicitly condemn any use that attempts to do so. If you're wondering why I made this public in the first place? I believe in freedom of *information* - this image classification model has various perfectly valid uses, and it's kinda useless to keep it private. | e2d5dea4193a30dfaa2389bdd1d38c58 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'model_for_talk', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event', 'ur'] | false | <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> infinitejoy/wav2vec2-large-xls-r-300m-urdu 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_7_0 - -UR dataset. It achieves the following results on the evaluation set: - Loss: NA - Wer: NA | 8d11592839b7bdc7576188ff0c7cbe58 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'model_for_talk', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event', 'ur'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-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_scheduler_warmup_steps: 2000 - num_epochs: 50.0 - mixed_precision_training: Native AMP | e6d4c3906ebeb8e4c770966ed5e07e5a |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'model_for_talk', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event', 'ur'] | false | Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_7_0` with split `test` ```bash python eval.py \ --model_id infinitejoy/wav2vec2-large-xls-r-300m-urdu --dataset speech-recognition-community-v2/dev_data \ --config ur --split validation --chunk_length_s 10 --stride_length_s 1 ``` | 2dd1380e5f4b24f6077cd6a81865c6b3 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'model_for_talk', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event', 'ur'] | false | Inference ```python import torch from datasets import load_dataset from transformers import AutoModelForCTC, AutoProcessor import torchaudio.functional as F model_id = "infinitejoy/wav2vec2-large-xls-r-300m-urdu" sample_iter = iter(load_dataset("mozilla-foundation/common_voice_7_0", "ur", split="test", streaming=True, use_auth_token=True)) sample = next(sample_iter) resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy() model = AutoModelForCTC.from_pretrained(model_id) processor = AutoProcessor.from_pretrained(model_id) input_values = processor(resampled_audio, return_tensors="pt").input_values with torch.no_grad(): logits = model(input_values).logits transcription = processor.batch_decode(logits.numpy()).text ``` | 0b9fc1dc695f65876c3cfac5a4c20764 |
mit | ['generated_from_trainer'] | false | suspicious_noyce This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets. | a70c18f189516c543449dd26b1884a55 |
mit | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', 'tomekkorbak/detoxify-pile-chunk3-200000-250000', 'tomekkorbak/detoxify-pile-chunk3-250000-300000', 'tomekkorbak/detoxify-pile-chunk3-300000-350000', 'tomekkorbak/detoxify-pile-chunk3-350000-400000', 'tomekkorbak/detoxify-pile-chunk3-400000-450000', 'tomekkorbak/detoxify-pile-chunk3-450000-500000', 'tomekkorbak/detoxify-pile-chunk3-500000-550000', 'tomekkorbak/detoxify-pile-chunk3-550000-600000', 'tomekkorbak/detoxify-pile-chunk3-600000-650000', 'tomekkorbak/detoxify-pile-chunk3-650000-700000', 'tomekkorbak/detoxify-pile-chunk3-700000-750000', 'tomekkorbak/detoxify-pile-chunk3-750000-800000', 'tomekkorbak/detoxify-pile-chunk3-800000-850000', 'tomekkorbak/detoxify-pile-chunk3-850000-900000', 'tomekkorbak/detoxify-pile-chunk3-900000-950000', 'tomekkorbak/detoxify-pile-chunk3-950000-1000000', 'tomekkorbak/detoxify-pile-chunk3-1000000-1050000', 'tomekkorbak/detoxify-pile-chunk3-1050000-1100000', 'tomekkorbak/detoxify-pile-chunk3-1100000-1150000', 'tomekkorbak/detoxify-pile-chunk3-1150000-1200000', 'tomekkorbak/detoxify-pile-chunk3-1200000-1250000', 'tomekkorbak/detoxify-pile-chunk3-1250000-1300000', 'tomekkorbak/detoxify-pile-chunk3-1300000-1350000', 'tomekkorbak/detoxify-pile-chunk3-1350000-1400000', 'tomekkorbak/detoxify-pile-chunk3-1400000-1450000', 'tomekkorbak/detoxify-pile-chunk3-1450000-1500000', 'tomekkorbak/detoxify-pile-chunk3-1500000-1550000', 'tomekkorbak/detoxify-pile-chunk3-1550000-1600000', 'tomekkorbak/detoxify-pile-chunk3-1600000-1650000', 'tomekkorbak/detoxify-pile-chunk3-1650000-1700000', 'tomekkorbak/detoxify-pile-chunk3-1700000-1750000', 'tomekkorbak/detoxify-pile-chunk3-1750000-1800000', 'tomekkorbak/detoxify-pile-chunk3-1800000-1850000', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25354], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048}, {'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'challenging_rtp', 'num_samples': 2048, 'prompts_path': 'resources/challenging_rtp.jsonl'}], 'scorer_config': {'device': 'cuda:0'}}, 'kl_gpt3_callback': {'force_call_on': [25354], 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'path_or_name': 'gpt2'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'suspicious_noyce', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output104340', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25354, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} | 3ea17a9c2b3d0fee39d17806da82c88a |
apache-2.0 | ['multilingual', 'PyTorch', 'Transformers', 'gpt3', 'gpt2', 'Deepspeed', 'Megatron', 'mGPT'] | false | mGPT: fine-tune on message data - 2E - This model is a fine-tuned version of [sberbank-ai/mGPT](https://huggingface.co/sberbank-ai/mGPT) on 80k messages. This builds on the minimum-working-example checkpoint [here](https://huggingface.co/pszemraj/mGPT-Peter-mwe). - 2E = 2 epochs | 98d820af37c51ee12b8ee9c6f2c0a435 |
apache-2.0 | ['multilingual', 'PyTorch', 'Transformers', 'gpt3', 'gpt2', 'Deepspeed', 'Megatron', 'mGPT'] | false | Model description - testing if fine-tuned personality data bleeds over to other languages without being trained in them explicitly **Interesting findings thus far:** - Passing a generic word after the `<name-identifier>` that is in a non-English language helps ensure the model responds in the question language (see: any example). - Model generations (in general) remain semantically consistent, even if the generations switch from `<language>`to English in the middle of the generated text. This demonstrates some sort of "universal concept understanding" | 2bfb5d2bfdc2b996c74615f8edf41144 |
apache-2.0 | ['multilingual', 'PyTorch', 'Transformers', 'gpt3', 'gpt2', 'Deepspeed', 'Megatron', 'mGPT'] | false | Usage in python Install the transformers library if you don't have it: ``` pip install -U transformers ``` load the model into a pipeline object: ``` from transformers import pipeline import torch device = 'cuda' if torch.cuda.is_available() else 'cpu' my_chatbot = pipeline('text-generation', 'pszemraj/mGPT-Peter-2E', device=0 if device == 'cuda' else -1, ) ``` | bbadd51cf9057f189ddba351d88dea9f |
apache-2.0 | ['multilingual', 'PyTorch', 'Transformers', 'gpt3', 'gpt2', 'Deepspeed', 'Megatron', 'mGPT'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 1 (in addition to all training on prior checkpoints) | 5521d5e00234e85fe034e5d0edd589ab |
mit | [] | false | wojaks-now on Stable Diffusion This is the `<red-wojak>` 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 your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`:    | 86869c4e8061ded20ad2e77afa735894 |
mit | ['audio', 'audio-to-audio'] | false | SpeechT5 (voice conversion task) SpeechT5 model fine-tuned for voice conversion (speech-to-speech) on CMU ARCTIC. This model was introduced in [SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing](https://arxiv.org/abs/2110.07205) by Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei. SpeechT5 was first released in [this repository](https://github.com/microsoft/SpeechT5/), [original weights](https://huggingface.co/mechanicalsea/speecht5-vc). The license used is [MIT](https://github.com/microsoft/SpeechT5/blob/main/LICENSE). Disclaimer: The team releasing SpeechT5 did not write a model card for this model so this model card has been written by the Hugging Face team. | 5c0714a0e89541a5b66f52a3d67baa2a |
mit | ['audio', 'audio-to-audio'] | false | Model Description Motivated by the success of T5 (Text-To-Text Transfer Transformer) in pre-trained natural language processing models, we propose a unified-modal SpeechT5 framework that explores the encoder-decoder pre-training for self-supervised speech/text representation learning. The SpeechT5 framework consists of a shared encoder-decoder network and six modal-specific (speech/text) pre/post-nets. After preprocessing the input speech/text through the pre-nets, the shared encoder-decoder network models the sequence-to-sequence transformation, and then the post-nets generate the output in the speech/text modality based on the output of the decoder. Leveraging large-scale unlabeled speech and text data, we pre-train SpeechT5 to learn a unified-modal representation, hoping to improve the modeling capability for both speech and text. To align the textual and speech information into this unified semantic space, we propose a cross-modal vector quantization approach that randomly mixes up speech/text states with latent units as the interface between encoder and decoder. Extensive evaluations show the superiority of the proposed SpeechT5 framework on a wide variety of spoken language processing tasks, including automatic speech recognition, speech synthesis, speech translation, voice conversion, speech enhancement, and speaker identification. | 30b477a5fd148188c7a41725cf38a993 |
mit | ['audio', 'audio-to-audio'] | false | Intended Uses & Limitations You can use this model for speech conversion. See the [model hub](https://huggingface.co/models?search=speecht5) to look for fine-tuned versions on a task that interests you. Currently, both the feature extractor and model support PyTorch. | 32e28cb2967dc935901527101a776dc7 |
mit | ['audio', 'audio-to-audio'] | false | Citation **BibTeX:** ```bibtex @inproceedings{ao-etal-2022-speecht5, title = {{S}peech{T}5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing}, author = {Ao, Junyi and Wang, Rui and Zhou, Long and Wang, Chengyi and Ren, Shuo and Wu, Yu and Liu, Shujie and Ko, Tom and Li, Qing and Zhang, Yu and Wei, Zhihua and Qian, Yao and Li, Jinyu and Wei, Furu}, booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, month = {May}, year = {2022}, pages={5723--5738}, } ``` | db6f26c5ca29b0b641d382a45796801f |
mit | ['audio', 'audio-to-audio'] | false | How to Get Started With the Model Use the code below to convert a mono 16 kHz speech waveform into another. ```python from transformers import SpeechT5Processor, SpeechT5ForSpeechToSpeech, SpeechT5HifiGan from datasets import load_dataset dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation") dataset = dataset.sort("id") sampling_rate = dataset.features["audio"].sampling_rate example_speech = dataset[0]["audio"]["array"] processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_vc") model = SpeechT5ForSpeechToSpeech.from_pretrained("microsoft/speecht5_vc") vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan") inputs = processor(audio=example_speech, sampling_rate=sampling_rate, return_tensors="pt") | c55205633976ae6cb17e4860b6fa4713 |
mit | ['audio', 'audio-to-audio'] | false | load xvector containing speaker's voice characteristics from a file import numpy as np import torch speaker_embeddings = np.load("xvector_speaker_embedding.npy") speaker_embeddings = torch.tensor(speaker_embeddings).unsqueeze(0) speech = model.generate_speech(inputs["input_values"], speaker_embeddings, vocoder=vocoder) import soundfile as sf sf.write("speech.wav", speech.numpy(), samplerate=16000) ``` | bed0e0c46c9fda07241f1985f46735a0 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xlsr-korean-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4534 - Wer: 0.3272 | 30025fa710acc32c7ccff33e6f5ea6cf |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP | dc59503680ddb08a59e4deda7c9e9753 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 17.4809 | 0.65 | 400 | 4.6145 | 1.0 | | 4.4863 | 1.29 | 800 | 4.3819 | 1.0 | | 4.2921 | 1.94 | 1200 | 4.1163 | 0.9970 | | 2.7971 | 2.59 | 1600 | 1.5376 | 0.8379 | | 1.5061 | 3.24 | 2000 | 1.0354 | 0.7299 | | 1.1123 | 3.88 | 2400 | 0.7909 | 0.6418 | | 0.9037 | 4.53 | 2800 | 0.6345 | 0.5698 | | 0.779 | 5.18 | 3200 | 0.5909 | 0.5571 | | 0.6834 | 5.83 | 3600 | 0.5339 | 0.5063 | | 0.6287 | 6.47 | 4000 | 0.5326 | 0.4954 | | 0.5518 | 7.12 | 4400 | 0.4930 | 0.4607 | | 0.5315 | 7.77 | 4800 | 0.4577 | 0.4451 | | 0.4867 | 8.41 | 5200 | 0.4547 | 0.4382 | | 0.4543 | 9.06 | 5600 | 0.4581 | 0.4371 | | 0.4089 | 9.71 | 6000 | 0.4387 | 0.4258 | | 0.3893 | 10.36 | 6400 | 0.4300 | 0.4100 | | 0.3751 | 11.0 | 6800 | 0.4265 | 0.4137 | | 0.3333 | 11.65 | 7200 | 0.4294 | 0.4011 | | 0.3039 | 12.3 | 7600 | 0.4187 | 0.3912 | | 0.2974 | 12.94 | 8000 | 0.4079 | 0.3805 | | 0.2658 | 13.59 | 8400 | 0.4273 | 0.3864 | | 0.2676 | 14.24 | 8800 | 0.4103 | 0.3734 | | 0.2466 | 14.89 | 9200 | 0.4122 | 0.3701 | | 0.2282 | 15.53 | 9600 | 0.4176 | 0.3650 | | 0.2186 | 16.18 | 10000 | 0.4199 | 0.3632 | | 0.2132 | 16.83 | 10400 | 0.4159 | 0.3671 | | 0.1962 | 17.48 | 10800 | 0.4321 | 0.3641 | | 0.1922 | 18.12 | 11200 | 0.4300 | 0.3535 | | 0.1827 | 18.77 | 11600 | 0.4244 | 0.3596 | | 0.1709 | 19.42 | 12000 | 0.4191 | 0.3518 | | 0.157 | 20.06 | 12400 | 0.4308 | 0.3496 | | 0.147 | 20.71 | 12800 | 0.4360 | 0.3457 | | 0.1502 | 21.36 | 13200 | 0.4329 | 0.3431 | | 0.1448 | 22.01 | 13600 | 0.4334 | 0.3432 | | 0.1407 | 22.65 | 14000 | 0.4392 | 0.3440 | | 0.1342 | 23.3 | 14400 | 0.4418 | 0.3399 | | 0.1325 | 23.95 | 14800 | 0.4360 | 0.3383 | | 0.1183 | 24.6 | 15200 | 0.4521 | 0.3359 | | 0.1174 | 25.24 | 15600 | 0.4426 | 0.3322 | | 0.1137 | 25.89 | 16000 | 0.4438 | 0.3356 | | 0.1129 | 26.54 | 16400 | 0.4547 | 0.3347 | | 0.1077 | 27.18 | 16800 | 0.4482 | 0.3300 | | 0.0999 | 27.83 | 17200 | 0.4491 | 0.3281 | | 0.0978 | 28.48 | 17600 | 0.4533 | 0.3281 | | 0.0997 | 29.13 | 18000 | 0.4542 | 0.3283 | | 0.0908 | 29.77 | 18400 | 0.4534 | 0.3272 | | 9ea8e1ac0ef651c1e5e0eaee67a8ebc5 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout 11890fdd9dd872edc50ce8eb7660d746c6ee160e pip install -e . cd egs2/stop/asr3 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/stop_hubert_slu_raw_en_bpe500 ``` <!-- Generated by scripts/utils/show_asr_result.sh --> | 57cb237eef1af5890b48302ed549aaaa |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Sun Dec 25 13:33:10 EST 2022` - python version: `3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]` - espnet version: `espnet 202205` - pytorch version: `pytorch 1.13.0+cu116` - Git hash: `11890fdd9dd872edc50ce8eb7660d746c6ee160e` - Commit date: `Sat Jun 18 17:05:39 2022 -0400` | 6c4bb487a9f5b9ff4c9dd1f246959f7d |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.acc.ave_10best/test|75636|728701|93.9|3.2|2.9|3.1|9.1|29.8| |decode_asr_asr_model_valid.acc.ave_10best/valid|33384|322094|0.0|0.0|100.0|0.0|100.0|100.0| |inference_asr_model_valid.acc.ave_10best/test|75636|728701|93.9|3.3|2.8|3.2|9.4|30.6| |inference_asr_model_valid.acc.ave_10best/valid|33384|322094|0.0|0.0|100.0|0.0|100.0|100.0| | e0ad9e7f9678c1107f54419c70b531dc |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.acc.ave_10best/test|75636|5745269|95.9|0.9|3.2|3.2|7.3|29.8| |decode_asr_asr_model_valid.acc.ave_10best/valid|33384|2537594|0.0|0.0|100.0|0.0|100.0|100.0| |inference_asr_model_valid.acc.ave_10best/test|75636|5745269|95.9|1.0|3.1|3.3|7.4|30.6| |inference_asr_model_valid.acc.ave_10best/valid|33384|2537594|0.0|0.0|100.0|0.0|100.0|100.0| | 246b35a3af99bd18f166aeef86d52650 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.acc.ave_10best/test|75636|2091389|95.1|1.5|3.4|3.1|8.0|29.8| |decode_asr_asr_model_valid.acc.ave_10best/valid|33384|921077|0.0|0.0|100.0|0.0|100.0|100.0| |inference_asr_model_valid.acc.ave_10best/test|75636|2091389|95.2|1.5|3.3|3.3|8.1|30.6| |inference_asr_model_valid.acc.ave_10best/valid|33384|921077|0.0|0.0|100.0|0.0|100.0|100.0| | 89b26ceb372fede0a6fe535619924d0d |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/train_asr2_hubert_lr0.002.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr2_hubert_lr0.002_raw_en_bpe500 ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: 4 dist_rank: 0 local_rank: 0 dist_master_addr: localhost dist_master_port: 57197 dist_launcher: null multiprocessing_distributed: true unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 50 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - acc - max keep_nbest_models: 10 nbest_averaging_interval: 0 grad_clip: 5.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 1 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_matplotlib: true use_tensorboard: true use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: - frontend.upstream num_iters_per_epoch: null batch_size: 128 valid_batch_size: null batch_bins: 1000000 valid_batch_bins: null train_shape_file: - exp/asr_stats_raw_en_bpe500/train/speech_shape - exp/asr_stats_raw_en_bpe500/train/text_shape.bpe valid_shape_file: - exp/asr_stats_raw_en_bpe500/valid/speech_shape - exp/asr_stats_raw_en_bpe500/valid/text_shape.bpe batch_type: folded valid_batch_type: null fold_length: - 80000 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/train/wav.scp - speech - sound - - dump/raw/train/text - text - text valid_data_path_and_name_and_type: - - dump/raw/valid/wav.scp - speech - sound - - dump/raw/valid/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.0004 weight_decay: 1.0e-06 scheduler: warmuplr scheduler_conf: warmup_steps: 25000 token_list: - <blank> - <unk> - ▁[ - ':' - ▁] - _ - SL - IN - GET - S - TIME - DATE - ▁THE - ▁TO - ▁FOR - ▁ - E - LOCATION - A - WEATHER - O - ▁ME - MUSIC - ▁MY - CREATE - ALARM - Y - D - ▁I - T - ▁AT - I - ▁A - TIMER - ▁IS - U - ▁IN - ▁ON - EVENT - M - ▁TIMER - TODO - REMINDER - R - ▁PM - P - ING - ▁WHAT - ▁THIS - ▁TODAY - ▁AM - N - ▁ALARM - ▁SET - NT - METHOD - ▁TOMORROW - ER - TYPE - B - ATTRIBUTE - DESTINATION - ▁MINUTES - REMINDED - PERSON - L - ▁HOW - NAME - K - ▁FIVE - ▁BE - ▁' - G - ▁NEXT - 'ON' - ▁IT - MESSAGE - H - ▁WILL - ▁S - ▁WEEK - ST - C - INFO - EN - CATEGORY - TRAFFIC - ▁F - LE - ▁AND - AR - SEND - RE - ▁P - ▁D - ▁FROM - RECIPIE - PLAY - ▁DO - ▁TRAFFIC - AN - ▁AN - AL - ▁SIX - ▁SONG - ▁ALL - ▁UP - CONTENT - ▁REMINDER - ▁WEEKEND - ▁REMIND - ▁OF - ▁T - RA - ▁WEATHER - ▁SEVEN - ▁PLEASE - ▁RE - ▁TONIGHT - EXACT - ▁EIGHT - ▁W - W - ▁TEN - F - SOURCE - ▁TIME - ESTIMATED - RECURRING - TH - DELETE - VE - ▁NEW - LL - ▁EVERY - ▁PLAY - ES - ▁THIRTY - ▁GET - ▁RAIN - CK - ▁TWO - ▁C - ▁CO - ▁ARE - ▁MESSAGE - RI - ▁G - ▁MORNING - CONTACT - ▁CAN - ▁NOW - ▁THREE - ▁THERE - ET - ▁MUSIC - TER - ▁TAKE - IC - CH - ▁J - V - ED - ▁FOUR - DURATION - LY - ▁E - ▁FRIDAY - UR - ▁YOU - ▁ANY - ▁NINE - ▁GO - UNSUPPORTED - OR - ▁SHOW - ▁O - ▁BA - ▁PA - ▁LONG - AT - ▁ONE - ND - ▁MA - ▁ST - ▁GOING - ▁LIKE - ▁ALARMS - ▁BY - ▁THAT - ▁TWENTY - ▁DAY - ▁CH - ▁MONTH - ▁K - ▁SH - UPDATE - ▁MONDAY - CE - IT - IL - AMOUNT - ▁SATURDAY - ▁BR - ▁NEED - ▁WORK - ID - ▁DRIVE - LA - ▁MO - ▁HAVE - ▁TUESDAY - ▁TELL - IR - HA - '''' - ▁IF - HOME - ▁HE - ▁LO - ▁LA - ▁WHEN - LO - ▁TH - ▁REMINDERS - IE - DISTANCE - ▁WE - ▁SA - ▁HOUR - OULD - NE - DEPARTURE - ▁HI - ▁LI - ARTIST - Z - TRAVEL - ▁OUT - PAUSE - EST - ARRIVAL - ▁CANCEL - ▁MI - ▁OFF - ▁FIFTEEN - POINT - ▁SNOW - NA - EL - ▁EVENTS - ▁CA - ▁SUNDAY - ▁LEAVE - TRACK - ▁SEND - ▁DELETE - ▁APPOINTMENT - ▁BO - RDINAL - ▁MAKE - ▁NEAR - ▁BEFORE - GE - ▁HOME - RELATION - ▁V - FR - ▁THURSDAY - ▁LAST - DIRECTIONS - ▁WEDNESDAY - ▁START - ▁FORECAST - ▁YORK - ▁RIGHT - UM - ▁WITH - USE - ▁MEETING - UT - LI - ▁CHANGE - ▁CAR - GENRE - ATION - X - ▁PICK - ▁WANT - ▁NIGHT - SKIP - ▁DE - ▁RO - ▁ABOUT - MAP - CO - MA - ▁HOUSE - ▁HOT - ▁PARTY - ▁WA - UNIT - ▁HERE - ▁SU - ▁AFTERNOON - ▁MUCH - ▁MOM - ▁TEMPERATURE - EQUENC - ▁ADD - ▁SAN - ▁HER - ▁CONCERTS - ▁CHRISTMAS - ▁DINNER - ▁MAR - LAND - ▁HOURS - ▁CURRENT - ▁TRACK - ▁SOME - ▁CITY - ▁FORTY - ATE - ▁ROUTE - SNOOZE - ▁TEXT - WORK - ▁COLD - RELATED - ▁OR - ▁NO - Q - ▁WAY - WAY - ▁MANY - ▁BIRTHDAY - ▁MINUTE - ▁PLAYLIST - ▁NOON - ▁ROAD - TITLE - PATH - ▁ASK - NAVIGATION - ▁LEFT - ▁ALBUM - ▁TURN - ▁LATE - ▁ELEVEN - NEW - ▁CELSIUS - ▁BUY - AVOID - LOW - NCE - SEARCH - ▁GAME - ▁STOP - ▁JO - ▁FIRST - ▁SHE - ▁DOCTOR - ▁BU - PERIOD - ▁WAKE - CONDITION - ▁EVENING - RADIUS - MODIFIE - ▁REPEAT - ▁SECOND - ▁CONCERT - ▁ANGELES - ▁DOWNTOWN - ▁UMBRELLA - TEMPERATURE - ASH - ▁YEAR - GROUP - ▁DRIVING - ▁GIVE - ▁HUNDRED - ▁HO - ▁MILES - PLAYLIST - ADD - RETRIEV - ▁TWELVE - EAD - ▁CLASS - ▁FREE - PORT - VILLE - ▁BETWEEN - ▁KNOW - ▁AROUND - ▁SCHOOL - ▁NINETY - PROVIDER - SILENCE - RESUME - ▁LET - TION - ▁AUGUST - ▁HAPPENING - ▁AFTER - ▁FAHRENHEIT - ▁EX - ▁VIDEO - ROAD - ▁PARK - ▁CHICAGO - ▁DAILY - ▁CHECK - ▁BEACH - ▁WHERE - ▁JUNE - ▁STREET - ▁FESTIVAL - ▁FLORIDA - ▁JOHN - ▁HAS - ▁SPOTIFY - ▁BILL - RESTART - ▁HIGHWAY - ▁SEATTLE - J - ▁LUNCH - ▁LOOK - ▁FRIEND - ▁COMING - ▁ALERT - IGHT - ▁PANDORA - ▁HEAVY - ▁KIDS - ▁MOVIE - ▁SOUTH - REACT - ▁CONSTRUCTION - PREVIOUS - ▁ORLANDO - ▁OVER - ▁MIAMI - REACTION - ▁ATLANTA - ▁ACCIDENT - ▁COUNTRY - ▁NORTH - ▁LIGHT - RADIO - ▁READ - ▁FAMILY - ▁AIRPORT - ▁EXPECT - ▁DEGREE - ▁PRO - ▁PARTIES - ▁FIFTY - ▁HIGH - ▁PLAN - ▁FOOD - ▁WARM - ▁SUNNY - ▁VEGAS - ▁HOLIDAY - ▁SCHEDULE - ▁STORM - ▁FIFTH - ▁BOSTON - ▁FRANCISCO - ▁LONDON - ATTENDEE - ▁JULY - ▁WALK - ▁COMMUTE - ▁CLEAN - ▁DENTIST - TOWN - ▁AGAIN - ▁DALLAS - ▁PORTLAND - ▁SEPTEMBER - ▁ARRIVE - ▁SISTER - ▁HOUSTON - Ã - É - Í - '*' - Á - Ç - Ó - ']' - '[' - Ú - Ü - <sos/eos> transcript_token_list: null two_pass: false pre_postencoder_norm: false init: null input_size: null ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: true joint_net_conf: null use_preprocessor: true token_type: bpe bpemodel: data/en_token_list/bpe_unigram500/bpe.model non_linguistic_symbols: null cleaner: null g2p: null speech_volume_normalize: null rir_scp: null rir_apply_prob: 1.0 noise_scp: null noise_apply_prob: 1.0 noise_db_range: '13_15' frontend: s3prl frontend_conf: frontend_conf: upstream: hubert_large_ll60k download_dir: ./hub multilayer_feature: true fs: 16k specaug: specaug specaug_conf: apply_time_warp: true time_warp_window: 5 time_warp_mode: bicubic apply_freq_mask: true freq_mask_width_range: - 0 - 30 num_freq_mask: 2 apply_time_mask: true time_mask_width_range: - 0 - 40 num_time_mask: 2 normalize: utterance_mvn normalize_conf: {} model: espnet model_conf: ctc_weight: 0.3 lsm_weight: 0.1 length_normalized_loss: false extract_feats_in_collect_stats: false preencoder: linear preencoder_conf: input_size: 1024 output_size: 80 encoder: conformer encoder_conf: output_size: 512 attention_heads: 8 linear_units: 2048 num_blocks: 12 dropout_rate: 0.1 positional_dropout_rate: 0.1 attention_dropout_rate: 0.1 input_layer: conv2d2 normalize_before: true macaron_style: true rel_pos_type: latest pos_enc_layer_type: rel_pos selfattention_layer_type: rel_selfattn activation_type: swish use_cnn_module: true cnn_module_kernel: 31 postencoder: null postencoder_conf: {} deliberationencoder: null deliberationencoder_conf: {} decoder: transformer decoder_conf: attention_heads: 8 linear_units: 2048 num_blocks: 6 dropout_rate: 0.1 positional_dropout_rate: 0.1 self_attention_dropout_rate: 0.1 src_attention_dropout_rate: 0.1 decoder2: null decoder2_conf: {} postdecoder: null postdecoder_conf: {} required: - output_dir - token_list version: '202205' distributed: true ``` </details> | 9df39bee883db8d6fe445570f5cf9f06 |
apache-2.0 | ['generated_from_keras_callback'] | false | aalogan/bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0170 - Validation Loss: 0.0546 - Epoch: 3 | dbe0fd8da205fcb8ac46a4c9f2b2c39c |
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': 3508, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 | 9b1c6778477e99eb960eb3cc1935e6ba |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.1722 | 0.0676 | 0 | | 0.0481 | 0.0531 | 1 | | 0.0270 | 0.0551 | 2 | | 0.0170 | 0.0546 | 3 | | 747d9b4e47f97ea5a98bc681fb728b1d |
apache-2.0 | ['automatic-speech-recognition', 'de'] | false | exp_w2v2t_de_unispeech-ml_s952 Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 5bb587e8c3cd40ff420726761c2866d0 |
mit | ['generated_from_trainer'] | false | camembert-ner-lr10e3 This model is a fine-tuned version of [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5566 - Overall Precision: 0.0 - Overall Recall: 0.0 - Overall F1: 0.0 - Overall Accuracy: 0.8840 - Humanprod F1: 0.0 - Loc F1: 0.0 - Org F1: 0.0 - Per F1: 0.0 | 299e77b49afa7e0e6936f2aaa02eaa6b |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.002 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 | 5c5d09aa38ae027f5d479816612ab2d7 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Humanprod F1 | Loc F1 | Org F1 | Per F1 | |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:------------:|:------:|:------:|:------:| | 0.5473 | 1.0 | 613 | 0.5626 | 0.0 | 0.0 | 0.0 | 0.8840 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.5299 | 2.0 | 1226 | 0.5566 | 0.0 | 0.0 | 0.0 | 0.8840 | 0.0 | 0.0 | 0.0 | 0.0 | | 184ba5878ca13c691379ecd60d49f6e1 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3 - mixed_precision_training: Native AMP | 366169b08a680803e76e4e0a3d7c125c |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.06 - num_epochs: 10 | a5d9a92abc0a20f0e9c7611551294d10 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-newsmodelclassification 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: 0.2177 - Accuracy: 0.928 - F1: 0.9278 | 62c050bc933a8895972de72d218f9dca |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8104 | 1.0 | 250 | 0.3057 | 0.9105 | 0.9084 | | 0.2506 | 2.0 | 500 | 0.2177 | 0.928 | 0.9278 | | 10f850a200dee01f443a67720fa4ea79 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-all 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: 0.1262 - F1: 0.8799 | 45aa8580f1889140b4963162ac7626f7 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2905 | 1.0 | 715 | 0.1625 | 0.8392 | | 0.1477 | 2.0 | 1430 | 0.1294 | 0.8688 | | 0.095 | 3.0 | 2145 | 0.1262 | 0.8799 | | 7780260cdae96398419f40a4babfbd2c |
gpl-3.0 | ['spacy', 'token-classification'] | false | Model description Catalan transformer (projecte-aina/roberta-large-ca-v2) pipeline by BSC. Components: transformer, morphologizer, parser, ner, attribute_ruler, lemmatizer, text classification. | Feature | Description | | --- | --- | | **Name** | `ca_bsc_demo_trf` | | **Version** | `3.4.2` | | **spaCy** | `3.4.1` | | **Default Pipeline** | `transformer`, `tagger`, `morphologizer`, `lemmatizer`, `parser`, `ner`, `textcat` | | **Components** | `transformer`, `tagger`, `morphologizer`, `lemmatizer`, `parser`, `ner`, `textcat` | | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) | | **Sources** |[roberta-large-ca-v2] (https://huggingface.co/projecte-aina/roberta-large-ca-v2) <br /> Ancora_UD_10 <br />[WikiCAT_ca] (https://huggingface.co/datasets/projecte-aina/WikiCAT_ca) | | **License** | `GNU GPL 3.0` | | **Author** | [AINA project](https://huggingface.co/projecte-aina) | | bcf1659c47d1063d9a780859534c940e |
gpl-3.0 | ['spacy', 'token-classification'] | false | Label scheme <details> <summary>View label scheme (342 labels for 5 components)</summary> | Component | Labels | | --- | --- | | **`tagger`** | `ADJ`, `ADP`, `ADV`, `AUX`, `CCONJ`, `DET`, `INTJ`, `NOUN`, `NUM`, `PART`, `PRON`, `PROPN`, `PUNCT`, `SCONJ`, `SYM`, `VERB`, `X` | | **`morphologizer`** | `Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `POS=PROPN`, `POS=PUNCT\|PunctSide=Ini\|PunctType=Brck`, `POS=PUNCT\|PunctSide=Fin\|PunctType=Brck`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Fem\|Number=Sing\|POS=NOUN`, `POS=ADP`, `NumType=Card\|Number=Plur\|POS=NUM`, `Gender=Masc\|Number=Plur\|POS=NOUN`, `Number=Sing\|POS=ADJ`, `POS=CCONJ`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `NumForm=Digit\|NumType=Card\|POS=NUM`, `NumForm=Digit\|POS=NOUN`, `Gender=Masc\|Number=Plur\|POS=ADJ`, `POS=PUNCT\|PunctType=Comm`, `POS=AUX\|VerbForm=Inf`, `Case=Acc,Dat\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `POS=PRON\|PronType=Rel`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Definite=Def\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Gender=Fem\|Number=Plur\|POS=NOUN`, `Gender=Fem\|Number=Plur\|POS=ADJ`, `POS=VERB\|VerbForm=Inf`, `Case=Acc,Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Plur\|POS=ADJ`, `POS=PUNCT\|PunctType=Peri`, `Number=Sing\|POS=PRON\|PronType=Rel`, `Gender=Masc\|Number=Sing\|POS=NOUN`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part`, `POS=SCONJ`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Definite=Def\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `POS=VERB\|VerbForm=Ger`, `POS=NOUN`, `Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Gender=Fem\|NumType=Ord\|Number=Plur\|POS=ADJ`, `POS=SYM`, `Gender=Masc\|Number=Sing\|POS=ADJ`, `Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `POS=ADV\|Polarity=Neg`, `POS=ADV`, `Number=Sing\|POS=PRON\|PronType=Dem`, `Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=NOUN`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=ADJ`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Tot`, `Case=Loc\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Degree=Cmp\|POS=ADV`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `NumType=Card\|POS=NUM`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Number=Sing\|POS=PRON\|PronType=Ind`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Number=Plur\|POS=DET\|PronType=Ind`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Sing\|POS=DET\|PronType=Ind`, `POS=PUNCT`, `Number=Sing\|POS=DET\|PronType=Rel`, `Case=Gen\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc,Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Degree=Cmp\|Number=Sing\|POS=ADJ`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem,Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|PronType=Rel`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `AdvType=Tim\|POS=NOUN`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=3\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `POS=PUNCT\|PunctType=Semi`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=3\|VerbForm=Fin`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `NumForm=Digit\|POS=SYM`, `Gender=Masc\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Part`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Int`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `POS=PRON\|PronType=Int`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin`, `POS=PART`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem`, `POS=ADJ`, `Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Degree=Cmp\|Number=Plur\|POS=ADJ`, `POS=PUNCT\|PunctType=Dash`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Int`, `Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Fut\|VerbForm=Fin`, `POS=PUNCT\|PunctType=Colo`, `Gender=Masc\|NumType=Card\|POS=NUM`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Sing\|POS=PRON\|PronType=Int`, `POS=PUNCT\|PunctType=Quot`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `POS=AUX\|VerbForm=Ger`, `Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Imp\|Number=Sing\|POS=AUX\|Person=3\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|PronType=Ind`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Acc,Dat\|Number=Sing\|POS=PRON\|Person=2\|Polite=Infm\|PrepCase=Npr\|PronType=Prs`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Int`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `NumForm=Digit\|NumType=Frac\|POS=NUM`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Dem`, `Gender=Fem\|POS=NOUN`, `Case=Acc,Dat\|Number=Sing\|POS=PRON\|Person=1\|PrepCase=Npr\|PronType=Prs`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Fut\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=2\|Polite=Infm\|PronType=Prs`, `POS=X`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=1\|VerbForm=Fin`, `Number=Sing\|POS=DET\|PronType=Dem`, `POS=DET`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `POS=DET\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `NumType=Ord\|Number=Sing\|POS=ADJ`, `Gender=Fem\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Part`, `Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Fem\|Number=Plur\|POS=AUX\|Tense=Past\|VerbForm=Part`, `Gender=Masc\|Number=Plur\|POS=AUX\|Tense=Past\|VerbForm=Part`, `Number=Plur\|POS=PRON\|PronType=Dem`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=1\|VerbForm=Fin`, `POS=PRON\|PronType=Ind`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PrepCase=Pre\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `POS=PUNCT\|PunctSide=Fin\|PunctType=Qest`, `NumForm=Digit\|NumType=Ord\|POS=ADJ`, `Case=Acc\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|Reflex=Yes`, `NumForm=Digit\|NumType=Frac\|POS=SYM`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `POS=PUNCT\|PunctSide=Ini\|PunctType=Qest`, `NumType=Card\|Number=Sing\|POS=NUM`, `Foreign=Yes\|POS=PRON\|PronType=Int`, `Foreign=Yes\|Mood=Ind\|POS=VERB\|VerbForm=Fin`, `Foreign=Yes\|POS=ADP`, `Gender=Masc\|Number=Sing\|POS=PROPN`, `POS=PUNCT\|PunctSide=Ini\|PunctType=Excl`, `POS=PUNCT\|PunctSide=Fin\|PunctType=Excl`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=1\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|Person=2\|Polite=Form\|PronType=Prs`, `Mood=Sub\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `POS=PUNCT\|PunctSide=Ini\|PunctType=Comm`, `POS=PUNCT\|PunctSide=Fin\|PunctType=Comm`, `Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Acc,Dat\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1\|VerbForm=Fin`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=1\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Number=Sing\|POS=PRON\|Person=2\|Polite=Form\|PronType=Prs`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `POS=VERB\|Tense=Past\|VerbForm=Part`, `Mood=Imp\|Number=Plur\|POS=AUX\|Person=3\|VerbForm=Fin`, `Case=Nom\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Definite=Ind\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `POS=AUX\|Tense=Past\|VerbForm=Part`, `Gender=Fem\|NumType=Card\|POS=NUM`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `AdvType=Tim\|Degree=Cmp\|POS=ADV`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|Polite=Infm\|PrepCase=Pre\|PronType=Prs`, `POS=DET\|PronType=Rel`, `Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin`, `POS=INTJ`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `POS=VERB\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Foreign=Yes\|POS=NOUN`, `Foreign=Yes\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Foreign=Yes\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Foreign=Yes\|POS=SCONJ`, `Foreign=Yes\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|POS=SYM`, `Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=PROPN`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Definite=Def\|Foreign=Yes\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Foreign=Yes\|POS=VERB`, `Foreign=Yes\|POS=ADJ`, `Foreign=Yes\|POS=DET`, `Foreign=Yes\|POS=ADV`, `Degree=Cmp\|POS=ADJ`, `AdvType=Tim\|POS=SYM`, `Number=Plur\|POS=DET\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin` | | **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `case`, `cc`, `ccomp`, `compound`, `conj`, `cop`, `csubj`, `dep`, `det`, `expl:pass`, `fixed`, `flat`, `iobj`, `mark`, `nmod`, `nsubj`, `nummod`, `obj`, `obl`, `parataxis`, `punct`, `xcomp` | | **`ner`** | `LOC`, `MISC`, `ORG`, `PER` | | **`textcat`** | `Economia`, `Enginyeria`, `Entreteniment`, `Història`, `Humanitats`, `Dret`, `Matemàtiques`, `Música`, `Filosofia`, `Política`, `Religió`, `Esport`, `Ciència_i_Tecnologia` | </details> | 67c8cf85eace5c77bcec6db009bdf142 |
gpl-3.0 | ['spacy', 'token-classification'] | false | Evaluation results | Type | Score | | --- | --- | | `TAG_ACC` | 96.35 | | `POS_ACC` | 96.36 | | `MORPH_ACC` | 95.71 | | `LEMMA_ACC` | 97.58 | | `DEP_UAS` | 95.16 | | `DEP_LAS` | 93.53 | | `SENTS_P` | 99.30 | | `SENTS_R` | 99.30 | | `SENTS_F` | 99.30 | | `ENTS_F` | 92.02 | | `ENTS_P` | 92.46 | | `ENTS_R` | 91.59 | | `TRANSFORMER_LOSS` | 2061930.61 | | `TAGGER_LOSS` | 462421.82 | | `MORPHOLOGIZER_LOSS` | 583505.58 | | `PARSER_LOSS` | 628332.01 | | `NER_LOSS` | 12427.23 | | be2d9ebdbe80107ec4f45340586a6cfd |
apache-2.0 | ['multiberts', 'multiberts-seed_0', 'multiberts-seed_0-step_600k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 0, Step 600k 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 random seeds, which causes variations in the initial weights and order of training instances. The aim is to distinguish findings that apply to a specific artifact (i.e., a particular instance of the model) from those that apply to the more general procedure. We also provide 140 intermediate checkpoints captured during the course of pre-training (we saved 28 checkpoints for the first 5 runs). The models were originally released through [http://goo.gle/multiberts](http://goo.gle/multiberts). We describe them in our paper [The MultiBERTs: BERT Reproductions for Robustness Analysis](https://arxiv.org/abs/2106.16163). This is model | 2e79b9bbf1074ddcd1746d41048a2328 |
apache-2.0 | ['multiberts', 'multiberts-seed_0', 'multiberts-seed_0-step_600k'] | 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_0-step_600k') model = TFBertModel.from_pretrained("google/multiberts-seed_0-step_600k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` PyTorch version: ``` from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_0-step_600k') model = BertModel.from_pretrained("google/multiberts-seed_0-step_600k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` | 09f20dc4396034d3e1ece61f5627f0b1 |
apache-2.0 | ['pythae', 'reproducibility'] | false | Downloading this model from the Hub This model was trained with pythae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from pythae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="clementchadebec/reproduced_aae") ``` | 75b6ab826032fafbb505945f16a7d078 |
apache-2.0 | ['pythae', 'reproducibility'] | false | Reproducibility This trained model reproduces the results of Table 1 in [1]. | Model | Dataset | Metric | Obtained value | Reference value | |:---:|:---:|:---:|:---:|:---:| | AAE | CELEBA 64 | FID | 43.3 | 42 | [1] Tolstikhin, O Bousquet, S Gelly, and B Schölkopf. Wasserstein auto-encoders. In 6th International Conference on Learning Representations (ICLR 2018), 2018. | 80c299cd9b9ccdc0c3108d628a13655f |
apache-2.0 | ['generated_from_trainer'] | false | DISO_bsc_test16 This model is a fine-tuned version of [PlanTL-GOB-ES/bsc-bio-ehr-es](https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1732 - Diso Precision: 0.7577 - Diso Recall: 0.7757 - Diso F1: 0.7666 - Diso Number: 4552 - Overall Precision: 0.7577 - Overall Recall: 0.7757 - Overall F1: 0.7666 - Overall Accuracy: 0.9732 | 87e17ec955891318a88fa59ab5c7a3e9 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 8e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 | ea71026c6e995ccb7453e3dc34d4d4a1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Diso Precision | Diso Recall | Diso F1 | Diso Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------------:|:-----------:|:-------:|:-----------:|:-----------------:|:--------------:|:----------:|:----------------:| | 0.0948 | 1.0 | 1400 | 0.0766 | 0.7157 | 0.7594 | 0.7369 | 4552 | 0.7157 | 0.7594 | 0.7369 | 0.9710 | | 0.0631 | 2.0 | 2800 | 0.0818 | 0.7442 | 0.7599 | 0.7520 | 4552 | 0.7442 | 0.7599 | 0.7520 | 0.9726 | | 0.0454 | 3.0 | 4200 | 0.0842 | 0.7544 | 0.7654 | 0.7599 | 4552 | 0.7544 | 0.7654 | 0.7599 | 0.9728 | | 0.0311 | 4.0 | 5600 | 0.1113 | 0.7678 | 0.7700 | 0.7689 | 4552 | 0.7678 | 0.7700 | 0.7689 | 0.9732 | | 0.0217 | 5.0 | 7000 | 0.1231 | 0.7745 | 0.7687 | 0.7716 | 4552 | 0.7745 | 0.7687 | 0.7716 | 0.9743 | | 0.015 | 6.0 | 8400 | 0.1482 | 0.7651 | 0.7733 | 0.7691 | 4552 | 0.7651 | 0.7733 | 0.7691 | 0.9735 | | 0.0101 | 7.0 | 9800 | 0.1498 | 0.7576 | 0.7709 | 0.7642 | 4552 | 0.7576 | 0.7709 | 0.7642 | 0.9730 | | 0.0073 | 8.0 | 11200 | 0.1732 | 0.7577 | 0.7757 | 0.7666 | 4552 | 0.7577 | 0.7757 | 0.7666 | 0.9732 | | 55cf5f19d86678c79196046aef5f5f01 |
gpl-3.0 | ['generated_from_trainer'] | false | IceBERT-finetuned-iec-sentence-bs16 This model is a fine-tuned version of [vesteinn/IceBERT](https://huggingface.co/vesteinn/IceBERT) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2508 - Matthews Correlation: 0.8169 | 3e4cfbdbcddd494623a37083033bce5d |
gpl-3.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:-----:|:---------------:|:--------------------:| | 0.5278 | 1.0 | 3640 | 0.4777 | 0.5396 | | 0.4648 | 2.0 | 7280 | 0.3886 | 0.6437 | | 0.3807 | 3.0 | 10920 | 0.3478 | 0.7060 | | 0.3061 | 4.0 | 14560 | 0.2523 | 0.8083 | | 0.2477 | 5.0 | 18200 | 0.2508 | 0.8169 | | e62002d3e9fa4df3dd36ed1ecdf71d85 |
apache-2.0 | ['generated_from_trainer'] | false | korean-aihub-learning-math-16batch This model is a fine-tuned version of [kresnik/wav2vec2-large-xlsr-korean](https://huggingface.co/kresnik/wav2vec2-large-xlsr-korean) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1497 - Wer: 0.5260 | b677324ec1bfdb3289ccdb2048696870 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP | 729f8e85b449a24202d18aa33106ac62 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 20 | 32.0718 | 1.0 | | No log | 2.0 | 40 | 24.7403 | 1.0808 | | No log | 3.0 | 60 | 5.8389 | 1.0 | | No log | 4.0 | 80 | 4.8543 | 1.0 | | 19.6583 | 5.0 | 100 | 4.4453 | 1.0 | | 19.6583 | 6.0 | 120 | 4.3923 | 1.0 | | 19.6583 | 7.0 | 140 | 4.2902 | 1.0 | | 19.6583 | 8.0 | 160 | 3.9026 | 0.9959 | | 19.6583 | 9.0 | 180 | 3.0616 | 0.9740 | | 3.7358 | 10.0 | 200 | 2.2049 | 0.8534 | | 3.7358 | 11.0 | 220 | 1.6666 | 0.7288 | | 3.7358 | 12.0 | 240 | 1.4123 | 0.6603 | | 3.7358 | 13.0 | 260 | 1.3113 | 0.6164 | | 3.7358 | 14.0 | 280 | 1.2269 | 0.6356 | | 0.8398 | 15.0 | 300 | 1.2349 | 0.5945 | | 0.8398 | 16.0 | 320 | 1.1970 | 0.5658 | | 0.8398 | 17.0 | 340 | 1.2144 | 0.5562 | | 0.8398 | 18.0 | 360 | 1.2551 | 0.5658 | | 0.8398 | 19.0 | 380 | 1.1971 | 0.5493 | | 0.2649 | 20.0 | 400 | 1.1967 | 0.5247 | | 0.2649 | 21.0 | 420 | 1.2796 | 0.5849 | | 0.2649 | 22.0 | 440 | 1.2156 | 0.5521 | | 0.2649 | 23.0 | 460 | 1.2118 | 0.5425 | | 0.2649 | 24.0 | 480 | 1.1637 | 0.5384 | | 0.1801 | 25.0 | 500 | 1.1846 | 0.5562 | | 0.1801 | 26.0 | 520 | 1.1927 | 0.5534 | | 0.1801 | 27.0 | 540 | 1.2015 | 0.5384 | | 0.1801 | 28.0 | 560 | 1.2077 | 0.5397 | | 0.1801 | 29.0 | 580 | 1.1554 | 0.5260 | | 0.1364 | 30.0 | 600 | 1.1497 | 0.5260 | | 75d03f8f62a423a59327a11fd64309f1 |
cc-by-sa-4.0 | [] | false | How to use You can use this model for masked language modeling as follows: ```python from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("nlp-waseda/roberta-large-japanese-seq512-with-auto-jumanpp") model = AutoModelForMaskedLM.from_pretrained("nlp-waseda/roberta-large-japanese-seq512-with-auto-jumanpp") sentence = '早稲田大学で自然言語処理を[MASK]する。' encoding = tokenizer(sentence, return_tensors='pt') ... ``` You can fine-tune this model on downstream tasks. | ed008e4c700337815f7fa56aea7f241c |
cc-by-sa-4.0 | [] | false | Tokenization `BertJapaneseTokenizer` now supports automatic tokenization for [Juman++](https://github.com/ku-nlp/jumanpp). However, if your dataset is large, you may take a long time since `BertJapaneseTokenizer` still does not supoort fast tokenization. You can still do the Juman++ tokenization by your self and use the old model [nlp-waseda/roberta-large-japanese-seq512](https://huggingface.co/nlp-waseda/roberta-large-japanese-seq512). Juman++ 2.0.0-rc3 was used for pretraining. Each word is tokenized into tokens by [sentencepiece](https://github.com/google/sentencepiece). | 34bbd0644119381ec80da2655ea2b082 |
cc-by-sa-4.0 | [] | false | Training procedure This model was trained on Japanese Wikipedia (as of 20210920) and the Japanese portion of CC-100 from the checkpoint of [nlp-waseda/roberta-large-japanese](https://huggingface.co/nlp-waseda/roberta-large-japanese). It took a week using eight NVIDIA A100 GPUs. The following hyperparameters were used during pretraining: - learning_rate: 6e-5 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 4120 (max_seq_length=128), 4032 (max_seq_length=512) - max_seq_length: 512 - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-6 - lr_scheduler_type: linear - training_steps: 670000 (max_seq_length=128) + 70000 (max_seq_length=512) - warmup_steps: 10000 - mixed_precision_training: Native AMP | 172a5810c53d0a65fea876bd0502f92c |
apache-2.0 | ['generated_from_keras_callback'] | false | malay-patel/bert-finetuned-squad-nq This model is a fine-tuned version of [nlpconnect/roberta-base-squad2-nq](https://huggingface.co/nlpconnect/roberta-base-squad2-nq) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.5461 - Train End Logits Accuracy: 0.6253 - Train Start Logits Accuracy: 0.6120 - Epoch: 2 | 6c26f48fe93c993d7bd98e43974ff9c7 |
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': 861, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 | 04b045dabd194d6c78f7c8b46bb8091a |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:-----:| | 1.5548 | 0.6236 | 0.6172 | 0 | | 1.5423 | 0.6286 | 0.6192 | 1 | | 1.5461 | 0.6253 | 0.6120 | 2 | | 7721bda51d2de2e2c811306a0d92448c |
apache-2.0 | ['generated_from_keras_callback'] | false | varun1/bert-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.2322 - Epoch: 0 | 58dbb8871f33bc4f76c10565850d63ed |
apache-2.0 | ['generated_from_trainer'] | false | distilbart-podimo-data-eval-1 This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.3983 - Rouge1: 34.6132 - Rouge2: 7.9113 - Rougel: 17.9418 - Rougelsum: 31.5251 - Gen Len: 141.5587 | 6bd482898e2f0df09940e0b36bb78270 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:--------:| | 4.1934 | 0.98 | 44 | 3.7592 | 32.8148 | 6.457 | 16.8696 | 29.6986 | 141.4441 | | 3.6362 | 1.98 | 88 | 3.5809 | 33.0442 | 6.851 | 17.1323 | 30.1382 | 141.324 | | 3.3554 | 2.98 | 132 | 3.4835 | 33.66 | 7.1375 | 17.5152 | 30.5783 | 141.2793 | | 3.1566 | 3.98 | 176 | 3.4301 | 34.524 | 7.757 | 17.995 | 31.5808 | 141.7151 | | 3.0107 | 4.98 | 220 | 3.4099 | 34.3459 | 7.7512 | 18.0605 | 31.4531 | 141.4106 | | 2.901 | 5.98 | 264 | 3.4073 | 35.028 | 7.9099 | 17.9907 | 31.8304 | 141.5419 | | 2.8246 | 6.98 | 308 | 3.3983 | 34.1937 | 7.8606 | 17.7858 | 31.1331 | 141.5279 | | 2.7306 | 7.98 | 352 | 3.3983 | 34.6132 | 7.9113 | 17.9418 | 31.5251 | 141.5587 | | ad9f3e4a69b584a52a3236d681025113 |
apache-2.0 | ['automatic-speech-recognition', 'et'] | false | exp_w2v2t_et_vp-it_s222 Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) 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 input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 6f9b1bf96f6e41165c16567d6808bf6c |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 64 - 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 | 563d8f0ab48c1372a9aa119eac651b90 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'text-to-image', 'dreambooth-hackathon', 'animal'] | false | Dreambooth Model for Animals trained on a custom dataset. This is a Stable Diffusion model fine-tuned on the animal concept with DreamBooth. It can be used by modifying the `instance_prompt`: **A photo of vishu cat** This model was created as part of the DreamBooth Hackathon 🔥. | 69ef4d810f0ab90fd93292d99cce1753 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'text-to-image', 'dreambooth-hackathon', 'animal'] | false | Examples Some examples of images generated with their prompts are (Guidance scale = 7.5 and Number of Inference steps = 50 for all): Prompt = A photo of vishu cat as a genshin impact character  Prompt = A photo of vishu cat shaking hands with Donald Trump  Prompt = A photo of vishu cat as a Disney Princess  Prompt = A photo of vishu cat cocking a gun  | 39130b94ecd0f03b8e432d06a3e85601 |
mit | ['exbert', 'commonsense', 'semeval2020', 'comve'] | false | Model description Finetuned model on Commonsense Validation and Explanation (ComVE) dataset introduced in [SemEval2020 Task4](https://competitions.codalab.org/competitions/21080) using a causal language modeling (CLM) objective. The model is able to generate a reason why a given natural language statement is against commonsense. | c4061d96158fb9ec01427e23bb302b40 |
mit | ['exbert', 'commonsense', 'semeval2020', 'comve'] | false | How to use You can use this model directly to generate reasons why the given statement is against commonsense using [`generate.sh`](https://github.com/AliOsm/SemEval2020-Task4-ComVE/tree/master/TaskC-Generation) script. *Note:* make sure that you are using version `2.4.1` of `transformers` package. Newer versions has some issue in text generation and the model repeats the last token generated again and again. | 721c26b373399df0f296dfcade4d93bb |
mit | ['exbert', 'commonsense', 'semeval2020', 'comve'] | false | Training data The model is initialized from the [distilgpt2](https://github.com/huggingface/transformers/blob/master/model_cards/distilgpt2-README.md) model and finetuned using [ComVE](https://github.com/wangcunxiang/SemEval2020-Task4-Commonsense-Validation-and-Explanation) dataset which contains 10K against commonsense sentences, each of them is paired with three reference reasons. | b5a3714229a1ae211c1ae014b6cf4d5a |
mit | ['exbert', 'commonsense', 'semeval2020', 'comve'] | false | Training procedure Each natural language statement that against commonsense is concatenated with its reference reason with `<|continue|>` as a separator, then the model finetuned using CLM objective. The model trained on Nvidia Tesla P100 GPU from Google Colab platform with 5e-5 learning rate, 15 epochs, 128 maximum sequence length and 64 batch size. <center> <img src="https://i.imgur.com/xKbrwBC.png"> </center> | bb382a35d9214f4c123f4ed3a4f3f1de |
mit | ['exbert', 'commonsense', 'semeval2020', 'comve'] | false | BibTeX entry and citation info ```bibtex @article{fadel2020justers, title={JUSTers at SemEval-2020 Task 4: Evaluating Transformer Models Against Commonsense Validation and Explanation}, author={Fadel, Ali and Al-Ayyoub, Mahmoud and Cambria, Erik}, year={2020} } ``` <a href="https://huggingface.co/exbert/?model=aliosm/ComVE-distilgpt2"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a> | d18e38dabd98ba9ca38c1ac2f69a6234 |
apache-2.0 | [] | false | Model Description This is a retriever model based on ColBERT v2 with [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) language model.<br> This model was trained with the OpenNQ data.<br> The architecture of the model and hyper parameters are described in the paper ‘Relevance-guided Supervision for OpenQA with ColBERT’. | 4048a24cbb0ce35c17ebd6ec17bfbbf2 |
apache-2.0 | [] | false | BibTeX entry and citation info ```bibtex @article{Khattab2021RelevanceguidedSF, title={Relevance-guided Supervision for OpenQA with ColBERT}, author={O. Khattab and Christopher Potts and Matei A. Zaharia}, journal={Transactions of the Association for Computational Linguistics}, year={2021}, } ``` ```bibtex @article{Lee2019LatentRF, title={Latent Retrieval for Weakly Supervised Open Domain Question Answering}, author={Kenton Lee and Ming-Wei Chang and Kristina Toutanova}, journal={ACL}, year={2019} } ``` | a135210ceae5a5b6cae45e5b035de619 |
apache-2.0 | ['generated_from_trainer'] | false | distilBERT_token_itr0_0.0001_all_01_03_2022-15_22_12 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2811 - Precision: 0.3231 - Recall: 0.5151 - F1: 0.3971 - Accuracy: 0.8913 | e90e8bcbd06ffaeae98837b1de4a90aa |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | c3b2a20470734e17ec0d91f0884c3c70 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 30 | 0.2881 | 0.2089 | 0.3621 | 0.2650 | 0.8715 | | No log | 2.0 | 60 | 0.2500 | 0.2619 | 0.3842 | 0.3115 | 0.8845 | | No log | 3.0 | 90 | 0.2571 | 0.2327 | 0.4338 | 0.3030 | 0.8809 | | No log | 4.0 | 120 | 0.2479 | 0.3051 | 0.4761 | 0.3719 | 0.8949 | | No log | 5.0 | 150 | 0.2783 | 0.3287 | 0.4761 | 0.3889 | 0.8936 | | 3900ef71352b4b23a4e3712cd4d2bc75 |
apache-2.0 | ['translation'] | false | opus-mt-bi-sv * source languages: bi * target languages: sv * OPUS readme: [bi-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/bi-sv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/bi-sv/opus-2020-01-20.zip) * test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/bi-sv/opus-2020-01-20.test.txt) * test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/bi-sv/opus-2020-01-20.eval.txt) | 16edafa117c4811bdd20116a452c2f77 |
mit | ['gpt_neo', 'code_synthesis'] | false | GPT-Neo-125M-APPS > **Please refer to our new [GitHub Wiki](https://github.com/ncoop57/gpt-code-clippy/wiki) which documents our efforts in detail in creating the open source version of GitHub Copilot** | ddca66f116a6774dfa64e6c209568fc5 |
mit | ['gpt_neo', 'code_synthesis'] | false | Training data The model is trained on the [Automated Programming Progress Standard (APPS) dataset](https://github.com/hendrycks/apps). The dataset consists of 10,000 coding problems in total, with 131,836 test cases for checking solutions and 232,444 ground-truth solutions written by humans. Problems can be complicated, as the average length of a problem is 293.2 words. The data are split evenly into training and test sets, with 5,000 problems each. | 76e490221b0aa0ef5ee45b51973f3cea |
mit | ['gpt_neo', 'code_synthesis'] | false | Training procedure The training script used to train this model can be found [here](https://github.com/ncoop57/gpt-code-clippy/blob/camera-ready/training/run_clm_apps.py). Training is done for 5 epochs using AdamW optimizer and leaner decay learning rate schedule with 800 warmup steps. To reproduce the training one can use this command with the above script: ```bash python run_clm_apps.py \ --output_dir $HOME/gpt-neo-125M-apps \ --model_name_or_path EleutherAI/gpt-neo-125M \ --dataset_name $HOME/gpt-code-clippy/data_processing/apps.py \ --dataset_config_name formatted \ --do_train --do_eval \ --block_size="1024" \ --per_device_train_batch_size="16" \ --per_device_eval_batch_size="16" \ --preprocessing_num_workers="16" \ --learning_rate="8e-5" \ --warmup_steps="800" \ --adam_beta1="0.9" \ --adam_beta2="0.98" \ --weight_decay="0.1" \ --overwrite_output_dir \ --num_train_epochs="5" \ --logging_steps="50" \ --eval_steps="2000" \ --report_to="wandb" \ --dtype="bfloat16" \ --save_strategy epoch \ --gradient_accumulation_steps 2 \ ``` | 71972a34885a666a9286180b1db34fd0 |
mit | ['gpt_neo', 'code_synthesis'] | false | How to use You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run: ```py from transformers import AutoModelForCausalLM, AutoTokenizer, FlaxAutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("flax-community/gpt-neo-125M-apps") tokenizer = AutoTokenizer.from_pretrained("flax-community/gpt-neo-125M-apps") prompt = """ A function to greet user. Given a user name it should say hello def greet(name): ANSWER: """ input_ids = tokenizer(prompt, return_tensors='pt').input_ids.to(device) start = input_ids.size(1) out = model.generate(input_ids, do_sample=True, max_length=50, num_beams=2, early_stopping=True, eos_token_id=tokenizer.eos_token_id, ) print(tokenizer.decode(out[0][start:])) ``` | 81cba947da4a7b8cf87137a84893ae15 |
apache-2.0 | ['generated_from_trainer'] | false | KoT5-test-add-data-from5ep This model is a fine-tuned version of [hyorea1/KoT5-test](https://huggingface.co/hyorea1/KoT5-test) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1737 - Rouge1: 11.8294 - Rouge2: 3.2314 - Rougel: 11.7891 - Rougelsum: 11.8237 - Gen Len: 35.2824 | 805464a086deceff3418ea499d91cae7 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 100 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 | 6e71e36de6d1eed9ddc9594c935f2c3b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 1.9029 | 0.16 | 400 | 1.1695 | 12.8243 | 3.2659 | 12.7542 | 12.8276 | 35.5743 | | 1.7971 | 0.32 | 800 | 1.1646 | 12.259 | 3.0668 | 12.1254 | 12.1927 | 35.2353 | | 1.4396 | 0.48 | 1200 | 1.1681 | 12.1151 | 3.1908 | 11.9507 | 12.0305 | 35.3125 | | 1.0945 | 0.64 | 1600 | 1.1703 | 12.0576 | 2.9688 | 11.9292 | 11.9792 | 35.0926 | | 1.1924 | 0.8 | 2000 | 1.1667 | 11.7835 | 2.9605 | 11.6755 | 11.7318 | 35.3596 | | 1.3711 | 0.97 | 2400 | 1.1668 | 11.9873 | 3.1107 | 11.9369 | 12.0207 | 34.5309 | | 1.6031 | 1.13 | 2800 | 1.1673 | 11.6049 | 3.1121 | 11.5527 | 11.5976 | 34.6551 | | 1.5254 | 1.29 | 3200 | 1.1693 | 11.6803 | 2.8527 | 11.6116 | 11.6829 | 34.8066 | | 1.641 | 1.45 | 3600 | 1.1737 | 11.8294 | 3.2314 | 11.7891 | 11.8237 | 35.2824 | | ec4bbd7812afdd7439b5ec26c6a9d05c |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-wnli This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.6950 - Accuracy: 0.5493 | 57b8527a2929891195aa170d160d2d04 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 40 | 0.6929 | 0.5211 | | No log | 2.0 | 80 | 0.6951 | 0.4789 | | No log | 3.0 | 120 | 0.6950 | 0.5493 | | No log | 4.0 | 160 | 0.6966 | 0.5352 | | No log | 5.0 | 200 | 0.6966 | 0.5352 | | f509b77ad8407963987b680c5e0b18a0 |
apache-2.0 | ['automatic-speech-recognition', 'ar'] | false | exp_w2v2t_ar_unispeech-sat_s504 Fine-tuned [microsoft/unispeech-sat-large](https://huggingface.co/microsoft/unispeech-sat-large) for speech recognition using the train split of [Common Voice 7.0 (ar)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 9f244147cbcddbfa3e94486d231322a4 |
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