modelId
stringlengths
4
112
sha
stringlengths
40
40
lastModified
stringlengths
24
24
tags
sequence
pipeline_tag
stringclasses
29 values
private
bool
1 class
author
stringlengths
2
38
config
null
id
stringlengths
4
112
downloads
float64
0
36.8M
likes
float64
0
712
library_name
stringclasses
17 values
__index_level_0__
int64
0
38.5k
readme
stringlengths
0
186k
softcatala/fullstop-catalan-punctuation-prediction
4b26218160a6efed8b99f7f5167269f57dcc25aa
2022-04-06T12:45:54.000Z
[ "pytorch", "roberta", "token-classification", "ca", "dataset:softcatala/Europarl-catalan", "transformers", "punctuation prediction", "punctuation", "autotrain_compatible" ]
token-classification
false
softcatala
null
softcatala/fullstop-catalan-punctuation-prediction
204
null
transformers
3,600
--- language: - ca tags: - punctuation prediction - punctuation datasets: softcatala/Europarl-catalan widget: - text: "Els investigadors suggereixen que tot i que es tracta de la cua d'un dinosaure jove la mostra revela un plomatge adult i no pas plomissol" example_title: "Catalan" metrics: - f1 --- This model predicts the punctuation of Catalan language. The model restores the following punctuation markers: **"." "," "?" "-" ":"** Based on the work https://github.com/oliverguhr/fullstop-deep-punctuation-prediction ## Results The performance differs for the single punctuation markers as hyphens and colons, in many cases, are optional and can be substituted by either a comma or a full stop. The model achieves the following F1 scores for Catalan language: | Label | CA | | ------------- | ----- | | 0 | 0.99 | | . | 0.93 | | , | 0.82 | | ? | 0.76 | | - | 0.89 | | : | 0.64 | | macro average | 0.84 | ## Contact Jordi Mas <[email protected]>
EMBO/BioMegatron345mUncased
ab9ac883b103dbb83e55f3b7a416fffcebff4e1b
2022-07-26T06:50:35.000Z
[ "pytorch", "megatron-bert", "en", "arxiv:2010.06060", "transformers", "language model", "license:cc-by-4.0" ]
null
false
EMBO
null
EMBO/BioMegatron345mUncased
204
1
transformers
3,601
--- license: cc-by-4.0 language: - en thumbnail: tags: - language model --- !--- # ############################################################################################## # # This model has been uploaded to HuggingFace by https://huggingface.co/drAbreu # The model is based on the NVIDIA checkpoint located at # https://catalog.ngc.nvidia.com/orgs/nvidia/models/biomegatron345muncased # # ############################################################################################## --> [BioMegatron](https://arxiv.org/pdf/2010.06060.pdf) is a transformer developed by the Applied Deep Learning Research team at NVIDIA. This particular Megatron model trained on top of the Megatron-LM model, adding a PubMed corpusto the Megatron-LM corpora(Wikipedia, RealNews, OpenWebText, and CC-Stories). BioMegatron follows a similar (albeit not identical) architecture as BERT and it has 345 million parameters: * 24 layers * 16 attention heads with a hidden size of 1024. More information available at [nVIDIA NGC CATALOG](https://catalog.ngc.nvidia.com/orgs/nvidia/models/biomegatron345muncased) # Running BioMegatron in 🤗 transformers In this implementation we have followed the commands of the [`nvidia/megatron-bert-uncased-345m`](https://huggingface.co/nvidia/megatron-bert-uncased-345m) repository to make BioMegatron available in 🤗. However, the file [`convert_megatron_bert_checkpoint.py`](https://github.com/huggingface/transformers/blob/main/src/transformers/models/megatron_bert/convert_megatron_bert_checkpoint.py) needed a modification. The reason is that the Megatron model shown in [`nvidia/megatron-bert-uncased-345m`](https://huggingface.co/nvidia/megatron-bert-uncased-345m) has included head layers, while the weights of the BioMegatron model that we upload to this repository do not contain a head. We provide in the repository an alternative version of the [python script](https://huggingface.co/EMBO/BioMegatron345mUncased/blob/main/convert_biomegatron_checkpoint.py) in order to any user to cross-check the validity of the model replicated in this repository. The code below is a modification of the original [`convert_megatron_bert_checkpoint.py`](https://github.com/huggingface/transformers/blob/main/src/transformers/models/megatron_bert/convert_megatron_bert_checkpoint.py). ```python import os import torch from convert_biomegatron_checkpoint import convert_megatron_checkpoint print_checkpoint_structure = True path_to_checkpoint = "/path/to/BioMegatron345mUncased/" # Extract the basename. basename = os.path.dirname(path_to_checkpoint).split('/')[-1] # Load the model. input_state_dict = torch.load(os.path.join(path_to_checkpoint, 'model_optim_rng.pt'), map_location="cpu") # Convert. print("Converting") output_state_dict, output_config = convert_megatron_checkpoint(input_state_dict, head_model=False) # Print the structure of converted state dict. if print_checkpoint_structure: recursive_print(None, output_state_dict) # Store the config to file. output_config_file = os.path.join(path_to_checkpoint, "config.json") print(f'Saving config to "{output_config_file}"') with open(output_config_file, "w") as f: json.dump(output_config, f) # Store the state_dict to file. output_checkpoint_file = os.path.join(path_to_checkpoint, "pytorch_model.bin") print(f'Saving checkpoint to "{output_checkpoint_file}"') torch.save(output_state_dict, output_checkpoint_file) ``` BioMegatron can be run with the standard 🤗 script for loading models. Here we show an example identical to that of [`nvidia/megatron-bert-uncased-345m`](https://huggingface.co/nvidia/megatron-bert-uncased-345m). ```python import os import torch from transformers import BertTokenizer, MegatronBertForMaskedLM, AutoModelForMaskedLM checkpoint = "EMBO/BioMegatron345mUncased" # The tokenizer. Megatron was trained with standard tokenizer(s). tokenizer = BertTokenizer.from_pretrained(checkpoint) # Load the model from $MYDIR/nvidia/megatron-bert-uncased-345m. model = AutoModelForMaskedLM.from_pretrained(checkpoint) device = torch.device("cpu") # Create inputs (from the BERT example page). input = tokenizer("The capital of France is [MASK]", return_tensors="pt").to(device) label = tokenizer("The capital of France is Paris", return_tensors="pt")["input_ids"].to(device) # Run the model. with torch.no_grad(): output = model(**input, labels=label) print(output) ``` # Limitations This implementation has not been fine-tuned in any task. It has only the weights of the official nVIDIA checkpoint. It needs to be trained to perform any downstream task. # Original code The original code for Megatron can be found here: [https://github.com/NVIDIA/Megatron-LM](https://github.com/NVIDIA/Megatron-LM).
SharpAI/mal_tls
735a8d55f96d3bbbb035834ed8c82df7c8ba9ae2
2022-07-27T18:04:04.000Z
[ "pytorch", "tf", "bert", "text-classification", "transformers", "generated_from_keras_callback", "model-index" ]
text-classification
false
SharpAI
null
SharpAI/mal_tls
204
null
transformers
3,602
--- tags: - generated_from_keras_callback model-index: - name: mal_tls results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # mal_tls This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.20.1 - TensorFlow 2.6.4 - Datasets 2.1.0 - Tokenizers 0.12.1
PlanTL-GOB-ES/roberta-base-bne-capitel-ner-plus
754e83ed79884f3ce74674711f002d95ae5f065e
2022-04-06T14:43:21.000Z
[ "pytorch", "roberta", "token-classification", "es", "dataset:bne", "dataset:capitel", "arxiv:1907.11692", "arxiv:2107.07253", "transformers", "national library of spain", "spanish", "bne", "capitel", "ner", "license:apache-2.0", "autotrain_compatible" ]
token-classification
false
PlanTL-GOB-ES
null
PlanTL-GOB-ES/roberta-base-bne-capitel-ner-plus
203
4
transformers
3,603
--- language: - es license: apache-2.0 tags: - "national library of spain" - "spanish" - "bne" - "capitel" - "ner" datasets: - "bne" - "capitel" metrics: - "f1" inference: parameters: aggregation_strategy: "first" --- # Spanish RoBERTa-base trained on BNE finetuned for CAPITEL Named Entity Recognition (NER) dataset. RoBERTa-base-bne is a transformer-based masked language model for the Spanish language. It is based on the [RoBERTa](https://arxiv.org/abs/1907.11692) base model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019. Original pre-trained model can be found here: https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne ## Dataset The dataset used is the one from the [CAPITEL competition at IberLEF 2020](https://sites.google.com/view/capitel2020) (sub-task 1). **IMPORTANT ABOUT THIS MODEL:** We modified the dataset to make this model more robust to general Spanish input. In the Spanish language all the name entities are capitalized, as this dataset has been elaborated by experts, it is totally correct in terms of Spanish language. We randomly took some entities and we lower-cased some of them for the model to learn not only that the named entities are capitalized, but also the structure of a sentence that should contain a named entity. For instance: "My name is [placeholder]", this [placeholder] should be a named entity even though it is not written capitalized. The model trained on the original capitel dataset can be found here: https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne-capitel-ner Examples: This model: - "Me llamo asier y vivo en barcelona todo el año." → "Me llamo {asier:S-PER} y vivo en {barcelona:S-LOC} todo el año." - "Hoy voy a visitar el parc güell tras salir del barcelona supercomputing center." → "Hoy voy a visitar el {park:B-LOC} {güell:E-LOC} tras salir del {barcelona:B-ORG} {supercomputing:I-ORG} {center:E-ORG}." Model trained on original data: - "Me llamo asier y vivo en barcelona todo el año." → "Me llamo asier y vivo en barcelona todo el año." (nothing) - "Hoy voy a visitar el parc güell tras salir del barcelona supercomputing center." → "Hoy voy a visitar el parc güell tras salir del barcelona supercomputing center." (nothing) ## Evaluation and results F1 Score: 0.8867 For evaluation details visit our [GitHub repository](https://github.com/PlanTL-GOB-ES/lm-spanish). ## Citing Check out our paper for all the details: https://arxiv.org/abs/2107.07253 ``` @article{gutierrezfandino2022, author = {Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Marc Pàmies and Joan Llop-Palao and Joaquin Silveira-Ocampo and Casimiro Pio Carrino and Carme Armentano-Oller and Carlos Rodriguez-Penagos and Aitor Gonzalez-Agirre and Marta Villegas}, title = {MarIA: Spanish Language Models}, journal = {Procesamiento del Lenguaje Natural}, volume = {68}, number = {0}, year = {2022}, issn = {1989-7553}, url = {http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6405}, pages = {39--60} } ``` ## Funding This work was partially funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL, and the Future of Computing Center, a Barcelona Supercomputing Center and IBM initiative (2020). ## Disclaimer The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions. When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of artificial intelligence. In no event shall the owner of the models (SEDIA – State Secretariat for digitalization and artificial intelligence) nor the creator (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models. Los modelos publicados en este repositorio tienen una finalidad generalista y están a disposición de terceros. Estos modelos pueden tener sesgos y/u otro tipo de distorsiones indeseables. Cuando terceros desplieguen o proporcionen sistemas y/o servicios a otras partes usando alguno de estos modelos (o utilizando sistemas basados en estos modelos) o se conviertan en usuarios de los modelos, deben tener en cuenta que es su responsabilidad mitigar los riesgos derivados de su uso y, en todo caso, cumplir con la normativa aplicable, incluyendo la normativa en materia de uso de inteligencia artificial. En ningún caso el propietario de los modelos (SEDIA – Secretaría de Estado de Digitalización e Inteligencia Artificial) ni el creador (BSC – Barcelona Supercomputing Center) serán responsables de los resultados derivados del uso que hagan terceros de estos modelos.
google/vit-large-patch16-384
4b143e77059a54c70b348a76677ab9946f584e13
2022-01-28T10:22:26.000Z
[ "pytorch", "tf", "jax", "vit", "image-classification", "dataset:imagenet", "dataset:imagenet-21k", "arxiv:2010.11929", "arxiv:2006.03677", "transformers", "vision", "license:apache-2.0" ]
image-classification
false
google
null
google/vit-large-patch16-384
203
2
transformers
3,604
--- license: apache-2.0 tags: - image-classification - vision datasets: - imagenet - imagenet-21k --- # Vision Transformer (large-sized model) Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384. It was introduced in the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Dosovitskiy et al. and first released in [this repository](https://github.com/google-research/vision_transformer). However, the weights were converted from the [timm repository](https://github.com/rwightman/pytorch-image-models) by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him. Disclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, at a higher resolution of 384x384. Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder. By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image. ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=google/vit) to look for fine-tuned versions on a task that interests you. ### 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 ViTFeatureExtractor, ViTForImageClassification from PIL import Image import requests url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Image.open(requests.get(url, stream=True).raw) feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-large-patch16-384') model = ViTForImageClassification.from_pretrained('google/vit-large-patch16-384') inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits # model predicts one of the 1000 ImageNet classes predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx]) ``` Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change. ## Training data The ViT model was pretrained on [ImageNet-21k](http://www.image-net.org/), a dataset consisting of 14 million images and 21k classes, and fine-tuned on [ImageNet](http://www.image-net.org/challenges/LSVRC/2012/), a dataset consisting of 1 million images and 1k classes. ## Training procedure ### Preprocessing The exact details of preprocessing of images during training/validation can be found [here](https://github.com/google-research/vision_transformer/blob/master/vit_jax/input_pipeline.py). Images are resized/rescaled to the same resolution (224x224 during pre-training, 384x384 during fine-tuning) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5). ### Pretraining The model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Pre-training resolution is 224. ## Evaluation results For evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance. ### BibTeX entry and citation info ```bibtex @misc{wu2020visual, title={Visual Transformers: Token-based Image Representation and Processing for Computer Vision}, author={Bichen Wu and Chenfeng Xu and Xiaoliang Dai and Alvin Wan and Peizhao Zhang and Zhicheng Yan and Masayoshi Tomizuka and Joseph Gonzalez and Kurt Keutzer and Peter Vajda}, year={2020}, eprint={2006.03677}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` ```bibtex @inproceedings{deng2009imagenet, title={Imagenet: A large-scale hierarchical image database}, author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li}, booktitle={2009 IEEE conference on computer vision and pattern recognition}, pages={248--255}, year={2009}, organization={Ieee} } ```
mrm8488/bert2bert_shared-spanish-finetuned-paus-x-paraphrasing
e5fcd441a0b0e6fc55c87b3c89915623941a9426
2021-07-31T05:12:47.000Z
[ "pytorch", "encoder-decoder", "text2text-generation", "es", "dataset:pausx", "transformers", "spanish", "paraphrasing", "paraphrase", "autotrain_compatible" ]
text2text-generation
false
mrm8488
null
mrm8488/bert2bert_shared-spanish-finetuned-paus-x-paraphrasing
203
2
transformers
3,605
--- language: es datasets: - pausx tags: - spanish - paraphrasing - paraphrase widget: - text: "El pionero suizo John Sutter (1803-1880) llegó a Alta California con otros colonos euroamericanos en agosto de 1839." --- # Spanish Bert2Bert (shared) fine-tuned on PAUS-X es for paraphrasing
HooshvareLab/bert-fa-base-uncased-sentiment-digikala
88db8245349b36b47ef1b92e49b8b80428b77d7c
2021-05-18T20:59:17.000Z
[ "pytorch", "tf", "jax", "bert", "text-classification", "fa", "transformers", "license:apache-2.0" ]
text-classification
false
HooshvareLab
null
HooshvareLab/bert-fa-base-uncased-sentiment-digikala
202
null
transformers
3,606
--- language: fa license: apache-2.0 --- # ParsBERT (v2.0) A Transformer-based Model for Persian Language Understanding We reconstructed the vocabulary and fine-tuned the ParsBERT v1.1 on the new Persian corpora in order to provide some functionalities for using ParsBERT in other scopes! Please follow the [ParsBERT](https://github.com/hooshvare/parsbert) repo for the latest information about previous and current models. ## Persian Sentiment [Digikala, SnappFood, DeepSentiPers] It aims to classify text, such as comments, based on their emotional bias. We tested three well-known datasets for this task: `Digikala` user comments, `SnappFood` user comments, and `DeepSentiPers` in two binary-form and multi-form types. ### 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 | # | |:---------------:|:------:| | no_idea | 10394 | | not_recommended | 15885 | | recommended | 36042 | **Download** You can download the dataset from [here](https://www.digikala.com/opendata/) ## 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 | - | ## How to use :hugs: | Task | Notebook | |---------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Sentiment Analysis | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/hooshvare/parsbert/blob/master/notebooks/Taaghche_Sentiment_Analysis.ipynb) | ### BibTeX entry and citation info Please cite in publications as the following: ```bibtex @article{ParsBERT, title={ParsBERT: Transformer-based Model for Persian Language Understanding}, author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri}, journal={ArXiv}, year={2020}, volume={abs/2005.12515} } ``` ## Questions? Post a Github issue on the [ParsBERT Issues](https://github.com/hooshvare/parsbert/issues) repo.
abhi1nandy2/EManuals_RoBERTa
b6fe473485f493db94fe67b8fa9d62bd3d0a43b9
2022-05-04T04:57:53.000Z
[ "pytorch", "roberta", "feature-extraction", "English", "transformers", "EManuals", "customer support", "QA" ]
feature-extraction
false
abhi1nandy2
null
abhi1nandy2/EManuals_RoBERTa
202
null
transformers
3,607
--- language: - English tags: - EManuals - customer support - QA - roberta --- Refer to https://aclanthology.org/2021.findings-emnlp.392/ for the paper and https://sites.google.com/view/emanualqa/home for the project website ## Citation Please cite the work if you would like to use it. ``` @inproceedings{nandy-etal-2021-question-answering, title = "Question Answering over Electronic Devices: A New Benchmark Dataset and a Multi-Task Learning based {QA} Framework", author = "Nandy, Abhilash and Sharma, Soumya and Maddhashiya, Shubham and Sachdeva, Kapil and Goyal, Pawan and Ganguly, NIloy", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021", month = nov, year = "2021", address = "Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.findings-emnlp.392", doi = "10.18653/v1/2021.findings-emnlp.392", pages = "4600--4609", abstract = "Answering questions asked from instructional corpora such as E-manuals, recipe books, etc., has been far less studied than open-domain factoid context-based question answering. This can be primarily attributed to the absence of standard benchmark datasets. In this paper, we meticulously create a large amount of data connected with E-manuals and develop a suitable algorithm to exploit it. We collect E-Manual Corpus, a huge corpus of 307,957 E-manuals, and pretrain RoBERTa on this large corpus. We create various benchmark QA datasets which include question answer pairs curated by experts based upon two E-manuals, real user questions from Community Question Answering Forum pertaining to E-manuals etc. We introduce EMQAP (E-Manual Question Answering Pipeline) that answers questions pertaining to electronics devices. Built upon the pretrained RoBERTa, it harbors a supervised multi-task learning framework which efficiently performs the dual tasks of identifying the section in the E-manual where the answer can be found and the exact answer span within that section. For E-Manual annotated question-answer pairs, we show an improvement of about 40{\%} in ROUGE-L F1 scores over most competitive baseline. We perform a detailed ablation study and establish the versatility of EMQAP across different circumstances. The code and datasets are shared at https://github.com/abhi1nandy2/EMNLP-2021-Findings, and the corresponding project website is https://sites.google.com/view/emanualqa/home.", } ```
af-ai-center/bert-base-swedish-uncased
5c7fb9dbad916c7c9738751e8ee117b186f7da91
2021-05-18T23:12:14.000Z
[ "pytorch", "tf", "jax", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
af-ai-center
null
af-ai-center/bert-base-swedish-uncased
202
null
transformers
3,608
Entry not found
suhasjain/DailoGPT-small-harrypotter
27baccd28f5226a79ac26499d6ed6e8feeaba056
2021-09-10T08:47:17.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
suhasjain
null
suhasjain/DailoGPT-small-harrypotter
202
null
transformers
3,609
--- tags: - conversational --- #Harry Potter DialoGPT Model
yoshitomo-matsubara/bert-base-uncased-mrpc
6a95d3c0ae14ab6bf2dccfaaa5bf6ee16b2a5172
2021-05-29T21:47:37.000Z
[ "pytorch", "bert", "text-classification", "en", "dataset:mrpc", "transformers", "mrpc", "glue", "torchdistill", "license:apache-2.0" ]
text-classification
false
yoshitomo-matsubara
null
yoshitomo-matsubara/bert-base-uncased-mrpc
202
null
transformers
3,610
--- language: en tags: - bert - mrpc - glue - torchdistill license: apache-2.0 datasets: - mrpc metrics: - f1 - accuracy --- `bert-base-uncased` fine-tuned on MRPC dataset, using [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_finetuning_and_submission.ipynb). The hyperparameters are the same as those in Hugging Face's example and/or the paper of BERT, and the training configuration (including hyperparameters) is available [here](https://github.com/yoshitomo-matsubara/torchdistill/blob/main/configs/sample/glue/mrpc/ce/bert_base_uncased.yaml). I submitted prediction files to [the GLUE leaderboard](https://gluebenchmark.com/leaderboard), and the overall GLUE score was **77.9**.
deepset/gelectra-base-generator
ba9ac3432c2c9904460f59343f1fb80d4c0a0740
2021-10-21T12:18:27.000Z
[ "pytorch", "tf", "electra", "fill-mask", "de", "dataset:wikipedia", "dataset:OPUS", "dataset:OpenLegalData", "arxiv:2010.10906", "transformers", "license:mit", "autotrain_compatible" ]
fill-mask
false
deepset
null
deepset/gelectra-base-generator
201
3
transformers
3,611
--- language: de license: mit datasets: - wikipedia - OPUS - OpenLegalData --- # German ELECTRA base generator Released, Oct 2020, this is the generator component of the German ELECTRA language model trained collaboratively by the makers of the original German BERT (aka "bert-base-german-cased") and the dbmdz BERT (aka bert-base-german-dbmdz-cased). In our [paper](https://arxiv.org/pdf/2010.10906.pdf), we outline the steps taken to train our model. The generator is useful for performing masking experiments. If you are looking for a regular language model for embedding extraction, or downstream tasks like NER, classification or QA, please use deepset/gelectra-base. ## Overview **Paper:** [here](https://arxiv.org/pdf/2010.10906.pdf) **Architecture:** ELECTRA base (generator) **Language:** German See also: deepset/gbert-base deepset/gbert-large deepset/gelectra-base deepset/gelectra-large deepset/gelectra-base-generator deepset/gelectra-large-generator ## Authors Branden Chan: `branden.chan [at] deepset.ai` Stefan Schweter: `stefan [at] schweter.eu` Timo Möller: `timo.moeller [at] deepset.ai` ## About us ![deepset logo](https://workablehr.s3.amazonaws.com/uploads/account/logo/476306/logo) We bring NLP to the industry via open source! Our focus: Industry specific language models & large scale QA systems. Some of our work: - [German BERT (aka "bert-base-german-cased")](https://deepset.ai/german-bert) - [GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")](https://deepset.ai/germanquad) - [FARM](https://github.com/deepset-ai/FARM) - [Haystack](https://github.com/deepset-ai/haystack/) Get in touch: [Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai) By the way: [we're hiring!](http://www.deepset.ai/jobs)
hakurei/lit-125M
f1e7a551a28ee8ad2e982d5425a07e1d59b4be65
2022-02-17T22:52:19.000Z
[ "pytorch", "gpt_neo", "text-generation", "en", "transformers", "causal-lm", "license:mit" ]
text-generation
false
hakurei
null
hakurei/lit-125M
201
null
transformers
3,612
--- language: - en tags: - pytorch - causal-lm license: mit --- # Lit-125M - A Small Fine-tuned Model For Fictional Storytelling Lit-125M is a GPT-Neo 125M model fine-tuned on 2GB of a diverse range of light novels, erotica, and annotated literature for the purpose of generating novel-like fictional text. ## Model Description The model used for fine-tuning is [GPT-Neo 125M](https://huggingface.co/EleutherAI/gpt-neo-125M), which is a 125 million parameter auto-regressive language model trained on [The Pile](https://pile.eleuther.ai/).. ## Training Data & Annotative Prompting The data used in fine-tuning has been gathered from various sources such as the [Gutenberg Project](https://www.gutenberg.org/). The annotated fiction dataset has prepended tags to assist in generating towards a particular style. Here is an example prompt that shows how to use the annotations. ``` [ Title: The Dunwich Horror; Author: H. P. Lovecraft; Genre: Horror; Tags: 3rdperson, scary; Style: Dark ] *** When a traveler in north central Massachusetts takes the wrong fork... ``` The annotations can be mixed and matched to help generate towards a specific style. ## Downstream Uses This model can be used for entertainment purposes and as a creative writing assistant for fiction writers. The small size of the model can also help for easy debugging or further development of other models with a similar purpose. ## Example Code ``` from transformers import AutoTokenizer, AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained('hakurei/lit-125M') tokenizer = AutoTokenizer.from_pretrained('hakurei/lit-125M') prompt = '''[ Title: The Dunwich Horror; Author: H. P. Lovecraft; Genre: Horror ] *** When a traveler''' input_ids = tokenizer.encode(prompt, return_tensors='pt') output = model.generate(input_ids, do_sample=True, temperature=1.0, top_p=0.9, repetition_penalty=1.2, max_length=len(input_ids[0])+100, pad_token_id=tokenizer.eos_token_id) generated_text = tokenizer.decode(output[0]) print(generated_text) ``` An example output from this code produces a result that will look similar to: ``` [ Title: The Dunwich Horror; Author: H. P. Lovecraft; Genre: Horror ] *** When a traveler takes a trip through the streets of the world, the traveler feels like a youkai with a whole world inside her mind. It can be very scary for a youkai. When someone goes in the opposite direction and knocks on your door, it is actually the first time you have ever come to investigate something like that. That's right: everyone has heard stories about youkai, right? If you have heard them, you know what I'm talking about. It's hard not to say you ``` ## Team members and Acknowledgements - [Anthony Mercurio](https://github.com/harubaru) - Imperishable_NEET
huggingface/funnel-small
3829c82f3a863632e1726bfe4ecf950a726689df
2020-08-31T21:06:56.000Z
[ "pytorch", "funnel", "feature-extraction", "transformers" ]
feature-extraction
false
huggingface
null
huggingface/funnel-small
201
null
transformers
3,613
Entry not found
hyunwoongko/blenderbot-9B
20049263e5130cc21015f3327085fb3adbc39939
2021-06-17T01:26:34.000Z
[ "pytorch", "blenderbot", "text2text-generation", "en", "dataset:blended_skill_talk", "arxiv:1907.06616", "transformers", "convAI", "conversational", "facebook", "license:apache-2.0", "autotrain_compatible" ]
conversational
false
hyunwoongko
null
hyunwoongko/blenderbot-9B
201
5
transformers
3,614
--- language: - en thumbnail: tags: - convAI - conversational - facebook license: apache-2.0 datasets: - blended_skill_talk metrics: - perplexity --- ## Model description + Paper: [Recipes for building an open-domain chatbot](https://arxiv.org/abs/1907.06616) + [Original PARLAI Code](https://parl.ai/projects/recipes/) ### Abstract Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.
lisaterumi/genia-biobert-ent
929329e6c4611a8add9409d97e0727ea8cfe9e63
2022-07-22T21:40:15.000Z
[ "pytorch", "bert", "token-classification", "en", "dataset:Genia", "transformers", "autotrain_compatible" ]
token-classification
false
lisaterumi
null
lisaterumi/genia-biobert-ent
201
null
transformers
3,615
--- language: "en" widget: - text: "Point mutation of a GATA-1 site at -230 reduced promoter activity by 37%." - text: "Electrophoretic mobility shift assays indicated that the -230 GATA-1 site has a relatively low affinity for GATA-1." - text: "Accordingly, the effects of the constitutively active PKCs were compared to the effects of mutationally activated p21ras." - text: "Activated Src and p21ras were able to induce CD69 expression." datasets: - Genia --- # Genia-BioBERT-ENT ## Citation ``` coming soon ```
xlm-mlm-17-1280
ed2e1c862c37217e1b185c33a282ed8f3ebdc3e2
2022-07-22T08:09:41.000Z
[ "pytorch", "tf", "xlm", "fill-mask", "multilingual", "en", "fr", "es", "de", "it", "pt", "nl", "sv", "pl", "ru", "ar", "tr", "zh", "ja", "ko", "hi", "vi", "arxiv:1901.07291", "arxiv:1911.02116", "arxiv:1910.09700", "transformers", "license:cc-by-nc-4.0", "autotrain_compatible" ]
fill-mask
false
null
null
xlm-mlm-17-1280
200
1
transformers
3,616
--- language: - multilingual - en - fr - es - de - it - pt - nl - sv - pl - ru - ar - tr - zh - ja - ko - hi - vi license: cc-by-nc-4.0 --- # xlm-mlm-17-1280 # Table of Contents 1. [Model Details](#model-details) 2. [Uses](#uses) 3. [Bias, Risks, and Limitations](#bias-risks-and-limitations) 4. [Training](#training) 5. [Evaluation](#evaluation) 6. [Environmental Impact](#environmental-impact) 7. [Technical Specifications](#technical-specifications) 8. [Citation](#citation) 9. [Model Card Authors](#model-card-authors) 10. [How To Get Started With the Model](#how-to-get-started-with-the-model) # Model Details xlm-mlm-17-1280 is the XLM model, which was proposed in [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau, trained on text in 17 languages. The model is a transformer pretrained using a masked language modeling (MLM) objective. ## Model Description - **Developed by:** See [associated paper](https://arxiv.org/abs/1901.07291) and [GitHub Repo](https://github.com/facebookresearch/XLM) - **Model type:** Language model - **Language(s) (NLP):** 17 languages, see [GitHub Repo](https://github.com/facebookresearch/XLM#the-17-and-100-languages) for full list. - **License:** CC-BY-NC-4.0 - **Related Models:** [xlm-mlm-17-1280](https://huggingface.co/xlm-mlm-17-1280) - **Resources for more information:** - [Associated paper](https://arxiv.org/abs/1901.07291) - [GitHub Repo](https://github.com/facebookresearch/XLM#the-17-and-100-languages) - [Hugging Face Multilingual Models for Inference docs](https://huggingface.co/docs/transformers/v4.20.1/en/multilingual#xlm-with-language-embeddings) # Uses ## Direct Use The model is a language model. The model can be used for masked language modeling. ## Downstream Use To learn more about this task and potential downstream uses, see the Hugging Face [fill mask docs](https://huggingface.co/tasks/fill-mask) and the [Hugging Face Multilingual Models for Inference](https://huggingface.co/docs/transformers/v4.20.1/en/multilingual#xlm-with-language-embeddings) docs. Also see the [associated paper](https://arxiv.org/abs/1901.07291). ## Out-of-Scope Use The model should not be used to intentionally create hostile or alienating environments for people. # Bias, Risks, and Limitations Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). ## Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. # Training This model is the XLM model trained on text in 17 languages. The preprocessing included tokenization and byte-pair-encoding. See the [GitHub repo](https://github.com/facebookresearch/XLM#the-17-and-100-languages) and the [associated paper](https://arxiv.org/pdf/1911.02116.pdf) for further details on the training data and training procedure. [Conneau et al. (2020)](https://arxiv.org/pdf/1911.02116.pdf) report that this model has 16 layers, 1280 hidden states, 16 attention heads, and the dimension of the feed-forward layer is 1520. The vocabulary size is 200k and the total number of parameters is 570M (see Table 7). # Evaluation ## Testing Data, Factors & Metrics The model developers evaluated the model on the XNLI cross-lingual classification task (see the [XNLI data card](https://huggingface.co/datasets/xnli) for more details on XNLI) using the metric of test accuracy. See the [GitHub Repo](https://arxiv.org/pdf/1911.02116.pdf) for further details on the testing data, factors and metrics. ## Results For xlm-mlm-17-1280, the test accuracy on the XNLI cross-lingual classification task in English (en), Spanish (es), German (de), Arabic (ar), and Chinese (zh): |Language| en | es | de | ar | zh | |:------:|:--:|:---:|:--:|:--:|:--:| | |84.8|79.4 |76.2|71.5|75 | See the [GitHub repo](https://github.com/facebookresearch/XLM#ii-cross-lingual-language-model-pretraining-xlm) for further details. # Environmental Impact Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** More information needed - **Hours used:** More information needed - **Cloud Provider:** More information needed - **Compute Region:** More information needed - **Carbon Emitted:** More information needed # Technical Specifications [Conneau et al. (2020)](https://arxiv.org/pdf/1911.02116.pdf) report that this model has 16 layers, 1280 hidden states, 16 attention heads, and the dimension of the feed-forward layer is 1520. The vocabulary size is 200k and the total number of parameters is 570M (see Table 7). # Citation **BibTeX:** ```bibtex @article{lample2019cross, title={Cross-lingual language model pretraining}, author={Lample, Guillaume and Conneau, Alexis}, journal={arXiv preprint arXiv:1901.07291}, year={2019} } ``` **APA:** - Lample, G., & Conneau, A. (2019). Cross-lingual language model pretraining. arXiv preprint arXiv:1901.07291. # Model Card Authors This model card was written by the team at Hugging Face. # How to Get Started with the Model More information needed. See the [ipython notebook](https://github.com/facebookresearch/XLM/blob/main/generate-embeddings.ipynb) in the associated [GitHub repo](https://github.com/facebookresearch/XLM#the-17-and-100-languages) for examples.
Helsinki-NLP/opus-mt-en-bg
f3d8448586d3626a38582ec5ff9e61f7d5940255
2021-01-18T08:05:27.000Z
[ "pytorch", "marian", "text2text-generation", "en", "bg", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-en-bg
200
null
transformers
3,617
--- language: - en - bg tags: - translation license: apache-2.0 --- ### eng-bul * source group: English * target group: Bulgarian * OPUS readme: [eng-bul](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-bul/README.md) * model: transformer * source language(s): eng * target language(s): bul bul_Latn * model: transformer * pre-processing: normalization + SentencePiece (spm32k,spm32k) * a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID) * download original weights: [opus-2020-07-03.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-bul/opus-2020-07-03.zip) * test set translations: [opus-2020-07-03.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-bul/opus-2020-07-03.test.txt) * test set scores: [opus-2020-07-03.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-bul/opus-2020-07-03.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.eng.bul | 50.6 | 0.680 | ### System Info: - hf_name: eng-bul - source_languages: eng - target_languages: bul - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-bul/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['en', 'bg'] - src_constituents: {'eng'} - tgt_constituents: {'bul', 'bul_Latn'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-bul/opus-2020-07-03.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-bul/opus-2020-07-03.test.txt - src_alpha3: eng - tgt_alpha3: bul - short_pair: en-bg - chrF2_score: 0.68 - bleu: 50.6 - brevity_penalty: 0.96 - ref_len: 69504.0 - src_name: English - tgt_name: Bulgarian - train_date: 2020-07-03 - src_alpha2: en - tgt_alpha2: bg - prefer_old: False - long_pair: eng-bul - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41
Helsinki-NLP/opus-mt-gem-en
a0433c2234bd6b0ee27e493c1d47ba46322312bc
2021-01-18T08:51:56.000Z
[ "pytorch", "marian", "text2text-generation", "da", "sv", "af", "nn", "fy", "fo", "de", "nb", "nl", "is", "en", "lb", "yi", "gem", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-gem-en
200
1
transformers
3,618
--- language: - da - sv - af - nn - fy - fo - de - nb - nl - is - en - lb - yi - gem tags: - translation license: apache-2.0 --- ### gem-eng * source group: Germanic languages * target group: English * OPUS readme: [gem-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/gem-eng/README.md) * model: transformer * source language(s): afr ang_Latn dan deu enm_Latn fao frr fry gos got_Goth gsw isl ksh ltz nds nld nno nob nob_Hebr non_Latn pdc sco stq swe swg yid * target language(s): eng * model: transformer * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus2m-2020-08-01.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/gem-eng/opus2m-2020-08-01.zip) * test set translations: [opus2m-2020-08-01.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/gem-eng/opus2m-2020-08-01.test.txt) * test set scores: [opus2m-2020-08-01.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/gem-eng/opus2m-2020-08-01.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newssyscomb2009-deueng.deu.eng | 27.2 | 0.542 | | news-test2008-deueng.deu.eng | 26.3 | 0.536 | | newstest2009-deueng.deu.eng | 25.1 | 0.531 | | newstest2010-deueng.deu.eng | 28.3 | 0.569 | | newstest2011-deueng.deu.eng | 26.0 | 0.543 | | newstest2012-deueng.deu.eng | 26.8 | 0.550 | | newstest2013-deueng.deu.eng | 30.2 | 0.570 | | newstest2014-deen-deueng.deu.eng | 30.7 | 0.574 | | newstest2015-ende-deueng.deu.eng | 32.1 | 0.581 | | newstest2016-ende-deueng.deu.eng | 36.9 | 0.624 | | newstest2017-ende-deueng.deu.eng | 32.8 | 0.588 | | newstest2018-ende-deueng.deu.eng | 40.2 | 0.640 | | newstest2019-deen-deueng.deu.eng | 36.8 | 0.614 | | Tatoeba-test.afr-eng.afr.eng | 62.8 | 0.758 | | Tatoeba-test.ang-eng.ang.eng | 10.5 | 0.262 | | Tatoeba-test.dan-eng.dan.eng | 61.6 | 0.754 | | Tatoeba-test.deu-eng.deu.eng | 49.7 | 0.665 | | Tatoeba-test.enm-eng.enm.eng | 23.9 | 0.491 | | Tatoeba-test.fao-eng.fao.eng | 23.4 | 0.446 | | Tatoeba-test.frr-eng.frr.eng | 10.2 | 0.184 | | Tatoeba-test.fry-eng.fry.eng | 29.6 | 0.486 | | Tatoeba-test.gos-eng.gos.eng | 17.8 | 0.352 | | Tatoeba-test.got-eng.got.eng | 0.1 | 0.058 | | Tatoeba-test.gsw-eng.gsw.eng | 15.3 | 0.333 | | Tatoeba-test.isl-eng.isl.eng | 51.0 | 0.669 | | Tatoeba-test.ksh-eng.ksh.eng | 6.7 | 0.266 | | Tatoeba-test.ltz-eng.ltz.eng | 33.0 | 0.505 | | Tatoeba-test.multi.eng | 54.0 | 0.687 | | Tatoeba-test.nds-eng.nds.eng | 33.6 | 0.529 | | Tatoeba-test.nld-eng.nld.eng | 58.9 | 0.733 | | Tatoeba-test.non-eng.non.eng | 37.3 | 0.546 | | Tatoeba-test.nor-eng.nor.eng | 54.9 | 0.696 | | Tatoeba-test.pdc-eng.pdc.eng | 29.6 | 0.446 | | Tatoeba-test.sco-eng.sco.eng | 40.5 | 0.581 | | Tatoeba-test.stq-eng.stq.eng | 14.5 | 0.361 | | Tatoeba-test.swe-eng.swe.eng | 62.0 | 0.745 | | Tatoeba-test.swg-eng.swg.eng | 17.1 | 0.334 | | Tatoeba-test.yid-eng.yid.eng | 19.4 | 0.400 | ### System Info: - hf_name: gem-eng - source_languages: gem - target_languages: eng - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/gem-eng/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['da', 'sv', 'af', 'nn', 'fy', 'fo', 'de', 'nb', 'nl', 'is', 'en', 'lb', 'yi', 'gem'] - src_constituents: {'ksh', 'enm_Latn', 'got_Goth', 'stq', 'dan', 'swe', 'afr', 'pdc', 'gos', 'nno', 'fry', 'gsw', 'fao', 'deu', 'swg', 'sco', 'nob', 'nld', 'isl', 'eng', 'ltz', 'nob_Hebr', 'ang_Latn', 'frr', 'non_Latn', 'yid', 'nds'} - tgt_constituents: {'eng'} - src_multilingual: True - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/gem-eng/opus2m-2020-08-01.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/gem-eng/opus2m-2020-08-01.test.txt - src_alpha3: gem - tgt_alpha3: eng - short_pair: gem-en - chrF2_score: 0.687 - bleu: 54.0 - brevity_penalty: 0.993 - ref_len: 72120.0 - src_name: Germanic languages - tgt_name: English - train_date: 2020-08-01 - src_alpha2: gem - tgt_alpha2: en - prefer_old: False - long_pair: gem-eng - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41
Helsinki-NLP/opus-mt-pa-en
e501d2bca4de5b63384fcdafc6b6185433efb4a9
2021-09-10T14:00:06.000Z
[ "pytorch", "marian", "text2text-generation", "pa", "en", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-pa-en
200
null
transformers
3,619
--- tags: - translation license: apache-2.0 --- ### opus-mt-pa-en * source languages: pa * target languages: en * OPUS readme: [pa-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/pa-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/pa-en/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/pa-en/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/pa-en/opus-2020-01-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.pa.en | 20.6 | 0.320 | | Tatoeba.pa.en | 29.3 | 0.464 |
ShannonAI/ChineseBERT-base
aa8b6fa9c3427f77b0911b07ab35f2b1b8bf248b
2022-06-19T08:14:46.000Z
[ "pytorch", "arxiv:2106.16038" ]
null
false
ShannonAI
null
ShannonAI/ChineseBERT-base
200
6
null
3,620
# ChineseBERT-base This repository contains code, model, dataset for **ChineseBERT** at ACL2021. paper: **[ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information](https://arxiv.org/abs/2106.16038)** *Zijun Sun, Xiaoya Li, Xiaofei Sun, Yuxian Meng, Xiang Ao, Qing He, Fei Wu and Jiwei Li* code: [ChineseBERT github link](https://github.com/ShannonAI/ChineseBert) ## Model description We propose ChineseBERT, which incorporates both the glyph and pinyin information of Chinese characters into language model pretraining. First, for each Chinese character, we get three kind of embedding. - **Char Embedding:** the same as origin BERT token embedding. - **Glyph Embedding:** capture visual features based on different fonts of a Chinese character. - **Pinyin Embedding:** capture phonetic feature from the pinyin sequence ot a Chinese Character. Then, char embedding, glyph embedding and pinyin embedding are first concatenated, and mapped to a D-dimensional embedding through a fully connected layer to form the fusion embedding. Finally, the fusion embedding is added with the position embedding, which is fed as input to the BERT model. The following image shows an overview architecture of ChineseBERT model. ![MODEL](https://raw.githubusercontent.com/ShannonAI/ChineseBert/main/images/ChineseBERT.png) ChineseBERT leverages the glyph and pinyin information of Chinese characters to enhance the model's ability of capturing context semantics from surface character forms and disambiguating polyphonic characters in Chinese.
facebook/wav2vec2-large-xlsr-53-dutch
ced00f8603b017d58cb3bcb3883e8c28f19ccdb4
2021-07-06T02:35:35.000Z
[ "pytorch", "jax", "wav2vec2", "automatic-speech-recognition", "nl", "dataset:common_voice", "transformers", "speech", "audio", "license:apache-2.0" ]
automatic-speech-recognition
false
facebook
null
facebook/wav2vec2-large-xlsr-53-dutch
200
null
transformers
3,621
--- language: nl datasets: - common_voice tags: - speech - audio - automatic-speech-recognition license: apache-2.0 --- ## Evaluation on Common Voice NL Test ```python import torchaudio from datasets import load_dataset, load_metric from transformers import ( Wav2Vec2ForCTC, Wav2Vec2Processor, ) import torch import re import sys model_name = "facebook/wav2vec2-large-xlsr-53-dutch" device = "cuda" chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"]' # noqa: W605 model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device) processor = Wav2Vec2Processor.from_pretrained(model_name) ds = load_dataset("common_voice", "nl", split="test", data_dir="./cv-corpus-6.1-2020-12-11") resampler = torchaudio.transforms.Resample(orig_freq=48_000, new_freq=16_000) def map_to_array(batch): speech, _ = torchaudio.load(batch["path"]) batch["speech"] = resampler.forward(speech.squeeze(0)).numpy() batch["sampling_rate"] = resampler.new_freq batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'") return batch ds = ds.map(map_to_array) def map_to_pred(batch): features = processor(batch["speech"], sampling_rate=batch["sampling_rate"][0], padding=True, return_tensors="pt") input_values = features.input_values.to(device) attention_mask = features.attention_mask.to(device) with torch.no_grad(): logits = model(input_values, attention_mask=attention_mask).logits pred_ids = torch.argmax(logits, dim=-1) batch["predicted"] = processor.batch_decode(pred_ids) batch["target"] = batch["sentence"] return batch result = ds.map(map_to_pred, batched=True, batch_size=16, remove_columns=list(ds.features.keys())) wer = load_metric("wer") print(wer.compute(predictions=result["predicted"], references=result["target"])) ``` **Result**: 21.1 %
maxidl/wav2vec2-large-xlsr-german
893c0ff874902327f50822f871ff78e4f584fbf5
2021-07-06T12:32:21.000Z
[ "pytorch", "jax", "wav2vec2", "automatic-speech-recognition", "de", "dataset:common_voice", "transformers", "audio", "speech", "xlsr-fine-tuning-week", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
maxidl
null
maxidl/wav2vec2-large-xlsr-german
200
null
transformers
3,622
--- language: de datasets: - common_voice metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: {XLSR Wav2Vec2 Large 53 CV-de} results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice de type: common_voice args: de metrics: - name: Test WER type: wer value: 12.77 --- # Wav2Vec2-Large-XLSR-53-German Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on German using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset. When using this model, make sure that your speech input is sampled at 16kHz. ## 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", "de", split="test[:8]") # use a batch of 8 for demo purposes processor = Wav2Vec2Processor.from_pretrained("maxidl/wav2vec2-large-xlsr-german") model = Wav2Vec2ForCTC.from_pretrained("maxidl/wav2vec2-large-xlsr-german") resampler = torchaudio.transforms.Resample(48_000, 16_000) """ Preprocessing the dataset by: - loading audio files - resampling to 16kHz - converting to array - prepare input tensor using the processor """ def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) # run forward with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"]) """ Example Result: Prediction: [ 'zieh durch bittet draußen die schuhe aus', 'es kommt zugvorgebauten fo', 'ihre vorterstrecken erschienen it modemagazinen wie der voge karpes basar mariclair', 'fürliepert eine auch für manachen ungewöhnlich lange drittelliste', 'er wurde zu ehren des reichskanzlers otto von bismarck errichtet', 'was solls ich bin bereit', 'das internet besteht aus vielen computern die miteinander verbunden sind', 'der uranus ist der siebinteplanet in unserem sonnensystem s' ] Reference: [ 'Zieht euch bitte draußen die Schuhe aus.', 'Es kommt zum Showdown in Gstaad.', 'Ihre Fotostrecken erschienen in Modemagazinen wie der Vogue, Harper’s Bazaar und Marie Claire.', 'Felipe hat eine auch für Monarchen ungewöhnlich lange Titelliste.', 'Er wurde zu Ehren des Reichskanzlers Otto von Bismarck errichtet.', 'Was solls, ich bin bereit.', 'Das Internet besteht aus vielen Computern, die miteinander verbunden sind.', 'Der Uranus ist der siebente Planet in unserem Sonnensystem.' ] """ ``` ## Evaluation The model can be evaluated as follows on the German test data of Common Voice: ```python import re import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor """ Evaluation on the full test set: - takes ~20mins (RTX 3090). - requires ~170GB RAM to compute the WER. Below, we use a chunked implementation of WER to avoid large RAM consumption. """ test_dataset = load_dataset("common_voice", "de", split="test") # use "test[:1%]" for 1% sample wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("maxidl/wav2vec2-large-xlsr-german") model = Wav2Vec2ForCTC.from_pretrained("maxidl/wav2vec2-large-xlsr-german") model.to("cuda") chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“]' resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): \tbatch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() \tspeech_array, sampling_rate = torchaudio.load(batch["path"]) \tbatch["speech"] = resampler(speech_array).squeeze().numpy() \treturn batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the audio files as arrays def evaluate(batch): \tinputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) \twith torch.no_grad(): \t\tlogits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits \tpred_ids = torch.argmax(logits, dim=-1) \tbatch["pred_strings"] = processor.batch_decode(pred_ids) \treturn batch result = test_dataset.map(evaluate, batched=True, batch_size=8) # batch_size=8 -> requires ~14.5GB GPU memory # non-chunked version: # print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) # WER: 12.900291 # Chunked version, see https://discuss.huggingface.co/t/spanish-asr-fine-tuning-wav2vec2/4586/5: import jiwer def chunked_wer(targets, predictions, chunk_size=None): if chunk_size is None: return jiwer.wer(targets, predictions) start = 0 end = chunk_size H, S, D, I = 0, 0, 0, 0 while start < len(targets): chunk_metrics = jiwer.compute_measures(targets[start:end], predictions[start:end]) H = H + chunk_metrics["hits"] S = S + chunk_metrics["substitutions"] D = D + chunk_metrics["deletions"] I = I + chunk_metrics["insertions"] start += chunk_size end += chunk_size return float(S + D + I) / float(H + S + D) print("Total (chunk_size=1000), WER: {:2f}".format(100 * chunked_wer(result["pred_strings"], result["sentence"], chunk_size=1000))) # Total (chunk=1000), WER: 12.768981 ``` **Test Result**: WER: 12.77 % ## Training The Common Voice German `train` and `validation` were used for training. The script used for training can be found [here](https://github.com/maxidl/wav2vec2). The model was trained for 50k steps, taking around 30 hours on a single A100. The arguments used for training this model are: ``` python run_finetuning.py \\ --model_name_or_path="facebook/wav2vec2-large-xlsr-53" \\ --dataset_config_name="de" \\ --output_dir=./wav2vec2-large-xlsr-german \\ --preprocessing_num_workers="16" \\ --overwrite_output_dir \\ --num_train_epochs="20" \\ --per_device_train_batch_size="64" \\ --per_device_eval_batch_size="32" \\ --learning_rate="1e-4" \\ --warmup_steps="500" \\ --evaluation_strategy="steps" \\ --save_steps="5000" \\ --eval_steps="5000" \\ --logging_steps="1000" \\ --save_total_limit="3" \\ --freeze_feature_extractor \\ --activation_dropout="0.055" \\ --attention_dropout="0.094" \\ --feat_proj_dropout="0.04" \\ --layerdrop="0.04" \\ --mask_time_prob="0.08" \\ --gradient_checkpointing="1" \\ --fp16 \\ --do_train \\ --do_eval \\ --dataloader_num_workers="16" \\ --group_by_length ```
pierrerappolt/cart
e1b8e44a537f4a3355859982f3327d4478eb4797
2022-03-27T02:53:44.000Z
[ "pytorch", "roberta", "question-answering", "en", "dataset:squadv2", "transformers", "html", "license:apache-2.0", "autotrain_compatible" ]
question-answering
false
pierrerappolt
null
pierrerappolt/cart
200
null
transformers
3,623
--- language: en tags: - html license: apache-2.0 datasets: - squadv2 inference: parameters: handle_impossible_answer: true --- Txt
facebook/deit-tiny-distilled-patch16-224
488ad407f8f6bcfc79829ece3507658c46d15563
2022-07-13T11:41:55.000Z
[ "pytorch", "tf", "deit", "image-classification", "dataset:imagenet", "arxiv:2012.12877", "arxiv:2006.03677", "transformers", "vision", "license:apache-2.0" ]
image-classification
false
facebook
null
facebook/deit-tiny-distilled-patch16-224
199
1
transformers
3,624
--- license: apache-2.0 tags: - image-classification - vision datasets: - imagenet --- # Distilled Data-efficient Image Transformer (tiny-sized model) Distilled data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) by Touvron et al. and first released in [this repository](https://github.com/facebookresearch/deit). However, the weights were converted from the [timm repository](https://github.com/rwightman/pytorch-image-models) by Ross Wightman. Disclaimer: The team releasing DeiT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description This model is a distilled Vision Transformer (ViT). It uses a distillation token, besides the class token, to effectively learn from a teacher (CNN) during both pre-training and fine-tuning. The distillation token is learned through backpropagation, by interacting with the class ([CLS]) and patch tokens through the self-attention layers. Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=facebook/deit) to look for fine-tuned versions on a task that interests you. ### How to use Since this model is a distilled ViT model, you can plug it into DeiTModel, DeiTForImageClassification or DeiTForImageClassificationWithTeacher. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name. 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 AutoFeatureExtractor, DeiTForImageClassificationWithTeacher from PIL import Image import requests url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Image.open(requests.get(url, stream=True).raw) feature_extractor = AutoFeatureExtractor.from_pretrained('facebook/deit-tiny-distilled-patch16-224') model = DeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-tiny-distilled-patch16-224') inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits # model predicts one of the 1000 ImageNet classes predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx]) ``` Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon. ## Training data This model was pretrained and fine-tuned with distillation on [ImageNet-1k](http://www.image-net.org/challenges/LSVRC/2012/), a dataset consisting of 1 million images and 1k classes. ## Training procedure ### Preprocessing The exact details of preprocessing of images during training/validation can be found [here](https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L78). At inference time, images are resized/rescaled to the same resolution (256x256), center-cropped at 224x224 and normalized across the RGB channels with the ImageNet mean and standard deviation. ### Pretraining The model was trained on a single 8-GPU node for 3 days. Training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper. ## Evaluation results | Model | ImageNet top-1 accuracy | ImageNet top-5 accuracy | # params | URL | |---------------------------------------|-------------------------|-------------------------|----------|------------------------------------------------------------------| | DeiT-tiny | 72.2 | 91.1 | 5M | https://huggingface.co/facebook/deit-tiny-patch16-224 | | DeiT-small | 79.9 | 95.0 | 22M | https://huggingface.co/facebook/deit-small-patch16-224 | | DeiT-base | 81.8 | 95.6 | 86M | https://huggingface.co/facebook/deit-base-patch16-224 | | **DeiT-tiny distilled** | **74.5** | **91.9** | **6M** | **https://huggingface.co/facebook/deit-tiny-distilled-patch16-224** | | DeiT-small distilled | 81.2 | 95.4 | 22M | https://huggingface.co/facebook/deit-small-distilled-patch16-224 | | DeiT-base distilled | 83.4 | 96.5 | 87M | https://huggingface.co/facebook/deit-base-distilled-patch16-224 | | DeiT-base 384 | 82.9 | 96.2 | 87M | https://huggingface.co/facebook/deit-base-patch16-384 | | DeiT-base distilled 384 (1000 epochs) | 85.2 | 97.2 | 88M | https://huggingface.co/facebook/deit-base-distilled-patch16-384 | Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance. ### BibTeX entry and citation info ```bibtex @misc{touvron2021training, title={Training data-efficient image transformers & distillation through attention}, author={Hugo Touvron and Matthieu Cord and Matthijs Douze and Francisco Massa and Alexandre Sablayrolles and Hervé Jégou}, year={2021}, eprint={2012.12877}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` ```bibtex @misc{wu2020visual, title={Visual Transformers: Token-based Image Representation and Processing for Computer Vision}, author={Bichen Wu and Chenfeng Xu and Xiaoliang Dai and Alvin Wan and Peizhao Zhang and Zhicheng Yan and Masayoshi Tomizuka and Joseph Gonzalez and Kurt Keutzer and Peter Vajda}, year={2020}, eprint={2006.03677}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` ```bibtex @inproceedings{deng2009imagenet, title={Imagenet: A large-scale hierarchical image database}, author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li}, booktitle={2009 IEEE conference on computer vision and pattern recognition}, pages={248--255}, year={2009}, organization={Ieee} } ```
liaad/srl-en_xlmr-large
93bf9d8074ab8794d705a1774ec2e7f33e25919c
2021-09-22T08:56:14.000Z
[ "pytorch", "xlm-roberta", "feature-extraction", "multilingual", "pt", "en", "dataset:PropBank.Br", "dataset:CoNLL-2012", "arxiv:2101.01213", "transformers", "xlm-roberta-large", "semantic role labeling", "finetuned", "license:apache-2.0" ]
feature-extraction
false
liaad
null
liaad/srl-en_xlmr-large
199
null
transformers
3,625
--- language: - multilingual - pt - en tags: - xlm-roberta-large - semantic role labeling - finetuned license: apache-2.0 datasets: - PropBank.Br - CoNLL-2012 metrics: - F1 Measure --- # XLM-R large fine-tuned on English semantic role labeling ## Model description This model is the [`xlm-roberta-large`](https://huggingface.co/xlm-roberta-large) fine-tuned on the English CoNLL formatted OntoNotes v5.0 semantic role labeling data. This is part of a project from which resulted the following models: * [liaad/srl-pt_bertimbau-base](https://huggingface.co/liaad/srl-pt_bertimbau-base) * [liaad/srl-pt_bertimbau-large](https://huggingface.co/liaad/srl-pt_bertimbau-large) * [liaad/srl-pt_xlmr-base](https://huggingface.co/liaad/srl-pt_xlmr-base) * [liaad/srl-pt_xlmr-large](https://huggingface.co/liaad/srl-pt_xlmr-large) * [liaad/srl-pt_mbert-base](https://huggingface.co/liaad/srl-pt_mbert-base) * [liaad/srl-en_xlmr-base](https://huggingface.co/liaad/srl-en_xlmr-base) * [liaad/srl-en_xlmr-large](https://huggingface.co/liaad/srl-en_xlmr-large) * [liaad/srl-en_mbert-base](https://huggingface.co/liaad/srl-en_mbert-base) * [liaad/srl-enpt_xlmr-base](https://huggingface.co/liaad/srl-enpt_xlmr-base) * [liaad/srl-enpt_xlmr-large](https://huggingface.co/liaad/srl-enpt_xlmr-large) * [liaad/srl-enpt_mbert-base](https://huggingface.co/liaad/srl-enpt_mbert-base) * [liaad/ud_srl-pt_bertimbau-large](https://huggingface.co/liaad/ud_srl-pt_bertimbau-large) * [liaad/ud_srl-pt_xlmr-large](https://huggingface.co/liaad/ud_srl-pt_xlmr-large) * [liaad/ud_srl-enpt_xlmr-large](https://huggingface.co/liaad/ud_srl-enpt_xlmr-large) For more information, please see the accompanying article (See BibTeX entry and citation info below) and the [project's github](https://github.com/asofiaoliveira/srl_bert_pt). ## Intended uses & limitations #### How to use To use the transformers portion of this model: ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("liaad/srl-en_xlmr-large") model = AutoModel.from_pretrained("liaad/srl-en_xlmr-large") ``` To use the full SRL model (transformers portion + a decoding layer), refer to the [project's github](https://github.com/asofiaoliveira/srl_bert_pt). #### Limitations and bias - This model does not include a Tensorflow version. This is because the "type_vocab_size" in this model was changed (from 1 to 2) and, therefore, it cannot be easily converted to Tensorflow. - The models were trained only for 5 epochs. - The English data was preprocessed to match the Portuguese data, so there are some differences in role attributions and some roles were removed from the data. ## Training procedure The models were trained on the CoNLL-2012 dataset, preprocessed to match the Portuguese PropBank.Br data. They were tested on the PropBank.Br data set as well as on a smaller opinion dataset "Buscapé". For more information, please see the accompanying article (See BibTeX entry and citation info below) and the [project's github](https://github.com/asofiaoliveira/srl_bert_pt). ## Eval results | Model Name | F<sub>1</sub> CV PropBank.Br (in domain) | F<sub>1</sub> Buscapé (out of domain) | | --------------- | ------ | ----- | | `srl-pt_bertimbau-base` | 76.30 | 73.33 | | `srl-pt_bertimbau-large` | 77.42 | 74.85 | | `srl-pt_xlmr-base` | 75.22 | 72.82 | | `srl-pt_xlmr-large` | 77.59 | 73.84 | | `srl-pt_mbert-base` | 72.76 | 66.89 | | `srl-en_xlmr-base` | 66.59 | 65.24 | | `srl-en_xlmr-large` | 67.60 | 64.94 | | `srl-en_mbert-base` | 63.07 | 58.56 | | `srl-enpt_xlmr-base` | 76.50 | 73.74 | | `srl-enpt_xlmr-large` | **78.22** | 74.55 | | `srl-enpt_mbert-base` | 74.88 | 69.19 | | `ud_srl-pt_bertimbau-large` | 77.53 | 74.49 | | `ud_srl-pt_xlmr-large` | 77.69 | 74.91 | | `ud_srl-enpt_xlmr-large` | 77.97 | **75.05** | ### BibTeX entry and citation info ```bibtex @misc{oliveira2021transformers, title={Transformers and Transfer Learning for Improving Portuguese Semantic Role Labeling}, author={Sofia Oliveira and Daniel Loureiro and Alípio Jorge}, year={2021}, eprint={2101.01213}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
facebook/regnet-y-320-seer
1b737c5ac08d98ca903d2c1d87d2c83e95df054e
2022-06-30T10:21:14.000Z
[ "pytorch", "tf", "regnet", "feature-extraction", "arxiv:2202.08360", "transformers", "vision", "license:apache-2.0" ]
feature-extraction
false
facebook
null
facebook/regnet-y-320-seer
199
null
transformers
3,626
--- license: apache-2.0 tags: - vision widgets: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace --- # RegNetModel RegNetModel model was introduced in the paper [Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision](https://arxiv.org/abs/2202.08360) and first released in [this repository](https://github.com/facebookresearch/vissl/tree/main/projects/SEER). Disclaimer: The team releasing RegNetModel did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description The authors trained [RegNets](https://huggingface.co/?models=regnet) models in a self-supervised fashion on bilion of random images from the internet ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/regnet_architecture.png) ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=regnet) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model: ```python >>> from transformers import AutoFeatureExtractor, RegNetModel >>> import torch >>> from datasets import load_dataset >>> dataset = load_dataset("huggingface/cats-image") >>> image = dataset["test"]["image"][0] >>> feature_extractor = AutoFeatureExtractor.from_pretrained("zuppif/regnet-y-040") >>> model = RegNetModel.from_pretrained("zuppif/regnet-y-040") >>> inputs = feature_extractor(image, return_tensors="pt") >>> with torch.no_grad(): ... outputs = model(**inputs) >>> last_hidden_states = outputs.last_hidden_state >>> list(last_hidden_states.shape) [1, 1088, 7, 7] ``` For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/regnet).
Chemsseddine/bert2gpt2SUMM-finetuned-mlsum
21c2f20b157fac797f5b45eb23163c1a5f8c4d59
2022-06-30T19:54:06.000Z
[ "pytorch", "tensorboard", "encoder-decoder", "text2text-generation", "fr", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
text2text-generation
false
Chemsseddine
null
Chemsseddine/bert2gpt2SUMM-finetuned-mlsum
199
null
transformers
3,627
--- language: fr license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: bert2gpt2_med_ml_orange_summ-finetuned_med_sum_new-finetuned_med_sum_new results: [] --- <img src="https://huggingface.co/Chemsseddine/bert2gpt2_med_ml_orange_summ-finetuned_med_sum_new-finetuned_med_sum_new/resolve/main/logobert2gpt2.png" alt="Map of positive probabilities per country." width="200"/> # bert2gpt2SUMM-finetuned-mlsum --- ## This model is used for french summarization - Problem type: Summarization - Model ID: 980832493 - CO2 Emissions (in grams): 0.10685501288084795 This model is a fine-tuned version of [Chemsseddine/bert2gpt2SUMM](https://huggingface.co/Chemsseddine/bert2gpt2SUMM) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.03749418258667 - Rouge1: 28.8384 - Rouge2: 10.7511 - RougeL: 27.0842 - RougeLsum: 27.5118 - Gen Len: 22.0625 ### 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: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 33199 | 4.03749 | 28.8384 | 10.7511 | 27.0842 | 27.5118 | 22.0625 |
justheuristic/test-bloomd-350m
e839f6d8cfb0061ee1100c3ddde71db8a7576f6c
2022-07-07T01:20:37.000Z
[ "pytorch", "bloom", "transformers" ]
null
false
justheuristic
null
justheuristic/test-bloomd-350m
199
null
transformers
3,628
Entry not found
Helsinki-NLP/opus-mt-yo-en
94685c1b0eb549569c394538ccd482b6132321a0
2021-09-11T10:52:45.000Z
[ "pytorch", "marian", "text2text-generation", "yo", "en", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-yo-en
198
null
transformers
3,629
--- tags: - translation license: apache-2.0 --- ### opus-mt-yo-en * source languages: yo * target languages: en * OPUS readme: [yo-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/yo-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/yo-en/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/yo-en/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/yo-en/opus-2020-01-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.yo.en | 33.8 | 0.496 |
aware-ai/byt5-german-grammar
8ab29798880b659a170a97a3ec9626a28e46ed3f
2021-06-23T12:20:22.000Z
[ "pytorch", "t5", "text2text-generation", "de", "transformers", "grammar", "autotrain_compatible" ]
text2text-generation
false
aware-ai
null
aware-ai/byt5-german-grammar
198
1
transformers
3,630
--- language: de tags: - grammar widget: - text: "correct german grammar: es ist schön so viele tolle menschen um sich zu haben denn ohne sie wäre es nicht so schön" --- example outputs: input: ich liebe das leben --> output: Ich liebe das Leben. input: es ist schön so viele tolle menschen um sich zu haben denn ohne sie wäre es nicht so schön --> output: Es ist schön, so viele tolle Menschen, um sich zu haben, denn ohne sie wäre es nicht so schön. input: der kunde hat ausdrücklich nach dirk verlangt weil er den rabatt haben möchte --> output: Der Kunde hat ausdrücklich nach Dirk verlangt, weil er den Rabatt haben möchte. the data can be prepared like this: the broken_text is used as input, while the text is the output ```python import re import phonetics import random chars_to_ignore_regex = "[^A-Za-z0-9\ö\ä\ü\Ö\Ä\Ü\ß\-,;.:?! ]+" broken_chars_to_ignore_regex = "[^A-Za-z0-9\ö\ä\ü\Ö\Ä\Ü\ß\- ]+" def do_manipulation(string): text = re.sub(chars_to_ignore_regex, '', string) broken_text = re.sub(broken_chars_to_ignore_regex, "", text.lower()) if(random.randint(0,100) >= 50): for xyz in range(int(len(broken_text.split(" "))/4)): if(random.randint(0,100) > 30): randc = random.choice(broken_text.split(" ")) if(random.randint(0,10) > 4): broken_text = broken_text.replace(randc, ''.join(random.choice('abcdefghijklmnopqrstuvxyz') for _ in range(len(randc))).lower()) else: broken_text = broken_text.replace(randc, phonetics.metaphone(randc).lower()) return text, broken_text ```
persiannlp/mt5-large-parsinlu-translation_en_fa
53fec354caa45718befa6f016a8f515a43ca8cd4
2021-09-23T16:20:29.000Z
[ "pytorch", "mt5", "text2text-generation", "fa", "multilingual", "dataset:parsinlu", "transformers", "machine-translation", "persian", "farsi", "license:cc-by-nc-sa-4.0", "autotrain_compatible" ]
text2text-generation
false
persiannlp
null
persiannlp/mt5-large-parsinlu-translation_en_fa
198
null
transformers
3,631
--- language: - fa - multilingual thumbnail: https://upload.wikimedia.org/wikipedia/commons/a/a2/Farsi.svg tags: - machine-translation - mt5 - persian - farsi license: cc-by-nc-sa-4.0 datasets: - parsinlu metrics: - sacrebleu --- # Machine Translation (ترجمه‌ی ماشینی) This is an mT5-based model for machine translation (English -> Persian). Here is an example of how you can run this model: ```python from transformers import MT5ForConditionalGeneration, MT5Tokenizer model_size = "large" model_name = f"persiannlp/mt5-{model_size}-parsinlu-translation_en_fa" tokenizer = MT5Tokenizer.from_pretrained(model_name) model = MT5ForConditionalGeneration.from_pretrained(model_name) def run_model(input_string, **generator_args): input_ids = tokenizer.encode(input_string, return_tensors="pt") res = model.generate(input_ids, **generator_args) output = tokenizer.batch_decode(res, skip_special_tokens=True) print(output) return output run_model("Praise be to Allah, the Cherisher and Sustainer of the worlds;") run_model("shrouds herself in white and walks penitentially disguised as brotherly love through factories and parliaments; offers help, but desires power;") run_model("He thanked all fellow bloggers and organizations that showed support.") run_model("Races are held between April and December at the Veliefendi Hippodrome near Bakerky, 15 km (9 miles) west of Istanbul.") run_model("I want to pursue PhD in Computer Science about social network,what is the open problem in social networks?") ``` which should output: ``` ['خدا را شکر که آفریننده و نگهدار جهان است.'] ['خود را با کفن سفید می پوشد و به شکل برادرانه ای در'] ['او از همه ی وبلاگ نویسان و سازمان هایی که از او حمایت کردند'] ['مسابقات بین آوریل و دسامبر در فرودگاه والی عبدین نزدیک بی'] ['من می خواهم پایان نامه دکتری را در رشته علوم کامپیوتر در'] ``` For more details, visit this page: https://github.com/persiannlp/parsinlu/
sismetanin/xlm_roberta_base-ru-sentiment-rusentiment
611b0acab81780953a2477cf1e17ddc4429b396f
2021-02-25T23:57:49.000Z
[ "pytorch", "xlm-roberta", "text-classification", "ru", "transformers", "sentiment analysis", "Russian" ]
text-classification
false
sismetanin
null
sismetanin/xlm_roberta_base-ru-sentiment-rusentiment
198
null
transformers
3,632
--- language: - ru tags: - sentiment analysis - Russian --- ## XML-RoBERTa-Base-ru-sentiment-RuSentiment XML-RoBERTa-Base-ru-sentiment-RuSentiment is a [XML-RoBERTa-Base](https://huggingface.co/xlm-roberta-base) model fine-tuned on [RuSentiment dataset](https://github.com/text-machine-lab/rusentiment) of general-domain Russian-language posts from the largest Russian social network, VKontakte. <table> <thead> <tr> <th rowspan="4">Model</th> <th rowspan="4">Score<br></th> <th rowspan="4">Rank</th> <th colspan="12">Dataset</th> </tr> <tr> <td colspan="6">SentiRuEval-2016<br></td> <td colspan="2" rowspan="2">RuSentiment</td> <td rowspan="2">KRND</td> <td rowspan="2">LINIS Crowd</td> <td rowspan="2">RuTweetCorp</td> <td rowspan="2">RuReviews</td> </tr> <tr> <td colspan="3">TC</td> <td colspan="3">Banks</td> </tr> <tr> <td>micro F1</td> <td>macro F1</td> <td>F1</td> <td>micro F1</td> <td>macro F1</td> <td>F1</td> <td>wighted</td> <td>F1</td> <td>F1</td> <td>F1</td> <td>F1</td> <td>F1</td> </tr> </thead> <tbody> <tr> <td>SOTA</td> <td>n/s</td> <td></td> <td>76.71</td> <td>66.40</td> <td>70.68</td> <td>67.51</td> <td>69.53</td> <td>74.06</td> <td>78.50</td> <td>n/s</td> <td>73.63</td> <td>60.51</td> <td>83.68</td> <td>77.44</td> </tr> <tr> <td>XLM-RoBERTa-Large</td> <td>76.37</td> <td>1</td> <td>82.26</td> <td>76.36</td> <td>79.42</td> <td>76.35</td> <td>76.08</td> <td>80.89</td> <td>78.31</td> <td>75.27</td> <td>75.17</td> <td>60.03</td> <td>88.91</td> <td>78.81</td> </tr> <tr> <td>SBERT-Large</td> <td>75.43</td> <td>2</td> <td>78.40</td> <td>71.36</td> <td>75.14</td> <td>72.39</td> <td>71.87</td> <td>77.72</td> <td>78.58</td> <td>75.85</td> <td>74.20</td> <td>60.64</td> <td>88.66</td> <td>77.41</td> </tr> <tr> <td>MBARTRuSumGazeta</td> <td>74.70</td> <td>3</td> <td>76.06</td> <td>68.95</td> <td>73.04</td> <td>72.34</td> <td>71.93</td> <td>77.83</td> <td>76.71</td> <td>73.56</td> <td>74.18</td> <td>60.54</td> <td>87.22</td> <td>77.51</td> </tr> <tr> <td>Conversational RuBERT</td> <td>74.44</td> <td>4</td> <td>76.69</td> <td>69.09</td> <td>73.11</td> <td>69.44</td> <td>68.68</td> <td>75.56</td> <td>77.31</td> <td>74.40</td> <td>73.10</td> <td>59.95</td> <td>87.86</td> <td>77.78</td> </tr> <tr> <td>LaBSE</td> <td>74.11</td> <td>5</td> <td>77.00</td> <td>69.19</td> <td>73.55</td> <td>70.34</td> <td>69.83</td> <td>76.38</td> <td>74.94</td> <td>70.84</td> <td>73.20</td> <td>59.52</td> <td>87.89</td> <td>78.47</td> </tr> <tr> <td>XLM-RoBERTa-Base</td> <td>73.60</td> <td>6</td> <td>76.35</td> <td>69.37</td> <td>73.42</td> <td>68.45</td> <td>67.45</td> <td>74.05</td> <td>74.26</td> <td>70.44</td> <td>71.40</td> <td>60.19</td> <td>87.90</td> <td>78.28</td> </tr> <tr> <td>RuBERT</td> <td>73.45</td> <td>7</td> <td>74.03</td> <td>66.14</td> <td>70.75</td> <td>66.46</td> <td>66.40</td> <td>73.37</td> <td>75.49</td> <td>71.86</td> <td>72.15</td> <td>60.55</td> <td>86.99</td> <td>77.41</td> </tr> <tr> <td>MBART-50-Large-Many-to-Many</td> <td>73.15</td> <td>8</td> <td>75.38</td> <td>67.81</td> <td>72.26</td> <td>67.13</td> <td>66.97</td> <td>73.85</td> <td>74.78</td> <td>70.98</td> <td>71.98</td> <td>59.20</td> <td>87.05</td> <td>77.24</td> </tr> <tr> <td>SlavicBERT</td> <td>71.96</td> <td>9</td> <td>71.45</td> <td>63.03</td> <td>68.44</td> <td>64.32</td> <td>63.99</td> <td>71.31</td> <td>72.13</td> <td>67.57</td> <td>72.54</td> <td>58.70</td> <td>86.43</td> <td>77.16</td> </tr> <tr> <td>EnRuDR-BERT</td> <td>71.51</td> <td>10</td> <td>72.56</td> <td>64.74</td> <td>69.07</td> <td>61.44</td> <td>60.21</td> <td>68.34</td> <td>74.19</td> <td>69.94</td> <td>69.33</td> <td>56.55</td> <td>87.12</td> <td>77.95</td> </tr> <tr> <td>RuDR-BERT</td> <td>71.14</td> <td>11</td> <td>72.79</td> <td>64.23</td> <td>68.36</td> <td>61.86</td> <td>60.92</td> <td>68.48</td> <td>74.65</td> <td>70.63</td> <td>68.74</td> <td>54.45</td> <td>87.04</td> <td>77.91</td> </tr> <tr> <td>MBART-50-Large</td> <td>69.46</td> <td>12</td> <td>70.91</td> <td>62.67</td> <td>67.24</td> <td>61.12</td> <td>60.25</td> <td>68.41</td> <td>72.88</td> <td>68.63</td> <td>70.52</td> <td>46.39</td> <td>86.48</td> <td>77.52</td> </tr> </tbody> </table> The table shows per-task scores and a macro-average of those scores to determine a models’s position on the leaderboard. For datasets with multiple evaluation metrics (e.g., macro F1 and weighted F1 for RuSentiment), we use an unweighted average of the metrics as the score for the task when computing the overall macro-average. The same strategy for comparing models’ results was applied in the GLUE benchmark. ## Citation If you find this repository helpful, feel free to cite our publication: ``` @article{Smetanin2021Deep, author = {Sergey Smetanin and Mikhail Komarov}, title = {Deep transfer learning baselines for sentiment analysis in Russian}, journal = {Information Processing & Management}, volume = {58}, number = {3}, pages = {102484}, year = {2021}, issn = {0306-4573}, doi = {0.1016/j.ipm.2020.102484} } ``` Dataset: ``` @inproceedings{rogers2018rusentiment, title={RuSentiment: An enriched sentiment analysis dataset for social media in Russian}, author={Rogers, Anna and Romanov, Alexey and Rumshisky, Anna and Volkova, Svitlana and Gronas, Mikhail and Gribov, Alex}, booktitle={Proceedings of the 27th international conference on computational linguistics}, pages={755--763}, year={2018} } ```
nlpaueb/sec-bert-shape
9829e591d1c801f9dcd887365d97da179a71b53e
2022-04-28T14:46:51.000Z
[ "pytorch", "tf", "bert", "pretraining", "en", "arxiv:2203.06482", "transformers", "finance", "financial", "license:cc-by-sa-4.0", "fill-mask" ]
fill-mask
false
nlpaueb
null
nlpaueb/sec-bert-shape
198
6
transformers
3,633
--- language: en pipeline_tag: fill-mask license: cc-by-sa-4.0 thumbnail: https://i.ibb.co/0yz81K9/sec-bert-logo.png tags: - finance - financial widget: - text: "Total net sales decreased [MASK]% or $[X.X] billion during [XXXX] compared to [XXXX]" - text: "Total net sales decreased [X]% or $[MASK] billion during [XXXX] compared to [XXXX]." - text: "Total net sales decreased [X]% or $[X.X] billion during [MASK] compared to [XXXX]." - text: "During [MASK], the Company repurchased $[XX.X] billion of its common stock and paid dividend equivalents of $[XX.X] billion." - text: "During 2019, the Company repurchased $[MASK] billion of its common stock and paid dividend equivalents of $[XX.X] billion." --- # SEC-BERT <img align="center" src="https://i.ibb.co/0yz81K9/sec-bert-logo.png" alt="sec-bert-logo" width="400"/> <div style="text-align: justify"> SEC-BERT is a family of BERT models for the financial domain, intended to assist financial NLP research and FinTech applications. SEC-BERT consists of the following models: * [**SEC-BERT-BASE**](https://huggingface.co/nlpaueb/sec-bert-base): Same architecture as BERT-BASE trained on financial documents. * [**SEC-BERT-NUM**](https://huggingface.co/nlpaueb/sec-bert-num): Same as SEC-BERT-BASE but we replace every number token with a [NUM] pseudo-token handling all numeric expressions in a uniform manner, disallowing their fragmentation) * **SEC-BERT-SHAPE** (this model): Same as SEC-BERT-BASE but we replace numbers with pseudo-tokens that represent the number’s shape, so numeric expressions (of known shapes) are no longer fragmented, e.g., '53.2' becomes '[XX.X]' and '40,200.5' becomes '[XX,XXX.X]'. </div> ## Pre-training corpus The model was pre-trained on 260,773 10-K filings from 1993-2019, publicly available at <a href="https://www.sec.gov/">U.S. Securities and Exchange Commission (SEC)</a> ## Pre-training details <div style="text-align: justify"> * We created a new vocabulary of 30k subwords by training a [BertWordPieceTokenizer](https://github.com/huggingface/tokenizers) from scratch on the pre-training corpus. * We trained BERT using the official code provided in [Google BERT's GitHub repository](https://github.com/google-research/bert)</a>. * We then used [Hugging Face](https://huggingface.co)'s [Transformers](https://github.com/huggingface/transformers) conversion script to convert the TF checkpoint in the desired format in order to be able to load the model in two lines of code for both PyTorch and TF2 users. * We release a model similar to the English BERT-BASE model (12-layer, 768-hidden, 12-heads, 110M parameters). * We chose to follow the same training set-up: 1 million training steps with batches of 256 sequences of length 512 with an initial learning rate 1e-4. * We were able to use a single Google Cloud TPU v3-8 provided for free from [TensorFlow Research Cloud (TRC)](https://sites.research.google/trc), while also utilizing [GCP research credits](https://edu.google.com/programs/credits/research). Huge thanks to both Google programs for supporting us! </div> ## Load Pretrained Model ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nlpaueb/sec-bert-shape") model = AutoModel.from_pretrained("nlpaueb/sec-bert-shape") ``` ## Pre-process Text <div style="text-align: justify"> To use SEC-BERT-SHAPE, you have to pre-process texts replacing every numerical token with the corresponding shape pseudo-token, from a list of 214 predefined shape pseudo-tokens. If the numerical token does not correspond to any shape pseudo-token we replace it with the [NUM] pseudo-token. Below there is an example of how you can pre-process a simple sentence. This approach is quite simple; feel free to modify it as you see fit. </div> ```python import re import spacy from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("nlpaueb/sec-bert-shape") spacy_tokenizer = spacy.load("en_core_web_sm") sentence = "Total net sales decreased 2% or $5.4 billion during 2019 compared to 2018." def sec_bert_shape_preprocess(text): tokens = [t.text for t in spacy_tokenizer(sentence)] processed_text = [] for token in tokens: if re.fullmatch(r"(\d+[\d,.]*)|([,.]\d+)", token): shape = '[' + re.sub(r'\d', 'X', token) + ']' if shape in tokenizer.additional_special_tokens: processed_text.append(shape) else: processed_text.append('[NUM]') else: processed_text.append(token) return ' '.join(processed_text) tokenized_sentence = tokenizer.tokenize(sec_bert_shape_preprocess(sentence)) print(tokenized_sentence) """ ['total', 'net', 'sales', 'decreased', '[X]', '%', 'or', '$', '[X.X]', 'billion', 'during', '[XXXX]', 'compared', 'to', '[XXXX]', '.'] """ ``` ## Using SEC-BERT variants as Language Models | Sample | Masked Token | | --------------------------------------------------- | ------------ | | Total net sales [MASK] 2% or $5.4 billion during 2019 compared to 2018. | decreased | Model | Predictions (Probability) | | --------------------------------------------------- | ------------ | | **BERT-BASE-UNCASED** | increased (0.221), were (0.131), are (0.103), rose (0.075), of (0.058) | **SEC-BERT-BASE** | increased (0.678), decreased (0.282), declined (0.017), grew (0.016), rose (0.004) | **SEC-BERT-NUM** | increased (0.753), decreased (0.211), grew (0.019), declined (0.010), rose (0.006) | **SEC-BERT-SHAPE** | increased (0.747), decreased (0.214), grew (0.021), declined (0.013), rose (0.002) | Sample | Masked Token | | --------------------------------------------------- | ------------ | | Total net sales decreased 2% or $5.4 [MASK] during 2019 compared to 2018. | billion | Model | Predictions (Probability) | | --------------------------------------------------- | ------------ | | **BERT-BASE-UNCASED** | billion (0.841), million (0.097), trillion (0.028), ##m (0.015), ##bn (0.006) | **SEC-BERT-BASE** | million (0.972), billion (0.028), millions (0.000), ##million (0.000), m (0.000) | **SEC-BERT-NUM** | million (0.974), billion (0.012), , (0.010), thousand (0.003), m (0.000) | **SEC-BERT-SHAPE** | million (0.978), billion (0.021), % (0.000), , (0.000), millions (0.000) | Sample | Masked Token | | --------------------------------------------------- | ------------ | | Total net sales decreased [MASK]% or $5.4 billion during 2019 compared to 2018. | 2 | Model | Predictions (Probability) | | --------------------------------------------------- | ------------ | | **BERT-BASE-UNCASED** | 20 (0.031), 10 (0.030), 6 (0.029), 4 (0.027), 30 (0.027) | **SEC-BERT-BASE** | 13 (0.045), 12 (0.040), 11 (0.040), 14 (0.035), 10 (0.035) | **SEC-BERT-NUM** | [NUM] (1.000), one (0.000), five (0.000), three (0.000), seven (0.000) | **SEC-BERT-SHAPE** | [XX] (0.316), [XX.X] (0.253), [X.X] (0.237), [X] (0.188), [X.XX] (0.002) | Sample | Masked Token | | --------------------------------------------------- | ------------ | | Total net sales decreased 2[MASK] or $5.4 billion during 2019 compared to 2018. | % | Model | Predictions (Probability) | | --------------------------------------------------- | ------------ | | **BERT-BASE-UNCASED** | % (0.795), percent (0.174), ##fold (0.009), billion (0.004), times (0.004) | **SEC-BERT-BASE** | % (0.924), percent (0.076), points (0.000), , (0.000), times (0.000) | **SEC-BERT-NUM** | % (0.882), percent (0.118), million (0.000), units (0.000), bps (0.000) | **SEC-BERT-SHAPE** | % (0.961), percent (0.039), bps (0.000), , (0.000), bcf (0.000) | Sample | Masked Token | | --------------------------------------------------- | ------------ | | Total net sales decreased 2% or $[MASK] billion during 2019 compared to 2018. | 5.4 | Model | Predictions (Probability) | | --------------------------------------------------- | ------------ | | **BERT-BASE-UNCASED** | 1 (0.074), 4 (0.045), 3 (0.044), 2 (0.037), 5 (0.034) | **SEC-BERT-BASE** | 1 (0.218), 2 (0.136), 3 (0.078), 4 (0.066), 5 (0.048) | **SEC-BERT-NUM** | [NUM] (1.000), l (0.000), 1 (0.000), - (0.000), 30 (0.000) | **SEC-BERT-SHAPE** | [X.X] (0.787), [X.XX] (0.095), [XX.X] (0.049), [X.XXX] (0.046), [X] (0.013) | Sample | Masked Token | | --------------------------------------------------- | ------------ | | Total net sales decreased 2% or $5.4 billion during [MASK] compared to 2018. | 2019 | Model | Predictions (Probability) | | --------------------------------------------------- | ------------ | | **BERT-BASE-UNCASED** | 2017 (0.485), 2018 (0.169), 2016 (0.164), 2015 (0.070), 2014 (0.022) | **SEC-BERT-BASE** | 2019 (0.990), 2017 (0.007), 2018 (0.003), 2020 (0.000), 2015 (0.000) | **SEC-BERT-NUM** | [NUM] (1.000), as (0.000), fiscal (0.000), year (0.000), when (0.000) | **SEC-BERT-SHAPE** | [XXXX] (1.000), as (0.000), year (0.000), periods (0.000), , (0.000) | Sample | Masked Token | | --------------------------------------------------- | ------------ | | Total net sales decreased 2% or $5.4 billion during 2019 compared to [MASK]. | 2018 | Model | Predictions (Probability) | | --------------------------------------------------- | ------------ | | **BERT-BASE-UNCASED** | 2017 (0.100), 2016 (0.097), above (0.054), inflation (0.050), previously (0.037) | **SEC-BERT-BASE** | 2018 (0.999), 2019 (0.000), 2017 (0.000), 2016 (0.000), 2014 (0.000) | **SEC-BERT-NUM** | [NUM] (1.000), year (0.000), last (0.000), sales (0.000), fiscal (0.000) | **SEC-BERT-SHAPE** | [XXXX] (1.000), year (0.000), sales (0.000), prior (0.000), years (0.000) | Sample | Masked Token | | --------------------------------------------------- | ------------ | | During 2019, the Company [MASK] $67.1 billion of its common stock and paid dividend equivalents of $14.1 billion. | repurchased | Model | Predictions (Probability) | | --------------------------------------------------- | ------------ | | **BERT-BASE-UNCASED** | held (0.229), sold (0.192), acquired (0.172), owned (0.052), traded (0.033) | **SEC-BERT-BASE** | repurchased (0.913), issued (0.036), purchased (0.029), redeemed (0.010), sold (0.003) | **SEC-BERT-NUM** | repurchased (0.917), purchased (0.054), reacquired (0.013), issued (0.005), acquired (0.003) | **SEC-BERT-SHAPE** | repurchased (0.902), purchased (0.068), issued (0.010), reacquired (0.008), redeemed (0.006) | Sample | Masked Token | | --------------------------------------------------- | ------------ | | During 2019, the Company repurchased $67.1 billion of its common [MASK] and paid dividend equivalents of $14.1 billion. | stock | Model | Predictions (Probability) | | --------------------------------------------------- | ------------ | | **BERT-BASE-UNCASED** | stock (0.835), assets (0.039), equity (0.025), debt (0.021), bonds (0.017) | **SEC-BERT-BASE** | stock (0.857), shares (0.135), equity (0.004), units (0.002), securities (0.000) | **SEC-BERT-NUM** | stock (0.842), shares (0.157), equity (0.000), securities (0.000), units (0.000) | **SEC-BERT-SHAPE** | stock (0.888), shares (0.109), equity (0.001), securities (0.001), stocks (0.000) | Sample | Masked Token | | --------------------------------------------------- | ------------ | | During 2019, the Company repurchased $67.1 billion of its common stock and paid [MASK] equivalents of $14.1 billion. | dividend | Model | Predictions (Probability) | | --------------------------------------------------- | ------------ | | **BERT-BASE-UNCASED** | cash (0.276), net (0.128), annual (0.083), the (0.040), debt (0.027) | **SEC-BERT-BASE** | dividend (0.890), cash (0.018), dividends (0.016), share (0.013), tax (0.010) | **SEC-BERT-NUM** | dividend (0.735), cash (0.115), share (0.087), tax (0.025), stock (0.013) | **SEC-BERT-SHAPE** | dividend (0.655), cash (0.248), dividends (0.042), share (0.019), out (0.003) | Sample | Masked Token | | --------------------------------------------------- | ------------ | | During 2019, the Company repurchased $67.1 billion of its common stock and paid dividend [MASK] of $14.1 billion. | equivalents | Model | Predictions (Probability) | | --------------------------------------------------- | ------------ | | **BERT-BASE-UNCASED** | revenue (0.085), earnings (0.078), rates (0.065), amounts (0.064), proceeds (0.062) | **SEC-BERT-BASE** | payments (0.790), distributions (0.087), equivalents (0.068), cash (0.013), amounts (0.004) | **SEC-BERT-NUM** | payments (0.845), equivalents (0.097), distributions (0.024), increases (0.005), dividends (0.004) | **SEC-BERT-SHAPE** | payments (0.784), equivalents (0.093), distributions (0.043), dividends (0.015), requirements (0.009) ## Publication <div style="text-align: justify"> If you use this model cite the following article:<br> [**FiNER: Financial Numeric Entity Recognition for XBRL Tagging**](https://arxiv.org/abs/2203.06482)<br> Lefteris Loukas, Manos Fergadiotis, Ilias Chalkidis, Eirini Spyropoulou, Prodromos Malakasiotis, Ion Androutsopoulos and George Paliouras<br> In the Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL 2022) (Long Papers), Dublin, Republic of Ireland, May 22 - 27, 2022 </div> ``` @inproceedings{loukas-etal-2022-finer, title = {FiNER: Financial Numeric Entity Recognition for XBRL Tagging}, author = {Loukas, Lefteris and Fergadiotis, Manos and Chalkidis, Ilias and Spyropoulou, Eirini and Malakasiotis, Prodromos and Androutsopoulos, Ion and Paliouras George}, booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL 2022)}, publisher = {Association for Computational Linguistics}, location = {Dublin, Republic of Ireland}, year = {2022}, url = {https://arxiv.org/abs/2203.06482} } ``` ## About Us <div style="text-align: justify"> [AUEB's Natural Language Processing Group](http://nlp.cs.aueb.gr) develops algorithms, models, and systems that allow computers to process and generate natural language texts. The group's current research interests include: * question answering systems for databases, ontologies, document collections, and the Web, especially biomedical question answering, * natural language generation from databases and ontologies, especially Semantic Web ontologies, text classification, including filtering spam and abusive content, * information extraction and opinion mining, including legal text analytics and sentiment analysis, * natural language processing tools for Greek, for example parsers and named-entity recognizers, machine learning in natural language processing, especially deep learning. The group is part of the Information Processing Laboratory of the Department of Informatics of the Athens University of Economics and Business. </div> [Manos Fergadiotis](https://manosfer.github.io) on behalf of [AUEB's Natural Language Processing Group](http://nlp.cs.aueb.gr)
AIDA-UPM/MSTSb_stsb-xlm-r-multilingual
eea01579b009257faffea3516c0da1f89f2d99ba
2021-07-21T18:32:31.000Z
[ "pytorch", "xlm-roberta", "feature-extraction", "sentence-transformers", "sentence-similarity", "transformers" ]
sentence-similarity
false
AIDA-UPM
null
AIDA-UPM/MSTSb_stsb-xlm-r-multilingual
197
null
sentence-transformers
3,634
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, max pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 1438 with parameters: ``` {'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "callback": null, "epochs": 1, "evaluation_steps": 1000, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 4e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 144, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
Intel/bert-large-uncased-sparse-90-unstructured-pruneofa
29c4f2d3eef81d3960cf87b214516a49398c09ed
2022-01-13T12:13:38.000Z
[ "pytorch", "tf", "bert", "pretraining", "en", "dataset:wikipedia", "dataset:bookcorpus", "arxiv:2111.05754", "transformers", "fill-mask" ]
fill-mask
false
Intel
null
Intel/bert-large-uncased-sparse-90-unstructured-pruneofa
197
null
transformers
3,635
--- language: en tags: fill-mask datasets: - wikipedia - bookcorpus --- # 90% Sparse BERT-Large (uncased) Prune OFA This model is a result from our paper [Prune Once for All: Sparse Pre-Trained Language Models](https://arxiv.org/abs/2111.05754) presented in ENLSP NeurIPS Workshop 2021. For further details on the model and its result, see our paper and our implementation available [here](https://github.com/IntelLabs/Model-Compression-Research-Package/tree/main/research/prune-once-for-all).
Norod78/hebrew-gpt_neo-xl
56f66a33c04fc5b0499b0f143ec08abc7ca436a2
2022-07-04T13:09:16.000Z
[ "pytorch", "jax", "gpt_neo", "text-generation", "he", "transformers", "license:mit" ]
text-generation
false
Norod78
null
Norod78/hebrew-gpt_neo-xl
197
1
transformers
3,636
--- language: he thumbnail: https://avatars1.githubusercontent.com/u/3617152?norod.jpg widget: - text: "עוד בימי קדם" - text: "קוראים לי דורון ואני מעוניין ל" - text: "קוראים לי איציק ואני חושב ש" - text: "החתול שלך מאוד חמוד ו" - text: "ובדרך ראינו שהגן" license: mit --- # hebrew-gpt_neo-xl Hebrew text generation model based on [EleutherAI's gpt-neo](https://github.com/EleutherAI/gpt-neo). Each was trained on a TPUv3-8 which was made avilable to me via the [TPU Research Cloud](https://sites.research.google/trc/) Program. ## Datasets 1. An assortment of various Hebrew corpuses - I have made it available [here](https://mega.nz/folder/CodSSA4R#4INvMes-56m_WUi7jQMbJQ) 2. oscar / unshuffled_deduplicated_he - [Homepage](https://oscar-corpus.com) | [Dataset Permalink](https://huggingface.co/datasets/viewer/?dataset=oscar&config=unshuffled_deduplicated_he) The Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture. 3. CC100-Hebrew Dataset [Homepage](https://metatext.io/datasets/cc100-hebrew) Created by Conneau & Wenzek et al. at 2020, the CC100-Hebrew This dataset is one of the 100 corpora of monolingual data that was processed from the January-December 2018 Commoncrawl snapshots from the CC-Net repository. The size of this corpus is 6.1G., in Hebrew language. ## Training Config Available [here](https://github.com/Norod/hebrew-gpt_neo/tree/main/hebrew-gpt_neo-xl/configs) <BR> ## Usage ### Google Colab Notebook Available [here ](https://colab.research.google.com/github/Norod/hebrew-gpt_neo/blob/main/hebrew-gpt_neo-xl/Norod78_hebrew_gpt_neo_xl_Colab.ipynb) <BR> #### Simple usage sample code ```python !pip install tokenizers==0.10.3 transformers==4.8.0 from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Norod78/hebrew-gpt_neo-xl") model = AutoModelForCausalLM.from_pretrained("Norod78/hebrew-gpt_neo-xl", pad_token_id=tokenizer.eos_token_id) prompt_text = "אני אוהב שוקולד ועוגות" max_len = 512 sample_output_num = 3 seed = 1000 import numpy as np import torch device = torch.device("cuda" if torch.cuda.is_available() else "cpu") n_gpu = 0 if torch.cuda.is_available()==False else torch.cuda.device_count() print(f"device: {device}, n_gpu: {n_gpu}") np.random.seed(seed) torch.manual_seed(seed) if n_gpu > 0: torch.cuda.manual_seed_all(seed) model.to(device) encoded_prompt = tokenizer.encode( prompt_text, add_special_tokens=False, return_tensors="pt") encoded_prompt = encoded_prompt.to(device) if encoded_prompt.size()[-1] == 0: input_ids = None else: input_ids = encoded_prompt print("input_ids = " + str(input_ids)) if input_ids != None: max_len += len(encoded_prompt[0]) if max_len > 2048: max_len = 2048 print("Updated max_len = " + str(max_len)) stop_token = "<|endoftext|>" new_lines = "\ \ \ " sample_outputs = model.generate( input_ids, do_sample=True, max_length=max_len, top_k=50, top_p=0.95, num_return_sequences=sample_output_num ) print(100 * '-' + "\ \t\tOutput\ " + 100 * '-') for i, sample_output in enumerate(sample_outputs): text = tokenizer.decode(sample_output, skip_special_tokens=True) # Remove all text after the stop token text = text[: text.find(stop_token) if stop_token else None] # Remove all text after 3 newlines text = text[: text.find(new_lines) if new_lines else None] print("\ {}: {}".format(i, text)) print("\ " + 100 * '-') ```
comodoro/wav2vec2-xls-r-300m-cs-250
5d17dcbb516fa3532bb9b6ce4dc20d4779993142
2022-03-23T18:26:50.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "cs", "dataset:mozilla-foundation/common_voice_8_0", "dataset:ovm", "dataset:pscr", "dataset:vystadial2016", "transformers", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_8_0", "robust-speech-event", "xlsr-fine-tuning-week", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
comodoro
null
comodoro/wav2vec2-xls-r-300m-cs-250
197
null
transformers
3,637
--- language: - cs license: apache-2.0 tags: - automatic-speech-recognition - generated_from_trainer - hf-asr-leaderboard - mozilla-foundation/common_voice_8_0 - robust-speech-event - xlsr-fine-tuning-week datasets: - mozilla-foundation/common_voice_8_0 - ovm - pscr - vystadial2016 model-index: - name: Czech comodoro Wav2Vec2 XLSR 300M 250h data results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 8 type: mozilla-foundation/common_voice_8_0 args: cs metrics: - name: Test WER type: wer value: 7.3 - name: Test CER type: cer value: 2.1 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Robust Speech Event - Dev Data type: speech-recognition-community-v2/dev_data args: cs metrics: - name: Test WER type: wer value: 43.44 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Robust Speech Event - Test Data type: speech-recognition-community-v2/eval_data args: cs metrics: - name: Test WER type: wer value: 38.5 --- # Czech wav2vec2-xls-r-300m-cs-250 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice 8.0 dataset as well as other datasets listed below. It achieves the following results on the evaluation set: - Loss: 0.1271 - Wer: 0.1475 - Cer: 0.0329 The `eval.py` script results using a LM are: - WER: 0.07274312090176113 - CER: 0.021207369275558875 ## Model description Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Czech using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset. When using this model, make sure that your speech input is sampled at 16kHz. 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("mozilla-foundation/common_voice_8_0", "cs", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("comodoro/wav2vec2-xls-r-300m-cs-250") model = Wav2Vec2ForCTC.from_pretrained("comodoro/wav2vec2-xls-r-300m-cs-250") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset[:2]["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset[:2]["sentence"]) ``` ## Evaluation The model can be evaluated using the attached `eval.py` script: ``` python eval.py --model_id comodoro/wav2vec2-xls-r-300m-cs-250 --dataset mozilla-foundation/common-voice_8_0 --split test --config cs ``` ## Training and evaluation data The Common Voice 8.0 `train` and `validation` datasets were used for training, as well as the following datasets: - Šmídl, Luboš and Pražák, Aleš, 2013, OVM – Otázky Václava Moravce, LINDAT/CLARIAH-CZ digital library at the Institute of Formal and Applied Linguistics (ÚFAL), Faculty of Mathematics and Physics, Charles University, http://hdl.handle.net/11858/00-097C-0000-000D-EC98-3. - Pražák, Aleš and Šmídl, Luboš, 2012, Czech Parliament Meetings, LINDAT/CLARIAH-CZ digital library at the Institute of Formal and Applied Linguistics (ÚFAL), Faculty of Mathematics and Physics, Charles University, http://hdl.handle.net/11858/00-097C-0000-0005-CF9C-4. - Plátek, Ondřej; Dušek, Ondřej and Jurčíček, Filip, 2016, Vystadial 2016 – Czech data, LINDAT/CLARIAH-CZ digital library at the Institute of Formal and Applied Linguistics (ÚFAL), Faculty of Mathematics and Physics, Charles University, http://hdl.handle.net/11234/1-1740. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - 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: 800 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:| | 3.4203 | 0.16 | 800 | 3.3148 | 1.0 | 1.0 | | 2.8151 | 0.32 | 1600 | 0.8508 | 0.8938 | 0.2345 | | 0.9411 | 0.48 | 2400 | 0.3335 | 0.3723 | 0.0847 | | 0.7408 | 0.64 | 3200 | 0.2573 | 0.2840 | 0.0642 | | 0.6516 | 0.8 | 4000 | 0.2365 | 0.2581 | 0.0595 | | 0.6242 | 0.96 | 4800 | 0.2039 | 0.2433 | 0.0541 | | 0.5754 | 1.12 | 5600 | 0.1832 | 0.2156 | 0.0482 | | 0.5626 | 1.28 | 6400 | 0.1827 | 0.2091 | 0.0463 | | 0.5342 | 1.44 | 7200 | 0.1744 | 0.2033 | 0.0468 | | 0.4965 | 1.6 | 8000 | 0.1705 | 0.1963 | 0.0444 | | 0.5047 | 1.76 | 8800 | 0.1604 | 0.1889 | 0.0422 | | 0.4814 | 1.92 | 9600 | 0.1604 | 0.1827 | 0.0411 | | 0.4471 | 2.09 | 10400 | 0.1566 | 0.1822 | 0.0406 | | 0.4509 | 2.25 | 11200 | 0.1619 | 0.1853 | 0.0432 | | 0.4415 | 2.41 | 12000 | 0.1513 | 0.1764 | 0.0397 | | 0.4313 | 2.57 | 12800 | 0.1515 | 0.1739 | 0.0392 | | 0.4163 | 2.73 | 13600 | 0.1445 | 0.1695 | 0.0377 | | 0.4142 | 2.89 | 14400 | 0.1478 | 0.1699 | 0.0385 | | 0.4184 | 3.05 | 15200 | 0.1430 | 0.1669 | 0.0376 | | 0.3886 | 3.21 | 16000 | 0.1433 | 0.1644 | 0.0374 | | 0.3795 | 3.37 | 16800 | 0.1426 | 0.1648 | 0.0373 | | 0.3859 | 3.53 | 17600 | 0.1357 | 0.1604 | 0.0361 | | 0.3762 | 3.69 | 18400 | 0.1344 | 0.1558 | 0.0349 | | 0.384 | 3.85 | 19200 | 0.1379 | 0.1576 | 0.0359 | | 0.3762 | 4.01 | 20000 | 0.1344 | 0.1539 | 0.0346 | | 0.3559 | 4.17 | 20800 | 0.1339 | 0.1525 | 0.0351 | | 0.3683 | 4.33 | 21600 | 0.1315 | 0.1518 | 0.0342 | | 0.3572 | 4.49 | 22400 | 0.1307 | 0.1507 | 0.0342 | | 0.3494 | 4.65 | 23200 | 0.1294 | 0.1491 | 0.0335 | | 0.3476 | 4.81 | 24000 | 0.1287 | 0.1491 | 0.0336 | | 0.3475 | 4.97 | 24800 | 0.1271 | 0.1475 | 0.0329 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.1+cu102 - Datasets 1.18.3 - Tokenizers 0.11.0
filco306/gpt2-switchboard-paraphraser
c12a7bd906a6d45768ed4860bc2b08a533c6e5dd
2021-08-28T23:33:47.000Z
[ "pytorch", "text-generation", "arxiv:2010.05700", "transformers" ]
text-generation
false
filco306
null
filco306/gpt2-switchboard-paraphraser
197
null
transformers
3,638
# GPT2 Switchboard style transfer paraphraser This is the trained Switchboard-model from the paper [Reformulating Unsupervised Style Transfer as Paraphrase Generation](https://arxiv.org/abs/2010.05700) by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by the main author. ## Citation If you found this model useful, please cite the original work: ``` @inproceedings{style20, author={Kalpesh Krishna and John Wieting and Mohit Iyyer}, Booktitle = {Empirical Methods in Natural Language Processing}, Year = "2020", Title={Reformulating Unsupervised Style Transfer as Paraphrase Generation}, } ```
neuropark/sahajBERT
e9159abbb5f8b352cc5ec59bababc66ba5382bb4
2021-06-24T16:49:26.000Z
[ "pytorch", "albert", "pretraining", "bn", "dataset:Wikipedia", "dataset:Oscar", "arxiv:1909.11942", "transformers", "collaborative", "bengali", "bangla", "license:apache-2.0", "fill-mask" ]
fill-mask
false
neuropark
null
neuropark/sahajBERT
197
7
transformers
3,639
--- language: bn tags: - collaborative - bengali - albert - bangla license: apache-2.0 datasets: - Wikipedia - Oscar widget: - text: "জীবনে সবচেয়ে মূল্যবান জিনিস হচ্ছে [MASK]।" pipeline_tag: fill-mask --- # sahajBERT <iframe width="100%" height="1100" frameborder="0" src="https://observablehq.com/embed/@huggingface/participants-bubbles-chart?cells=c_noaws%2Ct_noaws%2Cviewof+currentDate"></iframe> Collaboratively pre-trained model on Bengali language using masked language modeling (MLM) and Sentence Order Prediction (SOP) objectives. ## Model description <!-- You can embed local or remote images using `![](...)` --> sahajBERT is a model composed of 1) a tokenizer specially designed for Bengali and 2) an [ALBERT](https://arxiv.org/abs/1909.11942) architecture collaboratively pre-trained on a dump of Wikipedia in Bengali and the Bengali part of OSCAR. <!-- Add more information about the collaborative training when we have time / preprint available --> ## Intended uses & limitations You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. We trained our model on 2 of these downstream tasks: [sequence classification](https://huggingface.co/neuropark/sahajBERT-NCC) and [token classification](https://huggingface.co/neuropark/sahajBERT-NER) #### How to use You can use this model directly with a pipeline for masked language modeling: ```python from transformers import AlbertForMaskedLM, FillMaskPipeline, PreTrainedTokenizerFast # Initialize tokenizer tokenizer = PreTrainedTokenizerFast.from_pretrained("neuropark/sahajBERT") # Initialize model model = AlbertForMaskedLM.from_pretrained("neuropark/sahajBERT") # Initialize pipeline pipeline = FillMaskPipeline(tokenizer=tokenizer, model=model) raw_text = "ধন্যবাদ। আপনার সাথে কথা [MASK] ভালো লাগলো" # Change me pipeline(raw_text) ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import AlbertModel, PreTrainedTokenizerFast # Initialize tokenizer tokenizer = PreTrainedTokenizerFast.from_pretrained("neuropark/sahajBERT") # Initialize model model = AlbertModel.from_pretrained("neuropark/sahajBERT") text = "ধন্যবাদ। আপনার সাথে কথা বলে ভালো লাগলো" # Change me encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` #### Limitations and bias <!-- Provide examples of latent issues and potential remediations. --> WIP ## Training data The tokenizer was trained on he Bengali part of OSCAR and the model on a [dump of Wikipedia in Bengali](https://huggingface.co/datasets/lhoestq/wikipedia_bn) and the Bengali part of [OSCAR](https://huggingface.co/datasets/oscar). ## Training procedure This model was trained in a collaborative manner by volunteer participants. <!-- Add more information about the collaborative training when we have time / preprint available + Preprocessing, hardware used, hyperparameters... (maybe use figures)--> ### Contributors leaderboard | Rank | Username | Total contributed runtime | |:-------------:|:-------------:|-------------:| | 1|[khalidsaifullaah](https://huggingface.co/khalidsaifullaah)|11 days 21:02:08| | 2|[ishanbagchi](https://huggingface.co/ishanbagchi)|9 days 20:37:00| | 3|[tanmoyio](https://huggingface.co/tanmoyio)|9 days 18:08:34| | 4|[debajit](https://huggingface.co/debajit)|8 days 14:15:10| | 5|[skylord](https://huggingface.co/skylord)|6 days 16:35:29| | 6|[ibraheemmoosa](https://huggingface.co/ibraheemmoosa)|5 days 01:05:57| | 7|[SaulLu](https://huggingface.co/SaulLu)|5 days 00:46:36| | 8|[lhoestq](https://huggingface.co/lhoestq)|4 days 20:11:16| | 9|[nilavya](https://huggingface.co/nilavya)|4 days 08:51:51| |10|[Priyadarshan](https://huggingface.co/Priyadarshan)|4 days 02:28:55| |11|[anuragshas](https://huggingface.co/anuragshas)|3 days 05:00:55| |12|[sujitpal](https://huggingface.co/sujitpal)|2 days 20:52:33| |13|[manandey](https://huggingface.co/manandey)|2 days 16:17:13| |14|[albertvillanova](https://huggingface.co/albertvillanova)|2 days 14:14:31| |15|[justheuristic](https://huggingface.co/justheuristic)|2 days 13:20:52| |16|[w0lfw1tz](https://huggingface.co/w0lfw1tz)|2 days 07:22:48| |17|[smoker](https://huggingface.co/smoker)|2 days 02:52:03| |18|[Soumi](https://huggingface.co/Soumi)|1 days 20:42:02| |19|[Anjali](https://huggingface.co/Anjali)|1 days 16:28:00| |20|[OptimusPrime](https://huggingface.co/OptimusPrime)|1 days 09:16:57| |21|[theainerd](https://huggingface.co/theainerd)|1 days 04:48:57| |22|[yhn112](https://huggingface.co/yhn112)|0 days 20:57:02| |23|[kolk](https://huggingface.co/kolk)|0 days 17:57:37| |24|[arnab](https://huggingface.co/arnab)|0 days 17:54:12| |25|[imavijit](https://huggingface.co/imavijit)|0 days 16:07:26| |26|[osanseviero](https://huggingface.co/osanseviero)|0 days 14:16:45| |27|[subhranilsarkar](https://huggingface.co/subhranilsarkar)|0 days 13:04:46| |28|[sagnik1511](https://huggingface.co/sagnik1511)|0 days 12:24:57| |29|[anindabitm](https://huggingface.co/anindabitm)|0 days 08:56:44| |30|[borzunov](https://huggingface.co/borzunov)|0 days 04:07:35| |31|[thomwolf](https://huggingface.co/thomwolf)|0 days 03:53:15| |32|[priyadarshan](https://huggingface.co/priyadarshan)|0 days 03:40:11| |33|[ali007](https://huggingface.co/ali007)|0 days 03:34:37| |34|[sbrandeis](https://huggingface.co/sbrandeis)|0 days 03:18:16| |35|[Preetha](https://huggingface.co/Preetha)|0 days 03:13:47| |36|[Mrinal](https://huggingface.co/Mrinal)|0 days 03:01:43| |37|[laxya007](https://huggingface.co/laxya007)|0 days 02:18:34| |38|[lewtun](https://huggingface.co/lewtun)|0 days 00:34:43| |39|[Rounak](https://huggingface.co/Rounak)|0 days 00:26:10| |40|[kshmax](https://huggingface.co/kshmax)|0 days 00:06:38| ### Hardware used <iframe width="100%" height="251" frameborder="0" src="https://observablehq.com/embed/@huggingface/sahajbert-hardware?cells=c1_noaws"></iframe> ## Eval results We evaluate sahajBERT model quality and 2 other model benchmarks ([XLM-R-large](https://huggingface.co/xlm-roberta-large) and [IndicBert](https://huggingface.co/ai4bharat/indic-bert)) by fine-tuning 3 times their pre-trained models on two downstream tasks in Bengali: - **NER**: a named entity recognition on Bengali split of [WikiANN](https://huggingface.co/datasets/wikiann) dataset - **NCC**: a multi-class classification task on news Soham News Category Classification dataset from IndicGLUE | Base pre-trained Model | NER - F1 (mean ± std) | NCC - Accuracy (mean ± std) | |:-------------:|:-------------:|:-------------:| |sahajBERT | 95.45 ± 0.53| 91.97 ± 0.47| |[XLM-R-large](https://huggingface.co/xlm-roberta-large) | 96.48 ± 0.22| 90.05 ± 0.38| |[IndicBert](https://huggingface.co/ai4bharat/indic-bert) | 92.52 ± 0.45| 74.46 ± 1.91| ### BibTeX entry and citation info Coming soon! <!-- ```bibtex @inproceedings{..., year={2020} } ``` -->
pucpr/clinicalnerpt-medical
0d889f90b203734b0ba45904781a6779c8eac2b9
2021-10-13T09:28:28.000Z
[ "pytorch", "bert", "token-classification", "pt", "dataset:SemClinBr", "transformers", "autotrain_compatible" ]
token-classification
false
pucpr
null
pucpr/clinicalnerpt-medical
197
3
transformers
3,640
--- language: "pt" widget: - text: "Hoje realizou avaliacao de mp-cdi, com eletrodos atrial e ventricular." - text: "Paciente encaminhado a câmera hiperbárica no período da tarde." datasets: - SemClinBr thumbnail: "https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/logo-biobertpr1.png" --- <img src="https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/logo-biobertpr1.png" alt="Logo BioBERTpt"> # Portuguese Clinical NER - Medical The Medical NER model is part of the [BioBERTpt project](https://www.aclweb.org/anthology/2020.clinicalnlp-1.7/), where 13 models of clinical entities (compatible with UMLS) were trained. All NER model from "pucpr" user was trained from the Brazilian clinical corpus [SemClinBr](https://github.com/HAILab-PUCPR/SemClinBr), with 10 epochs and IOB2 format, from BioBERTpt(all) model. ## Acknowledgements This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001. ## Citation ``` @inproceedings{schneider-etal-2020-biobertpt, title = "{B}io{BERT}pt - A {P}ortuguese Neural Language Model for Clinical Named Entity Recognition", author = "Schneider, Elisa Terumi Rubel and de Souza, Jo{\~a}o Vitor Andrioli and Knafou, Julien and Oliveira, Lucas Emanuel Silva e and Copara, Jenny and Gumiel, Yohan Bonescki and Oliveira, Lucas Ferro Antunes de and Paraiso, Emerson Cabrera and Teodoro, Douglas and Barra, Cl{\'a}udia Maria Cabral Moro", booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.7", pages = "65--72", abstract = "With the growing number of electronic health record data, clinical NLP tasks have become increasingly relevant to unlock valuable information from unstructured clinical text. Although the performance of downstream NLP tasks, such as named-entity recognition (NER), in English corpus has recently improved by contextualised language models, less research is available for clinical texts in low resource languages. Our goal is to assess a deep contextual embedding model for Portuguese, so called BioBERTpt, to support clinical and biomedical NER. We transfer learned information encoded in a multilingual-BERT model to a corpora of clinical narratives and biomedical-scientific papers in Brazilian Portuguese. To evaluate the performance of BioBERTpt, we ran NER experiments on two annotated corpora containing clinical narratives and compared the results with existing BERT models. Our in-domain model outperformed the baseline model in F1-score by 2.72{\%}, achieving higher performance in 11 out of 13 assessed entities. We demonstrate that enriching contextual embedding models with domain literature can play an important role in improving performance for specific NLP tasks. The transfer learning process enhanced the Portuguese biomedical NER model by reducing the necessity of labeled data and the demand for retraining a whole new model.", } ``` ## Questions? Post a Github issue on the [BioBERTpt repo](https://github.com/HAILab-PUCPR/BioBERTpt).
raynardj/wenyanwen-ancient-translate-to-modern
164ab975150c37dcdf1bd2e782e2edd4b1add10c
2022-01-08T04:22:30.000Z
[ "pytorch", "encoder-decoder", "text2text-generation", "zh", "transformers", "translation", "古文", "文言文", "ancient", "classical", "autotrain_compatible" ]
translation
false
raynardj
null
raynardj/wenyanwen-ancient-translate-to-modern
197
3
transformers
3,641
--- language: - zh - zh tags: - translation - 古文 - 文言文 - ancient - classical widget: - text: "此诚危急存亡之秋也" --- # From Classical(ancient) Chinese to Modern Chinese > This model translate Classical(ancient) Chinese to Modern Chinese, so I guess who's interested in the problemset can speak at least modern Chinese, hence... let me continue the documentation in Chinese # 文言文(古文)到现代文的翻译器 > 这个模型已有做成应用, [【随无涯】](https://huggingface.co/spaces/raynardj/duguwen-classical-chinese-to-morden-translate)是一个huggingface spaces + streamlit 的古文阅读应用(含海量书籍), 可以在阅读时翻译 > 输入文言文, 可以是断句 或者 未断句的文言文, 模型会预测现代文的表述。 其他模型: * 从[现代文翻译到文言文](https://huggingface.co/raynardj/wenyanwen-chinese-translate-to-ancient) > 从文言文到现代文的翻译器, 欢迎前往[我的github文言诗词项目页面探讨、加⭐️ ](https://github.com/raynardj/yuan) > 训练语料是就是九十多万句句对, [数据集链接📚](https://github.com/BangBOOM/Classical-Chinese)。 训练时source序列(古文序列), 按照50%的概率整句去除所有标点符号。 ## 推荐的inference 通道 **注意** * 你必须将```generate```函数的```eos_token_id```设置为102就可以翻译出完整的语句, 不然翻译完了会有残留的语句(因为做熵的时候用pad标签=-100导致)。 目前huggingface 页面上compute按钮会有这个问题, 推荐使用以下代码来得到翻译结果 * 请设置```generate```的参数```num_beams>=3```, 以达到较好的翻译效果 * 请设置```generate```的参数```max_length```256, 不然结果会吃掉句子 ```python from transformers import ( EncoderDecoderModel, AutoTokenizer ) PRETRAINED = "raynardj/wenyanwen-ancient-translate-to-modern" tokenizer = AutoTokenizer.from_pretrained(PRETRAINED) model = EncoderDecoderModel.from_pretrained(PRETRAINED) def inference(text): tk_kwargs = dict( truncation=True, max_length=128, padding="max_length", return_tensors='pt') inputs = tokenizer([text,],**tk_kwargs) with torch.no_grad(): return tokenizer.batch_decode( model.generate( inputs.input_ids, attention_mask=inputs.attention_mask, num_beams=3, max_length=256, bos_token_id=101, eos_token_id=tokenizer.sep_token_id, pad_token_id=tokenizer.pad_token_id, ), skip_special_tokens=True) ``` ## 目前版本的案例 > 当然, 拿比较熟知的语句过来, 通常会有些贻笑大方的失误, 大家如果有好玩的调戏案例, 也欢迎反馈 ```python >>> inference('非我族类其心必异') ['不 是 我 们 的 族 类 , 他 们 的 心 思 必 然 不 同 。'] >>> inference('肉食者鄙未能远谋') ['吃 肉 的 人 鄙 陋 , 不 能 长 远 谋 划 。'] # 这里我好几批模型都翻不出这个**输**字(甚至有一个版本翻成了秦始皇和汉武帝), 可能并不是很古朴的用法, >>> inference('江山如此多娇引无数英雄竞折腰惜秦皇汉武略输文采唐宗宋祖稍逊风骚') ['江 山 如 此 多 , 招 引 无 数 的 英 雄 , 竞 相 折 腰 , 可 惜 秦 皇 、 汉 武 , 略 微 有 文 采 , 唐 宗 、 宋 祖 稍 稍 逊 出 风 雅 。'] >>> inference("清风徐来水波不兴") ['清 风 慢 慢 吹 来 , 水 波 不 兴 。'] >>> inference("无他唯手熟尔") ['没 有 别 的 事 , 只 是 手 熟 罢 了 。'] >>> inference("此诚危急存亡之秋也") ['这 实 在 是 危 急 存 亡 的 时 候 。'] ``` ## 其他文言诗词的资源 * [项目源代码 🌟, 欢迎+star提pr](https://github.com/raynardj/yuan) * [跨语种搜索 🔎](https://huggingface.co/raynardj/xlsearch-cross-lang-search-zh-vs-classicical-cn) * [现代文翻译古汉语的模型 ⛰](https://huggingface.co/raynardj/wenyanwen-chinese-translate-to-ancient) * [古汉语到现代文的翻译模型, 输入可以是未断句的句子 🚀](https://huggingface.co/raynardj/wenyanwen-ancient-translate-to-modern) * [断句模型 🗡](https://huggingface.co/raynardj/classical-chinese-punctuation-guwen-biaodian) * [意境关键词 和 藏头写诗🤖](https://huggingface.co/raynardj/keywords-cangtou-chinese-poetry)
tals/albert-base-vitaminc_wnei-fever
01a830a7f0c8cf62df0b5d29b473179cdf21c525
2022-06-22T23:56:28.000Z
[ "pytorch", "albert", "text-classification", "python", "dataset:fever", "dataset:glue", "dataset:tals/vitaminc", "transformers" ]
text-classification
false
tals
null
tals/albert-base-vitaminc_wnei-fever
197
null
transformers
3,642
--- language: python datasets: - fever - glue - tals/vitaminc --- # Details Model used in [Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence](https://aclanthology.org/2021.naacl-main.52/) (Schuster et al., NAACL 21`). For more details see: https://github.com/TalSchuster/VitaminC When using this model, please cite the paper. # BibTeX entry and citation info ```bibtex @inproceedings{schuster-etal-2021-get, title = "Get Your Vitamin {C}! Robust Fact Verification with Contrastive Evidence", author = "Schuster, Tal and Fisch, Adam and Barzilay, Regina", booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jun, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.naacl-main.52", doi = "10.18653/v1/2021.naacl-main.52", pages = "624--643", abstract = "Typical fact verification models use retrieved written evidence to verify claims. Evidence sources, however, often change over time as more information is gathered and revised. In order to adapt, models must be sensitive to subtle differences in supporting evidence. We present VitaminC, a benchmark infused with challenging cases that require fact verification models to discern and adjust to slight factual changes. We collect over 100,000 Wikipedia revisions that modify an underlying fact, and leverage these revisions, together with additional synthetically constructed ones, to create a total of over 400,000 claim-evidence pairs. Unlike previous resources, the examples in VitaminC are contrastive, i.e., they contain evidence pairs that are nearly identical in language and content, with the exception that one supports a given claim while the other does not. We show that training using this design increases robustness{---}improving accuracy by 10{\%} on adversarial fact verification and 6{\%} on adversarial natural language inference (NLI). Moreover, the structure of VitaminC leads us to define additional tasks for fact-checking resources: tagging relevant words in the evidence for verifying the claim, identifying factual revisions, and providing automatic edits via factually consistent text generation.", } ```
unicamp-dl/ptt5-small-portuguese-vocab
79a744f213b1f3a1a95f27f2230202440be253d1
2021-06-23T14:34:42.000Z
[ "pytorch", "tf", "jax", "t5", "text2text-generation", "pt", "dataset:brWaC", "transformers", "tensorflow", "pt-br", "license:mit", "autotrain_compatible" ]
text2text-generation
false
unicamp-dl
null
unicamp-dl/ptt5-small-portuguese-vocab
197
null
transformers
3,643
--- language: pt license: mit tags: - t5 - pytorch - tensorflow - pt - pt-br datasets: - brWaC widget: - text: "Texto de exemplo em português" inference: false --- # Portuguese T5 (aka "PTT5") ## Introduction PTT5 is a T5 model pretrained in the BrWac corpus, a large collection of web pages in Portuguese, improving T5's performance on Portuguese sentence similarity and entailment tasks. It's available in three sizes (small, base and large) and two vocabularies (Google's T5 original and ours, trained on Portuguese Wikipedia). For further information or requests, please go to [PTT5 repository](https://github.com/unicamp-dl/PTT5). ## Available models | Model | Size | #Params | Vocabulary | | :-: | :-: | :-: | :-: | | [unicamp-dl/ptt5-small-t5-vocab](https://huggingface.co/unicamp-dl/ptt5-small-t5-vocab) | small | 60M | Google's T5 | | [unicamp-dl/ptt5-base-t5-vocab](https://huggingface.co/unicamp-dl/ptt5-base-t5-vocab) | base | 220M | Google's T5 | | [unicamp-dl/ptt5-large-t5-vocab](https://huggingface.co/unicamp-dl/ptt5-large-t5-vocab) | large | 740M | Google's T5 | | [unicamp-dl/ptt5-small-portuguese-vocab](https://huggingface.co/unicamp-dl/ptt5-small-portuguese-vocab) | small | 60M | Portuguese | | **[unicamp-dl/ptt5-base-portuguese-vocab](https://huggingface.co/unicamp-dl/ptt5-base-portuguese-vocab)** **(Recommended)** | **base** | **220M** | **Portuguese** | | [unicamp-dl/ptt5-large-portuguese-vocab](https://huggingface.co/unicamp-dl/ptt5-large-portuguese-vocab) | large | 740M | Portuguese | ## Usage ```python # Tokenizer from transformers import T5Tokenizer # PyTorch (bare model, baremodel + language modeling head) from transformers import T5Model, T5ForConditionalGeneration # Tensorflow (bare model, baremodel + language modeling head) from transformers import TFT5Model, TFT5ForConditionalGeneration model_name = 'unicamp-dl/ptt5-base-portuguese-vocab' tokenizer = T5Tokenizer.from_pretrained(model_name) # PyTorch model_pt = T5ForConditionalGeneration.from_pretrained(model_name) # TensorFlow model_tf = TFT5ForConditionalGeneration.from_pretrained(model_name) ``` # Citation If you use PTT5, please cite: @article{ptt5_2020, title={PTT5: Pretraining and validating the T5 model on Brazilian Portuguese data}, author={Carmo, Diedre and Piau, Marcos and Campiotti, Israel and Nogueira, Rodrigo and Lotufo, Roberto}, journal={arXiv preprint arXiv:2008.09144}, year={2020} }
hellonesh/test_fine_tuned_crossencoder
a93a54a1b47b64fafab55a53cc80c7879096541b
2022-07-20T23:21:23.000Z
[ "pytorch", "bert", "feature-extraction", "transformers" ]
feature-extraction
false
hellonesh
null
hellonesh/test_fine_tuned_crossencoder
197
null
transformers
3,644
Entry not found
KoichiYasuoka/roberta-classical-chinese-large-char
6cb9aed2abae0da5fd17c5751598bdaeeb81cc3d
2021-10-30T00:38:19.000Z
[ "pytorch", "roberta", "fill-mask", "lzh", "transformers", "classical chinese", "literary chinese", "ancient chinese", "masked-lm", "license:apache-2.0", "autotrain_compatible" ]
fill-mask
false
KoichiYasuoka
null
KoichiYasuoka/roberta-classical-chinese-large-char
196
null
transformers
3,645
--- language: - "lzh" tags: - "classical chinese" - "literary chinese" - "ancient chinese" - "masked-lm" license: "apache-2.0" pipeline_tag: "fill-mask" mask_token: "[MASK]" widget: - text: "孟子[MASK]梁惠王" --- # roberta-classical-chinese-large-char ## Model Description This is a RoBERTa model pre-trained on Classical Chinese texts, derived from [GuwenBERT-large](https://huggingface.co/ethanyt/guwenbert-large). Character-embeddings are enhanced into traditional/simplified characters. You can fine-tune `roberta-classical-chinese-large-char` for downstream tasks, such as [sentence-segmentation](https://huggingface.co/KoichiYasuoka/roberta-classical-chinese-large-sentence-segmentation), [POS-tagging](https://huggingface.co/KoichiYasuoka/roberta-classical-chinese-large-upos), [dependency-parsing](https://github.com/KoichiYasuoka/SuPar-Kanbun), and so on. ## How to Use ```py from transformers import AutoTokenizer,AutoModelForMaskedLM tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-classical-chinese-large-char") model=AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/roberta-classical-chinese-large-char") ``` ## See Also [SuPar-Kanbun](https://github.com/KoichiYasuoka/SuPar-Kanbun): Tokenizer POS-tagger and Dependency-parser for Classical Chinese
asapp/sew-tiny-100k-ft-ls100h
a750ca77c83e5595081eddb05926c9b432684116
2022-05-24T12:53:02.000Z
[ "pytorch", "sew", "automatic-speech-recognition", "en", "dataset:librispeech_asr", "arxiv:2109.06870", "transformers", "audio", "speech", "hf-asr-leaderboard", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
asapp
null
asapp/sew-tiny-100k-ft-ls100h
196
null
transformers
3,646
--- language: en datasets: - librispeech_asr tags: - audio - speech - automatic-speech-recognition - hf-asr-leaderboard license: apache-2.0 widget: - example_title: Librispeech sample 1 src: https://cdn-media.huggingface.co/speech_samples/sample1.flac - example_title: Librispeech sample 2 src: https://cdn-media.huggingface.co/speech_samples/sample2.flac model-index: - name: sew-tiny-100k-ft-ls100h results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: LibriSpeech (clean) type: librispeech_asr config: clean split: test args: language: en metrics: - name: Test WER type: wer value: 10.61 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: LibriSpeech (other) type: librispeech_asr config: other split: test args: language: en metrics: - name: Test WER type: wer value: 23.74 --- # SEW-tiny [SEW by ASAPP Research](https://github.com/asappresearch/sew) The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Classification, Emotion Recognition, etc... Paper: [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) Authors: Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi **Abstract** This paper is a study of performance-efficiency trade-offs in pre-trained models for automatic speech recognition (ASR). We focus on wav2vec 2.0, and formalize several architecture designs that influence both the model performance and its efficiency. Putting together all our observations, we introduce SEW (Squeezed and Efficient Wav2vec), a pre-trained model architecture with significant improvements along both performance and efficiency dimensions across a variety of training setups. For example, under the 100h-960h semi-supervised setup on LibriSpeech, SEW achieves a 1.9x inference speedup compared to wav2vec 2.0, with a 13.5% relative reduction in word error rate. With a similar inference time, SEW reduces word error rate by 25-50% across different model sizes. The original model can be found under https://github.com/asappresearch/sew#model-checkpoints . # Usage To transcribe audio files the model can be used as a standalone acoustic model as follows: ```python from transformers import Wav2Vec2Processor, SEWForCTC from datasets import load_dataset import soundfile as sf import torch # load the model and preprocessor processor = Wav2Vec2Processor.from_pretrained("asapp/sew-tiny-100k-ft-ls100h") model = SEWForCTC.from_pretrained("asapp/sew-tiny-100k-ft-ls100h") # load the dummy dataset with speech samples ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") # preprocess input_values = processor(ds[0]["audio"]["array"], return_tensors="pt").input_values # Batch size 1 # retrieve logits logits = model(input_values).logits # take argmax and decode predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(predicted_ids) ``` ## Evaluation This code snippet shows how to evaluate **asapp/sew-tiny-100k-ft-ls100h** on LibriSpeech's "clean" and "other" test data. ```python from datasets import load_dataset from transformers import SEWForCTC, Wav2Vec2Processor import torch from jiwer import wer librispeech_eval = load_dataset("librispeech_asr", "clean", split="test") model = SEWForCTC.from_pretrained("asapp/sew-tiny-100k-ft-ls100h").to("cuda") processor = Wav2Vec2Processor.from_pretrained("asapp/sew-tiny-100k-ft-ls100h") def map_to_pred(batch): input_values = processor(batch["audio"][0]["array"], sampling_rate=16000, return_tensors="pt", padding="longest").input_values with torch.no_grad(): logits = model(input_values.to("cuda")).logits predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(predicted_ids) batch["transcription"] = transcription return batch result = librispeech_eval.map(map_to_pred, batched=True, batch_size=1, remove_columns=["audio"]) print("WER:", wer(result["text"], result["transcription"])) ``` *Result (WER)*: | "clean" | "other" | | --- | --- | | 10.61 | 23.74 |
federicopascual/finetuning-sentiment-model-3000-samples
11f7d327123ebcddd97304c57084c6365628dda5
2021-12-30T20:59:20.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "dataset:imdb", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
federicopascual
null
federicopascual/finetuning-sentiment-model-3000-samples
196
null
transformers
3,647
--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb args: plain_text metrics: - name: Accuracy type: accuracy value: 0.8666666666666667 - name: F1 type: f1 value: 0.8734177215189873 --- <!-- 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. --> # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3404 - Accuracy: 0.8667 - F1: 0.8734 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### 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: 2 ### Training results ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
flax-community/t5-base-wikisplit
2aafb7c5b0409872cea248eb548aba7405797d83
2021-07-21T08:56:00.000Z
[ "pytorch", "tf", "jax", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
flax-community
null
flax-community/t5-base-wikisplit
196
1
transformers
3,648
Entry not found
nandinib1999/quote-generator
43fed603ac9d6e99afba01ae8924b57630a3142c
2022-03-06T12:04:44.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "en", "dataset:quotes-500K", "transformers", "text generation", "license:cc" ]
text-generation
false
nandinib1999
null
nandinib1999/quote-generator
196
2
transformers
3,649
--- language: - en thumbnail: tags: - text generation license: cc datasets: - quotes-500K metrics: - perplexity --- # Quotes Generator ## Model description This is a GPT2 model fine-tuned on the Quotes-500K dataset. ## Intended uses & limitations For a given user prompt, it can generate motivational quotes starting with it. #### How to use ```python from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("nandinib1999/quote-generator") model = AutoModelWithLMHead.from_pretrained("nandinib1999/quote-generator") ``` ## Training data This is the distribution of the total dataset into training, validation and test dataset for the fine-tuning task. <table style="width:30%"> <tr> <th>train</th> <td>349796</td> </tr> <tr> <th>validation</th> <td>99942</td> </tr> <tr> <th>test</th> <td>49971</td> </tr> </table> ## Training procedure The model was fine-tuned using the Google Colab GPU for one epoch. The weights of the pre-trained GPT2 model were used as a base. ## Eval results <table style="width:30%"> <tr> <th>Epoch</th> <th>Perplexity</th> </tr> <tr> <td>1</td> <td>15.180</td> </tr> </table>
qanastek/pos-french
b377b7ff5d5b93286e8b3103104d7e5135d5f5be
2022-07-06T23:48:25.000Z
[ "pytorch", "fr", "dataset:qanastek/ANTILLES", "flair", "token-classification", "sequence-tagger-model" ]
token-classification
false
qanastek
null
qanastek/pos-french
196
1
flair
3,650
--- tags: - flair - token-classification - sequence-tagger-model language: fr datasets: - qanastek/ANTILLES widget: - text: "George Washington est allé à Washington" --- # POET: A French Extended Part-of-Speech Tagger - Corpora: [ANTILLES](https://github.com/qanastek/ANTILLES) - Embeddings: [FastText](https://fasttext.cc/) - Sequence Labelling: [Bi-LSTM-CRF](https://arxiv.org/abs/1011.4088) - Number of Epochs: 115 **People Involved** * [LABRAK Yanis](https://www.linkedin.com/in/yanis-labrak-8a7412145/) (1) * [DUFOUR Richard](https://cv.archives-ouvertes.fr/richard-dufour) (2) **Affiliations** 1. [LIA, NLP team](https://lia.univ-avignon.fr/), Avignon University, Avignon, France. 2. [LS2N, TALN team](https://www.ls2n.fr/equipe/taln/), Nantes University, Nantes, France. ## Demo: How to use in Flair Requires [Flair](https://pypi.org/project/flair/): ```pip install flair``` ```python from flair.data import Sentence from flair.models import SequenceTagger # Load the model model = SequenceTagger.load("qanastek/pos-french") sentence = Sentence("George Washington est allé à Washington") # Predict tags model.predict(sentence) # Print predicted pos tags print(sentence.to_tagged_string()) ``` Output: ![Preview Output](preview.PNG) ## Training data `ANTILLES` is a part-of-speech tagging corpora based on [UD_French-GSD](https://universaldependencies.org/treebanks/fr_gsd/index.html) which was originally created in 2015 and is based on the [universal dependency treebank v2.0](https://github.com/ryanmcd/uni-dep-tb). Originally, the corpora consists of 400,399 words (16,341 sentences) and had 17 different classes. Now, after applying our tags augmentation we obtain 60 different classes which add linguistic and semantic information such as the gender, number, mood, person, tense or verb form given in the different CoNLL-03 fields from the original corpora. We based our tags on the level of details given by the [LIA_TAGG](http://pageperso.lif.univ-mrs.fr/frederic.bechet/download.html) statistical POS tagger written by [Frédéric Béchet](http://pageperso.lif.univ-mrs.fr/frederic.bechet/index-english.html) in 2001. The corpora used for this model is available on [Github](https://github.com/qanastek/ANTILLES) at the [CoNLL-U format](https://universaldependencies.org/format.html). Training data are fed to the model as free language and doesn't pass a normalization phase. Thus, it's made the model case and punctuation sensitive. ## Original Tags ```plain PRON VERB SCONJ ADP CCONJ DET NOUN ADJ AUX ADV PUNCT PROPN NUM SYM PART X INTJ ``` ## New additional POS tags | Abbreviation | Description | Examples | |:--------:|:--------:|:--------:| | PREP | Preposition | de | | AUX | Auxiliary Verb | est | | ADV | Adverb | toujours | | COSUB | Subordinating conjunction | que | | COCO | Coordinating Conjunction | et | | PART | Demonstrative particle | -t | | PRON | Pronoun | qui ce quoi | | PDEMMS | Demonstrative Pronoun - Singular Masculine | ce | | PDEMMP | Demonstrative Pronoun - Plural Masculine | ceux | | PDEMFS | Demonstrative Pronoun - Singular Feminine | cette | | PDEMFP | Demonstrative Pronoun - Plural Feminine | celles | | PINDMS | Indefinite Pronoun - Singular Masculine | tout | | PINDMP | Indefinite Pronoun - Plural Masculine | autres | | PINDFS | Indefinite Pronoun - Singular Feminine | chacune | | PINDFP | Indefinite Pronoun - Plural Feminine | certaines | | PROPN | Proper noun | Houston | | XFAMIL | Last name | Levy | | NUM | Numerical Adjective | trentaine vingtaine | | DINTMS | Masculine Numerical Adjective | un | | DINTFS | Feminine Numerical Adjective | une | | PPOBJMS | Pronoun complements of objects - Singular Masculine | le lui | | PPOBJMP | Pronoun complements of objects - Plural Masculine | eux y | | PPOBJFS | Pronoun complements of objects - Singular Feminine | moi la | | PPOBJFP | Pronoun complements of objects - Plural Feminine | en y | | PPER1S | Personal Pronoun First-Person - Singular | je | | PPER2S | Personal Pronoun Second-Person - Singular | tu | | PPER3MS | Personal Pronoun Third-Person - Singular Masculine | il | | PPER3MP | Personal Pronoun Third-Person - Plural Masculine | ils | | PPER3FS | Personal Pronoun Third-Person - Singular Feminine | elle | | PPER3FP | Personal Pronoun Third-Person - Plural Feminine | elles | | PREFS | Reflexive Pronoun First-Person - Singular | me m' | | PREF | Reflexive Pronoun Third-Person - Singular | se s' | | PREFP | Reflexive Pronoun First / Second-Person - Plural | nous vous | | VERB | Verb | obtient | | VPPMS | Past Participle - Singular Masculine | formulé | | VPPMP | Past Participle - Plural Masculine | classés | | VPPFS | Past Participle - Singular Feminine | appelée | | VPPFP | Past Participle - Plural Feminine | sanctionnées | | DET | Determinant | les l' | | DETMS | Determinant - Singular Masculine | les | | DETFS | Determinant - Singular Feminine | la | | ADJ | Adjective | capable sérieux | | ADJMS | Adjective - Singular Masculine | grand important | | ADJMP | Adjective - Plural Masculine | grands petits | | ADJFS | Adjective - Singular Feminine | française petite | | ADJFP | Adjective - Plural Feminine | légères petites | | NOUN | Noun | temps | | NMS | Noun - Singular Masculine | drapeau | | NMP | Noun - Plural Masculine | journalistes | | NFS | Noun - Singular Feminine | tête | | NFP | Noun - Plural Feminine | ondes | | PREL | Relative Pronoun | qui dont | | PRELMS | Relative Pronoun - Singular Masculine | lequel | | PRELMP | Relative Pronoun - Plural Masculine | lesquels | | PRELFS | Relative Pronoun - Singular Feminine | laquelle | | PRELFP | Relative Pronoun - Plural Feminine | lesquelles | | INTJ | Interjection | merci bref | | CHIF | Numbers | 1979 10 | | SYM | Symbol | € % | | YPFOR | Endpoint | . | | PUNCT | Ponctuation | : , | | MOTINC | Unknown words | Technology Lady | | X | Typos & others | sfeir 3D statu | ## Evaluation results The test corpora used for this evaluation is available on [Github](https://github.com/qanastek/ANTILLES/blob/main/ANTILLES/test.conllu). ```plain Results: - F-score (micro): 0.952 - F-score (macro): 0.8644 - Accuracy (incl. no class): 0.952 By class: precision recall f1-score support PPER1S 0.9767 1.0000 0.9882 42 VERB 0.9823 0.9537 0.9678 583 COSUB 0.9344 0.8906 0.9120 128 PUNCT 0.9878 0.9688 0.9782 833 PREP 0.9767 0.9879 0.9822 1483 PDEMMS 0.9583 0.9200 0.9388 75 COCO 0.9839 1.0000 0.9919 245 DET 0.9679 0.9814 0.9746 645 NMP 0.9521 0.9115 0.9313 305 ADJMP 0.8352 0.9268 0.8786 82 PREL 0.9324 0.9857 0.9583 70 PREFP 0.9767 0.9545 0.9655 44 AUX 0.9537 0.9859 0.9695 355 ADV 0.9440 0.9365 0.9402 504 VPPMP 0.8667 1.0000 0.9286 26 DINTMS 0.9919 1.0000 0.9959 122 ADJMS 0.9020 0.9057 0.9039 244 NMS 0.9226 0.9336 0.9281 753 NFS 0.9347 0.9714 0.9527 560 YPFOR 0.9806 1.0000 0.9902 353 PINDMS 1.0000 0.9091 0.9524 44 NOUN 0.8400 0.5385 0.6562 39 PROPN 0.8605 0.8278 0.8439 395 DETMS 0.9972 0.9972 0.9972 362 PPER3MS 0.9341 0.9770 0.9551 87 VPPMS 0.8994 0.9682 0.9325 157 DETFS 1.0000 1.0000 1.0000 240 ADJFS 0.9266 0.9011 0.9136 182 ADJFP 0.9726 0.9342 0.9530 76 NFP 0.9463 0.9749 0.9604 199 VPPFS 0.8000 0.9000 0.8471 40 CHIF 0.9543 0.9414 0.9478 222 XFAMIL 0.9346 0.8696 0.9009 115 PPER3MP 0.9474 0.9000 0.9231 20 PPOBJMS 0.8800 0.9362 0.9072 47 PREF 0.8889 0.9231 0.9057 52 PPOBJMP 1.0000 0.6000 0.7500 10 SYM 0.9706 0.8684 0.9167 38 DINTFS 0.9683 1.0000 0.9839 61 PDEMFS 1.0000 0.8966 0.9455 29 PPER3FS 1.0000 0.9444 0.9714 18 VPPFP 0.9500 1.0000 0.9744 19 PRON 0.9200 0.7419 0.8214 31 PPOBJFS 0.8333 0.8333 0.8333 6 PART 0.8000 1.0000 0.8889 4 PPER3FP 1.0000 1.0000 1.0000 2 MOTINC 0.3571 0.3333 0.3448 15 PDEMMP 1.0000 0.6667 0.8000 3 INTJ 0.4000 0.6667 0.5000 6 PREFS 1.0000 0.5000 0.6667 10 ADJ 0.7917 0.8636 0.8261 22 PINDMP 0.0000 0.0000 0.0000 1 PINDFS 1.0000 1.0000 1.0000 1 NUM 1.0000 0.3333 0.5000 3 PPER2S 1.0000 1.0000 1.0000 2 PPOBJFP 1.0000 0.5000 0.6667 2 PDEMFP 1.0000 0.6667 0.8000 3 X 0.0000 0.0000 0.0000 1 PRELMS 1.0000 1.0000 1.0000 2 PINDFP 1.0000 1.0000 1.0000 1 accuracy 0.9520 10019 macro avg 0.8956 0.8521 0.8644 10019 weighted avg 0.9524 0.9520 0.9515 10019 ``` ## BibTeX Citations Please cite the following paper when using this model. ANTILLES corpus and POET taggers: ```latex @inproceedings{labrak:hal-03696042, TITLE = {{ANTILLES: An Open French Linguistically Enriched Part-of-Speech Corpus}}, AUTHOR = {Labrak, Yanis and Dufour, Richard}, URL = {https://hal.archives-ouvertes.fr/hal-03696042}, BOOKTITLE = {{25th International Conference on Text, Speech and Dialogue (TSD)}}, ADDRESS = {Brno, Czech Republic}, PUBLISHER = {{Springer}}, YEAR = {2022}, MONTH = Sep, KEYWORDS = {Part-of-speech corpus ; POS tagging ; Open tools ; Word embeddings ; Bi-LSTM ; CRF ; Transformers}, PDF = {https://hal.archives-ouvertes.fr/hal-03696042/file/ANTILLES_A_freNch_linguisTIcaLLy_Enriched_part_of_Speech_corpus.pdf}, HAL_ID = {hal-03696042}, HAL_VERSION = {v1}, } ``` UD_French-GSD corpora: ```latex @misc{ universaldependencies, title={UniversalDependencies/UD_French-GSD}, url={https://github.com/UniversalDependencies/UD_French-GSD}, journal={GitHub}, author={UniversalDependencies} } ``` LIA TAGG: ```latex @techreport{LIA_TAGG, author = {Frédéric Béchet}, title = {LIA_TAGG: a statistical POS tagger + syntactic bracketer}, institution = {Aix-Marseille University & CNRS}, year = {2001} } ``` Flair Embeddings: ```latex @inproceedings{akbik2018coling, title={Contextual String Embeddings for Sequence Labeling}, author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland}, booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics}, pages = {1638--1649}, year = {2018} } ``` ## Acknowledgment This work was financially supported by [Zenidoc](https://zenidoc.fr/)
google/ddpm-church-256
14e3c702d021d2563e924f2682ac4bd31464f888
2022-07-21T15:00:14.000Z
[ "diffusers", "arxiv:2006.11239", "pytorch", "unconditional-image-generation", "license:apache-2.0" ]
unconditional-image-generation
false
google
null
google/ddpm-church-256
196
null
diffusers
3,651
--- license: apache-2.0 tags: - pytorch - diffusers - unconditional-image-generation --- # Denoising Diffusion Probabilistic Models (DDPM) **Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239) **Authors**: Jonathan Ho, Ajay Jain, Pieter Abbeel **Abstract**: *We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN.* ## Inference **DDPM** models can use *discrete noise schedulers* such as: - [scheduling_ddpm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddpm.py) - [scheduling_ddim](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddim.py) - [scheduling_pndm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_pndm.py) for inference. Note that while the *ddpm* scheduler yields the highest quality, it also takes the longest. For a good trade-off between quality and inference speed you might want to consider the *ddim* or *pndm* schedulers instead. See the following code: ```python # !pip install diffusers from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline model_id = "google/ddpm-church-256" # load model and scheduler ddpm = DDPMPipeline.from_pretrained(model_id) # you can replace DDPMPipeline with DDIMPipeline or PNDMPipeline for faster inference # run pipeline in inference (sample random noise and denoise) image = ddpm()["sample"] # save image image[0].save("ddpm_generated_image.png") ``` For more in-detail information, please have a look at the [official inference example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb) ## Training If you want to train your own model, please have a look at the [official training example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb) ## Samples 1. ![sample_1](https://huggingface.co/google/ddpm-church-256/resolve/main/images/generated_image_0.png) 2. ![sample_2](https://huggingface.co/google/ddpm-church-256/resolve/main/images/generated_image_1.png) 3. ![sample_3](https://huggingface.co/google/ddpm-church-256/resolve/main/images/generated_image_2.png) 4. ![sample_4](https://huggingface.co/google/ddpm-church-256/resolve/main/images/generated_image_3.png)
Geotrend/distilbert-base-es-cased
d1a2feac7e5bdd65b5d659d766a80315a1e075a5
2021-08-16T13:26:35.000Z
[ "pytorch", "distilbert", "fill-mask", "es", "dataset:wikipedia", "transformers", "license:apache-2.0", "autotrain_compatible" ]
fill-mask
false
Geotrend
null
Geotrend/distilbert-base-es-cased
195
null
transformers
3,652
--- language: es datasets: wikipedia license: apache-2.0 --- # distilbert-base-es-cased We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages. Our versions give exactly the same representations produced by the original model which preserves the original accuracy. For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf). ## How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-es-cased") model = AutoModel.from_pretrained("Geotrend/distilbert-base-es-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers). ### How to cite ```bibtex @inproceedings{smallermdistilbert, title={Load What You Need: Smaller Versions of Mutlilingual BERT}, author={Abdaoui, Amine and Pradel, Camille and Sigel, Grégoire}, booktitle={SustaiNLP / EMNLP}, year={2020} } ``` ## Contact Please contact [email protected] for any question, feedback or request.
SkolkovoInstitute/xlmr_formality_classifier
b491648728ab0fab087598e4eea3e123be7155e6
2022-01-09T17:55:13.000Z
[ "pytorch", "xlm-roberta", "text-classification", "en", "fr", "it", "pt", "transformers", "formal or informal classification" ]
text-classification
false
SkolkovoInstitute
null
SkolkovoInstitute/xlmr_formality_classifier
195
0
transformers
3,653
--- language: - en - fr - it - pt tags: - formal or informal classification licenses: - cc-by-nc-sa --- XLMRoberta-based classifier trained on XFORMAL. all | | precision | recall | f1-score | support | |--------------|-----------|----------|----------|---------| | 0 | 0.744912 | 0.927790 | 0.826354 | 108019 | | 1 | 0.889088 | 0.645630 | 0.748048 | 96845 | | accuracy | | | 0.794405 | 204864 | | macro avg | 0.817000 | 0.786710 | 0.787201 | 204864 | | weighted avg | 0.813068 | 0.794405 | 0.789337 | 204864 | en | | precision | recall | f1-score | support | |--------------|-----------|----------|----------|---------| | 0 | 0.800053 | 0.962981 | 0.873988 | 22151 | | 1 | 0.945106 | 0.725899 | 0.821124 | 19449 | | accuracy | | | 0.852139 | 41600 | | macro avg | 0.872579 | 0.844440 | 0.847556 | 41600 | | weighted avg | 0.867869 | 0.852139 | 0.849273 | 41600 | fr | | precision | recall | f1-score | support | |--------------|-----------|----------|----------|---------| | 0 | 0.746709 | 0.925738 | 0.826641 | 21505 | | 1 | 0.887305 | 0.650592 | 0.750731 | 19327 | | accuracy | | | 0.795504 | 40832 | | macro avg | 0.817007 | 0.788165 | 0.788686 | 40832 | | weighted avg | 0.813257 | 0.795504 | 0.790711 | 40832 | it | | precision | recall | f1-score | support | |--------------|-----------|----------|----------|---------| | 0 | 0.721282 | 0.914669 | 0.806545 | 21528 | | 1 | 0.864887 | 0.607135 | 0.713445 | 19368 | | accuracy | | | 0.769024 | 40896 | | macro avg | 0.793084 | 0.760902 | 0.759995 | 40896 | | weighted avg | 0.789292 | 0.769024 | 0.762454 | 40896 | pt | | precision | recall | f1-score | support | |--------------|-----------|----------|----------|---------| | 0 | 0.717546 | 0.908167 | 0.801681 | 21637 | | 1 | 0.853628 | 0.599700 | 0.704481 | 19323 | | accuracy | | | 0.762646 | 40960 | | macro avg | 0.785587 | 0.753933 | 0.753081 | 40960 | | weighted avg | 0.781743 | 0.762646 | 0.755826 | 40960 | ## How to use ```python from transformers import XLMRobertaTokenizerFast, XLMRobertaForSequenceClassification # load tokenizer and model weights tokenizer = XLMRobertaTokenizerFast.from_pretrained('SkolkovoInstitute/xlmr_formality_classifier') model = XLMRobertaForSequenceClassification.from_pretrained('SkolkovoInstitute/xlmr_formality_classifier') # prepare the input batch = tokenizer.encode('ты супер', return_tensors='pt') # inference model(batch) ``` ## Licensing Information [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa]. [![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa] [cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/ [cc-by-nc-sa-image]: https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png
aubmindlab/araelectra-base-generator
3783d1be2a43dfede443a934410539f753122c41
2022-04-07T11:31:12.000Z
[ "pytorch", "tf", "tensorboard", "electra", "fill-mask", "ar", "dataset:wikipedia", "dataset:OSIAN", "dataset:1.5B Arabic Corpus", "dataset:OSCAR Arabic Unshuffled", "arxiv:2012.15516", "transformers", "autotrain_compatible" ]
fill-mask
false
aubmindlab
null
aubmindlab/araelectra-base-generator
195
null
transformers
3,654
--- language: ar datasets: - wikipedia - OSIAN - 1.5B Arabic Corpus - OSCAR Arabic Unshuffled widget: - text: " عاصمة لبنان هي [MASK] ." --- # AraELECTRA <img src="https://raw.githubusercontent.com/aub-mind/arabert/master/AraELECTRA.png" width="100" align="left"/> **ELECTRA** is a method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to the discriminator of a [GAN](https://arxiv.org/pdf/1406.2661.pdf). AraELECTRA achieves state-of-the-art results on Arabic QA dataset. For a detailed description, please refer to the AraELECTRA paper [AraELECTRA: Pre-Training Text Discriminators for Arabic Language Understanding](https://arxiv.org/abs/2012.15516). ## How to use the generator in `transformers` ```python from transformers import pipeline fill_mask = pipeline( "fill-mask", model="aubmindlab/araelectra-base-generator", tokenizer="aubmindlab/araelectra-base-generator" ) print( fill_mask(" عاصمة لبنان هي [MASK] .) ) ``` # Preprocessing It is recommended to apply our preprocessing function before training/testing on any dataset. **Install farasapy to segment text for AraBERT v1 & v2 `pip install farasapy`** ```python from arabert.preprocess import ArabertPreprocessor model_name="aubmindlab/araelectra-base" arabert_prep = ArabertPreprocessor(model_name=model_name) text = "ولن نبالغ إذا قلنا إن هاتف أو كمبيوتر المكتب في زمننا هذا ضروري" arabert_prep.preprocess(text) ``` # Model Model | HuggingFace Model Name | Size (MB/Params)| ---|:---:|:---: AraELECTRA-base-generator | [araelectra-base-generator](https://huggingface.co/aubmindlab/araelectra-base-generator) | 227MB/60M | AraELECTRA-base-discriminator | [araelectra-base-discriminator](https://huggingface.co/aubmindlab/araelectra-base-discriminator) | 516MB/135M | # Compute Model | Hardware | num of examples (seq len = 512) | Batch Size | Num of Steps | Time (in days) ---|:---:|:---:|:---:|:---:|:---: AraELECTRA-base | TPUv3-8 | - | 256 | 2M | 24 # Dataset The pretraining data used for the new AraELECTRA model is also used for **AraGPT2 and AraELECTRA**. The dataset consists of 77GB or 200,095,961 lines or 8,655,948,860 words or 82,232,988,358 chars (before applying Farasa Segmentation) For the new dataset we added the unshuffled OSCAR corpus, after we thoroughly filter it, to the previous dataset used in AraBERTv1 but with out the websites that we previously crawled: - OSCAR unshuffled and filtered. - [Arabic Wikipedia dump](https://archive.org/details/arwiki-20190201) from 2020/09/01 - [The 1.5B words Arabic Corpus](https://www.semanticscholar.org/paper/1.5-billion-words-Arabic-Corpus-El-Khair/f3eeef4afb81223df96575adadf808fe7fe440b4) - [The OSIAN Corpus](https://www.aclweb.org/anthology/W19-4619) - Assafir news articles. Huge thank you for Assafir for giving us the data # TensorFlow 1.x models **You can find the PyTorch, TF2 and TF1 models in HuggingFace's Transformer Library under the ```aubmindlab``` username** - `wget https://huggingface.co/aubmindlab/MODEL_NAME/resolve/main/tf1_model.tar.gz` where `MODEL_NAME` is any model under the `aubmindlab` name # If you used this model please cite us as : ``` @inproceedings{antoun-etal-2021-araelectra, title = "{A}ra{ELECTRA}: Pre-Training Text Discriminators for {A}rabic Language Understanding", author = "Antoun, Wissam and Baly, Fady and Hajj, Hazem", booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop", month = apr, year = "2021", address = "Kyiv, Ukraine (Virtual)", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2021.wanlp-1.20", pages = "191--195", } ``` # Acknowledgments Thanks to TensorFlow Research Cloud (TFRC) for the free access to Cloud TPUs, couldn't have done it without this program, and to the [AUB MIND Lab](https://sites.aub.edu.lb/mindlab/) Members for the continous support. Also thanks to [Yakshof](https://www.yakshof.com/#/) and Assafir for data and storage access. Another thanks for Habib Rahal (https://www.behance.net/rahalhabib), for putting a face to AraBERT. # Contacts **Wissam Antoun**: [Linkedin](https://www.linkedin.com/in/wissam-antoun-622142b4/) | [Twitter](https://twitter.com/wissam_antoun) | [Github](https://github.com/WissamAntoun) | <[email protected]> | <[email protected]> **Fady Baly**: [Linkedin](https://www.linkedin.com/in/fadybaly/) | [Twitter](https://twitter.com/fadybaly) | [Github](https://github.com/fadybaly) | <[email protected]> | <[email protected]>
koala/kykim-bert-kor-base-ko
d0944e73af133a5e221634d0063bf9af21eef197
2021-12-10T12:31:20.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
koala
null
koala/kykim-bert-kor-base-ko
195
null
transformers
3,655
Entry not found
l3cube-pune/MarathiSentiment
61900f644a268997fcf84e4529cec46986daeaf0
2021-05-18T07:35:10.000Z
[ "pytorch", "tf", "albert", "text-classification", "mr", "dataset:L3CubeMahaSent", "arxiv:2103.11408", "transformers", "license:cc-by-4.0" ]
text-classification
false
l3cube-pune
null
l3cube-pune/MarathiSentiment
195
2
transformers
3,656
--- language: mr tags: - albert license: cc-by-4.0 datasets: - L3CubeMahaSent widget: - text: "I like you. </s></s> I love you." --- ## MarathiSentiment MarathiSentiment is an IndicBERT(ai4bharat/indic-bert) model fine-tuned on L3CubeMahaSent - a Marathi tweet-based sentiment analysis dataset. [dataset link] (https://github.com/l3cube-pune/MarathiNLP) More details on the dataset, models, and baseline results can be found in our [paper] (http://arxiv.org/abs/2103.11408) ``` @inproceedings{kulkarni2021l3cubemahasent, title={L3CubeMahaSent: A Marathi Tweet-based Sentiment Analysis Dataset}, author={Kulkarni, Atharva and Mandhane, Meet and Likhitkar, Manali and Kshirsagar, Gayatri and Joshi, Raviraj}, booktitle={Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis}, pages={213--220}, year={2021} } ```
monologg/bert-base-cased-goemotions-group
8db941660d9c02eeca1357aa6e9e98844f73b5e1
2021-05-19T23:48:19.000Z
[ "pytorch", "bert", "transformers" ]
null
false
monologg
null
monologg/bert-base-cased-goemotions-group
195
2
transformers
3,657
Entry not found
yuchenlin/BART0
ed9861f2002cc16f79ad3953364d764668d52874
2022-05-03T01:31:34.000Z
[ "pytorch", "bart", "text2text-generation", "en", "dataset:bigscience/P3", "transformers", "license:apache-2.0", "autotrain_compatible" ]
text2text-generation
false
yuchenlin
null
yuchenlin/BART0
195
2
transformers
3,658
--- datasets: - bigscience/P3 language: en license: apache-2.0 widget: - text: "A is the son's of B's uncle. What is the family relationship between A and B?" - text: "Reorder the words in this sentence: justin and name bieber years is my am I 27 old." - text: "Task: copy but say the opposite.\n PSG won its match against Barca." - text: "Is this review positive or negative? Review: Best cast iron skillet you will every buy." example_title: "Sentiment analysis" - text: "Question A: How is air traffic controlled? \nQuestion B: How do you become an air traffic controller?\nPick one: these questions are duplicates or not duplicates." - text: "Barack Obama nominated Hilary Clinton as his secretary of state on Monday. He chose her because she had foreign affairs experience as a former First Lady. \nIn the previous sentence, decide who 'her' is referring to." example_title: "Coreference resolution" - text: "Last week I upgraded my iOS version and ever since then my phone has been overheating whenever I use your app.\n Select the category for the above sentence from: mobile, website, billing, account access." - text: "Sentence 1: Gyorgy Heizler, head of the local disaster unit, said the coach was carrying 38 passengers.\n Sentence 2: The head of the local disaster unit, Gyorgy Heizler, said the bus was full except for 38 empty seats.\n\n Do sentences 1 and 2 have the same meaning?" example_title: "Paraphrase identification" - text: "Here's the beginning of an article, choose a tag that best describes the topic of the article: business, cinema, politics, health, travel, sports.\n\n The best and worst fo 007 as 'No time to die' marks Daniel Craig's exit.\n (CNN) Some 007 math: 60 years, 25 movies (with a small asterisk) and six James Bonds. For a Cold War creation, Ian Fleming's suave spy has certainly gotten around, but despite different guises in the tuxedo and occasional scuba gear, when it comes to Bond ratings, there really shouldn't be much argument about who wore it best." - text: "Max: Know any good websites to buy clothes from?\n Payton: Sure :) LINK 1, LINK 2, LINK 3\n Max: That's a lot of them!\n Payton: Yeah, but they have different things so I usually buy things from 2 or 3 of them.\n Max: I'll check them out. Thanks.\n\n Who or what are Payton and Max referring to when they say 'them'?" - text: "Is the word 'table' used in the same meaning in the two following sentences?\n\n Sentence A: you can leave the books on the table over there.\n Sentence B: the tables in this book are very hard to read." - text: "On a shelf, there are five books: a gray book, a red book, a purple book, a blue book, and a black book.\n The red book is to the right of the gray book. The black book is to the left of the blue book. The blue book is to the left of the gray book. The purple book is the second from the right.\n\n Which book is the leftmost book?" example_title: "Logic puzzles" - text: "The two men running to become New York City's next mayor will face off in their first debate Wednesday night.\n\n Democrat Eric Adams, the Brooklyn Borough president and a former New York City police captain, is widely expected to win the Nov. 2 election against Republican Curtis Sliwa, the founder of the 1970s-era Guardian Angels anti-crime patril.\n\n Who are the men running for mayor?" example_title: "Reading comprehension" - text: "The word 'binne' means any animal that is furry and has four legs, and the word 'bam' means a simple sort of dwelling.\n\n Which of the following best characterizes binne bams?\n - Sentence 1: Binne bams are for pets.\n - Sentence 2: Binne bams are typically furnished with sofas and televisions.\n - Sentence 3: Binne bams are luxurious apartments.\n - Sentence 4: Binne bams are places where people live." --- A BART-large version of T0. Please check https://inklab.usc.edu/ReCross/ for more details.
patrickramosobf/bert-base-japanese-v2-wrime-fine-tune
060beffbf77befb673463d6c742af900f95d879d
2022-05-22T16:34:55.000Z
[ "pytorch", "tf", "bert", "text-classification", "ja", "dataset:wrime", "transformers", "license:cc-by-sa-3.0" ]
text-classification
false
patrickramosobf
null
patrickramosobf/bert-base-japanese-v2-wrime-fine-tune
195
null
transformers
3,659
--- license: cc-by-sa-3.0 language: - ja tag: - emotion-analysis datasets: - wrime widget: - text: "車のタイヤがパンクしてた。。いたずらの可能性が高いんだって。。" --- # WRIME-fine-tuned BERT base Japanese This model is a [Japanese BERT<sub>BASE</sub>](https://huggingface.co/cl-tohoku/bert-base-japanese-v2) fine-tuned on the [WRIME](https://github.com/ids-cv/wrime) dataset. It was trained as part of the paper ["Emotion Analysis of Writers and Readers of Japanese Tweets on Vaccinations"](https://aclanthology.org/2022.wassa-1.10/). Fine-tuning code is available at this [repo](https://github.com/PatrickJohnRamos/BERT-Japan-vaccination). # Intended uses and limitations This model can be used to predict intensities scores for eight emotions for writers and readers. Please refer to the `Fine-tuning data` section for the list of emotions. Because of the regression fine-tuning task, it is possible for the model to infer scores outside of the range of the scores of the fine-tuning data (`score < 0` or `score > 4`). # Model Architecture, Tokenization, and Pretraining The Japanese BERT<sub>BASE</sub> fine-tuned was `cl-tohoku/bert-base-japanese-v2`. Please refer to their [model card](https://huggingface.co/cl-tohoku/bert-base-japanese-v2) for details regarding the model architecture, tokenization, pretraining data, and pretraining procedure. # Fine-tuning data The model is fine-tuned on [WRIME](https://github.com/ids-cv/wrime), a dataset of Japanese Tweets annotated with writer and reader emotion intensities. We use version 1 of the dataset. Each Tweet is accompanied by a set of writer emotion intensities (from the author of the Tweet) and three sets of reader emotions (from three annotators). The emotions follow Plutchhik's emotions, namely: * joy * sadness * anticipation * surprise * anger * fear * disgust * trust These emotion intensities follow a four-point scale: | emotion intensity | emotion presence| |---|---| | 0 | no | | 1 | weak | | 2 | medium | | 3 | strong | # Fine-tuning The BERT is fine-tuned to directly regress the emotion intensities of the writer and the averaged emotions of the readers from each Tweet, meaning there are 16 outputs (8 emotions per writer/reader). The fine-tuning was inspired by common BERT fine-tuning procedures. The BERT was fine-tuned on WRIME for 3 epochs using the AdamW optimizer with a learning rate of 2e-5, β<sub>1</sub>=0.9, β<sub>2</sub>=0.999, weight decay of 0.01, linear decay, a warmup ratio of 0.01, and a batch size of 32. Training was conducted with an NVIDIA Tesla K80 and finished in 3 hours. # Evaluation results Below are the MSEs of the BERT on the test split of WRIME. | Annotator | Joy | Sadness | Anticipation | Surprise | Anger | Fear | Disgust | Trust | Overall | |---|---|---|---|---|---|---|---|---|---| | Writer | 0.658 | 0.688 | 0.746 | 0.542 | 0.486 | 0.462 | 0.664 | 0.400 | 0.581 | | Reader | 0.192 | 0.178 | 0.211 | 0.139 | 0.032 | 0.147 | 0.123 | 0.029 | 0.131 | | Both | 0.425 | 0.433 | 0.479 | 0.341 | 0.259 | 0.304 | 0.394 | 0.214 | 0.356 |
ckb/en-toki-mt
546b3df927463696d71dbee5b6028148f46f99b1
2022-07-09T09:48:35.000Z
[ "pytorch", "marian", "text2text-generation", "en", "tok", "transformers", "generated_from_trainer", "translation", "license:apache-2.0", "model-index", "autotrain_compatible" ]
translation
false
ckb
null
ckb/en-toki-mt
195
null
transformers
3,660
--- license: apache-2.0 language: - en - tok tags: - generated_from_trainer - translation model-index: - name: en-toki-mt results: [] widget: - text: "Hello, my name is Tom." - text: "Can the cat speak English?" --- # en-toki-mt This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ROMANCE](https://huggingface.co/Helsinki-NLP/opus-mt-en-ROMANCE) on the English - toki pona translation dataset on Tatoeba. ## Model description toki pona is a minimalist constructed language created in 2014 by Sonja Lang. The language features a very small volcabulary (~130 words) and a very simple grammar structure. ## Intended uses & limitations This model aims to translate English to Toki pona. ## Training and evaluation data The training data is acquired from all En-Toki sentence pairs on [Tatoeba](https://tatoeba.org/en) (~20000 pairs), without any filtering. Since this dataset mostly only includes core words (pu), it may produce inaccurate results when encountering more complex words. The model achieved a BLEU score of 54 on the testing set. ## Training procedure ### 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: 10 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.3.2 - Tokenizers 0.12.1
DaNLP/da-bert-ner
9fe6e2483f6a38b6800b63273e4f9848cf334aeb
2021-09-17T11:18:07.000Z
[ "pytorch", "tf", "bert", "token-classification", "da", "dataset:DaNE", "transformers", "ner", "license:cc-by-sa-4.0", "autotrain_compatible" ]
token-classification
false
DaNLP
null
DaNLP/da-bert-ner
194
null
transformers
3,661
--- language: - da tags: - ner - bert - pytorch - transformers license: cc-by-sa-4.0 datasets: - DaNE metrics: - f1 widget: - text: "Jens Peter Hansen kommer fra Danmark" --- # BERT fine-tuned for Named Entity Recognition in Danish The model tags tokens (in Danish sentences) with named entity tags (BIO format) [PER, ORG, LOC, MISC]. The pretrained language model used for fine-tuning is the [Danish BERT](https://github.com/certainlyio/nordic_bert) by BotXO. See the [DaNLP documentation](https://danlp-alexandra.readthedocs.io/en/latest/docs/tasks/ner.html#bert) for more details. Here is how to use the model: ```python from transformers import BertTokenizer, BertForTokenClassification model = BertForTokenClassification.from_pretrained("DaNLP/da-bert-ner") tokenizer = BertTokenizer.from_pretrained("DaNLP/da-bert-ner") ``` ## Training Data The model has been trained on the [DaNE](https://danlp-alexandra.readthedocs.io/en/latest/docs/datasets.html#dane).
ans/vaccinating-covid-tweets
ddb15134bf56cda36aea875c56822ab9074d768d
2021-06-18T04:12:08.000Z
[ "pytorch", "roberta", "text-classification", "en", "dataset:tweets", "transformers", "license:apache-2.0" ]
text-classification
false
ans
null
ans/vaccinating-covid-tweets
194
1
transformers
3,662
--- language: en license: apache-2.0 datasets: - tweets widget: - text: "Vaccines to prevent SARS-CoV-2 infection are considered the most promising approach for curbing the pandemic." --- # Disclaimer: This page is under maintenance. Please DO NOT refer to the information on this page to make any decision yet. # Vaccinating COVID tweets A fine-tuned model for fact-classification task on English tweets about COVID-19/vaccine. ## Intended uses & limitations You can classify if the input tweet (or any others statement) about COVID-19/vaccine is `true`, `false` or `misleading`. Note that since this model was trained with data up to May 2020, the most recent information may not be reflected. #### How to use You can use this model directly on this page or using `transformers` in python. - Load pipeline and implement with input sequence ```python from transformers import pipeline pipe = pipeline("sentiment-analysis", model = "ans/vaccinating-covid-tweets") seq = "Vaccines to prevent SARS-CoV-2 infection are considered the most promising approach for curbing the pandemic." pipe(seq) ``` - Expected output ```python [ { "label": "false", "score": 0.07972867041826248 }, { "label": "misleading", "score": 0.019911376759409904 }, { "label": "true", "score": 0.9003599882125854 } ] ``` - `true` examples ```python "By the end of 2020, several vaccines had become available for use in different parts of the world." "Vaccines to prevent SARS-CoV-2 infection are considered the most promising approach for curbing the pandemic." "RNA vaccines were the first vaccines for SARS-CoV-2 to be produced and represent an entirely new vaccine approach." ``` - `false` examples ```python "COVID-19 vaccine caused new strain in UK." ``` #### Limitations and bias To conservatively classify whether an input sequence is true or not, the model may have predictions biased toward `false` or `misleading`. ## Training data & Procedure #### Pre-trained baseline model - Pre-trained model: [BERTweet](https://github.com/VinAIResearch/BERTweet) - trained based on the RoBERTa pre-training procedure - 850M General English Tweets (Jan 2012 to Aug 2019) - 23M COVID-19 English Tweets - Size of the model: >134M parameters - Further training - Pre-training with recent COVID-19/vaccine tweets and fine-tuning for fact classification #### 1) Pre-training language model - The model was pre-trained on COVID-19/vaccined related tweets using a masked language modeling (MLM) objective starting from BERTweet. - Following datasets on English tweets were used: - Tweets with trending #CovidVaccine hashtag, 207,000 tweets uploaded across Aug 2020 to Apr 2021 ([kaggle](https://www.kaggle.com/kaushiksuresh147/covidvaccine-tweets)) - Tweets about all COVID-19 vaccines, 78,000 tweets uploaded across Dec 2020 to May 2021 ([kaggle](https://www.kaggle.com/gpreda/all-covid19-vaccines-tweets)) - COVID-19 Twitter chatter dataset, 590,000 tweets uploaded across Mar 2021 to May 2021 ([github](https://github.com/thepanacealab/covid19_twitter)) #### 2) Fine-tuning for fact classification - A fine-tuned model from pre-trained language model (1) for fact-classification task on COVID-19/vaccine. - COVID-19/vaccine-related statements were collected from [Poynter](https://www.poynter.org/ifcn-covid-19-misinformation/) and [Snopes](https://www.snopes.com/) using Selenium resulting in over 14,000 fact-checked statements from Jan 2020 to May 2021. - Original labels were divided within following three categories: - `False`: includes false, no evidence, manipulated, fake, not true, unproven and unverified - `Misleading`: includes misleading, exaggerated, out of context and needs context - `True`: includes true and correct ## Evaluation results | Training loss | Validation loss | Training accuracy | Validation accuracy | | --- | --- | --- | --- | | 0.1062 | 0.1006 | 96.3% | 94.5% | # Contributors - This model is a part of final team project from MLDL for DS class at SNU. - Team BIBI - Vaccinating COVID-NineTweets - Team members: Ahn, Hyunju; An, Jiyong; An, Seungchan; Jeong, Seokho; Kim, Jungmin; Kim, Sangbeom - Advisor: Prof. Wen-Syan Li <a href="https://gsds.snu.ac.kr/"><img src="https://gsds.snu.ac.kr/wp-content/uploads/sites/50/2021/04/GSDS_logo2-e1619068952717.png" width="200" height="80"></a>
hfl/cino-large-v2
39af3c16b2256141ad3306fc2be6841f6cc76aec
2022-01-24T10:40:50.000Z
[ "pytorch", "tf", "xlm-roberta", "fill-mask", "zh", "bo", "kk", "ko", "mn", "ug", "yue", "transformers", "license:apache-2.0", "autotrain_compatible" ]
fill-mask
false
hfl
null
hfl/cino-large-v2
194
2
transformers
3,663
--- language: - zh - bo - kk - ko - mn - ug - yue license: "apache-2.0" --- ## CINO: Pre-trained Language Models for Chinese Minority Languages(中国少数民族预训练模型) Multilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding. We have seen rapid progress on building multilingual PLMs in recent year. However, there is a lack of contributions on building PLMs on Chines minority languages, which hinders researchers from building powerful NLP systems. To address the absence of Chinese minority PLMs, Joint Laboratory of HIT and iFLYTEK Research (HFL) proposes CINO (Chinese-miNOrity pre-trained language model), which is built on XLM-R with additional pre-training using Chinese minority corpus, such as - Chinese,中文(zh) - Tibetan,藏语(bo) - Mongolian (Uighur form),蒙语(mn) - Uyghur,维吾尔语(ug) - Kazakh (Arabic form),哈萨克语(kk) - Korean,朝鲜语(ko) - Zhuang,壮语 - Cantonese,粤语(yue) Please read our GitHub repository for more details (Chinese): https://github.com/ymcui/Chinese-Minority-PLM You may also interested in, Chinese MacBERT: https://github.com/ymcui/MacBERT Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA Chinese XLNet: https://github.com/ymcui/Chinese-XLNet Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer More resources by HFL: https://github.com/ymcui/HFL-Anthology
jchen/DialoGPT-evan
161625a480f8188bdd82787fd0d3d43373f2dd44
2021-10-23T04:12:00.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
jchen
null
jchen/DialoGPT-evan
194
null
transformers
3,664
--- tags: - conversational --- # Evan Model
uer/chinese_roberta_L-12_H-256
4aeb03283d2d0ff43cffec9674a1ac4621055f68
2022-07-15T08:15:47.000Z
[ "pytorch", "tf", "jax", "bert", "fill-mask", "zh", "dataset:CLUECorpusSmall", "arxiv:1909.05658", "arxiv:1908.08962", "transformers", "autotrain_compatible" ]
fill-mask
false
uer
null
uer/chinese_roberta_L-12_H-256
194
null
transformers
3,665
--- language: zh datasets: CLUECorpusSmall widget: - text: "北京是[MASK]国的首都。" --- # Chinese RoBERTa Miniatures ## Model description This is the set of 24 Chinese RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). [Turc et al.](https://arxiv.org/abs/1908.08962) have shown that the standard BERT recipe is effective on a wide range of model sizes. Following their paper, we released the 24 Chinese RoBERTa models. In order to facilitate users to reproduce the results, we used the publicly available corpus and provided all training details. You can download the 24 Chinese RoBERTa miniatures either from the [UER-py Modelzoo page](https://github.com/dbiir/UER-py/wiki/Modelzoo), or via HuggingFace from the links below: | | H=128 | H=256 | H=512 | H=768 | | -------- | :-----------------------: | :-----------------------: | :-------------------------: | :-------------------------: | | **L=2** | [**2/128 (Tiny)**][2_128] | [2/256][2_256] | [2/512][2_512] | [2/768][2_768] | | **L=4** | [4/128][4_128] | [**4/256 (Mini)**][4_256] | [**4/512 (Small)**][4_512] | [4/768][4_768] | | **L=6** | [6/128][6_128] | [6/256][6_256] | [6/512][6_512] | [6/768][6_768] | | **L=8** | [8/128][8_128] | [8/256][8_256] | [**8/512 (Medium)**][8_512] | [8/768][8_768] | | **L=10** | [10/128][10_128] | [10/256][10_256] | [10/512][10_512] | [10/768][10_768] | | **L=12** | [12/128][12_128] | [12/256][12_256] | [12/512][12_512] | [**12/768 (Base)**][12_768] | Here are scores on the devlopment set of six Chinese tasks: | Model | Score | douban | chnsenticorp | lcqmc | tnews(CLUE) | iflytek(CLUE) | ocnli(CLUE) | | -------------- | :---: | :----: | :----------: | :---: | :---------: | :-----------: | :---------: | | RoBERTa-Tiny | 72.3 | 83.0 | 91.4 | 81.8 | 62.0 | 55.0 | 60.3 | | RoBERTa-Mini | 75.7 | 84.8 | 93.7 | 86.1 | 63.9 | 58.3 | 67.4 | | RoBERTa-Small | 76.8 | 86.5 | 93.4 | 86.5 | 65.1 | 59.4 | 69.7 | | RoBERTa-Medium | 77.8 | 87.6 | 94.8 | 88.1 | 65.6 | 59.5 | 71.2 | | RoBERTa-Base | 79.5 | 89.1 | 95.2 | 89.2 | 67.0 | 60.9 | 75.5 | For each task, we selected the best fine-tuning hyperparameters from the lists below, and trained with the sequence length of 128: - epochs: 3, 5, 8 - batch sizes: 32, 64 - learning rates: 3e-5, 1e-4, 3e-4 ## How to use You can use this model directly with a pipeline for masked language modeling (take the case of RoBERTa-Medium): ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='uer/chinese_roberta_L-8_H-512') >>> unmasker("中国的首都是[MASK]京。") [ {'sequence': '[CLS] 中 国 的 首 都 是 北 京 。 [SEP]', 'score': 0.8701988458633423, 'token': 1266, 'token_str': '北'}, {'sequence': '[CLS] 中 国 的 首 都 是 南 京 。 [SEP]', 'score': 0.1194809079170227, 'token': 1298, 'token_str': '南'}, {'sequence': '[CLS] 中 国 的 首 都 是 东 京 。 [SEP]', 'score': 0.0037803512532263994, 'token': 691, 'token_str': '东'}, {'sequence': '[CLS] 中 国 的 首 都 是 普 京 。 [SEP]', 'score': 0.0017127094324678183, 'token': 3249, 'token_str': '普'}, {'sequence': '[CLS] 中 国 的 首 都 是 望 京 。 [SEP]', 'score': 0.001687526935711503, 'token': 3307, 'token_str': '望'} ] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('uer/chinese_roberta_L-8_H-512') model = BertModel.from_pretrained("uer/chinese_roberta_L-8_H-512") text = "用你喜欢的任何文本替换我。" encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('uer/chinese_roberta_L-8_H-512') model = TFBertModel.from_pretrained("uer/chinese_roberta_L-8_H-512") text = "用你喜欢的任何文本替换我。" encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Training data [CLUECorpusSmall](https://github.com/CLUEbenchmark/CLUECorpus2020/) is used as training data. We found that models pre-trained on CLUECorpusSmall outperform those pre-trained on CLUECorpus2020, although CLUECorpus2020 is much larger than CLUECorpusSmall. ## Training procedure Models are pre-trained by [UER-py](https://github.com/dbiir/UER-py/) on [Tencent Cloud](https://cloud.tencent.com/). We pre-train 1,000,000 steps with a sequence length of 128 and then pre-train 250,000 additional steps with a sequence length of 512. We use the same hyper-parameters on different model sizes. Taking the case of RoBERTa-Medium Stage1: ``` python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \ --vocab_path models/google_zh_vocab.txt \ --dataset_path cluecorpussmall_seq128_dataset.pt \ --processes_num 32 --seq_length 128 \ --dynamic_masking --data_processor mlm ``` ``` python3 pretrain.py --dataset_path cluecorpussmall_seq128_dataset.pt \ --vocab_path models/google_zh_vocab.txt \ --config_path models/bert/medium_config.json \ --output_model_path models/cluecorpussmall_roberta_medium_seq128_model.bin \ --world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \ --total_steps 1000000 --save_checkpoint_steps 100000 --report_steps 50000 \ --learning_rate 1e-4 --batch_size 64 \ --data_processor mlm --target mlm ``` Stage2: ``` python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \ --vocab_path models/google_zh_vocab.txt \ --dataset_path cluecorpussmall_seq512_dataset.pt \ --processes_num 32 --seq_length 512 \ --dynamic_masking --data_processor mlm ``` ``` python3 pretrain.py --dataset_path cluecorpussmall_seq512_dataset.pt \ --vocab_path models/google_zh_vocab.txt \ --pretrained_model_path models/cluecorpussmall_roberta_medium_seq128_model.bin-1000000 \ --config_path models/bert/medium_config.json \ --output_model_path models/cluecorpussmall_roberta_medium_seq512_model.bin \ --world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \ --total_steps 250000 --save_checkpoint_steps 50000 --report_steps 10000 \ --learning_rate 5e-5 --batch_size 16 \ --data_processor mlm --target mlm ``` Finally, we convert the pre-trained model into Huggingface's format: ``` python3 scripts/convert_bert_from_uer_to_huggingface.py --input_model_path models/cluecorpussmall_roberta_medium_seq512_model.bin-250000 \ --output_model_path pytorch_model.bin \ --layers_num 8 --type mlm ``` ### BibTeX entry and citation info ``` @article{devlin2018bert, title={Bert: Pre-training of deep bidirectional transformers for language understanding}, author={Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina}, journal={arXiv preprint arXiv:1810.04805}, year={2018} } @article{liu2019roberta, title={Roberta: A robustly optimized bert pretraining approach}, author={Liu, Yinhan and Ott, Myle and Goyal, Naman and Du, Jingfei and Joshi, Mandar and Chen, Danqi and Levy, Omer and Lewis, Mike and Zettlemoyer, Luke and Stoyanov, Veselin}, journal={arXiv preprint arXiv:1907.11692}, year={2019} } @article{turc2019, title={Well-Read Students Learn Better: On the Importance of Pre-training Compact Models}, author={Turc, Iulia and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina}, journal={arXiv preprint arXiv:1908.08962v2 }, year={2019} } @article{zhao2019uer, title={UER: An Open-Source Toolkit for Pre-training Models}, author={Zhao, Zhe and Chen, Hui and Zhang, Jinbin and Zhao, Xin and Liu, Tao and Lu, Wei and Chen, Xi and Deng, Haotang and Ju, Qi and Du, Xiaoyong}, journal={EMNLP-IJCNLP 2019}, pages={241}, year={2019} } ``` [2_128]:https://huggingface.co/uer/chinese_roberta_L-2_H-128 [2_256]:https://huggingface.co/uer/chinese_roberta_L-2_H-256 [2_512]:https://huggingface.co/uer/chinese_roberta_L-2_H-512 [2_768]:https://huggingface.co/uer/chinese_roberta_L-2_H-768 [4_128]:https://huggingface.co/uer/chinese_roberta_L-4_H-128 [4_256]:https://huggingface.co/uer/chinese_roberta_L-4_H-256 [4_512]:https://huggingface.co/uer/chinese_roberta_L-4_H-512 [4_768]:https://huggingface.co/uer/chinese_roberta_L-4_H-768 [6_128]:https://huggingface.co/uer/chinese_roberta_L-6_H-128 [6_256]:https://huggingface.co/uer/chinese_roberta_L-6_H-256 [6_512]:https://huggingface.co/uer/chinese_roberta_L-6_H-512 [6_768]:https://huggingface.co/uer/chinese_roberta_L-6_H-768 [8_128]:https://huggingface.co/uer/chinese_roberta_L-8_H-128 [8_256]:https://huggingface.co/uer/chinese_roberta_L-8_H-256 [8_512]:https://huggingface.co/uer/chinese_roberta_L-8_H-512 [8_768]:https://huggingface.co/uer/chinese_roberta_L-8_H-768 [10_128]:https://huggingface.co/uer/chinese_roberta_L-10_H-128 [10_256]:https://huggingface.co/uer/chinese_roberta_L-10_H-256 [10_512]:https://huggingface.co/uer/chinese_roberta_L-10_H-512 [10_768]:https://huggingface.co/uer/chinese_roberta_L-10_H-768 [12_128]:https://huggingface.co/uer/chinese_roberta_L-12_H-128 [12_256]:https://huggingface.co/uer/chinese_roberta_L-12_H-256 [12_512]:https://huggingface.co/uer/chinese_roberta_L-12_H-512 [12_768]:https://huggingface.co/uer/chinese_roberta_L-12_H-768
DemangeJeremy/4-sentiments-with-flaubert
1795c955197e73fe016a4a1b03073eb68d7f8430
2021-03-29T00:03:14.000Z
[ "pytorch", "flaubert", "text-classification", "fr", "transformers", "sentiments", "french", "flaubert-large" ]
text-classification
false
DemangeJeremy
null
DemangeJeremy/4-sentiments-with-flaubert
193
null
transformers
3,666
--- language: fr tags: - sentiments - text-classification - flaubert - french - flaubert-large --- # Modèle de détection de 4 sentiments avec FlauBERT (mixed, negative, objective, positive) Les travaux sont actuellement en cours. Je modifierai le modèle ces prochains jours. ### Comment l'utiliser ? ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification from transformers import pipeline loaded_tokenizer = AutoTokenizer.from_pretrained('flaubert/flaubert_large_cased') loaded_model = AutoModelForSequenceClassification.from_pretrained("DemangeJeremy/4-sentiments-with-flaubert") nlp = pipeline('sentiment-analysis', model=loaded_model, tokenizer=loaded_tokenizer) print(nlp("Je suis plutôt confiant.")) ``` ``` [{'label': 'OBJECTIVE', 'score': 0.3320835530757904}] ``` ## Résultats de l'évaluation du modèle | Epoch | Validation Loss | Samples Per Second | |:------:|:--------------:|:------------------:| | 1 | 2.219246 | 49.476000 | | 2 | 1.883753 | 47.259000 | | 3 | 1.747969 | 44.957000 | | 4 | 1.695606 | 43.872000 | | 5 | 1.641470 | 45.726000 | ## Citation Pour toute utilisation de ce modèle, merci d'utiliser cette citation : > Jérémy Demange, Four sentiments with FlauBERT, (2021), Hugging Face repository, <https://huggingface.co/DemangeJeremy/4-sentiments-with-flaubert>
mbien/recipenlg
b690df9e40f6bf58c7cf8d96e1a91e101134167b
2021-05-23T08:56:58.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "transformers" ]
text-generation
false
mbien
null
mbien/recipenlg
193
1
transformers
3,667
# RecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation Model accompanying our INLG 2020 paper: [RecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation](https://www.aclweb.org/anthology/2020.inlg-1.4.pdf) ## Where is the dataset? Please visit the website of our project: [recipenlg.cs.put.poznan.pl](https://recipenlg.cs.put.poznan.pl/) to download it. ## How to use the model? Could you explain X andy Y? Yes, sure! If you feel some information is missing in our paper, please check first in our [thesis](https://www.researchgate.net/publication/345308878_Cooking_recipes_generator_utilizing_a_deep_learning-based_language_model), which is much more detailed. In case of further questions, you're invited to send us a github issue, we will respond as fast as we can!
persiannlp/mt5-base-parsinlu-translation_en_fa
3a107aa2674d8f097adea1c7e1c0332dfc5236f5
2021-09-23T16:20:09.000Z
[ "pytorch", "mt5", "text2text-generation", "fa", "multilingual", "dataset:parsinlu", "transformers", "machine-translation", "persian", "farsi", "license:cc-by-nc-sa-4.0", "autotrain_compatible" ]
text2text-generation
false
persiannlp
null
persiannlp/mt5-base-parsinlu-translation_en_fa
193
null
transformers
3,668
--- language: - fa - multilingual thumbnail: https://upload.wikimedia.org/wikipedia/commons/a/a2/Farsi.svg tags: - machine-translation - mt5 - persian - farsi license: cc-by-nc-sa-4.0 datasets: - parsinlu metrics: - sacrebleu --- # Machine Translation (ترجمه‌ی ماشینی) This is an mT5-based model for machine translation (English -> Persian). Here is an example of how you can run this model: ```python from transformers import MT5ForConditionalGeneration, MT5Tokenizer model_size = "base" model_name = f"persiannlp/mt5-{model_size}-parsinlu-translation_en_fa" tokenizer = MT5Tokenizer.from_pretrained(model_name) model = MT5ForConditionalGeneration.from_pretrained(model_name) def run_model(input_string, **generator_args): input_ids = tokenizer.encode(input_string, return_tensors="pt") res = model.generate(input_ids, **generator_args) output = tokenizer.batch_decode(res, skip_special_tokens=True) print(output) return output run_model("Praise be to Allah, the Cherisher and Sustainer of the worlds;") run_model("shrouds herself in white and walks penitentially disguised as brotherly love through factories and parliaments; offers help, but desires power;") run_model("He thanked all fellow bloggers and organizations that showed support.") run_model("Races are held between April and December at the Veliefendi Hippodrome near Bakerky, 15 km (9 miles) west of Istanbul.") run_model("I want to pursue PhD in Computer Science about social network,what is the open problem in social networks?") ``` which should output: ``` ['خدا را شکر که عامل خطرناک و محافظ دنیاست.'] ['خود را سفید می کند و به شکل برادرانه ای در کارخانه ها و'] ['او از تمامی همکاران و سازمان هایی که از او حمایت می کردند تشکر'] ['برگزاری مسابقات بین آوریل تا دسامبر در هیپوگریم والی'] ['من می خواهم تحصیل دکترای علوم کامپیوتری را در مورد شب'] ``` For more details, visit this page: https://github.com/persiannlp/parsinlu/
helliun/polhol
20558877f2958c1c49a6f11440e6594ed59d9d5b
2022-06-17T20:18:08.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
helliun
null
helliun/polhol
193
null
transformers
3,669
Entry not found
DeepPavlov/distilrubert-tiny-cased-conversational-5k
b81d5917df739af393e4aef61b7199fb04c502a7
2022-06-28T17:05:02.000Z
[ "pytorch", "distilbert", "ru", "arxiv:2205.02340", "transformers" ]
null
false
DeepPavlov
null
DeepPavlov/distilrubert-tiny-cased-conversational-5k
193
null
transformers
3,670
--- language: - ru --- # distilrubert-tiny-cased-conversational-5k Conversational DistilRuBERT-tiny-5k \(Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 3.6M parameters, 5k vocab\) was trained on OpenSubtitles\[1\], [Dirty](https://d3.ru/), [Pikabu](https://pikabu.ru/), and a Social Media segment of Taiga corpus\[2\] (as [Conversational RuBERT](https://huggingface.co/DeepPavlov/rubert-base-cased-conversational)). Our DistilRuBERT-tiny-5k is highly inspired by \[3\], \[4\] and architecture is very close to \[5\]. Namely, we use * MLM loss (between token labels and student output distribution) * KL loss (between averaged student and teacher hidden states) The key feature is: * reduced vocabulary size (5K vs 30K in *tiny* vs. 100K in *base* and *small*) Here is comparison between teacher model (`Conversational RuBERT`) and other distilled models. | Model name | \# params, M | \# vocab, K | Mem., MB | |---|---|---|---| | `rubert-base-cased-conversational` | 177.9 | 120 | 679 | | `distilrubert-base-cased-conversational` | 135.5 | 120 | 517 | | `distilrubert-small-cased-conversational` | 107.1 | 120 | 409 | | `cointegrated/rubert-tiny` | 11.8 | 30 | 46 | | `cointegrated/rubert-tiny2` | 29.3 | 84 | 112 | | `distilrubert-tiny-cased-conversational-v1` | 10.4 | 31 | 41 | | `distilrubert-tiny-cased-conversational-5k` | **3.6** | 5 | **14** | DistilRuBERT-tiny was trained for about 100 hrs. on 7 nVIDIA Tesla P100-SXM2.0 16Gb. We used `PyTorchBenchmark` from `transformers` to evaluate model's performance and compare it with other pre-trained language models for Russian. All tests were performed on NVIDIA GeForce GTX 1080 Ti and Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz | Model name | Batch size | Seq len | Time, s || Mem, MB || |---|---|---|------||------|| | | | | CPU | GPU | CPU | GPU | | `rubert-base-cased-conversational` | 16 | 512 | 5.283 | 0.1866 | 1550 | 1938 | | `distilrubert-base-cased-conversational` | 16 | 512 | 2.335 | 0.0553 | 2177 | 2794 | | `distilrubert-small-cased-conversational` | 16 | 512 | 0.802 | **0.0015** | 1541 | 1810 | | `cointegrated/rubert-tiny` | 16 | 512 | 0.942 | 0.0022 | 1308 | 2088 | | `cointegrated/rubert-tiny2` | 16 | 512 | 1.786 | 0.0023 | 3054 | 3848 | | `distilrubert-tiny-cased-conversational-v1` | 16 | 512 | **0.374** | **0.002** | **714** | **1158** | | `distilrubert-tiny-cased-conversational-5k` | 16 | 512 | **0.354** | **0.0018** | **664** | **1126** | To evaluate model quality, we fine-tuned DistilRuBERT-tiny-5k on classification (RuSentiment, ParaPhraser), NER and question answering data sets for Russian. The results could be found in the [paper](https://arxiv.org/abs/2205.02340) Table 4 as well as performance benchmarks and training details. # Citation If you found the model useful for your research, we are kindly ask to cite [this](https://arxiv.org/abs/2205.02340) paper: ``` @misc{https://doi.org/10.48550/arxiv.2205.02340, doi = {10.48550/ARXIV.2205.02340}, url = {https://arxiv.org/abs/2205.02340}, author = {Kolesnikova, Alina and Kuratov, Yuri and Konovalov, Vasily and Burtsev, Mikhail}, keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Knowledge Distillation of Russian Language Models with Reduction of Vocabulary}, publisher = {arXiv}, year = {2022}, copyright = {arXiv.org perpetual, non-exclusive license} } ``` \[1\]: P. Lison and J. Tiedemann, 2016, OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles. In Proceedings of the 10th International Conference on Language Resources and Evaluation \(LREC 2016\) \[2\]: Shavrina T., Shapovalova O. \(2017\) TO THE METHODOLOGY OF CORPUS CONSTRUCTION FOR MACHINE LEARNING: «TAIGA» SYNTAX TREE CORPUS AND PARSER. in proc. of “CORPORA2017”, international conference , Saint-Petersbourg, 2017. \[3\]: Sanh, V., Debut, L., Chaumond, J., & Wolf, T. \(2019\). DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108. \[4\]: <https://github.com/huggingface/transformers/tree/master/examples/research_projects/distillation> \[5\]: <https://habr.com/ru/post/562064/>, <https://huggingface.co/cointegrated/rubert-tiny>
samroni/model_gpt
3821d526bd9c83a658314707909ed089293b5220
2022-06-28T18:36:24.000Z
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
false
samroni
null
samroni/model_gpt
193
null
transformers
3,671
Entry not found
KoboldAI/fairseq-dense-1.3B
35812b21ae79688d634c285fb617640b4e5a9eda
2022-02-01T22:50:09.000Z
[ "pytorch", "xglm", "text-generation", "transformers" ]
text-generation
false
KoboldAI
null
KoboldAI/fairseq-dense-1.3B
192
1
transformers
3,672
Entry not found
congcongwang/gpt2_medium_fine_tuned_coder
636a8814222562b9b5517bada923fee23b8a73f6
2021-05-21T15:06:28.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "transformers" ]
text-generation
false
congcongwang
null
congcongwang/gpt2_medium_fine_tuned_coder
192
1
transformers
3,673
Entry not found
facebook/s2t-small-mustc-en-it-st
90f09c8fb0ba436e3ce1c1d5dbe9fb14a1b4cd7e
2022-02-07T15:01:15.000Z
[ "pytorch", "tf", "speech_to_text", "automatic-speech-recognition", "en", "it", "dataset:mustc", "arxiv:2010.05171", "arxiv:1904.08779", "transformers", "audio", "speech-translation", "license:mit" ]
automatic-speech-recognition
false
facebook
null
facebook/s2t-small-mustc-en-it-st
192
1
transformers
3,674
--- language: - en - it datasets: - mustc tags: - audio - speech-translation - automatic-speech-recognition license: mit pipeline_tag: automatic-speech-recognition widget: - example_title: Librispeech sample 1 src: https://cdn-media.huggingface.co/speech_samples/sample1.flac - example_title: Librispeech sample 2 src: https://cdn-media.huggingface.co/speech_samples/sample2.flac --- # S2T-SMALL-MUSTC-EN-IT-ST `s2t-small-mustc-en-it-st` is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST). The S2T model was proposed in [this paper](https://arxiv.org/abs/2010.05171) and released in [this repository](https://github.com/pytorch/fairseq/tree/master/examples/speech_to_text) ## Model description S2T is a transformer-based seq2seq (encoder-decoder) model designed for end-to-end Automatic Speech Recognition (ASR) and Speech Translation (ST). It uses a convolutional downsampler to reduce the length of speech inputs by 3/4th before they are fed into the encoder. The model is trained with standard autoregressive cross-entropy loss and generates the transcripts/translations autoregressively. ## Intended uses & limitations This model can be used for end-to-end English speech to Italian text translation. See the [model hub](https://huggingface.co/models?filter=speech_to_text) to look for other S2T checkpoints. ### How to use As this a standard sequence to sequence transformer model, you can use the `generate` method to generate the transcripts by passing the speech features to the model. *Note: The `Speech2TextProcessor` object uses [torchaudio](https://github.com/pytorch/audio) to extract the filter bank features. Make sure to install the `torchaudio` package before running this example.* You could either install those as extra speech dependancies with `pip install transformers"[speech, sentencepiece]"` or install the packages seperatly with `pip install torchaudio sentencepiece`. ```python import torch from transformers import Speech2TextProcessor, Speech2TextForConditionalGeneration from datasets import load_dataset import soundfile as sf model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-mustc-en-it-st") processor = Speech2TextProcessor.from_pretrained("facebook/s2t-small-mustc-en-it-st") def map_to_array(batch): speech, _ = sf.read(batch["file"]) batch["speech"] = speech return batch ds = load_dataset( "patrickvonplaten/librispeech_asr_dummy", "clean", split="validation" ) ds = ds.map(map_to_array) inputs = processor( ds["speech"][0], sampling_rate=16_000, return_tensors="pt" ) generated_ids = model.generate(input_ids=inputs["input_features"], attention_mask=inputs["attention_mask"]) translation = processor.batch_decode(generated_ids, skip_special_tokens=True) ``` ## Training data The s2t-small-mustc-en-it-st is trained on English-Italian subset of [MuST-C](https://ict.fbk.eu/must-c/). MuST-C is a multilingual speech translation corpus whose size and quality facilitates the training of end-to-end systems for speech translation from English into several languages. For each target language, MuST-C comprises several hundred hours of audio recordings from English TED Talks, which are automatically aligned at the sentence level with their manual transcriptions and translations. ## Training procedure ### Preprocessing The speech data is pre-processed by extracting Kaldi-compliant 80-channel log mel-filter bank features automatically from WAV/FLAC audio files via PyKaldi or torchaudio. Further utterance-level CMVN (cepstral mean and variance normalization) is applied to each example. The texts are lowercased and tokenized using SentencePiece and a vocabulary size of 8,000. ### Training The model is trained with standard autoregressive cross-entropy loss and using [SpecAugment](https://arxiv.org/abs/1904.08779). The encoder receives speech features, and the decoder generates the transcripts autoregressively. To accelerate model training and for better performance the encoder is pre-trained for English ASR. ## Evaluation results MuST-C test results for en-it (BLEU score): 22.7 ### BibTeX entry and citation info ```bibtex @inproceedings{wang2020fairseqs2t, title = {fairseq S2T: Fast Speech-to-Text Modeling with fairseq}, author = {Changhan Wang and Yun Tang and Xutai Ma and Anne Wu and Dmytro Okhonko and Juan Pino}, booktitle = {Proceedings of the 2020 Conference of the Asian Chapter of the Association for Computational Linguistics (AACL): System Demonstrations}, year = {2020}, } ```
won/DialoGPT-small-harrypotter
4701f16a4d0c0c19ad226e4506e01f335c3e2100
2022-01-15T11:58:54.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
won
null
won/DialoGPT-small-harrypotter
192
null
transformers
3,675
--- tags: - conversational --- # Harry Potter DialoGPT Model
yechen/question-answering-chinese
46beedecafca28a1aeb696b18408e1eb76262c87
2021-05-20T09:25:57.000Z
[ "pytorch", "tf", "jax", "bert", "question-answering", "zh", "transformers", "autotrain_compatible" ]
question-answering
false
yechen
null
yechen/question-answering-chinese
192
null
transformers
3,676
--- language: zh ---
projecte-aina/roberta-base-ca-v2-cased-qa
0ab7044d72b9cd2cd91b869ad9854026189faab1
2022-07-25T06:50:23.000Z
[ "pytorch", "roberta", "question-answering", "ca", "dataset:projecte-aina/catalanqa", "dataset:projecte-aina/xquad-ca", "arxiv:1907.11692", "transformers", "catalan", "qa", "license:apache-2.0", "model-index", "autotrain_compatible" ]
question-answering
false
projecte-aina
null
projecte-aina/roberta-base-ca-v2-cased-qa
192
null
transformers
3,677
--- language: - ca license: apache-2.0 tags: - "catalan" - "qa" datasets: - "projecte-aina/catalanqa" - "projecte-aina/xquad-ca" model-index: - name: roberta-base-ca-v2-cased-qa results: - task: type: question-answering dataset: type: projecte-aina/catalanqa name: CatalanQA metrics: - name: F1 type: f1 value: 0.8950 - task: type: question-answering dataset: type: projecte-aina/xquad-ca name: XQuAD-Ca metrics: - name: F1 type: f1 value: 0.7364 metrics: - "f1" - "exact match" widget: - text: "Quan va començar el Super3?" context: "El Super3 o Club Super3 és un univers infantil català creat a partir d'un programa emès per Televisió de Catalunya des del 1991. Està format per un canal de televisió, la revista Súpers!, la Festa dels Súpers i un club que té un milió i mig de socis." - text: "Quants eren els germans Marx?" context: "Els germans Marx van ser un grup de còmics dels Estats Units que originàriament estava compost per cinc germans (entre parèntesis els noms artístics): Leonard (Chico), Adolph (Harpo), Julius (Groucho), Milton (Gummo) i Herbert (Zeppo)." - text: "On van ser els Jocs Olímpics de 1992?" context: "Els Jocs Olímpics d'estiu de 1992, oficialment Jocs Olímpics de la XXV Olimpíada, es van celebrar a la ciutat de Barcelona entre els dies 25 de juliol i 9 d'agost de 1992. Hi participaren 9.356 atletes (6.652 homes i 2.704 dones) de 169 comitès nacionals, que competiren en 32 esports i 286 especialitats." - text: "Qui va dissenyar la Sagrada Família?" context: "El Temple Expiatori de la Sagrada Família, conegut habitualment com la Sagrada Família, és una basílica catòlica situada a la ciutat de Barcelona. És un dels exemples més coneguts del modernisme català i un edifici únic al món, que ha esdevingut tot un símbol de la ciutat. Obra inacabada de l'arquitecte català Antoni Gaudí, és al barri de la Sagrada Família, al districte de l'Eixample de la ciutat." - text: "Quin és el tercer volcà més gran de la Terra?" context: "El Teide (o Pic del Teide) és un estratovolcà i muntanya de Tenerife, Illes Canàries (28.27 N, 16.6 O). Amb una altitud de 3718 m sobre el nivell del mar i amb aproximadament uns 7000 m sobre el llit marí adjacent, és la muntanya més alta d'Espanya, la muntanya més alta de totes les illes atlàntiques i el tercer volcà més gran de la Terra." --- # Catalan BERTa-v2 (roberta-base-ca-v2) finetuned for Question Answering. ## Table of Contents - [Model Description](#model-description) - [Intended Uses and Limitations](#intended-uses-and-limitations) - [How to Use](#how-to-use) - [Training](#training) - [Training Data](#training-data) - [Training Procedure](#training-procedure) - [Evaluation](#evaluation) - [Variable and Metrics](#variable-and-metrics) - [Evaluation Results](#evaluation-results) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Funding](#funding) - [Contributions](#contributions) ## Model description The **roberta-base-ca-v2-cased-qa** is a Question Answering (QA) model for the Catalan language fine-tuned from the [roberta-base-ca-v2](https://huggingface.co/projecte-aina/roberta-base-ca-v2) model, a [RoBERTa](https://arxiv.org/abs/1907.11692) base model pre-trained on a medium-size corpus collected from publicly available corpora and crawlers (check the roberta-base-ca-v2 model card for more details). ## Intended Uses and Limitations **roberta-base-ca-v2-cased-qa** model can be used for extractive question answering. The model is limited by its training dataset and may not generalize well for all use cases. ## How to Use Here is how to use this model: ```python from transformers import pipeline nlp = pipeline("question-answering", model="projecte-aina/roberta-base-ca-v2-cased-qa") text = "Quan va començar el Super3?" context = "El Super3 o Club Super3 és un univers infantil català creat a partir d'un programa emès per Televisió de Catalunya des del 1991. Està format per un canal de televisió, la revista Súpers!, la Festa dels Súpers i un club que té un milió i mig de socis." qa_results = nlp(text, context) print(qa_results) ``` ## Training ### Training data We used the QA dataset in Catalan called [CatalanQA](https://huggingface.co/datasets/projecte-aina/catalanqa) for training and evaluation, and the [XQuAD-ca](https://huggingface.co/datasets/projecte-aina/xquad-ca) test set for evaluation. ### Training Procedure The model was trained with a batch size of 16 and a learning rate of 5e-5 for 5 epochs. We then selected the best checkpoint using the downstream task metric in the corresponding development set and then evaluated it on the test set. ## Evaluation ### Variable and Metrics This model was finetuned maximizing F1 score. ### Evaluation results We evaluated the _roberta-base-ca-v2-cased-qa_ on the CatalanQA and XQuAD-ca test sets against standard multilingual and monolingual baselines: | Model | CatalanQA (F1/EM) | XQuAD-Ca (F1/EM) | | ------------|:-------------:| -----:| | roberta-base-ca-v2-cased-qa | **89.50**/76.63 | **73.64/55.42** | | roberta-base-ca-cased-qa | 89.17/**77.14** | 69.20/51.47 | | mBERT | 86.90/74.19 | 68.79/50.80 | | XLM-RoBERTa | 88.17/75.93 | 72.55/54.16 | For more details, check the fine-tuning and evaluation scripts in the official [GitHub repository](https://github.com/projecte-aina/club). ## Licensing Information [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0) ## Citation Information If you use any of these resources (datasets or models) in your work, please cite our latest paper: ```bibtex @inproceedings{armengol-estape-etal-2021-multilingual, title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}atalan", author = "Armengol-Estap{\'e}, Jordi and Carrino, Casimiro Pio and Rodriguez-Penagos, Carlos and de Gibert Bonet, Ona and Armentano-Oller, Carme and Gonzalez-Agirre, Aitor and Melero, Maite and Villegas, Marta", booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.findings-acl.437", doi = "10.18653/v1/2021.findings-acl.437", pages = "4933--4946", } ``` ### Funding This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina). ## Contributions [N/A]
dim/dialogpt-medium-persona-chat
d857cbe74c45efede87b707cd4ffc3e2dd851baa
2022-07-12T11:39:20.000Z
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
false
dim
null
dim/dialogpt-medium-persona-chat
192
null
transformers
3,678
Entry not found
codeparrot/codeparrot-small-code-to-text
28a105e14da86230e3b92e6c6f55d2d398083208
2022-07-19T15:46:25.000Z
[ "pytorch", "gpt2", "text-generation", "code", "dataset:codeparrot/codeparrot-clean", "dataset:codeparrot/github-jupyter-code-to-text", "transformers", "generation", "license:apache-2.0" ]
text-generation
false
codeparrot
null
codeparrot/codeparrot-small-code-to-text
192
null
transformers
3,679
--- language: - code license: apache-2.0 tags: - code - gpt2 - generation datasets: - "codeparrot/codeparrot-clean" - "codeparrot/github-jupyter-code-to-text" --- # CodeParrot 🦜 small for text-t-code generation This model is [CodeParrot-small](https://huggingface.co/codeparrot/codeparrot-small) (from `branch megatron`) Fine-tuned on [github-jupyter-code-to-text](https://huggingface.co/datasets/codeparrot/github-jupyter-code-to-text), a dataset where the samples are a succession of Python code and its explanation as a docstring, originally extracted from Jupyter notebooks parsed in this [dataset](https://huggingface.co/datasets/codeparrot/github-jupyter-parsed).
SEBIS/code_trans_t5_base_code_documentation_generation_java_multitask_finetune
f692aced9d13bbf9213a1418bd1177a73480a28f
2021-06-23T04:24:36.000Z
[ "pytorch", "jax", "t5", "feature-extraction", "transformers", "summarization" ]
summarization
false
SEBIS
null
SEBIS/code_trans_t5_base_code_documentation_generation_java_multitask_finetune
191
null
transformers
3,680
--- tags: - summarization widget: - text: "public static < T , U > Function < T , U > castFunction ( Class < U > target ) { return new CastToClass < T , U > ( target ) ; }" --- # CodeTrans model for code documentation generation java Pretrained model on programming language java using the t5 base model architecture. It was first released in [this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized java code functions: it works best with tokenized java functions. ## Model description This CodeTrans model is based on the `t5-base` model. It has its own SentencePiece vocabulary model. It used multi-task training on 13 supervised tasks in the software development domain and 7 unsupervised datasets. It is then fine-tuned on the code documentation generation task for the java function/method. ## Intended uses & limitations The model could be used to generate the description for the java function or be fine-tuned on other java code tasks. It can be used on unparsed and untokenized java code. However, if the java code is tokenized, the performance should be better. ### How to use Here is how to use this model to generate java function documentation using Transformers SummarizationPipeline: ```python from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline pipeline = SummarizationPipeline( model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_base_code_documentation_generation_java_multitask_finetune"), tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_base_code_documentation_generation_java_multitask_finetune", skip_special_tokens=True), device=0 ) tokenized_code = "public static < T , U > Function < T , U > castFunction ( Class < U > target ) { return new CastToClass < T , U > ( target ) ; }" pipeline([tokenized_code]) ``` Run this example in [colab notebook](https://github.com/agemagician/CodeTrans/blob/main/prediction/multitask/fine-tuning/function%20documentation%20generation/java/base_model.ipynb). ## Training data The supervised training tasks datasets can be downloaded on [Link](https://www.dropbox.com/sh/488bq2of10r4wvw/AACs5CGIQuwtsD7j_Ls_JAORa/finetuning_dataset?dl=0&subfolder_nav_tracking=1) ## Training procedure ### Multi-task Pretraining The model was trained on a single TPU Pod V3-8 for half million steps in total, using sequence length 512 (batch size 4096). It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture. The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training. ### Fine-tuning This model was then fine-tuned on a single TPU Pod V2-8 for 2000 steps in total, using sequence length 512 (batch size 256), using only the dataset only containing java code. ## Evaluation results For the code documentation tasks, different models achieves the following results on different programming languages (in BLEU score): Test results : | Language / Model | Python | Java | Go | Php | Ruby | JavaScript | | -------------------- | :------------: | :------------: | :------------: | :------------: | :------------: | :------------: | | CodeTrans-ST-Small | 17.31 | 16.65 | 16.89 | 23.05 | 9.19 | 13.7 | | CodeTrans-ST-Base | 16.86 | 17.17 | 17.16 | 22.98 | 8.23 | 13.17 | | CodeTrans-TF-Small | 19.93 | 19.48 | 18.88 | 25.35 | 13.15 | 17.23 | | CodeTrans-TF-Base | 20.26 | 20.19 | 19.50 | 25.84 | 14.07 | 18.25 | | CodeTrans-TF-Large | 20.35 | 20.06 | **19.54** | 26.18 | 14.94 | **18.98** | | CodeTrans-MT-Small | 19.64 | 19.00 | 19.15 | 24.68 | 14.91 | 15.26 | | CodeTrans-MT-Base | **20.39** | 21.22 | 19.43 | **26.23** | **15.26** | 16.11 | | CodeTrans-MT-Large | 20.18 | **21.87** | 19.38 | 26.08 | 15.00 | 16.23 | | CodeTrans-MT-TF-Small | 19.77 | 20.04 | 19.36 | 25.55 | 13.70 | 17.24 | | CodeTrans-MT-TF-Base | 19.77 | 21.12 | 18.86 | 25.79 | 14.24 | 18.62 | | CodeTrans-MT-TF-Large | 18.94 | 21.42 | 18.77 | 26.20 | 14.19 | 18.83 | | State of the art | 19.06 | 17.65 | 18.07 | 25.16 | 12.16 | 14.90 | > Created by [Ahmed Elnaggar](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/) and Wei Ding | [LinkedIn](https://www.linkedin.com/in/wei-ding-92561270/)
SkolkovoInstitute/roberta-base-formality-ranker-v1
507700d5f40abdcee951bd2f859d48aed67367ba
2022-07-20T21:29:23.000Z
[ "pytorch", "roberta", "text-classification", "en", "dataset:GYAFC", "dataset:Pavlick-Tetreault-2016", "transformers", "formality" ]
text-classification
false
SkolkovoInstitute
null
SkolkovoInstitute/roberta-base-formality-ranker-v1
191
null
transformers
3,681
--- language: - en tags: - formality datasets: - GYAFC - Pavlick-Tetreault-2016 --- The model has been trained [here](https://git.mts.ai/ai/ml_lab/skoltech-nlp_lab/skoltech/task_oriented_TST/-/blob/main/transfer/formality_ranker_v1.ipynb) to predict for English sentences, whether they are formal or informal. Base model: `roberta-base` Datasets: [GYAFC](https://github.com/raosudha89/GYAFC-corpus) from [Rao and Tetreault, 2018](https://aclanthology.org/N18-1012) and [online formality corpus](http://www.seas.upenn.edu/~nlp/resources/formality-corpus.tgz) from [Pavlick and Tetreault, 2016](https://aclanthology.org/Q16-1005). Data augmentation: changing texts to upper or lower case; removing all punctuation, adding dot at the end of a sentence. It was applied because otherwise the model is over-reliant on punctuation and capitalization and does not pay enough attention to other features. Loss: binary classification (on GYAFC), in-batch ranking (on PT data). Performance metrics on the validation data: | dataset | ROC AUC | accuracy | Spearman R| | - | - | - | - | | GYAFC | 0.9779 | 0.9087 | 0.8233 | | GYAFC normalized (lowercase + remove punct.) | 0.9234 | 0.8218| 0.7294 | | P&T subset | Spearman R | | - | - | news | 0.4003 answers | 0.7500 blog | 0.7334 email | 0.7606
readerbench/RoGPT2-large
5d3292f4fb85e0dd83e1a31399f766b520c9b811
2021-07-22T11:23:50.000Z
[ "pytorch", "tf", "gpt2", "text-generation", "ro", "transformers" ]
text-generation
false
readerbench
null
readerbench/RoGPT2-large
191
null
transformers
3,682
Model card for RoGPT2-large --- language: - ro --- # RoGPT2: Romanian GPT2 for text generation All models are available: * [RoBERT-base](https://huggingface.co/readerbench/RoGPT2-base) * [RoBERT-medium](https://huggingface.co/readerbench/RoGPT2-medium) * [RoBERT-large](https://huggingface.co/readerbench/RoGPT2-large) For code and evaluation check out [GitHub](https://github.com/readerbench/RoGPT2). #### How to use ```python # TensorFlow from transformers import AutoTokenizer, TFAutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained('readerbench/RoGPT2-large') model = TFAutoModelForCausalLM.from_pretrained('readerbench/RoGPT2-large') inputs = tokenizer.encode("Este o zi de vara", return_tensors='tf') text = model.generate(inputs, max_length=1024, no_repeat_ngram_size=2) print(tokenizer.decode(text[0])) # PyTorch from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained('readerbench/RoGPT2-large') model = AutoModelForCausalLM.from_pretrained('readerbench/RoGPT2-large') inputs = tokenizer.encode("Este o zi de vara", return_tensors='pt') text = model.generate(inputs, max_length=1024, no_repeat_ngram_size=2) print(tokenizer.decode(text[0])) ``` ## Training --- ### Corpus Statistics | Corpus | Total size | Number of words | Number of sentences | |:------:|:----------:|:---------------:|:-------------------:| |OSCAR| 11.54 GB | 1745M | 48.46M | |Wiki-Ro | 0.46 GB | 68M | 1.79M | |Debates | 0.5 GB | 73M | 3.61M | |Books | 4.37 GB | 667M | 37.39M | |News | 0.15 GB | 23M | 0.77M | ### Training Statistics | Version | Number of parameters | Number of epoch | Duration of an epoch | Context size | Batch size | PPL | |:-------:|:--------------------:|:---------------:|:--------------------:|:----------:|:----------:|:---:| | Base | 124M | 15 | 7h | 1024 | 72 | 22.96 | | Medium | 354M | 10 | 22h | 1024 | 24 | 17.64 | | Large | 774M | 5 | **45h** | 512 | 16 | **16.77**| ## Evaluation --- ### 1. MOROCO | Model | Dialect | Md to Ro | Ro to Md | |:-----------------:|:-------:|:--------:|:--------:| | KRR + SK | 94.06 | 67.59 | 75.47 | | BERT-base-ro | 95.98 | 69.90 | 78.08 | | RoBERT-small | 95.76 | 69.05 | 80.15 | | RoBERT-base |**97.24**| 68.80 | 82.37 | | RoBERT-large | 97.21 | 69.50 | **83.26**| | RoGPT2-base | 96.69 | 69.82 | 77.55 | | RoGPT2-medium | 96.42 | 69.77 | 80.51 | | RoGPT2-large | 96.93 |**71.07** | 82.56 | ### 2. LaRoSeDa | Model | Binary: Accuracy | Binary: F1-Score | Multi-Class: Accuracy | Multi-Class: F1-Score | |:------------:|:----------------:|:----------------:|:---------------------:|:---------------------:| |BERT-base-ro | 98.07 | 97.94 | - |79.61 | | RoDiBERT |**98.40** |**98.31** | - |83.01 | | RoBERT-small | 97.44 | 97.43 | 89.30 |84.23 | | RoBERT-base | 98.27 | 98.26 | 90.59 |86.27 | | RoBERT-large | 98.20 | 98.19 |**90.93** |**86.63** | | RoGPT2-base | 97.89 | 97.88 |89.65 |84.68 | |RoGPT2-medium | 98.03 |98.04 | 90.29 | 85.37 | | RoGPT2-large | 98.06 |98.07 | 90.26 | 84.89 | ### 3. RoSTS | Model | Spearman dev-set | Spearman test-set | Pearson dev-set | Pearson test-set | |:------------:|:----------------:|:-----------------:|:---------------:|:----------------:| |BERT-base-ro | 84.26 | 80.86 | 84.59 | 81.59 | |RoDiBERT | 77.07 | 71.47 | 77.13 | 72.25 | |RoBERT-small | 82.06 | 78.06 | 81.66 | 78.49 | |RoBERT-base | 84.93 | 80.39 | 85.03 | 80.39 | |RoBERT-large |**86.25** |**83.15** |**86.58** |**83.76** | |RoGPT2-base | 83.51 | 79.77 | 83.74 | 80.56 | |RoGPT2-medium | 85.75 | 82.25 | 86.04 | 83.16 | |RoGPT2-large | 85.70 | 82.64 | 86.14 | 83.46 | ### 4. WMT16 | Model | Decoder method | Ro-En | En-Ro | |:------------:|:--------------:|:------:|:------:| |mBART | - |**38.5**|**38.5**| |OpenNMT | - | - | 24.7 | |RoGPT2-base |Greedy | 30.37 | 20.27 | |RoGPT2-base |Beam-search-4 | 31.26 | 22.31 | |RoGPT2-base |Beam-search-8 | 31.39 | 22.95 | |RoGPT2-medium |Greedy | 32.48 | 22.18 | |RoGPT2-medium |Beam-search-4 | 34.08 | 24.03 | |RoGPT2-medium |Beam-search-8 | 34.16 | 24.13 | |RoGPT2-large |Greedy | 33.69 | 23.31 | |RoGPT2-large |Beam-search-4 |34.40 |24.23 | |RoGPT2-large |Beam-search-8 |34.51 |24.32 | ### 5. XQuAD | Model |Decoder method | EM | F1-Score | |:------------:|:-------------:|:-----:|:--------:| |BERT-base-ro | - | 47.89 | 63.74 | |RoDiBERT | - | 21.76 | 34.57 | |RoBERT-small | - | 30.84 | 45.17 | |RoBERT-base | - | 53.52 | 70.04 | |RoBERT-large | - | 55.46 | 69.64 | |mBERT | - | 59.9 | 72.7 | |XLM-R Large | - |**69.7**|**83.6**| |RoGPT2-base | Greedy | 23.69 | 35.97 | |RoGPT2-base | Beam-search-4 | 24.11 | 35.27 | |RoGPT2-medium | Greedy | 29.66 | 44.74 | |RoGPT2-medium | Beam-search-4 | 31.59 | 45.32 | |RoGPT2-large | Greedy | 29.74 | 42.98 | |RoGPT2-large | Beam-search-4 | 29.66 | 43.05 | |RoGPT2-base-en-ro | Greedy | 23.86 | 34.27 | |RoGPT2-base-en-ro | Beam-search-4 | 25.04 | 34.51 | |RoGPT2-medium-en-ro | Greedy | 27.05 | 39.75 | |RoGPT2-medium-en-ro | Beam-search-4 | 27.64 | 39.11 | |RoGPT2-large-en-ro | Greedy | 28.40 | 39.79 | |RoGPT2-large-en-ro | Beam-search-4 | 28.73 | 39.71 | |RoGPT2-large-en-ro-mask | Greedy | 31.34 | 44.71 | |RoGPT2-large-en-ro-mask| Beam-search-4 | 31.59 | 43.53 | ### 6. Wiki-Ro: LM | Model | PPL dev | PPL test | |:------------:|:-------:|:--------:| |BERT-base-ro | 29.0897 | 28.0043| |RoGPT2-base | 34.3795 | 33.7460| |RoGPT2-medium | 23.7879 | 23.4581| |RoGPT2-large | **21.7491** | **21.5200** | ### 7. RoGEC | Model | Decoder mothod | P | R | F<sub>0.5</sub> | |:-----:|:--------------:|:---:|:---:|:------:| |Transformer-tiny | Beam-search | 53.53 | 26.36 | 44.38 | |Transformer-base Finetuning | Beam-search | 56.05 | 46.19 | 53.76 | |Transformer-base Finetuning | Beam-search-LM | 50.68 | 45.39 | 49.52 | |Transformer-base Finetuning | Beam-search-norm-LM | 51.06 | 45.43 | 49.83 | |RoGPT2-base | Greedy | 59.02 | 49.35 | 56.80 | |RoGPT2-base | Beam-search-4 | 65.23 | 49.26 | 61.26 | |RoGPT2-base |Beam-search-8 | 65.88 | 49.64 | 61.84 | |RoGPT2-medium | Greedy | 69.97 | 57.94 | 67.18 | |RoGPT2-medium | Beam-search-4 | **72.46** | **57.99** | **69.01** | |RoGPT2-medium | Beam-search-8 | 72.24 | 57.69 | 68.77 | |RoGP2-large | Greedy | 61.90 | 49.09 | 58.83 | |RoGP2-large | Beam-search-4 | 65.24 | 49.43 | 61.32 | |RoGP2-large | Beam-search-8 | 64.96 | 49.22 | 61.06 | |RoGPT2-base* | Greedy | 68.67 | 49.60 | 63.77 | |RoGPT2-base* | Beam-search-4 | 71.16 | 50.53 | 65.79 | |RoGPT2-base* | Beam-search-8 | 71.68 | 50.65 | 66.18 | |RoGPT2-medium* | Greedy | 58.21 | 43.32 | 54.47 | |RoGPT2-medium* | Beam-search-4 | 68.31 | 43.78 | 61.43 | |RoGPT2-medium* | Beam-search-8 | 68.68 | 43.99 | 61.75 | |RoGPT2-large* | Greedy | 64.86 | 41.30 | 58.22 | |RoGPT2-large* | Beam-search-4 | 65.57 | 41.00 | 58.55 | |RoGPT2-large* | Beam-search-8 | 65.44 | 41.09 | 58.50 | **__Note__**: * the models were trained using the dataset of 3,000,000 artificially generated pairs ## Acknowledgments --- Research supported with [Cloud TPUs](https://cloud.google.com/tpu/) from Google's [TensorFlow Research Cloud (TFRC)](https://www.tensorflow.org/tfrc) ## How to cite --- Niculescu, M. A., Ruseti, S., and Dascalu, M. (submitted). RoGPT2: Romanian GPT2 for Text Generation
StanfordAIMI/stanford-deidentifier-only-i2b2
45c5edfa4dd56d2d65a7b23eb2f7e4aee4a44584
2022-07-18T03:49:38.000Z
[ "pytorch", "bert", "en", "dataset:radreports", "transformers", "token-classification", "sequence-tagger-model", "pubmedbert", "uncased", "radiology", "biomedical", "license:mit" ]
token-classification
false
StanfordAIMI
null
StanfordAIMI/stanford-deidentifier-only-i2b2
191
1
transformers
3,683
--- widget: - text: "PROCEDURE: Chest xray. COMPARISON: last seen on 1/1/2020 and also record dated of March 1st, 2019. FINDINGS: patchy airspace opacities. IMPRESSION: The results of the chest xray of January 1 2020 are the most concerning ones. The patient was transmitted to another service of UH Medical Center under the responsability of Dr. Perez. We used the system MedClinical data transmitter and sent the data on 2/1/2020, under the ID 5874233. We received the confirmation of Dr Perez. He is reachable at 567-493-1234." - text: "Dr. Curt Langlotz chose to schedule a meeting on 06/23." tags: - token-classification - sequence-tagger-model - pytorch - transformers - pubmedbert - uncased - radiology - biomedical datasets: - radreports language: - en license: mit --- Stanford de-identifier was trained on a variety of radiology and biomedical documents with the goal of automatising the de-identification process while reaching satisfactory accuracy for use in production. Manuscript in-proceedings.
Cameron/BERT-rtgender-opgender-annotations
30c103f3f0b41b05c9b1814be20f59c87b7fbb42
2021-05-18T17:34:57.000Z
[ "pytorch", "jax", "bert", "text-classification", "transformers" ]
text-classification
false
Cameron
null
Cameron/BERT-rtgender-opgender-annotations
190
null
transformers
3,684
Entry not found
FPTAI/velectra-base-discriminator-cased
f594fee501933d92d8c13285c969495681d0cc00
2020-09-30T03:52:16.000Z
[ "pytorch", "electra", "pretraining", "transformers" ]
null
false
FPTAI
null
FPTAI/velectra-base-discriminator-cased
190
null
transformers
3,685
Entry not found
Harveenchadha/vakyansh-wav2vec2-indian-english-enm-700
6889649cf67fd629a47fbdd9e11560626d22ac64
2021-08-02T18:42:21.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]
automatic-speech-recognition
false
Harveenchadha
null
Harveenchadha/vakyansh-wav2vec2-indian-english-enm-700
190
3
transformers
3,686
Entry not found
arampacha/wav2vec2-xls-r-1b-hy
5a34466c5a07b932732d804e02d2a29ea608bebd
2022-03-23T18:34:38.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "hy-AM", "dataset:common_voice", "transformers", "generated_from_trainer", "hf-asr-leaderboard", "hy", "mozilla-foundation/common_voice_8_0", "robust-speech-event", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
arampacha
null
arampacha/wav2vec2-xls-r-1b-hy
190
1
transformers
3,687
--- language: - hy-AM license: apache-2.0 tags: - automatic-speech-recognition - generated_from_trainer - hf-asr-leaderboard - hy - mozilla-foundation/common_voice_8_0 - robust-speech-event datasets: - common_voice model-index: - name: wav2vec2-xls-r-1b-hy-cv results: - task: type: automatic-speech-recognition name: Speech Recognition dataset: type: mozilla-foundation/common_voice_8_0 name: Common Voice hy-AM args: hy-AM metrics: - type: wer value: 10.811865729898516 name: WER LM - type: cer value: 2.2205361659079412 name: CER LM - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Robust Speech Event - Dev Data type: speech-recognition-community-v2/dev_data args: hy metrics: - name: Test WER type: wer value: 18.219363037089988 - name: Test CER type: cer value: 7.075988867335752 --- <!-- 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. --> # This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the /WORKSPACE/DATA/HY/NOIZY_STUDENT_4/ - NA dataset. It achieves the following results on the evaluation set: - Loss: 0.1693 - Wer: 0.2373 - Cer: 0.0429 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 64 - seed: 842 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - training_steps: 5000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 1.255 | 7.24 | 500 | 0.2978 | 0.4294 | 0.0758 | | 1.0058 | 14.49 | 1000 | 0.1883 | 0.2838 | 0.0483 | | 0.9371 | 21.73 | 1500 | 0.1813 | 0.2627 | 0.0457 | | 0.8999 | 28.98 | 2000 | 0.1693 | 0.2373 | 0.0429 | | 0.8814 | 36.23 | 2500 | 0.1760 | 0.2420 | 0.0435 | | 0.8364 | 43.47 | 3000 | 0.1765 | 0.2416 | 0.0419 | | 0.8019 | 50.72 | 3500 | 0.1758 | 0.2311 | 0.0398 | | 0.7665 | 57.96 | 4000 | 0.1745 | 0.2240 | 0.0399 | | 0.7376 | 65.22 | 4500 | 0.1717 | 0.2190 | 0.0385 | | 0.716 | 72.46 | 5000 | 0.1700 | 0.2147 | 0.0382 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2 - Datasets 1.18.4.dev0 - Tokenizers 0.11.0
monologg/koelectra-base-v3-goemotions
9b9385c0190252577bc51bf222426e726fea51d0
2021-02-09T14:40:17.000Z
[ "pytorch", "electra", "transformers" ]
null
false
monologg
null
monologg/koelectra-base-v3-goemotions
190
null
transformers
3,688
Entry not found
mrm8488/roberta-base-1B-1-finetuned-squadv1
4a0a7703a4ca3bc57af44753073ccfcb03b45a1f
2021-05-20T18:26:13.000Z
[ "pytorch", "jax", "roberta", "question-answering", "en", "transformers", "autotrain_compatible" ]
question-answering
false
mrm8488
null
mrm8488/roberta-base-1B-1-finetuned-squadv1
190
null
transformers
3,689
--- language: en --- # RoBERTa-base (1B-1) + SQuAD v1 ❓ [roberta-base-1B-1](https://huggingface.co/nyu-mll/roberta-base-1B-1) fine-tuned on [SQUAD v1.1 dataset](https://rajpurkar.github.io/SQuAD-explorer/explore/1.1/dev/) for **Q&A** downstream task. ## Details of the downstream task (Q&A) - Model 🧠 RoBERTa Pretrained on Smaller Datasets [NYU Machine Learning for Language](https://huggingface.co/nyu-mll) pretrained RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). They released 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: They combine English Wikipedia and a reproduction of BookCorpus using texts from smashwords in a ratio of approximately 3:1. ## Details of the downstream task (Q&A) - Dataset 📚 **S**tanford **Q**uestion **A**nswering **D**ataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. SQuAD v1.1 contains **100,000+** question-answer pairs on **500+** articles. ## Model training 🏋️‍ The model was trained on a Tesla P100 GPU and 25GB of RAM with the following command: ```bash python transformers/examples/question-answering/run_squad.py \ --model_type roberta \ --model_name_or_path 'nyu-mll/roberta-base-1B-1' \ --do_eval \ --do_train \ --do_lower_case \ --train_file /content/dataset/train-v1.1.json \ --predict_file /content/dataset/dev-v1.1.json \ --per_gpu_train_batch_size 16 \ --learning_rate 3e-5 \ --num_train_epochs 10 \ --max_seq_length 384 \ --doc_stride 128 \ --output_dir /content/output \ --overwrite_output_dir \ --save_steps 1000 ``` ## Test set Results 🧾 | Metric | # Value | | ------ | --------- | | **EM** | **72.62** | | **F1** | **82.19** | ```json { 'exact': 72.62062440870388, 'f1': 82.19430877136834, 'total': 10570, 'HasAns_exact': 72.62062440870388, 'HasAns_f1': 82.19430877136834, 'HasAns_total': 10570, 'best_exact': 72.62062440870388, 'best_exact_thresh': 0.0, 'best_f1': 82.19430877136834, 'best_f1_thresh': 0.0 } ``` ### Model in action 🚀 Fast usage with **pipelines**: ```python from transformers import pipeline QnA_pipeline = pipeline('question-answering', model='mrm8488/roberta-base-1B-1-finetuned-squadv1') QnA_pipeline({ 'context': 'A new strain of flu that has the potential to become a pandemic has been identified in China by scientists.', 'question': 'What has been discovered by scientists from China ?' }) # Output: {'answer': 'A new strain of flu', 'end': 19, 'score': 0.04702283976040074, 'start': 0} ``` > Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/) > Made with <span style="color: #e25555;">&hearts;</span> in Spain
facebook/data2vec-vision-large-ft1k
9933efaca13b79863d36579c9a339c84cb0e9eca
2022-05-03T15:22:49.000Z
[ "pytorch", "tf", "data2vec-vision", "image-classification", "dataset:imagenet", "dataset:imagenet-1k", "arxiv:2202.03555", "arxiv:2106.08254", "transformers", "vision", "license:apache-2.0" ]
image-classification
false
facebook
null
facebook/data2vec-vision-large-ft1k
190
2
transformers
3,690
--- license: apache-2.0 tags: - image-classification - vision datasets: - imagenet - imagenet-1k --- # Data2Vec-Vision (large-sized model, fine-tuned on ImageNet-1k) BEiT model pre-trained in a self-supervised fashion and fine-tuned on ImageNet-1k (1,2 million images, 1000 classes) at resolution 224x224. It was introduced in the paper [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language](https://arxiv.org/abs/2202.03555) by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli and first released in [this repository](https://github.com/facebookresearch/data2vec_vision/tree/main/beit). Disclaimer: The team releasing Facebook team did not write a model card for this model so this model card has been written by the Hugging Face team. ## Pre-Training method ![model image](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/data2vec.png) For more information, please take a look at the [official paper](https://arxiv.org/abs/2202.03555). ## Abstract *While the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because they were developed with a single modality in mind. To get us closer to general self-supervised learning, we present data2vec, a framework that uses the same learning method for either speech, NLP or computer vision. The core idea is to predict latent representations of the full input data based on a masked view of the input in a selfdistillation setup using a standard Transformer architecture. Instead of predicting modality-specific targets such as words, visual tokens or units of human speech which are local in nature, data2vec predicts contextualized latent representations that contain information from the entire input. Experiments on the major benchmarks of speech recognition, image classification, and natural language understanding demonstrate a new state of the art or competitive performance to predominant approaches.* ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=data2vec-vision) to look for fine-tuned versions on a task that interests you. ### 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 BeitFeatureExtractor, Data2VecVisionForImageClassification from PIL import Image import requests url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Image.open(requests.get(url, stream=True).raw) feature_extractor = BeitFeatureExtractor.from_pretrained('facebook/data2vec-vision-large-ft1k') model = Data2VecVisionForImageClassification.from_pretrained('facebook/data2vec-vision-large-ft1k') inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits # model predicts one of the 1000 ImageNet classes predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx]) ``` Currently, both the feature extractor and model support PyTorch. ## Training data The BEiT model was pretrained and fine-tuned on [ImageNet-1k](http://www.image-net.org/), a dataset consisting of 1,2 million images and 1k classes. ## Training procedure ### Preprocessing The exact details of preprocessing of images during training/validation can be found [here](https://github.com/microsoft/unilm/blob/master/beit/datasets.py). Images are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5). ### Pretraining For all pre-training related hyperparameters, we refer to the [original paper](https://arxiv.org/abs/2106.08254) and the [original codebase](https://github.com/facebookresearch/data2vec_vision/tree/main/beit) ## Evaluation results For evaluation results on several image classification benchmarks, we refer to tables 1 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution. Of course, increasing the model size will result in better performance. We evaluated the model on `ImageNet1K` and got top-1 accuracy = **86.50** while in the original paper it was reported top-1 accuracy = 86.2. If you want to reproduce our evaluation process you can use [This Colab Notebook](https://colab.research.google.com/drive/1xl8hcdoDYVt5aSk1AYH-nLm1Sgvhac4L?usp=sharing) ### BibTeX entry and citation info ```bibtex @misc{https://doi.org/10.48550/arxiv.2202.03555, doi = {10.48550/ARXIV.2202.03555}, url = {https://arxiv.org/abs/2202.03555}, author = {Baevski, Alexei and Hsu, Wei-Ning and Xu, Qiantong and Babu, Arun and Gu, Jiatao and Auli, Michael}, keywords = {Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language}, publisher = {arXiv}, year = {2022}, copyright = {arXiv.org perpetual, non-exclusive license} } ```
jorge-henao/gpt2-small-spanish-historias-conflicto-col
0cd442c9fd4fb23202c25f79718ababf200f8ac6
2022-07-10T17:30:28.000Z
[ "pytorch", "tensorboard", "gpt2", "text-generation", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-generation
false
jorge-henao
null
jorge-henao/gpt2-small-spanish-historias-conflicto-col
190
null
transformers
3,691
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: gpt2-small-spanish-historias-conflicto-col results: [] --- <!-- 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. --> # gpt2-small-spanish-historias-conflicto-col This model is a fine-tuned version of [datificate/gpt2-small-spanish](https://huggingface.co/datificate/gpt2-small-spanish) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2388 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.8576 | 2.49 | 30000 | 1.2388 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
SajjadAyoubi/distil-bigbird-fa-zwnj
98fd06440980957e6428dc823e16d56593fb805c
2021-10-28T13:14:34.000Z
[ "pytorch", "big_bird", "fill-mask", "arxiv:1810.04805", "arxiv:2005.12515", "arxiv:2007.14062", "transformers", "autotrain_compatible" ]
fill-mask
false
SajjadAyoubi
null
SajjadAyoubi/distil-bigbird-fa-zwnj
189
null
transformers
3,692
<span align="center"> <a href="https://huggingface.co/SajjadAyoubi/"><img src="https://img.shields.io/static/v1?label=%F0%9F%A4%97%20Hugging%20Face&message=SajjadAyoubi&color=yellow"></a> <a href="https://colab.research.google.com/github/sajjjadayobi/PersianQA/blob/main/notebooks/Demo.ipynb"><img src="https://img.shields.io/static/v1?label=Colab&message=Fine-tuning Example&logo=Google%20Colab&color=f9ab00"></a> </span> # ParsBigBird: Persian Bert For **Long-Range** Sequences The [Bert](https://arxiv.org/abs/1810.04805) and [ParsBert](https://arxiv.org/abs/2005.12515) algorithms can handle texts with token lengths of up to 512, however, many tasks such as summarizing and answering questions require longer texts. In our work, we have trained the [BigBird](https://arxiv.org/abs/2007.14062) model for the Persian language to process texts up to 4096 in the Farsi (Persian) language using sparse attention. ## Evaluation: 🌡️ We have evaluated the model on three tasks with different sequence lengths | Name | Params | SnappFood (F1) | Digikala Magazine(F1) | PersianQA (F1) | | :--------------------------------------------------------------: | :----: | :-----------------: | :---------------: | :--------------: | | [distil-bigbird-fa-zwnj](https://github.com/sajjjadayobi/ParsBigBird) | 78M | 85.43% | **94.05%** | **73.34%** | | [bert-base-fa](https://github.com/hooshvare/parsbert) | 118M | **87.98%** | 93.65% | 70.06% | - Despite being as big as distill-bert, the model performs equally well as ParsBert and is much better on PersianQA which requires much more context - This evaluation was based on `max_lentgh=2048` (It can be changed up to 4096) ## How to use❓ ### As Contextualized Word Embedding ```python from transformers import BigBirdModel, AutoTokenizer MODEL_NAME = "SajjadAyoubi/distil-bigbird-fa-zwnj" # by default its in `block_sparse` block_size=32 model = BigBirdModel.from_pretrained(MODEL_NAME, block_size=32) # you can use full attention like the following: use this when input isn't longer than 512 model = BigBirdModel.from_pretrained(MODEL_NAME, attention_type="original_full") text = "😃 امیدوارم مدل بدردبخوری باشه چون خیلی طول کشید تا ترین بشه" tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) tokens = tokenizer(text, return_tensors='pt') output = model(**tokens) # contextualized embedding ``` ### As Fill Blank ```python from transformers import pipeline MODEL_NAME = 'SajjadAyoubi/distil-bigbird-fa-zwnj' fill = pipeline('fill-mask', model=MODEL_NAME, tokenizer=MODEL_NAME) results = fill('تهران پایتخت [MASK] است.') print(results[0]['token_str']) >>> 'ایران' ``` ## Pretraining details: 🔭 This model was pretrained using a masked language model (MLM) objective on the Persian section of the Oscar dataset. Following the original BERT training, 15% of tokens were masked. This was first described in this [paper](https://arxiv.org/abs/2007.14062) and released in this [repository](https://github.com/google-research/bigbird). Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token. Model is warm started from `distilbert-fa`’s [checkpoint](https://huggingface.co/HooshvareLab/distilbert-fa-zwnj-base). - For more details, you can take a look at config.json at the model card in 🤗 Model Hub ## Fine Tuning Recommendations: 🐤 Due to the model's memory requirements, `gradient_checkpointing` and `gradient_accumulation` should be used to maintain a reasonable batch size. Considering this model isn't really big, it's a good idea to first fine-tune it on your dataset using Masked LM objective (also called intermediate fine-tuning) before implementing the main task. In block_sparse mode, it doesn't matter how many tokens are input. It just attends to 256 tokens. Furthermore, original_full should be used up to 512 sequence lengths (instead of block sparse). ### Fine Tuning Examples 👷‍♂️👷‍♀️ | Dataset | Fine Tuning Example | | ------------------------------------- | ------------------------------------------------------------ | | Digikala Magazine Text Classification | <a href="https://colab.research.google.com/github/sajjjadayobi/PersianQA/blob/main/notebooks/Demo.ipynb"><img src="https://img.shields.io/static/v1?label=Colab&message=Fine-tuning Example&logo=Google%20Colab&color=f9ab00"></a> | ## Contact us: 🤝 If you have a technical question regarding the model, pretraining, code or publication, please create an issue in the repository. This is the fastest way to reach us. ## Citation: ↩️ we didn't publish any papers on the work. However, if you did, please cite us properly with an entry like one below. ```bibtex @misc{ParsBigBird, author = {Ayoubi, Sajjad}, title = {ParsBigBird: Persian Bert For Long-Range Sequences}, year = 2021, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/SajjjadAyobi/ParsBigBird}}, } ```
eduardofv/stsb-m-mt-es-distiluse-base-multilingual-cased-v1
dfd98175cd8ebb4da3ebd46c9409f78d4c7e0b73
2021-07-06T18:06:30.000Z
[ "pytorch", "distilbert", "feature-extraction", "es", "dataset:stsb_multi_mt", "sentence-transformers", "sentence-similarity" ]
feature-extraction
false
eduardofv
null
eduardofv/stsb-m-mt-es-distiluse-base-multilingual-cased-v1
189
null
sentence-transformers
3,693
--- language: es datasets: - stsb_multi_mt tags: - sentence-similarity - sentence-transformers --- This is a test model that was fine-tuned using the Spanish datasets from [stsb_multi_mt](https://huggingface.co/datasets/stsb_multi_mt) in order to understand and benchmark STS models. ## Model and training data description This model was built taking `distiluse-base-multilingual-cased-v1` and training it on a Semantic Textual Similarity task using a modified version of the training script for STS from Sentece Transformers (the modified script is included in the repo). It was trained using the Spanish datasets from [stsb_multi_mt](https://huggingface.co/datasets/stsb_multi_mt) which are the STSBenchmark datasets automatically translated to other languages using deepl.com. Refer to the dataset repository for more details. ## Intended uses & limitations This model was built just as a proof-of-concept on STS fine-tuning using Spanish data and no specific use other than getting a sense on how this training works. ## How to use You may use it as any other STS trained model to extract sentence embeddings. Check Sentence Transformers documentation. ## Training procedure This model was trained using this [Colab Notebook](https://colab.research.google.com/drive/1ZNjDMFdy_lKhnD9BtbqzSbQ4LNz638ZA?usp=sharing) ## Evaluation results Evaluating `distiluse-base-multilingual-cased-v1` on the Spanish test dataset before training results in: ``` 2021-07-06 17:44:46 - EmbeddingSimilarityEvaluator: Evaluating the model on dataset: 2021-07-06 17:45:00 - Cosine-Similarity : Pearson: 0.7662 Spearman: 0.7583 2021-07-06 17:45:00 - Manhattan-Distance: Pearson: 0.7805 Spearman: 0.7772 2021-07-06 17:45:00 - Euclidean-Distance: Pearson: 0.7816 Spearman: 0.7778 2021-07-06 17:45:00 - Dot-Product-Similarity: Pearson: 0.6610 Spearman: 0.6536 ``` While the fine-tuned version with the defaults of the training script and the Spanish training dataset results in: ``` 2021-07-06 17:49:22 - EmbeddingSimilarityEvaluator: Evaluating the model on stsb-multi-mt-test dataset: 2021-07-06 17:49:24 - Cosine-Similarity : Pearson: 0.8265 Spearman: 0.8207 2021-07-06 17:49:24 - Manhattan-Distance: Pearson: 0.8131 Spearman: 0.8190 2021-07-06 17:49:24 - Euclidean-Distance: Pearson: 0.8129 Spearman: 0.8190 2021-07-06 17:49:24 - Dot-Product-Similarity: Pearson: 0.7773 Spearman: 0.7692 ``` In our [STS Evaluation repository](https://github.com/eduardofv/sts_eval) we compare the performance of this model with other models from Sentence Transformers and Tensorflow Hub using the standard STSBenchmark and the 2017 STSBenchmark Task 3 for Spanish. ## Resources - Training dataset [stsb_multi_mt](https://huggingface.co/datasets/stsb_multi_mt) - Sentence Transformers [Semantic Textual Similarity](https://www.sbert.net/examples/training/sts/README.html) - Check [sts_eval](https://github.com/eduardofv/sts_eval) for a comparison with Tensorflow and Sentence-Transformers models - Check the [development environment to run the scripts and evaluation](https://github.com/eduardofv/ai-denv)
sultan/BioM-ELECTRA-Large-SQuAD2
7108315378d3adb4168a79035366e1b9a563f0b3
2021-08-06T22:27:10.000Z
[ "pytorch", "electra", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
sultan
null
sultan/BioM-ELECTRA-Large-SQuAD2
189
1
transformers
3,694
# BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA # Abstract The impact of design choices on the performance of biomedical language models recently has been a subject for investigation. In this paper, we empirically study biomedical domain adaptation with large transformer models using different design choices. We evaluate the performance of our pretrained models against other existing biomedical language models in the literature. Our results show that we achieve state-of-the-art results on several biomedical domain tasks despite using similar or less computational cost compared to other models in the literature. Our findings highlight the significant effect of design choices on improving the performance of biomedical language models. # Model Description We fine-tuned BioM-ELECTRA-Large, which was pre-trained on PubMed Abstracts, on the SQuAD2.0 dataset. Fine-tuning the biomedical language model on the SQuAD dataset helps improve the score on the BioASQ challenge. If you plan to work with BioASQ or biomedical QA tasks, it's better to use this model over BioM-ELECTRA-Large. This model (TensorFlow version ) took the lead in the BioASQ9b-Factoid challenge (Batch 5) under the name of (UDEL-LAB2). To see the full details of BioASQ9B results, please check this link http://participants-area.bioasq.org/results/9b/phaseB/ ( you need to register). Huggingface library doesn't implement Layer-Wise decay feature, which affects the performance on SQuAD task. The reported result of BioM-ELECTRA-SQuAD in our paper is 88.3 (F1) since we use ELECTRA open-source code with TF checkpoint, which uses Layer-Wise decay. Training Script ```python run_qa.py --model_name_or_path sultan/BioM-ELECTRA-Large-Discriminator \ --dataset_name squad_v2 \ --do_train \ --do_eval \ --dataloader_num_workers 20 \ --preprocessing_num_workers 20 \ --version_2_with_negative \ --num_train_epochs 2 \ --learning_rate 5e-5 \ --max_seq_length 512 \ --doc_stride 128 \ --per_device_train_batch_size 8 \ --gradient_accumulation_steps 6 \ --per_device_eval_batch_size 128 --fp16 \ --fp16_opt_level O1 \ --logging_steps 50 \ --save_steps 1000 \ --overwrite_output_dir \ --output_dir out ``` Evaluation results on SQuAD2.0 Dev Dataset ``` exact = 84.33420365535248 f1 = 87.49354241889522 total = 11873 HasAns_exact = 80.43184885290148 HasAns_f1 = 86.75958656200127 HasAns_total = 5928 NoAns_exact = 88.22539949537426 NoAns_f1 = 88.22539949537426 NoAns_total = 5945 best_exact = 84.33420365535248 best_exact_thresh = 0.0 best_f1 = 87.49354241889522 best_f1_thresh = 0.0 epoch = 2.0 ``` To reproduce results in Google Colab: - Make sure you have GPU enabled. - Clone and install required libraries through this code !git clone https://github.com/huggingface/transformers !pip3 install -e transformers !pip3 install sentencepiece !pip3 install -r /content/transformers/examples/pytorch/question-answering/requirements.txt - Run this python code: ```python python /content/transformers/examples/pytorch/question-answering/run_qa.py --model_name_or_path sultan/BioM-ELECTRA-Large-SQuAD2 \ --do_eval \ --version_2_with_negative \ --per_device_eval_batch_size 8 \ --dataset_name squad_v2 \ --overwrite_output_dir \ --fp16 \ --output_dir out ``` - You don't need to download the SQuAD2 dataset. The code will download it from the HuggingFace datasets hub. - Check our GitHub repo at https://github.com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints. - We added examples to fine-tune BioM-ELECTRA-Large on SQuAD and BioASQ7B using TensorFlow and TPU here https://github.com/salrowili/BioM-Transformers/tree/main/examples . In this example we show that we achieve 88.22 score in SQuAD2.0 since Tensor Flow code has Layer-wise decay feature. # Acknowledgment We would like to acknowledge the support we have from Tensorflow Research Cloud (TFRC) team to grant us access to TPUv3 units. # Citation ```bibtex @inproceedings{alrowili-shanker-2021-biom, title = "{B}io{M}-Transformers: Building Large Biomedical Language Models with {BERT}, {ALBERT} and {ELECTRA}", author = "Alrowili, Sultan and Shanker, Vijay", booktitle = "Proceedings of the 20th Workshop on Biomedical Language Processing", month = jun, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2021.bionlp-1.24", pages = "221--227", abstract = "The impact of design choices on the performance of biomedical language models recently has been a subject for investigation. In this paper, we empirically study biomedical domain adaptation with large transformer models using different design choices. We evaluate the performance of our pretrained models against other existing biomedical language models in the literature. Our results show that we achieve state-of-the-art results on several biomedical domain tasks despite using similar or less computational cost compared to other models in the literature. Our findings highlight the significant effect of design choices on improving the performance of biomedical language models.", } ```
uer/roberta-base-finetuned-jd-binary-chinese
b270d7d29fb2c4596908f2502a48a04cf459eb57
2022-02-20T07:57:21.000Z
[ "pytorch", "tf", "jax", "bert", "text-classification", "zh", "arxiv:1909.05658", "arxiv:1708.02657", "transformers" ]
text-classification
false
uer
null
uer/roberta-base-finetuned-jd-binary-chinese
189
2
transformers
3,695
--- language: zh widget: - text: "这本书真的很不错" --- # Chinese RoBERTa-Base Models for Text Classification ## Model description This is the set of 5 Chinese RoBERTa-Base classification models fine-tuned by [UER-py](https://arxiv.org/abs/1909.05658). You can download the 5 Chinese RoBERTa-Base classification models either from the [UER-py Modelzoo page](https://github.com/dbiir/UER-py/wiki/Modelzoo) (in UER-py format), or via HuggingFace from the links below: | Dataset | Link | | :-----------: | :-------------------------------------------------------: | | **JD full** | [**roberta-base-finetuned-jd-full-chinese**][jd_full] | | **JD binary** | [**roberta-base-finetuned-jd-binary-chinese**][jd_binary] | | **Dianping** | [**roberta-base-finetuned-dianping-chinese**][dianping] | | **Ifeng** | [**roberta-base-finetuned-ifeng-chinese**][ifeng] | | **Chinanews** | [**roberta-base-finetuned-chinanews-chinese**][chinanews] | ## How to use You can use this model directly with a pipeline for text classification (take the case of roberta-base-finetuned-chinanews-chinese): ```python >>> from transformers import AutoModelForSequenceClassification,AutoTokenizer,pipeline >>> model = AutoModelForSequenceClassification.from_pretrained('uer/roberta-base-finetuned-chinanews-chinese') >>> tokenizer = AutoTokenizer.from_pretrained('uer/roberta-base-finetuned-chinanews-chinese') >>> text_classification = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer) >>> text_classification("北京上个月召开了两会") [{'label': 'mainland China politics', 'score': 0.7211663722991943}] ``` ## Training data 5 Chinese text classification datasets are used. JD full, JD binary, and Dianping datasets consist of user reviews of different sentiment polarities. Ifeng and Chinanews consist of first paragraphs of news articles of different topic classes. They are collected by [Glyph](https://github.com/zhangxiangxiao/glyph) project and more details are discussed in corresponding [paper](https://arxiv.org/abs/1708.02657). ## Training procedure Models are fine-tuned by [UER-py](https://github.com/dbiir/UER-py/) on [Tencent Cloud](https://cloud.tencent.com/). We fine-tune three epochs with a sequence length of 512 on the basis of the pre-trained model [chinese_roberta_L-12_H-768](https://huggingface.co/uer/chinese_roberta_L-12_H-768). At the end of each epoch, the model is saved when the best performance on development set is achieved. We use the same hyper-parameters on different models. Taking the case of roberta-base-finetuned-chinanews-chinese ``` python3 run_classifier.py --pretrained_model_path models/cluecorpussmall_roberta_base_seq512_model.bin-250000 \ --vocab_path models/google_zh_vocab.txt \ --train_path datasets/glyph/chinanews/train.tsv \ --dev_path datasets/glyph/chinanews/dev.tsv \ --output_model_path models/chinanews_classifier_model.bin \ --learning_rate 3e-5 --epochs_num 3 --batch_size 32 --seq_length 512 ``` Finally, we convert the pre-trained model into Huggingface's format: ``` python3 scripts/convert_bert_text_classification_from_uer_to_huggingface.py --input_model_path models/chinanews_classifier_model.bin \ --output_model_path pytorch_model.bin \ --layers_num 12 ``` ### BibTeX entry and citation info ``` @article{devlin2018bert, title={BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding}, author={Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina}, journal={arXiv preprint arXiv:1810.04805}, year={2018} } @article{liu2019roberta, title={Roberta: A robustly optimized bert pretraining approach}, author={Liu, Yinhan and Ott, Myle and Goyal, Naman and Du, Jingfei and Joshi, Mandar and Chen, Danqi and Levy, Omer and Lewis, Mike and Zettlemoyer, Luke and Stoyanov, Veselin}, journal={arXiv preprint arXiv:1907.11692}, year={2019} } @article{zhang2017encoding, title={Which encoding is the best for text classification in chinese, english, japanese and korean?}, author={Zhang, Xiang and LeCun, Yann}, journal={arXiv preprint arXiv:1708.02657}, year={2017} } @article{zhao2019uer, title={UER: An Open-Source Toolkit for Pre-training Models}, author={Zhao, Zhe and Chen, Hui and Zhang, Jinbin and Zhao, Xin and Liu, Tao and Lu, Wei and Chen, Xi and Deng, Haotang and Ju, Qi and Du, Xiaoyong}, journal={EMNLP-IJCNLP 2019}, pages={241}, year={2019} } ``` [jd_full]:https://huggingface.co/uer/roberta-base-finetuned-jd-full-chinese [jd_binary]:https://huggingface.co/uer/roberta-base-finetuned-jd-binary-chinese [dianping]:https://huggingface.co/uer/roberta-base-finetuned-dianping-chinese [ifeng]:https://huggingface.co/uer/roberta-base-finetuned-ifeng-chinese [chinanews]:https://huggingface.co/uer/roberta-base-finetuned-chinanews-chinese
d0r1h/LEDBill
714bfe06554698b4945e72ffb0ef406df0347c11
2022-07-12T08:11:45.000Z
[ "pytorch", "led", "text2text-generation", "dataset:billsum", "arxiv:2004.05150", "transformers", "summarization", "license:apache-2.0", "model-index", "autotrain_compatible" ]
summarization
false
d0r1h
null
d0r1h/LEDBill
189
null
transformers
3,696
--- license: apache-2.0 datasets: billsum tags: - summarization widget: - text: "The people of the State of California do enact as follows: SECTIONHEADER Section 1170.02 is added to the Penal Code, to read: 1170.02. A prisoner is not eligible for resentence or recall pursuant to subdivision (e) of Section 1170 if he or she was convicted of first-degree murder if the victim was a peace officer, as defined in Section 830.1, 830.2, 830.3, 830.31, 830.32, 830.33, 830.34, 830.35, 830.36, 830.37, 830.4, 830.5, 830.6, 830.10, 830.11, or 830.12, who was killed while engaged in the performance of his or her duties, and the individual knew, or reasonably should have known, that the victim was a peace officer engaged in the performance of his or her duties, or the victim was a peace officer or a former peace officer under any of the above-enumerated sections, and was intentionally killed in retaliation for the performance of his or her official duties. SECTIONHEADER Section 3550 of the Penal Code is amended to read: 3550. Notwithstanding any other law, except as provided in subdivision (b), if the head physician of an institution in which a prisoner is incarcerated determines, as provided in this section, that the prisoner is permanently medically incapacitated with a medical condition that renders him or her permanently unable to perform activities of basic daily living, and results in the prisoner requiring 24-hour care, and that incapacitation did not exist at the time of sentencing, the prisoner shall be granted medical parole if the Board of Parole Hearings determines that the conditions under which he or she would be released would not reasonably pose a threat to public safety. This section does not alter or diminish the rights conferred under the Victims Bill of Rights Act of 2008 . Subdivision (a) does not apply to any of the following: A prisoner sentenced to death or life in prison without possibility of parole. A prisoner who is serving a sentence for which parole, pursuant to subdivision (a), is prohibited by any initiative statute. A prisoner who was convicted of first-degree murder if the victim was a peace officer, as defined in Section 830.1, 830.2, 830.3, 830.31, 830.32, 830.33, 830.34, 830.35, 830.36, 830.37, 830.4, 830.5, 830.6, 830.10, 830.11, or 830.12, who was killed while engaged in the performance of his or her duties, and the individual knew, or reasonably should have known, that the victim was a peace officer engaged in the performance of his or her duties, or the victim was a peace officer or a former peace officer under any of the above-enumerated sections, and was intentionally killed in retaliation for the performance of his or her official duties. When a physician employed by the Department of Corrections and Rehabilitation who is the primary care provider for a prisoner identifies a prisoner that he or she believes meets the medical criteria for medical parole specified in subdivision (a), the primary care physician shall recommend to the head physician of the institution where the prisoner is located that the prisoner be referred to the Board of Parole Hearings for consideration for medical parole. Within 30 days of receiving that recommendation, if the head physician of the institution concurs in the recommendation of the primary care physician, he or she shall refer the matter to the Board of Parole Hearings using a standardized form and format developed by the department, and if the head physician of the institution does not concur in the recommendation, he or she shall provide the primary care physician with a written explanation of the reasons for denying the referral. Notwithstanding any other provisions of this section, the prisoner or his or her family member or designee may independently request consideration for medical parole by contacting the head physician at the prison or the department. Within 30 days of receiving the request, the head physician of the institution shall, in consultation with the prisoners primary care physician, make a determination regarding whether the prisoner meets the criteria for medical parole as specified in subdivision (a) and, if the head physician of the institution determines that the prisoner satisfies the criteria set forth in subdivision (a), he or she shall refer the matter to the Board of Parole Hearings using a standardized form and format developed by the department. If the head physician of the institution does not concur in the recommendation, he or she shall provide the prisoner or his or her family member or designee with a written explanation of the reasons for denying the application. The Department of Corrections and Rehabilitation shall complete parole plans for inmates referred to the Board of Parole Hearings for medical parole consideration. The parole plans shall include, but not be limited to, the inmates plan for residency and medical care. Notwithstanding any other law, medical parole hearings shall be conducted by two-person panels consisting of at least one commissioner. In the event of a tie vote, the matter shall be referred to the full board for a decision. Medical parole hearings may be heard in absentia. Upon receiving a recommendation from the head physician of the institution where a prisoner is located for the prisoner to be granted medical parole pursuant to subdivision (c) or (d), the board, as specified in subdivision (f), shall make an independent judgment regarding whether the conditions under which the inmate would be released pose a reasonable threat to public safety, and make written findings related thereto. Notwithstanding any other law, the board or the Division of Adult Parole Operations shall have the authority to impose any reasonable conditions on prisoners subject to medical parole supervision pursuant to subdivision (a), including, but not limited to, the requirement that the parolee submit to electronic monitoring. As a further condition of medical parole, pursuant to subdivision (a), the parolee may be required to submit to an examination by a physician selected by the board for the purpose of diagnosing the parolees current medical condition. In the event such an examination takes place, a report of the examination and diagnosis shall be submitted to the board by the examining physician. If the board determines, based on that medical examination, that the persons medical condition has improved to the extent that the person no longer qualifies for medical parole, the board shall return the person to the custody of the department. Notwithstanding any other law establishing maximum periods for parole, a prisoner sentenced to a determinate term who is placed on medical parole supervision prior to the earliest possible release date and who remains eligible for medical parole, shall remain on medical parole, pursuant to subdivision (a), until that earliest possible release date, at which time the parolee shall commence serving that period of parole provided by, and under the provisions of, Chapter 8 of Title 1. Notwithstanding any other law establishing maximum periods for parole, a prisoner sentenced to an indeterminate term who is placed on medical parole supervision prior to the prisoners minimum eligible parole date, and who remains eligible for medical parole, shall remain on medical parole pursuant to subdivision (a) until that minimum eligible parole date, at which time the parolee shall be eligible for parole consideration under all other provisions of Chapter 8 of Title 1. The Department of Corrections and Rehabilitation shall, at the time a prisoner is placed on medical parole supervision pursuant to subdivision (a), ensure that the prisoner has applied for any federal entitlement programs for which the prisoner is eligible, and has in his or her possession a discharge medical summary, full medical records, parole medications, and all property belonging to the prisoner that was under the control of the department. Any additional records shall be sent to the prisoners forwarding address after release to health care-related parole supervision. The provisions for medical parole set forth in this title shall not affect an inmates eligibility for any other form of parole or release provided by law. (1) Notwithstanding any other law, the Department of Corrections and Rehabilitation shall give notice to the county of commitment and the proposed county of release, if that county is different than the county of commitment, of any medical parole hearing as described in subdivision (f), and of any medical parole release as described in subdivision (g). Notice shall be made at least 30 days, or as soon as feasible, prior to the time any medical parole hearing or medical parole release is scheduled for an inmate receiving medical parole consideration, regardless of whether the inmate is sentenced either determinately or indeterminately." model-index: - name: d0r1h/LEDBill results: - task: type: summarization name: Summarization dataset: name: billsum type: billsum config: default split: test metrics: - name: ROUGE-1 type: rouge value: 38.6502 verified: true - name: ROUGE-2 type: rouge value: 18.5458 verified: true - name: ROUGE-L type: rouge value: 25.6561 verified: true - name: ROUGE-LSUM type: rouge value: 33.1575 verified: true - name: loss type: loss value: 2.1305277347564697 verified: true - name: gen_len type: gen_len value: 288.372 verified: true --- # Longformer Encoder-Decoder (LED) fine-tuned on Billsum This model is a fine-tuned version of [led-base-16384](https://huggingface.co/allenai/led-base-16384) on the [billsum](https://huggingface.co/datasets/billsum) dataset. As described in [Longformer: The Long-Document Transformer](https://arxiv.org/pdf/2004.05150.pdf) by Iz Beltagy, Matthew E. Peters, Arman Cohan, *led-base-16384* was initialized from [*bart-base*](https://huggingface.co/facebook/bart-base) since both models share the exact same architecture. To be able to process 16K tokens, *bart-base*'s position embedding matrix was simply copied 16 times. ## How to use ```Python from transformers import AutoModelForSeq2SeqLM, AutoTokenizer device = "cuda" if torch.cuda.is_available() else "cpu" tokenizer = AutoTokenizer.from_pretrained("d0r1h/LEDBill") model = AutoModelForSeq2SeqLM.from_pretrained("d0r1h/LEDBill", return_dict_in_generate=True).to(device) case = "......." input_ids = tokenizer(case, return_tensors="pt").input_ids.to(device) global_attention_mask = torch.zeros_like(input_ids) global_attention_mask[:, 0] = 1 sequences = model.generate(input_ids, global_attention_mask=global_attention_mask).sequences summary = tokenizer.batch_decode(sequences, skip_special_tokens=True) ``` ## Evaluation results When the model is used for summarizing Billsum documents(10 sample), it achieves the following results: | Model | rouge1-f | rouge1-p | rouge2-f | rouge2-p | rougeL-f | rougeL-p | |:-----------:|:-----:|:-----:|:------:|:-----:|:------:|:-----:| | LEDBill | **34** | **37** | **15** | **16** | **30** | **32** | | led-base | 2 | 15 | 0 | 0 | 2 | 15 | [This notebook](https://colab.research.google.com/drive/1iEEFbWeTGUSDesmxHIU2QDsPQM85Ka1K?usp=sharing) shows how *led* can effectively be used for downstream task such summarization.
Finnish-NLP/roberta-large-wechsel-finnish
d12d05c3dd60b277728436a5cea6e50262f2d749
2022-06-13T16:13:27.000Z
[ "pytorch", "jax", "tensorboard", "roberta", "fill-mask", "fi", "dataset:Finnish-NLP/mc4_fi_cleaned", "dataset:wikipedia", "arxiv:1907.11692", "arxiv:2112.06598", "transformers", "finnish", "license:apache-2.0", "autotrain_compatible" ]
fill-mask
false
Finnish-NLP
null
Finnish-NLP/roberta-large-wechsel-finnish
188
1
transformers
3,697
--- language: - fi license: apache-2.0 tags: - finnish - roberta datasets: - Finnish-NLP/mc4_fi_cleaned - wikipedia widget: - text: "Moikka olen <mask> kielimalli." --- # RoBERTa large model trained with WECHSEL method for Finnish Pretrained RoBERTa model on Finnish language using a masked language modeling (MLM) objective with WECHSEL method. RoBERTa was introduced in [this paper](https://arxiv.org/abs/1907.11692) and first released in [this repository](https://github.com/pytorch/fairseq/tree/master/examples/roberta). WECHSEL method (Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models) was introduced in [this paper](https://arxiv.org/abs/2112.06598) and first released in [this repository](https://github.com/CPJKU/wechsel). This model is case-sensitive: it makes a difference between finnish and Finnish. ## Model description Finnish RoBERTa is a transformers model pretrained on a large corpus of Finnish 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 inputs and labels from those texts. More precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. This way, the model learns an inner representation of the Finnish language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the RoBERTa model as inputs. ## WECHSEL method Using the WECHSEL method, we first took the pretrained English [roberta-large](https://huggingface.co/roberta-large) model, changed its tokenizer with our Finnish tokenizer and initialized model's token embeddings such that they are close to semantically similar English tokens by utilizing multilingual static word embeddings (by fastText) covering English and Finnish. We were able to confirm the WECHSEL paper's findings that using this method you can save pretraining time and thus computing resources. To get idea of the WECHSEL method's training time savings you can check the table below illustrating the MLM evaluation accuracies during the pretraining compared to the [Finnish-NLP/roberta-large-finnish-v2](https://huggingface.co/Finnish-NLP/roberta-large-finnish-v2) which was trained from scratch: | | 10k train steps | 100k train steps | 200k train steps | 270k train steps | |------------------------------------------|------------------|------------------|------------------|------------------| |Finnish-NLP/roberta-large-wechsel-finnish |37.61 eval acc |58.14 eval acc |61.60 eval acc |62.77 eval acc | |Finnish-NLP/roberta-large-finnish-v2 |13.83 eval acc |55.87 eval acc |58.58 eval acc |59.47 eval acc | Downstream finetuning text classification tests can be found from the end but there this model trained with WECHSEL method didn't significantly improve the downstream performances. However, based on tens of qualitative fill-mask task example tests we noticed that for fill-mask task this WECHSEL model significantly outperforms our other models trained from scratch. ## Intended uses & limitations You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='Finnish-NLP/roberta-large-wechsel-finnish') >>> unmasker("Moikka olen <mask> kielimalli.") [{'sequence': 'Moikka olen hyvä kielimalli.', 'score': 0.07757357507944107, 'token': 763, 'token_str': ' hyvä'}, {'sequence': 'Moikka olen suomen kielimalli.', 'score': 0.05297883599996567, 'token': 3641, 'token_str': ' suomen'}, {'sequence': 'Moikka olen kuin kielimalli.', 'score': 0.03747279942035675, 'token': 523, 'token_str': ' kuin'}, {'sequence': 'Moikka olen suomalainen kielimalli.', 'score': 0.031031042337417603, 'token': 4966, 'token_str': ' suomalainen'}, {'sequence': 'Moikka olen myös kielimalli.', 'score': 0.026489052921533585, 'token': 505, 'token_str': ' myös'}] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('Finnish-NLP/roberta-large-wechsel-finnish') model = RobertaModel.from_pretrained('Finnish-NLP/roberta-large-wechsel-finnish') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import RobertaTokenizer, TFRobertaModel tokenizer = RobertaTokenizer.from_pretrained('Finnish-NLP/roberta-large-wechsel-finnish') model = TFRobertaModel.from_pretrained('Finnish-NLP/roberta-large-wechsel-finnish', from_pt=True) text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ### Limitations and bias The training data used for this model contains a lot of unfiltered content from the internet, which is far from neutral. Therefore, the model can have biased predictions. ## Training data This Finnish RoBERTa model was pretrained on the combination of five datasets: - [mc4_fi_cleaned](https://huggingface.co/datasets/Finnish-NLP/mc4_fi_cleaned), the dataset mC4 is a multilingual colossal, cleaned version of Common Crawl's web crawl corpus. We used the Finnish subset of the mC4 dataset and further cleaned it with our own text data cleaning codes (check the dataset repo). - [wikipedia](https://huggingface.co/datasets/wikipedia) We used the Finnish subset of the wikipedia (August 2021) dataset - [Yle Finnish News Archive](http://urn.fi/urn:nbn:fi:lb-2017070501) - [Finnish News Agency Archive (STT)](http://urn.fi/urn:nbn:fi:lb-2018121001) - [The Suomi24 Sentences Corpus](http://urn.fi/urn:nbn:fi:lb-2020021803) Raw datasets were cleaned to filter out bad quality and non-Finnish examples. Together these cleaned datasets were around 84GB of text. ## Training procedure ### Preprocessing The texts are tokenized using a byte version of Byte-Pair Encoding (BPE) and a vocabulary size of 50265. The inputs of the model take pieces of 512 contiguous token that may span over documents. The beginning of a new document is marked with `<s>` and the end of one by `</s>` The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `<mask>`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. Contrary to BERT, the masking is done dynamically during pretraining (e.g., it changes at each epoch and is not fixed). ### Pretraining The model was trained on TPUv3-8 VM, sponsored by the [Google TPU Research Cloud](https://sites.research.google/trc/about/), for 270k steps (a bit over 1 epoch, 512 batch size) with a sequence length of 128 and continuing for 180k steps (batch size 64) with a sequence length of 512. The optimizer used was Adafactor (to save memory). Learning rate was 2e-4, \\(\beta_{1} = 0.9\\), \\(\beta_{2} = 0.98\\) and \\(\epsilon = 1e-6\\), learning rate warmup for 2500 steps and linear decay of the learning rate after. ## Evaluation results Evaluation was done by fine-tuning the model on downstream text classification task with two different labeled datasets: [Yle News](https://github.com/spyysalo/yle-corpus) and [Eduskunta](https://github.com/aajanki/eduskunta-vkk). Yle News classification fine-tuning was done with two different sequence lengths: 128 and 512 but Eduskunta only with 128 sequence length. When fine-tuned on those datasets, this model (the first row of the table) achieves the following accuracy results compared to the [FinBERT (Finnish BERT)](https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1) model and to our previous [Finnish-NLP/roberta-large-finnish-v2](https://huggingface.co/Finnish-NLP/roberta-large-finnish-v2) and [Finnish-NLP/roberta-large-finnish](https://huggingface.co/Finnish-NLP/roberta-large-finnish) models: | | Average | Yle News 128 length | Yle News 512 length | Eduskunta 128 length | |------------------------------------------|----------|---------------------|---------------------|----------------------| |Finnish-NLP/roberta-large-wechsel-finnish |88.19 |**94.91** |95.18 |74.47 | |Finnish-NLP/roberta-large-finnish-v2 |88.17 |94.46 |95.22 |74.83 | |Finnish-NLP/roberta-large-finnish |88.02 |94.53 |95.23 |74.30 | |TurkuNLP/bert-base-finnish-cased-v1 |**88.82** |94.90 |**95.49** |**76.07** | To conclude, this model didn't significantly improve compared to our previous models which were trained from scratch instead of using the WECHSEL method as in this model. This model is also slightly (~ 1%) losing to the [FinBERT (Finnish BERT)](https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1) model. ## Acknowledgements This project would not have been possible without compute generously provided by Google through the [TPU Research Cloud](https://sites.research.google/trc/). ## Team Members - Aapo Tanskanen, [Hugging Face profile](https://huggingface.co/aapot), [LinkedIn profile](https://www.linkedin.com/in/aapotanskanen/) - Rasmus Toivanen [Hugging Face profile](https://huggingface.co/RASMUS), [LinkedIn profile](https://www.linkedin.com/in/rasmustoivanen/) Feel free to contact us for more details 🤗
Helsinki-NLP/opus-mt-ha-en
a42c55010565f550c110703534b61b32a37fda8c
2021-09-09T21:59:59.000Z
[ "pytorch", "marian", "text2text-generation", "ha", "en", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-ha-en
188
null
transformers
3,698
--- tags: - translation license: apache-2.0 --- ### opus-mt-ha-en * source languages: ha * target languages: en * OPUS readme: [ha-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ha-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://object.pouta.csc.fi/OPUS-MT-models/ha-en/opus-2020-01-09.zip) * test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ha-en/opus-2020-01-09.test.txt) * test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ha-en/opus-2020-01-09.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.ha.en | 35.0 | 0.506 | | Tatoeba.ha.en | 39.0 | 0.497 |
pki/t5-small-finetuned_xsum
65e211b0e8840a485ac8cbfc0f49ba4b8988f6e7
2022-03-15T13:42:02.000Z
[ "pytorch", "tensorboard", "t5", "text2text-generation", "dataset:xsum", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
text2text-generation
false
pki
null
pki/t5-small-finetuned_xsum
188
1
transformers
3,699
--- tags: - generated_from_trainer datasets: - xsum metrics: - rouge model-index: - name: t5-small-finetuned_xsum results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: xsum type: xsum args: default metrics: - name: Rouge1 type: rouge value: 34.0559 --- <!-- 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. --> # t5-small-finetuned_xsum This model is a fine-tuned version of [pki/t5-small-finetuned_xsum](https://huggingface.co/pki/t5-small-finetuned_xsum) on the xsum dataset. It achieves the following results on the evaluation set: - Loss: 2.0479 - Rouge1: 34.0559 - Rouge2: 12.7506 - Rougel: 27.6762 - Rougelsum: 27.68 - Gen Len: 18.7924 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### 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: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 2.1176 | 1.0 | 12753 | 2.0913 | 33.1548 | 11.8434 | 26.7805 | 26.7751 | 18.7805 | | 2.1019 | 2.0 | 25506 | 2.0875 | 33.231 | 11.9329 | 26.8674 | 26.861 | 18.7992 | | 2.1044 | 3.0 | 38259 | 2.0846 | 33.3643 | 11.9807 | 26.9817 | 26.9764 | 18.773 | | 2.0874 | 4.0 | 51012 | 2.0832 | 33.3562 | 12.0681 | 27.0178 | 27.0189 | 18.7988 | | 2.0791 | 5.0 | 63765 | 2.0803 | 33.38 | 12.081 | 27.0368 | 27.0344 | 18.7844 | | 2.0894 | 6.0 | 76518 | 2.0787 | 33.2549 | 11.9662 | 26.8674 | 26.8669 | 18.7975 | | 2.0802 | 7.0 | 89271 | 2.0777 | 33.3978 | 12.0828 | 27.0461 | 27.0443 | 18.7757 | | 2.0719 | 8.0 | 102024 | 2.0743 | 33.4083 | 12.1141 | 27.0523 | 27.0457 | 18.7928 | | 2.0782 | 9.0 | 114777 | 2.0748 | 33.3673 | 12.1637 | 27.0696 | 27.0663 | 18.7902 | | 2.0736 | 10.0 | 127530 | 2.0713 | 33.5771 | 12.2219 | 27.1707 | 27.1706 | 18.7945 | | 2.0816 | 11.0 | 140283 | 2.0703 | 33.5099 | 12.2069 | 27.1822 | 27.1835 | 18.8002 | | 2.057 | 12.0 | 153036 | 2.0693 | 33.5853 | 12.2427 | 27.2096 | 27.2109 | 18.806 | | 2.0584 | 13.0 | 165789 | 2.0676 | 33.4883 | 12.2674 | 27.1582 | 27.154 | 18.7857 | | 2.0475 | 14.0 | 178542 | 2.0662 | 33.5529 | 12.2765 | 27.1897 | 27.1901 | 18.79 | | 2.0426 | 15.0 | 191295 | 2.0643 | 33.6543 | 12.3545 | 27.2946 | 27.2928 | 18.8036 | | 2.0373 | 16.0 | 204048 | 2.0648 | 33.6671 | 12.349 | 27.2649 | 27.2707 | 18.7905 | | 2.0178 | 17.0 | 216801 | 2.0637 | 33.6794 | 12.4545 | 27.3015 | 27.3079 | 18.7948 | | 2.0235 | 18.0 | 229554 | 2.0626 | 33.7635 | 12.423 | 27.3475 | 27.3446 | 18.7892 | | 2.0296 | 19.0 | 242307 | 2.0622 | 33.7574 | 12.4651 | 27.3879 | 27.3882 | 18.8134 | | 2.0319 | 20.0 | 255060 | 2.0595 | 33.9093 | 12.5389 | 27.5003 | 27.5001 | 18.7915 | | 2.0208 | 21.0 | 267813 | 2.0583 | 33.7875 | 12.4912 | 27.4243 | 27.4332 | 18.7982 | | 2.0151 | 22.0 | 280566 | 2.0581 | 33.8516 | 12.4805 | 27.46 | 27.4647 | 18.816 | | 2.0188 | 23.0 | 293319 | 2.0575 | 33.7744 | 12.4548 | 27.381 | 27.382 | 18.802 | | 2.0087 | 24.0 | 306072 | 2.0579 | 33.8953 | 12.4984 | 27.4675 | 27.4727 | 18.7819 | | 2.0186 | 25.0 | 318825 | 2.0557 | 33.7766 | 12.4414 | 27.4025 | 27.4024 | 18.8005 | | 2.0051 | 26.0 | 331578 | 2.0555 | 33.8973 | 12.5796 | 27.5338 | 27.5339 | 18.8153 | | 2.0024 | 27.0 | 344331 | 2.0557 | 33.8709 | 12.5116 | 27.4684 | 27.4664 | 18.7911 | | 1.9947 | 28.0 | 357084 | 2.0545 | 33.8499 | 12.5242 | 27.4677 | 27.4716 | 18.8025 | | 1.9931 | 29.0 | 369837 | 2.0545 | 33.7957 | 12.5272 | 27.4129 | 27.4174 | 18.8 | | 1.9826 | 30.0 | 382590 | 2.0548 | 33.9723 | 12.6665 | 27.5598 | 27.5662 | 18.7958 | | 1.999 | 31.0 | 395343 | 2.0522 | 33.9702 | 12.6435 | 27.5788 | 27.579 | 18.795 | | 1.9872 | 32.0 | 408096 | 2.0525 | 33.9546 | 12.638 | 27.5985 | 27.5949 | 18.7976 | | 1.991 | 33.0 | 420849 | 2.0520 | 33.9792 | 12.6073 | 27.5686 | 27.5707 | 18.8056 | | 2.0044 | 34.0 | 433602 | 2.0504 | 34.0736 | 12.6511 | 27.647 | 27.6472 | 18.8093 | | 1.9972 | 35.0 | 446355 | 2.0513 | 34.0506 | 12.711 | 27.6533 | 27.6537 | 18.7984 | | 1.9901 | 36.0 | 459108 | 2.0504 | 33.9991 | 12.638 | 27.626 | 27.6272 | 18.7996 | | 1.9742 | 37.0 | 471861 | 2.0507 | 33.9357 | 12.6636 | 27.5673 | 27.5716 | 18.8064 | | 1.984 | 38.0 | 484614 | 2.0502 | 33.9476 | 12.6589 | 27.58 | 27.5813 | 18.8037 | | 1.9864 | 39.0 | 497367 | 2.0499 | 34.0733 | 12.7198 | 27.6926 | 27.6992 | 18.8061 | | 1.9734 | 40.0 | 510120 | 2.0492 | 33.9483 | 12.6486 | 27.5571 | 27.5598 | 18.8033 | | 1.9895 | 41.0 | 522873 | 2.0490 | 33.9753 | 12.684 | 27.6058 | 27.6086 | 18.8011 | | 1.964 | 42.0 | 535626 | 2.0487 | 33.9528 | 12.6376 | 27.576 | 27.5824 | 18.7919 | | 1.9849 | 43.0 | 548379 | 2.0487 | 33.9868 | 12.6936 | 27.6116 | 27.6158 | 18.7966 | | 1.9798 | 44.0 | 561132 | 2.0491 | 34.0379 | 12.7161 | 27.6227 | 27.6315 | 18.7889 | | 1.9837 | 45.0 | 573885 | 2.0473 | 34.0046 | 12.6559 | 27.5931 | 27.5988 | 18.7996 | | 1.9556 | 46.0 | 586638 | 2.0483 | 34.0378 | 12.712 | 27.6346 | 27.6446 | 18.7942 | | 1.9844 | 47.0 | 599391 | 2.0479 | 34.0301 | 12.7121 | 27.6492 | 27.6554 | 18.7999 | | 1.9869 | 48.0 | 612144 | 2.0474 | 34.0463 | 12.7151 | 27.6542 | 27.6604 | 18.7919 | | 1.9851 | 49.0 | 624897 | 2.0476 | 34.0549 | 12.7384 | 27.6542 | 27.6555 | 18.7924 | | 1.9912 | 50.0 | 637650 | 2.0479 | 34.0559 | 12.7506 | 27.6762 | 27.68 | 18.7924 | ### Framework versions - Transformers 4.12.0.dev0 - Pytorch 1.10.1 - Datasets 1.14.0 - Tokenizers 0.10.3