license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
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mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:| | 1.2699 | 1.0 | 59 | 1.1005 | 0.2718 | 0.5214 | 0.3573 | | 1.0852 | 2.0 | 118 | 0.8127 | 0.6403 ... | 5eb461b2296832dba1253fc90492a8e1 |
mit | [] | false | abstract concepts on Stable Diffusion This is the `<art-style>` 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 ca... | 95940335260c2e117022a367975f31dd |
apache-2.0 | ['automatic-speech-recognition', 'et'] | false | exp_w2v2t_et_vp-sv_s807 Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (et)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | 277629911ea4301be1eba2df0d2541e9 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Swedish Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Swedish using the [Common Voice](https://huggingface.co/datasets/common_voice) and parts for the [NST Swedish ASR Database](https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb... | 750fbffff8536769aedc924148734aa6 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "sv-SE", split="test[:2%]") | 0748b698af81abbf40f286544fd3bec6 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | TODO: replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site. processor = Wav2Vec2Processor.from_pretrained("vasilis/wav2vec2-large-xlsr-53-swedish") | 29b08a50cb65aad0e2331f80c3745372 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic` model = Wav2Vec2ForCTC.from_pretrained("vasilis/wav2vec2-large-xlsr-53-swedish") | 49e8788648fd72a97276e561c730c877 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic` resampler = torchaudio.transforms.Resample(48_000, 16_000) | dca00825ba1294890be8fb063c789a81 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Swedish test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "sv-SE", split="test") ... | 840521c213a91ddf59a5704d8c20c425 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | TODO: adapt this list to include all special characters you removed from the data resampler = { 48_000: torchaudio.transforms.Resample(48_000, 16_000), 44100: torchaudio.transforms.Resample(44100, 16_000), 32000: torchaudio.transforms.Resample(32000, 16_000) } | 8c41e905356e03e862c05e3dad631f34 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler[sampling_rate](speech_array).squeeze().numpy() return b... | 56e347285f991855877a5cdea2bfe26f |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.a... | 817787681fb1f89149c6f95301ea2277 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Training As first step used Common Voice train dataset and parts from NST as can be found [here](https://github.com/se-asr/nst/tree/master). Part of NST where removed using this mask ```python mask = [(5 < len(x.split()) < 20) and np.average([len(entry) for entry in x.split()]) > 5 for x in dataset['transcript'].tol... | 5a46d55e3359b0dc477f3d7b2162a978 |
apache-2.0 | ['question-answering', 'generated_from_trainer'] | false | Training and evaluation data Training: BioASQ 10B with SQUAD sampled evenly to match the same samples as BioASQ 10B Eval: BioASQ 9B Eval with SQUAD Eval sampled evenly to match the same samples as BioASQ 9B Eval | 30cae63b6d692cca83d43071aa83917c |
apache-2.0 | ['question-answering', 'generated_from_trainer'] | false | Training results Went from untrained exact match: 60.9% (f1 71.8%) to exact match: 95.2% (96.6% f1) on BioASQ 9B held out training set. Scores on SQUAD+BioASQ remained stable at exact match: 72.5% (f1 81.4%) to 88.5% (f1 93.3%). | 9c880356324d8d70307234f2f034e42c |
apache-2.0 | ['azureml', 't5', 'summarization', 'deepspeed'] | false | `t5-large-samsum-deepspeed` This model was trained using Microsoft's `AzureML` and `DeepSpeed`'s ZeRO 2 optimization. It was fine-tuned on the `SAMSum` corpus from `t5-large` checkpoint. More information on the fine-tuning process (includes samples and benchmarks): *(currently still WIP, major updates coming soon: ... | 95a7949426c3b8f0d28b95b3e6e7141c |
apache-2.0 | ['azureml', 't5', 'summarization', 'deepspeed'] | false | Resource Usage These results are retrieved from AzureML Studio's resource monitoring module. All experiments were ran on AzureML's low priority clusters. | key | value | | --- | ----- | | AzureML SKU | ND40rs_v2 (8 X V100 32GB) | | Region | US West 2 | | Run Duration | 12m 47.13s | | Compute Cost (LowPriority/Dedicat... | 639a9daf0496e3c73f8cbf48d71dd756 |
apache-2.0 | ['azureml', 't5', 'summarization', 'deepspeed'] | false | Carbon Emissions These results are obtained using `codecarbon`. The carbon emission is estimated from training runtime only (excluding setup and evaluation runtime). CodeCarbon: https://github.com/mlco2/codecarbon | key | value | | --- | ----- | | timestamp | 2021-07-08T06:29:27 | | duration | 515.5018835067749 |... | 48fbe36996c90137873aa3145b894575 |
apache-2.0 | ['azureml', 't5', 'summarization', 'deepspeed'] | false | DeepSpeed Optimizer = `AdamW`, Scheduler = `WarmupDecayLR`, Offload = `none` ```json "zero_optimization": { "stage": 2, "allgather_partitions": true, "allgather_bucket_size": 1300000000, "overlap_comm": true, "reduce_scatter": true, "reduce_bucket_size": 1300000000, "contiguous_gradients"... | 47e58e6bb798b02a85534ee529017f59 |
apache-2.0 | ['azureml', 't5', 'summarization', 'deepspeed'] | false | Usage ```python from transformers import pipeline summarizer = pipeline("summarization", model="henryu-lin/t5-large-samsum-deepspeed") conversation = '''Kevin: Hey man, are you excited to watch Finding Nemo tonight? Henry: Yea, I can't wait to watch that same movie for the 89th time. Is Nate coming over to watch ... | c0afaaeb38a1379d1c9590b4f6f66f0c |
apache-2.0 | ['azureml', 't5', 'summarization', 'deepspeed'] | false | Results | ROUGE | Score | | ----- | ----- | | eval_rouge1 | 53.0823 | | eval_rouge2 | 28.7097 | | eval_rougeL | 43.939 | | eval_rougeLsum | 49.067 | | predict_rouge1 | 51.6716 | | predict_rouge2 | 26.5372 | | predict_rougeL | 42.9681 | | predict_rougeLsum | 47.4084 | | Metric | Value | | ------ | ----- | | eval_gen_l... | 7de1589dadf9f2bef274d3994958d419 |
mit | ['GPT-2', 'Spanish', 'review', 'fake'] | false | Model description GPT-2 is a transformers model pretrained on a very large corpus of text data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inp... | 1d44d8cffc2814457239c250e6aa67cc |
mit | ['GPT-2', 'Spanish', 'review', 'fake'] | false | How to use You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility: ```python from transformers import pipeline, set_seed generator = pipeline('text-generation', model='Amloii/gpt2-reviewspanish', ... | 4b61ed667536bc11f3b746f3efbd408c |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Medium Greek - Robust This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0 el dataset. It achieves the following results on the evaluation set: - Loss: 0.2807 - Wer: 17.7099 **IMPORTANT** The model has been tra... | c0b7308dc7e678a0875d85a0f7bab442 |
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: 8 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 20000 - mixed_precisi... | dbe49dbc52b75c06285867b675277c0a |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 0.0407 | 4.69 | 2000 | 0.2484 | 20.8767 | | 0.0128 | 9.39 | 4000 | 0.2795 | 21.2017 | | 0.0041 | 14.08 | 6000 | 0.2744 | 1... | bfb52cd2347c11fadb20171fe212f197 |
mit | ['text-classication'] | false | WTWM Newsroom Mentions Detector Please node that this model originates from the ["What's there, what's missing"](https://interaktiv.br.de/ai-detect-newsroom-mentions-in-comments/) collaboration of [AI & Automation Labl of Bayerischer Rundfunk (BR hereafter)](https://www.br.de/extra/ai-automation-lab/index.html) and [... | c50d12ef7818840929121ea800c57333 |
mit | ['text-classication'] | false | The task This is a model for the task of classifying whether or not a articles comment addresses the moderation team/authors of the media house that published the article. In this prototype stage the media houses are Bayerischer Rundfunk and Mitteldeutscher Rundfunk. This classification task is implemented as a bina... | 6c80b72ad8c1fea467663e6c03f1d3a0 |
mit | ['text-classication'] | false | Dataset & preprocessing This model was finetuned on a corpus of 18.860 user comments with a share of user comments from BR and mdr websites and social media channels. The ratio of comments without mentions and with mentions is 92% to 8%. With the initial annotated data the share of comments with mentions was 2% of th... | 0d860bcf953cf0f9ecb8b08aa0c0074b |
mit | ['text-classication'] | false | Training After multiple test runs of finetuning the present model was further trained using the following parameters: - foundation_model: [german-gpt2](https://huggingface.co/dbmdz/german-gpt2) - num_train_epochs: 4 - learning_rate: 2e-7 - weight_decay: 0.1 - metric_for_best_model: precision | 55c330bc67642d13d1a1f7c737a68b96 |
mit | ['text-classication'] | false | Example: Direct model evaluation ```python from transformers import ( AutoModelForSequenceClassification, AutoTokenizer, pipeline, ) comment = "The preprocessed comment to classify" tokenizer = AutoTokenizer.from_pretrained(model_path) tokenizer.pad_token = tokenizer.eos_token model = AutoModelForSequen... | 75bbe45da19e37e9c712ee8e37c1fe77 |
mit | ['text-classication'] | false | Limitations Clearly, the amount of training data was to small for a state of the art result. This can be seen in the evaluation chapter. Future rounds of retraining have to be performed. For the sake of completeness we publish this model here within [the projects documentation](https://interaktiv.br.de/ai-detect-news... | 0f5a9ded1718e660c41a5581a7acbd26 |
mit | ['text-classication'] | false | Quantitative As a general training approach we decided to optimize for the precision of the detection of the mentions in comments. This strategy best fits the high speed moderation challenge the moderation team's faces in everyday work. Our goal is to focus their attention only to comments that are very likely to cont... | 630936aa9e84529b71ccb62b47e80ef0 |
mit | ['text-classication'] | false | Conclusion The qualitative evaluation of [this project](https://interaktiv.br.de/ai-detect-newsroom-mentions-in-comments/) makes us confident that the mediocre quantitative results can be overcome with a sufficiently large corpus and that the overall prototype of the project can be a usefull addition to comment moder... | 0f6ad1a30b378ff35486e2bd7182480b |
mit | ['text-classication'] | false | The fellowship [JournalismAI](https://www.lse.ac.uk/media-and-communications/polis/JournalismAI) is a project of [Polis](https://www.lse.ac.uk/media-and-communications/polis) – the journalism think-tank at the London School of Economics and Political Science – and it’s sponsored by the [Google News Initiative](https:... | ffd9eff5eab4fd8a11ddff65053f8775 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-for-tweet-sentiment 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: 0.2161 - Accuracy: 0.925 - F1: 0.9249 | a41878d4134846c96113b5f8aefb0103 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.3561 | 1.0 | 250 | 0.3072 | 0.9115 | 0.9098 | | 0.2195 | 2.0 | 500 | 0.2161 | 0.925 | 0.9249 | | cbe533e71c63e8a3502b0b91f3775bed |
apache-2.0 | ['tapas'] | false | TAPAS tiny model fine-tuned on Sequential Question Answering (SQA) This model has 2 versions which can be used. The default version corresponds to the `tapas_sqa_inter_masklm_tiny_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas). This model was pre-trained on MLM and an ... | ced5b64e1d8338c1b0a96d8fd3476960 |
apache-2.0 | ['tapas'] | false | Results on SQA - Dev Accuracy Size | Reset | Dev Accuracy | Link -------- | --------| -------- | ---- LARGE | noreset | 0.7223 | [tapas-large-finetuned-sqa (absolute pos embeddings)](https://huggingface.co/google/tapas-large-finetuned-sqa/tree/no_reset) LARGE | reset | 0.7289 | [tapas-large-finetuned-sqa](https... | 796e7cba377d907503aa0a1c58f938d0 |
apache-2.0 | ['tapas'] | false | Model description TAPAS is a BERT-like transformers model pretrained on a large corpus of English data from Wikipedia in a self-supervised fashion. This means it was pretrained on the raw tables and associated texts only, with no humans labelling them in any way (which is why it can use lots of publicly available da... | c26f29716a0bc714dad2ca20fe14edfc |
apache-2.0 | ['tapas'] | false | Intended uses & limitations You can use this model for answering questions related to a table in a conversational set-up. For code examples, we refer to the documentation of TAPAS on the HuggingFace website. | dde664609de7a045ba2b135cd67fb45b |
apache-2.0 | ['tapas'] | false | Preprocessing The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are then of the form: ``` [CLS] Question [SEP] Flattened table [SEP] ``` | 3cc20c3fdac32f32779c4978a8c2d968 |
apache-2.0 | ['tapas'] | false | Fine-tuning The model was fine-tuned on 32 Cloud TPU v3 cores for 200,000 steps with maximum sequence length 512 and batch size of 128. In this setup, fine-tuning takes around 20 hours. The optimizer used is Adam with a learning rate of 1.25e-5, and a warmup ratio of 0.2. An inductive bias is added such that the mod... | 6f31251ea16e760cf2e8f20d3cfa4b73 |
apache-2.0 | ['tapas'] | false | BibTeX entry and citation info ```bibtex @misc{herzig2020tapas, title={TAPAS: Weakly Supervised Table Parsing via Pre-training}, author={Jonathan Herzig and Paweł Krzysztof Nowak and Thomas Müller and Francesco Piccinno and Julian Martin Eisenschlos}, year={2020}, eprint={2004.02349}, ... | 4911e71f3e2b80d979bf463d04d80243 |
apache-2.0 | ['automatic-speech-recognition', 'uk'] | false | exp_w2v2t_uk_unispeech-sat_s335 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 (uk)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your spee... | bf8bd0493174d3f04101fdd6cfbe2fc4 |
apache-2.0 | ['translation'] | false | opus-mt-srn-es * source languages: srn * target languages: es * OPUS readme: [srn-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/srn-es/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | edad4332fa993825f1395fef7a60d742 |
creativeml-openrail-m | ['text-to-image'] | false | Duskfalls Artificial Photography Dreambooth model trained by Duskfallcrew with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/gi... | ea501b84267b9a9821bdb544f076fe86 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Estonian Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Estonian using the [Common Voice](https://huggingface.co/datasets/common_voice). When using this model, make sure that your speech input is sampled at 16kHz. | 2710644406b4ccd95ff8bc546a857133 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "et", split="test[:2%]") | 3487f6c5f634c232cc1e1d7921d8df96 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | TODO: replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site. processor = Wav2Vec2Processor.from_pretrained("vasilis/wav2vec2-large-xlsr-53-Estonian") | 9f3c80a35c8ff3333cbbcbb10bcf259c |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic` model = Wav2Vec2ForCTC.from_pretrained("vasilis/wav2vec2-large-xlsr-53-Estonian") | 62d21b0483e3d09bae1cdd630b64d3f3 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Estonian test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "et", split="test") we... | 9d98ebdc78e228e4c4a37f85a0d1c8e6 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.a... | d6f08ad4db9cae951cada1ed2f2f2981 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Training Common voice `train` and `validation` sets were used for finetuning for 20000 steps (approx. 116 epochs). Both the `feature extractor` (`Wav2Vec2FeatureExtractor`) and `feature projection` (`Wav2Vec2FeatureProjection`) layer were frozen. Only the `encoder` layer (`Wav2Vec2EncoderStableLayerNorm`) was finetu... | 254862b344961ee95a698f7cfab280e6 |
cc-by-4.0 | ['bert'] | false | bert-rand-base A BERT base Language Model with a **random** pre-training objective. For more details about the pre-training objective and the pre-training hyperparameters, please refer to [How does the pre-training objective affect what large language models learn about linguistic properties?](https://aclanthology.org... | f6a0ef2e169881176c858a7fdd44bd01 |
cc-by-4.0 | ['bert'] | false | Citation If you use this model, please cite the following paper: ``` @inproceedings{alajrami2022does, title={How does the pre-training objective affect what large language models learn about linguistic properties?}, author={Alajrami, Ahmed and Aletras, Nikolaos}, booktitle={Proceedings of the 60th Annual Meeting... | d21b4c354bdafc92218d066263f4d594 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Medium Japanese This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0 ja dataset. It achieves the following results on the evaluation set: - Loss: 0.2165 - Wer: 62.6897 | 1c584ea81efe10c1663f143bac5d3f1a |
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: 2 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precisio... | 3076442137a9c45aaee3f9eb7ca7873c |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.2264 | 0.2 | 1000 | 0.3102 | 79.3588 | | 0.3195 | 0.4 | 2000 | 0.2830 | 78.1955 | | 0.3905 | 0.6 | 3000 | 0.2508 | 72.918... | 001a63182bb04a59030bb9a1e0d13597 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 | b18a3294a680e82e287376afebacdba6 |
apache-2.0 | ['generated_from_keras_callback'] | false | javilonso/Mex_Rbta_Opinion_Polarity This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.4033 - Validation Loss: 0.5572 - Epoch: 1 | f9072257e6616fb94446e98a27089d46 |
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': 5986, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay':... | 0d436b1b87593d7d9f7d42006e332590 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-wnli-target-glue-qqp This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-wnli](https://huggingface.co/muhtasham/tiny-mlm-glue-wnli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4204 - Accuracy: 0.7892 - F1: 0.7460 | 23a3bcad616d5d4274e917eea72d96fe |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.5839 | 0.04 | 500 | 0.5193 | 0.7299 | 0.6543 | | 0.5179 | 0.09 | 1000 | 0.4861 | 0.7508 | 0.6874 | | 0.5047 |... | 5562df458a42ff84ec0d29f824db6e2d |
apache-2.0 | ['t5-small', 'text2text-generation', 'dialog state tracking', 'conversational system', 'task-oriented dialog'] | false | t5-small-dst-multiwoz21_sgd_tm1_tm2_tm3 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on [MultiWOZ 2.1](https://huggingface.co/datasets/ConvLab/multiwoz21), [Schema-Guided Dialog](https://huggingface.co/datasets/ConvLab/sgd), [Taskmaster-1](https://huggingface.co/datasets/ConvLab/t... | f1239ed90964ea1fab4eabea8d3d3af2 |
apache-2.0 | ['t5-small', 'text2text-generation', 'dialog state tracking', 'conversational system', 'task-oriented dialog'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - optimizer: Adafactor - lr_scheduler_type: linear - num_epochs: 10.0 | c07eb6abc8d3f14086f8c1264cf29953 |
apache-2.0 | ['generated_from_trainer'] | false | DepressionAnalysis 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: 0.4023 - Accuracy: 0.8367 | ff985c065050e21646eae794a9e7dce3 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 5 - mixed_precision_tra... | 5f4c71f9400204b2ccabc4fd6cbf3ca2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6091 | 1.0 | 151 | 0.5593 | 0.7082 | | 0.4041 | 2.0 | 302 | 0.4295 | 0.8055 | | 0.3057 | 3.0 | 453 | 0.4023 | 0.... | 35d716a9dcd95538651f81bba9af3f23 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small Vietnamese 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 vi dataset. It achieves the following results on the evaluation set: - Loss: 0.8001 - Wer: 27.7034 | 81edfaeddd42107e3d56db481a293e33 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0002 | 124.0 | 1000 | 0.8001 | 27.7034 | | 0.0001 | 249.0 | 2000 | 0.8835 | 33.8561 | | 0.0 | 374.0 | 3000 | 0.9383 | 36.038... | 6d044a0ff14061aa85855da202b63552 |
agpl-3.0 | ['generated_from_trainer'] | false | XLMR-ENIS-finetuned-ner This model is a fine-tuned version of [vesteinn/XLMR-ENIS](https://huggingface.co/vesteinn/XLMR-ENIS) on the mim_gold_ner dataset. It achieves the following results on the evaluation set: - Loss: 0.0891 - Precision: 0.8804 - Recall: 0.8517 - F1: 0.8658 - Accuracy: 0.9837 | 111ba7fc424f0d7e83e1dd0292b973b9 |
agpl-3.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0573 | 1.0 | 2904 | 0.1024 | 0.8608 | 0.8003 | 0.8295 | 0.9799 | | 0.0307 | 2.0 |... | c703bd091fdd62a164156ac056a5d1e4 |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-TINY-NH32 (Deep-Narrow version) T5-Efficient-TINY-NH32 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 an... | cfd22f4d933034f1e5b42d503dd0a01e |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-tiny-nh32** - is of model type **Tiny** with the following variations: - **nh** is **32** It has **37.6** million parameters and thus requires *ca.* **150.41 MB** of memory in full precision (*fp32*) or **75.2 MB** of memory in half precision (*fp16... | 2fa76070eaaa9f22ee0ebd5f384e41a4 |
mit | ['generated_from_keras_callback'] | false | botModel77k_sequence_weightDecay This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.0033 - Validation Loss: 1.6919 - Epoch: 24 | c80fe2664019fb93eb6cf9472769d944 |
mit | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps... | 962ab8138f91c9e2a0e5a20fbe86933c |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 10.2991 | 8.7086 | 0 | | 8.0817 | 7.3113 | 1 | | 7.2128 | 6.7962 | 2 | | 6.7796 | 6.3600 | 3 | | 6.1967 | 5.6402 | 4 | | 5.4418 |... | 9122ce281940e4871a5759c3d9709e12 |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | roberta-base-academic This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on a combination of Elsevier OA CC-by dataset and other corpora of university essays such as [BAWE](https://www.coventry.ac.uk/research/research-directories/current-projects/2015/british-academic-written-en... | 69fe549d3af910d6cf01f64d006edf2f |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.99) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | 94e1da75207056d61918f475db51a1ad |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.671 | 1.0 | 338 | 1.5581 | | 1.6395 | 1.99 | 676 | 1.5276 | | 1.5991 | 2.99 | 1014 | 1.5108 | | 1.5659 | 3.99 | 1352 | 1.4903 ... | 3788a3b7802cfa06ff3937a3c6d3fcd1 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 256 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon... | 92af608f826232f78de83cb926418082 |
apache-2.0 | ['ner', 'ncbi', 'disease', 'pubmed', 'bioinfomatics'] | false | NER to find Gene & Gene products > The model was trained on ncbi-disease, BC5CDR dataset, pretrained on this [pubmed-pretrained roberta model](/raynardj/roberta-pubmed) All the labels, the possible token classes. ```json {"label2id": { "O": 0, "Disease":1, } } ``` Notice, we removed the 'B-','I-' etc from... | 5dac6f60c1f2345738a9831776938ecc |
apache-2.0 | ['ner', 'ncbi', 'disease', 'pubmed', 'bioinfomatics'] | false | This is the template we suggest for using the model ```python from transformers import pipeline PRETRAINED = "raynardj/ner-disease-ncbi-bionlp-bc5cdr-pubmed" ner = pipeline(task="ner",model=PRETRAINED, tokenizer=PRETRAINED) ner("Your text", aggregation_strategy="first") ``` And here is to make your output more consecu... | 7816e02bd71cc95db536d21a726b4fc1 |
apache-2.0 | ['ner', 'ncbi', 'disease', 'pubmed', 'bioinfomatics'] | false | make to sub group by position for output in outputs: if output["index"]-1==last_idx: current.append(output) else: results.append(current) current = [output, ] last_idx = output["index"] if len(current)>0: results.append(current) | daf7dd4d06a27544644d6901a0b1363e |
apache-2.0 | ['ner', 'ncbi', 'disease', 'pubmed', 'bioinfomatics'] | false | from tokens to string strings = [] for c in results: tokens = [] starts = [] ends = [] for o in c: tokens.append(o['word']) starts.append(o['start']) ends.append(o['end']) new_str = tokenizer.convert_tokens_to_string(tokens) if... | 7ff365159bcaa9a44ff7404eadc06438 |
apache-2.0 | ['ner', 'ncbi', 'disease', 'pubmed', 'bioinfomatics'] | false | will return a dataframe entity_table(ner)(YOUR_VERY_CONTENTFUL_TEXT) ``` > check our NER model on * [gene and gene products](/raynardj/ner-gene-dna-rna-jnlpba-pubmed) * [chemical substance](/raynardj/ner-chemical-bionlp-bc5cdr-pubmed). * [disease](/raynardj/ner-disease-ncbi-bionlp-bc5cdr-pubmed) | fddfe741701fc035011dc05fa4e5d903 |
mit | [] | false | Neon Pastel on Stable Diffusion This is the `<neon-pastel>` style 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... | fd6e6bfad6b96a974598c49d524a4757 |
mit | ['generated_from_trainer'] | false | bert-base-portuguese-cased_harem-sm-first-ner This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the harem dataset. It achieves the following results on the evaluation set: - Loss: 0.1952 - Precision: 0.7456 - Recall: 0.8053 -... | b77d629a44f0c6c6f615dc9fc5f787ee |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 | b22194ef31df40b8443d6ef6c0701853 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1049 | 1.0 | 2517 | 0.1955 | 0.6601 | 0.7710 | 0.7113 | 0.9499 | | 0.0622 | 2.0 |... | f1af6167862ee54ab5dd07bd0a42f1ed |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2t_es_hubert_s251 Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is... | 1b7d6c31697526d2508e8ba029472893 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.7773 - Accuracy: 0.9152 | 61d2c355bdd240d4d15bf94d81ab8a3f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 4.293 | 1.0 | 318 | 3.2831 | 0.7432 | | 2.6252 | 2.0 | 636 | 1.8743 | 0.8310 | | 1.5406 | 3.0 | 954 | 1.1575 | 0.... | 5899e3c06c2c8eca7d50c1c08609ad86 |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0453 - Precision: 0.9275 - Recall: 0.9492 - F1: 0.9382 - Accuracy: 0.9934 | f164649c49e5d89119a31550b8883b3c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 407 | 0.0539 | 0.8283 | 0.8758 | 0.8514 | 0.9866 | | 0.1524 | 2.0 ... | 5cf53497b8376f86580d869aa3a8945c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.2823 | 1.0 | 2767 | 1.1980 | | 1.0336 | 2.0 | 5534 | 1.1334 | | 0.8513 | 3.0 | 8301 | 1.1476 | | 3dae68a5e5dab84d4218d26d9ad4b327 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_s... | 4ad5f5819cd30ba7829b359108a12cde |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 3.8623 | 3.12 | 100 | 1.7653 | | 1.6403 | 6.24 | 200 | 1.5635 | | 1.5806 | 9.37 | 300 | 1.5326 | | 1.5433 | 12.49 | 400 | 1.4568 ... | 88cc4e20f0aac9de40ddf91e05f7c724 |
bsd-3-clause | [] | false | Model description CodeGen is a family of autoregressive language models for **program synthesis** from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The models a... | 8b787f517e44b2937b412f8b3f3df89b |
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