license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
apache-2.0 | ['generated_from_trainer'] | false | bert-large-uncased_cls_sst2 This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3787 - Accuracy: 0.9255 | 455ffbff5afefb8e6b55b6eb7540e624 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 433 | 0.4188 | 0.8578 | | 0.3762 | 2.0 | 866 | 0.4894 | 0.8968 | | 0.3253 | 3.0 | 1299 | 0.3313 | 0.... | 26346d0e2e3039a4101607839fcebcad |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1457 - F1: 0.8665 | 87e5ad85e2000a3fe238f8e2ad82bce8 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2537 | 1.0 | 1049 | 0.1758 | 0.8236 | | 0.1335 | 2.0 | 2098 | 0.1442 | 0.8494 | | 0.0811 | 3.0 | 3147 | 0.1457 | 0.8665 | ... | e5a60392a3451b1cf23ea080a63bc1e5 |
mit | [] | false | model by thesun1094224 This your the Stable Diffusion model fine-tuned the yakuza-0-kiryu-kazuma concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **photo of sks kiryu** You can also train your own concepts and upload them to the library by using [this notebook](ht... | 6f2f0e5291df248ce13ec9074f65d1d1 |
apache-2.0 | ['generated_from_keras_callback'] | false | avialfont/dummy-translation-marian-kde4-en-to-fr This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.9807 - Validation Loss: 0.8658 - Epoch: 0 | 98ac924799f7458112b4fb52552d3b5f |
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': 5e-05, 'decay_steps': 17733, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay'... | 1f15c866833bee3df83839a68c4bb814 |
apache-2.0 | [] | false | Pretrained models for our paper (https://arxiv.org/abs/2210.08431) ```bibtex @inproceedings{wu-etal-2022-modeling, title = "Modeling Context With Linear Attention for Scalable Document-Level Translation", author = "Zhaofeng Wu and Hao Peng and Nikolaos Pappas and Noah A. Smith", booktitle = "Findings of th... | 7992128ac3e3d8275176c60d88bca179 |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2r_es_vp-100k_accent_surpeninsular-5_nortepeninsular-5_s324 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_... | 18e8f65a1b4fca2f3fce60d3d84cda9c |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | Zu-En_update This model is a fine-tuned version of [kabelomalapane/model_zu-en_updated](https://huggingface.co/kabelomalapane/model_zu-en_updated) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9399 - Bleu: 27.9608 | d733d2f4c0b475392d50c4acf58e50dd |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 2.1017 | 1.0 | 1173 | 1.8404 | 29.1031 | | 1.7497 | 2.0 | 2346 | 1.8318 | 28.9036 | | 1.523 | 3.0 | 3519 | 1.8250 | 2... | c4c6e9be77e4a5a197f9bc917dcbebdc |
apache-2.0 | ['vision', 'image-classification'] | false | ConvNeXT (large-sized model) ConvNeXT model trained on ImageNet-22k at resolution 224x224. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt). Disclaimer: The team releasing C... | a7ae0b6592d266ff7f10981c57e8de99 |
apache-2.0 | ['vision', 'image-classification'] | false | How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import ConvNextFeatureExtractor, ConvNextForImageClassification import torch from datasets import load_dataset dataset = load_dataset("huggingface/cats-image") i... | 44af6d816b746581cb9e6dbc506191dd |
apache-2.0 | ['generated_from_trainer'] | false | CR_ELECTRA_5E This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3778 - Accuracy: 0.9133 | d2cdda44b9b5d935750ca5915d82510e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.643 | 0.33 | 50 | 0.6063 | 0.66 | | 0.5093 | 0.66 | 100 | 0.4277 | 0.84 | | 0.2986 | 0.99 | 150 | 0.3019 | 0.... | 8a3424fb7c6d062cc33cd17b24ea347e |
apache-2.0 | [] | false | Model Description This model accepts as input un-punctuated, lower-case text and performs the following analytics: * Punctuation restoration * True-casing * Sentence-boundary detection. Quick example of input/output: ``` Input: hello world what's up Outputs: Hello, World. What's up? ``` (note that, technically,... | 236b55d669e5240f3753c8f5619fd7f5 |
apache-2.0 | [] | false | Usage This model is driven by code that lives in a personal branch of NeMo. This branch must be installed to run this model. If you have NeMo and the Github CLI installed, the code can be pulled from the corresponding pull request: ```shell $ cd /path/to/nemo/ $ gh pr checkout 4637 ``` Or to pull the full codebase... | d011f19d1638c4f7e65da797198ef049 |
apache-2.0 | [] | false | Example Input ``` from typing import List import nemo.collections.nlp as nemo_nlp hf_model_name = "1-800-BAD-CODE/punct_cap_seg_22lang1pass_xlmr" hf_model = nemo_nlp.models.PunctCapSegModel.from_pretrained(hf_model_name, map_location="cpu") | 3b1624a64553405c0c1de5c2fb7ac93a |
apache-2.0 | [] | false | function expects a batch. def print_inputs_outputs(inputs: List[str], outputs: List[List[str]]): for next_input, next_outputs in zip(inputs, outputs): print(f"Input: {next_input}") print(f"Outputs: ") for output_text in next_outputs: print(f" {output_text}") print() | ece469e0a8962104c09e4ab3a2c1d2e4 |
apache-2.0 | [] | false | For Spanish, the model should predict inverted punctuation as well. input_texts = ["no he perdonado ni a ti ni a maria por qué no juegas conmigo"] output_texts = hf_model.infer(input_texts) print_inputs_outputs(input_texts, output_texts) | fb88e2e775c597931b35fd86f09172de |
apache-2.0 | [] | false | Acronyms should be punctuated and true-cased, as well. Ideally, sentence boundaries will not be predicted at these characters. input_texts = [ "george w bush was the president of the us for 8 years he left office in january 2009 and was succeeded by barack " "obama prior to his presidency he was the governor o... | b2d0ffe510e4b126b9ee79c850e5b285 |
apache-2.0 | [] | false | Large blob of text from LibriSpeech dev clean input_texts = [ "ardent in the prosecution of heresy cyril auspiciously opened his reign by oppressing the novatians the most innocent and harmless of the sectaries without any legal sentence without any royal mandate the patriarch at the dawn of day led a seditious mu... | f84b68c622aff9af4c6a03f9f438ec8c |
apache-2.0 | [] | false | Expected output (sans logging): ``` Input: i saw you at the park yesterday what were you doing there Outputs: I saw you at the park yesterday. What were you doing there? Input: رأيتك في الحديقة أمس ماذا كنت تفعل هناك Outputs: رأيتك في الحديقة أمس؟ ماذا كنت تفعل هناك؟ Input: video sam te juče u parku šta s... | dfe63ddc647f3a4c1a8b5e3445f22556 |
apache-2.0 | [] | false | Training data This model was trained on a subset of [News Crawl](https://data.statmt.org/news-crawl/). Data was filtered using heuristics to provide clean training data for these particular analytics, e.g., whether each line ended with a full stop, whether the first character was capitalized, etc. | 77a83c9d37766b6397912f58fbe9c8ed |
apache-2.0 | [] | false | Limitations * This model was trained on news data, so it may perform worse on informal or conversational data. * This model was trained with limited hardware, and was not trained to convergence. It was trained for ~100k steps with a global batch size of 64. * This model was trained on a maximum sequence length of 80... | bb69bfe71e2524a17653d6756f13f4a8 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1080 - Precision: 0.9631 - Recall: 0.9740 - F1: 0.9685 - Accuracy: 0.9774 | 09c7a0aa2ef388a82d8e3f19625b982e |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-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: 14 | d8c838228dc4c6921e399e61541ac6b4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1714 | 1.0 | 1492 | 0.1178 | 0.9209 | 0.9470 | 0.9337 | 0.9613 | | 0.1018 | 2.0 ... | 8683d89f9eb7ac402f0c67e7c6f18263 |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | finetuned_HelsinkiNLP-opus-mt-en-vi_PhoMT This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-vi](https://huggingface.co/Helsinki-NLP/opus-mt-en-vi) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3069 - Bleu: 42.4251 | 20f8e558b43bdadbe47377b971c6d30d |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | |:-------------:|:-----:|:------:|:---------------:|:-------:| | 1.4437 | 1.0 | 186125 | 1.3648 | 40.6353 | | 1.3748 | 2.0 | 372250 | 1.3362 | 41.4991 | | 1.3182 | 3.0 | 558375 | 1.3224 ... | aef4e2f4502a265e7d8e791de33f0b75 |
apache-2.0 | ['whisper-event'] | false | Whisper Telugu Tiny This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Telugu data available from multiple publicly available ASR corpuses. It has been fine-tuned as a part of the Whisper fine-tuning sprint. | 0d0b2c94d1f813b10ab4ce26713e8245 |
apache-2.0 | ['whisper-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 88 - eval_batch_size: 88 - seed: 22 - optimizer: adamw_bnb_8bit - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 15000 - training_steps: 14652 (terminated upon convergence. Initially se... | b117a7a837ebde060f76a2b8bd8eb62d |
mit | [] | false | M2M100 418M M2M100 is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation. It was introduced in this [paper](https://arxiv.org/abs/2010.11125) and first released in [this](https://github.com/pytorch/fairseq/tree/master/examples/m2m_100) repository. The model that can d... | cef57e996aea6ab2ad9761ccf3c29f00 |
apache-2.0 | ['translation'] | false | opus-mt-tr-en * source languages: tr * target languages: en * OPUS readme: [tr-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/tr-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | c320b8b300613c9f9cf3e74cf16477c8 |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newsdev2016-entr.tr.en | 27.6 | 0.548 | | newstest2016-entr.tr.en | 25.2 | 0.532 | | newstest2017-entr.tr.en | 24.7 | 0.530 | | newstest2018-entr.tr.en | 27.0 | 0.547 | | Tatoeba.tr.en | 63.5 | 0.760 | | 95129b4e653f75682ab9805fb2cb42bb |
apache-2.0 | ['generated_from_trainer'] | false | recipe-distilbert-upper-Is 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: 0.8565 | e8b635ed8946edbfebe5b71bc13d0f00 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.6309 | 1.0 | 1305 | 1.2607 | | 1.2639 | 2.0 | 2610 | 1.1291 | | 1.1592 | 3.0 | 3915 | 1.0605 | | 1.0987 | 4.0 | 5220 | 1.0128 ... | 1f31497c629052cb6179a2f59edc87c1 |
apache-2.0 | ['text-classification', 'generated_from_trainer'] | false | distilroberta-base-mrpc-glue-oscar-salas7 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the datasetX dataset. It achieves the following results on the evaluation set: - Loss: 1.7444 - Accuracy: 0.2143 | a270fc9fd3be7d13aaecdd6d905df03d |
apache-2.0 | ['generated_from_trainer'] | false | swin-tiny-patch4-window7-224-finetuned-woody_130epochs This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.4550 - Accuracy: 0.8921 | 044a2b0b7e6c37bdde125907e7e56fd4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6694 | 1.0 | 58 | 0.6370 | 0.6594 | | 0.6072 | 2.0 | 116 | 0.5813 | 0.7030 | | 0.6048 | 3.0 | 174 | 0.5646 | 0.... | 39d23384e7f98ade4c193896a91035ac |
apache-2.0 | ['translation'] | false | opus-mt-sv-id * source languages: sv * target languages: id * OPUS readme: [sv-id](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-id/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | c078515adedcdb0128a3015acd5a9f3e |
apache-2.0 | [] | false | Unam_tesis_ROBERTA_GOB_finnetuning: Unam's thesis classification with PlanTL-GOB-ES/roberta-large-bne
This model is created from the finetuning of the pre-model
for RoBERTa large trained with data from the National Library of Spain (BNE) [
PlanTL-GOB-ES] (https://huggingface.co/PlanTL-GOB-ES/roberta-large-bne),... | 1505c1377ceefa9c907938c03f56761d |
apache-2.0 | [] | false | Training Dataset
1000 documents (Thesis introduction, Author´s first name, Author´s last name, Thesis title, Year, Career )
| Careers | Size |
|--------------|----------------------|
| Actuaría | 200 |
| Derecho| 200 |
| Economía| 200 |
| Psicología| 200 |
| Quí... | 34301f06514589cc52c1ec02e3eaa6a4 |
apache-2.0 | [] | false | Example of use
For further details on how to use unam_tesis_ROBERTA_GOB_finnetuning you can visit the Huggingface Transformers library, starting with the Quickstart section. Unam_tesis models can be accessed simply as 'hackathon-pln-e/unam_tesis_beto_finnetuning' by using the Transformers library. An example of how... | 34500adb6cd7fb22274c9d65db61c44f |
apache-2.0 | [] | false | Citation
[UNAM's Tesis with PlanTL-GOB-ES/roberta-large-bne ](https://huggingface.co/hackathon-pln-es/unam_tesis_ROBERTA_GOB_finnetuning)
To cite this resource in a publication please use the following:
```
@inproceedings{SpanishNLPHackaton2022,
title={Unam's thesis with PlanTL-GOB-ES/roberta-large-bne cl... | 45cdff548f3d35467e8a571153526bfe |
apache-2.0 | [] | false | Team members
- Isaac Isaías López López ([MajorIsaiah](https://huggingface.co/MajorIsaiah))
- Dionis López Ramos ([inoid](https://huggingface.co/inoid))
- Yisel Clavel Quintero ([clavel](https://huggingface.co/clavel))
- Ximyer Yeraldin López López ([Ximyer](https://huggingface.co/Ximyer))
| aeee56206cc55f3b81a145483779f347 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | kantoku-face Dreambooth model trained by KuroTuyuri with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stab... | 89753e612f46dac19395371df23e4dc5 |
apache-2.0 | ['text-classification', 'pytorch'] | false | Model description: This model was created with the purpose to detect toxic or potentially harmful comments. For this model, we finetuned a multilingual distilbert model [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the translated [Jigsaw Toxicity dataset](https://w... | bc61982b22d8b84ceb03294f92f33473 |
apache-2.0 | ['text-classification', 'pytorch'] | false | Model Performance: Model evaluation was done on 1/10th of the dataset, which served as the test dataset. | Accuracy | F1 Score | Recall | Precision | | --- | --- | --- | --- | | 95.75 | 78.88 | 77.23 | 80.61 | | 826fd4e8dfdd61a9b311529ce1d1db71 |
apache-2.0 | ['generated_from_keras_callback'] | false | Mohan515/t5-small-finetuned-medical This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.8018 - Validation Loss: 0.5835 - Train Rouge1: 43.3783 - Train Rouge2: 35.1091 - Train Rougel: 41.6332 -... | 1a4b665d1465428f55710edc8d9704f5 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch | |:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:| | 0.8018 | 0.5835 | 43.3783 | 35.1091 ... | 07fdab2c9a615ff8791ec66b86799110 |
apache-2.0 | ['generated_from_trainer'] | false | convnext-tiny-224-finetuned-eurosat-att-auto This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.5076 - Accuracy: 0.9506 | 2b66c6821558c2742fa4d151e8044e44 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sched... | a36454033b24a1e0b8a8e004296e8a33 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.5583 | 0.97 | 23 | 1.6008 | 0.7160 | | 1.2953 | 1.97 | 46 | 1.2957 | 0.7531 | | 0.9488 | 2.97 | 69 | 1.0720 | 0.... | abc4cf0b4932eb68e5c2134b2d2aa36f |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - eval_loss: 0.3049 - eval_accuracy: 0.9252 - eval_runtime: 423.5074 - eval_samples_per_s... | 20ca1d5f4b94d271e8ae6f15c0740446 |
apache-2.0 | ['generated_from_trainer'] | false | my_awsome_wnut_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset. It achieves the following results on the evaluation set: - Loss: 0.2858 - Precision: 0.4846 - Recall: 0.2632 - F1: 0.3411 - Accuracy: 0.9386 | bb2190c20ddc047092c447a45febc628 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 213 | 0.2976 | 0.3873 | 0.1974 | 0.2615 | 0.9352 | | No log | 2.0 |... | c43c6fa25da55a1acf8cb21f12c6183b |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | JBandMBsession1 Dreambooth model trained by Jackie 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-stabl... | 4a24d7999867b8e1639f16a5d7f9bf17 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1495 - Accuracy: 0.9385 - F1: 0.9383 | 410a010fc9af8f599d6ab07900bf3be4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.1739 | 1.0 | 250 | 0.1827 | 0.931 | 0.9302 | | 0.1176 | 2.0 | 500 | 0.1567 | 0.9325 | 0.9326 | | 0.0994 |... | bd2b6e2067ba2c24d4b5d639eb611ed4 |
mit | ['generated_from_trainer'] | false | xlnet-base-cased_fold_3_binary This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3616 - F1: 0.7758 | d050cb87bb10c813c14c51e0f91443a9 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 289 | 0.4668 | 0.7666 | | 0.4142 | 2.0 | 578 | 0.4259 | 0.7631 | | 0.4142 | 3.0 | 867 | 0.6744 | 0.7492 | |... | 2500c80361a31e97f52a0dbb4ca13219 |
cc-by-4.0 | [] | false | GujaratiBERT-Scratch GujaratiBERT is a Gujarati BERT model trained on publicly available Gujarati monolingual datasets from scratch. Preliminary details on the dataset, models, and baseline results can be found in our [<a href='https://arxiv.org/abs/2211.11418'> paper </a>]. Citing: ``` @article{joshi2022l3cubehind... | d92c7d88ec41ecd3696e76c8abc8d7eb |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | imal_workshop_collage Dreambooth model trained by mrcyme 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... | 9882a55b9098a5dd8df32951fe912f7a |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'Citrinet', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard', 'Riva'] | false | deployment-with-nvidia-riva) | This model transcribes speech in lowercase Ukrainian alphabet including spaces and apostrophes, and is trained on 69 hours of Ukrainian speech data. It is a non-autoregressive "large" variant of Streaming Citrinet, with around 141 million parameters. Model is fine-tuned from pre-trained ... | 0b2fe3a8dd5c5377ac63821ef7aad04c |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'Citrinet', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard', 'Riva'] | false | Transcribing many audio files ```shell python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py pretrained_name="nvidia/stt_uk_citrinet_1024_gamma_0_25" audio_dir="<DIRECTORY CONTAINING AUDIO FILES>" ``` | b9ed24b66a994ba2f9d53bf1e04c8bb1 |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'Citrinet', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard', 'Riva'] | false | Training The NeMo toolkit [3] was used for training the model for 1000 epochs. This model was trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_ctc/speech_to_text_ctc_bpe.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/citrinet/citrinet_... | 0ac81dad79687f44bc34e10e66940016 |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'Citrinet', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard', 'Riva'] | false | Datasets This model has been trained using validated Mozilla Common Voice Corpus 10.0 dataset (excluding dev and test data) comprising of 69 hours of Ukrainian speech. The Russian model from which this model is fine-tuned has been trained on the union of: (1) Mozilla Common Voice (V7 Ru), (2) Ru LibriSpeech (RuLS), (... | cc2bd9761200380667dae82380a4e6b6 |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'Citrinet', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard', 'Riva'] | false | Performance The list of the available models in this collection is shown in the following table. Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding. | Version | Tokenizer | Vocabulary Size | MCV-10 test | MCV-10 dev | MCV-9 test | MCV-9 dev | MCV-8 ... | eefaed88e5ab385cc4c82e5c89e110d8 |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'Citrinet', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard', 'Riva'] | false | Limitations Since this model was trained on publicly available speech datasets, the performance of this model might degrade for speech that includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech. | 637d1d9b4602bd8e0ccdeeb0129c8b14 |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'Citrinet', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard', 'Riva'] | false | Deployment with NVIDIA Riva For the best real-time accuracy, latency, and throughput, deploy the model with [NVIDIA Riva](https://developer.nvidia.com/riva), an accelerated speech AI SDK deployable on-prem, in all clouds, multi-cloud, hybrid, at the edge, and embedded. Additionally, Riva provides: * World-class out... | a0b4b88de278dae1c249f87dc876063b |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'Citrinet', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard', 'Riva'] | false | References [1] [Citrinet: Closing the Gap between Non-Autoregressive and Autoregressive End-to-End Models for Automatic Speech Recognition](https://arxiv.org/abs/2104.01721) <br /> [2] [Google Sentencepiece Tokenizer](https://github.com/google/sentencepiece) <br /> [3] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/... | 9141cbc7c27b5b24f939e343bfa1cbba |
apache-2.0 | ['generated_from_trainer'] | false | demo_irony_1234567 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 1.2905 - F1: 0.6858 | 42ce87a5673b7871ec283c6812af76d0 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-colab40 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7341 - Wer: 0.5578 | 2695923a342da98bb4f20a5245f22454 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.0438 | 13.89 | 500 | 3.0671 | 1.0 | | 1.0734 | 27.78 | 1000 | 0.7341 | 0.5578 | | b1121e3f106431b407c0f197f2738bf4 |
mit | [] | false | model by tulto This your the Stable Diffusion model fine-tuned the the-witcher-game-ciri concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of a sks woman with white hair** You can also train your own concepts and upload them to the library by using [this ... | 297fa5df519ca46fc63c2a0ad6fda68b |
apache-2.0 | ['multiberts', 'multiberts-seed_0', 'multiberts-seed_0-step_200k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 0, Step 200k 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 ... | e02427792cf134fc2f1b9ad551872a60 |
apache-2.0 | ['multiberts', 'multiberts-seed_0', 'multiberts-seed_0-step_200k'] | 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_200k') model = TFBertModel.from_pretrained("google/multibe... | 3dbd89a45c7d3f92648049ac55509f6e |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 2.7341 | 2efe023f79842ce69c447980bd0eba23 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_ep... | a8764f6b2b667629f3091d202e6622e0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.9667 | 1.0 | 156 | 2.7795 | | 2.8612 | 2.0 | 312 | 2.6910 | | 2.8075 | 3.0 | 468 | 2.7044 | | 82d3efb9ed1256cb195b333549861484 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Turkish Note: This model is trained with 5 Turkish movies additional to common voice dataset. Although WER is high (50%) per common voice test dataset, performance from "other sources " seems pretty good. Disclaimer: Please use another wav2vec2-tr model in hub for "clean environment" dialogue... | cb22d0d2c6f853f38afff4c350edf5e4 |
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 import pydub from pydub.utils import mediainfo import array from pydub import AudioSegment from pydub.utils import get_array_type import numpy as np from datasets import load_dataset from transformer... | bf8f6c466aae9a634f9151e1cfd4020b |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | gets current samplerate sound = pydub.AudioSegment.from_file(file=batch["path"]) sampling_rate = new_sample_rate sound = sound.set_frame_rate(new_sample_rate) left = sound.split_to_mono()[0] bit_depth = left.sample_width * 8 array_type = pydub.utils.get_array_type(bit_depth) numeric_ar... | 930f44e10fe40fc1774d72a4586c8f22 |
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 = audio_resampler(batch, new_sample_rate = new_sample_rate) return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", pa... | bdcd6a447141fe258b87a3dce1e6c205 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Turkish test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re import pydub import array import numpy as np test_dataset = load_da... | cb54226f5e19551b9a937287da51d436 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | gets current samplerate sound = pydub.AudioSegment.from_file(file=batch["path"]) sound = sound.set_frame_rate(new_sample_rate) left = sound.split_to_mono()[0] bit_depth = left.sample_width * 8 array_type = pydub.utils.get_array_type(bit_depth) numeric_array = np.array(array.array(array_ty... | 6e3a9a990773d8acf6a7066bf965a898 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | load and conversion done in resampler , takes and returns batch speech_array, sampling_rate = audio_resampler(batch, new_sample_rate = new_sample_rate) batch["speech"] = speech_array batch["sampling_rate"] = sampling_rate batch["target_text"] = batch["sentence"] return batch test_dataset = test_da... | 70b7743eaf374b0c466dd4c6d3e8ea31 |
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.... | 83a222a08c4e88ce219dd956e4684634 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | for 8GB RAM on GPU best is batch_size 2 for windows, 4 may fit in linux only result = test_dataset.map(evaluate, batched=True, batch_size=2) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 50.41 % | bfb9579163528009a9c6e021851166b7 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Training The Common Voice `train` and `validation` datasets were used for training. Additional 5 Turkish movies with subtitles also used for training. Similar training model used as base fine-tuning, additional audio resampler is on above code. Putting model building and merging code below for reference ```python... | 82762d12e15ea183e0c6eefe63eeab6f |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Walk all subdirectories of base_set_path and find csv files base_set_path = r'C:\\dataset_extracts' csv_files = [] for path, subdirs, files in os.walk(base_set_path): for name in files: if name.endswith(".csv"): deckfile= os.path.join(path, name) csv_files.append(deckfile) def get_d... | 617d9a09f9dbbdca5eed6e64ef7a488c |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Pandas has bug reading non-ascii file names, make sure use open with encoding df=pd.read_csv(open(path, 'r', encoding='utf-8'), delimiter=csv_delimiter,header=None , names=names, encoding='utf8') return Dataset.from_pandas(df) custom_datasets= [] for csv_file in csv_files: this_dataset=get_dataset_from_csv_file(... | 5de857212fb4aaab4176e04b90d73916 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Large-V2 Nepali This model is a fine-tuned version of [DrishtiSharma/whisper-large-v2-hindi-3k-steps](https://huggingface.co/DrishtiSharma/whisper-large-v2-hindi-3k-steps) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.9961 - Wer: 9.7561 | 88e94e57f0445258e118bd60368ab880 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 0.0 | 1000.0 | 1000 | 0.9961 | 9.7561 | | 7201c2d707d17d72c61aaf2672386ca5 |
apache-2.0 | ['generated_from_keras_callback'] | false | Ddaow/distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.9692 - Train End Logits Accuracy: 0.7314 - Train Start Logits Ac... | 8575962878431147c0d194e2c3a166dc |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | 222638459d6ff99326a08bb0473f0dae |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | fi_core_news_lg Finnish pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner. | Feature | Description | | --- | --- | | **Name** | `fi_core_news_lg` | | **Version** | `3.5.0` | | **spaCy** | `>=3.5.0,<3.6.0` | | **Default Pipeline** | `tok2vec`... | eda6bc750b0f3b1f082cfaacba6c1c34 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_ACC` | 100.00 | | `TOKEN_P` | 99.79 | | `TOKEN_R` | 99.90 | | `TOKEN_F` | 99.85 | | `TAG_ACC` | 97.09 | | `POS_ACC` | 96.28 | | `MORPH_ACC` | 92.22 | | `MORPH_MICRO_P` | 96.26 | | `MORPH_MICRO_R` | 95.17 | | `MORPH_MICRO_F` | 95.71 | | `SENTS_P` | 91.96 | | `SENTS_R` |... | 97301c9f35506dde0a3dad8cdba9f679 |
cc-by-4.0 | [] | false | UoM&MMU at TSAR-2022 Shared Task - Prompt Learning for Lexical Simplification: prompt-ls-pt-2 We present **PromptLS**, a method for fine-tuning large pre-trained masked language models to perform the task of Lexical Simplification. This model is part of a series of models presented at the [TSAR-2022 Shared Task](htt... | 8efb833ac39367674207d7eb60bcdd92 |
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